# document

id: 9cbd85295cb5ed4d02cd3c8dc7f745c0
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
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index: climate_gpt_only_giec
vector_id: 22195
content:
"02 PgC yr -1), 23% (2.5 +- 0.6 PgC yr-1) was taken up by the ocean and 31% \r\n(3.4 +- 0.9 PgC yr-1) was removed by terrestrial ecosystems (high confidence). {5.2.1, 5.2.2, 5.2.3}"


id: 47a42c70046e4714d99b56957d9dc9bc
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
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vector_id: 8660
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"In AR6, ESM projections are assessed with CO2 concentrations by 2100 from about 400 ppm (SSP1-1.9) to above 1100 ppm (SSP5-8.5). \r\nMost simulations are performed with prescribed atmospheric CO2 concentrations, which already account for a central estimate of \r\nclimate-carbon feedback effects. Carbon dioxide emissions-driven simulations account for uncertainty in these feedbacks, but do \r\nnot significantly change the projected global surface temperature changes (high confidence). Although land and ocean sinks absorb \r\nmore CO2 under high emissions than low emissions scenarios, the fraction of emissions removed from the atmosphere decreases \r\n(high confidence). This means that the more CO2 that is emitted, the less efficient the ocean and land sinks become (high confidence), \r\nan effect which compensates for the logarithmic relationship between CO2 and its radiative forcing, which means that for each unit \r\nincrease in additional atmospheric CO2 the effect on global temperature decreases"


id: cdec03bdd6b800652cd382b26620f9b2
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 29923
content:
"Ocean and land sinks show similar responses for a given scenario, but the land sink has a much higher interannual variability \r\nand wider model spread. Under SSP3-7.0 and SSP5-8.5, the initial growth of both sinks in response to increasing atmospheric \r\nconcentrations of CO2 is subsequently limited by emerging carbon-climate feedbacks (high confidence) (Box TS.5, Figure  1f). \r\nProjections show that the ocean and land sinks will stop growing from the second part of the 21st century under all emissions \r\nscenarios, but with different drivers for different emissions scenarios. Under SSP3-7.0 and SSP5-8.5, the weakening growth rate \r\nof the ocean CO2 sink in the second half of the century is primarily linked to the strengthening positive feedback from reduced \r\ncarbonate buffering capacity, ocean warming and altered ocean circulation (e.g., AMOC changes). In contrast, for SSP1-1.9, SSP1-2.6 \r\nand SSP2-4"


id: f376dd843c2d65dc4c69a7c6704d03d0
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
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vector_id: 35814
content:
"5, the weakening growth rate of the ocean carbon sink is a response to the stabilizing or declining atmospheric \r\nCO2 concentrations. Under SSP1-1.9, models project that combined land and ocean sinks will turn into a weak source by 2100 \r\n(medium confidence). Under high CO2 emissions scenarios, it is very likely that the land carbon sink will grow more slowly due to \r\nwarming and drying from the mid-21st century, but it is very unlikely that it will switch from being a sink to a source before 2100. "


id: 1b480f46155ef62375eceeaeed98b4c2
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
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vector_id: 1754
content:
"Box TS.5, Figure 1 (continued): The intent of this figure is to show the response of the carbon cycle to carbon dioxide (CO2) emissions and climate and its \r\nrole in determining future CO2 levels through projected changes to sinks and sink fractions. The figure shows changes in carbon storage in response to elevated \r\nCO2 (a, b) and the response to climate warming (c, d). Maps show spatial patterns of changes in carbon uptake during simulations with 1% per year increase in CO2\r\n(Section 5.4.5.5), and zonal mean plots show distribution of carbon changes is dominated by the land (green lines) in the tropics and Northern Hemisphere and ocean \r\n(blue lines) in the Southern Hemisphere. Hatching indicates regions where fewer than 80% of models agree on the sign of response. (e) Future CO2 projections: \r\nprojected CO2 concentrations in the Shared Socio-economic Pathway (SSP) scenarios in response to anthropogenic emissions, results from coupled Earth system \r\nmodels for SSP5-8"


id: e54ce01417f4814f3977adf1b2f24b36
created_at: 2023-03-16 14:01:55
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"5 and from the MAGICC7 emulator for other scenarios (Section 4.3.1). (f) Future carbon fluxes: projected combined land and ocean fluxes (positive \r\ndownward) up to 2100 for the SSP scenarios, and extended to 2300 for available scenarios, 5-95% uncertainty plumes shown for SSP1-2.6 and SSP3-7.0 (Sections \r\n4.3.2.4, 5.4.5.4 and 5.4.10). The numbers near the top show the number of model simulations used. (g) Sink fraction: the fraction of cumulative emissions of CO2\r\nremoved by land and ocean sinks. The sink fraction is smaller under conditions of higher emissions. {Figure 4.3; 5.4.5; Figures 5.25, 5.27 and 5.30}"


id: c21fb773e883e099e1aece7e795514cf
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
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"Hemisphere extratropics since the mid-20th century (high \r\nconfidence), are consistent with large-scale warming. At the \r\nsame time an increase in the amplitude of the seasonal cycle of \r\natmospheric CO2 poleward of 45degN since the early 1960s (high \r\nconfidence) and a global-scale increase in vegetation greenness \r\nof the terrestrial surface since the early 1980s (high confidence) \r\nhave been observed. Increasing atmospheric CO2, warming at high \r\nlatitudes, and land management interventions have contributed \r\nto the observed greening trend, but there is low confidence in \r\ntheir relative roles. There is medium confidence that increased \r\nplant growth associated with CO2 fertilization is the main driver \r\nof the observed increase in amplitude of the seasonal cycle of \r\natmospheric CO2 in the Northern Hemisphere"


id: 141f6a53be5c67762bacaf10f60a837a
created_at: 2023-03-16 14:01:55
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content_type: text
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content:
" Reactive nitrogen, \r\nozone and aerosols affect terrestrial vegetation and carbon cycle \r\nthrough deposition and effects on large-scale radiation (high \r\nconfidence), but the magnitude of these effects on the land carbon \r\nsink, ecosystem productivity and indirect CO2 forcing remains \r\nuncertain. {2.3.4, 3.6.1, 5.2.1, 6.4.5, 12.3.7, 12.4}"


id: b4dca5e2d37166f4a9ba15677c38ea59
created_at: 2023-03-16 14:01:55
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content:
"Over the last century, there has been a poleward and upslope shift \r\nin the distribution of many land species (very high confidence) \r\nas well as increases in species turnover within many ecosystems \r\n(high confidence). There is high confidence that the geographical \r\ndistribution of climate zones has shifted in many parts of the \r\nworld in the last half century. The SRCCL concluded that continued \r\nwarming will exacerbate desertification processes (medium \r\nconfidence) and that ecosystems will become increasingly exposed \r\nto climates beyond those that they are currently adapted to (high \r\nconfidence). There is medium confidence that climate change will \r\nincrease disturbance by, for example, fire and tree mortality, across \r\nseveral ecosystems. Increases are projected in drought, aridity and \r\nfire weather in some regions (Section TS.4.3; high confidence). \r\nThere is low confidence in the magnitude of these changes, but \r\nthe probability of crossing uncertain regional thresholds (e.g"


id: 916cbe111497528ab77065a04f45792b
created_at: 2023-03-16 14:01:55
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", fires, \r\nforest dieback) increases with further warming (high confidence). \r\nThe response of biogeochemical cycles to the anthropogenic \r\nperturbation can be abrupt at regional scales, and irreversible on \r\ndecadal to century time scales (high confidence). {2.3.4, 5.4.3, \r\n5.4.9, 11.6, 11.8, 12.5, SRCCL 2.2, SRCCL 2.5, SR1.5 3.4}"


id: bcce32240fb0f757a224629b176c5362
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
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vector_id: 27275
content:
"There is high confidence that the global water cycle has intensified since at least 1980 expressed by, for example, increased atmospheric \r\nmoisture fluxes and amplified precipitation minus evaporation patterns. Global land precipitation has likely increased since 1950, \r\nwith a faster increase since the 1980s (medium confidence), and a likely human contribution to patterns of change, particularly for \r\nincreases in high-latitude precipitation over the Northern Hemisphere. Increases in global mean precipitation are determined by a \r\nrobust response to global surface temperature (very likely 2-3% per degC) that is partly offset by fast atmospheric adjustments to \r\natmospheric heating by greenhouse gases (GHGs) and aerosols (Section TS.3.2.2). The overall effect of anthropogenic aerosols is \r\nto reduce global precipitation through surface radiative cooling effects (high confidence)"


id: 46b85de4d134f14de8d96ab767e8cd29
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
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content:
" Over much of the 20th century, opposing \r\neffects of GHGs and aerosols on precipitation have been observed for some regional monsoons (high confidence) (Box TS.13). Global \r\nannual precipitation over land is projected to increase on average by 2.4% (-0.2% to +4.7% likely range) under SSP1-1.9, 4.6% (1.5% \r\nto 8.3% likely range) under SSP2-4.5, and 8.3% (0.9% to 12.9% likely range) under SSP5-8.5 by 2081-2100 relative to 1995-2014 \r\n(Box TS.6, Figure 1). Inter-model differences and internal variability contribute to a substantial range in projections of large-scale and \r\nregional water cycle changes (high confidence). The occurrence of volcanic eruptions can alter the water cycle for several years (high \r\nconfidence). Projected patterns of precipitation change exhibit substantial regional differences and seasonal contrast as global surface \r\ntemperature increases over the 21st century (Box TS.6, Figure 1). {2.3.1, 3.3.2, 3.3.3, 3.5.2, 4.3.1, 4.4.1, 4.5.1, 4.6.1, Cross-Chapter \r\nBox 4"


id: d56dafc82af222d09c6134fdb72cf891
created_at: 2023-03-16 14:01:55
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vector_id: 31102
content:
"Global total column water vapour content has very likely increased since the 1980s, and it is likely that human influence has contributed \r\nto tropical upper tropospheric moistening. Near-surface specific humidity has increased over the ocean (likely) and land (very likely) \r\nsince at least the 1970s, with a detectable human influence (medium confidence). Human influence has been detected in amplified \r\nsurface salinity and precipitation minus evaporation (P-E) patterns over the ocean (high confidence). It is virtually certain that \r\nevaporation will increase over the ocean and very likely that evapotranspiration will increase over land, with regional variations under \r\nfuture surface warming (Box TS.6, Figure 1). There is high confidence that projected increases in precipitation amount and intensity will \r\nbe associated with increased runoff in northern high latitudes (Box TS.6, Figure 1). In response to cryosphere changes (Section TS.2"


id: ef3720e0474755f61f1f3538ffed2da1
created_at: 2023-03-16 14:01:55
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content:
"5), \r\nthere have been changes in streamflow seasonality, including an earlier occurrence of peak streamflow in high-latitude and mountain \r\ncatchments (high confidence). Projected runoff (Box TS.6, Figure 1c) is typically decreased by contributions from small glaciers because \r\nof glacier mass loss, while runoff from larger glaciers will generally increase with increasing global warming levels until their mass \r\nbecomes depleted (high confidence). {2.3.1, 3.3.2, 3.3.3, 3.5.2, 8.2.3, 8.4.1, 11.5}"


id: a78f3d7c1e7d828a48a812b1f2af70c6
created_at: 2023-03-16 14:01:55
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vector_id: 23867
content:
"Warming over land drives an increase in atmospheric evaporative demand and in the severity of drought events (high confidence). Greater \r\nwarming over land than over the ocean alters atmospheric circulation patterns and reduces continental near-surface relative humidity, \r\nwhich contributes to regional drying (high confidence). A very likely decrease in relative humidity has occurred over much of the global \r\nland area since 2000. Projected increases in evapotranspiration due to growing atmospheric water demand will decrease soil moisture \r\nover the Mediterranean region, south-western North America, South Africa, South-Western South America and south-western Australia \r\n(high confidence) (Box TS.6, Figure 1). Some tropical regions are also projected to experience enhanced aridity, including the Amazon \r\nbasin and Central America (high confidence)"


id: 1b6b3301b7a3bb0ba9bfecb8661935be
created_at: 2023-03-16 14:01:55
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" The total land area subject to increasing drought frequency and severity will expand (high \r\nconfidence), and in the Mediterranean, South-Western South America, and Western North America, future aridification will far exceed the \r\nmagnitude of change seen in the last millennium (high confidence). {4.5.1, 8.2.2, 8.2.3, 8.4.1, Box 8.2, 11.6, 11.9}"


id: ff1194d045e94e16c40e37834e2d854
created_at: 2023-03-16 14:01:55
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content:
"Water cycle variability and related extremes are projected to increase faster than mean changes in most regions of the world \r\nand under all emissions scenarios (high confidence). A warmer climate increases moisture transport into weather systems, which \r\nintensifies wet seasons and events (high confidence). The magnitudes of projected precipitation increases and related extreme \r\nevents depend on model resolution and the representation of convective processes (high confidence). Increases in near-surface \r\natmospheric moisture capacity of about 7% per 1oC of warming lead to a similar response in the intensification of heavy precipitation \r\nfrom sub-daily up to seasonal time scales, increasing the severity of flood hazards (high confidence). The average and maximum \r\nrain-rates associated with tropical and extratropical cyclones, atmospheric rivers and severe convective storms will therefore also \r\nincrease with future warming (high confidence)"


id: d715375b8c980221bdee37ba9b17826
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
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vector_id: 31349
content:
" For some regions, there is medium confidence that peak tropical cyclone rain\u0002rates will increase by more than 7% per 1degC of warming due to increased low-level moisture convergence caused by increases \r\nin wind intensity. In the tropics year-round and in the summer season elsewhere, interannual variability of precipitation and \r\nrunoff over land is projected to increase at a faster rate than changes in seasonal mean precipitation (Figure TS.12e,f) (medium \r\nconfidence). Sub-seasonal precipitation variability is also projected to increase, with fewer rainy days but increased daily mean \r\nprecipitation intensity over many land regions (high confidence). {4.5.3, 8.2.3, 8.4.1, 8.4.2, 8.5.1, 8.5.2, 11.4, 11.5, 11.7, 11.9}"


id: 5512e554865996585ce12f3d085bc8a6
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
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"Box TS.6, Figure 1 | Projected water cycle changes. The intent of this figure is to give a geographical overview of changes in multiple components of the global \r\nwater cycle using an intermediate emissions scenario. Important key message: without drastic reductions in greenhouse gas emissions, human-induced global warming \r\nwill be associated with widespread changes in all components of the water cycle. Long-term (2081-2100) projected annual mean changes (%) relative to present-day \r\n(1995-2014) in the SSP2-4.5 emissions scenario for (a) precipitation, (b) surface evapotranspiration, (c) total runoff and (d) surface soil moisture. Numbers in top \r\nright of each panel indicate indicate the number of Coupled Model Intercomparison Project Phase 6 (CMIP6) models used for estimating the ensemble mean. For other \r\nscenarios, please refer to relevant figures in Chapter 8"


id: c0fe59a2064ccb7cc397b4b9be880cb2
created_at: 2023-03-16 14:01:55
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" Uncertainty is represented using the simple approach: No overlay indicates regions with high model agreement, \r\nwhere >=80% of models agree on sign of change; diagonal lines indicate regions with low model agreement, where <80% of models agree on sign of change. For \r\nmore information on the simple approach, please refer to the Cross-Chapter Box Atlas.1. {8.4.1; Figures 8.14, 8.17, 8.18, and 8.19}"


id: 4de9236d3c1c04671b095708d85e0bbb
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 9679
content:
"The best estimate ERF of 2.72 W m-2 has increased by 0.43 \r\nW m-2 relative to that given in AR5 (for 1750-2014) due \r\nto an increase in the greenhouse gas ERF that is partly \r\ncompensated by a more negative aerosol ERF compared \r\nto AR5. The greenhouse gas ERF has been revised due to \r\nchanges in atmospheric concentrations and updates to \r\nforcing efficiencies, while the revision to aerosol ERF is due \r\nto increased understanding of aerosol-cloud interactions \r\nand is supported by improved agreement between \r\ndifferent lines of evidence. Improved quantifications \r\nof ERF, the climate system radiative response, and the \r\nobserved energy increase in the Earth system for the \r\nperiod 1971-2018 demonstrate improved closure of the \r\nglobal energy budget (i.e., the extent to which the sum \r\nof the integrated forcing and the integrated radiative \r\nresponse equals the energy gain of the Earth system) \r\ncompared to AR5 (high confidence). (See FAQ 7.1). {7.2.2, \r\n7.3.5, 7.5.2, Box 7.2, Table 7"


id: 6eec36f5e828deec8b2c5fec4e94cef1
created_at: 2023-03-16 14:01:55
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vector_id: 15075
content:
"Aerosols contributed an ERF of -1.3 [-2.0 to -0.6] W m-2 over the \r\nperiod 1750 to 2014 (medium confidence). The ERF due to aerosol-\r\ncloud interactions (ERFaci) contributes most to the magnitude of \r\nthe total aerosol ERF (high confidence) and is assessed to be -1.0 \r\n[-1.7 to -0.3] W m-2 (medium confidence), with the remainder due \r\nto aerosol-radiation interactions (ERFari), assessed to be -0.3 [-0.6 \r\nto 0.0] W m-2 (medium confidence). There has been an increase in \r\nthe estimated magnitude - but a reduction in the uncertainty - of \r\nthe total aerosol ERF relative to AR5, supported by a combination \r\nof increased process-understanding and progress in modelling and \r\nobservational analyses (Figure TS.15c). Effective radiative forcing \r\nestimates from these separate lines of evidence are now consistent \r\nwith each other, in contrast to AR5, and support the assessment that \r\nit is virtually certain that the total aerosol ERF is negative"


id: 34cfc4894bb78bfd2db98f0a5d1c7979
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
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vector_id: 5715
content:
" Compared \r\nto AR5, the assessed magnitude of ERFaci has increased, while that \r\nof ERFari has decreased. {7.3.3, 7.3.5}"


id: 45d5e45aee78d9d9cda06c560c3d3b7a
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
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vector_id: 8376
content:
"On average, CMIP6 models have higher mean ECS and TCR values \r\nthan the CMIP5 generation of models and also have higher mean \r\nvalues and wider spreads than the assessed best estimates and very \r\nlikely ranges within this Report. These higher mean ECS and TCR \r\nvalues can be traced to a positive net cloud feedback that is larger in \r\nCMIP6 by about 20%. The broader ECS and TCR ranges from CMIP6 \r\nalso lead the models to project a range of future warming that \r\nis wider than the assessed future warming range, which is based \r\non multiple lines of evidence (Cross-Section Box TS.1). However, \r\nsome of the high-sensitivity CMIP6 models (Section TS.1.2.2) are \r\nless consistent with observed recent changes in global warming \r\nand with paleoclimate proxy records than models with ECS within \r\nthe very likely range. Similarly, some of the low-sensitivity models \r\nare less consistent with the paleoclimate data"


id: d86f2c2fa50c679249d2004c582b8f6a
created_at: 2023-03-16 14:01:55
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" The CMIP6 models \r\nwith the highest ECS and TCRs values provide insights into low\u0002likelihood, high-impact futures, which cannot be excluded based on \r\ncurrently available evidence (Cross-Section Box TS.1). {4.3.1, 4.3.4, \r\n7.4.2, 7.5.6}"


id: 246ea8911938c02bf84e822069615ae4
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
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vector_id: 3179
content:
"Earth system feedbacks can be categorized into three broad groups: \r\nphysical feedbacks, biogeophysical and biogeochemical feedbacks, \r\nand feedbacks associated with ice sheets. In previous assessments, \r\nthe ECS has been associated with a distinct set of physical feedbacks \r\n(Planck response, water vapour, lapse rate, surface albedo, and cloud \r\nfeedbacks). In this assessment, a more general definition of ECS is \r\nadopted whereby all biogeophysical and biogeochemical feedbacks \r\nthat do not affect the atmospheric concentration of CO2 are included. \r\nThese include changes in natural CH4 emissions, natural aerosol \r\nemissions, N2O, ozone, and vegetation, which all act on time scales of \r\nyears to decades and are therefore relevant for temperature change \r\nover the 21st century"


id: c5a70669e782194ad112530a1a284668
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
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" Because the total biogeophysical and non-CO2\r\nbiogeochemical feedback is assessed to have a central value that is \r\nnear zero (low confidence), including it does not affect the assessed \r\nECS but does contribute to the net feedback uncertainty. The \r\nbiogeochemical feedbacks that affect the atmospheric concentration \r\nof CO2 are not included because ECS is defined as the response to \r\na sustained doubling of CO2. Moreover, the long-term feedbacks \r\nassociated with ice sheets are not included in the ECS owing to their \r\nlong time scales of adjustment. {5.4, 6.4, 7.4, 7.5, Box 7.1}"


id: b8ecdca51553ea95697bc219adcde3a3
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
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vector_id: 26627
content:
"The combined effect of all known radiative feedbacks (physical, \r\nbiogeophysical, and non-CO2 biogeochemical) is to amplify the \r\nbase climate response (in the absence of feedbacks), also known \r\nas the Planck temperature response20 (virtually certain). Combining \r\nthese feedbacks with the Planck response, the net climate feedback \r\nparameter is assessed to be -1.16 [-1.81 to -0.51] W  m-2 degC-1, \r\nwhich is slightly less negative than that inferred from the overall ECS \r\nassessment. The combined water vapour and lapse rate feedback \r\nmakes the largest single contribution to global warming, whereas \r\nthe cloud feedback remains the largest contribution to overall \r\nuncertainty. Due to the state-dependence of feedbacks, as evidenced \r\nfrom paleoclimate observations and from models, the net feedback \r\nparameter will increase (become less negative) as global temperature \r\nincreases"


id: 2bca4b8958a0726818db2433cd1e076c
created_at: 2023-03-16 14:01:55
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vector_id: 4316
content:
" Furthermore, on long time scales the ice-sheet feedback \r\nparameter is very likely positive, promoting additional warming on \r\nmillennial time scales as ice sheets come into equilibrium with the \r\nforcing. (high confidence) {7.4.2, 7.4.3, Figure 7.14, Table 7.10}"


id: 81cdee4066d1011fcfc98df9d436b12f
created_at: 2023-03-16 14:01:55
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vector_id: 17947
content:
"The net effect of changes in clouds in response to global warming is \r\nto amplify human-induced warming, that is, the net cloud feedback \r\nis positive (high confidence). Compared to AR5, major advances \r\nin the understanding of cloud processes have increased the level \r\nof confidence and decreased the uncertainty range in the cloud \r\nfeedback by about 50% (Figure TS.17a). An assessment of the low\u0002altitude cloud feedback over the subtropical ocean, which was \r\npreviously the major source of uncertainty in the net cloud feedback, \r\nis improved owing to a combined use of climate model simulations, \r\nsatellite observations, and explicit simulations of clouds, altogether \r\nleading to strong evidence that this type of cloud amplifies global \r\nwarming. The net cloud feedback is assessed to be +0.42 [-0.10 \r\nto 0.94] W m-2 degC-1. A net negative cloud feedback is very unlikely"


id: 2010f603625ff39c99fbb160aa63054a
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 2489
content:
" \r\nThe CMIP5 and CMIP6 ranges of cloud feedback are similar to this \r\nassessed range, with CMIP6 having a slightly more positive median \r\ncloud feedback (high confidence). The surface albedo feedback and \r\ncombined water vapour-lapse rate feedback are positive (Figure \r\nTS.17a), with high confidence in the estimated value of each based \r\non multiple lines of evidence, including observations, models and \r\ntheory (Box TS.6). {7.4.2, Figure 7.14, Table 7.10}"


id: 5e565fe85b0acac47639f0bdaad6095f
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 12376
content:
"Figure TS.17 | An overview of physical and biogeochemical feedbacks in the climate system. The intent of this figure is to summarize assessed estimates of physical, \r\nbiogeophysical and biogeochemical feedbacks on global temperature based on Chapters 5, 6 and 7. (a) Synthesis of physical, biogeophysical and non-carbon dioxide (CO2) \r\nbiogeochemical feedbacks that are included in the definition of equilibrium climate sensitivity (ECS) assessed in this Technical Summary. These feedbacks have been assessed \r\nusing multiple lines of evidence including observations, models and theory. The net feedback is the sum of the Planck response, water vapour and lapse rate, surface albedo, \r\ncloud, and biogeophysical and non-CO2 biogeochemical feedbacks. Bars denote the mean feedback values, and uncertainties represent very likely ranges; (b) Estimated values of \r\nindividual biogeophysical and non-CO2 biogeochemical feedbacks"


id: f41cc3de961fef80f45cc686937221c7
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 35914
content:
" The atmospheric methane (CH4) lifetime and other non-CO2 biogeochemical feedbacks have been calculated \r\nusing global Earth system model simulations from AerChemMIP, while the CH4 and nitrous oxide (N2O) source responses to climate have been assessed for the year 2100 using \r\na range of modelling approaches using simplified radiative forcing equations. The estimates represent the mean and 5-95% range. The level of confidence in these estimates \r\nis low owing to the large model spread. (c) Carbon-cycle feedbacks as simulated by models participating in the C4MIP of the Coupled Model Intercomparison Project Phase 6 \r\n(CMIP6). An independent estimate of the additional positive carbon-cycle climate feedbacks from permafrost thaw, which is not considered in most C4MIP models, is added. \r\nThe estimates represent the mean and 5-95% range"


id: 9692104a00528af0711fa24b42163287
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 21205
content:
" Note that these feedbacks act through modifying the atmospheric concentration of CO2 and thus are not included in the \r\ndefinition of ECS, which assumes a doubling of CO2, but are included in the definition and assessed range of the transient climate response to cumulative CO2 emissions (TCRE). \r\n{5.4.7, 5.4.8, Box 5.1, Figure 5.29, 6.4.5, Table 6.9, 7.4.2, Table 7.10}"


id: 2cff085e015512182aad5d722290aefb
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 4513
content:
"Thawing terrestrial permafrost will lead to carbon release (high \r\nconfidence), but there is low confidence in the timing, magnitude \r\nand the relative roles of CO2 versus CH4 as feedback processes. An \r\nensemble of models projects CO2 release from permafrost to be \r\n3-41 PgC per 1oC of global warming by 2100, leading to warming \r\nstrong enough that it must be included in estimates of the remaining \r\ncarbon budget but weaker than the warming from fossil fuel burning. \r\nHowever, the incomplete representation of important processes, \r\nsuch as abrupt thaw, combined with weak observational constraints, \r\nonly allow low confidence in both the magnitude of these estimates \r\nand in how linearly proportional this feedback is to the amount of \r\nglobal warming. There is emerging evidence that permafrost thaw \r\nand thermokarst give rise to increased CH4 and N2O emissions, which \r\nleads to the combined radiative forcing from permafrost thaw being \r\nlarger than from CO2 emissions only"


id: 576f147d7f8d082be91ca1b39274651b
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 11250
content:
" However, the quantitative \r\nunderstanding of these additional feedbacks is low, particularly for \r\nN2O. These feedbacks, as well as potential additional carbon losses \r\ndue to climate-induced fire feedback are not routinely included in \r\nEarth system models. {Box 5.1, 5.4.3, 5.4.7, 5.4.8}"


id: 3831abd870aff8ee45558ca8d62f5978
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 6213
content:
"Limiting further climate change would require substantial and \r\nsustained reductions of GHG emissions. Without net zero CO2\r\nemissions, and a decrease in the net non-CO2 forcing (or sufficient \r\nnet negative CO2 emissions to offset any further warming from \r\nnet non-CO2 forcing), the climate system will continue to warm. \r\nThere is high confidence that mitigation requirements for limiting \r\nwarming to specific levels over this century can be estimated using \r\na carbon budget that relates cumulative CO2 emissions to global \r\nmean temperature increase (Figure TS.18, Table TS.3). For the period \r\n1850-2019, a total of 2390 +- 240 GtCO2 of anthropogenic CO2 has \r\nbeen emitted. Remaining carbon budgets (starting from 1  January \r\n2020) for limiting warming to 1.5degC, 1.7degC and 2.0degC are estimated \r\nat 500 GtCO2, 850 GtCO2 and 1350 GtCO2, respectively, based on \r\nthe 50th percentile of TCRE. For the 67th percentile, the respective \r\nvalues are 400 GtCO2, 700 GtCO2 and 1150 GtCO2"


id: 64cb136c515761bda422d2bba12c2cb
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 13427
content:
" The remaining \r\ncarbon budget estimates for different temperature limits assume \r\nthat non-CO2 emissions are mitigated consistent with the median \r\nreductions found in scenarios in the literature as assessed in SR1.5, "


id: f6bca1f8ad17d1f6ca1ae7b531fc7633
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 36331
content:
"Methodological improvements and new evidence result in updated \r\nremaining carbon budget estimates. The assessment in AR6 applies \r\nthe same methodological improvements as in SR1.5, which uses \r\na recent observed baseline for historic temperature change and \r\ncumulative emissions. Changes compared to SR1.5 are therefore \r\nsmall: the assessment of new evidence results in updated median \r\nremaining carbon budget estimates for limiting warming to 1.5degC \r\nand 2degC being the same and about 60 GtCO2 smaller, respectively, \r\nafter accounting for emissions since SR1.5. Meanwhile, remaining \r\ncarbon budgets for limiting warming to 1.5degC would be about \r\n300-350 GtCO2 larger if evidence and methods available at the \r\ntime of AR5 would be used"


id: f10946caf9682a7c9a991da0205b0b2a
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 35420
content:
" If a specific remaining carbon budget \r\nis exceeded, this results in a lower probability of keeping warming \r\nbelow a specified temperature level and higher irreversible global \r\nwarming over decades to centuries, or alternatively a need for net \r\nnegative CO2 emissions or further reductions in non-CO2 greenhouse \r\ngases after net zero CO2 is achieved to return warming to lower levels \r\nin the long term. {5.5.2, 5.6, Box 5.2}"


id: 2080a3a66de75cee7580e40d23fe85bc
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 2563
content:
"Carbon dioxide removal (CDR) refers to anthropogenic activities \r\nthat deliberately remove CO2 from the atmosphere and durably \r\nstore it in geological, terrestrial or ocean reservoirs, or in products. \r\nCarbon dioxide is removed from the atmosphere by enhancing \r\nbiological or geochemical carbon sinks or by direct capture of CO2\r\nfrom air. Emissions pathways that limit global warming to 1.5degC or \r\n2degC typically assume the use of CDR approaches in combination \r\nwith GHG emissions reductions. CDR approaches could be used to \r\ncompensate for residual emissions from sectors that are difficult or \r\ncostly to decarbonize. CDR could also be implemented at a large scale \r\nto generate global net negative CO2 emissions (i.e., anthropogenic \r\nCO2 removals exceeding anthropogenic emissions), which could \r\ncompensate for earlier emissions as a way to meet long-term climate \r\nstabilization goals after a temperature overshoot"


id: ee5eda6d1524dc3fc78a937f6506a32d
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 34972
content:
" This Report assesses \r\nthe effects of CDR on the carbon cycle and climate. Co-benefits and \r\ntrade-offs for biodiversity, water and food production are briefly \r\ndiscussed for completeness, but a comprehensive assessment of the \r\necological and socio-economic dimensions of CDR options is left to \r\nthe WGII and WGIII reports. {4.6.3, 5.6}"


id: 79d0a89e8087bcddddcb4fb78c5a73ea
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 16737
content:
"CDR methods have the potential to sequester CO2 from the \r\natmosphere (high confidence). In the same way part of current \r\nanthropogenic net CO2 emissions are taken up by land and ocean \r\ncarbon stores, net CO2 removal will be partially counteracted by CO2\r\nrelease from these stores, such that the amount of CO2 sequestered \r\nby CDR will not result in an equivalent drop in atmospheric CO2 (very\r\nhigh confidence). The fraction of CO2 removed from the atmosphere \r\nthat is not replaced by CO2 released from carbon stores - a measure \r\nof CDR effectiveness - decreases slightly with increasing amounts of \r\nremoval (medium confidence) and decreases strongly if CDR is applied \r\nat lower atmospheric CO2 concentrations (medium confidence). The \r\nreduction in global surface temperature is approximately linearly \r\nrelated to cumulative CO2 removal (high confidence)"


id: d9a5888ce8fbf9b5b77d404c3f459fd8
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 31755
content:
" Because of this \r\nnear-linear relationship, the amount of cooling per unit CO2 removed \r\nis approximately independent of the rate and amount of removal \r\n(medium confidence). {4.6.3, 5.6.2.1, Figure 5.32, Figure 5.34}"


id: 493761d9cd5ca8b2c773be4e81e98841
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 8908
content:
"Carbon dioxide removal methods have a range of side effects that \r\ncan either weaken or strengthen the carbon sequestration and \r\ncooling potential of these methods and affect the achievement \r\nof sustainable development goals (high confidence). Biophysical \r\nand biogeochemical side-effects of CDR methods are associated \r\nwith changes in surface albedo, the water cycle, emissions of CH4\r\nand N2O, ocean acidification and marine ecosystem productivity \r\n(high confidence). These side-effects and associated Earth system \r\nfeedbacks can decrease carbon uptake and/or change local and \r\nregional climate and in turn limit the CO2 sequestration and cooling \r\npotential of specific CDR methods (medium confidence). Deployment \r\nof CDR, particularly on land, can also affect water quality and quantity, \r\nfood production and biodiversity (high confidence). These effects are \r\noften highly dependent on local context, management regime, prior \r\nland use, and scale (high confidence)"


id: 503fbb8b11625bf8cfc557071b060964
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 10066
content:
" The largest co-benefits are \r\nobtained with methods that seek to restore natural ecosystems or \r\nimprove soil carbon sequestration (medium confidence). The climate \r\nand biogeochemical effects of terminating CDR are expected to be \r\nsmall for most CDR methods (medium confidence). {4.6.3, 5.6.2.2, \r\nFigure 5.36, 8.4.3, 8.6.3}"


id: 2b067c31f9e9351998abb3a176bd22c
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 4201
content:
"Simulations with ESMs indicate that under scenarios where CO2\r\nemissions gradually decline, reach net zero and become net negative \r\nduring the 21st century (e.g., SSP1-2.6), land and ocean carbon sinks \r\nbegin to weaken in response to declining atmospheric CO2\r\nconcentrations, and the land sink eventually turns into a source \r\n(Figure TS.19). This sink-to-source transition occurs decades to a few \r\ncenturies after CO2 emissions become net negative. The ocean \r\nremains a sink of CO2 for centuries after emissions become net \r\nnegative. Under scenarios with large net negative CO2 emissions \r\n(e.g., SSP5-3.4-OS) and rapidly declining CO2 concentrations, the land \r\nsource is larger than for SSP1-2.6 and the ocean also switches to a \r\nsource. While the general response is robust across models, there is \r\nlow confidence in the timing of the sink-to-source transition and the \r\nmagnitude of the CO2 source in scenarios with net negative CO2\r\nemissions"


id: 2b0306d6a22627cb5cb1998270d59ca9
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 4198
content:
" Carbon dioxide removal could reverse some aspects \r\nclimate change if CO2 emissions become net negative, but some \r\nchanges would continue in their current direction for decades to \r\nmillennia. For instance, sea level rise due to ocean thermal expansion \r\nwould not reverse for several centuries to millennia (high confidence) \r\n(Box TS.4). {4.6.3, 5.4.10, 5.6.2.1, Figure 5.30, Figure 5.33}"


id: fbd60f894e06790511581d76dc4337cf
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 37119
content:
"Emissions metrics are needed to aggregate baskets of gases to \r\ndetermine net zero GHG emissions. Generally, achieving net zero CO2\r\nemissions and declining non-CO2 radiative forcing would halt human\u0002induced warming. Reaching net zero GHG emissions quantified by \r\nGWP-100 typically leads to declining temperatures after net zero \r\nGHGs emissions are achieved if the basket includes short-lived gases, \r\nsuch as CH4. Net zero GHG emissions defined by CGTP or GWP* \r\nimply net zero CO2 and other long-lived GHG emissions and constant \r\n(CGTP) or gradually declining (GWP*) emissions of short-lived gases. \r\nThe warming evolution resulting from net zero GHG emissions \r\ndefined in this way corresponds approximately to reaching net zero \r\nCO2 emissions, and would thus not lead to declining temperatures \r\nafter net zero GHG emissions are achieved but to an approximate \r\ntemperature stabilization (high confidence)"


id: ff83aaecec5f5554d0a48a7440744988
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 37745
content:
" The choice of emissions \r\nmetric hence affects the quantification of net zero GHG emissions, \r\nand therefore the resulting temperature outcome of reaching and \r\nsustaining net zero GHG emissions levels (high confidence). {7.6.1.4, \r\n7.6.2, 7.6.3}"


id: 8228145bb64770248607bee7cee6a189
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 18004
content:
"The relative climate effects of different forcing agents are typically \r\nquantified using emissions metrics that compare the effects of \r\nan idealised pulse of 1 kg of some climate forcing agent against \r\na reference climate forcing agent, almost always CO2. The two most \r\nprominent pulse emissions metrics are the global warming potential \r\n(GWP) and global temperature change potential (GTP) (see Glossary). \r\nThe climate responses to CO2 emissions by convention include the \r\neffects of warming on the carbon cycle, so for consistency these also \r\nneed to be determined for non-CO2 emissions. The methodology \r\nfor doing this has been placed on a more robust scientific footing \r\ncompared to AR5 (high confidence). Methane from fossil fuel sources \r\nhas slightly higher emissions metric values than those from biogenic \r\nsources since it leads to additional fossil CO2 in the atmosphere \r\n(high confidence). Updates to the chemical adjustments for CH4 and \r\nN2O emissions (Section TS.3"


id: dea4989f205ed20e4d08089ef2340e45
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 32526
content:
"1) and revisions in their lifetimes result \r\nin emissions metrics for GWP and GTP that are slightly lower than \r\nin AR5 (medium confidence). Emissions metrics for the entire suite \r\nof GHGs assessed in the AR6 have been calculated for various time \r\nhorizons. {7.6.1, Table 7.15, Table 7.SM.7}"


id: f1c2383e3d2ffe5135d65bcd81d557ce
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 35549
content:
"The global surface temperature response following a climate change \r\nmitigation measure that affects emissions of both short- and long\u0002lived climate forcers depends on their lifetimes, their ERFs, how \r\nfast and for how long the emissions are reduced, and the thermal \r\ninertia in the climate system. Mitigation, relying on emissions \r\nreductions and implemented through new legislation or technology \r\nstandards, implies that emissions reductions occur year after year. \r\nGlobal temperature response to a year's worth of current emissions \r\nfrom different sectors informs about the mitigation potential (Figure \r\nTS.20). Over 10- to 20-year time scales, the influence of SLCFs is \r\nat least as large as that of CO2, with sectors producing the largest \r\nwarming being fossil fuel production and distribution, agriculture, \r\nand waste management"


id: 4d46e20bf88d3142630d732ea1a22d28
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 9587
content:
" Because the effects of the SLCFs decay \r\nrapidly over the first few decades after emission, the net long-term \r\ntemperature effect from a single year's worth of current emissions is \r\npredominantly determined by CO2. Fossil fuel combustion for energy, \r\nindustry and land transportation are the largest contributing sectors \r\non a 100-year time scale (high confidence). Current emissions of CO2, \r\nN2O and SLCFs from East Asia and North America are the largest \r\nregional contributors to additional net future warming on both \r\nshort (medium confidence) and long time scales (10 and 100 years, \r\nrespectively) (high confidence). {6.6.1, 6.6.2, Figure 6.16}"


id: b14fe64a17a11574824e086840fcaa4a
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 25401
content:
"In the next two decades, it is very likely that SLCF emissions changes will cause a warming relative to 2019, across the WGI core set of \r\nSSPs (see Section TS.1.3.1), in addition to the warming from long-lived GHGs. The net effect of SLCF and HFC changes in global surface \r\ntemperature across the SSPs is a likely warming of 0.06degC-0.35degC in 2040 relative to 2019. This near-term global mean warming \r\nlinked to SLCFs is quite similar in magnitude across the SSPs due to competing effects of warming (CH4, ozone) and cooling (aerosols) \r\nforcers (Box TS.7, Figure 1). There is greater diversity in the end-of-century response among the scenarios. SLCF changes in scenarios \r\nwith no climate change mitigation (SSP3-7.0 and SSP5-8.5) will cause a warming in the likely range of 0.4degC-0.9degC in 2100 relative to \r\n2019 due to increases in CH4, tropospheric ozone and HFC levels. For the stringent climate change and pollution mitigation scenarios \r\n(SSP1-1.9 and SSP1-2"


id: 284e0398f4f48cdecd617065790b6057
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 3752
content:
"6), the cooling from reductions in CH4, ozone and HFCs partially balances the warming from reduced aerosols, \r\nprimarily sulphate, and the overall SLCF effect is a likely increase in global surface temperature of 0.0degC-0.3degC in 2100, relative to \r\n2019. With intermediate climate change and air pollution mitigations, SLCFs in SSP2-4.5 add a likely warming of 0.2degC-0.5degC to global \r\nsurface temperature change in 2100, with the largest warming resulting from reductions in aerosols. {4.4.4, 6.7.3}"


id: 7e02b06050ed197c29bd32cd99e9fff3
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 17398
content:
"Since AR5, further modelling work has been conducted on aerosol-based solar radiation modification (SRM) options \r\nsuch as stratospheric aerosol injection, marine cloud brightening, and cirrus cloud thinning21 and their climate and \r\nbiogeochemical effects. These investigations have consistently shown that SRM could offset some of the effects of \r\nincreasing greenhouse gases on global and regional climate, including the carbon and water cycles (high confidence). \r\nHowever, there would be substantial residual or overcompensating climate change at the regional scales and seasonal \r\ntime scales (high confidence), and large uncertainties associated with aerosol-cloud-radiation interactions persist. \r\nThe cooling caused by SRM would increase the global land and ocean CO2 sinks (medium confidence), but this \r\nwould not stop CO2 from increasing in the atmosphere or affect the resulting ocean acidification under continued \r\nanthropogenic emissions (high confidence)"


id: 245be906957ae19554c49ad322299303
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 3166
content:
" It is likely that abrupt water cycle changes will occur if SRM techniques \r\nare implemented rapidly. A sudden and sustained termination of SRM in a high CO2 emissions scenario would cause \r\nrapid climate change (high confidence). However, a gradual phase-out of SRM combined with emissions reduction and \r\ncarbon dioxide removal (CDR) would avoid these termination effects (medium confidence). {4.6.3, 5.6.3. 6.4.6, 8.6.3}."


