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Reliable subseasonal-to-seasonal (S2S) precipitation prediction is highly desired due to the great socioeconomical implications, yet it remains one of the most challenging topics in the weather/climate prediction research area. As part of the Impact of Initialized Land Temperature and Snowpack on Sub-seasonal to Seasonal Prediction (LS4P) project of the Global Energy and Water Exchanges (GEWEX) program, twenty-one climate models follow the LS4P protocol to quantify the impact of the Tibetan Plateau (TP) land surface temperature/subsurface temperature (LST/SUBT) springtime anomalies on the global summertime precipitation. We find that nudging towards reanalysis winds is crucial for climate models to generate atmosphere and land surface initial conditions close to observations, which is necessary for meaningful S2S applications. Simulations with nudged initial conditions can better capture the summer precipitation responses to the imposed TP LST/SUBT spring anomalies at hotspot regions all over the world. Further analyses show that the enhanced S2S prediction skill is largely attributable to the substantially improved initialization of the Tibetan Plateau-Rocky Mountain Circumglobal (TRC) wave train pattern in the atmosphere. This study highlights the important role that initial condition plays in the S2S prediction and suggests that data assimilation technique (e.g., nudging) should be adopted to initialize climate models to improve their S2S prediction.

ABSTRACT The inherent model uncertainty in precipitation projections is found to be more dominant over tropical regions thereby reducing the reliability of using them in climate change impact assessment studies. To address such issues, a subset of well performing global climate models (GCMs) can provide narrow range of possible future outcomes, which can be helpful in formulating mitigation and adaptation strategies that are more targeted and efficient. In this study, climate models are selected based on their performance in simulating relative humidity and vertical velocity since these variables play an important role in precipitation simulation and significantly contribute toward the intermodel spread. The models are evaluated by using various statistical performance measures and ranked using multi‐criteria decision‐making approaches. Finally, based on Jenks natural breaks optimization algorithm, subset of GCMs consisting of ACCESS1.0, ACCESS1.3 and INM‐CM4 models, are considered as the best possible subset for precipitation simulation over tropical land regions. Two observational precipitation datasets are further considered to investigate the effectiveness of the proposed framework. The proposed methodology is validated to be effective in identifying the best climate models since the resulting subset is capable of both capturing observed precipitation and minimizing the uncertainty in future projections. Hence, this methodology can be utilized further for performance evaluation of GCMs focusing different geography and climatic drivers. [ABSTRACT FROM AUTHOR]

Cloud feedback is the largest source of uncertainty in climate sensitivity. This feedback is generally discussed in terms of radiative flux at the top of the atmosphere, but the vertical distribution of the contribution of cloud changes to the top‐of‐atmosphere cloud feedback should be discussed to understand the feedback in greater detail. We have developed a simple analysis method called "vertical profile analysis of cloud feedback," which simply uses radiative flux data from each level of the model. The analysis is applied for typical cloud regimes and the results are discussed together with cloud fraction change profiles. The advantages and disadvantages of the vertical profile analysis, compared with the commonly used International Satellite Cloud Climatology Project (ISCCP) histogram kernel method, are discussed. Our analysis has the advantage of resolving the detailed vertical profiles of the contribution to the top‐of‐atmosphere cloud feedback from the changes in cloud regimes associated with warming, such as reduced cloud cover and upward shifts of the cloud layer in subtropical low‐cloud regions as well as the increased height of the melting layer in tropical deep convection. The main disadvantage is that the vertical profile analysis cannot represent the feedback components sorted by cloud optical thickness as in the ISCCP histogram kernel method. Plain Language Summary: Rising temperature caused by global warming will depend to a large extent on how clouds will change in the future climate. It is therefore important to know in detail how clouds and radiation will change in the future, not only horizontally but also vertically, in order to understand the mechanism. We have developed a simple but useful method for analyzing the vertical profile of changes in radiation and clouds. This detailed information is useful for understanding the mechanism of changes in clouds and radiation. We have compared this analysis method with the conventional method and discussed the advantages and disadvantages of the two methods in detail, based on simulation results. Although our vertical profile analysis provides more detailed vertical profile information, both methods can be used effectively, depending on the purpose of the analysis. Key Points: A simple analysis method is proposed to obtain a detailed vertical profile of cloud feedbackApplications are shown for typical regimes including stratocumulus, shallow convection, mid‐latitude clouds, and tropical deep convectionThe advantages and disadvantages of this analysis method relative to the conventional method are presented [ABSTRACT FROM AUTHOR]

The Lorenz Energy Cycle (LEC) in the Taiwan Earth System Model Version 1 (TaiESM1) historical simulation is calculated and compared with ERA5 to evaluate the model performance from the thermodynamic aspect. The future change of LEC is accessed by comparing the SSP5‐8.5 and historical simulations in TaiESM1. TaiESM1 reasonably simulates the global mean, seasonal cycle, and spatial patterns of the energy reservoirs with larger values in the mean energy components and smaller in the eddy energy components. The energy cycle in TaiESM1 is about 35%–45% stronger than ERA5, except from December to February. The impact of global warming on the LEC is different at the vertical levels. The influence of meridional temperature gradient change is the dominant factor in the intensity of the energy cycle, and the change in static stability only contributes to the lower troposphere. Lifting the tropopause in the tropics increases the meridional temperature gradient and produces more zonal mean potential energy (PM) in the upper troposphere. PM is the primary driver of the LEC and leads to a more active energy cycle in the upper troposphere. As the tropical tropospheric depth increases and the mid‐latitude eddy activities become more active, more (less) energy is stored in the upper (lower) troposphere, and the energy conversion processes tend to become stronger (weaker) in the upper (lower) troposphere. Plain Language Summary: The Lorenz Energy Cycle represents the atmosphere as a heat engine. TaiESM1 is a newly developed climate model. Therefore, we evaluate the model performance and future projection from the thermodynamic aspect to understand the model's overall capability and characteristics. The global mean values of energy reservoirs are in a reasonable range. Energy reservoirs have good spatial patterns and a proper seasonal cycle, but the model has a much stronger energy cycle than reanalysis data due to a stronger meridional temperature gradient. After warming, the energy cycle tends to be stronger (weaker) in the warming scenario's upper (lower) troposphere. This research shows that TaiESM1 is a reliable research tool and provides insights for future improvements for the model development team. Key Points: The newly developed TaiESM1 can simulate atmospheric energy reservoirs regarding global and seasonal means and spatial patternsThe energy cycle in TaiESM1 is 35%–45% stronger than ERA5, mainly contributed by the more active mean flows and eddiesIn the SSP5‐8.5 scenario, the energy cycle intensifies (weakens) in the upper (lower) troposphere due to changes in temperature gradient [ABSTRACT FROM AUTHOR]

Global climate models often simulate atmospheric conditions incorrectly due to their coarse grid resolution, flaws in their dynamics, and biases resulting from parameterization schemes. Here we document a bias in the magnitude and extent of minimum temperature extremes in the CMIP6 model ensemble, relative to ERA5. The bias is present in the southern Cascadia region (i.e., Pacific Northwestern United States and southwestern British Columbia, Canada, spanning from the coast to the Rocky Mountains), with some models showing a bias magnitude in excess of −10°C in the first percentile of daily winter minimum temperature. The sea level pressure pattern for these events is similar in CMIP6 models and ERA5, showing high anomalies in the Northeast Pacific that are indicative of an atmospheric blocking pattern and consequently more northerly flow. Though this atmospheric blocking pattern is typically concurrent with cold winter temperatures across much of North America, Rocky and Cascade mountain ranges prevent the cold air from reaching the southern Cascadia region as confirmed by the observations and reanalysis. Our results suggest that the bias in CMIP6 minimum temperatures is a result of unresolved topography in the Rockies and Cascade mountain ranges, such that the terrain does not adequately block cold air advection from reaching the southern Cascadia region. Plain Language Summary: Global climate models continue to struggle with recreating some of the observed behaviors of our Earth system for a variety of reasons. Here we document one such issue: daily minimum temperatures in Southwestern British Columbia and western Washington that are much colder than are observed. We find that these temperatures occur when extremely cold air is moved from the north into southwestern British Columbia and western Washington. In reality, the Rocky and Cascade mountain ranges prevent this air from reaching western Washington and southwestern British Columbia by acting as barriers. However, in the global climate models these mountain ranges are lower and less jagged, making it easier for cold air to move across them. Key Points: CMIP6 models show a pronounced cold bias in the coldest daily minimum temperatures for the southern Cascadia region of North AmericaWe find no evidence suggestion that differences in large‐scale dynamics are causing the cold bias in the southern Cascadia regionPoorly resolved topography in the CMIP6 models allows excessive cold advection into southern Cascadia during atmospheric blocking events [ABSTRACT FROM AUTHOR]

Global climate models (GCMs) and Earth system models (ESMs) exhibit biases, with resolutions too coarse to capture local variability for fine-scale, reliable drought and climate impact assessment. However, conventional bias correction approaches may cause implausible climate change signals due to unrealistic representations of spatial and intervariable dependences. While purely data-driven deep learning has achieved significant progress in improving climate and earth system simulations and predictions, they cannot reliably learn the circumstances (e.g., extremes) that are largely unseen in historical climate but likely becoming more frequent in the future climate (i.e., climate non-stationarity). This study shows an integrated trend-preserving deep learning approach that can address the spatial and intervariable dependences and climate non-stationarity issues for downscaling and bias correcting GCMs/ESMs. Here we combine the super-resolution deep residual network (SRDRN) with the trend-preserving quantile delta mapping (QDM) to downscale and bias correct six primary climate variables at once (including daily precipitation, maximum temperature, minimum temperature, relative humidity, solar radiation, and wind speed) from five state-of-the-art GCMs/ESMs in the Coupled Model Intercomparison Project Phase 6 (CMIP6). We found that the SRDRN-QDM approach greatly reduced GCMs/ESMs biases in spatial and intervariable dependences while significantly better-reducing biases in extremes compared to deep learning. The estimated drought based on the six bias-corrected and downscaled variables captured the observed drought intensity and frequency, which outperformed state-of-the-art multivariate bias correction approaches, demonstrating its capability for correcting GCMs/ESMs biases in spatial and multivariable dependences and extremes. [ABSTRACT FROM AUTHOR]

