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To evaluate future changes in the climate system, the outputs from coarse‐resolution global climate models (GCMs) need to be downscaled to a finer scale, making them more directly applicable for impact assessment. Here we focus on examining the projected changes of three key climate variables (precipitation, air temperature, and wet bulb temperature) across Arizona (south‐western United States). We use daily outputs from GCMs from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) and bias correct and downscale them to a 4‐km resolution. Through leave‐one‐out cross‐validation, we compare various bias correction methods and identify that the empirical quantile mapping approach performs the best regardless of the climate variable. Then, we analyse the projected bias‐corrected outputs for two future periods (Mid‐of‐Century: 2015–2048; End‐of‐Century: 2067–2100) with respect to the 1981–2014 period, under four shared socioeconomic pathway scenarios (SSP1‐2.6, SSP2‐4.5, SSP3‐7.0 and SSP5‐8.5). Our results show that Arizona's climate is projected to become overall warmer and wetter, more so towards the end of this century and for higher emission scenarios. Additionally, our findings project an increase in wet bulb temperature and cooling degree days, implying the ongoing warming climate's potential impacts on public health and the economy. These results provide a baseline understanding of climate change impacts in the state and highlight the projected changes in the future in response to the climate scenarios. [ABSTRACT FROM AUTHOR]

Climate change has myriad impacts on ecosystems, but the mechanisms by which it affects individual species can be difficult to pinpoint. One strategy to discover such mechanisms is to identify a specific ecological factor related to survival or reproduction and determine how that factor is affected by climate. Here we used Landsat imagery to calculate water clarity for 127 lakes in northern Wisconsin from 1995 to 2021 and thus investigate the effect of clarity on the body condition of an aquatic visual predator, the common loon (Gavia immer). In addition, we examined rainfall and temperature as potential predictors of water clarity. Body mass tracked July water clarity strongly in loon chicks, which grow chiefly in that month, but weakly in adult males and females. Long‐term mean water clarity was negatively related to chick mass but positively related to adult male mass, suggesting that loons foraging in generally clear lakes enjoy good foraging conditions in the long run but might be sensitive to perturbations in clarity during chick‐rearing. Finally, chick mass was positively related to the density of docks, perhaps because angling removes large fishes and thus boosts the abundance of the small fishes on which chicks depend. Water clarity itself declined strongly from 1995 to 2021, was negatively related to July rainfall, and was positively related to July air temperature. Our findings identified both long‐term and short‐term water clarity as strong predictors of loon foraging efficiency, and suggest that climate change, through water clarity, impacts freshwater ecosystems profoundly. Moreover, our results identified the recent decrease in water clarity as a likely cause of population decline in common loons. [ABSTRACT FROM AUTHOR]

Humid heatwaves, characterized by high temperature and humidity combinations, challenge tropical societies. Extreme wet‐bulb temperatures (TW) over tropical land are coupled to the warmest sea surface temperatures by atmospheric convection and wave dynamics. Here, we harness this coupling for seasonal forecasts of the annual maximum of daily maximum TW (TWmax). We develop a multiple linear regression model that explains 80% of variance in tropical mean TWmax and significant regional TWmax variances. The model considers warming trends and El Niño and Southern Oscillation indices. Looking ahead, the strong‐to‐very‐strong El Niño at the end of 2023, with an Oceanic Niño Index of ∼2.0, suggests a 2024 tropical land mean TWmax of 26.2°C (25.9–26.4°C), and a 68% chance (24%–94%) of breaking existing records. This method also predicts regional TWmax in specific areas. Plain Language Summary: The heat and humidity in the tropics can be particularly challenging for people to stay comfortable and healthy. This combination of heat and moisture is described using a measure called the wet‐bulb temperature (TW). We found that these extremely humid and hot conditions on land can be predicted about 5 months in advance using a physics‐based statistical model. The forecast is possible because the peak of El Niño comes before the peak in the warmest sea surface temperatures, which affects the maximum TW on land. This prediction can help tropical societies to better prepare for extreme heat. Key Points: Tropical wet‐bulb temperatures (TW) peak around 5 months after El Niño wintersA multiple linear regression model considering the El Niño‐Southern Oscillation index and the long‐term warming trend effectively explains TWmax variabilityOur model quantifies the likelihood of strong El Niño and human‐induced warming pushing TWmax to record‐breaking levels [ABSTRACT FROM AUTHOR]

The diurnal temperature range (DTR) has generally decreased at global scale according to the IPCC reports and previous publications. Here, we examined the change in DTR within the past four decades and found that DTR decreased in wet areas, but it increased in dry areas. This is because the daily minimum air temperature (Tmin) increased at a slower rate than the daily maximum air temperature (Tmax) in the dry areas, while it increased more rapidly in the wet areas. The changes in cloud cover, water vapour and moisture flux were observed to have contributed to the observed change in DTR. The change in moisture flux largely contributed to this change through the indirect effect on the change in water vapour across the aridity gradient. The significant impact of moisture flux is linked to the differential change in moisture flux between the dry and wet zones. [ABSTRACT FROM AUTHOR]

Climate change presents a major threat to species distribution and persistence. Understanding what abiotic or biotic factors influence the thermal tolerances of natural populations is critical to assessing their vulnerability under rapidly changing thermal regimes. This study evaluates how body mass, local climate, and pathogen intensity influence heat tolerance and its population‐level variation (SD) among individuals of the solitary bee Xenoglossa pruinosa. We assess the sex‐specific relationships between these factors and heat tolerance given the differences in size between sexes and the ground‐nesting behavior of the females. We collected X. pruinosa individuals from 14 sites across Pennsylvania, USA, that varied in mean temperature, precipitation, and soil texture. We measured the critical thermal maxima (CTmax) of X. pruinosa individuals as our proxy for heat tolerance and used quantitative PCR to determine relative intensities of three parasite groups—trypanosomes, Spiroplasma apis (mollicute bacteria), and Vairimorpha apis (microsporidian). While there was no difference in CTmax between the sexes, we found that CTmax increased significantly with body mass and that this relationship was stronger for males than for females. Air temperature, precipitation, and soil texture did not predict mean CTmax for either sex. However, population‐level variation in CTmax was strongly and negatively correlated with air temperature, which suggests that temperature is acting as an environmental filter. Of the parasites screened, only trypanosome intensity correlated with heat tolerance. Specifically, trypanosome intensity negatively correlated with the CTmax of female X. pruinosa but not males. Our results highlight the importance of considering size, sex, and infection status when evaluating thermal tolerance traits. Importantly, this study reveals the need to evaluate trends in the variation of heat tolerance within and between populations and consider implications of reduced variation in heat tolerance for the persistence of ectotherms in future climate conditions. [ABSTRACT FROM AUTHOR]

Understanding how different organisms cope with changing temperatures is vital for predicting future species' distributions and highlighting those at risk from climate change. As ectotherms, butterflies are sensitive to temperature changes, but the factors affecting butterfly thermoregulation are not fully understood.We investigated which factors influence thermoregulatory ability in a subset of the Mediterranean butterfly community. We measured adult thoracic temperature and environmental temperature (787 butterflies; 23 species) and compared buffering ability (defined as the ability to maintain a consistent body temperature across a range of air temperatures) and buffering mechanisms to previously published results from Great Britain. Finally, we tested whether thermoregulatory ability could explain species' demographic trends in Catalonia.The sampled sites in each region differ climatically, with higher temperatures and solar radiation but lower wind speeds in the Catalan sites. Both butterfly communities show nonlinear responses to temperature, suggesting a change in behaviour from heat‐seeking to heat avoidance at approximately 22°C. However, the communities differ in the use of buffering mechanisms, with British populations depending more on microclimates for thermoregulation compared to Catalan populations.Contrary to the results from British populations, we did not find a relationship between region‐wide demographic trends and butterfly thermoregulation, which may be due to the interplay between thermoregulation and the habitat changes occurring in each region. Thus, although Catalan butterfly populations seem to be able to thermoregulate successfully at present, evidence of heat avoidance suggests this situation may change in the future. [ABSTRACT FROM AUTHOR]

Many cold water‐dependent aquatic organisms are experiencing habitat and population declines from increasing water temperatures. Identifying mechanisms which drive local and regional stream thermal regimes facilitates restoration at ecologically relevant scales. Stream temperatures vary spatially and temporally both within and among river basins. We developed a modeling process to identify statistical relationships between drivers of stream temperature and covariates representing landscape, climate, and management‐related processes. The modeling process was tested in three study areas of the Pacific Northwest United States during the growing season (May [start], August [warmest], September [end]). Across all months and study systems, covariates with the highest relative importance represented the physical landscape (elevation [1st], catchment area [3rd], main channel slope [5th]) and climate covariates (mean monthly air temperature [2nd] and discharge [4th]). Two management covariates (groundwater use [6th] and riparian shade [7th]) also had high relative importance. Across the growing season (for all basins), local reach slope had high relative importance in May, but transitioned to a regional main channel slope covariate in August and September. This modeling process identified regionally similar and locally unique relationships among drivers of stream temperature. High relative importance of management‐related covariates suggested potential restoration actions for each system. [ABSTRACT FROM AUTHOR]

