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This study investigates the spontaneous self‐aggregation of convection in non‐rotating Radiative‐Convective Equilibrium (RCE) simulations performed by the CNRM‐CM6‐1 General Circulation Model within the framework of the RCE Model Intercomparison Project (RCEMIP). In this model, the level of convection self‐aggregation at equilibrium, as quantified by metrics based on moisture or moist static energy, strongly increases with sea surface temperature (SST). As it gets warmer, the troposphere gets drier, high cloud cover diminishes in dry regions, the top of high cloud rises and their thickness increases in moist regions, and low‐cloud cover increases. At high SSTs, the large‐scale circulation exhibits a shallow component, stronger than its deep counterpart. The transition toward self‐aggregation has a similar first 20‐day phase for all SSTs within the 295–305 K range. It primarily involves radiative positive feedback processes. Then, for SSTs above approximately 298 K, a new, slower, transition toward higher levels of self‐aggregation occurs. It is concomitant with a shift from a top‐heavy to a more bottom‐heavy large‐scale circulation, a strengthening of the shallow circulation and a reduced mobility of convective aggregates. This second transition is mostly driven by the dry regions, still involves longwave radiative positive feedbacks, but also advective positive feedbacks in the driest regions. It is argued that boundary‐layer radiative cooling difference between moist and dry regions, which is stronger at high SSTs, is instrumental in this second phase of self‐aggregation. The sensitivity of deep convection to environmental dry air also likely acts as a positive feedback on the system. Plain Language Summary: In idealized configurations of the Earth, convective clouds can spontaneously organize into large clusters: this is convective self‐aggregation. We investigate the sensitivity of this process to surface temperature in the atmospheric component of the state‐of‐the‐art global climate model CNRM‐CM6‐1. For surface temperatures spanning a typical range of tropical conditions (295–305 K), the model exhibits an aggregated state when equilibrium is reached. As the surface gets warmer, convection is more aggregated, the troposphere gets drier, high clouds get less frequent in dry regions and low‐cloud cover increases. When starting from homogeneous conditions, an initial rapid phase of self‐aggregation occurs in all experimented sea surface temperatures. Radiative processes are instrumental in leading to self‐aggregation. For warm surface temperature above approximately 298 K, a second, slower, transition occurs and leads to higher levels of self‐aggregation. It is associated with an adjustment of the large‐scale circulation, in which shallow circulations in the lower troposphere (surface‐700 hPa) and between dry and moist regions strengthen. The radiative loss of energy within the boundary layer, and its unbalanced state between dry and moist regions after the initial transition is argued to be the main process at play. Key Points: The sensitivity to surface temperature of convective self‐aggregation under radiative‐convective equilibrium is documented in CNRM‐CM6‐1Increase of self‐aggregation with surface temperature is associated to an efficient shallow circulation between dry and moist regionsA second slower transition toward self‐aggregation at high surface temperature is primarily driven by radiative processes in dry regions [ABSTRACT FROM AUTHOR]

Using the daily 2 m maximum temperature (Tmax), 2 m minimum temperature (Tmin) and cloud cover data measured at ground sites of the China Meteorological Administration in North China from 2000 to 2017, this study investigates the influence of clouds on the daily temperature range (DTR) defined as the difference between Tmax and Tmin. As expected, the cloud cover shows the similar averaged spatial distribution and monthly variation with Tmin. Surprisingly, it also shows the similar average spatial distribution and monthly variation with Tmax, suggesting the more important roles of regions (latitude) and seasons associated with the variations of land surface temperature, which is further related to solar radiation absorbed and surface heat capacity. By comparing monthly variations of temperature between cloudy and clear skies, we find that clouds can weaken Tmax and increase Tmin, and thus decrease DTR. As a result, the spatial distribution of DTR is opposite to the cloud cover. The clouds have relatively stronger impact on Tmin and DTR over mountain region, which is most likely caused by the stronger longwave cloud radiative forcing associated with higher cloud tops over the mountain region. [ABSTRACT FROM AUTHOR]

Atmospheric warming results in an intensification of annual precipitation over the globe but large uncertainties remain regionally and at seasonal scales, especially for extremes. Using 29 models from the Sixth Phase of the Coupled Model Intercomparison Project (CMIP6), we investigate future seasonal changes in extreme precipitation (under the scenario SSP5‐8.5) and how it compares to changes in mean precipitation. Over land, we find a strong intensification of the wettest day in all seasons over the mid and high latitudes of the Northern Hemisphere and over India during the monsoon. Extreme intensity decreases in the subtropics for some seasons, including over large regions around the Mediterranean basin and Southern Africa, and these drying patterns are not apparent in annual results. The key finding is that the CMIP6 multi‐model mean always shows that seasonal changes in mean and extreme precipitation align where there is high model agreement. That is, in all seasons by the end of the 21st century, extremes intensify in regions where mean precipitation increases and decline where mean precipitation decreases. This should not hide inherent uncertainties associated, namely the large range of changes intensity that can be found across the models, and an important modulation of the changes by internal variability. Yet, this study shows that the multi‐model mean shows broad consistency such that future seasonal changes in mean precipitation could be used to infer future changes in extremes (and vice versa), thus providing valuable information for risk planning and mitigation strategies. Plain Language Summary: The rise in surface air temperature due to human influence results in an intensification of the most extreme events of precipitation over the globe. Although this is shown by both observations and Earth System Models at the global scale, more uncertainties remain regionally and at the seasonal scales. In this study, we use a large ensemble of state‐of‐the‐art Earth System Models and investigate future seasonal changes in extreme precipitation and how it compares to changes in total precipitation. Our key finding is that, in all seasons by the end of the 21st century, extremes intensify in regions where mean precipitation increases and decline where mean precipitation decreases. This is found locally and in regions where there is high agreement between the models with projected changes of a same sign. Hence, when future seasonal changes in precipitation can be assessed with high confidence, we find that changes in totals and extremes align in the future. Our results demonstrate that there is potential added value from examining concomitant projected changes in totals and extremes of precipitation on seasonal and regional scales which could help better inform decision‐making in relevant climate‐sensitive sectors. Key Points: We present projected seasonal changes in Coupled Model Intercomparison Project (CMIP6) total and extreme precipitation and show that they scale into the futureCMIP6 multi‐model mean seasonal change is of same sign for total and extreme precipitation when there is high agreement between modelsThis is found locally (at the grid cell scale) and regionally and is generally supported by the 29 individual models [ABSTRACT FROM AUTHOR]

The model uncertainty, one of the major sources of projection uncertainty, is still a challenge in the climate simulation. In this study, we investigated the inter‐model spread of the simulated winter surface air temperature (SAT) over the Eurasian continent and the physical link to the upper‐level jet streams from the Coupled Model Inter‐comparison Project Phase 6 models. Extracted by the inter‐model empirical orthogonal function analysis, the leading inter‐model spread of SAT over the Eurasian continent is characterized by a cold pattern, particularly over high latitudes, which is closely associated with the out‐of‐phase variation in the intensity of the East Asian polar front jet (EAPJ) and East Asian subtropical jet (EASJ). The weakened‐EAPJ‐enhanced‐EASJ pattern is linked to the intensified Siberian high, the strengthened Aleutian low, and the deepened East Asian trough. All the variations would benefit the cold air invasion, resulting in the leading inter‐model spread of the SAT. The possible mechanism for the inter‐model uncertainty in the out‐of‐phase variation in the intensity of the two jets would be traced to the cooling over the Northwest Pacific oceans, which has reduced (enhanced) the meridional temperature gradient over high (low) latitudes and thereby contributed to the out of phase variation in the two jets. Our findings would help provide a potential metric for understanding the winter SAT change over the Eurasian continent. Plain Language Summary: The winter surface air temperature (SAT) over the Eurasian continent has profound economic and social impacts, however, there are large uncertainties in the climate model simulations. In this study, the inter‐model spread is utilized to derive the inter‐model uncertainty in the winter SAT over the Eurasian continent. The leading mode is a cold pattern over the Eurasian continent, which is closely related to the out‐of‐phase concurrent variation in the intensity of the upper‐level jet streams, resulting in the strengthened East Asian Winter monsoon. With an emphasis on the physical link between the East Asian jet streams and the winter SAT over the Eurasian continent, our findings provide a perspective of physical linkage to understand the traces of model uncertainty. Key Points: The leading inter‐model spread in winter surface air temperature over the Eurasian continent is a cold pattern from the CMIP6 modelsThe weakened‐East Asian polar front jet‐enhanced‐East Asian subtropical jet variation would result in this inter‐model spread due to the strengthened East Asian winter monsoonThe cooling spreads in the Northwest Pacific have physically linked this out‐of‐phase variation in the intensity of the two jets [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]

Irrigation cools near surface air temperature by increasing evapotranspiration from wetter soil. However, elevated evapotranspiration can also increase atmospheric albedo and enhance the local greenhouse effect via increased atmospheric water vapor. Their net effects on daily air temperature remains controversial. Here we show that in several considered regions, Northwest India and Central Valley of California, irrigation could result in warmer air temperature if night‐time warming is stronger than daytime cooling by irrigation. During the daytime, air temperature reduces through evaporative cooling and reduced solar radiation from increased atmospheric albedo outweighing the local greenhouse effect. At night‐time, the increased atmospheric water vapor by irrigation tends to make a stronger local greenhouse effect that increases night‐time temperature. Our results highlight the possible increase of air temperature by irrigation and the importance of considering sub‐daily processes when assessing the impact of irrigation on daily air temperature and temperature related socioeconomic phenomena. Plain Language Summary: Irrigation can influence air temperature in two contrasting ways. Evaporative cooling from wetter soil can reduce air temperature. However, evaporated water is a greenhouse gas, which can increase air temperature. The impact of these two influences differs for daytime and night‐time air temperature. Here, we examine the impact of irrigation on daytime and night‐time air temperature under two different assumptions. Our simulations show that the warming influence of irrigation on night‐time temperature could be greater than the cooling influence on daytime temperature in several considered regions, Northwest India and Central Valley of California. Our results underline that the impact of irrigation is a complex interplay of evaporative cooling and greenhouse warming and still needs to be further investigated. Key Points: Irrigation may result in warmer night‐time temperature by enhancing the local greenhouse effectThe temperature response of local irrigation can be influenced by irrigation practices over nearby areasHighlight the importance of water vapor as a greenhouse gas in climate change [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]

