Exploring the Influence of Improved Horizontal Resolution on Extreme Precipitation in Southern Africa Major River Basins: Insights from CMIP6 HighResMIP Simulations DOI Creative Commons
Sydney Samuel, Gizaw Mengistu Tsidu, Alessandro Dosio

и другие.

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Фев. 21, 2024

Abstract This study examines the impact of enhanced horizontal resolution on simulating mean and precipitation extremes in major river basins southern Africa. Seven global climate models (GCMs) from High-Resolution Model Intercomparison Project (HighResMIP) within Coupled Phase 6 (CMIP6) are employed. The available at both high-resolution (HR) low-resolution (LR) resolutions. Three datasets used to assess for period 1983-2014 during December-January-February. distributions daily HR nearly identical those their LR counterparts. However, bias intense is not uniform across three observations. Most reasonably simulate precipitation, maximum consecutive dry days (CDD), number rainy (RR1), albeit with some biases. Improvements due realised CDD, RR1 as noted high spatial correlation coefficients (SCCs), low root square errors, CMIP6 HighResMIP tend overestimate very extreme wet (R95p R99p), one-day (Rx1day), simple intensity (SDII) a pronounced R95p R99p. outperform counterparts R95p, R99p, SDII. Our results indicate that under either improvements (e.g., increased SCC) or deterioration decreased SCC), depending extremes, basin, model. findings this important scientists policymakers.

Язык: Английский

Improving CMIP6 Atmospheric River Precipitation Estimation by Cycle‐Consistent Generative Adversarial Networks DOI
Yuan Tian, Yang Zhao, Jianping Li

и другие.

Journal of Geophysical Research Atmospheres, Год журнала: 2024, Номер 129(14)

Опубликована: Июль 12, 2024

Abstract Given the important role of Atmospheric River precipitation (ARP) in global hydrological cycle, accurate representation ARP is significant. However, general circulation models (GCMs) demonstrate bias simulating ARP. The target this study to quantify performance intensity/frequency for CMIP6 simulations, and further improve estimation using Cycle‐Consistent Generative Adversarial Networks (CycleGAN) with highlighting more features under warming background. findings are as follows: (a) although reserved‐optimal overall reproduces observation, it still underestimated at stronger river (AR) scales, particularly AR highly active mid‐latitude regions. (b) CycleGAN‐based correction approach markedly diminishes simulations within most scales among both four Moreover, regions significant improvement, which mainly due reduction strongest scale. (c) Relative reference period (1986–2005), scale increase notably 3°C level, an average value 373.3% intensity 415.9% frequency key before correction, 451.9% 492.5% after correction. results illustrate that CycleGAN can effectively GCMs, early warning implies future strong extreme should potentially surpass current expected.

Язык: Английский

Процитировано

17
Anatomy and assessment of surface water and energy balance components simulated by CMIP6 models in Pan Third Pole DOI
Zhu Liu,

Bohan Huang,

Su Liu

и другие.

Journal of Hydrology, Год журнала: 2025, Номер 652, С. 132656 - 132656

Опубликована: Янв. 4, 2025

Язык: Английский

Процитировано

2
Exploring the influence of improved horizontal resolution on extreme precipitation in Southern Africa major river basins: insights from CMIP6 HighResMIP simulations DOI
Sydney Samuel, Gizaw Mengistu Tsidu, Alessandro Dosio

и другие.

Climate Dynamics, Год журнала: 2024, Номер 62(8), С. 8099 - 8120

Опубликована: Июль 4, 2024

Язык: Английский

Процитировано

4
Improving Daily CMIP6 Precipitation in Southern Africa Through Bias Correction— Part 2: Representation of Extreme Precipitation DOI Open Access
Amarech Alebie Addisuu, Gizaw Mengistu Tsidu, Lenyeletse Vincent Basupi

и другие.

Climate, Год журнала: 2025, Номер 13(5), С. 93 - 93

Опубликована: Май 2, 2025

Accurate simulation of extreme precipitation events is crucial for managing climate-vulnerable sectors in Southern Africa, as such directly impact agriculture, water resources, and disaster preparedness. However, global climate models frequently struggle to capture these phenomena, which limits their practical applicability. This study investigates the effectiveness three bias correction techniques—scaled distribution mapping (SDM), quantile (QDM), QDM with a focus on above below 95th percentile (QDM95)—and daily outputs from 11 Coupled Model Intercomparison Project Phase 6 (CMIP6) models. The Climate Hazards Group Infrared Precipitation Stations (CHIRPS) dataset was served reference. bias-corrected native were evaluated against observational datasets—the CHIRPS, Multi-Source Weighted Ensemble (MSWEP), Global Climatology Center (GPCC) datasets—for period 1982–2014, focusing December-January-February season. ability generate eight indices developed by Expert Team Change Detection Indices (ETCCDI) evaluated. results show that captured similar spatial patterns precipitation, but there significant changes amount episodes. While generally improved representation its varied depending reference used, particularly maximum one-day (Rx1day), consecutive wet days (CWD), dry (CDD), extremely (R95p), simple intensity index (SDII). In contrast, total rain (RR1), heavy (R10mm), (R20mm) showed consistent improvement across all observations. All techniques enhanced accuracy indices, demonstrated higher pattern correlation coefficients, Taylor skill scores (TSSs), reduced root mean square errors, fewer biases. ranking using comprehensive rating (CRI) indicates no single model consistently outperformed others relative GPCC, MSWEP datasets. Among methods, SDM QDM95 variety criteria. strategies, best-performing EC-Earth3-Veg, EC-Earth3, MRI-ESM2, multi-model ensemble (MME). These findings demonstrate efficiency improving modeling extremes ultimately boosting assessments.

