Downscaling Research Articles

Examining future changes in coastal low-level jet properties offshore California through dynamical downscaling

Abstract The coastal low-level jet, or CLLJ, is a synoptically-forced meteorological feature frequently present offshore the western United States (U.S.). Characterized by a wind speed maximum that resides at the top of the marine boundary layer, the CLLJ is largely controlled by the location and strength of the North Pacific High (NPH) as well as the coastal geometry. Considering the rich wind resource available in this offshore region, the Bureau of Ocean Energy Management identified wind energy lease areas offshore California and supported the deployment of two U.S. Department of Energy wind lidar buoys near Morro Bay and Humboldt. Despite our relatively good understanding of the fundamental mechanisms responsible for large-scale CLLJ properties offshore the western U.S., future changes in CLLJ characteristics are less clear. To address this research challenge, and ultimately to better inform future wind turbine deployments, we use simulations driven by three global climate models (GCMs). We apply self-organizing maps to the model outputs for a historical and two future climate periods to show the range of NPH regimes that support CLLJ conditions during the warm seasons, as well as the subtle contribution from land-falling cyclones approaching the mainland during the cold seasons. Compared to the historical period, the three GCM-driven simulations agree that CLLJ conditions will become more (less) prevalent from central California northward (southward). They agree less with respect to future changes in maximum CLLJ wind speeds and CLLJ heights. However, after considering model biases present during the historical period, wind speeds between the models are actually more similar during the 2070-2095 period than during the historical period. The potential combination of more frequent CLLJ conditions characterized by relatively consistent wind speeds occurring at lower heights across northern California suggests that the Humboldt lease area may be ideal for a long-term wind turbine deployment.

Assessment of the Rainfed Wheat Productivity in a Changing Climate in Irbid, Jordan Using Statistical Downscaling and Random Forest Regression Prediction Under RCP4.5 & 8.5 Pathways.

Jordan faces risks associated with climate change and endeavors to sustain resources through the SDGs. This study examines the impacts of climate change on rainfed wheat productivity in Irbid Governorate during the historical period (1994-2021) and simulated for the future (2024-2100) using climate models (SDGs 1, 2, 11, 12, 13, & 15). Monthly precipitation, mean temperature, and annual wheat yield data were collected for the period (1994-2021) and analyzed using the Mann-Kendall test with Sen’s slope to investigate the historical trends and elaborate Phi-k correlation coefficient to determine their association. Monthly precipitation and mean temperature projections were collected from CSIRO-MK3.6.0 & GFDL-ESM2M CMIP5 ensemble models under two concentration pathways: RCP4.5 and RCP8.5. The projections were biased and corrected by the dynamic bilinear interpolation method and Delta, EQM, and QM statistical downscaling to enhance the projections' reliability and performance in capturing the region's climate. Taylor diagram was utilized to choose the best model to represent observed data. Cumulative Distribution Function and Probability Density Function Curves were plotted to describe the changes over decades based on climate models. Future wheat yields were predicted using the Random Forest Regression model. The results revealed a non-significant increase in total annual precipitation of 1.8% and 13.8% and a rise in annual mean temperature of 5.4% and 2.7% for Irbid and Baqura stations, respectively during the baseline timeframe on which wheat yield increased by 21.3%. The CSIRO model outperformed the GFDL model with greater fidelity in simulating historical monthly precipitation and mean temperatures. The CSIRO-MK3.6.0 model indicates precipitation and temperature shifts for near and far future periods. Temperature increases are expected to have more severe impacts in the far future (2073-2100) while projected decreases in precipitation of 0.06 mm/day and 0.10 mm/day under RCP4.5 and RCP8.5 affect yield reductions by 11.42 kg/ha and 12.21 kg/ha. Temperature increases of 2.7°C and 4.1°C under RCP 4.5 & 8.5 correspond to yield decreases of 24.825 kg/ha and 33.395 kg/ha, respectively. The study highlights the need for enhanced adaptation strategies; cultivating resilient wheat varieties, promoting crop rotation, improving meteorological monitoring and risk response approaches, and providing timely weather forecasts to mitigate the adverse effects of climate change on wheat productivity and ensure sustainable agriculture in Jordan.