id: 8991a091c28f8a65389f14d4557ae135
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 19179
content:
"Some climate system components are slow to respond, such as the deep ocean overturning circulation and the ice sheets. It is likely that \r\nunder stabilization of global warming at 1.5degC, 2.0degC or 3.0degC relative to 1850-1900, the AMOC will continue to weaken for several \r\ndecades by about 15%, 20% and 30% of its strength and then recover to pre-decline values over several centuries (medium confidence). \r\nAt sustained warming levels between 2degC and 3degC, there is limited evidence that the Greenland and West Antarctic ice sheets will be lost \r\nalmost completely and irreversibly over multiple millennia; both the probability of their complete loss and the rate of mass loss increases \r\nwith higher surface temperatures (high confidence)"


id: 751181e4507e46827646095b344a1a6a
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 16011
content:
" At sustained warming levels between 3degC and 5degC, near-complete loss of the \r\nGreenland Ice Sheet and complete loss of the West Antarctic Ice Sheet is projected to occur irreversibly over multiple millennia (medium \r\nconfidence); with substantial parts or all of Wilkes Subglacial Basin in East Antarctica lost over multiple millennia (low confidence). Early\u0002warning signals of accelerated sea level rise from Antarctica could possibly be observed within the next few decades. For other hazards \r\n(e.g., ice-sheet behaviour, glacier mass loss and global mean sea level change, coastal floods, coastal erosion, air pollution, and ocean \r\nacidification) the time and/or scenario dimensions remain critical, and a simple and robust relationship with global warming level cannot \r\nbe established (high confidence). {4.3.2, 4.7.2, 5.4.3, 5.4.5, 5.4.8, 8.6, 9.2, 9.4, Box 9.3, Cross-Chapter Box 12.1}"


id: fe4d6e2a1b641a6ec310239c7deb59b4
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"For global climate indicators, evidence for abrupt change is limited. For global warming up to 2degC above 1850-1900 levels, paleoclimate \r\nrecords do not indicate abrupt changes in the carbon cycle (low confidence). Despite the wide range of model responses, uncertainty \r\nin atmospheric CO2 by 2100 is dominated by future anthropogenic emissions rather than uncertainties related to carbon-climate \r\nfeedbacks (high confidence). There is no evidence of abrupt change in climate projections of global temperature for the next century: \r\nthere is a near-linear relationship between cumulative CO2 emissions and maximum global mean surface air temperature increase \r\ncaused by CO2 over the course of this century for global warming levels up to at least 2degC relative to 1850-1900. The increase in global \r\nocean heat content (Section TS.2"


id: 6849cb5cb4b76bb6dc86992f821cd876
created_at: 2023-03-16 14:01:55
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"4) will likely continue until at least 2300 even for low emissions scenarios, and global mean sea level \r\nwill continue to rise for centuries to millennia following cessation of emissions (Box TS.4) due to continuing deep ocean heat uptake \r\nand mass loss of the Greenland and Antarctic ice sheets (high confidence). {2.2.3; Cross-Chapter Box 2.1; 5.1.1; 5.4; Cross-Chapter \r\nBox 5.1; Figures 5.3, 5.4, 5.25, and 5.26; 9.2.2; 9.2.4}"


id: afb9c5ec3a244145f8f39ecbbe606b73
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"confidence). It is very unlikely that gas clathrates (mostly methane) in deeper terrestrial permafrost and subsea clathrates will lead to a \r\ndetectable departure from the emissions trajectory during this century. Possible abrupt changes and tipping points in biogeochemical \r\ncycles lead to additional uncertainty in 21st century atmospheric GHG concentrations, but future anthropogenic emissions remain the \r\ndominant uncertainty (high confidence). There is potential for abrupt water cycle changes in some high emissions scenarios, but there \r\nis no overall consistency regarding the magnitude and timing of such changes. Positive land surface feedbacks, including vegetation, \r\ndust, and snow, can contribute to abrupt changes in aridity, but there is only low confidence that such changes will occur during \r\nthe 21st century"


id: d3e77870ac6dea2ac1c1f3e37098f00f
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" Continued Amazon deforestation, combined with a warming climate, raises the probability that this ecosystem will \r\ncross a tipping point into a dry state during the 21st century (low confidence). (Section TS.3.2.2) {5.4.3, 5.4.5, 5.4.8, 5.4.9, 8.6.2, 8.6.3, \r\nCross-Chapter Box 12.1}"


id: 61780feada6a0ace84f2eea142365c1c
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"Climate change information at regional scale is generated using a range \r\nof data sources and methodologies (Section TS.1.4). Understanding \r\nof observed regional climate change and variability is based on the \r\navailability and analysis of multiple observational datasets that are \r\nsuitable for evaluating the phenomena of interest (e.g., extreme \r\nevents), including accounting for observational uncertainty (Section \r\nTS.1.2.1). These datasets are combined with climate model simulations \r\nof observed changes and events to attribute causes of those changes \r\nand events to large- and regional-scale anthropogenic and natural \r\ndrivers and to assess the performance of the models. Future simulations \r\nwith many climate models (multi-model ensembles) are then used to \r\ngenerate and quantify ranges of projected regional climate responses \r\n(Section TS.4.2)"


id: a9e4d1cc32b36809dbc03862f6cf537d
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" Discarding models that fundamentally misrepresent \r\nrelevant processes improves the credibility of regional climate \r\ninformation generated from these ensembles (high confidence). \r\nHowever, multi-model mean and ensemble spread are not a full \r\nmeasure of the range of projection uncertainty and are not sufficient \r\nto characterize low-likelihood, high-impact changes (Box TS.3) or \r\nsituations where different models simulate substantially different \r\nor even opposite changes (high confidence). Large single-model \r\nensembles are now available and provide a more comprehensive \r\nspectrum of possible changes associated with internal variability (high \r\nconfidence) (Section TS.1.2.3). {1.5.1, 1.5.4, 10.2, 10.3.3, 10.3.4, 10.4.1, \r\n10.6.2, 11.2, Box 11.2, Cross-Chapter Box 11.1, 12.4, Atlas.1.4.1}"


id: 31576e350c6282e87d1ae3d8d7a67469
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"The regional extremes and events that have been studied are geographically uneven (Section TS.4.1). A few events, for example, \r\nextreme rainfall events in the United Kingdom, heatwaves in Australia, or Hurricane Harvey that hit Texas in 2017, have been heavily \r\nstudied. Many highly impactful extreme weather events have not been studied in the event attribution framework, particularly in \r\nthe developing world where studies are generally lacking. This is due to various reasons, including lack of observational data, lack of \r\nreliable climate models, and lack of scientific capacity"


id: be72c41ac71fee00af852fbb6648350
created_at: 2023-03-16 14:01:55
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" While the events that have been studied are not representative of all extreme \r\nevents that have occurred, and results from these studies may also be subject to selection bias, the large number of event attribution \r\nstudies provide evidence that changes in the properties of these local and individual events are in line with expected consequences \r\nof human influence on the climate and can be attributed to external drivers. {Cross-Working Group Box: Attribution, 11.1.4, 11.2.2}"


id: 66b2fdaf725bbdf4509b0e86619ce367
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"Box TS.10, Figure 1 (continued): The IPCC AR6 WGI inhabited regions are displayed as hexagons of identical sizes in their approximate geographical location (see \r\nlegend for regional acronyms). All assessments are made for each region as a whole and for the 1950s to the present. Assessments made on different time scales or \r\nmore local spatial scales might differ from what is shown in the figure. The colours in each panel represent the four outcomes of the assessment on observed changes. \r\nStriped hexagons (white and light-grey) are used where there is low agreement in the type of change for the region as a whole, and grey hexagons are used when \r\nthere is limited data and/or literature that prevents an assessment of the region as a whole. Other colours indicate at least medium confidence in the observed change"


id: ff43988fb658b31a0d81edc19bab2cdf
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" \r\nThe confidence level for the human influence on these observed changes is based on assessing trend detection and attribution and event attribution literature, \r\nand it is indicated by the number of dots: three dots for high confidence, two dots for medium confidence and one dot for low confidence (single, filled dot: limited \r\nagreement; single, empty dot: limited evidence)."


id: bc2a66141754894e2c819b26c00289f8
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"Panel (b) For heavy precipitation, the evidence is mostly drawn from changes in indices based on one-day or five-day precipitation amounts using global and \r\nregional studies. Green hexagons indicate regions where there is at least medium confidence in an observed increase in heavy precipitation. \r\nPanel (c) Agricultural and ecological droughts are assessed based on observed and simulated changes in total column soil moisture, complemented by evidence \r\non changes in surface soil moisture, water balance (precipitation minus evapotranspiration) and indices driven by precipitation and atmospheric evaporative demand. \r\nYellow hexagons indicate regions where there is at least medium confidence in an observed increase in this type of drought and green hexagons indicate regions \r\nwhere there is at least medium confidence in an observed decrease in agricultural and ecological drought.\r\nFor all regions, Table TS.5 shows a broader range of observed changes besides the ones shown in this figure"


id: 815c3d06b627c568474abe2d75721672
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" Note that Southern South America (SSA) is the only \r\nregion that does not display observed changes in the metrics shown in this figure, but is affected by observed increases in mean temperature, decreases in frost and \r\nincreases in marine heatwaves."


id: c3d329c969250a8ed0e16d93dfede2ab
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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vector_id: 28376
content:
"Since AR5, physical climate storyline approaches have emerged as \r\na complementary instrument to provide a different perspective, or \r\nadditional climate information, to facilitate communication of the \r\ninformation or provide a more flexible consideration of risk. Storylines \r\nthat condition climatic events and processes on a set of plausible \r\nbut distinct large-scale climatic changes enable the exploration of \r\nuncertainties in regional climate projections. For example, they can \r\nexplicitly address low-likelihood, high-impact outcomes, which would \r\nbe less emphasized in a probabilistic approach, and can be embedded \r\nin a user's risk landscape, taking account of socio-economic factors \r\nas well as physical climate changes"


id: 68969766bc01a3c52beabd209685e7a0
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" Storylines can also be used to \r\ncommunicate climate information by narrative elements describing \r\nand contextualizing the main climatological features and the relevant \r\nconsequences in the user context and, as such, can be used as part \r\nof a climate information distillation process. {1.4.4., Box 10.2, 11.2, \r\nBox 11.2, Cross-Chapter Box 12.2}"


id: a4275c664460421f32c24f23ff304477
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"The distillation process can vary substantially, as it needs to consider \r\nmultiple lines of evidence on all physically plausible outcomes \r\n(especially when they are contrasting) relevant to a specific decision \r\nrequired in response to a changing climate. Confidence in the \r\ndistilled regional climate information is enhanced when there is \r\nagreement across multiple lines of evidence, so the outcome can \r\nbe limited if these are inconsistent or contradictory. For example, in \r\nthe Mediterranean region the agreement between different lines of \r\nevidence, such as observations, projections by regional and global \r\nmodels, and understanding of the underlying mechanisms, provides \r\nhigh confidence in summer warming that exceeds the global average \r\n(see Box TS.12)"


id: cfd54ba9caaff21b1ab6aa9fe709daf
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"A significant innovation in the AR6 WGI Report is the Atlas. Part of its remit is to provide region-by-region assessment on changes in \r\nmean climate and to link with other WGI chapters to generate climate change information for the regions. An important component \r\nis the new online interactive tool, the Interactive Atlas, with flexible spatial and temporal analyses of much of the observed, simulated \r\npast and projected future climate change data underpinning the WGI assessment. This includes the ability to generate global maps \r\nand a number of regionally aggregated products (time series, scatter plots, tables, etc.) for a range of observations and ensemble \r\nclimate change projections of variables (such as changes in the climatic impact-drivers summarized in Table TS.5) from the Coupled \r\nModel Intercomparison Project Phases 5 and 6 (CMIP5, CMIP6) and the Coordinated Regional Climate Downscaling Experiment \r\n(CORDEX)"


id: dc80e484c270d1babf23c13c84d12da9
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" The data can be displayed and summarized under a range of SSP-RCP scenarios and future time slices and also for different \r\nglobal warming levels, relative to several different baseline periods. The maps and various statistics can be generated for annual mean \r\ntrends and changes or for any user-specified season. A new set of WGI reference regions is used for the regional summary statistics \r\nand applied widely throughout the report (with the regions, along with aggregated datasets and the code to generate these, available \r\nat the ATLAS GitHub: https://github.com/IPCC-WG1/Atlas)."


id: 5f2444416f9d50571f641fe32cf37bde
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"Box TS.12, Figure 1 (continued): The intent of this figure is to provide an example of using different lines of evidence to assess the confidence in or likelihood \r\nof a projected change in regional climate and which of these lines of evidence are available to view and explore in the Interactive Atlas. (a) Mechanisms and \r\nfeedbacks involved in enhanced Mediterranean summer warming. (b) Locations of observing stations from different datasets. (c) Distribution of 1960-2014 summer \r\ntemperature trends (degC per decade) for observations (black crosses), CMIP5 (blue circles), CMIP6 (red circles), HighResMIP (orange circles), CORDEX EUR-44 (light \r\nblue circles), CORDEX EUR-11 (green circles), and selected single model initial-condition large ensembles (SMILEs; grey boxplots, MIROC6, CSIRO-Mk3-6-0, MPI-ESM \r\nand d4PDF)"


id: d05e704e07f1eb72389a58a6c95e77c7
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" (d) Time series of area averaged (25degN-50degN, 10degW-40degE) land point summer temperature anomalies (degC, baseline period is 1995-2014): the boxplot \r\nshows long term (2081-2100) temperature changes of different CMIP6 scenarios in respect to the baseline period. (e) Projected Mediterranean summer warming \r\nin comparison to global annual mean warming of CMIP5 (RCP2.6, RCP4.5, RCP6.0 and RCP8.5) and CMIP6 (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5) ensemble \r\nmeans (lines) and spread (shading). {Figure 10.20, Figure 10.21, Figure Atlas.8}"


id: 56d94f55c00078e3caff686de12352d8
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"Since the late 19th century, major MoVs (Table TS.4) show \r\nno sustained trends, exhibiting fluctuations in frequency and \r\nmagnitude at multi-decadal time scales, except for the Southern \r\nAnnular Mode (SAM), which has become systematically more \r\npositive (high confidence) (Table TS.4). It is very likely that human \r\ninfluence has contributed to this trend from the 1970s to the \r\n1990s, and to the associated strengthening and southward shift \r\nof the Southern Hemispheric extratropical jet in austral summer. \r\nThe influence of stratospheric ozone forcing on the SAM trend has \r\nbeen reduced since the early 2000s compared to earlier decades, \r\ncontributing to the weakening of its positive trend observed over \r\n2000-2019 (medium confidence). By contrast, the cause of the \r\nNorthern Annular Mode (NAM) trend toward its positive phase \r\nsince the 1960s and associated northward shifts of Northern \r\nHemispheric extratropical jet and storm track in boreal winter \r\nis not well understood"


id: 287782ee53fca2e16771770a15edc172
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content:
" The evaluation of model performance on \r\nsimulating MoVs is assessed in Section TS.1.2.2. {2.3.3, 2.4, 3.3.3, \r\n3.7.1, 3.7.2}"


id: 6bad2e7bb95b8475c5c65811417c50bd
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"In the near term, the forced change in SAM in austral summer is likely\r\nto be weaker than observed during the late 20th century under all \r\nfive SSPs assessed. This is because of the opposing influence in the \r\nnear to mid-term from stratospheric ozone recovery and increases in \r\nother greenhouse gases on the Southern Hemisphere summertime \r\nmid-latitude circulation (high confidence). In the near term, forced \r\nchanges in the SAM in austral summer are therefore likely to be \r\nsmaller than changes due to natural internal variability. In the long \r\nterm (2081-2100) under the SSP5-8.5 scenario, the SAM index is \r\nlikely to increase in all seasons relative to 1995-2014. The CMIP6 \r\nmulti-model ensemble projects a long-term (2081-2100) increase \r\nin the boreal wintertime NAM index under SSP3-7.0 and SSP5-8"


id: 3410b3f5ce83956ef7081605a9a5fbc9
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"5, \r\nbut regional associated changes may deviate from a simple shift \r\nin the mid-latitude circulation due to a modified teleconnection \r\nresulting from interaction with a modified mean background state. \r\n{4.3.3, 4.4.3, 4.5.1, 4.5.3, 8.4.2}"


id: ea5d2c9046811e8ddbb97f1f9f1a7643
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"Human influence has not affected the principal tropical modes of \r\ninterannual climate variability (Table TS.4) and their associated \r\nregional teleconnections beyond the range of internal variability \r\n(high confidence). It is virtually certain that the El Nino-Southern \r\nOscillation (ENSO) will remain the dominant mode of interannual \r\nvariability in a warmer world. There is no consensus from models for \r\na systematic change in amplitude of ENSO sea surface temperature \r\n(SST) variability over the 21st century in any of the SSP scenarios \r\nassessed (medium confidence). However, it is very likely that \r\nrainfall variability related to ENSO will be enhanced significantly \r\nby the latter half of the 21st century in the SSP2-4.5, SSP3-7.0 \r\nand SSP5-8.5 scenarios, regardless of the amplitude changes in \r\nSST variability related to the mode"


id: e2acbaa1af3cb09a697562632ccfa34d
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content:
" It is very likely that rainfall \r\nvariability related to changes in the strength and spatial extent of \r\nENSO teleconnections will lead to significant changes at regional \r\nscale. {3.7.3, 3.7.4, 3.7.5, 4.3.3, 4.5.3, 8.4.2, 10.3.3}"


id: 1019d93a3211bdcb30963f87805a8a27
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content:
"Table TS.4 | Summary of the assessments on modes of variability (MoVs) and associated teleconnections. (a) Assessments on observed changes since the start \r\nof instrumental records, Coupled Model Intercomparison Project Phases 5 and 6 (CMIP5 and CMIP6) model performance, human influence on the observed changes, and \r\nnear-term (2021-2040) and mid- to long-term (2041-2100) changes. Curves schematically illustrate the assessed overall changes, with the horizontal axis indicating time, \r\nand are not intended to precisely represent the time evolution. (b) Fraction of surface air temperature (SAT) and precipitation (pr) variance explained at interannual time scale \r\nby each MoV for each AR6 region (numbers in each cell; in percent). Values correspond to the average of significant explained variance fractions based on HadCRUT, GISTEMP, \r\nBerkeleyEarth and CRU-TS (for SAT) and GPCC and CRU-TS (for precipitation)"


id: 1fd8c4a6b4753e83b9939d022576ffce
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content:
" Significance is tested based on F-statistics at the 95% level confidence, and a slash indicates that \r\nthe value is not significant in more than half of the available data sets. The colour scale corresponds to the sign and values of the explained variance as shown at the bottom. The \r\ncorresponding anomaly maps are shown in Annex IV. DJF: December-January-February. MAM: March-April-May. JJA: June-July-August. SON: September-October-November. \r\nIn (b), Northern Annular Mode (NAM) and El Nino-Southern Oscillation (ENSO) teleconnections are evaluated for 1959-2019, Southern Annular Mode (SAM) for 1979-2019, \r\nIndian Ocean Basin (IOB), Indian Ocean Dipole (IOD), Atlantic Zonal Mode (AZM) and Atlantic Meridional Mode (AMM) for 1958-2019, and Pacific Decadal Variability (PDV) \r\nand Atlantic Multi-decadal Variability (AMV) for 1900-2019. All data are linearly detrended prior to computation. (Section TS.1.2.2) {2.4, 3.7, 4.3.3, 4.4.3, 4.5.3, Table Atlas.1, \r\nAnnex IV}"


id: ae27cf1c3de595b2a9e4fd97830293a4
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content:
"which are relevant for the region. In fact, local changes over SES \r\nin terms of moisture convergence, ascending motion and storm\u0002track locations depend on these climate phenomena, and they are \r\noverall responsible for the observed precipitation trends. Projections \r\nsuggest continuing positive trends in rainfall over SES in the near\u0002term in response to GHG emissions scenarios. Multi-model mean and \r\nensemble spread are not sufficient to characterize situations where \r\ndifferent models simulate substantially different or even opposite \r\nchanges (high confidence). In such cases, physical climate storylines \r\naddressing possible outcomes for climate phenomena shown to \r\nplay a role in the variability of the region of interest can aid the \r\ninterpretation of projection uncertainties"


id: 61e4019742b2c881a8d3081241e63bd0
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content:
" In addition, single-model \r\ninitial-condition large ensembles of many realizations of internal \r\nvariability are required to separate internal variability from forced \r\nchanges (high confidence) and to partition the different sources of \r\nuncertainties as a function of future assessed periods. {10.3.4, 10.4.2, \r\nFigure 10.12a}"


id: ff2f50d8a122ddb4d1ffbbb358571078
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vector_id: 37704
content:
"Projections of regional monsoons during the 21st century indicate contrasting (region-dependent) and uncertain precipitation and \r\ncirculation changes. The annual contrast between the wettest and driest month of the year is likely to increase by 3-5% per degree \r\nCelsius in most monsoon regions in terms of precipitation, precipitation minus evaporation, and runoff (medium confidence). For the \r\nNorth American monsoon, projections indicate a decrease in precipitation, whereas increased monsoon rainfall is projected over South \r\nand South East Asia and over East Asia (medium confidence) (Box TS.13, Figure 1). West African monsoon precipitation is projected \r\nto increase over the central Sahel and decrease over the far western Sahel (medium confidence). There is low confidence in projected \r\nprecipitation changes in the South American and Australian-Maritime Continent regional monsoons (for both magnitude and sign) \r\n(Box TS.13, Figure 1)"


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content:
" There is medium confidence that the monsoon season will be delayed in the Sahel and high confidence that it \r\nwill be delayed in North and South America. {8.2.2, 8.4.2.4, Box 8.2}"


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"the amplitude in the projected direction of change in CIDs at a given \r\ntime horizon depends on climate change mitigation efforts over the \r\n21st century. It is evident from Table TS.5 that many heat, cold, snow \r\nand ice, coastal, and oceanic CID changes are projected with high \r\nconfidence in most regions starting from a global warming level \r\n(GWL) of 2degC, indicating worldwide challenges. Changes in many \r\nother regional CIDs have higher confidence later in the 21st century \r\nor at higher GWLs (high confidence), and another small subset are \r\nprojected with high confidence for the 1.5degC GWL. This section \r\nfocuses on the 2degC GWL and mid-century time period because the \r\nsignal emerges from natural variability for a wider range of CIDs \r\nat this higher warming level. Figure TS.22 shows the geographical \r\nlocation of regions belonging to one of five groups characterized \r\nby a specific combination of changing CIDs"


id: f3adf6b1025c45074f36f5a7bd16925d
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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content:
" The Regional Synthesis \r\ncomponent of the Interactive Atlas provides comprehensive synthesis \r\ninformation about changes in all of the individual CIDs across all of \r\nthe AR6 WGI reference regions. {10.5, Cross-Chapter Box 10.3, 11.1, \r\n11.9, Box 11.1, 12.1, 12.2, 12.4, 12.5}"


id: 3e0e1ecc1ef811ebbdddc54343264ec
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
content_type: text
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vector_id: 7123
content:
"Figure TS.22 (continued): Panel (a) shows the geographical location of regions belonging to one of five groups characterized by a specific combination of changing climatic \r\nimpact-drivers (CIDs). The five groups are represented by the five different colours, and the CID combinations associated with each group are represented in the corresponding \r\n'fingerprint' and text below the map. Each fingerprint comprises a set of CIDs projected to change with high confidence in every region in the group and a second set of CIDs, \r\none or more of which are projected to change in each region with high or medium confidence. The CID combinations follow a progression from those becoming hotter and drier \r\n(group 1) to those becoming hotter and wetter (group 5). In between (groups 2-4), the CIDs that change include some becoming drier and some wetter and always include \r\na set of CIDs which are getting hotter"


id: f8c94ae96a71eda47b334eae45a26f1
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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content:
" Tropical cyclones and severe wind CID changes are represented on the map with black dots in the regions affected. Regions affected \r\nby coastal CID changes are described by text on the map. The five groups are chosen to provide a reasonable level of detail for each region while not overwhelming the map \r\nwith a full summary of all aspects of the assessment, which is available in Table TS.5 and can be visualized in the Regional Synthesis component of the Interactive Atlas. The \r\nCID changes summarized in the figure represent high and medium confidence changes for the mid-21st century, considering scenarios SSP2-4.5, RCP4.5, SRES A1B, or above \r\n(SSP3-7.0, SSP5-8.5, RCP6.0, RCP8.5, SRES A2), which approximately encompasses global warming levels of 2.0degC to 2.4degC."


id: dc7d8f2e545903cfb52735c4039f965
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
content_type: text
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vector_id: 32193
content:
"The bar chart in panel (b) shows the numbers of regions where each CID is increasing or decreasing with medium or high confidence for all land regions and ocean regions \r\nlisted in Table TS.5. The colours represent the direction of change and the level of confidence in the change: purple indicates an increase while brown indicates a decrease; darker \r\nand lighter shades refer to high and medium confidence, respectively. Lighter background colours represent the maximum number of regions for which each CID is broadly \r\nrelevant. Sub-panel (i) shows the 30 CIDs relevant to the land and coastal regions while sub-panel (ii) shows the 5 CIDs relevant to the open ocean regions. Marine heatwaves \r\nand ocean acidity are assessed for coastal ocean regions in panel (i) and for open ocean regions in panel (ii)"


id: ddd23662b88d63ee5c7e4d4bc6b4c70d
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
content_type: text
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vector_id: 32395
content:
" Changes refer to a 20- to 30-year period centred around 2050 \r\nand/or consistent with 2degC global warming compared to a similar period within 1960-2014, except for hydrological drought and agricultural and ecological drought, which \r\nis compared to 1850-1900. Definitions of the regions are provided in Atlas.1, the Interactive Atlas (https://interactive-atlas.ipcc.ch/) and Chapter 12. (Table TS.5, Figure TS.24) \r\n{11.9, 12.2, 12.4, Atlas.1}"


id: 526140d3f389847c1300e1d1a84ff067
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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vector_id: 10416
content:
"Heat and cold: Changes in temperature-related CIDs such as mean \r\ntemperatures, growing season length, and extreme heat and frost have \r\nalready occurred (high confidence), and many of these changes have \r\nbeen attributed to human activities (medium confidence). Over all \r\nland regions with sufficient data (i.e., all except Antarctica), observed \r\nchanges in temperature have already clearly emerged outside the \r\nrange of internal variability, relative to 1850-1900 (Figure TS.23). In \r\ntropical regions, recent past temperature distributions have already \r\nshifted to a range different to that of the early 20th century (high \r\nconfidence) (Section TS.1.2.4). Most land areas have very likely\r\nwarmed by at least 0.1degC per decade since 1960, and faster in recent \r\ndecades. On regional-to-continental scales, trends of increased \r\nfrequency of hot extremes and decreased frequency of cold extremes \r\nare generally consistent with the global-scale trends in mean \r\ntemperature (high confidence)"


id: 820481dcf2a67989f3736c00c1f668e3
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
content_type: text
index: climate_gpt_only_giec
vector_id: 17973
content:
" In a few regions, trends are difficult \r\nto assess due to limited data availability. {2.3.1.1, 11.3, 11.9, 12.4, \r\nAtlas.3.1}"


id: b4f2e4dc37b539855dd426b78a2026e1
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
content_type: text
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vector_id: 26008
content:
"Wet and dry: Compared to the global scale, precipitation internal \r\nvariability is stronger at the regional scale while uncertainties in \r\nobservations, models and external forcing are all larger. However, \r\nGHG forcing has driven increased contrasts in precipitation amounts \r\nbetween wet and dry seasons and weather regimes over tropical land \r\nareas (medium confidence), with a detectable precipitation increase \r\nin the northern high latitudes (high confidence) (Box TS.6). The \r\nfrequency and intensity of heavy precipitation events have increased \r\nover a majority of land regions with good observational coverage \r\n(high confidence). A majority of land areas have experienced \r\ndecreases in available water in dry seasons due to human-induced \r\nclimate change associated with changes in evapotranspiration \r\n(medium confidence). Global hydrological models project a larger \r\nfraction of land areas to be affected by an increase rather than by \r\na decrease in river floods (medium confidence)"


id: 7800d638f581bbe666c82c54e1203ea6
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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content:
" Extreme precipitation \r\nand pluvial flooding will increase in many regions around the world \r\non almost all continents (high confidence), but regional changes \r\nin river floods are more uncertain than changes in pluvial floods \r\nbecause complex hydrological processes, including land cover and \r\nhuman water management, are involved. {8.2.2.1, 8.3.1, Box  8.2, \r\n10.4.1, 11.5, 11.6, 11.9, 12.4, 12.5.1, Atlas.3.1}"


id: e69e2e8ffca24683657863bc9aee50b7
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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vector_id: 33763
content:
"Snow and ice: Many aspects of the cryosphere either have seen \r\nsignificant changes in the recent past or will see them during the \r\n21st century (high confidence). Glaciers will continue to shrink \r\nand permafrost to thaw in all regions where they are present (high \r\nconfidence). Also, it is virtually certain that snow cover will experience \r\na decline over most land regions during the 21st century, in terms \r\nof water equivalent, extent and annual duration. There is high \r\nconfidence that the global warming-induced earlier onset of spring \r\nsnowmelt and increased melting of glaciers have already contributed \r\nto seasonal changes in streamflow in high-latitude and low-elevation \r\nmountain catchments. Nevertheless, it is very likely that some high\u0002latitude regions will experience an increase in winter snow water \r\nequivalent due to the effect of increased snowfall prevailing over \r\nwarming-induced increased snowmelt. (Section TS.2.5) {8.2.2.1, \r\n8.3.1, Box 8.2, 9.4, 9.5.1, 9.5.2, 12"


id: e9cf1f1a3944d3a0e06c443daad1eac5
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
content_type: text
index: climate_gpt_only_giec
vector_id: 34280
content:
"Other variables and concurrent CID changes: It is virtually certain\r\nthat atmospheric CO2 and oceanic pH will increase in all climate \r\nscenarios, until net zero CO2 emissions are achieved (Section TS.2.2). \r\nIn nearly all regions, there is low confidence in changes in hail, ice \r\nstorms, severe storms, dust storms, heavy snowfall, and avalanches, \r\nalthough this does not indicate that these CIDs will not be affected \r\nby climate change. For such CIDs, observations are often short\u0002term or lack homogeneity, and models often do not have sufficient \r\nresolution or accurate parametrizations to adequately simulate them \r\nover climate change time scales"


id: 47f5c6641de0eed3045d5c93585fd169
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
content_type: text
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vector_id: 8697
content:
" The probability of compound events \r\nhas increased in the past due to human-induced climate change and \r\nwill likely continue to increase with further global warming, including \r\nfor concurrent heatwaves and droughts, compound flooding, and the \r\npossibility of connected sectors experiencing multiple regional extreme \r\nevents at the same time (for example, in multiple breadbaskets) (high \r\nconfidence). {5.3.4.2, 11.8, Box 11.3, Box 11.4, 12.4}"


id: b72724a5f74c0ecbce2fef6abcf3ab0
created_at: 2023-03-16 14:01:56
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index: climate_gpt_only_giec
vector_id: 26349
content:
"Wet and dry: Mean precipitation changes have been observed \r\nover Africa, but the historical trends are not spatially coherent (high \r\nconfidence). North Eastern Africa, East Southern Africa and Central \r\nAfrica have experienced a decline in rainfall since about 1980 and \r\nparts of West Africa an increase (high confidence). Increases in the \r\nfrequency and/or the intensity of heavy rainfall have been observed in \r\nEast and West Southern Africa, and the eastern Mediterranean region \r\n(medium confidence). Increasing trends in river flood occurrence can \r\nbe identified beyond 1980 in East and West Southern Africa (medium \r\nconfidence) and Western Africa (high confidence). However, Northern \r\nAfrica and West Southern Africa are likely to have a reduction in \r\nprecipitation. Over West Africa, rainfall is projected to decrease \r\nin the western Sahel subregion and increase along the Guinea Coast \r\nsubregion (medium confidence)"


id: 5f4fee9b8ac1bdc5e30a6cac49fddfeb
created_at: 2023-03-16 14:01:56
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content_type: text
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vector_id: 12566
content:
" Rainfall is projected to increase over \r\nEastern Africa (medium confidence). {8.3.1.6, 11.4, 11.9, 12.4.1.2, \r\nAtlas.4.2, Atlas.4.4, Atlas.4.5}"


id: 96de15e0af97c3076873299a3aaa9b1d
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vector_id: 21254
content:
"With 2degC global warming, and as early as the mid-21st \r\ncentury, a wide range of CIDs, particularly related to the \r\nwater cycle and storms, are expected to show simultaneous \r\nregion-specific changes relative to the recent past with high\r\nor medium confidence. In a number of regions (Southern \r\nAfrica, the Mediterranean, North Central America, Western \r\nNorth America, the Amazon regions, South-Western South \r\nAmerica, and Australia), increases in one or more of drought, \r\naridity and fire weather (high confidence) will affect a wide \r\nrange of sectors, including agriculture, forestry, health and \r\necosystems"


id: 57cb8108d7818ee18f0c3e937076280b
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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vector_id: 11312
content:
" In another group of regions (North-Western, \r\nCentral and Eastern North America, Arctic regions, North\u0002Western South America, Northern, Western and Central \r\nand Eastern Europe, Siberia, Central, South and East Asia, \r\nSouthern Australia and New Zealand), decreases in snow \r\nand/or ice or increases in pluvial/river flooding (high \r\nconfidence) will affect sectors such as winter tourism, \r\nenergy production, river transportation and infrastructure. \r\n{11.9, 12.3, 12.4, 12.5, Table 12.2}"


id: 21de7a7ad1d149f7ee1e9185f211def7
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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vector_id: 2768
content:
"Additional regional changes in Central and South America, \r\nbesides those features described in Section TS.4.3.1, include \r\nincreases in mean and extreme precipitation in South\u0002Eastern South America since the 1960s (high confidence) \r\n(Section TS.4.2.3). Decreasing trends in mean precipitation \r\nand increasing trends in agricultural and ecological drought \r\nare observed over North-Eastern South America (medium \r\nconfidence). The intensity and frequency of extreme \r\nprecipitation and pluvial floods is projected to increase \r\nover South-Eastern South America, Southern South America, \r\nNorthern South America, South American Monsoon and \r\nNorth-Eastern South America (medium confidence) for a 2degC \r\nGWL and above"


id: 34c2f404683e1c42176145df51744944
created_at: 2023-03-16 14:01:56
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vector_id: 5702
content:
" Increases of agricultural and ecological \r\ndrought are projected in South America Monsoon and \r\nSouthern South America, and fire weather is projected to \r\nincrease over several regions (Northern South America, the \r\nSouth American Monsoon, North-Eastern South America \r\nand South-Western South America) (high confidence). {8.3, \r\n8.4, 11.3, 11.4, 11.9, Table 11.13, Table 11.14, Table 11.15, \r\n12.4.4.2, Atlas.7.1, Atlas.7.2}"


id: 8513662b1cf4b362d542a8be57919f79
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updated_at: 2023-03-16 14:01:56
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vector_id: 18463
content:
"Additional regional changes in Europe, besides those features \r\ndescribed in Section TS.4.3.1, include observed increases \r\nin pluvial flooding in Northern Europe and hydrological \r\nand agricultural/ecological droughts in the Mediterranean \r\n(high confidence), which have been attributed to human \r\ninfluence with high and medium confidence, respectively. \r\nIncreased mean precipitation amounts at high latitudes \r\nin boreal winter and reduced summer precipitation in \r\nsouthern Europe are projected starting from a 2degC GWL \r\n(high confidence). Aridity, agricultural and hydrological \r\ndroughts and fire weather conditions will increase in \r\nthe Mediterranean region starting from 2degC GWL (high \r\nconfidence). Pluvial flooding will increase everywhere \r\nwith high confidence except for medium confidence in the \r\nMediterranean; in Western and Central Europe this also \r\napplies to river flooding starting from a 2degC GWL (high \r\nconfidence)"


id: 1dae44fbefbff99bcb85dc1634af8375
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vector_id: 2108
content:
" Most periglacial processes in Northern Europe \r\nare projected to disappear by the end of the 21st century, \r\neven for a low warming scenario (medium confidence). {8.3, \r\n11.3, 11.9, 12.4.5, 12.5.2, Atlas.8.2, Atlas.8.4}"


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vector_id: 27145
content:
"Hui-Wen Lai (Sweden), Tania Villasenor (Chile), Rondrotiana Barimalala (South Africa/\r\nMadagascar), Rosario Carmona (Chile), Peter M. Cox (United Kingdom), Wolfgang Cramer (France/\r\nGermany), Francisco J. Doblas-Reyes (Spain), Hans Dolman (The Netherlands), Alessandro Dosio \r\n(Italy), Veronika Eyring (Germany), Gregory M. Flato (Canada), Piers Forster (United Kingdom), \r\nDavid Frame (New Zealand), Katja Frieler (Germany), Jan S. Fuglestvedt (Norway), John C. Fyfe \r\n(Canada), Mathias Garschagen (Germany), Joelle Gergis (Australia), Nathan P. Gillett (Canada), \r\nMichael  Grose (Australia), Eric Guilyardi (France), Celine Guivarch (France), Susan Hassol \r\n(United  States of America), Zeke Hausfather (United States of America), Hans Hersbach \r\n(United Kingdom/The Netherlands), Helene T. Hewitt (United Kingdom), Mark Howden (Australia), \r\nChristian Huggel (Switzerland), Margot Hurlbert (Canada), Christopher Jones (United Kingdom), \r\nRichard G. Jones (United Kingdom), Darrell S"


id: c8e41d01daf382db62b9efa0ca0ccb30
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vector_id: 29167
content:
" Kaufman (United States of America), Robert E. Kopp \r\n(United States of America), Anthony Leiserowitz (United States of America), Robert J. Lempert \r\n(United States of America), Jared Lewis (Australia/New Zealand), Hong Liao (China), \r\nNikki  Lovenduski (United  States of America), Marianne  T.  Lund (Norway), Katharine  Mach \r\n(United States of America), Douglas Maraun (Austria/Germany), Jochem Marotzke (Germany), \r\nJan Minx (Germany), Zebedee  R.J.  Nicholls (Australia), Brian  C.  O'Neill (United  States \r\nof America), M. Giselle Ogaz (Chile), Friederike Otto (United Kingdom/Germany), Wendy Parker \r\n(United  Kingdom), Camille  Parmesan (France, United Kingdom/United States of America), \r\nWarren Pearce (United Kingdom), Roque Pedace (Argentina), Andy Reisinger (New Zealand), \r\nJames  Renwick (New  Zealand), Keywan  Riahi (Austria), Paul  Ritchie (United Kingdom), \r\nJoeri  Rogelj (United Kingdom/Belgium), Rodolfo  Sapiains (Chile), Yusuke Satoh (Japan), \r\nSonia I"


id: d7b5dcc9687a3bba1c82bb965e7bbc98
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content:
" Seneviratne (Switzerland), Theodore G. Shepherd (United Kingdom/Canada), Jana Sillmann \r\n(Norway/Germany), Lucas Silva (Portugal/Switzerland), Aimee B.A. Slangen (The Netherlands), "


id: 1ad8a6b81e07bb905383184bd27162c2
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updated_at: 2023-03-16 14:01:56
content_type: text
index: climate_gpt_only_giec
vector_id: 1695
content:
"Understanding of the fundamental features of the climate \r\nsystem is robust and well established. Scientists in the 19th century \r\nidentified the major natural factors influencing the climate system. \r\nThey also hypothesized the potential for anthropogenic climate \r\nchange due to carbon dioxide (CO2) emitted by fossil fuel combustion. \r\nThe principal natural drivers of climate change, including changes in \r\nincoming solar radiation, volcanic activity, orbital cycles, and changes \r\nin global biogeochemical cycles, have been studied systematically \r\nsince the early 20th  century. Other major anthropogenic drivers, \r\nsuch as atmospheric aerosols (fine solid particles or liquid droplets), \r\nland-use change and non-CO2 greenhouse gases, were identified \r\nby the 1970s. Since systematic scientific assessments began in the \r\n1970s, the influence of human activity on the warming of the climate \r\nsystem has evolved from theory to established fact"


id: bec18a0dd2d1707c5ffa698e07f94ee2
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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vector_id: 27566
content:
" Past projections \r\nof global surface temperature and the pattern of warming are \r\nbroadly consistent with subsequent observations (limited evidence,\r\nhigh agreement), especially when accounting for the difference in \r\nradiative forcing scenarios used for making projections and the \r\nradiative forcings that actually occurred. {1.3.1-1.3.6}"


id: 1daaff8492eef34795dcf4dd706fc5fc
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updated_at: 2023-03-16 14:01:56
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vector_id: 2106
content:
"The WGI contribution to the IPCC Sixth Assessment Report (AR6) \r\nassesses new scientific evidence relevant for a world whose \r\nclimate system is rapidly changing, overwhelmingly due to \r\nhuman influence. The five IPCC assessment cycles since 1990 have \r\ncomprehensively and consistently laid out the rapidly accumulating \r\nevidence of a changing climate system, with the Fourth Assessment \r\nReport (AR4, 2007) being the first to conclude that warming of \r\nthe climate system is unequivocal. Sustained changes have been \r\ndocumented in all major elements of the climate system, including \r\nthe atmosphere, land, cryosphere, biosphere and ocean"


id: a60145f56cc491701e5c06c18f5248a7
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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vector_id: 23670
content:
" Multiple \r\nlines of evidence indicate the unprecedented nature of recent large\u0002scale climatic changes in the context of all human history, and that \r\nthese changes represent a millennial-scale commitment for the slow\u0002responding elements of the climate system, resulting in continued \r\nworldwide loss of ice, increase in ocean heat content, sea level rise \r\nand deep ocean acidification. {1.2.1, 1.3, Box 1.2, Appendix 1.A}"


id: d75c55f05a0336f2ea10ef86221aa30b
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updated_at: 2023-03-16 14:01:56
content_type: text
index: climate_gpt_only_giec
vector_id: 31401
content:
"Since the IPCC Fifth Assessment Report (AR5), the international \r\npolicy context of IPCC reports has changed. The UN Framework \r\nConvention on Climate Change (UNFCCC, 1992) has the overarching \r\nobjective of preventing 'dangerous anthropogenic interference \r\nwith the climate system'. Responding to that objective, the Paris \r\nAgreement (2015) established the long-term goals of 'holding the \r\nincrease in global average temperature to well below 2degC above pre\u0002industrial levels and pursuing efforts to limit the temperature increase \r\nto 1.5degC above pre-industrial levels' and of achieving 'a balance \r\nbetween anthropogenic emissions by sources and removals by sinks \r\nof greenhouse gases in the second half of this century'. Parties to \r\nthe Agreement have submitted Nationally Determined Contributions \r\n(NDCs) indicating their planned mitigation and adaptation strategies"


id: aee51bb38e1654147755ae38f038ee0d
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updated_at: 2023-03-16 14:01:56
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vector_id: 25053
content:
" \r\nHowever, the NDCs submitted as of 2020 are insufficient to reduce \r\ngreenhouse gas emissions enough to be consistent with trajectories \r\nlimiting global warming to well below 2degC above pre-industrial levels \r\n(high confidence). {1.1, 1.2}"


id: 2db208d92708503e869bd83cd8ea5439
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
content_type: text
index: climate_gpt_only_giec
vector_id: 4627
content:
"The latest generation of complex climate models has an \r\nimproved representation of physical processes, and a  wider \r\nrange of Earth system models now represent biogeochemical \r\ncycles. Since AR5, higher-resolution models that better capture \r\nsmaller-scale processes and extreme events have become \r\navailable. Key model intercomparisons supporting this Assessment \r\ninclude the Coupled Model Intercomparison Project Phase 6 (CMIP6) \r\nand the Coordinated Regional Climate Downscaling Experiment \r\n(CORDEX), for global and regional models respectively. Results \r\nusing CMIP Phase 5  (CMIP5) simulations are also assessed. Since \r\nAR5, large ensemble simulations, where individual models perform \r\nmultiple simulations with the same climate forcings, are increasingly \r\nused to inform understanding of the relative roles of internal \r\nvariability and forced change in the climate system, especially \r\non regional scales"