This study investigates the interdecadal variation of the intensity of the interannual variability (IIV) of surface air temperature (SAT) over mid‐ and high‐latitude Asia (MHLA) in winter months (December–February) during 1980–2015 and the role of the Arctic sea‐ice. Results show only the IIV of February SAT is significantly enhanced around the late‐1990s, which partly results from enhanced IIV of the frequency of extreme warm and cold events (FEWE and FECE). The influence of the sea‐ice in the Barents Sea from December to February on the February SAT, FEWE and FECE over MHLA is remarkably enhanced around the late‐1990s. During 1998–2015, the sea‐ice anomalies contribute to the enhanced IIV of February SAT over MHLA by modulating the monthly and synoptic tripole patterns over northern Eurasia, the positive phase of which exhibits an anomalous low over Eurasia north of 60°N and anomalous highs over southern Europe and MHLA. On the monthly average, the increased sea‐ice induces Rossby waves and then a positive phase of the monthly tripole pattern, inducing weakened East Asian trough and Siberian high, enhanced westerlies over 50°–70°N, and resultant decreased blocking high frequency over Eurasia north of 60°N. These anomalies favor increased February SAT over MHLA. On synoptic timescales, the increased blocking over the north side of the Ural Mountains favors more (fewer) extreme positive (negative) phases of the synoptic tripole pattern and hence increased FEWE (decreased FECE) over MHLA, further strengthening the increased SAT. Numerical experiments basically confirm the mechanisms above. Key Points: Intensity of interannual variability of February surface air temperature over mid‐ and high‐latitude Asia reinforces after 1998Intensity of interannual variability of frequency of February extreme warm and cold events over the region is enhanced after 1998Sea‐ice anomalies in the Barents Sea play an important role via modulating monthly and synoptic circulation over northern Eurasia [ABSTRACT FROM AUTHOR]

We present a near surface air temperature (NSAT) fused data product over the contiguous United States using Level 2 data from the Atmospheric Infrared Sounder, on the Aqua satellite, and the Cross‐track Infrared Microwave Sounding Suite (CrIMSS), on the Suomi National Polar‐orbiting Partnership satellite. We create the fused product using Spatial Statistical Data Fusion, a procedure for fusing multiple data sets by modeling spatial dependence in the data, along with ground station data from NOAA's Integrated Surface Database (ISD) which is used to estimate bias and variance in the input satellite data sets. Our fused NSAT product is produced twice daily and on a 0.25° latitude‐longitude grid. We provide detailed validation using withheld ISD data and comparison with ERA5‐Land reanalysis. The fused gridded product has no missing data; has improved accuracy and precision relative to the input satellite data sets, and comparable accuracy and precision to ERA5‐Land; and includes improved uncertainty estimates. Over the domain of our study, the fused product decreases daytime bias magnitude by 1.7 and 0.5 K, nighttime bias magnitude by 1.5 and 0.2 K, and overall RMSE by 35% and 15% relative to the AIRS and CrIMSS input data sets, respectively. Our method is computationally fast and generalizable, capable of data fusion from multiple data sets estimating the same quantity. Finally, because our product reduces bias, it produces long‐term data sets across multi‐instrument remote sensing records with improved bias stationarity, even as individual missions and their data records begin and end. Plain Language Summary: We have used a data fusion technique called spatial statistical data fusion (SSDF) to create an improved near surface air temperature (NSAT) data set by fusing two separate satellite data sets. NSAT is important for a variety of applications, such as drought, wildfire, and extreme heat research and prediction. The two input NSAT data sets come from the Atmospheric Infrared Sounder (AIRS) instrument on the Aqua satellite, and the Cross‐track Infrared Microwave Sounding suite on the Suomi National Polar‐orbiting Partnership satellite. Our fused NSAT product is produced twice daily and on a 0.25° latitude‐longitude grid. We also performed a detailed validation using withheld reference data (which was not included in the bias‐correction data) and comparison with ERA5‐Land reanalysis. The new fused product has no missing data; has improved accuracy and precision relative to the input satellite data sets, and comparable accuracy and precision to ERA5‐Land; and includes improved uncertainty estimates. SSDF is computationally fast and generalizable, capable of data fusion from multiple data sets so long as they estimate the same quantity. Finally, because our product reduces bias, it provides a means of creating high‐quality continuous long‐term data sets across the years, as individual satellite missions and their data records begin and end. Key Points: We demonstrate spatial statistical fusion for Level 2 remote sensing data sets which estimate the same observableWe introduce a new daily and nightly fused near‐surface air temperature product from satellite hyperspectral sounders over contiguous United StatesThe fused product decreases bias and RMSE by 1 K and 25% respectively relative to input data sets, averaged over the domain of the study [ABSTRACT FROM AUTHOR]

Australia experienced record‐breaking wildfires during 2019–2020, which emitted large amounts of carbonaceous aerosols (CAs) to the atmosphere. In this study, we explored the atmospheric dynamics and thermodynamic mechanism of the long‐range transport of CAs during November 2019–February 2020. The results indicate that the emitted CAs had a ternary advective spreading pattern across the Pacific Ocean in the Southern Hemisphere (SH), influenced mainly by the westerlies and anticyclonic systems. Upward vertical motion over the convergence zone and heat released from smoke pyro‐cumulonimbus clouds and strong light absorption of CAs provided strong vertical lift as the predominant injection pathway of the CAs into the stratosphere in the central Pacific Ocean in the SH. The abundant tropospheric CAs induced atmospheric heating (and potentially surface cooling), with increasing air temperature and decreasing of surface temperature. The anomalous thermal structure led to changes in atmospheric circulation, which weakened the upward vertical motion. Plain Language Summary: During the summer of 2019–2020, Australia suffered unprecedented wildfires. These wildfires emitted large amounts of pollutants into the free atmosphere. The pollutants are mainly composed of carbonaceous aerosols (CAs). Under the effects of meteorological conditions, the CAs experienced a long‐range spread expanding from the western Pacific Ocean to the eastern Pacific Ocean even arriving at the Atlantic Ocean. During the transportation, the CAs were accumulated over the central Pacific Ocean under the trapping effect of the anticyclone and then exhibited significant lift owing to upward motion over the convergence zone between westerlies and anticyclone and heat buoyancy released from smoke pyro‐cumulonimbus clouds and strong light absorption of CAs. As a response, the strong absorption of CAs resulted in the warming of the atmosphere and cooling of the surface, which enhanced atmospheric stratification and constrained the development of atmospheric instability. Thus, the upward vertical motion was weakened. Key Points: The westerlies and anticyclonic system dominated the advective spread of carbonaceous aerosols (CAs)The upward vertical motion, heat released from smoke pyro‐cumulonimbus clouds, and strong CAs light absorption contributed to CAs liftThe CAs caused warming of air temperature and cooling of surface temperature, which weakened the upward vertical motion [ABSTRACT FROM AUTHOR]

The diversity of El Niño events is commonly described by two distinct flavors, the Eastern Pacific (EP) and Central Pacific (CP) type. While the remote impacts, that is, teleconnections, of EP and CP events have been studied for different regions individually, a global picture of their structure is still lacking. Here, we use Forman‐Ricci curvature applied on climate networks constructed from surface air temperature data to distinguish regional links from teleconnections. Our results confirm that both El Niño types influence the teleconnection patterns, however, with different spatial manifestations. Our analysis suggests that EP El Niños alter the general circulation which changes the teleconnection structure to primarily tropical teleconnections. In contrast, the teleconnection pattern of CP El Niños show only subtle changes to normal conditions. Moreover, this work identifies the dynamics of the Eastern Pacific as a proxy for the remote impact of both El Niño types. Plain Language Summary: El Niño events, characterized by anomalous sea surface temperatures (SSTs) in the Tropical Pacific, come in two flavors; Eastern Pacific (EP) and Central Pacific (CP) types, depending on the longitudinal location of the strongest SST anomalies. Their remote impacts, known as teleconnections, differ. Although there are many studies investigating teleconnections of EP and CP events for individual target regions, a global analysis of the spatial distribution of their teleconnections is still lacking. In this study, we use the theory of complex networks to study EP and CP El Niño teleconnections. We construct "climate networks" from global surface air temperature data and use the notion of "curvature" of a network link to uncover their spatial organization. We show that the most negatively curved links highlight important teleconnection patterns that differ depending on the El Niño type. EP events change the teleconnection structure to the tropics while CP and Normal year conditions reveal teleconnections to all latitudes. Interestingly, the Central Pacific does not show many teleconnections, even during CP El Niño events which we attribute to the varying location of warm water anomalies in the Central Pacific. The Eastern Pacific changes more consistently allowing identifying remote impacts of both El Niños types. Key Points: Ricci curvature of boreal winter climate networks reveals long‐range teleconnection structureEastern Pacific (EP) El Niños show primarily teleconnections in tropical while Central Pacific El Niños teleconnections on all latitudesThe EP contains robust teleconnections for both El Niño types [ABSTRACT FROM AUTHOR]

European climate variability is shaped by atmospheric dynamics over the North Atlantic and local processes. Better understanding their future seasonality is essential to anticipate changes in weather conditions for human and natural systems. We explore atmospheric seasonality over 1979–2017 and 1979–2100 with seasonal circulation regimes (SCRs), by clustering year‐round daily fields of Z500 from the ERA‐Interim reanalysis and 12 Coupled Model Intercomparison Project phase 5 (CMIP5) climate models (historical and RCP8.5 runs). The spatial and temporal variability of SCR structures and associated patterns of surface air temperature are investigated. Climate models have biases but reproduce structures and evolutions of SCRs similar to the reanalysis over 1979–2017: decreasing frequency of winter conditions (starting later and ending earlier in the year) and the opposite for summer conditions. These changes are stronger over 1979–2100 than over 1979–2017, associated with a large increase of North Atlantic seasonal mean Z500 and temperature. When using more SCRs (more freedom in definition of seasonality), the changes over 1979–2100 correspond to a long‐term swap between SCRs, resulting in similar structures (annual cycle and spatial patterns) relative to the evolution of seasonal mean Z500 and temperature. To understand whether the evolution of SCRs is linked to uniform warming, or to changes in circulation patterns, we remove the calendar trend in the Z500 regional average to define SCRs based on detrended data (d‐SCRs). The temporal properties of d‐SCRs appear almost constant whereas their spatial patterns change, indicating that the calendar Z500 regional trend drives the evolutions of SCRs, and that changing spatial patterns in d‐SCRs account for the heterogeneity of this trend. Our study suggests that historical winter conditions will continue to decrease in the future while historical summer conditions continue to increase. However, it also suggests that the spatial and temporal patterns of SCRs would remain similar, relatively to the year‐round Z500 increase. [ABSTRACT FROM AUTHOR]

