Search

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.

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.

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]

ABSTRACT 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]

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]

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]

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]

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 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]

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]

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]

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.;

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]

Climate change is significantly altering precipitation patterns, leading to spatiotemporal changes throughout the world. In particular, the increased frequency and intensity of extreme weather events, leading to heavy rainfall, floods, and droughts, have been a cause of concern. A comprehensive understanding of these changes in precipitation patterns on a regional scale is essential to enhance resilience against the adverse effects of climate change. The present study, focused on the state of Bihar in India, uses a long-term (1901–2020) gridded precipitation dataset to analyze the effect of climate change. Change point detection tests divide the time series into two epochs: 1901–1960 and 1961–2020, with 1960 as the change point year. Modified Mann–Kendall (MMK) and Sen's slope estimator tests are used to identify trends in seasonal and annual time scales, while Centroidal Day (CD) analysis is performed to determine changes in temporal patterns of rainfall. The results show significant variability in seasonal rainfall, with the nature of pre-monsoon and post-monsoon observed to have flipped in second epoch. The daily rainfall intensity during the monsoon season has increased considerably, particularly in north Bihar, while the extreme rainfall has increased by 60.6 mm/day in the second epoch. The surface runoff increased by approximately 13.43% from 2001 to 2020. Further, 13 Global Climate Models (GCMs) evaluate future scenarios based on Shared Socioeconomic Pathways (SSP) 370 and SSP585. The suitability analysis of these GCMs, based on probability density function (PDF), monthly mean absolute error (MAE), root mean square error (RMSE) and percentage bias (P-Bias), suggests that EC-Earth3-Veg-LR, MIROC6, and MPI-ESM1-2-LR are the three best GCMs representative of rainfall in Bihar. A Bayesian model-averaged (BMA) multi-model ensemble reflects the variability expected in the future with the least uncertainty. The present study's findings clarify the current state of variability, patterns and trends in precipitation, while suggesting the most appropriate GCMs for better decision-making and preparedness. [ABSTRACT FROM AUTHOR]

Coarse‐grid weather and climate models rely particularly on parameterizations of cloud fields, and coarse‐grained cloud fields from a fine‐grid reference model are a natural target for a machine‐learned parameterization. We machine‐learn the coarsened‐fine cloud properties as a function of coarse‐grid model state in each grid cell of NOAA's FV3GFS global atmosphere model with 200 km grid spacing, trained using a 3 km fine‐grid reference simulation with a modified version of FV3GFS. The ML outputs are coarsened‐fine fractional cloud cover and liquid and ice cloud condensate mixing ratios, and the inputs are coarse model temperature, pressure, relative humidity, and ice cloud condensate. The predicted fields are skillful and unbiased, but somewhat under‐dispersed, resulting in too many partially cloudy model columns. When the predicted fields are applied diagnostically (offline) in FV3GFS's radiation scheme, they lead to small biases in global‐mean top‐of‐atmosphere (TOA) and surface radiative fluxes. An unbiased global‐mean TOA net radiative flux is obtained by setting to zero any predicted cloud with grid‐cell mean cloud fraction less than a threshold of 6.5%; this does not significantly degrade the ML prediction of cloud properties. The diagnostic, ML‐derived radiative fluxes are far more accurate than those obtained with the existing cloud parameterization in the nudged coarse‐grid model, as they leverage the accuracy of the fine‐grid reference simulation's cloud properties. Plain Language Summary: Weather and climate models typically use simplified means of predicting clouds, and these methods are particularly important for models that run at coarse resolution (200 km pixels) and relatively quickly. Machine learning is a natural way to improve upon the methods of predicting cloud. We first show that detailed cloud information from an accurate model run at much finer resolution (3 km pixels) can produce more accurate radiative energy fluxes at earth's surface and top of atmosphere when used in the coarse model, instead of its own predicted clouds. We show that by training on the temperature, humidity, and other characteristics of the coarse model, machine learning can replicate much of this skill in predicting clouds, and provide better radiation estimates than traditional methods. Because solar and thermal radiation is sensitive to small amounts of cloud, we find that we need to remove small amounts erroneously predicted by machine learning to achieve the right amounts of radiation at earth's surface and top of atmosphere. The machine‐learning based approach could be used as a replacement for the current statistical methods of predicting cloud fields in fast, coarse‐resolution weather and climate models. Key Points: Fine‐grid model clouds, when coarsened, can produce more accurate radiative fluxes than a coarse model's own parameterized cloudsMachine learning can capture the coarsened fine‐grid clouds as functions of coarse model state (temperature, pressure, and humidity)With appropriate post‐processing, the machine‐learned clouds also outperform the coarse model's clouds in terms of diagnostic radiation [ABSTRACT FROM AUTHOR]

