1Key Laboratory of Alpine Ecology, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China.
2University of Chinese Academy of Sciences, Beijing, China.
3CAS Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences, Beijing, China.
4Institut des Géosciences de l'Environnement, CNRS, Université Grenoble Alpes, Grenoble, France.
5International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria.
6Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China.
Key Laboratory of Alpine Ecology, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China.University of Chinese Academy of Sciences, Beijing, China.
Key Laboratory of Alpine Ecology, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China.CAS Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences, Beijing, China.
Institut des Géosciences de l'Environnement, CNRS, Université Grenoble Alpes, Grenoble, France.
Key Laboratory of Alpine Ecology, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China.
International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria.
Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China.
Key Laboratory of Alpine Ecology, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China.University of Chinese Academy of Sciences, Beijing, China.CAS Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences, Beijing, China.
CAS Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences, Beijing, China.
Substantial progress has been made in understanding how Eurasian snow cover variabilities affect the Indian summer monsoon, but the snow-monsoon relationship in a warming atmosphere remains controversial. Using long-term observational snow and rainfall data (1967–2015), we identified that the widely recognized inverse relationship of central Eurasian spring snow cover with the Indian summer monsoon rainfall has disappeared since 1990. The apparent loss of this negative correlation is mainly due to the central Eurasian spring snow cover no longer regulating the summer mid-tropospheric temperature over the Iranian Plateau and surroundings, and hence the land-ocean thermal contrast after 1990. A reduced lagged snow-hydrological effect, resulting from a warming-induced decline in spring snow cover, constitutes the possible mechanism for the breakdown of the snow-air temperature connection after 1990. Our results suggest that, in a changing climate, Eurasian spring snow cover may not be a faithful predictor of the Indian summer monsoon rainfall.
Seasonal snow cover plays a notable role in the Earth's climate system because it directly influences the proportion of solar radiation absorbed (1, 2) and creates soil moisture anomalies that may persist for periods of a few weeks to months (3–5). It has been recognized as an important contributor to seasonal climate prediction in several regions, particularly in the Indian subcontinent, where the extent of spring snow cover was first proposed as a useful tool for forecasting the following Indian summer monsoon rainfall more than a century ago (6, 7).
The Indian summer monsoon rainfall during June to September contributes more than 80% of the total annual rainfall over India, and its interannual variation of around 10% can have substantial impacts on agriculture and the economy in general (8–10). Skillful prediction of this variation months in advance would allow improved agricultural planning, drought mitigation, and flood warning (11, 12). Numerous observational (13–18) and modeling studies (19–24) have shown that positive Eurasian snow cover anomalies during winter and spring tend to be followed by an anomalous deficit of rainfall over the Indian subcontinent in the subsequent summer monsoon season, while negative snow cover anomalies tend to be followed by abundant rainfall. However, there are studies noting that this negative snow-monsoon teleconnection only holds true when the snow cover in particular regions, such as western Eurasia, is considered (16, 25, 26) or when the effect of the El Niño Southern Oscillation (ENSO) is excluded (25, 27, 28). After years of extensive research, exactly which part of the Eurasian region is primarily responsible for the inverse correlation between snow cover and the following Indian summer monsoon rainfall and how robust the relationship is in a changing climate remain unclear. This uncertainty is probably partly due to the use of different period lengths and different snow proxies in different studies (14, 16, 17).
The extent of spring Eurasian snow cover has been shrinking in recent years, as a consequence of rapid climate warming at mid to high latitudes (29). It would seem reasonable to suppose that this large-scale spring snow retreat should lead to higher land temperature and an increased land-sea thermal contrast that would be expected to strengthen the Indian summer monsoon. However, the Indian summer monsoon rainfall has experienced a clear downward trend in the last several decades (30), suggesting that our comprehension of the snow-monsoon relationship in a changing climate is incomplete. Previous studies have documented that the snow-monsoon relationship shows secular changes (28, 31, 32), but the mechanisms through which the altered snow cover affects the snow-monsoon relationship have not been identified. Here, we aim to unravel the causes of an evolving relationship between the Eurasian spring snow cover and the subsequent Indian summer monsoon rainfall at the interannual time scale by synthesizing long-term satellite snow cover data, instrumental rainfall records, and reanalysis datasets.
We found that changes in spring snow cover fraction (SCF; March to May) over central Eurasia (40 to 80°E, 35 to 65°N; the red box in Fig. 1A), including eastern Europe, western Siberia, central Asia, and the western Himalaya, had the strongest negative impact on the subsequent all-Indian summer monsoon rainfall (AISMR; June to September) for the entire study period. This result is consistent with previous findings, although the time periods considered are different (6, 16, 28, 33). Another region with obvious negative correlation is northeast Eurasia (100° to 140°E, 65° to 73°N; the black box in Fig. 1A). However, the spring SCF over this region exhibits smaller interannual variability in comparison with that in central Eurasia (fig. S1), meaning that its importance in the seasonal forecast of the AISMR changes should be limited.
Fig. 1 Shift in the snow-monsoon relationship.
(A) Spatial distribution of correlation coefficients (R) of spring SCF over Eurasia with AISMR for the period 1967–2015. (B and C) Same as (A) but for the periods 1967–1990 and 1991–2015, respectively. (D) Time series of detrended AISMR and CESCF. AISMR is all-Indian summer monsoon rainfall (June to September) from the Indian Institute of Tropical Meteorology (IITM), and CESCF denotes the area-weighted average spring (March to May) SCF over central Eurasia. Note that the order of the right-side axis is reversed to enable easier comparison of the time series. The line chart embedded in (D) denotes a 15-year sliding correlation between AISMR and CESCF. Significant R values are identified as gray dashed lines (P < 0.05). The black dots in (A) to (C) indicate that the R is statistically significant (P < 0.05). The red boxes represent the region of central Eurasia (40° to 80°E, 35° to 65°N), and the black box denotes the northeast Eurasia (100° to 140°E, 65° to 73°N).
Next, we focus on the central Eurasian region only and examine the stability of the interannual relationship between central Eurasian spring SCF and the subsequent AISMR for the period 1967–2015. For this analysis, the area-weighted average spring SCF over central Eurasia (hereafter CESCF) was calculated as an index of spring snow cover variabilities. Both CESCF and AISMR have a prominent interannual variability (Fig. 1D). Correlation analysis between CESCF and AISMR, using a 15-year sliding window, indicated that the relationship between the two variables underwent a dramatic shift starting around 1990 (see inset in Fig. 1D). Consequently, we split the study period into two subperiods (1967–1990 and 1991–2015) for further inspection. We found that RCESCF-AISMR (both time series are detrended; see Materials and Methods) is significantly negative during the period 1967–1990 (R = −0.61, P < 0.01) but becomes nonsignificant and positive for the period 1991–2015 (R = 0.18, P = 0.39) (Fig. 1D). This phenomenon was also reflected in the correlation patterns, where spring SCF over central Eurasia displays a coherent strong negative correlation with AISMR before 1990 but shows no significant correlation thereafter (Fig. 1, B and C).
