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Emerging Infectious Disease journal ISSN: 1080-6059
Volume 13, Number 8—August 2007
Invasive Meningococcal Disease, Utah, 1995–2005
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Rachelle B. Boulton*† , Stephen C. Alder*, Susan Mottice†, A. Peter Catinella*, and Carrie L. Byington†
Author affiliations: *University of Utah, Salt Lake City, Utah, USA; †Utah Department of Health, Salt Lake City, Utah, USA;
Trends in invasive meningococcal disease in Utah during 1995–2005 have differed substantially from US trends in incidence rate and serogroup and age distributions. Regional surveillance is essential to identify high-risk populations that might benefit from targeted immunization efforts.
Invasive meningococcal disease (IMD) refers to the many illnesses caused by infection with Neisseria meningitidis. IMD is an immediately reportable disease in Utah and a nationally reportable disease in the United States. A preliminary review of IMD in Utah suggests that, since 2000, epidemiologic trends have occurred that are distinct from trends reported elsewhere in the United States. We describe the change in incidence rates, serogroup distribution, and age distribution of IMD in Utah, based on cases reported from 1995 through 2005, and compare our results with US trend data from the same period.
We studied cases of IMD that occurred from January 1, 1995, through December 31, 2005, and were reported to the Utah Department of Health. Cases were classified as confirmed, probable, or suspected, based on the case definition for N. meningitidis infection in the Centers for Disease Control and Prevention and the Council of State and Territorial Epidemiologists 2005 case definition guidelines for IMD (1). Suspected cases, in which an isolate was not obtained, were not included in the final analysis because this study emphasized serotyping.
Utah incidence rates were calculated by using population estimates determined by Utah's Indicator-Based Information System for Public Health (2). Incidence rates and serogroup distributions published in the N. meningitidis Active Bacterial Core surveillance (ABCs) reports were used to estimate US trends (3).
The Pearson χ2 test and Fisher exact test were used to test the statistical significance of the prevalence of serogroups by period for Utah and US data. Statistical analysis was performed with SAS (version 9.1; SAS Institute, Cary, NC, USA).
Figure. Incidence of invasive meningococcal disease by year, Utah, 1995–2005.
In the 10-year study period, 128 reported cases met the criteria of either confirmed or probable. Yearly incidence rates were calculated and ranged from a high of 0.95/100,000 population/year to a low of 0.21/100,000 population/year (Figure). Because the number of annual cases dropped after 1999, the data were divided into 2 periods. The mean incidence rate decreased significantly, by 63%, from 0.80/100,000 population/year from 1995 through 1999 (hereafter period 1) to 0.30/100,000 population/year from 2000 through 2005 (hereafter period 2).
Incidence rates by period were stratified by age (Table 1). A rate difference was calculated by subtracting the average incidence rate for period 1 from the average incidence rate for period 2. The highest rate for both periods was for infants <1 year of age (period 1, average incidence rate 7.98/100,000 population/year; period 2, average incidence rate 3.07/100,000 population/year). The greatest rate difference also occurred for this age group, a decrease of 4.91/100,000 population/year between the 2 periods' mean incidence rates (Table 1).
The serogroup distribution in Utah changed substantially over the course of the 2 study periods. Before 2000, Utah meningococcal serogroup distribution reflected that of the United States; that is, serogroups B, C, and Y each caused ≈30% of IMD (4). Beginning in 2000, however, the percentage of serogroup B infections in Utah decreased significantly to 11.3%, while serogroup Y infections increased to 50.0% (p = 0.0102, Fisher exact test; χ2 = 7.2562, p = 0.0071). A similar change was not seen in US data. Whereas no significant difference was observed between Utah and ABCs data during period 1, a significant difference was seen for serogroups B (p = 0.0002) and Y (p < 0.0001) when period 2 data were compared (Table 2). Because of an ongoing outbreak of serogroup B disease in Oregon, Utah data were compared with US data both with and without Oregon's numbers. For both comparisons, the conclusions were the same, and therefore Oregon's numbers were not removed from the final analysis.
No Utah cases identified during the study period involved residents of military barracks or college dormitories, in which an increased risk for meningococcal disease is well documented (5,6). However, 5 (3.9%) patients were residents at a Job Corps facility (a residential job-training facility for young adults similar to a college dormitory). Of the 5 Job Corps cases, all were caused by serogroup Y infection, and 3 patients had bacteremic pneumonia.
The reduction in incidence rate could have several possible causes. One such cause could be a systematic change in reporting. However, no evidence to support this conclusion was found. Although the total number of reported cases declined between the 2 periods for most reporting hospitals, no single decline was strong enough to account for the observed decrease in reported cases. Underreporting of cases is another possible cause, but also is unlikely. Data from cases of IMD reported to the Utah Department of Health with onset dates from January 1, 2002, through December 31, 2005, were compared with data extracted from computerized laboratory records of a large hospital corporation in Utah for the same period. Ten cases of IMD were identified in each system, and demographic information confirmed that they were the same 10 patients.
Vaccination is unlikely to be the cause of the reduction in incidence rate as well. Over the study period, the percentage of vaccine-preventable strains causing disease in Utah increased, while infections caused by serogroup B, which is not included in the vaccine formula, decreased. Additionally, the greatest decrease in age-specific incidence rates occurred in age groups for which vaccination was not indicated.
Therefore, the decrease in the incidence rate seen is most likely the result of fluctuations in the community incidence rate, for which oscillations with a cyclical pattern have been documented (7–9). The incidence rate of IMD in Utah in 2005 increased substantially from the rate observed in 2004 (Figure). Although this rate is still much lower than rates seen for any year in period 1, it is still much greater than any other rate observed in period 2; the incidence rate appears to be increasing again, while the serogroup distribution is not changing. Due to the cyclical pattern of meningococcal disease, variability is expected, but the increase in serogroup Y cases and decrease in serogroup B cases appear unique to Utah.
During the second study period (2000–2005), the incidence rate and age and serogroup distributions for IMD in Utah have differed from US trends. In Utah, the decrease in serogroup B infections, the most common cause of IMD in infants, resulted in an overall decrease in infections in infants and increased infection rates in adolescents and young adults ages 15 to 24 years. Furthermore, of the 38 serogrouped isolates from period 2, 31 (82%) were vaccine-preventable strains. This suggests that recommendations by the Advisory Committee on Immunization Practices (ACIP) for routine vaccination of selected cohorts with meningococcal conjugate vaccine (MCV-4) would be beneficial in Utah. ACIP recommendations, however, may not reflect regional epidemiologic trends. For example, Job Corp residents were identified as a high-risk population for IMD in Utah but have not been identified as a high-risk group in the United States. Because IMD is so rare, routine vaccination is costly (10), and vaccine supply is limited, we believe regional surveillance is a key factor in determining groups at high risk for IMD. The identification of serogroup Y disease among Job Corps residents influenced Utah's vaccine policy. This study emphasizes the need for continued regional surveillance to help direct vaccine policy especially in regions of the United States not represented in ABCs.
