Extreme temperature changes and firm ESG performance

Extreme climate events are often of great interest because of their close relationship with meteorological and hydrological disasters. In this study, four RCM simulations driven by different GCMs (CSIRO‐Mk3.6.0, EC‐EARTH, HadGEM2‐ES, and MPI‐ESM‐MR) are used as an ensemble to project changes in extreme high and low temperatures in China as represented by selected Expert Team on Climate Change Detection indices. Each simulation is performed at 25 km resolution from 1980 to 2099 using RegCM4.4 RCM. Results show the following: (a) All four reasonably reproduce climatological maximum daily maximum temperature (TX), warm spell duration, minimum daily minimum temperature (TN), and cold spell duration, but the majority shows relatively weak ability to capture climatological warm day and cold night frequencies. (b) Although the RCM homogenizes much of the variance originally shown between driving GCM simulations, marked dissimilarities are still shown in western China across the four downscaling simulations, implying a dependency on the individual GCM‐driven RCM simulation for this region. (c) All of China shows an increase in maximum TX, warm days, warm spell duration, minimum TN, and a decrease in cold nights and cold spell duration, during the near‐term, middle‐term, and long‐term. The Tibetan Plateau and its adjacent regions show more significant changes in extreme temperature, indicating stronger sensitivity to changes in extremes. RCM downscaling projects less significant change in extreme temperatures than the driving GCM. (d) The ensemble of RCM simulations projects that area‐averaged maximum TX, warm days, warm spell duration, and minimum TN increase over China as a whole by 2.5°C, 21.1%, 43.6 days, and 3.3°C, respectively, while cold nights and cold spell duration decrease by 6.8% and 2.0 days, respectively during the long‐term under the RCP4.5 scenario relative to 1986–2005. Future episodes of extreme high temperature are likely to require more research as they become more common.

Projecting changes in regional temperature and precipitation extremes in the United States

Spatiotemporal distribution variation of extreme temperatures under elevation-dependent warming and its response characteristics to regional warming in Qinling-Daba Mountains, China.

Historical and projected trends in temperature and precipitation extremes in Australia in observations and CMIP5

Climate extremes are widely projected to become more severe as the global climate continues to warm due to anthropogenic greenhouse gas emissions. These extremes often cause the most severe impacts on society. Therefore, the extent to which the extremes might change at regional level as the global climate warms from current levels to proposed policy targets of 1.5 and 2.0 °C above preindustrial levels need to be understood to allow for better preparedness and informed policy formulation. This paper analysed projected changes in temperature and precipitation extremes at 1.0, 1.5 and 2.0 °C warming over Botswana, a country highly vulnerable to the impacts of climate change. Projected changes in temperature extremes are significantly different from each other at the three levels of global warming, across three main climatic zones in the country. Specifically, at 2.0 °C global warming relative to preindustrial, for the ensemble median: (a) country average warm spell duration index increases by 80, 65, 62 days per year across different climatic zones, approximately three (and two) times the change at 1.0 (1.5) °C; (b) cold night (TN10P) and cold day (TX10P) frequencies decrease by 12 and 9 days per year across all regions, respectively, while hot nights (TN90P) and hot days (TX90P) both increase by 8–9 days across all regions. Projected changes in drought-related indices are also distinct at different warming levels. Specifically: (a) projected mean annual precipitation decreases across the country by 5%–12% at 2 °C, 3%–8% at 1.5 °C and 2%–7% at 1.0 °C; (b) dry spell length (ALTCDD) increases by 15–19 days across the three climatic zones at 2.0 °C, about three (and two) times as much as the increase at 1.0 (1.5) °C. Ensemble mean projections indicate increases in heavy rainfall indices, but the inter-model spread is large, with no consistent direction of change, and so changes are not statistically significant. The implications of these changes in extreme temperature and precipitation for key socio-economic sectors are explored, and reveal progressively severe impacts, and consequent adaptation challenges for Botswana as the global climate warms from its present temperature of 1.0 °C above preindustrial levels to 1.5 °C, and then 2.0 °C.

