Earth System Model Evaluation Tool (ESMValTool) v2.0 – diagnostics for extreme events, regional and impact evaluation, and analysis of Earth system models in CMIP

Weigel, Katja; Gier, Bettina K.; Lauer, Axel; Righi, Mattia; Schlund, Manuel; Adeniyi, Kemisola; Andela, Bouwe; Arnone, Enrico; Berg, Peter; Caron, Louis-Philippe; Cionni, Irene; Corti, Susanna; Drost, Niels; Hunter, Alasdair; Lledó, Llorenç; Mohr, Christian Wilhelm; Paçal, Aytaç; Pérez-Zañón, Núria; Predoi, V.; Sandstad, Marit; Sillmann, Jana; Sterl, Andreas; Vegas-Regidor, Javier; Hardenberg, Jost von; Eyring, Veronika

doi:10.5194/gmd-14-3159-2021

Cited by 35 publications

(30 citation statements)

References 61 publications

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“…All analyses in this study were made with the open-source community diagnostics and performance metrics tool for the evaluation of the Earth system models "Earth System Model Evaluation Tool" (ESMValTool [66][67][68][69]). For use with the ESMValTool, the variables and metadata of the precipitation datasets were reformatted following the CMOR (Climate Model Output Rewriter; https://pcmdi.github.io/cmor-site/media/pdf/cmor_users_gui de.pdf (accessed on 18 June 2020) [70]) tables and definitions (e.g., https://github.com/P CMDI/cmip6-cmor-tables/tree/master/Tables (accessed on 7 November 2019) [71] for CMIP6).…”

Section: Earth System Model Evaluation Toolmentioning

confidence: 99%

Comparison of Reanalysis and Observational Precipitation Datasets Including ERA5 and WFDE5

Haßler

Lauer

2021

Atmosphere

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Precipitation is a key component of the hydrological cycle and one of the most important variables in weather and climate studies. Accurate and reliable precipitation data are crucial for determining climate trends and variability. In this study, eleven different precipitation datasets are compared, six reanalysis and five observational datasets, including the reanalysis datasets ERA5 and WFDE5 from the ECMWF family, to quantify the differences between the widely used precipitation datasets and to identify their particular strengths and shortcomings. The comparisons are focused on the common time period 1983 through 2016 and on monthly, seasonal, and inter-annual times scales in regions representing different precipitation regimes, i.e., the Tropics, the Pacific Inter Tropical Convergence Zone (ITCZ), Central Europe, and the South Asian Monsoon region. For the analysis, satellite-gauge precipitation data from the Global Precipitation Climatology Project (GPCP-SG) are used as a reference. The comparison shows that ERA5 and ERA5-Land are a clear improvement over ERA-Interim and show in most cases smaller biases than the other reanalysis datasets (e.g., around 13% high bias in the Tropics compared to 17% for MERRA-2 and 36% for JRA-55). ERA5 agrees well with observations for Central Europe and the South Asian Monsoon region but underestimates very low precipitation rates in the Tropics. In particular, the tropical ocean remains challenging for reanalyses with three out of four products overestimating precipitation rates over the Atlantic and Indian Ocean.

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Section: Earth System Model Evaluation Toolmentioning

confidence: 99%

Comparison of Reanalysis and Observational Precipitation Datasets Including ERA5 and WFDE5

Haßler

Lauer

2021

Atmosphere

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“…If smaller scale meteorology model data are available, historical model data can be corrected from historical site data to obtain more accurate future model data. The ideal data may take a regional climate model (RCM) to simulate the precipitation for Qinghai province in the future, but the RCM parameter setting needs field experiment and experience estimation (Weigel et al, 2021), and this could also bring the subjective experience of error and uncertainty. In order to reduce the uncertainty, this study uses 22 models of collection and data processing methods.…”

Section: Discussionmentioning

confidence: 99%

Spatial and Temporal Analysis of Quantitative Risk of Flood due to Climate Change in a China’s Plateau Province

Zhang²,

Guo³

et al. 2022

Front. Earth Sci.

