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While previous studies have largely focused on anthropogenic warming characterized by surface air temperature, little is known about the behaviors of human‐perceived temperature (HPT), which describe the "feels‐like" equivalent temperature by considering the joint effects of temperature, humidity and/or wind speed. Here we adopted an optimal fingerprinting method to compare seasonal mean HPTs in China with those from simulations conducted with multiple climate models participating in the Coupled Model Intercomparison Project Phase 6. We found clear anthropogenic signals in the observational records of changes in both summer and winter HPTs over the period 1971–2020. Moreover, the anthropogenic greenhouse gas influence was robustly detected, with clear separation from natural and anthropogenic aerosol forcings. The anthropogenic greenhouse gas forcing plays the dominant role (>90%) of human‐perceived warming. Urbanization effects contribute slightly and moderately to the estimated trends in summer and winter HPTs, respectively, in addition to the effects of external forcing. Plain Language Summary: Human influences have been identified in the observed warming quantified by surface air temperature (SAT), but SAT alone is inadequate as a metric for human thermal comfort. Here we focus on human‐perceived temperature (HPT), which describes the "feels‐like" equivalent temperature by considering the joint effects of temperature, humidity, and/or wind speed. We isolate anthropogenic impacts on the observed increase in summer and winter HPTs in China during 1971–2020 by comparing observations with state‐of‐the‐art climate models. Results show that the influence of anthropogenic greenhouse gas is detected, with clear separation from other external forcings such as solar and volcanic activities and anthropogenic aerosols. The human‐induced greenhouse gas increases are also found to explain most (>90%) of the observed human‐perceived warming. Along with the effects of large‐scale anthropogenic forcing, urbanization effects also have a slight to moderate influence on the estimated trends in summer and winter HPTs. Our work is an early attempt to provide quantitative evidence for the physiological impacts of anthropogenic global warming and local urbanization on human beings. Key Points: The warming is quantified by human‐perceived temperature that considers the joint effects of temperature, humidity and/or wind speedHuman influence could be robustly detected in both summer and winter human‐perceived warmingThe observed increase in human‐perceived temperature is mostly attributed to anthropogenic greenhouse gas increases [ABSTRACT FROM AUTHOR]

The Arctic is notable as a region where the greatest rate of increase in precipitation associated with global warming is anticipated. The Arctic precipitation simulated by the Coupled Model Intercomparison Project Phase 6 models showed a strong increasing trend since the 1980s. We found that the forcing factor of the trend is a combination of the continued strengthening of greenhouse gas forcing and the leveling off of aerosol forcing dominated in earlier periods. From an energetic perspective, we found that the increased atmospheric radiative cooling and reduced sensible heat transport from lower latitudes contributed equally to the recent increase in Arctic precipitation. The combination of these two energetic factors suggests a doubling of the Arctic amplification factor for precipitation relative to that for temperature. Future Arctic precipitation will change in proportion to the temperature change, and the fractional contributions of the energetic factors will remain stable across various scenarios. Plain Language Summary: The Arctic region is inherently a low‐precipitation area. However, because of global warming, precipitation is expected to increase substantially in the Arctic region compared with the global average when viewed as a percentage change from the original precipitation. This severely affects climate change in the Arctic environment. The latest climate model simulations show that there has been a rapid increase in precipitation in the Arctic region in recent decades. The driving factors behind the rapid increase are the effects of the accelerating growth of greenhouse gas concentrations, which were previously suppressed by the increasing anthropogenic aerosol emissions before the 1980s. Based on the heat budget of the atmosphere, we identified important factors contributing to these precipitation changes. These include enhanced radiative cooling (responding locally to increased air temperature) and reduced heat transport from lower latitudes due to greater temperature increases at higher latitudes. Future precipitation will change in proportion to the temperature change while maintaining consistent fractional contributions across different scenarios. Key Points: Trends in Arctic precipitation in the recent and future decades are examined from multimodel simulationsThe recent rapid increase is driven by accelerating greenhouse gas concentrations and plateauing growth in anthropogenic aerosol emissionsIncreased radiative cooling and reduced poleward sensible heat transport equally contributed to the Arctic precipitation changes [ABSTRACT FROM AUTHOR]

The efficacious forecasting of single‐station atmospheric temperature profiles can provide essential support for the structural design and flight missions of spacecrafts in near space. However, empirical models and reference atmospheric models most are calculations of the average state of the atmosphere profiles. Numerical assimilation models require expensive computational costs to improve the accuracy for medium and long‐term forecasting. It has been still a challenge to refined predict short‐term temperature profiles of near space at a low‐cost. We present a temperature profile operator method for refined modeling in the stratosphere by fusing the ability of Long Short‐Term Memory (LSTM) networks or its variants‐ bidirectional LSTM (BiLSTM) to exploit time series correlated information and deep operator networks (DeepONets) to approximate the solution operator of temperature profiles. It consists of three subnetworks. The first subnetwork is used to approximate the discrete temperature profile function, the second net is applied to represent the spatial information of pressure heights, and the third branch is utilized to encode the time domain of the temperature profile operator. We first use the hourly low latitude temperature data (20–50 km) from ERA5 for training, verification and iterative testing in the next 48 hr. The results denote that the temperature profile operator network has a stable and low error of cumulative generalization, and the BiLSTM operator significantly outperform the other models. We also apply two scenarios to testing the refined applicability of the high latitude temperature profile operator and the mid latitude wind profile operator in the stratosphere. This work provides a novel perspective for us to study the refined single‐station modeling of the upper and middle atmosphere. Plain Language Summary: The temporal and spatial distribution mechanism of physical parameters in near space is very complex. It is of great significance to carry out fine modeling and forecasting of atmospheric temperature profiles for spacecrafts in near space. In this study, taking a single‐station temperature profile in the stratosphere as the object, the mathematical mapping problem of temperature operator is realized by a new deep learning method (deep operator networks). This provides a new research perspective for fine spatiotemporal modeling of temperature profiles. The proposed temperature operator shows low cumulative error performance in forecasting low latitude temperature profiles, and is also suitable for high latitude temperature profiles and mid latitude wind profiles in the stratosphere. Key Points: A deep neural operator method is utilized to forecast hourly atmospheric temperature profiles in the stratosphereThe temperature profile operator network based on BiLSTM denotes lower cumulative error in multi‐step iterative forecastingThe proposed method exhibits potential performance in different locations and atmospheric variables [ABSTRACT FROM AUTHOR]

Since 1980, the Arctic surface has warmed four times faster than the global mean. Enhanced Arctic warming relative to the global average warming is referred to as Arctic Amplification (AA). While AA is a robust feature in climate change simulations, models rarely reproduce the observed magnitude of AA, leading to concerns that models may not accurately capture the response of the Arctic to greenhouse gas emissions. Here, we use CMIP6 data to train a machine learning algorithm to quantify the influence of internal variability in surface air temperature trends over both the Arctic and global domains. Application of this machine learning algorithm to observations reveals that internal variability increases the Arctic warming but slows global warming in recent decades, inflating AA since 1980 by 38% relative to the externally forced AA. Accounting for the role of internal variability reconciles the discrepancy between simulated and observed AA. Plain Language Summary: The Arctic has been warming four times as quickly as the global mean since 1980. This so‐called Arctic Amplification (AA) has unprecedented impacts on Arctic environments and livelihoods. AA is robustly simulated by climate models, but simulations rarely reproduce the observed levels of AA for 1980–2022. This may be due to a model misrepresentation of the Arctic's sensitivity to increasing greenhouse gases. Another possibility is that the large, observed value of AA is inflated by natural fluctuations in the climate system. Here, we use machine learning to quantify the contribution of natural fluctuations to observed AA. We show that natural fluctuations have inflated AA by 38%, and thus reconcile model‐observation differences and suggest that the observed large AA over 1980 to present would not persist into the future. Key Points: Internally generated and externally forced temperature trends over the Arctic and globe can be partitioned using machine learning methodsInternal variability has enhanced Arctic warming while damping global warming over 1980‐2022Accounting for internal variability in observations reconciles discrepancies between simulated and observed Arctic Amplification [ABSTRACT FROM AUTHOR]