Orographic gravity waves (OGWs) are an important mechanism for coupling of the free atmosphere with the surface, mediating the momentum and energy transport and influencing the dynamics and circulation especially in the middle-atmosphere. Current global climate models are not able to resolve a large part of the OGW spectrum and hence, OGW effects have to be parameterized in the models. Typically, the only parameterized effect is the OGW induced drag. Despite producing the same quantity as an output and relying on similar assumptions (e.g. instantaneous vertical propagation), the individual OGW parameterization schemes differ in many aspects such as handling of the orography, the inclusion of non-linear effects near the surface and the tuning of the emergent free parameters. In this study, we have reviewed 7 different parameterizations, which are used in 9 different CMIP6 models. We report pronounced differences in the vertical distribution and magnitude of the parameterized OGW drag between the models and study to what extent the inter-model differences can be traced back to the difference in the type and tuning of the schemes. Finally, we demonstrate how the OGW drag differences project to the intermodel differences in the stratospheric dynamics. The study can pave the way for a more systematic research of the OGW parameterizations in the future, with an ultimate goal of lowering the amount of uncertainty of the future climate projections connected with the parameterized effects of unresolved orography. [ABSTRACT FROM AUTHOR]

Abstract 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.

The increasingly frequent occurrence of urban heatwaves has become a significant threat to human health. To quantitatively analyze changes in heatwave characteristics and to investigate the return periods of future heatwaves in Wuhan City, China, this study extracted 9 heatwave definitions and divided them into 3 mortality risk levels to identify and analyze historical observations and future projections of heatwaves. The copula functions were employed to derive the joint distribution of heatwave severity and duration and to analyze the co-occurrence return periods. The results demonstrate the following. (1) As the concentration of greenhouse gas emissions increases, the severity of heatwaves intensifies, and the occurrence of heatwaves increases significantly; moreover, a longer duration of heatwaves correlated with higher risk levels in each emission scenario. (2) Increasing concentrations of greenhouse gas emissions result in significantly shorter heatwave co-occurrence return periods at each level of risk. (3) In the 3 risk levels under each emission scenario, the co-occurrence return periods for heatwaves become longer as heatwave severity intensifies and duration increases. Under the influence of climate change, regional-specific early warning systems for heatwaves are necessary and crucial for policymakers to reduce heat-related mortality risks in the population, especially among vulnerable groups.

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.

General circulation models are widely used to predict changes in regional climate under various pathways of projected carbon emissions. These global models are spatially coarse and are commonly downscaled to provide useful predictions at regional scales. As part of this process, any regional biases should be removed. Our research compares the accuracy of downscaling and bias removal applied to daily maximum temperature via two methods, empirical quantile mapping and Gaussian process regression. We find that the temporal stationarity assumptions within empirical quantile mapping result in a higher RMSE and distributional mismatch between observed and predicted bias than the application of Gaussian processes when applied to a dynamically downscaled global circulation model. The area of study is a sub-Arctic region covering a portion of the Copper River watershed in Alaska.

