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Global climate models (GCMs) are extensively used to calculate standardized drought indices. However, inaccuracies in GCM simulations and uncertainties inherent in the standardization methodology limit the precision of drought evaluations. The objective of this research is to remove bias in GCMs for improving drought monitoring and assessment. Consequently, this article proposes a new framework for drought index under the ensemble of GCMs—Multi‐Model Quantile Mapped Standardized Precipitation Index (MMQMSPI). In accordance of Standardized Precipitation Index (SPI), the second stage derives a new index by assessing the feasibility of parametric and nonparametric models during standardization. In the application, we used 18 GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) data of precipitation across 32 grid points within the Tibetan Plateau region. The comparative findings reveal that the integration of KCGMD is the most suitable choice compared to other best‐fitted univariate distributions in both features of the proposed framework. In this research, we assess the implications of evaluating future patterns of drought for the years 2015–2100 using seven different time periods and three different future scenarios. Temporal behavior clearly shows monthly variations in the pattern of MMQMSPI, and these variations differ on each time scale, but a drastic change can be seen over the long term, i.e., extreme dry and wet conditions, with a higher probability in all scenarios. [ABSTRACT FROM AUTHOR]

The Coordinated Regional Downscaling Experiment (CORDEX) offers a framework for regional climate downscaling experiments over continental‐based domains that overlap; this allows for conducting multimodel analysis. However, a question is raised about the uncertainty due to domain selection. This study aims to evaluate the sensitivity of CORDEX simulations to the choices of domain, downscaling regional climate model (RCM) and driving global model (GCM) over a Middle East study region, including Jordan. Understanding these sensitivities helps assess simulation uncertainties and enhance regional climate change projections. Taylor diagrams and variance decomposition analyses were used to analyse seasonal temperature and precipitation climatologies. The results indicate that the domain contribution to variance is negligible, whereas the choice of GCM and RCM strongly influences simulations. Variance in temperature is significantly impacted by the driving model (GCM), whereas RCM has a higher influence on precipitation, which reflects their large‐scale versus local nature. The results of this study support the development of multidomain ensembles since projections produced by the same GCM–RCM model combination are consistent across different overlapping domains. On the other hand, this approach facilitates the consideration as well as a comprehensive quantification of the uncertainties arising from the utilization of multiple GCMs and RCMs within such an ensemble. This improves the reliability of regional climate information, thus facilitating the development of effective adaptation strategies and mitigation plans. [ABSTRACT FROM AUTHOR]

Northwest China is much more sensitive to climate warming, and the climate has varied rapidly from warm and drought to warm and humid conditions. In addition, due to the complex terrain of Northwest China, the methods and parameterization schemes of different CMIP6 models, these models are mostly applied to arid areas in Northwest China or Central Asia, lacking climate data for plateau areas and eastern Lanzhou, specifically in filtering CMIP6 models and evaluating applicable models. In this paper, 34 CMIP6 climate models are used to evaluate and forecast future trends in Northwest China under the SSP126, SSP245 and SSP585 scenarios in the short, medium and long term. CMIP6 models of temperature and precipitation are identified by applying the interannual variability skill score (IVS) between CN05.1 datasets and historical CMIP6 models, which are suitable for Northwest China. Then, we assess the characteristics, warming and wetting deviations, and uncertainties in the prediction of climatic change according to CMIP6 models over Northwest China. The results show that CMIP6 models in precipitation and temperature applicable to Northwest China are AWI‐CM‐1‐1‐MR, BCC‐CSM2‐MR, FGOALS‐g3, INM‐CM4‐8, INM‐CM5‐0 and MRI‐ESM2‐0. The multi‐model ensemble mean (MMEM) has better capability than individual CMIP6 models in precipitation and temperature prediction. Spatiotemporal climatic change over Northwest China shows overall warming and wetting trends. The IVS provides the ability to estimate CMIP6 model simulation performance both temporally and spatially. The temperature simulation is quite good in the Tarim Basin and Hexi Corridor region, and the precipitation simulation is quite good in the plateau region, Altai Mountains, Tianshan Mountains and Hexi Corridor region. Cold and wet deviations occur in Northwest China due to the topography and few stations, which are common reasons. The main sources of uncertainties in temperature prediction during this century are model uncertainty (before the 2090s) and scenario variability (after the 2090s), and model uncertainty in precipitation for CMIP6 becomes the main source of uncertainty. [ABSTRACT FROM AUTHOR]

Ultrahot Jupiters (UHJs), being the hottest class of exoplanets known, provide a unique laboratory for testing atmospheric interactions with internal planetary magnetic fields at a large range of temperatures. Thermal ionization of atmospheric species on the dayside of these planets results in charged particles becoming embedded in the planet's mostly neutral wind. The charges will resist flow across magnetic field lines as they are dragged around the planet and ultimately alter the circulation pattern of the atmosphere. We model this process to study this effect on high-resolution emission and transmission spectra in order to identify observational signatures of the magnetic circulation regime that exist across multiple UHJs. Using a state-of-the-art kinematic MHD/active drag approach in a 3D atmospheric model, we simulate three different UHJs with and without magnetic effects. We postprocess these models to generate high-resolution emission and transmission spectra and explore trends in the net Doppler shift as a function of phase. In emission spectra, we find that the net Doppler shift before and after secondary eclipse can be influenced by the presence of magnetic drag and the wavelength choice. Trends in transmission spectra show our active drag models consistently produce a unique shape in their Doppler shift trends that differs from the models without active drag. This work is a critical theoretical step to understanding how magnetic fields shape the atmospheres of UHJs and provides some of the first predictions in high-resolution spectroscopy for observing these effects. [ABSTRACT FROM AUTHOR]

Synoptic climatology, which connects atmospheric circulation with regional environmental conditions, is pivotal to understanding climate dynamics. While regional climate models (RCMs) can reproduce key mesoscale precipitation patterns, biases related to synoptic circulation from the driving model, typically global climate models (GCMs), often remain unaddressed. This study examines the influence of correcting systematic bias in RCM boundaries on the representation of Australian synoptic systems. We utilize a structural self‐organizing map to evaluate the frequency, persistence, and transitions of daily synoptic systems. Our findings reveal that an RCM with multivariate bias‐corrected boundaries improves the representation of synoptic systems compared to the driving GCM, or an RCM with uncorrected or simply bias‐corrected boundaries, particularly in reference to the frequency of systems identified. This demonstrates that appropriately correcting RCM boundary conditions helps correct many of the circulation errors inherited from the driving GCM but not all. Plain Language Summary: Global climate models (GCMs) have limitations in accurately simulating precipitation characteristics at finer scales. Regional climate models (RCMs), driven by GCM data, are employed to overcome these limitations. However, GCM biases in regional circulations are inherited by the RCM, leading to errors in the simulated weather. This study investigates the effects of multivariate bias correction of RCM boundary conditions on the model's ability to simulate daily synoptic systems. Results indicate that RCMs with corrected boundaries better represent weather systems, overcoming some of the circulation errors in the GCM. Key Points: Regional climate models (RCMs) with uncorrected and simple bias‐corrected boundaries show large errors when simulating climatological frequency of synoptic systemsAn RCM with multivariate bias‐corrected boundaries demonstrates enhanced accuracy in the simulation of synoptic systemsThese findings demonstrate that correcting RCM boundaries improves the RCM representation of weather systems as well as the climate [ABSTRACT FROM AUTHOR]

Previous work has shown that estimates of climate sensitivity vary over time in response to abrupt CO2 forcing in climate model simulations. The energy fluxes that drive warming in response to increasing CO2 also influence precipitation, which prompts the question: Does the precipitation response therefore also vary over time? We investigate by examining the response of precipitation to warming forced by greenhouse gases—the hydrological sensitivity—in a set of millennial‐length climate simulations with multiple climate models, Long Run Model Intercomparison Project (LongRunMIP). We compare hydrological sensitivity calculated from three different timescales of the simulations: years 1–20, 21–150, and 151–1000. We show that the hydrological sensitivity lacks a consistent dependence on timescale, in contrast to climate sensitivity. Decomposition of the surface energy budget reveals that the relative muting of the multi‐model mean hydrological sensitivity is driven by surface downwelling shortwave flux. Plain Language Summary: Previous work has shown that when carbon dioxide concentrations are abruptly quadrupled, the response of the global energy imbalance between the Earth and space is a fast change for the first couple of decades, followed by a slower response as time goes on. Precipitation is also an important part of the energy budget, so perhaps its response to increasing carbon dioxide might depend on timescale as well. This study investigates using a recent set of very long model simulations. We find that there is less of a dependence for precipitation than for the global energy imbalance. Because the effect is smaller, it is also more subject to the details of the statistical analysis, so particular attention is paid there. Key Points: The trend across timescales of hydrological sensitivity is less consistent and has a smaller magnitude compared to climate sensitivityDownwelling surface shortwave flux is the component of the energy budget that contributes most consistently to this difference [ABSTRACT FROM AUTHOR]

