A nonstationary bias‐correction technique to remove bias in GCM simulations

Miao, Chiyuan; Su, Lei; Sun, Qing; Duan, Qingyun

doi:10.1002/2015jd024159

Cited by 115 publications

(78 citation statements)

References 48 publications

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“…Miao et al . () reviewed the methods to deal with non‐stationarity in bias correction of outputs from GCMs, proposed by Li et al . () and Wang and Chen ().…”

Section: Discussionmentioning

confidence: 99%

Evaluation of a new satellite‐based precipitation data set for climate studies in the Xiang River basin, southern China

Zhu

Hsu

et al. 2017

Intl Journal of Climatology

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A new satellite‐based precipitation data set, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks‐Climate Data Record (PERSIANN‐CDR), with long‐term time series dating back to 1983, can be one valuable data set for climate studies. This study investigates the feasibility of using PERSIANN‐CDR as a reference data set for climate studies. Sixteen Coupled Model Intercomparison Projection Phase 5 (CMIP5) models are evaluated over the Xiang River basin, southern China, by comparing their performance on precipitation projection and streamflow simulation, particularly on extreme precipitation and streamflow events. The results show PERSIANN‐CDR is a valuable data set for climate studies, even on extreme precipitation events. The precipitation estimates and their extreme events from CMIP5 models are improved significantly compared with rain gauge observations after bias correction by the PERSIANN‐CDR precipitation estimates. Given streamflows simulated with raw and bias‐corrected precipitation estimates from 16 CMIP5 models, 10 out of 16 are improved after bias correction. The impact of bias correction on extreme events for streamflow simulations are unstable, with 8 out of 16 models can be clearly claimed they are improved after the bias correction. Concerning the performance of raw CMIP5 models on precipitation, IPSL‐CM5A‐MR excels the other CMIP5 models, while MRI‐CGCM3 outperforms on extreme events with its better performance on six extreme precipitation metrics. Case studies also show that raw CCSM4, CESM1‐CAM5, and MRI‐CGCM3 outperform other models on streamflow simulation, while MIROC5‐ESM‐CHEM, MIROC5‐ESM, and IPSL‐CM5A‐MR behave better than the other models after bias correction.

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“…Miao et al . () reviewed the methods to deal with non‐stationarity in bias correction of outputs from GCMs, proposed by Li et al . () and Wang and Chen ().…”

Section: Discussionmentioning

confidence: 99%

Evaluation of a new satellite‐based precipitation data set for climate studies in the Xiang River basin, southern China

Zhu

Hsu

et al. 2017

Intl Journal of Climatology

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“…As mentioned, this holds particularly for precipitation. In view of these deficiencies, a new class of statistical downscaling methods, so-called QM bias-correction methods have been proposed in recent years (e.g., [26]) which appear to do a better job than classical SDSM in predicting local climate.…”

Section: Updated Quantile Mapping (Qm) Bias Correction Techniquementioning

confidence: 99%

“…The modified non-stationary cumulative distribution function (CDF)-matching (CNCDFm) technique and equidistant CDF matching (EDCDFm) [26,29] are used, respectively, for the bias correction of the daily precipitation and temperatures (minimum and maximum). For further details of the QM method, the reader is referred to Emami and Koch (2017) [10].…”

Section: Updated Quantile Mapping (Qm) Bias Correction Techniquementioning

confidence: 99%

Evaluation of Statistical-Downscaling/Bias-Correction Methods to Predict Hydrologic Responses to Climate Change in the Zarrine River Basin, Iran

Emami

Koch

2018

Climate

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Abstract:Modeling the hydrologic responses to future changes of climate is important for improving adaptive water management. In the present application to the Zarrine River Basin (ZRB), with the major reach being the main inflow source of Lake Urmia (LU), firstly future daily temperatures and precipitation are predicted using two statistical downscaling methods: the classical statistical downscaling model (SDSM), augmented by a trend-preserving bias correction, and a two-step updated quantile mapping (QM) method. The general circulation models (GCM) input to SDSM are climate predictors of the Canadian Earth System Model (CanESM2) GCM under the representative concentration pathway (RCP) emission scenarios, RCP45 and RCP85, whereas that to the QM is provided by the most suitable of several Climate Model Intercomparison Project Phase 5 (CMIP5) GCMs under RCP60, in addition. The performances of the two downscaling methods are compared to each other for a past "future" period (2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016) and the QM is found to be better and so is selected in the subsequent ZR streamflow simulations by means of the Soil and Water Assessment Tool (SWAT) hydrological model, calibrated and validated for the reference period . The impacts of climate change on the hydrologic response of the river basin, specifically the inflow to the Boukan Reservoir, the reservoir-dependable water release (DWR), are then compared for the three RCPs in the near-(2020-2038), middle-(2050-2068) and far-(2080-2098) future periods assuming (1) the "current" consumptive demand to be continued in the future, and (2) a more conservative "recommended" demand. A systematic future shortage of the available water is obtained for case (1) which can be mitigated somewhat for (2). Finally, the SWAT-predicted ZRB outflow is compared with the Montana-based estimated environmental flow of the ZR. The latter can successfully be sustained at good and fair levels for the near-and middle-future periods, but not so for the summer months of the far-future period, particularly, for RCP85.

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“…The geographic settings, remarkable topographic gradients, landscape, and dominating East Asian monsoon make China one of the most vulnerable regions worldwide (Sun et al, ; Miao et al, ). Therefore, accurate climate information is of crucial importance for climate adaption and mitigation in China.…”

Section: Introductionmentioning

confidence: 99%

More realistic land‐use and vegetation parameters in a regional climate model reduce model biases over China

Gou

Wang

Jin

et al. 2019

Intl Journal of Climatology

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Accurate vegetation cover data are important for realistic simulation of regional climate. The default vegetation parameters from Global Land Cover 2000, currently incorporated into global climate models and used in regional climate model RegCM, are not realistic for China, which may have contributed to serious bias in surface climate simulation. In this study, a new set of vegetation parameters considering the Plant Functional Type (PFT) fractions and the corresponding monthly leaf area index (PFT_LAI), were developed based on the land cover and MODIS LAI data sets. The regional climate model RegCM4.5 coupled with the land surface model CLM4.5 were utilized to test the performance of the new vegetation parameters by comparing simulations with observations using different surface parameters. The surface energy balance was analysed to examine the effects of changed vegetation parameters on regional climate. The results showed that the new parameters were more accurate than the GLC2000 parameters when describing the distribution of crops, grassland, and forests over China. The improved vegetation parameters reduced model biases for winter air temperature and precipitation over southern China by 0.9°C and 8%, respectively, and reduced the winter temperature and summer precipitation biases over northeastern China by approximately 0.7°C and 8%, respectively. More accurate surface albedo are the main reasons for reductions in model bias. However, certain biases, such as the cold and dry bias over the Tibetan Plateau, still remained in the simulation results using our new vegetation data.

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A nonstationary bias‐correction technique to remove bias in GCM simulations

Cited by 115 publications

References 48 publications

Evaluation of a new satellite‐based precipitation data set for climate studies in the Xiang River basin, southern China

Evaluation of a new satellite‐based precipitation data set for climate studies in the Xiang River basin, southern China

Evaluation of Statistical-Downscaling/Bias-Correction Methods to Predict Hydrologic Responses to Climate Change in the Zarrine River Basin, Iran

More realistic land‐use and vegetation parameters in a regional climate model reduce model biases over China

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