Assessing Green Strategies for Urban Cooling in the Development of Nusantara Capital City, Indonesia

The relocation of Indonesia’s capital to Nusantara in East Kalimantan has raised concerns about microclimatic impacts resulting from proposed land use and land cover (LULC) changes. This study explored strategies to mitigate these impacts by using dynamical downscaling with the Weather Research and Forecasting model integrated with the urban canopy model (WRF-UCM). Numerical experiments at a 1 km spatial resolution were used to evaluate the impacts of green and mitigation strategies on the proposed master plan. In this process, five scenarios were analyzed, incorporating varying proportions of blue–green spaces and modifications to building walls and roof albedos. Among them, scenario 5, with 65% blue‒green spaces, exhibited the highest cooling potential, reducing average urban surface temperatures by approximately 2 °C. In contrast, scenario 4, which allocated equal shares of built-up areas and mixed forests (50% each), achieved a more modest reduction of approximately 1 °C. The adoption of nature-based solutions and sustainable urban planning in Nusantara underscores the feasibility of climate-resilient urban development. This framework could inspire other cities worldwide, showcasing how urban growth can align with environmental sustainability.

Impact of Climate Change on the Dynamics of the Southern Senegal Upwelling Center

AbstractThe Canary current upwelling System (CCS) is one the most productive marine ecosystems. CMIP5 simulations under the RCP8.5 scenario for the end of the 21st century project a modest upwelling‐favorable wind decrease over the CCS southern outpost, that is, the southern senegalese upwelling center (SSUC). We explore the coastal‐scale physical manifestations of climate change in the SSUC through dynamical downscaling of projected changes from nine CMIP5 models selected for their realistic representation of present‐day thermohaline structure. We find that coastal upwelling reduction due to wind changes is projected to be aggravated by geostrophic/pressure adjustments related, in large part, to changes in upper ocean stratification. The reduction could reach 25% of present‐day upwelling rates. The intensity of the poleward boundary current offshore of the SSUC is projected to decrease. Together with upper ocean warming this opens vast possibilities of ecological evolutions with large impact on neighboring societies.

Flood modeling prior to the instrumental era reveals limited magnitude of 1931 Yangtze flood

The global flood risk urges an improved understanding of flood magnitude and its mechanism, which needs insights from pre-instrumental flood investigations. Due to data scarcity, reconstructing pre-instrumental flood magnitudes relies on statistical downscaling, failing to capture nonlinear and dynamic characteristics. We developed a dynamical approach, NorESM-WRF-SWAT, integrating a global climate, a regional, and a hydrologic model to investigate the 1931 Yangtze River flood (the deadliest in the world) and compared it with the 1998’s. Through validation, our method outperforms the statistical method in simulating precipitations and river discharges. For the first time, we presented detailed insights into the intensity and duration of the 1931 flood, revealing a smaller magnitude but associated with an amplified loss, likely due to social vulnerability and reduced societal resilience compared to the 1998’s. While successful simulation can be interfered with by model variability, our dynamical method shows promise for simulating pre-instrumental flood and building a long-term pre-instrumental-hydrology database.

Climate model downscaling in central Asia: a dynamical and a neural network approach