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The Qinghai–Tibet Plateau is a region sensitive to climate change with significant changes in topography, and the increase in extreme precipitation in the region easily leads to landslides and debris flows. To mitigate the effects of climate change, the Chinese government has pledged to an emission reduction target that achieves a “carbon peak’” in 2030 and being “carbon neutral” in 2060 at a huge cost. In order to explore the quantitative flood risk at the county level in Qinghai province (the core province of the Qinghai–Tibet Plateau) and the contribution of emission reduction efforts to risk mitigation, this article constructs a loss rate curve based on the data of more than 1,000 single flood disasters from 2009 to 2019 through government cooperation and the data of observed cumulative precipitation. Combined with the latest CMIP6 climate model data, the temporal and spatial variation characteristics of the flood loss ratio in Qinghai province from 2020 to 2060 are quantitatively characterized. The results show the following: 1) the curve of the flood loss rate in Qinghai province from 2020 to 2060 may present a trend of “rising in the early stage, stable in the middle stage, and declining in the late stage” under low (SSP126) and medium (SSP245) emission scenarios and presents a trend of rising fluctuation under the high-emission (SSP585) scenario. The flood loss ratio under SSP585 is 10 times higher than that under SSP126; 2) the flood risk of 44 counties from 2020 to 2060 in Qinghai province may be concentrated in the southeast region, with Jiuzhi county, Banma county, Nangqian county, Gande county, and Dari county being the most prominent; (3) compared with SSP585, the Qinghai province in SSP126 may cumulatively avoid about 24 billion CNY in economic losses from 2020 to 2060 and avoid nearly 600 million CNY in losses per year, which is equivalent to 93% of Qinghai province’s special funds for ecological and environmental protection in 2019. The research aims to provide theoretical and data support for flood disaster risk prevention and management in China’s high-altitude areas and to promote the initiative of emission reduction in China and even the world.

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“…The meteorological input is pre-processed within the eWaterCycle platform using the Earth System Model Evaluation Tool (ESMValTool) V2.0 (Righi et al, 2020;Weigel et al, 2021). Before further processing the data is aggregated to daily val- ues.…”

Section: Meteorological Input and Pre-processingmentioning

confidence: 99%

Large-sample assessment of spatial scaling effects of the distributed wflow_sbm hydrological model shows that finer spatial resolution does not necessarily lead to better streamflow estimates

Aerts

Hut

Giesen

et al. 2021

Preprint

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Abstract. Distributed hydrological modelling moves into the realm of hyper-resolution modelling. This results in a plethora of scaling related challenges that remain unsolved. In light of model result interpretation, finer resolution output might implicate to the user an increase in understanding of the complex interplay of heterogeneity within the hydrological system. Here we investigate spatial scaling in the realm of hyper-resolution by evaluating the streamflow estimates of the distributed wflow_sbm hydrological model based on 454 basins from the large-sample CAMELS data set. Model instances were derived at 3 spatial resolutions, namely 3 km, 1 km, and 200 m. The results show that a finer spatial resolution does not necessarily lead to better streamflow estimates at the basin outlet. Statistical testing of the objective function distributions (KGE score) of the 3 model instances show only a statistical difference between the 3 km and 200 m streamflow estimates. However, results indicate strong locality in scaling behaviour between model instances expressed by differences in KGE scores of on average 0.22. This demonstrates the presence of scaling behavior throughout the domain and indicates where locality in results is strong. The results of this study open up research paths that can investigate the changes in flux and state partitioning due to spatial scaling. This will help further understand the challenges that need to be resolved for hyper resolution hydrological modelling.

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Earth System Model Evaluation Tool (ESMValTool) v2.0 – diagnostics for extreme events, regional and impact evaluation, and analysis of Earth system models in CMIP

Cited by 35 publications

References 61 publications

Comparison of Reanalysis and Observational Precipitation Datasets Including ERA5 and WFDE5

Comparison of Reanalysis and Observational Precipitation Datasets Including ERA5 and WFDE5

Spatial and Temporal Analysis of Quantitative Risk of Flood due to Climate Change in a China’s Plateau Province

Large-sample assessment of spatial scaling effects of the distributed wflow_sbm hydrological model shows that finer spatial resolution does not necessarily lead to better streamflow estimates

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