Change Scenarios Research Articles

Enhancing Water Management and Urban Flood Resilience using Hazard Capacity Factor Design (HCFD) Model: Case Study of Eco-Delta City, Busan

This study evaluated future climate scenarios and the changes in urban flood resistance capacity in Busan Eco-Delta City using the hazard capacity factor design (HCFD) model. It analyzed the flood reduction effects of both gray and green infrastructure. Despite existing flood safety systems, there is a growing need to enhance urban flood resilience due to increasing heavy rainfall, unpredictable precipitation, and frequent typhoons driven by climate change. The HCFD model predicted urban flood volumes of Busan Eco-Delta City and analyzed the effectiveness of gray and green infrastructure in flood control. A stormwater management model (SWMM) simulated urban flood resistance capacity under the SSP1-2.6 climate change scenario. Results indicate that for an anticipated 500 mm rainfall over 3 h, green infrastructure can mitigate floods by 9% to 17.6%, while gray infrastructure can reduce flooding by 24% to 32.1%. The integration of gray and green infrastructure leads to an overall flood mitigation ranging from 47.1% to 63.3%. A notable contribution of this research is its predictive analysis of future flood scenarios using model-based scenario analysis and decision support algorithms, offering valuable insights into changes in urban flood resistance capacity and strategies for effective flood control decision-making.

Statistical Modeling of Main Inflation Drivers During the Post-Pandemic Period: The Case of Lithuania

The main inflation factors were analyzed of relatively high inflation in Lithuania after the COVID-19 pandemic in the context of European Union and euro area. The system dynamics model was developed mathematically describing the interrelations between inflation and the set of macroeconomic variables. The hypothetical reduced inflation rates were calculated modeling the different scenarios of macroeconomic changes under the inflationary environment.

Drosophila suzukii (Diptera: Drosophilidae) Population Dynamics and Infestation in a Raspberry Orchard of Loukkos Area, Morocco.

Raspberries (Rosales: Rosaceae) are considered to be one of most important crops in northwestern Morocco. However, this sector is seriously affected by the attack of Drosophila suzukii, which impairs the production and the export. Furthermore, the eco-ethology and population dynamics of D. suzukii under Moroccan conditions are still poorly understood. In Larache region, we monitored the population dynamics of D. suzukii adults using 4 traps baited with mashed ripe banana mixed with yeast, and reported the infestation levels during spring of 2022, 2023, and 2024 on raspberry cultivar Rubus idaeus var. Yazmin. Our results indicate that a maximum of 14, 20, and 28 D. suzukii adults per trap were caught weekly at the end of April 2022, in the middle of March 2023, and in the middle of April 2024, respectively. Furthermore, three peaks of D. suzukii adult flies were observed each year, whereas a total of six generations were predicted according to the accumulated degree-days. The male sex ratio of trapped D. suzukii was 1:0.32, 1:0.38, and 1:0.42 in 2022, 2023, and 2024, respectively. Raspberry fruit infestation reached a maximum of 76%, 75%, and 64% at the beginning of May 2022, middle of April 2023, and end of April 2024, respectively. Under the climate change scenario, knowledge of the eco-ethology of this insect and its population dynamics is essential for developing an IPM control strategy in Morocco, and further studies are ongoing to establish a biological and reasoned chemical approach based on degree-days.

An online tool with Google Earth Engine and cellular automata for seamlessly simulating global urban expansion at high resolutions

Projecting global urban expansion is crucial for environmental assessment under climate change scenarios. However, existing global future urban land products are typically provided at coarse resolutions (1 km) due to data and computing limitations. This hinders the accurate assessment of the impacts of global urban development at finer scales. Thus, we develop the first Cellular Automata (CA) online tool for simulating future global urban expansion in Google Earth Engine (GEE-CA), which can simulate future urban land change at a 30 m resolution under different SSP scenarios. GEE-CA enables seamless simulations of future urban land at high resolution through a partitioned parallel strategy. Seven large urban agglomerations are simulated under shared socioeconomic pathways as representative regions to present our fine-scale results. Comparatively, our datasets preserve significantly more spatial details than existing global urban land products. The improvement in resolution from 1 km to 30 m reduces errors by a range from 7.11% to 21.27% in the estimation of future urban area. So far, the proposed GEE-CA tool allows users to generate urban land projection maps for any defined region with a resolution as high as 30 m.

