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Wet and cold biases on the Tibetan Plateau (TP) commonly exist in global and regional climate simulations. Previous studies have explored the possible causes of wet and cold biases and contributed to reducing these biases. However, the connection between wet and cold biases remains insufficiently addressed. Our research indicates that the TP wet bias converts into positive snow amount bias not continually but efficiently and concentratedly, under the control of snow phenology in different regions. Furthermore, the complex relationship between snow amount, snow coverage and surface albedo restricts the transformation of snow amount to surface albedo bias, and thus to cold bias. Our research highlights the spatio‐temporally divergent transformation of wet to cold bias on the TP during snow season, providing a novel perspective to understand the intrinsic connection between wet and cold biases and improve climate simulations on the TP. Plain Language Summary: Due to the harsh environment, observational data are limited on the Tibetan Plateau (TP), so climate model is one of necessary tools for climate researches. However, both global and regional climate models generally overestimate precipitation (wet bias) and underestimate near surface air temperature (cold bias) on the TP. Previous studies have pointed out the possible causes of the wet and cold biases, and partly reduced these biases, leaving the connection between these biases unaddressed. Our study finds that the conversion of wet to cold bias during snow season is divergent in different areas of the TP. Under the control of snow duration in different areas, the wet bias efficiently and concentratedly transforms into larger snow amount. Furthermore, the complex relationship between snow amount, snow coverage and surface albedo restricts the transformation of snow amount to surface albedo bias, and thus to cold bias. We reveal the spatio‐temporally divergent transformation of wet to cold bias on the TP during snow season, and these conclusions will contribute to a better improvement of snow and climate modeling on the TP. Key Points: WRF simulations significantly overestimate snow amount and delay snow phenology on the Tibetan Plateau (TP)Snowfall bias is not always transformable into snow amount bias, depending on snow phenologyComplex relationship between snow amount and surface albedo restricts the transformation of wet to cold bias on the TP [ABSTRACT FROM AUTHOR]

Due to their significant influence on large‐scale atmospheric circulation and climate anomalies, the variability of Arctic sea ice and Eurasian snow cover during late autumn and their combined effects have garnered increasing attention. This study aims to investigate the physical mechanism underlying the covariation among the Barents‐Kara Seas (BKS) sea ice concentration (SIC), Eurasian snow cover extent (SCE) and the ensuing winter Eurasian surface air temperature (SAT). The statistics results of singular value decomposition suggest a significant linkage between the decreased BKS SIC, zonal “negative–positive” dipole SCE anomalies over Eurasia in November and cold Eurasian SAT in January–February (JF). Observational diagnosis analyses about the meridional moisture, heat transport and surface heat flux demonstrate that subpolar Eurasian anticyclonic circulation plays a crucial role in connecting the predominant modes of SIC and SCE. Furthermore, the BKS SIC and Eurasian SCE anomalies can jointly excite upward‐propagating planetary waves into the stratosphere, while simultaneously reducing the subpolar meridional temperature gradient. This results in westerly wind deceleration and favours the continuous planetary wave propagation. Consequently, the stratospheric polar vortex is significantly weakened, along with negative Northern Annular Mode anomalies propagating downward from the stratosphere to troposphere. Negative‐phase Arctic Oscillation anomalies correspondingly develop during JF, resulting in widespread cold anomalies over the Eurasian continent. These results are further confirmed by numerical sensitivity experiments from the Community Atmosphere Model forced by the above mentioned SIC and SCE anomalies. The empirical hindcast model analyses further suggest that the prediction skill of JF Eurasian SAT is enhanced when both the November BKS SIC and Eurasian SCE signals are considered. [ABSTRACT FROM AUTHOR]

The mechanisms by which clouds impact the variability of the mid‐latitude atmosphere are poorly understood. We use an idealized, dry atmospheric model to investigate the relationship between Atmospheric Cloud Radiative Effects (ACRE) and annular mode persistence. We force the model with time‐varying diabatic heating that mimics the observed ACRE response to the Southern Annular Mode (SAM). Realistic ACRE forcing reduces annular mode persistence by 5 days (−16%), which we attribute to a weakening of low‐frequency eddy forcing via modified low‐level temperature gradients, though this effect is partly compensated by reduced frictional damping due to zonal wind anomalies becoming more top‐heavy. The persistence changes are nonlinear with respect to the amplitude of ACRE forcing, reflecting nonlinearities in the response of the eddy forcing. These results highlight the ACRE's impact on low‐frequency eddy forcing as the dominant cause of changes in annular mode persistence. Plain Language Summary: In this study, we conduct novel modeling experiments aimed at understanding how clouds affect the unforced variability of jet streams—concentrated bands of eastward wind in the mid‐latitude atmosphere. We focus on direct heating and cooling of the atmosphere by clouds, called Atmospheric Cloud Radiative Effects (ACRE), and how these impact the annular mode—the most significant type of jet stream variability, which consists of north‐south shifts of the jet's position. Using a simplified atmospheric model, we investigate how the persistence and structure of the annular mode are impacted by heatings and coolings that mimic the observed ACRE response to the Southern Hemisphere Annular Mode (SAM). We find that the ACRE reduces the persistence of the annular mode (i.e., deviations of the jet stream from its mean position tend to persist for less time), which we attribute to a weakening of the feedback mechanism by which atmospheric waves strengthen the jet where it is already strongest. We identify some compensating effects, but, overall, our results highlight that ACRE impacts jet streams via low‐altitude temperature gradients and atmospheric wave generation. Key Points: The response of Atmospheric Cloud Radiative Effects (ACRE) to the Southern Annular Mode (SAM) reduces SAM's persistenceThe decrease in SAM's persistence is attributed to weakened low‐frequency eddy forcing via disruption of low‐level temperature gradientsThe change in persistence is nonlinear with respect to the amplitude of ACRE forcing [ABSTRACT FROM AUTHOR]

In this study, the Atmospheric Infrared Sounder (AIRS) Observations for Model Intercomparison Projects (Obs4MIPs) V2.1 tropospheric air temperature, specific humidity, and relative humidity data are utilized to evaluate the global tropospheric temperature and humidity simulations in the fully coupled global climate models from the Coupled Model Intercomparison Project phases 3, 5, and 6 (CMIP3, CMIP5, and CMIP6), and possible simulation improvement in CMIP6 models in comparison to CMIP3 and CMIP5 models. Our analyses indicate that all three phases of CMIP models share similar tropospheric air temperature, specific humidity, and relative humidity biases in their multi‐model ensemble means relative to AIRS. Cold biases up to 4 K and positive relative humidity biases up to 20% are found in the free troposphere almost globally with maxima over the mid‐latitude storm tracks. Warm biases up to 2 K are seen over the Southern Ocean in the lower troposphere. Positive specific and relative humidity biases exist over the off‐equatorial oceans while negative specific and relative humidity biases are seen near the equator in the tropical free troposphere, which are related to the double‐intertropical convergence zone bias in the models. Both the air temperature and specific humidity biases are important to the relative humidity biases except in the tropical free troposphere where the specific humidity biases dominate. The tropospheric air temperature, specific humidity, and relative humidity biases are reduced from CMIP3 to CMIP5 and to CMIP6 at almost all pressure levels except at 300 hPa for specific humidity and in the boundary layer for relative humidity. Plain Language Summary: In this study, we use the Atmospheric Infrared Sounder (AIRS) data to assess how well the fully coupled global climate models simulate temperature and humidity in the troposphere. We focus on the climate models from the recent three phases of Coupled Model Intercomparison Project (CMIP3, CMIP5, and CMIP6) and investigate if there is any bias reduction in the recent CMIP6 models compared to the earlier CMIP3 and CMIP5 models. To conduct our evaluation, we compare the CMIP model ensemble averages with the AIRS data and find that all three phases of CMIP models exhibit similar biases in tropospheric temperature and humidity. For example, cold biases up to 4 K and positive relative humidity biases up to 20% are seen in the free troposphere over most regions, with the maximum biases over the midlatitudes. Positive specific and relative humidity biases are found over the off‐equatorial oceans while negative specific and relative humidity biases are seen near the equator in the tropical free troposphere. We also note a possible temperature and humidity bias reduction from the CMIP3 models to the CMIP5 models and then to the CMIP6 models at most pressure levels. Key Points: CMIP Phase 3, 5 and 6 climate models share similar tropospheric air temperature, specific and relative humidity biases relative to Atmospheric Infrared SounderCold biases and positive relative humidity biases are prevalent in free troposphere almost globally with maxima over the mid‐latitude oceansDouble‐intertropical convergence zone bias‐related specific and relative humidity biases are evident in tropical free troposphere: positive (negative) off (near) the equator [ABSTRACT FROM AUTHOR]