This study presents an analysis of daily temperature and precipitation extremes with return periods ranging from 2 to 50 years in phase 6 of the Coupled Model Intercomparison Project (CMIP6) multimodel ensemble of simulations. Judged by similarity with reanalyses, the new-generation models simulate the present-day temperature and precipitation extremes reasonably well. In line with previous CMIP simulations, the new simulations continue to project a large-scale picture of more frequent and more intense hot temperature extremes and precipitation extremes and vanishing cold extremes under continued global warming. Changes in temperature extremes outpace changes in global annual mean surface air temperature (GSAT) over most landmasses, while changes in precipitation extremes follow changes in GSAT globally at roughly the Clausius–Clapeyron rate of ~7% °C−1. Changes in temperature and precipitation extremes normalized with respect to GSAT do not depend strongly on the choice of forcing scenario or model climate sensitivity, and do not vary strongly over time, but with notable regional variations. Over the majority of land regions, the projected intensity increases and relative frequency increases tend to be larger for more extreme hot temperature and precipitation events than for weaker events. To obtain robust estimates of these changes at local scales, large initial-condition ensemble simulations are needed. Appropriate spatial pooling of data from neighboring grid cells within individual simulations can, to some extent, reduce the needed ensemble size.

This study evaluated the effect of extreme temperatures on events requiring emergency room visits (ERVs) for hypertensive disease, ischemic heart disease (IHD), cerebrovascular disease, and chronic kidney disease (CKD) for population stratified by sex and age living in Taiwan's metropolitan city from 2000 to 2014. The distributed lag non-linear model was adopted to examine the association between ambient temperature and area-age-sex-disease-specific ERVs for a population aged 40 years and above. The reference temperature was defined by a percentile value to describe the temperature in each city. Area-age-sex-disease-specific relative risk (RR) and 95% confidence intervals (CI) were estimated in association with extreme high (99th percentile) and low (5th percentile) temperatures. Temperature-related ERV risks varied by area, age, sex, and disease. Patients with CKD tend to have comorbidities with hypertensive disease. All study populations with hypertensive disease have significant risk associations with extreme low temperatures with the highest RR of 2.64 (95% CI: 2.08, 3.36) appearing in New Taipei City. The risk of IHD was significantly associated with extreme high temperature for male subpopulation aged 40-64 years. A less significant association was observed between the risks of cerebrovascular disease with extreme temperature. The risk of CKD was most significantly associated with extreme high temperature especially for a subpopulation aged 40-64 years. All study subpopulations with hypertensive disease have significant risk associations with extreme low temperature. Male subpopulations were more vulnerable to extreme temperatures, especially for those aged 40-64 years.

