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Abstract: Aerosol‐cloud interactions (ACI) in warm clouds are the primary source of uncertainty in effective radiative forcing (ERF) during the historical period and, by extension, inferred climate sensitivity. The ERF due to ACI (ERFaci) is composed of the radiative forcing due to changes in cloud microphysics and cloud adjustments to microphysics. Here, we examine the processes that drive ERFaci using a perturbed parameter ensemble (PPE) hosted in CAM6. Observational constraints on the PPE result in substantial constraints in the response of cloud microphysics and macrophysics to anthropogenic aerosol, but only minimal constraint on ERFaci. Examination of cloud and radiation processes in the PPE reveal buffering of ERFaci by the interaction of precipitation efficiency and radiative susceptibility.

Given the important role of the land surface for climate, this study aims at (1) to evaluate the quality of the simulation of surface climate by the land-surface component of the EC-Earth3 ESM and (2) to assess the role of the coupling of the land surface with the atmosphere for the simulation of the surface climate in EC-Earth3. It is based on three simulations with different configurations of the EC-Earth3 ESM: an offline simulation with the land-surface component, a partially coupled simulation with the atmospheric component and a fully coupled simulation with the atmospheric component of EC-Earth3. The land-surface component of EC-Earth3 shows a characteristic geographical distribution of the biases for the different variables describing surface climate. As for the land-surface temperature, the model is characterized by warm biases in the tropics and the mid- and high latitudes of the Northern Hemisphere and a cold bias in the subtropics. For surface soil moisture, on the other hand, the model shows wet biases in the tropics and the mid- and high latitudes of the Northern Hemisphere and a dry bias in the subtropics. As for the surface energy fluxes, net radiation and sensible heat flux are underestimated in the tropics and the mid- and high latitudes of the Northern Hemisphere and overestimated in the subtropics, and the opposite behaviour is found for latent heat flux. The land-surface component of EC-Earth3 is characterised by an overall cold bias and a general underestimation of net radiation and sensible heat flux. Neither the coupling with the atmosphere nor the feedback between the land surface and the atmosphere affect the geographical distribution of the biases in surface climate characterising the offline simulation with the land-surface component of EC-Earth3 but have impacts on the magnitude of the local biases and regional details. The coupling with the atmosphere decreases land-surface temperature in the tropics and the mid- and high latitudes of the Northern Hemisphere and increases land-surface temperature in the subtropics, resulting in colder land-surface temperature and, thus, amplifying the overall cold bias found in the offline simulation. The feedback between the land surface and the atmosphere, on the other hand, increases land-surface temperature in the tropics and the mid-latitudes of the Northern Hemisphere and decreases land-surface temperature in the subtropics and the high latitudes of the Northern Hemisphere. The overall effect of the feedback between the land surface and the atmosphere is notably smaller than the effect of the coupling with the atmosphere for land-surface temperature and net radiation, similar for the fluxes of sensible and latent heat and stronger for surface soil moisture. The results of this study emphasize the need to improve the quality of the land-surface component of EC-Earth3 parallel with other components of the ESM. [ABSTRACT FROM AUTHOR]

Long-term changes in climate variability represent an important aspect of climate change, with various impacts on society and environment. In this study, we analyze outputs from 13 CMIP6 global climate models (GCMs) across the North Atlantic–European domain, focusing on their simulations of precipitation probability and short-term variability in both historical and future climates. Precipitation probability denotes the probability of a wet day (> 1 mm), and precipitation variability reflects the tendency to cluster wet days into sequences. By comparing against the ERA5 reanalysis, we found that the GCMs tend to overestimate precipitation probability across Europe in winter, whereas in summer, they have a tendency to underestimate it around 50°N. Precipitation variability is, on average, underestimated by the GCMs in summer, while overestimated in several regions in winter. Projections for the end of the twenty-first century indicate significant changes in both precipitation probability and variability which are more pronounced under the more pessimistic emission scenario compared to the moderate one. We found that the changes in probability and variability are mutually independent: the former being more latitudinal-dependent while the latter differs between the west and east. After identifying atmospheric circulation conducive and non-conducive to precipitation occurrence, we found that GCMs overestimating the frequency of conducive circulation tend to overestimate precipitation probability, and vice versa. Furthermore, increased precipitation variability is associated with higher circulation variability. Finally, our analysis reveals that projected changes in precipitation probability and variability are often linked to projected changes in atmospheric circulation, especially in winter. [ABSTRACT FROM AUTHOR]

The Huaihe River Basin is particularly vulnerable to climate change. This paper first evaluated interpolation methods for different meteorological elements, followed by an assessment of the simulation performance of various Coupled Model Intercomparison Project 5 (CMIP5) Global Climate Models (GCMs) for these elements. We then applied the Improved Quantile Mapping (IQM) method for bias correction of the GCMs. Finally, we analyzed the characteristics of future climate change in the Huaihe River Basin. The results show the following: (1) The radial basis function interpolation method is the most effective for rainfall, while Kriging performs best for air temperature. (2) The HadGEM2-AO model provides the most accurate rainfall simulations, MIROC-ESM best simulates maximum air temperature, and HadGEM2-ES is most effective for minimum air temperature. (3) The IQM method outperforms other approaches for bias correction of climate variables in the basin. (4) Future projections show an increase in both rainfall and air temperature, with more pronounced rises under the RCP8.5 scenario. Additionally, rainfall and maximum air temperature show considerable spatial variation across emission scenarios, while minimum air temperature consistently exhibits an upward trend. [ABSTRACT FROM AUTHOR]

Pakistan, like many other regions around the world, is experiencing the impacts of climate change, particularly in its northern region. These changes have adverse impacts on ecosystems and biodiversity. Herein, we have investigated future projections of temperature and precipitation trends for three periods historical (HT = 1975–2005), near-term (NT = 2010–2029), and mid-term (MT = 2030–2050) using climate model intercomparison projects along with global climate models (GCMs) including RCP4.5 and RCP8.5. The historical records spanning from 1975 to 2005 reveal that the Chilas region has a notable rise of 8 °C in maximum temperature (Tmax), whereas the Astore district exhibited a trend of decreasing temperatures. When examining the projected temperature trends using GCMs (RCP4.5 and RCP8.5), the Chilas region is predicted to undergo a further increase of 6 °C in Tmax. In contrast, the Babusar region is projected to observe a significant decrease of 2 °C for the period between 2010 and 2050. Additionally, the precipitation results obtained from historical-based analysis for the period 1975 to 2005 indicated that the Babusar area exhibited increased precipitation patterns to 20 mm on an annual basis. Similarly, the Astore region has the most significant decline in precipitation, with a reduction of 40 mm annually. The predicted precipitation patterns for the period between 2010 and 2050 under the RCP8.5 revealed that the Babusar region has maximum precipitation (25 mm). Conversely, the Astore region exhibited reduced precipitation patterns, recording minimum precipitation (40 mm). In the results from RCP4.5, the precipitation showed a similar pattern with a maximum of 35 mm and a minimum of 15 mm in the Babusar and Astore, respectively. The region's glaciers, snow cover, and land use systems are deteriorated by these changes in temperature and precipitation patterns. The increased winter and decreased summer precipitation under varied temperatures and precipitation cause land degradation, forest, and water resources. The cumulative impacts result in individuals experiencing poverty and raising concerns about the region's long-term viability. [ABSTRACT FROM AUTHOR]

