An improved multivariable integrated evaluation method and tool (MVIETool) v1.0 for multimodel intercomparison

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

doi:10.5194/gmd-14-3079-2021

Cited by 12 publications

(5 citation statements)

References 26 publications

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“…To assess the overall performance of the climate model in simulating multiple fields, we also used a multivariable integrated skill score (MISS). The MISS is defined based on vector field statistics and can summarize the overall performance of the model in simulating multiple fields 41 – 43 . The model performance improves monotonically with an increase in the MISS.…”

Section: Methodsmentioning

confidence: 99%

Bias-corrected CMIP6 global dataset for dynamical downscaling of the historical and future climate (1979–2100)

et al. 2021

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Dynamical downscaling is an important approach to obtaining fine-scale weather and climate information. However, dynamical downscaling simulations are often degraded by biases in the large-scale forcing itself. We constructed a bias-corrected global dataset based on 18 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and the European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) dataset. The bias-corrected data have an ERA5-based mean climate and interannual variance, but with a non-linear trend from the ensemble mean of the 18 CMIP6 models. The dataset spans the historical time period 1979–2014 and future scenarios (SSP245 and SSP585) for 2015–2100 with a horizontal grid spacing of (1.25° × 1.25°) at six-hourly intervals. Our evaluation suggests that the bias-corrected data are of better quality than the individual CMIP6 models in terms of the climatological mean, interannual variance and extreme events. This dataset will be useful for dynamical downscaling projections of the Earth’s future climate, atmospheric environment, hydrology, agriculture, wind power, etc.

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Section: Methodsmentioning

confidence: 99%

Bias-corrected CMIP6 global dataset for dynamical downscaling of the historical and future climate (1979–2100)

et al. 2021

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“…Four centered statistics were used to measure model performance in terms of the scalar fields, i.e., mean error (ME), standard deviation (SD), correlation coefficient (CORR), and root mean square error (RMSE). Similarly, we also used the vector mean error (VME), the centered root mean square length (cRMSL), the centered vector similarity coefficient (cVSC), and the root mean square vector difference (cRMSVD) 48 – 50 for the vector fields, such as wind fields. Note that these four statistics take the wind speed and wind direction into consideration simultaneously 48 – 50 .…”

Section: Methodsmentioning

confidence: 99%

“…Similarly, we also used the vector mean error (VME), the centered root mean square length (cRMSL), the centered vector similarity coefficient (cVSC), and the root mean square vector difference (cRMSVD) 48 – 50 for the vector fields, such as wind fields. Note that these four statistics take the wind speed and wind direction into consideration simultaneously 48 – 50 . ME (VME) and RMSE (RMSVE) measure the mean bias and the overall bias for a scalar (vector) variable, respectively.…”

Section: Methodsmentioning

confidence: 99%

Bias-corrected NESM3 global dataset for dynamical downscaling under 1.5 °C and 2 °C global warming scenarios

Zhang,

Han,

et al. 2024

Sci Data

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Dynamical downscaling is vital for generating finer-scale climate projections. Recently, a set of simulations under four types of 1.5/2 °C global warming scenarios are available with Nanjing University of Information Science and Technology Earth System Model (NESM). However, NESM3’s bias in large-scale driving variables would degrade downscaled simulations. We corrected NESM3 bias in terms of climate mean and inter-annual variance against ERA5 using a novel bias correction method and then produced a set of bias-corrected datasets for dynamical downscaling. The bias-corrected NESM3 spans the historical period for 1979–2014 and four future scenarios (i.e., 1.5 °C overshoot for 2070–2100, stabilized 1.5/2 °C for 2070–2100, and transient 2 °C for 2031–2061) with 1.25° × 1.25° horizontal resolution at six-hourly intervals. Our evaluation suggests that bias-corrected NESM3 outperforms the original NESM3 in the climatological mean of seasonal mean and variability, as well as climate extreme events during the historical period. This bias-corrected dataset is expected to generate more reliable projections for regional climate and environment under 1.5/2 °C global warming.

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“…In some cases, such as for SAM_SON, the models overestimate the observed amplitude. Other authors have used Portrait plots to synthesize CMIP performance of simulated variability (e.g., Sillmann et al, 2013;Bellenger et al, 2014;Cannon 2020;Kim et al, 2020;Planton et al, 2020;Zhang et al, 2021;Ahn et al, 2022Ahn et al, , 2023. The PMP's ETMoV metrics have been used in several model evaluation studies.…”

Section: Extratropical Modes Of Variabilitymentioning

confidence: 99%

Objective Evaluation of Earth System Models: PCMDI Metrics Package (PMP) version 3

Lee,

Gleckler,

Ahn

et al. 2023

Preprint

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Abstract. Systematic, routine, and comprehensive evaluation of Earth System Models (ESMs) facilitates benchmarking improvement across model generations and identifying the strengths and weaknesses of different model configurations. By gauging the consistency between models and observations, this endeavor is becoming increasingly necessary to objectively synthesize thousands of simulations contributed to the Coupled Model Intercomparison Project (CMIP) to date. The PCMDI Metrics Package (PMP) is an open-source Python software package that provides "quick-look" objective comparisons of ESMs with one another and with observations. The comparisons include metrics of large- to global-scale climatologies, tropical inter-annual and intra-seasonal variability modes such as El Niño-Southern Oscillation (ENSO) and Madden-Julian Oscillation (MJO), extratropical modes of variability, regional monsoons, cloud radiative feedbacks, and high-frequency characteristics of simulated precipitation, including extremes. The PMP results are produced in the context of all model simulations contributed to CMIP6 and earlier CMIP phases. An important priority of the PMP is to document evaluation statistics for all Historical and AMIP simulations submitted to recent phases of CMIP, providing version-controlled information for all data sets and software packages being used. Among other purposes, this also enables modeling groups to assess performance changes during the ESM development cycle in the context of the error distribution of the multi-model ensemble. In this paper, we present an overview of the PMP including its history to date, capabilities, recent updates, and future direction.

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An improved multivariable integrated evaluation method and tool (MVIETool) v1.0 for multimodel intercomparison

Cited by 12 publications

References 26 publications

Bias-corrected CMIP6 global dataset for dynamical downscaling of the historical and future climate (1979–2100)

Bias-corrected CMIP6 global dataset for dynamical downscaling of the historical and future climate (1979–2100)

Bias-corrected NESM3 global dataset for dynamical downscaling under 1.5 °C and 2 °C global warming scenarios

Objective Evaluation of Earth System Models: PCMDI Metrics Package (PMP) version 3

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