The North Adriatic Dense Water (NAddW)—the densest Mediterranean water generated by extreme cooling during wintertime hurricane‐strength winds—drives the thermohaline circulation, ventilates the deep layers, and changes the biogeochemical properties of the Adriatic Sea. However, modeling the dynamical properties of such dense water at the climate scale has been a challenge for decades due to the complex coastal geomorphology of the Adriatic basin not properly reproduced by existing climate models. To overcome these deficiencies, a 31‐year‐long simulation (1987–2017) of the Adriatic Sea and Coast (AdriSC) kilometer‐scale atmosphere‐ocean model is used to analyze the main NAddW dynamical phases (i.e., generation, spreading and accumulation). The study highlights four key results. First, during winter, the NAddW densities are higher in the shallow northern Adriatic shelf than in the deeper Kvarner Bay—where 25%–35% of the overall NAddW are found to be generated—due to a median bottom temperature difference of 2°C between the two generation sites. Second, the NAddW mass transported across most of the Adriatic peaks between February and May, except along the western side of the Otranto Strait. Third, for the accumulation sites, the bottom layer of the Kvarner Bay is found to be renewed annually while the renewal occurs every 1–3 years in the Jabuka Pit and every 5–10 years in the deep Southern Adriatic Pit. Fourth, the NAddW cascading and accumulation is more pronounced during basin‐wide high‐salinity conditions driven by circulation changes in the northern Ionian Sea. Plain Language Summary: The densest water in the Mediterranean Sea known as the North Adriatic Dense Water (NAddW) forms during severe winter wind events in the northern Adriatic. This phenomenon which brings oxygen‐rich waters to the sea bottom, plays a vital role in sustaining life in the Adriatic Sea. However, due to the complex geography of the Adriatic, accurate representation of NAddW is very challenging and requires fine‐resolution atmosphere‐ocean models such as the Adriatic Sea and Coast (AdriSC) model. Here, the AdriSC historical simulation is used to study NAddW generation, spreading and accumulation. The findings reveal that about a third of NAddW is produced in the Kvarner Bay which is a deeper and warmer than the Northern Adriatic shelf. Further, it is found that NAddW spreads over most of the Adriatic between February and May bringing dense waters to the bottom of the accumulation sites, which are renewed annually in the northern Adriatic, up to every 3 years in the middle Adriatic pit, and up to every decade in the Southern Adriatic Pit. Lastly, the study highlights that NAddW spreading and accumulation are significantly enhanced during periods of higher salinity across the Adriatic, a phenomenon driven by water exchanges with the Ionian Sea. Key Points: The kilometer‐scale dense water generation, spreading and accumulation is quantified in the Adriatic Sea during the 1987–2017 periodAbout a third of the dense water is generated within the Kvarner Bay which is deeper and warmer than the well‐studied main generation siteThe bottom layer renewal of the accumulation sites is annual in the Kvaner Bay and up to decadal in the deepest Adriatic pits [ABSTRACT FROM AUTHOR]

We propose the lag‐1 autocorrelation of daily precipitation as a simple diagnostic of tropical precipitation variability in climate models. This metric generally has a relatively uniform distribution of positive values across the tropics. However, selected land regions are characterized by exceptionally low autocorrelation values. Low values correspond to the dominance of high frequency variance in precipitation, and specifically of high frequency convectively coupled equatorial waves. Consistent with previous work, we show that CMIP6 climate models overestimate the autocorrelation. Global kilometer‐scale models capture the observed autocorrelation when deep convection is explicitly simulated. When a deep convection parameterization is used, though, the autocorrelation increases over land and ocean, suggesting that land surface‐atmosphere interactions are not responsible for the changes in autocorrelation. Furthermore, the metric also tracks the accuracy of the representation of the relative importance of high frequency and low frequency convectively coupled equatorial waves in the models. Plain Language Summary: Rainfall in the tropics is influenced by many atmospheric processes that depend on geographic location. We use the lag‐1 autocorrelation as a metric for the day‐to‐day persistence of rainfall. We find that rainfall is very persistent in most parts of the tropics with a few exceptions over land, for example, the Sahel, where high frequency rainfall events dominate. Our results show that models with a horizontal resolution of a few kilometers reproduce the autocorrelation, in contrast to coarser climate models. We also analyze atmospheric waves and find that they are important for the autocorrelation pattern in the observations and the simulations. Key Points: The lag‐1 autocorrelation pattern of daily precipitation in the tropics is robust across different observation‐based data setsThe lag‐1 autocorrelation reflects the relative variance of high frequency and low frequency convectively coupled equatorial wavesKilometer‐scale models capture the observed autocorrelation, but models with parameterized deep convection overestimate it [ABSTRACT FROM AUTHOR]

Climate models enable an analysis of observation data related to global warming and climate changes, and they allow making predictions for near the future progress of climate changes. In this study, basics of the climate model, which is used in the analysis of global warming and climate change topics, and the analyses that can be carried out by using these models, are reviewed. Simplified energy balance model considers the balance state between the incoming solar energy from the sun and the radiation energy outgoing from the earth to space. In this perspective, the model allows considering the factors, which can influence global warming, as radiative forces that are acting on the balance state. Thus enabling simplified analyzes of climate changes based on the energy budget even though the climate changes depend on very complex and dynamically changing meteorological and geological factors. In this study, within the framework of basic radiation laws, such as Stefan-Boltzmann and Wien Displacement, the absorption and reflection rates of the solar radiation by the earth, and the atmospheric window concepts, which provide energy escape from the earth's atmosphere to the space within the long-wavelength of the electromagnetic spectrum, were reviewed. In this context, the radiative forcing due to the Albedo effect and the radiative forcing effects due to the accumulation of CO2 molecules in the atmosphere, which have the potential to alter depending on human activities, were explained. Illustrative examples and observation data-based calculations and analyzes are presented to make basic concepts and presented models easy-to-understand. This article is intended to be a tutorial that explains some basic concepts on the topics of global warming and climate change. [ABSTRACT FROM AUTHOR]

Soil salinization is a manifestation of land degradation caused by the combined effect of natural processes and human activities. This issue is particularly common in irrigation districts, where various factors exacerbated by frequent human interventions influence soil salinity. To prevent soil salinization, it is essential to have a comprehensive understanding of soil water-salt transport mechanisms in irrigation districts with frequent human activities. After reviewing the factors that influence soil water-salt transport in irrigation districts and the characteristics and applications of water-salt transport model, we propose a potential direction for simulating water-salt transport in irrigation areas. The formation of salinization in irrigation areas is closely related to natural factors such as climatic aridity, terrain, seasonal soil freeze-thaw cycles, groundwater salinity, and parent material of soils, as well as anthropogenic factors such as irrational irrigation practices, agricultural methodologies, and fertilization regimes. To understand the process of water- salt transport, soil water- salt transport models are effective tools. The commonly used models for water-salt transport include water-salt balance models, physical models, and statistical models. They are mainly used to optimize irrigation water-saving regimes and drainage management for salinity control at the field scale. However, applying these models at the regional scale presents challenges due to the difficulty of obtaining observational data to calibrate and validate models that involve soil water-salt transport processes and crop growth. The rapid development of modern large-scale irrigation districts has changed the spatiotemporal distribution of soil water-salt transport. However, the continuous improvements of the model and the rapid development of computer technology have provided possibilities for investigating the spatiotempo-ral evolution mechanisms of water-salt transport in large-scale irrigation districts. Future models should focus on strengthening the soil water-salt transport mechanisms based on ecological safety. It is recommended to develop a multi-process water-salt transport model that is coupled with a climate model or an economic model. [ABSTRACT FROM AUTHOR]

This study attempts to answer the question: If global warming were to increase by 1.5°, 2° and 3°C (compared to the pre-industrial baseline of 1850–1900), at what rate would South Asia be warming? A basis for influencing climate policy-making in India and countries part of the South Asian Association for Regional Cooperation (i.e., SAARC, including Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan and Sri Lanka) has been put forth. IPCC provides tools for analyzing mean near-surface air temperature data using CMIP6 (global climate model) and CORDEX-SA (regional climate model). This study reports the generated outputs from both projection models by analyzing and comparing data to better understand climate change in the region. While CMIP6 provided a coherent overall picture of South Asia, CORDEX-SA projected the average temperature variations of Indian states better. The decreasing order in which Indian states are expected to be affected by average temperature rise is Himalayan states from north to north-east; northwestern and western states along the Pakistan border; western coastline along the Arabian Sea; southern states along the eastern coastline (Bay of Bengal); central and eastern states. Both models projected the temperature rise in the Indian state of West Bengal to be the highest in the coming decades. Among SAARC nations, Afghanistan was projected to be the most affected and Bangladesh the least, following the same pattern of temperature variation as the Indian regions neighbouring them. Further, it was found that a mean temperature rise of 1.5° and 3°C would occur over South Asia in the timeframe of 2035–2038 and 2062–2068, respectively. Regarding monthly variation, temperature rise is expected to affect the period of January (average 1.35°C rise) to April (average 1.05°C rise) the most. [ABSTRACT FROM AUTHOR]