The future behavior of synoptic-scale cyclones remains uncertain due to various factors; in terms of modeling it relies on the global climate models (GCMs) settings, such as their dynamical core, parameterizations, horizontal and vertical resolutions, etc. To cover one part of these examples, this study aims to understand how high (~ 25/50 km) and medium (~ 50/100 km) horizontal resolutions in HighResMIP-CMIP6 GCMs affect the climatology of cyclones over South America and southwestern South Atlantic Ocean. Present climate simulations of six GCMs are compared with CFSR and ERA5 reanalyses, the simulations reproduce the three main cyclogenetic regions near the eastern coast of South America very well. Nevertheless, high-resolution (HR) simulations slightly overestimate the frequency of cyclones compared to medium-resolution (MR) simulations. Models have smaller errors in simulating cyclone’s intensity than lifecycle, since they overestimate the long-lived events. The centered composites reveal that synoptic patterns (mean sea level pressure, 10-m winds and precipitation) of the cyclones show more organized and stronger fields in the simulations than reanalyses, especially close to the cyclone’s center. Mid-upper levels cyclogenesis mechanisms are well captured by simulations. While the differences between HR and MR composites are not visually pronounced, the Kling–Gupta efficiency (KGE) index indicates that HR performs better than MR in the representation of the cyclone spatial structures for almost all variables in all cyclogenetic regions. Although there are notable improvements in the HR simulations, synoptic-scale studies in South America using MR simulations (which are refined compared to most CMIP6 GCMs) can also be trustful. [ABSTRACT FROM AUTHOR]

Using the simulation results from the CMIP6 global climate models, we calculate the projected changes of different kinds of Arctic cyclones (ACs) in the twenty-first century and examine the characteristics related to the Arctic cyclones under two shared socio-economic pathways (SSP1-2.6, SSP5-8.5). There is a significant decline of ACs during winter over southern Greenland, the Barents Sea, and the Gulf of Alaska. In summer, the number of Arctic cyclones shows a significant circular decrease across most continental regions. By the end of the twenty-first century, the proportion of stronger, large-radius, and long-lifespan ACs is expected to increase, while the number of extreme Arctic cyclones will decrease in the future. However, trends in the intensity of Arctic cyclones depends on the measure of cyclone intensity used. Weaker baroclinic instability in the future is the primary reason for the decline of cyclone density in winter. In contrast, the situation in summer is more complicated. The number of Arctic cyclones in summer is influenced by factors such as the tropopause polar vortex and mid-latitude cyclones entering the Arctic, while positive anomalies in the Eady growth rate can lead to explosive cyclone development. [ABSTRACT FROM AUTHOR]

Climate change, one of the most pressing issues of the twenty-first century, threatens the long-term stability and short-term variability of water resources. Variations in precipitation and temperature will influence runoff and water availability, creating significant challenges as demand for potable water increases. This study addresses a critical literature gap by employing the Statistical Downscaling Model (SDSM) to downscale Global Climate Model (GCM) outputs for the Indravati River Basin, India. Maximum temperature (Tmax), minimum temperature (Tmin), and precipitation (PCP) were statistically downscaled, improving the spatial resolution of coarse GCM data. The model established strong predictor-predictand relationships, offering enhanced local-scale climate projections for the basin. This work provides critical insights into regional climate change impacts in a previously underexplored area. The study projected the meteorological variables (Tmax, Tmin, and PCP) for Chindnar, Jagdalpur, and Pathagudem stations using four GCMs, namely CanESM5, MPI-ESM1-2-HR, EC-Earth3, and NorESM2-LM for the baseline period (1990–2014). The Correlation Coefficient-values (R-values) range from 0.75 to 0.91 for maximum temperature, 0.85 to 0.96 for minimum temperature, and 0.71 to 0.83 for precipitation were achieved using SDSM. The best-performed MPI-ESM1-2-HR model was used to project maximum temperature, minimum temperature, and precipitation for 2024–2054 (2040s) and 2055–2085 (2070s) under SSP4.5 and SSP8.5 scenarios using SDSM. The downscaled results revealed significant shifts in meteorological patterns, highlighting the basin's sensitivity to different socio-economic pathways and future climate conditions. The percentage monthly, seasonal, and annual variations of Tmax, Tmin, and PCP were analysed based on each scenario and time period to suggest remedial measures for future floods and droughts. [ABSTRACT FROM AUTHOR]

Abstract: The influence of climate feedbacks on regional hydrological changes under warming is poorly understood. Here, a moist energy balance model (MEBM) with a Hadley Cell parameterization is used to isolate the influence of climate feedbacks on changes in zonal‐mean precipitation‐minus‐evaporation (P − E) under greenhouse‐gas forcing. It is shown that cloud feedbacks act to narrow bands of tropical P − E and increase P − E in the deep tropics. The surface‐albedo feedback shifts the location of maximum tropical P − E and increases P − E in the polar regions. The intermodel spread in the P − E changes associated with feedbacks arises mainly from cloud feedbacks, with the lapse‐rate and surface‐albedo feedbacks playing important roles in the polar regions. The P − E change associated with cloud feedback locking in the MEBM is similar to that of a climate model with inactive cloud feedbacks. This work highlights the unique role that climate feedbacks play in causing deviations from the “wet‐gets‐wetter, dry‐gets‐drier” paradigm.

Excessive dryness of arid regions has increased the intensity and spread of desertification. Investigating spatial and temporal patterns of desertification caused by climate change in the northwest of Yazd province using the Iranian model of desertification potential assessment (IMDPA) is one of the main objectives of this research. In this study, a twenty-year statistical period (2001-2020) was also selected as the base period to reveal climate change. And the precipitation and average temperature data collected from selected stations were downscaled with the BCC-CSM1-1 model from the CMIP5 series, under three radiative forcing scenarios RCP2.6, RCP4.5, and RCP8.5 using the LARS-WG6 simulator for the near future (period 2026-2055) and the far future (2071-2100). And the results of predicting climatic elements on the increase in the area of ​​areas prone to desertification in the studied region were evaluated. The results showed that the rainfall in the final decades of this century is lower than in the period 2026-2055 and in some places is increasing or decreasing compared to the average of the base period. Temperatures will increase relative to baseline for both future periods. Also, based on the IMDPA model, 80.54 percent of the area of ​​the region is in the severe desertification class in the base period. The intensity of climate-driven desertification in the distant future is more severe than in the near future and the base period. The largest changes in desertification classes in the near future are related to RCP2.6 and RCP4.5, and in the distant future are related to all scenarios. So that during this period, we will witness the transition and change of moderate and severe risk classes to severe and very severe classes, especially in RCP4.5. Therefore, at the end of this century, the intensity of desertification in the region will be more severe than in the base period and the near future.