Potential Changes in Distribution of Major Conifers and Their Seed Mass across Siberia by the Mid-Twenty-First Century in a Warming Climate

Research highlights: At the turn of the 21st century, there were more forest territories found disturbed by both natural processes (climate change, wildfires, insect outbreaks, permafrost thawing, etc.) and anthropogenic interferences (air pollution, clearcuts, etc.). Seed collecting, then growing seedlings in forest nurseries, and then planting seedlings over lost forest areas are the forestry measures needed to restore the forest after disturbances. Goals were to construct bioclimatic models of ranges and seed mass of major Siberian conifers (Siberian pine (Pinus sibirica Du Tour), Siberian fir (Abies sibirica Ledeb.), Siberian spruce (Picea obovata Ledeb.), Siberian larches (Larix sibirica Ledeb., L. gmelini (Rupr) Rupr, and L. cajanderi Mayr.) and Pinus sylvestris L.) and predict their potential change in a warming climate by the mid-century. Methods: Multi-year seed mass data were derived from the literature, seed station data, and were collected in the field. Climate data (January and July data and annual precipitation) were derived from published Russian reference books and websites on climate. Bioclimatic indices (growing degree-days > 5 C, negative degree-days < 0 C, and annual moisture index) were calculated from January and July temperatures and annual precipitation for both contemporary and the 2050s (2040–2060) climates using the general circulation model INM-CM5-0 and two climate change scenarios, ssp126 and ssp585, from CMIP6. Our bioclimatic range models (envelope and MaxEnt models) and regression seed mass models for major conifers were built based on these bioclimatic indices. Additionally, their ranges were limited by the permafrost border, which divided the forest area into the permafrost-free zone, where five conifers are able to grow, and the permafrost zone, where only one conifer, Dahurian larch, is able to survive. Results: Under warmed climates, the ranges of all Siberian conifers would expand 1.5-fold due to the decrease in the permafrost zone, except Dahurian larch, which would lose 5–20% of its coverage due to permafrost retreat. Conifers shifting northward would be slower than predicted only by warmed climates because permafrost would thaw slower than climates would warm. Scots pine may expand by up to 60%, covering dryer lands in the south. Future climates were found to favor seed mass increase for major Siberian conifers and for heavier seed to shift northward. Our major conifers differ by the type of seed dispersal mode: zoochoric, animal (Siberian pine) and anemochoric, and wind-dispersed (other five trees). The seed masses of the five anemochoric conifers varied within the range of 1.5–15 g of 1000 seeds, which is about 40–50-fold less than that of zoochoric Siberian pine. Site climate explained about 28–65% of the seed mass variation for the five anemochoric trees and only 11% for Siberian pine (zoochoric tree). This finding needs additional research to explain the reasons. Conclusions: Warmed climates would favor the expansion of the ranges of major Siberian conifers and their seed mass to be heavier, which would support the high-quality seed production for forest well-being and its restoration in Siberia.

Development of an inclusive, scalable, and flexible hydrologic modeling system: Establishing integrated flood simulation system at agricultural watersheds

In this study, we developed a comprehensive hydrological modeling system to address the diverse needs of hydrologists and researchers. The system comprised nine modules, each serving a specific purpose. These modules include a multiplicative random cascade model, frequency analysis, inflow simulation, Hydrologic Engineering Center – 5, Hydrologic Engineering Center – River Analysis System, and farmland drainage simulations. The system follows a structured sequence within a large framework, beginning with user inputs for the initial condition information and period specifications for frequency analysis. It then calculates the time-disaggregated precipitation data utilizing the Dask distributed server for efficient computation. Subsequent module computations were conducted on dedicated mask workers. The Integrated Database serves as a comprehensive repository for simulation studies, encompassing historical precipitation data, shared socioeconomic pathways, climate change scenario data, and reservoir and farmland survey data. This system has been used in several studies and has provided cohesive and reliable results for flood simulations.