To check the robustness of the observed shift in RCESCF-AISMR between the periods 1967–1990 and 1991–2015, we performed the following tests. First, we randomly selected 20 years for each period (1967–1990 and 1991–2015) and found that RCESCF-AISMR shifted from −0.61 ± 0.07 (with 98.6% of values being statistically significant and negative during 1967–1990) to 0.18 ± 0.11 (with 99.4% of values being statistically nonsignificant during 1991–2015) (Fig. 2A). The results suggest that the observed shift was not due to certain singular values.
Fig. 2 Robustness tests of the shift in the snow-monsoon relationship.
(A) Frequency distributions of the correlation coefficients of CESCF with AISMR for 1967–1990 and 1991–2015. We calculated the correlation coefficients by randomly selecting 20 years in each corresponding period. Significant correlation coefficients are indicated by the gray dashed lines (P < 0.05). (B) Partial correlation coefficients of CESCF and AISMR (RCESCF-AISMR) for 1967–1990 and 1991–2015, calculated by statistically controlling for the effect of ENSO, NAO, and IOD in turn. The original RCESCF-AISMR denotes the correlation analysis without excluding any other effects. The asterisks indicate that the correlations are statistically significant (P < 0.05). (C and D) Linear regression of CRU-derived Indian summer monsoon rainfall (June to September) with respect to CESCF for 1967–1990 and 1991–2015, respectively. The black dots represent significant rainfall anomalies at the 95% confidence level based on a Student's t test.
Second, we tested whether the observed shift in the snow-monsoon relationship is due to the weakened ENSO-monsoon association. This is an important test, given that previous studies have indicated that ENSO could affect both the Indian summer monsoon and Eurasian snow cover through atmospheric teleconnections (34–36), and the fact that the ENSO-AISMR relationship has also collapsed since the 1990s (37). To make the test, we calculated a partial correlation between CESCF and AISMR after statistically controlling for the ENSO effect for the two periods. RCESCF-AISMR calculated by this method also shows a shift from −0.55 (P < 0.01) during the earlier period to 0.21 (P = 0.34) during the later period (Fig. 2B). Moreover, the correlation patterns of spring SCF with AISMR after removing the ENSO signal during the two periods (fig. S2, A and B) are consistent with those that include the ENSO effect (Fig. 1, B and C). Similar results are also obtained when the same analyses are carried out to exclude the effects of NAO (North Atlantic Oscillation) and IOD (Indian Ocean Dipole) (Fig. 2B and fig. S2, C to F). These analyses illustrate that the shift in the snow-monsoon relationship is not an artifact but a real phenomenon.
Third, the change in the snow-monsoon relationship was also found when the Climate Research Unit (CRU) precipitation dataset was adopted to represent the Indian summer monsoon rainfall intensity (Fig. 2, C and D). To obtain further insight into the shift in the snow-monsoon relationship, we performed linear regression analyses to determine how the spatial pattern of Indian summer monsoon rainfall anomalies covaries with CESCF for the two periods. These analyses show that positive anomalies of spring snow cover over central Eurasia precede a significant decline in summer rainfall especially over northwestern and central India (also known as the core Indian monsoon region) during the period 1967–1990 (Fig. 2C), while it precedes an increase in summer rainfall after 1990 (Fig. 2D). Although the shift in RCESCF-AISMR was originally found when using the time series of area-averaged precipitation over India, the shift in summer monsoon rainfall anomalies in response to CESCF between the two study periods is also observed throughout most of the Indian subcontinent, suggesting that the shift could be related to large-scale changes in monsoon circulation.
Do current climate models capture such a shift in the snow-monsoon connection? To answer this question, we examine the simulation outputs of 30 models participating in the fifth phase of the Coupled Model Intercomparison Project (CMIP5) (table S1). We show that the key region with the strongest impact of spring snow cover on the Indian summer monsoon rainfall varies among models (fig. S3). In terms of the multimodel mean, the strongest impact lies in central Russia rather than central Eurasia (fig. S3). These differences across models and the differences between the model outputs and observations could arise from a combination of several factors and should be interpreted with caution. The CMIP5 climate models have systematic biases in their simulation of snow states (38, 39). Such biases could propagate through the models and lead to errors in the models' atmospheric responses to the underlying snow patterns (4), thus contributing to the model-data mismatch. Although the key region with the strongest impact of snow on the monsoon was not captured, around 8 of 30 models and the multimodel mean could reproduce the shift in the snow-monsoon relationship (fig. S4), if their own region where spring SCF strongly affected the Indian summer monsoon was used.
It is widely believed that the Indian summer monsoon originates from the meridional thermal contrast between the Asian continent and the Indian Ocean (40, 41). Changes in spring snow cover could induce anomalous warming or cooling of the atmospheric temperature over the Asian continent through the snow-albedo and snow-hydrological effects (4, 5, 19, 24), which would affect the land-sea temperature gradient and thereby the Indian summer monsoon intensity (23, 24, 42). Snow-induced atmospheric temperature anomalies can thus act as a "bridge" linking snow to monsoon. Moreover, both Eurasian snow cover and air temperature underwent an interdecadal shift around 1990 (43). We hypothesize that the weakened inverse snow-monsoon relationship may result from the loss of the coupling between spring snow cover and summer atmospheric thermal conditions after 1990.
To test this hypothesis, we examined the relationships of summer (June to September) air temperature with CESCF and AISMR for both study periods. Since the mid-upper level tropospheric temperature plays a more important role in driving the Indian summer monsoon (44, 45), we used the air temperature at 500 hPa (AT500) to indicate the atmospheric thermal conditions. The regression analyses showed that AT500 over southwestern and northeastern Eurasia has a significant negative association with CESCF during the period 1967–1990 (Fig. 3A), but almost no significant connection occurred after 1990 (Fig. 3B). In contrast, AISMR was positively linked with AT500 over large parts of the Eurasian continent during both periods (Fig. 3, C and D). We further calculated the correlation coefficients of CESCF and AISMR with the area-averaged AT500 over the Iranian Plateau and surroundings (IPS; 55° to 80°E, 25° to 40°N), where the air temperature exerts the strongest influence on the Indian summer monsoon (fig. S5). Our results again showed that CESCF is significantly negatively correlated with AT500 over the IPS during the earlier period (R = −0.51, P < 0.01), but the correlation becomes insignificant in the later period (R = 0.14, P = 0.50) (Fig. 3E). The observed shift in the snow-air temperature relationship coincides with that in the snow-monsoon relationship (Fig. 3F). However, the AISMR is significantly correlated with AT500 during both earlier (R = −0.70, P < 0.01) and later periods (R = −0.84, P < 0.01) (Fig. 3G). This result was also robust to the use of temperature data from the Japanese 55-year Reanalysis (JRA-55) (fig. S6). The analyses presented above support our hypothesis that the failure of the impact of CESCF on AISMR could be attributed to a decoupling between snow cover and atmospheric thermal conditions over the Asian landmass after 1990.
Fig. 3 The relationships between air temperature, atmospheric circulation, AISMR, and CESCF for the periods 1967–1990 and 1991–2015.
