Bias correction and projection of surface air temperature in LMDZ multiple simulation over central and eastern China

Guo, Lianyi; Gao, Qian; Jiang, Zhihong; Li, Laurent

doi:10.1016/j.accre.2018.02.003

Cited by 38 publications

(27 citation statements)

References 28 publications

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“…Limiting warming to 1.5 °C relative to 2 °C leads to a marked reduction by 32–34% of the increases in the intensity and frequency of temperature extremes, especially over Northeast China, where there occur the strongest avoided impacts ranging from 35% to 44% due to a 0.5 °C less warming. This result agrees with Guo et al (), who demonstrated that the key region of avoided change is Northeast China. However, the avoided impacts on hot extremes are larger than those in H. Chen and Sun (), and slightly smaller than those in Li, Zhou, et al ().…”

Section: Resultssupporting

confidence: 93%

Differential Impacts of 1.5 and 2 °C Warming on Extreme Events Over China Using Statistically Downscaled and Bias‐Corrected CESM Low‐Warming Experiment

Yang

Tang

Wang

et al. 2018

Geophysical Research Letters

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Using statistically downscaled and bias-corrected CESM (Community Earth System Model) 11-member ensemble, we investigate the changes in precipitation and temperature extremes over China in 1.5 and 2°C warmer climates. Most regions will experience a significant increase in wet extreme indices accompanied by fewer consecutive dry days under both warming targets. An additional 0.5°C warming may result in stronger increases in wet extreme indices over most subregions and weak decreases over North China, although the grid point-level changes between the two scenarios are mostly nonsignificant. A robust increase in extreme high-temperature events and TNn (the coldest night) is found across China at a 2°C relative to 1.5°C scenario. Compared to a 2°C world, a 0.5°C less warming avoids 32-34% of the increases in the frequency and intensity of temperature extremes in China, particularly over Northeast China.Plain Language Summary Extreme precipitation and temperature events have serious impacts on natural ecosystems and human health over China. It is therefore crucial to assess how the extreme events may change in China under different warming levels. A set of simulations for the 1.5 and 2°C targets are applied to investigate the changes in climate extremes over China and the benefits of limiting warming to 1.5°C instead of 2°C. Although the differences between the two warming levels are nonsignificant, a general wetting condition is expected for most areas when shifting from the 1.5 to 2°C scenario. If a 1.5°C scenario is followed, there will be a substantial reduction in the increases in hot extremes relative to 2°C warming across the domain. The reduced impacts on the frequency and intensity of temperature extremes are estimated to be about 33% with 0.5°C less global warming.

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Section: Resultssupporting

confidence: 93%

Differential Impacts of 1.5 and 2 °C Warming on Extreme Events Over China Using Statistically Downscaled and Bias‐Corrected CESM Low‐Warming Experiment

Yang

Tang

Wang

et al. 2018

Geophysical Research Letters

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“…Multivariate BC methods can be implemented in different dimensional configurations, depending on the need of the users to correct inter-variable and/or spatial correlations. However, in most cases, multivariate BC methods are applied grid cell by grid cell by practitioners to correct inter-variable properties of climate simulations, disregarding spatial structures (e.g., in Meyer et al, 2019;Guo et al, 2019). We not only tested and assessed this approach for each method but also expanded the study to include high-dimensional configurations of MBC to adjust spatial and full (i.e., spatial and intervariable jointly) dependence structures of climate simulations.…”

Section: Settings Of Mbcsmentioning

confidence: 99%

Multivariate bias corrections of climate simulations: which benefits for which losses?