Global Climate Models (GCMs) are globally accepted simulations used for modeling and forecasting of precipitation and other parameters of climate. However, discrepancies among GCMs reduce the scope of individual models. In this regard, a multimodel ensemble provides a more accurate picture than individual models. However, the presence of extreme values and outliers in GCMs makes ensemble inappropriate for statistical analysis and reanalysis. In this article, we propose a novel weighting scheme that integrates the Exponentially Weighted Moving Average (EWMA) chart to obtain optimum weights for ensemble means of multiple GCMs – EWMA-based Ensemble (EWMABE). To investigate the validity of EWMABE, we used precipitation data of eighteen GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6). In this study, the performance of EWMABE is compared with Multi-Model Simple Averaging Ensemble (MMSAE). The comparative statistics show that the EWMABE scheme has significantly improved the multimodel estimates. These results suggests the potential candidacy of EWMABE for the estimation of multimodel ensemble. [ABSTRACT FROM AUTHOR]

Mountain lee waves present significant hazards to aviation, often inducing turbulence and aircraft icing. The current study focuses on understanding the potential impact of global climate change on the precursor environments to mountain lee wave cloud episodes over central Iberia. We examine the suitability of several Global Climate Models (GCMs) from CMIP6 in predicting these environments using the ERA5 reanalysis as a benchmark for performance. The dataset is divided into two periods: historical data (2001–2014) and projections for the SSP5–8.5 future climate scenario (2015–2100). The variations and trends in precursor environments between historical data and future climate scenarios are exposed, with a particular focus on the expansion of the Azores High towards the Iberian Peninsula, resulting in increased zonal winds throughout the Iberian Peninsula in the future. However, the increase in zonal wind is insufficient to modify the wind pattern, so future mountain lee wave cloud events will not vary significantly. The relative humidity trends reveal no significant changes. Moreover, the risk of icing precursor environments connected with mountain lee wave clouds is expected to decrease in the future, due to rising temperatures. Our results highlight that the EC-EARTH3 GCM reveals the closest alignment with ERA5 data, and statistically significant differences between the historical and future climate scenario periods are presented, making EC-EARTH3 a robust candidate for conducting future studies on the precursor environments to mountain lee wave cloud events. [ABSTRACT FROM AUTHOR]

Based on the results of calculations with the CMIP6 ensemble of global climate models, quantitative estimates of changes in the snow cover extent (SCE) in Eurasia in the 21st century are obtained under SSP2-4.5 and SSP5-8.5 scenarios of anthropogenic impacts using the Bayesian approach. The contribution (weight) of the models to the overall ensemble estimates has been determined by the accuracy of reproduction of the long-term average, trend, and interannual variability of the SCE in Eurasia from satellite data. The largest intermodel variations in the estimates, increasing during the summer and fall months, are associated with the reproduction of the trend component and interannual variability (variance) of the SCE in Eurasia, as well as with equal-weighted averaging. It is shown that, when Bayesian weights are used, the uncertainty of snow cover area estimates can be halved compared to the ensemble average with model weights being equal. The ensemble estimates of the SCE obtained by using combined Bayesian weights exceed the corresponding estimates during equal-weighted averaging. [ABSTRACT FROM AUTHOR]

Drought as a natural disaster, can have devastating effects on various sectors. This study proposes a new weighting scheme called weighted aggregation (WA) and introduces the Multi-model weighted drought severity index (MMWDSI) as an improved indicator for drought assessment. The methodology of MMWDSI involves ensemble modeling using the WA scheme, incorporating the K-components Gaussian mixture model (K-CGMM) for appropriate distribution fitting. It employs a multi-stage statistical procedure that considers point-to-point variations and the past performance of climate models through the Taylor Skill Score in the initial stage. Subsequent stages involve linear models and K-CGMM for prediction and standardization. Similar to other standardized drought indices, the proposed index allows for inferring the probabilistic behavior of extreme events, such as extreme drought or extreme wet conditions, and assessing trends using various statistical techniques. For the application of the index, historical precipitation data from 1961 to 2014 was utilized from 32 grid points on the Tibetan Plateau as the reference dataset. Additionally, simulations from 18 models of the Coupled Model Intercomparison Project phase 6, both past and future, were employed for the estimation procedure. The findings demonstrate that the developed weighting scheme surpasses the Equal Weighted Averaging approach. In conclusion, the MMWDSI approach proves to be a flexible and effective method that enhances accuracy in drought monitoring. [ABSTRACT FROM AUTHOR]