Meteorological records at Signy Station in the South Orkney Islands (SOIs) have recently been digitized to cover the period of 1947–1995. This study compares the newly available near‐surface air temperatures at Signy with those from a nearby station, Orcadas and with reanalysis datasets to provide a more comprehensive picture of the weather and climate variability in the SOIs. Temperatures from both stations show a higher degree of variability in winter than summer, but the variability differs in terms of its relationship to the dominant wind directions and sea ice influences. The two stations differ markedly in terms of their respective warm temperature events, largely due to orography‐induced föhn winds at Signy Station as northwesterlies flow over Coronation Island. ERA5 reproduces the monthly to annual averages exceedingly well but underestimates both cold and warm tails of station temperatures. Temperature trends in the SOIs are also considered in terms of changes in large‐scale circulation and the sea surface temperature over the Brazil‐Falkland Confluence. However, caution is required in interpreting the long‐term temperature trends estimated from reanalysis data as most of the reanalyses show a cold bias before 1979, which is most likely caused by misrepresentation of the sea ice. [ABSTRACT FROM AUTHOR]

In spring 2022, strong temperature whiplash events occurred in eastern China (EC), characterized by enhanced subseasonal variability in surface air temperature (SAT). Our results show that extreme temperature fluctuations are dominated by the amplified Eurasian wave train on a biweekly timescale as a possible response to increased sea ice concentrations (SICs) over the Barents‐Kara Seas (BK) in previous winter. Increased winter SICs weaken the upward planetary waves and enhance the stratospheric polar vortex. In spring, the downward propagation of enhanced polar vortex intensifies the polar front jet, increasing kinetic energy gain of the North Atlantic wave train and guiding it to travel farther eastward. The Eurasian wave train strengthens through an upstream development and enhanced local baroclinicity and enhances subseasonal SAT variability over EC. Therefore, the state of BK SICs in previous winter could be a useful signal for the enhanced likelihood of spring extreme temperature whiplash events. Plain Language Summary: In spring 2022, extremely cold and warm spells alternately affected eastern China (EC) and caused drastic temperature whiplash events, featured by the enhanced subseasonal variability of surface air temperature (SAT). We find the subseasonal SAT variability is directly associated with the Eurasian wave train, and its intensity is correlated with sea ice concentrations (SICs) over the Barents‐Kara Seas (BK) in previous winter. Increased SICs reduce the heat transfer from ocean to atmosphere, induce a cyclone anomaly aloft, and diminish the climatological planetary wavenumber‐1 component. The weakened upward wave activity flux strengthens the stratospheric polar vortex, which spreads downward until spring. Hence, the polar front jet in the North Atlantic is enhanced, and the meridional temperature gradient in northern Europe increases, providing a favorable background for atmospheric disturbance development. The North Atlantic wave train propagates farther eastward after gaining more kinetic energy from the accelerated jet and intensifies the following Eurasian wave train. Due to stronger local baroclinic energy conversion, the Eurasian wave train further strengthens, resulting in enhanced subseasonal SAT variability over EC. Therefore, the extremely increased winter BK SICs could be a useful initial signal for the probability of temperature whiplash events over EC in the following spring. Key Points: Record‐breaking subseasonal variability of surface air temperature (SAT) prevails in eastern China in spring 2022Sea ice concentrations over the Barents‐Kara Seas in previous winter strengthen mean state of North Atlantic circulation via the delayed role of the stratosphereThe upstream interaction between mean state and eddies reinforces the Eurasian wave train and enhances subseasonal SAT variability [ABSTRACT FROM AUTHOR]

Quantitative methods to pinpoint the origin of atmospheric temperature anomalies (T′) associated with heatwaves are pivotal for the construction of physically plausible synoptic storylines of heatwave formation and their evaluation in models. Here, we combine a Lagrangian T′ decomposition with concepts from moisture tracking techniques to identify where and when the principal physical processes generate T′ and to attribute these sources to synoptic weather systems. Applying this framework to near‐surface and free‐tropospheric T′ associated with the record‐shattering 2021 heatwave in the Pacific Northwest shows that ascending, diabatic air streams in North Pacific cyclones contribute more than 50% of free‐tropospheric T′, whereas near‐surface T′ is mainly produced by local subsidence and diabatic heating with only marginal upstream contributions. Since free‐tropospheric T′ facilitates near‐surface accumulation of locally produced T′ by rendering the atmosphere stable to moist convection, our findings corroborate the notion of top‐down induced heatwave formation fueled by upstream diabatic processes. Plain Language Summary: Heatwaves, characterized by strongly above normal air temperatures, are amongst the most severe weather related threats to human lives and economic activities. Understanding the processes causing heatwaves and evaluating their representation in climate models requires suitable quantitative methods to diagnose the process' contributions to temperature anomalies. While it is well established which physical processes can create positive temperature anomalies, that is, transport of air from climatologically warmer regions, warming of air as it is compressed in sinking motion, and heating, for example, by a hot surface, it remains challenging to diagnose where and when these processes create temperature anomalies. Here, we present a quantitative framework based on the tracking of air parcels that allows us to obtain this kind of information. Applying this method to a recent devastating heatwave in the Pacific Northwest, we show that even though the air at the surface acquired its temperature anomaly mainly by local heating, storms over the North Pacific contributed substantially to warming the air aloft. This anomalously warm air aloft, in turn, was a pre‐requisite for the extreme accumulation of heat near the surface by suppressing the formation of thunderstorms, which would have had a cooling effect. Key Points: Novel diagnostic framework quantifies sources of atmospheric temperature anomalies (T′) generated by principal physical processesUpstream warm conveyor belts (WCBs) contributed >50% of free‐tropospheric T′ and <15% of near‐surface T′ to the 2021 Pacific Northwest (PNW) heatwaveImportance of upstream sources of free‐tropospheric T′ corroborate top‐down induced formation of PNW heatwave fueled by WCBs [ABSTRACT FROM AUTHOR]

Many freshwater turtle species are threatened with extinction, but the implementation of effective conservation measures may be hindered by ignorance of their biology and ecology. We analysed the available monitoring data for a listed Endangered but poorly known species, the Manning River helmeted turtle (Myuchelys purvisi: Chelidae), to elucidate its geographic distribution and evaluate its relationships to broad‐scale environmental factors. We found M. purvisi to be common and widely distributed in the Manning River system, with a balanced population structure including a wide mix of size classes and a substantial juvenile component. The occurrence of the species was significantly positively associated with a larger stream size, which probably confers greater habitat availability and lower vulnerability to drying during drought. Mean carapace length was significantly lower in stream segments with higher average annual air temperature, suggesting that warmer conditions favour recruitment. However, occurrence and mean carapace length were not related to the proportion of conservation land in the immediate stream environs, suggesting that persistence of the species is not favoured simply by occurrence in protected areas, and threats may need active management. We provide some suggestions for further investigation of the species' ecology and vulnerability to potential threats. [ABSTRACT FROM AUTHOR]

Thermoregulation is critical to the survival of animals. Tropical environments can be particularly thermally challenging as they reach very high, even lethal, temperatures. The thermoregulatory responses of tropical freshwater turtles to these challenges are poorly known. One common thermoregulatory behaviour is diurnal basking, which, for many species, facilitates heat gain. Recently, however, a north‐eastern Australian population of Krefft's river turtles (Emydura macquarii krefftii) has been observed basking nocturnally, possibly to allow cooling. To test this, we determined the thermal preference (central 50% of temperatures selected) of E. m. krefftii in an aquatic thermal gradient in the laboratory. We then conducted a manipulative experiment to test the effects of water temperatures, both lower and higher than preferred temperature, on diurnal and nocturnal basking. The preferred temperature range fell between 25.3°C (±SD: 1.5) and 27.6°C (±1.4) during the day, and 25.3°C (±2.4) and 26.8°C (±2.5) at night. Based on this, we exposed turtles to three 24 h water temperature treatments ('cool' [23°C], 'preferred' [26°C] and 'warm' [29°C]) while air temperature remained constant at 26°C. Turtles basked more frequently and for longer periods during both the day and night when water temperatures were above their preferred range (the 'warm' treatment). This population frequently encounters aquatic temperatures above the preferred thermal range, and our results support the hypothesis that nocturnal basking is a mechanism for escaping unfavourably warm water. Targeted field studies would be a valuable next step in understanding the seasonal scope of this behaviour in a natural environment. [ABSTRACT FROM AUTHOR]