The earliest Asian summer monsoon onset (SMO) occurs in the Bay of Bengal (BoB), heralding the coming of the rainy season. In late April or early May, the strong sea surface temperature (SST) diurnal variation accompanied by ocean surface warming triggers the SMO. However, this observed diurnal cycle intensity cannot be reasonably simulated by state‐of‐the‐art climate models, resulting in a spurious delayed SMO. To address this issue, the SST diurnal cycle parameterized by a diagnostic sublayer scheme was incorporated into a climate model named FIO‐ESM v2.0. The large diurnal amplitude of SST contributes to surface warming and changes atmospheric circulation. Consequently, the high‐pressure anomaly at high levels and an inverted trough at low levels promote more convective activity, triggering an earlier SMO. Our findings improve the ability of climate models in simulating the evolution of the Asian monsoon system. Plain Language Summary: Asian summer monsoon onset (SMO) accompanied by the coming of the rainy season has a significant impact on the lives and livelihoods of more than half of the world's population. However, state‐of‐the‐art climate models struggle to capture the major features of SMO accurately. The simulated diurnal amplitude of sea surface temperature (SST) is much smaller compared with observation, resulting in a cold bias in SST and a delayed SMO in the Bay of Bengal (BoB). Here, we incorporated a diagnostic sublayer parameterization into the climate model to account for the effect of a strong diurnal cycle. The SST in the BoB increases, and more convective activities are promoted in response to the warmer underlying surface. Changes in monsoon circulation create favorable environmental conditions for an advanced SMO. Considering the effect of strong diurnal variation provides a solution to reduce common biases of the climate model in simulating SMO. Key Points: The spurious delayed Asian summer monsoon onset (SMO) is improved by including the effect of large sea surface temperature (SST) diurnal amplitude in a climate modelStrong diurnal variation of SST contributes to upper ocean warming in the Bay of Bengal before the Asian SMOLarge‐scale circulation with favorable conditions for convection activity in response to ocean warming triggers an advanced monsoon onset [ABSTRACT FROM AUTHOR]

In August 2017, a smoke plume from wildfires in British Columbia and the Northwest Territories recirculated and persisted over northern Canada for over two weeks. We compared a full‐factorial set of NASA Goddard Institute for Space Studies ModelE simulations of the plume to satellite retrievals of aerosol optical depth and carbon monoxide, finding that ModelE performance was dependent on the model configuration, and more so on the choice of injection height approach, aerosol scheme and biomass burning emissions estimates than to the choice of horizontal winds for nudging. In particular, ModelE simulations with free‐tropospheric smoke injection, a mass‐based aerosol scheme and comparatively high fire NOx emissions led to unrealistically high aerosol optical depth. Using paired simulations with and without fire emissions, we estimated that for 16 days over an 850,000 km2 region, the smoke decreased planetary boundary layer heights by between 253 and 547 m, decreased downward shortwave radiation by between 52 and 172 Wm−2, and decreased surface temperature by between 1.5°C and 4.9°C, the latter spanning an independent estimate from operational weather forecasts of a 3.7°C cooling. The strongest surface climate effects were for ModelE configurations with more detailed aerosol microphysics that led to a stronger first indirect effect. Plain Language Summary: Smoke from biomass burning is known to have effects on surface weather. We used the NASA GISS ModelE to estimate these effects for a large 2017 smoke plume over northern Canada that persisted for two weeks. We first found that the height of the smoke release at the source was the most important factor influencing agreement between ModelE and satellite retrievals of aerosols and carbon monoxide, and that specific, plausible configurations of the model led to unrealistically high aerosol amounts. By comparing simulations with and without fire, we estimated a 16‐day cooling over a 850,000 km2 region of between 1.5°C and 4.9°C, depending on the model configuration. Key Points: We captured the overall pattern and magnitude of a large 2017 smoke plume over Canada with the NASA GISS ModelEOf the sixteen plausible model configurations tested under a full‐factorial design, two with higher NOx emissions, free‐tropospheric smoke release and mass‐based aerosols led to unrealistically high aerosol optical depthOver an 850,000 km2 region, we estimated a 16‐day surface cooling of between 1.5°C and 4.9°C [ABSTRACT FROM AUTHOR]

Nitrogen is one of the most abundant chemical elements on the Earth and plays an important role in global environmental change. Leading Earth system models include coupled carbon and nitrogen cycle modules of varying complexity, but the INM RAS climate model family has not yet included an explicit N-cycle description. This paper presents a parameterization of the terrestrial N-cycle based on a simplification of the JULES-CN model, adapted for coupled use with the INM-CM land C-cycle module. Numerical simulations were carried out with a standalone carbon cycle model with nitrogen feedback disabled and enabled versions for the period 1850–2100. The simulated global pools show good agreement with results of other models with an implemented N-cycle. Taking into account the N-limitation of the C-cycle, the modelled dynamics of total carbon storage in terrestrial ecosystems from 1850 to the mid-20th century is specified. [ABSTRACT FROM AUTHOR]

Machine learning (ML)‐based parameterizations have been developed for Earth System Models (ESMs) with the goal to better represent subgrid‐scale processes or to accelerate computations. ML‐based parameterizations within hybrid ESMs have successfully learned subgrid‐scale processes from short high‐resolution simulations. However, most studies used a particular ML method to parameterize the subgrid tendencies or fluxes originating from the compound effect of various small‐scale processes (e.g., radiation, convection, gravity waves) in mostly idealized settings or from superparameterizations. Here, we use a filtering technique to explicitly separate convection from these processes in simulations with the Icosahedral Non‐hydrostatic modeling framework (ICON) in a realistic setting and benchmark various ML algorithms against each other offline. We discover that an unablated U‐Net, while showing the best offline performance, learns reverse causal relations between convective precipitation and subgrid fluxes. While we were able to connect the learned relations of the U‐Net to physical processes this was not possible for the non‐deep learning‐based Gradient Boosted Trees. The ML algorithms are then coupled online to the host ICON model. Our best online performing model, an ablated U‐Net excluding precipitating tracer species, indicates higher agreement for simulated precipitation extremes and mean with the high‐resolution simulation compared to the traditional scheme. However, a smoothing bias is introduced both in water vapor path and mean precipitation. Online, the ablated U‐Net significantly improves stability compared to the non‐ablated U‐Net and runs stable for the full simulation period of 180 days. Our results hint to the potential to significantly reduce systematic errors with hybrid ESMs. Plain Language Summary: Due to their computational costs, it is currently not feasible to run more accurate high‐resolution climate models on a global domain on climate (century) time‐scales. However, high‐accuracy climate simulations are needed for more robust and detailed projections of our future climate. Here, we develop and evaluate various machine learning‐based convection parameterizations learned on reconstructed and coarse‐grained high‐resolution subgrid fluxes to solve this problem, and benchmark their performance. The data set is chosen from simulations of the Icosahedral Non‐hydrostatic modeling framework (ICON) in a realistic setting of the tropical Atlantic and at storm‐resolving resolutions. We focus only on convective subgrid fluxes that are isolated from other components. We improve the best ML algorithms further by excluding variables that cause unphysical correlations. Finally, we explain the learned relations of the best data‐driven schemes based on physical process understanding, test their performance when coupled to the ICON model, and achieve stable coupled simulations for 180 days as well as improved precipitation predictions. Key Points: We train/benchmark machine learning models on convective fluxes derived from realistic coarse‐grained data of storm‐resolving simulationsShapley values reveal that the best offline model, a U‐Net, learns non‐causal links to precipitation and shows poor online performanceA model, without non‐causal precipitation connections, runs more stable coupled to ICON and indicates better precipitation predictions [ABSTRACT FROM AUTHOR]

Climate change and climate variation influence the occurrence of natural disasters, especially water-related disasters. Landslides are a serious hydrogeohazard in Thailand. Hence, this study aims to project the spatial changes in the landslide susceptibility area in the Nakhon Si Thammarat province of southern Thailand. The study utilizes five climate models of CMIP6 (INM-CM4-8, IITM-ESM, MPI-ESM1-2-LR, CNRM-ESM2-1 and CNRM-CM6.-1) and conducts analyses based on 2 shared socioeconomic pathways (SSPs) of tier 1, which include SSP2-4.5 and SSP5-8.5. The linear scaling bias correction technique is applied to adjust for any biases in the climate models, and the model results are combined using the ensemble mean method. To achieve the study aims, a GIS-aided physically based landslide susceptibility model is used to predict changes in spatial aspects of the landslide susceptibility area. The analysis predicted spatial changes in the landslide susceptibility area over the next 20 years (2023 to 2042). According to the analysis, the results showed that the maximum increase in landslide susceptibility areas is approximately 29%, and the maximum decrease in landslide susceptibility areas is approximately 25% under SSP2-4.5. For SSP5-8.5, the maximum increase in landslide susceptibility areas is approximately 31%, and the maximum decrease in landslide susceptibility areas is approximately 35%. Moreover, the fluctuation of rainfall strongly influences the increase or decrease in landslide susceptibility areas. However, the landslide-prone areas remain located in areas that have experienced landslides in the past. [ABSTRACT FROM AUTHOR]