Reliable projections of the tropical Pacific sea surface temperature (SST) warming (TPSW) patterns are critically important for exploring the future climate change. However, climate models suffer from long‐standing common biases in simulating the present‐day climate, raising doubts about the model projected TPSW patterns. Here by using outputs from 30 CMIP6 models, we find the projected TPSW patterns are significantly correlated with the simulated present‐day SST in the tropical North Atlantic (TNA), with higher present‐day TNA SSTs tending to project more weakened zonal SST gradients by producing more present‐day low‐level clouds and the resultant positive cloud–shortwave–SST feedbacks over the eastern equatorial Pacific. An emergent constraint using observed TNA SST reveals a consistent El Niño‐like warming pattern in all models with more weakened zonal SST gradient than before in most models, together with a reduction of the inter‐model uncertainty in the zonal SST gradient change by more than 20%. Plain Language Summary: Future projections of the tropical Pacific SST warming (TPSW) pattern remain highly uncertain. One of the key reasons is that climate models suffer from several long‐standing common biases in simulating the current climate state. Here we find that the remote common cold SST bias in the tropical North Atlantic acts to suppress the future SST warming over the eastern equatorial Pacific through a trans‐basin atmospheric connection. By removing the effect of the TNA cold SST bias from model projections, the corrected TPSW displays a more El Niño‐like SST warming pattern with more weakened zonal SST gradient, together with a reduction of the inter‐model uncertainty by more than 20%. Key Points: Models with a higher (lower) present‐day tropical North Atlantic (TNA) sea surface temperature (SST) tend to project a more (less) El Niño‐like SST warming patternThe TNA cold SST bias leads to a lack of low‐level cloud over the eastern Pacific by weakening the regional Hadley‐type circulationSpatial constraints on the projected tropical Pacific SST warming from the observed TNA SST suggest a more El Niño‐like warming pattern [ABSTRACT FROM AUTHOR]

Aerodynamic roughness length (z0 ${z}_{0}$) fundamentally affects land surface momentum loss and wind resource simulation, but ground truth data of z0 ${z}_{0}$ are sparse in space, causing z0 ${z}_{0}$ datasets used in atmospheric models are empirically estimated from land cover types through a look‐up table. In this study, we derived z0 ${z}_{0}$ values from 101 anemometer towers in China. Taking them as ground truth, we show that existing gridded z0 ${z}_{0}$ datasets determined from either a look‐up table or a machine‐learning method contain considerable uncertainty and fail to capture the variability of z0 ${z}_{0}$ within each land cover type, although the latter performs better. Even for the widely used ERA5, its z0 ${z}_{0}$ is overestimated in wind‐rich regions of China, causing an underestimation of near‐surface wind speed. This highlights the necessity to improve z0 ${z}_{0}$ data in atmospheric models. Current rapidly expanding anemometer towers may substantially enrich z0 ${z}_{0}$ truth data and thus provide potential to improve wind resource modeling. Plain Language Summary: Aerodynamic roughness length (z0 ${z}_{0}$) is a key parameter for atmospheric modeling and wind energy assessment. However, z0 ${z}_{0}$ is a virtual height that cannot be measured directly, and its truth values are sparse in space. Gridded z0 ${z}_{0}$ datasets are generally estimated from land cover types. Their accuracy and applicability remain uncertain. The increasing number of anemometer towers built for wind resource assessment presents an opportunity to enrich z0 ${z}_{0}$ truth values. In this study, we have derived z0 ${z}_{0}$ values at 101 selected anemometer towers in China. Taking them as ground truth, we evaluated gridded z0 ${z}_{0}$ datasets that were specified by a look‐up table with land cover types or derived by a machine‐learning method. The latter has better accuracy than the former, but they all contain considerable uncertainty and fail to capture the evident variability of z0 ${z}_{0}$ within each land cover type. Such uncertainty of z0 ${z}_{0}$ can cause systematic biases in the simulated near‐surface wind speed. Taking the widely used ERA5 as an example, we show that its z0 ${z}_{0}$ has been overestimated in wind‐rich regions of China, resulting in an underestimation of near‐surface wind speed. This study highlights the potential of the emerging tens of thousands of anemometer towers to improve wind resource modeling. Key Points: Ground truth data of aerodynamic roughness length can be substantially enriched with the data of rapidly expanding anemometer towersCurrent datasets of aerodynamic roughness length determined by land cover types or machine‐learning methods have large uncertaintiesBiases in the aerodynamic roughness length of ERA5 have led to its underestimation of wind power resources in China [ABSTRACT FROM AUTHOR]

The ocean response to Antarctic Ice Sheet (AIS) mass loss has been extensively studied using numerical models, but less attention has been given to the atmosphere. We examine the global atmospheric response to AIS meltwater in an ensemble of experiments performed using two fully coupled climate models under a pre‐industrial climate. In response to AIS meltwater, the experiments yield cooling from the surface to the tropopause over the subpolar Southern Ocean, warming in the Southern Hemisphere polar stratosphere, and cooling in the upper tropical troposphere. Positive feedbacks, initiated by disrupted ocean‐atmosphere heat exchange, result in a change in the top‐of‐atmosphere radiative balance caused primarily through surface and near‐surface albedo changes. Changes in the atmospheric thermal structure alter the jet streams aloft. The results highlight the global influence of AIS melting on the climate system and the potential for impacts on mid‐latitude climate patterns and delayed regional warming signals. Plain Language Summary: The Antarctic Ice Sheet is expected to melt as the climate warms and many studies have focused on understanding how the ocean state might respond. How the structure of the global atmosphere, including temperature and global wind patterns, will respond to this added meltwater and associated ocean state changes remains unclear. We investigate changes in atmospheric properties including temperature, clouds, radiation, water vapor, and global wind patterns that occur when meltwater is added to the Antarctic coast in two climate models. We find temperatures within the troposphere to generally decrease, whereas temperatures in the stratosphere increase over the Southern Hemisphere polar region. Temperatures also decrease in the high‐altitude troposphere near the equator. Changes throughout the atmosphere, including clouds and water vapor, in combination with changes in atmosphere‐ocean heat exchange, impact the amount of energy within the climate system. Changes in the temperature structure of the atmosphere have far‐reaching effects on global wind patterns. The response to isolated Antarctic Ice Sheet melting, which we present, generally opposes that found for typical global warming scenarios lacking the ice sheet response. All of this highlights the remote consequences of Antarctic Ice Sheet melting. Key Points: Freshwater is added along the coast of Antarctica in two climate models and changes in the full‐column global atmosphere are investigatedTemperature changes extend from the surface to the stratosphere and from the Southern Ocean to the upper tropical troposphereMeltwater‐induced changes in the atmosphere's thermal structure result in altered large‐scale wind patterns [ABSTRACT FROM AUTHOR]

End of 21st‐century hydroclimate projections suggest an expansion of subtropical dry zones, with Mediterranean and Sahel regions becoming much drier. However, paleobotanical assemblage evidence from the middle Miocene (17‐12 Ma), suggests both regions were instead humid environments. Here we show that by modifying regional sea surface temperatures (SST) in an Earth System Model (CESM1.2) simulation of the middle Miocene, the increased ocean evaporation and integrated water vapor flux overrides any drying effects associated with warming‐induced land‐surface evaporation driven by atmospheric CO2 concentrations. These modifications markedly reduce the bias in the model‐data comparison for this period. A vegetation model (BIOME4) forced with simulated climatologies predicts both regions were dominated by mixed forest, which is largely consistent with the paleobotanical record. This study unveils the potential for wetter subtropical Mediterranean climates associated with warming, presenting an alternative scenario from future drying projections with localized SST warming governing regional climate change. Plain Language Summary: Climate models project drier conditions over Europe and Northern Africa due to global warming. However, evidence from a past warm climate period, the middle Miocene (∼15 million years ago), finds wetter rather than drier environments. We refine climate model boundary conditions by reconstructing warmer ocean waters in the North Atlantic based on proxy evidence. The warmer ocean produces wetter environments by enhancing North Atlantic precipitation events and the North African monsoon. The increased rainfall and surface temperature cause a vegetation model to predict more forest coverage over Europe and Northern Africa, which is consistent with fossil evidence from ∼15 million years ago. This study unveils the potential for wetter climates associated with warming, presenting an alternative scenario from future drying projections, with localized sea surface warming governing regional climate. Key Points: Middle Miocene Europe rainfall discrepancy between herpetological (dry) and paleobotanical (wet) records complicate climate model validationSimulations that produce dry environments also produce cool North Atlantic sea surface temperatures which are inconsistent with proxiesSSTs informed by North Atlantic proxies produce wetter environments consistent with paleobotanical evidence [ABSTRACT FROM AUTHOR]