Aim: Climate and land use change can independently affect food web structure. However, their direct and indirect relationships with food webs are not well‐understood. This is particularly relevant for freshwater ecosystems, which are strongly affected by global threats and differ substantially in attributes and functioning. Here, we investigated direct and indirect relationships between climate and land use changes and freshwater food web structure, while considering the differences between lakes and streams. Location: Multicontinental. Time period: Current. Major taxa studied: Freshwater taxa. Methods: We compiled 51 food webs, spanning 12 countries and six continents to investigate the direct and indirect relationships between land use intensity, changes in air temperature and precipitation and food web structure, using structural equation modelling (SEM). We further tested how lake and stream food webs differ in their responses to environmental change by applying a multigroup analysis to our SEM. Results: We found that connectance was positively related to both land use intensity and increases in air temperature and precipitation in stream food webs, whereas in lakes, it was only related to temperature. These findings suggest a dominance of generalists where conversion to agriculture and urbanization has been intensified, and temperature has increased. Food web vertical structure was negatively related to land use and increases in air temperature and precipitation, but climatic predictors were only linked to network structure through a negative relationship with the fraction of top consumers. Main conclusions: While land use intensity was directly related to food web structure, complex relationships between changes in climate and network topology were mediated by the fraction of top consumers. Also, as food webs in lakes and streams responded differently to land use and climate change, we suggest that their intrinsic characteristics must be explicitly considered to fully understand the effects of global changes on freshwater biodiversity. [ABSTRACT FROM AUTHOR]

Six gridded temperature datasets (T‐datasets) were evaluated for the first time over the South American continent, through the case study of Bolivia, by comparing them with temperature records acquired from 82 meteorological stations spanning the 1995–2010 period. The comparisons were carried out at the daily time step considering different seasons (annual scale, austral summer and austral winter) and regions (Amazon, La Plata and Altiplano basins). Overall, the climate hazards group infrared temperature with stations (CHIRTS) and the climate prediction centre (CPC) T‐datasets provided the most reliable mean daily temperature (Tmean) and also described well the temporal variability of minimum and maximum daily temperature estimates (Tn and Tx). Tmean, Tn and Tx trends were analysed over the 1983–2016 period to observe temperature temporal evolution across the three regions. Despite some general agreements between the trends (Tmean, Tx and Tn), large discrepancies are also observed. It was found that CPC overestimates and CHIRTS underestimates mean temperature trends and that CPC (CHIRTS) was better than CHIRTS (CPC) to estimate Tx (Tn) trends, both in magnitude and space. Furthermore, opposing trends (i.e., warming and cooling) are described by CPC and CHIRTS for some specific regions, which call into question their reliability for such analyses. These findings highlight the need to validate gridded temperature products with reliable ground data for the regions under study, particularly if they have a wide elevation range. [ABSTRACT FROM AUTHOR]

Changes to ambient temperatures under climate change may detrimentally impact small ectotherms that rely on their environment for thermoregulation; however, there is currently a limited understanding of insect larval thermoregulation. As holometabolous insects, Lepidoptera differ in morphology, ecology and behaviour across the life cycle, and so it is likely that adults and larvae differ in their capacity to thermoregulate. In this study, we investigated the thermoregulatory capacity (buffering ability) of 14 species of day‐flying Lepidoptera, whether this is influenced by body length or gregariousness, and whether it differs between adult and larval life stages. We also investigated what thermoregulation mechanisms are used: microclimate selection (choosing locations with a particular temperature) or behavioural thermoregulation (controlling temperature through other means, such as basking). We found that Lepidoptera larvae differ in their buffering ability between species and body lengths, but gregariousness did not influence buffering ability. Larvae are worse at buffering themselves against changes in air temperature than adults. Therefore Lepidoptera may be more vulnerable to adverse temperature conditions during their larval life stage. Adults and larvae rely on different thermoregulatory mechanisms; adults primarily use behavioural thermoregulation, whereas larvae use microclimate selection. This implies that larvae are highly dependent on the area around their foodplant for effective thermoregulation. These findings have implications for the management of land and species, for example, highlighting the importance of creating and preserving microclimates and vegetation complexity surrounding Lepidoptera foodplants for larval thermoregulation under future climate change. [ABSTRACT FROM AUTHOR]

Herein, we report a one‐pot viable protocol to synthesize tetrasubstituted 2‐aminothiophenes engaging readily accessible α‐enolic dithioesters and abundant fumaronitrile under transition metal‐free conditions at room temperature in open air. The reaction proceeds via successive Michael‐type addition/intramolecular cyclization/isomerization cascades. The added features are benign conditions, exclusive regio‐ and chemoselectivity, excellent atom‐/step‐economy, easy purification, and tolerance of wide range of functional groups of a diverse electronic and steric nature. This protocol not only provided a robust and modular approach to various 2‐aminothiophenes in moderate to excellent yields, but also demonstrated the potential of dithioesters and fumaronitrile in the challenging intermolecular cross‐coupling reactions widening the arsenal of synthetic methods. [ABSTRACT FROM AUTHOR]

Haematocrit, the proportion of blood comprising erythrocytes, is often used as a proxy of individual condition. Nestling haematocrit is influenced by several factors but ambient temperature is generally agreed as a key driver. It is unclear which day(s) in embryonic or nestling development are most influential in determining pre‐fledgling haematocrit. This is important, because if we are able to identify what day(s) nestlings are physiologically most vulnerable to climatic conditions, this may inform future conservation management and help mitigate the effects. We investigated the effect of ambient temperature, precipitation, body size, brood size, age, food abundance and habitat on nestling haematocrit in Aphrastura spinicauda (thorn‐tailed rayaditos). We collected this data from two climatically different locations in Chile: northern Patagonia (Pucón) and Sub‐Antarctic Patagonia (Navarino Island), located ~ 1800 km further south. We aimed to identify the key drivers of nestling haematocrit and find when nestlings are physiologically most vulnerable to climatic conditions. We confirm that ambient temperature is the key driver of nestling haematocrit, with increasing ambient temperatures nestling haematocrit decreased. However, precipitation also affected haematocrit in the late incubation stage. At low and high precipitation, nestling haematocrit was low and is optimum in light rainfall. Our results show a delayed effect of ambient temperature and precipitation in late incubation and on the day of hatching which can determine pre‐fledgling haematocrit, which may cause lower post‐fledgling fitness. We found that climatic conditions on the day haematocrit was collected was not important in determining haematocrit. We found that haematocrit was higher in the sub‐Antarctic area and that higher temperatures and precipitation on the day of hatching will result in lower nestling haematocrit and poorer pre‐fledgling condition. As atmospheric temperatures rise, nestling fitness is at risk. This is a threat to many bird species, especially those in locations vulnerable to climate breakdown. [ABSTRACT FROM AUTHOR]

Arctic outflow winds bring cold air from the continent to the coastline through mountain passes. Using observational data and a 2‐D model, we show that a February 2019 outflow event caused the upper 100 m in Bute Inlet, British Columbia (within the traditional territory of the Homalco Nation) to cool up to 1.9°C and gain up to 4.1 mLL−1 of oxygen. The cold, oxygenated water persisted for almost 1 year within the 1,023–1,023.5 kgm−3 isopycnal range (∼50–150 m). Atmospheric (from 1929 to 2022) and oceanographic (from 1951 to 2022) data showed a statistically significant relationship between continental air temperature at Tatlayoko Lake and temperature and oxygen in Bute Inlet. This local mechanism that counters some effects of climate change could create a biological refugium as surrounding waters warm and lose oxygen at a faster rate. The number of outflow events decreased from 1951 to 2018, and increased since. Plain Language Summary: There are times during most winters that Arctic air masses settle over the British Columbia (BC) interior east of the coast mountain range. Some of this cold air is transported to the coast as an outflow wind via coast mountain valleys. Using observational air and ocean data and a model, we examine an outflow event in February 2019. During this event, we find that outflow winds can cause mixing of the upper 100 m in Bute Inlet and this mixing can cause the ocean to rapidly cool and gain oxygen. Historical air data from 1929 to 2022 and ocean data from 1951 to 2022 were examined and it was found that the number of outflow events have generally decreased from the 1950s to 2018s, and increased afterward. This work identifies an important mechanism that could protect parts of BC's coastal ocean from climate change induced warming and loss of oxygen. Key Points: In February 2019, Arctic outflow winds caused the upper 100 m in Bute Inlet to cool by up to 1.9°C and gain up to 4.1 mLL−1 of oxygenApril to October Bute Inlet intermediate water properties are significantly correlated continental air temperature from the previous winterArctic outflow events decreased since the 1950s, but disruption of the polar vortex may increase these events with climate change [ABSTRACT FROM AUTHOR]

The homogenization of climate observational series is a needed process before undertaking confidently any study of their internal variability, since changes in the observation methods or in the surroundings of the observatories, for instance, can introduce biases in the data of the same order of magnitude than the underlying climate variations and trends. Many methods have been proposed in the past to remove the unwanted perturbations from the climatic series, and some of them have been implemented in software packages freely available on the Internet. The Spanish project MULTITEST was intended to test their performance in an automatic way with synthetic monthly series of air temperature and atmospheric precipitation, to update inter‐comparison results from former projects, especially those of the COST Action ES0601. Several networks representing different climates and station densities were used to test a variety of homogenization packages on hundreds of random samples. Results were evaluated mainly in the form of root mean squared errors and errors in the trend of the series, showing that ACMANT, followed by Climatol, minimized these errors. However, other packages performed also relatively well, even outperforming them when there were simultaneous biases of the same sign in most or all the test series. [ABSTRACT FROM AUTHOR]