Background: This study evaluated the effect of extreme temperatures on events requiring emergency room visits (ERVs) for hypertensive disease, ischemic heart disease (IHD), cerebrovascular disease, and chronic kidney disease (CKD) for population stratified by sex and age living in Taiwan’s metropolitan city from 2000 to 2014. Methods: The distributed lag nonlinear model was adopted to examine the association between ambient temperature and area–age–sex–disease-specific ERVs for a population aged 40 years and above. The reference temperature was defined by a percentile value to describe the temperature in each city. Area–age–sex–disease-specific relative risk (RR) and 95% confidence intervals (CI) were estimated in association with extreme high (99th percentile) and low (5th percentile) temperatures. Results: Temperature-related ERV risks varied by area, age, sex, and disease. Patients with CKD tend to have comorbidities with hypertensive disease. All study populations with hypertensive disease have significant risk associations with extreme low temperatures with the highest RR of 2.64 (95% CI: 2.08, 3.36) appearing in New Taipei City. The risk of IHD was significantly associated with extreme high temperature for male subpopulation aged 40–64 years. A less significant association was observed between the risks of cerebrovascular disease with extreme temperature. The risk of CKD was most significantly associated with extreme high temperature especially for a subpopulation aged 40–64 years. Conclusions: All study subpopulations with hypertensive disease have significant risk associations with extreme low temperature. Male subpopulations were more vulnerable to extreme temperatures, especially for those aged 40–64 years. Funding Statement: This study was supported by grants from the Taiwan Ministry of Science and Technology (MOST 108-2625-M-033-002- and MOST 106-2221-E-033-006-MY2), National Health Research Institutes (NHRI-107A1-EMCO-3617191, NHRI-106A1-PDCO-3617191 and MOHW105-TDU-M-212-113003), and Taiwan CWB (MP10501-0125), the Ministry of Health and Welfare (MOHW109-TDU-B-212-114004). Declaration of Interests: The authors declare that they have no competing interests. Ethics Approval Statement: All methods were carried out in accordance with relevant guidelines and all protocols were approved by Taiwan Research Ethics Committee, National Health Research Institutes (code: EC1061209-F-E). All the health data were extracted from National Health Insurance database that provided to scientist in Taiwan for research purpose.

This study develops a generalized extreme value (GEV) distribution analysis approach, namely, a GEV tree approach that allows for both stationary and nonstationary cases. This approach is applied to a century‐long homogenized daily temperature data set for Australia to assess changes in temperature extremes from 1910 to 2010. Changes in 20 year return values are estimated from the most suitable GEV distribution chosen from a GEV tree. Twenty year return values of extreme low minimum temperature are found to have warmed strongly over the century in most parts of the continent. There is also a tendency toward warming of extreme high maximum temperatures, but it is weaker than that for minimum temperatures, with the majority of stations not showing significant trends. The observed changes in extreme temperatures are broadly consistent with observed changes in mean temperatures and in the frequency of temperatures above the ninetieth and below the tenth percentile (i.e., extreme indices). The GEV tree analysis provides insight into behavior of extremes with re‐occurrence times of several years to decades that are of importance to engineering design/applications, while extreme indices represent moderately extreme events with re‐occurrence times of a year or shorter.

BackgroundThe effects of extreme temperature on infectious diseases are complex and far-reaching. There are few studies to access the relationship of pulmonary tuberculosis (PTB) with extreme temperature. The study aimed to identify whether there was association between extreme temperature and the reported morbidity of PTB in Shandong Province, China, from 2005 to 2016.MethodsA generalized additive model (GAM) was firstly conducted to evaluate the relationship between daily reported incidence rate of PTB and extreme temperature events in the prefecture-level cities. Then, the effect estimates were pooled using meta-analysis at the provincial level. The fixed-effect model or random-effect model was selected based on the result of heterogeneity test.ResultsAmong the 446,016 PTB reported cases, the majority of reported cases occurred in spring. The higher reported incidence rate areas were located in Liaocheng, Taian, Linyi and Heze. Extreme low temperature had an impact on the reported incidence of PTB in only one prefecture-level city, i.e., Binzhou (RR = 0.903, 95% CI: 0.817–0.999). While, extreme high temperature was found to have a positive effect on reported morbidity of PTB in Binzhou (RR = 0.924, 95% CI: 0.856–0.997) and Weihai (RR = 0.910, 95% CI: 0.843–0.982). Meta-analysis showed that extreme high temperature was associated with a decreased risk of PTB (RR = 0.982, 95% CI: 0.966–0.998). However, extreme low temperature was no relationship with the reported incidence of PTB.ConclusionOur findings are suggested that extreme high temperature has significantly decreased the risk of PTB at the provincial levels. The findings have implications for developing strategies to response to climate change.