The internal variability pertains to fluctuations originating from processes inherent to the climate component and their mutual interactions. On the other hand, forced variability delineates the influence of external boundary conditions on the physical climate system. A methodology is formulated to distinguish between internal and forced variability within the surface air temperature. The noise‐to‐noise approach is employed for training a neural network, drawing an analogy between internal variability and image noise. A large training data set is compiled using surface air temperature data spanning from 1901 to 2020, obtained from an ensemble of Atmosphere‐Ocean General Circulation Model simulations. The neural network utilized for training is a U‐Net, a widely adopted convolutional network primarily designed for image segmentation. To assess performance, comparisons are made between outputs from two single‐model initial‐condition large ensembles, the ensemble mean, and the U‐Net's predictions. The U‐Net reduces internal variability by a factor of four, although notable discrepancies are observed at the regional scale. While demonstrating effective filtering of the El Niño Southern Oscillation, the U‐Net encounters challenges in capturing the changes in the North Atlantic Ocean. This methodology holds potential for extension to other physical variables, facilitating insights into the climate change triggered by external forcings over the long term. Plain Language Summary: To anticipate future climate change, it is crucial to detect and understand the impacts of human activities. However, distinguishing the effects of anthropogenic forcing from natural climate variations in observational data is challenging. Natural climate variability, known as internal variability, arises from the chaotic nature of atmospheric and oceanic circulation, and from the interactions among the ocean, atmosphere, and land. Here, a novel approach is introduced to distinguish the changes caused by human activities from internal variability. It is applied to the surface air temperature evolution from 1901 to 2020. This method uses an artificial neural network designed to separate the internal from the human‐induced variability. An unprecedented number of climate model simulations are used, enabling precise estimation of human‐forced variability in these climate models. The spatio‐temporal variations are distinguished by applying a well‐known methodology previously used to remove noise from images. The method's performance is evaluated, revealing errors regarding the internal variability that are typically one‐fourth of the actual variations. Regions with important internal variability or with low agreement among models exhibit the largest errors. Overall, the skills are comparable to other existing approaches, but improvements are anticipated. Key Points: We present a new method to separate the forced and internal variability of the surface air temperatureWe utilize a U‐Net trained with global climate models outputs and implement a noise to noise methodology to eliminate internal variabilityThe results are assessed through the utilization of very large ensemble simulations of two distinct climate models [ABSTRACT FROM AUTHOR]

Various studies identified possible drivers of extremes of Arctic sea ice reduction, such as observed in the summers of 2007 and 2012, including preconditioning, local feedback mechanisms, oceanic heat transport and the synoptic- and large-scale atmospheric circulations. However, a robust quantitative statistical analysis of extremes of sea ice reduction is hindered by the small number of events that can be sampled in observations and numerical simulations with computationally expensive climate models. Recent studies tackled the problem of sampling climate extremes by using rare event algorithms, i.e., computational techniques developed in statistical physics to reduce the computational cost required to sample rare events in numerical simulations. Here we apply a rare event algorithm to ensemble simulations with the intermediate complexity coupled climate model PlaSim-LSG to investigate extreme negative summer pan-Arctic sea ice area anomalies under pre-industrial greenhouse gas conditions. Owing to the algorithm, we estimate return times of extremes orders of magnitude larger than feasible with direct sampling, and we compute statistically significant composite maps of dynamical quantities conditional on the occurrence of these extremes. We find that extremely low sea ice summers in PlaSim-LSG are associated with preconditioning through the winter sea ice-ocean state, with enhanced downward longwave radiation due to an anomalously moist and warm spring Arctic atmosphere and with enhanced downward sensible heat fluxes during the spring-summer transition. As a consequence of these three processes, the sea ice-albedo feedback becomes active in spring and leads to an amplification of pre-existing sea ice area anomalies during summer. [ABSTRACT FROM AUTHOR]

Despite ongoing climate change and warming, extreme cold events still negatively affect human society. Since cold air incursions are related to specific circulation patterns, the main aims of this study are (1) to validate how well current EURO-CORDEX regional climate models (RCMs) reproduce these synoptic links and (2) to assess possible future changes in atmospheric circulation conducive to cold events. Using anomalies of daily minimum temperature, we define cold days (CDs) in central Europe and analyse their characteristics over the historical (1979−2020) and future (2070−2099) periods. We classify wintertime atmospheric circulation by applying a novel technique based on Sammon mapping to the state-of-the-art ERA5 reanalysis output. We discover that circulation types (CT) conducive to CDs are characterised by easterly advection and/or clear-sky anticyclonic conditions. While the RCM ensemble generally reproduces these synoptic links relatively well, we observe biases in the occurrence of CDs in individual simulations. These biases can be attributed to inadequately reproduced frequencies of CTs conducive to CDs (primarily propagating from driving data), as well as to deviations in the conduciveness within these CTs (primarily originating in the RCMs). Interestingly, two competing trends are identified for the end of the twenty-first century: (1) most RCMs project an increased frequency of CTs conducive to CDs, suggesting more frequent CDs, while (2) the same CTs are projected to warm faster compared to their counterparts, suggesting weaker CDs. The interplay between these opposing trends contributes to the overall uncertainty surrounding the recurrence and severity of future winter extremes in central Europe. [ABSTRACT FROM AUTHOR]

The acceleration of Greenland ice sheet melting over the past decades is raising concern regarding the impacts on ocean circulation in the North Atlantic and Arctic regions. Global climate models struggle to assess these impacts as they do not include a realistic amount of meltwater from the Greenland ice sheet. Using an extended observation-based dataset of runoff and solid ice discharge for the recent historical period (1920–2019), we force the EC-Earth3 climate model following the ensemble approach and protocol of a previous study using a different model. We observe a slight increase of the ensemble mean AMOC with a large spread in the response: 0.20 ± 0.81 Sv for the maximum AMOC at 45 ∘ N. We notice that members with a strong initial AMOC state (18 Sv) show a strengthening of the AMOC, while members with intermediate AMOC strength between 16 and 18 Sv are not affected by the freshwater forcing. Weaker initial AMOC members respond with a mean weakening of - 0.21 ± 0.58 Sv of the AMOC. The AMOC ensemble spread is reduced by half in the freshwater forced ensemble at the end of the experiment and the negative trend is stronger compared to historical simulations without the forcing. This suggests the possible ability of the freshwater to constrain AMOC variability on multi-decadal time-scales. Comparison of the two ten member ensembles with ORAS5 reanalysis shows a reduction of the surface temperature and salinity biases in the freshwater forced ensemble in key regions such as the North Atlantic subpolar gyre and the Beaufort Gyre relative to the historical simulations. Recent trends are also more aligned with reanalysis in those regions. Arctic Ocean Atlantic Water subsurface core temperature is also closer to reanalysis but still strongly biassed. Members with a high AMOC initial state display an unrealistic Atlantic water layer core depth. [ABSTRACT FROM AUTHOR]

Present study examined the overall performance of 12 CMIP6 GCMs for rainfall, maximum and minimum temperatures for rice crop-growing seasons i.e., Boro (January to May) and Kharif (June to October) over the new alluvial zone of West Bengal. A wide range of indices i.e., index of agreement, error indices and bias estimators were utilized to put more confidence on the results. Results indicated that CMIP6 models were able to reproduce observed mean climatology and inter-annual variability of maximum and minimum temperature adequately for both seasons while a smaller number of models (3-4 models) out of a total of 12 GCM-CMIP6 models showed satisfactory performance for rainfall. The ranks assigned to the models revealed that CNRM–ESM2–1 was the best-performing model for Kharif and MRI-ESM2-0 showed the highest skill for Boro. ACCESS-CM2 and MPI-ESM1-2-LR performed worst for Kharif and Boro seasons respectively. Further, CNRM–ESM2–1 and MRIESM2-0 were used to project the future climate for Kharif and Boro seasons respectively under both moderate (SSP2-4.5) and extreme scenarios (SSP5-8.5). Higher warming was projected during Boro season than Kharif. Projections revealed increasing rainfall during Kharif season but decreasing rainfall in Boro season in both the moderate and extreme future scenarios. [ABSTRACT FROM AUTHOR]

This study examined climate change dynamics in the lower Mahanadi River basin by integrating observed and climate model data. Historical precipitation and temperature data (1979–2020) from the India Meteorological Department (IMD) and monthly climate model data from the CORDEX-SMHI-MIROC model via the Earth System Grid Federation (ESGF) are utilized. Four machine learning models (Fbprophet, Holt-Winters, LSTM RNN, and SARIMAX) are applied to forecast precipitation, Tmax, and Tmin, and are compared across different representative concentration pathway (RCP 2.6, 4.5, and 8.5) scenarios. Diverse trajectories emerge, highlighting potential shifts in precipitation and temperature dynamics over near, mid, and far-term intervals. Fbprophet and SARIMAX are identified as superior models through performance evaluation metrics (R2, RMSE, r, P-bias, and NSE). Spatial analysis using ArcGIS and IDW interpolation reveals spatial variations in climate projections, aiding in visualizing future climate trends within the Mahanadi Basin. This study acknowledges limitations such as historical data uncertainties, socio-economic indicators, and unpredictable RCP trajectories, introducing a novel method to integrate machine learning with climate model data for assessing reliability. It also explores anticipated shifts in monthly precipitation and temperature patterns, providing insights into future climate variations.

Abstract We propose the lag‐1 autocorrelation of daily precipitation as a simple diagnostic of tropical precipitation variability in climate models. This metric generally has a relatively uniform distribution of positive values across the tropics. However, selected land regions are characterized by exceptionally low autocorrelation values. Low values correspond to the dominance of high frequency variance in precipitation, and specifically of high frequency convectively coupled equatorial waves. Consistent with previous work, we show that CMIP6 climate models overestimate the autocorrelation. Global kilometer‐scale models capture the observed autocorrelation when deep convection is explicitly simulated. When a deep convection parameterization is used, though, the autocorrelation increases over land and ocean, suggesting that land surface‐atmosphere interactions are not responsible for the changes in autocorrelation. Furthermore, the metric also tracks the accuracy of the representation of the relative importance of high frequency and low frequency convectively coupled equatorial waves in the models.