Asian carp (bighead carp, Hypophthalmichthys nobilis; grass carp, Ctenopharyngodon idella and silver carp, H. molitrix) are a group of invasive species that are predicted to cause ecological effects if they invade the Great Lakes basin. Although Asian carp age at maturity is known to be an important factor in the risk of establishing a population, there is relatively little maturity data for North America. We found that air temperature can be used to predict the age at maturity of Asian carp. Nonlinear regressions using mean annual air temperature and annual degree days to predict age at maturity explain 60% and 62% of the variation respectively. These models predict that maturation is possible in locations that were previously excluded from Asian carp spawning range based on data from the Amur River. As expected, we find faster maturation in more southern areas of North America, although there are relatively large errors predicting age at maturity in the Mississippi River population. We conclude that due to the effect of faster maturation on population growth rates, southern Great Lakes locations (e.g. Lake Erie) may be at greater risk of faster population establishment. [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]

At most latitudes, the seasonal cycle of zonal‐mean surface air temperature is notably asymmetric: the length of the warming season is not equal to the length of the cooling season. The asymmetry varies spatially, with the cooling season being ∼40 days shorter than the warming season in the subtropics and the warming season being ∼100 days shorter than the cooling season at the poles. Furthermore, the asymmetry differs between the Northern Hemisphere and the Southern Hemisphere. Here, we show that these observed features are broadly captured in a simple model for the evolution of temperature forced by realistic insolation. The model suggests that Earth's orbital eccentricity largely determines the hemispheric contrast, and obliquity broadly dictates the meridional structure. Clouds, atmospheric heat flux convergence, and time‐invariant effective surface heat capacity have minimal impacts on seasonal asymmetry. This simple, first‐order picture has been absent from previous discussions of the surface temperature seasonal cycle. Plain Language Summary: Away from the equator, most places on Earth experience a noticeable seasonal cycle in surface air temperature. The seasonal cycle of temperature governs many aspects of human and ecosystem behavior. Observations show that, at most latitudes, the length of the warming season is not equal to the length of the cooling season, that is, the seasonal cycle is asymmetric. For example, at 25°N, the average warming season is 35 days longer than the cooling season. Here, we look at how this asymmetry varies with distance from the equator. We find that the asymmetry can largely be explained as a result of variations in the amount of radiation from the sun that reaches Earth, which are determined by Earth's orbit. Key Points: The seasonal cycle in zonal‐mean surface air temperature is asymmetricThe warming period tends to be shorter than 6 months in high latitudes and longer than 6 months in low latitudesThe asymmetry can largely be explained by variations in top‐of‐atmosphere insolation, dictated by Earth's orbit [ABSTRACT FROM AUTHOR]

A changing climate reshapes the range distribution of many organisms, and species with relatively low thermal optima, like many salmonids, are increasingly expected to face local population extinctions at lower latitudes. Understanding where and how fast these changes are happening is of pivotal importance for successful mitigation and conservation efforts. We used an extensive electrofishing database to explore temporal trends of juveniles of brown trout Salmo trutta in 218 locations from 174 Swedish streams, over the last 30 years (1991–2020). We hypothesized that 1) declines in abundance have occurred predominately in the warmer, southern regions, while increases have occurred in the colder, northern regions, 2) larger stream sizes may partly offset negative effects of climate, and 3) migrating and resident populations are affected differently by a warming climate. We found that abundance of brown trout juveniles generally declined in warmer regions especially in smaller streams (≤ 6 m wide), while the abundance increased in colder regions. In larger streams, negative effects of higher temperatures were seemingly buffered, as we found lower rates of decline or even positive trends. The rate of change (i.e. the slopes of the trends in abundance) was more pronounced towards the climate extremes, and was on average zero in regions with a normal annual air temperature (average temperature over 30 year period) around 5–6 °C. Warmer climate had stronger effects on migrating compared to resident populations, suggesting that climate‐induced loss of stream connectivity could be an additional factor that hinders recruitment in anadromous populations in a changing climate. Considering predictions of increasing temperatures and frequency of summer droughts, management of cold‐water salmonid populations should focus on conserving and restoring riparian vegetation, wetlands, climate and thermal refugia, and habitat integrity overall. Such measures may, however, not suffice for small streams at lower latitudes, unless hydrological connectivity is maintained. [ABSTRACT FROM AUTHOR]

Climate change is altering species’ traits across the globe. To predict future trait changes and understand the consequences of those changes, we need to know the environmental drivers of phenotypic change. In the present study, we use multidecadal long datasets to determine periods of within-year environmental variation that predict growth of three seabird species. We evaluate whether these periods changed over time and use them to predict future growth under climate change. We find that predictions of trait change could be improved by considering that 1) the timing of environmental factors used to predict traits (predictive-environmental features) can change over time, and 2) the type of predictive-environmental features can change over time. We find evidence of changes in the timing of environmental predictors in all populations studied and evidence for a change in the type of predictor in the studied Arctic murre population. Environmental models of growth predict that warming conditions will decrease growth rates and bird body sizes in two species (black-legged kittiwake Rissa tridactyla and glaucous-winged gull Larus Larus glaucescens), but not the third (thick-billed murre Uria lomvia). Consequently, climate change is likely to decrease fledging rates in the gulls and kittiwakes. Further, we find that sea icecover historically predicted murre chick growth well, but no longer does – instead air temperature is now a better predictor of murre growth. Our study highlights a need to investigate whether environmental determinants of trait variation commonly shift in a changing climate and whether such changes have implications for adaptation to novel environments. [ABSTRACT FROM AUTHOR]

Using 2001 and 2016 land use (LU) data and three developed deforestation LU scenarios, namely Deforest I, II and III, the possible impacts of the ongoing land use land cover change (LULCC) over West Africa (WA) and the uncertainty regarding the impacts on regional climate were assessed. Twelve Weather Research and Forecasting (WRF) model simulations were configured with the default and integrated MODIS satellite dataset for two sets of 3 months (December–February DJF 2011–2012 and July–September JAS 2012) over WA. Between 2001 and 2016, changes that occurred in precipitation, temperature and dew point temperature due to LULCC are mostly not significant at the 95% level with a decrease of 0.8, 0.0 and 0.1%, respectively. Deforest I, II and III caused significant changes with an increase of 44.4, 44.7, 41.5%; 0.8, 0.9, 1.1%; and 0.3, 0.1, −0.2%, respectively, for precipitation, temperature and dew point temperature. Precipitation was lower in the simulation with 2016 LU than it was with 2001 LU, which may have also been influenced by the reforestation that occurred between the periods. However, Deforest III resulted in a widespread nonsignificant rise in precipitation, as the modified areas showed a significant decrease during JAS period. Areas where forests were converted into barren lands revealed severe increase in the surface albedo which is a strong factor that its alteration is capable of influencing precipitation and other variables over WA. LULCC also altered the atmospheric column by modifying the strength of the African Easterly Jet (AEJ), Tropical Easterly Jet (TEJ), wind speed, and monsoonal, vertical, meridional and zonal winds. Heat related fields, such as dew point and air temperature, and moisture fields such as relative humidity were also modified by LULCC. Therefore, as WA reacts strongly to LULCC, sustainable land management practices should be encouraged to control its adverse effects. [ABSTRACT FROM AUTHOR]

Future global temperature change will have significant effects on society and ecosystems. Earth system models (ESM) are the primary tools to explore future climate change. However, ESMs have great uncertainty and often run at a coarse spatial resolution (usually about 2°). Accurate high‐spatial‐resolution temperature dataset are needed to improve our understanding of temperature variations and for many other applications. We apply Super resolution (SR) in computer vision using deep learning (DL) to merge 31 ESMs data. The proposed method performs data merging, bias‐correction, and spatial downscaling simultaneously. The CRU TS (Climate Research Unit gridded Time Series) data is used as reference data in the model training process. To find a suitable DL method, we select five SR methodologies with different structures. We compare the performances of the methods based on mean square error (MSE), mean absolute error (MAE) and Pearson correlation coefficient (R). The best method is used to merge the projected monthly data (1850–1900), and monthly future scenarios data (2015–2100), at the high spatial resolution of 0.5°. Results show that the merged data have considerably improved performance compared with individual ESM data and their ensemble mean (EM), both spatially and temporally. The MAE shows significant improvement; the spatial distribution of the MAE widens along the latitudes in the Northern Hemisphere. The MAE of merged data is ranging from 0.60 to 1.50, the South American (SA) has the lowest error and the Europe has the highest error. The merged product has excellent performance when the observation data is smooth with few fluctuations in the time series. This work demonstrates the applicability and effectiveness of the DL methods in data merging, bias‐correction and spatial downscaling when enough training data are available. Data can be accessed at https://doi.org/10.5281/zenodo.5746632. [ABSTRACT FROM AUTHOR]