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content:
" The  broader availability of ensemble model \r\nsimulations has contributed to better estimations of uncertainty \r\nin projections of future change (high confidence). A  broad set of \r\nsimplified climate models is assessed and used as emulators to \r\ntransfer climate information across research communities, such as for \r\nevaluating impacts or mitigation pathways consistent with certain \r\nlevels of future warming. {1.4.2, 1.5.3, 1.5.4, Cross-Chapter Box 7.1}"


id: 50bc7305e95ac87d5124dd69023a424d
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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content:
"Assessments of future climate change are integrated within \r\nand across the three IPCC Working Groups through the use of \r\nthree core components: scenarios, global warming levels, and \r\nthe relationship between cumulative CO2 emissions and global \r\nwarming. Scenarios have a long history in the IPCC as a method for \r\nsystematically examining possible futures. A new set of illustrative \r\nscenarios that cover the range of possible future developments of \r\nanthropogenic drivers of climate change found in the literature, \r\nderived from the Shared Socio-economic Pathways (SSPs), is used \r\nto synthesize knowledge across the physical sciences and impact, \r\nadaptation and mitigation research. The core set of SSP scenarios \r\nused in the WGI report, SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and \r\nSSP5-8.5, cover a broad range of emissions pathways, including new \r\nlow-emissions pathways. They start in 2015 and include scenarios \r\nwith high and very high greenhouse gas (GHG) emissions (SSP3-7"


id: f13fde7a857789a19b756a97cf8df80
created_at: 2023-03-16 14:01:56
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content:
"0 \r\nand SSP5-8.5) and CO2 emissions that roughly double from current \r\nlevels by 2100 and 2050, respectively; scenarios with intermediate "


id: 67ff39df38f0ff4906255d446a12341a
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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content:
"GHG emissions (SSP2-4.5) and CO2 emissions remaining around \r\ncurrent levels until the middle of the century; and scenarios with very \r\nlow and low GHG emissions and CO2 emissions declining to net zero \r\naround or after 2050, followed by varying levels of net negative CO2\r\nemissions (SSP1-1.9, SSP1-2.6). Emissions vary between scenarios \r\ndepending on socio-economic assumptions, levels of climate change \r\nmitigation and, for aerosols and non-methane ozone precursors, \r\nair pollution controls. Alternative assumptions may result in similar \r\nemissions and climate responses, but the socio-economic assumptions \r\nand the feasibility or likelihood of individual scenarios are not part of \r\nthis assessment, which focuses on the climate response to possible, \r\nprescribed emissions futures"


id: b975d47c50cc163c34d6f2961a580aa8
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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index: climate_gpt_only_giec
vector_id: 26721
content:
" Levels of global surface temperature \r\nchange (global warming levels), which are closely related to a range \r\nof hazards and regional climate impacts, also serve as reference \r\npoints within and across IPCC Working Groups. Cumulative carbon \r\nemissions, which have a  nearly linear relationship to increases \r\nin global surface temperature, are also used. {1.6.1-1.6.4, \r\nCross-Chapter Box 1.5, Cross-Chapter Box 11.1}"


id: c140724dd0f414522f24fafa063456d6
created_at: 2023-03-16 14:01:56
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vector_id: 27993
content:
"Regional Information (Chapters  10, 11, 12 and Atlas). New \r\nknowledge on climate change at regional scales is reflected in this \r\nreport with four chapters covering regional information. Chapter 10 \r\nprovides a framework for assessment of regional climate information, \r\nincluding methods, physical processes, an assessment of observed \r\nchanges at regional scales, and the performance of regional models. \r\nChapter 11 addresses extreme weather and climate events, including \r\ntemperature, precipitation, flooding, droughts and compound events. \r\nChapter 12 provides a comprehensive, region-specific assessment of \r\nchanging climatic conditions that may be hazardous or favourable \r\n(hence influencing climate risk) for various sectors to be assessed \r\nin WGII. Lastly, the Atlas assesses and synthesizes regional climate \r\ninformation from the whole report, focussing on the assessments of \r\nmean changes in different regions and on model assessments for \r\nthe regions"


id: 830a3acd1361e8018684f59b5a8f37d8
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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index: climate_gpt_only_giec
vector_id: 18144
content:
" It also introduces the online Interactive Atlas, a novel \r\ncompendium of global and regional climate change observations and \r\nprojections. It includes a visualization tool, which combines various \r\nwarming levels and scenarios on multiple scales of space and time."


id: 51abb0f5515afe82b4ee17bed5b9b4f4
created_at: 2023-03-16 14:01:56
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content:
"The chapter comprises seven sections (Figure 1.3). Section 1.2 describes \r\nthe present state of Earth's climate, in the context of reconstructed \r\nand observed long-term changes and variations caused by natural \r\nand anthropogenic factors. It also provides context for the present \r\nAssessment by describing recent changes in international climate \r\nchange governance and fundamental scientific values. The evolution \r\nof knowledge about climate change and the development of earlier \r\nIPCC assessments are presented in Section 1.3. Approaches, methods \r\nand key concepts of this Assessment are introduced in Section 1.4. \r\nNew developments in observing networks, reanalyses, modelling \r\ncapabilities and techniques since AR5 are discussed in Section 1.5. The \r\nthree main 'dimensions of integration' across Working Groups in AR6, \r\nthat is, emissions scenarios, global warming levels and cumulative \r\ncarbon emissions, are described in Section 1.6"


id: 922d252adba60c270f7793707e3e1c27
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content:
" The Chapter closes \r\nwith a discussion of opportunities and gaps in knowledge integration \r\nin Section 1.7."


id: 55751fc75511500eb2ad6f6f3477527f
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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vector_id: 10920
content:
"The physical climate system comprises all processes that combine to \r\nform weather and climate. The early chapters of this report broadly \r\norganize their assessments according to overarching realms: the \r\natmosphere, the biosphere, the cryosphere (surface areas covered \r\nby frozen water, such as glaciers and ice sheets), and the ocean. \r\nElsewhere in the report, and in previous IPCC assessments, the land is \r\nalso used as an integrating realm that includes parts of the biosphere \r\nand the cryosphere. These overarching realms have been studied \r\nand measured in increasing detail by scientists, institutions and \r\nthe general public since the 18th  century, throughout the era of \r\ninstrumental observation (Section 1.3). Today, observations include \r\nthose taken by numerous land surface stations, ocean surface \r\nmeasurements from ships and buoys, underwater instrumentation, \r\nsatellite and surface-based remote sensing, and in situ atmospheric \r\nmeasurements from aeroplanes and balloons"


id: f33e3f33e38036172e54eda0264a586b
created_at: 2023-03-16 14:01:56
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content:
" These instrumental \r\nobservations are combined with paleoclimate reconstructions and \r\nhistorical documentations to produce a  highly detailed picture \r\nof the past and present state of the whole climate system, and to \r\nallow assessments about rates of change across the different realms \r\n(Chapter 2 and Section 1.5)."


id: b69e3f65d633f6aba7225935ee7ae2c7
created_at: 2023-03-16 14:01:56
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vector_id: 26251
content:
"Figure  1.5 (continued): (a) Measurements of CO2 in air enclosed in Antarctic ice cores (Luthi et al., 2008; Bereiter et al., 2015 [a compilation]; uncertainty \r\n+-1.3 ppm; see Sections 2.2.3 and 5.1.2 for an assessment) and direct air measurements (Tans and Keeling, 2020; uncertainty +-0.12 ppm). Projected CO2 concentrations \r\nfor five Shared Socio-economic Pathways (SSP) scenarios are indicated by dots on the right-hand side of each panel (grey background; (Meinshausen et al., 2020; SSPs are \r\ndescribed in Section 1.6). (b) Reconstruction of GMST from marine paleoclimate proxies (light-grey line: Snyder (2016); dark grey line: Hansen et al. (2013); see Section 2.3.1 \r\nfor an assessment). Observed and reconstructed temperature changes since 1850 are the AR6 assessed mean (referenced to 1850-1900; Box TS.3; 2.3.1"


id: b5b65abefac7359f1463aed39bbc6740
created_at: 2023-03-16 14:01:56
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vector_id: 26118
content:
"1); dots/whiskers \r\non the right-hand panels (grey background) indicate the projected mean and ranges of warming derived from Coupled Model Intercomparison Project Phase 6 (CMIP6) SSP\u0002based (2081-2100) and Model for the Assessment of Greenhouse Gas Induced Climate Change (MAGICC7; 2300) simulations (Tables 4.5 and 4.9). (c) Sea level changes \r\nreconstructed from a  stack of oxygen isotope measurements on seven ocean sediment cores (Spratt and Lisiecki, 2016; see Chapter  2, Section  2.3.3.3 and Chapter  9, \r\nSection 9.6.2 for an assessment). The sea level record from 1850-1900 is from Kopp et al. (2016), while the 20th century record is an updated ensemble estimate of GMSL \r\nchange (Palmer et al., 2021; Sections 2.3.3.3 and 9.6.1.1). Dots/whiskers on the right-hand panels of the figure (grey background) indicate the projected median and ranges \r\nderived from SSP-based simulations (2081-2100: Table 9.9; 2300: Section 9.6.3.5)"


id: f4d08431773d706fdf59a9b3bbaa9d
created_at: 2023-03-16 14:01:56
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vector_id: 36023
content:
" Best estimates (dots) and uncertainties (whiskers), as assessed in Chapter 2, are included \r\nin the left and middle panels for each of the three indicators and selected paleo-reference periods used in this report (CO2: Table 2.1; GMST: Section 2.3.1.1 and Cross-Chapter \r\nBox 2.3, Table 1; GMSL: Sections 2.3.3.3 and 9.6.2. See also Cross-Chapter Box 2.1). Selected paleo-reference periods: LIG - Last Interglacial; LGM - Last Glacial Maximum; \r\nMH - mid-Holocene (Cross-Chapter Box 2.1, Table 1). The non-labelled best estimate in panel (c) corresponds to the sea level high-stand during Marine Isotope Stage 11, about \r\n410 ka (410,000 years ago; Section 9.6.2). Further details on data sources and processing are available in the chapter data table (Table 1.SM.1)."


id: d27fbc2a8dfb6534d3cec41277a50281
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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index: climate_gpt_only_giec
vector_id: 30645
content:
"This establishes an essential long-term context for the climate change \r\nof the past 150 years and the projected changes in the 21st century \r\nand beyond (Chapter  3; IPCC, 2013a; Masson-Delmotte et al., 2013). \r\nFigure 1.5 shows reconstructions of three key indicators of climate change \r\nover the past 800,000 years (800 kyr)2 - atmospheric CO2 concentrations, \r\nglobal mean surface temperature (GMST) and global mean sea level \r\n(GMSL) - comprising at least eight complete glacial-interglacial cycles \r\n(EPICA Community Members, 2004; Jouzel et al., 2007), which are largely \r\ndriven by oscillations in the Earth's orbit and consequent feedbacks \r\non multi-millennial time scales (Berger, 1978; Laskar et al., 1993). The \r\ndominant cycles  - recurring approximately every 100 kyr  - can be \r\nfound imprinted in the natural variations of these three key indicators"


id: f7f7d01affb8bfcd79d3f9ffa0bd15c
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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vector_id: 36528
content:
" \r\nBefore industrialisation, atmospheric CO2 concentrations varied between \r\n174 ppm and 300 ppm, as measured directly in air trapped in ice at \r\nDome Concordia, Antarctica (Bereiter et al., 2015; Nehrbass-Ahles et al., \r\n2020). Relative to 1850-1900 CE, the reconstructed GMST changed in \r\nthe range of -6degC to +1degC across these glacial-interglacial cycles (see \r\nChapter 2, Section 2.3.1 for an assessment of different paleo-reference \r\nperiods). GMSL varied between about -130 m during the coldest glacial \r\nmaxima and +5 to +25 m  during the warmest interglacial periods \r\n(Chapter 2; Spratt and Lisiecki, 2016). They represent the amplitudes of \r\nnatural, global-scale climate variations over the last 800 kyr prior to the \r\ninfluence of human activity. Further climate information from a variety of \r\npaleoclimatic archives is assessed in Chapters 2, 5, 7 and 9."


id: 7ed3302e6441fabf22f3921fe433781c
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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content:
"The rate, scale and magnitude of anthropogenic changes in the \r\nclimate system since the mid-20th century suggested the definition \r\nof a new geological epoch: the Anthropocene (Crutzen and Stoermer, \r\n2000; Steffen et al., 2007), referring to an era in which human \r\nactivity is altering major components of the Earth system and leaving \r\nmeasurable imprints that will remain in the permanent geological \r\nrecord (Figure  1.5; IPCC, 2018). These alterations include not only \r\nclimate change itself, but also chemical and biological changes in \r\nthe Earth system such as rapid ocean acidification due to uptake \r\nof anthropogenic CO2, massive destruction of tropical forests, \r\na  worldwide loss of biodiversity and the sixth mass extinction of \r\nspecies (Hoegh-Guldberg and Bruno, 2010; Ceballos et al., 2017; \r\nIPBES, 2019)"


id: b2dde90c2e62b3c8d459b50e8960dded
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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index: climate_gpt_only_giec
vector_id: 25654
content:
" According to the key messages of the last global \r\nassessment of the Intergovernmental Science-Policy Platform on \r\nBiodiversity and Ecosystem Services (IPBES, 2019), climate change is \r\na 'direct driver that is increasingly exacerbating the impact of other \r\ndrivers on nature and human well-being', and 'the adverse impacts \r\nof climate change on biodiversity are projected to increase with \r\nincreasing warming.'"


id: f7d1dd58371a06415f131163a976e667
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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content:
"Numerous studies of the NDCs submitted since adoption of the PA \r\nin 2015 (Fawcett et al., 2015; UNFCCC, 2015, 2016; Lomborg, 2016; \r\nRogelj et al., 2016, 2017; Benveniste et al., 2018; Gutschow et al., \r\n2018; UNEP, 2019) conclude that they are insufficient to meet the \r\nParis temperature goal. In the present IPCC Sixth Assessment Cycle, \r\na  Special Report on Global Warming of 1.5degC (SR1.5, IPCC, 2018) \r\nfound, with high agreement, that current NDCs 'are not in line with \r\npathways that limit warming to 1.5degC by the end of the century.' The \r\nPA includes a ratcheting mechanism designed to increase the ambition \r\nof voluntary national pledges over time. Under this mechanism, NDCs \r\nwill be communicated or updated every five years. Each successive \r\nNDC will represent a 'progression beyond' the 'then current' NDC and \r\nreflect the 'highest possible ambition' (Article 4)"


id: c67e5e4717c857a56f2d4a441e2e0c31
created_at: 2023-03-16 14:01:56
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vector_id: 28800
content:
"The SED of the first periodic review (2013-2015) provided an \r\nimportant opportunity for face-to-face dialogue between decision \r\nmakers and experts on review themes, based on 'the best available \r\nscientific knowledge, including the assessment reports of the IPCC.' \r\nThat SED was instrumental in informing the long-term global goal of \r\nthe PA and in providing the scientific argument for the consideration \r\nof limiting warming to 1.5degC warming (UNFCCC, 2015; Fischlin, \r\n2017). The SED of the second periodic review, initiated in the second \r\nhalf of 2020, focuses on, among other things, 'enhancing Parties' \r\nunderstanding of the long-term global goal and the scenarios \r\ntowards achieving it in the light of the ultimate objective of the \r\nConvention'. The second SED provides a  formal venue for the \r\nscientific and the policy communities to discuss the requirements \r\nand benchmarks to achieve the 'long-term temperature goal' (LTTG) \r\nof 1.5degC and well below 2degC global warming"


id: 4d66dd41dde2a6bf4123defe7c13df3c
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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index: climate_gpt_only_giec
vector_id: 9612
content:
" The discussions also \r\nconcern the associated timing of net zero emissions targets and the \r\ndifferent interpretations of the PA LTTG, including the possibility \r\nof overshooting the 1.5deg C warming level before returning to it by \r\nmeans of negative emissions (e.g.,  Section  1.6; Schleussner and \r\nFyson, 2020). The second periodic review is planned to continue until \r\nNovember 2022 and its focus includes the review of the progress \r\nmade since the first review, while minimising 'possible overlaps' and \r\nprofiting from 'synergies with the global stocktake'."


id: 6c112112a7d6a0333a318d4a068b62f
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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index: climate_gpt_only_giec
vector_id: 14632
content:
"The global stocktake under the Paris Agreement (PA) evaluates the collective progress of countries' actions towards \r\nattaining the Agreement's purpose and long-term goals every five years. The first global stocktake is due in 2023, and then \r\nevery five years thereafter, unless otherwise decided by the Conference of the Parties. The purpose and long-term goals of the PA \r\nare captured inter alia in Article 2: to 'strengthen the global response to the threat of climate change, in the context of sustainable \r\ndevelopment and efforts to eradicate poverty, including by': mitigation3 specifically, 'holding the increase in the global average \r\ntemperature to well below 2degC above pre-industrial levels and to pursue efforts to limit the temperature increase to 1"


id: 8958e1c23b9a9a7ac1e0b5b1f52d761f
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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index: climate_gpt_only_giec
vector_id: 19148
content:
"5degC above \r\npre-industrial levels, recognizing that this would significantly reduce the risks and impacts of climate change'; adaptation, that is, \r\n'increasing the ability to adapt to the adverse impacts of climate change and foster climate resilience and low greenhouse gas (GHG) \r\nemissions development, in a manner that does not threaten food production'; and means of implementation and support, that is, \r\n'making finance flows consistent with a pathway towards low GHG emissions and climate-resilient development.'"


id: 5975bde4c3101a720fe18fcff3d2a9ec
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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index: climate_gpt_only_giec
vector_id: 11569
content:
"Many interactions among environmental problems and development \r\nare addressed in the United Nations 2030 Agenda for Sustainable \r\nDevelopment and its Sustainable Development Goals. The 2030 \r\nAgenda, supported by the finance-oriented Addis Ababa Action \r\nAgenda (UN DESA, 2015), calls on nations to 'take the bold and \r\ntransformative steps which are urgently needed to shift the world \r\nonto a sustainable and resilient path.' The 2030 Agenda recognizes \r\nthat 'climate change is one of the greatest challenges of our time and \r\nits adverse impacts undermine the ability of all countries to achieve \r\nsustainable development.' SDG 13 deals explicitly with climate \r\nchange, establishing several targets for adaptation, awareness\u0002raising and finance. Climate and climate change are also highly \r\nrelevant to most other SDGs, and UNFCCC is acknowledged as the \r\nmain forum to negotiate the global response to climate change"


id: 973c125ed80e34e4e5f7ec6391c17513
created_at: 2023-03-16 14:01:56
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content:
" \r\nFor example, both long-lived GHGs (through mitigation decisions), \r\nand SLCFs (through air quality), are relevant to SDG 11 (sustainable \r\ncities and communities). Chapter  6  assesses the effects of SLCFs \r\non climate and the implications of changing climate for air quality, \r\nincluding opportunities for mitigation relevant to the SDGs (Box 6.2). \r\nAlso, the UN Conference on Housing and Sustainable Development \r\nestablished a New Urban Agenda (United Nations, 2017) envisaging \r\ncities as part of the solutions for sustainable development, climate \r\nchange adaptation and mitigation."


id: ab9a77d0e20ffca4e70b4179bf46f9d0
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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index: climate_gpt_only_giec
vector_id: 24521
content:
"Addressing climate change alongside other environmental problems, \r\nwhile simultaneously supporting sustainable socio-economic \r\ndevelopment, requires a  holistic approach. Since AR5, there is \r\nincreasing attention on the need for coordination among previously \r\nindependent international agendas, and a  recognition that climate \r\nchange, disaster risk, economic development, biodiversity conservation \r\nand human well-being are tightly interconnected. The  current \r\nCOVID-19 pandemic provides an example of the need for such \r\ninterconnection, with its widespread impacts on economy, society and \r\nenvironment (e.g., Shan et al., 2021). Cross-Chapter Box 6.1 assesses \r\nthe consequences of the COVID-19 lockdowns for emissions of GHGs \r\nand SLCFs, and related implications for the climate. Another example \r\nof the interconnected nature of these issues is the close link between \r\nSLCF emissions, climate change and air quality concerns (Chapter 6)"


id: fc88b2b6348471e5f09dd8a27e39e9ad
created_at: 2023-03-16 14:01:56
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index: climate_gpt_only_giec
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content:
" \r\nEmissions of halocarbons have previously been successfully regulated \r\nunder the Montreal Protocol and its Kigali Amendment. This has \r\nbeen achieved in an effort to reduce ozone depletion that has also \r\nmodulated other anthropogenic climate influence (Estrada et al., \r\n2013; Wu et al., 2013). In the process, emissions of some SLCFs were \r\njointly regulated to reduce environmental and health impacts from air \r\npollution (e.g., Gothenburg Protocol; Reis et al., 2012). Considering the \r\nrecognized importance of SLCFs in climate change processes, the IPCC \r\ndecided in May 2019 to approve that the IPCC Task Force on National \r\nGreenhouse Gas Inventories produces an IPCC Methodology Report on \r\nSLCFs to develop guidance for national SLCF inventories."


id: 827131d29e2159dc90d5518aa1f0d371
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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index: climate_gpt_only_giec
vector_id: 18040
content:
"Political cultures also give rise to variation in how climate science \r\nknowledge is interpreted, used and challenged (Leiserowitz, 2006; \r\nOreskes and Conway, 2010; Brulle et al., 2012; Dunlap and Jacques, \r\n2013; Mahony, 2014, 2015; Brulle, 2019). A  meta-analysis of \r\n87  studies carried out between 1998 and 2016 (62 USA national, \r\n16 non-USA national, 9 cross-national) found that political orientation \r\nand political party identification were the second most important \r\npredictors of views on climate change after environmental values \r\n(McCright et al. 2016). Ruiz et al. (2020) systematically reviewed \r\n34 studies of non-US nations or clusters of nations and 30 studies \r\nof the USA alone. They found that in the non-US studies, 'changed \r\nweather' and 'socio-altruistic values' were the most important \r\ndrivers of public attitudes. For the USA case, by contrast, political \r\naffiliation and the influence of corporations were most important"


id: e172007bfd359d87a5b9898d15bc54d4
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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index: climate_gpt_only_giec
vector_id: 32961
content:
" \r\nWidely varying media treatment of climate issues also affects public \r\nresponses (Section 1.2.3.4). In summary, environmental and socio\u0002altruistic values are the most significant influences on public opinion \r\nabout climate change globally, while political views, political party \r\naffiliation, and corporate influence also had strong effects, especially \r\nin the USA (high confidence)."


id: 9714e914ad70722e3512ad94fb4a5782
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
content_type: text
index: climate_gpt_only_giec
vector_id: 21288
content:
"If the author team concludes that there is sufficient evidence and agreement, the level of confidence can be evaluated. In this \r\nstep, assessments of evidence and agreement are combined into a single metric (steps 3-5). The assessed level of confidence is \r\nexpressed using five qualifiers: very low, low, medium, high, and very high. Step 4 depicts how summary statements for evidence and \r\nagreement relate to confidence levels. For a given evidence and agreement statement, different confidence levels can be assigned \r\ndepending on the context, but increasing levels of evidence and degrees of agreement correlate with increasing confidence. When \r\nconfidence in a finding is assessed to be low, this does not necessarily mean that confidence in its opposite is high, and vice versa. \r\nSimilarly, low confidence does not imply distrust in the finding; instead, it means that the statement is the best conclusion based on \r\ncurrently available knowledge"


id: 13b7a26cfb5b1fc82982ab1e5093b1f3
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" Further research and methodological progress may change the level of confidence in any finding in \r\nfuture assessments."


id: 72765aec55a9120c7d4283e1c7a29063
created_at: 2023-03-16 14:01:56
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vector_id: 15626
content:
"Budescu et al. (2014) surveyed 25 samples in 24 countries (a total \r\nof 10,792 individual responses), finding that even when shown IPCC \r\nuncertainty guidance, lay readers systematically misunderstood \r\nIPCC likelihood statements. When presented with a 'high likelihood' \r\nstatement, they understood it as indicating a lower likelihood than \r\nintended by the IPCC authors. Conversely, they interpreted 'low \r\nlikelihood' statements as indicating a  higher likelihood than \r\nintended. In another study, British lay readers interpreted uncertainty \r\nlanguage somewhat differently from IPCC guidance, but Chinese \r\nlay people reading the same uncertainty language translated into \r\nChinese differed much more in their interpretations (Harris et al., \r\n2013)"


id: 66877d3cf3bc50a905ffdd7e809f89c0
created_at: 2023-03-16 14:01:56
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content:
" Further, even though it is objectively more probable that wide \r\nuncertainty intervals will encompass true values, wide intervals were \r\ninterpreted by lay people as implying subjective uncertainty or lack \r\nof knowledge on the part of scientists (Lohre et al., 2019). Mach \r\net al. (2017) investigated the advances and challenges in approaches \r\nto expert judgment in AR5. Their analysis showed that the shared \r\nframework increased the overall comparability of assessment \r\nconclusions across all Working Groups and topics related to climate \r\nchange, from the physical science basis to resulting impacts, risks, and \r\noptions for response. Nevertheless, many challenges in developing \r\nand communicating assessment conclusions persist, especially for \r\nfindings drawn from multiple disciplines and Working Groups, for \r\nsubjective aspects of judgements, and for findings with substantial \r\nuncertainties (Adler and Hirsch Hadorn, 2014)"


id: f6800c30a0b7d6e88c48a234518ace99
created_at: 2023-03-16 14:01:56
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content:
" In summary, the \r\ncalibrated language cannot entirely prevent misunderstandings, \r\nincluding a tendency to systematically underestimate the probability \r\nof the IPCC's higher-likelihood conclusions and overestimate the \r\nprobability of the lower-likelihood ones (high confidence). However, \r\na  consistent and systematic approach across Working Groups to \r\ncommunicate the assessment outcomes is an important characteristic \r\nof the IPCC."


id: 43bf886946db4f90eade707d5c2a4fb3
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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vector_id: 8069
content:
"As noted above, values  - fundamental attitudes about what \r\nis important, good, and right  - play critical roles in all human \r\nendeavours, including climate science. In AR5, Chapters 3 and 4 of \r\nthe WGIII Assessment addressed the role of cultural, social and ethical \r\nvalues in climate change mitigation and sustainable development \r\n(Fleurbaey et al., 2014; Kolstad et al., 2014). These values include \r\nwidely accepted concepts of human rights, enshrined in international \r\nlaw, that are relevant to climate impacts and policy objectives (Hall \r\nand Weiss, 2012; Peel and Osofsky, 2018; Setzer and Vanhala, 2019). \r\nSpecific values  - human life, subsistence, stability, and equitable \r\ndistribution of the costs and benefits of climate impacts and policies - \r\nare explicit in the texts of the UNFCCC and the PA (Breakey et al., \r\n2016; Dooley and Parihar, 2016). Here we address the role of values \r\nin how scientific knowledge is created, verified and communicated"


id: 150d7b1b45575e4d6121229b909ccff8
created_at: 2023-03-16 14:01:56
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content:
" \r\nChapters 10, 12 and Cross-Chapter Box 12.2 address how the specific \r\nvalues and contexts of users can be addressed in the co-production \r\nof climate information."


id: 262f8e9c92e792c1f33391ec4e4accfa
created_at: 2023-03-16 14:01:56
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content:
"The epistemic (knowledge-related) values of science include \r\nexplanatory power, predictive accuracy, falsifiability, replicability, \r\nand justification of claims by explicit reasoning (Popper, 1959; \r\nKuhn, 1977). These are supported by key institutional values, \r\nincluding openness, 'organized scepticism', and objectivity or \r\n'disinterestedness' (Merton, 1973), operationalized as well-defined \r\nmethods, documented evidence, publication, peer review, and \r\nsystems for institutional review of research ethics (COSEPUP, 2009; \r\nElliott, 2017)"


id: e45981e11577dbed163a0cd69312874
created_at: 2023-03-16 14:01:56
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content:
" In recent decades, open data, open code and scientific \r\ncyber-infrastructure (notably the Earth System Grid Federation, \r\na partnership of climate modelling centers dedicated to supporting \r\nclimate research by providing secure, web-based, distributed access \r\nto climate model data) have facilitated scrutiny from a larger range \r\nof participants, and FAIR data stewardship principles  - making \r\ndata Findable, Accessible, Interoperable and Reusable (FAIR)  - are \r\nbeing mainstreamed in many fields (Wilkinson et al., 2016). Climate \r\nscience norms and practices embodying these scientific values and \r\nprinciples include the publication of data and model code, multiple \r\ngroups independently analysing the same problems and data, model \r\nintercomparison projects (MIPs), explicit evaluations of uncertainty, \r\nand comprehensive assessments by national academies of science \r\nand the IPCC."


id: 68a35211b015cfb4367388d09a42049c
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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content:
"The framework for communicating uncertainties does not allow \r\nfor indicating cases where 'deep uncertainty' is identified in the \r\nassessment (Adler and Hirsch Hadorn, 2014). The definition of deep \r\nuncertainty in IPCC assessments has been described in the context \r\nof SROCC (IPCC, 2019b; Box 5 in Abram et al., 2019): a situation of \r\ndeep uncertainty exists when experts or stakeholders do not know \r\nor cannot agree on: (i) appropriate conceptual models that describe \r\nrelationships among key driving forces in a system; (ii) the probability \r\ndistributions used to represent uncertainty about key variables and \r\nparameters; and/or (iii) how to weigh and value desirable alternative \r\noutcomes (Cross-Chapter Box  1.2 and Annex VII: Glossary; Abram \r\net al., 2019)"


id: 7483d8b38d214fb2029eaa45b310b419
created_at: 2023-03-16 14:01:56
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content:
" Since AR5, 'storylines' or 'narratives' approaches have \r\nbeen used to address issues related to deep uncertainty, for example \r\nlow-likelihood events that would have high impact if they occurred, \r\nto better inform risk assessment and decision making (Section 1.4.4). "


id: 2de9260f06b32a07a36df2a390c24a2f
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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content:
"Responding to national and regional policymakers' needs for \r\ntailored information relevant to risk assessment and adaptation, \r\nAR6 emphasizes assessment of regional information more than \r\nearlier reports. Here the phrase 'regional climate information' refers \r\nto predefined reference sets of land and ocean regions; various \r\ntypological domains (such as mountains or monsoons); temporal \r\nframes including baseline periods as well as near term (2021-2040), \r\nmedium term (2041-2060) and long term (2081-2100); and global \r\nwarming levels (Chapters 10 and 12, Sections 1.4.1 and 1.4.5, and \r\nAtlas). Regional climate change information is constructed from \r\nmultiple lines of evidence including observations, paleoclimate \r\nproxies, reanalyses, attribution of changes and climate model \r\nprojections from both global and regional climate models \r\n(Sections 1.5.3 and 10.2-10.4)"


id: 90c63411e45bf3bbcb5491256ce50ce5
created_at: 2023-03-16 14:01:56
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vector_id: 20324
content:
" The constructed regional information \r\nneeds to take account of user context and values for risk assessment, \r\nadaptation and policy decisions (Sections 1.2.3 and 10.5)."


id: aaddabc4508dc85957fb0948bcf93722
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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vector_id: 24401
content:
"Social values are implicit in many choices made during the \r\nconstruction, assessment and communication of climate science \r\ninformation (Heymann et al., 2017; Skelton et al., 2017). Some \r\nclimate science questions are prioritized for investigation, or given \r\na specific framing or context, because of their relevance to climate \r\npolicy and governance. One example is the question of how the \r\neffects of a 1.5degC global warming would differ from those of a 2degC \r\nwarming, an assessment specifically requested by Parties to the PA. \r\nThe SR1.5 (2018) explicitly addressed this issue 'within the context of \r\nsustainable development; considerations of ethics, equity and human \r\nrights; and the problem of poverty' (Chapters 1 and 5; see also Hoegh\u0002Guldberg et al., 2019) following the outcome of the approval of the \r\noutline of the Special Report by the IPCC during its 44th Session \r\n(Bangkok, Thailand, 17-20 October 2016)"


id: 70e46f3bb38f0c5580a5fa130b46faf6
created_at: 2023-03-16 14:01:56
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content:
" Likewise, particular metrics \r\nare sometimes prioritized in climate model improvement efforts \r\nbecause of their practical relevance for specific economic sectors or \r\nstakeholders. Examples include reliable simulation of precipitation in \r\na specific region, or attribution of particular extreme weather events \r\nto inform rebuilding and future policy (Chapters 8 and 11; Intemann, \r\n2015; Otto et al., 2018; James et al., 2019). Sectors or groups whose \r\ninterests do not influence research and modelling priorities may thus \r\nreceive less information in support of their climate-related decisions \r\n(Parker and Winsberg, 2018)."


id: f31a1df53565fc3395d2c5046d2c3b24
created_at: 2023-03-16 14:01:56
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vector_id: 35763
content:
"As detailed in Chapter 10, scientific climate information often requires \r\n'tailoring' to meet the requirements of specific decision-making \r\ncontexts. In a study of the UK Climate Projections 2009 (UKCP09) \r\nproject, researchers concluded that climate scientists struggled to \r\ngrasp and respond to users' information needs because they lacked \r\nexperience interacting with users, institutions and scientific idioms \r\noutside the climate science domain (Porter and Dessai, 2017). \r\nEconomic theory predicts the value of 'polycentric' approaches \r\nto climate change informed by specific global, regional and local \r\nknowledge and experience (Ostrom, 1996, 2012). This is confirmed \r\nby numerous case studies of extended, iterative dialogue among \r\nscientists, policymakers, resource managers and other stakeholders \r\nto produce mutually understandable, usable, task-related information \r\nand knowledge, policymaking and resource management around the \r\nworld (Lemos and Morehouse, 2005; Lemos et al"


id: 549650542a7c9ad7c6a8519d6a51f2c3
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content:
", 2012, 2014, 2018; \r\nsee Vaughan and Dessai, 2014 for a critical view). The SR1.5 (2018) \r\nassessed that 'education, information, and community approaches, \r\nincluding those that are informed by indigenous knowledge and \r\nlocal knowledge, can accelerate the wide-scale behaviour changes \r\nconsistent with adapting to and limiting global warming to 1.5degC. \r\nThese approaches are more effective when combined with other \r\npolicies and tailored to the motivations, capabilities and resources \r\nof specific actors and contexts (high confidence).' These extended \r\ndialogic co-production and education processes have thus been \r\ndemonstrated to improve the quality of both scientific information \r\nand governance (high confidence) (Section  10.5 and Cross \r\nChapter Box 12.2)."


id: fc4c7b8396052a5186142cc338c526b1
created_at: 2023-03-16 14:01:56
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content:
"Recent work also recognizes that choices made throughout the \r\nresearch process can affect the relative likelihood of false alarms \r\n(overestimating the probability and/or magnitude of hazards) or \r\nmissed warnings (underestimating the probability and/or magnitude \r\nof hazards), known respectively as Type I and Type II errors. Researchers \r\nmay choose different methods depending on which type of error they \r\nview as most important to avoid, a  choice that may reflect social \r\nvalues (Douglas, 2009; Knutti, 2018; Lloyd and Oreskes, 2018). This \r\nreflects a fundamental trade-off between the values of reliability and \r\ninformativeness. When uncertainty is large, researchers may choose to \r\nreport a wide range as very likely, even though it is less informative \r\nabout potential consequences. By contrast, high-likelihood statements \r\nabout a  narrower range may be more informative, yet also prove \r\nless reliable if new evidence later emerges that widens the range"


id: 9a463aa0befbe418948e2d1026192d30
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updated_at: 2023-03-16 14:01:56
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index: climate_gpt_only_giec
vector_id: 21798
content:
" \r\nFurthermore, the difference between narrower and wider uncertainty \r\nintervals has been shown to be confusing to lay readers, who often \r\ninterpret wider intervals as less certain (Lohre et al., 2019)."


id: 2e0ee13482a8a9ee8e198c7cad2d0cfb
created_at: 2023-03-16 14:01:56
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content:
"Since AR5, social media platforms have dramatically altered the mass\u0002media landscape, bringing about a shift from uni-directional transfer \r\nof information and ideas to more fluid, multi-directional flows \r\n(Pearce et al., 2019). A survey covering 18 Latin American countries \r\n(StatKnows-CR2, 2019) found that the main sources of information \r\nabout climate change mentioned were the Internet (52% of mentions), \r\nfollowed by social media (18%). There are well-known challenges \r\nwith social media, such as misleading or false presentations of \r\nscientific findings, incivility that diminishes the quality of discussion \r\naround climate change topics, and 'filter bubbles' that restrict \r\ninteractions to those with broadly similar views (Anderson and \r\nHuntington, 2017). However, at certain moments (such as at the \r\nrelease of the AR5 WGI report), Twitter studies have found that more \r\nmixed, highly-connected groups existed, within which members \r\nwere less polarized (Pearce et al"


id: 33098690411edb96a074d829b0da5177
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content:
", 2014; Williams et al., 2015). Thus, \r\nsocial media platforms may in some circumstances support dialogic \r\nor co-production approaches to climate communication. Because the \r\ncontents of IPCC reports speak not only to policymakers, but also to \r\nthe broader public, the character and effects of media coverage are \r\nimportant considerations across Working Groups."


id: 8608ec2fad5a055c87736cfd915d66c3
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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content:
"Climate services focus on users with specific needs for climate \r\ninformation, but most people learn about climate science findings \r\nfrom media coverage. Since AR5, research has expanded on how \r\nmass media report climate change and how their audiences respond \r\n(Dewulf, 2013; Jaspal and Nerlich, 2014; Jaspal et al., 2014). \r\nFor  example, in five European Union (EU) countries, television \r\ncoverage of AR5 used 'disaster' and 'opportunity' as its principal \r\nthemes, but virtually ignored the 'risk' framing introduced by \r\nAR5 WGII (Painter, 2015) and now extended by the AR6 (Cross\u0002Chapter Box 1.3). Other studies show that people react differently \r\nto climate change news when it is framed as a  catastrophe (Hine \r\net al., 2016), as associated with local identities (Sapiains et al., \r\n2016), or as a social justice issue (Howell, 2013)"


id: 337e744c7901bd69cd6799c59219ec88
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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content:
" Similarly, audience \r\nsegmentation studies show that responses to climate change vary \r\nbetween groups of people with different, although not necessarily \r\nopposing, views on this phenomenon (e.g.,  Maibach et al., 2011; \r\nSherley et al., 2014; Detenber et al., 2016). In Brazil, two studies \r\nhave shown the influence of mass media on the high level of public \r\nclimate change concern in that country (Rodas and Di Giulio, 2017; \r\nDayrell, 2019). In the USA, analyses of television network news show \r\nthat climate change receives minimal attention, is most often framed \r\nin a political context, and largely fails to link extreme weather events \r\nto climate change using appropriate probability framing (Hassol \r\net al., 2016). However, recent evidence suggests that Climate Matters \r\n(an Internet resource to help US television weather forecasters link \r\nweather to climate change trends) may have had a positive effect \r\non public understanding of climate change (Myers et al., 2020)"


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content:
" \r\nAlso, some media outlets have recently adopted and promoted \r\nterms and phrases stronger than the more neutral 'climate change' \r\nand 'global warming', including 'climate crisis', 'global heating', \r\nand 'climate emergency' (Zeldin-O'Neill, 2019). Google searches on \r\nthose terms, and on 'climate action', increased 20-fold in 2019, when "


id: 11adbb9abfe9a9afdda366a1eac471f4
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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index: climate_gpt_only_giec
vector_id: 272
content:
"Data at altitude came initially from scattered mountain summits, \r\nballoons and kites, but the upper troposphere and stratosphere \r\nwere not systematically observed until radiosonde (weather balloon) \r\nnetworks emerged in the 1940s and 1950s. These provide the \r\nlongest continuous quasi-global record of the atmosphere's vertical \r\ndimension (Stickler et al., 2010). New methods for spatial and temporal \r\nhomogenisation (intercalibration and quality control) of radiosonde \r\nrecords were introduced in the 2000s (Sherwood et al., 2008, 2015; \r\nHaimberger et al., 2012). Since 1978, Microwave Sounding Units \r\n(MSU) mounted on Earth-orbiting satellites have provided a second \r\nhigh-altitude data source, measuring temperature, humidity, ozone, \r\nand liquid water throughout the atmosphere. Over time, these satellite \r\ndata have required numerous adjustments to account for such factors \r\nas orbital precession and decay (Edwards, 2010)"


id: 8907c473f2f2f3e585bcd18516f9c90e
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content:
" Despite repeated \r\nadjustments, however, marked differences remain in the temperature \r\ntrends from surface, radiosonde, and satellite observations; between \r\nthe results from three research groups that analyse satellite data \r\n(University of Alabama in Huntsville (UAH), Remote Sensing Systems \r\n(RSS), and NOAA); and between modelled and satellite-derived \r\ntropospheric warming trends (Thorne et al., 2011; Santer et al., 2017). \r\nThese differences are the subject of ongoing research (Maycock \r\net al., 2018). In the 2000s, Atmospheric Infrared Sounder (AIRS) and \r\nradio occultation (GNSS-RO) measurements provided new ways to \r\nmeasure temperature at altitude, complementing data from the MSU. \r\nGNSS-RO is a new independent, absolutely calibrated source, using \r\nthe refraction of radio-frequency signals from the Global Navigation \r\nSatellite System (GNSS) to measure temperature, pressure and water \r\nvapour (Section 2.3.1.2.1; Foelsche et al., 2008; Anthes, 2011)."