Ms Boulton is an epidemiologist at the Utah Department of Health. Her interests include vaccine-preventable disease epidemiology and outbreak investigation.
We are indebted to the nurses and epidemiologists at local health departments in Utah and health information services staff at many hospitals for their help in obtaining data for this study. We also thank the Utah Public Health Laboratory for their assistance in gathering serogoup data and the staff of the Communicable Disease Epidemiology Program at the Utah Department of Health.
Centers for Disease Control and Prevention. Meningococcal disease (Neisseria meningitidis) 2005 case definition. [cited 2006 Jun 26]. Available from http://www.cdc.gov/epo/dphsi/casedef/meningococcalcurrent.htm
Center for Health Data, Utah Department of Health. Utah's Indicator-Based Information System for Public Health. [cited 2006 Jun 27]. Available from http://ibis.health.utah.gov/home/welcome.html
Division of Bacterial and Mycotic Diseases, Centers for Disease Control and Prevention. Active Bacterial Core surveillance reports. [cited 2006 Jun 23]. Available from http://www.cdc.gov/ncidod/dbmd/abcs/survreports.htm
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Table 1. Rates of invasive meningococcal disease by age group, Utah, 1995–2005
Table 2. Serogroup distribution of invasive meningococcal disease by period
DOI: 10.3201/eid1308.061406
Table of Contents – Volume 13, Number 8—August 2007
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Boulton RB, Alder SC, Mottice S, Catinella AP, Byington CL. Invasive Meningococcal Disease, Utah, 1995–2005. Emerg Infect Dis. 2007;13(8):1225. https://doi.org/10.3201/eid1308.061406
Boulton RB, Alder SC, Mottice S, et al. Invasive Meningococcal Disease, Utah, 1995–2005. Emerging Infectious Diseases. 2007;13(8):1225. doi:10.3201/eid1308.061406.
Boulton, R. B., Alder, S. C., Mottice, S., Catinella, A. P., & Byington, C. L. (2007). Invasive Meningococcal Disease, Utah, 1995–2005. Emerging Infectious Diseases, 13(8), 1225. https://doi.org/10.3201/eid1308.061406.
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Home > News > Project Update October 2019
Project Update October 2019
Works update
In Kearsney Abbey, work is taking place to fit the internal fixtures of the café, including the electrics and the fittings. Once the café building has been finished, our landscape contractors will start work on the terrace at the front of the building.
The roof lantern on top of the old billiard room has been restored. A new cover to protect the roof lantern is being built off site and will be installed shortly.
When Kearsney Abbey was a private house, there was an 'eyecatcher' bridge at the end of the boating lake. A new bridge is being built off site. This will be installed at the end of the lake early next year. This will allow people to do a circular route.
In Russell Gardens, frames are being installed for the new wooden play equipment. There will be a basket swing, a double slide and a tree house. The play equipment, new pathways and repairs to the lily pond, pergolas, boathouse and other brickwork should be finished by the end of this year. We are working closely with DDC's Principal Heritage Officer.
The slopes above the old tennis courts are being cleared so that an orchard can be planted this winter. The work has been overseen by an ecologist. The new summerhouse is being fitted out with a disabled access toilet. We aim to open this, and the rest of Russell Gardens, early next year.
While the work is taking place, there is limited parking available on site. Please think about using public transport, cycling or walking to the parks. If you do drive, please think about the nearby residents. Please park considerately, and do not block emergency access routes. Thank you.
Kent County Council's Highways Department is continuing work on installing a puffin crossing on Alkham Road between Kearsney Abbey and Russell Gardens. The crossing should be finished by the end of October.
Russell Gardens pond
A tender is out for repairing the pond in Russell Gardens. This is not part of the Kearsney Parks project but is being dealt with by DDC's Parks and Open Spaces team. Once costs have been received, the Parks and Open Spaces team will decide on the next steps.
Kearsney Parks 2020 Calendar
Thank you to everyone who sent in photos for the Kearsney Parks 2020 Calendar! The calendar is now on sale at the mobile café in Kearsney Abbey for £5. It's a perfect stocking filler for Christmas. From November, you'll also be able to buy the calendar online at www.kearsneyparks.co.uk/order-calendar. The money raised will be put back into Kearsney Abbey and Russell Gardens.
We've had some lovely images and couldn't use them all. If you'd like to have a photo considered for next year's calendar, please email it to Kearsney.parks@dover.gov.uk. Photos should be 'landscape' layout and be high-resolution files.
Restoring the chalk grassland
Once a month, we're holding volunteer tasks to clear some of the scrub on Coxhill Mount. This will help to restore the area to chalk grassland. The tasks are being led by the White Cliffs Countryside Partnership. The next task is taking place on Thursday 7 November. More information is at www.kearsneyparks.co.uk/events
'Happy to chat' benches
'Happy to chat' benches are beginning to pop up across the world. They were the idea of a woman in Cardiff who saw an elderly man sitting alone. She wanted to make people feel less isolated and designed a simple sign that says 'Happy to chat bench. Sit here if you don't mind someone stopping to say hello'. We've got a couple of these signs in Kearsney Abbey – why not say hello to someone next time you're in the park?
Little Wild Things
Little Wild Things, the toddlers' group with a Forest School ethos, will be back in Kearsney Abbey on Wednesday 30 October. Bring your little one along to explore the park, make new discoveries and get creative with nature! It costs £5 a child (no charge for non-walkers and babies). More information is at www.kearsneyparks.co.uk/events
November surveys
From 11-17 November, we'll be carrying out a survey in Kearsney Abbey and Russell Gardens to find out how many people are using the parks. We do this survey three times a year to give an update to our funders, the National Lottery. If you'd like to help out, please email kearsney.parks@dover.gov.uk - it only takes an hour or two and is a great opportunity to get out in the fresh air!
If you have any comments or questions, please email us on kearsney.parks@dover.gov.uk or follow us on Twitter @Kearsneyparks
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Seasonal evolution of Niño-3 index (°C) for composite short decaying (thick solid blue line) and the long decaying (thick dashed red line) El Niños and for individual cases (thin lines) in observations.
Scatter diagram of the correlation coefficients for short (abscissa) and long (ordinate) decaying El Niño evolution between observations and individual coupled models. Nine models that have a correlation coefficient above 0.8 for both short and long decay phases are chosen as best models (inside the solid rectangle).
Evolution of (a) short and (b) long decaying El Niño cases in observations (blue line) and in the B9MME (red line) for the historical period of 1950–2005. Shading indicates one standard deviation for the samples (57 short decaying and 53 long decaying cases) in the B9MME. The El Niño event is referred to as the Niño-3 index, being defined as the SST anomalies averaged over the region of 5°S–5°N, 90°–150°W.