Changes in climate in the South Atlantic region and adjacent regions have been described in numerous works using projections from global climate models from CMIP5 and CMIP6. This paper presents an evaluation of the ability of these models to reproduce the atmospheric circulation patterns (weather types) and their seasonal and inter‐annual variability. The analyses are performed based on the probability of occurrence of weather types in the historical period and in future projections. The scatter index and the relative entropy are the statistical parameters used to evaluate the models' performance in the historical period. Future projections consist of RCP2.6, 4.5 and 8.5 scenarios for the CMIP5 models and the SSP126, 245, 370 and 585 scenarios for the CMIP6 and are assessed at different time intervals: short term (2015–2039), mid‐term (2040–2069) and long term (2070–2100). The performance of projections is measured by analysing their consistency, that is, based on the similarity between projections of the same scenario in different models. The results show that the reproduction of the probability of occurrence of historical weather types and their seasonal and interannual variability was better performed by ACCESS1‐0, HadGEM2‐ES, HadGEM2‐CC, CMCC‐CM and MPI‐ESM‐P when assessing the models from CMIP5, and by HadGEM3‐GC31‐MM, ACCESS‐ESM1‐5, ACCESS‐ CM2 and MRI‐ESM‐P when assessing the models from CMIP6. As for future projections, only the BESM‐AO2‐5, GFDL‐ESM4 and HadGEM3‐GC31‐MM models showed inconsistency in one or more scenarios. [ABSTRACT FROM AUTHOR]

The assessment of trends in hydroclimatic characteristics averaged over the watershed area and changes in the fields of the hydrological cycle components in the 21st century in a large river basin of the Nizhnekamskoye Reservoir was carried out on the basis of a model approach combining regional space distributed hydrological model and global climate models. It is shown that a slight decrease by 8% is expected in the area-averaged specific runoff by the end of the century according to the Representative Concentration Pathway 8.5 scenario. However, in various parts of the watershed both a slight increase by 3–6% and a significant decrease in the mean annual runoff by 22% can occur, depending on physiographic conditions. Moreover, climatic changes can increase contrasts in the ecological status of the territory: warm regions in the steppe zone become even hotter and more arid, and the moisture availability of wetter territories in the centre and east of the basin increases even more. [ABSTRACT FROM AUTHOR]

Excessive dryness of arid regions has increased the intensity and spread of desertification. Investigating spatial and temporal patterns of desertification caused by climate change in the northwest of Yazd province using the Iranian model of desertification potential assessment (IMDPA) is one of the main objectives of this research. In this study, a twenty-year statistical period (2001- 2020) was also selected as the base period to reveal climate change. And the precipitation and average temperature data collected from selected stations were downscaled with the BCC-CSM1-1 model from the CMIP5 series, under three radiative forcing scenarios RCP2.6, RCP4.5, and RCP8.5 using the LARS-WG6 simulator for the near future (period 2026-2055) and the far future (2071-2100). And the results of predicting climatic elements on the increase in the area of areas prone to desertification in the studied region were evaluated. The results showed that the rainfall in the final decades of this century is lower than in the period 2026-2055 and in some places is increasing or decreasing compared to the average of the base period. Temperatures will increase relative to baseline for both future periods. Also, based on the IMDPA model, 80.54 percent of the area of the region is in the severe desertification class in the base period. The intensity of climate-driven desertification in the distant future is more severe than in the near future and the base period. The largest changes in desertification classes in the near future are related to RCP2.6 and RCP4.5, and in the distant future are related to all scenarios. So that during this period, we will witness the transition and change of moderate and severe risk classes to severe and very severe classes, especially in RCP4.5. Therefore, at the end of this century, the intensity of desertification in the region will be more severe than in the base period and the near future. [ABSTRACT FROM AUTHOR]

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]

A fundamental characteristic of extreme precipitation events (EPEs) is their horizontal scale. This horizontal scale can influence the intensity of an EPE through its effect on the timescale of an EPE as well as its effect on the strength of convective feedbacks. Thus, to have confidence in future projections of extreme precipitation, the horizontal scales of EPEs in global climate models (GCMs) should be evaluated. Analyzing daily output from 27 models participating in the Coupled Model Intercomparison Project phase 6 (CMIP6), including 13 models participating in the High Resolution Model Intercomparison Project (HighResMIP), we computed the horizontal scales of EPEs and extreme ascent for annual maximum EPEs during 1981–2000. We found that the horizontal scales of both EPEs and the associated ascending motion are resolution‐dependent: for a factor of seven increase in horizontal resolution, the horizontal scale decreases by a factor of approximately two to five, with higher sensitivity in the tropics than in the midlatitudes. Further analysis in the southern hemisphere midlatitudes reveals that this resolution dependence results from precipitation during the simulated EPEs that is almost entirely resolved rather than parameterized. However, the EPEs are not simply grid box storms, and analysis of the horizontal scales of geopotential anomalies suggests that the planetary‐scale dynamics in GCMs is not resolution‐dependent. Thus, the dominance of resolved precipitation during EPEs is more likely due to convection on the model grid or formation of strong, poorly resolved fronts, and additional work is needed to explore these possibilities and find a remedy for this resolution dependence. Plain Language Summary: Extreme precipitation is of great human interest because of the potentially severe flooding that it can cause. We are reliant on global climate models (GCMs) to project future changes in extreme precipitation, so there is a need to assess the realism of extreme precipitation in GCMs. One fundamental characteristic of an extreme precipitation event (EPE) is its horizontal size or "scale." In this study, we analyze a large archive of output from simulations using state‐of‐the‐art GCMs and compute the horizontal scale of EPEs during 1981–2000. We found that the horizontal scale depends on the spacing between grid points in the model. These grid points are like pixels in a digital photo, and the smaller the distance between grid points, the higher the model resolution. The fact that the EPE horizontal scale in GCMs is resolution‐dependent is concerning because the horizontal scale should be dictated by realistic physical processes rather than artificial aspects of the model configuration. While we have provided some initial insights into the reasons for this resolution dependence, additional work is needed to develop more complete explanations and improve models. Key Points: The simulated horizontal scale of the annual maximum of daily precipitation anomalies is sensitive to a model's horizontal resolutionThis extreme precipitation horizontal scale is determined almost entirely by resolved, not parameterized, precipitationThe simulated horizontal scale of geopotential disturbances during these extreme precipitation events is not resolution dependent [ABSTRACT FROM AUTHOR]