id: 97c90f64fbd5aaa985240bc290899f1f
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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content:
"National and colonial weather services built networks of surface \r\nstations in the 19th  century. By the mid-19th  century, semi\u0002standardized naval weather logs recorded winds, currents, \r\nprecipitation, air pressure, and temperature at sea, initiating the \r\nlongest continuous quasi-global instrumental record (Maury, 1849, \r\n1855, 1860). Because the ocean covers over 70% of global surface \r\narea and constantly exchanges energy with the atmosphere, both air \r\nand sea surface temperatures (SST) recorded in these naval logs are \r\ncrucial variables in climate studies. Dove (1853) mapped seasonal \r\nisotherms over most of the globe. By 1900, a patchy weather data\u0002sharing system reached all continents except Antarctica. Regular \r\ncompilation of climatological data for the world began in 1905 with \r\nthe Reseau Mondial (Air Ministry - Meteorological Office, 1921), and \r\nsimilar compilations  - the World Weather Records (Clayton, 1927) \r\nand Monthly Climatic Data for the World (est"


id: 58257032c34d4754dda75ac4294b82d3
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updated_at: 2023-03-16 14:01:56
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index: climate_gpt_only_giec
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content:
"Land and ocean surface temperature data have been repeatedly \r\nevaluated, refined and extended (Section 1.5.1). As computer power \r\nincreased and older data were recovered from handwritten records, \r\nthe number of surface station records used in published global land \r\ntemperature time series grew. A  pioneering study for 1880-1935 \r\nused fewer than 150 stations (Callendar, 1938). A benchmark study \r\nof 1880-2005 incorporated 4300 stations (Brohan et al., 2006). \r\nA  study of the 1753-2011 period included previously unused \r\nstation data, for a  total of 36,000 stations (Rohde et al., 2013); \r\nrecent versions of this dataset comprise over 40,000 land stations \r\n(Rohde and Hausfather, 2020). Several centres, including the National \r\nOceanic and Atmospheric Administration (NOAA), Hadley, and Japan \r\nMeteorological Agency (JMA), produce SST datasets independently \r\ncalculated from instrumental records"


id: 10c65b0f5d3fa4f7cf461e43e68821c9
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updated_at: 2023-03-16 14:01:56
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vector_id: 113
content:
" In the 2000s, adjustments \r\nfor bias due to different measurement methods (buckets, engine \r\nintake thermometers, moored and drifting buoys) resulted in major \r\nimprovements of SST data (Thompson et al., 2008), and these \r\nimprovements continue (Huang et al., 2017; Kennedy et al., 2019). \r\nSST  and land-based data are incorporated into global surface \r\ntemperature datasets calculated independently by multiple research \r\ngroups, including NOAA, NASA, Berkeley Earth, Hadley-CRU, JMA, and \r\nChina Meteorological Administration (CMA). Each group aggregates \r\nthe raw measurement data, applies various adjustments for non\u0002climatic biases such as urban heat-island effects, and addresses \r\nunevenness in geospatial and temporal sampling with various \r\ntechniques (see Section 2.3.1.1.3 and Table 2.4 for references). Other \r\nresearch groups provide alternative interpolations of these datasets \r\nusing different methods (e.g., Cowtan and Way, 2014; Kadow et al., \r\n2020)"


id: ca40d1c691032f2149c4735608b1329e
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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index: climate_gpt_only_giec
vector_id: 29382
content:
" Using the then available global surface temperature datasets, \r\nAR5 WGI assessed that the GMST increased by 0.85degC from 1880 \r\nto 2012 and found that each of the three decades following 1980 \r\nwas successively warmer at the Earth's surface than any preceding \r\ndecade since 1850 (IPCC, 2013b). Marine air temperatures, especially \r\nthose measured during nighttime, are increasingly also used to \r\nexamine variability and long-term trends (e.g., Rayner et al., 2006; \r\nKent et al., 2013; Cornes et al., 2020; Junod and Christy, 2020). \r\nCross-Chapter Box 2.3 discusses updates to the global temperature \r\ndatasets, provides revised estimates for the observed changes and \r\nconsiders whether marine air temperatures are changing at the \r\nsame rate as SSTs."


id: 15283ddae542e73f29c3e03a9a71900e
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content:
"reaching full equilibrium with the atmosphere only after hundreds \r\nto thousands of years (Yang and Zhu, 2011). The earliest subsurface \r\nmeasurements in the open ocean date to the 1770s (Abraham et al., \r\n2013). From 1872-76, the research ship HMS Challenger measured \r\nglobal ocean temperature profiles at depths up to 1700 m along its \r\ncruise track. By 1900, research ships were deploying instruments \r\nsuch as Nansen bottles and mechanical bathythermographs \r\n(MBTs) to develop profiles of the upper 150 m in areas of interest \r\nto navies and commercial shipping (Abraham et al., 2013). Starting \r\nin 1967, eXpendable BathyThermographs (XBTs) were deployed by \r\nscientific and commercial ships along repeated transects to measure \r\ntemperature to 700 m  (Goni et al., 2019). Ocean data collection \r\nexpanded in the 1980s with the Tropical Ocean Global Experiment \r\n(TOGA; Gould, 2003)"


id: 55d55b2fb16082817981f547dad41deb
created_at: 2023-03-16 14:01:56
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content:
" Marine surface observations for the globe, \r\nassembled in the mid-1980s in the International Comprehensive \r\nOcean-Atmosphere Data Set (ICOADS; Woodruff et al., 1987, 2005), \r\nwere extended to 1662-2014 using newly recovered marine records \r\nand metadata (Woodruff et al., 1998; Freeman et al., 2017). The Argo \r\nsubmersible float network, developed in the early 2000s, provided "


id: ad5d5c84c60cbdac4446faadb8f6f9c
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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content:
"With the gradual acceptance of evidence for geological 'deep time' in \r\nthe 19th century came investigation of fossils, geological strata, and \r\nother evidence pointing to large shifts in the Earth's climate, from ice \r\nages to much warmer periods, across thousands to billions of years. \r\nThis awareness set off a search for the causes of climatic changes. \r\nThe long-term perspective provided by paleoclimate studies is essential \r\nto understanding the causes and consequences of natural variations \r\nin climate, as well as crucial context for recent anthropogenic climatic \r\nchange. The reconstruction of climate variability and change over \r\nrecent millennia began in the 1800s (Bruckner, 1890; Stehr and von \r\nStorch, 2000; Coen, 2018, 2020)"


id: 1fd3b510b1e35d57d9eb16c6e0f2f489
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vector_id: 2442
content:
" In brief, paleoclimatology reveals \r\nthe key role of CO2 and other greenhouse gases in past climatic \r\nvariability and change, the magnitude of recent climate change in \r\ncomparison to past glacial-interglacial cycles, and the unusualness \r\nof recent climate change (Section 1.2.1.2 and Cross-Chapter Box 2.1; \r\nTierney et al., 2020a). FAQ 1.3 provides a plain-language summary of \r\nits importance."


id: 15f011fa04777b03fcb70329dc5ff61b
created_at: 2023-03-16 14:01:56
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vector_id: 974
content:
"Water expands as it warms. This thermal expansion, along with glacier \r\nmass loss, were the dominant contributors to GMSL rise during the \r\n20th century (high confidence) according to AR5 (IPCC, 2013b). Sea \r\nlevel can be measured by averaging across tide gauges, some of which \r\ndate to the 18th century. However, translating tide gauge readings \r\ninto GMSL is challenging, since their spatial distribution is limited to \r\ncontinental coasts and islands, and their readings are relative to local \r\ncoastal conditions that may shift vertically over time. Satellite radar \r\naltimetry, introduced operationally in the 1990s, complements the \r\ntide gauge record with geocentric measurements of GMSL at much \r\ngreater spatial coverage (Katsaros and Brown, 1991; Fu et al., 1994). \r\nThe AR5 WGI assessed that GMSL rose by 0.19 [0.17 to 0.21] m over \r\nthe period 1901-2010, and that the rate of sea level rise increased \r\nfrom 2.0 [1.7 to 2.3] mm yr-1 in 1971-2010 to 3.2 [2.8 to 3"


id: ac444f51dfb88c378950c86d81a920bb
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content:
"6] mm yr-1\r\nfrom 1993-2010. Warming of the ocean very likely contributed 0.8 \r\n[0.5 to 1.1] mm yr-1 of sea level change during 1971-2010, with the \r\nmajority of that contribution coming from the upper 700 m (IPCC, \r\n2013b). Chapter 2 (Section 2.3.3.3) assesses current understanding \r\nof the extent and rate of sea level rise, past and present."


id: 7e1a5af0ed7c263c7aabeb7a21bbeaf9
created_at: 2023-03-16 14:01:56
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content:
"Satellite remote sensing also revolutionized studies of the cryosphere \r\n(Sections  2.3.2 and 9.3-9.5), particularly near the poles, where \r\nconditions make surface observations very difficult. Satellite mapping \r\nand measurement of snow cover began in 1966, with land and \r\nsea ice observations following in the mid-1970s. Yet prior to the \r\nThird Assessment Report, researchers lacked sufficient data to tell \r\nwhether the Greenland and Antarctic ice sheets were shrinking or \r\ngrowing. Through a combination of satellite and airborne altimetry \r\nand gravity measurements, and improved knowledge of surface \r\nmass balance and perimeter fluxes, a  consistent signal of ice loss \r\nfor both ice sheets was established by the time of AR5 (Shepherd \r\net al., 2012). After 2000, satellite radar interferometry revealed rapid \r\nchanges in surface velocity at ice-sheet margins, often linked to \r\nreduction or loss of ice shelves (Scambos et al., 2004; Rignot and \r\nKanagaratnam, 2006)"


id: 13db1272541dcf3eb028acc87da07367
created_at: 2023-03-16 14:01:56
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vector_id: 628
content:
" Whereas sea ice area and concentration have \r\nbeen continuously monitored since 1979 via microwave imagery, \r\ndatasets for ice thickness emerged later from upward sonar profiling \r\nby submarines (Rothrock et al., 1999) and radar altimetry of sea ice \r\nfreeboards (Laxon et al., 2003). A  recent reconstruction of Arctic \r\nsea ice extent back to 1850 found no historical precedent for the \r\nArctic sea ice minima of the 21st century (Walsh et al., 2017). Glacier \r\nlength has been monitored for decades to centuries; internationally \r\ncoordinated activities now compile worldwide glacier length and \r\nmass balance observations (World Glacier Monitoring Service, Zemp \r\net al., 2015), global glacier outlines (Randolph Glacier Inventory, \r\nPfeffer et al., 2014), and ice thickness data for about 1100 glaciers "


id: 29fc9d0b8e7576bca058416883bf256d
created_at: 2023-03-16 14:01:56
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content:
"On longer time scales, tiny air bubbles trapped in polar ice sheets \r\nprovide direct evidence of past atmospheric composition, including \r\nCO2 levels (Petit et al., 1999), and the 18O isotope in frozen precipitation \r\nserves as a  proxy marker for temperature (Dansgaard, 1954). \r\nSulphate deposits in glacier ice and as ash layers within sediment \r\nrecord major volcanic eruptions, providing another mechanism for \r\ndating. The first paleoclimate reconstructions used an almost 100-kyr \r\nice core taken at Camp Century, Greenland (Dansgaard et al., 1969; \r\nLangway Jr, 2008). Subsequent cores from Antarctica extended this \r\nclimatic record to 800 kyr (EPICA Community Members, 2004; Jouzel, \r\n2013). Comparisons of air contained in these ice samples against \r\nmeasurements from the recent past enabled AR5 WGI to assess that \r\natmospheric concentrations of CO2, methane (CH4), and nitrous oxide \r\n(N2O) had all increased to levels unprecedented in at least the last \r\n800,000 years (Figure 1"


id: bfb9aff243b2e9c4e98e2dde66523ee9
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content:
"The natural and anthropogenic factors responsible for climate \r\nchange are known today as radiative 'drivers' or 'forcers'. The net \r\nchange in the energy budget at the top of the atmosphere, resulting \r\nfrom a  change in one or more such drivers, is termed 'radiative \r\nforcing' (RF; Glossary) and measured in watts per square metre (W \r\nm-2). The total radiative forcing over a given time interval (often since \r\n1750) represents the sum of positive drivers (inducing warming) and \r\nnegative ones (inducing cooling). Past IPCC reports have assessed \r\nscientific knowledge of these drivers, quantified their range for the \r\nperiod since 1750, and presented the current understanding of how \r\nthey interact in the climate system"


id: 971c52411a92eaddde7feec219121ee2
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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vector_id: 21291
content:
" Like all previous IPCC reports, AR5 \r\nassessed that total radiative forcing has been positive at least since \r\n1850-1900, leading to an uptake of energy by the climate system, \r\nand that the largest single contribution to total radiative forcing is \r\nthe rising atmospheric concentration of CO2 since 1750 (Chapter 7, \r\nand Cross-Chapter Box 1.2; IPCC, 2013a)."


id: ad0752a9540c748f35415ee7d309070e
created_at: 2023-03-16 14:01:56
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content:
"others indirect (e.g., cloud droplet nucleation); specific effects may \r\ncause either positive or negative radiative forcing. Major volcanic \r\neruptions inject SO2 (a negative driver) into the stratosphere, creating \r\naerosols that can cool the planet for years at a time by reflecting some \r\nincoming solar radiation. The history and climatic effects of volcanic \r\nactivity have been traced through historical records, geological \r\ntraces, and observations of major eruptions by aircraft, satellites and \r\nother instruments (Dorries, 2006). The negative RF of major volcanic \r\neruptions was considered in the First Assessment Report (FAR; IPCC, \r\n1990a). In subsequent assessments, the negative RF of smaller \r\neruptions has also been considered (e.g., Cross-Chapter Box 4.1 in \r\nChapter 4 of this Report; Section 2.4.3 in IPCC, 1996). Dust and other \r\nnatural aerosols have been studied since the 1880s (e.g"


id: 66dfc90144b74bc8f62574c49583160
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content:
",  Aitken, \r\n1889; Angstrom, 1929, 1964; Twomey, 1959), particularly in relation \r\nto their role in cloud nucleation, an aerosol indirect effect whose \r\nRF may be either positive or negative depending on such factors \r\nas cloud altitude, depth and albedo (Stevens and Feingold, 2009; \r\nBoucher et al., 2013)."


id: 3a29dab04a9e6b111ea9c7d836e1ff8e
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content:
"Radioactive fallout from atmospheric nuclear weapons testing \r\n(1940s-1950s) and urban smog (1950s-1960s) first provoked \r\nwidespread attention to anthropogenic aerosols and ozone in the \r\ntroposphere (Edwards, 2012). Theory, measurement and modelling \r\nof these substances developed steadily from the 1950s (Hidy, 2019). \r\nHowever, the radiative effects of anthropogenic aerosols did not \r\nreceive sustained study until around 1970 (Bryson and Wendland, \r\n1970; Rasool and Schneider, 1971), when their potential as cooling \r\nagents was recognized (Peterson et al., 2008). The US Climatic \r\nImpact Assessment Program (CIAP) found that proposed fleets of \r\nsupersonic aircraft, flying in the stratosphere, might cause substantial \r\naerosol cooling and depletion of the ozone layer, stimulating efforts \r\nto understand and model stratospheric circulation, atmospheric \r\nchemistry, and aerosol radiative effects (Mormino et al., 1975; Toon \r\nand Pollack, 1976)"


id: 1a4825093d9647802f77610d62e93fe3
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content:
" Since the 1980s, aerosols have increasingly been \r\nintegrated into comprehensive modelling studies of transient climate \r\nevolution and anthropogenic influences, through treatment of volcanic \r\nforcing, links to global dimming and cloud brightening, and their \r\ninfluence on cloud nucleation and other properties (e.g., thickness, \r\nlifetime and extent), and precipitation (e.g.,  Hansen et al., 1981; \r\nCharlson et al., 1987, 1992; Albrecht, 1989; Twomey, 1991)."


id: 4fe51259d41900f8209c03eeddb50fdc
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updated_at: 2023-03-16 14:01:56
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vector_id: 10008
content:
"Anthropogenic drivers of climatic change were hypothesized as early as \r\nthe 17th century, with a primary focus on forest clearing and agriculture \r\n(Grove, 1995; Fleming, 1998). In the 1890s, Arrhenius was first to \r\ncalculate the effects of increased or decreased CO2 concentrations on \r\nplanetary temperature, and Hogbom estimated that worldwide coal \r\ncombustion of about 500 Mt yr-1 had already completely offset the \r\nnatural absorption of CO2 silicate rock weathering (Hogbom, 1894; \r\nArrhenius, 1896; Berner, 1995; Crawford, 1997). As coal consumption \r\nreached 900 Mt yr-1 only a  decade later, Arrhenius wrote that \r\nanthropogenic CO2 from fossil fuel combustion might eventually \r\nwarm the planet (Arrhenius, 1908). In 1938, analysing records from \r\n147  stations around the globe, Callendar calculated atmospheric \r\nwarming over land at 0.3degC-0.4degC from 1880-1935 and attributed \r\nabout half of this warming to anthropogenic CO2 (Figure 1"


id: bff6a23be7ca748bcedf84064a6221c6
created_at: 2023-03-16 14:01:56
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vector_id: 27757
content:
"The FAR (1990) focused attention on human emissions of CO2, CH4, \r\ntropospheric O3, chlorofluorocarbons (CFCs), and N2O. Of these, at \r\nthat time only the emissions of CO2 and CFCs were well measured, \r\nwith methane sources known only 'semi-quantitatively' (IPCC, \r\n1990a). The FAR assessed that some other trace gases, especially \r\nCFCs, have global warming potentials hundreds to thousands of \r\ntimes greater than CO2 and CH4, but are emitted in much smaller \r\namounts. As a  result, CO2 remains by far the most important \r\npositive anthropogenic driver, with CH4 next most significant \r\n(Section 1.6.3); anthropogenic methane stems from such sources as \r\nfossil fuel extraction, natural gas pipeline leakage, agriculture and \r\nlandfills. In 2001, increased greenhouse forcing attributable to CO2, \r\nCH4, O3, CFC-11 and CFC-12 was detected by comparing satellite \r\nmeasurements of outgoing longwave radiation measurements taken \r\nin 1970 and in 1997 (Harries et al., 2001)"


id: f80aab0281a35744b2499eb3003a32cf
created_at: 2023-03-16 14:01:56
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vector_id: 36539
content:
" AR5 assessed that the 40% \r\nincrease in atmospheric CO2 contributed most to positive RF since \r\n1750. Together, changes in atmospheric concentrations of CO2, CH4, \r\nN2O and halocarbons from 1750-2011 were assessed to contribute \r\na positive RF of 2.83 [2.26 to 3.40] W m-2 (IPCC, 2013b)."


id: e70c6112e3f3c91ab358ce65d1a23055
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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index: climate_gpt_only_giec
vector_id: 33845
content:
"Studies of radiocarbon (14C) in the 1950s established that increasing \r\natmospheric CO2 concentrations were due to fossil fuel combustion. \r\nSince all the 14C once contained in fossil fuels long ago decayed \r\ninto non-radioactive 12C, the CO2 produced by their combustion \r\nreduces the overall concentration of atmospheric 14C (Suess, 1955). \r\nRelated work demonstrated that while the ocean was absorbing \r\naround 30% of anthropogenic CO2, these emissions were also \r\naccumulating in the atmosphere and biosphere (Section 1.3.1 and \r\nChapter  5, Section  5.2.1.5). Further work later established that \r\natmospheric oxygen levels were decreasing in inverse relation to the \r\nanthropogenic CO2 increase, because combustion of carbon consumes \r\noxygen to produce CO2 (Chapters 2 and 6; Keeling and Shertz, 1992; \r\nIPCC, 2013a)"


id: 7e4b04f74f5cfaf221785669c1aed40d
created_at: 2023-03-16 14:01:56
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index: climate_gpt_only_giec
vector_id: 17431
content:
" Revelle and Suess (1957) famously described fossil fuel \r\nemissions as a 'large scale geophysical experiment', in which 'within \r\na  few centuries we are returning to the atmosphere and ocean \r\nthe concentrated organic carbon stored in sedimentary rocks over \r\nhundreds of millions of years.' The 1960s saw increasing attention \r\nto other radiatively active gases, especially ozone (O3; Manabe and \r\nMoller, 1961; Plass, 1961). Methane and nitrous oxide (N2O) were \r\nnot considered systematically until the 1970s, when anthropogenic \r\nincreases in those gases were first noted (Wang et al., 1976). In the \r\n1970s and 1980s, scientists established that synthetic halocarbons "


id: 6c7f32730b79c9f041b6011bec2f99e0
created_at: 2023-03-16 14:01:56
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vector_id: 14693
content:
"Since controlled experiments at planetary scale are impossible, \r\nclimate simulations provide one important way to explore the \r\ndifferential effects and interactions of variables such as solar \r\nirradiance, aerosols and GHGs. To assess their quality, models or \r\ncomponents of models may be compared with observations. For this \r\nreason, they can be used to attribute observed climatic effects to \r\ndifferent natural and human drivers (Hegerl et al., 2011). As early \r\nas Arrhenius (1896), simple mathematical models were used to \r\ncalculate the effects of doubling atmospheric carbon dioxide over pre\u0002industrial concentrations (approximately 550 ppm vs approximately \r\n275 ppm respectively). In the early 20th century Bjerknes formulated \r\nthe Navier-Stokes equations of fluid dynamics for motion of the \r\natmosphere (Bjerknes, 1906; Bjerknes et al., 1910), and Richardson \r\n(1922) developed a system for numerical weather prediction based \r\non these equations"


id: 69a5497fb67e3b0fd9a7b4681e9d930b
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vector_id: 14221
content:
" When electronic computers became available \r\nin the late 1940s, the methods of Bjerknes and Richardson were \r\nsuccessfully applied to weather forecasting (Charney et al., 1950; \r\nNebeker, 1995; Harper, 2008)."


id: 9cc9d71e50dbc68929d92b2e554b5c62
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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vector_id: 22203
content:
"In the 1990s, AOGCMs were state of the art. By the 2010s, Earth \r\nsystem models (ESMs, also known as coupled carbon-cycle climate \r\nmodels) incorporated land surface, vegetation, the carbon cycle, \r\nand other elements of the climate system. Since the 1990s, some \r\nmajor modelling centres have deployed 'unified' models for \r\nboth weather prediction and climate modelling, with the goal \r\nof a  seamless modelling approach that uses the same dynamics, \r\nphysics and parameterisations at multiple scales of time and space \r\n(Section  10.1.2; Cullen, 1993; Brown et al., 2012; NRC, 2012; \r\nWMO, 2015). Because weather forecast models make short-term \r\npredictions that can be frequently verified, and improved models are \r\nintroduced and tested iteratively on cycles as short as 18 months, this \r\napproach allows major portions of the climate model to be evaluated \r\nas a  weather model and more frequently improved"


id: 2d1a1ad771e254b22e8b4192d7d9215f
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vector_id: 4535
content:
" However, all \r\nclimate models exhibit biases of different degrees and types, and \r\nthe practice of 'tuning' parameter values in models to make their \r\noutputs match variables such as historical warming trajectories has \r\ngenerated concern throughout their history (Section 1.5.3.2; Randall \r\nand Wielicki, 1997; Edwards, 2010; Hourdin et al., 2017). Overall, \r\nAR5 WGI assessed that climate models had improved since previous \r\nreports (IPCC, 2013b)."


id: dd9503dfd561b917bf8533480056195b
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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vector_id: 32367
content:
"modelling community developed increasingly sophisticated model \r\nintercomparison projects (MIPs; Gates et al., 1999; Covey et al., \r\n2003). MIPs prescribe standardized experiment designs, time periods, \r\noutput variables or observational reference data to facilitate direct \r\ncomparison of model results. This aids in diagnosing the reasons for \r\nbiases and other differences among models, and furthers process \r\nunderstanding (Section  1.5). Both the CMIP3 and CMIP5 model \r\nintercomparison projects included experiments testing the ability \r\nof models to reproduce 20th-century global surface temperature \r\ntrends both with and without anthropogenic forcings. Although some \r\nindividual model runs failed to achieve this (Hourdin et al., 2017), the \r\nmean trends of multi-model ensembles did so successfully (Meehl \r\net al., 2007a; Taylor et al., 2012)"


id: 96a427db0f4223407e2ed1c3ea313794
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vector_id: 21222
content:
" When only natural forcings were \r\nincluded (creating the equivalent of a 'control Earth' without human \r\ninfluence), similar multi-model ensembles could not reproduce the \r\nobserved post-1970 warming at either global or regional scales \r\n(Edwards, 2010; Jones et al., 2013). The GCMs and ESMs compared \r\nin CMIP6 (used in this Report) offer more explicit documentation and \r\nevaluation of tuning procedures (Section 1.5; Schmidt et al., 2017; \r\nBurrows et al., 2018; Mauritsen and Roeckner, 2020)."


id: 8a9573855e29f0e36fac3c402ad8272c
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updated_at: 2023-03-16 14:01:56
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vector_id: 19341
content:
"The Appendix to Chapter  1  (Appendix 1A) lists the key detection \r\nand attribution statements in the Summaries for Policymakers of \r\nWGI reports since 1990. The evolution of these statements over \r\ntime reflects the improvement of scientific understanding and the \r\ncorresponding decrease in uncertainties regarding human influence. \r\nThe Second Assessment Report (SAR) stated that 'the balance of \r\nevidence suggests a discernible human influence on global climate' \r\n(IPCC, 1995b). Five years later, the Third Assessment Report (TAR) \r\nconcluded that 'there is new and stronger evidence that most \r\nof the warming observed over the last 50  years is attributable to \r\nhuman activities' (IPCC, 2001b). The AR4 further strengthened \r\nprevious statements, concluding that 'most of the observed increase \r\nin global average temperatures since the mid-20th century is very \r\nlikely due to the observed increase in anthropogenic greenhouse \r\ngas concentrations' (IPCC, 2007b)"


id: 4d5dc61926207b6587a9c80c0dcb4146
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vector_id: 9604
content:
" The AR5 assessed that a human \r\ncontribution had been detected in: changes in warming of the \r\natmosphere and ocean; changes in the global water cycle; reductions \r\nin snow and ice; global mean sea level rise; and changes in some \r\nclimate extremes. The AR5 concluded that 'it is extremely likely that \r\nhuman influence has been the dominant cause of the observed \r\nwarming since the mid-20th century' (IPCC, 2013b)."


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content:
"The FAR (IPCC, 1990a) concluded that while both theory and models \r\nsuggested that anthropogenic warming was already well underway, \r\nits signal could not yet be detected in observational data against the \r\n'noise' of natural variability (see also Section 1.4.2; and Barnett and \r\nSchlesinger, 1987). Since then, increased warming and progressively \r\nmore conclusive attribution studies have identified human activities \r\nas the 'dominant cause of the observed warming since the mid\u000220th  century' (IPCC, 2013b). 'Fingerprint' studies seek to detect \r\nspecific observed changes - expected from theoretical understanding \r\nand model results - that could not be explained by natural drivers \r\nalone, and to attribute statistically the proportion of such changes \r\nthat is due to human influence"


id: 17b18240d543442a11e9b213e70f9eeb
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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vector_id: 1225
content:
" These include global-scale surface \r\nwarming, nights warming faster than days, tropospheric warming \r\nand stratospheric cooling, a rising tropopause, increasing ocean heat \r\ncontent, changed global patterns of precipitation and sea level air \r\npressure, increasing downward longwave radiation, and decreasing \r\nupward longwave radiation (Hasselmann, 1979; Karoly et al., 1994; \r\nSchneider, 1994; Santer et al., 1995, 2013; Hegerl et al., 1996, 1997; \r\nGillett et al., 2003; Santer, 2003; Zhang et al., 2007; Stott et al., 2010; \r\nDavy et al., 2017; Mann et al., 2017). The Cross-Working Group Box \r\non Attribution outlines attribution methods and uses from across \r\nAR6, now including event attribution (specifying the influence of \r\nclimate change on individual extreme events such as floods, or on \r\nthe frequency of classes of events such as tropical cyclones). Overall, \r\nthe evidence for human influence has grown substantially over time \r\nand from each IPCC report to the next."


id: a06a27e8068bcf267ccc9506130a7d18
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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index: climate_gpt_only_giec
vector_id: 22777
content:
"Many different sets of climate projections have been produced over \r\nthe past several decades, so it is valuable to assess how well those \r\nprojections have compared against subsequent observations. Consistent \r\nfindings build confidence in the process of making projections for the \r\nfuture. For example, Stouffer and Manabe (2017) compared projections \r\nmade in the early 1990s with subsequent observations. They found that \r\nthe projected surface pattern of warming, and the vertical structure of \r\ntemperature change in both the atmosphere and ocean, were realistic. \r\nRahmstorf et al. (2007, 2012) examined projections of global surface \r\ntemperature and GMSL assessed by TAR and AR4 and found that \r\nthe global surface temperature projections were in good agreement \r\nwith the subsequent observations, but that sea level projections \r\nwere underestimates compared to subsequent observations"


id: 6cf29f5353d4512c4e6fb2593dd7b7c3
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
content_type: text
index: climate_gpt_only_giec
vector_id: 14765
content:
" The \r\nAR5 WGI also examined earlier IPCC assessment reports to evaluate \r\ntheir projections of how global surface temperature and GMSL would \r\nchange (Cubasch et al., 2013) with similar conclusions."


id: def724c1999ad18591be66c086c1be13
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
content_type: text
index: climate_gpt_only_giec
vector_id: 32574
content:
"The AR5 WGI assessed that there is a close relationship of cumulative \r\ntotal emissions of CO2 and GMST response that is approximately \r\nlinear (IPCC, 2013b). This finding implies that continued emissions \r\nof CO2 will cause further warming and changes in all components of \r\nthe climate system, independent of any specific scenario or pathway. \r\nScenario-based climate projections using the Representative \r\nConcentration Pathways (RCPs) assessed in AR5 WGI result in \r\ncontinued warming over the 21st century in all scenarios except \r\na  strong climate change mitigation scenario (RCP2.6). Similarly, \r\nunder all RCP scenarios, AR5 assessed that the rate of sea level rise \r\nover the 21st  century will very likely exceed that observed during \r\n1971-2010 due to increased ocean warming and increased loss of \r\nmass from glaciers and ice sheets. Further increases in atmospheric \r\nCO2 will also lead to further uptake of carbon by the ocean, which will \r\nincrease ocean acidification"


id: 4c9b22083e3a80b6b182728bd97abb12
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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index: climate_gpt_only_giec
vector_id: 9478
content:
" By the mid-21st century the magnitudes \r\nof the projected changes are substantially affected by the choice of \r\nscenario. The set of scenarios used in climate change projections \r\nassessed as part of AR6 is discussed in Section 1.6."


id: 4e565e54896d4933dd5a2c782af4fb9b
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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index: climate_gpt_only_giec
vector_id: 9756
content:
"Although this approach has limitations when the modelled forcings \r\ndiffer greatly from the forcings subsequently experienced, they \r\nwere generally able to project actual future global warming when \r\nthe mismatches between forecast and observed radiative forcings \r\nare accounted for. For example, Scenario B  presented in Hansen \r\net al. (1988) projected around 50% more warming than has been \r\nobserved during the 1988-2017 period, but this is largely because \r\nit overestimated subsequent radiative forcings. Similarly, while FAR \r\n(IPCC, 1990a) projected a higher rate of global surface temperature \r\nwarming than has been observed, this is largely because it \r\noverestimated future GHG concentrations: FAR's projected increase in \r\ntotal anthropogenic forcing between 1990 and 2017 was 1.6 W m-2, \r\nwhile the observational estimate of actual forcing during that \r\nperiod is 1.1 W m-2 (Dessler and Forster, 2018)"


id: 9316e16f595bd7aae8d886a984246741
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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index: climate_gpt_only_giec
vector_id: 20655
content:
" Under these actual \r\nforcings, the change in temperature in FAR aligns with observations \r\n(Hausfather et al., 2020)."


id: ab7faed68098d74b75498dc46da10e67
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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index: climate_gpt_only_giec
vector_id: 24494
content:
"The SR1.5 estimated with high confidence that human activities caused a  global warming of approximately 1degC between the \r\n1850-1900 period and 2017. For the period 2006-2015, observed global mean surface temperature (GMST7) was 0.87degC +- 0.12degC \r\nhigher than the average over the 1850-1900 period (very high confidence). Anthropogenic global warming was estimated to be \r\nincreasing at 0.2 +- 0.1degC per decade (high confidence) and likely matches the level of observed warming to within +-20%. The SRCCL \r\nfound with high confidence that over land, mean surface air temperature increased by 1.53degC +- 0.15degC between 1850-1900 and \r\n2006-2015, or nearly twice as much as the global average"


id: a124b6f81a22abd07963f0c6fb2b8a9c
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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index: climate_gpt_only_giec
vector_id: 22911
content:
" This observed warming has already led to increases in the frequency and \r\nintensity of climate and weather extremes in many regions and seasons, including heat waves in most land regions (high confidence), \r\nincreased droughts in some regions (medium confidence), and increases in the intensity of heavy precipitation events at the global \r\nscale (medium confidence). These climate changes have contributed to desertification and land degradation in many regions (high \r\nconfidence). Increased urbanization can enhance warming in cities and their surroundings (heat island effect), especially during heat \r\nwaves (high confidence), and intensify extreme rainfall (medium confidence)."


id: 41d462b0ba314e82ed845e1ebd728615
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
content_type: text
index: climate_gpt_only_giec
vector_id: 7761
content:
"Concerning the cryosphere, SROCC reported widespread continued shrinking of nearly all components. Mass loss from the Antarctic \r\nIce Sheet tripled over the period 2007-2016 relative to 1997-2006, while mass loss doubled for the Greenland Ice Sheet (likely,\r\nmedium confidence). The Report concludes with very high confidence that due to the combined increased loss from the ice sheets, \r\nglobal mean sea level (GMSL) rise has accelerated (extremely likely). The rate of recent GMSL rise (3.6 +- 0.5 mm yr-1 for 2006-2015) is \r\nabout 2.5 times larger than for 1901-1990. The report also found that Arctic sea ice extent has very likely decreased for all months of \r\nthe year since 1979 and that September sea ice reductions of 12.8 +- 2.3% per decade are likely unprecedented for at least 1000 years"


id: bc891684d7f0394c522644d109cbb5ca
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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index: climate_gpt_only_giec
vector_id: 27242
content:
" \r\nFeedbacks from the loss of summer sea ice and spring snow cover on land have contributed to amplified warming in the Arctic (high \r\nconfidence), where surface air temperature likely increased by more than double the global average over the last two decades. By \r\ncontrast, Antarctic sea ice extent overall saw no statistically significant trend for the period 1979-2018 (high confidence)."


id: a6d869f3e2f8be2ee56f23f09c062da9
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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vector_id: 23768
content:
"The SROCC projected that global-scale glacier mass loss, permafrost thaw, and decline in snow cover and Arctic sea ice extent will \r\ncontinue in the period 2031-2050 due to surface air temperature increases (high confidence). The Greenland and Antarctic ice sheets \r\nare projected to lose mass at an increasing rate throughout the 21st century and beyond (high confidence). Sea level rise will also \r\ncontinue at an increasing rate. For the period 2081-2100 with respect to 1986-2005, the likely ranges of GMSL rise are projected \r\nat 0.26-0.53 m for RCP2.6 and 0.51-0.92 m for RCP8.5. For the RCP8.5 scenario, projections of GMSL rise by 2100 are higher by \r\n0.1 m than in AR5 due to a larger contribution from the Antarctic Ice Sheet (medium confidence). Extreme sea level events that \r\noccurred once per hundred years in the recent past are projected to occur at least once per year at many locations by 2050, especially \r\nin tropical regions, under all RCP scenarios (high confidence)"


id: 47b3819fb1db4329c8777ae3dcd009f5
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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index: climate_gpt_only_giec
vector_id: 8662
content:
" According to SR1.5, by 2100 GMSL rise would be around 0.1 m lower \r\nwith 1.5degC global warming compared to 2degC (medium confidence). If warming is held to 1.5degC, GMSL will still continue to rise well \r\nbeyond 2100, but at a slower rate and a lower magnitude. However, instability and/or irreversible loss of the Greenland and Antarctic \r\nice sheets, resulting in a multi-metre rise in sea level over hundreds to thousands of years, could be triggered at 1.5degC-2degC of global \r\nwarming (medium confidence). According to SROCC, sea level rise in an extended RCP2.6 scenario would be limited to around 1 m in \r\n2300 (low confidence) while under RCP8.5 multi-metre sea level rise is projected by then (medium confidence)."


id: 3fb3e4d3eee4d01991d7a1aa3703ceab
created_at: 2023-03-16 14:01:56
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content:
"The AR6 WGI builds on previous assessments using well established \r\nfoundations and concepts. This section highlights some of the cross\u0002cutting methods applied in the climate change literature and topics \r\ndiscussed repeatedly throughout this Report. First, the choices related \r\nto 'baselines', or 'reference periods', are highlighted (Section 1.4.1), \r\nincluding a specific discussion on the pre-industrial baseline used in \r\nAR6 WGI (Cross-Chapter Box 1.2). The relationships between long\u0002term trends, climate variability and the concept of 'emergence of \r\nchanges' (Section  1.4.2) and the sources of uncertainty in climate \r\nsimulations (Section  1.4.3) are discussed next. The topic of low\u0002likelihood outcomes, storylines, abrupt changes and surprises follows \r\n(Section  1.4.4), including a  description of AR6 WGI risk framing \r\n(Cross-Chapter Box 1.3). The Cross-Working Group Box on Attribution \r\ndescribes attribution methods, including those for extreme events"


id: 2d249b064ab3db0888af52feec56d093
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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vector_id: 4545
content:
" \r\nVarious sets of geographical regions used in later chapters are also \r\ndefined and introduced (Section 1.4.5)."


id: 12d5e391e410a3cf8a0ce1c2f79393d0
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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vector_id: 468
content:
"Third, different datasets can have different absolute values for the \r\nsame climate variable that should be removed to allow effective \r\ncomparisons of variations over time. This is often required when \r\ncomparing climate simulations with each other, or when comparing \r\nsimulations with observations, as simulated climate variables are \r\nalso affected by model bias that can be removed when they are \r\npresented as anomalies. It can also be required when comparing \r\nobservational datasets or reanalyses (Section 1.5.2) with each other, \r\ndue to systematic differences in the underlying measurement system \r\n(Figure 1.11). Understanding the reasons for any absolute difference \r\nis important, but whether the simulated absolute value matters when \r\nprojecting future change will depend on the variable of interest"


id: b921d385faae18aaf94b2bdc76a0f1d6
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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index: climate_gpt_only_giec
vector_id: 26659
content:
" For \r\nexample, there is not a strong relationship between climate sensitivity \r\nof a model (which is an indicator of the degree of future warming) \r\nand the simulated absolute global surface temperature (Mauritsen \r\net al., 2012; Hawkins and Sutton, 2016)."


id: 83c58f1e2ea2a9390d5c9cc29e301ace
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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index: climate_gpt_only_giec
vector_id: 18264
content:
"The Paris Agreement aims to limit global temperatures to specific thresholds 'above pre-industrial levels'. In AR6 WGI, as in previous \r\nIPCC reports, observations and projections of changes in global temperature are generally expressed relative to 1850-1900 as an \r\napproximate pre-industrial state (SR1.5, IPCC, 2018). This is a pragmatic choice based upon data availability considerations, though \r\nboth anthropogenic and natural changes to the climate occurred before 1850. The remaining carbon budgets, the chance of crossing \r\nglobal temperature thresholds, and projections of extremes and sea level rise at a  particular level of global warming can all be \r\nsensitive to the chosen definition of the approximate pre-industrial baseline (Millar et al., 2017b; Schurer et al., 2017; Pfleiderer et al., \r\n2018; Rogelj et al., 2019; Tokarska et al., 2019)"


id: 6e57faa49d85cd299758c39d486c780f
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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content:
" This Cross-Chapter Box assesses the evidence on change in radiative forcing and global \r\ntemperature from the period around 1750 to 1850-1900; variations in the climate before 1750 are discussed in Chapter 2."


id: 4978b61e3e18ec3a731114e52ebeef15
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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index: climate_gpt_only_giec
vector_id: 8948
content:
"Although there is some evidence for human influence on climate before 1750 (e.g., Ruddiman and Thomson, 2001; Koch et al., 2019), \r\nthe magnitude of the effect is still disputed (Section 5.1.2.3; e.g., Joos et al., 2004; J. Beck et al., 2018), and most studies analyse \r\nthe human influence on climate over the industrial period. Historically, the widespread use of coal-powered machinery started the \r\nIndustrial Revolution in Britain in the late 18th century (Ashton, 1997), but the global effects were small for several decades. In line \r\nwith this, previous IPCC assessment reports considered changes in radiative forcing relative to 1750, and temperature changes were \r\noften reported relative to the 'late 19th century'. The AR5 and SR1"


id: a1bc16a1f19fbfa6c54abbcf46145514
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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content:
"5 made the specific pragmatic choice to approximate pre-industrial \r\nglobal temperatures by using the average of the 1850-1900 period, when permanent surface observing networks emerged that \r\nprovide sufficiently accurate and continuous measurements on a near-global scale (Sections 1.3.1 and 2.3.1.1), and because model \r\nsimulations of the historical period used 1850 as their start date. For the same reasons, to ensure continuity with previous assessments, \r\nand because of larger uncertainties and lower confidence in climatic changes before 1850 than after, AR6 makes the same choice to \r\napproximate pre-industrial global temperatures by using the the average of the 1850-1900 period."