The unprecedented 2021 June‐July heatwave in Western North America resulted in record‐breaking human‐perceived heat stress across the region, measured by the humidex considering both air temperature and humidity. During extended summer (June‐September), both 95th percentiles of daily maximum humidex (HX95) and air temperature (TX95) have increased over the 1940–2022 period, with even faster intensification in the last two decades (2001–2022). HX95 has increased more than TX95 because of the positive monotonic nonlinear relationship between humidex and air temperature at a given level of relative humidity. The Canadian Earth System Model version 5 (CanESM5) projects a larger increase in human‐perceived heat stress than air temperature across the region under low to high emission scenarios (HX95 increases 4.40–7.04°C and TX95 increases 2.92–4.65°C between 1981–2010 and 2041–2060). Moreover, CanESM5 projects significant increases in the frequency of HX and TX conditions that exceed the levels reached in 2021 under intermediate and high emission scenarios. Plain Language Summary: Using humidex, a measure that combines air temperature and humidity, we assessed the human‐perceived heat stress during the record‐breaking 2021 summer heatwave in Western North America. The 2021 heatwave broke records for both air temperature and humidex, and these two measures have been increasing in the past decades. The heatwave was drier than usual due to high pressure in the atmosphere, clear skies, and more heat warming up the air instead of evaporating water. Humidex will rise more than air temperature under various emission scenarios, making people feel even hotter in the region. The climate model used suggests that humidex values and temperatures that exceed the levels observed in 2021 will happen much more often and be much more intense across the region in the future, with estimates of three to ten times for humidex and two to seven times for air temperature more frequent under intermediate to high emission scenarios by the end of the century. However, the likelihood of extreme humidex in dry conditions, as observed in 2021, will remain low, like historical levels. Key Points: Unprecedented 2021 heatwave in Western North America resulted in record‐breaking human‐perceived heat stress, considering both air temperature and humidityExtreme human‐perceived heat stress increased at a faster rate than extreme air temperature, both showing rapid increases in recent decadesFor events exceeding 2021 level, a larger future increase in extreme human‐perceived heat stress is projected compared to air temperature [ABSTRACT FROM AUTHOR]

This study investigated ice cloud formation associated with marine bioaerosols over the Southern Ocean (SO) using a combination of cloud particle sensor (CPS) sonde observations, satellite products, reanalysis data, and backward trajectory analysis. The CPS sonde detected ice clouds at temperatures higher than −10°C in the mid‐troposphere near an atmospheric river at high‐latitudes over the SO. Backward trajectory analyses indicated that a mid‐latitude air mass with a high concentration of atmospheric dimethylsulfide (DMS) in the atmospheric boundary layer (<1 km) arrived at the ice cloud formation layer over the high‐latitudes. The DMS in the boundary layer began to increase under high wave conditions, coincident with the highest chlorophyll‐a concentrations in the ocean. These results suggest that bioaerosols emitted from the ocean over the mid‐latitudes acted as ice‐nucleating particles for ice cloud formation over high‐latitudes. Plain Language Summary: Polar region clouds play a key role in Earth's climate. Knowledge of the cloud phase (i.e., liquid water, ice, or mixed) is important for determining the surface heat budget because the reflection of solar radiation at the cloud top depends on cloud phase. Although the development of numerical climate models allows the investigation of clouds globally, there still is a cloud phase (water or ice) bias in the models. Therefore, an observational study is required to investigate cloud formation environments. During a cruise in the SO by a research ship, ice clouds were observed at relatively high temperatures in the mid‐troposphere at high‐latitudes. Analysis of the trajectory of the air mass at the ice formation height indicated that the air mass had traveled from the surface in the mid‐latitudes to the mid‐troposphere over high‐latitudes. Biogenic material emitted from the ocean under strong wind and high wave conditions was transferred to the air mass near the surface in the mid‐latitudes and eventually reached the mid‐troposphere at the observation point. These results suggest that marine biogenic material transported from the mid‐latitudes influenced ice cloud formation under relatively high temperatures. Key Points: A cloud particle sensor sonde measured ice clouds at air temperatures higher than −10 °C in the mid‐troposphere near an atmospheric riverMid‐latitude air mass with high dimethylsulfide (DMS) in the boundary layer reached the ice cloud formation layer over high‐latitudesSimulated high surface atmospheric DMS was associated with observed high chlorophyll‐a concentration under simulated high wave conditions [ABSTRACT FROM AUTHOR]

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

Radiative feedbacks govern the Earth's climate sensitivity and elucidate the geographic patterns of climate change in response to a carbon‐dioxide forcing. We develop an analytical model for patterned radiative feedbacks that depends only on changes in local surface temperature. The analytical model combines well‐known moist adiabatic theory with the radiative‐advective equilibrium that describes the energy balance in high latitudes. Together with a classic analytical function for surface albedo, all of the non‐cloud feedbacks are represented. The kernel‐based analytical feedbacks reproduce the feedbacks diagnosed from global climate models at the global, zonal‐mean, and seasonal scales, including in the polar regions, though with less intermodel spread. The analytical model thus provides a framework for a quantitative understanding of radiative feedbacks from simple physics, independent of the detailed atmospheric and cryospheric responses simulated by comprehensive climate models. Plain Language Summary: Given an increase in carbon dioxide concentration, individual radiative feedbacks stabilize or amplify the climate response. When diagnosed from comprehensive climate models, these feedbacks exhibit considerable variability, yet, single atmospheric columns have been successfully used as minimal models to quantify the effect of temperature and water vapor changes on global climate. Here, we bring together these perspectives by developing an analytical model for radiative feedbacks that, for projected changes, knows only of local surface temperature. That is, thermodynamic expressions for atmospheric temperature, water vapor, and sea‐ice albedo are combined with radiative kernels, which characterize the top‐of‐atmosphere radiative response to a small perturbation, to yield estimates of radiative feedbacks independent of the simulated atmospheric and cryospheric changes from a climate model. The analytical model captures the equator‐to‐pole and seasonal feedback structure, including in the ice‐covered polar regions, as well as the global feedback across an ensemble of global climate models. This research thus provides a framework for a quantitative understanding of radiative feedbacks from simple physics combined with geographic patterns of surface warming. Key Points: Analytical approximations of atmospheric temperature and surface albedo are used to estimate feedbacks in comprehensive climate modelsMeridional, seasonal, and global variations in feedbacks are well‐represented by the analytical model, including in the polar regionsAnalytical feedbacks have less intermodel spread than general circulation model feedbacks, suggesting that deviations from an adiabat contribute to uncertainty [ABSTRACT FROM AUTHOR]

