Evaluation of bias correction methods for regional climate models: Downscaled Rainfall analysis over diverse Agroclimatic zones of India

Health impact assessments of future environmental exposures are routinely conducted to quantify population burdens associated with the changing climate. It is well-recognized that simulations from climate models need to be bias-corrected against observations to estimate future exposures. Quantile mapping (QM) is a technique that has gained popularity in climate science because of its focus on bias-correcting the entire exposure distribution. Even though improved bias-correction at the extreme tails of exposure may be particularly important for estimating health burdens, the application of QM in health impact projection has been limited. In this paper we describe and apply five QM methods to estimate excess emergency department (ED) visits due to projected changes in warm-season minimum temperature in Atlanta, USA. We utilized temperature projections from an ensemble of regional climate models in the North American-Coordinated Regional Climate Downscaling Experiment (NA-CORDEX). Across QM methods, we estimated consistent increase in ED visits across climate model ensemble under RCP 8.5 during the period 2050 to 2099. We found that QM methods can significantly reduce between-model variation in health impact projections (50–70% decreases in between-model standard deviation). Particularly, the quantile delta mapping approach had the largest reduction and is recommended also because of its ability to preserve model-projected absolute temporal changes in quantiles.

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New Approach for Bias Correction and Stochastic Downscaling of Future Projections for Daily Mean Temperatures to a High-Resolution Grid

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-19-0086.1 ◽

2019 ◽

Vol 58 (12) ◽

pp. 2617-2632 ◽

Cited By ~ 1

Author(s):

Qifen Yuan ◽

Thordis L. Thorarinsdottir ◽

Stein Beldring ◽

Wai Kwok Wong ◽

Shaochun Huang ◽

...

Keyword(s):

High Resolution ◽

Bias Correction ◽

Climate Models ◽

Regional Climate ◽

Regional Climate Models ◽

Climate Change Signal ◽

Climate Projections ◽

Data Product ◽

Temporal Features ◽

Stochastic Downscaling

AbstractIn applications of climate information, coarse-resolution climate projections commonly need to be downscaled to a finer grid. One challenge of this requirement is the modeling of subgrid variability and the spatial and temporal dependence at the finer scale. Here, a postprocessing procedure for temperature projections is proposed that addresses this challenge. The procedure employs statistical bias correction and stochastic downscaling in two steps. In the first step, errors that are related to spatial and temporal features of the first two moments of the temperature distribution at model scale are identified and corrected. Second, residual space–time dependence at the finer scale is analyzed using a statistical model, from which realizations are generated and then combined with an appropriate climate change signal to form the downscaled projection fields. Using a high-resolution observational gridded data product, the proposed approach is applied in a case study in which projections of two regional climate models from the Coordinated Downscaling Experiment–European Domain (EURO-CORDEX) ensemble are bias corrected and downscaled to a 1 km × 1 km grid in the Trøndelag area of Norway. A cross-validation study shows that the proposed procedure generates results that better reflect the marginal distributional properties of the data product and have better consistency in space and time when compared with empirical quantile mapping.

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Performance evaluation of bias adjustment methods for wave climate projections in the Mediterranean Sea

10.5194/egusphere-egu21-13557 ◽

2021 ◽

Author(s):

Andrea Lira Loarca ◽

Giovanni Besio

Keyword(s):