id: 3461f75cb092ae00df246254ed6771bd
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
content_type: text
index: climate_gpt_only_giec
vector_id: 5657
content:
"Here we assess improvements in our understanding of climatic changes in the period 1750-1850. Anthropogenic influences on \r\nclimate between 1750 and 1900 were primarily increased anthropogenic GHG and aerosol emissions, and changes in land use. \r\nBetween 1750 and 1850 atmospheric CO2 levels increased from about 278 ppm to about 285 ppm (equivalent to around 3 years \r\nof current rates of increase; Chapter 2, Section 2.2.3), corresponding to about 55 GtCO2 in the atmosphere. Estimates of emissions \r\nfrom fossil fuel burning (about 4 GtCO2, Boden et al., 2017) cannot explain the pre-1850 increase, so CO2 emissions from land-use \r\nchanges are implicated as the dominant source. The atmospheric concentration of other GHGs also increased over the same period, \r\nand there was a cooling influence from other anthropogenic radiative forcings (such as aerosols and land-use changes), but with \r\na  larger uncertainty than for GHGs (Sections  2.2.6 and 7.3.5.2, and Cross-Chapter Box  1"


id: 203e57b0c9b2964038a2011e16c423b4
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
content_type: text
index: climate_gpt_only_giec
vector_id: 2522
content:
"Several studies since AR5 have estimated changes in global temperatures following industrialisation and before 1850. Hawkins \r\net al. (2017) used observations, radiative forcing estimates and model simulations to estimate the warming from 1720-1800 until \r\n1986-2005 and assessed a  likely range of 0.55degC-0.80degC, slightly broader than the equivalent range starting from 1850-1900 \r\n(0.6degC-0.7degC). From proxy evidence, PAGES 2k Consortium (2019) found that GMST for 1850-1900 was 0.02 [-0.22 to +0.16] degC \r\nwarmer than the 30-year period centred on 1750. Schurer et al. (2017) used climate model simulations of the last millennium to \r\nestimate that the increase in GHG concentrations before 1850 caused an additional likely range of 0.0degC -0.2degC global warming \r\nwhen considering multiple reference periods. Haustein et al. (2017) implies an additional warming of around 0.05degC attributable \r\nto human activity from 1750 to 1850-1900, and the AR6 emulator (Section 7.3.5"


id: ebacf9d94568fc9243be6337b1483afe
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
content_type: text
index: climate_gpt_only_giec
vector_id: 34579
content:
"see Section  1.5.4.2). A  set of illustrative examples using one such \r\nlarge ensemble (Maher et al., 2019) demonstrates how variability \r\ncan influence trends on decadal time scales (Figure 1.13). The long\u0002term anthropogenic trends in this set of climate indicators are clearly \r\napparent when considering the ensemble as a whole (grey shading), \r\nand all the individual ensemble members have very similar trends \r\nfor ocean heat content (OHC), which is a robust estimate of the total \r\nenergy stored in the climate system (e.g., Palmer and McNeall, 2014). \r\nHowever, the individual ensemble members can exhibit very different \r\ndecadal trends in global surface air temperature (GSAT), UK summer \r\ntemperatures, and Arctic sea ice variations. More specifically, for \r\na representative 11-year period, both positive and negative trends \r\ncan be found in all these surface indicators, even though the long\u0002term trend is for increasing temperatures and decreasing sea ice"


id: f234e23c9bbd5e803859d2cbd997a997
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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index: climate_gpt_only_giec
vector_id: 35637
content:
" \r\nPeriods in which the long-term trend is substantially masked or \r\nenhanced for more than 20 years are also visible in these regional \r\nexamples. This highlights the fact that observations are expected \r\nto exhibit short-term trends which are larger or smaller than the \r\nlong-term trend or that differ from the average projected trend from \r\nclimate models, especially on continental spatial scales or smaller \r\n(Cross-Chapter Box  3.1). The actual observed trajectory can be \r\nconsidered as one realization of many possible alternative worlds \r\nthat experienced different weather; this is also demonstrated by "


id: 3a40de96b5bb150e4f17be8606740b9
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
content_type: text
index: climate_gpt_only_giec
vector_id: 6534
content:
"of a  climate change signal or trend refers to when a  change in \r\nclimate (the 'signal') becomes larger than the amplitude of natural \r\nor internal variations (defining the 'noise'). This concept is often \r\nexpressed as a 'signal-to-noise' ratio (S/N) and emergence occurs at \r\na defined threshold of this ratio (e.g., S/N >1 or 2). Emergence can be \r\nestimated using observations and/or model simulations and can refer \r\nto changes relative to a historical or modern baseline (Section 12.5.2 \r\nand Glossary). The concept can also be expressed in terms of time \r\n(the 'time of emergence'; Glossary) or in terms of a global warming \r\nlevel (Section  11.2.5; Kirchmeier-Young et al., 2019) and is also \r\nused to refer to a time when we can expect to see a response of \r\nmitigation activities that reduce emissions of GHGs or enhance \r\ntheir sinks (emergence with respect to mitigation; Section 4.6.3.1)"


id: cec822d7b0f4c5989cd8206e382b40fb
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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index: climate_gpt_only_giec
vector_id: 30069
content:
" \r\nWhenever possible, emergence should be discussed in the context \r\nof a clearly defined level of S/N or other quantification, such as 'the \r\nsignal has emerged at the level of S/N >2', rather than as a simple \r\nbinary statement. For an extended discussion, see Chapter  10 \r\n(Section 10.4.3)."


id: 869d806ed1ec2425f046f4e17ae0fe50
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
content_type: text
index: climate_gpt_only_giec
vector_id: 18716
content:
"Although the magnitude of any change is important, regions which \r\nhave a larger signal of change relative to the background variations \r\nwill potentially face greater risks than other regions, as they will \r\nsee unusual or novel climate conditions more quickly (Frame et al., \r\n2017). As in Figure 1.14, the signal of temperature change is often \r\nsmaller in tropical countries, but their lower amplitude of variability \r\nmeans they may experience the effects of climate change earlier \r\nthan the mid-latitudes. In addition, these tropical countries are \r\noften among the most exposed, due to large populations (Lehner \r\nand Stocker, 2015), and often more vulnerable (Harrington et al., \r\n2016; Harrington and Otto, 2018; Russo et al., 2019). Higher levels \r\nof exposure and vulnerability increase the risk from climate\u0002related impacts (Cross-Chapter Box 1.3). The rate of change is also \r\nimportant for many hazards (e.g., Loarie et al., 2009)"


id: d7890075120921c062e1eeb7026fdeb
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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index: climate_gpt_only_giec
vector_id: 31431
content:
" Providing more \r\ninformation about changes and variations on regional scales, and \r\nthe associated attribution to particular causes (Cross-Working Group \r\nBox: Attribution), is therefore important for adaptation planning."


id: f143a1824d89b469ebdfa0c90c6e48e8
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
content_type: text
index: climate_gpt_only_giec
vector_id: 35461
content:
"Since AR5, the emergence of projected future changes has also \r\nbeen extensively examined, in variables including surface air \r\ntemperature (Hawkins and Sutton, 2012; Kirtman et al., 2013; \r\nTebaldi and Friedlingstein, 2013), ocean temperatures and salinity \r\n(Banks and Wood, 2002), mean precipitation (Giorgi and Bi, 2009; \r\nMaraun, 2013), drought (Orlowsky and Seneviratne, 2013), extremes \r\n(Diffenbaugh and Scherer, 2011; Fischer et al., 2014; King et al., 2015; \r\nSchleussner and Fyson, 2020), and regional sea level change (Lyu \r\net al., 2014). The concept has also been applied to climate change \r\nimpacts such as effects on crop growing regions (Rojas et al., 2019). \r\nIn AR6, the emergence of oceanic signals such as regional sea \r\nlevel change and changes in water mass properties is assessed in \r\nChapter 9 (Section 9.6.1.4); emergence of future regional changes is \r\nassessed in Chapter 10 (Section 10.4"


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content:
"Uncertainties also exist regarding past emissions and radiative \r\nforcings. These are especially important for simulations of paleoclimate \r\ntime periods, such as the Pliocene, Last Glacial Maximum or the last \r\nmillennium, but are also relevant for the CMIP historical simulations \r\nof the instrumental period since 1850. In particular, historical \r\nradiative forcings due to anthropogenic and natural aerosols are less \r\nwell constrained by observations than the GHG radiative forcings. \r\nThere is also uncertainty in the size of large volcanic eruptions (and \r\nin the location for some that occurred before around 1850), and the \r\namplitude of changes in solar activity, before satellite observations. \r\nThe role of historical radiative forcing uncertainty was considered \r\npreviously (Knutti et al., 2002; Forster et al"


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", 2013) but, since AR5, \r\nspecific simulations have been performed to examine this issue, \r\nparticularly for the effects of uncertainty in anthropogenic aerosol \r\nradiative forcing (e.g.,  Jimenez-de-la-Cuesta and Mauritsen, 2019; \r\nDittus et al., 2020)."


id: 60fbc50ca6ef2ad19b118fc90040dba5
created_at: 2023-03-16 14:01:56
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content:
"Under any particular scenario (Section 1.6.1), there is uncertainty in \r\nhow the climate will respond to the specified emissions or radiative \r\nforcing combinations. A  range of climate models is often used to \r\nestimate the range of uncertainty in our understanding of the key \r\nphysical processes and to define the 'model response uncertainty' \r\n(Sections 1.5.4 and 4.2.5). However, this range does not necessarily \r\nrepresent the full 'climate response uncertainty' in how the climate \r\nmay respond to a particular radiative forcing or emissions scenario. \r\nThis is because, for example, the climate models used in CMIP \r\nexperiments have structural uncertainties not explored in a typical \r\nmulti-model exercise (e.g., Murphy et al., 2004) and are not entirely \r\nindependent of each other (Section 1.5.4.8; Masson and Knutti, 2011; \r\nAbramowitz et al"


id: aa053520e2c93ce33ddfe9b17e6dfbef
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content:
", 2019); there are small spatial-scale features which \r\ncannot be resolved; and long time-scale processes or tipping points \r\nare not fully represented. Section 1.4.4 discusses how some of these \r\nissues can still be considered in a risk assessment context. For some \r\nmetrics, such as equilibrium climate sensitivity (ECS), the CMIP6 \r\nmodel range is found to be broader than the very likely range assessed \r\nby combining multiple lines of evidence (Sections 4.3.4 and 7.5.6)."


id: 98ca73032f4c67b5ef2c585189092864
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content:
"The relative uncertainty due to internal variability and model \r\nuncertainty increases for smaller spatial scales. In the regional \r\nexample shown in Figure 1.15 for changes in temperature, the same \r\nscenario and model combination has produced two simulations \r\nwhich differ by 1degC in their projected 2081-2100 averages due solely \r\nto internal climate variability. For regional precipitation changes, \r\nemissions scenario uncertainty is often small relative to model \r\nresponse uncertainty. In the example shown in Figure 1.15, the SSPs \r\noverlap considerably, but SSP1-1.9 shows the largest precipitation \r\nchange in the near term, even though global mean temperature \r\nwarms the least; this is due to differences between regional aerosol \r\nemissions projected in this and other scenarios (Wilcox et al., 2020). \r\nThese cascades of uncertainty would branch out further if applying the \r\nprojections to derive estimates of changes in hazard (e.g"


id: a956d1be49b1d3d6973af41221a10d80
created_at: 2023-03-16 14:01:56
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content:
"In the broader IPCC context, the term 'scenario storyline' refers to \r\na narrative description of one or more scenarios, highlighting their \r\nmain characteristics, relationships between key driving forces and \r\nthe dynamics of their evolution (e.g., emissions of short-lived climate \r\nforcers assessed in Chapter  6  are driven by 'scenario storylines'; \r\nsee Section  1.6). The AR6 WGI is mainly concerned with 'physical \r\nclimate storylines'. A  physical climate storyline is a  self-consistent \r\nand plausible physical trajectory of the climate system, or a weather \r\nor climate event, on time scales from hours to multiple decades \r\n(T.G. Shepherd et al., 2018). This approach can be used to constrain \r\nprojected changes or specific events on specified explanatory \r\nelements such as projected changes of large-scale indicators \r\n(Box  10.2). For example, Hazeleger et al"


id: 6ab97b1ca5a08aea965c68244c07ad1b
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content:
" (2015) suggested using \r\n'tales of future weather', blending numerical weather prediction with \r\na  climate projection to illustrate the potential behaviour of future \r\nhigh-impact events (also see Hegdahl et al., 2020). Several studies \r\ndescribe how possible large changes in atmospheric circulation "


id: 19dd33c97bafa13d410cf31f22f87f2c
created_at: 2023-03-16 14:01:56
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content:
"In the AR6 WGI Assessment Report, these different storyline \r\napproaches are used in several places (see Table 1.1). Chapter 4 uses \r\na  storyline approach to assess the upper tail of the distribution \r\nof global warming levels (the storylines of high global warming \r\nlevels) and their manifestation in global patterns of temperature \r\nand precipitation changes. Chapter  9  uses a  storyline approach to \r\nexamine the potential for, and early warning signals of, a high-end \r\nsea level scenario, in the context of deep uncertainty related to our \r\ncurrent understanding of the physical processes that contribute to \r\nlong-term sea level rise. Chapter  10 assesses the use of physical \r\nclimate storylines and narratives as a way to explore uncertainties \r\nin regional climate projections, and to link to the specific risk and \r\ndecision context relevant to a  user, for developing integrated and \r\ncontext-relevant regional climate change information"


id: 67d5f7262e6d3a9ef77951b6159d9b7a
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content:
" Chapter  11 \r\nuses the term storyline in the framework of extreme event attribution. \r\nChapter 12 assesses the use of a storylines approach with narrative \r\nelements for communicating climate (change) information in the \r\ncontext of climate services (Cross-Chapter Box 12.2)."


id: f081389f982a02fb1e66d5e2f4000558
created_at: 2023-03-16 14:01:56
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content:
"As examples of the use of this terminology, the term 'flood risk' should not be used if it only describes changes in the frequency and \r\nintensity of flood events (a hazard); the risk from flooding to human and ecological systems is caused by the flood hazard, the exposure \r\nof the system affected (e.g., topography, human settlements or infrastructure in the area potentially affected by flooding) and the \r\nvulnerability of the system (e.g., design and maintenance of infrastructure, existence of early warning systems). As another example, \r\nclimate-related risk to food security can arise from both potential climate change impacts and responses to climate change and can \r\nbe exacerbated by other stressors. Drivers for risks related to climate change impacts include climatic impact- drivers (e.g., drought, \r\ntemperature extremes, humidity) mediated by other climatic impact-drivers (e.g"


id: 6b2cd5d74f7bdd15d2de33851cb2bd32
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content:
", increased CO2 fertilization of certain types of crops \r\nmay help increase yields), the potential for indirect climate-related impacts (e.g., pest outbreaks triggered by ecosystem responses to \r\nweather patterns), exposure of people (e.g., how many people depend on a particular crop) and vulnerability or adaptability (how able \r\nare affected people to substitute other sources of food, which may be related to financial access and markets). "


id: 333224427c583a18f6846541e272df8e
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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content:
"Figure 1.17 | Illustration of two types of tipping points: noise-induced (a, b) and bifurcation (c, d). (a) and (c) are example time-series (coloured lines) through \r\nthe tipping point, with solid-black lines indicating stable climate states (e.g., low or high rainfall) and dashed lines representing the boundary between stable states. (b) and \r\n(d) are stability landscapes, which provide an intuitive understanding of the different types of tipping point. The 'valleys' represent different climate states the system can occupy, \r\nwith 'hilltops' separating the stable states. The resilience of a climate state is implied by the depth of the valley. The current state of the system is represented by a ball. Both \r\nscenarios assume that the ball starts in the left-hand valley (dashed-black lines) and then through different mechanisms dependent on the type of tipping transitions to the \r\nright-hand valley (coloured lines)"


id: 447cbd3c2a899d9ba37f6b40d49d94d7
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content:
" Noise-induced tipping events (a, b), for instance drought events causing sudden dieback of the Amazon rainforest, develop from fluctuations \r\nwithin the system. The stability landscape in this scenario remains fixed and stationary. A series of perturbations in the same direction, or one large perturbation, are required to \r\nforce the system over the hilltop and into the alternative stable state. Bifurcation tipping events (c, d), such as a collapse of the thermohaline circulation in the Atlantic Ocean \r\nunder climate change, occur when a critical level in the forcing is reached. Here the stability landscape is subjected to a change in shape. Under gradual anthropogenic forcing \r\nthe left-hand valley begins to shallow and eventually vanishes at the tipping point, forcing the system to transition to the right-hand valley."


id: 8a341a245b3203581f94f7fde5e4d1ac
created_at: 2023-03-16 14:01:56
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content:
"The unambiguous framing of what changes are being attributed to what causes is a crucial first step for an assessment (Easterling \r\net al., 2016; Hansen et al., 2016; Stone et al., 2021), followed by the identification of the possible and plausible drivers of change and \r\nthe development of a hypothesis or theory for the linkage (Cross-Working Group Box: Attribution, Figure 1). The next step is to clearly \r\ndefine the indicators of the observed change or event and note the quality of the observations. There has been significant progress in \r\nthe compilation of fragmented and distributed observational data, broadening and deepening the data basis for attribution research \r\n(WGI Section 1.5; e.g., Poloczanska et al., 2013; Ray et al., 2015; Cohen et al., 2018). The quality of the observational record of drivers \r\nshould also be considered (e.g., volcanic eruptions: WGI Section 2.2.2)"


id: 9e76f032880aeb737b45d03afc1ee80e
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content:
" Impacted systems also change in the absence of climate \r\nchange; this baseline and its associated modifiers - such as agricultural developments or population growth - need to be considered, \r\nalongside the exposure and vulnerability of people depending on these systems."


id: bfb46aa02b14b9e34c7b01a13443e739
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content:
"Changes in large-scale climate variables (e.g., global mean temperature) have been reliably attributed to anthropogenic and natural \r\nforcings (WGI Section 1.3.4; e.g., Hegerl et al., 2010; Bindoff et al., 2013). The most established method is to identify the 'fingerprint' \r\nof the expected space-time response to a particular climate forcing agent such as the concentration of anthropogenically induced \r\nGHGs or aerosols, or natural variation of solar radiation. This technique disentangles the contribution of individual forcing agents to \r\nan observed change (e.g., Gillett et al., 2021). New statistical approaches have been applied to better account for internal climate \r\nvariability and the uncertainties in models and observations (WGI Section 3.2; e.g., Naveau et al., 2018; Santer et al., 2019)"


id: 6d5b161695bd70f466d58abe85a28a3
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content:
" There \r\nare many other approaches, for example, global mean sea level change has been attributed to anthropogenic climate forcing by \r\nattributing the individual contributions from, for example, glacier melt or thermal expansion, while also examining which aspects of \r\nthe observed change are inconsistent with internal variability (WGI Sections 3.5.2 and 9.6.1.4)."


id: 99ba536eacadd597543f6812387e0555
created_at: 2023-03-16 14:01:56
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vector_id: 21706
content:
"Attribution of weather and climate events to anthropogenic forcing\r\nNew methods have emerged since AR5 to attribute the change \r\nin likelihood or characteristics of weather or climate events or \r\nclasses of events to underlying drivers (WGI Sections 10.4.1 and \r\n11.2.2; NA SEM, 2016; Stott et al., 2016; Jezequel et al., 2018; \r\nWehner et al., 2018; Wang et al., 2021). Typically, historical \r\nchanges, simulated under observed forcings, are compared \r\nto a  counterfactual climate simulated in the absence of \r\nanthropogenic forcing. Another approach examines facets of the \r\nweather and thermodynamic status of an event through process\u0002based attribution (WGI Chapter  11 and Section  10.4.1; Hauser \r\net al., 2016; Shepherd et al., 2018; Grose et al., 2019)"


id: 9a3f27123193ba01d4a1dfa9b0681d94
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content:
" Events \r\nwhere attributable human influence have been found include hot \r\nand cold temperature extremes (including some with widespread \r\nimpacts), heavy precipitation, and certain types of droughts \r\nand tropical cyclones (AR6 WGI Section 11.9; e.g., Vogel et al., \r\n2019; Herring et al., 2021). Event attribution techniques have \r\nsometimes been extended to 'end-to-end' assessments from \r\nclimate forcing to the impacts of events on natural or human \r\nsystems (Otto, 2017)."


id: f0615abf38094489648b327ee0a0d384
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content:
"Impact attribution covers a diverse set of qualitative and quantitative approaches, building on experimental approaches, observations \r\nfrom remote sensing, long-term in situ observations, and monitoring efforts, teamed with local knowledge, process understanding \r\nand empirical or dynamical modelling (WGII Section  16.2; Stone et al., 2013; Cramer et al., 2014). The attribution of a  change in \r\na natural or human system (e.g., wild species, natural ecosystems, crop yields, economic development, infrastructure or human health) \r\nto changes in climate-related systems (i.e., climate, ocean acidification, permafrost thawing or sea level rise) requires accounting for \r\nother potential drivers of change, such as technological and economic changes in agriculture affecting crop production (Hochman \r\net al., 2017; Butler et al., 2018), changes in human population patterns and vulnerability affecting flood- or wildfire-induced damages \r\n(Huggel et al., 2015; Sauer et al"


id: 13a2a9197bd004d97fd82b1c169702f3
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", 2021), or habitat loss driving declines in wild species (IPBES, 2019). These drivers are accounted \r\nfor by estimating a baseline condition that would exist in the absence of climate change. The baseline might be stationary and be \r\napproximated by observations from the past, or it may change over time and be simulated by statistical or process-based impact models \r\n(WGII Section 16.2; Cramer et al., 2014). Assessment of multiple independent lines of evidence, taken together, can provide rigorous \r\nattribution when more quantitative approaches are not available (Parmesan et al., 2013). These include paleodata, physiological \r\nand ecological experiments, natural 'experiments' from very long-term datasets indicating consistent responses to the same climate \r\ntrend/event, and 'fingerprints' in species' responses that are uniquely expected from climate change (e.g"


id: 3ae3ea0360af406395871feadd54140e
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content:
" poleward range boundaries \r\nexpanding and equatorial range boundaries contracting in a coherent pattern worldwide; Parmesan and Yohe, 2003). Meta-analyses \r\nof species/ecosystem responses, when conducted with wide geographic coverage, also provide a globally coherent signal of climate \r\nchange at an appropriate scale for attribution to anthropogenic climate change (Parmesan and Yohe, 2003; Parmesan et al., 2013)."


id: c70ec17db4f946ffa728e9ffd03650c2
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content:
"aggregation of climate variables was implicitly achieved through the \r\nconsideration of biomes, giving rise to the traditional vegetation\u0002based classification of Koppen (1936). In the last decades, the \r\nsubstantial increases in climate observations, climate modelling, and \r\ndata processing capabilities have allowed new approaches to climate \r\nclassification, for example through interpolation of aggregated \r\nglobal data from thousands of stations (Peel et al., 2007; Belda \r\net al., 2014; Beck et al., 2018) or through data-driven approaches \r\napplied to delineate ecoregions that behave in a coherent manner \r\nin response to  climate variability (Papagiannopoulou et al., 2018). \r\nExperience shows that each method has strengths and weaknesses \r\nthrough trade-offs between detail and convenience"


id: b93681e70fb8ffb897b182ea1c71a05d
created_at: 2023-03-16 14:01:56
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content:
" For instance, \r\na  very detailed classification, with numerous complexly shaped \r\nregions derived from a  large set of variables, may be most useful \r\nfor the evaluation of climate models (Rubel and Kottek, 2010; Belda \r\net al., 2015; Beck et al., 2018) and climate projections (Feng et al., \r\n2014; Belda et al., 2016). In contrast, geometrically simple regions \r\nare often best suited for regional climate modelling and downscaling \r\n(e.g.,  the Coordinated Regional Climate Downscaling Experiment \r\n(CORDEX) domains; Section 1.5.3; Giorgi and Gutowski, 2015)."


id: de4aa883bcc89ff76b436dbfb0c2c360
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content:
"The Reference Sets of Land and Ocean Regions are polygonal, \r\nsub-continental domains, defined through a  combination of \r\nenvironmental, climatic and non-climatic (e.g., pragmatic, technical, \r\nhistorical) factors, in accordance with the literature and climatological \r\nreasoning based on observed and projected future climate. Merging \r\nthe diverse functions and purposes of the regions assessed in the \r\nliterature into a common reference set implies a certain degree of \r\ncompromise between simplicity, practicality and climate consistency. \r\nFor instance, Spain is fully included in the Mediterranean (MED) \r\nReference Region, but is one of the most climatically diverse \r\ncountries in the world. Likewise, a  careful comparison of panels \r\na and b of Figure 1.18 reveals that the simplified southern boundary \r\nof the Sahara (SAH) Reference Region slightly overlaps the northern \r\nboundary of the West African Monsoon Typological Region"


id: 8ffd846022cb287b06edd7f55c111c80
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content:
" As \r\nsuch, the resulting Reference Regions are not intended to precisely \r\nrepresent climates, but rather to provide simple domains suitable for \r\nregional synthesis of observed and modelled climate and climate \r\nchange information (Iturbide et al., 2020). In particular, CMIP6 model \r\nresults averaged over Reference Regions are presented in the Atlas."


id: 27e6614521c264e58c389e2caf348ee
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content:
"Observed large-scale climatic changes assessed in Chapter  2, \r\nattribution of these changes in Chapter 3, and regional observations \r\nof specific physical or biogeochemical processes presented in other \r\nChapters, are supported by improvements in observational capacity \r\nsince AR5. Attribution assessments can be made at a higher likelihood \r\nlevel than in AR5, due in part to the availability of longer observational \r\ndatasets (Chapter  3). Updated assessments are made based on \r\nnew and improved datasets, for example of global temperature \r\nchange (Cross-Chapter Box  2.3) or regional climate information \r\n(Section 10.2). Of particular relevance to the AR6 assessment are the \r\nEssential Climate Variables (ECVs; Hollmann et al., 2013; Bojinski et al., \r\n2014), and Essential Ocean Variables (EOVs; Lindstrom et al., 2012), \r\ncompiled by the Global Climate Observing System (GCOS; WMO, \r\n2016), and the Global Ocean Observing System (GOOS), respectively"


id: 2622928d8eb0868e68ebe760ada709a0
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content:
" \r\nThese variables include physical, chemical and biological variables \r\nor groups of linked variables, and underpin 'headline indicators' \r\n(a selected set of essential parameters representing the state of the \r\nclimate system) for climate monitoring (Trewin et al., 2021)."


id: 3127e105a53c5f42e33b1c7ba6e1f406
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content:
"Assessments of the hydrological cycle in Chapters  2  and 8  are \r\nsupported by longer time series and new developments. Examples \r\nare new satellites (McCabe et al., 2017) and measurements of water \r\nvapour using commercial laser absorption spectrometers and water \r\nvapour isotopic composition (Steen-Larsen et al., 2015; Zannoni \r\net al., 2019). Data products of higher quality have been developed \r\nsince AR5, such as the multi-source weighted ensemble precipitation \r\n(Beck et al., 2017) and multi-satellite terrestrial evaporation \r\nproducts(Fisher et al., 2017). Longer series are available for satellite\u0002derived global inundation data (Prigent et al., 2020). Observations \r\nof soil moisture are now available via the Soil Moisture and Ocean \r\nSalinity (SMOS) and the Soil Moisture Active Passive (SMAP) satellite \r\nretrievals, filling critical gaps in the observation of hydrological \r\ntrends and variability over land (Dorigo et al., 2017)"


id: df693ba55f5f7242e33191d8e8a0669e
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content:
" Similarly, the \r\nGravity Recovery and Climate Experiment GRACE and GRACE\u0002FO satellites (Tapley et al., 2019) have provided key constraints \r\non groundwater variability and trends around the world (Frappart \r\nand Ramillien, 2018). The combination of new observations with \r\nother sources of information has led to updated estimates of heat \r\nstorage in inland waters (Vanderkelen et al., 2020), contributing to \r\nrevised estimates of heat storage on the continents (Section 7.2.2.3; \r\nvon Schuckmann et al., 2020)."


id: 6c6447b931fdba49a391e33224aba6e9
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vector_id: 14681
content:
"Satellites provide observations of a large number of key atmospheric \r\nand land-surface variables, ensuring sustained observations over \r\nwide areas. Since AR5, such observations have expanded to include \r\nsatellite retrievals of atmospheric CO2 via the NASA Orbiting Carbon \r\nObservatory satellites (OCO-2 and OCO-3; Eldering et al., 2017), \r\nfollowing on from similar efforts employing the Greenhouse Gases \r\nObserving Satellite (GOSat; Yokota et al., 2009; Inoue et al., 2016). \r\nBy combining remote sensing and in situ measurements, knowledge \r\nof fluxes between the atmosphere and land surface has improved \r\n(Rebmann et al., 2018). FLUXNET (https://fluxnet.org/) has been \r\nproviding eddy covariance measurements of carbon, water, and \r\nenergy fluxes between the land and the atmosphere, with some of \r\nthe stations operating for over 20 years (Pastorello et al"


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content:
", 2017), while \r\nthe Baseline Surface Radiation Network (BSRN) has been maintaining \r\nhigh-quality radiation observations since the 1990s (Ohmura et al., \r\n1998; Driemel et al., 2018)."


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"The ongoing collection of information about the atmosphere as it \r\nevolves is supplemented by the reconstruction and digitization \r\nof data about past conditions. Programmes aimed at recovering \r\ninformation from sources such as handwritten weather journals and \r\nships' logs continue to make progress, and are steadily improving \r\nspatial coverage and extending our knowledge backward in time. \r\nFor example, Bronnimann et al. (2019a) has recently identified \r\nseveral thousand sources of climate data for land areas in the pre\u00021890 period, with many from the 18th  century. The vast majority \r\nof these data are not yet contained in international digital data \r\narchives, and substantial quantities of undigitized ships' weather \r\nlog data exist for the same period (Kaspar et al., 2015). Since AR5 \r\nthere has been a growth of 'citizen science' activities, making use \r\nof volunteers to rapidly transcribe substantial quantities of weather \r\nobservations. Examples of projects include: oldWeather"


id: d54fc47530713dd79c0f1905a4ea00
created_at: 2023-03-16 14:01:56
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"org and \r\nSouthernWeatherDiscovery.org (both of which used ship-based \r\nlogbook sources); the DRAW project (Data Rescue: Archival and \r\nWeather, which recovered land-based station data from Canada); \r\nWeatherRescue.org (land-based data from Europe); JungleWeather.org\r\n(data from the Congo); and the Climate History Australia project \r\n(data from Australia; e.g.,  Park et al., 2018; Hawkins et al., 2019). \r\nUndergraduate students have also been recruited to successfully \r\ndigitize rainfall data in Ireland (Ryan et al., 2018). Such observations \r\nare an invaluable source of weather and climate information for the \r\nearly historical period that continues to expand the digital archives \r\n(e.g., Freeman et al., 2017) which underpin observational datasets \r\nused across several Chapters."


id: 9cc01ea7d60cc67460ecca641c7dc673
created_at: 2023-03-16 14:01:56
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vector_id: 22197
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"Observations of the composition of the atmosphere have been \r\nfurther improved through expansions of existing surface observation \r\nnetworks (Bodeker et al., 2016; De Maziere et al., 2018) and through \r\nin situ measurements such as aircraft campaigns (Sections  2.2, \r\n5.2 and Section  6.2). Examples of expanded networks include the \r\nAerosols, Clouds and Trace Gases Research Infrastructure (ACTRIS; \r\nPandolfi et al., 2018), which focuses on short-lived climate forcers, \r\nand the Integrated Carbon Observation System (ICOS), which \r\nallows scientists to study and monitor the global carbon cycle \r\nand GHG emissions (Colomb et al., 2018)"


id: 1c5376fb253f077a31b81b200294728c
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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content:
" Examples of recent \r\naircraft observations include the Atmospheric Tomography Mission \r\n(ATom), which has flown repeatedly along the north-south axis of \r\nboth the Pacific and Atlantic oceans, and the continuation of the \r\nIn-service Aircraft for a  Global Observing System (IAGOS) effort, \r\nwhich measures atmospheric composition from commercial aircraft \r\n(Petzold et al., 2015)."


id: 466879c0729332f6239f67f3e770c104
created_at: 2023-03-16 14:01:56
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content:
"The value of gravity-based estimates of changes in ice-sheet mass \r\nhas increased, as the time series from the GRACE and GRACE-FO \r\nsatellites  - homogenized and absolutely calibrated  - is close to \r\n20 years in length. The European Space Agency's (ESA's) Cryosat-2 \r\nradar altimetry satellite mission has continued to provide \r\nmeasurements of the changes in the thickness of sea ice and the \r\nelevation of the Greenland and Antarctic ice sheets (Tilling et al., \r\n2018). Other missions include NASA's Operation IceBridge, collecting \r\nairborne remote-sensing measurements to bridge the gap between \r\nICESat (Ice, Cloud and land Elevation Satellite) and the upcoming \r\nICESat-2 laser altimetry missions. Longer time series from multiple \r\nmissions have led to considerable advances in understanding the \r\norigin of inconsistencies between the mass balances of different \r\nglaciers and reducing uncertainties in estimates of changes in \r\nthe Greenland and Antarctic ice sheets (Bamber et al"


id: 29920bcf250e0eeca9dbaeb45fb5b158
created_at: 2023-03-16 14:01:56
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content:
", 2018; A. \r\nShepherd et al., 2018; Shepherd et al., 2020). Last, the first observed \r\nclimatology of snowfall over Antarctica was obtained using the cloud/\r\nprecipitation radar onboard NASA's CloudSat (Palerme et al., 2014)."


id: 7ba137bb2d3af9b7c4ebb390c356e83f
created_at: 2023-03-16 14:01:56
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content:
"Satellite observations have recently expanded to include data on the \r\nfluorescence of land plants as a measure of photosynthetic activity \r\nvia the Global Ozone Monitoring Experiment (GOME; Guanter et al., \r\n2014; Yang et al., 2015) and OCO-2 satellites (Sun et al., 2017). \r\nClimate data records of leaf area index (LAI), characterizing the area \r\nof green leaves per unit of ground area, and the fraction of absorbed \r\nphotosynthetically active radiation (FAPAR) - an important indicator \r\nof photosynthetic activity and plant health (Gobron et al., 2009) - are \r\nnow available for over 30 years (Claverie et al., 2016). In addition, \r\nkey indicators such as fire disturbances/burned areas are now \r\nretrieved via satellite (Chuvieco et al., 2019)"


id: ef3ea6a4826b5d58fdb869e426706e4e
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content:
" In the US, the National \r\nEcological Observational Network (NEON) provides continental-scale \r\nobservations relevant to the assessment of changes in aquatic and \r\nterrestrial ecosystems via a wide variety of ground-based, airborne, \r\nand satellite platforms (Keller et al., 2008). All these long-term \r\nrecords reveal range shifts in ecosystems (Section 2.3.4)."


id: 65355c400c060412ec673e0d17503323
created_at: 2023-03-16 14:01:56
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vector_id: 13491
content:
"Threats to observational capacity also include the loss of natural \r\nclimate archives that are disappearing as a  direct consequence of \r\nwarming temperatures. Ice-core records from vulnerable alpine \r\nglaciers in the tropics (Permana et al., 2019) and the mid-latitudes \r\n(Gabrielli et al., 2016; Winski et al., 2018; Moreno et al., 2021) \r\ndocument more frequent melt layers in recent decades, with glacial \r\nretreat occurring at a rate and geographic scale that is unusual in \r\nthe Holocene (Solomina et al., 2015). The scope and severity of coral \r\nbleaching and mortality events have increased in recent decades \r\n(Hughes et al., 2018), with profound implications for the recovery \r\nof coral climate archives from new and existing sites. An observed \r\nincrease in the mortality of larger, long-lived trees over the last century \r\nis attributed to a  combination of warming, land-use change, and \r\ndisturbance (e.g., McDowell et al., 2020)"


id: dd978fdc8d07df88733df9c187f27a20
created_at: 2023-03-16 14:01:56
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content:
" The ongoing loss of these \r\nnatural, high-resolution climate archives endanger an end in their \r\ncoverage over recent decades, given that many of the longest \r\nmonthly- to annually-resolved paleoclimate records were collected \r\nin the 1960s to 1990s (e.g., the PAGES2K database as represented \r\nin PAGES 2k Consortium, 2017). This gap presents a barrier to the \r\ncalibration of existing decades-to-centuries-long records needed to \r\nconstrain past temperature and hydrology trends and extremes."


id: a4eb95d81a5470e987b411ec0615939d
created_at: 2023-03-16 14:01:56
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content:
"The lockdowns and societal outcomes arising from the COVID-19 \r\npandemic pose a  new threat to observing systems. For example, \r\nWMO and UNESCO-IOC (Intergovernmental Oceanographic \r\nCommission) published a summary of the changes to Earth system \r\nobservations during COVID-19 (WMO, 2020b). Fewer aircraft flights \r\n(down 75-90% in May 2020, depending on region) and ship transits \r\n(down 20% in May 2020) mean that onboard observations from \r\nthose networks have reduced in number and frequency (James \r\net al., 2020; Ingleby et al., 2021). Europe has deployed more \r\nradiosonde soundings to account for the reduction in data from air \r\ntraffic. Fewer ocean observing buoys were deployed during 2020, \r\nand reductions have been particularly prevalent in the tropics and \r\nSouthern Hemisphere. The full consequences of the pandemic, and \r\nresponses to it, will come to light over time"


id: ab4c2adc3b34f24acb5a0115fae560c2
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content:
" Estimates of the effect \r\nof the reduction in aircraft data assimilation on weather forecasting \r\nskill are small (James et al., 2020; Ingleby et al., 2021), potentially \r\nalleviating concerns about veracity of future atmospheric reanalyses \r\nof the COVID-19 pandemic period."


id: 474c2ec9d3013e523e4763cdd0bbeaa7
created_at: 2023-03-16 14:01:56
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content:
"Historical archives of weather and climate observations contained \r\nin ships' logs, weather diaries, observatory logbooks and other \r\nsources of documentary data also risk being lost, for example \r\nto natural disasters or accidental destruction. These archives \r\ninclude measurements of temperature (air and sea surface), \r\nrainfall, surface pressure, wind strength and direction, sunshine \r\namount, and many other variables back into the 19th  century. \r\nWhile internationally coordinated data-rescue efforts are focused \r\non recovering documentary sources of past weather and climate \r\ndata (e.g.,  Allan et al., 2011), no such coordinated efforts exist \r\nfor vulnerable paleoclimate archives"


id: 3c9b9b0f0f43eaf37356bd778f8ece52
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" Furthermore, oral traditions \r\nabout local and regional weather and climate from indigenous \r\npeoples represent valuable sources of information, especially when \r\nused in combination with instrumental climate data (Makondo \r\nand Thomas, 2018), but are in danger of being lost as indigenous \r\nknowledge-holders pass away."


id: 81fe83e9da89296d078420f5bcd60e4
created_at: 2023-03-16 14:01:56
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content:
"The intercomparison of reanalyses with each other, or with earlier \r\nversions, is often done for particular variables or aspects of the \r\nsimulation. ERA5 is assessed as the most reliable reanalysis for \r\nclimate trend assessment (Section  2.3). Compared to ERA-Interim, \r\nthe ERA5 forecast model and assimilation system, as well as the \r\navailability of improved reprocessing of observations, resulted in \r\nrelatively smaller errors when compared to observations, including \r\na better representation of global energy budgets, radiative forcing \r\nfrom volcanic eruptions (e.g., Mt. Pinatubo: Allan et al., 2020), the \r\npartitioning of surface energy (Martens et al., 2020), and wind (Kaiser\u0002Weiss et al., 2015, 2019; Borsche et al., 2016; Scherrer, 2020). In \r\nERA5, higher resolution means a better representation of Lagrangian \r\nmotion convective updrafts, gravity waves, tropical cyclones, and \r\nother meso- to synoptic-scale features of the atmosphere (Hoffmann \r\net al., 2019; Martens et al"


id: 57f54b5ee1a76c0c9d57543b72865692
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", 2020). Low-frequency variability is found \r\nto be generally well represented and, from 10 hPa downwards, \r\npatterns of anomalies in temperature match those from the ERA\u0002Interim, MERRA-2 and JRA-55 reanalyses. Inhomogeneities in the \r\nwater cycle have also been reduced (Hersbach et al., 2020)."


id: 50b3b687138058b58d4e8166a67863fe
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content:
"The first Ocean Reanalyses Intercomparison project (ORA-IP; \r\nBalmaseda et al., 2015) focussed on the uncertainty in key climate \r\nindicators, such as ocean heat content (Palmer et al., 2017), \r\nthermosteric sea level (Storto et al., 2017, 2019), salinity (Shi \r\net al., 2017), sea ice extent (Chevallier et al., 2017), and the AMOC \r\n(Karspeck et al., 2017). Reanalysis uncertainties occur in areas of \r\ninhomogeneous or sparse observational data sampling, such as for \r\nthe deep ocean, the Southern Ocean, and western boundary currents \r\n(Lellouche et al., 2018; Storto et al., 2019). Intercomparisons have \r\nalso been dedicated to specific variables such as mixed-layer depths \r\n(Toyoda et al., 2017), eddy kinetic energy, globally (Masina et al., \r\n2017) and in the polar regions (Uotila et al., 2019). Karspeck et al. \r\n(2017) found disagreement in the AMOC variability and strength in \r\nreanalyses over observation-sparse periods, whereas Jackson et al"


id: 2fc0613ca690dbb4f58db82413370859
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content:
" \r\n(2019) reported a lower spread in AMOC strength across an ensemble \r\nof ocean reanalyses of the recent period (1993-2010), linked to \r\nimproved observation availability for assimilation. Reanalyses also \r\nhave a larger spread of ocean heat uptake than data-only products \r\nand can produce spurious overestimates of heat uptake (Palmer \r\net al., 2017), which is important in the context of estimating climate \r\nsensitivity (Storto et al., 2019). The ensemble approach for ocean \r\nreanalyses provides another avenue for estimating uncertainties \r\nacross ocean reanalyses (Storto et al., 2019)."