Regarded as the Asian Water Tower, the Tibetan Plateau (TP) collects atmospheric precipitation from a vast area of land and feeds into major rivers that sustain the livelihood of billions of people in East, South and Central Asia. It is critical to reasonably simulate the hydrological cycle over the TP in order to assess future climate risks to agriculture, water resources and ecosystem services. To address the chronic wet biases over the TP in state‐of‐the‐art climate models, we have compared 12 high‐resolution (HR) climate models (25–50 km) to their corresponding low‐resolution versions (100–200 km) with respect to the 1979–2014 climatology. It is found that the HR models consistently reduce about half of the wet biases over the TP, mainly from better resolved orography. The wet biases are reduced by 41% over the northern and western TP, mainly contributed by decreased frequency of light precipitation (0.1–10 mm day−1), which is attributed to reduced evaporation because of weakened surface wind by raised orography. The most significant reduction of biases (53%) rising from decreased frequency of mid‐heavy precipitation (10–50 mm day−1), appears over the southern and eastern TP, on the leeside of elevated orography where steeper orography enhances rain shadow effect by stronger downward motion along the sharper slope, while partly compensated by air column convergence due to vertical stretching of the downward flow for potential vorticity conservation. This study highlights the importance of surface processes and resolving complex orography in simulating precipitation and large‐scale hydrology around the TP which potentially benefits the future hydrological projection. Plain Language Summary: Tibetan Plateau (TP), the largest area with highest altitude, is well known as "the Asian Water Tower" because precipitation is stored in many glaciers and lakes and feeds into major rivers in Asia. Reasonably modeling the precipitation over the TP is very important but too much simulated precipitation is a long‐standing problem for several decades. By analyzing 12‐pair high‐ and low‐resolution (HR and LR) versions of the same climate models, we attribute about half of the biases to coarse model resolution, especially to the overly mosaic orography. The higher altitudes on the edges of TP for the finer orography decelerate winds and then suppress evaporations on the northern and western TP, meanwhile enhance the rain shadow effect on the southern and eastern TP. All the above improved hydrological processes reduce the wet biases in LR simulations. The application of HR modeling could benefit the prediction and projection of water resources in regions with complex orography. Key Points: High‐resolution Atmospheric General Circulation Models reduce wet biases over the Tibetan Plateau (TP) for better resolved orographySuppressed evaporation mainly reduces the wet biases in the western‐northern TP due to higher and rougher orography weakening surface windsImproved precipitation in the southern and eastern TP are dominated by an enhanced rain shadow effect on the leeside of elevated orography [ABSTRACT FROM AUTHOR]

Unraveling plausible future rainfall change (ΔPr) patterns is crucial for tailoring societies' responses to climate change‐induced hazards. This study compares rainfall projections from the regionally coupled ocean model (ROM) and its atmospheric component, the regional atmospheric model REMO, over Central Equatorial Africa (CEA). Both models are forced by the Earth system model MPI‐ESM‐LR following the Representative Concentration Pathway 8.5. Results reveal increased rainfall across most of CEA, with ROM projecting more widespread and intensified wetting than REMO, although REMO produces more precipitation under future conditions, underscoring the influence of historical biases on REMO's projection. Examining processes underpinning changes unveils strong controls of sea and land surface temperature changes in ΔPr differences between the two models. Specifically, ROM mitigates warming more over the Atlantic than over CEA landmass compared to REMO, inducing enhancement of the Congo Basin cell and increased precipitable water content through specific humidity, affecting deep convection. Both models project enhanced Sahel and Kalahari thermal lows, with ROM better depicting the Kalahari low's warmer nature than the Sahel low. The resulting temperature gradients strengthen the northern and southern shallow meridional Hadley overturning circulation. ROM simulates the wetter conditions than REMO, attributed to its weaker northern Hadley Cell, which restricts the likelihood of northward moisture divergence toward the Sahel. Additionally, differences in mid‐tropospheric moisture convergence differentiate between ROM and REMO's wetness relative to the historical period and under future conditions. ROM projections are more plausible, in association with the reliability of its added value under the historical climate and mechanisms underlying Δpr. Plain Language Summary: Discriminating models that project plausible rainfall change signals become increasingly necessary in a context where climate information surrounding future rainfall patterns is fraught with important uncertainties. It is not always straightforward to differentiate plausible projected changes from imperfect simulations as climate models are approximated solutions to mathematical and physical differential equations often solved with limited orders. One of the suggested approaches involves process‐based assessments of projections. This approach requires that for example, rainfall change patterns must be underpinned by plausible mechanisms of change. A mechanism of change is plausible when it is identifiable in observational or reanalysis data sets. We applied such a method over Central Equatorial Africa using the regionally coupled ocean model (ROM) and its uncoupled version, the regional climate model REMO. The results show that projections based on ROM are more plausible thanks to a better depiction of the atmospheric baroclinicity associated with rainfall changes. Key Points: The coupled ROM and uncoupled REMO project increased precipitation for the future, with ROM projecting wetter conditions than REMOChanges in sea and land surface temperatures underpin rainfall changes with the interplay of changes in atmospheric baroclinicityROM shows more plausible precipitation change in association with its improved historical climatology and reliable mechanisms of change [ABSTRACT FROM AUTHOR]

Arctic amplification (AA), characterized by a more rapid surface air temperature (SAT) warming in the Arctic than the global average, is a major feature of global climate warming. Various metrics have been used to quantify AA based on SAT anomalies, trends, or variability, and they can yield quite different conclusions regarding the magnitude and temporal patterns of AA. This study examines and compares various AA metrics for their temporal consistency in the region north of 70°N from the early twentieth to the early 21st century using observational data and reanalysis products. We also quantify contributions of different radiative feedback mechanisms to AA based on short‐term climate variability in reanalysis and model data using the Kernel‐Gregory approach. Albedo and lapse rate feedbacks are positive and comparable, with albedo feedback being the leading contributor for all AA metrics. The net cloud feedback, which has large uncertainties, depends strongly on the data sets and AA metrics used. By quantifying the influence of internal variability on AA and related feedbacks based on global climate model ensemble simulations, we find that water vapor and cloud feedbacks are most heavily affected by internal variability. Plain Language Summary: The Arctic has been observed to warm at a faster pace than the rest of the world, especially over the recent few decades. This phenomenon is known as Arctic amplification (AA), which has connections with different components of the Earth's climate system. Various metrics are used in the science community to measure AA in different data sets and to assess the performance of climate models in reproducing the observed history of AA. In this study, we apply a collection of AA metrics from the literature to the same historical data sets, based on observations and climate model experiments, to identify differences/similarities and pros/cons of these metrics. We also study how radiatively active processes in the climate system, like changes in snow/ice surface reflectivity (albedo), air temperature changes with height (lapse rate), humidity increase, and cloud changes, can in turn amplify (positive feedback) or dampen (negative feedback) this overall Arctic warming. Our study shows that changes in the Arctic surface albedo and lapse rate are the main drivers of AA. We also find that natural climate variations can greatly influence Arctic warming in climate models and their ability to reproduce the observed AA by affecting regional water vapor and cloud patterns. Key Points: Arctic amplification is quantified by applying various metrics to observational data, reanalysis products, and model simulations.Radiative feedback analysis shows that albedo and lapse rate feedbacks are the main contributors to Arctic amplification for all metrics.Water vapor and cloud feedbacks are most heavily influenced by internal variability. [ABSTRACT FROM AUTHOR]

Clouds play a pivotal role in determining Earth's climate, impacting several atmospheric processes influencing the weather. The study explores the temporal variations in cloud base height (CBH) over Pune, a tropical station, from 2017 to 2023 using a Laser-Based Ceilometer with a range of 0–7500 m. Our analysis shows seasonal trends in CBH, with the monsoon season exhibiting unique characteristics showing the lowest average CBH and minimal diurnal changes during the study period. Conversely, the pre-monsoon, post-monsoon, and winter season show a gradual rise of CBH in the morning, maintaining higher levels until late afternoon, followed by a decline in the evening. Analysis of the diurnal frequency of cloud occurrence at 500 m intervals reveals the monsoon season's dominance in the 500 m to 1000 m layer. Throughout the year, low cloud bases (CBH <2000 m) are most frequent, outpacing the frequency of mid and high cloud bases. High cloud base (7500 < CBH > 6000 m) is the least common, showing a semi-diurnal pattern in winter with peaks around noon and midnight. These findings not only enhance our understanding of Pune's atmospheric behaviour but also offer valuable information for weather prediction, climate modelling, and environmental monitoring. [ABSTRACT FROM AUTHOR]

The evaluation of climate models is a crucial step in climate studies. It consists of quantifying the resemblance of model outputs to reference data to identify models with superior capacity to replicate specific climate variables. Clearly, the choice of the evaluation indicator significantly impacts the results, underscoring the importance of selecting an indicator that properly captures the characteristics of a "good model". This study examines the behaviour of six indicators, considering spatial correlation, distribution mean, variance and shape. Monthly data for precipitation, temperature and teleconnection patterns in Central America were utilized in the analysis. A new multicomponent measure was selected based on these criteria to assess the performance of 32 CMIP6 models in reproducing the annual seasonal cycle of these variables. The top six models were determined using multicriteria methods. It was found that even the best model reproduces one derived climatic variable poorly in this region. The proposed measure and selection method can contribute to enhancing the accuracy of climatological research based on climate models. [ABSTRACT FROM AUTHOR]

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

The theory of radiative transfer in the atmosphere is crucial in the study of climate, because radiative exchanges are at the origin of the atmospheric dynamics. It is therefore important to evaluate this phenomenon in order to be able to take effective measures to tackle climate change. The objective of this work is to evaluate the capability of the RegCM5 climate model to reproduce radiative transfer over Central Africa. The analysis is carried out over a 10‐year period, from January 2002 to December 2011 preceded by 1 year as spin‐up. RegCM5 model were evaluated using the ERA5 dataset for the radiative transfer parameters (the shortwave radiation [SWR], longwave radiation [LWR], cloud cover [CLT], surface albedo [ALB] and surface temperature), as well as CHIRPS dataset for precipitation. Three subregions were identified for more specific analysis of the model, namely the Sahel, Congo basin and Cameroon highlands. Two radiative schemes were used: the radiative scheme of the community climate model (CCM) and Rapid Radiative Transfer Model (RRTM). The assessment of radiative transfer parameters was carried out by examining their seasonal variability and annual cycles using data from two RegCM5 experiments, RegCM5‐CCM3 and RegCM5‐RRTM. Before this assessment, a sensibility analysis to convective schemes carried out with the default RegCM5 radiative scheme (CCM3) shows that Grell scheme with Arakawa and Shulber closure is the best scheme to represent key radiation parameters (LWR and SWR). This convective scheme is therefore used for assessing the two Radiative transfer schemes. Results show that both RegCM5 experiments simulate relatively well the variables linked to radiative transfer for the four seasons of the year. However, RegCM5 with RRTM as radiative scheme depicts better performance over all subregions and seasons, suggesting that the choice of this scheme does not depend on land cover, topography and rainfall regimes in a complex region such as Central Africa. [ABSTRACT FROM AUTHOR]