China's provincial optimum population sizes under SSP‐RCP scenarios

AbstractClimate change poses new challenges to achieving a balanced population distribution. Predicting the impact of development patterns using climate change scenarios can offer more precise insights into environmental and social risks. By adopting the possibility–satisfiability (P–S) model, this study investigates the optimum population sizes for the 31 provincial districts of China by 2035 and 2050 under seven shared socioeconomic pathway‐representative concentration pathway (SSP‐RCP) scenarios. Results indicate that under SSP2‐4.5 scenario, which maintains current development patterns, the optimum human population (OHP) ranges from 985 million to 717 million in 2035 and from 902 million to 623 million by 2050 at different P–S values, respectively. The SSP1‐1.9 and SSP1‐2.6 scenarios offer promising prospects by carrying the largest optimum population size. Therefore, China's future development depends on choosing a sustainable path that prioritizes the growth of the OHP. The optimum population sizes of provinces under unbalanced development routes SSP4‐3.4 and SSP4‐6.0 scenarios are mainly limited by economic development, while those of the regional competition route SSP3‐7.0 scenario are limited by both economic and climatic pressures. Under the fossil fuel route SSP5‐8.5 scenario, climate deterioration imposes severe constraints on optimum population size. The optimum population scales of the eastern coastal area with high urbanization levels and the northwest area with harsh environments are subject to natural conditions. In addition, the central and western regions encounter population size limitations due to insufficient economic development. Notably, China's population distribution remains concentrated in the east and sparse in the west, flanking the Hu Huanyong Line (Hu Line), with minimal expected changes in the future. By studying the spatial distribution of the optimum population under different SSP‐RCP scenarios, this study provides theoretical support for government decision‐making in the context of future climate change.

Effects of Climate Change and Changes in Land Use and Cover on Water Yield in an Equatorial Andean Basin

Ecosystem services contribute significantly to human development, with water production being a crucial component. Climate and land use changes can impact water availability within a basin. In this context, researching water-related areas is essential for formulating policies to protect and manage hydrological services. The objective of this study was to estimate water yield in the sub-basins of the Tabacay and Aguilán rivers under climate change scenarios in 2030, 2040, and 2050, combined with scenarios of changes in land cover and land use. The InVEST model was employed to analyze water yield. The results show that crop areas were identified as the lowest water yield in future scenarios, and forested areas, particularly the region where the Cubilán Protected Forest is located, contribute the most to water yield in the subbasin. Besides, water yield has increased in the historic period (2016–2018) due to the conservation and reforestation initiatives carried out by the Municipal Public Service Company for Drinking Water, Sewerage, and Environmental Sanitation of the city of Azogues in 2018, the so-called Reciprocal Agreements for Water. Additionally, an increase in water yield is projected for future scenarios. This study can serve as a basis for decision-makers to identify areas that should prioritize protection and conservation.

The urban island: climatic suitability of Linepithema humile (Hymenoptera: Formicidae) and the role of cities in the invasion of the Western Palearctic

AbstractUrban environments often present environmental conditions that facilitate the introduction and establishment of nonnative and invasive species. These can expand their range into areas with unfavorable climates by taking advantage of the ecological and climatic homogenization of cities, bypassing the ecological barriers presented by the surrounding environment. One way to monitor the expansion of these species is using potential distribution models. We used as a model species the Argentine ant, Linepithema humile (Hymenoptera: Formicidae) whose invasion has caused serious consequences for biodiversity and economic losses worldwide. We used the average result of six different algorithms and used climatic variables and population density as a proxy for the urbanization level in the Western Palearctic to build the predictive model. The model indicates this ant prefers to inhabit areas with Mediterranean and Temperate Oceanic climates and that its suitability depends on two main factors: the continentality (temperature annual range) and the degree of urbanization. The species is predicted to be absent in areas with large temperature contrasts throughout the year, particularly in rural and peri‐urban areas (i.e. adjacent to urban areas) of inland regions. Conversely, the species has a predilection for coastal and urban areas where environmental conditions are attenuated by the influence of the sea or the “urban heat island” effect in the case of inland cities. In this sense, cities act as “bioclimatic islands” facilitating the establishment of the Argentine ant as a reservoir, enlarging its distribution into climatically nonoptimal areas, and promoting its future expansion in a scenario of global warming and socioeconomic change.