Composite SST anomalies (°C) for the (a)–(c) short and (d)–(f) long decaying El Niño events from (top to bottom) peak winter D(0)JF(1) to the following summer JJA(1) in the B9MME. The region within the black lines indicates the region where the anomalies are significant at the 95% confidence level by the t test. The numbers in the upper left corners are the pattern correlation coefficient between the observed and simulated composite anomalies over the entire region (20°S–40°N, 40°–270°E).
As in Fig. 4, but for 850-hPa streamfunction anomalies (106 m2 s−1).
As in Fig. 4, but for precipitation anomalies (shading; mm day−1) and 850-hPa wind anomalies (vectors; m s−1). Either the zonal wind or the meridional wind anomalies significant at the 95% confidence level by t test are shown.
Evolution of (a) short and (b) long decaying El Niño cases in the B9MME (red line) for the RCP4.5 period of 2050–99 in comparison to those (blue line) for the historical period of 1950–2005. Dark gray indicates one standard deviation for the samples (45 short decaying and 53 long decaying cases) in the RCP4.5 run and the light gray indicates one standard deviation in the historical run (as in Fig. 3).
As in Fig. 4, but for the RCP4.5 period of 2050–99.
Differences in the (a),(d) SST (°C), (b),(e) 850-hPa streamfunction (106 m2 s−1), and (c),(f) 850-hPa precipitation (shading; mm day−1) and wind (vectors; m s−1) anomalies between RCP4.5 and historical runs during JJA(1) for the (left) short decaying and (right) long decaying El Niño.
Differences between the RCP4.5 run and the historical run for (a) the interannual standard deviation of precipitation anomalies (mm day−1) in JJA and (b) the JJA-mean climatological precipitation (mm day−1). The contours represent the regions where the differences are significant at the 95% confidence level by the t test. The dashed rectangle is referenced in Figs. 13 and 14.
Scatter diagram of the mean state precipitation over the central tropical Pacific (5°S–5°N, 160°E–150°W; the dashed rectangle labeled in Fig. 12; abscissa) and correlation coefficients between JJA-mean WNPAC index and DJF-mean Niño-3 index (ordinate) in the RCP4.5 run (red circles) and the historical run (blue circles) for the nine best models (filled circles) and the B9MME (open circles).
Scatter diagram of the differences for the mean state precipitation (abscissa) and standard deviation of precipitation anomaly (ordinate) over the central tropical Pacific (5°S–5°N, 160°E–150°W; the dashed rectangle labeled in Fig. 12) between the RCP4.5 run and the historical run for the 20 CGCMs in CMIP5.
Intensification of the Western North Pacific Anticyclone Response to the Short Decaying El Niño Event due to Greenhouse Warming
Wei Chen1, June-Yi Lee2, Kyung-Ja Ha3, Kyung-Sook Yun3, and Riyu Lu4
1 State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
| 2 Research Center for Climate Sciences, Pusan National University, Busan, South Korea
| 3 Research Center for Climate Sciences, and Division of Earth Environmental System, Pusan National University, Busan, South Korea
| 4 State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
https://doi.org/10.1175/JCLI-D-15-0195.1
Two types of El Niño evolution have been identified in terms of the lengths of their decaying phases: the first type is a short decaying El Niño that terminates in the following summer after the mature phase, and the second type is a long decaying one that persists until the subsequent winter. The responses of the western North Pacific anticyclone (WNPAC) anomaly to the two types of evolution are remarkably different. Using experiments from phase 5 of the Coupled Model Intercomparison Project (CMIP5), this study investigates how well climate models reproduce the two types of El Niño evolution and their impacts on the WNPAC in the historical period (1950–2005) and how they will change in the future under anthropogenic global warming. To reduce uncertainty in future projection, the nine best models are selected based on their performance in simulating El Niño evolution. In the historical run, the nine best models' multimodel ensemble (B9MME) well reproduces the enhanced (weakened) WNPAC that is associated with the short (long) decaying El Niño. The comparison between results of the historical run for 1950–2005 and the representative concentration pathway 4.5 run for 2050–99 reveals that individual models and the B9MME tend to project no significant changes in the two types of El Niño evolution for the latter half of the twenty-first century. However, the WNPAC response to the short decaying El Niño is considerably intensified, being associated with the enhanced negative precipitation anomaly response over the equatorial central Pacific. This enhancement is attributable to the robust increase in mean and interannual variability of precipitation over the equatorial central Pacific under global warming.
Corresponding author address: Dr. June-Yi Lee, Research Center for Climate Sciences, Pusan National University, Busan 46241, South Korea. E-mail: juneyi@pusan.ac.kr
Keywords: Circulation/ Dynamics; Atmospheric circulation; ENSO; Teleconnections; Models and modeling; Climate models; Coupled models; Variability; Interannual variability
As the most significant source of interannual variability and seasonal predictability in the tropics, the El Niño–Southern Oscillation (ENSO) has attracted much attention because of its pronounced global impacts (e.g., Hoerling et al. 1997; McPhaden et al. 2006; Cai et al. 2011; Wang et al. 2009, 2015; J.-Y. Lee et al. 2011, 2013). The conventional El Niño is generally characterized by a positive sea surface temperature (SST) anomaly over the eastern tropical Pacific that is triggered in March–May (MAM), develops in June–August (JJA), peaks in December–February (DJF), and declines in the following MAM (Nigam and Shen 1993; Wang et al. 2003; Li et al. 2007).
The El Niño evolution varies from case to case because of the difference in decaying phase: a short decaying El Niño that terminates in the following boreal summer and a long one that persists until the subsequent boreal winter (Chen et al. 2012; Ha et al. 2016). Li et al. (2007) pointed out that the short decaying phase tends to occur with an El Niño with strong amplitude, whereas the long decaying phase tends to appear with a moderate one. Several studies also proposed a quasi-biennial El Niño that declines in the following summer, which is in contrast with a low-frequency El Niño that maintains into the subsequent year (Barnett 1991; Kim and Kim 2002; Bejarano and Jin 2008; Yun et al. 2015). Previous studies primarily focused on observed analyses or single model evaluations for the two types. Thus, an evaluation of how the current climate models that participated in phase 5 of Coupled Model Intercomparison Project (CMIP5) capture the two different evolutions is needed. The two El Niño evolutions mentioned above are based on the El Niño events that occurred mainly over the eastern tropical Pacific. They are different from those with SST anomalies that occurred over the central Pacific (e.g., Ashok et al. 2007; Kug et al. 2009, 2012; Yeh et al. 2009).