Several damaging tropical cyclones (TCs) have occurred recently over the South Indian Ocean (SIO) region, causing enormous social and economic losses. Yet, while many studies have examined SIO TC characteristics using observations and reanalysis, only a few have assessed these characteristics specifically for this region in climate models, and fewer have investigated their projections under climate change. Here we do this for a historical (1980–2010) and future (2020–2050) period, using multimodel simulations from the High Resolution Model Intercomparison Project, as well as examine biases in the historical period relative to a reanalysis (ERA5). The models have horizontal resolutions of 25–50 km, which has enabled an improved ability to represent tropical cyclones globally in previous studies. TempestExtremes software is employed to detect tropical storm and cyclone tracks. In cases where TempestExtremes cannot be applied due to a lack of requisite variables in a dataset, we instead examine extreme wind speeds in that dataset. For the historical period, we find considerable variation in model biases compared to ERA5, which itself exhibits realistic spatial patterns of tracks and their monthly distribution. Models do at least agree on positive biases in track frequency east of Madagascar and somewhat in the Mozambique Channel. However, the models and ERA5 only produce Category 3 tropical cyclones at best. Wind speeds for 25 km resolution models have much larger positive biases than for 50 km ones, suggesting the former can simulate even higher‐category tropical cyclones. Considerable intermodel variation is also found in track changes between the future and historical periods. No systematic intercategory pattern of change exists, and low signal‐to‐noise may obscure any such patterns in the limited timespan of available data. Thus, no meaningful conclusions can be drawn regarding changes in track intensity. Nevertheless, track frequency broadly decreases across models for the region, as does accumulated cyclone energy. An east‐to‐west shift in track location from east of Madagascar toward the Mozambique Channel is also implied by track frequency and wind speed changes. Our findings provide information to potentially improve storm resiliency in this vulnerable region. [ABSTRACT FROM AUTHOR]

Guanabara Bay, located in the Metropolitan Region of Rio de Janeiro, one of the largest urban coastal areas in the Southern Hemisphere, is subject to intense maritime traffic due to the presence of several ports. These facilities are affected by sea level fluctuations, influenced by atmospheric and astronomical forces, which motivates synoptic and climatological analysis, including meteorological and astronomical tidal phenomena. This study aimed to assess the evolution of tidal components and the relative mean sea level (MSL) between 1990 and 2021, as well as the atmospheric influence on extreme meteorological tide events, in which the MSL exceeded ±2 and ±3 (±29.98 and ±44.97 cm) standard deviations. The results have shown that, albeit small, the main tidal components (M2, S2, O1, M4, Q1, K1, K2 and N2) indicated positive trends in the increase of amplitude and phase, despite the small quantities. For the relative MSL, an increase of 0.30 cm·year−1 was observed in agreement with estimates from global series from satellite altimeters and climate model predictions. Pressure and wind field analyses led to the identification of seasonal variability and the pattern of evolution of atmospheric systems associated with extreme events. In the case of positive surges, very intense winds from the SW, extending along the S/SE Brazilian coast, are caused by the presence of a high‐pressure centre over the continent and low‐pressure centres over the ocean, leading to water piling up. In turn, negative surges are caused by the presence of an anticyclone over the ocean, generating NE winds parallel to the coast of Rio de Janeiro, inducing Ekman transport and lowering the sea level. Therefore, the extreme sea level events in the Guanabara Bay are not triggered by local forces, but rather depend on the temporal persistence and direction of winds along the S/SE Brazilian coast. [ABSTRACT FROM AUTHOR]

Ecological simulations including forest and vegetation growth models require climate inputs that match the resolution and extent of the process being modelled. Climate inputs are often derived at resolutions coarser than the scale of many ecosystem processes. Machine learning models can be trained to spatially downscale climate data to fine (30 m) resolution using topographic variables such as elevation, aspect and other site‐specific factors. Statistically downscaled climate models will have spatially varying uncertainty that is not usually incorporated into downscaling techniques for error propagation into later models, are often applied on smaller areas, are not fine enough resolutions for many modelling techniques, or are not always scalable to large spatial extents. There remains opportunity to leverage machine learning advancements to downscale climate to very fine (30 m) resolutions with associated spatially explicit uncertainty to represent microclimatic variation in ecological models. In this study, we used quantile machine learning to produce 30 m downscaled temperature and precipitation data and associated model prediction uncertainty for the state of California. Temperature models were accurate at downscaling 4 km climate data to 30 m, performing better than the 4 km data at high and low slope positions and at high elevations, especially where there were fewer weather observations. Precipitation model predictions did not show global improvement over the 4 km scale, but were more accurate at high elevations, slopes with higher solar radiation and in valleys. For all climate variables, the added detail of spatial explicit uncertainty via 90% prediction intervals provides critical insight into the utility of empirically downscaled climate. The resulting 30 m spatially contiguous outputs can be used as ecological model inputs with uncertainty propagation, to illuminate climate trends over time as a function of fine‐scale spatial factors, and to highlight areas of spatially explicit uncertainty. [ABSTRACT FROM AUTHOR]

Westerly wind bursts (WWBs) in the western–central equatorial Pacific are critical in El Niño–Southern Oscillation (ENSO) dynamics. Understanding how they may change with global warming has important implications for future projections of El Niño. In this study, we investigate how the enhanced eastern equatorial Pacific warming pattern, emerging in future climate projections, can influence WWB characteristics in an atmospheric general circulation model, CAM6. Changes in three main factors affecting WWBs—El Niño-conditions mean westerly wind stress anomalies, the Madden–Julian oscillation (MJO), and tropical cyclones (TCs)—are analyzed. We find that during El Niño onset (December–April), the WWB wind stress intensity remains largely unchanged but WWBs shift westward by about 20° of longitude. In contrast, during El Niño development (May–November), the WWB intensity increases by 41% or 79% in two warming scenarios considered [shared socioeconomic pathways (SSP5-8.5) and SSP2-4.5, respectively], which is mainly caused by a higher frequency of TC occurrence in the central tropical Pacific within 15°N/S. Further, we find that westerly wind speed anomalies associated with El Niño and the MJO also increase during El Niño development. However, as trade winds weaken in the central-eastern equatorial Pacific, the mean strength of the resulting westerly wind stress anomalies does not change much, and no significant eastward shift of WWBs is observed. Thus, our atmospheric model simulations suggest a strong TC-driven increase in WWB wind stress anomalies during El Niño development and hence a more important role of TCs in WWB generation, which in a coupled system could lead to stronger ENSO events, enhanced TC–ENSO coupling, and even greater intensification of WWBs. [ABSTRACT FROM AUTHOR]

Assessing if CMIP models can detect observed climate change signals is crucial for evaluating their realism and strengthening confidence in future projections. These signals can only be compared once distinguishable from the background climate "noise." Built for impact studies but not detection purpose, the time of emergence (ToE) estimates when this occurs using a fixed signal-to-noise ratio. To more accurately assess models' ability to reproduce observed climate change signals, we introduce the time of detection (ToD), which employs a statistical significance test of the nonlinear trend, accounting for the time series length. ToD and ToE are computed for sea surface temperature (SST) and relative SST (RSST) across four observational and 69 CMIP5/6 historical and shared socioeconomic pathways 5–8.5 (SSP5–8.5) scenario datasets. ToD precedes ToE and is less sensitive to the chosen threshold. While already detectable in most of the tropics, SST warming in the equatorial Pacific is detectable in ∼90% of models but only one out of four observational datasets, due to a stronger modeled warming. Because RSST signals are weaker than SST signals, they do not emerge anywhere but are detectable in two regions. The enhanced warming in the western Indian Ocean, detected in ∼45% of models, is not robust in observations when excluding pre-1960 dubious data. Conversely, the subdued southeast Pacific warming detected in observations and ∼80% of models is robust and consistent with a poleward extension of the Southern Hemisphere Hadley cell. The ToD's ability to detect subtle signals allows for assessing the realism of the CMIP warming pattern in this region. Significance Statement: Recent studies emphasize the importance of the climate change warming pattern and its apparent discrepancy between climate models and observations in the equatorial Pacific. We argue that our newly proposed time of detection (ToD) is better suited than the well-known time of emergence (ToE) for investigating where modeled and observed warming patterns can be confidently compared. In agreement with previous studies, our analysis indicates model–observations disagreement over the equatorial Pacific. Using ToD instead of ToE allows for detecting a robust subdued warming (i.e., weaker than the tropical average) in the southeast Pacific and therefore confirms the realism of the CMIP warming pattern in this region. [ABSTRACT FROM AUTHOR]

