Added value of <scp>CMIP6</scp> models over <scp>CMIP5</scp> models in simulating the climatological precipitation extremes in China

Luo, Neng; Guo, Yan; Chou, Jieming; Gao, Zhibo

doi:10.1002/joc.7294

Cited by 47 publications

(20 citation statements)

References 44 publications

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“…Fifty‐two models from CMIP6 (Eyring et al ., 2016) were used to generate climate classifications for the present (1980–2014) and future climate (2015–2100). A new feature of CMIP6 is the finer resolution of GCMs, allowing a more precise representation of physical processes involved in earth's climate (Srivastava et al ., 2020; Luo et al ., 2021). This is advantageous because previously parameterized processes are now directly resolved by models (Haarsma et al ., 2016).…”

Section: Datamentioning

confidence: 99%

Towards better characterization of global warming impacts in the environment through climate classifications with improved global models

Navarro

Merino

Sánchez

et al. 2022

Intl Journal of Climatology

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Climate classifications are useful to synthesize the physical state of the climate with a proxy that can be directly related to biota. However, they present a potential drawback, namely a strong sensitivity because of the use of hard thresholds (step functions). Thus, minor discrepancies in the base data produce large differences in the type of climate. However, such an a priori limitation is also a strength because such sensitivity can be used to better gauge model performance. Although previous attempts of classifying climates of the world using global climate model outputs were encouraging, the applicability of their classifications to impact studies has been limited by past model issues. Notwithstanding the persistence of certain imperfections and limitations in current models, the high‐quality physical simulations from phase six of the Coupled Intercomparison Project (CMIP6) has opened new possibilities in the field, thanks to their improved representation of atmospheric and oceanic physics. The purpose of this paper is twofold: to show that climate classifications from CMIP6 are sufficiently robust for use in impact studies, and to use those classifications for identifying error sources and potential issues that deserve further attention in models. Thus, 52 CMIP6 climate models were evaluated by using three climate classifications schemes, classical Köppen, extended‐Köppen, and modified Thornthwaite. We first assessed model ability to reproduce present climate types by comparing their outputs with observational data. Models performed best for the Köppen and extended‐Köppen classification methods (Cohen's kappa κ = 0.7), and had moderate scores for the Thornthwaite climate classification (κ = 0.4). By tracing back the observed biases, we were able to pinpoint the misrepresentation of dry climates as a major source of error. Another finding was that most models still had some difficulties in representing the seasonal variability of precipitation over several monsoonal regions, thereby yielding the wrong climate type there. Models were also evaluated for future climate. Substantial changes in climate types are projected in the SSP5‐8.5 scenario. These changes include a shrinkage of polar/frigid climates (22%) and an increase of dry climates (7%).

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Section: Datamentioning

confidence: 99%

Towards better characterization of global warming impacts in the environment through climate classifications with improved global models

Navarro

Merino

Sánchez

et al. 2022

Intl Journal of Climatology

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“…Nowadays, benefiting from the Coupled Model Intercomparison Project (CMIP) established and promoted by the World Climate Research Program (WCRP), global climate models (GCMs) have become available tools for understanding current and future climate change variations (Li et al, 2013;Sillmann et al, 2013;Stanfield et al, 2016;Sun et al, 2022). Previous studies have shown that the models commonly overestimate precipitation in mountainous areas relative to observed data, especially in the eastern of the TP (Su et al, 2013;Lin et al, 2018;Luo et al, 2022). Also, the precipitation intensity is generally underestimated in the Sichuan Basin (He et al, 2017;Tao et al, 2020;Hu and Yuan, 2021).…”

Section: Introductionmentioning

confidence: 99%

To what extent horizontal resolution improves the simulation of precipitation in CMIP6 HighResMIP models over Southwest China?

Jin

Chen

et al. 2023

Front. Earth Sci.

