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Aim: We aimed to assess potential climate change impacts on the distribution of a cold‐adapted terrestrial amphibian that shows strong intraspecific differentiation. Location: Alps and Dinarides. Taxon: Alpine salamander (Salamandra atra). Methods: We built SDMs for each of two major intraspecific lineages (S. a. atra, which is found throughout the Northern Alps, and S. a. prenjensis, which appears sparsely along the Dinarides) and for the entire species, using occurrence points from a carefully curated database, climate data with 1 km2 spatial resolution and eight modelling techniques. We projected climatically suitable areas to 2070 under two climate scenarios (RCP2.6 and RCP8.5), using 24 general circulation models. Results: For S. a. atra, under RCP2.6 scenario, we detected a 3% increase in the extent of climatically suitable areas in the Alps (although with low model agreement), yet a 6% decrease under the RCP8.5 scenario. For S. a. prenjensis, we detected a decrease in the extent of climatically suitable areas in the Dinarides between 7% and 45% (RCP2.6 and RCP8.5, respectively). The SDM predictions for the entire species were most in line with those of the S. a. atra model. Although climate variables were not equally strong in predicting the climatic suitability for both lineages, the minimum temperature of the coldest month revealed an important predictor for the entire species, with clear decreases in suitability towards higher temperature. Main Conclusions: All models reveal a considerable decrease in climate suitability throughout most of the range of S. atra, which is particularly visible in the Dinarides. We highlight the importance of considering intraspecific variation when modelling climate change impacts on geographically differentiated species whose populations vary in their climatic niche. We provide our study results (occurrence data and maps) via a web application that can be useful for guiding conservation efforts. [ABSTRACT FROM AUTHOR]

This paper deals with the existence of forced waves for an epidemic model of West Nile virus in a shifting environment. Here a forced wave is a traveling wave with wave speed the same as the environmental shifting speed. The forced waves we constructed have the property that the waves tend to the positive endemic state of the epidemic model as the time tends to infinity. The derivation of these forced waves relies on a careful construction of a suitable lower solution with the help of Schauder's fixed point theorem. [ABSTRACT FROM AUTHOR]

In this paper we study the influence of atmospheric and oceanic resolutions on the sea surface temperature (SST) under climate change and the related mechanisms. To this end, a set of historical period and future simulations in which two different atmospheric resolutions are combined with two different oceanic resolutions, one of them eddy-permitting, are used. We found that the magnitude of the warming is more dependent on atmospheric resolution, while the influence of oceanic resolution is stronger on the spatial pattern of SST changes, leading to the enhancement of the climate change signal in coastal regions in the higher resolution configuration. The mechanisms related to these changes involve both the atmosphere and the ocean, with the latter playing the main role. We found that changes in upwelling are the primary drivers of SST changes in regions close to the coast, and these changes in SST exhibit a clear seasonal pattern, precisely associated with the marked seasonality of the Benguela upwelling system. The radiative and turbulent fluxes adjust to the increase of greenhouse gases and are also influenced by the resolution and SST patterns. Additionally, the net longwave fluxes into the ocean increase, while the shortwave fluxes decrease due to the formation of low clouds. [ABSTRACT FROM AUTHOR]

Climate science has long explored whether higher resolution regional climate models (RCMs) provide improved simulation of regional climates over global climate models (GCMs). The advent of convective-permitting RCMs (CPRCMs), where sufficiently fine-scale grids allow explicitly resolving rather than parametrising convection, has created a clear distinction between RCM and GCM formulations. This study investigates the simulation of tropical-extratropical (TE) cloud bands in a suite of pan-South America convective-permitting Met Office Unified Model (UM) and Weather Research and Forecasting (WRF) climate simulations. All simulations produce annual cycles in TE cloud band frequency within 10–30% of observed climatology. However, too few cloud band days are simulated during the early summer (Nov–Dec) and too many during the core summer (Jan–Feb). Compared with their parent forcing, CPRCMs simulate more dry days but systematically higher daily rainfall rates, keeping the total rain biases low. During cloud band systems, the CPRCMs correctly reproduced the observed changes in tropical rain rates and their importance to climatology. Circulation analysis suggests that simulated lower subtropical rain rates during cloud bands systems, in contrast to the higher rates in the tropics, are associated with weaker northwesterly moisture flux from the Amazon towards southeast South America, more evident in the CPRCMs. Taken together, the results suggest that CPRCMs tend to be more effective at producing heavy daily rainfall rates than parametrised simulations for a given level of near-surface moist energy. The extent to which this improves or degrades biases present in the parent simulations is strongly region-dependent. [ABSTRACT FROM AUTHOR]

Atmospheric blocking is a phenomenon that can lead to extreme weather events over a large region, yet its causes are not fully understood. Global climate models show limitations in representing Northern Hemisphere blocking, especially its frequency, and decadal variability in Greenland blocking in summer in the recent decades. In this study we evaluate the ability of high-resolution (HighResMIP) Earth System Models (ESMs) to simulate summer blocking over the Greenland area, using different but complementary methods to describe the characteristics of blocking. We find that the HighResMIP ensemble can reproduce the spatial pattern of Greenland blocking events, albeit with systematic biases, and capture the relative frequencies of the main blocking patterns: namely the wave breaking structure, North Atlantic ridge, and omega-type blocking. However, the HighResMIP ensemble fails to simulate the observed temporal variations of Greenland blocking index (GB2) and the extremely high values of daily GB2 observed in recent decades. In addition, we do not find clearly superior representation of blocking features from higher-resolution in HighResMIP models compared with lower-resolution models. We also find large sea surface temperature (SST) biases over the North Atlantic and seas surrounding Greenland, and biases in moisture transport over the North Atlantic toward Greenland, especially over the western flank of blocking areas, which might together contribute to model biases in the representation of blocking magnitude. [ABSTRACT FROM AUTHOR]

Poor land management coupled with increasing climate extremes is affecting the livelihoods of Ethiopian communities. So, evaluating the possible impact of climate change on water resource is essential for planning a sustainable water use system. As a result, this study aimed to investigate the impact of climatic variable changes on streamflow in the Borkena River Catchment, Awash Basin, Ethiopia, using projected climate data of canESM2 (Canadian Earth System Model of second generation) global climate model. Future scenario, analysis was performed for the 2020s, 2050s and 2080s for the Representative Concentration Pathway of RCP4.5 and RCP8.5. Impact assessment of climatic variable changes on streamflow was done by HBV Light hydrological models. The model was calibrated using a semi-automated method and the performance was measured by the coefficient of determination (R2), Nash–Sutcliffe efficiency (NSE) and mean difference. The performance of HBV Light hydrological model during the calibration and validation period showed a satisfactory agreement between observed and simulated flow with R2, NSE and mean difference. The value of R2, NSE and mean differences was 0.84, 0.82 and 0 for calibration and 0.83, 0.78 and 8.23 for validation periods, respectively. The downscaled precipitation and temperature result reveals decrease and increase value in all future three-time horizon for both RCP8.5 and RCP4.5 scenarios in an annual basis respectively. This condition was expected to result decreased in mean annual streamflow of Borkena River in the future three-time horizon with the value of –16.81%,–17.25% and –18.28% for RCP8.5 scenario and –6.88%, –9.89% and –10.66% for RCP4.5 scenario in the 2020s, 2050s and 2080s, respectively. Inoculation future climate variation has a significant impact on the surface water potential of the catchment. So, for sustainable development of the River catchment in water use the streamflow of the catchment should be harvested. [ABSTRACT FROM AUTHOR]

