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An analysis of climate extremes indices was conducted for maximum temperatures (TMax), minimum temperatures (TMin), and daily rainfall data (PRCP) over the northeast region of Brazil for the period of 1961–2014. The indices were calculated for 96 weather stations using RClimDex software in a sub-regional study based on cluster analysis, as well as for each individual weather station, after a rigorous process of quality control, gap filling of missing values, and homogenization. The Mann-Kendall non-parametric trend test was employed to assess the statistical significance of the series. The results indicate unequivocal signs of heating. In the past decades, there were predominant trends of decrease in the percentage of cold nights (−8.4% days/decade), increase in the percentage of the number of warm nights (10.6% days/decade), increase in the number of days per decade in which the minimum temperature exceeded the threshold of 20 °C, and a trend showing an increase in heat waves. This was corroborated by the declining trend in the number of consecutive days in which TMin did not exceed the 10th percentile, and the increase in the number of consecutive days in which TMax exceeded the 90th percentile in the data distribution. With regard to PRCP, in most weather stations where there is a significant increase in consecutive dry days, there is also a trend of significant increase in consecutive wet days, thereby intensifying the seasonality, with the dry seasons becoming drier and the rainy seasons wetter. In most weather stations, a reduction was found in the total annual precipitation and in the frequency of rainy days. Moreover, in many stations, an increase in rainfall events that surpasses the threshold of 95% and 99% in the distribution was discovered. These factors highlight concerns of a region of Brazil marked by the scarcity of perennial water resources and the threat of desertification.

Temperature extreme indices were analyzed for five continental regions of southern South America defined according to their climatic characteristics. Gridded observations, reanalysis, and global-coupled climate models from CMIP5 were used with the approach of temperature extreme trend attribution analysis on fixed-threshold and percentile-based temperature extremes indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI). The largest positive trends are exhibited in the tropical nights index, and a clear anthropogenic signal is evident in the subtropical region. In the subtropical central Andes, there is a decrease in the frost days index and increases in the tropical nights and summer days indices, and an anthropogenic signal is evident. In the Patagonian region, all trends from the historical runs were significant, while the ones from the natural experiment were nonsignificant, showing the marked effect of anthropogenic forcing in this region in the extreme temperature events. Projected changes in extreme indices for the 21st century are consistent with a warming climate, and larger changes are expected in the warm nights index., (© 2021 New York Academy of Sciences.)

Unlike sporadic daytime heat spikes, a consistent increase in night-time temperatures can potentially derail the genetic gains being achieved. Ten winter wheat genotypes were exposed to six different night-time temperatures (15-27°C) during flowering and grain-filling stages in controlled environment chambers. We identified the night-time temperature of 23 o C as the critical threshold beyond which a consistent decline in yields and quality was observed. Confocal laser scanning micrographs of central endosperm, bran, and germ tissue displayed differential accumulation of protein, lipid, and starch with increasing night-time temperatures. KS07077M-1 recorded a decrease in starch and an increase in protein and lipid in central endosperm with increasing night-time temperatures, whereas the same was significantly lower in the tolerant SY Monument. Expression analysis of genes encoding 21 enzymes (including isoforms) involved in grain-starch metabolism in developing grains revealed a high night-time temperature (HNT)-induced reduction in transcript levels of adenosine diphosphate glucose pyrophosphorylase small subunit involved in starch synthesis and a ≥2-fold increase in starch degrading enzymes isoamylase III, alpha-, and beta-amylase. The identified critical threshold, grain compositional changes, and the key enzymes in grain starch metabolism that lead to poor starch accumulation in grains establish the foundational knowledge for enhancing HNT tolerance in wheat., (© 2019 John Wiley & Sons Ltd.)

In this study, the daily maximum and minimum temperatures measured over the period 1961–2020 by 18 stations located near the Mediterranean coast are analysed to evaluate temperature trends and to compute 10 climate indices, selected among those proposed by the Expert Team on Climate Change Detection and Indices to monitor the occurrence of climate extremes of temperature. The trend analysis is performed using the Seasonal‐Kendall test. The results show statistically significant positive trends in both above‐mentioned variables throughout the Mediterranean, although the rate of warming is more marked in the minimum than in the maximum temperature and is more evident in the western portion of the Mediterranean Basin from 1990 onward. The climate extremes indices are evaluated assuming two different base periods (1961–1990 and 1991–2020). The shift forward of the base period involves a general rise in the percentile‐based thresholds used for identifying temperature extremes, as a natural consequence of the current atmospheric warming, resulting in fewer warm events and more cold events since the 1990s using a warmer/later base period. The application of the Seasonal‐Kendall test to the occurrence of extreme warm and cold events reveals that the western portion of the Mediterranean and the Adriatic Sea are the subregions most influenced by the base period update. This study demonstrates that the selection of the base period for the identification of extreme temperature events significantly impacts the results, and that the choice of a recent base period partially masks the ongoing atmospheric warming. The results suggest that the base period 1961–1990 might be more appropriate for climatological studies, as it provides a solid and stable baseline, and that attention must be paid when scientific results are shared with stakeholders, so as not to alter the communication of warnings and specific risks for the population. [ABSTRACT FROM AUTHOR]

In this work, the climatology, observed trends, and future projections of temperature and precipitation extremes are analysed in the drainage area of the Belo Monte Hydropower Plant in the Xingu River basin. Observed data come from gridded information for the period 1980–2013. The climate projections until the end of the 21st century are provided by the regional climate model Eta‐20 km nested to the global climate model MIROC5. Seventeen climate indicators were selected for this assessment, and statistical tests were used to evaluate the significance and magnitude of trends. A tropical climate predominates in the whole basin but with differences in the climatology of extreme temperature. The average annual rainfall (PRCPTOT) presents values between 1,500 and 2,200 mm. Remarkable contrasts of consecutive dry days (CDD) can also be seen. We found a warming signal during the examined period in much of the Xingu River basin, with an increase in the frequency of extremely warm days and nights. In the northern (south‐central) area of the basin, there is an increase (reduction) in precipitation. There is a contrasted and local distribution of detected trends in all climate extremes indices related to rainfall. CDD has displayed a considerable elevation in the south‐central area over the last decades. The study area exhibits statistically significant warming projections to the future climate. As for the precipitation projections, future changes are toward a dryer climate. We also found that dry periods may last longer in the following decades. Thus, heatwaves can be excited by subsequent days without precipitation in the basin in the future climate. The impacts of climate change on the balance of different environmental and socioeconomic sectors in this area must be wholly investigated. [ABSTRACT FROM AUTHOR]

A broad spectrum of model-derived weather datasets are available in the US. Because each product integrates atmospheric conditions with different model processes, each produces different statistical biases. This study validated air temperature from NLDAS-2 and a novel statistically downscaled NLDAS-2 against observational weather station data for the state of Florida. We statistically downscaled NLDAS-2 to a 1-km grid product using MODIS land surface temperature. We investigated mean biases and Pearson correlation coefficients between daily observational data and the two model-derived datasets. We then calculated multiple Climate Extremes Indices to further scrutinize differences in capturing extreme temperatures. Finally, we quantified potential causes of systematic NLDAS-2 biases related to distance from the coast, urban heat island, land cover, and type of observational stations. Two model-derived datasets showed similar mean biases and correspondence with observational data, underestimating maximum temperature by 1°C and overestimating minimum temperature by 2°C. Extreme temperatures were well simulated in both datasets. However, we still found overestimated extreme minimum temperatures and underestimated extreme maximum temperatures. Systematic biases tended to be higher for coastal stations and grids having a high fraction of water. Our study suggests that including physical processes covering land surface and ocean interactions may improve the model accuracy. [ABSTRACT FROM AUTHOR]

The occurrence of extreme climate events (ECEs) in the Amazon basin (AMZ) and northeast Brazil (NEB), such as torrential rains and severe droughts, varies in both spatial and temporal scales. Spatial analysis of trends allows observing changes in behaviour and determining in which regions a particular variable has been experiencing changes over time. Thus, the objective of this study is to analyse trends of 21 climate extremes indices, relative to maximum and minimum precipitation and temperature, as defined by the World Meteorological Organization, for the AMZ and NEB. The 21 indices were selected according to meteorological and climate characteristics of the regions. Through annual analysis it was possible to observe an increase in most of the climate extremes indices for air temperature in all AMZ and NEB subregions. As for extreme precipitation, only a few of the selected indices presented significant increase and/or decrease in their values. Overall, the eastern Amazon subregion in the AMZ presented the highest significant indices for temperature and precipitation. In NEB, both Northern Coast and Southern Coast subregions presented substantial increase or decrease in precipitation and temperature indices. Location of the reference points selected for each of the 11 homogeneous precipitation subregions in the AMZ and the NEB. [ABSTRACT FROM AUTHOR]