Drought is one of the most devastating threats to the livelihoods of the southern African population, who mainly rely on rain‐fed agriculture for income. Previous studies have highlighted that the Botswana High influences drought over the region; however, its influence on the spatial modes of drought remains unknown. This study examines the spatiotemporal structures of drought modes (DMs) over southern Africa and their link with the Botswana High in observation, reanalysis and Model for Prediction Across Scales (MPAS). To characterize droughts, the study uses the 3‐month scale standardized precipitation index (SPI) and the standardized precipitation evapotranspiration index (SPEI). Spatiotemporal characteristics of the DMs are identified using empirical orthogonal function (EOF) analysis on SPI and SPEI. EOF analysis is also used to identify the spatiotemporal characteristics of the Botswana High. The relationship between each DM and the Botswana High is quantified using correlation and R2 analysis. In all the datasets (Climate Research Unit (CRU), European Centre for Medium‐Range Weather Forecasts version 5 (ERA5), 20th Century reanalysis II (20C) and MPAS), the most dominant five DMs (hereafter DM1–DM5) over southern Africa jointly explain more than 60% of the interannual variability in the 3‐month scale summer droughts for SPEI and SPI. CRU, ERA5 and MPAS agree that the Botswana High correlates with the interannual variability of DM1, with a stronger correlation in ERA5 (r = −0.85) compared to MPAS (r = −0.42) and CRU (r = −0.35). Additionally, wet years (+ve SPEI and SPI) are characterized by a weak Botswana High and drought years (−ve SPEI and SPI) by a strong Botswana High. The wet and dry years correspond to the −ve and +ve phases of El Niño–Southern Oscillation (ENSO), respectively. Given this, the results of this study suggest that the Botswana High might be a teleconnection pattern through which ENSO signals influence DM1 over the region. [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]

Abstract It has been proposed that two isomers of the SO dimer (cis‐ and trans‐OSSO) are candidates for the unknown UV absorber in Venus' atmosphere because they have a good spectral match with the absorber, despite the low concentrations predicted by 1D photochemical models. Here OSSO chemistry (production from SO and loss by photolysis, thermal decomposition, and reaction with O and Cl) has been included in the photochemistry scheme of a 3D planetary climate model (PCM‐Venus) along with sulfur injection due to meteoric ablation. 1D multiple scattering radiative transfer modeling is then used to predict the resulting top‐of‐the‐atmosphere reflectance produced by OSSO. The modeled OSSO concentrations are shown to be ∼3 orders of magnitude too low to explain the observed absorbance levels, and the predicted ratio of the OSSO isomers provides an unsatisfactory match to the spectral shape of the unknown absorber.

Drought prediction is often fraught with significant uncertainties due to various factors. It is crucial to identify the key contribution source of uncertainty, and therefore enhancing the reliability of drought prediction and risk assessment. This study focuses on Beijing as a case study, employing multivariate analysis of variance (MVANOVA) to assess the contributions of uncertainties from three primary sources: Global Climate Models (GCMs), Shared Socioeconomic Pathway (SSP) scenarios, and drought indices. These factors were analyzed to predict meteorological drought characteristics such as duration, peak, and intensity, while also examining their spatiotemporal variations. Additionally, the minimum number of GCMs was explored by using the systematic sampling and derivative function methods to ensure the uncertainty triggered by GCMs reasonably estimated. The results indicated that, GCMs, and the interactions between SSPs and GCMs, are the two most significant sources of uncertainty affecting drought characteristic prediction in Beijing area. The main sources of uncertainty remain consistent across spatial and temporal scales. Furthermore, when the number of GCMs is less than seven, the uncertainty caused by the GCMs may be underestimated in Beijing area. These findings underscore the importance of reducing GCM-related uncertainties for reliable drought predictions and highlight the need to account for the interactions among factors in uncertainty research. [ABSTRACT FROM AUTHOR]

Zonal extensions of the Western Pacific subtropical high (WPSH) strongly modulate extreme rainfall activity and tropical cyclone (TC) landfall over the Western North Pacific (WNP) region. These zonal extensions are primarily forced on seasonal timescales by inter‐basin zonal sea surface temperature (SST) gradients. However, despite the presence of large‐scale zonal SST gradients, the WPSH response to SSTs varies from year to year. In this study, we force the atmosphere‐only NCAR Community Earth System Model version 2 simulations with two real‐world SST patterns, both featuring the large‐scale zonal SST gradient characteristic of decaying El Niño‐developing La Niña summers. For each of these patterns, we performed four experimental sets that tested the relative contributions of the tropical Indian Ocean, Pacific, and Atlantic basin SSTs to simulated westward extensions over the WNP during June–August. Our results indicate that the subtle differences between the two SST anomaly patterns belie two different mechanisms forcing the WPSH's westward extensions. In one SST anomaly pattern, extratropical North Pacific SST forcing suppresses the tropical Pacific zonal SST gradient forcing, resulting in tropical Atlantic and Indian Ocean SSTs being the dominant driver. The second SST anomaly pattern drives a similar westward extension as the first pattern, but the underlying SST gradient driving the WPSH points to intra‐basin forcing mechanisms originating in the Pacific. The results of this study have implications for understanding and predicting the impact of the WPSH's zonal variability on tropical cyclones and extreme rainfall over the WNP. Plain Language Summary: Westward extensions of the Western North Pacific subtropical high (WPSH) drive rainfall extremes over the Western North Pacific basin, and is important for the prediction of summer rainfall, including monsoonal rainfall and tropical cyclone activity. Studies have previously highlighted the importance of tropical large‐scale zonal sea surface temperature (SST) gradient—warm tropical Indian Ocean in conjunction with cold equatorial eastern Pacific Ocean—in developing and maintaining the summer WPSH and westward extensions. Here, we further show that even with very similar SST patterns, the large‐scale zonal SST pattern may belie forcing from inter‐basin SSTs versus intra‐basin SSTs. We find that the net influence from the Pacific basin determines whether inter‐basin remote SST gradients versus intra‐basin Pacific SST gradients are the predominant driver of westward extensions. Key Points: Two similar SST patterns belie two different mechanisms forcing the subtropical high's westward extensionsTropical Pacific SST gradient forcing of westward extensions can be suppressed by its extratropical SST forcingRemote Atlantic SST forcing drives westward extensions when the local net Pacific forcing is weak [ABSTRACT FROM AUTHOR]

Selecting appropriate Global Climate Models (GCMs) presents a significant challenge for accurate climate projections. To address this, a novel framework based on information theory based minimum redundancy and maximum relevancy (MRMR) method identifies top-performing GCMs across the entire study region using multicriteria decision analysis methodology. A subset of the ten best-performing models out of twenty-two GCMs is chosen for multi-model ensemble analysis. Five MME methods are selected to assess the ensemble performance of the ten selected GCMs, categorized into simple, regression-based, geometric-based, and machine learning ensembles. This study evaluates the effectiveness of the MME method based on a comprehensive index called the extended distance between indices of simulation and observation. An Adaptive Multimodel Standardized Drought Index (AMSDI) has been developed based on the optimal MME method. For the application of the framework and the proposed index, historical precipitation data from 1950 to 2014 were utilized from 28 grid points in the Punjab province of Pakistan as the reference dataset. Additionally, simulations from 22 models of the Coupled Model Intercomparison Project phase 6, both past and future, were employed for the estimation procedure. In AMSDI indicator, we used improved multimodel ensemble of precipitation for future drought characterization under various future scenarios. Outcome associated with this research show that AMSDI effectively have ability to effectively identifiy extreme drought events for all three future scenarios. In conclusion, the AMSDI method is shown to be effective and flexible, improving accuracy in monitoring droughts. [ABSTRACT FROM AUTHOR]