Atmospheric instability affects the formation of convective storms, but how it has changed during recent decades is unknown. Here we analyze the occurrence frequency of stable and unstable atmospheric conditions over land using homogenized radiosonde data from 1979 to 2020. We show that atmospheric stable (unstable) conditions have decreased (increased) significantly by ∼8%–32% (of time) from 1979 to 2020 over most land areas. In boreal summer, the mean positive buoyancy (i.e., convective available potential energy [CAPE]) also increases over East Asia while mean negative buoyancy (i.e., convective inhibition [CIN]) strengthens over Europe and North America from midnight‐dawn for unstable cases. The increased unstable cases and mean CAPE result from increased low‐level specific humidity and air temperature, which increase the buoyancy of a lifted parcel. The stronger CIN results from decreased near‐surface relatively humidity and decreased lapse rate in the lower troposphere. Our results suggest that the atmosphere has become increasingly unstable, which could lead to more convective storms. Plain Language Summary: Severe weather events such as tornados and intense thunderstorms often cause significant loss of life and property. Their formation requires instability or unstable conditions in the atmosphere. Climate models project increased unstable conditions under greenhouse gas‐induced global warming, but how atmospheric instability has changed during recent decades is unclear. Through analysis of newly homogenized radiosonde data, here we show that the atmosphere has become increasingly unstable over most land areas over the Northern Hemisphere from 1979 to 2020, with an increasing number of unstable conditions (i.e., with positive buoyancy) but a decreasing number of stable conditions (i.e., with zero or negative buoyancy). These changes result mainly from increased low‐level moisture content and warmer air temperature. Such instability changes favor increased occurrence of convective storms. Key Points: Newly homogenized radiosonde humidity and temperature data allow quantitative assessment of historical changes in atmospheric instabilityThere are increasingly more unstable conditions with positive buoyancy in all seasons over most land areas from 1979 to 2020The increased unstable conditions mainly result from increased low‐level moisture content and warmer air temperature [ABSTRACT FROM AUTHOR]

Reliable precipitation simulation over the Tibetan Plateau (TP) remains a challenge, manifested by a prominent systematic wet bias in the warm season. Previous studies have generally neglected the potential linkage between surface radiation energy budget and precipitation bias. Prevalent scattered cumulus and thunderstorms over the TP in summer strongly influence surface radiation. A cloud fraction scheme considering subgrid temperature and humidity fluctuations is implemented in the WRF model and tested for a month‐long simulation. It is found that the scheme better reproduces the surface solar radiation compared to a default cloud fraction scheme in the WRF model. Using abundant surface observations, we find that overestimation of the downward surface shortwave radiation (DSSR) would lead to wet bias. DSSR overestimation contributes to higher surface temperature and larger evaporation and enhanced atmospheric instability, which favor more simulated convective precipitation. The study suggests that a better simulation of clouds and surface radiation would benefit precipitation simulation over the plateau. Plain Language Summary: Summer precipitation over the Tibetan Plateau (TP) is generally overestimated in general circulation models as well as regional weather forecasts and climate models. The prevailing "popcorn‐like" clouds over the TP in summer would strongly impact surface radiation. Considering the potential linkage between surface radiation energy budget and precipitation bias, a statistical cloud fraction and subgrid condensation scheme (GS‐PDF scheme) is implemented in the WRF model and tested for a month‐long simulation. The GS‐PDF scheme better captures clouds over the TP with more mid‐low cloud water (ice) than the default Xu‐Randall scheme. Increased mid‐low cloud water in the GS‐PDF scheme reduced simulated downward surface shortwave radiation, reduced simulated surface temperatures, atmospheric instabilities and convective precipitation, leading to alleviated wet bias over the TP. This study suggests that better simulations of cloud and surface radiation would be beneficial for simulations of hydrological cycles over the TP. Key Points: A new cloud macrophysics scheme considering subgrid temperature and humidity fluctuations is implemented in the WRF modelThe new macrophysics scheme reduced simulated downward surface shortwave radiation, surface temperatures and atmospheric instabilitiesA better simulation of clouds and surface radiation would benefit precipitation simulation over the Tibetan Plateau [ABSTRACT FROM AUTHOR]

A new detection and attribution method is presented and applied to the global mean surface air temperature (GSAT) from 1900 to 2014. The method aims at attributing the climate changes to the variations of greenhouse gases, anthropogenic aerosols, and natural forcings. A convolutional neural network (CNN) is trained using the simulated GSAT from historical and single‐forcing simulations of 12 climate models. Then, we perform a backward optimization with the CNN to estimate the attributable GSAT changes. Such a method does not assume additivity in the effects of the forcings. The uncertainty in the attributable GSAT is estimated by sampling different starting points from single‐forcing simulations and repeating the backward optimization. To evaluate this new method, the attributable GSAT changes are also calculated using the regularized optimal fingerprinting (ROF) method. Using synthetic non‐additive data, we first find that the neural network‐based method estimates attributable changes better than ROF. When using GSAT data from climate model, the attributable anomalies are similar for both methods, which might reflect that the influence of forcing is mainly additive for the GSAT. However, we found that the uncertainties given both methods are different. The new method presented here can be adapted and extended in future work, to investigate the non‐additive changes found at the local scale or on other physical variables. Plain Language Summary: In order to design effective adaptation policies, it is essential to have reliable estimates of the effect of anthropogenic activities on the climate. For that purpose, a new attribution method based on a neural network is designed and evaluated. The method estimates the past global mean surface air temperatures anomalies caused by the changes in the greenhouse gases concentration, the variation of anthropogenic aerosols, and the variations driven by naturally occurring phenomena. To build this estimation, the data from observations and climate models are used. This methodology is compared with another state‐of‐the‐art method. The results of both methods are evaluated and discussed. The proposed method provides better estimations in the case of large non‐additivity of the causes of climate change and can be applied to other physical variables or at the regional scale. In the case of the global mean surface air temperature, the method presented provides estimation similar to other methods. Key Points: We present a non linear method based on neural network to attribute the global mean surface air temperature variability to different forcingsWe use a convolutional neural network associated with a backward optimization to estimate the climate response to the different external forcingsThe attributable forcings are consistent with those obtained using another state‐of‐the‐art method [ABSTRACT FROM AUTHOR]

Since climate model studies project a decline of the Atlantic Meridional Overturning Circulation (AMOC) in the 21st century, monitoring AMOC changes remains essential. While AMOC variability is expected to be coherent across latitudes on longer than decadal timescales, connectivity on inter‐annual and seasonal timescales is less clear. Model studies and observational estimates disagree on the regions and timescales of meridional connectivity and AMOC observations at multiple latitudes are needed to study its connectivity. We calculate basin‐wide AMOC volume transports (1993–2018) from measurements of the North Atlantic Changes (NOAC) array at 47°N, combining data from moored instruments with hydrography and satellite altimetry. The mean NOAC AMOC is 17.2 Sv exhibiting no long‐term trend. Both the unfiltered and low‐pass filtered NOAC AMOC show a significant correlation with the RAPID‐MOCHA‐WBTS AMOC at 26°N when the NOAC AMOC leads by about one year. Plain Language Summary: In the North Atlantic Ocean, currents transport heat and salt from the warm subtropical regions to the colder and less saline subpolar regions. This current system is part of the Atlantic Meridional Overturning Circulation (AMOC). This enormous northward heat transport associated with the AMOC impacts regional and global climate. In a warming world, climate models project a reduction of the AMOC, affecting, for example, air temperature and rain patterns over the continents. Continuous observations are required to investigate whether climate models realistically simulate the AMOC and assess the validity of these projections. We calculate the AMOC from moored observations, hydrography, and satellite data at 47°N from 1993 to 2018. In this period, the AMOC at 47°N does not show a trend. The 26°N AMOC lags the 47°N AMOC by about one year, indicating that the AMOC evolution at these two latitudes is connected. Key Points: We present a 25‐year observational Atlantic Meridional Overturning Circulation (AMOC) volume transport record at 47°N with a mean basin‐wide AMOC transport of 17.2 SvThe inter‐annual variability at 47°N is similar to the RAPID AMOC at 26°N, while the monthly variability is much stronger at 47°NThe AMOC time series at 47°N and 26°N show a significant lag correlation when the AMOC at 47°N leads by about one year [ABSTRACT FROM AUTHOR]