Earth system models (ESMs) are essential for understanding the interaction between human activities and the Earth's climate. However, the computational demands of ESMs often limit the number of simulations that can be run, hindering the robust analysis of risks associated with extreme weather events. While low‐cost climate emulators have emerged as an alternative to emulate ESMs and enable rapid analysis of future climate, many of these emulators only provide output on at most a monthly frequency. This temporal resolution is insufficient for analyzing events that require daily characterization, such as heat waves or heavy precipitation. We propose using diffusion models, a class of generative deep learning models, to effectively downscale ESM output from a monthly to a daily frequency. Trained on a handful of ESM realizations, reflecting a wide range of radiative forcings, our DiffESM model takes monthly mean precipitation or temperature as input, and is capable of producing daily values with statistical characteristics close to ESM output. Combined with a low‐cost emulator providing monthly means, this approach requires only a small fraction of the computational resources needed to run a large ensemble. We evaluate model behavior using a number of extreme metrics, showing that DiffESM closely matches the spatio‐temporal behavior of the ESM output it emulates in terms of the frequency and spatial characteristics of phenomena such as heat waves, dry spells, or rainfall intensity. Plain Language Summary: Ideally, to study how damaging phenomena like heatwaves, droughts and downpours will change in the future under global warming, we would want a large number of climate model runs producing many realizations of climate futures that we can analyze and from which the new characteristics of climate extremes can be quantified. Currently, emulators can rapidly generate simulations of future climate, but often to relatively low frequencies, as decadal, annual or monthly output at best in most cases, which is insufficient for studying extreme events that occur on a daily timescale. We show how it is possible to train a machine learning model to produce daily series of temperature or precipitation from monthly averages, thus facilitating a more robust investigation into how extreme events will change in the future. Key Points: Earth system models (ESMs) are key devices for understanding how human actions will affect the future global climateComputational demands prevent us from running them for more than a handful of scenarios. ESM emulators are most commonly limited to the monthly frequencyWe present DiffESM as a data‐driven emulator of ESMs that closely matches the spatiotemporal distributions of ESMs at daily frequency [ABSTRACT FROM AUTHOR]

BACKGROUND: Weather variability is associated with enteric infections in people through a complex interaction of human, animal, and environmental factors. Although Campylobacter infections have been previously associated with precipitation and temperature, the association between precipitation and drought on campylobacteriosis has not been studied. OBJECTIVE: Using data from Arizona, Colorado, New Mexico, and counties in Utah, this ecological study aimed to assess the association between precipitation and the incidence of campylobacteriosis by county from 2009 to 2021 and to determine how this association is modified by prior drought level and animal operations. METHODS: We merged 38,782 cases of campylobacteriosis reported in 127 counties with total precipitation (in inches), temperature (in average degrees Fahrenheit), Palmer Drought Severity Index (PDSI, category), and animal census data (presence, density per square mile) by week from 2009 to 2021. Negative binomial generalized estimating equations adjusted for temperature with a 3-wk lag were used to explore the association between precipitation on campylobacteriosis with resulting incidence rate ratios (IRRs). Stratified analyses explored the association with precipitation following antecedent drought, presence of farm operations, and animal density. RESULTS: A 1-in (25.4 mm) increase in precipitation was associated with a 3% increase in campylobacteriosis reported 3 wks later (IRR=1.03; 95% CI: 1.02, 1.04) after adjusting for average temperature and PDSI. Compared with normal conditions, there were significantly more cases when precipitation followed antecedent extremely wet (IRR=1.15; 95% CI: 1.04, 1.26), very wet (IRR=1.09; 95% CI: 1.01, 1.18), moderately wet (IRR=1.06; 95% CI: 1.01, 1.12), moderate drought (IRR=1.11; 95% CI: 1.07, 1.16), and severe drought (IRR=1.06; 95% CI: 1.02, 1.11) conditions, whereas there were significantly fewer cases (IRR=0.89; 95% CI: 0.85, 0.94) for antecedent extreme drought. Compared to counties with no animal operations, counties with animal operations had significantly more cases following precipitation for every PDSI category except extreme drought. Counties with a higher density of beef cattle, goats for meat, chicken broilers, and chicken layers had significantly higher rates of campylobacteriosis following precipitation than those with no such operations, whereas those with dairy cattle and goats for milk, did not. DISCUSSION: In this majority arid and semiarid environment, precipitation following prior wet conditions and moderate and severe drought were significantly associated with increased rates of campylobacteriosis, and only in prior extreme drought did rates decrease. Where the precipitation fell made a difference; after precipitation, counties with farm operations had significantly more cases compared to counties without farm operations. Further work should assess individual-level risk factors within environmental exposure pathways for Campylobacter. [ABSTRACT FROM AUTHOR]

The responses of Ulva species to diurnal temperature difference remain poorly understood. In this present study, we cultured Ulva prolifera under different diurnal temperature treatments with 22°C for photoperiod and 22, 20, 18, 16, 14, and12°C for dark period, respectively (noted as 22-22, 22-20, 22-18, 22-16, 22-14, and 22-12°C treatments). The growth, pigment contents, photosynthesis, superoxide dismutase (SOD) activity, soluble proteins, and sugars were measured. The main results were shown as follows: (1) The growth of U. prolifera was enhanced by the moderate diurnal temperature difference, and the highest growth rate was observed at 22-18°C. (2) Compared with 22-22°C treatment, the thalli grown under 22-18°C condition showed lower chlorophyll a (Chla) content, respiration rate (Rd), the ratio of Rd, and gross photosynthetic rate (Rd/Pg) as well as the net photosynthetic rate (Pn), while the lowest Pn was observed at 22-12°C. (3) The maximum quantum yield (Fv/Fm) was enhanced by diurnal temperature difference, while the effective quantum yield (Fv′/Fm′) decreased with the decreased in temperature in the nighttime. (4) With the increase of the diurnal temperature difference gradient, the SOD activity decreased and then increased, with the lowest value observed at 22-18 °C, and the soluble protein content showed similar trend. Then we cultured this species at 22-22°C and 22-16°C both under 250 and 60 μmol m−2s−1 conditions in order to study the combined effects of diurnal temperature change and light intensities. It was found that under both two light levels, 22-16°C-grown thalli showed higher growth rate, while the SOD activity was lower than that grown under 22-22°C condition. Overall, the suitable range of diurnal temperature difference for the growth of U. prolifera was about 4–6°C and also was mediated by light intensity. [ABSTRACT FROM AUTHOR]

Based on the daily average temperature, daily maximum temperature and daily minimum temperature from the 18 global climate models that participated in the phase 6 of the Coupled Model Intercomparison Project (CMIP6) and their precedent phase project (CMIP5), and the 84 observational stations during 1986-2005, the capabilities of CMIP6 on simulating the temperature over Guizhou by Portrait and Taylor chat were evaluated. The best simulation results were selected to project the characteristics of temperature in Guizhou under three scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) from 2023 to 2100. The results showed that the CMIP6-MME (multi-model ensemble) generally had better performance in simulating the indices including the numbers of forest days (FD), the length of the growing season (GSL), the numbers of summer days (SU), the lowest value of the minimum temperature (TNN) and the mean temperature (Tav), respectively during 1986-2005. Compared to the reference period (1995-2014), FD decreased significantly, but GSL, SU, TNN and Tav increased obviously from 2023 to 2100 under three scenarios. Furthermore, it was indicated that FD (GSL, SU, TNN and Tav) were less (more) than that in 1995-2014 during the 21st century under three scenarios over Guizhou, and the decrease (increase) was proportional to the emission scenario, with the amplitude of FD (GSL) reducing (growing) from south to north, SU rising from northeast to southwest, TNN/Tav going up from southwest to northeast. [ABSTRACT FROM AUTHOR]

A significant average difference between the observed and modeled precipitation in the warm months is registered in Odesa for 1970–2005. This difference is probably determined by complex orography and inappropriate parameterization methods for convective processes climate models. In the last 15 years (2006–2020), the average temperature has increased by about 1 °C in winter and by 2 °C in summer compared with 1970–2005. Considering decreasing precipitation during summer months, it seems that the climate of Odesa is moving towards the Mediterranean climate—warm to hot, dry summers and mild, moderately wet winters. The approach based on selecting representative simulations with minimum average bias and adjusting the choice to the present-day climate is described and applied for Odesa using data from the RCP8.5 scenario simulations of the EURO-CORDEX project and ERA5-Land reanalysis. The approach can be applied separately for monthly near-surface temperature and total precipitation, as well as jointly for these variables, and provides the satisfactory ability to select models for use in impact studies. The output variables of simulations selected are close to observed ones in recent years and are well to coincide with the ensemble-mean values in the near future, 2021–2050. On the other hand, the scatter of output variables in the selected simulations adequately describes the uncertainty of the future climate. [ABSTRACT FROM AUTHOR]