Abstract. High-resolution climate projections are essential for estimating future climate change impacts. Statistical and dynamical downscaling methods, or a hybrid of both, are commonly employed to generate input datasets for impact modelling. In this study, we employ COSMO-CLM (CCLM) version 6.0, a regional climate model, to explore the benefits of dynamically downscaling a general circulation model (GCM) from the Coupled Model Intercomparison Project Phase 6 (CMIP6), focusing on climate change projections for central Asia (CA). The CCLM, at 0.22° horizontal resolution, is driven by the MPI-ESM1-2-HR GCM (at 1° spatial resolution) for the historical period of 1985–2014 and the projection period of 2019–2100 under three Shared Socioeconomic Pathways (SSPs), namely the SSP1-2.6, SSP3-7.0, and SSP5-8.5 scenarios. Using the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) gridded observation dataset as a reference, we evaluate the performance of CCLM driven by ERA-Interim reanalysis over the historical period. The added value of CCLM, compared to its driving GCM, is evident over mountainous areas in CA, which are at a higher risk of extreme precipitation events. The mean absolute error and bias of climatological precipitation (mm d−1) are reduced by 5 mm d−1 for summer and 3 mm d−1 for annual values. For winter, there was no error reduction achieved. However, the frequency of extreme precipitation values improved in the CCLM simulations. Additionally, we employ CCLM to refine future climate projections. We present high-resolution maps of heavy precipitation changes based on CCLM and compare them with the CMIP6 GCM ensemble. Our analysis indicates an increase in the intensity and frequency of heavy precipitation events over CA areas already at risk of extreme climatic events by the end of the century. The number of days with precipitation exceeding 20 mm increases by more than 90 by the end of the century, compared to the historical reference period, under the SSP3-7.0 and SSP5-8.5 scenarios. The annual 99th percentile of total precipitation increases by more than 9 mm d−1 over mountainous areas of central Asia by the end of the century, relative to the 1985–2014 reference period, under the SSP3-7.0 and SSP5-8.5 scenarios. Finally, we train a convolutional neural network (CNN) to map a GCM simulation to its dynamically downscaled CCLM counterpart. The CNN successfully emulates the GCM–CCLM model chain over large areas of CA but shows reduced skill when applied to a different GCM–CCLM model chain. The scientific community interested in downscaling CMIP6 models could use our downscaling data, and the CNN architecture offers an alternative to traditional dynamical and statistical methods.

Climate Projection of Tropical Cyclone Lifetime in the Western North Pacific Basin

Abstract In this study, the potential changes in tropical cyclone (TC) lifetime in the western North Pacific basin are examined for different future climates. Using homogeneous 9-km-resolution dynamical downscaling with the Weather Research and Forecasting (WRF) Model, we show that TC-averaged lifetime displays insignificant change under both low and high greenhouse gas concentration scenarios. However, more noticeable changes in the tails of TC lifetime statistics are captured in our downscaling simulations, with more frequent long-lived TCs (lifetime of 8–11 days) and less short-lived TCs (lifetime of 3–5 days). Unlike present-day simulations, it is found that the correlation between TC lifetime and the Niño index is relatively weak and insignificant in all future downscaling simulations, thus offering little explanation for these changes in TC lifetime statistics based on El Niño–Southern Oscillation. More detailed analyses of TC track distribution in the western North Pacific basin reveal, nevertheless, a noticeable shift of TC track patterns toward the end of the twenty-first century. Such a change in TC track climatology results in an overall longer duration of TCs over the open ocean, which is consistent across future scenarios and periods examined in this study. This shift in the TC track pattern is ultimately linked to changes in the western North Pacific subtropical high, which retreats to the south during July and to the east during August–September. The results obtained in this study provide new insights into how large-scale circulations can affect TC lifetime in the western North Pacific basin in warmer climates. Significance Statement Using high-resolution dynamical downscaling with the Weather Research and Forecasting (WRF) Model under low- and high-emission scenarios, this study shows that the basin-averaged tropical cyclone (TC) lifetime in the western North Pacific (WNP) basin has no noticeable change under both warmer climate scenarios, despite an overall increase in TC maximum intensity. However, the tails of the TC lifetime distribution display significant changes, with more long-lived (6–20 days) TCs but less short-lived (3–5 days) TCs in the future. These changes in TC lifetime statistics are caused by the shift of the North Pacific subtropical high, which alters large-scale steering flows and TC track patterns. These results help explain why previous studies on TC lifetime projections have been inconclusive in the WNP basin and provide new insights into how large-scale circulations can modulate TC lifetime in a warmer climate.