Copyright of Atmosphere -- Ocean (Taylor & Francis Ltd) is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The effects of global warming on the regional climate of southern South America (SSA) during the recent decades have been exhaustively documented with consistency throughout the literature. However, the projected changes on temperature- and precipitation-related climate hazards depict an important uncertainty, mostly in the intensity of the changes. This work assessed a set of CMIP5 and CMIP6 global climate models (GCMs) in reproducing the observed atmospheric circulation patterns (CPs) over SSA and their expected changes for the twenty-first century. Furthermore, the attribution of the seasonal precipitation changes to changes in the CPs was explored for the late future (2070–2100). GCMs were generally able to represent the variety of CPs and their seasonal frequencies, although presenting more deficiencies in capturing their respective precipitation patterns over SSA. Larger model agreement was found in the increasing and/or decreasing frequency of specific CPs for the near future (2040–2070) than for the late future, when model spread became more noticeable. Particularly, CPs associated with larger positive rainfall anomalies over southeastern South America—a hotspot for precipitation extremes—are expected to become more frequent in the near future, whereas their changes in the longer term are more uncertain. When performing an attributional study, precipitation changes showed important differences between GCMs and were often associated with changes in the intrapattern variability rather than in the CP changing frequency. In this way, the modification of the precipitation regime of SSA may not be explained only by changes in the large-scale circulation but probably also by other regional-to-local features. [ABSTRACT FROM AUTHOR]

Abstract 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.

Abstract 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.

Global warming induces spatially heterogeneous changes in precipitation patterns, highlighting the need to assess these changes at regional scales. This assessment is particularly critical for Afghanistan, where agriculture serves as the primary livelihood for the population. New global climate model (GCM) simulations have recently been released for the recently established shared socioeconomic pathways (SSPs). This requires evaluating projected precipitation changes under these new scenarios and subsequent policy updates. This research employed six GCMs from the CMIP6 to project spatial and temporal precipitation changes across Afghanistan under all SSPs, including SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. The employed GCMs were bias-corrected using the Global Precipitation Climatological Center's (GPCC) monthly gridded precipitation data with a 1.0° spatial resolution. Subsequently, the climate change factor was calculated to assess precipitation changes for both the near future (2020–2059) and the distant future (2060–2099). The bias-corrected projections' multi-model ensemble (MME) revealed increased precipitation across most of Afghanistan for SSPs with higher emissions scenarios. The bias-corrected simulations showed a substantial increase in summer precipitation of around 50%, projected under SSP1-1.9 in the southwestern region, while a decline of over 50% is projected in the northwestern region until 2100. The annual precipitation in the northwest region was projected to increase up to 15% for SSP1-2.6. SSP2-4.5 showed a projected annual precipitation increase of around 20% in the southwestern and certain eastern regions in the far future. Furthermore, a substantial rise of approximately 50% in summer precipitation under SSP3-7.0 is expected in the central and western regions in the far future. However, it is crucial to note that the projected changes exhibit considerable uncertainty among different GCMs.

Abstract Coarse‐grid weather and climate models rely particularly on parameterizations of cloud fields, and coarse‐grained cloud fields from a fine‐grid reference model are a natural target for a machine‐learned parameterization. We machine‐learn the coarsened‐fine cloud properties as a function of coarse‐grid model state in each grid cell of NOAA's FV3GFS global atmosphere model with 200 km grid spacing, trained using a 3 km fine‐grid reference simulation with a modified version of FV3GFS. The ML outputs are coarsened‐fine fractional cloud cover and liquid and ice cloud condensate mixing ratios, and the inputs are coarse model temperature, pressure, relative humidity, and ice cloud condensate. The predicted fields are skillful and unbiased, but somewhat under‐dispersed, resulting in too many partially cloudy model columns. When the predicted fields are applied diagnostically (offline) in FV3GFS's radiation scheme, they lead to small biases in global‐mean top‐of‐atmosphere (TOA) and surface radiative fluxes. An unbiased global‐mean TOA net radiative flux is obtained by setting to zero any predicted cloud with grid‐cell mean cloud fraction less than a threshold of 6.5%; this does not significantly degrade the ML prediction of cloud properties. The diagnostic, ML‐derived radiative fluxes are far more accurate than those obtained with the existing cloud parameterization in the nudged coarse‐grid model, as they leverage the accuracy of the fine‐grid reference simulation's cloud properties.