The UK contribution to the Detection and Attribution Model Intercomparison Project (DAMIP), part of the sixth phase of the Climate Model Intercomparison Project (CMIP6), is described. The lower atmosphere and ocean resolution configuration of the latest Hadley Centre global environmental model, HadGEM3‐GC3.1, is used to create simulations driven either with historical changes in anthropogenic well‐mixed greenhouse gases, anthropogenic aerosols, or natural climate factors. Global mean near‐surface air temperatures from the HadGEM3‐GC31‐LL simulations are consistent with CMIP6 model ensembles for the equivalent experiments. While the HadGEM3‐GC31‐LL simulations with anthropogenic and natural forcing factors capture the overall observed warming, the lack of marked simulated warming until the 1990s is diagnosed as due to aerosol cooling mostly offsetting the well‐mixed greenhouse gas warming until then. The model has unusual temperature variability over the Southern Ocean related to occasional deep convection bringing heat to the surface. This is most prominent in the model's aerosol only simulations, which have the curious feature of warming in the high southern latitudes, while the rest of the globe cools, a behavior not seen in other CMIP6 models. This has implications for studies that assume model responses, from different climate drivers, can be linearly combined. While DAMIP was predominantly designed for detection and attribution studies, the experiments are also very valuable for understanding how different climate drivers influence a model, and thus for interpretating the responses of combined anthropogenic and natural driven simulations. We recommend institutions provide model simulations for the high priority DAMIP experiments. Plain Language Summary: We describe the UK submission to the Detection and Attribution Model Intercomparison Project (DAMIP), using the HadGEM3‐GC3.1 climate model. The model's near‐surface temperature responses to different human and natural climate drivers are compared with other climate models and observed temperature changes. The experiments help to understand the evolution of the model's simulated historical global temperatures. One of the more interesting model features is the variability in the Southern Ocean which manifests itself as occasional surface warming due to deep ocean heat coming to the surface. This behavior, which occurs more often in simulations that cool than in simulations that warm, appears to be unusual compared to other models. The investigation of this model behavior demonstrates that DAMIP model experiments are not just useful for climate change detection and attribution, but also for understanding how a model responds to different climate drivers. Climate model participation in DAMIP is encouraged. Key Points: The UK's contribution to the Detection and Attribution Model Intercomparison project (DAMIP) is describedThe climate model's global temperature response to different anthropogenic and natural drivers is examined and compared to other modelsSouthern Ocean temperature variability is unusual and sensitive to climate driver [ABSTRACT FROM AUTHOR]

The atmosphere‐ocean is a highly coupled system with significant diurnal and hourly variations. However, current coupled models usually lack sub‐diurnal scale processes at the air‐sea interface due to the finite vertical resolution for ocean discretization. Previous modeling studies showed that sub‐diurnal scale air‐sea interaction processes are important for ocean mixing. Here, by designing an integrated sub‐diurnal parameterization (ISDP) scheme which combines different temperature profiling functions, we stress sub‐diurnal air‐sea interactions to better represent the local ocean mixing. This scheme has been implemented into two coupled models which contributed to the Climate Model Intercomparison Project (CMIP), referenced by the Intergovernmental Panel on Climate Change—Community Earth System Model and Coupled Model version 2. The results show that the ISDP scheme improves model simulations with better climatology and more realistic spectra, especially in the tropics and North Pacific Ocean. With the scheme, the tropical cold tongue bias is significantly relaxed by reducing the overestimation of ocean upper mixing, and the cold bias of North Pacific Ocean is reduced due to the improvement on currents and net heat fluxes. Our scheme may help better the simulation and prediction skills of coupled models when their horizontal resolution becomes fine but vertical resolution remains relatively coarse as it describes high‐frequency air‐sea interactions more realistically. Plain Language Summary: The atmosphere and ocean interact with each other. In these interactions, changes may occur over different time periods. Some changes take years or months, while others happen in days or hours. The atmosphere changes quickly throughout the day, such as air temperature, wind, and rain. These quick changes in the atmosphere can affect the ocean rapidly. Similarly, there are quick changes in the ocean. These quick changes also affect the atmosphere in return. Understanding these quick changes is important. However, it is difficult to perfectly capture the quick changes in the climate models because they do not always represent the ocean behavior accurately. In this study, we developed a new scheme (named the sub‐diurnal scale parameterization, ISDP) to better represent these quick changes. We added this new scheme into two widely‐used climate models. These models are important tools for studying climate change. Our new scheme represents better the ocean mixing and interaction based on the local weather conditions. When we use ISDP, the climate models get better at simulating climate. They're more accurate in showing ocean temperatures in the tropics. These models make the tropical oceans seem colder than they really are, but our scheme, to some extent, fixes this problem. The results also show the more accurate ocean temperatures in the North Pacific Ocean. Our new scheme has a great potential to make climate models more accurate. Key Points: An integrated sub‐diurnal scale parameterization (ISDP) scheme has a more appropriate representation for local ocean mixingThe ISDP scheme has been implemented into two models which contributed to the Climate Model Intercomparison Project, referenced by IPCC: Community Earth System Model and Coupled Model version 2The model tropical cold tongue bias with ISDP is relaxed by reducing the overestimation of ocean upper mixing [ABSTRACT FROM AUTHOR]