A Reliable Generative Adversarial Network Approach for Climate Downscaling and Weather Generation

AbstractAnticipating climate impacts and risks in present or future climates requires predicting the statistics of high‐impact weather events at fine‐scales. Direct numerical simulations of fine‐scale weather are computationally too expensive for many applications. While deterministic‐based (deep‐learning or statistical) downscaling of low‐resolution climate simulations are several orders of magnitude faster than direct numerical simulations, it suffers from several limitations. These limitations include the tendency to regress to the mean, which produces excessively smooth predictions and underestimates the magnitude of extreme events. They also fail to preserve statistical measures that are key for climate research. We use a conditional GAN (cGAN) architecture to downscale daily precipitation as a Regional Climate Model (RCM) emulator. The cGAN generates plausible residuals on top of the predictable expectation state produced by a deterministic deep learning algorithm. The skill of cGANs is highly sensitive to a hyperparameter known as the weight of the adversarial loss (), where the value of required for accurate results varies with season and performance metric, casting doubt on the reliability of cGANs as usually implemented. However, by applying a simple intensity constraint to the loss function, it is possible to obtain reliable performance results across spanning two orders of magnitude. CGANs are considerably more skillful in capturing climatological statistics, including the distribution and spatial characteristics of extreme events. With this modification, we expect cGANs to be readily transferable to other applications and time periods, making them a useful weather generator for representing extreme event statistics in present and future climates.

Modelling climate change and aridity for climate impact studies in semi-arid regions: The case of Giba basin, northern Ethiopia.

of long-term and future climate variability is crucial for impact assessment studies in drought-prone areas like the Giba basin in northern Ethiopia. This study has applied the statistical downscaling model (SDSM) and (De Martonne and Pinna combinative) aridity index methods to evaluate the climate system of the Giba basin. Historical data (1961-2019) from seven meteorological stations and global grided data were used for future climate projections (2020-2100) under the three emission scenarios (RCPs 2.6, 4.5, and 8.5) for the three-time horizons (2040s, 2060s, and 2080s). Analysis of results showed that rainfall and temperature projection on a monthly and/or seasonal basis has more significance than on an annual basis for impact studies particularly, in areas where irrigation practices are common like in the Giba basin. Seasonal projection of rainfall in the basin showed a slightly decreasing trend during the spring season (MAM), and a significant increment in the main rainy season (JJA) under all scenarios and for the whole projection year. On an annual basis, a maximum increase of rainfall, up to +285mm/year and +298mm/year was expected to increase at Abyi Adi and Mekelle Obs stations, respectively, under RCP 8.5 in the 2080s. Temperature projection showed a consistent rise throughout the basin that ranges from a minimum increase of Tmax by +0.29°C in the 2040s (RCP 2.6) at Mekelle Obs station to a maximum increase of Tmin by +2.35°C in the 2080s (RCP8.5) at Abyi Adi station. In general, it is observed that the rate of increment of projected Tmin was more than that of Tmax in all stations in the Giba basin, which showed a continuous contraction of the gap between Tmin and Tmax, hence, the prevalence of global warming. This has led to a considerable increment of aridity till the end of the 21st century. Hence, the implementation of locally-suited climate change resilient strategies is crucial to enhance the sustainability of the ecosystem and ensure food security in the basin.

Impact of Climate Change on Water Resources in the Wadi Guir Watershed: Adaptation Strategies for Sustainable Management

The Wadi Guir Watershed, located in the eastern basins of Morocco, spans approximately 4,231 km². One of the major challenges both globally and in Morocco, particularly in oasis regions such as the Guir River watershed, is ensuring water availability while preserving ecosystems in arid zones affected by climate change. This challenge is further compounded by the development of modern intensive agriculture alongside traditional oasis systems.This study aims to evaluate the impact of climate change on water resources in the Wadi Guir watershed, focusing on the most critical scenario, RCP 8.5, for the period from 2019 to 2060. A statistical downscaling model and the non-parametric Pettitt test were employed to analyze precipitation trends in the region. The results reveal a decline in precipitation under the selected scenario, coupled with an increase in temperatures. Consequently, a reduction in discharge is projected, from 0.045 Mm³ in 2019 to 0.030 Mm³ in 2060, alongside intensified evaporation.This research highlights the urgent need to develop adaptation strategies and decision-making frameworks tailored to the impacts of climate change to ensure the sustainable management of natural resources and ecosystems.