Early summer (May–June) is the season where the surface air temperature (SAT) variability is largest and may result in extreme temperature conditions over Pakistan. Therefore, we analysed the early summer interannual SAT variability over Pakistan for the period 1981–2018 using observational dataset and model simulations. We noted that upper‐level anticyclonic circulation anomalies over Pakistan, which are associated with upper to the middle tropospheric descending motion, favour clear skies with an increase in net shortwave radiation that leads to extreme surface warming over the region. Moreover, the El Niño–Southern Oscillation (ENSO) based on Niño3.4 SST index is negatively associated with SAT anomalies over Pakistan. The cold ENSO phase favours the positive geopotential height anomalies due to the strengthening of the Walker circulation that enhance the sinking motion and result in less cloudiness leading to the extreme higher surface temperature conditions over Pakistan, while the opposite happens in the warm ENSO phase. Moreover, the Saudi‐KAU Atmospheric Global Climate Model (AGCM) simulated large‐scale patterns that are in good agreement with the observations. Sensitivity experiments with the AGCM confirm that cold SST anomalies in the ENSO region significantly favour above‐normal SAT anomalies over Pakistan, while the opposite happens in the warm ENSO phase. These results are important to understand and potentially predict regional heatwaves over the South Asian region, particularly over Pakistan. [ABSTRACT FROM AUTHOR]

Long‐term satellite land surface temperature (LST) data are desirable to augment 2m air temperatures (T2m) measured in situ and as an independent measure of surface temperature change. However, previous studies show variable agreement between LST and T2m time series. The objective of this study is to assess the stability and trends in six new LST data sets from the European Space Agency's Climate Change Initiative for LST (LST_cci). LST anomalies are compared with homogenized station T2m anomalies over Europe, which verifies all six data sets are well coupled (LST vs T2m anomaly correlations and slopes: 0.6–0.9). The temporal stability of the LST_cci data is assessed through a comparison with the T2m anomaly time series. Only the LST_cci data sets for the MODerate resolution Imaging Spectroradiometer (MODIS) onboard Aqua and the Advanced Along‐Track Scanning Radiometer (AATSR) appear stable; the MODIS/Terra, ATSR‐2, and multisensor InfraRed and MicroWave data sets show non‐climatic discontinuities associated with changes in sensor and/or drift over time. For MODIS/Aqua (2002–2018), significant trends in LST of 0.64–0.66 K/decade compare well with the equivalent T2m trends of 0.52–0.59 K/decade. The LST and T2m trends for AATSR (2002–2012) are found to be statistically insignificant, likely due to the comparatively short study period and specific years available for analysis. No evidence is found to suggest that trends calculated using cloud‐free InfraRed observations are affected by clear‐sky bias. This study suggests that satellite LST data can be used to assess warming trends over land and for other climate applications if the required homogeneity is assured. Plain Language Summary: Long‐term changes in surface temperature due to climate change are usually studied using near‐surface air temperatures (T2m) from weather stations. However, these data are not available everywhere and changes in T2m are poorly understood in some regions (e.g., parts of Africa). This study assesses the potential for satellite‐observed land surface temperatures (LST) to help fill these gaps. The study finds that LST and T2m provide very similar information on surface temperatures and therefore LST can provide useful information where T2m data are unavailable. However, some of the LST data sets are found to be "unstable" and suffer from jumps and changes over time that result from for example, changes in satellite instrument or a change in the orbit of the satellite platform. These unstable LST data sets cannot be used to measure changes in surface temperature. However, the study finds that for "stable" LST data sets, which do not suffer from these non‐climatic jumps and changes, the changes in LST over time are very similar to, or even the same as, the changes in T2m. Therefore, this work suggests that satellite LST data could be used to measure changes in surface temperature where there are not enough stations to make these measurements using T2m. Key Points: The stability of six satellite land surface temperature data sets is assessed using homogenized station air temperatures over EuropeOnly two data sets are stable; the other four show non‐climatic discontinuities associated with a change in sensor and/or drift over timeSignificant trends in land surface temperature of ∼0.65 K/decade over Europe are found for one of the stable data sets over 2002–2018 [ABSTRACT FROM AUTHOR]

The lake surface water temperature (LSWT) on Tibetan Plateau (TP) is sensitive to climate change. Based on a 1‐D lake model, we have investigated the interdecadal variation and long‐term trend of LSWT in Lake Nam Co (LNC) on TP during 1980–2018 and quantified the relative contributions of atmospheric factors to the LSWT trend. Results show LNC was warmed with a rate of 0.29°C decade−1, which is smaller than the warming of ambient air (0.45°C decade−1). The weakened wind speed, rising air temperature, increased downward longwave radiation, and decreased shortwave radiation contributed about 35%, 30%, 20%, and −15% of the estimated long‐term LSWT trend during 1980–2018, respectively. The contribution rate of 32.5% was from the interactions among all forcing variables. The primary warming was completed before 1997 (0.30°C decade−1) and was followed by a hiatus that the LSWT jumped to a warm level with a slightly negative trend (−0.08°C decade−1) after 1997. During this hiatus, the wind speed recovered from decrease and the deceleratingly increased downward longwave radiation slowed down the LNC warming. Particularly, the specific humidity shifted from increasing to decreasing trend played a key role in the hiatus of LNC warming after 1997, despite its slight contribution (−2.5%) to the LSWT trend during 1980–2018. The results showed the particularity of warming for a TP lake compared to the other ice‐covered lakes worldwide and provided a quantitative perspective for understanding the relative contributions of atmospheric factors to the long‐term trend of LSWT. Key Points: As an ice‐covered lake, Lake Nam Co (LNC) warmed with a smaller rate than the local surface air temperature during 1980–2018The interdecadal shift of surface specific humidity played a key role in the hiatus of LNC warming after 1997The individual and cross contributions of different atmospheric factors to the long‐term trend of surface water temperature are quantified [ABSTRACT FROM AUTHOR]

Observations have shown subseasonal reversal of temperature anomalies between early and late winter over Eurasia, which is distinct from the seasonal mean condition. Based on the reanalysis data, the 1800‐year control simulation and the 40‐member ensemble simulations in 1920–2100 from the Community Earth System Model (CESM) Large Ensemble (CESM‐LE), this study reveals that the reversal of surface air temperature (SAT) anomalies between early and late winter is one of the dominant and intrinsic features of the Arctic‐Eurasian winter climate. Such a reversal is characterized by "colder Arctic, warmer Eurasia" in December (January–February) and "warmer Arctic, colder Eurasia" in January–February (December). Robust climate dynamic processes associated with the reversal of SAT anomalies, including subseasonal reversals of anomalies in the Ural blocking, midlatitude westerlies, and stratospheric polar vortex, are found in both reanalysis data and CESM simulations, indicating the important role of internal atmospheric variability. Further analysis reveals that the reversal of Ural blocking anomalies in late December can be a potential precursor for the reversal of SAT anomalies in late winter. The reversal of midlatitude westerly wind anomalies associated with the Ural blocking can affect upward propagation of planetary‐scale waves especially with wavenumber 1, subsequently promoting the contribution of stratospheric polar vortex to the reversal of SAT anomalies in late winter over the Arctic‐Eurasian regions. Such a troposphere‐stratosphere pathway triggered by the perturbation of tropospheric circulations is confirmed by the CESM‐LE simulations, and it may be useful for the prediction of subseasonal reversal of SAT anomalies. Plain Language Summary: Observations show that Eurasia may experience subseasonal reverse temperature anomalies from early to late winter. For example, the warmer‐than‐normal conditions in early winter can change to colder‐than‐normal conditions in late winter. Such a reverse temperature anomaly in Eurasia is usually concurrent with a reverse one in the Arctic. The "colder Arctic, warmer Eurasia" pattern in early winter followed by "warmer Arctic, colder Eurasia" pattern in late winter is robustly detected in climate model simulations, indicating that the subseasonal reversal of temperature anomalies is one of the intrinsic features of the Arctic‐Eurasian climate. This feature is associated with robust climate dynamic processes, including subseasonal reversals of the anomalies in the Ural blocking, the midlatitude westerly winds, and the stratospheric polar vortex. Because the Ural blocking can trigger the troposphere‐stratosphere interaction, the reversal of Ural blocking anomalies in late December can be a potential precursor for the reversal of surface air temperature anomalies in late winter. Key Points: Reversal of surface air temperature (SAT) anomalies between early and late winter is an intrinsic feature of the Arctic‐Eurasian climateThe changes of Ural blocking in late December may indicate whether the SAT in late winter will be reversed to early winterRobust climate dynamic processes associated with such a reversal of SAT are found in both reanalysis data and long‐term model simulations [ABSTRACT FROM AUTHOR]

This paper uses monthly surface air temperature (SAT) data from the Climatic Research Unit dataset and the Coupled Model Intercomparison Project phase 6 multimodel ensemble simulations to investigate the variations in the annual‐mean and four seasons SAT across the Eurasian and North Africa landmasses and the possible causes. The SAT over multiple timescales has significant seasonal and regional differences. The trend, variance of decadal variation (DV), and variance of interannual variability (IV) show that the differences between Siberia and mid‐low latitudes are much larger in winter and spring than in summer and autumn. The SAT exhibits a robust warming trend over Siberia in winter (0.21–0.42°C·decade−1) than over mid–low latitudes in summer (<0.07°C·decade−1). The cause is the combined diversity effects of greenhouse gases (GHG) and anthropogenic aerosols (AA) in four seasons over different regions. The warming trend of Siberia in winter is affected by the combination of strong warming effect by GHG and the weak cooling effect by AA, and the trend of mid–low latitudes in summer is opposite. The leading modes of the DV show a consistent spatial pattern over the whole Eurasia in four seasons while the IV show a meridional dipole pattern across 40°N except in summer. The internal climate variability is the main driver of the SAT IV and DV, but the effect of anthropogenic activity and natural forcing may reinforce the IV and DV in some regions. The combined role of GHG and AA forcings can significantly enlarge the DV in many parts of Eurasia, and the AA and natural forcing have significant effect on the IV in the high latitudes. [ABSTRACT FROM AUTHOR]

We investigated interdecadal relationship changes between the Arctic oscillation (AO) and Aleutian low (AL) for the period 1980–2021 along with the linkage on surface air temperature (SAT) over Eastern Eurasia during the boreal winter (December–January–February). The AO–AL relationship has weakened since the mid‐1990s. To explore a possible explanation, we compared atmospheric fields for two contrasting periods, one when the AO–AL relationship is the strongest (1986–2006) and one when it is the weakest (2000–2020). In both periods, atmospheric circulation over the Arctic region for the two AO–AL in‐phase relationships (i.e., either +AO/+AL or –AO/−AL) evolves with negative AO‐like patterns, contributing to a weakening AO–AL relationship in concurrent with a cool SAT in Eastern Eurasia. We found that the correlation between AL and SAT over Eastern Eurasia decreases, while the AO and SAT correlation remains unchanged. This implies that low‐frequency atmospheric circulation changes such as those of the AO and AL should be considered when studying SAT changes in Eastern Eurasia. [ABSTRACT FROM AUTHOR]