Accurately and reliably predicting droughts under multiple models of Global Climate Models (GCMs) is a challenging task. To address this challenge, the Multimodel Ensemble (MME) method has become a valuable tool for merging multiple models and producing more accurate forecasts. This paper aims to enhance drought monitoring modules for the twenty-first century using multiple GCMs. To achieve this goal, the research introduces a new weighing paradigm called the Multimodel Homo-min Pertinence-max Hybrid Weighted Average (MHmPmHWAR) for the accurate aggregation of multiple GCMs. Secondly, the research proposes a new drought index called the Condensed Multimodal Multi-Scalar Standardized Drought Index (CMMSDI). To assess the effectiveness of MHmPmHWAR, the research compared its findings with the Simple Model Average (SMA). In the application, eighteen different GCM models of the Coupled Model Intercomparison Project Phase 6 (CMIP6) were considered at thirty-two grid points of the Tibet Plateau region. Mann–Kendall (MK) test statistics and Steady States Probabilities (SSPs) of Markov chain were used to assess the long-term trend in drought and its classes. The analysis of trends indicated that the number of grid points demonstrating an upward trend was significantly greater than those displaying a downward trend in terms of spatial coverage, at a significance level of 0.05. When examining scenario SSP1-2.6, the probability of moderate wet and normal drought was greater in nearly all temporal scales than other categories. The outcomes of SSP2-4.5 demonstrated that the likelihoods of moderate drought and normal drought were higher than other classifications. Additionally, the results of SSP5-8.5 were comparable to those of SSP2-4.5, underscoring the importance of taking effective actions to alleviate drought impacts in the future. The results demonstrate the effectiveness of the MHmPmHWAR and CMMSDI approaches in predicting droughts under multiple GCMs, which can contribute to effective drought monitoring and management. [ABSTRACT FROM AUTHOR]

Characterizing climate change impacts on water resources typically relies on Global Climate Model (GCM) outputs that are bias‐corrected using observational data sets. In this process, two pivotal decisions are (a) the Bias Correction Method (BCM) and (b) how to handle the historically observed time series, which can be used as a continuous whole (i.e., without dividing it into sub‐periods), or partitioned into monthly, seasonal (e.g., 3 months), or any other temporal stratification (TS). Here, we examine how the interplay between the choice of BCM, TS, and the raw GCM seasonality may affect historical portrayals and projected changes. To this end, we use outputs from 29 GCMs belonging to the CMIP6 under the Shared Socioeconomic Pathway 5–8.5 scenario, using seven BCMs and three TSs (entire period, seasonal, and monthly). The results show that the effectiveness of BCMs in removing biases can vary depending on the TS and climate indices analyzed. Further, the choice of BCM and TS may yield different projected change signals and seasonality (especially for precipitation), even for climate models with low bias and a reasonable representation of precipitation seasonality during a reference period. Because some BCMs may be computationally expensive, we recommend using the linear scaling method as a diagnostics tool to assess how the choice of TS may affect the projected precipitation seasonality of a specific GCM. More generally, the results presented here unveil trade‐offs in how BCMs are applied, regardless of the climate regime, urging the hydroclimate community to carefully implement these techniques. Plain Language Summary: Global Climate Models (GCMs) are useful tools to characterize the historical and future evolution of the Earth's climate and its impacts on water resources. Because these models contain errors and their horizontal resolution is too coarse for local impact assessments, spatial downscaling, and bias correction are required steps. In particular, bias correction methods can be trained and applied using all the available historical data or by splitting the time series (e.g., by season or months). Since there are no guidelines for selecting a temporal stratification (TS), we analyze bias‐corrected GCM outputs obtained using three types of strategies (entire period, seasons, and months) and seven bias‐correction techniques over continental Chile. We show that the choice of BCM and the TS applied can modify the projected precipitation signal and seasonality. We also propose using a simple statistical technique to identify if the TS may be a relevant decision for climate impact assessments for a given climate model. Key Points: The choice of temporal stratification (TS) for GCM bias correction is crucial for removing biases, even for GCMs with good raw seasonalityDifferent temporal stratifications used for GCM bias correction may yield different future seasonalities and signals in projected changesThe linear scaling approach can be used to easily identify GCMs whose projections are sensitive to the choice of TS [ABSTRACT FROM AUTHOR]

Gravity‐wave (GW) parameterizations from 12 general circulation models (GCMs) participating in the Quasi‐Biennial Oscillation initiative (QBOi) are compared with Strateole 2 balloon observations made in the tropical lower stratosphere from November 2019–February 2020 (phase 1) and from October 2021–January 2022 (phase 2). The parameterizations employ the three standard techniques used in GCMs to represent subgrid‐scale non‐orographic GWs, namely the two globally spectral techniques developed by Warner and McIntyre (1999) and Hines (1997), as well as the "multiwaves" approaches following the work of Lindzen (1981). The input meteorological fields necessary to run the parameterizations offline are extracted from the ERA5 reanalysis and correspond to the meteorological conditions found underneath the balloons. In general, there is fair agreement between amplitudes derived from measurements for waves with periods less than 1 h and parameterizations. The correlation between the daily observations and the corresponding results of the parameterization can be around 0.4, which is 99% significant, since 1200 days of observations are used. Given that the parameterizations have only been tuned to produce a quasi‐biennial oscillation (QBO) in the models, the 0.4 correlation coefficient of the GW momentum fluxes is surprisingly good. These correlations nevertheless vary between schemes and depend little on their formulation (globally spectral versus multiwaves for instance). We therefore attribute these correlations to dynamical filtering, which all schemes take into account, whereas only a few relate the gravity waves to their sources. Statistically significant correlations are mostly found for eastward‐propagating waves, which may be due to the fact that during both Strateole 2 phases the QBO is easterly at the altitude of the balloon flights. We also found that the probability density functions (pdfs) of the momentum fluxes are represented better in spectral schemes with constant sources than in schemes ("spectral" or "multiwaves") that relate GWs only to their convective sources. [ABSTRACT FROM AUTHOR]