Validation of the Ensemble Generalized Analog Regression Downscaling (En-GARD) Model to Downscale Near-Surface Wind Speed for the Northeast United States

Abstract Wind, often overlooked in climate change impact assessments, is now a crucial atmospheric variable due to our imminent reliance on renewable energy sources like wind energy. Therefore, establishing a wind speed database in higher temporal and spatial resolution is of great importance to assess wind power in historical periods and future projections. This study focuses on enhancing wind data representation and reliability of statistically downscaled model outputs by refining grid spacing from coarser to a desirable finer scale. Employing the Ensemble Generalized Analog Regression Downscaling (En-GARD) technique, we downscaled near-surface wind speed across a northeast (NE) U.S. domain that combines onshore and offshore wind farm areas. We tested multiple atmospheric variable combinations and identified near-surface wind speed (wind speed at 10 m), longitudinal and latitudinal wind at 10 m, and atmospheric temperature at 2 m, as the set that effectively guides the selection of analog days for the analog regression downscaling approach. Validation involved using ERA5 atmospheric reanalysis products (31 km) and high-resolution (4 km) Weather Research and Forecasting (WRF) Model simulations. Downscaling was conducted using a leave-one-year-out approach over 12 years (2001–12). Statistical analysis of the downscaled output over this period demonstrated a significant correlation and low error metrics (less than 20% for normalized RMSE and 16% for normalized MAE), indicating the reliability of the method and paving the way for its future application to downscale climate projection outputs. Significance Statement Wind speed has become a significant atmospheric variable due to our society’s imminent reliance on renewable energy resources like wind power. Often overlooked in climate change assessments, wind speed at a local scale over wind farm locations plays a crucial role in assessing and planning wind power resources in a future climate. We are providing the successful validation of a statistical downscaling methodology for near-surface wind speed at 4-km grid spacing using reanalysis data. The validity of the methodology gives confidence to downscale climate projection models and assess changes in wind speed for future climate scenarios in a follow-up study.

Characterizing Droughts During the Rice Growth Period in Northeast China Based on Daily SPEI Under Climate Change.

In agricultural production, droughts occurring during the crucial growth periods of crops hinder crop development, while the daily-scale standardized precipitation evapotranspiration index (SPEI) can be applied to accurately identify the drought characteristics. In this study, we used the statistical downscaling method to obtain the daily precipitation (Pr), maximum air temperature (Tmax) and minimum air temperature (Tmin) during the rice growing season in Heilongjiang Province from 2015 to 2100 under the SSP1-2.6, SSP2-4.5 and SSP5-8.5 in CMIP6, to study the spatial and temporal characteristics of drought during the rice growing season in cold region and the effect of climate change on drought characteristics. The potential evapotranspiration (PET0) was calculated using the regression correction method of the Hargreaves formula recommended by the FAO, and the daily SPEI was calculated to quantitatively identify the drought classification. The Pearson correlation coefficient was used to analyze the correlation between the meteorological factors (Pr, Tmax, Tmin), PET0 and SPEI. The results showed that: (1) Under 3 SSP scenarios, Pr showed an increasing trend from the northwest to the southeast, Tmax showed an increasing trend from the northeast to the southwest, and higher Tmin was mainly distributed in the east and west regions. (2) PET0 indicated an overall interannual rise in the three future SSP scenarios, with higher values mainly distributed in the central and western regions. The mean daily PET0 values ranged from 4.8 to 6.0 mm/d. (3) Under SSP1-2.6, rice mainly experienced mild drought and moderate drought (-0.5 ≥ SPEI > -1.5). The predominant drought classifications experienced were mild, moderate, and severe drought under SSP2-4.5 and SSP8.5 (-0.5 ≥ SPEI > -2.0). (4) The tillering stage experienced the highest drought frequency and drought intensity, with the longest drought lasting 24 days. However, the heading flower stage had the lowest drought frequency and drought intensity. The drought barycenter was mainly in Tieli and Suihua. (5) The PET0 was most affected by the Tmax, while the SPEI was most affected by the Pr. This study offers a scientific and rational foundation for understanding the drought sensitivity of rice in Northeast China, as well as a rationale for the optimal scheduling of water resources in agriculture in the future.