The impact of the quasi‐biennial oscillation (QBO) on the surface air temperature (SAT) in the Northern Hemisphere extratropics is investigated. It is found that the QBO, defined as 70‐hPa zonal wind in the deep tropics, is negatively correlated with the SAT over the western North Pacific in February and March. Cold temperature anomaly appears during the QBO westerly phase. Such relationship is likely mediated by the subtropical jet. During the QBO westerly phase, a horseshoe‐shaped zonal wind anomaly forms in the upper troposphere and lower stratosphere and is connected to the equatorward shift of the Asia‐Pacific jet. This equatorward jet shift is accompanied by a cyclonic circulation anomaly in the subtropical North Pacific and an anticyclonic circulation anomaly over northern Eurasia in the troposphere. The resultant temperature advection brings cold air to East Asia and the western North Pacific. This regional downward coupling in February and March, which is not sensitive to El Niño‐Southern Oscillation, has become statistically significant in recent decades. [ABSTRACT FROM AUTHOR]

The North Atlantic Oscillation (NAO) index is often characterized by independent positive and negative NAO events with a characteristic spatial pattern and a typical lifetime of around 1 week. These events are separated by periods of near‐neutral NAO conditions. Here, we challenge this view by showing in reanalysis and observed data that the strength and spatial shape of NAO events depends on the NAO index prior to the window of 1 week and this dependency is most pronounced for negative NAO events. The influence is seen in the mean sea level pressure, and in other important features, including blocking frequency and jet stream characteristics, and also in air surface temperature and precipitation in parts of Europe. This new appreciation is important for efforts to improve methods for subseasonal‐to‐seasonal predictions of NAO. Plain Language Summary: Northern European winters differ. Some are mild and moist with prevailing westerly winds and some are cold and dry, dominated by the Siberian air masses from East. When the Northern European winters are mild, the winter in Greenland is cold, and vice versa. This is the North Atlantic Oscillation (NAO) and its companion, the Europe–Greenland temperature seesaw. They were described in qualitative terms already in the late eighteenth century. Nowadays, the NAO is often described as a series of independent positive or negative events with a duration of around 1 week with intermittent periods of near‐neutral conditions and variable duration. The net result of these events determines the NAO strength of the winter. We show that this picture is too simplified because the NAO outside the time horizon of 1 week influences the strength of the individual NAO events. This is most pronounced for the negative events where this influence can both distort and amplify or diminish the pattern of the event. Also the typical temperatures and precipitation patterns associated with NAO are influenced significantly by this effect. One important consequence for our discovery is the improved prospects of doing subseasonal‐to‐seasonal predictions. Key Points: The daily North Atlantic Oscillation (NAO) index consists of positive and negative events of duration around 1 week separated by periods of near‐neutral conditionsThe strength and spatial shape of negative NAO events depends on NAO‐index values prior to the eventBlocking frequencies, jet strength and position, and surface temperature and precipitation have similar dependencies [ABSTRACT FROM AUTHOR]

The surface of the Great Lakes interacts with the atmosphere, influencing the weather and climate over the Great Lakes Region (GLR). However, most climate models were not designed with sufficient emphasis on lake–atmosphere interactions, which could potentially cause model biases over the GLR. To understand how lake surface temperature (LST) affects the regional summer climate over the GLR, we conducted twin experiments using the Weather Research and Forecasting model at a spatial resolution of 4 km using two different LST data sets as the bottom boundary condition over the Great Lakes. Our simulations include 10 ensemble members for the summer of 2018 and a single multiyear run for the summers of 2014–2020. Results show that variations in LST influence atmospheric temperature and moisture at a local scale—while affecting the convective environment and precipitation processes over a much larger spatial scale. In particular, an LST that is only 1°C–3°C warmer (depending on the lake) increases near‐surface air temperature by 1.93°C and 0.97°C over Lake Superior and Lake Erie, respectively, and increases evaporation over the lakes by 0.23 and 1.1 mm day−1. The warmer LST reduces mesoscale convective precipitation upstream of the GLR; however, it increases isolated deep convective precipitation and nonconvective precipitation downstream of GLR due to increased local instability and enhancement of moisture transport. Our analyses confirm the robustness of these impacts, which is at least 2 times larger than the model internal variability and is seen across all simulated summer seasons. Key Points: Small differences in lake surface temperature (LST) can change summer climate at both local and nonlocal scales over the Great Lakes Region (GLR)Warmer LSTs create an unstable environment and enhance isolated deep convection downwind of the GLRThe influences of the difference in LST are robust and significantly larger than the model's internal variability [ABSTRACT FROM AUTHOR]

This study investigates the role of internal climate variability on the wintertime surface air temperature (SAT) trend over Eurasia during 1970–2004 based on outputs from the Community Earth System Model Large Ensemble Project. The SAT trends vary significantly in both spatial pattern and magnitude among ensemble members, indicating the essential role of internal climate variability on SAT trends. The leading modes of the spread of SAT trends are extracted via inter‐member empirical orthogonal function (EOF) analysis of the SAT trend patterns among 40 ensemble members. The first EOF mode shows widespread warming over northern Eurasia and slight cooling southward of 40°N during its positive phase. The second EOF mode displays a zonal dipole, with one center located in Europe and the other of the opposite sign over Siberia. The Arctic Oscillation and Scandinavian patterns are key dynamical factors associated with the two dominant modes, respectively. A dynamical adjustment method is further employed to decompose the internal climate variability into those related to large‐scale atmospheric circulation and residuals. The spread of SAT trends among ensemble members is primarily associated with the various large‐scale atmospheric circulation and second by the residual component that is closely tied to diverse snow cover trends over Eurasia, especially over northern Eurasia. These results may help understand the relationship between internal climate variability and winter SAT trends over Eurasia via dynamical and thermodynamical processes. Key Points: Internal climate variability plays a crucial role in the Eurasian winter surface air temperature trends of the past decadesArctic Oscillation and Scandinavian patterns are two essential internal dynamical factors associated with the temperature trendsSnow cover is the key internal thermodynamical factor associated with the temperature trends [ABSTRACT FROM AUTHOR]

Climate reconstructions by data assimilation need to accommodate for sensitivities to proxies and prior estimates because models are uncertain and proxies are spatiotemporally limited. This study examines these sensitivities using multiple climate model simulations and different combinations of proxies (i.e., corals, ice cores, and tree‐ring cellulose). Experiments were conducted using an offline data assimilation approach; the results showed annual variations in the global distribution of surface air temperature and precipitation amount from 850 to 2000. Standard deviations of surface air temperature and precipitation amount during the entire period differed by up to 36% due to prior estimates. Experiments with different types of proxies showed that the El Niño‐like distribution of positive anomalies in the central to eastern tropical Pacific may only be adequately reproduced in experiments with corals, and not experiments without corals. The correlation coefficient of the NINO.3 index from 1971 to 2000 between experiments with corals and the Japanese 55‐year Reanalysis (JRA‐55) was 0.81 at maximum. By contrast, the correlation coefficient between experiments without corals and JRA‐55 was a maximum of 0.19. Key Points: Climate reconstruction by data assimilation illustrates annual variations in surface air temperature and precipitation from 850 to 2,000Spatiotemporal differences in climate reconstruction were compared based on prior estimatesThe impact of proxies on the results of past reproduced El Niño cases from 1971 to 2000 was validated [ABSTRACT FROM AUTHOR]

Southeast Asian (SEA) river basins, particularly Huang He, Yangtze and Mekong river basins, are home for large share of global population. The region is experiencing rapid socio‐economic development, urbanization and industrialization. Using Community Earth System Model large ensemble data, seasonal mean surface air temperature (SAT) in summer is projected to increase by ~6°C under Representative Concentration Pathway (RCP) 8.5 scenario. As a consequence, in the long term (2071–2100), record‐breaking extreme hot and precipitation events are projected to increase by ~90–100% and ~ 60–70%, respectively. The climate mean SAT will increase by 4 ~ 5.5°C by the end of 21st century. Extreme events occur due to the combined influence of external forcing factors and internal variability. The relative strength of both the factors are estimated by signal‐to‐noise ratio (SNR), that is, the ratio of external forcings and internal variability. Until 2000/1998/2000, SNR < 1 over Huang He/Yangtze/Mekong river basins, that is, internal variability dominates over external forcings, while from 2001/1999/2001 onwards, SNR > 1, that is, external forcings strongly override the internal climate variability. Interestingly, the SAT variability at 50th (mean state) and 95th (record‐breaking hot/wet extreme) percentile will decrease significantly as climate warms in the future. It contradicts the existing hypothesis that as the climate warms in the future, variability will increase and weather will get more volatile everywhere. Interestingly, in our analysis, SAT variability appears to decrease over SEA river basins, which is opposite to the current understanding. It makes the result a novel one. We analyse the temperature–precipitation relationship under RCP8.5 scenario, which displays a peak‐shaped structure, that is, precipitation is increasing at low–mid‐range of temperature rise, while it decreases at higher temperatures. [ABSTRACT FROM AUTHOR]

An important metric for communicating the context and impact of climate change is regionally or nationally averaged temperature anomalies. Quantifying the associated uncertainty adds confidence in the reporting of temperature records and trends. For area‐averaged temperature anomalies derived from point‐source observations we model the uncertainty by a three‐step process: (1) observation random error; (2) spatial analysis grid point uncertainty; and (3) area‐average uncertainty. Each step should reflect the method used to calculate the area average. For Australian area‐average temperature anomalies, the observation random error is modelled as the sum of the sampling uncertainty and the uncertainty in the adjustment process. Daily maximum and minimum temperature time series from the 104 non‐urban locations of the Australian Climate Observation Reference Network—Surface Air Temperature (ACORN‐SAT) version 2 dataset for 1981–2010 are used to estimate the sampling uncertainty, based on predicted location pair zero‐distance correlations having values of less than 1.0. ACORN‐SAT version 2 breakpoint information is used to estimate the uncertainty from the selection of reference stations used in the detection and homogenization processes. Monthly area‐average temperature uncertainty is estimated for the 1910–2018 period from Monte Carlo simulations using the observation uncertainty and the available ACORN‐SAT network for each month. The uncertainty in annual area‐average temperature anomalies is then obtained from the monthly uncertainties and inter‐month correlations modelled with a periodic auto‐regressive model. The ±2σ annual mean temperature area‐average uncertainty is ±0.084°C in 2018 and ± 0.176°C in 1910. The larger value in 1910 is primarily due to decreased ACORN‐SAT coverage over areas of inland Australia. The Australian annual mean temperature change and associated uncertainty from ACORN‐SAT version 2 over 1910–2018 is found to be 1.42 ± 0.28°C. This is consistent with global air temperature warming by around 1°C since 1850. [ABSTRACT FROM AUTHOR]