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Up-to-date regional and local assessments of changing climate extremes are important to allow countries to make informed decisions on mitigation and adaptation strategies, and to put these changes into a global context. A workshop for countries from the Indo-Pacific region has brought together daily observations from 13 countries for an analysis of climate extremes between 1971 and 2005. This paper makes use of the workshop outcomes and post-workshop analyses to build on previous work in Southeast Asia to update the assessment of changing climate extremes using newly available station data. We utilise a consistent and widely tested methodology to allow a direct comparison of the results with those from other parts of the world. The relationship of inter-annual variability in the climate extremes indices with sea surface temperature (SST) patterns has been investigated with a focus on the influence of the El Niño-Southern Oscillation phenomenon. The results support findings from elsewhere around the globe that warm extremes, particularly at night, are increasing and cold extremes are decreasing. Trends in precipitation extremes are less spatially consistent across the region. © Royal Meteorological Society and Crown Copyright 2010. [ABSTRACT FROM AUTHOR]

Concurrent compound dry and hot events (CDHEs) amplified more damange on the ecosystems and human society than individual extremes. Under climate change, compound dry and hot events become more frequent on a global scale. This paper proposes a mathematical method to quantitatively attribute changes of CDHEs to changes of precipitation, change in temperature and change in the dependence between precipitation and temperature. The attribution is achieved by formulating the total differential equation of the return period of CDHEs among Meta-gaussian model. A case study of China is devised based on monthly precipitation and temperature data during the period from 1921 to 2020 for 80 major river basins. It is found that temperature is the main driving factor of increases in CDHEs for 49 major river basins in China, except for the upper and middle reaches of the Yangtze River. In West China, precipitation changes drove the increase in CDHEs in 18 river basins (23%), particularly in parts of North Xinjiang, Qinghai and Gansu. On the other hand, dependence between precipitation and temperature dominated changes of CDHEs in 13 river basins (16%) of China with other factors, including parts of South China, East China and Northwestern China. Furthermore, changes in both the mean and spread of precipitation and temperature can also contribute to changes in CDHEs. [ABSTRACT FROM AUTHOR]

The Okavango River Basin (ORB), including the World Heritage site Okavango Delta, is a region of high biodiversity projected to suffer increased early summer drying under climate change. Little work has been done on drought over this sensitive region. Here, various drought metrics are analysed over the ORB. These include a cumulative drought intensity index, based on the product of maximum temperature anomaly and maximum duration of a dry spell, and the Standardized Precipitation‐Evapotranspiration Index. Strong gradients in dry spell and hot day frequencies shift south over the ORB from August to November as the tropical rain‐belt moves increasingly south of the equator, the Congo Air Boundary declines and the Botswana High strengthens and moves southwestwards. By December, the tropical gradient in dry spell frequencies has vanished while that across the Limpopo River and southern ORB region, where the Botswana High is centred, is prominent. sub‐seasonal analyses highlight October–November 2013–2021 as particularly dry and hot over the Okavango Delta region. This dry and hot epoch appears related to a stronger and southward shifted Botswana High and reduced low‐level moisture convergence. On interannual scales, strong relationships were found with the Botswana High and El Niño‐Southern Oscillation (ENSO). The early summer shows a strong drying‐warming trend, related to a significant strengthening of the Botswana High. These trends, together with the Coupled Model Intercomparison Project Phase 6 projected early summer drying over southern Africa, may impact severely on the sensitive ecosystems of the ORB and on agriculture, with important implications for the management of agricultural activities, water resources and biodiversity. [ABSTRACT FROM AUTHOR]

In the present study, the climate change indices of the Lacantún climate station, Mexico, and their influence on the agricultural field (milpa system) in two communities of the Lacandon Rainforest region were analyzed. The detection of changes and identification of temperature and precipitation trends in the study area was carried out by calculating the indices proposed by the expert team on climate change detection and indices through the RClimdex program. In addition, through interviews with the local population, the perception of climate changes and their impact on agriculture was known. The results indicate that, from 1980 to 2016, there was an increase of 1.5 °C in the maximum temperature, an increase of 0.5 °C in the minimum temperature, precipitation decreased by 500 mm, dry days increased, and wet days did not vary. According to the perception of the population, there is a delay and a decrease in rains, delaying sowings for up to 20-30 days, the dog days have extended 10 or 15 more days, there is a greater presence of pests and diseases (fall armyworm and bean rust); these changes have caused yield reductions of 20-25% in corn and 20-30% in beans. [ABSTRACT FROM AUTHOR]

Summary: Global nocturnal temperatures are rising more rapidly than daytime temperatures and have a large effect on crop productivity. In particular, stomatal conductance at night (gsn) is surprisingly poorly understood and has not been investigated despite constituting a significant proportion of overall canopy water loss.Here, we present the results of 3 yr of field data using 12 spring Triticum aestivum genotypes which were grown in NW Mexico and subjected to an artificial increase in night‐time temperatures of 2°C.Under nocturnal heating, grain yields decreased (1.9% per 1°C) without significant changes in daytime leaf‐level physiological responses. Under warmer nights, there were significant differences in the magnitude and decrease in gsn, values of which were between 9 and 33% of daytime rates while respiration appeared to acclimate to higher temperatures. Decreases in grain yield were genotype‐specific; genotypes categorised as heat tolerant demonstrated some of the greatest declines in yield in response to warmer nights.We conclude the essential components of nocturnal heat tolerance in wheat are uncoupled from resilience to daytime temperatures, raising fundamental questions for physiological breeding. Furthermore, this study discusses key physiological traits such as pollen viability, root depth and irrigation type may also play a role in genotype‐specific nocturnal heat tolerance. [ABSTRACT FROM AUTHOR]

Global warming has been accelerating the frequency and intensity of climate extremes, and has had an immense influence on the economy and society, but attention is seldom paid to future Antarctic temperature extremes. This study investigates five surface extreme temperature indices derived from the multimodel ensemble mean (MMEM) based on 14 models from phase 6 of the Coupled Model Intercomparison Project (CMIP6) under the shared socioeconomic pathways (SSPs) of SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. In Antarctica, the variations in extreme temperature indices exhibit regional and seasonal differences. The diurnal temperature range (DTR) usually illustrates a downward trend, particularly for the Antarctic Peninsula and Antarctic coast, and the strongest change occurs in austral summer. In all cases, the annual highest minimum/maximum temperature (TNx/TXx) increases faster in inland Antarctica. Antarctic amplification of extreme temperature indices is detected and is strongest at the lowest maximum temperature (TXn). At the Antarctic Peninsula, TXx amplification only appears in winter. Great DTR amplification appears along the Antarctic coast and is strongest in summer and weakest in winter. The changes in extreme temperature indices indicate the accelerated Antarctic warming in future scenarios. [ABSTRACT FROM AUTHOR]

According to data obtained from meteorological towers, Brazil has significantly increased temperature in the past 20 years, particularly in the North and Midwest regions. Vapor pressure deficit and evapotranspiration were also analyzed, showing an increase across the entire country, confirming that the air is becoming drier. This warming trend is part of the global climate change phenomenon caused by the rise of greenhouse gases in the atmosphere, fires, poor soil management practices, deforestation, and logging. The increase in temperature and dryness has profoundly impacted Brazil's climate and ecosystems, leading to intensified extreme weather events and changes in the distribution of both animal and plant species. This study highlights the importance of utilizing meteorological tower data to monitor and understand the effects of climate change in Brazil. It emphasizes the need for immediate action to address its causes and mitigate its negative impacts. [ABSTRACT FROM AUTHOR]

In this study, we attempt to quantify the potential impacts of two global warming levels (i.e., 1.5 °C and 2.0 °C) on extreme temperature indices across China. The CMIP6 dataset is first evaluated against the CN05.1 observation for the historical period of 1995–2014. Then, future spatiotemporal patterns of changes in extreme temperature at two global warming levels under two shared socio-economic pathway scenarios (SSP245 and SSP585) are further analyzed. Overall, China will experience more frequent and intense high temperature events, such as summer days (SU), tropical nights (TR), warm days (TX90p) and nights (TN90p). On the other hand, under the SSP585, the number of icing days and frost days is projected to decrease at two global warming levels, with the maximal days of decrease (exceeding 20 days) seen in the west of China. Our results suggest that limiting global warming to 1.5 °C rather than 2.0 °C is beneficial to reduce extreme temperature risks. As temperature increases to 1.5 °C and then 2.0 °C above preindustrial levels, the most extreme temperature indices are expected to increase proportionately more during the final 0.5° than during the first 1.5° across most regions of China. For some warm indices, such as the warmest day (TXx), summer days (SU), and warm days (TX90p), the largest incremental changes (from 1.5° to 2.0°) tend to be found in the southwest. Under the SSP585, the incremental changes are similar to the change in the SSP245, but smaller magnitude and spatial extent. [ABSTRACT FROM AUTHOR]