Multi‐Model Projection of Climate Extremes under 1.5°C–4°C Global Warming Levels across Iran

ABSTRACTThis study investigates the spatial and temporal patterns of climate extremes in Iran and projects future changes using data from seven General Circulation Models (GCMs) that participated in the Coupled Model Intercomparison Project phase 6 (CMIP6). We assess the impacts of climate change under the SSP2‐4.5 and SSP5‐8.5 emission scenarios, considering global warming levels of 1.5°C, 2°C, 3°C, and 4°C above preindustrial levels. Gridded observations are derived from ground measurements, using the SYMAP algorithm at a 1/8° latitude–longitude resolution. Subsequently, statistical downscaling of GCMs is performed using the Multivariate Bias Correction (MBC) and Bias Correction Constructed Analogues with Quantile Mapping Reordering (BCCAQ) approaches. Projected changes in extreme temperature and precipitation events are evaluated using the CLIMDEX indices. The findings indicate consistent rises in annual temperatures across Iran, with temperature indices such as warm spell duration and the monthly minimum value of daily temperature exhibiting substantial increases, about twofold by the +4.0°C period. Additionally, the study highlights a potential intensification in precipitation extremes (Rx1day, Rx5day, R90p, R95p), suggesting a heightened risk of more frequent and severe floods, particularly in the western, northern, and northwestern regions. These insights underline the critical need for region‐specific adaptation strategies to address the risks associated with climate change in Iran.

An Evaluation of Dynamical Downscaling Methods Used to Project Regional Climate Change

AbstractIn the past decade, dynamical downscaling using “pseudo‐global‐warming” (PGW) techniques has been applied frequently to project regional climate change. Such techniques generate signals by adding mean global climate model (GCM)‐simulated climate change signals in temperature, moisture, and circulation to lateral and surface boundary conditions derived from reanalysis. An alternative to PGW is to downscale GCM data directly. This technique should be advantageous, especially for simulation of extremes, since it incorporates the GCM's full spectrum of changing synoptic‐scale dynamics in the regional solution. Here, we test this assumption, by comparing simulations in Europe and Western North America. We find that for warming and changes in temperature extremes, PGW often produces similar results to direct downscaling in both regions. For mean and extreme precipitation changes, PGW generally also performs surprisingly well in many cases. Moisture budget analysis in the Western North America domain reveals why. Large fractions of the downscaled hydroclimate changes arise from mean changes in large‐scale thermodynamics and circulation, that is, increases in temperature, moisture, and winds, included in PGW by design. The one component PGW may have difficulty with is the contribution from changes in synoptic‐scale variability. When this component is large, PGW performance could be degraded. Global analysis of GCM data shows there are regions where it is large or dominant. Hence, our results provide a road map to identify, through GCM analyses, the circumstances when PGW would not be expected to accurately regionalize GCM climate signals.

Dynamic downscaling of climate projections using WRF-CCSM4: future soil temperature predictions for the Arabian Peninsula and Kuwait

This study employs the Weather Research and Forecasting (WRF) model to dynamically downscale climate projections from the Community Climate System Model version 4 (CCSM4) using 12 km and 4 km high grid resolutions. The performance of the WRF-CCSM4 configuration is evaluated against observational data from the automated weather observing systems of Kuwait (AWOSK). The analysis focuses on future predictions for maximum soil temperature during the summer months from May to September for the period 2050–2060. The findings indicate a projected increase in average soil temperatures of 1–3°C across the Arabian Peninsula and Kuwait. Notably, the results demonstrate that the 4-km high-resolution WRF domain, is more effective framework for accurate weather and climate predictions in this region. These insights underscore the importance of high-resolution modeling in understanding and mitigating the impacts of climate change, particularly in arid environments like Kuwait and the broader Arabian Peninsula.