The Community Earth System Model (CESM) is widely used for the prediction and understanding of climate variability and change. Accurate simulation of the behavior of near surface air temperature (T2m) is critical in such a model for addressing societally relevant problems. However, previous versions of CESM suffered from an overestimation of wintertime T2m variability in Northern Hemisphere (NH) land regions. Here, it is shown that the latest version of CESM (CESM2) exhibits a much improved representation of wintertime T2m variability compared to its predecessor and it now compares well with observations. A series of targeted experiments reveal that an important contributor to this improvement is the local effects of changes to the representation of snow density within the land surface component. Increased snow densities in CESM2 lead to enhanced conductance of the snow layer. As a result, larger heat fluxes across the snow layer are induced in the presence of T2m anomalies, leading to a greater dampening of surface and near surface atmospheric temperature anomalies. The implications for future projections with CESM2 are also considered through comparison of the CESM1 and CESM2 large ensembles. Aligned with the reduction in surface temperature variability, compared to CESM1, CESM2 exhibits reduced ensemble spread in future projections of NH winter mean temperature and a smaller decline in daily wintertime T2m variability under climate change. Overall, this improvement has increased the accuracy of CESM2 as a tool for the study of wintertime T2m variability and change. Plain Language Summary: A societally relevant quantity that is predicted by Earth System Models is near surface air temperature (T2m). Accurate simulation of this quantity requires accurate representation of atmospheric circulation, boundary layer processes and land‐atmosphere interaction. Here we show substantial improvements in the representation of wintertime T2m variability in the latest version of the Community Earth System Model and isolate the roles of different aspects of the model development. Increases in the density of snow in the new model are shown to reduce T2m variability. Snow density governs the heat fluxes induced across the snow layer via conductance and increased snow density in the model has increased the ability of heat fluxes across the snow layer to dampen T2m variability. The implications of this change for future climate projections are explored and it is found that CESM2 exhibits a reduced uncertainty in future projections of mean T2m over Northern Hemisphere land regions, as might be expected given the reduced sampling uncertainty in T2m due to reduced internal variability. Furthermore, wintertime T2m variability is expected to decrease under climate change and this decrease is larger in the older version of the model that exhibited greater T2m variability in its historical simulation. Key Points: Northern Hemisphere wintertime surface temperature variability was overestimated in Community Earth System Model Version 1 and is improved in Community Earth System Model Version 2 (CESM2)This improvement has arisen through an increase in snow density and associated snow conductanceA reduced ensemble spread and reduced decline in daily variance is also found in future projections of surface temperature with CESM2 [ABSTRACT FROM AUTHOR]

Decadal climate prediction has been recognized as the important information for policy makers for agriculture, health and energy sectors and the general public for any near‐term planning. The modelling community is determined to put forward a reliable near‐term climate forecasting system, to predict annual to decadal state and variability of climate. However, deriving reliable information from the decadal prediction/hindcast is still a challenge. This study examines the decadal hindcast simulations of surface air temperature (SAT) over India in seven different ocean–atmosphere coupled models from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Each decadal hindcast is available for the next 10‐years period from the initialized climate states of 1961–2006. The performance of models is assessed using different evaluation metrics, such as absolute mean difference, root mean square error, skill score and uncertainty in terms of the range of hindcasts. The multimodel ensemble mean displayed considerable skill in representing the spatial distribution of SAT over the Indian region except over the western Himalaya and Northeast India. Our results indicate that the decadal hindcasts skills improved noticeably when quantile mapping (QM) approach is used for the bias corrections. The major improvements are seen in terms of reducing absolute mean difference and uncertainty, regardless of lead time and region. The present study advocates that QM approach is useful not only for reducing bias but also for improving the decadal hindcast skill for SAT over India in the coupled models. [ABSTRACT FROM AUTHOR]

This study examines the sub‐seasonal forecast skill of the dominant mode of atmospheric circulation variability in the Northern Hemisphere (NH) mid‐latitudes, the Middle Latitudes Seasonal Oscillation (MLSO), and the influence of MLSO on the forecast skill of surface weather in this area. The analysis is conducted using reforecasts produced by four forecast systems, which archive their output in the Subseasonal‐to‐Seasonal (S2S) Data Base. A reduced ensemble of reforecasts for each model is verified against the ERA‐Interim reanalysis. The S2S models display significant ability to predict the MLSO on the S2S time scale. The MLSO prediction skill is higher in boreal winter than in summer. A relationship is found between the MLSO and the forecast skill of 2‐m temperature (T2m) in the NH mid‐latitudes during the extended boreal winter. The prediction of T2m improves when MLSO events are in the initial conditions and the improvement depends on the phase of MLSO. Plain Language Summary: Many phenomena are routinely predicted, yet prediction on time scales longer than two weeks is regarded as limited because of the chaotic nature of atmospheric dynamics. Phenomena with life cycles longer than two weeks can enhance the predictability of phenomena with shorter life cycles if there is a linkage between the two, and especially if the former is characterized by large‐scale patterns and the latter consists of small‐scale patterns. The forecast skill or how successful a prediction can be, depends also on the ability of the forecast model to predict the longer time scale variability. In this study, the forecast skill of four state‐of‐the‐art forecast models is evaluated to assess their ability to predict an atmospheric oscillation with a life cycle of 120 days coined as the Middle Latitudes Seasonal Oscillation (MLSO) and whether this oscillation can improve the forecast skill of surface air temperature over the Northern Hemisphere. We find that models show a skillful forecast of the 120‐day oscillation especially during boreal winter, and MLSO is a source of predictability for the surface air temperature. Key Points: The Middle Latitudes Seasonal Oscillation (MLSO) can be predicted by the S2S models up to four weeksThe forecast skill of MLSO is higher during boreal winter than summerMLSO is an important source of predictability for the surface air temperature during boreal winter [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive analysis of the effect of urbanization on the surface air temperature (SAT) from 1951 to 2018 in the Philippines. The daily minimum temperature (Tmin) and daily maximum temperature (Tmax) records from 34 meteorological stations were used to derive extreme temperature indices. These stations were then classified as urban or rural based on satellite night‐lights. The results showed a significant difference in the SAT trends between urban and rural stations, indicative of the effect of urbanization in the country. Larger and more significant warming trends were observed in indices related to Tmin than those related to Tmax. In particular, the effects of urbanization were significant in the annual index series of Tmin, diurnal temperature range, minimum Tmin, percentage of days when Tmin was less than the 10th percentile (TN10p), percentage of days when Tmin was greater than 90th percentile (TN90p), and the number of coldest nights. The effects of urbanization were not as clear on the index series of maximum Tmax (TXx), minimum Tmax (TXn), percentage of days when Tmax was less than 10th percentile (TX10p), and the number of hottest days. The effects of urbanization on the annual series of extreme temperature indices were statistically significant at the 95% confidence level, with the exception of Tmax, TXn, TXx, TX10p, and the number of hottest days. Further analysis revealed that the effect of urbanization was the greatest during the DJF (December–January–February) season. These findings serve as a baseline study that focuses on the countrywide effect of urbanization on SAT trends in the Philippines. [ABSTRACT FROM AUTHOR]

Large‐scale cold and warm spells successively attacked North Asia (NA) and resulted in a subseasonal alternation of extreme surface air temperature (SAT) before and after 15 January 2021. Our results show the synergistic effect of annual cycle and intraseasonal oscillation component (AC and ISO) of SAT and circulation anomalies caused temperature extremes. Before 15 January 2021, an eastward‐propagating Rossby wave train strengthened the cold circulation regime with an enhanced Ural blocking high (UBH) and surface Siberian high (SH). Circulation anomalies of the negative ISO were superimposed on the counterpart of AC, which easily resulted in cold extremes via nonlinear interaction of meridional temperature gradient. After mid‐January 2021, however, weakened UBH and SH caused warm advection and spells over NA. This synergistic effect of AC and ISO anomalies in SAT accounted for most historical temperature extremes, implying that AC anomalies have important indications for subseasonal alternation of temperature extremes. Plain Language Summary: Large‐scale cold and warm spells successively occurred in North Asia (NA) and resulted in the intraseasonal oscillation (ISO) of surface air temperature (SAT) extremes during 2020/21 winter. Here, we find the annual cycle (AC) of SAT and circulation anomalies provided a favorable background in regulating the subseasonal alternation of cold and warm extremes. The negative (positive) anomalies of AC (denoted PACN (PACP)) are characterized by enhanced (weakened) Ural blocking high (UBH) and Siberian high (SH), corresponding to cold (warm) NA circulation regimes before (after) 15 January 2021. During PACN (negative anomalies of ISO, PISON), a quasi‐barotropic Rossby wave train in the midlatitudes of Eurasia enhanced AC (ISO) of the UBH and SH, causing cold extremes in NA. While during the PACP and positive anomalies of ISO (PISOP), circulation anomalies like negative Arctic oscillation (AO) weakened UBH and SH, jointly resulting in warm extremes in NA. The subseasonal temperature change process in NA was mainly attributed to the conveyance of AC anomalies of meridional temperature gradient by subseasonal winds. Historical temperature extremes were almost attributed to the synergistic effect of AC and ISO of SAT anomalies, implying that the AC anomalies have important indications for subseasonal alternation of cold and warm extremes. Key Points: The successive cold and warm spells caused a subseasonal alternation of extreme surface air temperature (SAT) in North Asia during the 2020/21 winterThe annual cycle (AC) of meridional temperature gradient dominated by the nonlinear interaction of subseasonal advection in this winterCold and warm extremes in historical winters were verified by the synergistic effect of AC and intraseasonal oscillation in SAT anomalies [ABSTRACT FROM AUTHOR]

Networks of observations ideally provide adequate sampling of parameters to be monitored for climate and weather forecasting applications. This is a challenge for any network, but is particularly difficult in the harsh environment of the Antarctic continent. We evaluate a network design method providing objective information on station siting for optimal sampling of a variable, here taken to be surface air temperature. The method uses the concept of ensemble sensitivity to predict locations reducing the most total ensemble variance, that is, uncertainty, across the continent. The method is applied to a network of frequently-reporting stations, and validation is performed using results from assimilating station observations. A cost-efficient “offline” data assimilation framework is used to allow testing over a large sample of experiments, including a large number of randomly chosen networks that serve as a null hypothesis. Network design predictions agree well with observed error reductions from assimilation. The important role of stations on the East Antarctic Plateau in monitoring surface air temperature is evident in network design and data assimilation results, followed by stations in West Antarctica and the Ross Ice Shelf region. Antarctic coastal and Peninsula stations are found to provide the smallest information content integrated over the continent. Validation results are also robust to covariance localization, an essential factor for ensemble methods. Optimal networks outperform randomly chosen-networks in all cases, by up to nearly 50%, depending on the size of the network and the covariance localization distance. [ABSTRACT FROM AUTHOR]