The variability of Arctic sea ice extent (SIE) on interannual and multidecadal time scales is examined in 29 models with historical forcing participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6) and in twentieth-century sea ice reconstructions. Results show that during the historical period with low external forcing (1850–1919), CMIP6 models display relatively good agreement in their representation of interannual sea ice variability (IVSIE) but exhibit pronounced intermodel spread in multidecadal sea ice variability (MVSIE), which is overestimated with respect to sea ice reconstructions and is dominated by model uncertainty in sea ice simulation in the subpolar North Atlantic. We find that this is associated with differences in models' sensitivity to Northern Hemispheric sea surface temperatures (SSTs). Additionally, we show that while CMIP6 models are generally capable of simulating multidecadal changes in Arctic sea ice from the mid-twentieth century to present day, they tend to underestimate the observed sea ice decline during the early twentieth-century warming (ETCW; 1915–45). These results suggest the need for an improved characterization of the sea ice response to multidecadal climate variability in order to address the sources of model bias and reduce the uncertainty in future projections arising from intermodel spread. Significance Statement: The credibility of Arctic sea ice predictions depends on whether climate models are capable of reproducing changes in the past climate, including patterns of sea ice variability which can mask or amplify the response to global warming. This study aims to better understand how latest-generation global climate models simulate interannual and multidecadal variability of Arctic sea ice relative to available observations. We find that models differ in their representation of multidecadal sea ice variability, which is overall larger than in observations. Additionally, models underestimate the sea ice decline during the period of observed warming between 1915 and 1945. Our results suggest that, to achieve better predictions of Arctic sea ice, the realism of low-frequency sea ice variability in models should be improved. [ABSTRACT FROM AUTHOR]

This paper assessed the effects of climate change and planting date adjustment on spring and summer potato in South Korea for the period 2061–2090. The study applied the SUBSTOR-Potato model and outputs of 24 general circulation models to capture future variability in climate conditions for four shared socioeconomic pathway-representative concentration pathway scenarios. Without planting date adjustment, tuber yield was projected to increase by approximately 20% for spring and summer potato, indicating that the CO2 fertilization effect would offset the adverse effect of rising temperature. The effect of planting date adjustment was significant only for spring potato, where overall climate change impact with the optimized planting date was approximately +60%. For spring potato, the effects of rising temperature were bidirectional: temperature increases early in the year extended the growing season, whereas the higher temperature increases in June under the most severe climate change condition accelerated leaf senescence and reduced tuber bulking rate. Based on these results, different adaptation strategies could be established for spring potato for different climate change conditions. For example, developing frost-tolerant cultivars would continue to be recommended to plant earlier under the mild climate change conditions, whereas breeding mid-late maturity cultivars with high-temperature tolerance would be needed to delay senescence and enhance late tuber growth under the severe climate change conditions. Unlike spring potato, the breeding goal for summer potato of increasing high-temperature tolerance holds across all climate change conditions. Finally, these optimistic results should be interpreted with caution as the current model does not fully capture the effect of high-temperature episodes and the interactive effect between CO2 and temperature, which may reduce beneficial projected climate change impacts. [ABSTRACT FROM AUTHOR]

Climate change is challenging sustainable agrifood production and food security, and encouraging farmers' climate change adaptation can help promote sustainable agrifood production and ensure food security. This study investigates farmers' climate change adaptation and its impact on agrifood production. We employ the propensity score matching (PSM) model to address the selection bias issue of climate change adaptation and estimate the survey data collected from 415 rice-producing households in rural China. We also estimate the inverse probability weighted regression adjustment (IPWRA) model for robustness check. The empirical results show that farmers' decisions on climate change adaptation are influenced by household heads' age, education level, life satisfaction, temperature perception, and transportation conditions. The treatment effect estimations of the PSM model reveal that climate change adaptation significantly increases land productivity by 41.24–44.29% and labor productivity by 55.06–63.72% in rice production. The IPWRA model estimation largely confirms the robustness of the PSM model estimation. We also find that climate change adaptation significantly increases the net returns of rice production. These findings have significant global implications. By understanding the factors influencing farmers' decisions to adapt to climate change, policymakers worldwide can design targeted interventions to encourage similar practices in other regions. Promoting farmers' climate change adaptation to increase farm productivity is crucial for ensuring global food security in the face of ongoing climate challenges. [ABSTRACT FROM AUTHOR]

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

ECOMAG runoff formation model was used to calculate physically based changes in river water regime in the Mountain Crimea with the use of data of an ensemble of climate models, taking into account various scenarios of global warming. The objects of the study were the rivers of Chernaya, Belbek, Derekoika, Alma, Salgir, Burulcha, Tonas, Kuchuk-Karasu, and Indol. The formation models of natural river runoff for this set of river basins were constructed based on homogeneous sources of data on the hydrometeorological regime and parameters of the land surface. The hydrological models were verified by comparing the actual and calculated daily and monthly water discharges at various hydrometric gages over a many-year period. Next, hydrological models and the data of an ensemble of global climate models for the base period of 2006–2020 were used to calculate future scenario changes in river runoff over a year and conventionally warm and cold seasons of a year. At the implementation of any of RCP-scenarios in the nearest future for 2021–2050, as well as for scenarios RCP 2.6 and RCP 4.5 in the late XXI century, deficiency of water resources can be faced mostly in river basins east of the Salgir upper reaches, however, not reaching catastrophic levels. According to more aggressive climate scenarios RCP 6.0 and RCP 8.5, in the late XXI century, a largest decrease in the river runoff of the mountain Crimea is likely, which will contribute to deficit of water resources due to atmospheric sources. [ABSTRACT FROM AUTHOR]

This paper provides a novel approach to integrate farmers' behavior in spatially explicit agricultural land use modeling to investigate climate change adaptation strategies. More specifically, we develop and apply a computationally efficient machine learning approach based on reinforcement learning to simulate the adoption of agroforestry practices. Using data from an economic experiment with crop farmers in Southeast Germany, our results show that a change in climate, market, and policy conditions shifts the spatial distribution of the uptake of agroforestry systems. Our modeling approach can be used to advance currently used models for ex ante policy analysis by upscaling existing knowledge about farmers behavioral characteristics and combine it with spatially explicit environmental and farm structural data. The approach presents a potential solution for researchers who aim to upscale information, potentially enriching and complementing existing land use modeling approaches. [ABSTRACT FROM AUTHOR]