id: 155087cf378ace676f57b3ca35ccd9d
created_at: 2023-03-16 14:01:56
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vector_id: 872
content:
"Although reanalyses such as ERA5 take advantage of new observational \r\ndatasets and present a great improvement in atmospheric reanalyses, \r\nthe issues introduced by the evolving observational network remain. \r\nSparse input reanalyses, where only a  limited set of reliable and \r\nlong-observed records are assimilated, address these issues, with \r\nthe limitation of fewer observational constraints. These efforts are \r\nsometimes called centennial-scale reanalyses. One example is the \r\natmospheric 20th century Reanalysis (Compo et al., 2011; Slivinski \r\net al., 2021) which assimilates only surface and sea-level pressure \r\nobservations, and is constrained by time-varying observed changes \r\nin atmospheric constituents, prescribed sea surface temperatures and \r\nsea ice concentration, creating a reconstruction of the weather over \r\nthe whole globe every three hours for the period 1806-2015. The \r\nERA-20C atmospheric reanalysis (covering 1900-2010; Poli et al"


id: f37fb9bd68c25acc511dfa1f6794182f
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", \r\n2016) also assimilates marine wind observations, and CERA-20C is \r\na centennial-scale reanalysis that assimilates both atmospheric and \r\noceanic observations for the 1901-2010 period (Laloyaux et al., \r\n2018). These centennial-scale reanalyses are often run as ensembles \r\nthat provide an estimate of the uncertainty in the simulated \r\nvariables over space and time. Slivinski et al. (2021) conclude that \r\nthe uncertainties in surface circulation fields in version 3  of the \r\n20th  century Reanalysis are reliable and that there is also skill in \r\nits tropospheric reconstruction over the 20th  century. Long-term \r\nchanges in other variables, such as precipitation, also agree well with \r\ndirect observation-based datasets (Sections 2.3.1.3 and 8.3.2.8)."


id: 7e9ed19bcedc6d7a2a43e6b39a025469
created_at: 2023-03-16 14:01:56
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vector_id: 17487
content:
"Longer reanalyses that extend further back in time than the \r\nbeginning of the instrumental record are being developed. They \r\ninclude the complete integration of paleoclimate archives and newly \r\navailable early instrumental data into extended reanalysis datasets. \r\nSuch integration leverages ongoing development of climate models \r\nthat can simulate paleoclimate records in their units of analysis \r\n(i.e., oxygen isotope composition, tree ring width, etc.), in many cases \r\nusing physical climate variables as input for so-called proxy system \r\nmodels (Evans et al., 2013; Dee et al., 2015). Ensemble Kalman filter \r\ndata assimilation approaches allow for combining paleoclimate \r\ndata and climate model data to generate annually resolved fields \r\n(Last Millenium Reanalysis, Hakim et al., 2016; Tardif et al., 2019) \r\nor even monthly fields (Franke et al., 2017)"


id: fa2d0214466a40379cc9f7d9cbacad35
created_at: 2023-03-16 14:01:56
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vector_id: 36877
content:
" This allows for a greater \r\nunderstanding of decadal variability (Parsons and Hakim, 2019) and \r\ngreater certainty around the full range of the frequency and severity \r\nof climate extremes. This, in turn, allows for better-defined detection \r\nof change. It also helps to identify the links between biogeochemical "


id: f958d9724da6a2c93aca8ada0c2166f9
created_at: 2023-03-16 14:01:56
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content:
"The developments in reanalyses described above mean that they \r\nare now used across a  range of applications. In AR6, reanalyses \r\nprovide information for fields and in regions where observations \r\nare limited. There is growing confidence that modern reanalyses can \r\nprovide another line of evidence in describing recent temperature \r\ntrends (Tables 2.4 and 2.5). As their spatial resolution increases, the \r\nexploration of fine-scale extremes in both space and time becomes \r\npossible (e.g., wind; Kaiser-Weiss et al., 2015). Longer reanalyses can \r\nbe used to describe the change in the climate over the last 100 to \r\n1000 years. Reanalyses have been used to help post-process climate \r\nmodel output, and drive impact models; however, they are often bias \r\nadjusted first (Cross-Chapter Box  10.2; e.g., Weedon et al., 2014). \r\nCopernicus Climate Change Service (C3S) provides a bias-adjusted \r\ndataset for global land areas based on ERA5 called WFDE5 (Cucchi \r\net al"


id: 9ed055af3d11a283e291969b60483909
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vector_id: 22529
content:
", 2020) which, combined with ERA5 information over the ocean \r\n(W5E5; Lange, 2019), is used as the AR6 Interactive Atlas reference \r\nfor the bias adjustment of model output."


id: 58ec76c1ac81949ed24b485a21d80bb6
created_at: 2023-03-16 14:01:56
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content:
"Earth system models are mathematical formulations of the natural \r\nlaws that govern the evolution of climate-relevant systems: \r\natmosphere, ocean, cryosphere, land, and biosphere, as well as the \r\ncarbon cycle (Flato, 2011). They build on the fundamental laws of \r\nphysics (e.g.,  Navier-Stokes or Clausius-Clapeyron equations) or \r\nempirical relationships established from observations and, when \r\npossible, they are constrained by fundamental conservation laws \r\n(e.g., mass and energy). The evolution of climate-relevant variables \r\nis computed numerically using high-performance computers (Andre \r\net al., 2014; Balaji et al., 2017), on three-dimensional discrete grids \r\n(Staniforth and Thuburn, 2012). The spatial (and temporal) resolution \r\nof these grids in both the horizontal and vertical directions determines \r\nwhich processes need to be parameterized or whether they can be \r\nexplicitly resolved"


id: c260cb9fae3c78761afc902d90ef7e32
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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index: climate_gpt_only_giec
vector_id: 28167
content:
" Developments since AR5 in model resolution, \r\nparameterizations and modelling of the land and ocean biosphere \r\nand of biogeochemical cycles are discussed below."


id: 8f63587e1188b0ba240f20f8d25c2b48
created_at: 2023-03-16 14:01:56
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vector_id: 20105
content:
"The horizontal resolution and the number of vertical levels in ESMs \r\nis generally higher in CMIP6 than in CMIP5 (Figure  1.19). Global \r\nmodels with finer horizontal grids better represent many aspects of \r\nthe circulation of the atmosphere (Gao et al., 2020; Schiemann et al., \r\n2020) and ocean (Bishop et al., 2016; Storkey et al., 2018), bringing \r\nimprovements in the simulation of the global hydrological cycle \r\n(Roberts et al., 2018). CMIP6 includes a dedicated effort (HighResMIP, \r\nHaarsma et al., 2016) to explore the effect of higher horizontal \r\nresolution, such as ~50 km, ~25 km and even ~10 km (Section 1.5.4.2 \r\nand Annex II, Table AII.6). Improvements are documented in the \r\nhighest-resolution coupled models used for HighResMip (Hewitt \r\net al., 2017; Roberts et al., 2019). Flexible grids allowing spatially \r\nvariable resolution in the atmosphere (McGregor, 2015; Giorgetta \r\net al., 2018) and in the ocean (Wang et al., 2014; Petersen et al"


id: f20fe88191c5e207b8f8cda00ad62d83
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vector_id: 35616
content:
"Since AR5, more sophisticated land-use and land-cover change \r\nrepresentations in ESMs have been developed to simulate the effects \r\nof land management on surface fluxes of carbon, water and energy \r\n(Lawrence et al., 2016), although the integration of many processes \r\n(e.g.,  wetland drainage, fire as a  management tool) remains \r\na  challenge (Pongratz et al., 2018). The importance of nitrogen \r\navailability to limit the terrestrial carbon sequestration has been \r\nrecognized (Section  5.4; Zaehle et al., 2014) and so an increasing \r\nnumber of models now include a prognostic representation of the \r\nterrestrial nitrogen cycle and its coupling to the land carbon cycle \r\n(Jones et al., 2016; Arora et al., 2020), leading to a  reduction in \r\nuncertainty for carbon budgets (Section 5.1; Jones and Friedlingstein, \r\n2020). As was the case in CMIP5 (Ciais et al"


id: 7b6c372992e9ac2a669564014628019b
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vector_id: 17010
content:
", 2013), the land surface \r\nprocesses represented vary across CMIP6 models, with at least \r\nsome key processes (fire, permafrost carbon, microbes, nutrients, \r\nvegetation dynamics, plant demography) absent from any particular \r\nESM land model (Table  5.4). Ocean biogeochemical models have \r\nevolved to enhance the consistency of the exchanges between ocean, \r\natmosphere and land, through riverine input and dust deposition \r\n(Stock et al., 2014; Aumont et al., 2015). Other developments include \r\nflexible plankton stoichiometric ratios (Galbraith and Martiny, 2015), \r\nimprovements in the representation of nitrogen fixation (Paulsen \r\net al., 2017), and the limitation of plankton growth by iron (Aumont \r\net al., 2015). Due to the long time scale of biogeochemical processes, \r\nhow the models are initialized (spun up) strategies has been shown \r\nto affect their performance in AR5 (Seferian et al., 2016)."


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content:
"The representation of ocean and cryosphere processes has also \r\nevolved significantly since CMIP5. The explicit representation of \r\nocean eddies, due to increased grid resolution (typically, from 1deg \r\nto  1/4 deg), is a  major advance in a  number of CMIP6 ocean model \r\ncomponents (Hewitt et al., 2017). Advances in sea ice models have \r\nbeen made, for example through correcting known shortcomings in \r\nCMIP5 simulations, in particular the persistent underestimation of \r\nthe rapid decline in summer Arctic sea ice extent (Rosenblum and \r\nEisenman, 2016, 2017; Turner and Comiso, 2017; Notz and Stroeve, \r\n2018). The development of glacier and ice-sheet models has been \r\nmotivated and guided by an improved understanding of key physical \r\nprocesses, including grounding line dynamics, stratigraphy and \r\nmicrostructure evolution, sub-shelf melting, and glacier and ice\u0002shelf calving, among others (Faria et al., 2014, 2018; Hanna et al., \r\n2020)"


id: f1f459f0814458f48f6320ec150abd05
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content:
" The resolution of ice-sheet models has continuously increased, \r\nincluding the use of nested grids, sub-grid interpolation schemes, \r\nand adaptive mesh approaches (Cornford et al., 2016), mainly for \r\na  more accurate representation of grounding-line migration and \r\ndata assimilation (Pattyn, 2018). Ice-sheet models are increasingly \r\ninteractively coupled with global and regional climate models, \r\naccounting for the height-mass-balance feedback (Vizcaino et al., \r\n2015; Le clec'h et al., 2019), and enabling a better representation of \r\nice-ocean processes, in particular for the Antarctic Ice Sheet (Asay\u0002Davis et al., 2017)."


id: eadfcfd93fad35a5f0ad2d1bd52f15cf
created_at: 2023-03-16 14:01:56
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content:
"An initial set of such choices is usually made by (often extensive) \r\ngroups of modellers working on individual components of the Earth \r\nsystem (e.g.,  ocean, atmosphere, land or sea ice). As components \r\nare assembled to build an ESM, the choices are refined so that the \r\nsimulated climate best represents a number of pre-defined climate \r\nvariables, or 'tuning targets'. When these are met the model is \r\nreleased for use in intercomparisons such as CMIP. Tuning targets \r\ncan be one of three types: mean climate; regional phenomena and \r\nfeatures; or historical trends (Hourdin et al., 2017). One example of \r\nsuch a goal is that when the simulated climate system receives energy \r\nfrom the sun in accordance with what we observe today, the resulting \r\nmean equilibrium temperature should also be close to observations"


id: 600a7dd37fda68fef8afe8dc9fbc55f6
created_at: 2023-03-16 14:01:56
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content:
"Each modelling group has its own strategy and, after AR5, a survey \r\nwas conducted to understand the tuning approach used in 23 CMIP5 \r\nmodelling centres. The results are discussed in Hourdin et al. (2017), \r\nwhich stresses that the behaviour of ESMs depends on the tuning \r\nstrategy. An important recommendation is that the calibration steps \r\nthat lead to particular model tuning should be carefully documented. \r\nIn CMIP6 each modelling group now describes the three levels of \r\ntuning, both for the complete ESM and for the individual components \r\n(available at https://explore.es-doc.org and in the published model \r\ndescriptions, Annex II: Models). The most important global tuning \r\ntarget for CMIP6 models is the net top-of-the-atmosphere (TOA) heat \r\nflux and its radiative components"


id: a682cdc86817ab82337ba0b64a100629
created_at: 2023-03-16 14:01:56
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content:
" Other global targets include: the \r\ndecomposition of the energy fluxes at TOA into a clear sky component \r\nand a component due to the radiative effect of clouds, global mean air \r\nand ocean temperature, sea ice extent, sea ice volume, glacial mass \r\nbalance, and the global root mean square error of precipitation. The TOA \r\nheat flux balance is achieved using a diversity of approaches, usually \r\nunique to each modelling group. Adjustments are made for parameters \r\nassociated with uncertain or poorly constrained processes (Schmidt \r\net al., 2017), for example the aerosol indirect effects, adjustments to \r\nocean albedo, marine dimethyl sulfide (DMS) parameterization, or \r\ncloud properties (Mauritsen and Roeckner, 2020)."


id: 59e13f3111fe82c57737ef61f92a64c9
created_at: 2023-03-16 14:01:56
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vector_id: 11644
content:
"More recently, a number of studies have pointed to the possibility \r\nof systematically different climate responses to external forcings in \r\nEMICs and complex ESMs (Frolicher and Paynter, 2015; Pfister and \r\nStocker, 2017, 2018) that need to be considered in the context of \r\nthis report. For example, Frolicher and Paynter (2015) showed that \r\nEMICs have a higher simulated realized warming fraction (i.e., the \r\nTCR/ECS ratio) than CMIP5 ESMs and speculated that this may bias \r\nthe temperature response to zero carbon emissions. But, in a recent \r\ncomprehensive multi-model analysis of the zero CO2 emissions \r\ncommitment, MacDougall et al. (2020) did not find any significant \r\ndifferences between EMICs and ESMs in committed temperatures \r\n90  years after halting emissions. While some EMICs contribute to \r\nparts of the CMIP6-endorsed MIPs, a coordinated EMICs modelling \r\neffort similar to those carried out for AR4 (Plattner et al., 2008) and \r\nAR5 (Eby et al., 2013; Zickfeld et al"


id: 214739b1fca6ba5760b24a9eb1845722
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content:
", 2013) is not in place for IPCC AR6; \r\nhowever, EMICs are assessed in a number of chapters. For example, \r\nChapters 4 and 5 use EMICs in the assessment of long-term climate \r\nchange beyond 2100 (Section  5.5); zero-emissions commitments, \r\novershoot and recovery (Section 4.7); consequences of CO2 removal \r\n(CDR) on the climate system and the carbon cycle (Sections 4.6 and \r\n5.6); and long-term carbon cycle-climate feedbacks (Section 5.4)."


id: 70dd093962005d5b3613df0212279012
created_at: 2023-03-16 14:01:56
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content:
"information at the regional scale (Roberts et al., 2018). However, \r\ndue to the large computational resources required by these models, \r\nonly a  limited number of simulations per model are available. \r\nIn  addition to CMIP global models, regional information can be \r\nderived using regional climate models (RCMs) and downscaling \r\ntechniques, presented in Chapter  10 and the Atlas. RCMs are \r\ndynamical models, similar to GCMs, that simulate a limited region \r\nand are forced with boudary conditions from a  global simulation, \r\noften correcting for biases (Section 10.3, Cross-Chapter Box 10.2 and \r\nAnnex II). This approach allows the use of a higher resolution within \r\nthe chosen domain, and thus better represents important drivers of \r\nregional climate such as mountain ranges, land management and \r\nurban effects. RCMs resolving atmospheric convection explicitly are \r\nnow included in intercomparisons (Coppola et al., 2020) and are \r\nused in Chapters 10, 11 and 12"


id: 8d1259614ef6fede44f9873dfd5f4da2
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content:
" Other approaches, such as statistical \r\ndownscaling, are also used to generate regional climate projections \r\n(Section 10.3; Maraun and Widmann, 2018)."


id: e615c257b92b1feb6ac7a48bf23d4e87
created_at: 2023-03-16 14:01:56
updated_at: 2023-03-16 14:01:56
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content:
"Physical emulators and simple climate models make up a broad \r\nclass of heavily parametrized models designed to reproduce the \r\nresponses of the more complex, process-based models, and provide \r\nrapid translations of emissions, via concentrations and radiative \r\nforcing, into probabilistic estimates of changes to the physical climate \r\nsystem. The main application of emulators is to extrapolate insights \r\nfrom ESMs and observational constraints to a larger set of emissions \r\nscenarios (Cross-Chapter Box  7.1). The computational efficiency of \r\nvarious emulating approaches opens new analytical possibilities, \r\ngiven that ESMs take a  lot of computational resources for each \r\nsimulation"


id: 67393138354b19e5b2300695f28668e5
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content:
" The applicability and usefulness of emulating approaches \r\nare however constrained by their skill in capturing the global mean \r\nclimate responses simulated by the ESMs (mainly limited to global \r\nmean or hemispheric land/ocean temperatures) and by their ability \r\nto extrapolate skilfully outside the calibrated range."


id: fb3f9aed6a93cb0f6bb1f5c6411b30f
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content:
"Earth system models of intermediate complexity (EMICs) \r\ncomplement the model hierarchy and fill the gap between conceptual, \r\nsimple climate models and complex GCMs or ESMs (Claussen \r\net al., 2002). EMICs are simplified; they include processes in a more \r\nparameterized, rather than explicitly calculated, form and generally \r\nhave lower spatial resolution compared to the complex ESMs. \r\nAs a result, EMICs require much less computational resource and can \r\nbe integrated for many thousands of years without supercomputers \r\n(Hajima et al., 2014). The range of EMICs used in climate change \r\nresearch is highly heterogeneous, ranging from zonally averaged or \r\nmixed-layer ocean models coupled to statistical-dynamical models \r\nof the atmosphere, to low-resolution three-dimensional ocean \r\nmodels coupled to simplified dynamical models of the atmosphere"


id: 111d2adab0339f07f68b59efc6955401
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content:
" \r\nAn increasing number of EMICs include interactive representations \r\nof the global carbon cycle, with varying levels of complexity and \r\nnumbers of processes considered (Plattner et al., 2008; Zickfeld \r\net al., 2013; MacDougall et al., 2020). Given the heterogeneity of \r\nthe EMIC community, modellers tend to focus on specific research \r\nquestions and develop individual models accordingly. As for any \r\ntype of models assessed in this Report, the set of EMICs undergoes \r\nthorough evaluation and fit-for-purpose testing before being applied \r\nto address specific climate aspects."


id: 3042baf0f564891564f937aa36e63af3
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content:
"The terms 'emulator' and 'simple climate model' (SCM) are different, \r\nalthough they are sometimes used interchangeably. SCM refers to \r\na  broad class of lower-dimensional models of the energy balance, \r\nradiative transfer, carbon cycle, or a  combination of such physical \r\ncomponents. SCMs can also be tuned to reproduce the calculations \r\nof climate-mean variables of a  given ESM, assuming that their \r\nstructural flexibility can capture both the parametric and structural \r\nuncertainties across process-oriented ESM responses. When run in \r\nthis setup, they are termed emulators. Simple climate models do not \r\nhave to be run in 'emulation' mode, though, as they can also be used \r\nto test consistency across multiple lines of evidence with regard to \r\nranges in ECS, TCR, TCRE and carbon cycle feedbacks (Chapters 5 and \r\n7)"


id: b7e2a034fffca7eeb32957dba8b058f1
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" Physical emulation can also be performed with very simple \r\nparameterizations ('one-or-few-line climate models'), statistical \r\nmethods like neural networks, genetic algorithms, or other artificial \r\nintelligence approaches, where the emulator behaviour is explicitly \r\ntuned to reproduce the response of a given ESM or model ensemble \r\n(Chapters 4, 5 and 7)."


id: 8285e584951d26babc3cb824aab4b0f0
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content:
"Typical strategies for enhancing the fitness-for-purpose of a model \r\ninclude increasing resolution in order to explicitly simulate key \r\nprocesses, improving relevant parameterizations, and careful tuning. \r\nChanges to a  model that enhance its fitness for one purpose can \r\nsometimes decrease its fitness for others, by upsetting a pre-existing \r\nbalance of approximations. When it is unclear whether a model is \r\nfit for a purpose of interest, there is often a closely related purpose \r\nfor which the evidence of fitness is clearer. For example, it might \r\nbe unclear whether a  model is fit for providing highly accurate \r\nprojections of precipitation changes in a region, but reasonable to \r\nthink that the model is fit for providing projections of precipitation \r\nchanges that cannot yet be ruled out (Parker, 2009). Such information \r\nabout plausible or credible changes can be useful to inform \r\nadaptation"


id: 34be2401e5cf0054780d7736298aba1
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content:
" Note that challenges associated with assessing models' \r\nfitness-for-purpose need not prevent reaching conclusions with high \r\nconfidence if there are multiple other lines of evidence supporting \r\nthose same conclusions."


id: 528fc1e4c69052a56dfa11d23b648ff3
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content:
"A key issue addressed in this Report is whether climate models \r\nare adequate or 'fit' for purposes of interest, that is, whether they \r\ncan be used to successfully answer particular research questions, \r\nespecially about the causes of recent climate change and the future \r\nevolution of climate (e.g.,  Parker, 2009; Notz, 2015; Knutti, 2018; \r\nWinsberg, 2018). Assessment of a  model's fitness-for-purpose can \r\nbe informed both by how the model represents relevant physical \r\nprocesses and by relevant performance metrics  (Baumberger \r\net al., 2017; Parker, 2020). The processes and metrics that are \r\nmost relevant can vary with the question of interest. For example, \r\na  question about changes in deep-ocean circulation compared \r\nwith a  question  about  changes  in  regional precipitation (Notz, \r\n2015; Gramelsberger et al., 2020). New model-evaluation \r\ntools (Section  1.5.4.5) and  emergent constraint methodologies \r\n(Section 1.5.4"


id: 379a75bcc89b05233afeebe8cabedf46
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content:
"7) can also aid the assessment of fitness-for-purpose, \r\nespecially in conjunction with process understanding (Klein and \r\nHall, 2015; Knutti, 2018). The broader availability of large model \r\nensembles may allow for novel tests of fitness that better account \r\nfor natural climate variability (Section 1.5.4.2). Fitness-for-purpose of \r\nmodels used in this Report is discussed in Chapter 3 (Section 3.8.4) \r\nfor the global scale, in Chapter 10 (Section 10.3) for regional climate, \r\nand in the other chapters for the process level."


id: 57e18813df51392e24a501ee96c2f289
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content:
"A key approach in climate science is the comparison of results from \r\nmultiple model simulations with each other and against observations. \r\nThese simulations have typically been performed by separate models \r\nwith consistent boundary conditions and prescribed emissions or \r\nradiative forcings, as in the Coupled Model Intercomparison Project \r\nphases (CMIP, Meehl et al., 2000, 2007a; Taylor et al., 2012; Eyring \r\net al., 2016). Such multi-model ensembles (MMEs) have proven \r\nhighly useful in sampling and quantifying model uncertainty, within \r\nand between generations of climate models. They also reduce the \r\ninfluence on projections of the particular sets of parametrizations \r\nand physical components simulated by individual models. The primary \r\nusage of MMEs is to provide a well-quantified model range, but when \r\nused carefully they can also increase confidence in projections (Knutti \r\net al., 2010)"


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content:
" Presently, however, many models also share provenance \r\n(Masson and Knutti, 2011) and may have common biases that should \r\nbe acknowledged when presenting and building on MME-derived \r\nconclusions (Section 1.5.4.6; Boe, 2018; Abramowitz et al., 2019)."


id: 33e8164672dfcf342a463620d36e14f0
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content:
"Since AR5, an increase in computing power has made it possible \r\nto investigate simulated internal variability and to provide \r\nrobust estimates of forced model responses, using large initial \r\ncondition  ensembles (ICEs), also referred to as single model \r\ninitial  condition large ensembles (SMILEs). Examples using GCMs \r\nor ESMs that support assessments in AR6 include the CESM \r\nLarge Ensemble (Kay et al., 2015), the MPI Grand Ensemble \r\n(Maher et al., 2019), and the CanESM2 large ensembles (Kirchmeier\u0002Young et al., 2017). Such ensembles employ a single GCM or ESM \r\nin a  fixed configuration, but starting from a  variety of different \r\ninitial states. In some experiments, these initial states only differ \r\nslightly. As the climate system is chaotic, such tiny changes in initial \r\nconditions lead to different evolutions for the individual realizations \r\nof the system as a  whole"


id: cd527c53d1243c919a850135649ca6a1
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content:
" Other experiments start from a  set of \r\nwell-separated ocean initial conditions to sample the uncertainty in \r\nthe circulation state of the ocean and its role in longer-time scale \r\nvariations. These two types of ICEs have been referred to as 'micro' \r\nand 'macro' perturbation ensembles respectively (Hawkins et al., \r\n2016). In support of this Report, most models contributing to CMIP6 \r\nhave produced ensembles of multiple realizations of their historical \r\nand scenario simulations (Chapters 3 and 4)."


id: 5ca27b8e7e39f1c27d11421df82194a4
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content:
"The CMIP DECK simulations form the basis for a range of assessments \r\nand projections in the following chapters. As in CMIP5, they consist of: \r\na 'pre-industrial' control simulation (piControl, where 'pre-industrial' \r\nis taken as fixed 1850 conditions in these experiments); an idealized, \r\nabrupt quadrupling of CO2 concentrations relative to piControl \r\n(to estimate equilibrium climate sensitivity); a 1% per year increase \r\nin CO2 concentrations relative to piControl (to estimate the transient \r\nclimate response); and a  transient simulation with prescribed sea\u0002surface temperatures for the period 1979-2014 (termed 'AMIP' for \r\nhistorical reasons). In addition, all participating models perform \r\na  historical simulation for the period 1850-2014. For the latter, \r\ncommon CMIP6 forcings are prescribed (Cross-Chapter Box  1.4, \r\nTable  2). Depending on the model setup, these include emissions \r\nand concentrations of short-lived species (Hoesly et al., 2018; Gidden \r\net al"


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", 2019), long-lived GHGs (Meinshausen et al., 2017), biomass \r\nburning emissions (van Marle et al., 2017), global gridded land-use \r\nforcing data (Ma et al., 2020), solar forcing (Matthes et al., 2017), \r\nand stratospheric aerosol data from volcanoes (Zanchettin et al., \r\n2016). The methods for generating gridded datasets are described \r\nin Feng et al. (2020). For AMIP simulations, common sea surface \r\ntemperatures (SSTs) and sea ice concentrations (SICs) are prescribed. \r\nFor simulations with prescribed aerosol abundances (i.e.,  not \r\ncalculated from emissions), optical properties and fractional changes \r\nin cloud droplet effective radius are generally prescribed in order to \r\nprovide a more consistent representation of aerosol forcing relative \r\nto earlier CMIP phases (Fiedler et al., 2017; Stevens et al., 2017). \r\nFor models without ozone chemistry, time-varying gridded ozone \r\nconcentrations and nitrogen deposition are also provided (Checa\u0002Garcia et al., 2018)."


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content:
"In addition, process- or regime-oriented evaluation of models has \r\nbeen expanded since AR5. By focusing on processes, causes of \r\nsystematic errors in the models can be identified and insights can \r\nbe gained as to whether a mean state or trend is correctly simulated \r\nand for the right reasons. This approach is commonly used for the \r\nevaluation of clouds (e.g., Williams and Webb, 2009; Konsta et al., \r\n2012; Bony et al., 2015; Dal Gesso et al., 2015; Jin et al., 2017), \r\ndust emissions (e.g.,  Parajuli et al., 2016; Wu et al., 2016) as well \r\nas aerosol-cloud (e.g., Gryspeerdt and Stier, 2012) and chemistry-\r\nclimate (SPARC, 2010) interactions. Process-oriented diagnostics \r\nhave also been used to evaluate specific phenomena such as the \r\nEl Nino-Southern Oscillation (ENSO; Guilyardi et al., 2016), the \r\nMadden-Julian Oscillation (MJO; Ahn et al., 2017; Jiang et al., 2018), \r\nSouthern Ocean clouds (Hyder et al., 2018), monsoons (Boo et al., \r\n2011; James et al"


id: 11c987f5c05ba091c9fce1a71b30da16
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content:
"Emergent constraints use the spread in model projections to estimate the \r\nsensitivities of the climate system to anthropogenic forcing, providing \r\nanother type of ensemble-wide information that is not readily available \r\nfrom simulations with one ESM alone. As emergent constraints depend on \r\nidentifying those observable aspects of the climate system that are most \r\nrelated to climate projections, they also help to focus model evaluation on \r\nthe most relevant observations (Hall et al., 2019). However, there is a chance \r\nthat indiscriminate data-mining of the multi-dimensional outputs from \r\nESMs could lead to spurious correlations (Caldwell et al., 2014; Wagman \r\nand Jackson, 2018) and less-than-robust emergent constraints on future \r\nchanges (Bracegirdle and Stephenson, 2013). To avoid this, emergent \r\nconstraints need to be tested 'out of sample' on parts of the dataset \r\nthat were not included in its construction (Caldwell et al"


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content:
", 2018) and\r\nshould also always be based on sound physical understanding \r\nand mathematical theory (Hall et al., 2019). Their conclusions should also \r\nbe reassessed when a new generation of MMEs becomes available, such \r\nas CMIP6. As an example, Chapter 7 (Section 7.5.4) discusses and assesses \r\nrecent studies where equilibrium climate sensitivities (ECS) diagnosed in \r\na multi-model ensemble are compared with the same models' estimates \r\nof an observable quantity, such as post-1970s global warming or \r\ntropical sea surface temperatures of past climates like the Last Glacial \r\nMaximum or the Pliocene. Assessments of other emergent constraints \r\nappear throughout later chapters, such as Chapter  4  (Section  4.2.5), \r\nChapter 5 (Section 5.4.6) and Chapter 7 (Section 7.5.4)."


id: 9491e16238a1820fbf0cbcc66c86fc58
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content:
"Concern has been raised about the large extent to which code is \r\nshared within the CMIP5 multi-model ensemble (Sanderson et al., \r\n2015a). Boe (2018) showed that a clear relationship exists between \r\nthe number of components shared by climate models and how similar \r\nthe simulations are. The resulting similarities in behaviour need to be \r\naccounted for in the generation of best-estimate multi-model climate \r\nprojections. This has led to calls to move beyond equally-weighted \r\nmulti-model means towards weighted means that take into account \r\nboth model performance and model independence (Sanderson et al., \r\n2015b, 2017; Knutti et al., 2017). Model independence has been \r\ndefined in terms of performance differences within an ensemble \r\n(Masson and Knutti, 2011; Knutti et al., 2013, 2017, Sanderson \r\net al., 2015a, b, 2017; Lorenz et al., 2018)"


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" However, this definition \r\nis sensitive to the choice of variable, observational dataset, metric, \r\ntime period, and region, and a  performance-ranked ensemble has \r\nbeen shown to sometimes perform worse than a random selection \r\n(Herger et al., 2018a). The adequacy of the constraint provided by the \r\ndata and experimental methods can be tested using a 'calibration\u0002validation' style partitioning of observations into two sets (Bishop and \r\nAbramowitz, 2013), or a 'perfect model approach' where one of the \r\nensemble members is treated as the reference dataset and all model \r\nweights are calibrated against it (Bishop and Abramowitz, 2013; \r\nWenzel et al., 2016; Knutti et al., 2017; Sanderson et al., 2017; Herger \r\net al., 2018a, b). Sunyer et al. (2014) use a Bayesian framework to \r\naccount for model dependencies and changes in model biases. Annan \r\nand Hargreaves (2017) provides a statistical, quantifiable definition of \r\nindependence that is independent of performance-based measures."


id: ee68c74ca7469e677c495c61cc395074
created_at: 2023-03-16 14:01:56
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"Assessments of climate model ensembles have commonly assumed \r\nthat each individual model is of equal value ('model democracy') \r\nand when combining simulations to estimate the mean and variance \r\nof quantities of interest, they are typically unweighted (Haughton \r\net al., 2015). This practice has been noted to diminish the influence \r\nof models exhibiting a  good match with observations (Tapiador \r\net al., 2020). However, exceptions to this approach exist, notably \r\nAR5 projections of sea ice, which only selected a few models which \r\npassed a model performance assessment (Collins et al., 2013), and \r\nmore studies on this topic have appeared since AR5 (e.g., Eyring et al., \r\n2019). Ensembles are typically sub-selected by removing either poorly \r\nperforming model simulations (McSweeney et al., 2015) or model \r\nsimulations that are perceived to add little additional information, \r\ntypically where multiple simulations have come from the same \r\nmodel"


id: 36c70b3e9f547575a26a806b2066cebf
created_at: 2023-03-16 14:01:56
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"The AR5 quantified uncertainty in CMIP5 climate projections by \r\nselecting one realization per model per scenario, and calculating \r\nthe 5-95% range of the resulting ensemble (Box 4.1) and the same \r\nstrategy is generally still used in AR6. Broadly, the following chapters \r\ntake the CMIP6 5-95% ensemble range as the likely uncertainty \r\nrange for projections,8 with no further weighting or consideration \r\nof model ancestry and as long as no universal, robust method for \r\nweighting a multi-model projection ensemble is available (Box 4.1). \r\nA  notable exception to this approach is the assessment of future \r\nchanges in global surface air temperature (GSAT), which also draws \r\non the updated best estimate and range of equilibrium climate \r\nsensitivity assessed in Chapter 7. For a thorough description of the \r\nmodel-weighting choices made in this Report, and the assessment \r\nof GSAT, see Chapter  4  (Box  4.1)"


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" Model selection and weighting \r\nin downscaling approaches for regional assessment is discussed in \r\nChapter 10 (Section 10.3.4)."


id: b3ad47e4b7839619ff39c16cec37827e
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"This section introduces three ways to synthesize climate \r\nchange  knowledge across topics and chapters. These 'dimensions \r\nof integration' include (i) emissions and concentration scenarios \r\nunderlying the climate change projections assessed in this \r\nReport,  (ii)  levels of global mean surface warming relative to the \r\n1850-1900 baseline ('global warming levels'), and (iii) cumulative \r\ncarbon emissions (Figure 1.24). All three dimensions can, in principle, \r\nbe used to synthesize physical science knowledge across WGI, and \r\nalso across climate change impacts, adaptation, and mitigation \r\nresearch. Scenarios, in particular, have a  long history of serving \r\nas a  common reference point within and across IPCC Working \r\nGroups and research communities. Similarly, cumulative carbon \r\nemissions and global warming levels provide key links between WGI \r\nassessments and those of the other WGs; these two dimensions \r\nframe the cause-effect chain investigated by WGI"


id: cb795ff1e68d13aba292f8fb5a4e8d68
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" The closest links \r\nto WGIII are the emissions scenarios, as WGIII considers drivers of \r\nemissions and climate change mitigation options. The links to WGII \r\nare the geophysical climate projections from the Earth system \r\nmodels, which are often used as the starting point in the literature on \r\nclimate impacts and adaptation."


id: ef18ffed4599f029e74c6f1d54f20505
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"Figure 1.25 | Global mean surface air temperature (GSAT) illustrated as warming stripes from blue (cold) to red (warm) over three different time periods.\r\nFrom 1750-1850 based on PAGES 2K reconstructions (PAGES 2k Consortium, 2017, 2019); from 1850-2018 showing the composite GSAT time series assessed in Chapter 2; \r\nand from 2020 onwards using the assessed GSAT projections for each Shared Socio-economic Pathway (SSP) (from Chapter 4). For the projections, the upper end of each arrow \r\naligns with the colour corresponding to the 95th percentile of the projected temperatures and the lower end aligns with the colour corresponding to the 5th percentile of the \r\nprojected temperature range. Projected temperatures are shown for five scenarios from 'very low' SSP1-1.9 to 'very high' SSP5-8.5 (see Cross-Chapter Box 1.4 for more details \r\non the scenarios). For illustrative purposes, natural variability has been added from a single CMIP6 Earth system model (MRI ESM2)"


id: 4a5a6254c14e03dc88294b609e32085b
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" The points in time when total CO2 emissions \r\npeak; reach halved levels of the peak; and reach net zero emissions are indicated with arrows, ' 1/2 ' and '0' marks, respectively. Further details on data sources and processing \r\nare available in the chapter data table (Table 1.SM.1)."


id: f162d01f0f33ea6b97c3127fd0f8380f
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"Figure 1.26 | Historical and projected future concentrations of carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) and global mean temperatures \r\n(GMST). GMST temperature reconstructions over the last 2000 years were compiled by the PAGES 2k Consortium (2017, 2019) (grey line, with 95% uncertainty range), joined \r\nby historical GMST time series assessed in Chapter 2 (black line) - both referenced against the 1850-1900 period. Future GSAT temperature projections are from CMIP6 ESM \r\nmodels across all concentration-driven SSP scenario projections (Chapter 4). The discontinuity around year 2100 for CMIP6 temperature projections results from the fact that not \r\nall ESM models ran each scenario past 2100. The grey vertical band indicates the future 2015-2300 period. The concentrations used to drive CMIP6 Earth system models are \r\nderived from ice core, firn and instrumental datasets (Meinshausen et al., 2017) and projected using an emulator (Cross-Chapter Box 7.1; Meinshausen et al"


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", 2020). The colours \r\nof the lines indicate the SSP scenarios used in this Report (Cross-Chapter Box 1.4, Figure 1). Further details on data sources and processing are available in the chapter data \r\ntable (Table 1.SM.1)."


id: 228196703edb5869fd16af0bc3e44dcf
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"The SSP scenarios can be used for either emissions- or concentration\u0002driven model experiments (Cross-Chapter Box 1.4). ESMs can be run \r\nwith emissions and concentrations data for GHGs and aerosols and \r\nland-use or landcover maps and calculate levels of radiative forcing \r\ninternally. The radiative forcing labels of the RCP and SSP scenarios, \r\nsuch as '2.6' in RCP2.6 or SSP1-2.6, are thus approximate labels \r\nfor the year 2100 only. The actual global mean effective radiative \r\nforcing varies across ESMs due to different radiative transfer \r\nschemes, uncertainties in aerosol-cloud interactions, and different \r\nfeedback mechanisms, among other reasons. Nonetheless, using \r\napproximate radiative forcing labels is advantageous because it \r\nestablishes a clear categorization of scenarios, with multiple climate \r\nforcings and different combinations in those scenarios summarized in \r\na single number. The classifications according to cumulative carbon \r\nemissions (Section  1.6"


id: d38e31077d5308fdb262b3560fcd6e2d
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"The core set of five illustrative SSP scenarios - SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 - was selected in this Report to \r\nalign with the objective that the new generation of SSP scenarios should fill certain gaps identified in the RCPs. For example, a scenario \r\nassuming reduced air-pollution control and thus higher aerosol emissions was missing from the RCPs. Likewise, nominally the only 'no\u0002additional-climate-policy' scenario in the set of RCPs was RCP8.5. The new SSP3-7.0 'no-additional-climate-policy' scenario fills both \r\nthese gaps. A very strong mitigation scenario in line with the 1.5degC goal of the Paris Agreement was also missing from the RCPs, and the \r\nSSP1-1.9 scenario now fills this gap, complementing the other strong mitigation scenario SSP1-2.6. The five core SSPs were also chosen \r\nto ensure some overlap with the RCP levels for radiative forcing at the year 2100 (specifically 2.6, 4.5, and 8.5; O'Neill et al., 2016; Tebaldi \r\net al"


id: 6f0af5e73ea1561a35f47c23fc5d8040
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", 2021), although effective radiative forcings are generally higher in the SSP scenarios compared to the equivalently named RCP \r\npathways (Section 4.6.2 and Cross-Chapter Box 1.4, Figure 1). In theory, running scenarios with similar radiative forcings would permit \r\nanalysis of the CMIP5 and CMIP6 outcomes for pairs of scenarios (e.g., RCP8.5 and SSP5-8.5) in terms of varying model characteristics \r\nrather than differences in the underlying scenarios. In practice, however, there are limitations to this approach (Sections 1.6.1.1 and 4.6.2)."


id: 228379192abefa3b3b1a413ae29df898
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"Cross-Chapter Box 1.4, Figure 1 | The SSP scenarios used in this Report, their indicative temperature evolution and radiative forcing categorization, \r\nand the five socio-economic storylines upon which they are built. The core set of scenarios used in this report - i.e., SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 \r\nand SSP5-8.5 - is shown together with an additional four SSPs that are part of ScenarioMIP, as well as previous RCP scenarios. In the left-hand panel, the indicative \r\ntemperature evolution is shown (adapted from Meinshausen et al., 2020). The black stripes on the respective scenario family panels on the left-hand side indicate \r\na larger set of IAM-based SSP scenarios that span the scenario range more fully, but are not used in this report. The SSP-radiative forcing matrix is shown on the \r\nright-hand panel, with the SSP socio-economic narratives shown as columns and the indicative radiative forcing categorization by 2100 shown as rows"


id: 3dcc8732ed949a9a5194296660956f40
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" Note that \r\nthe descriptive labels for the five SSP narratives refer mainly to the reference scenario futures without additional climate policies. For example, SSP5 can accommodate \r\nstrong mitigation scenarios leading to net zero emissions; these do not match a 'fossil-fuelled development' label. Further details on data sources and processing are \r\navailable in the chapter data table (Table 1.SM.1)."


id: 427712a38a397ad9b137362cac074435
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"Cross-Chapter Box 1.4, Table 1 | Overview of SSP scenarios used in this report. The middle column briefly describes the SSP scenarios and the right-hand \r\ncolumn indicates the previous RCP scenarios that most closely match that SSP's assessed global surface air temperature (GSAT) trajectory. RCP scenarios are generally \r\nfound to result in larger modelled warming for the same nominal radiative forcing label (Section 4.6.2.2). The five core SSP scenarios used most commonly in this report \r\nare highlighted in bold . Further SSP scenarios are used where they allow assessment of specific aspects, e.g., air pollution policies in Chapter 6 (SSP3-7.0-lowNTCF). \r\nRCPs are used in this report wherever the relevant scientific literature makes substantial use of regional or domain-specific model output that is based on these previous \r\nRCP pathways, such as sea level rise projections in Chapter 9 (Section 9.6.3.1) or regional climate aspects in Chapters 10 and 12. See Chapter 4 (Section 4.3"


id: 6a8a069ca3dce2e7b855e5f1216aa399
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"4) for the \r\nGSAT assessment for the SSP scenarios and Section 4.6.2.2 for a comparison between SSPs and RCPs in terms of both radiative forcing and global surface temperature."


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"ESMs are driven by either emissions or concentrations scenarios. Inferring concentration changes from emissions time series requires \r\nusing carbon cycle and other gas cycle models. To aid comparability across ESMs, and in order to allow participation of ESMs that do \r\nnot have coupled carbon and other gas cycle models in CMIP6, most of the CMIP6 ESM experiments are so-called 'concentration\u0002driven' runs, with concentrations of CO2, CH4, N2O and other well-mixed GHGs prescribed in conjunction with aerosol emissions, \r\nozone changes and effects from human-induced land-cover changes that may be radiatively active via albedo changes (Cross-Chapter \r\nBox 1.4, Figure 2). In these concentration-driven climate projections, the uncertainty in projected future climate change resulting from \r\nour limited understanding of how the carbon cycle and other gas cycles will evolve in the future is not captured"


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" For example, when \r\nderiving the default concentrations for these scenarios, permafrost and other carbon cycle feedbacks are considered using default \r\nsettings, with a single time series prescribed for all ESMs (Meinshausen et al., 2020). Thus, associated uncertainties (Joos et al., 2013; \r\nSchuur et al., 2015) are not considered."