Spring snow depth (SSD) in the Tibetan Plateau (TP) plays an essential role in snow water estimation, vegetation growth, and climate projections for Southwest China and the surrounding Asian countries. Previous studies have reported a decline in SSD that is associated with increasing surface air temperature (Ts) across the TP over the past decades. However, only a few studies have explored the potential feedback of SSD to Ts, which has hindered the elucidation of the vulnerability of snow cover in the TP against the backdrop of rapid climate change. Based on data from 69 meteorological stations in the central and eastern parts of the TP, this study analyses changes in SSD and its feedback to Ts from 1961 to 2013. Overall, a slightly negative trend in SSD was observed from 1961 to 2013, during which two contrasting trends were detected using a piecewise regression approach: an initial increase from 1961 to 1981 (0.12 cm decades−1, p <.01) followed by a decrease from 1982 to 2013 (−0.36 cm decades−1, p <.01). Sensitivity analysis revealed that the SSD decline was systematically correlated with a positive Ts anomaly, with sensitivities of −0.050 cm °C−1 (p <.01) and −0.069 cm °C−1 (p <.01) for 1961–2013 and 1982–2013, respectively. However, following the accelerated SSD decline during 1982–2013, its potential feedback to Ts was attenuated, with averaged feedback values from −1.328°C cm−1 during 1961–2013 to −0.936°C cm−1 during 1982–2013 in latitudes below 3,500 m, distributed in the Qaidam and Yellow River basins, and the upper reaches of the Yangtze River basin. The decrease in feedback from SSD to Ts may lead to changes in the mechanism controlling Ts dynamics across the TP, which should be considered in climate change projections in future studies. [ABSTRACT FROM AUTHOR]

Studies have indicated that marine heatwaves (MHWs) have had severe impacts on the marine ecosystem in the Pacific, Atlantic, and Indian Oceans, but there have been few studies focused on MHWs in the Arctic. On the other hand, amplified rapid warming in the Arctic region makes it a hotspot strategically and economically worldwide. In this study, we documented that the average intensity of MHWs in the Arctic was comparable with that in the other regions of the global oceans. The annual intensity, frequency, duration, and areal coverage of MHWs have increased significantly in recent decades. The increase of the annual duration is mainly owing to the postponed end time, thus the prolonged periods, of the MHW seasons. Our analysis indicates that the increasing trends of the annual intensity, frequency, duration, and areal coverage in the Arctic are closely associated with the increasing surface air temperature and decreasing sea‐ice concentration under the global warming environment. These features are robust across three different sea surface temperature (SST) products and using different MHW criteria. Plain Language Summary: Events of extremely warm waters in the oceans are known as marine heatwaves (MHWs). Past MHW research has used sea surface temperature (SST) to diagnose MHWs and has focused on the tropical and subtropical oceans. The questions we address here are: (a) Are there any MHW events in the Arctic region where SSTs are generally low? and (b) Are the MHWs weaker or stronger in the Arctic than in the other oceans? Our study indicates that: (a) MHWs do exist in the Arctic, (b) their strengths increase with time, and (c) they are stronger than those in the other oceans in the most recent decades. These MHWs may have a strong impact on the Arctic bio‐ecosystem due to their low heat tolerance since the seasonal variation of SST in the Arctic is small. Key Points: The average marine heatwaves (MHWs) in the Arctic are as strong as in the other ocean basins in 1982–2020The annual intensity are stronger in 2000–2020 than in 1982–2000, which is consistent with changes in air temperature, sea‐ice, and cloudThe increase of annual duration is largely associated with the postponed end time of the MHW seasons [ABSTRACT FROM AUTHOR]

In tropical regions, particularly in Central and South America (CSA), the projections of climate seasonality under climate change are still uncertain. This is especially true for ecologically‐relevant variables such as precipitation and temperature. However, assessments of model‐based projections of seasonal climate for this region are scarce. We analyzed the simulation of seasonal precipitation and air surface temperature in CSA and six sub‐regions within from 49 models included in the Coupled Intercomparison Project Phase 5 (CMIP5) and 33 models from CMIP6. In general, continental patterns and seasonality of both variables are moderately well resembled, while most models show systematic biases over the oceans, producing unrealistic spatial patterns. To quantify how well CMIP5/CMIP6 models simulate these variables, we used Taylor diagrams with respect to TRMM for precipitation and ERA5 for temperature. Precipitation shows the largest spread among models. Conversely, temperature shows a better simulation. CMIP5/CMIP6 models exhibit a better performance simulating both variables during December–January–February and March–April–May than during the other seasons. This is partly due to the reduced model biases in representing the Intertropical Convergence Zone during these two seasons. In general, biases are reduced in the CMIP6 models with respect to CMIP5. Regarding regional evaluations, precipitation patterns for Mesoamerica, Cerrado and Chaco regions are better reproduced compared to TRMM, while the annual cycles for the Andes hotspot, Central Chile and Guianas are not well simulated, mainly during their wet seasons. However, these biases are reduced in CMIP6 models. In regard to precipitation projections, models only agree over most of the regions with decreasing precipitation. Conversely, temperature exhibits a general consensus on persistent warming even during the historical period, with an average increase of 6°C by the end of the century, according to the CMIP6 models. [ABSTRACT FROM AUTHOR]

Ecological research heavily relies on coarse‐gridded climate data based on standardized temperature measurements recorded at 2 m height in open landscapes. However, many organisms experience environmental conditions that differ substantially from those captured by these macroclimatic (i.e. free air) temperature grids. In forests, the tree canopy functions as a thermal insulator and buffers sub‐canopy microclimatic conditions, thereby affecting biological and ecological processes. To improve the assessment of climatic conditions and climate‐change‐related impacts on forest‐floor biodiversity and functioning, high‐resolution temperature grids reflecting forest microclimates are thus urgently needed. Combining more than 1200 time series of in situ near‐surface forest temperature with topographical, biological and macroclimatic variables in a machine learning model, we predicted the mean monthly offset between sub‐canopy temperature at 15 cm above the surface and free‐air temperature over the period 2000–2020 at a spatial resolution of 25 m across Europe. This offset was used to evaluate the difference between microclimate and macroclimate across space and seasons and finally enabled us to calculate mean annual and monthly temperatures for European forest understories. We found that sub‐canopy air temperatures differ substantially from free‐air temperatures, being on average 2.1°C (standard deviation ± 1.6°C) lower in summer and 2.0°C higher (±0.7°C) in winter across Europe. Additionally, our high‐resolution maps expose considerable microclimatic variation within landscapes, not captured by the gridded macroclimatic products. The provided forest sub‐canopy temperature maps will enable future research to model below‐canopy biological processes and patterns, as well as species distributions more accurately. [ABSTRACT FROM AUTHOR]

In this study, a dynamically downscaled regional climate model (RegCM4.3) is used to study the Indian summer monsoon (ISM) surface air temperature over the South‐Asia CORDEX domain using six convection schemes during 1986–2010. The spatial and temporal variability of mean surface air temperature has been analysed with reference to the India Meteorological Department (IMD) analysis data using various statistical scores. The sensitivity experiments in selecting the best convective parameterized schemes have been performed in simulating the surface air temperature during the summer monsoon season (June–September) over India and its five sub‐regions such as Northwest India, Northcentral India, West Peninsular India, Eastern Peninsular India, and Southern Peninsular India. The model results show the tendency of overestimation of surface air temperature mainly in four cumulus parameterization schemes (CPSs) that is, Tiedtke, Emanuel, Mix98, and Mix99 of RegCM4.3 during the JJAS, where Grell and Kuo CPSs show better agreement with the IMD data. Overall, Grell CPS has a close resemblance to the observation data with a minimum root mean square error, mean absolute error, lowest mean absolute percentage error (MAPE), and higher correlation coefficient. The model simulated results have also been investigated further using modified Nash Sutcliffe efficiency and modified Willmott's degree of index. These analyses confirm the potentiality of the Grell CPS followed by the Kuo CPS in simulating interannual variability of the surface air temperature over Indian and its five sub‐regions. The MAPE in Grell and Kuo CPSs are 0.004 and 0.013°C during monsoon season over India, respectively. The inter‐scheme difference in simulating surface air temperature is linked with the generation of low cloud convection and warming‐induced atmospheric moisture advection in the schemes. Therefore, Emanuel, Tiedtke, and Mix98 CPSs have shown a persistent nature of overestimation in surface air temperature variability during JJAS. It is also inferred that after removing the systematic mean bias from the RegCM4.3 model simulated outputs; the skill of Emanuel, Mix98, and Mix99 could be useful over the Indian subcontinent except for the southern peninsular region. [ABSTRACT FROM AUTHOR]

Previous studies show that the environment in the Guangdong, Hong Kong, and Macao region is under the double stress of global warming and urbanization. Here, we show that due to the increase of regional greenness, the effect of urbanization warming on surface air temperature (SAT) decreased with time and became statistically insignificant from 2004 to 2018, compared to 1979 onward; while the urbanization itself has significantly warmed land surface temperature (LST), with a warming rate of 0.14°C ± 0.04°C/10a at daytime and 0.02°C ± 0.02°C/10a at nighttime during 2004–2018, respectively. The anthropogenic heat was found to have a limited influence on SAT, but more significant and tangible effects on LST. It is essential to improve the control of additional warming effects caused by urbanization. Plain Language Summary: The Guangdong, Hong Kong, and Macao region is the most economically developed region in China, and the urbanization impact on the regional warming is frequently discussed during recent decades. We found that the urbanization warming on surface air temperature (SAT) has decreased to insignificant during the recent decade due to regional greening, while the warming of land surface temperature (LST) remains tangible. This result is related to the different effects of two moderating drivers (anthropogenic heat and vegetation index) on both SAT and LST. Thus, it is essential to increase urban greenness and reduce anthropogenic heat fluxes in parallel. Key Points: The effect of urbanization on the surface air temperature (SAT) decreases over time as regional vegetation greening increasesThe urbanization effect on the land surface temperatures (LSTs) from the long time series of satellite retrievals remains significantThe anthropogenic heat was found to have a limited influence on SAT, but more significant and tangible effects on LST [ABSTRACT FROM AUTHOR]