Tropical overshooting deep convections (ODCs) play a vital role in vertical transport of boundary layer pollutants, especially short‐lived species, to upper troposphere and lower stratosphere, with important implications for stratospheric ozone and climate. We use simulations from a global cloud‐system resolving model, Nonhydrostatic Icosahedral Atmosphere Model (NICAM), to study ODC changes from historical period to the end of the 21st century. NICAM well reproduces Tropical Rainfall Measuring Mission‐satellite observed ODC spatiotemporal patterns. The future occurrences of ODCs with cloud top height above 15.5, 16.9, and 18.4 km scaled by the global temperature increase will increase by 7%/K, 27%/K, and 90%/K, respectively, over ocean where the atmosphere is becoming warmer and wetter. The corresponding changes are −1%/K, 10%/K, and 37%/K over land where the atmosphere will become hotter but drier. Relative to tropical cold point tropopause height, ODCs will only change by 3%/K, with 6%/K over the ocean but −3%/K on land. Plain Language Summary: Tropical overshooting deep convection (ODC) plays an important role in transporting short‐lived chemical species rapidly from troposphere to stratosphere. This study shows that the simulations from a global cloud‐system resolving model, Nonhydrostatic Icosahedral Atmosphere Model (NICAM), can well capture observed spatiotemporal variations of tropical ODCs. The NICAM simulations predict that by the end of the 21st century (2075–2104) versus the historical period (1979–2008) with a global‐mean surface air temperature increase of 2.67 K, ODC occurrences with cloud tops reaching above 15.5, 16.9, and 18.4 km will increase by 14%, 59%, and 189%, respectively. Thus ODCs with higher cloud tops increase by a larger fraction than ODCs with lower cloud tops. The corresponding changes in ODC occurrences over the future warmer (hotter) and wetter (drier) oceanic (terrestrial) environments will be 20% (−2%), 72% (27%), and 240% (98%). Thus ODCs over ocean generally increase at a faster rate than over land. With tropical cold point tropopause height as a reference level, which will increase from 17.2 to 18.1 km, ODCs will increase by only 8% over the tropics, with 15% over ocean but decrease by −8% over land. Key Points: The Nonhydrostatic Icosahedral Atmosphere Model well reproduces observed spatiotemporal distributions of tropical overshooting deep convections (ODCs)Tropical ODC increases with global temperature on ocean (land) by 7%/K (−1%/K) for ODCs above 15.5 km, 27%/K (10%/K) above 16.9 km, and 90%/K (37%/K) above 18.4 kmResponse of tropical ODCs that penetrate the tropical cold point tropopause height to global warming is 3%/K, with 6%/K over the ocean and −3%/K on land [ABSTRACT FROM AUTHOR]

Radiation energy balance at the top of the atmosphere (TOA) is a critical boundary condition for the Earth climate. It is essential to validate it in the global climate models (GCM) on both global and regional scales. However, the comparison of overall radiation field is known to conceal compensating errors. Here we use a new set of radiative kernels to diagnose the radiation biases caused by different geophysical variables in the GCMs of the Coupled Model Intercomparison Project. We find although clouds remain a primary cause of radiation biases, the radiation biases caused by non‐cloud variables are of comparable magnitudes. Many GCMs have a cold air temperature bias and a moist tropospheric humidity bias, which lead to considerable biases in TOA radiation budget but are compensated by cloud biases. These findings signify the importance of validating the GCM simulations in terms of both the overall and component radiation biases. Plain Language Summary: Radiation energy balance at the top of atmosphere is crucial to the Earth climate system and is routinely examined in climate model validations. Here, we show that seemingly good agreements between model and observation can be due to compensating errors and propose the use of a kernel method for separately identifying component radiation biases due to different geophysical variables, which may afford a more stringent test of climate models. Key Points: Climate models are subject to compensating radiation biases caused by cloud and non‐cloud variablesMany models have a cold temperature bias and a moist tropospheric humidity biasKernel diagnosis of component radiation biases affords a stricter test for climate models [ABSTRACT FROM AUTHOR]

Observations show Arctic sea ice has declined and midlatitude storminess has weakened during Northern Hemisphere (NH) summertime. It is currently unclear whether Arctic sea ice loss impacts summertime storminess because most previous work focuses on other seasons. Here we quantify the impact of Arctic sea ice loss on NH summertime storminess using equilibrium and transient climate model simulations. The equilibrium simulations show mid‐to‐late 21st century Arctic sea ice loss weakens summertime storminess, but only in the presence of ocean coupling. With ocean coupling, the equator‐to‐pole temperature and atmospheric energy gradients significantly weaken due to increased surface turbulent flux in the polar region following Arctic sea ice loss. The transient simulations show Arctic sea ice loss does not significantly weaken summertime storminess until the late 21st century. Furthermore, Arctic Amplification, which is dominated by Arctic sea ice loss in the present day, does not significantly impact the present‐day weakening of summertime storminess. Plain Language Summary: Present‐day summertime climate change in the NH is characterized by rapid Arctic sea ice loss and weakening of weather systems. While these trends are projected to continue throughout the 21st century, their connection is not well understood. Using climate model simulations, we show mid‐to‐late 21st century Arctic sea ice loss weakens the summertime weather system by decreasing the equator‐to‐pole energy (and temperature) difference. In addition, we demonstrate transient Arctic sea ice loss does not significantly contribute to weakening summertime weather system until the late 21st century. The results suggest that present‐day Arctic sea ice loss and Arctic Amplification have not contributed significantly to the observed weakening of weather systems. Key Points: Equilibrium and transient climate model simulations are used to quantify the summertime storminess response to Arctic sea ice lossThe equilibrium response to mid‐to‐late 21st century Arctic sea ice loss involves weaker summertime storminess due to ocean couplingThe transient weakening of present‐day summertime storminess is not significantly affected by Arctic sea ice loss and Arctic Amplification [ABSTRACT FROM AUTHOR]

Four climate parameters (i.e. maximum, mean and minimum air temperature and precipitation amount) from 10 regional climate models, provided by the EURO‐CORDEX initiative, are adjusted using as reference the ROCADA gridded dataset. The adjustment was performed on a daily temporal resolution for the historical period (1971–2005), as well as for climate change scenarios based on two Representative Concentration Pathways (RCP4.5 and RCP8.5). The most accurately method for bias‐correction (BC) was selected following a 2‐fold cross‐validation approach, which was performed on historical data using two methods: Quantile Mapping (QMAP) and Multivariate Bias Correction with N‐dimensional probability (MBCn). The performances of the two methods are very similar when analysing the frequency distribution of each selected variable, whereas the comparison between (1) the intervariables correlation of the adjusted datasets, and (2) the reference dataset revealed much smaller differences for the dataset adjusted with the multivariate method, hence this was used for producing the BC scenario dataset. Based on the MBCn adjusted dataset, a climate change analysis over Romania was tested at the seasonal and annual scale. Overall, for the multimodel ensemble mean, at the country level, a substantial temperature increase is reported for both scenarios and no significant trend is revealed for precipitation amount. The adjusted RCMs are provided without any restrictions via an open‐access repository in netCDF CF‐1.4‐compliant file format. The BC climate models are archived at the 0.1° spatial resolution, in the WGS‐84 coordinate system, at a daily temporal resolution. [ABSTRACT FROM AUTHOR]

