Reproducibility and Trends of Extreme Climate Indices in Japan: Insights From Dynamical JRA‐55 Downscaling DOI
Kazuyo Murazaki, Tosiyuki Nakaegawa, Hiroaki Kawase

et al.

International Journal of Climatology, Journal Year: 2025, Volume and Issue: unknown

Published: May 9, 2025

ABSTRACT Dynamical downscaling (DDS) datasets play a crucial role in understanding regional climate patterns and extreme weather events. This study evaluates the reproducibility of indices Japan using two DDS based on JRA‐55 reanalysis for period 1979–2012. A total 48 were analysed to assess biases, interannual variability, trends precipitation temperature by comparing with AMeDAS observations, high‐resolution automated meteorological observation network Japan. Both reasonably captured correlation coefficients exceeding 0.6 many indices. However, systematic biases underestimations trend magnitudes observed. For indices, DS‐run (DDS Non‐Hydrostatic Model, NHM) generally exhibited consistent tendency toward negative across most areas, while RC‐run Regional Climate NHRCM) showed relatively smaller some regions but larger Southwest Islands (area 7). both runs successfully reproduced variability. pronounced TX‐related particularly TXm TXn, slightly TXx. Positive more common TN‐related especially area 1. Trend analyses revealed regionally varying patterns. direction observed all regions, high agreement sign. magnitude statistical significance varied depending index type region. Although each run distinct characteristics, shared highlighting need further improvements model performance. These findings suggest importance careful evaluation when outputs impact assessments offer useful insights future improvement development strategies

Language: Английский

The policy dimension of energy transition: The Brazilian case in promoting renewable energies (2000–2022) DOI
Deborah Werner, Lira Luz Benites Lázaro

Energy Policy, Journal Year: 2023, Volume and Issue: 175, P. 113480 - 113480

Published: Feb. 8, 2023

Language: Английский

Citations

50
Bias Correction in CMIP6 Models Simulations and Projections for Brazil’s Climate Assessment DOI

Livia Maria Brumatti,

Luiz Felipe Sant’Anna Commar,

N Neumann

et al.

Earth Systems and Environment, Journal Year: 2024, Volume and Issue: 8(1), P. 121 - 134

Published: Jan. 1, 2024

Language: Английский

Citations

11
Extreme weather events and crop diversification: climate change adaptation in Brazil DOI Creative Commons
Elena Beatriz Piedra-Bonilla, Dênis Antônio da Cunha, Marcelo José Braga

et al.

Mitigation and Adaptation Strategies for Global Change, Journal Year: 2025, Volume and Issue: 30(5)

Published: April 21, 2025

Language: Английский

Citations

1
Current and Future Climate Extremes Over Latin America and Caribbean: Assessing Earth System Models from High Resolution Model Intercomparison Project (HighResMIP) DOI Creative Commons
Álvaro Ávila-Díaz, Roger Rodrigues Torres, Cristian Felipe Zuluaga

et al.

Earth Systems and Environment, Journal Year: 2022, Volume and Issue: 7(1), P. 99 - 130

Published: Dec. 19, 2022

Extreme temperature and precipitation events are the primary triggers of hazards, such as heat waves, droughts, floods, landslides, with localized impacts. In this sense, finer grids Earth System models (ESMs) could play an essential role in better estimating extreme climate events. The performance High Resolution Model Intercomparison Project (HighResMIP) is evaluated using Expert Team on Climate Change Detection Indices (ETCCDI) over 1981-2014 period future changes (2021-2050) under Shared Socioeconomic Pathway SSP5-8.5, ten regions Latin America Caribbean. impact increasing horizontal resolution variability a regional scale first compared against reference gridded datasets, including reanalysis, satellite, merging products. We used three different groups based model's grid (sg): (i) low (0.8° ≤ sg 1.87°), (ii) intermediate (0.5° 0.7°), (iii) high (0.23° ≥ 0.35°). Our analysis indicates that there was no clear evidence to support posit improves model performance. ECMWF-IFS family appears be plausible choice represent extremes, followed by ensemble mean HighResMIP their resolution. For climate, projections indicate consensus extremes increase across most regions. Despite uncertainties presented study, have been will continue important tool for assessing risk face events.The online version contains supplementary material available at 10.1007/s41748-022-00337-7.

Language: Английский

Citations

35
Comparison of multi-model ensembles of global and regional climate model projections for daily characteristics of precipitation over four major river basins in southern Africa. Part II: Future changes under 1.5 °C, 2.0 °C and 3.0 °C warming levels DOI Open Access
Sydney Samuel, Alessandro Dosio, Kgakgamatso Mphale

et al.

Atmospheric Research, Journal Year: 2023, Volume and Issue: 293, P. 106921 - 106921

Published: July 16, 2023

Language: Английский

Citations

21
Evaluation of the performance of CMIP6 models in simulating extreme precipitation and its projected changes in global climate regions DOI Creative Commons

B. Zhang,

Songbai Song, Huimin Wang

et al.

Natural Hazards, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 20, 2024

Extreme precipitation events usually lead to economic, agricultural, and social losses globally. The bias of different global circulation models (GCMs) is a major challenge in the projection extreme climate regions. Revealing Coupled Model Intercomparison Project (CMIP) GCMs helpful for providing reference predicting understanding performance CMIP Phase 6 (CMIP6) GCMs. Eight indices were used describe based on daily data retrieved from Global Precipitation Climatology (GPCP) 19 CMIP6 Six evaluation metrics adopted assess ability CMIP6-determined precipitation. results showed that half overestimated Sahara, Arabian Peninsula, Central Asia, underestimated northern North America Asia. In general, multimodel ensemble (MME) achieved greater simulating than did individual considered was relatively small tropical regions, especially equatorial future, will increase, under high emission scenarios (i.e., SSP5-8.5). notably increase cold polar Our could improve simulations, they are very important reliable future predictions.