Current climate models project that Antarctic sea ice will decrease by the end of the twenty-first century. Previous studies have suggested that Antarctic sea ice changes have impacts on atmospheric circulation and the mean state of the Southern Hemisphere. However, little is known about whether Antarctic sea ice loss may have a tangible impact on climate extremes over the southern continents and whether ocean–atmosphere coupling plays an important role in changes of climate extremes over the southern continents. In this study, we conduct a set of fully coupled and atmosphere-only model experiments forced by present and future Antarctic sea ice cover. It is found that the projected Antarctic sea ice loss by the end of the twenty-first century leads to an increase in the frequency and duration of warm extremes (especially warm nights) over the southern continents and a decrease in cold extremes over most regions. The frequency and duration of wet extremes are projected to increase over South America and Antarctica, whereas changes in dry days and the longest dry spell vary with regions. Further Antarctic sea ice loss under a quadrupling of CO2 leads to similar but larger changes. Comparison between the coupled and atmosphere-only model experiments suggests that ocean dynamics and their interactions with the atmosphere induced by Antarctic sea ice loss play a key role in driving the identified changes in temperature and precipitation extremes over southern continents. By comparing with global warming experiments, we find that Antarctic sea ice loss may affect temperature and precipitation extremes for some regions under greenhouse warming, especially Antarctica. [ABSTRACT FROM AUTHOR]

Climate extremes pose threats to lives and properties. It is therefore critical to assess the dynamics of climate extremes in the future. Knowledge of the appropriate models to be used for projection helps reduces uncertainties in model projections. The study assesses the evolution of 8 extreme temperature indices over the Volta Basin for the 1985–2014 period. Consequently, the ability of 40 Coupled Model Intercomparison Project Phase 6 (CMIP6) models in reproducing these extremes is assessed. The biases and the M-scores of the annual spatial distribution of indices are determined. It is observed that the temperature extremes are frequent and profound in the Sahel, indicating that the Sahel is susceptible to extreme temperature changes. The warm indices showed positive trends, while the cold indices mainly showed negative trends. Most of the models had difficulty reproducing the climatologies of the indices. However, the CMCC-ESM2, MPI-ESM-1-2-HAM, MIROC6, and AWI-CM-1-1-MR reproduced the TNN, TNX, TXN, and TXX well while the MIROC6, MRI-ESM2-0, INM-CM4-8, and MIROC6 showed robust performance in reproducing the TN10P, TN90P, TX10P, and TX90P respectively. Poor performance of models may be due to poor simulation of sub-grid features such as vegetation cover, and clouds as a result of coarse grid sizes. Warming of the basin poses a significant impact on agriculture, drought occurrence, and seasonal to annual scale precipitation distribution. Southern sections of the basin may be vulnerable to temperature-related extremes as a result of deforestation and urbanization. [ABSTRACT FROM AUTHOR]

Changes in the frequency of temperature extremes are often attributed to global warming. The recent availability of near‐surface temperature data records from reference networks, such as the U.S. Climate Reference Network (USCRN), enables the quantification of measurement uncertainties. Within an activity of the Copernicus Climate Change Service, the estimation of the measurement uncertainty has been provided for USCRN temperature data, using metadata made available by the National Oceanic and Atmospheric Administration (NOAA). In this paper, four climate extreme indices (Frost Days, Summer Days, Ice Days, Tropical Nights) and the related uncertainties are calculated for the period 2006–2020 from the USCRN data set and compared with traditional indices. Moreover, the asymmetric USCRN measurement uncertainties are propagated to estimate the uncertainties of climate indices. The comparison shows expanded uncertainties homogeneously distributed with the latitude and typically within 15 days per year for Frost Days and within 10 days for Ice Days, while smaller uncertainties are estimated for Summer Days and Tropical Nights, with values typically within six to seven days per year. Positive uncertainties are typically larger than negative ones for all the indices. The values of Frost and Ice Days with the related uncertainties for USCRN have also been compared with the corresponding values calculated from reanalyses data, showing differences typically within 60 days for median values, quite often smaller than USCRN and inconsistent within the related uncertainties, Overall, the results show that USCRN measurement uncertainties increase confidence in the estimation of climate extreme indices and decisions for adaptation. Plain Language Summary: The relationship between the intensity and frequency of extremes and climate change as well as their attribution to human activities is fundamental for improving the assessment of risk and the elaboration of adaptation strategies. Temperature extremes are often reported and estimated using observations or model data using indices, which are widely adopted in the research community and by decision‐makers. However, the number of temperature extremes is quantified assuming input observations as perfect, whereas these are always affected by uncertainties due to instrumental noise and systematic effects that cannot be always properly accounted for. This also implies that climate extreme indices may under or over‐represent the number of temperature extremes. The advent of reference measurement networks, as well as the overall increase in observational data quality due to recent technological improvements, allows us to quantify measurement uncertainties in detail. In this paper, temperature extremes over the US are estimated from near‐surface temperature measurements provided by the USCRN network in the period 2006–2020 with related uncertainties. The use of uncertainty illustrates the range of values that climate extreme indices may assume. Possible sources of uncertainties and comparisons with data from atmospheric reanalysis are also discussed. Key Points: An extensive assessment of uncertainties for four climate extreme indices is provided using reference near‐surface temperaturesEstimate uncertainties of climate indices for reanalysis validation and quantification of extremes by propagating measurement uncertaintiesUSCRN traceable measurements with quantified uncertainties increase confidence in estimating extreme indices and decisions for adaptation [ABSTRACT FROM AUTHOR]

The 2021 IPCC report found that most studies show declining trends for the global diurnal temperature range (DTR) since the 1950s, decreasing mainly during 1960–1980. This issue is revisited here using an up‐to‐date in‐situ data set, Hadley Center Integrated Surface Database, constrained by rigorous station selection conditions. The global observed DTR trend was found to reverse during 1980–2021, increasing significantly at a rate of 0.091 ± 0.008°C decade−1. The trend was dominated by a faster rate of increasing daily maximum air temperature. This increasing observed trend in the past four decades was not fully captured in raw CMIP6 models, as models only partially capture the spatial patterns. With global CMIP6 outputs and regionally‐available observations, the global land DTR was then estimated, through emergent constraints, to be 0.063 ± 0.012°C decade−1. The study raises concern for risks of increasing DTR globally and provides new insights into global DTR assessment. Plain Language Summary: In 2021, the IPCC reported a decrease in the near‐surface diurnal air temperature range (DTR) since the 1950s. However, using the in‐situ surface air temperature observations, the global DTR trend was found to reverse after the 1980s, as daily maximum air temperature increased faster than the daily minimum air temperature did between 1980 and 2021. The observed results for 1980–2014 were used to assess the historical simulations within CMIP6. Models generally depicted similar spatial variability as observed results but high variation existed between models. Most of the models did not capture the reversal of the global DTR trend and underestimated regional results. To narrow down the uncertainty and produce a complete global land DTR estimation, we applied the emergent constraint approach by combining observation data and model results. The global DTR trend from 1980 to 2014 was 0.063 ± 0.012°C decade−1. The constraining data was also used at a regional scale. It was shown that DTR trends for North America retained high uncertainty (−0.011 ± 0.026°C decade−1), while Europe and Asia showed reduced uncertainty with increasing DTR. Key Points: The up‐to‐date Hadley Center Integrated Surface Database (HadISD) in‐situ data reveals a reversed global diurnal air temperature range (DTR) trend, increasing for 1980–2021The observed spatial patterns are partially captured but the reversal trends are not shown by CMIP6 modelsThe emergent constraint for CMIP6 models with HadISD estimates increasing DTR at the global scale [ABSTRACT FROM AUTHOR]

The primary metabolism and respiration of the hop strobilus has not been quantified in response to daily temperature fluctuations. The objective of this study was to assess strobilus gas exchange, specifically the response to temperature fluctuations. Hop strobilus were measured under controlled environment conditions to assess the organ's contribution to carbon assimilation and respiration during the maturation phase. Strobilus-specific daily carbon budgets were estimated in response to temperature fluctuation. The optimal temperature for net carbon gain occurred at 15.7 °C. Estimated strobilus carbon uptake decreased approximately 41% per 5 °C increase in temperature above 20 °C. Daily temperatures within 10–27 °C resulted in a net positive strobilus daily carbon balance, whereas temperature increases beyond 27 °C increasingly exhaust strobilus carbon reserves. The Q10 temperature coefficient (the rate respiration increases every 10 °C rise in temperature) approximately doubled per 10 °C rise in temperature from 7–40 °C (1.94–2) with slightly reduced values at lower temperatures. In conclusion, we show that photosynthetically active bracts maintain a positive strobilus carbon balance at moderate temperatures and as mean daily temperatures progressively exceed 27 °C, strobilus net carbon reserves are precipitously exhausted due to ever-increasing respiration rates. [ABSTRACT FROM AUTHOR]