(A and B) Linear regression of summer (June to September) air temperature (AT) (°C) and wind fields (m/s) at 500 hPa with respect to CESCF for 1967–1990 and 1991–2015, respectively. (C and D) Linear regression of air temperature with respect to AISMR for 1967–1990 and 1991–2015, respectively. The pink dashed lines represent significant air temperature anomalies at the 95% confidence level, and black arrows denote that the wind anomalies are statistically significant (P < 0.05) based on a Student's t test. (E to G) Correlation coefficients of CESCF with air temperature averaged over the IPS, that of CESCF with AISMR, and that of AISMR with air temperature over the IPS, respectively. The IPS is denoted by the red box in (A). The asterisks indicate that the correlation coefficients are statistically significant (P < 0.05). All the temperature and wind data are derived from the NCEP-NCAR reanalysis.
Changes in Eurasian spring snow cover and the resultant alterations of land surface conditions could exert strong impacts on the hemispheric atmospheric circulation and hence the climate during the following seasons (5, 22, 32). Therefore, we explored whether the observed shift in the snow-air temperature relationship is explained by changes in the snow-related summer atmospheric circulation. We performed regression analyses of summer wind field at 500 hPa with respect to the CESCF. Our analyses illustrated that there is a prominent anomalous cyclonic circulation centered over the IPS following the excessive anomaly of CESCF during the earlier period (Fig. 3A). Such an anomalous cyclonic circulation, on the one hand, acts to transport cold air from midlatitudes into the IPS via the anomalous northeasterly winds. On the other hand, the cyclonic flow could probably further lower the mid-tropospheric air temperature by favoring upward air motion that leads to an increase in cloud cover and a reduction of downward solar radiation (46, 47). The cooling in the mid-upper troposphere over the IPS would contribute to a decrease of the meridional thermal gradient and thereby a depressed Indian summer monsoon. In the later period, however, there are almost no significant anomalies of circulations in response to the variability of CESCF (Fig. 3B). Particularly, the cyclonic circulation over the IPS formed in the earlier period has vanished but was replaced by an anomalous weak southerly wind, which is conducive to the advection of warm air from lower latitudes to the IPS and may thus destroy the inverse snow-air temperature correlation (Fig. 3B). These results were found to be similar when using the JRA-55 dataset (fig. S6, A and B).
The potential mechanism responsible for the shift in snow-related circulations and the resultant snow-air temperature decoupling could be related to the change in the lagged snow-hydrological effect. Numerous observational and modeling studies indicated that the snow-hydrological effect has as a crucial role in determining the land-atmosphere coupling through its effects on the soil moisture (4, 5, 48). The soil moisture anomalies formed from spring snowmelt may take weeks or months to dissipate, known as the "memory," providing a lagged impact on the summer climate both locally and remotely through changing the surface energy budget (5, 23). However, as for global warming, changes in snow mass and snow phenology (29) could alter the soil moisture memory, which may disrupt the delayed effect of spring snow on summer climate.
As illustrated in Fig. 4, in the period 1991–2015, mean spring SCF over central Eurasia decreased by 16% (Fig. 4A), and the snowmelt date advanced by at least 2 weeks (Fig. 4B) in comparison to those in the period 1967–1990. Such a decline in spring snow cover and an advance in snowmelt date could lead to less infiltration of meltwater into the soils, resulting in the soil moisture anomalies becoming more susceptible to rainfall and evaporation during summer and hence losing the memory. To test this mechanism, we calculated the memory of spring (March to May) soil moisture anomalies over central Eurasia as the number of months until the correlation coefficient of soil moisture between spring and the following months dropped below the insignificant level (P > 0.05). Our analysis showed that the memory length reduced from 4 months (June to September) in the earlier period to 2 months (June and July) in the later period (Fig. 4C). As a result of the decline in soil moisture memory since 1990, the anomaly of CESCF can only efficiently affect the atmospheric circulations and air temperature over Asian landmass during June and July rather than during the whole monsoon season (June to September) (fig. S7). Therefore, the reduced snow-hydrological effect can be regarded as a plausible mechanism for the observed decoupled relationship between snow and air temperature in the later period.
Fig. 4 Changes in spring snow cover and soil moisture memory over central Eurasia for 1967–1990 and 1991–2015.
(A) Time series of the CESCF. The blue and red lines represent mean CESCF for 1967–1990 and 1991–2015, respectively. (B) Seasonal evolution of CESCF during two periods. (C) Correlation coefficients (R) of monthly mean soil moisture with spring (March, April, and May) soil moisture averaged over central Eurasia for the periods 1967–1990 and 1991–2015. The gray dashed line denotes the R significant at 95% confidence level.
In addition, we note that the atmospheric circulation pattern associated with the anomaly of CESCF during 1991–2015 (Fig. 3B) closely resembled the pattern triggered by the variability of the west-east dipole mode of Eurasian spring snow cover anomalies (fig. S8C) (48, 49). This dipole structure is found to dominate the variations of Eurasian spring snow cover anomalies since 1990 (fig. S8, A and B) (32, 49). It therefore tentatively suggests that the emergence of the west-east dipole pattern may lead to changes in the CESCF-related atmospheric circulations, which, in turn, contribute to the decoupling between CESCF and air temperature over the IPS in the later period. However, uncertainties remain in understanding the linkage between snow and circulation anomalies based on statistical analysis. We could not rule out the possible interference of internal atmospheric dynamics and other boundary conditions, such as sea surface temperature over the North Atlantic, with the influence of snow (32). Further model simulation works are needed to quantify the actual role of the west-east dipole pattern of snow cover in shaping the circulation.
In summary, we have used the long-term satellite-based snow cover data and instrumental rainfall records (1967–2015) to show that the classical inverse relationship between Eurasian spring snow cover and subsequent Indian summer monsoon rainfall has disappeared after 1990. This shift in the snow-monsoon relationship is found to be related to the reduced lagged snow-hydrological effect, which could be further attributed to the reduction of spring snow cover and an advance in snowmelt date due to the climate warming. However, our understanding of the mechanisms behind the loss of the snow-monsoon correlation is still limited. Further studies using the state-of-the-art earth system models, which include a physical representation of the interactions between snow and atmospheric dynamics, are required to develop our comprehension of these mechanisms. In addition, similar behavior to the observed weakening of the snow-monsoon correlation is found in the relationship between the Indian summer monsoon and ENSO, suggesting a changing paradigm for Indian summer monsoon precipitation in a warming atmosphere. The CMIP5 models fail to capture the connection between snow cover and the monsoon and its shift at the decadal time scale, emphasizing the necessity to improve the models' ability to represent snow status and atmospheric responses to snow anomalies in a warming world.
We used AISMR data from the Indian Institute of Tropical Meteorology (IITM) to represent the variability of the Indian summer monsoon rainfall (June to September). This dataset was derived from the area-weighted average of 306 rain-gauge stations that are almost uniformly distributed throughout India (50). To confirm the reliability of the IITM data, we also used rainfall datasets from the Climatic Research Unit Time Series (0.5° × 0.5°;CRU TS v4.00) (51) and the Global Precipitation Climatology Centre (0.5° × 0.5°; GPCC v7) (52).