et al. 2020

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Abstract. Climate models are the major tools to study the climate system and its evolutions in the future. However, climate simulations often present statistical biases and have to be corrected against observations before being used in impact assessments. Several bias correction (BC) methods have therefore been developed in the literature over the last 2 decades, in order to adjust simulations according to historical records and obtain climate projections with appropriate statistical attributes. Most of the existing and popular BC methods are univariate, i.e., correcting one physical variable and one location at a time and, thus, can fail to reconstruct inter-variable, spatial or temporal dependencies of the observations. These remaining biases in the correction can then affect the subsequent analyses. This has led to further research on multivariate aspects for statistical postprocessing BC methods. Recently, some multivariate bias correction (MBC) methods have been proposed, with different approaches to restore multidimensional dependencies. However, these methods are not yet fully apprehended by researchers and practitioners due to differences in their applicability and assumptions, therefore leading potentially to different results. This study is intended to intercompare four existing MBCs to provide end users with aid in choosing such methods for their applications. For evaluation and illustration purposes, these methods are applied to correct simulation outputs from one climate model through a cross-validation method, which allows for the assessment of inter-variable, spatial and temporal criteria. Then, a second cross-validation method is performed for assessing the ability of the MBC methods to account for the multidimensional evolutions of the climate model. Additionally, two reference datasets are used to assess the influence of their spatial resolution on (M)BC results. Most of the methods reasonably correct inter-variable and intersite correlations. However, none of them adjust correctly the temporal structure as they generate bias-corrected data with usually weak temporal dependencies compared to observations. Major differences are found concerning the applicability and stability of the methods in high-dimensional contexts and in their capability to reproduce the multidimensional changes in the model. Based on these conclusions, perspectives for MBC developments are suggested, such as methods to adjust not only multivariate correlations but also temporal structures and allowing multidimensional evolutions of the model to be accounted for in the correction.

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“…The "Cumulative Distribution Function -Transform" (CDF-t) method is a univariate BC method initially proposed by Michelangeli et al (2009) to correct the univariate distribution of a modeled climate variable. Since then, CDF-t has been applied for various studies (e.g., Tramblay et al, 2013;Tobin et al, 2015;Defrance et al, 2017;Famien et al, 2018;Guo et al, 2018) and specific variants have been developed (e.g., Kallache et al, 2011;Vrac et al, 2016). The CDF-t approach applies, independently to each variable, a univariate transfer function T , which permits to relate the cumulative distribution function (CDF) of a variable of interest in the model simulations to that of the reference dataset.…”

Section: "Cumulative Distribution Function -Transform" (Cdf-t)mentioning

confidence: 99%

Multivariate bias corrections of climate simulations: Which benefits for which losses?

François¹,

Vrac²,

Cannon³

et al. 2020

Preprint

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Abstract. Climate models are the major tools to estimate climate variables evolutions in the future. However, climate simulations often present statistical biases and have to be corrected against observations before being used in impact assessments. Several bias correction (BC) methods have therefore been developed in the literature over the last two decades, in order to adjust simulations according to historical records and obtain climate projections with appropriate statistical attributes. Most of the existing and popular BC methods are univariate, i.e., correcting one physical variable and one location at a time, and thus can fail to reconstruct inter-variable, spatial or temporal dependencies of the observations. These remaining biases in the correction can then affect the subsequent analyses. This has led to further research on multivariate aspects for statistical post-processing BC methods. Recently, some multivariate bias correction (MBC) methods have been proposed, with different approaches to restore multidimensional dependencies. However, these methods are not well apprehended yet by researchers and practitioners due to differences in their applicability and assumptions, therefore leading potentially to different results. This study is intended to intercompare four existing MBCs to provide end-users with aid in choosing such methods for their applications. For evaluation and illustration purposes, these methods are applied to correct simulation outputs from one climate model through a cross-validation methodology, which allows for the assessment of inter-variable, spatial and temporal criteria. Then, a second methodology is performed for assessing the ability of the MBC methods to account for the multi-dimensional evolutions of the climate model. Additionally, two reference datasets are used to assess the influence of their spatial resolution on (M)BC results. Most of the methods reasonably correct inter-variable and inter-site correlations. However, none of them adjust correctly the temporal structure as they generate bias corrected data with usually weak temporal dependencies compared to observations. Major differences are found concerning the applicability and stability of the methods in high-dimensional contexts, and in their capability to reproduce the multi-dimensional changes of the model. Based on these conclusions, perspectives for MBC developments are suggested, such as methods to adjust not only multivariate correlations but also temporal structures and allowing to account for multi-dimensional evolutions of the model in the correction.

show abstract

Bias correction and projection of surface air temperature in LMDZ multiple simulation over central and eastern China

Cited by 38 publications

References 28 publications

Differential Impacts of 1.5 and 2 °C Warming on Extreme Events Over China Using Statistically Downscaled and Bias‐Corrected CESM Low‐Warming Experiment

Differential Impacts of 1.5 and 2 °C Warming on Extreme Events Over China Using Statistically Downscaled and Bias‐Corrected CESM Low‐Warming Experiment

Multivariate bias corrections of climate simulations: which benefits for which losses?

Multivariate bias corrections of climate simulations: Which benefits for which losses?

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