Evaluating downscaled products with expected hydroclimatic co-variances

Abstract. There has been widespread adoption of downscaled products amongst practitioners and stakeholders to ascertain risk from climate hazards at the local scale (e.g., ∼ 5 km resolution). Such products must nevertheless be consistent with physical laws to be credible and of value to users. Here we evaluate statistically and dynamically downscaled products by examining local co-evolution of downscaled temperature and precipitation during convective and frontal precipitation events (two mechanisms testable with just temperature and precipitation). We find that two widely used statistical downscaling techniques (Localized Constructed Analogs version 2, LOCA2, and Seasonal Trends and Analysis of Residuals Empirical Statistical Downscaling Model, STAR-ESDM) generally preserve expected co-variances during convective precipitation events over the historical and future projected intervals as compared to European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) and two observation-based data products (Livneh and nClimGrid-Daily). However, both techniques dampen future intensification of frontal precipitation that is otherwise robustly captured in global climate models (i.e., prior to downscaling) and with process-based dynamical downscaling across five different regional climate models. In the case of LOCA2, this leads to appreciable underestimation of future frontal precipitation event intensity. This study is one of the first to quantify a likely ramification of the stationarity assumption underlying statistical downscaling methods and identify a phenomenon where projections of future change diverge depending on data production method employed. Finally, our work proposes expected co-variances during convective and frontal precipitation as useful evaluation diagnostics that can be universally applied to a wide range of statistically downscaled products.

Future Changes in the Contribution of Gulf of Mexico-Caribbean Sea Moisture Source: Impacts on Regional Precipitation Patterns

AbstractCentral America (CA) and the eastern United States (US) is a region with a significant contribution from the Gulf of Mexico–Caribbean Sea (GM_CS) moisture source. In the context of climate change, sink patterns associated with this source may change implying significant changes in this region. Hence, this study determined future changes in the contributions to precipitation (PCs) associated with GM_CS. So, a dynamic downscaling methodology was considered using the regional climate model WRF-ARW and FLEXPART-WRF, forced with the CESM2 climate model. Specifically, a decrease in the GM_CS contribution in the CA region was projected for all seasons considered, showing a southward shift in the contribution pattern. Additionally, the sink pattern was projected to increase, but the area was projected to decrease, mainly by the end of the century, except in the autumn. In addition, for the southeastern US region, an increase in PCs in spring and autumn was projected for end-century, but a decrease in PCs by more than 40% in the summer months was projected. However, for the northeastern US, changes in PCs were amplified for both the mid- and end-century relative to the southeastern US region, with an increase projected for most periods, except for a slight decrease in the summer of 10%.

Building high-resolution projections of temperature potential changes using statistical downscaling for the future period 2026-2100 in the Highland region of Yemen – A supportive approach for empowering environmental planning and decision-making