Many countries and regions across the globe broke their surface temperature records in recent years. Recent crippling heat waves swept across the Earth, further sparking concerns about the impending arrival of “tipping points” later in the 21st century. This study analyzes observed global surface temperature trends in three target latitudinal regions: the Arctic Circle, Tropics, and the Antarctic Circle. We show that global warming is accelerating unevenly across the planet, with the Arctic warming at more than three times the average rate of our world. We also analyzed the reliability of latitude-dependent surface temperature simulations from a suite of Coupled Model Intercomparison Project Phase 6 (CMIP6) models and their multi-model mean (MMM) by comparing their outputs to observational data sets. We selected the best-performing models based on their statistical abilities to reproduce historical, latitude-dependent values adapted from these data sets. The surface temperature projections were calculated from ensemble simulations of the Shared Socioeconomic Pathway 2–4.5 (SSP2–4.5) by the selected CMIP6 models. We estimate the calendar years of when surface temperatures will increase by 1.5, 2.0, and 2.5°C relative to the preindustrial period, both globally and in the three target regions. Our results reaffirm a dramatic, upward trend in projected surface temperatures, with unprecedented acceleration in the Arctic Circle, which could lead to catastrophic consequences across the Earth. Further studies are necessary to determine the most efficient solutions to reduce global warming acceleration and maintain a low SSP, both globally and regionally.

The Atlantic meridional overturning circulation (AMOC) has caused significant climate changes over the past 90 000 years. Prior work has hypothesized that these millennial-scale climate variations effected past and contemporary biodiversity, but the effects are understudied. Moreover, few biogeographic models have accounted for uncertainties in palaeoclimatic simulations of millennial-scale variability. We examine whether refuges from millennial-scale climate oscillations have left detectable legacies in the patterns of contemporary species richness in eastern North America. We analyse 13 palaeoclimate estimates from climate simulations and proxy-based reconstructions as predictors for the contemporary richness of amphibians, passerine birds, mammals, reptiles and trees. Results suggest that past climate changes owing to AMOC variations have left weak but detectable imprints on the contemporary richness of mammals and trees. High temperature stability, precipitation increase, and an apparent climate fulcrum in the southeastern United States across millennial-scale climate oscillations aligns with high biodiversity in the region. These findings support the hypothesis that the southeastern United States may have acted as a biodiversity refuge. However, for some taxa, the strength and direction of palaeoclimate-richness relationships varies among different palaeoclimate estimates, pointing to the importance of palaeoclimatic ensembles and the need for caution when basing biogeographic interpretations on individual palaeoclimate simulations. This article is part of the theme issue 'Ecological novelty and planetary stewardship: biodiversity dynamics in a transforming biosphere'. [ABSTRACT FROM AUTHOR]

All climate models project a weakening of the Atlantic Meridional Overturning Circulation (AMOC) strength in response to greenhouse gas forcing. However, the climate impacts of the AMOC decline alone cannot be isolated from other drivers of climate change using existing Coupled Model Intercomparison Project simulations. To address this issue, we conduct idealized experiments using the EC‐Earth3 climate model. We compare an abrupt 4×CO2 simulation with the same experiment, except we artificially fix the AMOC strength at preindustrial levels. With this design, we can formally attribute differences in climate change impacts between these two experiments to the AMOC decline. In addition, we quantify the state‐dependence of AMOC impacts by comparing the aforementioned experiments with a preindustrial simulation in which we artificially reduce the AMOC strength. Our findings demonstrate that AMOC decline impacts are state‐dependent, thus understanding AMOC impacts on future climate change requires targeted model experiments. Plain Language Summary: Climate models predict that the Atlantic Ocean's major circulation system, known as the Atlantic Meridional Overturning Circulation (AMOC), will weaken during the 21st century. This weakening could have significant impacts on the climate. However, it is challenging to isolate the AMOC's effects because other factors, such as rising greenhouse gas levels, also affect the climate. To better understand the AMOC's role, in this study we use a climate model to conduct numerical experiments. We compare a simulation of the preindustrial climate with one in which we artificially decrease the strength of the AMOC. Then, we compare the preindustrial climate with two forced simulations: one with a fourfold increase in atmospheric carbon dioxide, where the AMOC weakens as expected, and another where we keep the AMOC at its preindustrial strength despite higher CO2 levels. By comparing these experiments, we determine that the impacts of an AMOC decline depend on the background climate state. This research demonstrates that ad‐hoc model experiments are needed to understand the impacts of a weakened AMOC in a changing climate. Key Points: We present new idealized experiments to assess the influence of a weakened Atlantic Meridional Overturning Circulation (AMOC) on future climate changeWe use the EC‐Earth3 climate model to carry out experiments imposing abrupt 4×CO2 forcing but fixing the AMOC strength, and then we compare them with preindustrial water hosing experimentsWe find that AMOC impacts on temperature and precipitation depend on the background climate state [ABSTRACT FROM AUTHOR]

Heavy precipitation, often associated with weather phenomena such as tropical cyclones, extratropical cyclones (ETCs), atmospheric rivers (ARs), and mesoscale convective systems (MCSs), can cause significant socio‐economic loss. In this study, we apply atmospheric feature trackers to quantify the contributions of these storm types in observational data sets and climate model short‐range hindcasts. We generate a global hourly storm data set at 0.25° spatial resolution covering 2006–2020, based on the tracking results from TempestExtremes and Python FLEXible object TRacKeR. Our analyses show that these four storm types account for 67% of global annual mean precipitation and 82% of top 1% precipitation extremes, with MCSs mainly over the tropics, and ARs and ETCs over the midlatitudes. The percentage of precipitation contributions from these storms also show strong seasonality over many geographical locations. We further apply the tracking results to the Energy Exascale Earth System Model (E3SM) short‐range hindcasts and evaluate how well these storms are simulated. The evaluation show that E3SM, with ∼1° resolution, significantly underestimates storm‐associated precipitation totals and extremes, especially for MCSs in the tropics. Our analysis also suggests that model fails to capture the correct mean diurnal phases and amplitude of MCS precipitation. This phenomenon‐based approach provides a better understanding of precipitation characteristics and can lead to enhanced model evaluation by revealing underlying problems in model physics related to precipitation processes associated with the heavy‐precipitating storms. Plain Language Summary: Earth system models are immensely useful for understanding how the climate system works. However, it is important to recognize that they have limitations including wet or dry precipitation biases caused by complicated factors. On the other hand, different storm types contribute to regional precipitation differently under varying conditions. Attributing precipitation to sourced storm types is a new approach to understanding model precipitation biases. Here we build a new data set to study precipitation from several storm types including tropical cyclones, extratropical cyclones, atmospheric rivers, and mesoscales convection systems. We find that these four storm types explain 67% of global mean precipitation and 82% of extreme precipitation. We also demonstrate the application of this tool for understanding biases in modeled precipitation. The future application of this new tool will shed light on the causes of modeled precipitation biases and underlying model problems. Key Points: A global observational database for tracking four major heavy‐rain storm systems is established for the 2006–2020These four storm systems contribute to over 67% of global annual mean precipitation and over 80% of top 1% precipitation extremesClimate model short‐range hindcasts underestimate the storm‐associated precipitation, especially for heavy precipitation extremes [ABSTRACT FROM AUTHOR]

Few studies have investigated the spatial patterns of the air temperature urban heat island (AUHI) and its controlling factors. In this study, the data generated by an urban climate model were used to investigate the spatial variations of the AUHI across China and the underlying climate and ecological drivers. A total of 355 urban clusters were used. We performed an attribution analysis of the AUHI to elucidate the mechanisms underlying its formation. The results show that the midday AUHI is negatively correlated with climate wetness (humid: 0.34 K; semi-humid: 0.50 K; semi-arid: 0.73 K). The annual mean midnight AUHI does not show discernible spatial patterns, but is generally stronger than the midday AUHI. The urban–rural difference in convection efficiency is the largest contributor to the midday AUHI in the humid (0.32 ± 0.09 K) and the semi-arid (0.36 ± 0.11 K) climate zones. The release of anthropogenic heat from urban land is the dominant contributor to the midnight AUHI in all three climate zones. The rural vegetation density is the most important driver of the daytime and nighttime AUHI spatial variations. A spatial covariance analysis revealed that this vegetation influence is manifested mainly through its regulation of heat storage in rural land. [ABSTRACT FROM AUTHOR]

General circulation models (GCMs) and their downscaled regional‐scale equivalents have been important tools for climate‐change studies. However, there has been limited assessment of the performance of GCMs and downscaled models in simulating extreme storms in temperate coastal environments. This study assesses the model characterization of extreme storms on the heavily populated coast of south‐east Australia. Twenty‐year average recurrence interval (ARI) storm intensities derived from generalized extreme value (GEV) distributions based on observed and large‐scale atmospheric model data are compared. Changes in extreme storms from past climate to a high‐emission future scenario are also investigated. Simulations of storm minimum surface pressures compared favourably with measured data. Both the GCMs and downscaled models reproduced the observed decrease with increasing latitude along the coast in the 20 year ARI of minimum surface pressure. Both indicated that the minimum storm surface pressure should change negligibly in a high‐emission future. Although the models underestimated the maximum daily precipitation significantly, models are improving significantly with CMIP epoch and downscaling. In the high‐emission future scenario, the GCMs and NARCliM projected the 20 year ARI maximum daily precipitation would increase in the order of 25%. GCMs and the corresponding downscaled products presently do not represent the extreme value distributions of historical wind speed data well, overestimating at smaller values of ARI and significantly underestimating in larger values of ARI. Significant changes in the magnitude of the 20 year ARI maximum daily‐average onshore wind speed are not projected for the high‐emission future. [ABSTRACT FROM AUTHOR]