The southern Great Plains experience fluctuating precipitation extremes that significantly impact agriculture and water management. Despite ongoing efforts to enhance forecast accuracy, the underlying causes of these climatic phenomena remain inadequately understood. This study elucidates the relative influence of the tropical Pacific and Atlantic basins on April–May–June precipitation variability in this region. Our partial ocean assimilation experiments using the Community Earth System Model unveil the prominent role of interbasin interaction, with the Pacific and Atlantic contributing approximately 70% and 30%, respectively, to these interbasin contrasts. Our statistical analyses suggest that these tropical interbasin contrasts could serve as a more reliable indicator for late-spring precipitation anomalies than El Niño–Southern Oscillation. The conclusions are reinforced by analyses of seven climate forecasting systems within the North American Multi-Model Ensemble, offering an optimistic outlook for enhancing real-time forecasting of late-spring precipitation in the southern plains. However, the current predictive skills of the interbasin contrasts across the prediction systems are hindered by the lower predictability of the tropical Atlantic Ocean, pointing to the need for future research to refine climate prediction models further. Significance Statement: Agriculture and infrastructure in the southern plains face challenges from severe late-spring precipitation extremes. Traditional predictors like El Niño–Southern Oscillation (ENSO) lose effectiveness during the critical spring-to-summer transition, creating a forecasting gap. This study introduces the concept of tropical interbasin interactions, known to enhance seasonal predictability for late-spring precipitation in the southern plains. Novel climate model experiments highlight contributions from the tropical Pacific and Atlantic, offering a promising predictability that potentially surpasses the limitations of ENSO-based predictions. These outcomes hold the potential for developing operational forecasts of late-spring precipitation anomalies in the southern plains, enabling proactive risk management. [ABSTRACT FROM AUTHOR]

Solar proxies are a valuable tool used in most atmospheric models to simulate or predict the state and variation of the different layers linked to the solar source. In this work, we analyze the performance, through correlation analysis, of six solar EUV proxies, and different combination of them, to reproduce annual ionospheric F2 critical frequency (foF2) from ten different stations. We find that certain combinations of traditional proxies outperform the capacity to reproduce interannual ionospheric foF2 variability, resulting in better statistical correlation and time stability, these new proxies could be a valuable tool to predict the state of the atmosphere and for interpolation in case of missing data. Different combinations of proxies such as F30, MgII, Sn or Lya, creates better indicators than using them alone. These new EUV proxies are also used to estimate long-term trends along the ten stations. As we increase the correlation, the estimated trend decrease as expected by definition. However, a key point is that the average trends, after filtering solar activity, result negative as was predicted by the increase of the greenhouse gases. [ABSTRACT FROM AUTHOR]

Climate change threatens agricultural sustainability, especially water resource management. This study comprehensively analyses how climate change impacts irrigation water needs in India's vital Girna command area, which lies in a semi-arid region, where a decision support tool with climate scenario analysis was integrated, incorporating five different climate models with region-specific agricultural data to evaluate future irrigation demands under various climate change scenarios. The maximum and minimum temperatures will rise under two representative concentration pathways (RCP) scenarios, with RCP 8.5 projecting a more significant increase than the RCP 4.5 scenario. Reference evapotranspiration (ETo) will also increase compared to the base period, reaching its maximum in May and minimum in December across all climate models. However, crop water requirements should remain stable or slightly decrease, with a maximum occurring in October and a minimum in March across all climate models. Depending on the climate model and RCP scenario, maximum effective precipitation will vary and most likely occur in July. Maximum gross irrigation requirements may decrease under RCP 8.5 for the base period, particularly in November. These findings significantly impact agricultural planning, water management, and climate change adaptation strategies. This study can assist stakeholders in making informed decisions for sustainably managing water resources in the Girna command area and beyond. [ABSTRACT FROM AUTHOR]

Historically, meteorological drought in the western United States (WUS) has been driven primarily by precipitation deficits. However, our observational analysis shows that, since around 2000, rising surface temperature and the resulting high evaporative demand have contributed more to drought severity (62%) and coverage (66%) over the WUS than precipitation deficit. This increase in evaporative demand during droughts, mostly attributable to anthropogenic warming according to analyses of both observations and climate model simulations, is the main cause of the increased drought severity and coverage. The unprecedented 2020-2022 WUS drought exemplifies this shift in drought drivers, with high evaporative demand accounting for 61% of its severity, compared to 39% from precipitation deficit. Climate model simulations corroborate this shift and project that, under the fossil-fueled development scenario (SSP5-8.5), droughts like the 2020-2022 event will transition from a one-in-more-than-a-thousand-year event in the pre-2022 period to a 1-in-60-year event by the mid-21st century and to a 1-in-6-year event by the late-21st century. [ABSTRACT FROM AUTHOR]

Extreme precipitation and flooding events are rising globally, necessitating a thorough understanding and sustainable management of water resources. One such setting is the Nile River's source areas, where high precipitation has led to the filling of Lake Nasser (LN) twice (1998–2003; 2019–2022) in the last two decades and the diversion of overflow to depressions west of the Nile, where it is lost mainly to evaporation. Using temporal satellite-based data, climate models, and continuous rainfall-runoff models, we identified the primary contributor to increased runoff that reached LN in the past two decades and assessed the impact of climate change on the LN's runoff throughout the twenty-first century. Findings include: (1) the Blue Nile subbasin (BNS) is the primary contributor to increased downstream runoff, (2) the BNS runoff was simulated in the twenty-first century using a calibrated (1965–1992) rainfall-runoff model with global circulation models (GCMs), CCSM4, HadGEM3, and GFDL-CM4.0, projections as model inputs, (3) the extreme value analysis for projected runoff driven by GCMs' output indicates extreme floods are more severe in the twenty-first century, (4) one adaptation for the projected twenty-first century increase in precipitation (25–39%) and flood (2%-20%) extremes is to recharge Egypt's fossil aquifers during high flood years. [ABSTRACT FROM AUTHOR]

The receptive field (RF) plays a crucial role in convolutional neural networks (CNNs) because it determines the amount of input information that each neuron in a CNN can perceive, which directly affects the feature extraction ability. As the number of convolutional layers in CNNs increases, there is a decay of the RF according to the two-dimensional Gaussian distribution. Thus, an effective receptive field (ERF) can be used to characterize the available part of the RF. The ERF is calculated by the kernel size and layer number within the neural network architecture. Currently, ERF calculation methods are typically applied to single-channel input data that are both independent and identically distributed. However, such methods may result in a loss of effective information if they are applied to more general (i.e., multi-channel) datasets. Therefore, we proposed a multi-channel ERF calculation method. By conducting a series of numerical experiments, we determined the relationship between the ERF and the convolutional kernel size in conjunction with the layer number. To validate the new method, we used the recently published global wave surrogate model for climate simulation (GWSM4C) and its accompanying dataset. According to the newly established relationship, we refined the kernel size and layer number in each neural network of the GWSM4C to produce the same ERF but lower RF attenuation rates than those of the original version. By visualizing the gradient map at several points in West African and East Pacific areas, the high gradient value regions confirmed the known swell sources, which indicated effective feature extraction in these areas. Furthermore, the new version of the GWSM4C yielded better prediction accuracy for significant wave height in global swell pools. The root mean square errors in the West African and East Pacific regions reduced from approximately 0.3 m, in the original model to about 0.15 m, in the new model. Moreover, these improvements were attributed to the higher efficiency of the newly modified neural network structure that allows the inclusion of more historical winds while maintaining acceptable computational consumption. [ABSTRACT FROM AUTHOR]

The field of extreme event attribution (EEA) has rapidly developed over the last two decades. Various methods have been developed and implemented, physical modelling capabilities have generally improved, the field of impact attribution has emerged, and assessments serve as a popular communication tool for conveying how climate change is influencing weather and climate events in the lived experience. However, a number of non-trivial challenges still remain that must be addressed by the community to secure further advancement of the field whilst ensuring scientific rigour and the appropriate use of attribution findings by stakeholders and associated applications. As part of a concept series commissioned by the World Climate Research Programme, this article discusses contemporary developments and challenges over six key domains relevant to EEA, and provides recommendations of where focus in the EEA field should be concentrated over the coming decade. These six domains are: (1) observations in the context of EEA; (2) extreme event definitions; (3) statistical methods; (4) physical modelling methods; (5) impact attribution; and (6) communication. Broadly, recommendations call for increased EEA assessments and capacity building, particularly for more vulnerable regions; contemporary guidelines for assessing the suitability of physical climate models; establishing best-practice methodologies for EEA on compound and record-shattering extremes; co-ordinated interdisciplinary engagement to develop scaffolding for impact attribution assessments and their suitability for use in broader applications; and increased and ongoing investment in EEA communication. To address these recommendations requires significant developments in multiple fields that either underpin (e.g., observations and monitoring; climate modelling) or are closely related to (e.g., compound and record-shattering events; climate impacts) EEA, as well as working consistently with experts outside of attribution and climate science more generally. However, if approached with investment, dedication, and coordination, tackling these challenges over the next decade will ensure robust EEA analysis, with tangible benefits to the broader global community. [ABSTRACT FROM AUTHOR]