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Southwest China (SWC) is located in the eastern part of Tibetan Plateau (TP) with large elevation differences and complex topography, which has always been a challenge to the simulation of precipitation in climate modeling community. In this study, the differences in the simulation of precipitation over the SWC are evaluated using the lower and higher resolution models (LR and HR) from the High–Resolution Model Intercomparison Project (HighResMIP) protocol in Coupled Model Intercomparison Project Phase 6 (CMIP6). Our results indicate that the spatial patterns of annual precipitation over the SWC for the period 1985–2014 are well reproduced in most of the HR and LR models, with an increasing tendency from the northwest to southeast. Compared with LR models, the wet biases over the eastern TP and the dry biases over the Sichuan Basin are significantly reduced in HR models. The bias for annual precipitation of the multi–model ensemble mean (MME) has been reduced from 0.97 mm/day (LR) to 0.72 mm/day (HR). In addition, the simulation of extreme precipitation is significantly improved in the finer horizontal resolution models, showing effectively reduced simulation biases in the Sichuan Basin compared with the LR models. The frequency and intensity of extremes are represented by heavy precipitation days (R10 mm) and maximum consecutive 5 days precipitation (Rx5day), which the relative changes have been decreased from 66% (LR) to 47% (HR) in R10 mm and decreased from 23% (LR) to 19% (HR) in Rx5day. We further examine the possible reasons for the difference between LR and HR models in precipitation simulation, showing that the HR models could generate “additional” cyclonic circulation and promote more upward motion with the water vapor convergence, thus correcting the dry biases of precipitation simulation over the Sichuan Basin. This indicates that atmospheric circulation and moisture conditions could be simulated more realistically in climate model with a finer resolution, further improving precipitation simulation performance.

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“…CMIP experiments provide historical and future climate projections from a large ensemble (approximately 30) of GCMs (Luo et al 2022). CMIP6 was launched as an improvement to CMIP5, particularly due to improved physical processes, parameterizations, increased spatial resolutions and additional biogeochemical processes (Eyring et al 2016).…”

Section: Introductionmentioning

confidence: 99%

Comparison of multimodel ensembles of global and regional climate models projections for extreme precipitation over four major river basins in southern Africa. Part I: Assessment of the historical simulations

Samuel

Dosio

Mphale

et al. 2022

Preprint

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This study assesses the performance of large ensembles of global (CMIP5, CMIP6) and regional (CORDEX, CORE) climate models in simulating extreme precipitation over four major river basins (Limpopo, Okavango, Orange and Zambezi) in southern Africa during the period 1983–2005. The ability of the model ensembles to simulate seasonal extreme precipitation indices is assessed using three high-resolution satellite-based datasets. The results show that all ensembles overestimate the annual cycle of mean precipitation over all basins, although the intermodal spread is large, with CORDEX being the closest to the observed values. Generally, all ensembles overestimate the mean and interannual variability of rainy days (RR1), maximum consecutive wet days (CWD), and heavy and very heavy precipitation days (R10mm and R20mm respectively) over all basins during all three seasons. Simple daily rainfall intensity (SDII) and the number of consecutive dry days (CDD) are generally underestimated. The lowest Taylor skill scores (TSS) and spatial correlation coefficients (SCC) are depicted for CDD over Limpopo compared with the other indices and basins, respectively. Additionally, the ensembles exhibit the highest normalized standard deviations (NSD) for CWD compared to other indices. The intermodal spread and performance of the RCM ensembles are lower and better, respectively, than those of GCM ensembles (except for the interannual variability of CDD). In particular, CORDEX performs better than CORE in simulating extreme precipitation over all basins. Although the ensemble biases are often within the range of observations, the statistically significant wet biases shown by all ensembles underline the need for bias correction when using these ensembles in impact assessments.

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Added value of CMIP6 models over CMIP5 models in simulating the climatological precipitation extremes in China

Cited by 47 publications

References 44 publications

Towards better characterization of global warming impacts in the environment through climate classifications with improved global models

Towards better characterization of global warming impacts in the environment through climate classifications with improved global models

To what extent horizontal resolution improves the simulation of precipitation in CMIP6 HighResMIP models over Southwest China?

Comparison of multimodel ensembles of global and regional climate models projections for extreme precipitation over four major river basins in southern Africa. Part I: Assessment of the historical simulations

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