The study of shifts in patterns of precipitation and temperatures is central to managing water resources and controlling damages yielded by natural hazards. Thus, this study investigated modifications in precipitation and temperature patterns using multifractal detrended fluctuation analysis and shuffling technique. The novelty of this study lies in exploring the influence of climate change on precipitation and temperatures from a novel nonlinear dynamic perspective. The temperatures and precipitation data regarding central England (1931–2019) were analyzed by multifractal theory. The results demonstrated that the sources of precipitation and temperature multifractal patterns altered owing to climate change on the daily and monthly scales. Concerning maximum temperature ( T max ), on a daily scale, multifractal patterns with probability density function (PDF) origin were intensified, whereas, on a monthly scale, multifractal patterns with long-range correlations (LRC) source were amplified. Regarding precipitation and minimum temperature ( T min ), on a daily scale, multifractal patterns with LRC origin were strengthened, whereas, on a monthly scale, multifractal patterns with PDF source were augmented. On a daily scale, multifractality of precipitation and T max increased by 85.66% and 87.37%, respectively, and regarding T min , decreased by 7.28%. The multifractal patterns of precipitation were dependent on the T min multifractal patterns. It was concluded that alterations in drought and flood patterns were consequences of variations in the multifractal source of T min patterns. The present study contributes to understanding the mechanism of climate change impacts on multifractal patterns of precipitation and temperatures and furnished references for multifractal studies of precipitation and temperatures. [ABSTRACT FROM AUTHOR]

Long-duration, sub-seasonal dry spells in combination with high temperature extremes during summer have led to extreme impacts on society and ecosystems in the past. Such events are expected to become more frequent due to increasing temperatures as a result of anthropogenic climate change. However, there is little information on how long-duration dry and hot spells are represented in global climate models (GCMs). In this study, we evaluate 33 CMIP5 (coupled model intercomparison project 5) GCMs in their representation of long-duration dry spells and temperatures during dry spells. We define a dry spell as a consecutive number of days with a daily precipitation of less than 1 mm. CMIP5 models tend to underestimate the persistence of dry spells in northern Europe, while a large variability exists between model estimates in central and southern Europe, where models have contrasting biases. Throughout Europe, we also find a large spread between models in their representation of temperature extremes during dry spells. In central and southern Europe this spread in temperature extremes between models is related to the representation of dry spells, where models that produce longer dry spells also produce higher temperatures, and vice versa. Our results indicate that this variability in model estimates is due to model differences and not internal variability. At latitudes between 50–60 ∘ N, the differences in the representation of persistent dry spells are strongly related to the representation of persistent anticyclonic systems, such as atmospheric blocking and subtropical ridges. Furthermore, models simulating a higher frequency of anticyclonic systems than ERA5 also simulate temperatures in dry spells that are between 1.4, and 2.8 K warmer than models with a lower frequency in these areas. Overall, there is a large spread between CMIP5 models in their representation of long-duration dry and hot events that is due to errors in the representation of large-scale anticyclonic systems in certain parts of Europe. This information is important to consider when interpreting the plausibility of future projections from climate models and highlights the potential value that improvements in the representation of anticyclonic systems may have for the simulation of impactful hazards. [ABSTRACT FROM AUTHOR]

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Extreme temperature events (ETEs) have evolved alongside the warming climate over most parts of the world. This study provides a statistical quantification of how human influences have changed the frequencies of extreme temperatures in New Zealand, depending on the synoptic weather types. We use the ensembles under pre‐industrial conditions (natural scenarios with no human‐induced changes) and present‐day conditions (anthropogenic scenarios) from the weather@home regional climate model. The ensemble simulations under these two scenarios are used to identify how human influences have impacted the frequency and intensity of extreme temperatures based on their connection to different large‐scale circulation patterns derived using self‐organizing maps (SOMs). Over New Zealand, an average two to three fold rise in frequencies of extremes occurs irrespective of seasons due to anthropogenic influence with a mean temperature increase close to 1°C. For some synoptic situations, the frequency of extremes are especially enhanced; in particular, for low‐pressure centres to the northeast of New Zealand where the frequency of occurrence of daily temperature extremes has increased by a factor of 7 between anthropogenic and natural ensembles for the winter season, though these synoptic patterns rarely occur. For low‐pressure centres to the northwest of New Zealand, we observe high temperatures frequently in both anthropogenic and natural ensembles which we expect is probably associated with warm air advection from the Tropics. The frequency of occurrence of high temperatures in these synoptic patterns has also increased by a factor of 2 between the natural and anthropogenic ensembles. For these synoptic states, the extremes are observed in the North Island and along the east coast of the country with the highest temperature along the Canterbury coast and Northland. The change between the natural and anthropogenic ensembles is largest on the west coast along the Southern Alps for all the synoptic circulation types. [ABSTRACT FROM AUTHOR]

Persistent hot and dry conditions could lead to serious impacts on society, economy, and human health. Using statistically downscaled and bias corrected data, we investigate the changes in compound long‐duration dry and hot (LDDH) events and the corresponding socioeconomic exposure over China in transient and stabilized warmer worlds. The transient response is identified with Coupled Model Intercomparison Project Phase 5 (CMIP5) and CMIP6 models, while the stabilized response is identified with the Community Earth System Model ensemble. Under 1.5°C and 2°C warming, the LDDH events in China will become more frequent and hotter. Substantial differences are found in LDDH features between transient and stabilized warming. For a given global temperature, the increase in frequency and temperature magnitude of LDDH events over most northern regions is significantly greater in a transient case than in a stabilized climate, while the increase over the southeastern China is substantially stronger under stabilized warming than transient warming. Future population exposure to LDDH events is projected to increase over China. For many regions, the aggregate exposure is two times greater in a transient climate than in a stabilized climate. Under transient warming, changes in LDDH events dominate the increase in population exposure whereas population change has smaller effects below 10%. For a stabilized warmer world, the negative population growth can largely offset the impact of climate change on exposure. Limiting global warming to 1.5°C instead of 2°C can reduce the exposure to LDDH events by 26.1% and 21.8% over China under transient and stabilized warming, respectively. Key Points: Compound events are more frequent and hotter over much of the northern regions in a transient world than the stabilized onePopulation exposure in a transient climate is 1.5–2.3 times higher than that in a stabilized climateThe increased exposure is driven by climate change alone (changes in climate and population) in a transient (stabilized) world [ABSTRACT FROM AUTHOR]

The rising intensity and frequency of extreme temperature events are caused due to climate change and are likely to affect the entire world. In this context, the Himalayas are reported to be very sensitive to changes in temperature extremes. In this study, we investigate the variability of temperature extremes over the Northwest Himalayas in the early 21st century (2000–2018). Here, we used 14 temperature indices recommended by ETCCDI (Expert Team on Climate Change Detection and Indices). The present study reveals the trends of extreme temperature indices on the spatial scale for the western part of the Northwest Himalayas. The 14 temperature indices were used to assess the behavior of extreme temperature trends with their significance. This study reports that the northwestern region of the study area has a cooling effect due to an increase in the trends of cold spells, cold days/nights, and frost days, while the southwestern region significantly shows the warming effects due to the increasing trends in warm spells, warm days/nights, and summer days. On the other hand, the eastern region of the study area shows mixed behavior, i.e., some places show warm effects while some reveal cold effects in the early 21st century. Overall, this study implies the northwestern parts have cooling trends while the southwestern and southeastern parts have warming trends during the early 21st century. [ABSTRACT FROM AUTHOR]

While global warming is mostly conceptualized in terms of increases in mean temperature, changes in the most extreme conditions encountered often have disproportionate impacts. Here, a measure of warming amplification is defined as the change in the highest yearly temperature (denoted TXx), representing extreme heat, minus that in the 80th percentile daily high temperature ( T max 80 ), which represents typical summer conditions. Based on the ERA5 reanalysis, over 1959–2021, warming of TXx averaged 1.56 K over land areas, whereas warming of T max 80 averaged 1.60 K. However, the population-weighted mean warming of TXx significantly exceeded warming of T max 80 (implying positive amplification) over Africa, South America, and Oceania. Where available, station temperature observations generally showed similar trends to ERA5. These findings provide a new target for climate model calibration and insight for evaluating the changing risk of temperature extremes. [ABSTRACT FROM AUTHOR]