The summer climate over the western North Pacific (WNP) is highly dependent on ENSO activity (e.g., Wang et al. 2000, 2015; S.-S. Lee et al. 2011, 2013; Stuecker et al. 2013; Chen et al. 2014; Lee and Ha 2015). The lower-tropospheric anticyclonic anomaly over the WNP (WNPAC), which frequently persists from the El Niño mature winter to the following summer, is one of the key bridges that links El Niño to the summer climate over the WNP and East Asia (Chang et al. 2000; Wang et al. 2000, 2013, 2015; Chou et al. 2003; Wu et al. 2003; Lee et al. 2010; S.-S. Lee et al. 2013). Wang et al. (2000, 2013) suggested that the WNPAC is the response to El Niño heating over the central and eastern equatorial Pacific, which is maintained by the local air–sea interaction. In addition, the Indian Ocean basinwide warming, which occurs after wintertime El Niño events, contributes to the persistence of the WNPAC (Yang et al. 2007; Li et al. 2008; Xie et al. 2009; Ding et al. 2010; Chowdary et al. 2010, 2014). The strength of the WNPAC is associated with different ENSO activities. The enhanced ENSO variability tends to strengthen the monsoon–ocean interaction over the WNP and then provide a favorable condition for the WNPAC to persist for a longer period during the El Niño decaying summer since the late 1970s (Wang et al. 2008). Based on coupled model experiments, Li et al. (2007) and Chen et al. (2014) reported that the strength of the WNPAC is closely related to the amplitude of El Niño.
Recent studies highlighted the role of El Niño evolution in the WNP summer climate. Chen et al. (2012) illustrated that the length of the El Niño decaying phase contributes to the intensity of the WNPAC. A short decaying phase leads to a strong WNPAC in the following summer. A long decaying phase, however, is related to a disappearance of WNPAC. They suggested that the positive SST anomalies in the Indian Ocean and the negative SST anomalies in the central and eastern tropical Pacific constructively induce significant WNPAC for the short decaying cases, whereas the roles of positive SSTs in both regions for the long decaying El Niño are destructively and lead to weaker WNP circulation anomalies. In addition, Yun et al. (2015) suggested that the quasi-biennial El Niño, rather than the low-frequency one, is associated with a strengthened WNPAC. The impact of different El Niño evolution on the WNPAC has been analyzed by using observational data (e.g., Chen et al. 2012; Yun et al. 2015). Whether these different impacts can be reproduced by CMIP5 coupled general circulation models (CGCMs) is still unanswered.
The effect of anthropogenic forcing on ENSO characteristics has been investigated by analyzing the CMIP3 models (Collins et al. 2005; Guilyardi 2006; Meehl et al. 2007; Yeh et al. 2009; Latif and Keenlyside 2009; Collins et al. 2010) and the CMIP5 models (Kim and Yu 2012; Guilyardi et al. 2012; Stevenson et al. 2012; Weare 2013; Chu et al. 2014; Lee et al. 2014). Future change in ENSO variability is not robust in the CMIP5 projection. Guilyardi et al. (2012) suggested that the CMIP5 models show intermodel disagreement in the change in ENSO intensity; there is no significant change in 6 of the 13 models, an increase in 5 models, and a decrease in 2 models. Stevenson (2012) also indicated that the ENSO amplitude change is close to zero based on the multimodel mean of 27 models. However, it was recently noted that the ENSO frequency tends to be slightly decreased but the extreme ENSO events are increased in response to global warming (Cai et al. 2014).
The following specific questions need to be addressed: 1) how do the CMIP5 CGCMs reproduce the two different El Niño evolutions and what is their impact on the WNP summer climate in the historical simulation, and 2) how do El Niño evolutions and their impacts change under anthropogenic global warming? This study aims to answer these questions by analyzing two different runs of the 20 CGCMs in CMIP5: the historical run and the representative concentration pathway 4.5 (RCP4.5) run. A detailed description of the models and the data are provided in section 2. Section 3 evaluates the 20 CGCMs in capturing the El Niño evolutions and their different impacts on the WNP summer climate for the optimal selection of reliable models to explore future changes. Section 4 discusses the future changes in the two types of El Niño evolution and their impacts. The results are summarized in section 5.
A total of 20 CGCMs that participated in the CMIP5 are utilized in this study, following Lee and Wang (2014). The model names and institutions are listed in Table 1. Two experiments are investigated: the historical run (i.e., the twentieth-century run) for the period from 1850 to 2005 and the RCP4.5 run for the period from 2006 to 2099. The historical run was imposed with conditions that resemble observed anthropogenic and natural forcings, such as atmospheric composition (including CO2) due to both anthropogenic and volcanic influences, solar forcing, emissions or concentrations of short-lived species and natural and anthropogenic aerosols or their precursors, and land use. The RCP4.5 run assumes that radiative forcing will increase and then stabilize at approximately 4.5 W m−2 after 2100; it is selected as a "medium" scenario in CMIP5 (Taylor et al. 2012). Detailed information about the CMIP5 models and experiments is available online (at http://cmip-pcmdi.llnl.gov/cmip5/experiment_design.html) and in other related papers (e.g., Taylor et al. 2012).
Description of CMIP5 models used in this study. Models in bold are the nine best models based on reproducibility of El Niño evolution in the historical run.
Generally, the CMIP5 CGCMs have a larger number of ensemble simulations for the historical run compared with the RCP4.5 run. For a fair comparison, we use the same number of ensemble members for the two experiment types in individual models. Table 1 shows the number of ensemble members for each model. If there are more than one ensemble member in an individual model experiment, we calculate the ensemble mean prior to applying any analyses. We analyze the historical run with a 56-yr period from 1950 to 2005 and the RCP4.5 run with a 50-yr period from 2050 to 2099. It should be mentioned that the same conclusions can be obtained using the RCP4.5 run with a longer period from 2006 to 2099, which indicates the statistical robustness in the results of this study.
The observational data include the National Oceanic and Atmospheric Administration (NOAA) Extended Reconstructed Sea Surface Temperature with a horizontal resolution of 2.0° by 2.0° (Smith and Reynolds 2004) and lower-tropospheric circulation data from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis data with a horizontal resolution of 2.5° by 2.5° (Kalnay et al. 1996) for the period from 1948 to 2005.
The observed evidence suggests that the evolution of El Niño varies from case to case, particularly in the decaying phase. A warm event is selected when the DJF-mean Niño-3 index (defined as the SST anomalies averaged over 5°S–5°N, 90°–150°W) exceeds 0.8 standard deviations from the time mean, following Chen et al. (2012). Figure 1 shows the seasonal evolution of 11 El Niño events in observations from the developing year (0) to the decaying year (1). Most of the El Niño events develop in MAM(0), peak in D(0)JF(1), and then begin to decline in the following year (Fig. 1). However, a remarkable spread exists in the decaying phases [measured by the standard deviation of JJA(1) Niño-3 index of 0.72°C], compared with a relatively small spread in the developing phases [measured by the standard deviation of MAM(0) Niño-3 index of 0.34°C]. The spread in the decaying phases is caused by the different lengths of the decaying phases: a short decaying phase that terminates in the following summer [JJA(1)] and a long decaying phase that maintains in the subsequent summer and persists until the next winter [D(1)JF(2)]. Thus, the length of the El Niño decaying phase is the most remarkable feature for El Niño evolution.