The snow cover data were from the Northern Hemisphere EASE-Grid 2.0 weekly snow cover and sea ice extent version 4, available at the National Snow and Ice Data Center (NSIDC). The data were stored as binary files in arrays of 720 columns by 720 rows and had a spatial resolution of 25 km. To make the comparison with other data easier, we converted the EASE-Grid 2.0 data to a 1° × 1° longitude-latitude grid. The snow cover data were in binary form (snow or no snow), and so we used the SCF, rather than snow cover extent, to represent the variability of snow cover. The monthly SCF was calculated by summing the number of weeks in that month for which snow was present for a pixel and by expressing this number as a fraction of the total number of weeks in that month (16). In this study, we used only the spring (March, April, and May) snow cover to investigate the snow-monsoon relationship. There are two reasons for this restriction. First, the snow cover has little interannual variability during the winter. Second, the snow-albedo effect was found to be high in the spring because of the rapid retreat of snow cover. In addition, the snow-hydrological effect, controlled by anomalies in snow water equivalent, emerges in spring, meaning that the degree to which the atmosphere responds to changes in snow should be strong at this time of year (4, 5).
The data of 500 hPa air temperature and wind fields with a resolution of 2.5° × 2.5° for the summer months (June to September) were obtained from the National Centers for Environmental Prediction (NCEP)–National Center for Atmospheric Research (NCAR) reanalysis (53). The air temperature and wind data from the JRA-55 were also used (54). The soil moisture data were provided by the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center (CPC) and had a resolution of 0.5° × 0.5°. In addition, we used the 3-month mean (June, July, and August) Oceanic Niño Index in the Niño 3.4 region to represent the ENSO, and the average monthly NAO index for January, February, and March to represent the cold season NAO index. Both the ENSO index and the NAO index are also available at the NOAA CPC. We used summer (June to September) Dipole Mode Index (DMI) from the NOAA Earth System Research Laboratory to represent the IOD. The IOD was considered since it exerts an important impact on both the Indian summer monsoon rainfall (55) and Eurasian snow cover (56).
The snow cover and precipitation data from 30 earth system models participating in the CMIP5 were also used in our study (table S1). Because of the uncertainties in the climate forcing data for CMIP5 after 2005 (57), we only used the historical simulations data (1967–2005). The CMIP5 models had various spatial resolutions, and so, for convenience, we converted all the CMIP5 model output to a 1° × 1° longitude-latitude grid.
To diagnose the impact of spring snow cover on the Indian summer monsoon rainfall, we performed a correlation analysis between AISMR (June to September) and satellite-based Eurasian spring (March to May) SCF for each grid square. We also computed the partial correlation between spring SCF and AISMR while controlling for the effects of ENSO, NAO, and IOD. In addition, we performed a linear regression analysis to reveal the response of the air temperature and atmospheric circulation to the snow anomalies. All variables were detrended before the statistical analyses. The significance of all the analysis was determined by the standard two-tailed Student's t test method.
Fig. S1. The 11-year sliding SD of spring SCF averaged over central (40° to 80°E, 35° to 65°N; the red box in Fig. 1A) and northeast (100° to 140°E, 65° to 73°N; the black box in Fig. 1A) Eurasia for the period 1967–2015.
Fig. S2. Spatial distribution of the partial correlation coefficients of Eurasian spring SCF with AISMR during 1967–1990 and 1991–2015.
Fig. S3. Spatial distribution of the correlation coefficients of Eurasian spring SCF with Indian summer (June to September) monsoon rainfall for the period of 1967–2005 in the CMIP5 models.
Fig. S4. Scatter plot of the correlations between Indian summer monsoon rainfall and area-averaged spring snow cover for 1967–1990 versus that for 1991–2005.
Fig. S5. Spatial distribution of the correlation coefficients of 500 hPa summer (June to September) air temperature over Eurasia with AISMR for 1967–2015.
Fig. S6. Same as Fig. 3 but using air temperature and wind fields from the JRA-55 reanalysis.
Fig. S7. Anomalies of air temperature and circulations related to the variabilities of central Eurasian spring snow cover.
Fig. S8. Shift in the dominant pattern of Eurasian spring snow cover anomalies and its associated atmospheric circulation anomalies after 1990.
Table S1. Summary of CMIP5 models used in this study.
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, Interactions between the Northern and Southern Hemispheric atmospheres and the essence of monsoon (in Chinese). Chin. J. Atmos. Sci. 26, 433–448 (2002).
, Decadal change of the spring snow depth over the Tibetan Plateau: The associated circulation and influence on the East Asian summer monsoon. J. Climate 17, 2780–2793 (2004).
, Interdecadal change of Eurasian snow, surface temperature, and atmospheric circulation in the late 1980s. J. Geophys. Res. Atmos. 120, 2738–2753 (2015).
, The relative roles of upper and lower tropospheric thermal contrasts and tropical influences in driving Asian summer monsoons. J. Geophys. Res. Atmos. 118, 7024–7045 (2013).
B. N. Goswami, S. Chakravorty, Dynamics of the Indian summer monsoon climate, in Oxford Research Encyclopedia of Climate Science (Oxford Univ. Press, 2017).
, Internal feedbacks from monsoon-midlatitude interactions during droughts in the Indian summer monsoon. J. Atmos. Sci. 66, 553–578 (2009).
, Circumglobal wave train and the summer monsoon over northwestern India and Pakistan: The explicit role of the surface heat low. Clim. Dynam. 37, 1045–1060 (2011).
, Impact of Eurasian Spring snow decrement on East Asian summer precipitation. J. Climate 30, 3421–3437 (2017).
, Two distinct patterns of spring Eurasian snow cover anomaly and their impacts on the East Asian summer monsoon. J. Geophys. Res. 115, D22113 (2010).
D. R. Kothawale, M. Rajeevan, Monthly, Seasonal and Annual Rainfall Time Series for All India, Homogeneous Regions and Meteorological Subdivisions: 1871–2016 (Technical Report RR-138, Indian Institute of Tropical Meteorology, 2017).
, Updated high-resolution grids of monthly climatic observations - the CRU TS3.10 Dataset. Int. J. Climatol. 34, 623–642 (2014).
, GPCC's new land surface precipitation climatology based on quality-controlled in situ data and its role in quantifying the global water cycle. Theor. Appl. Climatol. 115, 15–40 (2014).
, The NCEP/NCAR 40-year reanalysis project. Bull. Am. Meteorol. Soc. 77, 437–471 (1996).
, The JRA-55 Reanalysis: General specifications and basic characteristics. J. Meteorol. Soc. Jpn. 93, 5–48 (2015).
, Impact of the Indian Ocean dipole on the relationship between the Indian monsoon rainfall and ENSO. Geophys. Res. Lett. 28, 4499–4502 (2001).
, Respective influences of IOD and ENSO on the Tibetan snow cover in early winter. Clim. Dynam. 33, 509–520 (2009).
, Evaluating adjusted forcing and model spread for historical and future scenarios in the CMIP5 generation of climate models. J. Geophys. Res. Atmos. 118, 1139–1150 (2013).
Acknowledgments: Funding: This study was supported by the Strategic Priority Research Program (A) of the Chinese Academy of Sciences (XDA20100300 and XDA20050101), the Key Research and Development Programs for Global Change and Adaptation (2017YFA0603604), the International Partnership Program of Chinese Academy of Sciences (131C11KYSB20160061), the 13th Five-year Informatization Plan of Chinese Academy of Sciences (grant XXH13505-06), and the Thousand Youth Talents Program in China. We also acknowledge the support of Kathmandu Center for Research and Education, Chinese Academy of Sciences–Tribhuvan University. Author contributions: T.W. designed the research; T.Z. performed the data analysis; T.W. and T.Z. drafted the paper; all authors contributed to the interpretation of the results and to the text. Competing interests: The authors declare that they have no competing interests. Data and materials availability: All data needed to evaluate the conclusions in this paper are present in the paper and/or the Supplementary Materials. Additional data related to this paper may be requested from the authors.
Eurasian spring snow cover may not be a faithful predictor of the Indian summer monsoon rainfall in a changing climate.