The frequency analysis method is commonly used to calculate design floods. Under the double challenge of the non‐stationary situation under the changing environment and the inadequate length of flood series, developing a new method to integrate the historical extraordinary floods into the non‐stationary frequency analysis is essential. First, the Multi‐Model Ensemble projections of temperature and precipitation based on Global Climate Model outputs were employed to drive the Soil & Water Assessment Tool hydrological model for runoff simulation. Then, the Integrated Time‐Varying Moment (ITVM) model was developed to re‐analyze the design floods based on the Pearson‐III distribution. The calibrated SWAT model can satisfactorily simulate the rainfall‐runoff relationship in the Yalong River basin. The developed ITVM model is effective to conduct the design flood frequency analysis to cope with the problems of insufficient length and non‐stationarity of the flood series. The design flood values of Maidilong station show an obvious increase, with variations of 6.5%–9.4%, 2.9%–12.3%, and 16%–33.7% for SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5, respectively. The significant increase of low frequencies (p = 0.2%, p = 0.1%) floods, especially for SSP5‐8.5 scenario, requires more attention, as the increased floods may exceed the discharge capacity of the reservoir determined at the design stage. [ABSTRACT FROM AUTHOR]

The number of studies on the urban heat island has been increasing in recent years. Developments in geospatial analytics, especially in recent years, have made frequent use of three-dimensional 3D modeling techniques in urban heat island research. There is a limited number of studies in the literature that deals with a systematic review of planning strategies and 3D modeling techniques used that are effective in reducing the formation of urban heat islands. The study presents an evaluation of a total of 33 studies that evaluated planning strategies that are effective in reducing the urban heat island effect with 3D modeling. The studies were evaluated in 4 sections. While the parameters used in the studies were evaluated in the first part, a summary of the studies and the criteria they used were evaluated in the second part, and the techniques of the studies were evaluated in the third part. In the last part, the programs used in 3D modeling were evaluated. As a result of the evaluations, it has been determined that buildings, green areas, and pavements, which are the most effective factors in the formation of urban heat islands, should be taken into account in planning strategies. In these 33 studies examined, the cooling effect of green areas on urban areas comes to the fore. These studies show that it has been concluded that the 3D indicators reflect the temperature, built environment, and land cover/use relationship in the best way. This result shows that 3D spatial modeling techniques should be preferred more in urban heat island research since they better reflect 2D land cover variables. [ABSTRACT FROM AUTHOR]

In regions characterized by snow and glaciers, the selection of appropriate methodologies and the use of suitable hydrological models are imperative due to heightened vulnerability to the impacts of global climate change. This study focuses on the Tamor River basin (TRB) in the Eastern Himalaya of Nepal, aiming to employ an advanced hydrological model to enhance understanding and prediction. We used the SPHY-cryo-hydro-climate-impact model to simulate the daily discharge and to estimate the contribution of different runoff components both annually and seasonally. The model was chosen based on physical processes representation, the ability to simulate snow and glacier melt, and the computational efficiency to project the daily discharge simulations in changing climate. Utilizing meteorological data with high temporal (daily) and spatial (250 m × 250 m grid cell size) resolution from 1995 to 2012, the model undergoes calibration (1996–2004) and validation (2005–2012) using a manual approach due to the limited parameters requiring calibration. The model's performance is evaluated using goodness of fit (GoF) functions, including NSE, KGE, and VD, with satisfactory results (NSE ≥ 70%, VD < 10%, KGE > 0.7) for both calibration and validation periods at two monitoring stations (690 and 684). The study reveals that snow and ice melt significantly contribute to pre-monsoon flow (March, April, and May), constituting approximately 35% of the total flow, while their influence diminishes during post-monsoon seasons (October and November). Sensitivity analyses indicate the model's responsiveness to both temperature and precipitation forcings, with temperature exerting a more pronounced impact, particularly on temperature-dependent melt modules and the state of precipitation at higher elevations. However, uncertainties persist in the model outputs, primarily attributed to input forcings, model structure, and parameters derived from literature rather than field measurements. The observed discrepancy between flow duration curves suggests that calibrated parameters remain partially optimized and subject to uncertainty. This study underscores the complexity of hydrological modeling in snow and glacier-dominated regions and emphasizes the need for continued refinement to enhance predictive accuracy amid evolving climatic conditions. [ABSTRACT FROM AUTHOR]

This study projects the changes in tropical cyclone (TC) landfalls in the western North Pacific under shared socioeconomic pathway (SSPs) scenarios during the TC peak season by using low‐resolution global climate models participating in the Coupled Model Intercomparison Project phase 6 (CMIP6). Projections are based on the relationship between mid‐ and lower‐level atmospheric circulation and TC landfall frequency during the historical period from 1985 to 2014 and the future climate period from 2015 to 2100. The landfall areas for TCs are divided into northern East Asia (NEA), middle East Asia (MEA) and southern East Asia (SEA); the TC peak seasons are July–September for NEA and MEA, and July–November for SEA. To evaluate reproducibility, both ensemble and individual model outputs for mid‐ and lower‐level atmospheric circulations associated with TC landfall in each East Asian subregion are compared to the reanalysis. An ensemble of seven models with stable results for all three regions is more reasonable in simulating atmospheric circulation patterns than an ensemble of all CMIP6 models. The findings suggest that TC landfall is projected to increase by about 12% and 32% in NEA and MEA, respectively, in the late 21st century under the SSP5‐8.5 scenario compared to the historical period, while decreasing by 13% in SEA. These changes are consistent under both warming scenarios, and are more pronounced in the SSP5‐8.5 scenario compared to SSP1‐2.6, particularly in the later period of this century. An analysis of future atmospheric circulations suggests that global warming will weaken the western North Pacific subtropical high and cause its boundary to retreat eastward. This will lead to changes in the steering flow, which is closely related to TC tracks, resulting in TC landfalls to increase or decrease depending on the East Asian subregion. [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]

The key locations of freshwater input driving Atlantic Meridional Overturning Circulation (AMOC) slowdown and their climate responses remain inconclusive. Using a state-of-the-art global climate model, we conduct freshwater hosing experiments to reexamine AMOC sensitivity and its climate impacts. The Irminger basin emerges as the most effective region for additional freshwater fluxes, causing the greatest AMOC weakening. While global temperature and precipitation responses are relatively homogeneous, subcontinental responses--especially in the northern mid-latitudes--are heterogeneous. At high latitudes, sea ice responses to freshwater fluxes and associated ice-albedo feedbacks determine temperature changes. In tropical and extratropical regions, temperature dynamics are shaped by atmospheric circulation and oceanic heat transport. Precipitation shows seasonal and regional variability due to altered surface turbulent heat flux and the southward movement of the Intertropical Convergence Zone (ITCZ). The widespread heterogeneity in climate extremes underscores the need to monitor freshwater release regions linked to AMOC slowdown. These findings hold vital implications for understanding paleoclimate and future AMOC impacts. [ABSTRACT FROM AUTHOR]