This study investigated the drivers and physical processes for the abrupt decadal summer surface warming and increases in hot temperature extremes that occurred over Northeast Asia in the mid-1990s. Observations indicate an abrupt increase in summer mean surface air temperature (SAT) over Northeast Asia since the mid-1990s. Accompanying this abrupt surface warming, significant changes in some temperature extremes, characterized by increases in summer mean daily maximum temperature (Tmax), daily minimum temperature (Tmin), annual hottest day temperature (TXx), and annual warmest night temperature (TNx) were observed. There were also increases in the frequency of summer days (SU) and tropical nights (TR). Atmospheric general circulation model experiments forced by changes in sea surface temperature (SST)/sea ice extent (SIE), anthropogenic greenhouse gas (GHG) concentrations, and anthropogenic aerosol (AA) forcing, relative to the period 1964–93, reproduced the general patterns of observed summer mean SAT changes and associated changes in temperature extremes, although the abrupt decrease in precipitation since the mid-1990s was not simulated. Additional model experiments with different forcings indicated that changes in SST/SIE explained 76% of the area-averaged summer mean surface warming signal over Northeast Asia, while the direct impact of changes in GHG and AA explained the remaining 24% of the surface warming signal. Analysis of physical processes indicated that the direct impact of the changes in AA (through aerosol–radiation and aerosol–cloud interactions), mainly related to the reduction of AA precursor emissions over Europe, played a dominant role in the increase in TXx and a similarly important role as SST/SIE changes in the increase in the frequency of SU over Northeast Asia via AA-induced coupled atmosphere–land surface and cloud feedbacks, rather than through a direct impact of AA changes on cloud condensation nuclei. The modelling results also imply that the abrupt summer surface warming and increases in hot temperature extremes over Northeast Asia since the mid-1990s will probably sustain in the next few decades as GHG concentrations continue to increase and AA precursor emissions over both North America and Europe continue to decrease.

A workshop was held at the University of the West Indies, Jamaica, in May 2012 to build capacity in climate data rescue and to enhance knowledge about climate change in the Caribbean region. Scientists brought their daily observational surface temperature and precipitation data from weather stations for an assessment of quality and homogeneity and for the calculation of climate indices helpful for studying climate change in their region. This study presents the trends in daily and extreme temperature and precipitation indices in the Caribbean region for records spanning the 1961–2010 and 1986–2010 intervals. Overall, the results show a warming of the surface air temperature at land stations. In general, the indices based on minimum temperature show stronger warming trends than indices calculated from maximum temperature. The frequency of warm days, warm nights and extreme high temperatures has increased while fewer cool days, cool nights and extreme low temperatures were found for both periods. Changes in precipitation indices are less consistent and the trends are generally weak. Small positive trends were found in annual total precipitation, daily intensity, maximum number of consecutive dry days and heavy rainfall events particularly during the period 1986–2010. Correlations between indices and the Atlantic multidecadal oscillation (AMO) index suggest that temperature variability and, to a lesser extent, precipitation extremes are related to the AMO signal of the North Atlantic surface sea temperatures: stronger associations are found in August and September for the temperature indices and in June and October for some of the precipitation indices.

Land use and land cover changes (LUCC) are thought to be amongst the most important impacts exerted by humans on climate. However, relatively little research has been carried out so far on the effects of LUCC on extreme climate change other than on regional temperatures and precipitation. In this paper, we apply a regional weather research and forecasting (WRF) climate model using LUCC data from Heilongjiang Province, that was collected between the 1900s and 2010s, to explore how changes in forest cover influence extreme temperature indexes. Our selection of extreme high, low, and daily temperature indexes for analysis in this study enables the calculation of a five-year numerical integration trail with changing forest space. Results indicate that the total forested area of Heilongjiang Province decreased by 28% between the 1900s and 2010s. This decrease is most marked in the western, southwestern, and northeastern parts of the province. Our results also reveal a remarkable correlation between change in forested area and extreme high and low temperature indexes. Further analysis enabled us to determine that the key factor explaining increases in extreme high temperature indexes (i.e., calculated using the number of warm days, warm nights, as well as tropical nights, and summer days) is decreasing forest area; data also showed that this factor caused a decrease in extreme low temperature indexes (i.e., calculated using the number of cold days and cold nights, as well as frost days, and ice days) and an increase in the maximum value of daily minimum temperature. Spatial data demonstrated that there is a significant correlation between forest-to-farmland conversion and extreme temperature indexes throughout most of our study period. Spatial data demonstrated that there is a significant correlation between forest-to-farmland conversion and extreme temperature indexes throughout most of our study period. Positive correlations are also present between decreasing forest area, the more frequent occurrence of extreme high temperature events, and a rise in the maximum value of daily minimum temperature. At the same time, we found clear negative correlations between decreasing forest area and less frequent occurrence of extreme low temperature events.