This study presents the model setup and results from the first calibrated, physically-based, spatially-distributed hydrological modelling of combined land cover and climate change impacts on a large sample of UK river catchments. The SHETRAN hydrological model was automatically calibrated for 698 UK catchments then driven by the 12 regional climate model projections from UKCP18, combined with urban development and natural flood management scenarios. The automatic calibration of SHETRAN produces a median Nash-Sutcliffe efficiency value of 0.82 with 581 catchments having a value greater than 0.7. 24 summary metrics were calculated to capture changes to important aspects of the flow regime. The UKCP18 realisations in SHETRAN indicate that a warming climate will cause river flows, on average, to decrease. These decreases are simulated to be greatest in the south and east of the UK, with droughts becoming longer and more severe. While high flows also decrease on average, an increased number of extremes are exhibited, implying a greater number of extreme flood events in the future, particularly in the north and west of the UK. In the urban development scenarios, for flood events there is an increase in flow with the increased urbanization, with the 1 in 3-year peak flow event showing the greatest increase. The natural flood management scenarios consider the effect of increasing woodland and adding surface water storage ponds. The inclusion of these features produces a complex response but overall, the modelling shows a reduction in low, median, and high flows, although the more extreme the flow event the smaller the percentage change in flow. Simulated timeseries and summary metric datasets are freely available on the CEDA archive. [ABSTRACT FROM AUTHOR]

While the tropical Pacific teleconnection to North America has been studied extensively, the impact of the Indian Ocean on North American climate has received less attention. Here, through observational analysis and hierarchy atmospheric model simulations with different complexity, we find that the Indian Ocean plays a crucial role in North American winter climate through a teleconnection termed the Indian Ocean - North America pattern. We show that in the warm Indian Ocean phase, this teleconnection contributes to anomalously cold winters along the west coast of the United States through advection with increased mountain snowfall, while simultaneously leading to warmer conditions over the Great Lakes region. Snow-albedo feedback amplifies these Rossby wave-induced surface anomalies. Remarkably, this teleconnection pattern is at work on both interannual and multi-decadal time scales, with its climatic impact being slightly less pronounced than that induced by tropical Pacific sea surface temperature anomalies. Our findings underscore the significance of the Indian Ocean in both the prediction and future projection of North American climate. This study shows that the Indian Ocean significantly affects North American winter climate through Rossby wave dynamics. During its warm phase, this teleconnection drives an eastern warming – western cooling anomaly in North America, along with snowfall changes. [ABSTRACT FROM AUTHOR]

Nowadays the phenomenon of global warming is unequivocal, as confirmed by the latest reports of the IPCC and studies of the climate-change impacts on ecosystems, global economy, and populations. The effect of climate change on groundwater is a very relevant task especially for regions dependent chiefly on groundwater availability, as for the southern Italy. In such a territorial framework, to achieve a detailed hydro-climatological characterization, an Ensemble of 15 RCMs (E15) derived from the EURO-CORDEX project was analyzed considering two IPCC Representative Concentration Pathways (RCP4.5 and RCP8.5). The E15 was calibrated over the period (1950–1996) by a statistical comparison with data observed by the regional meteorological network managed by the former National Hydrological Service (SIMN), Department of Naples. The effects of climate change on air temperature (T), precipitation (P) and, consequently, on actual evapotranspiration (ETR) and effective precipitation Pe (P – ETR) were analyzed until 2100. The latter was considered as a proxy of groundwater recharge of the principal aquifer systems, represented chiefly by the karst aquifers. As a principal result, it was found that the E15 is basically able to reproduce the observed annual precipitation (OBSP) and mean annual air temperature (OBST), being characterized by a very similar frequency distribution. Accordingly, an inferential statistical approach was performed for calibrating E15 precipitation (E15P) and air temperature (E15T) based on the compensation of the difference with OBSP (+ 7%) and OBST (− 16%). The E15 projects a reduction in precipitation and an increase in air temperature under both RCPs, with a divergence point between the two scenarios occurring by about 2040. As a principal result, Pe shows declining trends for both RCP scenarios, reaching a decrease of the 11-yrs moving average down to − 20%, for RCP4.5, and − 50%, for RCP8.5, even if characterized by relevant inter-annual fluctuations. [ABSTRACT FROM AUTHOR]

ABSTRACT The Cerrado biome, renowned for its biodiversity and threatened by rapid land transformation, encompasses a vast savanna ecosystem in Brazil. The region is characterised by a seasonal climate, influenced by a myriad of meteorological systems creating diverse and non‐homogeneous rainfall regimes across the region. To account for this heterogeneity, we propose a novel classification of the Cerrado using rainfall data to delineate three distinct climatic regions: Eastern, Southern and Central‐West Cerrado. The Eastern region exhibited the driest and most seasonal climate, marked by high vapour pressure deficit (VPD) and predominantly open‐canopy vegetation. Conversely, the Southern region, characterised by lower seasonality, boasts a higher proportion of forest cover and lower mean VPD. The Central‐West region, encompassing diverse landscapes, featured areas with higher precipitation levels, particularly along the Amazônia border. Furthermore, we conducted trend analyses on observed station data and used CMIP6 models to evaluate future scenarios under differing emissions trajectories. While observed trends in mean rainfall were marginal, VPD demonstrated a notable upward trend of approximately 1% annually throughout the biome. Climate models indicated a substantial drying close to the Amazônia border, and wetter conditions in the southeast. All Cerrado regions are anticipated to experience amplified seasonality and VPD, with VPD projected to surge by approximately 30% (60%) under low (high) emissions scenarios by the end of the century. Notably, the transition from the dry to wet season was the most affected. Our study provides critical insights on how the climatic heterogeneity of the Cerrado shapes vegetation structure distribution and how future changes will exacerbate water stress throughout the biome. These findings underscore the importance of understanding climate variability for effective conservation and management strategies in the region. [ABSTRACT FROM AUTHOR]

Urbanization is one of the important drivers of increasing local temperatures. As cities and urban areas evolve, extensive land use and land cover (LULC) changes alter the physical characteristics of surface materials. This modification results in reduced evapotranspiration rates, ultimately contributing to higher surface and air temperatures. This study investigated the impact of urbanization on urban temperature in Jakarta. Urban temperature was simulated for a 20-year time period (1995–2014) by the urban boundary layer climate model UrbClim, using LULC data for both 1995 and 2014. Temperature changes were analysed by assessing the temperature anomaly across different LULC change classes divided into four main classes namely no built-up changes (BB), no green spaces changes (GG), built-up to green spaces (BG), and green spaces to built-up (GB). The study revealed that the conversion of green spaces to built-up areas (GB) had the most significant impact on the increase in air temperature. This was indicated by the mean values of the temperature anomaly of GB of about 0.24°C followed by GG, BB, and BG with the mean values of the temperature anomaly of about 0.20°C, 0.19°C, 0.17°C, respectively. The different temperature anomalies of the LULC change classes indicate that green spaces have an important role in maintaining local climate. Hence, it is important for local government to effectively manage the composition, the quantity, as well as the distribution of green spaces within a city. By looking at temperature anomalies of LULC change classes, the present study provides an alternative approach to many existing methods that provide general information about temperature changes, without specifically analyzing the effects of LULC transformations. [ABSTRACT FROM AUTHOR]

The US Antarctic Search for Meteorites (ANSMET) discovered a dense cluster of 88 ordinary chondrites with a total mass of more than 100 kg on a blue ice area (BIA) of 1.6 × 0.3 km2 near the Otway Massif, Grosvenor Mountains, Antarctica. The larger masses (weighing up to 29 kg) were found at one end of an oval‐shaped pattern and the smaller masses (50–200 g) at the other end. We measured concentrations of the cosmogenic radionuclides 10Be (half‐life—1.36 × 106 year) and 36Cl (3.01 × 105 year) in the metal fraction of 17 H chondrites, including 14 fragments of this cluster, to verify the hypothesis that this meteorite cluster on the Otway Massif BIA represents a meteorite strewn field produced by the atmospheric breakup of a single meteoroid. The 10Be and 36Cl concentrations confirm that 10 out of 14 H chondrites from different locations within this small area are paired fragments of the same meteorite fall, while the four other H chondrites represent two additional—smaller—falls. The radionuclides suggest a pre‐atmospheric mass of 200–400 kg for the large pairing group, suggesting that 25%–50% of the meteoroid survived atmospheric entry. Based on the distribution of the paired H chondrites and evidence of their common cosmic‐ray exposure history in space, we conclude that most of the 88 meteorites within this small area represent a meteorite strewn field. The small size of the strewn field suggests that the meteoroid entered at a steep angle (>60°), while the low amount of fusion crust on most meteorite surfaces most likely indicates atmospheric break up at low altitude, while additional fragmentation of a large surviving fragment may have occurred during impact on the ice. This well‐documented strewn field provides a good opportunity to apply model simulations of the atmospheric fragmentation of this object as a function of entry angle, velocity, and meteoroid strength. Cosmogenic 14C analyses in two members of the Otway Massif pairing group yield a terrestrial age of 15.5 ± 1.5 kyr, which represents the time elapsed since this meteorite fell on Earth. The excellent preservation of an Antarctic meteorite strewn field suggests that the Otway Massif BIA represents a relatively stagnant blue ice field. [ABSTRACT FROM AUTHOR]