Язык: Английский

Процитировано

0
Projecting Daily Maximum Temperature Using an Enhanced Hybrid Downscaling Approach in Fujian Province, China DOI Open Access

Pangpang Gao,

Ying Sun, Zhihao Liu

и другие.

Sustainability, Год журнала: 2025, Номер 17(10), С. 4360 - 4360

Опубликована: Май 12, 2025

The rise in global temperatures and increased extreme weather events, such as heatwaves, underscore the need for accurate regional projections of daily maximum temperature (Tmax) to inform effective adaptation strategies. This study develops CNN-BMA-QDM model, which integrates convolutional neural networks (CNNs), Bayesian model averaging (BMA), quantile delta mapping (QDM) downscale project Tmax under future climate scenarios. stands out its ability capture nonlinear relationships between atmospheric circulation factors, reduce uncertainty, correct bias, thus improving simulation accuracy. is applied Fujian Province, China, using three CMIP6 GCMs four shared socioeconomic pathways (SSPs) from 2015 2100. results show that outperforms CNN-BMA, CNNs, other downscaling methods (e.g., RF, BPNN, SVM, LS-SVM, SDSM), particularly simulating value at 99% 95% percentiles. Projections indicate consistent warming trends across all SSP scenarios, with spatially averaged rates 0.0077 °C/year SSP126, 0.0269 SSP245, 0.0412 SSP370, 0.0526 SSP585. Coastal areas experience most significant warming, an increase 4.62–5.73 °C SSP585 by 2071–2100, while inland regions a smaller 3.64–3.67 °C. Monthly December sees largest (5.30 2071–2100), July experiences smallest (2.40 °C). On seasonal scale, winter highest reaching 4.88 SSP585, whereas summer shows more modest 3.10 Notably, greatest discrepancy south north occurs during summer. These findings emphasize importance developing tailored strategies based on spatial variations. provide valuable insights policymakers contribute advancement projection research.

Язык: Английский

Процитировано

0
Advancing the Reliability of Future Hydrological Projections in a Snow‐Dominated Alpine Watershed: Integrating Uncertainty Decomposition and CycleGAN Bias Correction DOI Creative Commons
Tao Su, Zhu Liu, Qingyun Duan

и другие.

Earth s Future, Год журнала: 2025, Номер 13(5)

Опубликована: Май 1, 2025

Abstract Given the sensitivity of snow to climate change and its critical role in hydrological cycle alpine regions, it is essential reduce biases meteorological forces for driving models. This study, taking Manas River Basin (MRB) Xinjiang China as test bed, aims quantify uncertainties hydrometeorological variables from 24 NASA Earth Exchange Global Daily Downscaled Projections (NEX‐GDDP‐CMIP6) simulations further these using a Cycle‐Consistent Generative Adversarial Network (CycleGAN). The bias‐corrected CMIP6 data are then used drive Soil Water Assessment Tool model calibrated with both runoff water equivalent (SWE) through dual‐objective approach future projections. results indicate that: (a) Model uncertainty brought by different models primary source original outputs. CycleGAN demonstrates substantial effectiveness reducing uncertainty; (b) Most subbasins MRB will experience absolute SWE reduction future, changes varying significantly across elevation bands, decreasing 30%–60% baseline levels end century; (c) has an increasing trend projected increases ranging 1.34% under SSP126 24.56% SSP585. As rain‐to‐snow ratio rises snowmelt shifts earlier, low flows increase during dry period, elevating spring flood risks. These findings provide crucial insights management resources snow‐dominated watersheds.

Язык: Английский

Процитировано

0
Exploring the Influence of Improved Horizontal Resolution on Extreme Precipitation in Southern Africa Major River Basins: Insights from CMIP6 HighResMIP Simulations DOI Creative Commons
Sydney Samuel, Gizaw Mengistu Tsidu, Alessandro Dosio

и другие.

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Фев. 21, 2024

Abstract This study examines the impact of enhanced horizontal resolution on simulating mean and precipitation extremes in major river basins southern Africa. Seven global climate models (GCMs) from High-Resolution Model Intercomparison Project (HighResMIP) within Coupled Phase 6 (CMIP6) are employed. The available at both high-resolution (HR) low-resolution (LR) resolutions. Three datasets used to assess for period 1983-2014 during December-January-February. distributions daily HR nearly identical those their LR counterparts. However, bias intense is not uniform across three observations. Most reasonably simulate precipitation, maximum consecutive dry days (CDD), number rainy (RR1), albeit with some biases. Improvements due realised CDD, RR1 as noted high spatial correlation coefficients (SCCs), low root square errors, CMIP6 HighResMIP tend overestimate very extreme wet (R95p R99p), one-day (Rx1day), simple intensity (SDII) a pronounced R95p R99p. outperform counterparts R95p, R99p, SDII. Our results indicate that under either improvements (e.g., increased SCC) or deterioration decreased SCC), depending extremes, basin, model. findings this important scientists policymakers.

Язык: Английский

Процитировано

0