Global air temperature increase has caused changes in the global climate such as droughts, floods and severe events. Land cover shifts and the increase in greenhouse gas emissions can intensify these impacts. Therefore, the application of Specific Warming Level 2 (SWL2) technique has been considered to evaluate the global warming issue in various climate scales, which may induce different responses in the climate system. The objective of this study is to evaluate the impacts of a doubling of current atmospheric CO2 concentrations as well as the influence of deforestation of the Amazon Forest on the climate of South America. In this study, the CPTEC‐BAM1.2 global model was used to simulate these processes. The first scenario of simulations considered doubled atmospheric CO2 concentration (2XCO2 = 776 ppm) and the second scenario is the total conversion of the Amazon Forest to pasture (DEF). In the third scenario both CO2 and deforestation are considered simultaneously (2XCO2 + DEF). A control simulation considers the natural Amazon Forest shape and a steady CO2 concentration of 388 ppm. Results suggest that the temperature increased in all scenarios, the highest increment in the 2XCO2 + DEF experiment (5.4°C in 2XCO2 + DEF, 5.1°C in DEF and 3.2°C in 2XCO2). The model simulated drastic negative rainfall anomalies in each of three scenarios (2XCO2 + DEF: −2.1 mm day−1, 2XCO2: −1.3 mm day−1 and DEF: −0.9 mm day−1). Besides less precipitation and evapotranspiration, shifts in moisture transport were identified in each of the three scenarios. The effect of doubled atmospheric CO2 produces results similar to deforestation for hydrological water budget changes, and total pasture conversion may intensify such changes. [ABSTRACT FROM AUTHOR]

Global air temperature increase has caused changes in the global climate such as droughts, floods and severe events. Land cover shifts and the increase in greenhouse gas emissions can intensify these impacts. Therefore, the application of Specific Warming Level 2 (SWL2) technique has been considered to evaluate the global warming issue in various climate scales, which may induce different responses in the climate system. The objective of this study is to evaluate the impacts of a doubling of current atmospheric CO2 concentrations as well as the influence of deforestation of the Amazon Forest on the climate of South America. In this study, the CPTEC‐BAM1.2 global model was used to simulate these processes. The first scenario of simulations considered doubled atmospheric CO2 concentration (2XCO2 = 776 ppm) and the second scenario is the total conversion of the Amazon Forest to pasture (DEF). In the third scenario both CO2 and deforestation are considered simultaneously (2XCO2 + DEF). A control simulation considers the natural Amazon Forest shape and a steady CO2 concentration of 388 ppm. Results suggest that the temperature increased in all scenarios, the highest increment in the 2XCO2 + DEF experiment (5.4°C in 2XCO2 + DEF, 5.1°C in DEF and 3.2°C in 2XCO2). The model simulated drastic negative rainfall anomalies in each of three scenarios (2XCO2 + DEF: −2.1 mm day−1, 2XCO2: −1.3 mm day−1 and DEF: −0.9 mm day−1). Besides less precipitation and evapotranspiration, shifts in moisture transport were identified in each of the three scenarios. The effect of doubled atmospheric CO2 produces results similar to deforestation for hydrological water budget changes, and total pasture conversion may intensify such changes. [ABSTRACT FROM AUTHOR]

Future global temperature change will have significant effects on society and ecosystems. Earth system models (ESM) are the primary tools to explore future climate change. However, ESMs have great uncertainty and often run at a coarse spatial resolution (usually about 2°). Accurate high‐spatial‐resolution temperature dataset are needed to improve our understanding of temperature variations and for many other applications. We apply Super resolution (SR) in computer vision using deep learning (DL) to merge 31 ESMs data. The proposed method performs data merging, bias‐correction, and spatial downscaling simultaneously. The CRU TS (Climate Research Unit gridded Time Series) data is used as reference data in the model training process. To find a suitable DL method, we select five SR methodologies with different structures. We compare the performances of the methods based on mean square error (MSE), mean absolute error (MAE) and Pearson correlation coefficient (R). The best method is used to merge the projected monthly data (1850–1900), and monthly future scenarios data (2015–2100), at the high spatial resolution of 0.5°. Results show that the merged data have considerably improved performance compared with individual ESM data and their ensemble mean (EM), both spatially and temporally. The MAE shows significant improvement; the spatial distribution of the MAE widens along the latitudes in the Northern Hemisphere. The MAE of merged data is ranging from 0.60 to 1.50, the South American (SA) has the lowest error and the Europe has the highest error. The merged product has excellent performance when the observation data is smooth with few fluctuations in the time series. This work demonstrates the applicability and effectiveness of the DL methods in data merging, bias‐correction and spatial downscaling when enough training data are available. Data can be accessed at https://doi.org/10.5281/zenodo.5746632. [ABSTRACT FROM AUTHOR]

Despite the availability of global and continental climate datasets and climate change information, locally relevant quantification of historic trends in climate variables is still lacking in developing countries, especially at local scales. This is particularly true in the Department of Arequipa, Peru. An arid region with a booming population, substantial mining activities, and large irrigated agriculture, which is highly susceptible to climate change. This study aims to evaluate climate trends from 1988 to 2017 in the Arequipa Department and provide information that can facilitate stakeholders' adaptation to the rapid‐changing climate. The daily precipitation (Prec), and maximum (Tmax) and minimum (Tmin) daily air temperature data used in this study came from the Servicio Nacional de Meteorología e Hidrología del Perú (SENAMHI) and the National Ocean and Atmospheric Administration's (NOAA) Global Summary of the Day (GSOD). Data passed through a quality checking process for removal of implausible data, data gap filling, and inhomogeneity detection. The Mann–Kendall test, at a significance level of 0.10, was used to determine trends and the Theil–Sen slope (Sen's slope) was used to estimate the magnitude of the change. Sen's slope was also calculated spatially, using the gridded Arequipa Climate Maps (ACM) dataset. Results indicate that precipitation seasonality has been increasing, as the observed increase in annual precipitation is happening mostly in the rainy season (December–March) and the start and end of the rainy season are delayed. Positive temperature trends were dominant in the whole region. Tmin is increasing more than Tmax, especially at higher altitudes. Exceptions to increasing temperatures were found in areas influenced by irrigation projects that underwent great expansion. The effect of increasing temperature on glaciers was evaluated by mapping the change in the area with average annual temperature below 0°C between the decades of 1988–1997 and 2008–2017, which reduced by 73.2%, with small areas disappearing and larger contiguous areas shrinking. [ABSTRACT FROM AUTHOR]

Extreme temperatures were considered natural hazards because they could increase morbidity and mortality. Understanding the extreme temperature changes at different warming levels is crucial to climate change mitigation and adoption for human health. This study projected climate change effects on the intensity, occurrence, and duration of extreme temperatures in Taiwan with 2 and 4°C global warming scenarios using the Weather Research and Forecasting model. The future climate simulations were conducted with the pseudo‐global warming approach, and the future climate changes were obtained from the ensemble mean of simulations in the Coupled Model Intercomparison Project 5. The simulated daily mean temperature increased by 1.40 and 3.09°C under 2 and 4°C global warmings. In a warming world, the daily maximum temperature was projected to increase by 1.35–3.00°C, whereas the daily minimum temperature was even higher, leading to weaker diurnal temperature variation in most regions. The simulation results show that intensified heatwaves with frequent and prolonged durations become par for the course, whereas extremely cold days disappear gradually. The occurrence of heatwaves in the future is projected to be five times that in the current climate. Comparing the global warming impacts over different land‐use types, the heatwave occurrence over urban areas rose more quickly than over other land‐use types; forests are less vulnerable to global warming. On the contrary, the changes in extremely cold days over urban areas were weaker than over other land‐use types. Overall, the effects of global warming on temperature revealed that extreme events were more severe with increased temperature than with the mean state of air temperature. Nonlinear behaviours indicated that global warming should be limited to 2°C, and the additional 2°C warming (from 2 to 4°C) should be addressed carefully. [ABSTRACT FROM AUTHOR]

Climate is a powerful driver of agricultural and natural systems, and spatial climate datasets are currently in great demand. This is especially true in the Arequipa Department of Peru, a region with low seasonal precipitation, remarkable topographic variability, and significant water demand in a highly managed water system. This paper presents the Arequipa Climate Maps (ACM) datasets, a high resolution (1 km) spatial 30‐year (1988–2017) climate dataset for the Arequipa Region, in Peru. Four interpolation methods, and combinations of those methods, were tested to produce 30 years of daily precipitation, maximum and minimum air temperature: Ordinary Kriging (OK), Thin Plate Splines (TPS), Regression Kriging (RK), and Regression Thin Plate Splines (RTPS). The mixed method RTPS‐TPS and RTPS using locally fitted polynomial and potential regressions were found to best represent the spatial variability of precipitation and daily extreme temperatures, respectively, and helped compensate the bias resulting from the lack of weather stations at higher elevations. These methods were then selected to create the ACM dataset, which contains climate maps of 30‐year annual and monthly climate normals (ACM–Normals) and 30 years of annual, monthly, and daily climate maps (ACM–YMD). In addition, insights on weather station gap filling in mountainous areas and bias corrections for avoidance of anomalous precipitation and to assure consistency between annual, monthly and daily data are presented, together with discussion about the quality and limitations of the dataset, and its comparison with other datasets. [ABSTRACT FROM AUTHOR]