Investigating Hydrological Drought Characteristics in Northeastern Thailand in CMIP5 Climate Change Scenarios

In this study, we analyzed the predictions of hydrological droughts in the Lam Chiang Kri Watershed (LCKW) by using the Soil and Water Assessment Tool (SWAT) and streamflow data for 2010–2021. The objective was to assess the streamflow drought index (SDI) for 5-, 10-, 25-, and 50-year return periods (RPs) in 2029 and 2039 in two representative concentration pathway (RCP) scenarios: the moderate climate change scenario (RCP 4.5) and the high-emission scenario (RCP 8.5). The SWAT model showed high accuracy (R2 = 0.82, NSE = 0.78). In RCP4.5, streamflow is projected to increase by 34.74% for 2029 and 18.74% for 2039, while in RCP8.5, a 37.06% decrease is expected for 2029 and 55.84% for 2039. A historical analysis indicated that there were frequent short-term droughts according to SDI-3 (3-month-period index), particularly from 2014 to 2015 and 2020 to 2021, and severe droughts according to SDI-6 (6-month-period index) in 2015 and 2020. The RCP8.5 projections indicate worsening drought conditions, with critical periods from April to June. A wavelet analysis showed that there is a significant risk of severe hydrological drought in the LCKW. Drought characteristic analysis indicated that high-intensity events occur with low frequency in the 50-year RP. Conversely, high-frequency droughts with lower intensity are observed in RPs of less than 50 years. The results of this study highlight an increase in severe drought risk in high emission scenarios, emphasizing the need for water management.

Distribution of euptyctimous mite Phthiracarus longulus (Acari: Oribatida) under future climate change in the Palearctic

The aim of this paper is to prepare, describe and discuss the models of the current and future distribution of Phthiracarus longulus (Koch, 1841) (Acari: Oribatida: Euptyctima), the oribatid mite species widely distributed within the Palearctic. We used the maximum entropy (MAXENT) method to predict its current and future (until the year 2100) distribution based on macroclimatic bio-variables. To our best knowledge, this is the first-ever prediction of distribution in mite species using environmental niche modelling. The main thermal variables that shape the current distribution of P. longulus are the temperature annual range, mean temperature of the coldest quarter and the annual mean temperature, while for precipitation variables the most important is precipitation of the driest quarter. Regardless of the climatic change scenario (SSP1-2.6, SSP2-4.5, SSP5-8.5) our models show generally the northward shift of species range, and in Southern Europe the loss of most habitats with parallel upslope shift. According to our current model, the most of suitable habitats for P. longulus are located in the European part of Palearctic. In general, the species range is mostly affected in Europe. The most stable areas of P. longulus distribution were the Jutland with surrounding southern coasts of Scandinavia, islands of the Danish Straits and the region of Trondheim Fjord.