Environmental resources and ecological systems are significantly affected by the steady rise of the global temperature. However, the degree of temperature change at the regional and local levels is uncertain. The uncertainty arises from various factors, but mostly due to the short length of ground data and dependency of local studies on the large-scale and spatially coarse output of Global Climate Models (GCMs). Therefore, the output of GCM cannot be directly used in impact assessment studies at a regional and local level. In this study, the Statistical Down-Scaling Model (SDSM) is employed to investigate the magnitude of temperature changes (Minimum and Maximum Temperature) for the future period 2026-2100. The SDSM builds relationships between large-scale predictors and local climate variables, allowing for finer-resolution projections at a regional level. The study utilized the Climate Hazard Infra-Red Temperature with Station (CHIRTS-daily) to complete daily missing records in more than 90 ground stations. Additionally, predictors of the National Center for Environmental Prediction (NCEP) for the historical period (1961-2010) and the Canadian Earth System Model (CanESM2) for the future period (2026-2100) are employed to calibrate SDSM and to build finer-resolution scenarios under two representative concentration pathways; RCP2.6 and RCP8.5. The methodology additionally involved validating the SDSM performance using observed historical data before applying it to future projections. The findings indicate that both minimum and maximum temperatures (T-min and T-max) will increase, with a more pronounced rise in minimum temperature (T-min). Over the future period (2026-2100), the projected average temperature rise is 1.10°C (T-max) and 1.43°C (T-min) under RCP2.6. For RCP8.5, the projected average increases are 1.56°C and 2.3°C for T-max and T-min, respectively. Overall, the most significant increase is projected to occur in the 2090s (2076-2100) under RCP8.5, particularly in the lowlands and wadis of Al Mahwit and Raymah governorate. In these areas, the minimum temperature (T-min) exhibited an increased absolute value of up to 3.2°C. This high rise in temperatures is expected to result in increased evapotranspiration, prolonged droughts, and possibly breakouts of some plant diseases and pests. This would require effective adaptation measures such as harvesting rainwater and growing short-time and heat-resistance crops. Engaging in field visits and social discussions added depth to the study by introducing various traditional methods and indigenous practices. Valuable resources for future efforts to mitigate the potential impacts of climate change are offered by these insights.

Super-resolution reconstruction of wind fields with a swin-transformer-based deep learning framework

Deep learning approaches that allow for the rapid simulation of high-resolution atmospheric turbulence are expected by using the super-resolution (SR) technique. Recently, the shifted window attention mechanism in Swin-Transformer offers a significant improvement compared with the vanilla attention mechanism. This method restricts the attention computation to a local neighborhood, reducing the computational load to a linear relationship with sequence length. However, its original architecture is unsuitable for the SR in turbulence due to the mismatch with classification, detection, and segmentation tasks. In this study, the hierarchical structure is redesigned, and a new relative position representing approach is introduced to facilitate the SR procedures of turbulent wind. The channel-shuffled perceptual loss is integrated into the loss function to guide the update of weight parameters. The experimental cases of idealized two-dimensional turbulent flow and realistic boundary layer wind are employed to validate the performance. The results suggest that the proposed approach remarkably outperformed the previous Super-Resolution Convolutional Neural Network, Deep Statistical Downscaling, and Regional Climate Model Emulator in wind vectors. It exhibits lower values than the other three networks whether in terms of point-wise metrics like mean square error, mean absolute error, mean absolute percentage error, or perceptual metrics, including structural similarity index measure and probability density functions. The reconstructed wind vectors closely match the target high-resolution results. This study will help advance the application of shifted window attention mechanisms in wind field SR.

Applying a coupled model framework to assess global climate change impacts on the river-type algal blooms in the middle and lower reaches of the Hanjiang River, China

Global climate change (GCC), characterized by warming, affects the hydrological conditions at the basin scale and whether harmful algal blooms (HABs) occur at the scale of river ecological systems. Research on HABs mainly focuses on oceans and lakes, and there is still less research on the effects of GCC on river-type HABs that differ from oceans and lakes in hydrodynamic, water temperature, and nutrient conditions. This study constructed a coupled model framework that includes the GCC model, downscaling model, hydrological model, hydrodynamic model, and eutrophication model, analyzing and exploring the effect of changes in the aquatic ecological environment caused by GCC on river-type HABs in the middle and lower reaches of the Hanjiang River (MLHR). Firstly, based on the three GCC models and statistical downscaling model in CMIP6, high-precision meteorological factors such as future precipitation and temperature were obtained. Secondly, a coupled model based on SWAT and MIKE21-ECOLab was used with the digital elevation model (DEM), land use, soil, meteorological, pollution source, and measured terrain data in the MLHR Basin, which was validated by observed data. Thirdly, there has not been a significant increase in Chl-a, and the impact of GCC has not fundamentally changed the temporal and spatial distribution of HABs. Fourthly, this study proposes to use 0.2 m/s (Corresponding discharge 1160 m3/s) as the hydrodynamic condition for preventing and controlling HABs in the Shayang section.