This work utilizes an analytic expression for a model of acoustic propagation in a two‐layered, inhomogeneous atmosphere developed by the authors. The model is used to study the atmospheres of Earth, Mars, and Titan. In particular, vertical wave propagation in these atmospheres is studied. The effect(s) of a two‐layered, inhomogeneous atmosphere on vertical, acoustic propagation due to a time‐harmonic, point source are examined. An adiabatic atmosphere is used for the bottom layer (troposphere) and an isothermal one for the top (stratosphere). The derived, analytic solution is expressed in terms of the acoustic pressure fluctuations. For the adiabatic layers, the solutions satisfy Bessel's equation for orders of χ=−3.5,−4.45$\chi =-3.5, -4.45$, and −3.63$-3.63$ for Earth, Mars, and Titan, respectively. The Bessel function's argument is 2Ωτ$2 \Omega \tau$, where Ω$\Omega$ and τ$\tau$ are dimensionless frequency and height, respectively. For the isothermal layer, the solution represents a damped, harmonic oscillator with a cutoff value of Ωc$\Omega _{c}$. Only values greater than Ωc$\Omega _{c}$ are considered. The analysis and results are reported for combinations of single‐ and double‐layer atmospheres in the presence of a source on given boundaries. Acoustic propagation and transmission loss results are shown and discussed for all three planetary bodies: Earth, Mars, and Titan. [ABSTRACT FROM AUTHOR]

Climate models predict at least another 1.5°C warming in the next 75 years. This warming drives increased atmospheric drying and a global increase in the severity and duration of ecological drought. Vegetation has the capacity to reduce microclimate temperatures and atmospheric aridity.All species of plants create shade, move water, evapotranspire, humidify the air around them, and affect the temperature and vapour pressure deficit of the environment. Vegetation can thus act as a nature‐based solution to warming and atmospheric drying.These microclimate modifications likely depend on the traits, functional groups and diversity of the plant community. Vegetative feedbacks on microclimate are strong enough to buffer some plants against the negative impacts of warming and drying (e.g. facilitation).Synthesis: Here we present, for the first time, a trait‐based framework that can be applied across study systems for assessing microclimate temperature and humidity under vegetation. This framework includes multiple new hypotheses for future work in this area. We emphasize that a systematic examination of trait–microclimate relationships will enable us to use vegetation as a nature‐based solution to warming and atmospheric drying in a changing climate. [ABSTRACT FROM AUTHOR]

Meteorological mesoscale models with different urban parametrization are used to predict the local urban climate at 250 m resolution. The authors propose a hybrid machine learning approach to improve the mesoscale prediction accuracy using measured air temperature data from a sensor network and remove simulation bias. The simulation of the urban climate of Zurich during a hot summer is used as case study showing the improvements of the simulation accuracy. Based on the hybrid model results, a cumulative heat exposure index is proposed to map local hotspots in the city and assess the difference of cooling loads between rural and urban environments. Furthermore, intra-urban microclimatic differences of a typical mid-latitude city are explored to highlight the benefits of detailed simulations for building physics purposes. [ABSTRACT FROM AUTHOR]

In current land surface models or satellite remote sensing retrievals, clear‐sky surface albedo (α) is usually assumed to be symmetrical and relies only on the solar elevation angle (SEA). Based on 1‐min high‐resolution measurements of surface radiation fluxes, this study demonstrated that the diurnal variations of clear‐sky surface albedo exhibited a significant asymmetrical pattern in both summer and winter seasons over a semi‐arid grassland of the China's Loess Plateau. The results indicated that α values in the morning were generally larger than those in the afternoon at the same SEA, and diurnal asymmetry of surface albedo was distinctly prominent with SEA <40° in summer (before 9:30 a.m.) or SEA <20° in winter (before 10:00 am), and tended to diminish at midday. The averaged morning/afternoon albedo differences under sunny days were 0.05 (30.4%) and 0.09 (37.8%) in summer and winter seasons, respectively. Air relative humidity was positively correlated with the diurnal asymmetry of surface albedo, ascribed to probable formation of dew in the morning. Depression of the dew point was negatively linked to the morning/afternoon albedo differences, which was attributed to the strong scattering of incident sunlight by dewdrops could enhance the morning surface albedo. Such diurnal asymmetry of surface albedo should be included in the parameterization scheme of mesoscale and region‐scale climate models in the semi‐arid areas of China's Loess Plateau. [ABSTRACT FROM AUTHOR]

Cities are particularly vulnerable to surface water flooding. It is also well‐known that they influence local rainfall themselves, which has important implications for climate change adaptation planning for cities. At km‐scale resolution, convection‐permitting climate models (CPCMs) better resolve cities and should better represent local urban temperature and rainfall modifications. However, using state‐of‐the‐art pan‐African CPCM simulations with the Met Office Unified Model (CP4), we show that for the city of Johannesburg, South Africa, this is not the case. A significant enhancement of rainfall occurs over the city compared with surrounding rural areas, which is not seen in available observations. We demonstrate this is associated with an overestimated urban heat island effect, which leads to additional triggering of rainfall. Urban signals in future rainfall change are small compared with changes in the wider surroundings, the latter of which we expect to be more reliable than in models with parameterized convection. This suggests that deficiencies in representation of urban processes are of secondary importance in terms of future percentage change in rainfall. We recommend urban planners apply relative changes in CP4 as an uplift to observations, where available, or treat absolute future rainfall as an upper estimate if used directly. [ABSTRACT FROM AUTHOR]

Intensified by climate change, extreme rainfall events are frequently increasing and have become severe particularly in tropical and subtropical regions. On 16 April 2024, the United Arab Emirates (UAE) experienced a record-breaking flood event, characterized by ~ 250 mm of rain within a 24-hour period and surpassed the previous record of 1949. Later, on 2 May 2024, another rainfall event occurred but with low rainfall (~20 mm in 12 hours). These consecutive incidents raised a need to understand convective mechanism, so present study focus to analyse it using various cloud properties over Dubai. Our findings inferred that the April event was mostly driven by extensive cloud such as cumulonimbus which stretched up to ~ 16 km, with prominent features of high optical thickness (~150), small-sized cloud droplets (~23.89 µm), and considerable liquid water content (~2185 g/m−2). Further, we found high (~400–1800 J kg−1) and comparatively low (~0–700 J kg−1) potential energies, which trigerred convection on April 16 and May 02 respectively. Thus our study gained insights related to occurrence of convective activities at regional level using atmospheric and cloud parameters. These outcomes can be used as an input in climate models to improve weather patterns even in arid environments like Dubai, where cloud seeding is a regular practice. [ABSTRACT FROM AUTHOR]

There is compelling evidence that atmospheric moisture may either increase or decrease midlatitude eddy kinetic energy (EKE). We reconcile these findings by using a hierarchy of idealized atmospheric models to demonstrate that moisture energizes individual eddies given fixed large-scale background winds and temperatures but makes those background conditions less favorable for eddy growth. For climates similar to the present day, the latter effect wins out, and moisture weakens midlatitude eddy activity. The model hierarchy includes a moist two-layer quasigeostrophic (QG) model and an idealized moist general circulation model (GCM). In the QG model, EKE increases when moisture is added to simulations with fixed baroclinicity, closely following a previously derived scaling. But in both models, moisture decreases EKE when environmental conditions are allowed to vary. We explain these results by examining the models' mean available potential energy (MAPE) and by calculating terms in the models' Lorenz energy cycles. In the QG model, the EKE decreases because precipitation preferentially forms on the poleward side of the jet, releasing latent heat where the model is relatively cold and decreasing the MAPE, hence the EKE. In the moist GCM, the MAPE primarily decreases because the midlatitude stability increases as the model is moistened, with reduced meridional temperature gradients playing a secondary role. Together, these results clarify moisture's role in driving the midlatitude circulation and also highlight several drawbacks of QG models for studying moist processes in midlatitudes. Significance Statement: Dry models of the atmosphere have played a central role in the study of large-scale atmospheric dynamics. But we know that moisture adds much complexity, associated with phase changes, its effect on atmospheric stability, and the release of latent heat during condensation. Here, we take an important step toward incorporating moisture into our understanding of midlatitude dynamics by reconciling two diverging lines of literature, which suggest that atmospheric moisture can either increase or decrease midlatitude eddy kinetic energy. We explain these opposing results by showing that moisture not only makes individual eddies more energetic but also makes the environment in which eddies form less favorable for eddy growth. For climates similar to the present day, the latter effect wins out such that moisture decreases atmospheric eddy kinetic energy. We demonstrate this point using several different idealized atmospheric models, which allow us to gradually add complexity and to smoothly vary between moist and dry climates. These results add fundamental understanding to how moisture affects midlatitude climates, including how its effects change in warmer and moisture climates, while also highlighting some drawbacks of the idealized atmospheric models. [ABSTRACT FROM AUTHOR]

The accurate flux calibration of observational data is vital for astrophysics and cosmology because absolute flux uncertainties of stellar standards propagate into scientific results. With the ever higher precision achieved by telescopic missions (e.g. JWST) in the infrared (IR), suitable calibrators are required for this regime. The basis of the Hubble Space Telescope (HST) flux scale is defined by model fits of three hot (⁠|${T_\mathrm{eff}} > 30\, 000$|  K) hydrogen-atmosphere (DA) white dwarfs, which achieve an accuracy better than 1 per cent at optical wavelengths but falls below this level in the IR range. We present a network of 17 cooler DA white dwarfs with |${T_\mathrm{eff}} < 20\, 000$|  K as spectrophotometric flux standards that are equally, if not more, accurate at IR wavelengths. Cooler white dwarfs do not suffer from non-local thermal equilibrium effects in continuum flux or from ultraviolet metal line blanketing, have a larger sky density, are generally closer to Earth with little or negligible interstellar reddening, and have energy distributions peaking in the optical or near-IR. Using the latest grid of DA local thermal equilibrium atmosphere models with three-dimensional convection, the observed Space Telescope Imaging Spectrometer (STIS) and Wide Field Camera 3 (WFC3) fluxes of our network are accurate to 3 per cent over most of the range |$1450\!\! - \!\!16\, 000$|  Å, with a median standard deviation of 1.41 per cent. Fitting the HST STIS and WFC3 white dwarf spectral energy distributions (SEDs) and Balmer lines independently yields SEDs that agree within |$3\sigma$|⁠ , which demonstrates the precision of the models for our network. [ABSTRACT FROM AUTHOR]