Sensitivity of surface water and groundwater contributions to streamflow in a tropical glacierized basin under climate change scenarios

Abstract While mountain water faces threats posed by climate change, particularly in snow-dominated and glacierized systems, the role of groundwater in sustaining streamflow in these systems remains elusive. Changing mountain headwaters, marked by reduced snowpacks, retreating glaciers, shifting precipitation patterns, and rising temperatures, pose a crucial question: what is the resilience of streamflow in these mountains, and what role does groundwater play in this resilience? This is particularly uncertain in tropical high mountains where the seasonality of precipitation and glacier melt govern streamflow generation. A glacio-hydrological model was created using the Cold Regions Hydrological Modelling platform to investigate cryosphere-surface water-groundwater interactions in the Quilcayhuanca Basin, in Peru’s Cordillera Blanca. The model was forced by in-situ meteorological observations and parameterized using numerous data sources and process-based studies in the basin. Model results show that during the dry season, 37% of streamflow is generated from groundwater discharge, increasing to 56% during the lowest flows. Evapotranspiration is the largest mass flux from the basin at the peak of the dry season. Precipitation, temperature, and glacier change scenarios were used to assess the sensitivity of basin hydrology to climate change and glacier retreat. In a warmer, wetter, and nearly deglaciated future, Quilcayhuanca basin streamflow is expected to decrease by 4-19% annually, with a larger volumetric change in overland and vadose zone flow than in groundwater flow. The range in values is more closely linked to uncertainty in precipitation change than temperature change. Despite a strong reduction in snow and ice contribution to streamflow with warming and deglaciation, the concomitant increase in precipitation can limit the changes in streamflow and groundwater flow, showcasing the resilience of the system to shifts in climate and glacier cover.

Climate change and hydrodynamic scenario of the Muni Pomadze Lagoon in Ghana

Climate change and hydrodynamic scenario of the Muni Pomadze Lagoon in Ghana

Projecting irrigation demand under IPCC climate change scenarios using WEAP modeling in the Rechna Doab, Pakistan

Pakistan is currently facing significant water scarcity issues, intensified by climate change. The main water source, the transboundary Indus River system, faces challenges such as water management, limited data availability, and inadequate management, leading to a gap between water demand and supply across various sectors. Agriculture, which consumes over 95 % of the country’s water resources, contributes nearly 25 % to the GDP, but is heavily dependent on irrigation due to limited rainfall. With rainfall meeting only 15 % of crop water requirements, groundwater plays a critical role, covering 40–60 % of irrigation needs. This study focuses on the Rechna Doab in the Indus Basin Irrigation System (IBIS) using the WEAP (Water Evaluation and Planning) model to assess the supply-demand gap under IPCC's climate change scenarios from Assessment Report Six (AR6). The main findings indicate: (1) Under SSP 8.5, unmet demand in the Upper Chenab Canal and other regions will increase by 33–47 % by mid-century; (2) demand site coverage will decline significantly, especially in Lower Gugera and Jhang branches; (3) groundwater dependency will increase substantially in response to the growing supply-demand gap. This work contributes to improving water management in Rechna Doab by providing a clear framework for adapting water resources to climate change using WEAP projections under various IPCC scenarios.

Potential contribution of land cover change on flood events in the Senegal River basin

The increase in flood events observed in West African countries, and often in specific river basins, can be influenced by several factors, including anthropogenic land use and land-cover changes. However, the potential contribution of land cover changes to flood events still needs to be explored, especially in West Africa. Here, the fully coupled atmosphere-hydrology WRF-Hydro system, which comprises an atmospheric model and additionally incorporates the surface, subsurface, overland flow, and channel routing, is used to investigate the potential impact of a land cover change scenario on flood events in the Senegal River basin. The simulation was performed from 2010 to 2020, with a calibration period spanning from 2011 to 2012 and a validation period from 2013 to 2020. Several skill scores, including Nash-Sutcliffe Efficiency (NSE), BIAS, and Kling-Gupta Efficiency (KGE), were utilized to assess the calibration and validation performances. Additionally, two planetary boundary layer schemes (PBL5 and PBL7) were used to determine their associated uncertainty. Our results show that the best calibration results (NSE = 0.70; KGE = 0.83; PBIAS = −7% and BE = 0.67) in the Senegal River basin are obtained with PBL5 when the calibration is performed with a SLOPE parameter 0.03. A similar good performance was also obtained for the validation with NSE = 0.74, KGE = 0.84, and PBIAS = −8%. Likewise, our findings indicate that converting savanna to woody savannas can elevate water resources, with a 2% rise in precipitation and a 4% increase in runoff. This transition also correlates with an increase in moderate flood events (3500–4000 m3/s), a decrease in severe floods (4000–5000 m3/s), and their associated occurrence of extreme floods (>5000 m3/s) in the Senegal River basin.