Global climate models (GCMs) are essential for studying the climate system and climate change projections. Due to their coarse spatial resolution, downscaling is necessary on the regional scale. Regional climate models (RCMs) represent a standard solution for this issue. Nevertheless, the boundary conditions provided by GCMs unavoidably influence the outputs of RCMs. This study evaluates CMIP6 GCMs regarding the variables relevant to RCM boundary conditions. Particular focus is on the simulation of CNRM-ESM2-1, which is being used as a driving model for convection-permitting ALARO-Climate RCM, used as one source feeding new Czech climate change scenarios. The analysis is conducted over the boundaries and inside the RCM integration domain. Firstly, an evaluation of CFSR and ERA5 reanalyses against radiosondes is performed to choose an appropriate reference dataset for upper air variables. A high correlation between the two studied reanalysis and radiosondes was revealed, and it slightly decreases at the upper tropospheric levels. ERA5 is then chosen as the reference for the boundary analysis. Over the inner region, the simulated mean annual cycle of impact-relevant variables is validated against E-OBS. The CNRM-ESM2-1 performs well regarding near-surface variables over the Czech Republic, but it exhibits larger errors along the boundaries, especially for air temperature and specific humidity. The GCM performance in simulating the upper air atmospheric variables used as RCM boundary conditions relates rather weakly to the GCM performance in simulating the near-surface parameters in the inner region in terms of parameters relevant for impact studies. [ABSTRACT FROM AUTHOR]

Designing buildings that will exist and be inhabited for at least 50 – 100 years in the face of climate change is an exciting and non-trivial challenge. Currently, when designing the “Energy Efficiency” part, it is usual to use climatic data (solar and temperature), obtained from measurements in a limited number of meteorological stations on the territory of Bulgaria in the past 30-year climatic periods. This makes the newly designed buildings suitable for the past and not the future with the expected significant change in temperatures, air humidity, and other climatic parameters. Considering different possible trajectories of human behavior that lead to different climate consequences, the Intergovernmental Panel on Climate Change IPCC defined four climate scenarios under which corresponding global and regional climate models were created. They can be used to investigate the behavior of newly designed buildings in the warmer climate of the late 21st century. This methodology was applied to the study of a newly designed building in Bulgaria. The heating energy in the period 2061 – 2080 with climate scenario RCP 4,5 (90 %) will decrease by about 32 %. The cooling energy demand will increase by about 150 % and the dry cooling load – by about 170 % due to the substantial increase in summer outdoor temperatures. Adding active blinds to the design reduces the increase in dry cooling load to 93 %. [ABSTRACT FROM AUTHOR]

Our understanding of spatiotemporal variability in reference evapotranspiration (ETo) and its long-term trends is of paramount importance for water cycle studies, modeling, and water resource management, especially in the context of climate change. Therefore, the primary aim of this study is to critically evaluate the performance of various CMIP5 global climate models in simulating the Penman–Monteith reference evapotranspiration and its associated climate variables (maximum and minimum air temperature, incident solar radiation, relative humidity, and wind speed). This evaluation is based on data from nine climate models and 33 automatic meteorological stations (AWSs) in the state of Mato Grosso, spanning the period 2007–2020, within the areas of the biomes Amazon and Cerrado and around the Pantanal biome. The statistical metrics used for evaluation include bias, root mean square error, and Pearson and Spearman correlation coefficients. The projections of the most accurate model were then used to analyze the spatial and temporal changes and trends in ETo under the Representative Concentration Pathways (RCPs) of 2.6, 4.5, and 8.5 scenarios from 2007 to 2100. The HadGEM2-ES model projections indicate static averages similar to current conditions until the end of the century in the RCP 2.6 scenario. However, in the RCP 4.5 and 8.5 scenarios, there is a continuous increase in ETo, with the most significant increase occurring during the dry period (May to September). The areas of the Amazon biome in the north of Mato Grosso exhibit the largest increases in ETo when comparing the observed (2007–2020) and projected (2020–2100) averages. The trend analysis reveals significant changes in ETo and its variables across the state of Mato Grosso in the RCP 4.5 and 8.5 scenarios. In the RCP 2.6 scenario, significant trends in ETo are observed only in the northern Amazon areas. Despite not being observed in all AWSs, the trend analysis of the observed data demonstrates more intense changes in ETo and the existence of the evapotranspiration paradox, with an increase in the Cerrado areas and reductions in the Pantanal and southern Amazon areas. [ABSTRACT FROM AUTHOR]

This study integrated climate projections from five global climate models (GCMs) into the soil and water assessment tool to evaluate the potential impact of climate alterations on the Rietspruit River sub-basin under two representative concentration pathways (RCP4.5 and 8.5). The model's performance was evaluated based on the coefficient of determination (R2), percent bias (PBIAS), Nash–Sutcliffe efficiency (NSE), probability (P)-factor and correlation coefficient (R)-factor. Calibration results showed an R2 of 0.62, NSE of 0.60, PBIAS of 20, P-factor of 0.86 and R-factor of 0.91, while validation produced an R2 of 0.64, NSE of 0.61, PBIAS of 40, P-factor of 0.85 and R-factor of 1.22. Precipitation is predicted to increase under both RCPs. Maximum temperature is projected to increase under both RCPs, with a major increase in the winter months. Minimum temperatures are projected to decrease under RCP4.5 in the near (−0.99 °C) and mid (−0.23 °C) futures, while the far future is projected to experience an increase of 0.14 °C. Precipitation and temperature changes correspond to increases in streamflow by an average of 53% (RCP4.5) and 47% (RCP8.5). These results indicate a need for an integrated approach in catchment water resource management amid potential climate and land use variations. HIGHLIGHTS An ensemble of five best-performing GCMs (MiroC5, CanESM2, SHMI-ESM, CSIRO and NorESM2) was employed.; P-factor and R-factor were used to assess model uncertainty particularly due to anthropogenic land use changes.; During winter, maximum and minimum temperatures are projected to increase and decrease, respectively.; The projected increase in streamflow seems to be aggravated by continued land use changes.;

This study investigates the environmental and financial implications of an offshore wind-driven green hydrogen generation system using Life Cycle Assessment (LCA) and Life Cycle Cost Analysis (LCCA) methodologies. Global Climate Models (GCM) are employed to predict wind speeds crucial for system efficacy, followed by sizing the electrolyser system based on projected offshore wind power output. As a result of this study, LCA utilizing the GREET 2023 reveals a Global Warming Potential (GWP) of 0.7 kgCO 2 -eq./kgH 2 and 0.753 kgCO 2 -eq./kgH 2 for GWP-20 and GWP-100, respectively. Fine Particulate Matter Formation (FPMF) is calculated as 0.24 gPM 2.5 /kgH 2 for FPMF-20 and 0.53 gPM 2.5 /kgH 2 for FPMF-100. In LCCA, under the base scenario, the Net Present Cost (NPC) and Levelized Cost of Hydrogen (LCOH) are estimated at $49.65 million and $4.36/kgH 2 , respectively. Despite various incentives and tax scenarios explored, it is revealed that Internal Rates of Return (IRRs) fall below the anticipated cost of equity, indicating non-profitability. • Environmental and economic evaluation of offshore wind-driven hydrogen production. • Among all components, the spar-buoy system is responsible for the highest GWPs. • GWPs of the electrolyser system are almost negligible compared to the wind turbine. • Hydrogen production from the proposed system is not feasible without any incentive. • IRRs fall below anticipated cost of equity; tax incentives crucial for profitability. [ABSTRACT FROM AUTHOR]