Climate change in the Arctic has substantial impacts on human life and ecosystems both within and beyond the Arctic. Our analysis of CMIP6 simulations shows that some climate models project much larger Arctic climate change than other models, including changes in sea ice, ocean mixed layer, air‐sea heat flux, and surface air temperature in wintertime. In particular, dramatic enhancement of Arctic Ocean convection down to a few hundred meters is projected in these models but not in others. Interestingly, these models employ the same ocean model family (NEMO) while the choice of models for the atmosphere and sea ice varies. The magnitude of Arctic climate change is proportional to the strength of the increase in poleward ocean heat transport, which is considerably higher in this group of models. Establishing the plausibility of this group of models with high Arctic climate sensitivity to anthropogenic forcing is imperative given the implied ramifications. Plain Language Summary: Arctic climate is projected to change dramatically in the future based on the latest climate models. However, there is a large discrepancy between these climate models. We find that simulated Arctic climate change tends to be much larger in models using the community ocean model "NEMO" as their ocean component. Future Arctic climate change in winter strongly depends on the strength of the increase in poleward ocean heat transport, which is considerably higher in the NEMO‐family climate models. There are some indications that this group of models can better reproduce the observed winter sea ice decline and mixed layer depth in the Barents Sea in their historical simulations. We suggest that investigating the model physics in the NEMO‐family climate models in comparison with other models may ultimately help to reduce the overall uncertainty in climate change projections. Key Points: Future Arctic climate change in winter strongly depends on the strength of the increase in poleward ocean heat transport (OHT)Arctic climate change in the NEMO‐family climate models is much more pronouncedNEMO‐family climate models have larger increases in poleward OHT [ABSTRACT FROM AUTHOR]

Over the Southern Ocean (SO, 40°S–70°S), climate models have consistently underestimated solar reflection. Here we evaluate the relationship between cloud profiles, cloud phase and radiation over the SO in Coupled Model Intercomparison Project Phase 6 (CMIP6) models against Clouds and the Earth's Radiant Energy System and Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observations. We find that the lack of solar reflection is slightly improved in CMIP6 models compared to CMIP5's, attributable to a better representation of cloud fraction and phase. We show that clouds have a different vertical structure and radiative effect south and north of where the 0°C isotherm meets the surface (∼55°S). Although the models capture the greater vertical extent of clouds south of 55°S, they fail to reproduce the observed increase in solar reflection, which we pinpoint to cloud phase biases. Increasing CMIP6 supercooled liquid cloud opacity should help reduce their persistent shortwave biases. Plain Language Summary: Over the Southern Ocean, defined as the latitudinal band between 40° and 70°S, climate models have consistently overestimated the amount of absorbed solar radiation, mostly because of biases in the representation of clouds. Such biases in climate models are particularly problematic because they may affect the radiative response of clouds to climate warming. We find that the newest generation of climate models better represents clouds than the previous one, compared to satellite observations. We show that clouds have a different vertical structure and radiative properties south and north of the 0°C surface isotherm, around 55°S. Although the models capture the greater vertical extent of clouds south of 55°S, they fail to reproduce the observed increase in solar reflection by clouds there. Finally, we report that increasing the opacity of liquid clouds at subzero temperatures (between 0° and −40°C) should help reduce persistent solar radiation biases attributable to clouds, south of 55°S, in the newest generation of climate models. Key Points: The lack of shortwave reflection is somewhat corrected in Coupled Model Intercomparison Project Phase 6 (CMIP6) models, attributable to a better representation of cloud fraction and cloud phaseThe cloud fraction and radiative effect behavior is different north and south of 55°S, which is where the 0°C isotherm meets the surfaceContrary to observations, CMIP6 clouds are less reflective south of 55°S, where boundary layer clouds are often topped by mid‐level clouds [ABSTRACT FROM AUTHOR]

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

The Bayesian model averaging (BMA) method has been widely used for generating probabilistic climate projections. However, the averaging weights used in BMA can only reflect the spatially‐ and temporally‐averaged performance of each ensemble member, without the ability to address the spatiotemporal variations of model biases. This can lead to inevitable exaggeration or understatement of the contributions of individual members to the ensemble mean, thus reducing the robustness of the resulting probabilistic projections. Here we propose a new method to help address the neglected spatiotemporal variations of model biases. Through the proposed method, the BMA weights are used as prior distributions to drive the Bayesian discriminant analysis in order to generate refined weights for individual ensemble models according to their spatially‐ and temporally‐clustered performance. Through applying the proposed method to Canada, we demonstrate its effectiveness in generating robust probabilistic climate projections (e.g., the average R2 increases from 0.82 to 0.89). Plain Language Summary: Since no climate model is superior to others under all conditions, taking the advantages of multiple models is desired for robust climate projections. However, there exist significant spatial and temporal variations regarding the model performances. For example, an overall poorly‐performed model can still show promising results over a specific region or during a specific time period, while an overall well‐performed model does not necessarily perform well over all regions or in all years. Here we propose a new method to robustly address the spatiotemporal variations in the modeling performance of each ensemble member. Then we apply the proposed method to generate the probabilistic temperature projections over Canada. The proposed method can improve both the accuracy and reliability in ensemble‐based climate projections. Key Points: A discriminant‐BMA ensemble modeling approach is developed to synthesize the spatiotemporal variations of model biasesThe accuracy and reliability of probabilistic projections are both improved (e.g., the average R2 increases from 0.82 to 0.89)The temperature probabilistic projections based on regional climate models in three future periods are generated over Canada [ABSTRACT FROM AUTHOR]

While the annual mean Arctic Amplification (AA) index varied between two and three during the 1970–2000 period, it reached values exceeding four during the first two decades of the 21st century. The AA did not change in a continuous fashion but rather in two sharp increases around 1986 and 1999. During those steps the mean global surface air temperature trend remained almost constant, while the Arctic trend increased. Although the "best" CMIP6 models reproduce the increasing trend of the AA in 1980s they do not capture the sharply increasing trend of the AA after 1999 including its rapid step‐like increase. We propose that the first sharp AA increase around 1986 is due to external forcing, while the second step close to 1999 is due to internal climate variability, which models cannot reproduce in the observed time. Plain Language Summary: The rate of Arctic warming varied in time, while the rate of global warming was nearly constant over the period 1960–2020. This led to a variable Arctic Amplification (AA) defined as a ratio of the Arctic warming trend to mean global warming trend. In addition, the Arctic experienced sudden changes in the rate of warming with step‐like changes in annual mean Arctic Amplification around the years 1986 and 1999. Climate models do not capture the second observed sharp increase of the AA. We suggest that the first AA increase around 1986 is primarily due to external forcing (increasing concentration of carbon dioxide) while the second sharp increase around 1999 is dominated by internal climate variability which current models cannot capture accurately within the time scale of the physical phenomena. Key Points: Annual mean Arctic Amplification (AA) within the period 1970–2020 changed in steep steps around 1986 and 1999. It reached values over 4.0Even those CMIP6 models best at reproducing the AA did not reproduce the second sudden increase near 1999We conjecture that the first AA sharp increase is due to external forcing, while the second is due to internal climate variability [ABSTRACT FROM AUTHOR]