The objective of this study was to devise an optimal method for estimating air temperatures outside pig farms to be able to evaluate the genetic performance of pigs. Using daily temperature data from Japan Meteorological Agency meteorological stations, we investigated the optimal number of observation weather stations (number of records), and methods of estimating outside temperature when temperature records are missing. We also considered the possibility of using relative humidity data. Our results showed that it is possible to use records from the three nearest weather stations to estimate off‐farm ambient temperatures. We also concluded that estimates of outside temperatures when records are missing can be made by using data from at least one weather station that holds a full set of data. The correlation coefficients between the true THI (temperature‐humidity index) and the estimated THI and the average daily temperature were almost the same, indicating that the daily average temperature can be used instead of estimated THI. [ABSTRACT FROM AUTHOR]

In situ measurements are the most important basis of obtaining precise meteorological datasets. However, it is difficult to accurately extrapolate the plausible meteorological field in certain regions based on in situ measurements alone, especially in areas with complex topography like the Tibetan Plateau (TP). Gridded products, remote sensing, and data assimilation technique overcome this problem but they have their own weaknesses, such as low resolution, huge computation, and time‐consuming. Here we applied the state‐of‐the‐art generative adversarial networks (GAN) in image inpainting to construct the surface temperature over the TP combined with high‐resolution China Meteorological Forcing Dataset (CMFD) air temperature product and in situ observations. In this study, the surface air temperature at 2 m dataset with grids of 0.1° over the TP from 1979 to 2020 based on GAN‐based model have been generated, which can capture the spatial patterns and trends of surface air temperature over the TP. Moreover, the GAN‐based daily temperature has higher correlation coefficients (0.99) and lower root‐mean‐square errors (1.06°C) than CMFD (0.98, 1.43°C) and reanalysis products when compared with in situ observations. Finally, the temperature fields over the TP from 2019 to 2020 is constructed, whereas CMFD is only available from 1979 to 2018. The results reflect the advantages of GAN on big‐data computation and fully utilizing effective information over the TP, and GAN‐based temperature dataset can be used to detect climate change over the region. This study demonstrated the potential and applicability of artificial intelligence for developing and extending practicable high‐resolution meteorological dataset over regions with sparse stations and rugged topography. [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]

Day-to-day (DTD) temperature variability is an important characteristic of air temperature and is closely related to the economy, ecology, and human health. According to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, the air temperature has increased particularly during the most recent decades. However, the temporal and spatial variations of DTD temperature variability have not been fully addressed. In this study, the DTD temperature variability is calculated using observed air temperature from meteorological stations in China. The regional and seasonal performances are evaluated among different climate regions and half-year periods. In addition, the relationship between DTD temperature variability and the Arctic Oscillation (AO) is evaluated. The national mean DTD temperature variability in 1961--2020 during the warming (cooling) half-year was 1.8°C (1.5°C), with minimum (maximum) values occurring in the southwestern (northeastern) regions of mainland China. Negative correlation was found between DTD temperature variability and the AO, particularly during the warming half-year in the northern region. The national mean trends of DTD temperature variability were -0.003°C and -0.007°C per decade during warming and cooling half-years, respectively, indicating that the inter-day variation is decreasing under the background of global warming, which may be ecologically beneficial. These results could be of great significance in understanding temperature change and can be used to quantify land--atmosphere interaction and climate change. [ABSTRACT FROM AUTHOR]

In order to improve the temperature and microstructure uniformity in the thickness direction of online‐quenching 960 MPa high strength steel, the finite element software ADINA and the experiments have been used to analyze the effect of internal air cooling on temperature variation in the thickness direction during the online quenching process. The results showed that the interval air cooling could effectively improve the uniformity of temperature, microstructure and hardness in the direction of thickness of steel plate during the online quenching process. When air cooled for 3.2 s, the temperature difference between the surface and the core of the steel plate could be reduced by 90.13 %, and the cooling rate in the core could be reduced by about 85.20 % compared with continuous water cooling condition. The hardness difference between the surface and the core could reduce from 23 HV 5 to 5 HV 5. The internal air cooling process had an influence on the matensite lath morphology compared with continuous water cooling process. The microstructures with the different cooling conditions were martensite and the properties could still meet the requirements of 960 MPa high strength steel. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) is essential to microbial nitrogen (N)‐cycling networks in terrestrial ecosystems. Studies have found that SM–atmosphere feedbacks dominate the changes in land carbon fluxes. However, the influence of SM–atmosphere feedbacks on the N fluxes changes, and the underlying mechanisms remain highly unsure, leading to uncertainties in climate projections. To fill this gap, we used in situ observation coupled with gridded and remote sensing data to analyze N2O fluxes emissions globally. Here, we investigated the synergistic effects of temperature, hydroclimate on global N2O fluxes, as the result of SM–atmosphere feedback impact on N fluxes. We found that SM–temperature feedback dominates land N2O emissions by controlling the balance between nitrifier and denitrifier genes. The mechanism is that atmospheric water demand increases with temperature and thereby reduces SM, which increases the dominant N2O production nitrifier (containing amoA AOB gene) and decreases the N2O consumption denitrifier (containing the nosZ gene), consequently will potential increasing N2O emissions. However, we find that the spatial variations of soil–water availability as a result of the nonlinear response of SM to vapor pressure deficit caused by temperature are some of the greatest challenges in predicting future N2O emissions. Our data‐driven assessment deepens the understanding of the impact of SM–atmosphere interactions on the soil N cycle, which remains uncertain in earth system models. We suggest that the model needs to account for feedback between SM and atmospheric temperature when estimating the response of the N2O emissions to climatic change globally, as well as when conducting field‐scale investigations of the response of the ecosystem to warming. [ABSTRACT FROM AUTHOR]

This study investigates the relative contributions of internal variability and external forcing to multidecadal variations in the winter (December–February) East Asian averaged surface air temperature (EASAT) since 1880 by analyzing three observational datasets and large ensemble simulations from 12 coupled models. The results indicate that the internal variability plays a dominant role in the EASAT multidecadal variations. Specifically, the Atlantic Multidecadal Oscillation (AMO) affects the EASAT via an atmospheric teleconnection across the Eurasian continent, and the correlation coefficient between internally generated EASAT variation and the AMO index is −0.49. In comparison, the external forcings only modulate the observed EASAT variations due to offsetting effects of the external forcing agents. That is, the multidecadal variations in the greenhouse gas concentrations and anthropogenic/volcanic aerosols make positive and negative contributions, respectively. Our new finding is conducive to EASAT predictions in the following decades due to the predictability of AMO and anthropogenic forcings. Plain Language Summary: Internal variability is one of the most important sources of uncertainty in predicting the East Asian winter temperature in future decades. It is thus essential to address associated reasons for the internally generated East Asian temperature variability. This study tries to separate internal variability from the externally forced climate response for the winter East Asian averaged surface air temperature (EASAT) based on three observational datasets and large ensemble simulations from 12 latest coupled models. We find that the internal variability explains 59% of the total EASAT multidecadal variations, in which the Atlantic Multidecadal Oscillation (AMO) plays a vital role. The external forcings play the secondary role. This means that accurate prediction of the AMO would be helpful for predicting internally generated EASAT variations at the multidecadal scale. Key Points: Internal variability has played a dominant role in the multidecadal variations of winter temperature averaged over East Asia since 1880The Atlantic Multidecadal Oscillation plays a vital role in internally generated East Asian winter temperature variationsChanges in greenhouse gas concentrations and anthropogenic/volcanic aerosols make positive and negative contributions, respectively [ABSTRACT FROM AUTHOR]