The 2021 heatwave over Western North America (WNA) led to record‐breaking air temperatures and human‐perceived heat stress (humidex) values. The event was accompanied by drier conditions driven by prolonged atmospheric blocking. During the heatwave, the maximum 6‐day means of humidex and temperature (HX‐6 and TX‐6) exhibited larger anomalies (6.70 and 5.57°C) compared to the 95th percentiles (HX95 and TX95) (4.12 and 3.73°C), relative to 1981–2021 extended summer (June‐September) averages. Extreme indices of humidex show faster and larger increases than those of temperature, reflecting the nonlinear positive relationship between humidex and temperature. Future projections from a multi‐model ensemble of 19 Coupled Model Intercomparison Project Phase six (CMIP6) Global Climate Models (GCMs) clearly show an increase in humidex and temperature extremes, especially under intermediate and high emissions scenarios. Humidex indices (HX‐6 and HX95) show faster and larger increases than temperature indices (TX‐6 and TX95) for the same future years and global warming levels. Controlling for differences in GCM climate sensitivity to greenhouse gas forcing yields robust projections at various global warming levels, reducing the ranges of projected changes from the multi‐model ensemble. At 3.0°C global warming from pre‐industrial, the multi‐model ensemble projects occurrences of HX‐6, TX‐6, HX95, and TX95 over WNA that exceed 2021 levels to occur every 3.9, 1.7, 1.4, and 2.2 years, respectively, increasing to almost annually at 4.0°C. Plain Language Summary: In 2021 summer, Western North America (WNA) experienced an unprecedented heatwave, breaking air temperature and human‐perceived heat stress (humidex) records despite lower humidity due to prolonged atmospheric blocking. During the heatwave, consecutive 6‐day means of heat stress (HX‐6) and temperature (TX‐6) showed larger anomalies (6.70 and 5.57°C) compared to the 95th percentiles (HX95 and TX95) (4.12 and 3.73°C) over the region, relative to their averages over the 1981–2010 summer period (June‐September). Heat stress increased faster and more significantly than air temperature due to their nonlinear relationship. Future projections from 19 climate models reveal clear increases in heat stress and temperature, especially under intermediate and high emission scenarios. Heat stress indices show larger and faster increases than temperature indices at the same future years and global warming levels. Accounting for uncertainty related to climate model sensitivity enhances projection reliability. At 3.0°C global warming from pre‐industrial levels, 2021‐level events over WNA are projected to occur every few years, becoming an almost annual occurrence at 4.0°C. The study underscores the critical need for robust projections using multi‐model ensembles. Key Points: Unprecedented 2021 summer heatwave over Western North America resulted in record‐breaking humidex and air temperature extremesLarger increases in humidex than temperature, exceeding their 2021 levels, under intermediate and high emissions scenariosRobust projections of multi‐model ensemble, mitigating climate sensitivity uncertainty of GCMs response to greenhouse gas emissions [ABSTRACT FROM AUTHOR]

Surface anticyclones connected to the ridge of an upper‐tropospheric Rossby wave are the main dynamical drivers of mid‐latitude summer heatwaves. It is, however, unclear to what extent an anomalously low zonal phase speed of the wave in the upper troposphere is necessary for persistent temperature extremes at the surface. Here, we use spectral decomposition to separate fast and slow synoptic‐scale waves. A composite analysis of ERA5 reanalysis data reveals that, while in some regions heatwaves become more frequent during episodes of weak or no phase propagation, temperature extremes in other regions are commonly associated with more rapidly eastward propagating Rossby waves. Reflected in the mean heatwave duration as well, this relationship is possibly linked to a longitudinal phase preference of slow and fast waves or a meridional storm track shift. These findings open up new questions about the influence of mid‐latitude dynamics on temperature extremes. Plain Language Summary: High pressure systems tend to be associated with mid‐latitude summer heatwaves, defined as multiple consecutive hot days. The persistence of heatwaves raises the question whether the associated high pressure system has to be anomalously persistent as well to cause a heatwave. Using a combination of observational data and atmospheric model output that is commonly regarded as ground truth for prediction purposes we find that this is not the case. By re‐distributing the location of on‐average high air pressure, some regions experience an increased heatwave frequency even if the atmospheric circulation is less persistent than usual. Understanding the link between persistent surface temperature extremes and the atmospheric circulation is important for the prediction and projection of extreme events in a warming climate. Key Points: Phase speed of synoptic‐scale waves is crucial for where, but less important for whether heatwaves occurA longitudinal phase preference of slow and fast waves is reflected in the mean heatwave durationChanges in phase speed are linked to a meridional storm track shift [ABSTRACT FROM AUTHOR]

This study examines the temporal and spatial variability of near‐surface air temperature and the canopy layer urban heat island (UHICL) of Kuwait City. Observations collected at 12 locations across the country of Kuwait and for the period 2010–2022 are analysed on an hourly and 3‐hourly basis to provide monthly and diurnal insights of the city's UHICL characteristics. Research on Kuwait's UHICL was first conducted by Nasrallah et al. (International Journal of Climatology, 10, 401–405). Results presented here have been afforded the benefit of additional stations and more extensive data compared with the earlier study. Mean positive UHICL intensities, ranging from 1.1°C to 3.8°C at night, are observed consistently across all months, owing to the prevalence of clear skies from winter to summer. Negative UHICL intensities, indicating a typical daytime urban cool island (UCICL), are most prominent on summer days, exhibiting a mean hourly magnitude range between 0.6°C and 2.6°C that extends into the early parts of the evening. Heat and cool island effects are maintained up to wind speeds approaching 10 m s−1 at the urban periphery. A coastal site near the city demonstrates strong influences of the Arabian Gulf temperatures and associated sea and land breeze effects on UHICL development. The results can be used for comparison with other desert locales, in the evaluation of urban climate models, for urban planning policies and improving local weather forecasts. This study honours in memoriam Dr. Hassan Nasrallah, who produced the first UHICL study in the Arab World. [ABSTRACT FROM AUTHOR]

Measurements of ground and near surface atmospheric temperatures at Jezero obtained during 700 sols by the Mars Environmental Dynamics Analyzer (MEDA) characterize the thermal behavior of the near surface Martian atmosphere during a full Martian Year. The seasonal evolution of MEDA measurements is compared with predictions from the Mars Climate Database and the solar irradiance at the surface. Thermal tides observed in the daily cycle of temperatures follow a seasonal cycle with additional variations greater than 2 K on time‐scales of tens of sols. We also observe sol‐to‐sol variations of about 1 K in mean daily air temperatures in autumn and winter with periodicities of 4–7 sols that might be related to baroclinic disturbances that are frequent in those seasons at high latitudes. We examine the evolution of the vertical thermal gradient and temperature fluctuations without finding a seasonal response to irradiance and dust load. We find that the convective boundary layer becomes isothermal and collapses 1 hr before sunset except during northern hemisphere winter, when the collapse occurs closer to sunset, implying a longer duration of the daytime convective instability. Around this period, the rover was located in the delta front in a location of complex topography where we observed stronger thermal gradients and intense daytime air temperature fluctuations. We also find in this place a nighttime event of gravity waves on near‐surface air temperatures, with amplitudes of 2 K and periods of 10 min. These waves possibly propagate downward through a near isothermal stable layer. Plain Language Summary: The Perseverance rover on Mars is carrying a meteorological station that among other measurements obtains air temperatures at three heights near the surface as well as ground temperatures. We analyze seasonal and local changes in temperatures measured over one Martian year (687 Earth days) in which Perseverance moved several kilometers across Jezero crater, comparing in situ observations with the output from a Global Climate Model of the atmosphere of Mars. Our results show that the diurnal cycle of temperatures is modulated by the solar irradiance on the surface, the amount of dust in the atmosphere, local dynamics and large‐scale weather systems. The temperature difference between the ground and the atmosphere greatly determines the meteorology in the lower atmosphere. Neither this temperature difference, nor temperature fluctuations, which during daytime are a proxy of atmospheric convection driven by the heating of the surface, show seasonal variations in line with the seasonal solar irradiance or dust load. Instead, the rover path toward a more complex topography led to the highest surface‐to‐air temperature difference during the northern hemisphere winter, despite the solar irradiance being minimum at that time. Close to this complex topography, the meteorological station recorded oscillations in near‐surface temperature related to gravity waves. Key Points: Seasonal variations in irradiance at Mars' surface drive observed daily mean near‐surface air temperatures with some differences from modelsThermal tides and long‐period traveling waves present a clear seasonal cycle with autumn and winter seasons more prone to variabilityAir temperatures underwent non‐seasonal changes near Jezero's delta, with strong daytime convection and nighttime gravity waves [ABSTRACT FROM AUTHOR]