Bias Correction ◽

Temporal Variability ◽

Climate Models ◽

Regional Climate ◽

Regional Climate Models ◽

Wave Climate ◽

Mapping Method ◽

Climate Projections ◽

Bias Adjustment ◽

Distribution Mapping

Global and regional climate models are the primary tools to investigate the climate system response to different scenarios and therefore allow to make future projections of different atmospheric variables which are used as input for wave generation models to assess future wave climate. Adequate projections of future wave climate are needed in order to analyze climate change impacts and hazards in coastal areas such as flooding and erosion with waves being the predominant factor with varied temporal variability.&#160;Bias adjustment methods are commonly used for climate impact variables dealing with systematic errors (biases) found in global and regional climate models.&#160; While bias correction techniques are extended in the climate and hydrological impact modeling scientific communities, there is still a lack of consensus regarding their use in sea climate variables (Parker & Hill, 2017; Lemos et al, 2020; Lira-Loarca et at, 2021)In these work we assess the performance of different bias-adjustment methods such as the Empirical Gumbel Quantile Mapping (EGQM) method as a standard method which takes into the account the extreme values of the distribution takes, the Distribution Mapping method using Stationary Mixture Distributions (DM-stMix) allowing for a better representation of each variable in the mean regime and tails and the Distribution Mapping method using Non-Stationary Mixture Distributions (DM-nonstMix) as an improved methods which allows to take into account the temporal variability of wave climate according to different baseline periods such as monthly, seasonal, yearly and decadal. The performance of the different bias adjustment methods will be analyzed with particular interest on the futural temporal behavior of wave climate. The advantages and drawbacks of each bias adjustment method as well as their complexity will be discussed.&#160;References:<ul><li>Lemos, G., Menendez, M., Semedo, A., Camus, P., Hemer, M., Dobrynin, M., Miranda, P.M.A. (2020). On the need of bias correction methods for wave climate projections, Global and Planetary Change, 186, 103109.</li> <li>Lira-Loarca, A., Cobos, M., Besio, G., Baquerizo, A. (2021) Projected wave climate temporal variability due to climate change. Stoch Environ Res Risk Assess.</li> <li>Parker, K. & Hill, D.F. (2017) Evaluation of bias correction methods for wave modeling output, Ocean Modelling 110, 52-65</li> </ul>

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Influence of Bias Correction Methods on Simulated Köppen−Geiger Climate Zones in Europe

Climate ◽

10.3390/cli7020018 ◽

2019 ◽

Vol 7 (2) ◽

pp. 18 ◽

Cited By ~ 3

Author(s):

Beáta Szabó-Takács ◽

Aleš Farda ◽

Petr Skalák ◽

Jan Meitner

Keyword(s):

Bias Correction ◽

Climate Models ◽

Regional Climate ◽

Generalized Pareto Distribution ◽

Regional Climate Models ◽

Climate Classification ◽

Quantile Mapping ◽

Temperature Bias ◽

Simulated Precipitation ◽

Precipitation And Temperature

Our goal was to investigate the influence of bias correction methods on climate simulations over the European domain. We calculated the Köppen−Geiger climate classification using five individual regional climate models (RCM) of the ENSEMBLES project in the European domain during the period 1961−1990. The simulated precipitation and temperature data were corrected using the European daily high-resolution gridded dataset (E-OBS) observed data by five methods: (i) the empirical quantile mapping of precipitation and temperature, (ii) the quantile mapping of precipitation and temperature based on gamma and Generalized Pareto Distribution of precipitation, (iii) local intensity scaling, (iv) the power transformation of precipitation and (v) the variance scaling of temperature bias corrections. The individual bias correction methods had a significant effect on the climate classification, but the degree of this effect varied among the RCMs. Our results on the performance of bias correction differ from previous results described in the literature where these corrections were implemented over river catchments. We conclude that the effect of bias correction may depend on the region of model domain. These results suggest that distribution free bias correction approaches are the most suitable for large domain sizes such as the pan-European domain.

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A method to preserve trends in quantile mapping bias correction of climate modeled temperature

Earth System Dynamics ◽

10.5194/esd-8-889-2017 ◽

2017 ◽

Vol 8 (3) ◽

pp. 889-900 ◽

Cited By ~ 19

Author(s):

Manolis G. Grillakis ◽

Aristeidis G. Koutroulis ◽

Ioannis N. Daliakopoulos ◽

Ioannis K. Tsanis

Keyword(s):

Bias Correction ◽

Climate Models ◽

Regional Climate ◽

Regional Climate Models ◽

Quantile Mapping ◽

Temperature Bias ◽

Mapping Bias ◽

Temperature Signal ◽

Change Impact

Abstract. Bias correction of climate variables is a standard practice in climate change impact (CCI) studies. Various methodologies have been developed within the framework of quantile mapping. However, it is well known that quantile mapping may significantly modify the long-term statistics due to the time dependency of the temperature bias. Here, a method to overcome this issue without compromising the day-to-day correction statistics is presented. The methodology separates the modeled temperature signal into a normalized and a residual component relative to the modeled reference period climatology, in order to adjust the biases only for the former and preserve the signal of the later. The results show that this method allows for the preservation of the originally modeled long-term signal in the mean, the standard deviation and higher and lower percentiles of temperature. To illustrate the improvements, the methodology is tested on daily time series obtained from five Euro CORDEX regional climate models (RCMs).