The change of groundwater storage (GWS) on the Tibetan Plateau (TP) is vital for water resources management and regional sustainability, but its estimation has large uncertainty due to insufficient hydrological measurements and diverse future climate scenarios. Here, we employ high-resolution land surface modeling, advanced satellite observations, global climate model data, and deep learning to estimate GWS changes in the past and future. We find a 3.51 ± 2.40 Gt yr−1 increase in GWS from 2002–2018, especially in exorheic basins, attributed to glacier melting. The GWS will persistently increase in the future, but the growth rate is slowing down (0.14 Gt yr−1 for 2079–2100 under a high emission scenario). Increasing GWS is projected over most endorheic basins, which is associated with increasing precipitation and decreasing shortwave radiation. In contrast, decreasing GWS is projected over the headwaters of Amu Darya, Yangtze, and Yellow river basins. These insights have implications for sustainable water resource management in a changing climate. [ABSTRACT FROM AUTHOR]

The Northwest Pacific Ocean is the most productive fishing ground in the Pacific Ocean, with a continuous rise in water temperature since 1990. We developed stacked species distribution models (SSDMs) to estimate the impacts of climate change on the distribution dynamics of economically significant species under three climate change scenarios for the periods 2040-2060 and 2080-2100. Overall, water temperature is the most important factor in shaping the distribution patterns of species, followed by water depth. The predictive results indicate that all the species show a northward migration in the future, and the migration distance varies greatly among species. Most pelagic species will expand their habitats under climate change, implying their stronger adaptability than benthic species. Tropical fishes are more adaptable to climate change than species in other climate zones. Though limitations existed, our study provided baseline information for designing a climate-adaptive, dynamic fishery management strategy for maintaining sustainable fisheries. [ABSTRACT FROM AUTHOR]

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

Capabilities of 43 models of the Coupled Model Intercomparison Project Phase 6 (CMIP6) to simulate the Madden‐Julian Oscillation (MJO) were compared in this study. Models with higher MJO simulation skills reproduce organized large‐scale convection more frequently over the equatorial western Pacific (EWP), suggesting that MJO simulations are tightly connected to organization of large‐scale precipitation over the EWP. MJO simulation skills are not significantly correlated with the mean sea surface temperature (SST) over the EWP, but tightly connected to SST threshold for convection there. The top simulations exhibit lower SST threshold and more realistic SST‐precipitation relationship over the EWP, suggesting that convection can be organized more efficiently and intensified more rapidly with the increasing SST in these simulations. Our results emphasize that reproducing the relationship between SST and large‐scale precipitation over the EWP is critical to MJO simulations. Plain Language Summary: The Madden‐Julian Oscillation (MJO) is one of the most important tropical phenomena that modulate weather and climate systems at the intraseasonal time scales. However, accurately reproducing the MJO in global climate models remains a challenge. The eastward propagation of the MJO convection is usually not well‐reproduced in models. Comparing to observations, they tend to underestimate the occurrence frequency of individual MJO events with lower amplitude and shorter propagation range. This study explore whether the MJO simulations in models are linked to the relationship between sea surface temperature (SST) and large‐scale precipitation they reproduced. It was found that MJO simulation skills are greatly dependent on the organized large‐scale precipitation over the equatorial western Pacific. We further revealed that realistically reproducing the SST‐precipitation relationship over this region is critical for simulating the MJO in models. Key Points: Madden‐Julian Oscillation (MJO) simulation skills depend on organized large‐scale precipitation over the equatorial western Pacific (EWP) reproduced in modelsModels with higher MJO simulation skills exhibit lower sea surface temperature (SST) threshold for convection over the EWPRealistically reproducing the relationship between the SST and organized large‐scale precipitation is critical to MJO simulations [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]

Mesoscale convective systems (MCSs) are pivotal in global energy/water cycles and typically produce extreme weather events. Despite their importance, our understanding of their future change remains limited, largely due to inadequate representation in current climate models. Here, using a global storm‐resolving model that accurately simulates MCSs, we conclude contrasting responses to increased SST in their occurrence, that is, notable decreases over land but increases over ocean. This land‐ocean contrast is attributed to the changes in convective available potential energy (CAPE) and convective inhibition (CIN). Over land, notable rises in CIN alongside moderate increases in CAPE effectively suppress (favor) weak to moderate (intense) MCSs, resulting in an overall reduction in MCS occurrences. In contrast, substantial increases in CAPE with minimal changes in CIN over ocean contribute to a significant rise in MCS occurrences. The divergent response in MCS occurrence has profound impacts on both mean and extreme precipitation. Plain Language Summary: Mesoscale convective systems (MCSs) account for more than half of tropical total rainfall and are the primary drivers of extreme rainfall and flooding events. However, understanding how the frequency of these systems will change in a warming climate remains a challenge. This is due to both the incomplete representation of these disturbances in global climate models and a lack of fundamental theory to explain their dynamics. In this study, we employ a global storm resolving model to shed light on the response of tropical MCS occurrences to climate warming. What emerges is a striking contrast: MCSs over tropical land are projected to decrease in frequency, while their occurrence over tropical oceans is anticipated to rise. This difference is linked to the relative changes in convective available potential energy and convective inhibition. The divergent response in MCS occurrences has significant implications for both average and extreme precipitation patterns. As a result, it presents challenges for developing regional climate adaptation and mitigation strategies in the face of a warming climate. Key Points: A global storm resolving model is employed to study the response of global mesoscale convective systems (MCSs) to a warming climateTropical MCS occurrence exhibits divergent responses to increased SST, declining over land while increasing over the oceanThis land‐ocean contrast is attributed to the changes in convective available potential energy and convective inhibition, on MCSs [ABSTRACT FROM AUTHOR]

Terrestrial processes influence the atmosphere by controlling land‐to‐atmosphere fluxes of energy, water, and carbon. Prior research has demonstrated that parameter uncertainty drives uncertainty in land surface fluxes. However, the influence of land process uncertainty on the climate system remains underexplored. Here, we quantify how assumptions about land processes impact climate using a perturbed parameter ensemble for 18 land parameters in the Community Earth System Model version 2 under preindustrial conditions. We find that an observationally‐informed range of land parameters generate biogeophysical feedbacks that significantly influence the mean climate state, largely by modifying evapotranspiration. Global mean land surface temperature ranges by 2.2°C across our ensemble (σ = 0.5°C) and precipitation changes were significant and spatially variable. Our analysis demonstrates that the impacts of land parameter uncertainty on surface fluxes propagate to the entire Earth system, and provides insights into where and how land process uncertainty influences climate. Plain Language Summary: Land processes can affect climate by controlling the transfer of energy and water from the land to the atmosphere. Previous research has shown that uncertainty surrounding land processes (e.g., photosynthesis and the movement of water through soils) can drive uncertainty in land‐to‐atmosphere fluxes. However, it remains unclear how much that land uncertainty can impact climate. Here, we quantify how climate is sensitive to assumptions about land processes by varying 18 land model parameters to create an ensemble of 36 possible worlds in a global climate model. Land temperature ranges by 2.2°C across this ensemble, mostly due to changes in how much water is evaporated from the land surface. Changing land parameters also drives regionally variable changes in mean precipitation. This study highlights a large and underappreciated impact of land processes in determining the mean climate state, and provides insights into how climate is influenced by land process uncertainty. Key Points: Land processes substantially impact the climatological mean state terrestrial temperature and precipitationLand parameters influence climate predominantly through changing evapotranspiration rather than through other mechanismsWarming driven by land processes activates different atmospheric feedbacks than radiatively‐driven warming [ABSTRACT FROM AUTHOR]