Soil Water Assessment Tool (SWAT), Hydrologic Engineering Center-Hydrologic Modelling System (HEC-HMS), and Hydrologic Simulation Program Fortran (HSPF) are explored for streamflow simulation of Lower Godavari Basin, India. The simulating ability of models is evaluated using four indicators. SWAT has shown exceptional simulating ability in calibration and validation compared to the other two. Accordingly, SWAT is used in the climate change framework using an ensemble of 13 Global Climate Models and four Shared Socioeconomic Pathways (SSPs). Three time segments, near-future (2021–2046), mid-future (2047–2072), and far-future (2073–2099), are considered for analysis. Four SSPs show a substantial increase in streamflow compared to the historical period (1982–2020). These deviations range from 17.14 (in SSP245) to 28.35% (in SSP126) (near-future), 31.32 (SSP370) to 43.28% (SSP585) (mid-future), and 30.41 (SSP126) to 70.8% (SSP585) (far-future). Across all timescales covering 948 months, the highest projected streamflows observed in SSP126, SSP245, SSP370, and SSP585 were 4962.36, 6,108, 6,821, and 6,845 m3/s, respectively. Efforts are also made to appraise the influence of multi-model combinations on streamflow. The present study is expected to provide a platform for holistic decision-making, which helps develop efficient basin planning and management alternatives. HIGHLIGHTS SWAT performed ahead of HEC-HMS and HSPF in training and testing for all chosen indicators.; Four SSPs show a substantial increase in streamflow compared to the historical period (1982–2020).; Across all timescales, the highest projected streamflows observed in SSP126, SSP245, SSP370, and SSP585 were 4962.36, 6,108, 6,821, and 6,845 m3/s, respectively.; Four multi-model combinations were developed.;

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.

The high computational cost of Global Climate Models (GCMs) is a problem that limits their use in many areas. Recently an inverse climate modeling (InvCM) method, which fixes the global mean sea surface temperature (SST) and evolves the CO2 mixing ratio to equilibrate climate, has been implemented in a cloud-resolving model. In this article, we apply InvCM to ExoCAM GCM aquaplanet simulations, allowing the SST pattern to evolve while maintaining a fixed global-mean SST. We find that InvCM produces the same climate as normal slab-ocean simulations but converges an order of magnitude faster. We then use InvCM to calculate the equilibrium CO2 for SSTs ranging from 290 to 340 K at 1 K intervals and reproduce the large increase in climate sensitivity at an SST of about 315 K at much higher temperature resolution. The speedup provided by InvCM could be used to equilibrate GCMs at higher spatial resolution or to perform broader parameter space exploration in order to gain new insight into the climate system. Additionally, InvCM could be used to find unstable and hidden climate states, and to find climate states close to bifurcations such as the runaway greenhouse transition.

In maize fields, few studies have been conducted to identify the temporal trend of soil salinity and formulate optimal irrigation plans under climate change. Therefore, the main goals of this study were to predict changes in soil salinity over 2022–2050 and to formulate an optimal supplemental irrigation plan preventing soil salinity in a South African rainfed maize field. The study used the Global Climate Model (GCM) MPI-ESM1-2-LR to obtain future climate data for the study area from 2022 to 2050 and applied the HYDRUS-1D model to project the effects of these future climate data on soil salinity over the same period and to identify the best irrigation plan under climate change. Two key findings were revealed: first, the combined use of GCMs (i.e., MPI-ESM1-2-LR model) and soil-water models (i.e., HYDRUS-1D) was a powerful tool to identify soil salinity trends and formulate optimal irrigation plan under climate change. Second, in addition to rainfall amount, supplying a limited supplemental irrigation amount equal to 8% of the actual evapotranspiration of maize at the mid-season stage of maize growth can significantly reduce soil salinity (

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.