The accurate partitioning of precipitation is important for snowpack and streamflow simulations in mountainous regions. However, most hydrologic models employ a partitioning method based on near‐surface air temperature (Ta) and the sensitivity of hydrologic simulations to the selection of the precipitation partitioning method (PPM) under climate change has been underexplored. To address this, we compared simulations using two PPMs, a conventional Ta‐based scheme and a wet‐bulb temperature (Tw)‐based approach (TA and TW) with the Variable Infiltration Capacity model. Our study focused on the Colorado River Basin (CRB) which is vulnerable to future snowfall and streamflow reductions due to climate warming. Historical simulations demonstrated the improved performance of the TW scheme in simulating snowfall fraction (SF) and snow water equivalent (SWE) in relation to in situ observations and a gridded SWE product. Subsequently, we evaluated the influence of the PPM selection on snowpack and streamflow projections from eight climate models under two emissions scenarios. The ensemble median results revealed more substantial decline in annual SF with the TW scheme as compared to the TA method (−12% vs. −9%) from the historical (1976–2005) to the far future (2066–2095) period, especially at lower elevations. Hydrologic simulations showed that the TA scheme underestimated reductions in SWE and streamflow (Q) under warming as compared to the TW scheme. This finding has important implications on future projections for the CRB and in other mountainous basins where climate warming can shift conditions from snowfall to rainfall. Key Points: The precipitation partitioning method (PPM) impacts the simulation of snowfall and snowpack dynamics in the Colorado River Basin (CRB)The selection of PPM also determines the sensitivity of streamflow projections in the CRB under climate change scenariosSpatial variations in the impact of PPM reveal distinct climate change sensitivities in high and low elevation regions in the CRB [ABSTRACT FROM AUTHOR]

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

Current‐generation climate models project that Africa will warm by up to 5°C in the coming century, severely stressing African populations. Past and ongoing work indicates, however, that the models used to create these projections do not match proxy records of past temperature in Africa during the mid‐Holocene (MH), raising concerns that their future projections may house large uncertainties. Rather than reproducing proxy‐based reconstructions of MH warming relative to the Pre‐Industrial (PI), models instead simulate MH temperatures very similar to or slightly colder than the PI. This data‐model mismatch could be due to a variety of factors, including biases in model surface energy budgets or inaccurate representation of the feedbacks between temperature and hydrologic change during the "Green Sahara." We focus on the differences among model simulations in the Paleoclimate Modeling Intercomparison Project Phases 3 and 4 (PMIP3 and PMIP4), examining surface temperature and energy budgets to investigate controls on temperature and the potential model sources of this paleoclimate data‐model mismatch. Our results suggest that colder conditions simulated by PMIP3 and PMIP4 models during the MH are in large part due to the joint impacts of feedback uncertainties in response to increased precipitation, a strengthened West African Monsoon (WAM) in the Sahel, and the Green Sahara. We extend these insights into suggestions for model physics and boundary condition changes, and discuss implications for the accuracy of future climate model projections over Africa. Key Points: We evaluate the simulation of African air temperatures in Paleoclimate Modeling Intercomparison Project Phases 3 and 4 simulations of the mid‐HoloceneEnergy balance decomposition analyses indicate the hydrologic cycle plays a key role in causing mid‐Holocene cooling in model simulations"Green Sahara" experiments show that dust and vegetation affect simulated temperatures, revealing pathways for refining model simulations [ABSTRACT FROM AUTHOR]

An increase in winter air temperature can amplify snowmelt and sublimation in mountain regions with implications to water resources and ecological systems. Winter Warm Spells (WWS) are defined as a winter period (December to February, DJF) of at least 3 consecutive days with daily maximum temperature anomaly above the 90th percentile (using a moving‐average of 15 days between 2001 and 2013). We calculate WWS for every 4‐km grid cell within an atmospheric model over western North America to characterize WWS and analyze snow ablation and their changes in a warmer climate. We find that days with ablation during WWS represent a small fraction of winter days (0.6 days), however, 49% of total winter ablation (33.4 mm/DJF) occurs during WWS. Greater extreme ablation rates (99th percentile) occur 18% more frequently during WWS than during non‐WWS days. Ablation rates during WWS in humid regions are larger (9 mm d−1) than in dry regions (7 mm d−1) in a warmer climate, which can be explained by differences in the energy balance and the snowpack's cold content. We find that warmer (0.8°C), longer (1.8 days) and more frequent (3.7 more events) WWS increase total winter ablation (on average 109% or 18 mm/DJF) in a warmer climate. Winter melt during WWS in warm and humid places is expected to increase about 3 times more than in the cold and dry region. This study provides a comprehensive description of WWS and their impact on snowpack dynamics, which is relevant to reservoir operations and water security. Plain Language Summary: Extreme maximum winter temperatures (Winter Warm Spells, WWS) can affect the snow in winter by increasing snowmelt, with consequences for mountain water supplies. In this study we quantify snowpack response to WWS in the mountains of western North America and its sensitivity to a warmer future climate using numerical models, observations, and statistical tools. We find that 49% of snow lost in winter happens during WWS, despite having only 0.6 days with WWS per winter. Across the mountains, the humid climatic regions are more affected by WWS than arid regions. We show that snowpack ablation during WWS will increase in a warmer climate, which will put more stress on reservoir and water security planning. Key Points: Winter Warm Spells account for 49% of snowpack winter ablation (16.5 mm), despite only representing 0.6 days on averageHistorical Winter Warm Spells increase in frequency (3.7 more events), duration (1.8 days) and magnitude (0.8°C) in a warmer climateWinter snow ablation driven by melt (93.6%) is expected to increase in a warmer climate (147%), especially in humid regions [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]

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 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]

This study investigates the response of surface air temperature (SAT) to strong tropical volcanic eruptions (STVEs), focusing on the seasonal anchoring of SAT recovery, using the Coupled Model Intercomparison Project 5 (CMIP5) and CMIP6 climate model outputs. The decrease in SAT was more significant in the Northern Hemisphere than in the Southern Hemisphere. After the STVEs, shortwave radiation at the surface (SW) decreased rapidly and reached a minimum in approximately 6 months, whereas the decrease in SAT lasted for approximately 1 year; thus, the SAT minima lagged behind those of SW. The recovery of SAT from the major minimum SAT after the STVEs occurred in the same seasons, the second boreal autumn and winter. The SAT recovery occurred during the climatologically low solar radiation season after the STVEs. During this season, the decreasing effects of the downward SW (DSRS) by volcanic aerosols could become negligible. The weakening of the decrease in SAT and DSRS had a seasonal cycle in the high‐latitude regions of both hemispheres, which could be a significant factor in the seasonal alignment of returning SATs. Furthermore, the SAT response to the El Niño–Southern Oscillation (ENSO) was examined using composite analysis based on the initial state of ENSO. The influence of ENSO on surface cooling after the STVE was at least 1/5 that of the STVEs and might exceed that of the STVEs, particularly in moderate STVEs. These results were commonly found in the CMIP5 and CMIP6 climate models. Plain Language Summary: Strong tropical volcanic eruptions (STVEs) transport volcanic sulfur aerosols into the stratosphere, affecting downward solar radiation and decreasing surface air temperature (SAT) for several years. After the 1991 Pinatubo eruption, the global average surface air temperature dropped by 0.5 K. We investigated the SAT response to the STVEs using the simulation results from multiple climate models. Analyzing many simulations increases the reliability of our results. As is well known from previous studies, a SAT drop has been clearly observed in the Northern Hemisphere. In addition, the SAT response to volcanic aerosols was weakened in the second northern winter after the STVEs, even when the STVE occurred in northern spring and fall. Because solar radiation at the Earth's surface is climatologically weak in northern winter, the effects of volcanic aerosols on SAT decrease can be negligibly small in northern winter. Therefore, the recovery of SAT after STVEs occurred in the northern winter. Key Points: Surface air temperature (SAT) responses to strong tropical volcanic eruptions (STVEs) were investigated using CMIP5/6 climate modelsDecrease in SAT after the STVEs tended to stop in the second winter, even though the timing of the eruption was differentSeasonally anchoring SAT recovery after STVEs is related to weak solar radiation in winter [ABSTRACT FROM AUTHOR]