Despite many years of extensive research, the evolution of Tropical Cyclone (TC) activity in our changing climate remains uncertain. This is partly because the answer to that question relies primarily on climate simulations with horizontal resolutions of a few tens of kilometers. Such simulations have only recently become accessible for most modeling centers, including the Institut Pierre-Simon Laplace (IPSL). Using recent numerical developments in the IPSL model, we perform a series of historical atmospheric-only simulations that follow the HighResMIP protocol. We assess the impact of increasing the resolution from ∼ 200 to 25 km on TC activity. In agreement with previous work, we find a systematic improvement of TC activity with increasing resolution with respect to the observations. However, a clear signature of TC frequencies convergence with resolution is still lacking. Cyclogenesis geographical distributions also improve at the scale of individual basins. This is particularly true of the North Atlantic, where the agreement with the observed distribution is impressive at 25 km. In agreement with the observations, TC activity correlates with the large-scale environment and ENSO in that basin. By contrast, TC frequencies remain too small in the Western North Pacific at 25 km, where significant biases of humidity and vorticity are found compared to the reanalysis. Despite the few minor weaknesses we identified, our results demonstrate that the IPSL model is a suitable tool for studying TCs on climate time scales. This work thus opens the way for further studies contributing to our understanding of TC climatology. [ABSTRACT FROM AUTHOR]

Stable water isotopes in inland Antarctic ice cores are powerful paleoclimate proxies; however, their relationship with dynamical atmospheric circulations remains controversial. Using a water isotope climate model (MIROC5‐iso), we assessed the influence of the Last Glacial Maximum (LGM; ∼21,000 years ago) sea surface temperatures (SST) and sea ice (SIC) on Antarctic precipitation isotopes (δ18Op) through atmospheric circulation. The results revealed that the synoptic circulation mostly maintained southward moisture transport, reaching inland Antarctica. The steepened meridional SST gradient in the mid‐latitudes increased δ18Op in inland Antarctica with the enhanced baroclinic instability and synoptic moisture transport. In contrast, expanded SIC distribution decreased δ18Op over Antarctica by enhanced preferential removal of heavy isotopes during vapor transport due to the increased transport distance and enhanced surface cooling. These findings propose to use Antarctic ice cores to describe the southern hemisphere atmospheric circulation, represented by the westerly jets, during the LGM and other past climates. Plain Language Summary: Stable water isotopes are widely used to reconstruct past variations of Earth's climate, such as the temperature in Antarctica during the Last Glacial Maximum (LGM) ∼21,000 years ago. This is an essential period for the climate community, given the order of magnitude of the temperature change between the LGM and today, which is similar to that of today's warming. However, the relationship between stable water isotopes and temperature is still subject to debate because of the influence of other climatic factors. Using an isotope‐enabled climate model, we found that the isotopic composition of Antarctic precipitation was not simply associated with the high and low average Antarctic temperature, but was substantially influenced by changes in dynamical atmospheric circulation related to sea surface temperature and sea ice expansion. We also suggest that representation of the past atmospheric circulations, such as westerly jets, can be constrained using water isotopic signals in Antarctic ice cores. Key Points: Synoptic circulations sustained the moisture transport toward inland AntarcticaMeridional sea surface temperature gradient enhanced moisture and heavy isotope precipitations on Antarctica, unlike sea ice expansionAntarctic ice core isotopes may refine southern hemisphere atmospheric circulations during the last glacial maximum [ABSTRACT FROM AUTHOR]

Scientists widely agree that anthropogenically driven climate change significantly impacts vegetation growth, particularly in floodplain areas, by altering river flow and flood regimes. This impact will accelerate in the future, according to climate change projections. For example, in Australia, climate change has been attributed to a decrease in winter precipitation in the range of 56% to 72.9% and an increase in summer from 11% to 27%, according to different climate scenarios. This research attempts to understand vegetation responses to climate change variability at the floodplain level. Further, this study is an effort to enlighten our understanding of temporal climate change impacts under different climate scenarios. To achieve these aims, a semi-distributed hydrological model was applied at a sub-catchment level to simulate the Leaf Area Index (LAI). The model was simulated against future time series of climate data according to Global Climate Model (GCM) projections. The time series data underwent a non-parametric Mann–Kendall test to detect trends and assess the magnitude of change. To quantify the model's performance, calibration and validation were conducted against the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI. The calibration and validation results show Nash–Sutcliffe efficiency (NSE) values of 0.85 and 0.78, respectively, suggesting the model's performance is very good. The modeling results reveal that the rainfall pattern fluctuates under climate projections within the study site, in which vegetation tends to be more vibrant during the warmer seasons. Moreover, the modeling results highlighted increases in the average projected future winter temperatures, which can help vegetation growth during winter. The results of this study may be employed for sustainable floodplain management, restoration, land-use planning, and policymaking, and help floodplain communities better prepare for and respond to changing flood patterns and related challenges under a future changing climate. [ABSTRACT FROM AUTHOR]

This paper describes the modification of a simple land snow cover module of the INM RAS climate model. The possible liquid water and refreezing of meltwater in the snow layer are taken into account by the proposed parameterization. This is particularly important for modelling the transition season, as this phenomenon is mainly observed during the formation and melting of the snow cover when the surface temperature fluctuates around 0   ° C. The snow density evolution simulation is also added. This parameterization is implemented in the INM-CM snow module and verified on observation data using the ESM-SnowMIP-like protocol. As a result, the INM-CM mean climate snow melt periods are refined, particularly in middle and high latitudes. The snow-covered area according to the model is also improved. In the future, a modified version of the land snow module can be used, coupled with a snow albedo model that takes into account snow metamorphism. This module can also be applied to sea ice snow. [ABSTRACT FROM AUTHOR]

The impact of mid‐latitude oceanic frontal zones with sharp meridional sea‐surface temperature (SST) gradients on the middle atmosphere circulation during austral winter is investigated by comparing two idealized experiments with a high‐top gravity wave (GW) permitting general circulation model. Control run is performed with realistic frontal SST gradients, which are artificially smoothed in no‐front run. The control run simulates active baroclinic waves and GW generation around the mid‐latitude SST front, with GWs propagating into the stratosphere and mesosphere. In the no‐front run, by contrast, baroclinic‐wave activity is significantly suppressed, and GWs with smaller amplitude are excited and then dissipated at higher altitudes in the mesosphere. Westward wave forcing in the winter hemisphere was more pronounced in the control run up to ∼0.03 hPa, resulting in a more realistic reproduction of the middle atmospheric polar vortex. The results demonstrate the importance of realistic mid‐latitude ocean conditions for simulating the middle atmosphere circulation. Plain Language Summary: The impact of the mid‐latitude oceanic fronts characterized by sharp sea‐surface temperature (SST) gradients is investigated using a global gravity‐wave permitting atmospheric model that represents the troposphere, stratosphere and mesosphere. Two idealized experiments were conducted with different SST profiles. Control run features a realistic SST profile characterized by frontal SST gradients in mid‐latitudes, while they are smoothed out artificially in the "no‐front" run. In winter the no‐front run simulates significantly suppressed generation of synoptic‐scale cyclones and anticyclones, which results in reduced upward propagation of higher‐frequency gravity waves into the stratosphere, exerting marked impact on the large‐scale circulation extending as high as the mesopause. Notably higher gravity wave activity in the control run leads to a weaker, and more realistic wintertime polar vortex in the stratosphere and mesosphere. This study emphasizes the potential influence of mid‐latitude oceanic conditions on the atmospheric circulation, not only in the troposphere but also throughout the stratosphere and mesosphere. Key Points: High‐top global model simulations are conducted to examine the impact of a mid‐latitude oceanic front on the atmospheric circulationThe oceanic front enhances tropospheric baroclinic‐wave activity and generation of gravity waves propagating into the middle atmosphereThe enhanced gravity waves act to reduce cold bias of the wintertime polar vortex in the Southern Hemisphere middle atmosphere [ABSTRACT FROM AUTHOR]

Abstract Machine learning (ML)‐based parameterizations have been developed for Earth System Models (ESMs) with the goal to better represent subgrid‐scale processes or to accelerate computations. ML‐based parameterizations within hybrid ESMs have successfully learned subgrid‐scale processes from short high‐resolution simulations. However, most studies used a particular ML method to parameterize the subgrid tendencies or fluxes originating from the compound effect of various small‐scale processes (e.g., radiation, convection, gravity waves) in mostly idealized settings or from superparameterizations. Here, we use a filtering technique to explicitly separate convection from these processes in simulations with the Icosahedral Non‐hydrostatic modeling framework (ICON) in a realistic setting and benchmark various ML algorithms against each other offline. We discover that an unablated U‐Net, while showing the best offline performance, learns reverse causal relations between convective precipitation and subgrid fluxes. While we were able to connect the learned relations of the U‐Net to physical processes this was not possible for the non‐deep learning‐based Gradient Boosted Trees. The ML algorithms are then coupled online to the host ICON model. Our best online performing model, an ablated U‐Net excluding precipitating tracer species, indicates higher agreement for simulated precipitation extremes and mean with the high‐resolution simulation compared to the traditional scheme. However, a smoothing bias is introduced both in water vapor path and mean precipitation. Online, the ablated U‐Net significantly improves stability compared to the non‐ablated U‐Net and runs stable for the full simulation period of 180 days. Our results hint to the potential to significantly reduce systematic errors with hybrid ESMs.