In the context of rapidly rising sea levels over the north Indian Ocean, the decadal variations play significant role and predicting them is of utmost important to the society. The present study evaluates the quality of all the available retrospective decadal sea level predictions from the state-of-the-art climate models of the Coupled Model Intercomparison Project (CMIP) Phase 6 (CMIP6). The models are skilful in predicting the sea level over the tropical Indo-Pacific for the lead year 1 and thereafter the skill persists mainly over the western Indian Ocean regions. Most of the models exhibit significant skill in the Arabian Sea region at higher leads including 2–5 years and 6–9 years. Half of the models display higher sea level prediction skill over the Arabian Sea at lead year 2–5, compared to lead year 1. Taking into account of the close association between the Indian Ocean decadal sea level variations and the decadal climate variability in the Pacific and Atlantic, the skill of models in predicting the Pacific Decadal Oscillation (PDO) and Atlantic Multidecadal Oscillation (AMO) up to a decade is also assessed in this study. The AMO is better predicted in most of the models up to lead year 9, whereas only few models display skill in predicting the PDO. Skilful representations of strong cross equatorial flow and convergence of southerly winds towards the Arabian Sea region are noted in the CMIP6 predictions, which are responsible for better sea level prediction skill over the Arabian Sea. This study highlights that the better representation of AMO in the models has primarily contributed to the skilful sea level predictions over the Arabian Sea region. [ABSTRACT FROM AUTHOR]

A significant fraction of multidecadal fluctuations in the reanalysis-based gridded estimates of the observed climate variability over the past century and a half lie outside of the envelope generated by ensembles of climate-model historical simulations. Several pattern-recognition methods have been previously used to map out a truly global reach of the observed vs. simulated climate-data differences; in our own work we dubbed these global discrepancies the stadium wave to highlight their most striking spatiotemporal characteristic. Here we used a novel combination of such methods in conjunction with a large multi-model ensemble and two popular twentieth-century reanalysis products to: (i) succinctly describe the geographical evolution of the observed stadium wave in the annually sampled near-surface atmospheric temperature and mean sea-level pressure fields in terms of three basic patterns; (ii) show the robustness of this identification with respect to methodological details, including the demonstration of the truly global character of the stadium wave; and (iii) provide essential clues to its dynamical origin. Part I of this study decomposed all input time series into the forced signal and the residual internal variability; multi-model forced-signal estimates were also decomposed into their common-evolution part and the individual-model residuals. Analysis of the latter residuals suggests a contribution to the stadium-wave dynamics from a delayed climate response to variable external forcing despite the observed stadium-wave patterns' exhibiting the magnitudes and the level of global teleconnectivity unmatched by the forced-signal residuals. Part III of this paper will compare the observed stadium wave with the model simulated internal patterns. [ABSTRACT FROM AUTHOR]

The relationship between the South Asian summer monsoon (SASM) and El Niño Southern Oscillation (ENSO) is crucial for understanding and predicting changes of monsoon rainfall, as water resources strongly affect the living of billions of populations in South Asia. Although many mechanisms have been proposed to understand variations of SASM-ENSO relationship in previous studies, the impact of internal variability and primary control factor are under debate. Here, the potential contribution of internal variability to SASM-ENSO relationship is analyzed by using large-ensemble historical simulations in which members only differ for initial conditions, from two coupled climate models FGOALS-g3 and CESM2. While both ensembles show reasonable skills in monsoon climatology and basic ENSO characteristics, there is considerable spread of SASM-ENSO correlation coefficient across ensemble members: −0.57 to -0.01 (−0.66 to −0.25, range between 5 and 95%) in FGOALS and −0.74 to −0.36 (−0.83 to −0.51) in CESM2 for precipitation (circulation). We find that ENSO intensity is the primary factor significantly influencing the diversity of the SASM-ENSO relationship by modulating the Walker circulation, which is associated with the spread in mean state of tropical Pacific sea surface temperature zonal gradient. The ENSO intensity can explain about 8% and 20% of the spread in the SASM-ENSO relationship in the FGOALS and CESM2 large ensembles, respectively. Taking the mean of 110 and 100 members as a referenced truth, approximately 75 and 60 members are necessary to accurately estimate the SASM-ENSO relationship for FGOALS and CESM2, respectively, affirming the importance of sufficiently large ensemble sizes to reasonably address the interannual SASM-ENSO relationship. [ABSTRACT FROM AUTHOR]

Under global warming, extreme events have been increasing in the last decade and are projected to increase in the future with every increment of global warming. The potential increase in compound drought and hot events may induce a complex web of impacts on societies, ecosystems, and economies, including crop failure, wildfires, and water scarcity. This is particularly concerning for Brazil, where it has been demonstrated to be vulnerable to recent extreme climate events. Using an ensemble of CORDEX-CORE simulations over Tropical Brazil, we investigate changes in compound events in response to changes in radiative forcing and their impact on climate extreme events, including drought and extreme heat. The simulations are conducted at a 25 km horizontal grid spacing using lateral and lower boundary forcing from three Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models. Each model covers the period from 1980 to 2100 under two Radiative Concentration Pathways (RCP2.6 and RCP8.5) in the 21st-century projection period. We used observed data from the Brazilian Daily Weather Gridded Data (BR-DWGD) to evaluate the simulations and perform a quantitative assessment of areas affected by these compound events during the present day. The study finds a generally good agreement between RCM simulations and observed data, with moderate to high correlation coefficients for precipitation, though the strength of these correlations varies across different regions and seasons. The analysis emphasizes the prevalence of compound climate events during the Austral summer season and projects a significant increase in both extreme heat and drought events in the coming decades. These findings underscore Brazil's vulnerability to compound climate events, highlighting the need for adaptive strategies and policy interventions to mitigate the socio-economic and environmental impacts across various sectors. [ABSTRACT FROM AUTHOR]

Land-atmosphere coupling is a key factor for understanding the mean hydroclimatological conditions in South America. In this study, we analyzed how land-atmosphere coupling modulates the aridity conditions in the South American continent through experiments performed with a regional climate model. We assessed changes in precipitation (P), potential evaporation (PET), aridity index (ɸ = PET / P) and vapor pressure deficit (VPD) in experiments with and without soil moisture - atmosphere coupling. Additionally, we analyzed how air dry enthalpy (Hd), moist enthalpy (Hw) and total enthalpy (Ht) change between the coupled and uncoupled experiments, which help to identify patterns in aridity conditions. When analyzing ɸ, we found opposite responses in central-eastern Brazil and southeastern South America (SESA), the two main hotspots of land-atmosphere coupling. On the other hand, a widespread amplification of VPD is found. Both VPD and enthalpy are locally driven by changes in temperature and water vapor content and reflect wetter conditions in central-eastern Brazil and drier conditions in SESA. Changes in regional circulation, described in previous studies, increase moisture convergence in Brazil and help to explain the response of each individual metric when coupling is enabled. [ABSTRACT FROM AUTHOR]

The diurnal temperature range (DTR) serves as a vital indicator reflecting both natural climate variability and anthropogenic climate change. This study investigates the historical and projected multitemporal DTR variations over the Tibetan Plateau. It assesses 23 climate models from phase 6 of the Coupled Model Intercomparison Project (CMIP6) using CN05.1 observational data as validation, evaluating their ability to simulate DTR over the Tibetan Plateau. Then, the evolution of DTR over the Tibetan Plateau under different shared socioeconomic pathway (SSP) scenarios for the near, middle, and long term of future projection are analyzed using 11 selected robustly performing models. Key findings reveal: (1) Among the models examined, BCC-CSM2-MR, EC-Earth3, EC-Earth3-CC, EC-Earth3-Veg, EC-Earth3-Veg-LR, FGOALS-g3, FIO-ESM-2-0, GFDL-ESM4, MPI-ESM1-2-HR, MPI- ESM1-2-LR, and INM-CM5-0 exhibit superior integrated simulation capability for capturing the spatiotemporal variability of DTR over the Tibetan Plateau. (2) Projection indicates a slightly increasing trend in DTR on the Tibetan Plateau in the SSP1-2.6 scenario, and decreasing trends in the SSP2-4.5, SSP3-7.0, and SPP5-8.5 scenarios. In certain areas, such as the southeastern edge of the Tibetan Plateau, western hinterland of the Tibetan Plateau, southern Kunlun, and the Qaidam basins, the changes in DTR are relatively large. (3) Notably, the warming rate of maximum temperature under SSP2-4.5, SSP3-7.0, and SPP5-8.5 is slower compared to that of minimum temperature, and it emerges as the primary contributor to the projected decrease in DTR over the Tibetan Plateau in the future. [ABSTRACT FROM AUTHOR]