Language: Английский

Citations

6
Using quantile mapping and random forest for bias‐correction of high‐resolution reanalysis precipitation data and CMIP6 climate projections over Iran DOI

Maryam Raeesi,

Ali Asghar Zolfaghari, S H Kaboli

et al.

International Journal of Climatology, Journal Year: 2024, Volume and Issue: 44(12), P. 4495 - 4514

Published: Aug. 15, 2024

Abstract Climate change is expected to cause important changes in precipitation patterns Iran until the end of 21st century. This study aims at evaluating projections climate over by using five model outputs (including ACCESS‐ESM1‐5, BCC‐CSM2‐MR, CanESM5, CMCC‐ESM2 and MRI‐ESM2‐0) Coupled Model Intercomparison Project phase 6 (CMIP6), performing bias‐correction a novel combination quantile mapping (QM) random forest (RF) between years 2015 2100 under three shared socioeconomics pathways (SSP2‐4.5, SSP3‐7.0 SSP5‐8.5). First, was performed on ERA5‐Land reanalysis data as reference period (1990–2020) QM method, then corrected considered measured data. Based historical simulations (1990–2014), future (2015–2100) were also bias‐corrected utilizing method. Next, accuracy method validated comparing with for overlapping 2020. comparison revealed persistent biases; hence, QM‐RF applied rectify result, highest RMSE both SSP2‐4.5 amounting 331.74 201.84 mm·year −1 , respectively. Particularly, exclusive use displayed substantial errors projecting annual based SSP5‐8.5, notably case ACCESS‐ESM1‐5 (RMSE = 431.39 ), while reduced after (197.75 ). Obviously, significant enhancement results observed upon implementing 139.30 ) 151.43 showcasing approximately reduction values 192.43 50.41 Although each output evaluated individually, multi‐model ensemble (MME) created project pattern Iran. By considering that lower correcting outputs, we used technique create MME. SSP2‐4.5, MME highlight imminent reductions (>10%) across large regions Iran, conversely increases ranging from 10% 20% southern areas SSP3‐7.0. Moreover, projected dramatic declines especially impacting central, eastern, northwest Notably, most pronounced possibly decline are arid (central plateau) eastern SSP5‐8.5.

Language: Английский

Citations

5
An ensemble-based projection of future hydro-climatic extremes in Iran DOI
Afshin Jahanshahi, Martijn J. Booij, Sopan Patil

et al.

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 642, P. 131892 - 131892

Published: Aug. 23, 2024

Language: Английский

Citations

5
Evaluation of CMIP6 historical simulations over IGAD region of Eastern Africa DOI Creative Commons
Paulino Omoj Omay, Nzioka John Muthama,

Christopher Oludhe

et al.

Discover Environment, Journal Year: 2023, Volume and Issue: 1(1)

Published: Aug. 31, 2023

Abstract The Accuracy of model simulations is critical for climate change and its socio-economic impact. This study evaluated23 Global models participating in the Coupled Model Intercomparison Project phase 6 (CMIP6). main objective was to identify top 10 best performance capturing patterns rainfall 1981–2014 period over Intergovernmental Authority on Development (IGAD) region Eastern Africa. total rainfall, annual cycle, continuous, categorical Volumatic statistical metrics, scatter plots, Cumulative Distribution Function (CDF), colored code portrait were used assess . Results indicate that most CMIP6 generally capture characteristics observed climatology pattern bimodal unimodal regimes. majority Arid Semi-Arid Lands (ASALs) Kenya, Somalia, Ethiopia, Sudan scored lowest skills, highest bias, over-estimated lower skills June–September (JJAS) compared March–May (MAM) October-December (OND). Quantitatively, a high percent bias exceeding 80% ASALs, correlation coefficient ranging between 0.6 0.7 across Ethiopia’s highlands, 5–40 as Root Mean Squared Error region. In addition, 21 out 23 parts ACCESS-ESM1-5 MIROC6 are opposed CNRM-CM6-1HR under-estimated RMSE values. regional sub-national analysis showed it inconclusive select best-performed based individual metrics analysis. Out models, INM-CM5-0, HadGEM3-GC31-MM, CMCC-CM2-HR4, IPSL-CM6A-LR, KACE-1-0-G, EC-Earth3, NorESM2-MM, GFDL-ESM4, TaiESM1, KIOST-ESM IGAD These findings highlight importance selecting mapping present future hotspots extreme events

Language: Английский

Citations

13
Toward Earth System Modeling with Resolved Clouds and Ocean Submesoscales on Heterogeneous Many-Core HPCs DOI Creative Commons
Shaoqing Zhang, Shiming Xu, Haohuan Fu

et al.

National Science Review, Journal Year: 2023, Volume and Issue: 10(6)

Published: March 20, 2023

With the aid of newly developed 'Sunway' heterogeneous-architecture supercomputer, which has world-leading HPC (high-performance computer) capability, a series high-resolution coupled Earth system models (SW-HRESMs) with up to 5 km atmosphere and 3 ocean have been developed. These can meet needs multiscale interaction studies different computational costs. Here we describe progress SW-HRESMs development, an overview major advancements made by international science community in HR-ESMs. We also show preliminary results regard capturing weather-climate extremes ocean, stressing importance permitted clouds submesoscale eddies modeling tropical cyclones eddy-mean flow interactions, paving way for further model development resolve finer scales even higher resolution more realistic physics. Finally, addition increasing resolution, procedure non-hydrostatic cloud resolved ESM is discussed, laying out scientific directions such huge advancement.

Language: Английский

Citations

12