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"The RCP scenarios (van Vuuren et al., 2011) then broke new ground \r\nby providing low-emissions pathways that implied strong climate \r\nchange mitigation, including an example with negative CO2 emissions \r\non a large scale, namely RCP2.6. As shown in Figure 1.28, the upper \r\nend of the scenario range has not substantially shifted. Building \r\non the SRES multi-gas scenarios, the RCPs include time series of \r\nemissions and concentrations of the full suite of GHGs, aerosols and \r\nchemically active gases, as well as land use and land cover (Moss \r\net al., 2010). The word 'representative' signifies that each RCP is \r\nonly one of many possible scenarios that would lead to the specific \r\nradiative forcing characteristics. The term 'pathway' emphasizes that \r\nnot only the long-term concentration levels are of interest, but also \r\nthe trajectory taken over time to reach that outcome (Moss et al., \r\n2010)"


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" RCPs usually refer to the concentration pathway extending to \r\n2100, for which IAMs produced corresponding emissions scenarios. \r\nFour RCPs produced from IAMs were selected from the published \r\nliterature and are used in AR5 as well as in this report, spanning \r\napproximately the range from below 2degC warming to high (above \r\n4degC) warming best-estimates by the end of the 21st century: RCP2.6, \r\nRCP4.5 and RCP6.0 and RCP8.5 (Cross-Chapter Box  1.4, Table  1). \r\nExtended Concentration Pathways (ECPs) describe extensions of \r\nthe RCPs from 2100 to 2300 that were calculated using simple rules \r\ngenerated by stakeholder consultations; these do not represent fully \r\nconsistent scenarios (Meinshausen et al., 2011b)."


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"In general, no likelihood is attached to the scenarios assessed in this \r\nReport. The use of different scenarios for climate change projections \r\nallows the exploration of 'scenario uncertainty' (Section 1.4.4; SR1.5; \r\nCollins et al., 2013). Scenario uncertainty is fundamentally different \r\nfrom geophysical uncertainties, which result from limitations in the \r\nunderstanding and predictability of the climate system (Smith and \r\nStern, 2011). In scenarios, by contrast, future emissions depend to \r\na large extent on the collective outcome of choices and processes \r\nrelated to population dynamics and economic activity, or on choices \r\nthat affect a given activity's energy and emissions intensity (Jones, \r\n2000; Knutti et al., 2008; Kriegler et al., 2012; van Vuuren et al., \r\n2014)"


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" Even if identical socio-economic futures are assumed, the \r\nassociated future emissions still face uncertainties, since different \r\nexperts and model frameworks diverge in their estimates of future \r\nemissions ranges (Ho et al., 2019)."


id: efc3f332a35e945fd7e263e53dc664cf
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"When exploring various climate futures, scenarios with no, or no \r\nadditional, climate policies are often referred to as 'baseline' or \r\n'reference scenarios' (Section 1.6.1.1 and Glossary). Among the five \r\ncore scenarios used most in this report, SSP3-7.0 and SSP5-8.5 are \r\nexplicit 'no-climate-policy' scenarios (Cross-Chapter Box 1.4, Table 1; \r\nGidden et al., 2019), assuming a carbon price of zero. These future \r\n'baseline' scenarios are hence counterfactuals that include fewer \r\nclimate policies compared to 'business-as-usual' scenarios  - given \r\nthat 'business-as-usual' scenarios could be understood to imply \r\na continuation of existing climate policies. Generally, future scenarios \r\nare meant to cover a broad range of plausible futures, due, for example \r\nto unforeseen discontinuities in development pathways  (Raskin \r\nand Swart, 2020), or to large uncertainties in underlying long\u0002term projections of economic drivers (Christensen et al., 2018)"


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" \r\nHowever, the likelihood of high-emissions scenarios such as RCP8.5 \r\nor SSP5-8.5 is considered low in light of recent developments in "


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"the  energy sector (Hausfather and Peters, 2020a, b). Studies that \r\nconsider possible future emissions trends in the absence of additional \r\nclimate policies, such as the recent IEA 2020 World Energy Outlook \r\n'stated policy' scenario (IEA, 2020), project approximately constant \r\nfossil fuel and industrial CO2 emissions out to 2070, approximately \r\nin line with the intermediate RCP4.5, RCP6.0 and SSP2-4.5 scenarios \r\n(Hausfather and Peters, 2020b) and the 2030 global emissions levels \r\nthat are pledged as part of the Nationally Determined Contributions \r\n(NDCs) under the Paris Agreement (Section  1.2.2; Fawcett et al., \r\n2015; Rogelj et al., 2016; UNFCCC, 2016; IPCC, 2018). On the other \r\nhand, the default concentrations aligned with RCP8.5 or SSP5-8.5 \r\nand resulting climate futures derived by ESMs could be reached by \r\nlower emissions trajectories than RCP8.5 or SSP5-8.5"


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" That is because \r\nthe uncertainty range on carbon cycle feedbacks includes stronger \r\nfeedbacks than assumed in the default derivation of RCP8.5 and \r\nSSP5-8.5 concentrations (Section 5.4; Ciais et al., 2013; Friedlingstein \r\net al., 2014; Booth et al., 2017)."


id: 1e06886312c2f562c4a9498af8d4f821
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"The global mean surface temperature change, or 'global warming \r\nlevel' (GWL), is a 'dimension of integration' that is highly relevant \r\nacross scientific disciplines and socio-economic actors. First, global \r\nwarming levels relative to pre-industrial conditions are the quantity \r\nin which the 1.5degC and 'well below 2degC' Paris Agreement goals were \r\nformulated. Second, global mean temperature change has been \r\nfound to be almost-linearly related to a number of regional climate \r\neffects (Mitchell et al., 2000; Mitchell, 2003; Tebaldi and Arblaster, \r\n2014; Seneviratne et al., 2016; Li et al., 2020; Seneviratne and Hauser, \r\n2020). Even where non-linearities are found, some regional climate \r\neffects can be considered to be almost scenario-independent for \r\na given level of warming (Sections 4.2.4, 4.6.1, 8.5.3 and 10.4.3.1, \r\nand Cross-Chapter Box  11.1)"


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" Finally, the evolution of aggregated \r\nimpacts with warming levels has been widely used and embedded \r\nin the assessment of the 'Reasons for Concern' (RFC) in IPCC WGII \r\n(Smith et al., 2009; IPCC, 2014a). The RFC framework was further \r\nexpanded in SR1.5 (2018), SROCC (2019) and SRCCL (2019) by \r\nexplicitly describing the differential impacts of half-degree warming \r\nsteps (Section 1.4.4 and Cross-Chapter Box 12.1; cf. King et al., 2017)."


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"To address long-term scenario uncertainties, scenario storylines \r\n(or  'narratives') are often used (see Section  1.4.4 for a  more \r\ngeneral discussion on 'storylines', also covering 'physical climate \r\nstorylines'; Rounsevell and Metzger, 2010; O'Neill et al., 2014). \r\nScenario storylines are descriptions of a  future world, and the \r\nrelated large-scale socio-economic development pathways towards \r\nthat world that are deemed plausible within the current state of \r\nknowledge and historical experience (Section 1.2.3; WGIII). Scenario \r\nstorylines attempt to 'stimulate, provoke, and communicate visions of \r\nwhat the future could hold for us' (Rounsevell and Metzger, 2010) in \r\nsettings where either limited knowledge or inherent unpredictability \r\nin social systems prevent a  forecast or numerical prediction"


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" \r\nScenario storylines have been used in previous climate research, \r\nand they are the explicit or implicit starting point of any scenario \r\nexercise, including for the SRES scenarios (IPCC, 2000) and the SSPs \r\n(e.g., O'Neill et al., 2017a)."


id: 32a8c0e4123f4079204a7adf2e86b2fa
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content:
"Recent technological or socio-economic trends might be informative \r\nfor bounding near-term future trends, for example, if technological \r\nprogress renders a  mitigation technology cheaper than previously \r\nassumed. However, short-term emissions trends alone do not \r\ngenerally rule out an opposite trend in the future (van Vuuren et al., \r\n2010). The ranking of individual RCP emissions scenarios from the \r\nIAMs with regard to emissions levels is different for different time \r\nhorizons, for example, 2020 compared with longer-term emissions \r\nlevels. For example, the strongest climate change mitigation scenario, \r\nRCP2.6, was in fact the second highest CO2 emissions scenario (jointly \r\nwith RCP4.5) before 2020 in the set of RCPs and the strong global \r\nemissions decline in RCP2.6 only followed after 2020. Implicitly, \r\nthis scenario feature was cautioning against the assumption that \r\nshort-term trends predicate particular long-term trajectories"


id: 3cefe94f31f40f275c6ceb3da18acfc6
created_at: 2023-03-16 14:01:57
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content:
" This is \r\nalso the case in relation to the COVID-19 related drop in 2020 \r\nemissions. Potential changes in underlying drivers of emissions, such \r\nas those potentially incentivized by COVID-19 recovery stimulus \r\npackages, are more significant for longer-term emissions than the \r\nshort-term deviation from recent emissions trends (Cross-Chapter \r\nBox 6.1 on COVID-19)."


id: 4134784913927e7a6e95014debffc1da
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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vector_id: 7646
content:
"In order to fully derive climate impacts, warming levels will need to be \r\ncomplemented by additional information, such as their associated CO2\r\nconcentrations (e.g., fertilization or ocean acidification), composition \r\nof the total radiative forcing (aerosols compared with GHGs, with \r\nvarying regional distributions) or socio-economic conditions (e.g., to \r\nestimate societal impacts). More fundamentally, while a  global \r\nwarming level is a good proxy for the state of the climate (Cross\u0002Chapter Box 11.1), it does not uniquely define a change in global or \r\nregional climate state. For example, regional precipitation responses \r\ndepend on the details of the individual forcing mechanisms that \r\ncaused the change (Samset et al., 2016); on whether the temperature \r\nlevel is stabilized or transient (King et al., 2020; Zappa et al., 2020); \r\non the vertical structure of the troposphere (Andrews et al"


id: d22edd2d5cad117f08c2523fdbb07a40
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content:
", 2010); \r\nand, in particular, on the global distribution of atmospheric aerosols \r\n(Frieler et al., 2012). Another aspect is how Earth system components \r\nwith  century-to-millennial response time scales, such as long-term \r\nsea level rise or permafrost thaw, are affected by global mean \r\nwarming. For example, sea level rise 50 years after a 1degC warming \r\nwill be lower than sea level rise 150  years after that same 1degC \r\nwarming (Chapter 9)."


id: 231389625d3037b9a46132fd0535ccd5
created_at: 2023-03-16 14:01:57
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content:
"There is also a  close relationship between cumulative total GHG \r\nemissions and cumulative CO2 emissions for scenarios in the \r\nSR1.5 scenario database (Figure 1.29; IPCC, 2018). The dominance \r\nof CO2 compared to other well-mixed GHGs (Figure  1.29 and \r\nSection 5.2.4) allows policymakers to make use of the carbon budget \r\nconcept (Section  5.5) in a  policy context, in which GWP-weighted \r\ncombinations of multiple GHGs are used to define emissions targets. \r\nA caveat is that cumulative GWP-weighted CO2 equivalent emissions \r\nover the next decades do not yield exactly the same temperature \r\noutcomes as the same amount of cumulative CO2 emissions, because \r\natmospheric perturbation lifetimes of the various GHGs differ. While \r\ncarbon budgets are not derived using GWP-weighted emissions \r\nbaskets but rather by explicit modelling of non-CO2-induced warming \r\n(Section  5.5 and Cross-Chapter Box  7"


id: e4f6c304c3406be44e2f079c65293401
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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content:
"1), the policy frameworks \r\nbased on GWP-weighted emissions baskets can still make use of the \r\ninsights from remaining cumulative carbon emissions for different \r\nwarming levels."


id: 9e1f03d56bc6cb8c92c4a8438c0109bd
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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vector_id: 22402
content:
"The same cumulative CO2 emissions could lead to a slightly different \r\nlevel of warming over time (Box  1.4). Rapid emissions followed \r\nby steep cuts and potentially net negative emissions would be \r\ncharacterized by a  higher maximum warming and faster warming \r\nrate, compared with the same cumulative CO2 emissions spread \r\nover a longer period. As further explored in the WGIII assessment, \r\none potential limitation when presenting emissions pathway \r\ncharacteristics in cumulative emissions budget categories is that path \r\ndependencies and lock-in effects (e.g.  today's decisions regarding \r\nfossil fuel-related infrastructure) play an important role in long-term \r\nmitigation strategies (Davis et al., 2010; Luderer et al., 2018). Similarly, \r\nhigh emissions early on might imply strongly net negative emissions \r\n(Minx et al., 2018) later on to reach the same target envelope for \r\ncumulative emissions and temperature by the end of the century \r\n(Box  1.4)"


id: af703bd6070498f1bbc0edf705df4f7b
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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vector_id: 25114
content:
" This report explores options to address some of those \r\npotential issues from a WGI perspective (Sections 5.5.2 and 5.6.2)."


id: b8e30a63e9bd14167937e1b980ba1497
created_at: 2023-03-16 14:01:57
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content:
"Figure 1.29 | The role of CO2 in driving future climate change in comparison to other greenhouse gases (GHGs). The GHGs included here are CH4, N2O, and \r\n40 other long-lived, well-mixed GHGs. The blue shaded area indicates the approximate forcing exerted by CO2 in Shared Socio-economic Pathways (SSP) scenarios, ranging from \r\nvery low SSP1-1.9 to very high SSP5-8.5 (Chapter 7). The CO2 concentrations under the SSP1-1.9 scenarios reach approximately 350 ppm after 2150, while those of SSP5-8.5 \r\nexceed 2000 ppm CO2 in the longer term (up to year 2300). Similar to the dominant radiative forcing share at each point in time (lower area plots), cumulative GWP-100-\r\nweighted GHG emissions happen to be closely correlated with cumulative CO2 emissions, allowing policymakers to make use of the carbon budget concept in a policy context \r\nwith multi-gas GHG baskets as it exhibits relatively low variation across scenarios with similar cumulative emissions until 2050 (inset panel)"


id: 240652c8347996a7c4b1667f15c668dc
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 3111
content:
" Further details on data sources \r\nand processing are available in the chapter data table (Table 1.SM.1)."


id: d6fc0d0bb3afb48a47d0f620932b0424
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 31326
content:
"CDR can be achieved through a  number of measures (Section  5.6; SRCCL, IPCC, 2019a). These include additional afforestation, \r\nreforestation, soil carbon management, biochar, direct air capture and carbon capture and storage (DACCS), and bioenergy with \r\ncarbon capture and storage (BECCS; de Coninck et al., 2018, SR1.5 Ch4; Minx et al., 2018; see also WGIII Chapters  7  and 12). \r\nDifferences between land use, land-use change and forestry (LULUCF) accounting rules, and scientific bookkeeping approaches for CO2\r\nemissions and removals from the terrestrial biosphere, can result in significant differences between the amount of CDR that is reported \r\nin different studies (Grassi et al., 2017). Different measures to achieve CDR come with different risks, negative side effects and \r\npotential co-benefits - also in conjunction with sustainable development goals - that can inform choices around their implementation \r\n(Section 5.6; Fuss et al., 2018; Roe et al., 2019)"


id: 7c6c24ee3738bf4dd1d2614910dd6b73
created_at: 2023-03-16 14:01:57
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vector_id: 17174
content:
" Technologies to achieve direct large-scale anthropogenic removals of non-CO2 GHGs \r\nare speculative at present (Yoon et al., 2009; Ming et al., 2016; Kroeger et al., 2017; Jackson et al., 2019)."


id: 1c144840058d3057baef024432e2f17a
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 1879
content:
"As scientists seek to refine our understanding of Earth's climate system and how it may evolve in coming decades \r\nto centuries, past climate states provide a wealth of insights. Data about these past states help to establish the \r\nrelationship between natural climate drivers and the history of changes in global temperature, global sea levels, \r\nthe carbon cycle, ocean circulation, and regional climate patterns, including climate extremes. Guided by such \r\ndata, scientists use Earth system models to identify the chain of events underlying the transitions between past \r\nclimatic states (FAQ 3.3). This is important because during present-day climate change, just as in past climate \r\nchanges, some aspects of the Earth system (e.g., surface temperature) respond to changes in greenhouse gases \r\non a time scale of decades to centuries, while others (e.g., sea level and the carbon cycle) respond over centuries \r\nto millennia (FAQ 5.3)"


id: 182257e791490dcfc2788484e9c880fa
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 1287
content:
" In this way, past climate states serve as critical benchmarks for climate model simulations, \r\nimproving our understanding of the sequences, rates, and magnitude of future climate change over the next \r\ndecades to millennia."


id: a95bd01d6b031c99f0a8e737defdceeb
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 24176
content:
"Analyzing previous warm periods caused by natural factors can help us understand how key aspects of the \r\nclimate system evolve in response to warming. For example, one previous warm-climate state occurred roughly \r\n125,000  years ago, during the Last Interglacial period, when slight variations in the Earth's orbit triggered \r\na sequence of changes that caused about 1degC-2degC of global warming and about 2-8 m of sea level rise relative \r\nto the 1850-1900, even though atmospheric carbon dioxide concentrations were similar to 1850-1900 values \r\n(FAQ 1.3, Figure 1). Modelling studies highlight that increased summer heating in the higher latitudes of the \r\nNorthern Hemisphere during this time caused widespread melting of snow and ice, reducing the reflectivity of \r\nthe planet and increasing the absorption of solar energy by the Earth's surface. This gave rise to global-scale \r\nwarming, which led in turn to further ice loss and sea level rise"


id: ce8a26d61ece009ddeaafe009d958f64
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 30035
content:
" These self-reinforcing positive feedback cycles are \r\na pervasive feature of Earth's climate system, with clear implications for future climate change under continued \r\ngreenhouse gas emissions. In the case of sea level rise, these cycles evolved over several centuries to millennia, \r\nreminding us that the rates and magnitude of sea level rise in the 21st century are just a fraction of the sea level \r\nrise that will ultimately occur after the Earth system fully adjusts to current levels of global warming."


id: e3c727bd355587207522f0ea1dc9a07d
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 33316
content:
"Roughly 3 million years ago, during the Pliocene Epoch, the Earth witnessed a prolonged period of elevated \r\ntemperatures (2.5degC-4degC higher than 1850-1900) and higher sea levels (5-25 m  higher than 1850-1900), in \r\ncombination with atmospheric carbon dioxide concentrations similar to those of the present day. The fact that \r\nPliocene atmospheric carbon dioxide concentrations were similar to the present, while global temperatures and \r\nsea levels were significantly higher, reflects the difference between an Earth system that has fully adjusted to \r\nchanges in natural drivers (the Pliocene) and one where greenhouse gases concentrations, temperature, and sea \r\nlevel rise are still increasing (present day)"


id: 452774430c3fe2b958138982ea8761f4
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 8303
content:
" Much about the transition into the Pliocene climate state - in terms of \r\nkey causes, the role of cycles that hastened or slowed the transition, and the rate of change in climate indicators \r\nsuch as sea level - remain topics of intense study by climate researchers, using a combination of paleoclimate \r\nobservations and Earth system models. Insights from such studies may help to reduce the large uncertainties \r\naround estimates of global sea level rise by 2300, which range from 0.3 m to 3 m above 1850-1900 (in a low\u0002emissions scenario) to as much as 16 m higher than 1850-1900 (in a very high-emissions scenario that includes \r\naccelerating structural disintegration of the polar ice sheets)."


id: 154f0394ed3b470f8332aec49ef46bfc
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 869
content:
"Gudfinna Adalgeirsdottir (Iceland), Samuel Albani (Italy), Richard P. Allan (United Kingdom), \r\nRichard A. Betts (United Kingdom), Lea Beusch (Switzerland), Kinfe Beyene (Ethiopia), Jason E. Box \r\n(Denmark/United States of America), Denise Breitburg (United States of America), Kevin D. Burke \r\n(United States of America), Michael P. Byrne (United Kingdom/Ireland), John A. Church (Australia), \r\nSloane Coats (United States of America), Naftali Cohen (United States of America), William Collins \r\n(United Kingdom), Owen R. Cooper (United States of America), Pedro Di Nezio (United States \r\nof America), Fabio Boeira Dias (Finland/Brazil), Ed J. Dlugokencky (United States of America), \r\nTimothy Dunkerton (United States of America), Paul J. Durack (United States of America/Australia), \r\nTamsin L. Edwards (United Kingdom), Veronika Eyring (Germany), Chris  Fairall (United States \r\nof America), Vitali Fioletov (Canada), Piers Forster (United Kingdom), Gavin  L"


id: 8b2046e0982642d59e6a623c521f3dff
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 19414
content:
"  Foster (United \r\nKingdom), Baylor Fox-Kemper (United States of America), Qiang Fu (United States of America), \r\nJan S. Fuglestvedt (Norway), John C. Fyfe (Canada), Marie-Jose Gaillard (Sweden/Switzerland, \r\nSweden), Joelle Gergis (Australia), Nathan P. Gillett (Canada), Hans Gleisner (Denmark/Sweden), \r\nNadine Gobron (EU/France), Nicholas R. Golledge (New Zealand/United Kingdom), Bradley Hall \r\n(United States of America), Ed Hawkins (United Kingdom), Alan M. Haywood (United Kingdom), \r\nArmand Hernandez (Spain), Forrest M. Hoffman (United States of America), Yongyun Hu (China), \r\nDale F. Hurst (United States of America), Masao Ishii (Japan), Samuel Jaccard (Switzerland), Dabang \r\nJiang (China), Christopher Jones (United Kingdom), Bror  Jonsson (United Kingdom/Sweden), \r\nAndreas Kaab (Norway/Germany), Ralph Keeling (United States of America), Noel S. Keenlyside \r\n(Norway/Australia, United Kingdom), John Kennedy (United Kingdom), Elizabeth Kent (United \r\nKingdom), Nichol S"


id: 285042cc3ec0d86df6bf54b42ddac84
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 3754
content:
" Khan (Hong Kong, China/United States of America), Wolfgang Kiessling \r\n(Germany), Stefan Kinne (Germany), Robert E. Kopp (United States of America), Svitlana Krakovska "


id: b294e1aa5516d43f216bdc4d10ebfad5
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 25618
content:
"(Ukraine), Elmar Kriegler (Germany), Gerhard Krinner (France/Germany, France), Natalie Krivova \r\n(Germany), Paul B. Krummel (Australia), Werner L. Kutsch (EU/Germany), Ron  Kwok (United \r\nStates of America), Florian Ladstadter (Austria), Peter Landschutzer (Germany/Austria), \r\nJune-Yi Lee (Republic of Korea), Andrew Lenton (Australia), Lisa A. Levin (United States of America), \r\nDaniel J. Lunt (United Kingdom), Jochem Marotzke (Germany), Gareth J. Marshall (United Kingdom), \r\nRobert A. Massom (Australia), Katja Matthes (Germany), H. Damon Matthews (Canada), Thorsten \r\nMauritsen (Sweden/Denmark), Gerard D. McCarthy (Ireland), Erin L. McClymont (United Kingdom), \r\nShayne  McGregor (Australia), Jerry F. McManus (United States of America), Walter  N.  Meier \r\n(United States of America), Alan Mix (United States of America), Olaf Morgenstern (New Zealand/\r\nGermany), Lawrence R. Mudryk (Canada), Jens Muhle (United States of America/Germany), \r\nDirk Notz (Germany), Lisa C"


id: b945b9b2a91cb56bb8b11d65d8f78c75
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 26685
content:
" Orme (Ireland/United Kingdom), Scott M. Osprey (United Kingdom), \r\nMatthew D. Palmer (United Kingdom), Camille Parmesan (France, United Kingdom/United States \r\nof America), Anna Pirani (Italy), Chris Polashenski (United States of America), Elvira Poloczsanka \r\n(Australia/United Kingdom), Marie-Fanny  Racault (United  Kingdom), Anthony  Richardson \r\n(Australia), Belen Rodriguez-Fonseca (Spain), Joeri  Rogelj (United Kingdom/Belgium), \r\nSteven K. Rose (United States of America), Yair Rosenthal (United States of America/Israel, United \r\nStates of America), Alessio Rovere (Germany/Italy), Lucas  Ruiz (Argentina), Ulrich  Salzmann \r\n(United Kingdom/Germany, United Kingdom), Bjorn  H.  Samset (Norway), Abhishek  Savita \r\n(Australia/India), Margit  Schwikowski (Switzerland), Sonia  I.  Seneviratne (Switzerland), \r\nDavid Schoeman (Australia), Isobel J. Simpson (Canada), Aimee B.A. Slangen (The Netherlands), \r\nChris Smith (United Kingdom), Olga N. Solomina (Russian Federation), Joshua H.P"


id: 408cd8f157e407ccc5a0841a301cd844
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 7535
content:
" Studholme \r\n(United States of America/United Kingdom, New Zealand), Alessandro Tagliabue (United \r\nKingdom), Claudia Tebaldi (United States of America), Jessica Tierney (United States of America), \r\nMatthew Toohey (Canada, Germany/Canada), Andrew Turner (United Kingdom), Osvaldo Ulloa \r\n(Chile), Caroline C. Ummenhofer (United States of America/Germany, United States of America), \r\nAxel von Engeln (Germany), Rachel Warren (United Kingdom), Kate Willett (United Kingdom), \r\nJohn W. Williams (United States of America)"


id: 4dc354d07f5e8cb0a4592965a0e5d3be
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 9663
content:
"Changes across the terrestrial cryosphere are widespread, with \r\nseveral indicators now in states unprecedented in centuries to \r\nmillennia (high confidence). Reductions in spring snow cover extent \r\nhave occurred across the Northern Hemisphere since at least 1978 \r\n(very high confidence). With few exceptions, glaciers have retreated \r\nsince the second half of the 19th century and have continued to \r\nretreat at increased rates since the 1990s (very high confidence); this \r\nbehaviour is unprecedented in at least the last 2000 years (medium \r\nconfidence). Greenland Ice Sheet (GrIS) mass loss has increased \r\nsubstantially since 2000 (high confidence). The Greenland Ice Sheet \r\nwas smaller than at present during the Last Interglacial period (high \r\nconfidence) and the mid-Holocene (high confidence). The Antarctic Ice \r\nSheet (AIS) lost mass between 1992 and 2020 (very high confidence), \r\nwith an increasing rate of mass loss over this period (medium \r\nconfidence)"


id: fcff754cc4293610cc9228d060c1b78b
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 37319
content:
" Although permafrost persists in areas of the Northern \r\nHemisphere where it was absent prior to 3000 years ago, increases in \r\ntemperatures in the upper 30 m over the past three to four decades \r\nhave been widespread (high confidence). {2.3.2}"


id: 80533cccefdc71ce9803fe230afd2b18
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 17717
content:
"Recent ocean changes are widespread, and key ocean \r\nindicators are in states unprecedented for centuries to \r\nmillennia (high confidence). Since 1971, it is virtually certain that \r\nglobal ocean heat content has increased for the upper (0-700 m) \r\nlayer, very likely for the intermediate (700-2000 m) layer and likely\r\nbelow 2000 m, and is currently increasing faster than at any point \r\nsince at least the last deglacial transition (18 to 11 thousand years \r\nago) (medium confidence). It is virtually certain that large-scale near\u0002surface salinity contrasts have intensified since at least 1950. The \r\nAtlantic Meridional Overturning Circulation (AMOC) was relatively \r\nstable during the past 8000 years (medium confidence) but declined \r\nduring the 20th century (low confidence). Ocean pH has declined \r\nglobally at the surface over the past four decades (virtually certain) \r\nand in all ocean basins in the ocean interior (high confidence) over \r\nthe past 2-3 decades"


id: 46a80ccdbf5cc1e36a97486524e2f86a
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 8502
content:
" A long-term increase in surface open ocean \r\npH occurred over the past 50 million years (high confidence), and \r\nsurface ocean pH as low as recent times is uncommon in the last \r\n2 million years (medium confidence). Deoxygenation has occurred in "


id: 364d705b3e427066b9165864923c6554
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 5937
content:
"Changes in the marine biosphere are consistent with large-scale \r\nwarming and changes in ocean geochemistry (high confidence). \r\nThe ranges of many marine organisms are shifting towards the \r\npoles and towards greater depths (high confidence), but a minority \r\nof organisms are shifting in the opposite directions. This mismatch \r\nin responses across species means that the species composition of \r\necosystems is changing (medium confidence). At multiple locations, \r\nvarious phenological metrics for marine organisms have changed \r\nin the last 50 years, with the nature of the changes varying with \r\nlocation and with species (high confidence). In the last two decades, \r\nthe concentration of phytoplankton at the base of the marine food \r\nweb, as indexed by chlorophyll concentration, has shown weak and \r\nvariable trends in low and mid-latitudes and an increase in high \r\nlatitudes (medium confidence)"


id: b477dfd2a116cd996b9f66c50f04d81f
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 25925
content:
" Global marine primary production \r\ndecreased slightly from 1998-2018, with increasing production in \r\nthe Arctic (medium confidence). {2.3.4.2}"


id: f1f87b459f08053542b2265001c53479
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updated_at: 2023-03-16 14:01:57
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vector_id: 35595
content:
"Since the late 19th century, major modes of climate variability \r\nshow no sustained trends but do exhibit fluctuations in \r\nfrequency and magnitude at inter-decadal time scales, with \r\nthe notable exception of the Southern Annular Mode, which \r\nhas become systematically more positive (high confidence). \r\nThere is high confidence that these modes of variability have \r\nexisted for millennia or longer, but low confidence in detailed \r\nreconstructions of most modes prior to direct instrumental \r\nrecords. Both polar annular modes have exhibited strong positive \r\ntrends toward increased zonality of mid-latitude circulation over \r\nmulti-decadal periods, but these trends have not been sustained for \r\nthe Northern Annular Mode since the early 1990s (high confidence)"


id: b8e9c45f8e1edebee8781b5ca1280f08
created_at: 2023-03-16 14:01:57
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content:
" \r\nFor tropical ocean modes, a sustained shift beyond multi-centennial \r\nvariability has not been observed for El Nino-Southern Oscillation \r\n(medium confidence), but there is limited evidence and low agreement \r\nabout the long-term behaviour of other tropical ocean modes. Modes \r\nof decadal and multi-decadal variability over the Pacific and Atlantic \r\noceans exhibit no significant trends over the period of observational \r\nrecords (high confidence). {2.4}"


id: 9e23abadcde3516b68ab1d3477d3c226
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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vector_id: 22403
content:
"This chapter assesses the evidence basis for large-scale past changes \r\nin selected components of the climate system. As such, it combines \r\nmuch of the assessment performed in Chapters 2 through  5 of \r\nthe Fifth Assessment Report (AR5) WGI contribution (IPCC, 2013) \r\nthat, taken together, supported a finding of unequivocal recent \r\nwarming of the climate system. The Sixth Assessment Report \r\n(AR6) WGI Report structure differs substantially from that in AR5 \r\n(Section  1.1.2). This  chapter focuses upon observed changes in \r\nclimate system drivers and changes in key selected large-scale \r\nindicators of climate change and in important modes of variability \r\n(Cross-Chapter Box 2.2), which allow for an assessment of changes \r\nin the global climate system in an integrated manner. This chapter \r\nis complemented by Chapters 3 and 4, which respectively consider \r\nmodel assessment/detection and attribution, and future climate \r\nprojections for subsets of these same indicators and modes"


id: 2223eff7c0b9848c67b775cbc9d1fdcb
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 2818
content:
" It does \r\nnot consider changes in observed extremes, which are assessed in \r\nChapter 11. The chapter structure is outlined in the visual abstract \r\n(Figure 2.1). "


id: efe0ac18a5e1d25eb634bef5fea5500
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 35246
content:
"Global mean surface temperature (GMST) is a key indicator of the changing state of the climate system. Earth's mean temperature \r\nhistory during the current geological era (Cenozoic, beginning 66 Ma (66 million years ago)) can be broadly characterized as follows \r\n(Cross-Chapter Box 2"


id: 1c848b7bcd7767036c13483d99025808
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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vector_id: 1942
content:
"GMST estimated for each of the reference periods based on proxy evidence (Section 2.3.1.1) can be compared with climate projections \r\nover coming centuries to place the range of possible futures into a longer-term context (Cross-Chapter Box 2.1, Figure 1). Here, the \r\nvery likely range of GMST for the warmer world reference periods are compared with the very likely range of GSAT projected for \r\nthe end the 21st century (2080-2100; Table 4.5) and the likely range for the end of the 23rd century (2300; Table 4.9) under multiple \r\nShared Socio-economic Pathway (SSP) scenarios. From this comparison, there is medium confidence in the following: GMST estimated \r\nfor the warmest long-term period of the Last Interglacial about 125 ka (125,000 years ago; 0.5degC-1.5degC relative to 1850-1900) \r\noverlaps with the low end of the range of temperatures projected under SSP1-2.6 including its negative emissions extension to the end \r\nof the 23rd century (1.0degC to 2.2degC)"


id: e4796758f7179cc78b9022351e298366
created_at: 2023-03-16 14:01:57
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vector_id: 33426
content:
" GMST estimated for a period of prolonged warmth during the mid-Pliocene Warm Period about \r\n3 Ma [2.5degC to 4.0degC] is similar to temperatures projected under SSP2-4.5 for the end of the 23rd century (2.3degC to 4.6degC). GMST \r\nestimated for the Miocene Climatic Optimum [5degC to 10degC] and Early Eocene Climatic Optimum [10degC to 18degC], about 15 and 50 Ma, \r\nrespectively, overlap with the range projected for the end of the 23rd century under SSP5-8.5 (6.6degC to 14.1degC)."


id: a8a373241ed2fda054b393b4984b97b5
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 24061
content:
"This section assesses the magnitude and rates of changes in both \r\nnatural and anthropogenically mediated climate drivers over \r\na range of time scales. First, changes in insolation (orbital and solar; \r\nSection 2.2.1), and volcanic stratospheric aerosol (Section 2.2.2) are \r\nassessed. Next, well-mixed greenhouse gases (GHGs; CO2, N2O and \r\nCH4) are covered in Section 2.2.3, with climate feedbacks and other \r\nprocesses involved in the carbon cycle assessed in Chapter 5. The \r\nsection continues with the assessment of changes in halogenated \r\nGHGs (Section 2.2.4), stratospheric water vapour, stratospheric \r\nand tropospheric ozone (Section 2.2.5), and tropospheric aerosols \r\n(Section  2.2.6). Short-lived climate forcers (SLCFs), their precursor \r\nemissions and key processes are assessed in more detail in Chapter 6. \r\nSection 2.2.7 assesses the effect of historical land cover change \r\non climate, including biophysical and biogeochemical processes. \r\nSection 2.2"


id: a3e92274d7facce572634d6f4039700b
created_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 23348
content:
"Direct observations of volcanic gas-phase sulphur emissions (mostly \r\nSO2), sulphate aerosols, and their radiative effects are available from \r\na variety of sources (Kremser et  al., 2016). New estimates of SO2\r\nemissions from explosive eruptions have been derived from satellite \r\n(beginning in 1979) and in situ measurements (Hopfner et  al., \r\n2015; Carn et al., 2016; Neely III and Schmidt, 2016; Bruhl, 2018). \r\nSatellite observations of aerosol extinction after recent eruptions \r\nhave uncertainties of about 15-25% (Vernier et al., 2011; Bourassa \r\net al., 2012). Additional uncertainties occur when gaps in the satellite \r\nrecords are filled by complementary observations or using statistical \r\nmethods (Thomason et al., 2018). Merged datasets (Thomason et al., \r\n2018) and sparse ground-based measurements (Stothers, 1997) allow \r\nfor volcanic forcing estimates back to 1850. In contrast to the CMIP5 \r\nhistorical volcanic forcing datasets (Ammann et al"


id: 17a908811b1a223cfc967340ad4bb786
created_at: 2023-03-16 14:01:57
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vector_id: 1221
content:
", 2003), updated \r\ntime series (Figure 2.2d; Luo, 2018) feature a more comprehensive \r\nset of optical properties including latitude-, height- and wavelength\u0002dependent aerosol extinction, single scattering albedo and asymmetry \r\nparameters. A series of small-to-moderate eruptions since 2000 \r\nresulted in perturbations in SAOD of 0.004-0.006 (Andersson et al., \r\n2015; Schmidt et al., 2018)."


id: e52408077d86e6ea7aa01cc6c23792b4
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 33543
content:
"The AR5 concluded that, on interannual time scales, the radiative \r\neffects of volcanic aerosols are a dominant natural driver of climate \r\nvariability, with the greatest effects occurring within the first \r\n2-5 years following a strong eruption. Reconstructions of radiative \r\nforcing by volcanic aerosols used in the Paleoclimate Modelling \r\nIntercomparison Project Phase III (PMIP3) simulations and in AR5 \r\nfeatured short-lived perturbations of a range of magnitudes, with \r\nevents of greater magnitude than -1 W m-2 (annual mean) occurring \r\non average every 35-40 years, although no associated assessment \r\nof confidence was given. This section focuses on advances in \r\nreconstructions of stratospheric aerosol optical depth (SAOD), \r\nwhereas Chapter 7 focuses on the ERF of volcanic aerosols, and \r\nChapter 5 assesses volcanic emissions of CO2 and CH4; tropospheric \r\naerosols are discussed in Section 2.2.6. Cross-Chapter Box 4"


id: f7114ef5437b84b81cd62abad4a6cd18
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 36383
content:
"1 \r\nundertakes an integrative assessment of volcanic effects including \r\npotential for 21st century effects."


id: 6b87c22c6025edfaaa841372c97b1c25
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 14535
content:
"Advances in analysis of sulphate records from the Greenland Ice Sheet \r\n(GrIS) and AIS have resulted in improved dating and completeness of \r\nSAOD reconstructions over the past 2.5 kyr (Sigl et al., 2015), a more \r\nuncertain extension back to 10 ka (Kobashi et al., 2017; Toohey and \r\nSigl, 2017), and a better differentiation of sulphates that reach high \r\nlatitudes via stratospheric (strong eruptions) versus tropospheric \r\npathways (A. Burke et  al., 2019; Gautier et  al., 2019). The PMIP4 \r\nvolcanic reconstruction extends the period analysed in AR5 by 1 kyr \r\n(Figure 2.2c; Jungclaus et  al., 2017) and features multiple strong \r\nevents that were previously misdated, underestimated or not detected, \r\nparticularly before about 1500 CE. The period between successive \r\nlarge volcanic eruptions (Negative ERF greater than -1 W m-2), ranges \r\nfrom 3-130 years, with an average of 43 +- 7.5 years between such \r\neruptions over the past 2.5 kyr (data from Toohey and Sigl, 2017)"


id: 7574b2eda0fef5925d7d4bd7b011a9b8
created_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 16066
content:
" The \r\nmost recent such eruption was that of Mt Pinatubo in 1991. Century\u0002long periods that lack such large eruptions occurred once every \r\n400 years on average. Systematic uncertainties related to the scaling \r\nof sulphate abundance in glacier ice to radiative forcing have been \r\nestimated to be about 60% (Hegerl et al., 2006). Uncertainty in the \r\ntiming of eruptions in the proxy record is +- 2 years (95% confidence \r\ninterval) back to 1.5 ka and +- 4 years before (Toohey and Sigl, 2017)."


id: c59a6e82e62797ebc28429acb429a3bf
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 28668
content:
"Well-mixed greenhouse gases generally have lifetimes of more than \r\nseveral years. The AR5 assigned medium confidence to the values \r\nof atmospheric CO2 concentrations (mixing ratios) during the warm \r\ngeological periods of the early Eocene and Pliocene. It concluded \r\nwith very high confidence that, by 2011, the mixing ratios of CO2, \r\nCH4, and N2O in the atmosphere exceeded the range derived from \r\nice cores for the previous 800 kyr, and that the observed rates of \r\nincrease of the greenhouse gases were unprecedented on centennial \r\ntimescales over at least the past 22 kyr. It reported that over \r\n2005-2011 atmospheric burdens of CO2, CH4, and N2O increased, \r\nwith 2011 levels of 390.5 parts per million (ppm), 1803.2 parts per \r\nbillion (ppb) and 324.2 ppb, respectively. Increases of CO2 and N2O \r\nover 2005-2011 were comparable to those over 1996-2005, while \r\nCH4 resumed increasing in 2007, after remaining nearly constant over \r\n1999-2006"


id: e461136c7d5d160d08477f698ca09ac6
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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vector_id: 33411
content:
" A comprehensive process-based assessment of changes \r\nin CO2, CH4, and N2O is undertaken in Chapter 5."


