Projection of Hot and Cold Extremes in the Amu River Basin of Central Asia using GCMs CMIP6

The extreme temperature has become more frequent and intense due to global warming, particularly in dry regions, causing devastating impacts on humans and ecosystems. The transboundary Amu river basin (ARB) is the most vulnerable region in Central Asia (CA) to extreme weather linked to climate change. This study aimed to project warm and cold extremes in ARB for three Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5) and two time-horizons, 2020–2059 and 2060-2099, using daily maximum (Tmax) and minimum temperature (Tmin) simulations of global climate models (GCMs) of Coupled Model Inter-comparison Project phase six (CMIP6). Results revealed that the basin's west experiences more hot extremes and the east more cold extremes. Climate change would cause a significant increase in the annual mean of Tmax and Tmin. However, the increase in mean Tmin would be much higher (5.0ºC ) than the mean Tmax (4.6ºC ). It would cause an increase in the hot extremes and a decrease in the cold extremes in the basin. The higher increase in the hot extremes would be in the west, while the higher decrease in the cold extreme in the basin's east. The number of days above 40℃ would increase from 45 to 60 days in the basin's west and northwest compared to the historical period. The number of days below -20℃ would decrease up to 45 days in the basin's east. Overall, the decrease in cold extremes would be much faster than the increase in hot extremes.

Frequency of winter temperature extremes over northern Eurasia dominated by AMOC

Widespread observed and projected increases in warm extremes, along with decreases in cold extremes, have been confirmed to be consistent with global and regional warming. However, in our study, decadal variations in surface air temperature (SAT) extremes over northern Eurasia in winter are primarily dominated by the Atlantic Meridional Overturning Circulation (AMOC), rather than an anthropogenically forcing. Based on reanalysis and state-of-the-art model simulations, we highlight that the decadal weakening of AMOC narrowed the generalized extreme value (GEV) distribution of winter SAT via diminishing its variance, and thus inducing fewer SAT extremes. On the contrary, the decadal enhancing of AMOC corresponded to the increase in the SAT variance over northern Eurasia, making a milder GEV distribution and generating more warm and cold extremes. These AMOC induced variations in SAT extremes may depend on the ocean heat transported into the atmosphere for air motion over northern Eurasia and the Arctic amplification caused by the heat advection from the northward-flowing Atlantic water.

Global Patterns of Hottest, Coldest and Extreme Diurnal Variability on Earth

Most previous studies of extreme temperatures have primarily focused on atmospheric temperatures. Using 18 years of the latest version of the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) data, we globally investigate the spatial patterns of hot and cold extremes as well as diurnal temperature range (DTR). We show that the world’s highest LST of 80.8 °C, observed in the Lut Desert in Iran and the Sonoran Desert in Mexico, is over ten degrees above the previous global record of 70.7 °C observed in 2005. The coldest place on Earth is Antarctica with the record low temperature of -110.9 °C. The world’s maximum DTR of 81.8 °C is observed in a desert environment in China. We see strong latitudinal patterns in hot and cold extremes as well as DTR. Biomes worldwide are faced with different levels of temperature extremes and DTR: we observe the highest zonal average maximum LST of 61.1 ± 5.3 °C in the deserts and xeric shrublands; the lowest zonal average minimum LST of -66.6 ± 14.8 °C in the Tundra; and the highest zonal average maximum DTR of 43.5 ± 9.9 °C in the montane grasslands and shrublands. This global exploration of extreme LST and DTR across different biomes sheds light on the type of extremes different ecosystems are faced with.

Climate Variability, Drought, and the Belief that High Gods Are Associated with Weather in Nonindustrial Societies

All societies have religious beliefs, but societies vary widely in the number and type of gods in which they believe as well as their ideas about what the gods do. In many societies, a god is thought to be responsible for weather events. In some of those societies, a god is thought to cause harm with weather and/or can choose to help, such as by bringing needed rain. In other societies, gods are not thought to be involved with weather. Using a worldwide, largely nonindustrial sample of 46 societies with high gods, this research explores whether certain climate patterns predict the belief that high gods are involved with weather. Our major expectation, largely supported, was that such beliefs would most likely be found in drier climates. Cold extremes and hot extremes have little or no relationship to the beliefs that gods are associated with weather. Since previous research by Skoggard et al. showed that greater resource stress predicted the association of high gods with weather, we also tested mediation path models to help us evaluate whether resource stress might be the mediator explaining the significant associations between drier climates and high god beliefs. The climate variables, particularly those pertaining to dryness, continue to have robust relationships to god beliefs when controlling on resource stress; at best, resource stress has only a partial mediating effect. We speculate that drought causes humans more anxiety than floods, which may result in the greater need to believe supernatural beings are not only responsible for weather but can help humans in times of need.

A study about the linearity of trends between extreme and mean temperatures in Argentina