text:
A few thoughts on the new draft proposals for PE in Welsh schools (from the girl who hated PE at school)
Current affairs Family life June 12, 2019
The irony of me – the least sportiest girl in the class who hated PE with a passion – turning up at BBC Radio Wales at 7.45am this morning, in full workout gear having already done an hour's session at the gym, to discuss draft proposals that some believe could see physical education dropped from the school curriculum, wasn't lost on me.
You see, younger me hated PE. Younger me thought she wasn't sporty. Younger me was crap at netball. Even worse at hockey. She could never get the racquet to hit the ball in tennis. Fell over the hurdles and couldn't get the javelin to go in a straight line. Always the last to be picked for team games.
Younger me – the book worm and member of the school orchestra and choir – would never in a million years have believed that older her would have not one but two jobs connected with the fitness industry. Younger me, who detested navy gym skirts and matching knickers with a passion, could never have imagined that when she grew up she'd not only be a qualified yoga teacher but also organise social runs for women with her job as This Mum Runs Cardiff community coordinator.
Younger me would have been the first to say drop PE from the curriculum entirely. That there were far better things she could be doing with her time.
Younger me thought sport wasn't for her.
But actually, younger me just hadn't found the right sports.
Because that's the thing about sports and fitness – it can and should be for everyone.
And for lots of us, that starts with our experiences at school.
Curriculum criticism
The new draft curriculum for Welsh schools has been in the news today because Baroness Tanni Grey-Thompson has criticised the curriculum for not specifying a set amount of physical activity every week. That's what I was invited onto the radio to discuss.
The proposals would leave it to individual schools to determine PE lessons, as part of a wider Health and Wellbeing remit.
This is one of six areas of learning and experience which could replace traditional subjects (the others being maths and numeracy; languages, literacy and communication; humanities; science and technology; and expressive arts), with the curriculum due to be introduced in primary and Year 7 classrooms from September 2022 before being rolled out to all year groups. It's the biggest reform to Welsh schools in decades and a huge change in culture.
However, some education campaigners have criticised the drafts for not being clear enough. (You can read more about it here.
Supporters of the draft curriculum say that because for the first time 'wellbeing' will be placed at the heart of school life, children will become more active and healthier.
However, Baroness Grey-Thompson – the former Paralympic gold medallist, who sits in the House of Lords – told BBC Wales Live, "If time is not carved out to do it, it will just disappear".
"Because PE is difficult to teach, it's one of those things that I really worry will slip away because there are other things that are easier to do," said Tanni, who was grew up in Cardiff.
"If sport is not explicitly mentioned, it will just drop off. Whatever the meaning and the intention, it won't have the same priority.
"We won't see the problem right now, we'll see it 15 or 20 years down the line when the NHS bill goes through the roof because we have a generation of young adults who are just not fit enough to be healthy."
She is supported by a group of AMs who recommend schools be required by law to provide at least two hours of PE every week.
Sport Wales says the new curriculum's success depends on teachers being adequately trained. Its chief executive Sarah Powell told the BBC, "The essential thing is to build up the confidence, motivation and skills of teachers to be able to deliver a high quality curriculum. But if we don't see that, then this is a curriculum that doesn't actually deliver the changes that we need to see."
It's a huge subject and one that deserves to be debated and discussed to find the best responses and solutions.
Focussing on wellbeing is a hugely positive step. Encouraging children to look holistically at how nutrition and fitness can benefit all areas of their lives, as outlined in the draft, can only be encouraged.
However, I echo Tanni's concerns that not specifically timetabling physical activity could see it fall off the curriculum in some schools. With school budgets being slashed and teachers under increasing demands for their pupils to hit targets, PE (and also the Arts) are the first things to be ditched when time and resources are low.
A country in crisis
It's no secret that we are in the midst of an obesity crisis. In fact, obesity has overtaken smoking as the biggest Welsh health crisis. More than a quarter of children in Wales are classed as overweight or obese. We also know that mental health in children is a huge issue – The Welsh Government has said it must be a priority
It's no secret that sport and fitness would help in both these areas.
It's easy to say parents need to take responsibility – and yes, in an ideal world that would be great. But for some children, those two hours of PE lessons a week may be the only physical activity they are getting.
Ensuring it's timetabled is a huge part of making physical activity a normal part of life.
But I also think we need to look at what physical activity children are offered.
Finding the right sport opens doors
Some children thrive with competitive team sports such as netball, hockey, football and rugby, finding it improves both their physical and mental wellbeing, their confidence, team skills and so on. Others grow up scarred by memories of team sports at school, with their confidence so low that the mere thought of a hockey stick gives them palpitations. It's a common feeling among the women I meet through This Mum Runs and my yoga classes. Many of the women I meet know they need to do something to help their physical and mental wellbeing – but putting themselves out there is petrifying. It's tough when you start – you're red-faced, puffy and sweaty. But then you begin to see the benefits, you slowly improve, and you achieve things you never thought possible. You realise sport doesn't always have to be fiercely competitive. You realise you can go at your own pace. You realise you can work together to achieve goals. You realise that it can be fun and sociable. You realise that someone else doing well doesn't make you a 'loser'.
For me, it was a couple of sessions on long distance running when in year 10, and our year 11 teacher shoving us in front of a step aerobics video so that she could concentrate on the GCSE PE students that made me think, actually, not all exercise is bad. No one was looking at me when I did step aerobics. Me doing something wrong didn't lose us the game. It was a hugely refreshing experience for someone who until then hated PE.
More than that though; it paved the way for me to join the gym while at university and to run along the seafront. It gave me the confidence to join a yoga class, not long after I graduated,
I'm reluctant to call yoga a sport, because it is so much more than that, but I came to it initially from a physical point of view, and my life has never been the same since. Likewise with running. I strive to see improvement in myself, whether that's getting faster or running longer distances. But above all, both make me feel mentally stronger, calmer and better prepared to deal with life. I have other friends who get the benefits same from Zumba or body pump or even walking up mountains, despite not being sporty at school.
Ever since they were babies, my three children have taken part in physical activity – baby yoga, baby swimming, toddler gymnastics and now they are older, swimming, netball, football, rugby, athletics, hockey and Junior Parkrun between them. I hope they will grow up appreciating the benefits of physical exercise and I hope they realise that just because they don't like or aren't 'good' (I hate that word) at certain sports, that doesn't mean they won't like other sports.
So yes, introducing wellbeing to the curriculum is a hugely positive step for schools – but we need to ensure physical education is timetabled into the curriculum, because being sat in a classroom learning about why exercising is good isn't the same as actually doing that exercise. And we also need to ensure the physical opportunities we provide inspire and excite children so that they grow up to become adults who want fitness to be a part of their lives.
I'd love to know your thoughts. Let me know in the comments below, on the Cardiff Mummy Says Facebook page, or you can tweet me on @cardiffmummy
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Homepage » Current affairs » A few thoughts on the new draft proposals for PE in Welsh schools (from the girl who hated PE at school)
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redpajama_set_name: RedPajamaCommonCrawl