Historical changes and possible future projections of temperature extremes in China, in terms of return values of annual extreme temperatures, are examined based on daily maximum and minimum temperatures from station observations and multiple models of the fifth and sixth phases of the Coupled Model Intercomparison Project (CMIP). The observations suggest that increases in temperature extremes are largely attributable to the changing mean climate, while the varying natural variability also has an important impact, which depends on the index of the variability. The models simulate warm extremes reasonably well but underestimate the spatial heterogeneity and temporal trend of cold extremes in China. In comparison, Coupled Model Intercomparison Project Phase 6 (CMIP6) models have higher skill in simulating temperature extremes in China, showing smaller biases and intermodel variability. MRI‐ESM2‐0 and NorESM2‐LM from CMIP6 and GFDL‐ESM2M and NorESM1‐M from CMIP5 are selected as reference models based on the better performance in reproducing observed temperature extremes in China. In the future, projections from CMIP6 multimodel ensemble (MME, represented as the multimodel median) and reference models all show a continued uptick in temperature extremes, with statistically significant increases in warm extremes mainly in the north and increases in cold extremes prominent in most parts of China. Different individual models, which have similar historical simulations, yield divergent future trends of temperature extremes, which may be associated with different climate sensitivities of models. In addition, MME usage should be treated with caution since its smoothing on spatial heterogeneity and possible information from poor models. Plain Language Summary: Given the serious hazards of extreme temperature events, understanding changes in extreme temperatures and effectively predicting their future changes are critical to climate mitigation and adaptation. According to station observations and model data from the Coupled Model Intercomparison Project Phase 5 (CMIP5) and 6 (CMIP6), we find besides the varying temperature mean, the varying temperature variance also has an important impact on changes in extreme temperatures, and CMIP6 models have overall better performance than CMIP5 models in reproducing changes in extreme temperatures in China. Our results also emphasize that good performances in historical simulation cannot guarantee better projections in the future and projections of extreme temperatures from the multimodel ensemble sacrifice the spatial heterogeneity relative to individual reference models. Key Points: The impact of the varying natural variability on changes in temperature extremes depends on the variability indexCMIP6 models perform better than CMIP5 in simulating temperature extremes changes in ChinaMultimodel ensemble projections of extreme temperatures sacrifice spatial heterogeneity [ABSTRACT FROM AUTHOR]

In this study, we evaluated the performance of the Brazilian Global Atmospheric Model (BAM), in its version 2.2.1, in the representation of the surface variables solar radiation, temperature (maximum, minimum, and average), and wind speed. Three experiments were carried out for the period from 2016 to 2022 under three different aerosol conditions (constant (CTE), climatological (CLIM), and equal to zero (ZERO)), discarding the first year as a spin-up period. The observations came from a high-resolution gridded analysis that provides Brazil with robust data based on observations from surface stations on a daily scale from 1961 to 2020; therefore, combining the BAM outputs with the observations, our intercomparison period took place from 2017 to 2020, for three timescales: daily, 10-day average, and monthly, targeting different applications. In its different simulations, BAM overestimated solar radiation throughout Brazil, especially in the Amazon; underestimated temperature in most of the northeast, southeast, and south regions; and overestimated in parts of the north and mid-west; while wind speed was only not overestimated in the Amazon region. In relative terms, the simulations with constant aerosol showed better performance than the others, followed by climatological conditions and zero aerosol. The dexterity indices applied in the intercomparison between BAM and observations indicate that BAM needs adjustments and calibration to better represent these surface variables. Where model deficiencies have been identified, these can be used to drive model development and further improve the predictive capabilities. [ABSTRACT FROM AUTHOR]

Recently, extreme climatic events have been a major concern across the world. Assessments on various climates and geographic regions are needed to understand the uncertainties in extreme climate events' responses to global warming. Developing countries particularly Ethiopia is highly affected by such extreme climate events due to its low adaptive capacity. The aim of this study was to investigate the trends of daily extreme temperatures indices in Ethiopia. The changes and trends in climate extremes of temperature were evaluated based on 14 indices of extreme temperatures over Ethiopia. The Mann-Kendall (MK) trend test is used to investigate the trends in the selected datasets. In the present study, three zones, which represent 34 stations, were selected based of extreme climate indices. The results of 14 extreme temperature indices showed that warm days (TX90p) and nights (TN90p) increased by 2.65 (P < 0.05) and 2.04 (P < 0.05) days/decade, respectively. The annual maximum and minimum temperatures (TXam and TNam) as well as the diurnal temperature range (DTR) were 2.68/decade, 2.04/decade and 0.62/decade, respectively. 73.5% and 26.5% of the stations show an increasing trend for (TXx and TNx), respectively. The extreme hot temperature indices (TXx, TXn, TX90, TN90 and SU) and cold temperature indices (ID, TNn, TXn, TX10P, TN10P and FD) exhibited contrasting variations over the stations. As far as spatial patterns concerned, most of the stations show significant increasing trends in northern and southeastern parts of Ethiopia. Thus, changes in climate extremes will likely increase the risk of severe extreme events and the society to become more vulnerable to the climate shocks. Generally, the findings of this study could help decision and policymakers to take immediate measures to tackle the climate change impacts. [ABSTRACT FROM AUTHOR]

The diurnal temperature range (DTR) has significantly decreased in many land areas as a consequence of global warming. The DTR spatial distribution and future projections of spatiotemporal variations in the case of global warming levels at 1.5 and 2.0°C under the RCP4.5 emission scenario was investigated using datasets from the Climatic Research Unit (CRU‐TS v.3.24.01) and the Coupled Model Intercomparison Project Phase 5 (CMIP5) in the Mongolian Plateau. And the main factors influencing DTR spatiotemporal variations were also evaluated using the Geo‐detector model at different time scales. The results showed that variations of the DTR significant decrease in snow‐winter dry‐warm summer climate region are higher than in other climate regions in spatial scale. As a result of the increasing rates of Tmin, which was more than double that of Tmax, the DTR decreased significantly on a temporal scale. In climate projections, the DTR was lower at global warming of 2.0°C (2037–2056) than 1.5°C (2017–2036). Precipitation was determined to be the predominant factor underlying the annual and seasonal (spring, summer, and autumn) DTR changes. Total cloud cover was the main factor in winter. Soil moisture was the key factor for the spatial variations in DTR in the warm and dry seasons. This study enhances the understanding of climate change in the Mongolian Plateau and provides a strong reference for other DTR variation studies in similar climates. [ABSTRACT FROM AUTHOR]

The use if the general circulation model (GCM) as a boundary condition for the dynamic regionalization process, without robust technical criteria, is one of the problems responsible for increasing the uncertainties of simulations and projections of climate scenarios, mainly affecting the observed trends and consequently the representation of future trends. Based on this premise, the present study evaluated the historical ability of 30 models that make up phase 5 of the Coupled Model Intercomparison Project using the Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) method. The meteorological variables used in this assessment were precipitation and near‐surface air temperature from 1975 to 2005 in the southern sectors of the Amazon Basin (SAMZ), Eastern Northeast Brazil (ENEB), and the intersection area between these regions, called MATOPIBA, was used to select the best GCM. The annual cycle, Taylor diagram and TOPSIS method were used as metrics of similarity between the models and the reference dataset. In general, the models simulated the temperature more accurately than the precipitation, with lower dispersions. According to the TOPSIS, over SAMZ, the HadGEM2‐ES (MIROC5) model was ranked with greater (less) ability to represent precipitation. In turn, for temperature the ensmean_cmip5 (IPSL‐CM5A‐MR) was the best (worst). Over ENEB, the model that showed the greatest (lowest) ability to simulate precipitation was CSIRO‐ACESS1.0 (HadGEM2‐AO). For temperature, the NorESM‐ME model (INMCM4) was ranked first (last). Over MATOPIBA, the CSIRO‐ACESS1.0 (NorESM‐ME) model was selected as having the best performance and the MPI‐ESM‐LR (MPI‐ESM‐LR) as the worst performance when representing precipitation (temperature). The similarities and discrepancies in the capacity of the GCM presented in the different metrics covered in this study can assist in the selection of more appropriate climate models in other regions for future studies of climate change. [ABSTRACT FROM AUTHOR]

Extreme heat events are a threat to human health, productivity, and food supply, so understanding their drivers is critical to adaptation and resilience. Anticyclonic circulation and certain quasi-stationary Rossby wave patterns are well known to coincide with heatwaves, and soil moisture deficits amplify extreme heat in some regions. However, the relative roles of these two factors in causing heatwaves is still unclear. Here we use constructed circulation analogs to estimate the contribution of atmospheric circulation to heatwaves in the United States in the Community Earth System Model version 1 (CESM1) preindustrial control simulations. After accounting for the component of the heatwaves explained by circulation, we explore the relationship between the residual temperature anomalies and soil moisture. We find that circulation explains over 85% of heatwave temperature anomalies in the eastern and western United States but only 75%–85% in the central United States. In this region, there is a significant negative correlation between soil moisture the week before the heatwave and the strength of the heatwave that explains additional variance. Further, for the hottest central U.S. heatwaves, positive temperature anomalies and negative soil moisture anomalies are evident over a month before heatwave onset. These results provide evidence that positive land–atmosphere feedbacks may be amplifying heatwaves in the central United States and demonstrate the geographic heterogeneity in the relative importance of the land and atmosphere for heatwave development. Analysis of future circulation and soil moisture in the CESM1 Large Ensemble indicates that, over parts of the United States, both may be trending toward greater heatwave likelihood. [ABSTRACT FROM AUTHOR]