Citation: Journal of Climate 29, 10; 10.1175/JCLI-D-15-0195.1
To distinguish the different lengths of El Niño decay, all El Niño events are divided into two categories: a short decaying El Niño when the JJA(1) Niño-3 index is less (greater) than 0°C according to Chen et al. (2012). Five short (1951/52, 1965/66, 1972/73, 1994/95, and 1997/98) and six long (1957/58, 1976/77, 1982/83, 1986/87, 1991/92, and 2002/03) decaying El Niño events in observations are obtained under this criterion. To test the robustness in the selected years, different El Niño indices are used, including the multivariate ENSO index, the detrended Niño-3 index, and the Niño-3 index after removing the covariation of Indian Ocean SST (not shown). The selected years are not sensitive to the use of different El Niño indices. It is further noted that there is no significant decadal change in the short and long decaying cases, although a decadal shift of El Niño characteristics around the mid-1970s has been reported by Xie et al. (2010) and Chowdary et al. (2011) and many others.
The composites of short (solid blue line) and long (dashed red line) decaying El Niño, which are shown in Fig. 1, indicate that they have quite similar length of the developing phase and intensity in the peak phase but significantly differ from each other in terms of the length of decaying phase. In the following section, we evaluate the CMIP5 models for capturing the short and long decaying El Niño events in the current climate.
The identical criterion for the observed analysis is applied to select the short and long decaying El Niño events in the ensemble mean of the historical simulation of individual CMIP5 CGCMs for the period from 1950 to 2006. The models' ability to capture the two El Niño evolutions is assessed in terms of the correlation coefficients for the short and long decaying El Niños, separately, between the observations and each model's ensemble mean from D(−2)JF(−1) to JJA(2) (Fig. 2). The correlation analysis is performed in a particular sequence: first, the composite of the Niño-3 index for short and long El Niño decaying events is applied in individual coupled models and observations, respectively. The correlation coefficients of the composite El Niño evolutions between the observations and individual models are then calculated.
It is noted that the models tend to reproduce the long decaying El Niño events better than the short ones. For the short decaying case (abscissa), the correlation coefficient ranges from 0.5 to 0.9. For the long decaying case (ordinate), however, all models, with the exception of one model, have correlation coefficients greater than 0.8. It is speculated that the longer persistency and relatively weak amplitude in the long decaying El Niño may be more favorable to model simulation. In this study, the nine best models are selected based on the correlation coefficients for both the short and long decaying cases being greater than 0.8 (inside the solid rectangle in Fig. 2). The selected models are ACCESS1.0, BCC_CSM1.1, CanESM2, CCSM4, CNRM-CM5, FGOALS-g2, IPSL-CM5A-MR, MRI-CGCM3, and NorESM1-M. The nine best models' multimodel ensemble (B9MME) is used for further assessment of the El Niño evolution.
Figure 3 shows the composite of the seasonal evolution of the short (Fig. 3a) and long (Fig. 3b) decaying cases in the B9MME (red dashed line) and observations (blue solid line). The shading represents the spread among the El Niño events in the B9MME. Considering the large spread in the ENSO amplitude among the models, we normalized the Niño-3 index in each model prior to calculating the MME. The B9MME has significantly high skill in capturing the evolution for both the short and the long decaying cases. The correlation coefficients for the El Niño evolution between observations and the B9MME are 0.93 and 0.97 for the short and the long decaying case, respectively. In particular, the lengths of the decaying phase for both the two cases is accurately reproduced by the B9MME, although it tends to underestimate the intensity of the El Niño peak, particularly for the short decaying El Niño.
Figure 4 investigates the B9MME's reproduction of the spatial distribution of SST anomalies over the Indo-Pacific region associated with the short and long decaying El Niños. The B9MME is able to simulate the associated SST patterns that correspond to the observed results shown in Chen et al. (2012). During the mature winter D(0)JF(1) and the following spring MAM(1), the short and long decaying El Niños show similar SST patterns with positive SST anomalies over the central and eastern tropical Pacific and the Indian Ocean. The positive SST anomalies over the central and eastern tropical Pacific are slightly stronger in D(0)JF(1) but are weaker in MAM(1) for the short decaying El Niño compared with the long decaying one. In particular, the warm SST over the Indian Ocean is stronger for the short decaying El Niño compared with the long decaying one during D(0)JF(1). The Indian Ocean SST anomalies, which are averaged over 10°S–20°N, 40°–110°E, are 0.26°C for the short decaying case, compared with 0.19°C for the long decaying one. The strong warm Indian Ocean SST for the short decaying El Niño is consistent with Chen et al. (2012), who proposed that the positive SST anomalies over the Indian Ocean during D(0)JF(1) favor a rapid decline of El Niño by inducing easterlies over the western to the central equatorial Pacific and therefore lead to a short decaying El Niño.
The significant difference in the SST patterns between short and long decaying El Niños is seen in JJA(1). The positive SST anomalies over the central and eastern Pacific still maintain for the long decaying case but transform into negative SST anomalies for the short decaying case. Meanwhile, the positive Indian Ocean SST anomaly after El Niño winter still persists into JJA(1) for both evolution cases.
As mentioned, these SST patterns are quite similar to those in observations in Chen et al. (2012). The pattern correlation coefficients (PCCs) for the SST anomalies over the Indo-Pacific Ocean between the B9MME and the observations exceed 0.6 for both decaying cases from the mature winter D(0)JF(1) to decaying summer JJA(1). The results from Figs. 3 and 4 indicate that the B9MME is capable of reproducing two types of El Niño evolution and their associated SST distribution over the Indo-Pacific region with better skills for capturing the long decaying El Niño and its associated spatial distribution of the Indo-Pacific SST compared with the short decaying case.
The responses of the WNP summer climate to the different categories of El Niño evolution in the B9MME are also evaluated. Figure 5 shows the circulation pattern that is associated with the two types of El Niño decays. The circulation response to the two cases is quite similar for D(0)JF(1) and MAM(1). However, during JJA(1), the WNPAC is sustained and considerably intensified for the short decaying case (Fig. 5c) but vanished for the long decaying one (Fig. 5f). The strength of the WNPAC that is associated with the short decaying El Niño is 1.13 × 106 m2 s−1 [measured by the 850-hPa streamfunction averaged over 10°–25°N, 110°–160°E, following Chen et al. (2014)], compared with 0.02 × 106 m2 s−1 for that associated with the long decaying one. The significant WNPAC anomaly that is associated with the short decaying El Niño in JJA(1) suggests a strong impact of the short decaying El Niño on the WNP summer climate, which is consistent with Chen et al. (2012). It is also noted that the B9MME well simulates the northeastward shift of the center of the WNPAC anomalies from D(0)JF(1) to MAM(1) for both short and long decaying El Niños (e.g., Wang et al. 2003; Li et al. 2007; Chen et al. 2012).