The cool season (November–March) of 2022–2023 was exceptional in the western United States (US), with the highest precipitation totals in ≥128 years in some areas. Recent precipitation extremes and expectations based on thermodynamics motivate us to evaluate the evidence for an anthropogenic intensification of western US cool‐season precipitation to date. Over cool seasons 1951–2023, trends in precipitation totals on the wettest cool‐season days were neutral or negative across the western US, and significantly negative in northern California and parts of the Pacific Northwest, counter to the expected net intensification effect from anthropogenic forcing. Multiple reanalysis data sets indicate a corresponding lack of increase in moisture transports into the western US, suggesting that atmospheric circulation trends over the North Pacific have counteracted the increases in atmospheric moisture expected from warming alone. The lack of precipitation intensification to date is generally consistent with climate model simulations. A large ensemble of 648 simulations from 35 climate models suggests it is too soon to detect anthropogenic intensification of precipitation across much of the western US. In California, the 35‐model median time of emergence for intensification of the wettest days is 2080 under a mid‐level emissions scenario. On the other hand, observed reductions of precipitation extremes in California and the Pacific Northwest are near the lower edge of the large ensemble of simulated trends, calling into question model representation of western US precipitation variability. Plain Language Summary: After warming, one of the best understood climate responses to greenhouse gasses is increased atmospheric moisture (from more evaporation) and resultant storm intensification. Given that hydrological infrastructure and policies are probably more attuned to past climate than future climate, intensified precipitation has important implications for management of flood hazards and water resources. In the western US, the very wet winter of 2023 and the extensive flooding that resulted indicated that this region may be highly vulnerable to increases in precipitation extremes. Are precipitation extremes already intensifying in the western US? We find no evidence for intensified western US winter precipitation thus far. In fact, precipitation intensity actually decreased in northern California and the Pacific Northwest over 1951–2023. In California, most climate models do not project intensified precipitation to become detectable from that region's wide range of natural variability until the 2060s even under heavy greenhouse‐gas emissions. By the end of this century, however, most climate models consistently project the strongest storms to be stronger than they were historically across most or all of the western US. Thus, the lack of precipitation intensification thus far should not dissuade planning for greater precipitation intensity and flood risks. Key Points: Over 1951–2023, precipitation on the wettest cool‐season days did not increase across most of the western US, and even declined in areasIn California, most climate models do not project human‐caused precipitation intensification to become detectable before the 2060sClimate models rarely simulate precipitation intensity to decline as much as it did in northern California and the Pacific Northwest [ABSTRACT FROM AUTHOR]

We describe and demonstrate a two-step approach for screening global climate models (GCMs) and produce robust annual and seasonal climate projections for Chile. First, we assess climate model simulations through a Past Performance Index (PPI) inspired by the Kling-Gupta Efficiency, which accounts for climatological averages, interannual variability, seasonal cycles, monthly probabilistic distribution, spatial patterns of climatological means, and the capability of the GCMs to reproduce teleconnection responses to El Niño Southern Oscillation (ENSO) and the Southern Annular Mode (SAM). The PPI formulation is flexible enough to include additional variables and evaluation metrics and weight them differently. Secondly, we use a recently proposed GCM classification based on model code genealogy to obtain a subset of independent model structures from the top 60% GCMs in terms of PPI values. We use this approach to evaluate 27 models from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) and generate projections in five regions with very different climates across continental Chile. The results show that the GCM evaluation framework is able to identify pools of poor-performing and well-behaved models at each macrozone. Because of its flexibility, the model features that may be improved through bias correction can be excluded from the model evaluation process to avoid culling GCMs that can replicate other climate features and observed teleconnections. More generally, the results presented here can be used as a reference for regional studies and GCM selection for dynamical downscaling, while highlighting the difficulty in constraining precipitation and temperature projections. [ABSTRACT FROM AUTHOR]

Argentina is a country with a variety of climates, where an increase in mean and extreme temperatures is currently on-going, demanding regional climate information to design and implement effective strategies for climate change adaptation. In this regard, the use of empirical statistical downscaling (ESD) procedures can help provide tailored climate information. In this work, a set of ESD models were tested and applied to generate plausible regional climate projections for daily maximum and minimum temperatures (Tx, Tn) in Argentina. ESD models were applied to an ensemble of CMIP5 and CMIP6 global circulation models (GCMs) to downscale historical and future RCP8.5 and SSP585 scenarios. The plausibility of the ESD projections was analysed by comparing them with their driving GCMs and with CORDEX regional climate models (RCMs). Generally, all ESD models added value during the historical period, in mean values as well as in extreme indices, especially for Tx. The climate projections depicted an extended signal of warming (both in the mean and in the frequency of extremes), consistent between all simulations (GCMs, RCMs and ESD) and strongest over northern Argentina. ESD models showed potential to produce plausible projections, although, depending on the technique considered (for Tx) and the predictor configurations (for Tn), differences in the change rates were identified. Nevertheless, the uncertainty in future changes was considerably reduced by RCMs and ESD when compared to their driving GCMs. Overall, this study evidences the potential of ESD in a climate change context and contributes to the assessment of the uncertainty on the future Argentine climate. [ABSTRACT FROM AUTHOR]

The intricate relationship between climate and society in a given region demands a profound understanding of climate patterns, especially in agricultural areas like Diamantina, Minas Gerais (MG), recognized by the Food and Agriculture Organization (FAO) as the birthplace of the first Globally Important Agricultural Heritage System (GIAHS) in Brazil, situated in the southwest region of the country. Given the growing concerns about climate change, we conducted a meticulous analysis of the climatic characteristics of Diamantina-MG. To achieve this, we examined historical meteorological data from 1973 to 2022, employing the Mann–Kendall and Sen's slope tests to analyze trends. Additionally, we utilized three global climate models (GCMs) under different shared socioeconomic pathways (SSPs) to predict future climate scenarios (2021–2100) based on the projections of the sixth phase of the Coupled Model Intercomparison Project (CMIP6). Furthermore, we used Köppen and Thornthwaite climate classification methodologies to characterize both the current and future climate conditions of the region. Our results indicate that, historically, Diamantina-MG has experienced significant increases in minimum temperature, indicating a warmer climate in recent decades. For temperature, the projections show a consensus among models, projecting a continuous increase, potentially reaching up to 5.8 °C above the historical average temperature (19.2 °C) by the end of the century. Regarding rainfall projections, they show greater uncertainty, with discrepancies among models observed until 2060. However, specifically for the second half of the century (2060–2100), the models agree that there will be increases in annual rainfall. Regarding the climatic types of the region, we found that the current Köppen Cwb and Thornthwaite B3rB'3a' classifications could shift to Aw and B1wA'a', representing a humid tropical savanna climate with longer periods of water deficiency, considering the impacts resulting from increased air temperature and evapotranspiration. In summary, the study's results indicate that climate changes are occurring and are likely to intensify in the Jequitinhonha Valley region, MG, in the future. The analysis of these data, from the perspective of the Brazilian GIAHS sustainability, reveals the importance of considering adaptation and mitigation measures to ensure the resilience of agricultural systems and local communities in the region that face these significant environmental changes. [ABSTRACT FROM AUTHOR]