Sediment load assessments under climate change scenarios and a lack of integration between climatologists and environmental modelers

Increasing precipitation accelerates soil erosion and boosts sediment loads, especially in mountain catchments. Therefore, there is significant pressure to deliver plausible assessments of these phenomena on a local scale under future climate change scenarios. Such assessments are primarily drawn from a combination of climate change projections and environmental model simulations, usually performed by climatologists and environmental modelers independently. Our example shows that without communication from both groups the final results are ambiguous. Here, we estimate sediment loads delivered from a Carpathian catchment to a reservoir to illustrate how the choice of meteorological data, reference period, and model ensemble can affect final results. Differences in future loads could reach up to even 6000 tons of sediment per year. We suggest there must be a better integration between climatologists and environmental modelers, focusing on introducing multi-model ensembles targeting specific impacts to facilitate an informed choice on climate information.

Wildfire Burnt Area and Associated Greenhouse Gas Emissions under Future Climate Change Scenarios in the Mediterranean: Developing a Robust Estimation Approach

Wildfires burn annually over 400,000 ha in Mediterranean countries. By the end of the 21st century, wildfire Burnt Area (BA) and associated Green House Gas (GHG) emissions may double to triple due to climate change. Regional projections of future BA are urgently required to update wildfire policies. We present a robust methodology for estimating regional wildfire BA and GHG emissions under future climate change scenarios in the Mediterranean. The Fire Weather Index, selected drought indices, and meteorological variables were correlated against BA/GHG emissions data to create area-specific statistical projection models. State-of-the-art regional climate models (horizontal resolution: 12 km), developed within the EURO-CORDEX initiative, simulated data under three climate change scenarios (RCP2.6, RCP4.5, and RCP8.5) up to 2070. These data drove the statistical models to estimate future wildfire BA and GHG emissions in three pilot areas in Greece, Montenegro, and France. Wildfire BA is projected to increase by 20% to 130% up to 2070, depending on the study area and climate scenario. The future expansion of fire-prone areas into the north Mediterranean and mountain environments is particularly alarming, given the large biomass present here. Fire-smart landscape management may, however, greatly reduce the projected future wildfire BA and GHG increases.

Advanced prediction of rice yield gaps under climate uncertainty using machine learning techniques in Eastern India

The current study focuses on applying machine learning approaches to forecast future Kharif rice yield gaps in eastern India while accounting for climate change implications. To achieve the United Nations Sustainable Development Goals (SDGs), food security must be prioritized. Rice yield has been predicted using Cubist, GBM, MARS, RF, SVM, and XGB machine learning methods, considering six factors: elevation, soil moisture, precipitation, temperature, soil temperature, and actual evapotranspiration. Climatic change scenarios were generated using the latest climatic Coupled Model Intercomparison Project Phase 6 (CMIP6 MIROC6) Shared Socioeconomic Pathways (SSP) 2–4.5 and SSP5-8.5 datasets between 1990 and 2030. Finally, machine learning algorithms were used to identify rice yield gaps to achieve sustainable agricultural intensification. The rice yield validation was completed with 1889 field-based farmer observation records. The results suggest that Murshidabad and Purba Bardhaman districts had very high rice yields (5.60–3.45 t/ha) when using the Cubist model compared to another model. The findings also reveal a poor rice-yielding zone (1.44–0.39 t/ha) in the western region (Purulia) and a northwestern region (half of the west of Birbhum). The Cubist and RF models' maximum and minimum R2 values were 0.73 and 0.72, respectively. The R2 values were also negligible for the XGB, GBM, SVM, and MARS, machine learning models. Projections for rice production in 2030 indicate that the northern and north-eastern regions (Murshidabad and Purba Bardhaman) as well as the southeastern areas (Jhargram and Paschim Medinipur) have the highest yields, categorized as extremely very high (5.56–3.49 t/ha) and high (3.49–2.49 t/ha). A significant rice yield gap (50-40 %) was found in the center and south-east areas (Bankura, Jhargram, and Paschim Medinipur), the northern region (Murshidabad and Birbhum), and the western region (Purulia). In 2030, the north-western region (Birbhum), as well as the middle and south-eastern regions (Bankura, Jhargram, and Paschim Medinipur districts), had the highest proportion of high (50%–40 %) and very high (60%–50 %) rice yield gap. Our findings can contribute to a new viewpoint on agricultural planning and management for sustainable growth in the face of changing climate circumstances.