The high PCCs for the 850-hPa streamfunction anomalies over the Indo-Pacific Ocean between the B9MME and observations shown in Fig. 5 indicate that the state-of-the-art models in the CMIP5 are capable of reproducing the different impacts of the two types of El Niño evolutions on the WNP summer climate. The B9MME captures the large-scale circulation pattern that is associated with El Niño: a pair of cyclonic anomalies center over the central to eastern Pacific and a pair of anticyclonic anomalies center over the Indian Ocean to the WNP in mature winter and the following spring. Furthermore, the B9MME well simulates the different influences on the WNPAC between short and long decaying El Niños and suggests that the short decaying El Niño, rather than the long decaying one, is associated with a strong WNPAC anomaly during summer.
Figure 6 shows the response of the WNP summer climate to the short and long decaying El Niños in terms of the 850-hPa wind and precipitation anomalies. The significant negative precipitation anomaly over the WNP in response to the short decaying El Niño in JJA(1) further indicates a strong relationship between the short decaying El Niño and the WNP summer climate (Fig. 6c). This negative precipitation anomaly is associated with the well-organized WNPAC with strong easterlies over the western equatorial Pacific. For the long decaying El Niño, however, the easterly anomalies in the northwestern subtropical Pacific are associated with the wind anomalies in the midlatitudes instead of those in the tropical region. These features of the lower-tropospheric wind anomalies are consistent with those in observations (Fig. 14 in Chen et al. 2012).
We also investigate the sensitivity of the results to the selection of the observation data using the Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST; Rayner et al. 2003) for SST and the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40; Uppala et al. 2005) data for the circulations (not shown). It is noted that the same results can be obtained regardless of using different observed data.
The previous section demonstrated that the B9MME is highly capable of reproducing the two types of El Niño evolutions and their impact on the WNP summer climate. This section investigates future changes in the El Niño evolutions and their impact by comparing the historical run for 1950–2005 and the RCP4.5 run for 2050–99 using the CMIP5 B9MME selected in section 3.
Figure 7 compares the seasonal evolution of the short (Fig. 7a) and the long (Fig. 7b) decaying El Niño between the historical run and the RCP4.5 run. Although the spread among the El Niño events in the RCP4.5 run is relatively large, the evolutions for both the short and the long decaying cases are consistent with those in the historical run; the short decaying El Niño terminates in the following summer and the long decaying one persists until the subsequent winter. The correlation coefficients for the El Niño evolution between the RCP4.5 run and the historical run are 0.96 and 0.97 for the short and the long decaying case, respectively. It is suggested that the El Niño can be separated into short and long decaying cases in the future, and no significant change in the two evolutions under anthropogenic global warming is projected.
Table 2 analyzes the occurrence frequency of the short and the long decaying El Niño cases in observations, the historical run, and the RCP4.5 run. In observations and the historical run, the frequency for the short or the long decaying is approximately half of all the El Niño events, which reveals no significant difference in the frequency between the short and the long decaying El Niño. In the RCP4.5 run, the frequency of the short (long) decaying one is slightly decreased (increased). However, these changes are not statistically significant with respect to the model uncertainty measured by the intermodel spread (16.2% and 20.4% in the historical run and RCP4.5 run, respectively). Thus, under the RCP4.5 scenario, robust changes in the frequency of the short and the long decaying El Niño are not expected. In addition, the interannual variability of El Niño, which is measured by the standard deviation of the DJF-mean Niño-3 index, is 0.77°C in the RCP4.5 run. It is slightly less than the standard deviation of 0.86°C in the historical run, but the difference between the present and future is far less than the intermodel spread. Thus, the B9MME projects that there is no significant change in the interannual variability of El Niño in the future under the RCP4.5 scenario.
Standard deviation of D(0)JF(1) Niño-3 index and fractional occurrence of short and long decaying El Niño in observation and in the B9MME during historical run and RCP4.5 run.
Figure 8 shows the spatial pattern of the SST anomalies that are associated with El Niño evolution in RCP4.5. These SST patterns are similar to those in the historical run (Fig. 4) with comparable intensity. The similarity further indicates an insignificant change in El Niño evolution between the historical run and the RCP4.5 run. The CMIP5 CGCMs tend to show diverse results for the ENSO variability changes (Guilyardi et al. 2012; Stevenson et al. 2012; Weare 2013). Further analysis reveals that the insignificant change in the ENSO evolution and the amplitude in the future scenario also can be obtained by using all 20 CGCMs, which indicates the robustness of the results. Our results are also consistent with the result from Stevenson (2012) by using the multimodel mean of 27 CMIP5 models.
Additionally, the strength of SST anomaly over the Indian Ocean is also close to that in the historical run (Figs. 4 and 8), particularly during JJA(1), when the Indian Ocean SST is suggested to be important to the WNPAC. The intensity of Indian Ocean SST anomalies is 0.17°C (0.24°C) in the RCP4.5 run, compared with 0.23°C (0.26°C) in the historical run for the short (long) decaying El Niño. Thus, the B9MME results indicate that there is also no significant change for the intensity of Indian Ocean SST under the anthropogenic global warming scenario.
We investigate whether the impacts of the two types of El Niño evolution could change under the RCP4.5 scenario. The lower-tropospheric circulation patterns that are associated with the short and the long decaying El Niño under the RCP4.5 (Fig. 9) scenario are similar to those in the historical run (Fig. 5): 1) during D(0)JF(1) and MAM(1), a pair of cyclonic anomalies over the central to eastern Pacific and a pair of anticyclonic anomalies over the Indian Ocean extend to the western Pacific for both short and long decaying cases, and 2) during JJA(1), a strong WNPAC maintains for the short decaying El Niño, in contrast with a disappearance of WNPAC for the long decaying case. The latter suggests that the short decaying El Niño still has a strong impact on the WNP summer climate in the future scenario.
On the other hand, the circulation anomaly over the WNP is significantly stronger in the RCP4.5 scenario than that in the historical run. The intensification of the WNPAC also exists in the mature winter and the subsequent spring for both the short and the long decaying El Niño, particularly in the subsequent summer for the short decaying case. In JJA(1), the strength of the WNPAC is 1.46 × 106 m2 s−1 in the RCP4.5 run with a growth ratio of 29.2% compared with that in the historical run. The enhanced WNPAC under the global warming scenario is further confirmed by the 20 CGCMs' MME, with an intensity of 1.31 × 106 m2 s−1 up from 0.89 × 106 m2 s−1. It should be noted that the intensification of WNPAC under the global warming scenario is not dependent on the definition of the WNPAC. Similar results can be obtained by using other definitions (results not shown). The intensification of the WNPAC indicates an enhancement of the response of the WNP summer climate to the short decaying El Niño in the future. Furthermore, the enhanced WNPAC coincides with an intensified relationship between ENSO and the WNP summer climate under the RCP4.5 scenario. The correlation coefficient between the JJA-mean WNPAC and the DJF-mean Niño-3 indices increases from 0.19 in the historical run to 0.26 in the RCP4.5 run for all integration years (both significant at the 95% confidence level). In particular, for the short decaying El Niño years, the correlation coefficient enhances from 0.58 to 0.70 (both significant at the 99% confidence level).