Vertical exchange between the atmospheric boundary layer (ABL) and free troposphere (FT) is a key link in coupling the earth's surface and upper atmosphere. This process is usually quantified by numerical simulations, while its reliability is not well assessed until now. Using space‐time intensified ABL observations, we evaluate the ABL‐FT air mass exchange flux derived from the Weather Research and Forecast (WRF) model. A six‐site sounding experiment is conducted in the North China Plain during the wintertime of 2019. The measured data is processed to provide enough information to derive the vertical exchange flux corresponding to the model‐based result, so that a systematic comparison is conducted. Three physical processes involved in ABL‐FT vertical exchange are quantitatively evaluated, that is, temporal variation of ABL height, advection across the inclined ABL top, and vertical motion at the ABL‐FT interface. Results show that the model‐based and observation‐based fluxes are generally agreed in temporal evolution (R = 0.67, p < 0.01), both characterized by 4–6 days periodicity and diurnal cycle. Their relative mean error was about 45% during the whole study period, mainly stemming from the vertical motion term and the advection crossing term. The model inaccuracy in representing these relevant processes at the ABL top is largely responsible for the discrepancy. Besides, the difference may also be attributed to the observational uncertainty (∼22%) that is caused by the measurement's difficulties in determining ABL spatial variation and acquiring vertical velocity. Through this study, the credibility and limitation of the WRF model in deriving ABL‐FT exchange flux are quantified. Plain Language Summary: The vertical exchange between the atmospheric boundary layer (ABL) and free troposphere (FT) plays a crucial role in atmospheric matter transport and energy flow, affecting weather systems, atmospheric chemistry, air pollution and climate change. This process is usually quantified by numerical simulation. This work aims to evaluate the reliability of the air exchange flux derived from a meteorological model, using comprehensive observation data in 1 month. The result shows that the model‐based exchange flux can reproduce the temporal evolution of the observation‐based exchange flux, including 4–6 days periodicity and diurnal variation. The overall relative difference between simulated and observed fluxes is about 45% during the whole study period. A more detailed comparison of each component term is conducted to investigate the causes of the discrepancy. We found that the difference is partly attributed to the model inaccuracy in representing the ABL top spatial variation and the vertical velocity at this layer. Additionally, a 22% uncertainty in observation‐based flux introduced by the relevant data processing is also an important reason. The evaluation result provides a quantitative reference to understand the biases in the meteorological model and their impact on ABL‐FT vertical exchange simulation study. Key Points: The air exchange flux between the boundary layer and free troposphere is reproduced by the WRF model with synoptic and diurnal variationsThe model‐derived exchange flux deviates from observation‐derived flux with a relative difference of 45%The deviation is mainly attributed to simulation‐observation differences in vertical motion and spatial gradient at the boundary layer top [ABSTRACT FROM AUTHOR]

1976 was one of the most acute droughts in the UK, exceptional due to the compounding effects of low rainfall and hot summer temperatures. In this study, we provide a multiperspective view of the likelihood of a 1976‐like compound event occurring now and into the future. We find a high level of consistency in the messages emerging across a range of different approaches and climate modelling tools, from convection‐permitting climate projections to decadal hindcasts and global coupled‐model attribution ensembles. 1976 summer average temperatures remain, at the time of writing, amongst the highest on record, but with warming are becoming increasingly common. The nine‐month rainfall deficit to August 1976 was incredibly rare. Analysis here indicates that compound extremes like 1976 are expected to occur on time‐scales of hundreds to thousands of years in the present‐day climate, decreasing slightly into the future. The probability remains very small even if we account for favourable sea surface temperatures and atmospheric circulation that occurred in 1976. Similar but less severe events with a 1% chance of occurring in the present day are five times more frequent from the 2040s under RCP8.5. The occurrence of such compound events is significantly (up to an order of magnitude) higher than expected if temperature and rainfall extremes occurred independently. In general, differences in likelihood estimates between approaches can begin to be understood from how dependence between variables is handled, differences in bias correction, and different levels of conditioning (i.e., the probability given particular atmospheric or ocean states). The appropriate choice of conditioning very much depends on the question being asked and its unconscious use may lead to apparent contradictions. Parallels can be drawn between 1976 and the recent summer of 2022, and results here suggest that with hotter summers we should be prepared for more severe droughts like 1976 in the future. [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]

The summer surface air temperatures (SAT) over Eurasia exhibit a non‐uniform multidecadal pattern since the 1990s, with amplified warming over Europe–West Asia (EWA) and Northeast Asia (NEA) but weak warming over Central Asia. However, current climate models may inadequately capture this regional pattern. In this study, we evaluated the performance of 44 coupled models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) in simulating the decadal variability of Eurasian temperatures. Results show that all the CMIP6 models generally reproduce the largest SAT trends from 1985 to 2020 over EWA and NEA. However, the models fail to capture the non‐uniform warming pattern and the SAT magnitudes are underestimated in most CMIP6 models. We compare "good" and "poor" ensemble, which represent the best and worst simulations of the non‐uniform warming, in order to understand the inter‐model spread. The BMME models capture the teleconnection wave trains from northwest Europe to East Asia, and the associated summer North Atlantic Oscillation like circulation changes. In contrast, the WMME models fail to reproduce such teleconnections. The contrast between the BMME and WMME simulations give a measure of confidence that the non‐uniform warming pattern over Eurasia is tightly coupled with the upper‐tropospheric wave‐like pattern over Eurasia. If such atmospheric circulation changes cannot be reproduced, the non‐uniform warming pattern over Eurasia will not be captured. Our results suggest a fundamental pattern of atmospheric circulation changes that determine the non‐uniform warming variations in model simulations. Plain Language Summary: Previous studies have documented pronounced multidecadal temperature variations in summer during the late 20th century over Eurasia, with accelerated warming over Europe–West Asia (EWA) and Northeast Asia (NEA), but weak warming over Central Asia. Whether Coupled Model Intercomparison Project Phase 6 (CMIP6) climate models are able to adequately reproduce this non‐uniform pattern remain unknown. Here we quantitatively evaluated the performance of 44 CMIP6 models in simulating the summer surface air temperatures decadal variations over Eurasia since 1950 and further investigated the key factors in causing the differences in the simulation ability among the models. Our results show that a good performance of the model is closely related to its ability in simulating the Silk Road Pattern, which can induce the intrinsic teleconnection wave trains, causing the anticyclonic circulations around EWA and NEA, and increase warm advection and downward shortwave radiation, leading to amplified warming there. Our findings provide a perspective of physical linkage to understand the model uncertainty in simulating the non‐uniform multidecadal pattern over Eurasia. Key Points: Evaluation of Coupled Model Intercomparison Project Phase 6 (CMIP6) models for the summer non‐uniform multidecadal temperature variations over EurasiaThe magnitudes of non‐uniform warming variations are substantially underestimated in most CMIP6 modelsSimulation of the upper‐tropospheric wave‐like pattern is key and significantly affects the model's performance in simulating the surface air temperatures [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]