This study provides a comprehensive bibliometric and in-depth analysis of artificial intelligence (AI) and machine learning (ML) applications in environmental monitoring, based on 4762 publications from 1991 to 2024. The research highlights a notable increase in publications and citations since 2010, with China, the United States, and India emerging as leading contributors. Key areas of research include air and water quality monitoring, climate change modeling, biodiversity assessment, and disaster management. The integration of AI with emerging technologies, such as the Internet of Things (IoT) and remote sensing, has significantly expanded real-time environmental monitoring capabilities and data-driven decision-making. In-depth analysis reveals advancements in AI/ML methodologies, including novel algorithms for soil mapping, land-cover classification, flood susceptibility modeling, and remote sensing image analysis. Notable applications include enhanced air quality predictions, water quality assessments, climate impact forecasting, and automated wildlife monitoring using AI-driven image recognition. Challenges such as the "black-box" nature of AI models, the need for high-quality data in resource-constrained regions, and the complexity of real-time disaster management are also addressed. The study highlights ongoing efforts to develop explainable AI (XAI) models, which aim to improve model transparency and trust in critical environmental applications. Future research directions emphasize improving data quality and availability, fostering interdisciplinary collaborations across environmental and computer sciences, and addressing ethical considerations in AI-driven environmental management. These findings underscore the transformative potential of AI and ML technologies for sustainable environmental management, offering valuable insights for researchers and policymakers in addressing global environmental challenges. [ABSTRACT FROM AUTHOR]

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

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]

Groundwater sustains human well-being and ecosystems functioning. Many regions in Europe have experienced declining groundwater levels caused by decreasing groundwater recharge (GWR) or increasing groundwater abstractions (GWAs). These changes can lead to groundwater-related stress, threatening ecosystems and water supplies. Existing groundwater stress indicators estimate stress during a given period but do not address how stress changes or show the uncertainty of future stress. We propose a novel indicator of future groundwater stress (GWSI) due to changes in GWR and GWA and, thus, the alteration of long-term mean annual groundwater discharge (GWD). Groundwater stress is defined as any alteration in GWD since ecosystems are adapted to an equilibrium state. Focusing on decreasing GWD, which is generally more harmful than increasing GWD, we quantified the future GWSI in Europe by integrating scenarios of GWR and GWA in 2070–2099. GWR was evaluated using an ISIMIP2b multi-model ensemble of eight global hydrological models driven by the output of four global climate models under two greenhouse gas emission scenarios. GWA scenarios for irrigation, domestic and manufacturing sectors were combined with the GWR projections to generate an ensemble of GWSIs, simplified into three groundwater stress scenarios (high, intermediate, low). Projected GWSIs vary significantly among the scenarios. For the high-stress scenario, 58% of Europe's land area is projected to experience a GWD decrease of at least 25% under RCP8.5 compared to 38% under RCP2.6, while the respective values are 26 and 1% for the intermediate-stress scenario. Groundwater demand management alone might not prevent GWD declines under the high-stress and intermediate scenarios, particularly under RCP8.5. Therefore, climate change mitigation might imperative for reducing the decline of GWD, especially in Eastern and Southeastern Europe, where changes in GWR are projected to be the primary cause of declining GWD (in the high abstraction scenario under RCP8.5). Under RCP2.6, reductions in GWAs by 25–75% might balance a GWD decline in parts of Spain and Italy where GWAs are high, even in the high-stress scenario. In line with the precautionary principle, we recommend adapting to the high-stress scenario to minimize harm to the beneficiaries of groundwater. [ABSTRACT FROM AUTHOR]

To address the challenge of limited training samples, this study employs the model-agnostic meta-learning (MAML) algorithm along with domain-knowledge-based data augmentation to predict winter temperatures on the Korean Peninsula. While data augmentation has been achieved by using global climate model simulations, the proposed augmentation is purely based on the observed data by defining the labels using large-scale climate variabilities associated with the Korean winter temperatures. The MAML-applied convolutional neural network (CNN) (referred to as the MAML model) demonstrates superior correlation skills for Korean temperature anomalies compared to a reference model (i.e., the CNN without MAML) and state-of-the-art dynamical forecast models across all target lead months during the boreal winter seasons. Sensitivity experiments show that the domain-knowledge-based data augmentation enhances the forecast skill of the MAML model. Moreover, occlusion sensitivity results reveal that the MAML model better captures the physical precursors that influence Korean winter temperatures, resulting in more accurate predictions. [ABSTRACT FROM AUTHOR]

ABSTRACT Precipitation changes dynamically in the Mediterranean region. Therefore, the projection of future precipitation and its historical distribution mechanism is essential for climate mitigation and adaptation. In this study, a stepwise clustered precipitation downscaling method (SCPD) was developed and adopted in the Mediterranean region to reveal the inherent variation rules and trends over the future 100 years under two SSP scenarios. A cutting and merging multivariate process is introduced to build a cluster tree for supporting further downscaling and projecting steps. The ensemble average from the global climate model (GCM) dataset is used for precipitation projections. The precipitation performance of SCPD, evaluated by R2, is fairly decent. The precipitation projections vary with the original rainfall patterns over the gauge stations. Dry places tend to become comparably drier in the future. Precipitation in the northern Mediterranean region shows a drier winter–spring and wetter summer–autumn. Opposite trends emerged in the southern part, with increasing winter precipitation and decreasing summer rainfall. The rising carbon dioxide concentration will further intensify the decrease in rainfall. However, the centres of these two EOFs are not identical. The contributions of NAO (positive) and Niño 3.4 (negative) to PC1 are relatively high. Accordingly, the strongest positive correlation with PC2 is SCAND, as well as negative correlations with AO, NAO and EAWR. Positive anomaly precipitation is attributed to PC1, whereas PC2 is responsible for most of the negative variance precipitation. [ABSTRACT FROM AUTHOR]

This study analyses the climate change and technological change impact on food production in Benin using data from the agriculture ministry and Climate Analytics over the period 1995–2015 and a bio‐economic model. The results suggest that land‐use changes may depend on crop types and prevailing future conditions. With respect to crop production, the results show that the climate change effect on different crops varies from one municipality to another. Moreover, crops such as rice and soybean will benefit more from climate change in several Benin's municipalities, while maize will suffer from climate change. Moreover, results showed technological change globally mitigates the negative impact of moderate climate change. The results suggest that Beninese's government must invest in agriculture technology through agriculture and engineering high schools' installation, which could improve farmers' productivity and efficiency. [ABSTRACT FROM AUTHOR]