Climate model simulations' outputs are prone to biases compared to observations; furthermore, climate projections can be very different in modelling future temperature characteristics. One possible solution for reducing uncertainty and eliminating possible systematic errors within climate projections is the bias‐adjustment of the raw climate model data. We used the quantile mapping method for bias‐adjustment of a mini ensemble consisting of 8‐member high‐resolution (0.11°) regional climate model simulations provided by the CORDEX community. As the method requires a reliable observational dataset serving as reference data, we used the quality controlled and homogenized observational dataset: CARPATCLIM. Quantile mapping bias‐adjustment technique was applied on the following variables: daily mean, minimum and maximum temperature. We analysed changes in mean temperature characteristics and of climate indices for future periods of 2021–2050 and 2070–2099 with respect to the reference period 1976–2005 for the Carpathian Region. The selected climate indices are based on minimum (frost days, FD) and maximum daily temperature data (summer days, SU). Our bias‐adjusted RCM results suggest a similar degree of mean temperature change as the raw RCMs' data. Bias‐adjusted and raw RCM data project a remarkable annual temperature increase on average under the RCP8.5 scenario (1.4°C and 3.9°C by 2021–2050 and 2070–2099, respectively). The highest temperature increase is likely to occur in summer: it is 4.3°C on average by the end of the 21st century. More pronounced differences were found for the projected changes of the number of summer and frost days based on the bias‐adjusted and the raw RCM data. Our results draw attention to the fact that bias‐adjusted RCM data are crucial for the provision of regional climate change impact and adaptation studies for the Carpathian Region. [ABSTRACT FROM AUTHOR]

In the context of global warming, the frequency and intensity of extreme weather and climate events are increasing. However, the impact of these changes that is directly felt by people is the day-to-day temperature change. Extreme temperature changes between neighboring days (ETCNs) carry substantial disease risks and socioeconomic impacts. Evaluation studies of ETCN events with global climate models (GCMs) remain unknown in China. This study quantitatively evaluates the performances of 35 GCMs and the multi-model ensemble (MME) of the Coupled Model Intercomparison Project 6 (CMIP6) in simulating the extreme cooling (EC) and extreme warming (EW) events of two consecutive days as defined by relative thresholds. The results showed that from 1981 to 2013, the annual average EW frequencies showed an increasing trend over China, but a decreasing trend for EC events, and the frequency of EW events was higher than that of EC events. EW events mostly occurred in spring, while EC events occurred in autumn. Additionally, the performances of the CMIP6 models were quite different between EC and EW events. The models could capture the annual cycle of EC and EW events well, and the simulations of EW events were generally more reliable than those of EC events. Furthermore, most CMIP6 models overestimated the frequency of EW events but underestimated the frequency of EC events in China. The CMIP6 models could capture the trends in EC events in China but fail to simulate them in EW events. The interannual variability of EW events exhibited relatively better performance than that of EC events. The CMIP6 MME effectively improved the capabilities of the models to simulate the climatology of ETCN events. Individual CMIP6 models exhibited better performances than the CMIP6 MME in terms of the trend and interannual variability. Finally, according to the overall ranking of the CMIP6 models, MPI-ESM-1–2-HAM and FGOALS-f3-L achieved the best performance in simulating EW and EC events, respectively. This study selected the optimal models in different regions at the seasonal and annual scales, providing theoretical support for the frequency projection and modeling improvement of ETCN events. [ABSTRACT FROM AUTHOR]

Knowledge of regional differences in future climate projections is important for effective adaptation strategies. Extreme events often arise regionally, but multiscale factors likely act together. Hence, we need discussion of multiple scales for the regional characteristics of future changes of extremes. In this study, using a large ensemble climate simulation database (d4PDF) created by global and regional climate models, the change in the temperature extreme defined as the top 10% of summertime daily maximum temperature in Japan is investigated under a globally 2- and 4-K-warmer climate, with emphasis on its regionality. Under global warming, the increase in extremely high temperature has a different spatial distribution from that of mean temperature. A simple composite analysis of extreme events shows that the high temperature occurs under a site-specific spatial pattern of sea level pressure (SLP), with a common feature of a warm anomaly up to the upper troposphere over the sites. The SLP pattern reflects the local topography and favors a foehnlike wind that increases the near-surface temperature. The impact of climate change in SLP on the foehn-inducing pattern varies with site, leading to regional differences in high-temperature changes. Therefore, the dynamic response of SLP to global warming results in a characteristic spatial distribution for the high-temperature change, which differs from the distribution for the mean-temperature change that generally shows the thermodynamic response. The characteristic is expected to appear in mountainous regions of the world, and this study helps in understanding future projections of high temperature there. Significance Statement: Regionality in future climate projections strongly influences the usefulness of adaptation strategies to climate change. This study indicates that the increase in extremely high temperature has a different spatial distribution from that of mean temperature. A site-specific spatial pattern of sea level pressure (SLP) reflecting the local topography contributes to the location of high temperature via a foehnlike wind. The impact of climate change in SLP on the pattern varies with site and leads to the regionality in high-temperature changes, which is the dynamic response to global warming unlike the thermodynamic response appearing on the mean-temperature change. This study helps us to understand future projections of temperature extreme in mountainous regions and their surroundings around the world. [ABSTRACT FROM AUTHOR]

A 22-member ensemble from CMIP6 is used to analyze the projected changes and seasonal behavior in surface air temperature over South America during the twenty-first century. In the future projections, CMIP6 models shown a high dependency to the socioeconomic pathway over each country of South America. The multimodel ensemble projects a continuous increase in the annual mean temperature over South America during the twenty-first century under the three future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5). Besides, it was possible to identify consistent positive trends across all the models, with values between 0.45 ± 0.05 and 2.05 ± 0.31 °C cy−1 under the historical experiment, however largest trends occurs for the projection periods (near, mid and far future), with values between − 0.87 ± 0.84 to 2.88 ± 0.60 °C cy−1 (SSP1-2.6), 1.41 ± 0.88 to 5.32 ± 0.81 °C cy−1 (SSP2-4.5) and 4.75 ± 0.58 to 8.76 ± 0.74 °C cy−1 (SSP5-8.5) with maximum values at Bolivia, Brasil, Paraguay and Venezuela whilst minimum values for Argentina and Uruguay, regardless of the SSP scenario used. From the seasonal behavior analysis was possible to identify maximum values between January and March whilst minimum between June and July, except in Brasil, Venezuela and Guyana–Surinam–French Guayana, with annual range decreasing as the latidude decreases. By the end of the twenty-first century the annual mean temperature over South america is projected to increase between 0.92–2.11 °C, 0.97–3.37 °C and 1.27–6.14 °C under SSP1-2.6, SSP2-4.5 and SSP5-8.5 projection scenarios respectively. This projected increase of temperature across the continent will produce negative repercussions in the social, economic and political spheres. The results obtained in this study provide insights about the CMIP6 performance over this region, which can be used to develop adaptation strategies and might be useful for the adaptation to the climate change. [ABSTRACT FROM AUTHOR]

Australia experiences a variety of climate extremes that result in loss of life and economic and environmental damage. This paper provides a first evaluation of the performance of state-of-the-art Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate models (GCMs) in simulating climate extremes over Australia. Here, we evaluate how well 37 individual CMIP6 GCMs simulate the spatiotemporal patterns of 12 climate extremes over Australia by comparing the GCMs against gridded observations (Australian Gridded Climate Dataset). This evaluation is crucial for informing, interpreting, and constructing multimodel ensemble future projections of climate extremes over Australia, climate-resilience planning, and GCM selection while conducting exercises like dynamical downscaling via GCMs. We find that temperature extremes (maximum-maximum temperature -TXx, number of summer days -SU, and number of days when maximum temperature is greater than 35 °C -Txge35) are reasonably well-simulated in comparison to precipitation extremes. However, GCMs tend to overestimate (underestimate) minimum (maximum) temperature extremes. GCMs also typically struggle to capture both extremely dry (consecutive dry days -CDD) and wet (99th percentile of precipitation -R99p) precipitation extremes, thus highlighting the underlying uncertainty of GCMs in capturing regional drought and flood conditions. Typically for both precipitation and temperature extremes, UKESM1-0-LL, FGOALS-g3, and GCMs from Met office Hadley Centre (HadGEM3-GC31-MM and HadGEM3-GC31-LL) and NOAA (GFDL-ESM4 and GFDL-CM4) consistently tend to show good performance. Our results also show that GCMs from the same modelling group and GCMs sharing key modelling components tend to have similar biases and thus are not highly independent. [ABSTRACT FROM AUTHOR]