The frequency and magnitude of global warming events varies greatly across different regions and countries. The climatic diversity for China and future warming features are projected across 12 climatic zones based on the ensemble of the five well‐performing high‐resolution downscaled climate models for each zone. There are warming patterns for the mean near‐surface air temperature (Tm), maximum near‐surface air temperature (Tmax), minimum near‐surface air temperature (Tmin) as well as heatwave and frost events. Under RCP4.5 and RCP8.5 scenarios, the three indices (i.e., Tm, Tmax and Tmin) countrywide are likely to increase at respective rates of 0.30–0.31 and 0.64–0.67°C·decade−1. The extent of frost event (FE) are projected to decrease at a rate of −1912 and −4442 day·km−2·decade−1 while the extent of heatwave event (HE) increase at a rate of 1116 and 3557 day·km−2·decade−1 under RCP4.5 and RCP8.5 scenarios, respectively. A higher increment in temperatures, a decreasing trend in diurnal temperature range (DTR), frost days and FE are highlighted in the western and northern zones, while these trends are opposite in southern zones. The warming is likely to get faster in the current colder zones (western and northern zones) while heatwave is more intense and severe in Jianghuai, Jianghan, the south Yangzi River, South China and Xinjiang. These potential changes indicate that adaption and mitigation strategies are necessary in response to future warming. [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]

Future changes in Sahel precipitation are uncertain because of large differences among projections from various models. In order to explore this uncertainty, we use a storyline approach which seeks to identify alternative plausible evolutions of Sahel precipitation and their driving factors. By analyzing projections from the CMIP6 climate models, we show that changes in North Atlantic and in Euro‐Mediterranean temperatures explain up to 60% of the central Sahel precipitation change uncertainty. We then construct several storylines of Sahel precipitation change based on future plausible changes in North Atlantic and Euro‐Mediterranean temperatures. In one storyline, an amplified warming of both the North Atlantic and the Euro‐Mediterranean areas promotes a northward shift of the West African monsoon, increasing precipitation over the central Sahel, while, in the opposite storyline, a moderate warming in both regions is associated with a small change in precipitation over the central Sahel and a decrease in precipitation over the western Sahel, at the end of the 21st century. These results indicate that Sahel precipitation uncertainty will not be substantially reduced unless the uncertainty in the future warming of the North Atlantic and the Euro‐Mediterranean areas is constrained. Plain Language Summary: Variations in the strength of the West African Monsoon (WAM) circulation have societal impacts on around 80 million people from Senegal to Chad. However, future projections of the WAM precipitation are uncertain for the end of the 21st century because of strong differences from one climate model to another. We show here that sources of uncertainty in Sahel precipitation depend on changes in North Atlantic and Euro‐Mediterranean temperature, relative to changes in global mean surface air temperature. We show that differences in how climate models simulate the future warming over the North Atlantic and Euro‐Mediterranean area explain a large proportion of the uncertainty in precipitation change over the central Sahel. We provide a method that could be helpful at selecting models for impact studies, based on their sensitivity to climate change over the North Atlantic ocean and Mediterranean Sea. Key Points: Future changes in Sahel precipitation are uncertainChanges in North Atlantic and Euro‐Mediterranean temperatures explain up to 60% of the Sahel precipitation change uncertaintyUncertainty in changes in Sahel precipitation is associated with uncertainty in the future northward shift of the Saharan heat low [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]

The NASA Goddard Earth Observing System (GEOS) employs a hybrid four‐dimensional ensemble‐variational (Hybrid‐4DEnVar) atmospheric data assimilation system to provide global, near‐real time weather analysis and forecast products. This study introduces a land analysis into the GEOS Hybrid‐4DEnVar system to additionally assimilate L‐band (1.4 GHz) brightness temperature observations over land from the NASA Soil Moisture Active Passive (SMAP) satellite mission, which are highly sensitive to surface (∼0–5 cm) soil moisture. This weakly coupled land analysis impacts the simulated and forecast atmosphere through the land–atmosphere exchange processes encoded in the atmospheric model. Retrospective assimilation experiments for boreal summer 2017 were conducted with the system at 50 km horizontal resolution. The SMAP assimilation is shown to mitigate errors in screen‐level (2 m) specific humidity (q2m) and temperature (T2m), with regional reductions in the root‐mean‐square error (RMSE) of q2m and maximum daily T2m by up to 0.4 g·kg−1 and 0.3 K, respectively. These improvements are somewhat smaller than those found in a precursor study that used the atmospheric analysis in the simpler, 3‐dimensional variational (3DVar) atmospheric assimilation configuration of the current GEOS reanalysis; Hybrid‐4DEnVar provides overall better near‐surface background estimates than 3DVar and therefore leaves less room for improvement. Finally, in the Hybrid‐4DEnVar system with SMAP assimilation, forecasts of q2m and T2m have significantly improved anomaly correlation and RMSE (99% confidence) at lead times out to 5 days compared to the Hybrid‐4DEnVar system without SMAP assimilation, with medium‐range lead times extended by ∼3 hr for q2m and ∼2 hr for T2m. Slight but nevertheless significant improvements are also seen for temperature forecasts at the 925 and 850 hPa levels and lead times out to 4 days. Humidity forecasts at 925 and 850 hPa are improved out to 1.5‐day lead time with 90% confidence. [ABSTRACT FROM AUTHOR]

The present research focuses on temperature change signals over the Carpathian Basin with a special focus on selected lowland and mountainous subregions. High‐resolution (0.11°) EURO‐ and Med‐CORDEX regional climate model (RCM) simulations of near‐surface air temperature are analysed based on raw and bias‐adjusted data. The mini‐ensemble consists of eight RCM simulations driven by five different general circulation models for the period 1976–2099 under the high‐end RCP8.5 scenario. The high‐resolution, homogenized and quality controlled CARPATCLIM was used as a reference dataset. The selected subregions cover eight municipalities located at diverse altitudes: Bratislava, Budapest, Brassov, Debrecen, Hoverla, Novi Sad, Pécs and Poprad. The following climate indices are assessed: summer days, ice days, frost days, tropical nights, the coldest day, the warmest day, the coldest night and the warmest night. In general, for the reference period (1976–2005) bias‐adjusted RCM data showed almost perfect match with observations. Accordingly, no best performing RCM is found for all indices. The ensemble mean of the bias‐adjusted RCM simulations projects an increase (decrease) of 32% and 112% (18% and 25%) in the annual number of summer days and tropical nights (frost days and ice days) for the period 2021–2050. For 2070–2099 we can expect more frequent tropical nights (about five times) with respect to the reference period and the frequency of frost days can be halved. Profound warming manifests in the increase of the warmest temperature of day of up to 2–3°C by the near future and of 5–7°C by the end of the 21st century, which means the absolute maximum temperature can reach 44–47°C for the period 2070–2099. Our results also highlight the need for bias‐adjusted data adapted by different sectors (human health, agriculture, transport, disaster management, heritage conservation) under the national adaptation strategies. [ABSTRACT FROM AUTHOR]