Sea ice thickness (SIT) is a critical and sensitive parameter in the climate system, with its dynamic changes profoundly influencing global climate models, navigational routes, and the potential for Arctic resource development. Given the widespread application of current satellite remote sensing technology in monitoring SIT, significant uncertainties remain. This study first underscores the importance of in-situ observations as a direct measurement method for SIT. However, the limitations of in-situ data in terms of acquisition cost, spatiotemporal coverage continuity, and distribution uniformity significantly hinder the effective evaluation of multi-source SIT products. To address this, the study innovatively introduces the Triple Collocation (TC) method, which effectively mitigates the impact of errors from individual data sources on the overall evaluation results through a mutual validation mechanism among multiple satellite data sources. This allows for a scientific assessment of multi-source SIT products even in the context of scarce in-situ observations. The findings indicate that the TC method not only successfully resolves the challenges of multi-source data evaluation but also facilitates data integration among these products, significantly enhancing the overall accuracy and spatiotemporal consistency of SIT data. [ABSTRACT FROM AUTHOR]

Global warming has caused the gradual degradation of permafrost, which may affect the vegetation water uptake from different depths. However, the water utilization strategies of different vegetation species during the thawing stages of permafrost regions need further study. To elucidate these differences, we selected the permafrost region in Northeast China as study area. We analyzed the water uptake from different depths of Larix gmelinii , a deciduous coniferous tree, Pinus sylvestris var. mongolica , an evergreen tree, and Betula platyphylla , a deciduous broadleaf tree, using stable isotopes of xylem water, soil water, and precipitation from June to October 2019. The results showed that L. gmelinii primarily used shallow soil water (0-40 cm) with the highest proportion at 64.1%, B. platyphylla generally used middle soil water (40-110 cm) with the highest proportion at 55.7%, and P. sylvestris mainly used middle (40-110cm) and deep soil water (110-150 cm) with the highest proportion at 40.4% and 56.9%. The water sources from different depths exhibited more frequent changes in P. sylvestris , indicating a higher water uptake capacity from different soil depths. L. gmelinii mainly uptakes water from shallow soils, suggesting that the water uptake of this species is sensitive to permafrost degradation. This study revealed the water uptake strategies from different depths of three tree species in a permafrost region, and the results suggested that water uptake capacity of different tree species should be considered in the prediction of vegetation changes in permafrost regions under a warming climate. [ABSTRACT FROM AUTHOR]

Maritime Antarctica's harsh abiotic conditions forged simple terrestrial ecosystems, mostly constituted of bryophytes, lichens, and vascular plants. Though biotic interactions are, together with abiotic factors, thought to help shape this ecosystem, influencing species' distribution and, indirectly, mediating their response to climate, the importance of these interactions is still fairly unknown. We modeled current and future abundance patterns of bryophytes, lichens, and vascular plants, accounting for biotic interactions and abiotic drivers, along a climatic gradient in maritime Antarctica. The influence of regional climate and other drivers was modeled using structural equation models, with and without biotic interactions. Models with biotic interactions performed better; the one offering higher ecological support was used to estimate current and future spatial distributions of vegetation. Results suggest that plants are confined to lower elevations, negatively impacting bryophytes and lichens, whereas at higher elevations both climate and other drivers influence bryophytes and lichens. Our findings strongly support the use of biotic interactions to predict the spatial distribution of Antarctic vegetation. [Display omitted] • Bryophytes, lichens, and plants constitute the bulk of Maritime Antarctic vegetation • Abiotic drivers only partially explained Antarctic vegetation abundance • Biotic interactions were key to further explain vegetation abundance patterns • These findings boost our understanding of climate change impact in Antarctica There is an urgent need to improve predictive ecological models of climate change effects in maritime Antarctic vegetation. Rocha et al. uncovered that the inclusion of biotic interactions is a vital step to better explain and predict vegetation abundance patterns. These findings contribute toward building better conservation tools for the region. [ABSTRACT FROM AUTHOR]

This paper explores the optimal temperature change in normalized difference vegetation index (NDVI) growth in the Huang-Huai-Hai Plain under the background of climate change, aiming to better cope with the impact of global warming on vegetation growth. The temporal and spatial variation characteristics of the NDVI and temperature factors were analyzed by using the NDVI, average temperature (Tavg), maximum temperature (Tmax) and minimum temperature (Tmin) datasets from 1982 to 2020. Through the Buishand U test and sliding slope detection, 1998 was determined to be a year of abrupt climate change. Furthermore, SHAP important feature analysis, a generalized additive model, correlation analysis, and other methods were used to identify the trend in the optimum temperature of vegetation growth before and after climate change. The results showed that (1) from 1982 to 2020, Tavg, Tmax, Tmin, and the NDVI in Huang-Huai-Hai Plain showed a significant upward trend. At the same time, the spatial distribution of these indicators shows the distribution characteristics of high in the south and low in the north. (2) The NDVI was positively correlated with Tavg, Tmax, and Tmin, and the correlation with Tmin was most significant. (3) The most suitable Tavg, Tmax, and Tmin intervals for vegetation growth were 20~30 °C, 25~35 °C, and 16~25 °C, respectively. (4) The optimum temperature range of vegetation growth was expanded after climate change, and the change rates of the Tavg and Tmax lower limits reached 24% and 25%, respectively, under the best condition of vegetation growth. (5) After abrupt climate change, the temperature suitable for vegetation growth increased significantly in the northern part of the plain but decreased slightly in the southern part. [ABSTRACT FROM AUTHOR]

Providing reliable and accurate high-resolution meteorological data is very significant to guide the rapid response to extreme weather conditions. However, due to the constraints of computing power and simulation time, the spatial resolution of existing all global climate models is low, and it is unable to provide meteorological data with more precise resolution at local scale. In this research, a deep learning downscaling model called two-stage multi-scale feature extraction network (TSMFN) is proposed. By combining ERA5 reanalysis data and terrain data, spatial downscaling of global precipitation measurement mission precipitation data is carried out. Specifically, in the first stage of the network, several multi-scale residual Inception blocks are used to extract multi-scale features of low-resolution precipitation data; several residual-based residual multi-scale cross blocks are used to fully excavate multi-scale features after the fusion of multiple data. In the second stage of the TSMFN, the output feature map after the fusion of the previous stage is fused with the high-resolution monthly average precipitation data, and several progressive multistage Swin Transformer blocks are constructed to overcome the problems that the reconstructed image of a general convolutional neural network is smooth and cannot reflect the real spatial distribution of precipitation. Finally, a hybrid loss function combining L 1 loss function and focal frequency loss function is proposed to alleviate the ill-posedness of the downscaling. By comparing the proposed algorithm with some advanced deep learning downscaling algorithms, the results of the experiment show that the TSMFN model is significantly better in terms of generated image quality and multiple evaluation indexes, and the model has stronger generalization ability. [ABSTRACT FROM AUTHOR]