CO2 absorbs and emits radiation, which allows it to act both as radiative forcing and feedback. Recent work has shown CO2's feedback effect becomes dominant in hothouse climates, giving rise to a non‐monotonic climate sensitivity around 310 K. However, CO2's feedback effect in colder climates is less clear. We use a line‐by‐line model to explore the CO2‐dependence of the longwave clear‐sky feedback and identify a dividing temperature. Above 290 K, feedback increases with CO2 concentration; below 290 K, feedback decreases with CO2 concentration. We explain this dependence in terms of spectral competition under CO2 increases. In hot climates, CO2's moderate feedback replaces near‐zero feedback from the H2O bands; in cold climates, CO2's moderate feedback replaces the large feedback from the surface. Given that global mean temperature is currently close to 290 K, our results suggest that feedback CO2‐dependence is weak at present but can be important in past and future climates. Plain Language Summary: CO2 traps heat, causing warming. But CO2 also emits heat to space, acting as radiative feedback. Recent work has shown CO2's feedback effect crucially helps to stabilize very hot climates, but how does it affect present‐day Earth? We show that in hot climates, more CO2 increases Earth's feedback, while in cold climates, more CO2 decreases it. To understand why, we explain that the surface is an effective emitter, CO2 is a moderate emitter, while H2O is a poor emitter. At high temperatures, adding CO2 to the atmosphere thus replaces feedback that would have otherwise come from H2O, increasing the overall feedback; at low temperatures, adding CO2 replaces feedback that would have otherwise come from the surface, decreasing the overall feedback. Currently, Earth's global‐mean temperature falls between these two temperature regimes, where CO2's effect on feedback is nearly zero. Our results explain why CO2's impact on feedback is small now but can be significant in past or future climates. Key Points: An increase in CO2 concentration strengthens Earth's feedback in hot climates, ∂λ/∂CO2 > 0, but weakens it in colder climates, ∂λ/∂CO2 < 0Whether feedback CO2‐dependence, ∂λ/∂CO2, is positive or negative primarily depends on the extent of the H2O windowFeedback CO2‐dependence and forcing temperature‐dependence, ∂λ/∂CO2 = −∂F2x/∂Ts, can be important for past or future climates [ABSTRACT FROM AUTHOR]

This work describes the implementation and evaluation of the Slab Ocean Model component of the Energy Exascale Earth System Model version 2 (E3SMv2‐SOM) and its application to understanding the climate sensitivity to ocean heat transports (OHTs) and CO2 forcing. E3SMv2‐SOM reproduces the baseline climate and Equilibrium Climate Sensitivity (ECS) of the fully coupled E3SMv2 experiments reasonably well, with a pattern correlation close to 1 and a global mean bias of less than 1% of the fully coupled surface temperature and precipitation. Sea ice extent and volume are also well reproduced in the SOM. Consistent with general model behavior, the ECS estimated from the SOM (4.5 K) exceeds the effective climate sensitivity obtained from extrapolation to equilibrium in the fully coupled model (4.0 K). The E3SMv2 baseline climate also shows a large sensitivity to OHT strengths, with a global surface temperature difference of about 4.0°C between high‐/low‐OHT experiments with prescribed forcings derived from fully coupled experiments with realistic/weak ocean circulation strengths. Similar to their forcing pattern, the surface temperature response occurs mainly over the subpolar regions in both hemispheres. However, the Southern Ocean shows more surface temperature sensitivity to high/low‐OHT forcing due to a positive/negative shortwave cloud radiative effect caused by decreases/increases in mid‐latitude marine low‐level clouds. This large temperature sensitivity also causes an overcompensation between the prescribed OHTs and atmosphere heat transports. The SOM's ECS estimate is also sensitive to the prescribed OHT and the associated baseline climate it is initialized from; the high‐OHT ECS is 0.5 K lower than the low‐OHT ECS. Plain Language Summary: The implementation and evaluation of the Slab Ocean Model (SOM) in the Energy Exascale Earth System Model version 2 (E3SMv2) is described in this study. The SOM is evaluated by comparing its climate simulation to that of the full version of the model that uses a dynamic ocean model instead of a SOM. The SOM reproduces the baseline climate of the full E3SMv2, as well as the equilibrium surface global temperature response to CO2 doubling of the full model reasonably well. The SOM is further used to test the sensitivity of the E3SM model to various ocean heat transport strengths. The results show that E3SMv2 has a large surface temperature sensitivity to ocean heat transport changes, particularly over the Southern Ocean. This large sensitivity occurs due to changes in marine low‐level clouds, which cause shortwave radiation changes that reach the surface and enhance the surface temperature changes. Atmosphere heat transport also responds to and compensates ocean heat transport changes, and as a result of the large temperature response in the Southern Ocean, this compensation is also greater there. We also find that increases in ocean heat transport reduce the equilibrium surface temperature response to CO2 doubling. Key Points: Slab Ocean Model implementation in E3SMv2 reproduces the model's baseline climate and equilibrium climate sensitivity very wellE3SMv2 Slab model experiments show a large surface temperature sensitivity to ocean heat transports, particularly in the Southern HemisphereTemperature sensitivity to ocean heat transports is enhanced by the shortwave cloud radiative effect due to marine low‐level cloud changes [ABSTRACT FROM AUTHOR]

This study presents a multiscale assessment of the springtime U.S. Mesoscale Convective Systems (MCSs) in the NOAA Geophysical Fluid Dynamics Laboratory (GFDL)'s Atmosphere Model version 4 (AM4). In AM4, MCSs exhibit lower intensity but longer duration, producing more precipitation compared to observation. The overall MCS activity demonstrates a "location bias" with its peak shifting from the Southern Great Plains to the Midwest in AM4, causing an eastward shift in associated precipitation. However, the dry bias of MCS precipitation over the Great Plains due to this shift is compensated by additional precipitation from amplified extratropical cyclone activities. Further analysis reveals that AM4 effectively reproduces the spatiotemporal distribution and relative frequency contribution of large-scale forcing patterns driving MCS genesis. The MCS location bias emerges under all forms of large-scale forcing patterns and is further attributed to local dynamic and thermodynamic factors including weaker surface lows, eastward-shifted fronts, and suppressed low-level jets (LLJs). Here we argue that the MCS location bias results from AM4 biases in both synoptic-mesoscale anomalies (i.e., fronts and LLJs) and seasonal mean circulations. The lack of two-way air-sea interaction in AM4 creates a hemispheric-scale sea level pressure bias, which is ultimately responsible for a seasonal mean northerly bias in lower-tropospheric winds and the subsequent weakening of LLJs. The existence of such biases in prescribed sea surface temperature (SST) experiments implies the need for extra caution when utilizing extended-range forecasts for MCSs over the continental U.S. [ABSTRACT FROM AUTHOR]