Variability of the North Atlantic Oscillation (NAO) drives wintertime temperature anomalies in the Northern Hemisphere. Dynamical seasonal prediction systems can skilfully predict the winter NAO. However, prediction of the NAO‐dependent air temperature anomalies remains elusive, partially due to the low variability of predicted NAO. Here, we demonstrate a hidden potential of a multi‐model ensemble of operational seasonal prediction systems for predicting wintertime temperature by increasing the variability of predicted NAO. We identify and subsample those ensemble members which are close to NAO index statistically estimated from initial autumn conditions. In our novel multi‐model approach, the correlation prediction skill for wintertime Central Europe temperature is improved from 0.25 to 0.66, accompanied by an increased winter NAO prediction skill of 0.9. Thereby, temperature anomalies can be skilfully predicted for the upcoming winter over a large part of the Northern Hemisphere through increased variability and skill of predicted NAO. Plain Language Summary: Wintertime temperature in the Northern Hemisphere is regulated by the variations of atmospheric pressure, represented by the so‐called North Atlantic Oscillation (NAO). The NAO's phase—negative or positive—is associated with the pathways of cold and warm air masses leading to cold or warm winters in Europe. While the NAO phase can be predicted well, predictions of the NAO‐dependent air temperature remain elusive. Specifically, it is challenging to predict the strength of the NAO, the most important requirement for the accurate prediction of wintertime temperature. Here, we improve wintertime temperature prediction by increasing the strength of the predicted NAO. We use observation based autumn Northern Hemisphere ocean and air temperature, as well as ice and snow cover for statistical estimation of the first guess NAO for the upcoming winter. Then, we sub‐select only those simulations from the multi‐model ensemble, which are consistent with our first guess NAO. As a result, based on these selected members, the wintertime temperature prediction is substantially improved over a large part of the Northern Hemisphere. Key Points: Amplitude and skill of predicted North Atlantic Oscillation (NAO) improve significantly by subsampling of ensemble of existing seasonal prediction systemsAmplified NAO variability leads to significant improvement in predicting the upcoming winter temperature anomalies in the Northern Hemisphere [ABSTRACT FROM AUTHOR]

Defined as the difference between the daily maximum (Tmax) and minimum surface air temperature (Tmin), the diurnal temperature range (DTR) is critical to local climate, ecosystem, and socio‐economic functioning. This study investigates how DTR responds to the future global warming, using a novel, fine‐resolution convection‐permitting regional climate model. Results demonstrate the asymmetrical impact of global warming, that is, the more substantial warming in Tmin, and the lower in Tmax. This asymmetry results in the DTR reduction of 0.5°K in the mid‐latitude and 0.25°K in the low‐altitude area (under 2°K warming scenario). The increase in daytime cloudiness under warmer climates is likely responsible for the reduction of incoming short‐wave radiation reduction, consequently causing Tmax "underwarming." Study results also suggest that atmospheric instability could play a vital role in the difference in DTR response between the tropical and mid‐latitude areas. Plain Language Summary: Diurnal temperature range, which is defined as the difference between the daily maximum (Tmax) and daily minimum air temperature (Tmin), is a critical factor that modulates local climate, ecosystem, and socio‐economic functioning. This study investigates how global warming impacts diurnal temperature range (DTR) using a high resolution numerical model. Results demonstrate the asymmetrical response to global warming, that is, the more substantial warming is confirmed in Tmin, and the lower in Tmax. This asymmetry results in the DTR reduction of approximately 0.5°K in the mid‐latitude and 0.25°K in the low‐altitude area. A cause of the DTR reduction is the daytime‐cloudiness increase under warmer climates, which reduces short‐wave solar radiation reaching the surface, consequently causing the Tmax "underwarming." Also, the difference in atmospheric instability in the mid‐ and low‐latitude areas explains the difference in the DTR response, that is, more moderate DTR reduction is confirmed in low latitude areas. Key Points: This study demonstrates the reduction of the diurnal temperature range (DTR) under future warmer climatesThe reduction of DTR is primarily attributed to the increase in cloudiness which reducing daytime incoming‐shortwave radiationDifference in intrinsic atmospheric stability between tropical and mid‐latitude areas could explain the distinct responses of DTR [ABSTRACT FROM AUTHOR]

The last millennium (LM, 1000–1850 AD) is crucial for studying historical climate change on decadal to multidecadal timescales. The summer surface air temperature (SAT) evolutions on regional scales (e.g. over China) are more uncertain than the globe/Northern Hemisphere, especially in response to external forcing factors and internal climate variability. Here, we provide one‐signal (full‐forcing) fingerprints of summer SAT in China derived from three large ensemble model archives with a multi‐proxy reconstruction during the LM, Little Ice Age (LIA, 1451–1850 AD), and Medieval Climate Anomaly (MCA, 1000–1250 AD), respectively. Our results show that (a) SATs in the northeast, southeast, northwest, and Tibetan Plateau (TP) regions of China show evident decreasing trends during the LM. External forcing response from all model archives agrees with the regional SAT reconstruction but underestimates variability in northwest China at the multidecadal timescale. (b) During the LIA, the summer regional SAT exhibits a cold condition in the reconstruction and simulations, especially in the northeast and northwest regions of China. External forcing responses in most model archives are the dominant factor on multidecadal SAT evolutions in the southeast, northeast, and TP regions of China and decadal SAT evolutions in northwest China. (c) During the MCA, detection and attribution of SAT shows that internal climate variability dominates in southeast, northeast, and TP regions of China, but external forcing dominates in northwest China at decadal to multidecadal timescales. These results contribute to a better understanding of the causes and mechanisms of regional climate change. [ABSTRACT FROM AUTHOR]

Under global warming, the frequent occurrence of Eurasian extreme cold events in the Northern Hemisphere in recent years has attracted widespread attention. Winter Eurasia near‐surface anticyclone activities are closely related to low temperatures. This study uses a new objective anticyclone identification algorithm based on a Mask region‐based convolutional neural network (Mask R‐CNN) to detect long‐term winter Eurasian anticyclone activities. We found that most tracks of Eurasian anticyclones (30.5%) pass Siberia in winter. Anticyclone tracks over Siberia can be well separated into the eastward moving type, the northwest–southeast type, and the local type, with distinct associated circulation patterns. The energy dispersions of upstream Rossby waves have an important influence on the movement, maintenance and intensification of the three types. In association with anticyclones intensifying in Siberia, surface air temperatures in the mid‐latitudes of Eurasia, especially Siberia, are significantly lower. In particular, the local type is the most active (56.1%) and is significantly correlated with the winter temperature anomaly in most areas of East Asia. Further research indicates that the preceding autumn sea ice condition in the Barents Sea can be used as a precursor factor to modulate the local type anticyclone activity and winter temperature anomaly in East Asia. In years of less ice, a quasi‐meridional wave activity flux originating from the Barents Sea is more active, and the centre SLP of the local type anticyclone is higher with more cold air mass carried, which in turn results in colder weather in Siberia, the Mongolian Plateau and East Asia. [ABSTRACT FROM AUTHOR]

Examining variation in fish populations over time is strengthened when climatic variations are understood and incorporated into analyses. We used a 18‐year brook trout (Salvelinus fontinalis) data set with samples across a ~4800 km2 spatial area in the Central Appalachian Mountains, combined with PRISM climate data at the HUC‐12 subwatershed level to investigate temporal trends of each. We found significant increases in air temperature (p <.01) with no significant trends in precipitation across time, including no trends in changes in variation of temperature and precipitation also. Extreme rainfall events (# of days above 95% percentile for total daily precipitation in a year) and consecutive dry days (cumulative # of consecutive days with <3 mm precipitation in a year) were also examined and produced no significantly changing trends over time. Using random forests, seasonal climate variables identified as changing over time explained 35.1% of the variability in young‐of‐the‐year brook trout abundance over the 18‐year monitoring period. The same procedure explained 25.1% of the variability in juvenile abundance and only 5.1% in adult abundance. Variable selection following random forest generation illustrated significant effects for winter and spring precipitation (both negative on young‐of‐the‐year abundance. Spring degree days and spring precipitation both had significant negative effects on juvenile abundance. Random effects for stream and year explained a maximum of 27.7% and 10.8% of the variation in abundance across all models. A significant, positive interaction between spring degree days and precipitation was also identified, lending support to the important balance between temperature and flow in these systems that is critical to brook trout persistence. This work provides long‐term evidence to help understand the dynamics of these sentinel headwater fish populations as they experience a changing climate. [ABSTRACT FROM AUTHOR]

The influences of environmental factors such as weather on the human brain are still largely unknown. A few neuroimaging studies have demonstrated seasonal effects, but were limited by their cross‐sectional design or sample sizes. Most importantly, the stability of the MRI scanner has not been taken into account, which may also be affected by environments. In the current study, we analyzed longitudinal resting‐state functional MRI (fMRI) data from eight individuals, where they were scanned over months to years. We applied machine learning regression to use different resting‐state parameters, including the amplitude of low‐frequency fluctuations (ALFF), regional homogeneity (ReHo), and functional connectivity matrix, to predict different weather and environmental parameters. For careful control, the raw EPI and the anatomical images were also used for predictions. We first found that daylight length and air temperatures could be reliably predicted with cross‐validation using the resting‐state parameters. However, similar prediction accuracies could also be achieved by using one frame of EPI image, and even higher accuracies could be achieved by using the segmented or raw anatomical images. Finally, the signals outside of the brain in the anatomical images and signals in phantom scans could also achieve higher prediction accuracies, suggesting that the predictability may be due to the baseline signals of the MRI scanner. After all, we did not identify detectable influences of weather on brain functions other than the influences on the baseline signals of MRI scanners. The results highlight the difficulty of studying long‐term effects using MRI. [ABSTRACT FROM AUTHOR]