Rewetting of disturbed peatlands is an important restoration strategy for climate change mitigation. Previous work primarily focuses on the biogeochemical processes altered by rewetting and few studies have investigated the biophysical impacts, which can diminish or amplify biogeochemical effects beyond the ecosystem scale. We used a paired flux tower approach in a restored peatland to collect year‐round eddy covariance data to assess the biophysical impacts of disturbance and management practices. The first site was actively rewetted and is characterized by Sphagnum and white beak‐rush with patches of open water. The second site represents a disturbed ecosystem, which underwent natural regeneration and is dominated by scrub pine, Sphagnum, and low shrubs. We found that the actively restored site had higher net radiation compared to the second site due to more surface water ponding; however, the higher aerodynamic conductance at the passively restored site contributed to enhanced daytime turbulent fluxes, and hence, both sites had similar aerodynamic temperatures during the daytime. The actively restored site experienced warmer nighttime and seasonal aerodynamic temperature as much of the excess radiation during the day was stored in the water column and released at night. To achieve restoration goals, higher water tables are now maintained throughout large sections of the bog. The study implies that water table manipulation has the potential to minimize greenhouse gas emissions from the bog, thereby allowing the biophysical impacts of peatland restoration to enhance the biogeochemical benefits. Therefore, it is important to consider both biophysical and biogeochemical changes in peatland restoration management. Plain Language Summary: Peatland restoration through rewetting influences the climate by altering land‐atmosphere greenhouse gas dynamics and energy and water exchanges. The energy and water exchanges altered by rewetting can influence surface and air temperature, and hence local and regional climate; however, they are understudied. This study analyzed year‐round water and energy exchange measurements at two sites in the Burns Bog peatland in British Columbia, Canada, which represent two different dominant ecosystem types that experienced different restoration strategies. We found that the rewetted site with more open water had warmer nighttime and seasonal surface temperature. The results indicate that water levels and vegetation type influenced by restoration strategies have a strong impact on energy partitioning, surface aerodynamic characteristics, and consequently surface and air temperatures. Peatland restoration management should recognize the implications of both greenhouse gas fluxes and energy and water exchanges to fully understand climatic impacts of restoration. Key Points: Significant surface temperature differences at night between the two restored peatland sites with different surface characteristicsThe warmer nighttime and seasonal temperatures at the actively rewetted site were due to energy stored in standing water during daytimeRestoration management has to consider biophysical impacts in addition to biogeochemical changes [ABSTRACT FROM AUTHOR]

Plain Language Summary: Melting of the Antarctic ice sheet and shelf in the future will be influenced by interannual changes in the surface air temperature (SAT) in Antarctica. The SAT changes in Antarctica are related to variations in the Southern Hemisphere Annular Mode (SAM) during the austral summer. The SAM is a dominant pattern of atmospheric variability in the Southern Hemisphere and influences the Antarctic SAT with opposite changes between the northern Antarctic Peninsula (AP) and Eastern Antarctica (EA). To project future changes in the Antarctic SAT, we analyzed historical and future simulations from the Climate Model Intercomparison Project 5 models. We found that the degree of opposite interannual SAT changes between EA and the AP increases in the future due to intensified magnitude of the SAM‐related circulation anomalies, and summers of warmer SAT in the northern AP and cooler SAT in EA increase by 4% in the future compared to the historical period. This finding has major consequences for glacier melting in the northern AP in the future because more days of extremely high SAT in the northern AP may occur in the future. In this study, using the Climate Model Intercomparison Project 5 (CMIP5) simulations and by empirical orthogonal function (EOF) analysis, the first mode of variability in interannual surface air temperature (SAT) in Antarctica (EOF1) was examined for the period between 1979–2004 and 2051–2099 during the austral summer. The ensemble mean of EOF1 of the CMIP5 models shows a positive SAT anomaly over the northern Antarctic Peninsula (AP) and a negative SAT anomaly over Eastern Antarctica (EA) in both periods. A poleward expansion of the AP positive anomaly and an increase in the negative anomaly over interior EA are expected in 2051–2099, resulting in a larger difference of interannual SAT between interior EA and the AP in 2051–2099 than in 1979–2004. The increasing difference in the interannual SAT is consistent with a larger magnitude of the SAM‐related circulation anomalies in the future. Key Points: An increasing summer interannual surface air temperature (SAT) variability in interior Eastern Antarctica (EA) and the Antarctic Peninsula (AP) simulated in 2051–2099An increasing difference in summer interannual SAT between EA and the AP simulated in 2051–2099 than in 1979–2004The increasing difference in summer interannual SAT caused by a larger Southern Hemisphere Annular Mode‐related circulation anomaly in 2051–2099 than in 1979–2004 [ABSTRACT FROM AUTHOR]

This study proposes a hybrid approach to improving subseasonal prediction skills by bridging a conventional statistical model and a dynamical ensemble forecast system. Based on the perfect prognosis method, the phase of the Arctic Oscillation (AO) from the European Centre for Medium‐range Weather Forecasts ensemble forecast system is used as a predictor in a composite based statistical model to predict the wintertime surface air temperature in the Northern Hemisphere. The hybrid model, which employs AO phases predicted by the dynamical model for weeks 1–4, generally outperforms the conventional statistical model for lead times of weeks 2–6. The improved skill score is due to the high accuracy of the AO forecast from the dynamical model and the strong lagged connection between the AO and temperature. This study thus lays the groundwork for the potential use of combining climate variability, statistical relation, and dynamical forecasting. Plain Language Summary: Climate prediction from 1 week in advance to a season is challenging despite increasing social demand and scientific interest for accurate and dependable predictions. Statistical climate prediction relies on observed relationship within the climate data, while dynamical climate prediction utilizes numerical models that is built based on physical principles. Here, we suggest combining those two types of models to enhance the prediction accuracy for the surface air temperature at weeks 2–6 ahead. The hybrid model is built upon a statistical phase model using one of major climate variabilities of Northern Hemisphere winter, so called the Arctic Oscillation (AO). Conventional AO phase model relies on the observed lagged relationship between the AO and the surface air temperature. In the hybrid model, information of forecasted AO from a dynamical model is provided to a statistical model to improve the accuracy and reliability of the conventional statistical model for all forecast lead times. This approach thus lays the groundwork for the potential use of combining climate variability, statistical relation, and dynamical forecasting. Key Points: A hybrid approach is taken by bridging a conventional statistical model and a dynamical ensemble forecast systemA hybrid model enhances the prediction skill of a conventional statistical model for temperature at weeks 2–6An increase in the skill score using the hybrid model arises from the high accuracy of Arctic Oscillation forecasting in the dynamical model [ABSTRACT FROM AUTHOR]

We investigate the impacts of external forcings and internal climate variability (ICV) on global‐mean surface air temperature (GMST) variations (1850–2014) in observation and an ensemble of sixth Coupled Model Intercomparison Project (CMIP6) historical simulations. Using multiple regression models, we estimate that 83% of the observed GMST variance is explained by global‐scale anthropogenic forcing. Most CMIP6 models underestimate this anthropogenic contribution (median explained variance is 67%), but compare better with observation when the North Atlantic regional contribution is included in the anthropogenic forcing (median 77%). The ICV contributions to the GMST variance are ∼8% for observation and ∼7% for the models (median). The models' GMST responses to the external forcings and ICV vary widely, with the two responses being oppositely related. The North Atlantic predominantly drives the free GMST variability in observations, whereas the Pacific drives this in most CMIP6 models due to stronger than observed simulated GMST‐Pacific ICV relationships. Plain Language Summary: Global surface temperature has significantly warmed over the last one and a‐half centuries due to the combined effects of human produced greenhouse gases and aerosols and naturally occurring volcanic eruptions and solar output changes. These externally forced temperature changes are modulated by the climate system's internal variability. Most climate models simulate these different temperature changes, but with significant differences between the models' simulations. Here, we analyze data from observations and from simulations by 42 state‐of‐the‐art climate models to quantify the effects of external forcings and internal variability on surface temperature change. We find that most of the climate models underestimate the observed warming arising from global climate forcing but overestimate the multidecadal variations arising from North Atlantic aerosol forcing. The effect on temperature change from internal variability is much smaller than that from the combined external forcing, in both observations and climate models. Also, the models showing the largest temperature responses to external forcing show the smallest responses to internal variability, and vice‐versa. Understanding the reasons for these differences between climate simulations may help improve the confidence in climate change projections. Key Points: Most models underestimate (overestimate) the surface temperature response to global‐scale anthropogenic (North Atlantic regional) forcingThe models' diverse responses to external forcing have a strong negative relationship with the responses to internal variabilityThe North Atlantic (Pacific) internal variability dominates the unforced surface temperature variations in observations (most models) [ABSTRACT FROM AUTHOR]

The recent summer surface air temperature (SAT) changes over densely populated Eurasia exhibit a non‐uniform pattern with amplified warming over Europe and East Asia (EA) but weak warming over Central Asia (CA), forming a wave train‐like structure. However, the key factors that determine this non‐uniform warming pattern remain unclear. By analyzing observations and model simulations, here, we show that more than half of the SAT multidecadal variations from 1950 to 2014 over Europe‐west Asia and EA may have resulted from external forcing, rather than from internal variability in the Atlantic as previously thought. In contrast, the recent SAT over CA is influenced mainly by internal variations in the Atlantic and Pacific oceans. Large ensemble model simulations suggest that the forced SAT multidecadal variations over Eurasia are mainly caused by changes in greenhouse gases and aerosols. Our findings provide strong evidence for major impacts of external forcing on multidecadal climate variations over Eurasia. Plain Language Summary: Previous studies have documented non‐uniform decadal warming rates in summer since the mid‐1990s over densely populated Eurasia, with amplified warming over Europe and East Asia (EA) but weak warming over Central Asia. This unique tripole warming pattern and its cause have attracted considerable attention. It may arise from internal variability (IV; e.g., Atlantic Multidecadal Oscillation [AMO]) and/or external forcing. Furthermore, it was found that the recent AMO resulted from both IV and decadal changes in aerosols. Thus, the relative contributions of IV and external forcing to the recent Eurasian warming pattern remain unclear. Here we show that the surface air temperature multidecadal variations since 1950 over Eurasia are caused by both IV and external forcing, with more than half of the variations over Europe‐west Asia and EA resulted from external forcing (i.e., greenhouse gases [GHGs] and aerosols), in contrast to the previous notion that the non‐uniform warming is mainly originated from the Atlantic Ocean. Looking ahead into the upcoming decades, aerosols will continue to decrease, whereas the AMO will likely be in its positive phase. These changes, together with continued increases in GHGs, will accelerate warming trends over Eurasia and further exacerbate summer heat waves. Key Points: More than half of the multidecadal surface air temperature (SAT) variations from 1950 to 2014 over Europe‐west Asia and East Asia resulted from external forcingThe forced SAT multidecadal variations over Eurasia are mainly caused by changes in greenhouse gases and aerosolsOur study helps reconcile human and natural drivers of the tripole pattern of multidecadal summer temperature variations over Eurasia [ABSTRACT FROM AUTHOR]