Abstract The earliest Asian summer monsoon onset (SMO) occurs in the Bay of Bengal (BoB), heralding the coming of the rainy season. In late April or early May, the strong sea surface temperature (SST) diurnal variation accompanied by ocean surface warming triggers the SMO. However, this observed diurnal cycle intensity cannot be reasonably simulated by state‐of‐the‐art climate models, resulting in a spurious delayed SMO. To address this issue, the SST diurnal cycle parameterized by a diagnostic sublayer scheme was incorporated into a climate model named FIO‐ESM v2.0. The large diurnal amplitude of SST contributes to surface warming and changes atmospheric circulation. Consequently, the high‐pressure anomaly at high levels and an inverted trough at low levels promote more convective activity, triggering an earlier SMO. Our findings improve the ability of climate models in simulating the evolution of the Asian monsoon system.

Rainfall and temperature are characterized by spatial and temporal variability in Ethiopia. However, less attention was given for the analysis of climate variability using advanced techniques and multiple sets of data. This study was conducted to examine spatiotemporal variability and trends in rainfall and temperature extremes in Ghibe III Dam watershed. Observational, ERA5, and regional simulation model data sets were used. The coefficient of variation (CV) and precipitation concentration index (PCI) were employed. The trends in rainfall and temperature extremes were examined using the modified Mann-Kendall test and the Sen Slope estimator in R-ClimDex in R 4.2.2 software. The warmest days exhibited temperature from 24.6°C to 40°C in Bele, 28.2°C to 35.43°C in Wolaita Sodo, 33.6 °C–44 °C in Areka, 31.64 °C–36.8 °C in Gesuba, and 29.19 °C–36.15 °C in Gena Bosa. The warmest nights showed temperature ranging from 14 °C to 18.74 °C in Bele and Gena Bosa, respectively. Annual warm days (TX90p) ranges from 11.34 to 57.1 days, with higher heating in the southern parts. The cool days (TX10p) range from 2.79 to 8.41, while the cool nights (TN10p) range from 0.04 to 8.26 days. The areal average temperature maximum and minimum range between 26.37 °C and 13.81 °C, respectively, with mean precipitation of 1446.92 mm.The rainfall extremes indices showed increasing and decreasing trends. The extreme temperature indices showed an overall warming trend. Based on ERA estimates, the rainfall in winter showed higher variability (CV = 72.4%–99.3 %) than the annual rainfall (CV = 33%–79.8 %). PCI showed a moderately (12 %) to very erratic (19.4 %) rainfall distribution. The climate model estimate showed high variability (CV = 20.65 %) in Climate Limited Area Modeling Community (CCLM) under representative concentration paths (RCP) 4.5 and 8.5 and extremely high variability (CV = 93.49 %) in the Regional Atmospheric Climate Model (RACMO) under RCP 4.5. Policymakers should design appropriate adaptation strategies applicable to farmers.

Abstract The internal variability pertains to fluctuations originating from processes inherent to the climate component and their mutual interactions. On the other hand, forced variability delineates the influence of external boundary conditions on the physical climate system. A methodology is formulated to distinguish between internal and forced variability within the surface air temperature. The noise‐to‐noise approach is employed for training a neural network, drawing an analogy between internal variability and image noise. A large training data set is compiled using surface air temperature data spanning from 1901 to 2020, obtained from an ensemble of Atmosphere‐Ocean General Circulation Model simulations. The neural network utilized for training is a U‐Net, a widely adopted convolutional network primarily designed for image segmentation. To assess performance, comparisons are made between outputs from two single‐model initial‐condition large ensembles, the ensemble mean, and the U‐Net's predictions. The U‐Net reduces internal variability by a factor of four, although notable discrepancies are observed at the regional scale. While demonstrating effective filtering of the El Niño Southern Oscillation, the U‐Net encounters challenges in capturing the changes in the North Atlantic Ocean. This methodology holds potential for extension to other physical variables, facilitating insights into the climate change triggered by external forcings over the long term.

Present study examined the overall performance of 12 CMIP6 GCMs for rainfall, maximum and minimum temperatures for rice crop-growing seasons i.e., Boro (January to May) and Kharif (June to October) over the new alluvial zone of West Bengal. A wide range of indices i.e., index of agreement, error indices and bias estimators were utilized to put more confidence on the results. Results indicated that CMIP6 models were able to reproduce observed mean climatology and inter-annual variability of maximum and minimum temperature adequately for both seasons while a smaller number of models (3-4 models) out of a total of 12 GCM-CMIP6 models showed satisfactory performance for rainfall. The ranks assigned to the models revealed that CNRM–ESM2–1 was the best-performing model for Kharif and MRI-ESM2-0 showed the highest skill for Boro. ACCESS-CM2 and MPI-ESM1-2-LR performed worst for Kharif and Boro seasons respectively. Further, CNRM–ESM2–1 and MRI-ESM2-0 were used to project the future climate for Kharif and Boro seasons respectively under both moderate (SSP2-4.5) and extreme scenarios (SSP5-8.5). Higher warming was projected during Boro season than Kharif. Projections revealed increasing rainfall during Kharif season but decreasing rainfall in Boro season in both the moderate and extreme future scenarios.

The excessive abstraction of lake water affects its sustainability and is common in Lake Tana. Hence this study aims to develop operation rule curves for optimum operation of Lake Tana reservoir by considering the effects of small and large-scale irrigation water abstraction, Hydropower demand, and climate change. The data used in this study were produced by two Global Circulation Models (GCMs) (MIROC5 and MPI) under two Representative Concentration Pathways (RCP4.5 and RCP8.5) in the 2040s (2020–2049) and 2070s (2050–2079). For RCP4.5 and RCP 8.5 scenarios of the 2040s- and 2070s-time domains, lake temperature exhibits an upward trend. The lake rainfall also will show an increase for the 2040s and 2070s under both RCP scenarios. The outcome showed that in all future scenarios, lake evaporation under RCP4.5 of 2040s and 2070s rise between 3.6 % to 7 % and 4.6 % to 19, respectively, while for RCP8.5 of 2040s and 2070s increases between 4.6 % to 9.9 % and 9.2 % to 24.7 %. The surface water inflow increased for RCP4.5 (2070s) and RCP8.5 (2040s), whereas it declined for RCP4.5 (the 2040s) and RCP8.5 (2070s. Under both scenarios, the average value of the time-based and volumetric reliability in the 2040s and 2070s is less than 80 %. The reservoir will take a very long time to recover from the shortfall as the resilience levels are below 50 %. Additionally, the dimensionless vulnerability was less than 50 %, indicating that there won't be a shortage of inflow to meet demand at the reservoir. Based on evaluation, the reservoir will not probably perform well in any way because of the rise in upstream abstractions. To guide the operation of the reservoir, operation rule curves were developed using the Hec-ResSim model. Generally, the government, decisions, and policymakers should set a water resources management plan not trigger normal functioning of the Lake system.

In climate science and applications, the term “metric” is used to describe the distillation of complex, multifaceted evaluations to summarize the overall quality of a model simulation, or other data product, and/or as a means to quantify some response to climate change. Metrics provide insights into the fidelity of processes and outcomes from climate models and can assist with both differentiating models' representation of variables or processes and informing whether models are “fit for purpose.” Metrics can also provide a valuable reference point for co-production of knowledge between climate scientists and climate impact practitioners. Although continued metric developments enable model developers to better understand the impacts of decisions made in the model design process, metrics also have implications for the characterization of uncertainty and facilitating analyses of underlying physical processes. As a result, comprehensive evaluation with multiple metrics enhances usability of climate information by both scientific and stakeholder communities. This paper presents examples of insights gained from the development and appropriate use of metrics, and provides examples of how metrics can be used to engage with stakeholders and inform decision-making. This article is categorized under: Climate Models and Modeling > Knowledge Generation with Models The Social Status of Climate Change Knowledge > Climate Science and Decision Making Assessing Impacts of Climate Change > Evaluating Future Impacts of Climate Change.

Abstract There is little consensus among global climate models (CGMs) regarding the response of lightning flash rates to past and future climate change, largely due to graupel not being included in models. Here a two-moment prognostic graupel scheme was incorporated into the MIROC6 GCM and applied in three experiments involving pre-industrial aerosol, present-day, and future warming simulations. The new microphysics scheme performed well in reproducing global distributions of graupel, convective available potential energy, and lightning flash rate against satellite retrievals and reanalysis datasets. The global mean lightning rate increased by 7.1% from the pre-industrial period to the present day, which was attributed to increased graupel occurrence. The impact of future warming on lightning activity was more evident, with the rate increasing by 18.4 $$\%\,\textrm{K}^{-1}$$ % K - 1 through synergistic contributions of destabilization and increased graupel. In the Arctic, the lightning rate depends strongly on the seasonality of graupel, emphasizing the need to incorporate graupel into GCMs for more accurate climate prediction.