Because downscaling methods can yield substantially different projections of future climate, it is imperative to constrain these projections with information from existing observations, while also recognizing observational uncertainty. California is a natural test case to develop observational constraints on future projections given prior studies that have purportedly found contrasting spatial patterns in late‐20th‐century trends of summertime daily‐maximum temperature: cooling along the coast and warming inland. Revisiting this claim, we find that coastal cooling is largely confined to non‐homogenized temperature records while homogenized observations show either non‐significant cooling or warming trends throughout the state. This finding has implications for weighting historical and future climate simulations downscaled using localized constructed analogs. Failure to consider out‐of‐sample skill results in weighted and unweighted Representative Concentration Pathway 8.5 temperature trend estimates differing by 2 K/century or more in California. However, weighted mean estimates that properly account for trend uncertainty do not differ significantly from the unweighted mean. Plain Language Summary: Downscaling methods and models yield climate projections on local scales relevant to entities such as cities and individual facilities. Weighted averages of future projections are needed so that the huge amount of existing climate model data is curated to a set that is compact enough to be widely‐accessible to end users. Observations are used to judge which models most faithfully represent historical climate. In California, previous work has ostensibly observed coastal cooling and inland warming over the later part of the 20th century. Upon closer inspection, we determine that this California coastal cooling finding is likely an artifact due to historical changes in observing practices at weather stations. We then evaluate and weight downscaled models using temperature records that show California coastal cooling and separately develop weights using temperature records that do not show such cooling. When we carefully consider the information in the observational data, we find that the two approaches to weighting produce about the same results. However, the weights can significantly differ when observational uncertainty is not considered. Together, these results highlight the importance, even over a heavily‐instrumented area like California, of carefully evaluating historical climate model simulations when producing future, localized climate projections for end users. Key Points: Twentieth‐century California coastal cooling estimates are likely an artifact of historical changes in weather stations' observing practicesErrors in measuring summertime extreme temperature trends have limited effect on model weights for localized extreme temperature projectionsFailure to consider out‐of‐sample skill leads to underestimated uncertainty in future trend estimates of extreme temperature in California [ABSTRACT FROM AUTHOR]

Projected increase in frequency and severity of extreme events are important threat brought by climate change. Thus, there is a need to understand the dynamics and magnitude of climate extreme at local and regional level. This study examines the patterns of annual trends and changes of extreme daily temperature and precipitation in Myanmar for the period of 1981 to 2015 using the RClimDex 1.1 software. The trends of maximum and minimum temperature show significant warming trends (p < 0.001) across Myanmar. From 2009 to 2015, the maximum temperature anomaly has continuously increased by 0.5 °C for all years except 2011. The larger rise in both maximum and minimum temperature observed after 2000 suggests that, overall, days and nights are becoming hotter for the entirety of Myanmar over this recent period. Furthermore, our works also show that the temperature extreme indices of warm days and warm nights have increased, whereas the frequency of cool days and cool nights have decreased. Our analysis also reveals that increasing trends in precipitation anomaly were not significant during 1981–2015. On the contrary, slight increasing trends towards wetter conditions were observed with a rate of 76.52 mm/decade during the study period. The other precipitation extreme indicators—namely, annual total precipitation (PRCPTOT), heavy precipitation days (R20mm), extreme wet days precipitation (R99p), and consecutive wet days (CWD)—are consistent with warming trends. Additionally, the relationship between inter-annual variability in the climate extremes indices and Oceanic Niño Index (ONI) patterns was also examined with a focus on the influence of the El Niño-Southern Oscillation (ENSO) phenomenon. [ABSTRACT FROM AUTHOR]

Due to increasing greenhouse gases, Iran is experiencing changes in patterns and trends of extreme climate events. Future climate extremes are one of the hottest topics affecting agricultural, economical, and environmental balances in many regions. Here, changes in temperature and precipitation extremes are investigated for the observational (1976–2005) and future (2025–2054) periods over Iran. For this, temperature and precipitation data of the CORDEX project acquired from three regional climate models with RCP4.5 and RCP8.5 scenarios are simulated using a developed multiscale bias correction downscaling method. For identifying trends and changes in 14 extreme climate indices used in this study from 54 climatic stations, the Mann–Kendall test and Sen's slope estimator are used. Results show that notable changes can be seen in the climate extremes across Iran for both observed and projected data. Overall, more warming and increasing trends are found for both maximum and minimum values of daily maximum and minimum temperatures, which rise from RCP4.5 to RCP8.5. The number of cold days and nights indicate decreasing trends; alternatively, the number of warm days and nights show opposite behaviors. Changes in the monthly maximum 1-day and consecutive 5-day precipitation illustrate an increasing trend in most model-scenarios for the projected data in the future compared to the observed one. The spatial analysis results reveal that changes in the climate extremes are more noticeable in the northwestern and western regions of Iran. These areas will probably experience heavy disastrous rainfall because the number of very wet days and heavy precipitation days will increase in the future. Changes in climate extremes will likely increase the risk of severe extreme events in the future in these areas and, consequently, make society more vulnerable to natural disasters. The findings of this study can help decision-makers consider appropriate management in the face of climate change consequences. [ABSTRACT FROM AUTHOR]

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Purpose: Three Coupled Model Intercomparison Project Phase 5 models involved in the G4 experiment of the Geoengineering Model Inter-comparison Project (GeoMIP) project were used to investigate the impact of stratospheric aerosol injection (SAI) on the mean surface air temperature and precipitation extremes in Africa. Design/methodology/approach: This impact was examined under G4 and Representative Concentration Pathway (RCP) 4.5 scenarios on the total precipitation, the number of rainy days (RR1) and of days with heavy rainfall (R20 mm), the rainfall intensity (SDII), the maximum length of consecutive wet (CWD) and dry (CDD) days and on the maximum rainfall in five consecutive days (Rx5day) across four regions: Western Africa (WAF), Eastern Africa (EAF), Northern Africa and Southern Africa (SAF). Findings: During the 50 years (2020–2069) of SAI, mean continental warming is −0.40°C lower in G4 than under RCP4.5. During the post-injection period (2070–2090), the temperature continues to increase, but at a lower rate (−0.19°C) than in RCP4.5. During SAI, annual rainfall in G4 is significantly greater than in RCP4.5 over the high latitudes (especially over SAF) and lower over the tropics. The termination of SAI leads to a significant increase of rainfall over Sahel and EAF and a decrease over SAF and Guinea Coast (WAF). Practical implications: Compared to RCP4.5, SAI will contribute to reducing significantly regional warming but with a significant decrease of rainfall in the tropics where rainfed agriculture account for a large part of the economies. After the SAI period, the risk of drought over the extratropical regions (especially in SAF) will be mitigated, while the risk of floods will be exacerbated in the Central Sahel. Originality/value: To meet the Paris Agreement, African countries will implement mitigation measures to contribute to keep the surface air temperature below 2°C. Geoengineering with SAI is suggested as an option to meet this challenge, but its implication on the African climate system needs a deep investigation in the aim to understand the impacts on temperature and precipitation extremes. To the best of the authors' knowledge, this study is the first to investigate the potential impact of SAI using the G4 experiment of GeoMIP on temperature and precipitation extremes of the African continent. [ABSTRACT FROM AUTHOR]

This article investigates seasonal and annual trends of near‐surface temperature (NST) during the last century (1901–2018) over Ghana. The ability of 24 global climate models (GCMs) from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) to simulate abrupt mean temperature changes in Ghana from 1980 to 2014 was assessed. The trends' magnitude and abrupt changes were detected using Sen Slope Estimator (SSE) and sequential Mann–Kendall (SQMK) tests, respectively. Root mean square difference, correlation coefficient, and mean bias (B) were used to determine the skilfulness of GCMs in simulating NST relative to Climatic Research Unit (CRU TS4.04) datasets. Ghana's Southern belt is characterized by low temperatures, averaging 25°C to 27°C, whilst the northern belt is characterized by high temperatures (29–31°C). The inter‐annual variability of temperature over Ghana exhibits an increasing trend. Findings depict a significant increase in both the annual and seasonal temperatures of Tmin and Tmean, with a steady increase in Tmax. Results revealed the rate of increase had been higher in the country's northern regions (0.5°C) than in the south (0.3–0.4°C) in recent decades. The model performances for the interannual variability of annual and FMAM temperatures are better than their JJAS seasonal performances. The study's overall model ranking shows that Ghana's best performing models for annual, FMAM and JJAS seasons are INM‐CM5‐0, NorCPM1, and KACE‐1‐0‐G, respectively. Observations and comparisons provide useful reference values for a comprehensive assessment of various applications. [ABSTRACT FROM AUTHOR]