Limiting excess atmospheric ammonia (NH3) is one of the great challenges for humanity in the 21st century but can only be achieved with adequate monitoring means in place. Here, we report the first NH3 measurements from the Geostationary Interferometric Infrared Sounder (GIIRS) onboard the Chinese FY‐4A satellite. The instrument scans almost all of Asia 10 times per day. We show that GIIRS′ unprecedented temporal sampling can be exploited to measure diurnal and nocturnal variations of NH3 and demonstrate this on two case studies over Punjab and the North China Plain. Day–night variations are found to be almost absent in winter, but can reach a factor 2–3 in the warmer months. These case studies are very promising given the future landscape of geostationary sounders, but also show that improved knowledge on lower tropospheric air temperatures and the vertical profile of NH3 is key to better exploit their measurements. Plain Language Summary: To reduce the worldwide concentrations of ammonia (NH3) in the air is one of the great challenges for humanity in the 21st century, but this can only be achieved with stringent policies in place, and with the support of accurate and frequent measurements. In this paper, we report the first ammonia measurements from the Geostationary Interferometric Infrared Sounder (GIIRS) onboard the Chinese FY‐4A satellite. The satellite is positioned above Indonesia and measures almost all of Asia 10 times per day. We show that GIIRS' measurements can be used to measure ammonia both during day and night. We look into more detail at two locations, over Punjab (Pakistan) and the North China Plain. Day‐night variations are found to be almost absent in the colder months, but can reach a factor 2–3 in the warmer months. These case studies are very promising given the future landscape of geostationary sounders, but demonstrate also that improved knowledge on the air temperature at the surface and the behavior of ammonia at higher altitudes will result in more accurate ammonia measurements. Key Points: We report the first observations of atmospheric NH3 from the geostationary satellite FY‐4A GIIRS over AsiaThe potential of GIIRS to reveal the diurnal and nocturnal variations of NH3 total columns is demonstrated over two sitesTo better exploit geostationary data, improved temperature profiles and knowledge on the vertical NH3 distribution is needed [ABSTRACT FROM AUTHOR]

The influence of the Madden‐Julian Oscillation (MJO) on surface air temperature over Iran is examined using daily data from meteorological stations from 1979 to 2015. Composites of daily surface air temperature anomalies are positive in MJO phases 1 and 8 and negative in MJO phases 3–4 with broader region positive tendencies also in MJO phases 2 and 7, and negative tendencies in MJO phases 5–6. This variability is associated with horizontal temperature advection, whereby the southerly (northward) winds act to heat and the northerly (southward) winds cool Iran, in association with the MJO. Further, we find that daily minimum surface air temperatures respond more strongly to the MJO than do daily maximum surface temperatures. These signals correspond to cloudy and humid conditions. [ABSTRACT FROM AUTHOR]

Drylands occupy about 41% of land surface and feed 38% of the world's population, where understanding drylands' water‐cycle is in urgent need. In this study, response of water‐cycle over the drylands to global warming from 1980 to 2015 and its mechanisms have been investigated in detail. The results show that, with the global surface air temperature (SAT) increasing at a rate of 0.03 K⋅year−1 over the drylands, an increased evaporation is found; however, the net water vapour transport over the global drylands appears an overall decreased rate of −0.24 mm⋅year−1. Due to global warming and atmospheric circulation anomalies, the evaporation increases by 1.1 mm⋅year−1, while the precipitation and runoff decrease by −2.32 and −0.8 mm⋅year−1, respectively. In addition, the precipitation conversion rate (PCR) tends to decrease during this period, while the precipitation recycling rate (PRR) shows slightly increasing trend, implying a more dependence of the precipitation on the local evaporation over the drylands. Overall, except the drylands of Africa, it indicates a weakened water‐cycle over the global drylands from 1980 to 2015. Though some findings have been revealed, there still remains some uncertainties due to the observation limitation on the water‐cycle components. Therefore, a comprehensive study including multi‐sourced data and model simulation is expected in the future. [ABSTRACT FROM AUTHOR]

Urban development exacerbates urban heat island effects of cities and thermal discomfort of the residents worldwide. However, such urban effects vary with the geographical and climatic conditions of the respective cities. This study is the first to quantitatively estimate the impact of past (1878), present (2012), and future (2050) urbanization on surface air temperatures and human thermal comfort in Sofia, the largest city of Bulgaria. We used the state-of-the-art Weather Research and Forecasting (WRF) climate model, with a 1-km horizontal resolution for three cases: LU1878, LU2012, and LU2050. We first validated the results of the control simulation (case LU2012) against actual observations. The model satisfactorily reproduced the diurnal variation of the observed monthly mean surface air temperatures for July (2011-2013) with a mean bias of ±0.6°C. We then estimated the potential impacts of past and future urbanization on surface temperature. Our results suggest that because of urbanization of Sofia, the surface air temperature at 0600 Local Standard Time (LST) has increased approximately 4.0°C in the past (1878-2012) and approximately 0.4°C in future (2012-2050). We also evaluated the impact of the urban thermal environment on human comfort levels. Considering the LU2050 case in the central part of Sofia, the temperature-humidity index during mid-afternoon (1400-1700 LST) is estimated to be 24-26, indicating that approximately 50% of the residents could feel perceptibly uncomfortable. [ABSTRACT FROM AUTHOR]

Water vapour is among the most critical variables that shape the Earth's energy balance and hydrologic cycle. Surface relative humidity (RH) and specific humidity (SH) are the main measures of atmospheric moisture. Long‐term changes in these variables in South Korea vary regionally as well as locally, but have not been explored using long‐term observation data. This study analyzes the annual trends of the means and SD of RH, surface air temperature, and SH in South Korea at 55 stations for the period 1973–2018. Three trend detection tests based on the Mann–Kendall test have been performed to detect trends at the 5% significance level for the annual and monthly statistics (means and SD) of these variables. Significant decreasing and increasing trends have been detected in the annual means of RH and air temperature, while the annual mean of SH has remained nearly constant. Overall, the surface air in South Korea has become drier over the study period, particularly in winter. The trend in RH and SH varies widely according to months to result in the increase in the SD over the annual cycle. The monthly SD of RH, SH, and surface air temperature show especially large positive trends for March and September, when the transition from winter to spring and summer to fall, respectively, occurs. [ABSTRACT FROM AUTHOR]

Surface air temperature trends and extreme events are of global concern and they are related. Here, we show that the occurrence of record hot years over different latitudes from 1960 to 2019 are more strongly correlated with the observational annual mean temperature trends normalized by internal variability. Compared with the raw trends showing Arctic amplification, the normalized trends show a tropical amplification over land. Two hot spots with more frequent occurrence of record hot years are identified: northern hemisphere ocean (vs. land) and southern hemisphere tropical land (vs. mid‐ and high‐latitude lands). Ensemble mean results from 32 Earth system models agree with observations better than individual models, but they do not reproduce observed large differences in correlations across latitudes between normalized trends and record‐breaking events over land versus ocean. Our results enable the quantification of record hot year occurrence through normalized warming trends and provide new metrics for model evaluation and improvement. Plain Language Summary: Surface air temperature trends and extreme hot events are of global concern and they are related. Based on generic time series, it was argued that the occurrence of extreme events depends on the normalized, rather than the raw, trends. However, it remains unknown whether this dependence is relevant to the comparison of extreme hot event occurrences over different regions, such as the Arctic versus Amazon. Here, we show that the occurrence of record hot years over different regions from 1960 to 2019 are more strongly correlated with the observational annual mean temperature trends normalized by internal variability. Compared with the raw trends showing Arctic amplification, the normalized trends show a tropical amplification over land. Two hot spots with more frequent occurrence of record hot years are identified: Northern hemisphere ocean (vs. land) and southern hemisphere tropical land (vs. mid‐ and high‐latitude lands). Results averaged from 32 Earth system models agree with observations better than individual models, but they do not reproduce observed large differences in correlations across latitudes between normalized trends and record‐breaking events over land versus ocean. Our results enable the quantification of record hot year occurrence through normalized warming trends and provide new metrics for model evaluation and improvement. Key Points: Occurrence of record hot years in different latitudes is better correlated with normalized, rather than raw, temperature trendsCompared with the raw trends showing Arctic amplification, the normalized trends show a tropical amplification over landEarth system models' correlations between normalized trends and record‐breaking events are as high over land as over ocean, unlike in observations [ABSTRACT FROM AUTHOR]

This study investigates the variability of summer surface air temperature (SAT) over northern Eurasia and its precursory signals in the tropical Atlantic and northern Asian land. The leading mode of summer SAT variations features a northern Eurasian SAT tripole (NEST) pattern, with two same‐sign SAT anomaly regions over eastern Europe–western Siberia and the Far East region and an opposite‐sign SAT anomaly region around the Baikal Lake. It is found that sea surface temperature (SST) or SAT anomalies in the tropical Atlantic and rainfall‐soil moisture anomalies around the Baikal Lake during May can modulate the NEST pattern. The SST anomalies in the tropical Atlantic persist from May to summer and induce a downstream zonal wave train across northern Eurasia, consequently causing the variation in the summer NEST pattern. May rainfall anomalies around the Baikal Lake affect the overlying atmospheric circulation during summer through the 'memory' effect of soil moisture and the soil moisture‐rainfall interaction, correspondingly modulating the downstream wave train and the associated NEST pattern. Based on the above results, a statistical prediction model is established using the two precursory signals, that is, SAT in the tropical Atlantic and rainfall around the Baikal Lake during May. The leave‐three‐out cross‐validation shows that the model has a high skill in predicting the summer NEST pattern, with a correlation coefficient of 0.51 (significant at the 99.8% confidence level) between observation and prediction during the period 1980–2016. [ABSTRACT FROM AUTHOR]

The Arctic has experienced prominent climate warming, at the beginning of the 20th century and currently. Comparing the driving mechanism during these periods helps to explain the causes of contemporary climate change. Our study explores the impact of regional circulation on Svalbard's surface air temperature (SAT, 2 m above ground). We used air temperature data from Svalbard Airport, Bjørnøya stations, and three regional circulation indices that describe the frequency of cyclonic conditions, zonal circulation, and meridional circulation. The indices were calculated for four circulation areas with differing circulation conditions and, therefore, may have various impacts on long‐term changes in SAT. This was checked for the entire study period (1920–2018), and 30‐year sub‐periods representing the most prominent climatic events: the early 20th‐century Arctic warming (ETCAW), contemporary Arctic warming (CAW), and a cold period between them (CAP). In autumn and winter, the deviations in SAT from the long‐term average during warm and cold periods were almost twice as large at Svalbard Airport as at Bjørnøya. In these seasons, the ETCAW was significantly warmer than the subsequent cold period, which was not the case for summer and spring. However, long‐term trends in the regional circulation indices were more evident in summer and spring than in autumn and winter. Air temperature was the most strongly influenced by meridional circulation over the eastern circulation areas, with the exception of spring, when air temperature variability was more affected by zonal circulation. The recent warming weakened the relationship between SAT and the indices in summer. We attributed the ETCAW in autumn to a southerly advection of sensible heat. During the same historical period, the impact of the indices was much weaker in winter. In winter during the CAP, there was a higher frequency of northern air advection, particularly over the northern part of the Greenland Sea. [ABSTRACT FROM AUTHOR]