Extreme precipitation events have substantial implications for water resources, ecosystems, and human populations in Central Africa (CA). Consequently, understanding the spatial variability of these events is crucial for effective climate change adaptation and water management strategies. In this study, we assess the performance of the state-of-the-art global climate models from Phase 6 of the Coupled Model Intercomparison Project (CMIP6) in simulating extreme precipitation events over CA. By considering three observational datasets, we evaluated the ability of sixteen CMIP6 models as well their multi-model ensemble (MME), to capture the patterns of extreme precipitation events. We then focus on key metrics such as duration and intensity, based on a total of ten indices of extreme precipitation events, defined by the Expert Team on Climate Change Detection and Indices (ETCCDI). The results showed that the individual models as well as the MME exhibited acceptable performance in reproducing the patterns of extreme precipitation events, especially when dealing with wet-day amount, frequency, dry spell duration and persistence of extreme precipitation over the two northern CA subregions (i.e., Sudano-Sahelian and Northern Equatorial). Moreover, the analyses revealed that CMIP6 models generally lack the ability of in simulating not only the wet spell duration, but also heavy precipitation indices, particularly over the central and southeastern CA subregions (i.e., Equatorial East and Southern Equatorial, respectively). These results highlight the remaining challenges in reproducing local-scale processes governing precipitation variability and extreme precipitation events over the region. The results of this study provide an understanding of both the strengths and limitations of the CMIP6 models over CA, which would help to improve regional climate projections and strengthen the ability of decision makers to assess the future risks associated with extreme precipitation events in the region. [ABSTRACT FROM AUTHOR]

To separate the contributions of climate change and human activities to alterations in flow for the Ahar Chay watershed in Iran, change points of the normalized difference vegetation index (NDVI) and evapotranspiration (ET) were distinguished before and after significant alterations. The Budyko model and Soil and Water Assessment Tool (SWAT) were used to find the reduction in runoff. New Penman-Monteith-Leuning (PML) ET data was used to consider plant types. Results revealed an increase in NDVI coinciding with the change point in 1998, as well as ET in 1999. The significance of the Sattarkhan Dam became evident when distinguishing the impacts upstream versus downstream of the dam. Results also indicated that climate change contributed significantly to upstream flow reduction (83.5%), whereas downstream reductions were primarily linked to human-related impacts (59.7–82.5%). Assuming no dam interception, 2007 became the change point, signifying a 37.5% reduction in average flow from before to after this point due to the increased vegetation cover. [ABSTRACT FROM AUTHOR]

Social Impact Statement: For hundreds of years, Agave marmorata plants have been used in the production of alcoholic beverages in Mexico. This species is very important in small‐scale rural economies because it is a large plant, yielding five liters of mezcal. However, the production of these beverages takes place when it reaches its reproductive stage, which takes up to 35 years. Due to its slow maturation and high demand, it is considered an endangered species. Therefore, as a conservation strategy, this study proposes the creation of nurseries, genetic breeding programs, and demographic monitoring of wild populations to counteract the extraction of wild plants and, the conservation of the genetic diversity. Summary: Agave marmorata Roezl., is an endemic species distributed in the states of Oaxaca and Puebla, Mexico, and locally is widely used to produce mezcal.We assessed the genomic diversity and differentiation using the RADseq method and 29,101 high‐quality single nucleotide polymorphisms (SNPs) in wild plants and grown under three different management types (cultivated, plants used as live fences, and young plants growing in nurseries). We examined the demographic history and used species distribution modeling to understand the future of A. marmorata under scenarios of climate change.We found high levels of genomic diversity (HS = 0.229) and moderate levels of inbreeding (FIS = 0.106 and Fhat3 = 0.190). The cultivated samples harbored less genetic diversity than the wild plants. Furthermore, we estimated low differentiation between cultivated and wild localities (FST = 0.037). In the wild samples, we identified two main genetic groups, one in the East and another in the West of its distribution area. This genetic structure possibly derived from a population contraction during the Pleistocene (~216,879.75 BP) and the formation of two refugia in small areas with climatic stability. Furthermore, the demographic reconstruction indicated that A. marmorata went through a recent population expansion event, with a large current Ne (Ne = 8,009). The future climate change models indicated contrasting possible changes in its distribution range, from an increase to the reduction of its suitable habitat, differences related to model parametrization, and future levels of CO2 production.We propose conservation measures for the different management types of the species while also considering the biotic and abiotic interactions of Agave marmorata. [ABSTRACT FROM AUTHOR]

The north and central coast of Chile is influenced by El Niño-Southern Oscillation (ENSO) through oceanic and atmospheric teleconnections. However, it also experiences episodic oceanic warmings off central Chile (30°S) lasting a few months that are not necessarily associated with ENSO. These episodes, called "Chile Niño" events, besides their ecological and socio-economical impacts, have also the potential to influence tropical Pacific variability. Here, we investigate how realistically the models in the Coupled Model Intercomparison Project (CMIP, Phases 5 and 6) simulate Chile Niño/Niña (CN) events, and quantify their changes under anthropogenic forcing. Despite limitations of the global models in simulating realistically coastal upwelling dynamics, we show that they simulate reasonably well the observed spatial pattern, amplitude and seasonal evolution of CN events. They however fail to properly represent the positive skewness from observations. The analysis of a sub-group of models (36) that simulate ENSO realistically reveals that CN events increase in amplitude and variance in the future climate with no changes in their frequency of occurence. This is interpreted as resulting from compensating effects amongst changes in remote drivers and local feedbacks. In particular, ENSO variance increases while that of the South Pacific Oscillation decreases. Conversely, we found that while the Wind-Evaporation-SST feedback tends to increase and the coupling between mixed-layer depth and SST weakens, favoring the development of CN events, the thermocline and wind-SST feedbacks decrease. However, only the change in the thermocline feedback is correlated to changes in CN variance amongst the models, suggesting a dominant role of local oceanic stratification changes in constraining the sensitivity of CN to global warming. [ABSTRACT FROM AUTHOR]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal 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.)

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal 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.)

In the upcoming decades, precipitation and temperature patterns are expected to shift in the Mediterranean basin due to global warming, potentially having an influence on the environment and the economy in the area. Using monthly precipitation and temperature data from 15 global climate models (GCMs) developed as part of the Coupled Model Intercomparison Project Phase 6 (CMIP6), the Mediterranean Climate Envelop (MCE), as defined by Daget's (1977) criteria, is projected under two climate change scenarios: SSP2-4.5 and SSP5-8.5, and for two future periods: 2050s and 2070s. According to the findings, the MCE is expected to expand by 3.51 and 4.93% in the 2050s under the SSP2-4.5 and SSP5-8.5 scenarios, respectively, in comparison to the current state. This expansion is expected to reach 5.28 and 9.87% for SSP2-4.5 and SSP5-8.5, respectively, in 2070s. For both situations and durations, MCE contraction would be minor, however, at less than 1%. More than 99% of the present MCE would stay stable proportionately. The northern Mediterranean region is mostly concerned by the MCE's expansion. The SSP2-4.5 scenario predicts that by the 2070s, expansion zones will occupy 674,183 km2, with 64% of the area located in Southern Europe and 36% in Western Asia. In SSP5-8.5 scenario, this area is expected to be significantly larger, estimated to be approximately 1,256,881 km2; 67% in Southern Europe and 33% in Western Asia. [ABSTRACT FROM AUTHOR]