This study evaluates the performance of Global Climate Models (GCMs) in simulating extreme climate in three northeastern provinces of China (TNPC). A total of 23 GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) were selected and compared with observations from 1961 to 2010, using the 12 extreme climate indices defined by the Expert Team on Climate Change Detection and Indicators. The Interannual Variability Skill Score (IVS), Taylor diagrams and Taylor Skill Scores (S) were used as evaluation tools to compare the outputs of these 23 GCMs with the observations. The results show that the monthly minimum of daily minimum temperature (TNn) is overestimated in 55.7% of the regional grids, while the percentage of time when the daily minimum temperature is below the 10th percentile (TN10p) and the monthly mean difference between the daily maximum and minimum temperatures (DTR) are underestimated in more than 95% of the regional grids. The monthly maximum value of the daily maximum temperature (TXx) and the annual count when there are at least six consecutive days of the minimum temperature below the 10th percentile (CSDI) have relatively low regional spatial biases of 1.17 °C and 1.91 d, respectively. However, the regional spatial bias of annual count when the daily minimum temperature is below 0 °C (FD) is relatively high at 9 d. The GCMs can efficiently capture temporal variations in CSDI and TN10p (IVS < 0.5), as well as the spatial patterns of TNn and FD (S > 0.8). For the extreme precipitation indices, GCMs overestimate the annual total precipitation from days greater than the 95th percentile (R95p) and the annual count when precipitation is greater than or equal to 10 mm (R10 mm) in more than 90% of the regional grids. The maximum number of consecutive days when precipitation is below 1 mm (CDD) and the ratio of annual total precipitation to the number of wet days (greater than or equal to 1 mm) (SDII) are underestimated in more than 80% and 54% of the regional grids, respectively. The regional spatial bias of the monthly maximum consecutive 5-day precipitation (RX5day) is relatively small at 10.66%. GCMs are able to better capture temporal variations in the monthly maximum 1-day precipitation (RX1day) and SDII (IVS < 0.6), as well as spatial patterns in R95p and R10mm (S > 0.7). The findings of this study can provide a reference that can inform climate hazard risk management and mitigation strategies for the TNPC. [ABSTRACT FROM AUTHOR]

European heatwaves at the 0.5, 1.0, 1.5 and 2.0 ∘ C global warming levels above the pre-industrial temperature are examined using bias-corrected daily-mean temperature data from 60 simulations performed with 25 global climate models (GCMs). A heatwave event is defined here to consist of at least three consecutive days above the 90th percentile of summer daily-mean temperatures and a break of 1 day is allowed. At the 2.0 ∘ C global warming level compared with 0.5 ∘ C, the multi-GCM mean annual count of heatwave days is projected to be three to fourfold in northern and more than sixfold in southern Europe. The total annual heatwave extremity index, or the sum of exceedings above the threshold temperature over all the heatwave days, becomes approximatively fourfold in the north and tenfold in the south. In central Europe, the date of the strongest heatwave of year is delayed by about 1 week. By concatenating the bias-corrected output data of all the GCM runs, 1200-year samples were created from which probabilities of occurrence of the strongest heatwaves can be determined very robustly under all four warming levels. An intense heatwave occurring once in 10 years at the 0.5 ∘ C warming level has an annual probability of about 50% in northern and 80–90% in southern Europe under 2.0 ∘ C. Corresponding probabilities for 100-year heatwaves would be nearly 20% in the north and about 60% in the south. Finally, we discuss factors that explain the stronger increase in heatwave duration and extremity in the south than north. [ABSTRACT FROM AUTHOR]