Climate change increases the frequency and intensity of heat waves, bringing along multiple adverse impacts on ecosystems, human health, societies, and the economy. Groundwater influences the near surface air temperature evolution through land–atmosphere interactions. Using simplified and shallow groundwater representations, reproducing heat waves in a regional climate model (RCM) is challenging. Currently, RCMs applied over Europe exhibit a warm bias. This study analyzes heat waves over a 13‐year evaluation period, comparing the terrestrial systems modeling platform (TSMP) with an explicit groundwater representation to a EURO‐CORDEX RCM ensemble, the ERA5 reanalysis, and observations. The TSMP multiannual heat wave statistics are consistent with observations and reanalysis data. We attribute the lower absolute deviations of heat wave metrics simulated by TSMP to the improved hydrology including 3D groundwater flow. The findings emphasize the importance of hydrological process representation in RCMs. Plain Language Summary: Heat waves severely impact the ecosystem, society, and the economy. Climate change will amplify these events in the near future, urging a correct representation of heat waves in climate models. Especially during warm and dry periods, there is a strong connection from the groundwater stored in the subsurface through the land surface to the atmosphere. Commonly applied models have a simplified representation of groundwater and cannot establish a correct connection to the atmosphere, simulating too high temperatures over Europe. To show the advantage of a model with a complex groundwater representation, we compare such a model with 12 representative climate models. The results indicate lower deviations from heat wave statistics of observations with the inclusion of complex groundwater. Our findings emphasize the importance of including groundwater dynamics representation in climate models. Key Points: Multiannual heat wave statistics from a regional climate model including groundwater dynamics are consistent with observationsRegional climate models with simplified groundwater representations overestimate observed multiannual heat wave statisticsExplicit groundwater treatment increases the reliability of heat wave simulations with implications for regional climate projections [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]

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

Global climate change is a change in the planetary energy (E) balance. It is usually expressed as a change in near‐surface air temperature (Ta), but changes in energy content due to Ta (ET) represent only part of the near‐surface energy balance, which also includes E changes due to specific humidity (ESH). We analyzed MERRA‐2 and ERA5 reanalysis data and 15 CMIP6 Atmospheric Model Intercomparison Project (AMIP) models from 1980 to 2014. Some 44%, 39%, and 50% of MERRA‐2 pixels showed significant increases in ET, ESH, and E, respectively. The average increase in ET (ESH) was 16.9 (13.6) J kg−1 year−1, but AMIP models estimated larger average ET (21.6 J kg−1 year−1) and ESH (16.7 J kg−1 year−1). Global Ta would have increased at nearly double the observed rate if energy was not partitioned into latent heat. Results demonstrate the critical role that specific humidity plays in recent climate changes. Plain Language Summary: Greenhouse gases trap longwave radiation, making it more difficult for energy to leave Earth. This increases air temperatures, but there is much more to global climate change than air temperature alone. Most of the excess energy caused by the increase in greenhouse gases has entered the ocean, because it takes a lot of energy to heat water. The water vapor content of the atmosphere near the surface has also increased, and it takes energy to heat and evaporate this water. We find that the energy needed to evaporate the extra water that has entered the atmosphere has helped buffer increases in global air temperatures, which would have increased by nearly double if this energy was used to increase air temperatures alone. Global climate models can simulate many aspects of these energy changes, but struggle in regions where regional climate is complex. Many of the largest increases in atmospheric energy have occurred in populous regions of the globe while the greatest temperature increases are near the poles. It is important to understand that global change is a change in the planetary energy balance, and that near surface air temperature alone is only a small (but important) part of this energy change. Key Points: The temperature and specific humidity of the near‐surface atmosphere are increasingBoth imply an increase in atmospheric energy content, and climate models often struggle to simulate regional changesThe rate of air temperature increase would have more than doubled without specific humidity change, all else being equal [ABSTRACT FROM AUTHOR]

The uncertainty in polar cloud feedbacks calls for process understanding of the cloud response to climate warming. As an initial step toward improved process understanding, we investigate the seasonal cycle of polar clouds in the current climate by adopting a novel modeling framework using large eddy simulations (LES), which explicitly resolve cloud dynamics. Resolved horizontal and vertical advection of heat and moisture from an idealized general circulation model (GCM) are prescribed as forcing in the LES. The LES are also forced with prescribed sea ice thickness, but surface temperature, atmospheric temperature, and moisture evolve freely without nudging. A semigray radiative transfer scheme without water vapor and cloud feedbacks allows the GCM and LES to achieve closed energy budgets more easily than would be possible with more complex schemes. This enables the mean states in the two models to be consistently compared, without the added complications from interaction with more comprehensive radiation. We show that the LES closely follow the GCM seasonal cycle, and the seasonal cycle of low‐level clouds in the LES resembles observations: maximum cloud liquid occurs in late summer and early autumn, and winter clouds are dominated by ice in the upper troposphere. Large‐scale advection of moisture provides the main source of water vapor for the liquid‐containing clouds in summer, while a temperature advection peak in winter makes the atmosphere relatively dry and reduces cloud condensate. The framework we develop and employ can be used broadly for studying cloud processes and the response of polar clouds to climate warming. Plain Language Summary: The polar regions are changing rapidly. Clouds and their feedbacks remain uncertain due to small‐scale unresolved processes in climate models, which contribute to uncertainties in polar climate projection. In order to understand the mechanisms that control polar clouds, we focus on their seasonal cycle in the current climate. We adopt an idealized framework for driving high‐resolution simulations by a global climate model. With minimal components represented, we find similar features between the simulated and observed polar clouds. In particular, liquid‐containing clouds reach maximum in summer, which coincides with the summer peak in moisture advection from lower latitudes. Therefore, projection of polar clouds will depend on future changes in heat and moisture advection. This framework will allow us to study the response of polar clouds to climate warming. Key Points: Large eddy simulations driven by time‐varying large‐scale forcings from an idealized general circulation model is used to simulate the seasonal cycle of Arctic cloudsSimulated low‐level cloud liquid is maximal in late summer to early autumn, and minimal in winter, consistent with observationsLarge‐scale advection provides the main moisture source for cloud liquid and shapes its seasonal cycle [ABSTRACT FROM AUTHOR]

Rapid urbanization in China in the past several decades has been shown to affect surface air temperature (SAT) locally, but its global impact is unclear. Using a global climate model, we have investigated the potential impact of urbanization in China on SAT at the global scale. The expansion of urban areas in China from 1985 to 2017 leads to winter surface air warming by as much as 0.8°C in North America (NA), largely caused by warm temperature advection due to global‐scale atmospheric circulation changes. The East Asian jet stream moves northward in eastern and northeastern China where the expansion of urban areas has increased SAT and sensible heat flux, forcing change of the atmospheric stationary waves and so the structure of the jet streams. Plain Language Summary: Rapid urbanization in China has been found to impact surface air temperature (SAT) at local and regional scales significantly through altered land surface properties. However, its impacts on global climate are less clear. Using a state‐of‐the‐art global climate model, we show that tremendous expansion of urban areas in China from 1985 to 2017 has resulted in global‐scale atmospheric circulation changes by altered surface properties and enhanced anthropogenic heating. Anomalous atmospheric circulation engenders changes of SAT by as much as 0.8°C in winter over NA, which is more pronounced than the local SAT changes in China for any season. This study provides a robust conceptual advance in understanding the climate implications of land use change especially on the global impacts of urbanization. Key Points: The fractional coverage of impermeable land surface has increased by six‐fold in China from 1985 to 2017Urbanization in China results in winter surface air warming by as much as 0.8°C in North AmericaThe altered atmospheric stationary waves and East Asian jet streams are responsible for winter warming in North America [ABSTRACT FROM AUTHOR]