Audizine Forums > Main Model-Line Discussion > B5 A4 > Seat harnesses for stock seats?
View Full Version : Seat harnesses for stock seats?
Schroth racing harnesses. thats the company.
i think its actually topspin. topspin is the one that had a bracket between the headrest to hold em in place.


text:
require 'test_helper'

class Organizations::ProfileControllerTest < ActionDispatch::IntegrationTest
  # test "the truth" do
  #   assert true
  # end
end

redpajama_set_name: RedPajamaGithub


text:
cool-planet.earth
The Planet Earth Coolkit
BECCS
Bioenergy with carbon capture and storage
Co2 removal
Link 1 : https://en.wikipedia.org/wiki/Bioenergy_with_carbon_capture_and_storage
Link 2 : https://psci.princeton.edu/tips/2020/11/15/preventing-climate-change-with-beccs-bioenergy-with-carbon-capture-and-storage
Quote from Wikipedia (Link 1)
Bioenergy with carbon capture and storage (BECCS) is the process of extracting bioenergy from biomass and capturing and storing the carbon, thereby removing it from the atmosphere.[1] The carbon in the biomass comes from the greenhouse gas carbon dioxide (CO2) which is extracted from the atmosphere by the biomass when it grows. Energy is extracted in useful forms (electricity, heat, biofuels, etc.) as the biomass is utilized through combustion, fermentation, pyrolysis or other conversion methods. Some of the carbon in the biomass is converted to CO2 or biochar which can then be stored by geologic sequestration or land application, respectively, enabling carbon dioxide removal (CDR) and making BECCS a negative emissions technology (NET).[2]
The IPCC Fifth Assessment Report by the Intergovernmental Panel on Climate Change (IPCC), suggests a potential range of negative emissions from BECCS of 0 to 22 gigatonnes per year.[3] As of 2019, five facilities around the world were actively using BECCS technologies and were capturing approximately 1.5 million tonnes per year of CO2.[4] Wide deployment of BECCS is constrained by cost and availability of biomass.[5][6]
Endquote
Picture from Princeton (Link 2)
BECCS process, Liam McGill
PrevPreviousPlanting and Growing Trees
NextMirrors for Earth´s Energy RebalancingNext
AMR AG
Lärchenstr. 5
contactform@amr.earth
atmospheric methane removal AG
AMR AG© 2023 All Rights Reserved | Website by LaChouetteInformatique


text:
Q: Deleting files one-by-one vs deleting the same set of files in one run? I'm assuming that this (batch deleting):
for(int i=0;i<10;i++){
    updateToStatus1(i, 1); //update table set status = 1 where id = :i
}
deleteAllStatus1();//delete from table where status = 1

is faster than this (deleting one-by-one):
for(int i=0;i<10;i++){
    delete(i); //delete from table where id = i
}

Is my assumption true no matter what index I create on "table"? I'm thinking there would be a lot of locking involved when deleting one-by-one.
Also, if anyone can recommend me good regarding materials regarding such SQL trade-offs, I would be very grateful.

A: I think updating the rows one by one will take about the same time as deleting them one by one (of course depending on indices) - so I can't imagine the combination update + batch-delete to be daster than deleting one by one.
The fastest way would be to have a method delete(int...) that issues a DELETE ... WHERE ID IN(...).

A:
I'm assuming that this (batch deleting):

 for(int i=0;i<10;i++){
        updateToStatus1(i, 1); //update table set status = 1 where id = :i
    }
    deleteAllStatus1();//delete from table where status = 1

is faster than this (deleting one-by-one):
for(int i=0;i<10;i++){
    delete(i); //delete from table where id = i
}

I can only assume that the time taken by first T1 > T2 (time taken by second )

*

*in loop1 update(find by id and update staus) is a write operation like delete (find by id and delete row) in loop2

*if you index status, update will become costly compared to before (as it changes the value of indexed key).

*indexed status will also have adverse effect on deletion based on status.

I'm thinking there would be a lot of locking involved when deleting
  one-by-one.

*

*That is same for updating one by one also

so your time comparison is between:
updating one by one + deleting all vs deleting one by one
A single transaction commit for same data would take less time than commit in multiple transactions. But here there is update overhead.

Also, if anyone can recommend me good regarding materials regarding
  such SQL trade-offs

for jdbc tips:
http://shop.oreilly.com/product/9780596000882.do
Chapter 19 Performance
for indexes (though for DB2 still useful): Indexing
I would also suggest you to search for performance tuning for sql queries and data imports/exports for the DB you are using. These are usually available in reference manuals. like: MySQL sql optimization
InnoDB performance tuning

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How Real Food Can Heal Almost Any Health Problem: Eating a real food diet can be so powerful it can transform your health. Read 10 .... For anyone who wants to run a mile and do 100 squats, nbd: | 9 Incredible Ways To Get A Cardio Workout That Aren't All Running .... 11 Of The Best Things To Eat Before A Workout.
К вашим услугам обширная коллекция стоковых изображений, векторных изображений и фотографий для cardio workout, которые можно купить на Shutterstock. Ищите качественные изображения, фотографии, графику и многое другое.
What and how long before a cardio workout should i eat drink q when i work out in the morning end up starving after if eat before and again am eating three times as many calories normally would protein shake if you wish to perform hiit cardio then a balanced meal of protein and carbohydrates is vital.
A common question about morning workouts is this: Should you eat before? And if yes, what should you eat?
I wish there were a simple answer to the question about whether you should eat before a morning cardio workout or if you should just work out in a fasted state on empty stomach.
What To Eat Before A Workout And After Shape Magazine. When Should I Eat Before Or After A Cardio Workout. Pre Post Workout Nutrition With Vegetarian Vegan Options. Best Pre Workout Snacks For Morning Exercise Men S Fitness.
early morning - what to eat before I workout?
someone at work said I should eat protein - this can't be right, can it? All I'm doing is about 30-45 minutes of cardio.
Research shows that it's very important to drink fluids and consume carbohydrates before a morning workout if you want to perform your best.
Morning Workouts. Your body burns calories and nutrients while you sleep.
Cardio. Workout Playlists & Songs.
If you're a morning exerciser, your clothes are laid out and ready for your run or workout, but you try to go at it on an empty stomach and bam, you crash again.


text:
@interface ThirdController : UIViewController

@end


text:
/*
 * To change this license header, choose License Headers in Project Properties.
 * To change this template file, choose Tools | Templates
 * and open the template in the editor.
 */
package pe.edu.upeu.modelo;

import java.io.Serializable;
import javax.persistence.Basic;
import javax.persistence.Column;
import javax.persistence.Entity;
import javax.persistence.GeneratedValue;
import javax.persistence.GenerationType;
import javax.persistence.Id;
import javax.persistence.JoinColumn;
import javax.persistence.ManyToOne;
import javax.persistence.NamedQueries;
import javax.persistence.NamedQuery;
import javax.persistence.Table;
import javax.xml.bind.annotation.XmlRootElement;

/**
 *
 * @author SistemasUpeu
 */
@Entity
@Table(name = "glo_estado_departamento")
@XmlRootElement
@NamedQueries({
    @NamedQuery(name = "GloEstadoDepartamento.findAll", query = "SELECT g FROM GloEstadoDepartamento g")})
public class GloEstadoDepartamento implements Serializable {
    private static final long serialVersionUID = 1L;
    @Id
    @GeneratedValue(strategy = GenerationType.IDENTITY)
    @Basic(optional = false)
    @Column(name = "id_estadodepartamento")
    private Integer idEstadodepartamento;
    @Basic(optional = false)
    @Column(name = "estadometa")
    private int estadometa;
    @Basic(optional = false)
    @Column(name = "estadoavance")
    private int estadoavance;
    @Basic(optional = false)
    @Column(name = "estadopoa")
    private int estadopoa;
    @Basic(optional = false)
    @Column(name = "estadopm")
    private int estadopm;
    @JoinColumn(name = "id_departamento_filial", referencedColumnName = "id_departamento_filial")
    @ManyToOne(optional = false)
    private GloDepartamentosFilial idDepartamentoFilial;
    @JoinColumn(name = "id_periodo", referencedColumnName = "id_periodo")
    @ManyToOne(optional = false)
    private ConfPeriodo idPeriodo;

    public GloEstadoDepartamento() {
    }

    public GloEstadoDepartamento(Integer idEstadodepartamento) {
        this.idEstadodepartamento = idEstadodepartamento;
    }

    public GloEstadoDepartamento(Integer idEstadodepartamento, int estadometa, int estadoavance, int estadopoa, int estadopm) {
        this.idEstadodepartamento = idEstadodepartamento;
        this.estadometa = estadometa;
        this.estadoavance = estadoavance;
        this.estadopoa = estadopoa;
        this.estadopm = estadopm;
    }