<p>Small variations in the mean state of the atmosphere can cause large changes in the frequency of extreme events. In order to deepen and extend previous results in time, in this work we analyzed the linear relationship between extreme and mean temperature (Τ) on a climate change scale in Argentina. Two monthly extreme indices, cold nights (TN10) and warm days (TX90), were calculated based on the quality-controlled daily minimum and maximum temperature data provided by the Argentine National Meteorological Service from 58 conventional weather stations located over Argentina in the 1977–2017 period. Subsequently, we evaluated the relationship between the linear trends of extremes and mean temperature on a seasonal basis (JFM, AMJ, JAS, and OND). Student's T-test was performed to analyze their statistical significance at 5%. Firstly, positive (negative) and significant linear regressions were found between TX90 (TN10) trends and mean temperature trends for the four studied seasons. Therefore, an increase in the Τ-trend maintains a linear relationship with significant increase (decrease) of warm days (cold nights). Moreover, we found that JFM was the one with the highest coefficient of determination (0.602 for hot extremes and 0.511 for cold extremes), implying that 60.2% (51.1%) of the TX90 (TN10) trend could be explained as a function of the Τ-trend by a linear regression. In addition, in the JFM (OND) quarter, the TX90 index increased by 7.02 (6.02) % of days each with a 1 ºC increase in the mean temperature. Likewise, the TN10 index decreased by 4.94 (and 4.99) % of days from a 1ºC increase in the mean temperature for the JFM (AMJ) quarter. Finally, it is worthwhile to highlight the uneven behavior between hot and cold extremes and the mean temperature. Specifically, it was observed that the slopes of the linear regression calculated for the TX90 index and Τ presented a higher absolute value than those registered for the TN10 index and Τ. Therefore, a change in the mean temperature affects hot extremes to a greater extent than cold ones in Argentina.</p>

Greenhouse gas and aerosol contributions to the observed global and regional changes in extreme temperature changes

<p>This study carried out an updated detection and attribution analysis of extreme temperature changes for 1951-2015. Four extreme temperature indices (warm extremes: annual maximum daily maximum/minimum temperatures; cold extremes: annual minimum daily maximum/minimum temperatures) were used considering global, continental (6 domains), and subcontinental (33 domains) scales. HadEX3 observations were compared with CMIP6 multi-model simulations using an optimal fingerprinting technique. Response patterns of extreme indices (fingerprints) to anthropogenic (ANT), greenhouse gas (GHG), anthropogenic aerosol (AA), and natural (NAT) forcings were estimated from corresponding CMIP6 forced simulations. Pre-industrial control simulations (CTL) were also used to estimate the internal variability. Results from two-signal detection analysis where the observations are simultaneously regressed onto ANT and NAT fingerprints reveal that ANT signals are robustly detected in separation from NAT in global and most continental regions for all extreme indices. At subcontinental scale, ANT detection occurs especially in warm extremes (more than 60% of regions). Results from three-signal detection analysis where observations are simultaneously regressed onto GHG, AA, and NAT fingerprints show that GHG signals are detected and separated from other external forcings over global, most continental, and several subcontinental (more than 60%) domains in warm extremes. In addition, AA influences are jointly detected in warm extremes over global, Europe and Asia. The detected GHG forcings are found to explain most of the observed warming while AA forcings contribute to the observed cooling for the early decades over globe, Europe, and Asia with a slight warming over Europe during recent decades. Overall, improved detection occurs compared to previous studies, especially in cold extremes, which is due to the use of extended period which increases signal-to-noise ratios.</p>

Anthropogenic Greenhouse Gas and Aerosol Contributions to Extreme Temperature Changes during 1951–2015

This study conducted a detection and attribution analysis of the observed global and regional changes in extreme temperatures during 1951–2015. HadEX3 observations were compared with multimodel simulations from the Coupled Model Intercomparison Project phase 6 (CMIP6) using an optimal fingerprinting technique. Annual maximum daily maximum and minimum temperatures (TXx and TNx; warm extremes) and annual minimum daily maximum and minimum temperatures (TXn and TNn; cold extremes) over land were analyzed considering global, continental, and subcontinental scales. Response patterns (fingerprints) of extreme temperatures to anthropogenic (ANT), greenhouse gases (GHG), aerosols (AA), and natural (NAT) forcings were obtained from CMIP6 forced simulations. The internal variability ranges were estimated from preindustrial control simulations. A two-signal detection analysis where the observations are regressed onto ANT and NAT fingerprints simultaneously reveals that ANT signals are robustly detected in separation from NAT over global and all continental domains (North and South America, Europe, Asia, and Oceania) for most of the extreme indices. ANT signals are also detected over many subcontinental regions, particularly for warm extremes (more than 60% of 33 subregions). A three-signal detection analysis that considers GHG, AA, and NAT fingerprints simultaneously demonstrates that GHG signals are detected in isolation from other external forcings over global, continental, and several subcontinental domains especially for warm extremes, explaining most of the observed warming. Moreover, AA influences are detected for warm extremes over Europe and Asia, indicating significant offsetting cooling contributions. Overall, human influences are detected more frequently, compared to previous studies, particularly for cold extremes, due to the extended period and the improved spatial coverage of observations.

Approaching environmental human thermophysiological thresholds for the case of Ankara, Turkey

The disclosed study undertook a ‘human centred-approach’ that ascertained and categorised environmental human thermophysiological risk factors by relating them to the human biometeorological system through the use of three widely utilised energy balance model (EBM) indices, the physiologically equivalent temperature (PET), the modified PET, and the universal thermal climate index (UTCI). The disclosed assessment was carried out over the past decade (i.e., 2010–2019) with a 3-h temporal resolution for the case of Ankara through two WMO meteorological stations to compare both local urban and peri-urban environmental conditions. The study recognised extreme annual variability of human physiological stress (PS) during the different seasons as a result of the biometeorological processing of the singular variables, which in the case of average PET for both stations, varied by up to 75 °C between the winter and summer for the same annual dataset (2012). In addition, all EBMs indicated higher heat stress within the city centre that were conducive of both urban extreme heatwaves and very hot days during the summer months, with extreme heat stress levels lasting for longer than a week with PET values reaching a maximum of 48 °C. Similar cold extremes were found for the winter months, with PET values reaching − 30 °C, and average PS levels varying lower in the case of the peri-urban station.Graphical abstract