id: 68a3b63ad3b6a3da8561665d974bba9d
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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vector_id: 14058
content:
"in ocean sediments has improved (Penman et al., 2014; Anagnostou \r\net al., 2016, 2020; Chalk et al., 2017; Gutjahr et al., 2017; Babila et al., \r\n2018; Dyez et al., 2018; Raitzsch et al., 2018; Sosdian et al., 2018; \r\nHenehan et  al., 2019, 2020; de la Vega et  al., 2020; Harper et  al., \r\n2020), the understanding of the alkenone CO2 proxy has increased \r\n(e.g.,  Badger et  al., 2019; Stoll et  al., 2019; Y. Zhang et  al., 2019; \r\nZhang et al., 2020; Rae et al., 2021) and new phytoplankton proxies \r\nhave been developed and applied (e.g.,  Witkowski et  al., 2018). \r\nUnderstanding of the boron isotope CO2 proxy has improved since \r\nAR5 with studies showing very good agreement between boron\u0002CO2 estimates and co-existing ice core CO2 (Honisch and Hemming, \r\n2005; Foster, 2008; Henehan et al., 2013; Chalk et al., 2017; Raitzsch \r\net al., 2018; see Figure 2.3c). Such independent validation has proven \r\ndifficult to achieve with the other available CO2 proxies (e.g., Badger \r\net al"


id: 2f675ab9391eb6ecef50b412b1b7cd8
created_at: 2023-03-16 14:01:57
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vector_id: 4878
content:
", 2019; Da et al., 2019; Stoll et al., 2019; Y. Zhang et al., 2019). \r\nRemaining uncertainties in these ocean sediment based proxies \r\n(Hollis et al., 2019) partly limit the applicability of the alkenone d13C \r\nand boron d11B proxies beyond the Cenozoic, although new records \r\nare emerging, for example, Jurikova et al. (2020). CO2 estimates from \r\nthe terrestrial CO2 proxies, such as stomatal density in fossil plants \r\nand d13C of palaeosol carbonates, are available for much of the \r\nlast 420 Myr. Given the low sampling density, relatively large CO2 \r\nuncertainty, and high age uncertainty (relative to marine sediments) "


id: ad96578fdf60e6fd767dcef5162ee73b
created_at: 2023-03-16 14:01:57
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content:
"Levels were close to 1750 values during at least one prolonged \r\ninterval during the Carboniferous and Permian (350-252 Ma). \r\nDuring the Triassic (251.9-201.3 Ma), atmospheric CO2 mixing ratios \r\nreached a maximum of between 2000-5000 ppm (200-220  Ma). \r\nDuring the PETM (56 Ma) CO2 rapidly rose from about 900 ppm \r\nto about 2000 ppm (Table 2.1; Schubert and Jahren, 2013; Gutjahr \r\net al., 2017; Anagnostou et al., 2020) in 3-20 kyr (Zeebe et al., 2016; \r\nGutjahr et al., 2017; Turner, 2018). Estimated multi-millennial rates \r\nof CO2 accumulation during this event range from 0.3-1.5 PgC yr -1\r\n(Gingerich, 2019), at least 4-5 times lower than current centennial \r\nrates (Section 5.3.1.1). Based on boron and carbon isotope data, \r\nsupported by other proxies (Hollis et  al., 2019), atmospheric CO2\r\nduring the EECO (50 Ma) was between 1150 and 2500 ppm (medium \r\nconfidence), and then gradually declined over the last 50 Myr at a \r\nlong-term rate of about 16 ppm Myr -1 (Figure 2.3)"


id: 79dbeaced31c142e63df0efda00c5229
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 16745
content:
" The last time the \r\nCO2 mixing ratio was as high as 1000 ppm (the level reached by some \r\nhigh emissions scenarios by 2100; Annex III) was prior to the Eocene\u0002Oligocene transition (33.5 Ma; Figure 2.3) that was associated with \r\nthe first major advance of the AIS (Pearson et  al., 2009; Pagani \r\net al., 2011; Anagnostou et al., 2016; Witkowski et al., 2018; Hollis "


id: 8a454a5fe9ed844326710bc21102db8e
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 19289
content:
"Table 2.1 | Concentration (mixing ratios) and, where applicable, century time-scale rate of change of atmospheric CO2 based on multiple datasets for \r\ntarget paleoclimate reference (Cross-Chapter Box 2.1, and Figure 2.34) and selected other periods. Modern data are from Section 2.2.3.3 and Annex III. 'AR6' \r\ndenotes best estimates assessed in this report and propagated to Figure 2.34. Units for the rate of change are given only for centennial periods characterized by rapid changes. \r\nConfidence levels are very high for instrumentally derived concentrations, high for values derived from air in glacier ice (back to LIG), medium for values supported by multiple \r\nproxy types (MPWP, EECO), and low for values from a single sedimentary proxy type (PETM). '-' indicates transition from the beginning to the end of the time interval. \r\nUncertainties for Modern are based on 2019 estimates"


id: e3fddcf73cb6c6b59ab5bb8f88c1c3ab
created_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 33346
content:
" Last Millennium rate of range shows lowest and highest values attained during this period; LDT shows highest rate of \r\nchange. N/A indicates that values are not available. See chapter data table for bibliographic citation and auxiliary information for each dataset (Table 2.SM.1)."


id: c8dc20276074f7525514929c44d30e10
created_at: 2023-03-16 14:01:57
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vector_id: 29161
content:
"Records of CO2 from the AIS formed during the last glacial period \r\nand the LDT show century-scale fluctuations of up to 9.6 ppm \r\n(Ahn et al., 2012; Ahn and Brook, 2014; Marcott et al., 2014; Bauska \r\net al., 2015; Rubino et al., 2019). Although these rates are an order \r\nof magnitude lower than those directly observed over 1919-2019 CE \r\n(Section 2.2.3.3.1), they provide information on non-linear responses \r\nof climate-biogeochemical feedbacks (Section 5.1.2). Multiple \r\nrecords for 0-1850 CE show CO2 mixing ratios of 274-285  ppm. \r\nOffsets among ice core records are about 1%, but the long-term \r\ntrends agree well and show coherent multi-centennial variations of \r\nabout 10 ppm (Ahn et al., 2012; Bauska et al., 2015; Rubino et al., \r\n2019). Multiple records show CO2 concentrations of 278.3 +- 2.9 ppm \r\nin 1750 and 285.5 +- 2.1 ppm in 1850 (Siegenthaler et  al., 2005; \r\nMacFarling Meure et al., 2006; Ahn et al., 2012; Bauska et al., 2015). \r\nCO2 concentration increased by 5.0 +- 0"


id: a69c6e6ecb3e24ef6a6188cce705e3df
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 23739
content:
"8 ppm during 970-1130 CE, \r\nfollowed by a decrease of 4.6 +- 1.7 ppm during 1580-1700 CE. \r\nThe greatest rate of change over the CE prior to 1750 is observed \r\nat about 1600 CE, and ranges from -6.9 to +4.7 ppm per century in \r\nmultiple high-resolution ice core records (Siegenthaler et al., 2005; \r\nMacFarling Meure et al., 2006; Ahn et al., 2012; Bauska et al., 2015; \r\nRubino et  al., 2019). Although ice core records present low-pass \r\nfiltered time series due to gas diffusion and gradual bubble close\u0002off in the snow layer over the ice sheet (Fourteau et al., 2020), the \r\nrate of increase since 1850 CE (about 125 ppm increase over about \r\n170 years) is far greater than implied for any 170-year period by ice \r\ncore records that cover the last 800 ka (very high confidence)."


id: 62c5ff624c7451ac6fd94975b743f02c
created_at: 2023-03-16 14:01:57
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vector_id: 13110
content:
"et  al., 2019). The compilation of Foster et  al. (2017) constrained \r\nCO2 concentration to between 290 and 450 ppm during the MPWP, \r\nbased primarily on the boron-isotope data reported by Martinez\u0002Boti et al. (2015b), consistent with the AR5 range of 300-450 ppm. \r\nA more recent high-resolution boron isotope-based study revealed \r\nthat CO2 cycled during the MPWP from about 330 to about 390 ppm \r\non orbital timescales, with a mean of about 370 ppm (de la Vega \r\net al., 2020). Although data from other proxy types (e.g., stomatal \r\ndensity or d13C of alkenones) have too low resolution to resolve the \r\norbital-related variability of CO2 during this interval (e.g., Kurschner \r\net al., 1996; Stoll et al., 2019) there is general agreement among \r\nthe different proxy types with the boron-derived mean (e.g., Stoll \r\net al., 2019)"


id: 5f9426975ab1b12c955be2a074898798
created_at: 2023-03-16 14:01:57
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vector_id: 12611
content:
" High-resolution sampling (about 1 sample per 3 kyr) \r\nwith the boron-isotope proxy indicates mean CO2 mixing ratios for \r\nthe Marine Isotope Stage KM5c interglacial were 360-420 ppm \r\n(medium confidence) (de la Vega et al., 2020). "


id: 22ed44d0a50074273e94ff1dc5452605
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 2942
content:
"CH4 concentrations over the past 110 kyr are higher in the Northern \r\nHemisphere (NH) than in the Southern Hemisphere (SH), but closely \r\ncorrelated on centennial and millennial timescales (Buizert et  al., \r\n2015). On glacial to interglacial cycles, approximately 450 ppb \r\noscillations in CH4 concentrations have occurred (Loulergue et  al., \r\n2008). On millennial timescales, most rapid climate changes observed \r\nin Greenland and other regions are coincident with rapid CH4 changes \r\n(Buizert et al., 2015; Rhodes et al., 2015, 2017). The variability of CH4 on \r\ncentennial timescales during the early Holocene does not significantly \r\ndiffer from that of the late Holocene prior to about 1850 (Rhodes et al., \r\n2013; Yang et al., 2017). The LGM concentration was 390.5 +- 6.0 ppb \r\n(Kageyama et  al., 2017). The global mean concentrations during \r\n0-1850 CE varied between 625 and 807 ppb"


id: 8242201218ff520aa8589a53a38d260c
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 18020
content:
" High-resolution ice core \r\nrecords from Antarctica and Greenland exhibit the same trends with \r\nan inter-polar difference of 36-47 ppb (Sapart et al., 2012; L. Mitchell \r\net al., 2013). There is a long-term positive trend of about 0.5 ppb per \r\ndecade during the CE until 1750 CE. The most rapid CH4 changes \r\nprior to industrialization were as large as 30-50  ppb on multi\u0002decadal timescales. Global mean CH4 concentrations estimated from \r\nAntarctic and Greenland ice cores are 729.2 +- 9.4 ppb in 1750 and \r\n807.6 +- 13.8 ppb in 1850 (L. Mitchell et al., 2013)."


id: 86a1d98a547bcf666d61abae9fe7cd05
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vector_id: 18718
content:
"New records show that N2O concentration changes are associated \r\nwith glacial-interglacial transitions (Schilt et  al., 2014). The most \r\nrapid change during the last glacial termination is a 30 ppb increase \r\nin a 200-year period, which is an order of magnitude smaller \r\nthan the modern rate (Section 2.2.3.3). During the LGM, N2O was \r\n208.5 +- 7.7 ppb (Kageyama et al., 2017). Over the Holocene the lowest \r\nvalue was 257 +- 6.6 ppb during 6-8 ka, but millennial variation is \r\nnot clearly detectable due to analytical uncertainty and insufficient \r\nice core quality (Fluckiger et al., 2002; Schilt et al., 2010). Recently \r\nacquired high-resolution records from Greenland and Antarctica for \r\nthe last 2 kyr consistently show multi-centennial variations of about \r\n5-10 ppb (Figure 2.4), although the magnitudes vary over time \r\n(Ryu  et  al., 2020). Three high temporal resolution records exhibit \r\na short-term minimum at about 600 CE of 261 +- 4 ppb (MacFarling \r\nMeure et al"


id: 11aa20428cb6f92e066e228a27fbef5
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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content:
", 2006; Ryu et al., 2020). It is very likely that industrial \r\nN2O increase started before 1900 CE (Machida et al., 1995; Sowers, \r\n2001; MacFarling Meure et al., 2006; Ryu et al., 2020). Multiple ice \r\ncores show N2O concentrations of 270.1 +- 6.0 ppb in 1750 and \r\n272.1 +- 5.7 ppb in 1850 (Machida et al., 1995; Fluckiger et al., 1999; \r\nSowers, 2001; Rubino et al., 2019; Ryu et al., 2020)."


id: 610d79282d3098c16a448ce56ed18679
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 12841
content:
"There has been a positive trend in globally averaged surface CO2 \r\nmixing ratios since 1958 (Figure 2.5a), that reflects the imbalance \r\nof sources and sinks (Section 5.2). The growth rate has increased \r\noverall since the 1960s (Figure 2.5a inset), while annual growth rates \r\nhave varied substantially, for example, reaching a peak during the \r\nstrong El Nino events of 1997-1998 and 2015-2016 (Bastos et al., \r\n2013; Betts et al., 2016). The average annual CO2 increase from 2000 \r\nthrough 2011 was 2.0 ppm yr-1 (standard deviation 0.3 ppm yr-1), \r\nsimilar to what was reported in AR5. From 2011 through 2019 it was \r\n2.4 ppm yr -1 (standard deviation 0.5 ppm yr -1), which is higher than \r\nthat of any comparable time period since global  measurements \r\nbegan. Global networks consistently show that the globally averaged \r\nannual mean CO2 has increased by 5.0% since 2011, reaching \r\n409.9 +- 0.4 ppm in 2019 (NOAA measurements)"


id: edb419a1113cee56fb32e32ec1881549
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 34861
content:
"Other radiatively important gases with predominantly anthropogenic \r\nsources also continue to increase in abundance. SF6, used in electrical \r\ndistribution systems, magnesium production, and semi-conductor \r\nmanufacturing, increased from 7.3 ppt in 2011 to 10.0 ppt in 2019 \r\n(+36%). Alternatives to SF6 or SF6-free equipment for electrical systems \r\nhave become available in recent years, but SF6 is still widely in use in \r\nelectrical switch gear (Simmonds et al., 2020). The global lifetime of \r\nSF6 has been revised from 3200 years to about 1000 years (Kovacs \r\net al., 2017; Ray et al., 2017) with implications for climate emissions \r\nmetrics (Section 7.6.2). NF3, which is used in the semi-conductor \r\nindustry, increased 147% over the same period to 2.05 ppt in 2019. \r\nIts contribution to ERF remains small, however, at 0.0004 W m-2. The \r\natmospheric abundance of SO2F2, which is used as a fumigant in place \r\nof ozone-depleting methyl bromide, reached 2"


id: 6447c58d7acc4f0135a50fd1075c1eff
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 13347
content:
"Recent studies of dynamical influences on SWV (Eguchi et  al., \r\n2015; Evan et  al., 2015; Tao et  al., 2015; Konopka et  al., 2016; \r\nDiallo et  al., 2018; Garfinkel et  al., 2018) have demonstrated that \r\nthe quasi-biennial oscillation (QBO), El Nino-Southern Oscillation \r\n(ENSO), Sudden Stratospheric Warming (SSW) events and possibly \r\nalso Pacific Decadal Variability (PDV; W. Wang et  al., 2016), can \r\nsignificantly influence SWV abundance and the tropical cold point \r\ntropopause temperatures that largely control water vapour entering \r\nthe stratosphere. It has also been shown that the convective lofting \r\nof ice can moisten the lower stratosphere over large regions (Dessler \r\net al., 2016; Anderson et al., 2017; Avery et al., 2017). Near-global \r\nobservations of SWV have revealed unusually strong and abrupt \r\ninterannual changes, especially in the tropical lower stratosphere. \r\nBetween December 2015 and November 2016, the tropical mean SWV \r\nanomaly at 82 hPa dropped from 0.9 +- 0"


id: 8dbefc7cc18e25e3ecf3549d7184eb95
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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vector_id: 19826
content:
"1 ppm to -1.0 +- 0.1 ppm, \r\naccompanied by highly anomalous QBO-related dynamics in the \r\ntropical stratosphere (P.A. Newman et al., 2016; Tweedy et al., 2017) \r\nand the transition of ENSO from strong El Nino to La Nina conditions \r\n(Davis et al., 2017). The tropical mean SWV anomaly then rose sharply \r\nto 0.7 +- 0.1 ppm in June 2017 as warm westerlies returned to the \r\ntropical lower stratosphere and ENSO neutral conditions prevailed \r\n(Davis et al., 2017). "


id: c6c4dd648813e16a5455a3767563f7c6
created_at: 2023-03-16 14:01:57
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vector_id: 28847
content:
"The earliest observations of free tropospheric ozone (1934-1955) \r\nare available from northern mid-latitudes where limited data \r\nindicate a tropospheric column ozone increase of 48 +- 30% up to \r\n1990-2012 (Tarasick et al., 2019). Starting in the 1960s, records from \r\nozonesondes show no significant changes in the free troposphere \r\nover the Arctic and mid-latitude regions of Canada, but trends are \r\nmainly positive elsewhere in the northern mid-latitudes (Oltmans \r\net  al., 2013; Cooper et  al., 2020). Tropospheric column and free \r\ntropospheric trends since the mid-1990s based on commercial aircraft, \r\nozonesonde observations and satellite retrievals (Figure 2.8b,c), are \r\noverwhelmingly positive across the northern mid-latitudes (2-7%; \r\n1-4 ppbv per decade) and tropics (2-14%; 1-5 ppbv per decade), with \r\nthe largest increases (8-14%; 3-6 ppbv per decade) in the northern \r\ntropics in the vicinity of southern Asia and Indonesia"


id: 5a7dcbabdd04ec12a1596674a226f783
created_at: 2023-03-16 14:01:57
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vector_id: 11750
content:
" Observations \r\nin the SH are limited, but indicate average tropospheric column \r\nozone increases of 2-12% (1-5 ppbv) per decade in the tropics \r\n(Figure 2.8c), and weak tropospheric column ozone increases (<5%, \r\n<1 ppbv per decade) at mid-latitudes (Cooper et al., 2020). Above \r\nAntarctica, mid-tropospheric ozone has increased since the late 20th \r\ncentury (Oltmans et al., 2013). The total ozone ERF from 1750 to 2019 \r\nbest estimate is assessed as 0.47 W m-2 (Section 7.3.2.5) and this is \r\ndominated by increases in the troposphere. The underlying modelled \r\nglobal tropospheric ozone column increase (Skeie et al., 2020) from \r\n1850 to 2010 of 40-60%, is somewhat higher than the isotope \r\nbased upper-limit of Yeung et al. (2019). At mid-latitudes (30deg-60degN) \r\nmodel increases of 30-40% since the mid-20th century are broadly \r\nconsistent with observations."


id: c165baa243af14f3eb5692dcebe149a3
created_at: 2023-03-16 14:01:57
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vector_id: 28016
content:
"Prior to 1850 ozone observations do not exist, but a recent analysis \r\nusing clumped-isotope composition of molecular oxygen (18O18O in O2) \r\ntrapped in polar firn and ice, combined with atmospheric chemistry \r\nmodel simulations, constrains the global tropospheric ozone increase \r\nto less than 40% between 1850 and 2005, with most of this increase \r\noccurring between 1950 and 1980 (Yeung et  al., 2019). Recently, \r\nthe Tropospheric Ozone Assessment Report identified and evaluated \r\n60 records of surface ozone observations collected at rural locations \r\nworldwide between 1896 and 1975, which were based on a range of \r\nmeasurement techniques with potentially large uncertainties (Tarasick \r\net al., 2019). They found that from the mid-20th century (1930s to \r\nthe early 1970s) to 1990-2014, rural surface ozone increased by \r\n30-70% across the northern extra-tropics"


id: b405f6f1bbe81ee1c4dce6a6ab8a7f67
created_at: 2023-03-16 14:01:57
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content:
" This is smaller than the \r\n100% 20th-century increase reported in AR5, which relied on far \r\nfewer measurement sites, all in Europe. In the northern tropics limited \r\nlow-elevation historical data (1954-1975) provide no clear indication \r\nof surface ozone increases (Tarasick et al., 2019). However, similar to \r\nthe northern mid-latitude increases, lower-free tropospheric ozone \r\nat Mauna Loa, Hawaii increased by approximately 50% from the \r\nlate 1950s to present (Cooper et  al., 2020). Historical observations \r\nare too limited to draw conclusions on surface ozone trends in "


id: c634975fa538a62c5086704b0d07f3c6
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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vector_id: 28760
content:
"Spatially resolved trends of AOD derived from Aqua/Terra MISR and \r\nMODIS instruments over 2000-2019 range between -2% and +2% \r\nper year (Figure 2.9c). Ground-based solar attenuation networks \r\nhelp to constrain and improve the satellite-derived retrievals of \r\nAOD, and trends derived from the AERONET network (Figure 2.9c,d) \r\ncorroborate satellite results (Georgoulias et  al., 2016; Wei et  al., \r\n2019; Bauer et al., 2020; H. Yu et al., 2020) in particular for declines \r\nover Europe (Stjern et al., 2011; Cherian et al., 2014; Li et al., 2014) \r\nand the USA (Li et al., 2014; Jongeward et al., 2016). The tendency in \r\nAOD over East Asia reversed from positive (2000-2010) to negative \r\n(since 2010) (Sogacheva et al., 2018; Filonchyk et al., 2019; Ma et al., \r\n2019; Samset et al., 2019). Over southern Asia, however, AOD from \r\nsatellite (MODIS/MISR) and AERONET retrievals show continuing \r\nincreases (Li et al., 2014; Zhao et al"


id: 422deaaf219be4e1c61c2786ec2c7cd3
created_at: 2023-03-16 14:01:57
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vector_id: 7812
content:
", 2017), with similar trends from \r\nUV-based aerosol retrievals from the Ozone Monitoring Instrument \r\n(OMI) on the Aura satellite (Dahutia et  al., 2018; Hammer et  al., \r\n2018). A comparison of MODIS and MISR radiometric observations \r\nwith the broadband CERES satellite instrument (Corbett and \r\nLoeb, 2015) showed that drifts in calibration are unlikely to affect \r\nthe satellite derived trends. CERES shows patterns for clear-sky \r\nbroadband radiation consistent with the aerosol spatio-temporal \r\nchanges (Loeb et al., 2018; Paulot et al., 2018). "


id: 8c767d52160bcea80a89fd3289878440
created_at: 2023-03-16 14:01:57
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vector_id: 19617
content:
"with a slightly slower rise thereafter (Ghimire et  al., 2014). Recent \r\nradiative forcing estimates arising from biophysical processes generally \r\nfall at the lower end of the AR5 assessed range. For instance, based on \r\nhistorical simulations from 13 CMIP6 models, C.J. Smith et al. (2020) \r\nestimated that the ERF from surface albedo changes (including snow \r\ncover and leaf area) was -0.08 [-0.22 to +0.06] W m-2 since 1850. \r\nSimilarly, based on simulations from 13 CMIP5 models, Lejeune et al. \r\n(2020) estimated the radiative forcing from transitions between trees, \r\ncrops, and grasslands was -0.11 [-0.16 to +0.04] W m-2 since 1860. \r\nAndrews et  al. (2017) identified an ERF of -0.40 W  m-2 since \r\n1860, ascribing much of the effect to increases in albedo (including \r\nthe unmasking of underlying snow cover); notably, however, the \r\nanalysis was based on a single model with a known tendency \r\nto overestimate the ERF (Collins et  al., 2011). Ward et  al"


id: 9a7b57499e31072a5d5c6bfea8cf4004
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 21830
content:
" (2014) \r\nexamined the combined effects of biophysical and biogeochemical \r\nprocesses, obtaining an RF of 0.9 +- 0.5 W m-2 since 1850 that was \r\ndriven primarily by increases in land-use related GHG emissions from \r\ndeforestation and agriculture (Ward and Mahowald, 2015). According \r\nto a large suite of historical simulations, the biophysical effects of \r\nchanges in land cover (i.e., increased surface albedo and decreased \r\nturbulent heat fluxes) led to a net global cooling of 0.10degC +- 0.14degC \r\nat the surface (SRCCL). Available model simulations suggest that \r\nbiophysical and biogeochemical effects jointly may have contributed \r\nto a small global warming of 0.078degC +- 0.093degC at the surface over \r\nabout the past two centuries (SRCCL), with a potentially even larger \r\nwarming contribution over the Holocene as a whole (He et al., 2014)."


id: f0846eb0dcd8c82f1c0aec81b61ce047
created_at: 2023-03-16 14:01:57
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vector_id: 35340
content:
"Much of the global land surface has been modified or managed to \r\nsome extent by human activities during the Holocene. Reconstructions \r\nbased on pollen data indicate that natural vegetation probably \r\ncovered most of the Earth's ice-free terrestrial surface until roughly \r\nthe mid-Holocene (Marquer et  al., 2017; Harrison et  al., 2020; \r\nF. Li et al., 2020). Reconstructions based on pollen, archaeological, \r\nand historical data indicate deforestation at the regional scale \r\nsince at least 6 ka (Marquer et  al., 2017; Stephens et  al., 2019; \r\nHarrison et al., 2020; F. Li et al., 2020). From a global perspective, \r\nland-use forcing datasets (Lawrence et  al., 2016) estimate that \r\nchanges in land use (and related deforestation) were small on the \r\nglobal scale until the mid-19th century and accelerated markedly \r\nthereafter, with larger uncertainties prior to industrialization (Kaplan \r\net  al., 2017)"


id: de771c8d3e541072e13dacc7f3ae6f34
created_at: 2023-03-16 14:01:57
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vector_id: 32503
content:
" Since the early 1980s, about 60% of all land cover \r\nchanges have been associated with direct human activities, with \r\nspatial patterns emphasizing the regional character of land use \r\nand land management, including tropical deforestation, temperate \r\nafforestation, cropland intensification, and increased urbanization \r\n(Song et al., 2018; Zeng et al., 2018). At present, nearly three-quarters \r\nof the ice-free terrestrial surface is under some form of human use \r\n(Venter et al., 2016; Erb et al., 2017), particularly in agriculture and \r\nforest management. "


id: d3c4f3436a3c8d63848a9793d1d291cb
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 30843
content:
"Monitoring surface temperatures is integral to the negotiations of UNFCCC and the global mean temperature goals of the Paris \r\nAgreement. Upper-air temperatures are a key indicator of different causal mechanisms underlying climate change and underpinned \r\nthe very first conclusion in the Second Assessment Report of a 'discernible human influence' (Santer et al., 1996). To ascertain large\u0002scale changes of and human influence on the global hydrological cycle (which includes terrestrial and oceanic components), a small \r\nsubset of indicators across this cycle are chosen: ocean and land precipitation-evaporation (P-E), global precipitation, total column \r\nwater vapour, surface humidity (specific and relative), and global river runoff. Chapter 8 performs a substantive and holistic assessment \r\nof a much broader range of components"


id: aedc72562b701edfe4a9154f20ffdaec
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 25047
content:
" Finally, a warming world may be accompanied by a change in large-scale circulation patterns \r\nlinked through energy/mass/momentum constraints such as the extent and strength of the Hadley circulation (HC), monsoon systems, \r\nand/or the position and strength of the sub-tropical and polar jets. "


id: d2c890f0a749330d64d2232b554c2f39
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 30684
content:
"For the PETM, new reconstructions agree with those assessed by AR5. \r\nA major new compilation of proxy temperature data (Hollis et  al., \r\n2019) analysed using multiple statistical approaches (Inglis et  al., \r\n2020) indicates that GMST was 10degC-25degC (90% range) warmer \r\nthan 1850-1900, or about 5degC warmer relative to the pre-PETM \r\nstate. A related synthesis study also estimates that PETM warmed \r\nby 5degC (no uncertainty assigned; Zhu et al., 2019). A recent benthic \r\nisotope compilation (Westerhold et al., 2020) transformed to GMST \r\nbased on the formulation by J. Hansen et al. (2013; Cross-Chapter \r\nBox 2.1, Figure 1), and adjusted to 1850-1900 by adding 0.36degC, \r\nshows an increase of GMST by about 10degC during the PETM. This \r\nreflects the expected higher variability at single sites that were used \r\nto splice together the composite time series, compared to the globally \r\naveraged composite time series of Zachos et al. (2008). The latter was \r\noriginally used by J"


id: 63c0849019ba6ebe0a2d7abadbde21d1
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 13265
content:
" Hansen et al. (2013) to reconstruct GMST, and \r\nis the preferred representation of the global average bottom water \r\nconditions, despite its less well-refined chronology."


id: 2d0cc7fd2d532cca63fb131370ab7ebf
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 4525
content:
"This assessment of the paleo reference periods (Cross-Chapter Box 2.1) \r\ndraws from studies based mostly or entirely on indirect observational \r\nevidence from geological archives (i.e.,  proxy records) rather than \r\nreconstructions that rely more heavily on modelled parameters and \r\nthose based on deep-ocean temperatures (e.g.,  Kohler et  al., 2015; \r\nFriedrich et  al., 2016). In contrast to AR5, temperature estimates \r\nfrom climate models are not included in the assessed values for \r\npaleo reference periods in this chapter. The AR5 concluded that the \r\nreconstructed GMST during the PETM was 4degC-7degC warmer than \r\npre-PETM mean climate (low confidence), and that the EECO and the \r\nMPWP were 9degC-14degC and 1.9degC-3.6degC warmer than pre-industrial, \r\nrespectively (medium confidence). The GMST during the LIG was \r\nassessed at 1degC-2degC warmer than pre-industrial (medium confidence), \r\nwhereas SROCC narrowed the range to 0.5degC-1"


id: 6c93ea712166d82484cf592179d5c8da
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vector_id: 14705
content:
"0degC warmer, but did \r\nnot state a confidence level. The AR5 further concluded that it was \r\nvery likely that the LGM was 3degC-8degC colder than pre-industrial, and \r\nlikely that the maximum rate of global warming during the subsequent \r\ndeglacial period was 1degC-1.5degC kyr-1."


id: 3511694a788c24568bfe713a2471a99f
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 5759
content:
"6.1degC +- 0.4degC (2 SD) cooler than the late Holocene. Assuming that the \r\n1850-1900 reference period was 0.2degC and 0.4degC cooler than the late \r\nand first part of the Holocene, respectively (Kaufman et al., 2020a), \r\nthe midpoints of these three new GMST reconstructions average \r\n-5.8degC relative to 1850-1900. The coldest multi-century period of the \r\nLGM in the J. Hansen et al. (2013) reconstruction is 4.3degC colder than \r\n1850-1900. This compares to land- and SST-only estimates of about \r\n-6.1degC +- 2degC and -2.2degC +- 1degC, respectively (2 SD), which are based \r\non AR5-generation studies that imply a warmer GMST than more \r\nrecent reconstructions (Figure 1c in Harrison et al., 2015; Figure 7 in \r\nHarrison et  al., 2016). A major new pollen-based data-assimilation \r\nreconstruction averages 6.9degC cooler over northern extratropical \r\nland (Cleator et al., 2020)"


id: eea00cee057e9b54a9365727f422c774
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 35014
content:
" LGM temperature variability on centennial \r\nscales was about four times higher globally than during the Holocene, \r\nand even greater at high latitudes (Rehfeld et al., 2018). In summary, \r\nGMST is estimated to have been 5degC-7degC lower during the LGM \r\n(around 23-19 ka) compared with 1850-1900 (medium confidence). "


id: ce7d354798907cf17a3d8a7597f701f6
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 30026
content:
"For the LIG (Cross-Chapter Box 2.1, Figure 1, and Figure 2.11), \r\na major new compilation of marine proxy data (Turney et al., 2020) \r\nfrom 203 sites indicates that the average SST from 129-125 ka was \r\n1.0degC +- 0.2degC (2 SD) warmer than 1850-1900 (reported relative to \r\n1981-2010 and adjusted here by 0.8degC). These temperatures represent \r\nthe time of peak warmth, which may not have been synchronous \r\namong these sites. This compares with two other SST estimates \r\nfor 125 ka of 0.5degC +- 0.3degC (+- 2 SD) warmer at 125 ka relative to \r\n1870-1889 (Hoffman et al., 2017), and about 1.4degC (no uncertainty \r\nstated) warmer at 125 ka relative to 1850-1900 (Friedrich and \r\nTimmermann, 2020; reported relative to 10-5 ka and adjusted here \r\nby 0.4degC; Kaufman et  al., 2020a). The average of these post-AR5 \r\nglobal SST anomalies is 1degC. Commensurately (Figure 3.2b), GMST \r\nis estimated to have been roughly 1"


id: 48617b689d24ab2c3a5a96e23f7adbaf
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 8761
content:
"1degC above 1850-1900 values, \r\nalthough this value could be too high if peak warmth was not globally \r\nsynchronous (Capron et al., 2017). A further estimate of peak GMST \r\nanomalies of 1.0degC-3.5degC (90% range; adjusted here to 1850-1900 \r\nby adding 0.2degC) based on 59 marine sediment cores (Snyder, 2016) is \r\nconsiderably warmer than remaining estimates and is therefore given \r\nless weight in the final assessment. The warmest millennium of the \r\nLIG GMST reconstruction in J. Hansen et  al. (2013) is 1.5degC above \r\n1850-1900. In summary, GMST during the warmest millennia of the \r\nLIG (within the interval of around 129-125 ka) is estimated to have \r\nreached 0.5degC-1.5degC higher values than the 1850-1990 reference \r\nperiod (medium confidence). "


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content:
"This section concerns the Holocene period prior to industrialization \r\nwhen GMST was overall highest. Whereas SR1.5 focussed upon the \r\n'Holocene thermal maximum' when regional temperatures were \r\nup to 1degC higher than 1850-1900, though peak warming occurred \r\nregionally at different times between around 10 and 5 ka greatly \r\ncomplicating interpretation. A multi-method reconstruction (Kaufman \r\net al., 2020a) based on a quality-controlled, multi-proxy synthesis of \r\npaleo-temperature records from 470 terrestrial and 209 marine sites \r\nglobally (Kaufman et al., 2020b) indicates that the median GMST of \r\nthe warmest two-century-long interval was 0.7 [0.3 to 1.8] degC warmer \r\nthan 1800-1900 (which averaged 0.03degC colder than 1850-1900; \r\nPAGES 2k Consortium, 2019), and was centred around 6.5 ka. This is \r\nsimilar to Marcott et al. (2013), which is based on a smaller dataset \r\n(73 sites) and different procedures to estimate a maximum warmth \r\nof 0.8degC +- 0.3degC (2 SD) at around 7"


id: 206d7d85f224d2daa087836bae664d30
created_at: 2023-03-16 14:01:57
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vector_id: 2551
content:
"0 ka, adjusted here by adding \r\n0.3degC to account for differences in reference periods. These may be "


id: f15aad03531877e80c744c87bf56d0e0
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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vector_id: 35477
content:
"proxy data syntheses (Luning et al., 2019a, b) have been published, \r\nextending back 1-2 kyr. In addition, a major new global compilation \r\nof multiproxy, annually resolved paleo-temperature records for the \r\nCE (PAGES 2k Consortium, 2017) has been analysed using a variety \r\nof statistical methods for reconstructing temperature (PAGES \r\n2k Consortium, 2019). The median of the multi-method GMST \r\nreconstruction from this synthesis (Figure 2.11a) generally agrees \r\nwith the AR5 assessment, while affording more robust estimates of \r\nthe following major features of GMST during the CE: (i) an overall \r\nmillennial-scale cooling trend of -0.18 [-0.28 to 0.00] degC kyr-1 prior \r\nto 1850; (ii) a multi-centennial period of relatively low temperature \r\nbeginning around the 15th century, with GMST averaging -0.03 \r\n[-0.30 to 0"


id: 9f77cd4ce718542033b087b41c91f110
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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vector_id: 22629
content:
"06] degC between 1450 and 1850 relative to 1850-1900; \r\n(iii) the warmest multi-decadal period occurring most recently; and \r\n(iv) the rate of warming during the second half of the 20th century \r\n(from instrumental data) exceeding the 99th percentile of all 51-year \r\ntrends over the past 2 kyr. Moreover, the new proxy data compilation \r\nshows that the warming of the 20th century was more spatially \r\nuniform than any other century-scale temperature change of the \r\nCE (medium confidence) (Neukom et al., 2019). A new independent \r\ntemperature reconstruction extending back to 1580 is based on an \r\nexpanded database of subsurface borehole temperature profiles, \r\nalong with refined methods for inverse modelling (Cuesta-Valero \r\net al., 2021). The borehole data, converted to GMST based on the \r\nmodelled relation between changes in land versus sea surface \r\ntemperature outlined previously, indicate that average GMST for \r\n1600-1650 was 0"


id: 80949e9423d4fe409f5126cab49c4193
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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vector_id: 17756
content:
"12degC colder than 1850-1900, which is similar to \r\nthe PAGES 2k reconstruction (0.09degC colder), although both estimates \r\nare associated with relatively large uncertainties (0.8degC (95% range) \r\nand 0.5degC (90% range), respectively). "


id: 52b2604c1442c818962c60afaf1149c6
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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vector_id: 10470
content:
"underestimates because averaging inherently smoothed proxy records \r\nwith uncertain chronologies reduces the variability in the temperature \r\nreconstruction (e.g.,  Dolman and Laepple, (2018) for sedimentary \r\narchives). However, the general coincidence between peak warmth \r\nand astronomically driven boreal summer insolation might reflect \r\na bias toward summer conditions (Liu et al., 2014; Hou et al., 2019; \r\nBova et  al., 2021), suggesting that the estimate is too high. This \r\npossibility is supported by AR5-generation proxy data focusing on 6 ka \r\n(Harrison et al., 2014), the long-standing MH modelling target (Cross\u0002Chapter  Box 2.1), that indicate surface temperatures for land and \r\nocean were indistinguishable from 'pre-industrial' climate (Figure 1c \r\nin Harrison et al., 2015; Figure 7 in Harrison et al., 2016). In contrast, \r\nthe GMST estimate from the multi-method global reconstruction \r\n(Kaufman et al., 2020a) for the millennium centred on 6 ka is only \r\nabout 0"


id: 4adc3eeaf0539702f72c1a65e62254c
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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vector_id: 9193
content:
"Improvements in global temperature datasets since AR5 have addressed two major systematic issues. First, new SST datasets (Huang \r\net al., 2017; Kennedy et al., 2019) address deficiencies previously identified in AR5 relating to the shift from predominantly ship-based \r\nto buoy-based measurements; these improvements result in larger warming trends, particularly in recent decades. Second, all datasets \r\nnow employ interpolation to improve spatial coverage. This is particularly important in the Arctic, which has warmed faster than the \r\nrest of the globe in recent decades (Atlas 5.9.2.2); under-sampling of the Arctic leads to a cool bias in recent decades (Simmons et al., \r\n2017; Benestad et al., 2019). Some datasets are now spatially complete (Cowtan and Way, 2014; Kadow et al., 2020) while others \r\nhave expanded spatial coverage (Lenssen et al., 2019; Rohde and Hausfather, 2020; Morice et al., 2021; Vose et al., 2021)"


id: 77e8c367c4e3adbe2623432e82ac255
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
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index: climate_gpt_only_giec
vector_id: 16426
content:
" Several \r\ninterpolation methods have been benchmarked against test cases (e.g., Lenssen et al., 2019), and comparisons with reanalyses further \r\nconfirm the value of such interpolation (Simmons et al., 2017). It is extremely likely that interpolation produces an improved estimate \r\nof the changes in GMST compared to ignoring data-void regions."


id: df1d11619aea93bd1c27af2018942655
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 32598
content:
"Effects of warming since AR5 and choice of metrics of global mean temperature change\r\nEach of the six years from 2015 to 2020 has likely been warmer than any prior year in the instrumental record. GMST for the decade \r\n2011-2020 has been 0.19 [0.16 to 0.22] degC warmer than 2003-2012, the most recent decade used in AR5 (Cross-Chapter Box 2.3, \r\nFigure 1). A linear trend has become a poorer representation of observed change over time since most of the sustained warming has \r\noccurred after the 1970s (Cross-Chapter Box 2.3, Figure 1) and all values since 2012 are at least 0.2degC above a linear trendline for \r\n1850-2020. For this reason, the primary method used to assess observed warming in this report is the change in temperature from \r\n1850-1900 to the most recent decade (2011-2020) or the recent past (1995-2014), replacing the trend-based methods used in AR5 \r\nand earlier assessments"


id: fc3cd88838e6ba01a2c071799dab8feb
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 37190
content:
" The effect of this change from trend-based to change-based metrics is currently relatively minor at -0.03degC \r\n(<5%) for the most recent decade, but this may not remain the case in future (high confidence)."


id: 8ea002a941ef87758e94610e0a4ce1d7
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 19964
content:
"Addressing the non-equivalence of GMST and GSAT \r\nGMST is a combination of land surface air temperatures (LSAT) and SSTs, whereas GSAT is a combination of LSAT and marine air \r\ntemperatures (MATs). Although GMST and GSAT are closely related, the two measures are physically distinct. The implications have \r\nbecome more apparent since AR5 (Merchant et al., 2013; Cowtan et al., 2015; Simmons et al., 2017; IPCC, 2018 (SR1.5); Richardson \r\net  al., 2018), and it has been shown (Rubino et  al., 2020) that MAT and SST can show distinct multi-decadal-scale trends and \r\npatterns of interannual variability. Although SR1.5 used GMST for observational-based and GSAT for model-based headline warming \r\nstatements, they noted the importance of the difference for their assessment (SR1.5 Section 1.2.1.1). The SR1.5 used information \r\nfrom CMIP5 models to estimate a GSAT equivalent from observation-based GMST for certain applications such as remaining carbon \r\nbudgets"


id: 63acf14089c7553272906da5c7e99f22
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 13252
content:
" The following subsections assess available lines of evidence related to the equivalence between GMST and GSAT. "


id: 4092c84570a807024ed5af3d5edecac
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 7536
content:
"heat flux effect cannot be considered in isolation. Attempts to further constrain the combination of fluxes (e.g., Lorenz et al., 2010; \r\nSiler et al., 2019) rely on parameterizations or output from Earth system models (ESMs) or reanalyses and so are not considered \r\nindependent. Apart from the above global considerations, regional and seasonal effects such as changes to the frequency and intensity \r\nof storms, sea state, cloudiness, sea ice cover, vegetation and land use may all affect the GSAT to GMST difference, either directly or \r\nby altering the relationships between gradients and energy fluxes. These changing energy flux relationships are monitored through \r\nobserving the stratification of the upper ocean (Section 9.2.1.3) and the response of upper ocean processes (Cross-Chapter Box 5.3) in \r\nESMs and reanalyses, but such monitoring tasks rival the observational challenge of directly observing SSTs and 2 m air temperature \r\nunder a wide range of conditions"


id: 9a8ee4aa646429ed83b0bad601ab2179
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 21850
content:
" In summary, because of the lack of physical constraints and the complexity of processes driving \r\nchanges in the GSAT to GMST temperature differences, there is no simple explanation based on physical grounds alone for how this \r\ndifference responds to climate change."


id: 4cb980ac1ae11500cdc54aa82f4ad27b
created_at: 2023-03-16 14:01:57
updated_at: 2023-03-16 14:01:57
content_type: text
index: climate_gpt_only_giec
vector_id: 9496
content:
"There is currently no regularly updated, entirely observation-based dataset for GSAT. The best available observations of near-surface \r\nair temperature over ocean are datasets of night-time marine air temperature (NMAT; e.g., Cornes et al., 2020; Junod and Christy, \r\n2020), though spatial coverage is less extensive than for SST. Night-time measurements are used to avoid potential biases from \r\ndaytime heating of ship superstructures. Kennedy et al. (2019) show little difference between HadNMAT2 and HadSST4 between \r\n1920 and 1990, but a warming of SST relative to NMAT manifesting as a step change of 0.05degC-0.10degC in the early 1990s, which \r\nmay reflect an actual change, the impact of increasingly divergent spatial coverage between SST and MAT measurements, or \r\nunresolved structural uncertainties in one or both datasets. This leads to NMAT warming around 10% more slowly than SST over \r\nthe last century"