High‐resolution (dx = 25 km) simulations of the regional climate model RegCM4.7 coupled with the Community Land Model version 4.5 (CLM4.5) under low (RCP2.6) and high (RCP8.5) emissions scenarios, which interact with the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (AR5‐IPCC), were carried out over tropical South America (TSA). These simulations were produced through boundary conditions from simulations driven by the general circulation model HadGEM2‐ES. With the goal of verifying the added value (AV) of RegCM4.7, reproducing in an adequate and coherent way the regional aspects of the historical period (1986–2005), as well as evaluating the regional aspects simulated by the model in the scope of the change projections for the far‐future (2080–2099). For this study, the climate in the TSA was characterized based on the variables of precipitation and near‐surface air temperature. For the evaluation of the spatial–temporal representation, frequency and distribution of the regional and global simulation, the high‐resolution observational dataset of the Climate Research Unit version ts4.02 (CRU) was used. Although some differences and biases still persist, RegCM4.7 presents AV in the spatial representation of precipitation and temperature over the northeast region of Brazil and part of the Andes, especially in winter. However, it does not adequately represent precipitation over the Amazon Basin, especially in summer. Results for future projections indicate that the more refined simulation of RegCM4.7 improves the projected change patterns of the coarser resolution simulation of HadGEM2‐ES and even modifies the precipitation signal in some cases. Both models project increase temperature with greater magnitude for RCP8.5. RegCM4.7 presents a much more refined and realistic spatial distribution. HadGEM2‐ES simulates the major aspects of climate enough to consider forcing RegCM4.7 to generate simulations with better performance and more realistic projections. [ABSTRACT FROM AUTHOR]

Biophysical effects of forest cover changes are often neglected by climate policies and recent state‐of‐the‐art climate models exhibit wide spreads in simulating the biophysical impacts of deforestation. By using the CMIP6‐LUMIP simulations, here we examined the biophysical impacts of deforestation on global temperature and attributed deforestation‐induced surface temperature change to different biophysical effects (i.e., radiative forcing, aerodynamic resistance, Bowen ratio and atmospheric feedbacks) at regional scales. Results show that models agree on the sign of temperature responses to different biophysical factors in the tropics, but exhibit wide divergence in the extratropical regions. Among the three local biophysical factors (i.e., radiative forcing, aerodynamic resistance, and Bowen ratio), aerodynamic resistance contributes largely to local surface warming in models. As the local effects rarely affect the areas away from the deforested regions, much of the modeled discrepancies result from non‐local atmospheric feedbacks in the middle and high latitudes. Our results suggest that climate responses to deforestation have a large spread in current models and highlight the need to improve our understanding and modeling of non‐local effects in the biophysical impacts of deforestation. Plain Language Summary: The Land Use Model Intercomparison Project (LUMIP) multimodel simulations with a common experimental protocol provide a great opportunity to explore the biophysical effects of land use and land cover change and allow a direct comparison of model responses to deforestation. By analyzing the CMIP6‐LUMIP multimodel simulations, we show that deforestation causes widespread summer warming in the tropics, but the simulated temperature changes in response to deforestation diverge in the middle and high latitudes. We further attributed deforestation‐induced surface temperature change to different biophysical effects (radiative forcing, aerodynamic resistance, Bowen ratio and atmospheric feedbacks). Our results indicate that atmospheric feedbacks play the dominant role in determining the temperature responses, and aerodynamic resistance also matters. The atmospheric feedbacks in the temperate and boreal regions are highly model‐dependent. Further efforts to explore the non‐local influence of temperate deforestation using state‐of‐the‐art climate models are needed. Key Points: Our study assesses different biophysical impacts of deforestation on surface temperature in CMIP6 modelsCMIP6 models show wide divergence on the surface temperature changes in the extratropical regions due to non‐local atmospheric feedbacksChanges in the aerodynamic resistance dominate the surface temperature response to local biophysical effects of deforestation [ABSTRACT FROM AUTHOR]

West Antarctica exhibits a pronounced sea ice variability in interannual timescale, and 20%–30% of the total variance can be explained by the El Niño–Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) during austral spring. The sea ice variation is primarily linked with anomalous atmospheric circulation in the Amundsen‐Bellingshausen Sea (ABS) that modulates poleward atmospheric temperature advection and radiative forcing. With a co‐varying relationship between ENSO and IOD, isolating their remote impacts on Antarctica has been limited in observations. An idealized experiment using the atmospheric model with a dry dynamical core suggests that the anticyclonic circulation anomaly in the ABS is primarily contributed by the ENSO in the Pacific Ocean, while the contribution of the IOD in the Indian Ocean is only one‐third large. This study implies that atmospheric teleconnection through the southern Pacific Ocean is crucial for understanding the West Antarctic sea ice concentration variability. Plain Language Summary: Antarctic sea ice, which affects the Earth's climate system, has gained considerable attention in recent years. Previous studies have shown that the El Nino‐Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) in the tropics affect the sea ice variation in West Antarctica through atmospheric teleconnections at interannual timescale. Unfortunately, due to the high positive correlation between these two climate modes, it is unclear how each phenomenon contributes to the West Antarctic sea ice variation. Therefore, this study investigates the underlying mechanisms related to the sea ice changes caused by ENSO and IOD during austral spring and quantifies the effect of each phenomenon on the West Antarctic sea ice through an idealized model experiment. It is found that the ENSO and the IOD account for 20%–30% of sea ice variance during austral spring, which is primarily contributed by the dynamic and thermodynamic mechanisms associated with atmospheric circulation in the Amundsen‐Bellingshausen Sea (ABS). In addition, we confirm through the model experiments that the effect of ENSO in the Pacific Ocean on the atmospheric circulation patterns of ABS is about three times stronger than that of IOD in the Indian Ocean. Key Points: El Niño–Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) account for 20%–30% of the variability in total sea ice concentration in austral springThe tropical variability affects circulation anomalies in the Amundsen‐Bellingshausen Sea, leading to sea ice variations in West AntarcticaAn idealized atmospheric model experiment suggests the ENSO effect on the West Antarctic circulation is much stronger than the IOD [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]

In this study, we focus on the features and predictability of winter 2‐m air temperature (t2m) over China under different intensities of La Niña events. The seasonal and intraseasonal prediction skills of winter t2m over China are examined by four state‐of‐art seasonal climate forecast models. The results show that the China winter t2m anomaly is sensitive to the intensity of La Niña. Strong and moderate (S/M) La Niña events are often accompanied by cold winter over China, while a positive anomaly of mean t2m over China frequently occurs in the weak La Niña events. The intensity difference of the East Asian winter monsoon (EAWM) results in this discrepancy. All models have a slightly higher anomaly correlation coefficient (ACC) for the S/M La Niña than that for the weak La Niña in leading 1 month (LT1). Both individual models and MME exhibit higher skills of anomaly sign consistency rate (ASCR) for the S/M La Niña, which implies that models predict t2m anomaly over China more accurately in the S/M La Niña events. Furthermore, the winter t2m over China presents larger intraseasonal variability for the S/M La Niña than that for the weak La Niña. Models can capture the t2m anomaly over China more accurately when the temperature varies gently with small intraseasonal variability during winter for the S/M La Niña. [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]

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

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]