In the current study, the temperature dependence of the Cl atom rate coefficient with 2‐methyl‐3‐butene‐2‐ol (MBO) is re‐assessed. Relative rate kinetic measurements were performed inside THALAMOS (thermally regulated atmospheric simulation chamber) facility under atmospheric pressure (760 Torr) of zero air in the temperature range of 256–353 K. The rate coefficient of the title reaction was found to be independent of temperature with a value of kMBO = (3.20 ± 0.10) × 10−10 cm3 molecule–1 s–1, where the quoted error corresponds to 2σ standard error of the mean value and includes the overall uncertainty of the measurements. Besides the kinetic study we have also determined the yields of the major products formed, in the temperature range of 273–353 K. Acetone and chloroacetaldehyde were identified as primary oxidation products with almost identical yields, ranging between 60% and 80%. Formaldehyde and methyl vinyl ketone were also observed with yields below 10% and 0.5%, respectively. Our experimental results indicate that the dominant reaction pathway is the addition of Cl atoms to the terminal carbon atom of MBO. [ABSTRACT FROM AUTHOR]

BACKGROUND Tocopherols and sterols are minor components of virgin olive oils that contribute to oil quality. Based on observations at different geographical locations, it has been suggested that environmental temperature during fruit growth affects tocopherol and sterol oil concentrations in olive fruit. However, controlled experiments have not been conducted to directly assess their responses to temperature. In this study, a manipulative experiment using open‐top chambers (OTCs) was performed in the field to evaluate the responses of these oil components to a moderate air temperature increase during oil accumulation in young trees of two olive cultivars (Arbequina, Coratina). The two temperature levels in the OTCs were a control about 1 °C above ambient temperature (T0) and a heated treatment (T+) with a target temperature of 4 °C above T0. RESULTS: Total tocopherol and sterol oil concentrations in olive fruit were generally higher in the T+ temperature treatment than in the control at the end of the oil accumulation period. The increase in total tocopherols in T+ appeared to be related to a decrease in fruit oil concentration with heating. Individual sterols showed both significant increases and decreases due to T+, and some differences in response occurred between the two cultivars. CONCLUSION: These findings provide evidence that growth temperature affects tocopherol and sterol oil concentrations in olive fruit at the end of the oil accumulation period. Cultivars should be carefully chosen for new olive‐growing regions, and the results could be relevant for global warming scenarios in existing growing regions. © 2021 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

We present a novel hierarchical Bayesian quantile regression model to estimate how precipitation scales with temperature depending on season, weather regime (WR; i.e., large‐scale patterns of atmospheric circulation), and precipitation percentile. The approach develops regional scaling estimates by partially pooling data across sites, accounting for uncertainty stemming from variable record lengths. Results using long‐term records of daily and hourly precipitation and both dry‐bulb and dew point temperature across the Northeast US suggest that regional precipitation scaling rates vary from 0% to 8% per °C. Scaling rate variability is driven most by season and the percentile of precipitation, with only modest variations across WRs. Daily scaling rates are highest in the winter and summer, while hourly rates at the highest percentiles are greatest in summer. Lower scaling rates for more extreme precipitation quantiles are driven by a handful of storms occurring at cooler temperatures but with strong vertical uplift and heavy precipitation. Plain Language Summary: The intensification of precipitation with warming is a major impact of climate change, and is driven by the increased moisture holding capacity of the air as temperatures rise. The rate of intensification (i.e., the percent increase in precipitation for every degree of warming) is an important scientific question with major implications for how communities should adapt to climate change. Previous work investigating how precipitation scales with temperature has often conducted analyses for specific seasons and on a site‐by‐site basis, ignoring the influence of large‐scale patterns of atmospheric circulation and failing to account for uncertainty arising from variable record lengths. This study forwards a hierarchical Bayesian quantile regression framework to address these challenges and develop the first set of regional scaling rate estimates that vary by season, pattern of atmospheric flow, and the intensity of precipitation events. The approach, applied to over 90 weather stations across the Northeast U.S., reveals that scaling rates are largest in the winter and summer and smaller in the shoulder seasons, but only vary modestly across patterns of atmospheric flow. Average precipitation tends to increase faster with warming than rare, extreme events, but this is due to a few intense storms that occur at cooler temperatures. Key Points: A Bayesian quantile regression framework is advanced to investigate regional daily and sub‐daily precipitation scaling with temperatureScaling rates across the Northeast US vary modestly by weather regime but significantly by season and the quantile of precipitationA handful of storms with strong uplift and cooler temperatures drive lower scaling rates at higher precipitation quantiles [ABSTRACT FROM AUTHOR]

Past studies based on univariate scaling analyses at the weather time scale documented a negative scaling of extreme precipitation intensity (EPI), which prevents EPI extrapolation from past climate. Here we present a bivariate scaling analysis and show that, contrary to the univariate scaling results, EPI monotonically increases with temperature and shows no negative scaling when controlled for saturation deficit. The observed EPI‐temperature relationship in saturated atmosphere is surprisingly similar among different regions and closely follows the Clausius‐Clapeyron scaling; climate models produce greater regional dependence of the scaling relationship with a wide range of scaling rate. For extratropical regions, the model‐simulated EPI‐temperature relationship under saturation shows a past‐to‐future continuity, which could potentially support extrapolation to a warmer climate. The scaling at saturation bridges the EPI‐temperature relationship between weather and climate time scales and may enable potential prediction of future precipitation extremes via extrapolation from past observations. Plain Language Summary: Predicting changes of extreme precipitation intensity (EPI) and their regional dependency is a crucial but challenging task. Extrapolation based on past observations has been hindered by the apparent decrease of EPI on hot days, a phenomenon often referred to as "negative scaling". Here we propose a bivariate scaling analysis to account for the impact of both temperature and atmospheric saturation deficit (SD), and show that under a constant SD, EPI increases monotonically with temperature and shows no negative scaling, which contradicts the findings from conventional scaling analysis. In saturated atmosphere, the EPI‐temperature relationship based on observations is surprisingly similar among different regions and is close to the thermodynamics‐based Clausius‐Clapeyron scaling; climate models produce a larger disparity among different regions with wide ranging scaling rates. As climate changes, the model simulated EPI‐temperature relationships in saturated atmosphere over extratropical regions show a certain degree of continuity between historical and warmer climates, contrasting the strong discontinuity in conventional analysis. The past‐to‐future continuity of the relationship makes it possible to derive future extreme precipitation through extrapolation from past observations. The scaling relationship in saturated atmosphere bridges the weather and climate time scales, and provides a new emergent constraint for climate model performance. Key Points: Under a constant saturation deficit, extreme precipitation intensity increases monotonically with temperature, showing no negative scalingThe observed precipitation scaling in saturated atmosphere shows little regional dependence and is close to the Clausius‐Clapeyron ratioClimate models produce larger regional dependence, but show some past‐to‐future continuity of the scaling relationship [ABSTRACT FROM AUTHOR]

Eemian pollen records from central Europe describe a transition from thermophilous tree taxa in the early Eemian to boreal tree taxa in the late Eemian with forest opening in the subsequent stadial. Available summer‐temperature reconstructions for the mid‐ to late Eemian transition show decreasing values during that time. We present a new chironomid record from southern Germany that covers the mid‐Eemian to the end of the first Würmian stadial (c. 125–105 ka) and also parts of the late Rissian glaciation and early Brörup interstadial of the early Würmian glaciation. Based on this record we describe lake development in the former Füramoos palaeolake and quantitatively reconstruct July air temperature during the examined interval. Late Rissian sediments are dominated by two chironomid taxa, Sergentia coracina‐type and Micropsectra radialis‐type, indicating very cold conditions. Following an uncertain interval, probably including a hiatus at the late Rissian/Eemian transition, mid‐Eemian sediments contain Tanytarsus glabrescens‐type and Tanytarsus mendax‐type suggesting relatively high July air temperatures. During the late Eemian, typically thermophilic taxa such as Tanytarsus glabrescens‐type disappear, suggesting decreasing temperatures. Stadial A is associated with increases in Microtendipes pedellus‐type suggesting more oligotrophic conditions. Early Brörup sediments contain Tanytarsus glabrescens‐type, suggesting a slight increase in July air temperature. Reconstructed July air temperatures show temperatures of 7–8 °C during the late Rissian and a decline from ~15.5–12 °C during the mid‐ to late Eemian associated with decreasing Northern Hemisphere July insolation. July air temperature values vary between 12 and 14 °C in the late Eemian, while reconstructed temperatures remain within 12–13.5 °C during Stadial A. Our new chironomid‐based temperature reconstruction provides valuable corroboration and new quantification of temperature development from the mid‐Eemian to the early Brörup interstadial as well as for sections of the late Rissian from the alpine foreland of southern Germany. [ABSTRACT FROM AUTHOR]