Evaluation of CMIP6 models toward dynamical downscaling over 14 CORDEX domains

Zhang, Meng-Zhuo; Xu, Zhongfeng; Han, Ying; Guo, Weidong

doi:10.1007/s00382-022-06355-5

Cited by 31 publications

(19 citation statements)

References 56 publications

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“…The Centre National de Recherches Météorologiques (CNRM), Commonwealth Science and Industrial Research Organisation (CSIRO), European Community Earth-System (EC-Earth), and Max Planck Institute (MPI) models were downscaled using the RegCM4 model (Giorgi et al, 2012) from CORDEX SEA (Ngo-Duc et al, 2017;Supari et al, 2020;Tangang et al, 2020). The HadGEM model was downscaled using the regional Weather Research and Forecasting (WRF3.5) model (Skamarock et al, 2008) from the Asia-Pacific Economic Cooperation Climate Centre (Yang, 2012). We will refer to this model generation as CORDEX.…”

Section: Model Resultsmentioning

confidence: 99%

Evaluation of extreme precipitation over Southeast Asia in the Coupled Model Intercomparison Project Phase 5 regional climate model results andHighResMIPglobal climate models

Hariadi

Schrier

Steeneveld

et al. 2022

Intl Journal of Climatology

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Modelling rainfall extremes and dry periods over the Southeast Asia (SEA) region is challenging due to the characteristics of the region, which consists of the Maritime Continent and a mountainous region; it also experiences monsoonal conditions, as it is located between the Asian summer monsoon and the Australian summer monsoon. Representing rainfall extremes is important for flood and drought assessments in the region. This paper evaluates extreme rainfall climatic indices from regional climate models from CORDEX Southeast Asia and compares them with the results of high‐resolution global climate models with a comparable spatial resolution from the HighResMIP experiment. Observations indicate a high intensity of rainfall over areas affected by tropical cyclones and long consecutive dry day periods over some areas in Indochina and the southern end of Indonesia. In the model simulations, we find that both coupled and sea surface temperature‐forced HighResMIP model experiments are more similar to the observations than CORDEX model results. However, the models produce a poorer simulation of precipitation intensity‐related indices due to model biases in the rainfall intensity. This bias is higher in CORDEX than in HighResMIP and is evident in both the low‐ and high‐resolution HighResMIP model versions. The comparable performances of HighResSST (atmosphere‐only runs) and Hist‐1950 (coupled ocean–atmosphere runs) demonstrate the accuracy of the ocean model. Comparable performances were also found for the two different resolutions of HighResMIP, suggesting that there is no improvement in the performance of the high‐resolution HighResMIP model compared to the low‐resolution HighResMIP model.

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Section: Model Resultsmentioning

confidence: 99%

Evaluation of extreme precipitation over Southeast Asia in the Coupled Model Intercomparison Project Phase 5 regional climate model results andHighResMIPglobal climate models

Hariadi

Schrier

Steeneveld

et al. 2022

Intl Journal of Climatology

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“…Differences among the GCMs' results are associated with various factors, such as the parameterizations of key physical processes like convection and the models' configuration, including numerical techniques and horizontal and vertical resolutions. However, a satisfactory simulation of the past climate yields more reliable future climate projections and justifies the model selection, given that skillfulness in historical simulation can translate into future predictions that are also consistent [34]. Furthermore, studies indicate that the ensemble mean composed of the best-performing GCMs produces results closer to observations and improves the quality of climate simulations compared to any individual model [3,116].…”

Section: General Analysismentioning

confidence: 99%

“…The weighting approach assesses the model's performance in simulating past climate and assigns a weight to this performance in future projections [29,33]. Although a good simulation of historical climate does not determine more accurate climate projections, if a model fails to simulate aspects of past climate, it will probably produce less reliable projections of future climate [29,34].…”

Section: Introductionmentioning

confidence: 99%

Assessment of precipitation and near-surface temperature simulation by CMIP6 models in South America