A novel statistical framework of drought projection by improving ensemble future climate model simulations under various climate change scenarios.

Unlike other natural disasters, drought is one of the most severe threats to all living beings globally. Due to global climate change, the frequency and duration of droughts have increased in many parts of the world. Therefore, accurate prediction and forecasting of droughts are essential for effective mitigation policies and sustainable research. In recent research, the use of ensemble global climate models (GCMs) for simulating precipitation data is common. The objective of this research is to enhance the multi-model ensemble (MME) for improving future drought characterizations. In this research, we propose the use of relative importance metric (RIM) to address collinearity effects and point-wise discrepancy weights (PWDW) in GCMs. Consequently, this paper introduces a new statistical framework for weighted ensembles called the discrepancy-enhanced beta weighting ensemble (DEBWE). DEBWE enhances the weighted ensemble data of precipitation simulated by multiple GCMs. In DEBWE, we addressed uncertainties in GCMs arising from collinearity and outliers. To evaluate the effectiveness of the proposed weighting framework, we compared its performance with the simple average multi-model ensemble (SAMME), Taylor skill score ensemble (TSSE), and mutual information ensemble (MIE). Based on the Kling-Gupta efficiency (KGE) metric, DEBWE outperforms all competitors across all evaluation criteria. These inferences are based on the analysis of historical simulated data from 22 GCMs in the CMIP6 project. The quantitative performance indicators strongly support the superiority of DEBWE. The median and mean KGE values for DEBWE are 0.2650 and 0.2429, compared to SAMME (0.1000, 0.0991), TSSE (0.2600, 0.2397), and MIE (0.1550, 0.1511). For drought assessment, we computed the adaptive standardized precipitation index (SPI) for three future scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.5. The steady-state probabilities suggest that normal drought (ND) is the most frequent condition, with extreme events (dry or wet) being less probable.

Impacts of Atlantic meridional overturning circulation weakening on Arctic amplification

Enhanced warming of the Arctic region relative to the rest of the globe, known as Arctic amplification, is caused by a variety of diverse factors, many of which are influenced by the Atlantic meridional overturning circulation (AMOC). Here, we quantify the role of AMOC changes in Arctic amplification throughout the twenty-first century by comparing two suites of climate model simulations under the same climate change scenario but with two different AMOC states: one with a weakened AMOC and another with a steady AMOC. We find that a weakened AMOC can reduce annual mean Arctic warming by 2 °C by the end of the century. A primary contributor to this reduction in warming is surface albedo feedback, related to a smaller sea ice loss due to AMOC slowdown. Another major contributor is the changes in ocean heat uptake. The weakened AMOC and its associated anomalous ocean heat transport divergence lead to increased ocean heat uptake and surface cooling. These two factors are inextricably linked on seasonal timescales, and their relative importance for Arctic amplification can vary by season. The weakened AMOC can also abate Arctic warming via lapse rate feedback, creating marked cooling from the surface to lower-to-mid troposphere while resulting in relatively weaker cooling in the upper troposphere. Additionally, the weakened AMOC increases the low-level cloud fraction over the North Atlantic warming hole, causing significant cooling there via shortwave (sw) cloud feedback despite the overall effect of sw cloud feedback being a slight warming of the average temperature over the Arctic.