The distribution of the 850-hPa wind and precipitation anomalies that are associated with El Niño evolution further indicates a strong response of the WNP summer climate to the short decaying El Niño rather than the long decaying one (Fig. 10), which is consistent with those in the historical run (Fig. 6). In general, both precipitation and wind anomaly patterns that are associated with El Niño evolutions in the RCP4.5 run (Fig. 10) are similar to those in the historical run (Fig. 6) from the mature winter to the following summer. However, the easterlies over the western equatorial Pacific that are associated with the short decaying El Niño shown in Fig. 10c are considerably stronger than those shown in Fig. 6c, which indicates a stronger WNPAC anomaly in the RCP4.5 run compared with that in the historical run.
Figure 11 clearly shows the differences between the RCP4.5 run and the historical run during the summer season. The insignificant anomalies for the SST pattern further suggest an insignificant change in the ENSO amplitude during summer for the short and the long decaying case (Figs. 11a,d). The strong positive streamfunction anomaly over the WNP shown in Fig. 11b indicates the intensified WNPAC response to the short decaying El Niño under the global warming scenario. A natural question arises as to possible causes of this intensification of the WNPAC because there is no robust change in the ENSO evolution and the amplitude under the RCP4.5 scenario. We propose that the stronger precipitation response in the equatorial central Pacific contributes to the WNPAC intensification (Fig. 11c). Although the Niño-3 SST anomaly in JJA(1) for the RCP4.5 run is nearly identical to that for the historical run, the negative precipitation anomaly over the equatorial central Pacific (0°–10°N, 160°E–150°W) is −0.81 mm day−1 [considerably increased from −0.47 mm day−1 in the present (Fig. 6c) to −1.28 mm day−1 in the future (Fig. 10c)]. Cai et al. (2014) also suggested that extreme ENSO events defined by the central Pacific precipitation response to ENSO will be increased under global warming. Recently, the central Pacific precipitation anomaly response to the developing La Niña during JJA(1) has been highlighted as an important source of the WNPAC intensification, differing from the Indo-Pacific air–sea coupling process associated with the decaying phase of El Niño (Chen et al. 2012; Fan et al. 2013; Wang et al. 2013). Thus, the enhanced negative precipitation anomaly over the equatorial central Pacific can be a possible source for the intensification of the WNPAC response under anthropogenic global warming.
For the physical process that is responsible for the intensification of the WNPAC response, it is speculated that the negative precipitation anomaly over the equatorial central Pacific induces the WNPAC over its western side as a Gill-pattern Rossby wave response. Moreover, the northeasterly on the eastern flank further reinforces the negative precipitation anomaly through enhancing the wind speed and dry advection. Thus, this strong positive feedback may further enhance the role of the strong negative precipitation anomaly over the central Pacific in the enhanced WNPAC.
It is further suggested that the stronger precipitation anomalies in JJA(1) are possibly associated with the enhancement of the mean and the interannual variability of precipitation over the equatorial central Pacific under global warming (Fig. 12). The difference between the present and future is 0.32 and 0.57 mm day−1 for the interannual standard deviation and climatology, respectively, of precipitation over the equatorial central Pacific (5°S–5°N, 160°E–150°W, the dashed rectangle region in Fig. 12).
The contribution of the enhanced mean state precipitation over the central tropical Pacific to the El Niño–related WNPAC is further supported by the positive correlation between the intensity of the mean state precipitation and the response of WNPAC to El Niño (Fig. 13). The results indicate that models that have stronger mean precipitation tend to have a higher correlation between WNPAC and El Niño for both the historical run and the RCP4.5 run. Thus, the increased mean state of precipitation under global warming may intensify the El Niño–related WNPAC, although there is no robust change in ENSO amplitude.
Figure 14 shows the increase in the mean and the interannual variability of the central Pacific precipitation anomaly in the individual models. There is a positive correlation between the changes in the mean and the variability of precipitation in individual model simulations (0.43 and 0.45 for all models and the nine best models, respectively). Of the 20 models, 13 models, including 5 of the 9 best models, project an increase in the mean and the variability of the precipitation in future. It remains unclear in this study whether the mean increase induces the intensification of interannual variability of precipitation. Nonetheless, Figs. 11c, 12, and 14 suggest that the significant increase in the variability and the mean of precipitation over the region of interest, which has also been noted by previous studies (Huang et al. 2013; Lee et al. 2014; Lee and Wang 2014), may provide favorable conditions for strengthening the negative precipitation response over the region and then, in turn, the WNPAC response under global warming.
In addition, we examined the relationship between the changes in interannual variability of the precipitation anomaly and the change in meridional gradient of mean state SST between the RCP4.5 and the historical runs (not shown) because previous research has suggested that a weakened SST gradient (a faster warming in the background state along the equatorial than in the off equatorial) under a global warming scenario enhances the precipitation intensity (Cai et al. 2014). The results suggest that 13 of the 20 models show both a weakened SST gradient and an intensified interannual variability of precipitation. However, it is difficult to conclude that the enhanced precipitation is induced by the weakened SST gradient because of their insignificant linear correlation.
This study investigates reproduction and future changes in two types of the El Niño evolution and their impact on the WNP summer climate, as projected by 20 CGCMs that participated in the CMIP5. Two runs are compared: the historical run under changing solar–volcanic forcing and anthropogenic influences for 1950–2005 and the RCP4.5 run for 2050–99. The El Niño evolutions are divided into two categories according to the length of their decaying phases: a short decaying El Niño that terminates in the following summer and a long decaying one that persists until the subsequent winter.
The capability of reproducing these two categories of El Niño evolution by the 20 CMIP5 models is evaluated by calculating the correlation coefficient for short and long decaying El Niño evolution between observations and each individual coupled model's ensemble mean in the historical run. The nine best models, which exhibit a high skill (correlation coefficient above 0.8) for simulating both the short and the long decaying El Niño, are selected for future change analysis. The B9MME not only well captures the seasonal evolution of the short and the long decaying El Niño but also has a high skill for reproducing the spatial patterns of SST anomalies that are associated with the two types of El Niño evolution.