Whether sea‐ice loss or lapse‐rate feedback dominates the Arctic amplification (AA) remains an open question. Analysis of data sets based upon observations reveals a 1.11 K per decade surface warming trend in the Arctic (70°–90°N) during 1979–2020 cold season (October–February) that is five times higher than the corresponding global mean. Based on surface energy budget analysis, we show that the largest contribution (∼82%) to this cold season warming trend is attributed to changes in clear‐sky downward longwave radiation. In contrast to that in Arctic summer and over tropics, a reduction in lower‐tropospheric inversions plays a unique role in explaining the reduction of the downward longwave radiation associated with atmospheric nonuniform temperature and corresponding moisture changes. Our analyses also suggest that Arctic lower‐tropospheric stability should be considered in conjunction with sea‐ice decline during the preceding warm season to explain AA. Plain Language Summary: Observations and climate models have consistently shown a stronger surface warming in the Arctic than the global mean, a.k.a. Arctic amplification (AA), which has a strong asymmetry between the cold season and warm season. Previous studies suggested that key contributors to AA are the positive surface‐albedo feedback and lapse‐rate feedback. However, the lapse‐rate feedback itself depends on temperature profiles and sea‐ice loss. Whether sea‐ice loss or lapse‐rate feedback dominates AA remains an open question. Here, by analyzing the latest generation of observationally‐based reanalysis data (1979–2020), we present a unique role of lower‐tropospheric temperature inversion changes in representing the contribution of vertically inhomogeneous atmospheric temperature and associated moisture changes to clear‐sky downward longwave radiation during the cold season. This unique role is not found either in the tropics or during Arctic summertime. We further link the inversion during the cold season to sea‐ice loss during the preceding warm season. These results reinforce previous findings not only that lapse‐rate feedback and sea‐ice loss play a key role in AA but also that lapse‐rate feedback in the cold season is likely a consequence of sea‐ice albedo feedback during the preceding warm season. Key Points: An increase in clear‐sky downward longwave radiation (rldsclr) explains 82% of the recent cold season Arctic surface warming trendReduced low inversion largely explains the Arctic rldsclr reduction due to vertical nonuniform temperature and related moisture changesLapse‐rate feedback in the Arctic cold season is likely a consequence of sea‐ice albedo feedback during the preceding warm season [ABSTRACT FROM AUTHOR]

The Kamchatka Peninsula (Russian Far East) remains among the least studied regions of eastern Asia. Recent studies revealed a high degree of palaeoenvironmental variability between different parts of the peninsula. We investigated semi‐aquatic (chironomids) and terrestrial (leaf wax biomarkers) proxies from a sediment core collected from Lake Sokoch (southern Kamchatka) to provide reconstruction of the mean July air temperature and variations in limnic conditions. The lake formed after 10.0 cal. ka BP as a result of postglacial warming and was fed by glacial meltwaters from neighbouring glaciers. Our data show a later beginning of the Holocene thermal maximum (HTM) relative to more northern sites in Kamchatka, Siberia and Chukotka and support climate model experiments that suggest that the HTM was delayed in southern and central Kamchatka by about 2000 years compared with Alaska and NE Siberia. Warm conditions prevailed between 10.0 and 6.4 cal. ka BP with a short spell of cool and dry climate around 8.2 cal. ka BP that might be related to the 8.2 ka cooling event. The HTM took place between 6.5 and 3.4 cal. ka BP with the warmest phase from 6.0 to 5.0 cal. ka BP. An onset of Neoglacial cooling at 3.4 cal. ka BP is consistent with the strengthening of both the Siberian High and the Aleutian Low. Warming between 1.2 and 0.9 cal. ka BP can be attributed to the Mediaeval Climate Anomaly. The LIA cooling is related to another strengthening of the Siberian High and the Aleutian Low. The modern warming, though weakly traced in our record, is consistent with the recent meteorological observations. The presented palaeoenvironment record confirms the earlier findings of spatial differences within Kamchatka in timing and magnitude of the major Holocene climate fluctuations and contributes towards understanding the expression of Holocene climate change in Kamchatka. [ABSTRACT FROM AUTHOR]

The nature of Holocene temperature changes is controversial because of the apparent discrepancy between global temperature reconstructions and climate modelling results. Here we present evidence indicating that the discrepancy can be attributed to the combination of seasonal biases in the proxy temperature records and insufficient understanding of the mechanisms within climate models. We obtained independently dated records of mean annual air temperature (MAAT) based on glycerol dialkyl glycerol tetraethers (GDGTs) from Bangong Co in the western Qinghai‐Tibetan Plateau (QTP) and two loess‐palaeosol sections in the northeastern QTP. One of the most prominent features in the composite MAAT record is the relatively cool Middle Holocene, ~2 °C cooler than the Early and Late Holocene. The MAAT variations are consistent with orbitally forced modelling results that show increasing regional temperatures since the Middle Holocene, but they contrast with global temperature reconstructions that mainly reflect summer temperature variations. Our findings suggest that changes in boreal summer insolation that led to variations in precipitation patterns and vegetation dynamics were the key factor modulating MAAT during the Holocene. Overall, our study sheds light on possible approaches to solving the Holocene temperature conundrum by considering internal feedbacks within the climate system. [ABSTRACT FROM AUTHOR]

Whether sea‐ice loss or lapse‐rate feedback dominates the Arctic amplification (AA) remains an open question. Analysis of data sets based upon observations reveals a 1.11 K per decade surface warming trend in the Arctic (70°–90°N) during 1979–2020 cold season (October–February) that is five times higher than the corresponding global mean. Based on surface energy budget analysis, we show that the largest contribution (∼82%) to this cold season warming trend is attributed to changes in clear‐sky downward longwave radiation. In contrast to that in Arctic summer and over tropics, a reduction in lower‐tropospheric inversions plays a unique role in explaining the reduction of the downward longwave radiation associated with atmospheric nonuniform temperature and corresponding moisture changes. Our analyses also suggest that Arctic lower‐tropospheric stability should be considered in conjunction with sea‐ice decline during the preceding warm season to explain AA. Plain Language Summary: Observations and climate models have consistently shown a stronger surface warming in the Arctic than the global mean, a.k.a. Arctic amplification (AA), which has a strong asymmetry between the cold season and warm season. Previous studies suggested that key contributors to AA are the positive surface‐albedo feedback and lapse‐rate feedback. However, the lapse‐rate feedback itself depends on temperature profiles and sea‐ice loss. Whether sea‐ice loss or lapse‐rate feedback dominates AA remains an open question. Here, by analyzing the latest generation of observationally‐based reanalysis data (1979–2020), we present a unique role of lower‐tropospheric temperature inversion changes in representing the contribution of vertically inhomogeneous atmospheric temperature and associated moisture changes to clear‐sky downward longwave radiation during the cold season. This unique role is not found either in the tropics or during Arctic summertime. We further link the inversion during the cold season to sea‐ice loss during the preceding warm season. These results reinforce previous findings not only that lapse‐rate feedback and sea‐ice loss play a key role in AA but also that lapse‐rate feedback in the cold season is likely a consequence of sea‐ice albedo feedback during the preceding warm season. Key Points: An increase in clear‐sky downward longwave radiation (rldsclr) explains 82% of the recent cold season Arctic surface warming trendReduced low inversion largely explains the Arctic rldsclr reduction due to vertical nonuniform temperature and related moisture changesLapse‐rate feedback in the Arctic cold season is likely a consequence of sea‐ice albedo feedback during the preceding warm season [ABSTRACT FROM AUTHOR]