    public Integer getIdEstadodepartamento() {
        return idEstadodepartamento;
    }

    public void setIdEstadodepartamento(Integer idEstadodepartamento) {
        this.idEstadodepartamento = idEstadodepartamento;
    }

    public int getEstadometa() {
        return estadometa;
    }

    public void setEstadometa(int estadometa) {
        this.estadometa = estadometa;
    }

    public int getEstadoavance() {
        return estadoavance;
    }

    public void setEstadoavance(int estadoavance) {
        this.estadoavance = estadoavance;
    }

    public int getEstadopoa() {
        return estadopoa;
    }

    public void setEstadopoa(int estadopoa) {
        this.estadopoa = estadopoa;
    }

    public int getEstadopm() {
        return estadopm;
    }

    public void setEstadopm(int estadopm) {
        this.estadopm = estadopm;
    }

    public GloDepartamentosFilial getIdDepartamentoFilial() {
        return idDepartamentoFilial;
    }

    public void setIdDepartamentoFilial(GloDepartamentosFilial idDepartamentoFilial) {
        this.idDepartamentoFilial = idDepartamentoFilial;
    }

    public ConfPeriodo getIdPeriodo() {
        return idPeriodo;
    }

    public void setIdPeriodo(ConfPeriodo idPeriodo) {
        this.idPeriodo = idPeriodo;
    }

    @Override
    public int hashCode() {
        int hash = 0;
        hash += (idEstadodepartamento != null ? idEstadodepartamento.hashCode() : 0);
        return hash;
    }

    @Override
    public boolean equals(Object object) {
        // TODO: Warning - this method won't work in the case the id fields are not set
        if (!(object instanceof GloEstadoDepartamento)) {
            return false;
        }
        GloEstadoDepartamento other = (GloEstadoDepartamento) object;
        if ((this.idEstadodepartamento == null && other.idEstadodepartamento != null) || (this.idEstadodepartamento != null && !this.idEstadodepartamento.equals(other.idEstadodepartamento))) {
            return false;
        }
        return true;
    }

    @Override
    public String toString() {
        return "pe.edu.upeu.modelo.GloEstadoDepartamento[ idEstadodepartamento=" + idEstadodepartamento + " ]";
    }

}


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text:
21 F. 3d 1008 - Brown v. Wa Perrill US
Federal Reporter, Third Series
21 F.3d
21 F3d 1008 Brown v. Wa Perrill US
21 F.3d 1008
Randy BROWN, Petitioner-Appellant,
W.A. PERRILL, Warden, and U.S. Bureau of Prisons,
Respondents-Appellees.
No. 93-1381.
Tenth Circuit.
Randy Brown, pro se.
James R. Allison, Interim U.S. Atty., and Elizabeth J. Ruffing, Sp. Asst. U.S. Atty., Denver, CO, for respondents-appellees.
Before LOGAN, SETH, and BARRETT, Circuit Judges.
SETH, Circuit Judge.
After examining the briefs and appellate record, this panel has determined unanimously that oral argument would not materially assist the determination of this appeal. See Fed.R.App.P. 34(a); Tenth Cir.R. 34.1.9. The cause is therefore ordered submitted without oral argument.
The facts in this case are essentially undisputed. On March 9, 1983, Appellant was released from a California state prison to federal authorities on a writ of habeas corpus ad prosequendum. At the time of release, Appellant was serving a sentence on a state conviction for sale of heroin. Appellant remained in federal custody while awaiting trial on federal charges of Conspiracy to Distribute Heroin until October 5, 1984 when he was released on bail. During this period Appellant's state sentence expired on September 23, 1984. Appellant returned to federal custody on April 2, 1985 and was finally convicted on the conspiracy charge on June 4, 1985.
At sentencing on the federal conviction, Appellant was awarded jail time credit for all of the time spent in federal custody prior to sentencing. At some later date approximately 562 days of jail credit were retracted. These days coincide with the period beginning on March 9, 1983 until September 23, 1984, the date upon which Appellant's state sentence terminated. Subsequently, Appellant exhausted his administrative remedies challenging the propriety of the jail credit reduction and filed the habeas corpus action underlying this appeal. The magistrate assigned to this matter recommended denial, and the district court agreed. This appeal followed.
We are mindful that because Appellant is proceeding pro se he is to be held to less stringent standards than professional attorneys, and we will liberally construe his pleadings. See Meade v. Grubbs, 841 F.2d 1512, 1526 (10th Cir.1988). After reading his brief, we find that the dispositive issue raised by Appellant is whether the district court relying on Bruss v. Harris, 479 F.2d 392 (10th Cir.1973), erroneously determined that he was not entitled to "double credit" pursuant to 18 U.S.C. Sec. 3568.
In Bruss petitioner was arrested on state charges and then released to federal authorities pursuant to a writ of habeas corpus ad prosequendum. On July 2, 1971, petitioner pled guilty to federal charges, was sentenced to three years, and was immediately returned to state custody. Thereafter, he pled guilty to the state charges, was sentenced to two years to run concurrently with the federal sentence, and returned to federal prison on October 28, 1971. The issue in Bruss was whether the time spent in state custody between July 2 and October 28 should be credited to the petitioner's federal sentence. We held that because petitioner was credited by the state for the time in question, he was not entitled to "double credit" on his federal sentence. Bruss, 479 F.2d at 394. We further opined that the "jail time in question was not spent in connection with the offense or acts for which the federal sentence was imposed, and, consequently, he is not entitled to credit against the federal sentence." Id.
The situation presented in the case before us is different from Bruss in several crucial ways. First, during the time period from March 9, 1983 to September 23, 1984, Appellant was in federal custody, not in state custody as was the scenario in Bruss. Second, the issue of whether the time spent in federal custody pursuant to the writ of habeas corpus ad prosequendum was not addressed in Bruss; rather, we were concerned in that case with the period of time petitioner was in custody after the writ of habeas corpus ad prosequendum was no longer in effect. We must conclude that Bruss is not dispositive of this case because the issues presented and the factual background are demonstratively dissimilar. Having made this determination, we now turn to the merits of Appellant's claim of error.
During the relevant time frame of this case, 18 U.S.C. Sec. 3568 provided that a federal prisoner's sentence does not commence until "such person is received at the penitentiary, reformatory, or jail for service of such sentence." However, a prisoner's sentence must be credited with "any days spent in custody in connection with the offense or acts for which sentence was imposed." Id. Consequently, the questions pertinent to this case are whether Appellant was "in custody" of the federal government during the time in question, and if so, whether the time spent in custody was in connection with the offense which led to his federal sentence. We note in passing that the magistrate relied on the amended version of Sec. 3568 which appears at 18 U.S.C. Sec. 3585(b) and explicitly states that no time may be credited if the same time has been credited to another sentence. However, this amended version did not become effective until 1987, well after Appellant was sentenced to federal imprisonment. Consequently, our review is not concerned with the express Congressional mandate found in Sec. 3585(b).
Unlike Bruss where there was a dispute over who had custody of petitioner because he was in a state prison, there is no dispute that Appellant was in a federal prison at the direction of federal authorities during the period of time at issue. We are convinced that this constitutes being "in custody" as contemplated by the statute. Next, we note that it is undisputed that the writ of habeas corpus ad prosequendum was issued to bring Appellant into federal custody for the purposes of prosecuting him on a federal charge of Conspiracy to Distribute Heroin. It was this precise charge for which Appellant was convicted. Therefore, we also must find that Appellant's custody satisfies the "in connection with" requirement of Sec. 3568 as he was held for an offense (conspiracy) for which he was ultimately convicted and sentenced. See also United States v. Haney, 711 F.2d 113 (8th Cir.1983); Roche v. Sizer, 675 F.2d 507 (2d Cir.1982). But see Crawford v. Jackson, 589 F.2d 693 (D.C.Cir.1978). The fact that the state continued to grant Appellant jail time credit does not impact on our analysis under the facts presented herein because Appellant clearly satisfied the requirements of Sec. 3568.
Although we need not and do not reach Appellant's other issues on appeal, we recognize that he presented a viable claim of a due process violation. Although the record is not absolutely clear, there appears to be no doubt that the federal district court that sentenced Appellant granted to him the disputed 562 days of jail time credit. We can find no authority justifying the subsequent deduction of that time by the prison administration regardless of the procedural safeguards employed by the prison. The reduction of such jail time credit is not within the discretionary powers of the prison, but more properly lies with the sentencing court.
Accordingly, the district court's decision is REVERSED, and the case is REMANDED with directions that Appellant be credited on his federal sentence for the period of time commencing on March 9, 1983 (the date Appellant was removed to federal custody) and ending on September 23, 1984 (the date Appellant's state sentence expired while still in federal custody). For purposes of clarity, we note that Appellant is not entitled to additional credit for the time spent in federal custody from September 24, 1984 until October 5, 1984 and from April 2, 1985 to June 3, 1985 because he has already been credited on his federal sentence for these periods of time.