The two types of El Niño behavior have different impacts on the WNP climate during the following summer: the short decaying El Niño is related to a strong WNPAC and a significant negative precipitation anomaly over the WNP, whereas the long decaying one responds to a disappearance of WNPAC. The B9MME well reproduces these different responses of the WNP summer climate to the short and the long decaying El Niño. Particularly, the PCC of 850-hPa streamfunction anomalies that are associated with the short decaying El Niño in the following summer between B9MME in the historical run and observations is as high as 0.88, which indicates a high skill of B9MME in simulating a strong relationship between the short decaying El Niño and the WNP summer climate.
In the RCP4.5 scenario, the B9MME projects a similar short and long El Niño evolution compared with those in the historical run. In addition, the frequency of the short and the long cases and the interannual variability of El Niño are also similar to those in the historical run. According to the RCP4.5 scenario and the B9MME analysis, the El Niño evolution, particularly the length of its decaying phase, may not be significantly changed under anthropogenic global warming.
Although there is no robust change in El Niño evolution and amplitude, the WNPAC response to the short decaying El Niño in JJA(1) is significantly intensified in the RCP4.5 run compared with that in the historical run. The intensification of the WNPAC is possibly induced by the strong negative precipitation anomaly over the central tropical Pacific, which is associated with the short decaying El Niño. This negative precipitation anomaly induces the WNPAC over its western side as a Gill-pattern Rossby wave response. Moreover, the northeasterly on the eastern flank further reinforces the negative precipitation anomaly through enhancing the wind speed and dry advection. Thus, this strong positive feedback explains the role of the strong negative precipitation anomaly over the central Pacific in intensifying the WNPAC.
It is suggested that the significant increase in variability and the mean of precipitation over the equatorial central Pacific may provide favorable conditions for strengthening the negative precipitation response over the region and then, in turn, the WNPAC response under global warming. The clear positive correlation between the strength of the mean state precipitation and the response of WNPAC to El Niño indicates that the stronger the mean state precipitation, the higher the correlation between WNPAC and El Niño in both the historical run and the RCP4.5 run.
It should be mentioned that the results obtained in this study are robust regardless of the selection of models and observed data. Using all 20 models and HadISST and ERA-40 data, we obtained similar results.
This study suggests that the influence of ENSO on the WNP summer climate may be enhanced under anthropogenic global warming, particularly for the short decaying El Niño, although no significant change in ENSO amplitude is projected. A key to understanding the change may lie in explaining the increased response of atmospheric convection over the equatorial central Pacific to the similar amplitude of ENSO. Cai et al. (2014) also suggested that the frequency of extreme El Niño, as defined by atmospheric convective response over the equatorial Pacific, will increase in the warmer climate, although El Niño frequency is projected to be slightly decreased. We suggested that an increase in the mean and the variability of precipitation over the equatorial central Pacific may be responsible for the enhanced response. However, further investigation is necessary for better understanding controlling mechanisms for the change.
This study was supported by the National Research Foundation of Korea (NRF) through Global Research Laboratory (GRL) Grant MEST 2011-0021927 and National Natural Science Foundation of China Grants 41105046 and 41320104007. We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups for producing and making available their model output. For CMIP the U.S. Department of Energy's Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led the development of software infrastructure in partnership with the Global Organization for Earth System Science Portals.
https://journals.ametsoc.org/view/journals/clim/29/10/jcli-d-15-0195.1.xml


The Pickled Chef, a local canning, preserving and pickling company, was started in the Spring of 2014 by Chef Greg Andrews and the family partnership team at The Supper Club restaurant in Greensburg, PA. The Pickled Chef features a variety of all natural preservative free, hand canned and hand packed products including classic Bread & Butter and Sour Dill Pickles and unique pickled vegetables, such as Green Beans, Radishes, Ramps, Beets, Squash, Okra and Kohlrabi along with a variety of condiments, conserves, jams, sauces, dressings, pesto's and fresh fermented sauerkraut and Kim Chi style items are also being produced. These "Farm to Jar" items utilize all local seasonal ingredients purchased from local area farmers and producers.
Opening the first season at Ligonier Country Market each Saturday morning, The Pickled Chef was one of the principal vendors of more than 100+ merchants selling between 150-200 items each week throughout the 21 weeks beginning in May 2014. The premier products and recipes have been highlighted in local publications, The Tribune Review, Edible Allegheny and Table Magazine. The company was showcased in Pittsburgh's Farm to Table Fall Harvest Tasting. Chef Greg was also a guest on WQED's Chris Fennimore's QED Cooks.
The 2015 season saw the addition of made-to-order Gourmet Grilled Cheese Sandwiches within the primary Saturday Ligonier Country Market along with participation in the yearly Ligonier Days Festival in October and the inaugural PicklesBurgh event in July hosted by the Pittsburgh Downtown Partnership.
Chef Greg also began teaching cooking classes at Crate Cooking School & Kitchen Supply in Greentree, Pa in Fall of 2015. In 2016-17 classes included Fermentation, Farm to Table Seafood, Gelato, Local Dairy, and private corporate team building instruction.
Throughout the 2016 & 2017 seasons The Pickled Chef product line was introduced into the regional and greater Pittsburgh Market and in several Pittsburgh area weekday farmers markets and annual regional festivals.
The Pickled Chef's FIRST 'brick and mortar' fast casual kitchen and retail 'farmers market' location opened in February 2018 in the Latrobe 30 Shoppes located in a Unity Township's main shopping center.
Offering lunch and brunch, Chef Greg's kitchen menus feature his famous 'made-to-order' grilled cheese sandwiches along with home made soups and sides. Being born and raised in Dallas, his dinner menus include Texas style BBQ, Southern style fried chicken and "blue plate specials" featuring home style comfort food.
The Farmers Market offers the Pickled Chef's full line of jarred products along with additional local producers' specialties such as Logan Family Farms Beef, Jamison Farms Lamb, Pleasant Lane Farms Honey, Paul Family Farms Maple Syrup, Clover Creek Cheese Cellars Artisanal Cheeses and Turner's Dairy full line of bottled milks, juices and teas. In addition, in the market, local artisans' products such as jewelry, soaps and candles are available. A full array of local farm fresh fruits and vegetables will be available for purchase beginning this Spring and Summer season.


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Q: How to make a hyphen, not minus sign, when using expression() to label in R? I'm using expression() in my plot's x-axis label to create a square-root symbol over my measure's name to indicate that the data has been transformed using square-root transformation. However, my measure's name ("CES-D") has a hyphen in it. When I write it in the expression(), the hyphen becomes a minus or en dash character with space around it.
qplot(1:10, 1:10) +
    labs(x = expression(sqrt(CES-D~scores)),
         y = "CES-D scores")

Notice the hyphens are different in the x and y axis labels. In the x-axis label, it looks like square root of "CES minus D scores".
How do I make a regular hyphen character inside an expression() for text?

A: Try backticks around CES-D:
qplot(1:10, 1:10) + labs(x = expression(sqrt(`CES-D`~scores)), y = "CES-D scores")

redpajama_set_name: RedPajamaStackExchange


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