Forest fire dynamics are expected to alter due to climate change. Despite the projected increase in precipitation, rising temperatures will amplify forest fire risk from the present to the end of the century. Here, we analysed changes in fire season, the number of fires, and burnt area in Fennoscandia from 1951 to 2100. Regional simulations from the JSBACH–SPITFIRE ecosystem model (where SPITFIRE stands for SPread and InTensity of FIRE) were performed under two climate change forcing scenarios (Representative Concentration Pathway (RCP) 4.5 and RCP 8.5) and three global climate driver models (CanESM2, CNRM-CM5, and MIROC5) with a 0.5° resolution. Simulations were forced by downscaled and bias-corrected EURO-CORDEX data. Generally, as a consequence of the projected longer fire season and drier fuel, the probability of fires is projected to increase. However, changes in fire season, the number of fires, and burnt area are highly dependent on climate projections and location. The fire season is estimated to increase on average from 20 ± 7 to 52 ± 12 d, starting from 10 ± 9 to 23 ± 11 d earlier and ending from 10 ± 10 to 30 ± 16 d later, compared to the reference period (1981–2010), by the end of the century (2071–2100). The results for Finland indicate a change in the number of fires, ranging from -7 ± 4 % to 98 ± 56 %, and a change in burnt area, ranging from -19 ± 24 % to 87 ± 42 %. These findings contribute to a better understanding of potential changes in the future fire seasons of northern Europe. [ABSTRACT FROM AUTHOR]

The age of air is an important transport diagnostic that can be derived from trace gas measurements and compared to global chemistry climate model output. We describe a new technique to calculate the age of air, measuring transport times from the Earth's surface to any location in the atmosphere based on simultaneous in situ measurements of multiple key long-lived trace gases. The primary benefits of this new technique include (1) optimized ages of air consistent with simultaneously measured SF6 and CO2 ; (2) age of air from the upper troposphere through the stratosphere; (3) estimates of the second moment of age spectra that have not been well constrained from measurements; and (4) flexibility to be used with measurements across multiple instruments, platforms, and decades. We demonstrate the technique on aircraft and balloon measurements from the 1990s, the last period of extensive stratospheric in situ sampling, and several recent missions from the 2020s, and compare the results with previously published and modeled values. [ABSTRACT FROM AUTHOR]

The impact of biomass burning aerosols (BBA) emitted in central Africa on the tropical African climate is studied using the ocean–atmosphere global climate model CNRM-CM, including prognostic aerosols. The direct BBA forcing, cloud feedbacks (semi-direct effects), effects on surface solar radiation, atmospheric dynamics and precipitation are analysed for the 1990–2014 period. During the June–July–August (JJA) season, the CNRM-CM simulations reveal a BBA semi-direct effect exerted on low-level clouds with an increase in the cloud fraction of ∼5 %–10 % over a large part of the tropical ocean. The positive effect of BBA radiative effects on low-level clouds is found to be mainly due to the sea surface temperature response (decrease of ∼0.5 K) associated with solar heating at 700 hPa, which increases the lower-tropospheric stability. Over land, results also indicate a positive effect of BBA on the low-cloud fraction, especially for the coastal regions of Gabon and Angola, with a potentially enhanced impact in these coupled simulations that integrates the response (cooling) of the sea surface temperature (SST). In addition to the BBA radiative effect on SST, the ocean–atmosphere coupled simulations highlight that the oceanic temperature response is noticeable (about -0.2 to -0.4 K) down to ∼80 m depth in JJA between the African coast and 10° W. In parallel to low-level clouds, reductions of ∼5 %–10 % are obtained for mid-level clouds over central Africa, mainly due to BBA-induced surface cooling and lower-tropospheric heating inhibiting convection. In terms of cloud optical properties, the BBA radiative effects induced an increase in the optical depth of about ∼2 –3 over the ocean south of the Equator. The result of the BBA direct effect and feedback on tropical clouds modulates the surface solar radiation over the whole of tropical Africa. The strongest surface dimming is over central Africa (∼-30 W m−2), leading to a large reduction in the continental surface temperature (by ∼1 to 2 K), but the solar radiation at the oceanic surface is also affected up to the Brazilian coast. With respect to the hydrological cycle, the CNRM-CM simulations show a negative effect on precipitation over the western African coast, with a decrease of ∼1 to 2 mm d−1. This study also highlights a persistent impact of BBA radiative effects on low-level clouds (increase in cloud fraction, liquid water content and optical depth) during the September–October–November (SON) period, mainly explained by a residual cooling of sea surface temperature over most of the tropical ocean. In SON, the effect on precipitation is mainly simulated over the Gulf of Guinea, with a reduction of ∼1 mm d−1. As for JJA, the analysis clearly highlights the important role of the slow response of the ocean in SON and confirms the need to use coupled modelling platforms to study the impact of BBA on the tropical African climate. [ABSTRACT FROM AUTHOR]

Phosphorus (P) is an important soil element for sustaining plant growth and the integrity of terrestrial ecosystems, and the soil P cycle is strongly influenced by climate change and agricultural activities. However, little is known about how soil P has evolved with climate change and intensive agriculture at mid‐high latitudes, where the soil P cycle is sensitive to climate change. To answer this question, an ecohydrological model (EcoHAT‐P) driven by remote sensing data was used in this study to calculate soil P concentration and loss and was calibrated and validated using 272 soil samples collected in the Sanjiang Plain, a typical mid‐high latitude region with a long history of strong agricultural activity. Soil P concentration and loss, and plant uptake of soil P, were analyzed for the years 2000–2019 and 2020–2040. The results showed that soil total P, soil P loss, and plant P uptake all increased under intensive agriculture. The soil P cycle at mid‐high latitudes was more sensitive to temperature than to precipitation. Increased temperature would increase soil P loss and plant P uptake by 93.94% and 8.16%, respectively, and soil legacy P from intensive agriculture would become the main source even if external P inputs were eliminated. The results highlight the evolution of soil P transport at mid‐high latitudes and clarify the response of soil P cycle to climate change under intensive agriculture. [ABSTRACT FROM AUTHOR]

Water scarcity is a global issue, especially in semi-arid and arid regions where precipitation is irregularly distributed over time and space. Predicting groundwater flow in heterogeneous karst terrains, which are essential water sources, presents a significant challenge. This article integrates geology, hydrology, and water monitoring to develop a pioneering conceptual and numerical model of groundwater flow in the Montes Claros Region (Vieira River Watershed, Brazil). This model was evaluated under various climate change scenarios, considering changes in rainfall, groundwater consumption, and population growth over the current century. The results indicate that a decline in water table levels is inevitable, primarily driven by population growth and high pumping rates rather than rainfall fluctuations. This underscores the urgent need for improved monitoring, model upgrading, and more importantly, targeted water resource management for Montes Claros. [ABSTRACT FROM AUTHOR]