16 He called down famine on the land and destroyed all their supplies of food; 17 and he sent a man before them--Joseph, sold as a slave. 18 They bruised his feet with shackles, his neck was put in irons, 19 till what he foretold came to pass, till the word of the LORD proved him true. 20 The king sent and released him, the ruler of peoples set him free. 21 He made him master of his household, ruler over all he possessed, 22 to instruct his princes as he pleased and teach his elders wisdom.
8 And being found in appearance as a man, he humbled himself and became obedient to death-even death on a cross! 9 Therefore God exalted him to the highest place and gave him the name that is above every name, 10 that at the name of Jesus every knee should bow, in heaven and on earth and under the earth, 11 and every tongue confess that Jesus Christ is Lord, to the glory of God the Father.
As a young boy, Joseph was the favorite of his father. He was treated special and his brothers came to hate him because of it. When Joseph had a dream that he and his brothers were working in the fields and their stalks of grain bowed down to his stalk of grain, it just made matters worse. Would his older brothers indeed bow down to him as their lord? They felt he was insolent and arrogant.
The time came when his brothers were keeping the sheep away from home and his father told him to go and check on them. They saw him coming, wearing the special coat his father had made for him and plotted to kill him. Reuben, who was the oldest of them, suggested that they put him in a dry well and not kill him. He was planning to come later and release him without harm.
While Reuben was gone, a band of traders came by and the brothers sold Joseph as a slave. He ended up in Egypt and was later put in prison on false accusations. But God had plans and exalted him to ruler over Egypt, second only to Pharaoh in power. A famine came and his brothers came to buy grain. They all bowed before Joseph, not knowing it was their brother. The dream God gave Joseph came true.
Lord God keep me from ever doubting you and your word. Through the story of Joseph I see that against all human odds, you can act and carry out your will. In fact I see that you actually allow what I see as evil to be used for your purpose and for my own good. What a glorious and wonderful God you are indeed! Use me in your service in whatever way you want me to be used. Open my heart to see your wisdom and to do your bidding. Use the ones in government to carry out your purpose for our nation. I pray in the name of Jesus my Lord and King.


Jaimi King Is Bachelor Nation's "IDGAF AF"
Bustle takes the Bachelor franchise pretty seriously. That's why we created the Bachelor Nation Class of 2018 — like high school superlatives, but Bachelor edition. Check out what Jaimi from Nick's Bachelor season had to say about being awarded "IDGAF AF."
"Wow, what can I say? I'm flattered to be recognized for IDGAF. I'll never stop representing."
After things didn't work out for her on The Bachelor, Jaimi tried her luck on Bachelor in Paradise, where she mused about whether to take a female or male contestant out on a date. Ultimately she chose to take a guy, and she had an open, honest conversation about her sexuality. She's just being authentic with herself and her feelings, wherever they may take her.
Jaimi is who she is and she won't apologize for it, nor should she. That's why Bustle is giving her the "IDGAF AF" superlative in our Bachelor Nation Class of 2018. No one gives fewer f*cks than Jaimi — and more people should take note.
Outside of the show, Jaimi is representing NOLA as a chef and baker of amazing cakes (seriously, check out her Instagram). She can also be seen chilling with her Bachelor Nation squad, advocating for love and laughter, and typing very relatable IG captions. To quote Glee, she keeps it real and she's hilarious.


that's me....the Homemade Problem Solver. At least today it is.
You see, I've had some problems. And I'm going to share them with you. Why? Because I am a blogger. And I get to post whatever I want to.
So I decided today was the day to solve them.
So I put the babies down for nap/room-time.
So I made an oatmeal mask for my face.
So I'll share the recipe with you. (Ok I'm done with the So's). (For Today).
I wanted something homemade sense I'm pregnant and all and the jury's still out on a lot of acne medication during pregnancy and I can't remember which ones I'm allowed to use and I don't want to give #3 birth defects just because I don't want to look like moon-crater-face.
I made my own little concoction based on some things I already knew and read about online.
I cooked 3/4 c oatmeal with 3/4 cup water over the stove for 3 minutes. (I knew oatmeal helps with acne and oily skin).
Then I set it aside and let it cool.
Then I added some honey. (because it's sticky and is actually a great moisturizer for your face that won't add oil).
Then it was still too thick and gloopy so I added some milk. Why milk? Because milk is actually a great facial cleanser and because it's oatmeal. What else am I going to add?
In the meantime, I heated up about 1/4 c. of olive oil in the microwave to treat my flaky itchy scalp.
Then I grabbed a baster brush and the two bowls and went to bathroom.
The first thing I did was wash my face really good with just plain water.
Next, I grabbed my clean baster brush and started applying the olive oil to my scalp. Allllll over my scalp. Let me just tell you, olive oil was dripping everywhere. All over my face and ears. After my scalp was saturated, I massaged it all in because it just seemed like the right thing to do. Then I wished I had plastic wrap to wrap around my head because it also just seemed like the thing to do. I didn't have any nor do I own a shower cap so I just pulled it back with a rubber band.
I washed my face again to clean off the oil that had dripped. I applied the oatmeal mask to my face. Very gloopy and messy and chunks of oatmeal fell off into the sink as I was massaging it into my skin. But I persevered and kept going.
At think point, I knew things needed to set and saturate so I piddled and picked up a few things, made my bed, and clipped my fingernails.
After about 15 minutes or so, I jumped in the shower, washed off the oatmeal mask and washed my hair 3 times with tea tree shampoo.
I was nervous that my hair would still be very oily and to be honest, even when I towel dried it, it still seemed heavy and oily but I decided to go ahead and blow-dry.
I have to tell you that my hair is amazingly SOFT, SHINY and FLAKE-FREE!!! I might just do this once a week.
After blow-drying, I straitened it like I normally do and then I applied a light moisturizer to my face. My face feels soft and oil-free without being too dry.
Here's a pic of my shiny hair and not shiny face and #3 finally starting to shine a little bit herself (himself)!


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