In this study, we implement a new frozen‐soil parameterization scheme into the climate system model CAS‐FGOALS‐g3 to investigate the dynamic changes of freezing and thawing fronts and the effects arising from thermal processes and climate. Simulations are conducted using the developed model to validate its performance relative to multi‐source observations. It is shown that the model could reasonably reproduce soil freezing and thawing processes, including dynamic changes in freezing and thawing fronts. The historical simulation shows that the maximum freeze depth increases with an increase of latitude in seasonally frozen ground, and the active layer thickness decreases with an increase of latitude in permafrost regions. The active layer thickness shows increasing trends while the maximum freeze depth shows decreasing trends, which is consistent with change in the 2‐m air temperature. In conclusion, these results have the potential to further deepen our understanding of the freeze‐thaw cycle process and the historical response of permafrost to climate change. Plain Language Summary: The dynamic changes of freezing and thawing fronts have a significant influence on the surface energy balance, hydrological processes, cold‐zone engineering practices and the terrestrial carbon cycle. In this study, a frozen‐ground algorithm that considers the dynamic changes of freezing and thawing fronts is implemented in a global climate system model to investigate the dynamic changes of freezing and thawing fronts and the effects arising from thermal processes and climate. It is shown that the developed model can reasonably reproduce soil freezing and thawing processes, including changes in freezing and thawing fronts. The active layer thickness shows increasing trends, and the maximum freeze depth shows decreasing trends. These results have the potential to further deepen our understanding of the freeze‐thaw cycle process and the historical response of permafrost. Key Points: A new frozen soil algorithm, including freezing and thawing fronts, is implemented in the climate model CAS‐FGOALS‐g3The updated model can capture changes of freezing and thawing fronts, which affects soil moisture and soil temperatureThe active layer thickness shows increasing trends, while the maximum freeze depth shows decreasing trends [ABSTRACT FROM AUTHOR]

The Institut Pierre‐Simon Laplace Climate Modeling Center has produced an ensemble of extended historical simulations using the IPSL‐CM6A‐LR climate model. This ensemble (referred to as IPSL‐EHS) is composed of 32 members over the 1850–2059 period that share the same external forcings but differ in their initial conditions. In this study, we assess the simulated decadal to multidecadal climate variability in the IPSL‐EHS. In particular, we examine the global temperature evolution and recent warming trends, and their consistency with ocean heat content and sea ice cover. The model exhibits a large low‐frequency internal climate variability. In particular, a quasi‐bicentennial mode of internal climate variability is present in the model and is associated with the Atlantic Meridional Overturning Circulation. Such variability modulates the global mean surface air temperature changes over the historical period by about ∼ 0.1K. This modulation is found to be linked to the phase present in the initial condition state of each member. This variability appears to decrease during the 1850–2018 period in response to external forcings. The analysis of the ocean heat content reveals furthermore an overestimation of the ocean stratification, which likely leads to an overestimation of the recent warming rate on average. Plain Language Summary: The Institut Pierre‐Simon Laplace (IPSL) developed an ensemble of 32 simulations over the 1850–2059 period using the IPSL‐CM6A‐LR climate model. Such a large ensemble allows a better sampling of the internally generated variability. Moreover, the ensemble averaging provides an estimation of the forced variability induced by the greenhouse gases and the aerosol concentration used as boundary conditions. In this study, we assess the simulated decadal to multidecadal climate variability in the IPSL ensemble. Relative to the large variability of the model, the evolution of observed surface temperature and sea ice cover is within the range of possibilities of the ensemble. The oceanic circulation and sea surface temperature over the North Atlantic are key players in the low‐frequency internal variability of the model. Key Points: A large part of the spread of temperature and sea ice trends in the IPSL ensemble is related to a large multicentennial internal variabilitySome members of the IPSL ensemble are consistent with the observed surface temperature, sea ice variations, and ocean heat content evolutionThe low‐frequency internal climate variability of IPSL‐CM6A‐LR decreases since the 2000s in response to external forcing [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]

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]

Amid reports from western US wildland fire managers that, compared to when many started their careers, fires are burning longer throughout the day before reducing in intensity overnight, we examined decadal changes in nighttime vapor pressure deficits over the western United States with a focus on the summer fire season. We calculated changes using a recently updated observation‐assimilating reanalysis (ERA5) available at hourly resolution over the 1980–2019 period. Analysis identifies the proximate cause (atmospheric temperature vs. moisture content) of the observed changes and the extent to which they have been captured in climate model simulations. Increases in nighttime vapor pressure deficits of >50% in 40 years are evident over foothills of mountains ranges adjacent to arid plateaus. The largest observed increases greatly exceed the forced climate‐model response. Correlation analysis reveals a broad link between the variability of western US summer‐nighttime vapor pressure deficit and the Pacific Decadal Oscillation. Plain Language Summary: Western US wildland fire managers have reported that fires are burning longer into the night and increasing in intensity earlier in the morning compared to when many started their careers. Increasing nighttime vapor pressure deficit—a measure of the drying power of air—is widely suspected to be responsible for these perceived changes in fire behavior. It is also suspected that human‐caused increases in nighttime temperatures are responsible. We used a recently released observation‐based data set to quantify the extent to which nighttime vapor pressure deficits have changed over the last 40 years and determine the proximate cause for the observed changes (particularly whether temperature effects on their own can explain them). We also explored how well the observed changes are captured in climate model simulations. Results highlight large increases in nighttime vapor pressure deficit over the foothills of mountain ranges next to arid plateaus, where the observation‐based increases greatly exceed the climate model projections, and both temperature and humidity play a role in driving the observed changes. Key Points: Nighttime vapor pressure deficit increased >50% in 40 years over the foothills of the Sierra Nevada and Rocky MountainsSaturation vapor pressure increases caused by rising temperatures and decreases in actual vapor pressure account for these changesThe magnitudes of the changes in the observation‐based records greatly exceed those projected by climate forecast models [ABSTRACT FROM AUTHOR]

We develop a post‐millennium mid‐tropospheric temperature time series from continuous observations by advanced microwave sounders onboard satellites in stable sun‐synchronous orbits. Such observations have high radiometric stability and do not experience diurnal sampling changes over time, allowing us to develop merged time series from multiple satellites with an accuracy better than 0.012 Kelvin/Decade. With such high accuracy, the resulting time series can be used as a reference measurement of climate variability and trends in atmospheric temperatures. The warming rate from this time series for the atmospheric layer between surface and 10 km is 0.230 ± 0.134 Kelvin/Decade during the period from 2002 to 2020, which is ∼14% higher than the existing satellite microwave sounder datasets. Our finding provides new insight on trend differences among microwave sounder temperature data sets developed by different research groups, and is also helpful in reconciling trend differences between satellite observations and climate model simulations. Plain Language Summary: Atmospheric temperature time series developed from satellite microwave sounder observations has been extensively used in climate change monitoring and verifying climate model simulations of climate change. However, uncertainties exist in the satellite merged products and their resulting atmospheric temperature trends, mainly caused by diurnal sampling changes over time and instrument calibration errors. Satellite products developed by different research groups produced different atmospheric temperature trends, undermining the capability of using satellite observations in global change monitoring. Here we develop a reference time series from 2002 to present using advanced satellite microwave sounder observations in stable sun‐synchronous orbits for the detection of global mid‐tropospheric temperature trends with accuracy better than 0.012 Kelvin/Decade. This high accuracy in trend detection was achieved because diurnal sampling drifting errors do not exit for satellites in stable orbits and that these measurements have high radiometric stability. This reference measurement is expected to help reconcile differences in climate trend comparisons among different satellite products and between climate model simulations and satellite observations during the post‐millennium periods. It may also be helpful in the development of atmospheric temperature time series with a better accuracy for satellites before the millennium when used as a reference. Key Points: A reference mid‐tropospheric temperature time series was developed using advanced satellite microwave sounder observations in stable orbitsThe reference time series can detect the mid‐tropospheric temperature trends with an accuracy better than 0.01 Kelvin/DecadeThe mid‐tropospheric temperature trends observed from the reference time series are 0.230 ± 0.134 Kelvin/Decade during 2002–2020 [ABSTRACT FROM AUTHOR]