This study aimed to assess the response of soil erosion to climate change in the Sululta catchment using the Revised Universal Soil Loss Equation (RUSLE) integrated with the geographic information system (GIS). The current rainfall erosivity factor (R) was computed from the current rainfall data (1989–2018). Regional climate models (RCMs) under representative concentration pathways RCP4.5 and RCP8.5 were used for future rainfall projection (2021–2080) to determine projected rainfall R factor. Rainfall data, soil map, digital elevation model and land use/land cover data were used to evaluate RUSLE factors in the ArcGIS environment. The results of this study showed that the current average annual soil loss rate was found to be 5.03 tons/ha/year. The average annual soil loss may decrease by 2.78 and 0.80% in 2021–2050 and 2051–2080, respectively, under the RCP4.5 scenario compared to the current average annual soil loss. Under the RCP8.5 scenario, the average annual soil loss may increase by 7.75 and 2.98% in 2021–2050 and 2051–2080, respectively, from the current average annual soil loss. The result reveals that the average annual soil loss decreases in both time periods under RCP4.5 and increases in both time periods under RCP8.5. HIGHLIGHTS Performing bias correction for RCM data.; Projection of future precipitation.; Determining RUSLE factors.; Determining rainfall erosivity factor under RCP4.5 and RCP8.5.; Evaluating the impact of current and future climate change on soil erosion.;

Although climate models can highlight potential shifts in intensity–duration–frequency (IDF) curves, their limited geographical and temporal resolutions limit their direct use in predicting sub-daily heavy precipitation. To use global or regional model outputs to predict urban short-term precipitation, approaches that give the requisite level of spatial and temporal downscaling are required, and these processes remain one of the difficulties that have demanded intensive effort in recent years. Although no novel methods are given in this work, there are few studies in the literature that investigate the impact of climate change on the analysis and design of infrastructure-related engineering structures. Therefore, the purpose of this research is to determine the potential changes in IDF curves because of climate change. The equidistance quantile matching method was used to turn future rainfall forecast data from global climate models (HadGEM2-ES, MPI-ESM-MR, and GFDL-ESM2M) corresponding to RCP4.5 and RCP8.5 scenarios into standard duration rainfall data, and new IDF curves were generated. These IDF curves corresponded very well with those generated from observed data (R2 ≈ 1). The HadGEM2-ES model predicts up to a 25% rise in rainfall intensity, whereas the other two models expect up to a 50% drop. HIGHLIGHTS Climate model data were used to update IDF curves under global climate change.; The equidistance quantile matching method is a highly successful method for obtaining shorter-term (minute and hourly) precipitation data.; It is important to select the correct global climate model to be used in modeling local climate.;

There is little consensus among global climate models (CGMs) regarding the response of lightning flash rates to past and future climate change, largely due to graupel not being included in models. Here a two-moment prognostic graupel scheme was incorporated into the MIROC6 GCM and applied in three experiments involving pre-industrial aerosol, present-day, and future warming simulations. The new microphysics scheme performed well in reproducing global distributions of graupel, convective available potential energy, and lightning flash rate against satellite retrievals and reanalysis datasets. The global mean lightning rate increased by 7.1% from the pre-industrial period to the present day, which was attributed to increased graupel occurrence. The impact of future warming on lightning activity was more evident, with the rate increasing by 18.4 % K - 1 through synergistic contributions of destabilization and increased graupel. In the Arctic, the lightning rate depends strongly on the seasonality of graupel, emphasizing the need to incorporate graupel into GCMs for more accurate climate prediction. [ABSTRACT FROM AUTHOR]

Globally emissions from aviation affect the Earth's climate via a complex set of processes. Contrail cirrus and carbon dioxide emissions are the largest factors from aviation in the radiative forcing on climate. Although contrail cirrus enhances the impact of natural clouds on the climate, there are still unresolved questions concerning their characteristics and life cycle. Despite extensive research in studying contrails, significant uncertainties persist. Contrail cirrus encompasses linear contrails and the associated cirrus clouds; these are characterized by ice particle properties, e.g., size, concentration, extinction, ice water content, optical depth, geometrical depth, and cloud coverage. The climate impact of contrails may intensify due to projected increases in air traffic. The radiative forcing from global contrail cirrus has the potential to triple and could reach as much as 160 mW m-2 by 2050. This projection is based on anticipated growth in air traffic and a potential shift to higher altitudes. The future climate impact of contrail cirrus is influenced by factors like the magnitude and geographical spread in air traffic, advancements in fuel efficiency, the effects from use of alternative fuels, and the effects of the changing climate on the background atmosphere. This study reviews the microphysical processes affecting contrail formation and the aging of contrails and contrail-cirrus. Furthermore, the study explores global observational datasets for contrails, current analyses, and future projections and will aid in evaluating the effectiveness and trade-offs associated with various mitigation strategies. The research highlights gaps in knowledge and uncertainties while outlining research priorities for the future. [ABSTRACT FROM AUTHOR]

To analyze the operation risk of check dams under extreme precipitation conditions, taking Ningxia area as an example, this paper carried out a risk assessment of check dams under extreme precipitation conditions in Ningxia through data collection, hydrological statistics, numerical simulation, and other methods. The conclusions are the following: (1) By the end of 2020, about 40% of the silt reservoir capacity of check dams in various water and soil conservation zones in Ningxia has been accumulated. During 1966–2020, the extreme precipitation and frequency of extreme precipitation in Ningxia increased while the intensity of extreme precipitation decreased. The extreme precipitation in Ningxia increased year by year and lasted longer. (2) Under two future scenarios of RCP4.5 (the full name of RCP is Representative Concentration Pathway) and RCP8.5, the extreme precipitation threshold in Ningxia is gradually decreasing from south to north. Extreme precipitation in the future will bring high risk to the operation of check dams in Ningxia. The results of this paper can provide a scientific basis for the operation and management of check dams in Ningxia. [ABSTRACT FROM AUTHOR]

The main apple-producing areas in northern China are located in the North China plain and the loess Plateau, and the apple blossom frost hazard events have had a severe impact on the income of fruit growers and the economy of the production areas. Future climate change will exacerbate the frequency and intensity of extreme weather and climate events. In this study, based on the climate model data shared by NEX-GDDP, the seven models with the best fitting ability for the minimum temperature during apple blossom were selected using Taylor diagrams, and the annual encounter values of the minimum temperature during apple blossom were calculated and revised using the transfer function correction method of the extreme value distribution, so as to predict the risk of frost disaster and yield reduction of apples in northern China under climate change. Taking the intensity of the apple blossom frost disaster with a 30-year occurring period event across the study area as an example, in the near and distant future under the RCP4.5 scenario, the regions of Henan, Shanxi, northern Shaanxi and, northern Ningxia were the main affected areas, dominated by the minimum temperature of -3 to -2°C, and the highest yield reduction rates were in the northern regions of Henan, Ningxia, Shanxi, and northern Shaanxi provinces, with the yield reduction rate of apples in northern China ranging from 2.47% to 5.22%. Under the RCP8.5 scenario, the frost disaster area with a minimum temperature of -3°C or less expands to the whole of Henan, central Shandong and other places, and the yield reduction rate of apples in northern China is 4.57%-12.39%. In the future, apple cultivation in these areas needs to strengthen the prevention of frost disaster risk. [ABSTRACT FROM AUTHOR]

The effective climate sensitivity in the Department of Energy's Energy Exascale Earth System Model (E3SM) has decreased from 5.3 K in version 1 to 4.0 K in version 2. This reduction is mainly due to a weaker positive cloud feedback that leads to a stronger negative radiative feedback. Present‐day atmosphere‐only experiments with uniform 4 K sea surface temperature warming are used to separate the contributions of individual model modifications to the reduced cloud feedback. We find that the reduced cloud feedback is mostly driven by changes over the tropical marine low cloud regime, mainly related to a new trigger function for the deep convection scheme and modifications in the cloud microphysics scheme. The new trigger function helps weaken the low cloud reduction by increasing the cloud water detrainment at low levels from deep convection under warming. Changes to the formula of autoconversion rate from liquid to rain and an introduced minimum cloud droplet number concentration threshold in cloud microphysical calculations help sustain clouds against dissipation by suppressing precipitation generation with warming. In the midlatitudes, the increased Wegener‐Bergeron‐Findeisen (WBF) efficiency strongly reduces present‐day liquid water and leads to a stronger negative cloud optical depth feedback. The reduced trade cumulus cloud feedback in v2 is closer to estimates from recent observational and large‐eddy modeling studies but might not be due to the right physical reasons. The reduced mid‐latitude cloud feedback may be more plausible because more realistic present‐day mixed‐phase clouds are produced through the change in the WBF efficiency. Plain Language Summary: Understanding how the Earth responds to greenhouse gas increases is important for climate change research. In the Department of Energy's Energy Exascale Earth System Model, the global temperature response to an abrupt quadrupling of atmospheric carbon dioxide has decreased from 5.3 K in version 1 to 4.0 K in version 2. This reduction is mainly because low‐level clouds over the tropical oceans decrease less as the planet warms in version 2: a weaker amplifying cloud feedback. To understand the reasons behind this reduction, warming simulations were conducted to separate the contributions of individual model changes. We find that the reduced cloud feedback is primarily due to changes in the representation of the vertical movement of air through the depth of the lower atmosphere and of the microscopic properties of clouds. The findings highlight some unexpected impacts on cloud feedback resulting from modifications to the model's physics and emphasize the importance of monitoring and understanding changes in cloud feedback during model development. Key Points: E3SM's effective climate sensitivity is lower in version 2 mainly due to the reduced positive cloud feedback over marine low cloud regionsThe feedback reduction is primarily due to altered cloud microphysical parameters and a new deep convection trigger functionProcess‐level analysis is conducted to understand the impact of these model modifications on cloud feedbacks [ABSTRACT FROM AUTHOR]