In 2012, the World Bank released a report and stated that a 4 °C increase in global temperature relative to the pre‐industrial levels would be devastating. However, there is a lack of scientific literature on the changes in climate associated with a 4 °C temperature increase. Based on the daily temperature and precipitation data from the Coupled Model Intercomparison Project Phase 5 (CMIP5), we project the global terrestrial changes in the extreme values of temperature and precipitation in association with a warming of 4 °C above the pre‐industrial levels using the Representative Concentration Pathways 8.5 (RCP8.5) scenario. The results indicated that cold extremes will decrease and warm extremes will increase. More remarkable signals in temperature extremes based on daily minimum temperature are expected, and local changes in temperature extremes generally extend beyond the natural internal variability. Significant signals in the frequency of temperature extremes appear in low latitudes, while the temperature changes of the coldest and hottest days are great in northern high latitudes. Large signal‐to‐noise ratios for warm extremes mainly occur in low latitudes, while those of cold extremes are not uniformly distributed. More intense and frequent heavy precipitation is also projected. Signals in high‐precipitation extremes are prominent in high latitudes and usually exceed the local natural internal variability. Changes in extreme dry events are high in the extratropics and generally lie within the background noise. Relative to the 1.5 °C global warming scenario, there would be more violent temperature and precipitation extremes with the 4 °C warming level, and these extremes would pose more threats to natural and human systems. Additionally, small inter‐model uncertainty occurs in the regions with remarkable signals for high precipitation‐related extremes and cold days and nights, while the opposite trend is seen for extreme dry events and other temperature extremes.

Changes in temperature and precipitation extremes are examined in transient climate change simulations performed with the second-generation coupled global climate model of the Canadian Centre for Climate Modelling and Analysis. Three-member ensembles were produced for the time period 1990–2100 using the IS92a, A2, and B2 emission scenarios of the Intergovernmental Panel on Climate Change. The return values of annual extremes are estimated from a fitted generalized extreme value distribution with time-dependent location and scale parameters by the method of maximum likelihood. The L-moment return value estimates are revisited and found to be somewhat biased in the context of transient climate change simulations.The climate response is of similar magnitude in the integrations with the IS92a and A2 emission scenarios but more modest for the B2 scenario. Changes in temperature extremes are largely associated with changes in the location of the distribution of annual extremes without substantial changes in its shape over most of the globe. Exceptions are regions where land and ocean surface properties change drastically, such as the regions that experience sea ice and snow cover retreat. Globally averaged changes in warm extremes are comparable to the corresponding changes in annual mean daily maximum temperature, while globally averaged cold extremes warm up faster than annual mean daily minimum temperature. There are considerable regional differences between the magnitudes of changes in temperature extremes and the corresponding annual means. Changes in precipitation extremes are due to changes in both the location and scale of the extreme value distribution and exceed substantially the corresponding changes in the annual mean precipitation. Generally speaking, the warmer model climate becomes wetter and hydrologically more variable. The probability of precipitation events that are considered extreme at the beginning of the simulations is increased by a factor of about 2 by the end of the twenty-first century.