Reboita,

Willian de Souza Ferreira,

Gabriel Martins Ribeiro

et al. 2024

Environ. Res.: Climate

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This study evaluated the performance of 50 global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) in simulating the statistical features of precipitation and air temperature in five subdomains of South America during the historical period (1995-2014). Monthly precipitation and temperature simulations were validated with data from the Climate Prediction Center Merged Analysis of Precipitation (CMAP), the Global Precipitation Climatology Project (GPCP), and the ERA5 reanalysis. The models’ performance was evaluated using a ranking analysis with statistical metrics such as mean, standard deviation, Pearson’s spatial correlation, annual cycle amplitude, and linear trend. The analyses considered the representation of precipitation and air temperature separately for each subdomain, the representation for all five regions together, and the joint representation of precipitation and air temperature for all five subdomains. In the Brazilian Amazon, the best-performing models were EC-Earth3-Veg, INM-CM4-8, and INMCM5-0 (precipitation), and IPSL-CM6A-LR, MPI-ESM2-0, and IITM-ESM (temperature). In the La Plata Basin, KACE-1-0-G, ACCESS-CM2, and IPSL-CM6A-LR (precipitation), and GFDL-ESM4, TaiESM1, and EC-Earth3-Veg (temperature) yielded the best simulations. In Northeast Brazil, SAM0-UNICON, CESM2, and MCM-UA-1-0 (precipitation), BCC-CSM2-MR, KACE-1-0-G, and CESM2 (temperature) showed the best results. In Argentine Patagonia, the GCMs ACCESS-CM2, ACCESS-ESM1-5 and EC-Earth3-Veg-LR (precipitation), and CAMS-CSM1-0, CMCC-CM2-HR4, and GFDL-ESM4 (temperature) outperformed. Finally, for Southeast Brazil, the models ACCESS-CM2, ACCESS-ESM1-5, and EC-Earth3-Veg-LR (precipitation), and CAMS-CSM1-0, CMCC-CM2-HR4, and GFDL-ESM4 (temperature) yielded the best simulations. The joint evaluation of the regions and variables indicated that the best models are CESM2, TaiESM1, CMCC-CM2-HR4, FIO-ESM-2-0, and MRI-ESM2-0.

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“…1 among them (from their Figure S1). Zhang et al (2022) evaluated the performance and interdependency of 37 GCMs from CMIP6 in terms of seven key large-scale driving fields over 14 CORDEX domains, and concluded that MPI-ESM1-2-HR and FIO-ESM-2-0 rank top two. The model performance of MPI-ESM1-2-HR is much better than that of MPI-ESM1-2-LR, indicating that fine resolution does improve the model.…”

Section: Ocean-to-climate Model Development and Applicationsmentioning

confidence: 99%

Ocean Waves in Large‐Scale Air‐Sea Weather and Climate Systems

Babanin

2023

JGR Oceans

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Ocean surface gravity waves are mechanical water waves propagating along the sea interface, with heights from centimeters to more than 10 m. They are one of the most significant dynamic processes in the upper ocean which have received a lot of attention in the fluid mechanics and ocean science community, starting from Stokes (1847) and peaking about half a century ago (e.g., see Yuan and Huang (2012) for a review). When moderate-to-strong wind blows over the sea surface, most of the energy and momentum are first transported to surface waves, to support their growth (e.g., Kudryavtsev & Makin, 2001), before those are passed to the ocean through wave breaking and wave-turbulence interactions (Babanin, 2011). That is, the waves are the "sink" of the energy and momentum flux for the low atmosphere and the "source" for the upper ocean, and play an important role in redistribution of the air-sea energy and momentum fluxes at the space-time scale of the storms. Moreover, such energy and momentum balance is not quite dependent on the instantaneous and local wind, because the waves are integral of the wind over large scales. Furthermore, in large areas dominated by swells, the waves can be unrelated to the local wind altogether, and in fact can be responsible for generating the local winds (e.g., Hanley et al., 2010).

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Evaluation of CMIP6 models toward dynamical downscaling over 14 CORDEX domains

Cited by 31 publications

References 56 publications

Evaluation of extreme precipitation over Southeast Asia in the Coupled Model Intercomparison Project Phase 5 regional climate model results andHighResMIPglobal climate models

Evaluation of extreme precipitation over Southeast Asia in the Coupled Model Intercomparison Project Phase 5 regional climate model results andHighResMIPglobal climate models

Assessment of precipitation and near-surface temperature simulation by CMIP6 models in South America

Ocean Waves in Large‐Scale Air‐Sea Weather and Climate Systems

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