We examine multiple factors in the representation of satellite‐retrieved atmospheric temperature diagnostics in historical simulations of climate change during the satellite era (specifically 1979–2021) using GISS ModelE contributions to the Coupled Model Intercomparison Project (Phase 6) (CMIP6). The tropospheric and stratospheric trends in these diagnostics are affected by greenhouse gases (notably carbon dioxide and ozone), coupling with the ocean, volcanic aerosols, solar activity and compositional and dynamic feedbacks. We explore the impacts of internal variability, changing forcing specifications, composition interactivity, the quality of the stratospheric circulation, vertical resolution, and possible impacts of the mis‐specification of volcanic aerosol optical depths. Overall temperature trends throughout the satellite period are well captured, but discrepancies at all levels exist and have multiple distinct causes. We find that stratospheric comparisons (using Stratospheric Sounding Unit (SSU) retrievals and successor instruments) are most affected by variations in the representation of ozone depletion and feedbacks, followed by the volcanic signals. Tropospheric skill (using the Microwave Sounding Unit (MSU) retrievals) is affected by the trends in ocean heat uptake and tropospheric aerosols, but also by the representation of stratospheric processes through the impact of the Brewer‐Dobson circulation on the height of the tropical tropopause. We demonstrate that no single factor is the dominant cause of the discrepancies and that almost all observations lie within the broad envelope of structural uncertainty. Plain Language Summary: The assessment of the ability of climate models to match trends and variability in the real world is a key factor in building the credibility of model projections under future scenarios. We focus on satellite‐derived atmosphere temperatures from the surface to the stratosphere whose trends at different levels reflect different processes and drivers. The satellite retrievals are weighted averages of atmospheric temperatures and so the vertical structure of the model trends matter in the comparison. We find that overall the trends throughout the atmosphere are well‐captured by the GISS models but that discrepancies can occur due to misspecified forcings, chaotic weather variability, and model structure. Key Points: Changes in forcing configurations have significant impact on agreement with satellite dataTropospheric model‐observation agreement is linked to ocean heat uptake, aerosols, sensitivity, and internal variabilityStratospheric discrepancies are mainly related to volcanic aerosol modeling and representations of ozone climatology and trends [ABSTRACT FROM AUTHOR]

The continued functioning of tropical forests under climate change depends on their resilience to drought and heat. However, there is little understanding of how tropical forests will respond to combinations of these stresses, and no field studies to date have explicitly evaluated whether sustained drought alters sensitivity to temperature. We measured the temperature response of net photosynthesis, foliar respiration and the maximum quantum efficiency of photosystem II (Fv/Fm) of eight hyper‐dominant Amazonian tree species at the world's longest‐running tropical forest drought experiment, to investigate the effect of drought on forest thermal sensitivity. Despite a 0.6°C–2°C increase in canopy air temperatures following long‐term drought, no change in overall thermal sensitivity of net photosynthesis or respiration was observed. However, photosystem II tolerance to extreme‐heat damage (T50) was reduced from 50.0 ± 0.3°C to 48.5 ± 0.3°C under drought. Our results suggest that long‐term reductions in precipitation, as projected across much of Amazonia by climate models, are unlikely to greatly alter the response of tropical forests to rising mean temperatures but may increase the risk of leaf thermal damage during heatwaves. [ABSTRACT FROM AUTHOR]

Impact of the similarity functions of surface layer parametrization in National Centre for Atmospheric Research Community Atmosphere Model version 5 (NCAR‐CAM5) is examined over the Indian region. The surface layer parametrization is based on the Monin–Obukhov similarity theory (MOST), which uses momentum and heat similarity functions to parametrize surface turbulent fluxes. In the present study, some of the well‐established similarity functions under stable as well as unstable stratification have been incorporated in the surface layer scheme of NCAR‐CAM5. Eight simulations have been conducted using different similarity functions with and without a limit on stability parameter along with the simulation using the default surface layer scheme. The impact of the incorporated functions on surface fluxes, near‐surface variables, and precipitation has been assessed in this climate model over different climatic zones for the boreal winter and summer seasons. The model represents the timing of diurnal variation of 2 m temperature and turbulent fluxes accurately with higher correlation in all simulations, but the performance of the schemes is found to be sensitive to the climatic zones and seasons. During the winter season, most of the schemes overestimate the night‐time surface air temperature over different climatic zones, but the simulation having a model performance index (MPI) of about 0.91 alleviates the bias. Under unstable stratification, the default scheme performs better than incorporated schemes in the winter season, whereas in boreal summer under unstable (stable) stratification, the incorporated functional form having an MPI of about 0.94 (0.97) does better than default and other schemes. As the performance of the functions is spatially and temporally dependent, based on the region and season of interest, the schemes may judiciously be chosen for the climate simulations. [ABSTRACT FROM AUTHOR]

Given the strong sensitivity of sweet cherry trees to air temperature and the foreseeable warming under future climates, it becomes urgent to analyse spatiotemporal variability of growing conditions in main production areas of China. Here, we estimated changes of three climate metrics, that is, winter chill, heat accumulation and the risk of spring frost events, by using 22 global climate models over the historical and future (represented by central years 2050s and 2085s) time stages. Statistically downscaled daily maximum and minimum temperatures at 14 sites under representative concentration pathway (RCP) 4.5 and 8.5 scenarios were used. The results show a general increase of available winter chill for most sites in Bohai and Lanzhou–Lianyungang Railway line agro‐climatic zones, and the increase in chill accumulation could reach up to 15.2% in these regions for RCP4.5 by 2085 s. However, the most dramatic winter chill decrease is projected to occur in the southwest region under the RCP8.5 scenario by 2085 s. Additionally, the increase rate of heat accumulation during the forcing period shows spatially consistency, and the most pronounced increase is found in the RCP8.5 by 2085 s. In the north region, median heat accumulation increases by 17.5%–21.0% in the 2050 s under RCP4.5. Similar increasing range could be found in the southwest station. High frost risk areas are found in the southwestern region for both baseline and future climate scenarios. Across the 14 study sites, Mengzi and Kunming have the highest meteorologically defined risk of spring damaging frost with accumulated 335 growing degree days (GDDs) and 264 GDDs before the last spring frost event, respectively. Overall, this study provides projected characteristics of site‐specific growing conditions for sweet cherry trees in main production regions. The results could be useful for decision‐making such as selection of appropriate species and varieties in adapting to future warming. [ABSTRACT FROM AUTHOR]

High‐resolution simulation is important for understanding climate change and its impact over regions with complex terrain. Northern Asia (NA) is such a region and sensitive to global warming. However, the performance of the regional model over NA is still unclear. In this study, we conducted a two‐decade‐long high‐resolution (25 km) simulation over NA using the Weather Research and Forecasting (WRF) model forced by the ERA‐Interim reanalysis and then evaluated the performance of WRF against the observations. The results show that the WRF simulation, set up with selections of the physical schemes, can reasonably reproduce the spatial and temporal features of surface air temperature and precipitation over NA. In addition, the WRF simulation can add more regional details on the climate features than the ERA‐Interim data, especially over the NA regions with complex terrain. The spatial‐pattern correlation coefficient of summer precipitation over NA between the WRF simulation and observations is higher than that of the ERA‐Interim data. WRF can reasonably reproduce the annual cycles of precipitation. Moreover, the observed year‐to‐year variations in summer surface air temperature and precipitation over most NA subregions could be generally captured by the WRF. In conclusion, although systemic model bias still exists, the evaluations in this study indicate that WRF shows reasonable performance in a high‐resolution simulation of climate over NA, consequently providing a scientific basis for future simulation studies over the region. [ABSTRACT FROM AUTHOR]

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