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Evaluation of a New Statistical Method—TIN-Copula–for the Bias Correction of Climate Models’ Extreme Parameters

Atmosphere ◽

10.3390/atmos11030243 ◽

2020 ◽

Vol 11 (3) ◽

pp. 243 ◽

Cited By ~ 1

Author(s):

Georgia Lazoglou ◽

Christina Angnostopoulou ◽

Konstantia Tolika ◽

Gräler Benedikt

Keyword(s):

Statistical Method ◽

Bias Correction ◽

Climate Models ◽

Regional Climate ◽

Extreme Temperature ◽

Regional Climate Models ◽

Climate Conditions ◽

Copula Theory ◽

Mean Temperature ◽

Copula Method

During the last decades, global and regional climate models have been widely used for the estimation of future climate conditions. Unfortunately, the models’ estimated values present important biases relative to the observed values, especially when the estimations refer to extremes. Consequently, several researchers have studied several statistical methods that are able to minimize the biases between climate models and observed values. The present study evaluates a new statistical method for bias correction: The triangular irregular network (TIN)-copula method. This method is a combination of the triangular irregular networks and the copula theory. In the present research, the new method is applied to ten Mediterranean stations and its results are compared with the bias-corrected values of three other widely used methods: The delta, the scaling, and the empirical quantile mapping methods. The analysis was made for maximum mean temperature (TMX) and minimum mean temperature (TMN) as well as for extreme precipitation (R99). According to the results, the TIN-copula method is able to correct extreme temperature and precipitation values, estimated by regional climate models, with high accuracy. Additionally, it is proven that the TIN-copula method is a useful tool for bias correction as it presents several advantages compared with the other methods, and it is recommended for future works.

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Simulation performance of selected global and regional climate models for temperature and rainfall in some locations in India

Journal of Agrometeorology ◽

10.54386/jam.v22i4.443 ◽

2021 ◽

Vol 22 (4) ◽

pp. 407-418

Author(s):

SHWETA PANJWANI ◽

S. NARESH KUMAR ◽

LAXMI AHUJA

Keyword(s):

Climate Change ◽

Bias Correction ◽

Climate Models ◽

Regional Climate ◽

India Meteorological Department ◽

Regional Climate Models ◽

Climate Data ◽

Simulation Performance ◽

Climate Change Assessment ◽

Better Than

Global and regional climate models are reported to have inherent bias in simulating the observed climatology of a region. This bias of climate models is the major source of uncertainties in climate change impact assessments. Therefore, use of bias corrected simulated climate data is important. In this study, the bias corrected climate data for 30 years’ period (1976-2005) from selected common fourGCMs and RCMs for six Indian locations are compared with the respective observed data of India Meteorological Department. The analysis indicated that the RCMs performance is much better than GCMs after bias correction for minimum and maximum temperatures. Also, RCMs performance is better than GCMs in simulating extreme temperatures. However, the selected RCMs and GCMs are found to either over estimate or under estimate the rainfall despite bias correction and also overestimated the rainfall extremes for selected Indian locations. Based on the overall performance of four models for the six locations, it was found that the GFDL_ESM2M and NORESM1-M RCMs performed comparatively better than CSIRO and IPSL models. After bias correction, the RCMs could represent the observed climatology better than the GCMs. And these RCMs viz., GFDL_ESM2M and NORESM1-M can be usedindividually after bias correction in the climate change assessment studies for the selected regions.

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Quantile Mapping Bias Correction on Rossby Centre Regional Climate Models for Precipitation Analysis over Kenya, East Africa

Water ◽

10.3390/w12030801 ◽

2020 ◽

Vol 12 (3) ◽

pp. 801 ◽

Cited By ~ 3

Author(s):

Brian Ayugi ◽

Guirong Tan ◽

Niu Ruoyun ◽

Hassen Babaousmail ◽

Moses Ojara ◽

...

Keyword(s):

Bias Correction ◽

Impact Analysis ◽

Climate Models ◽

Regional Climate ◽

Regional Climate Models ◽

Absolute Error ◽

Intermediate Step ◽

Climate Data ◽

Quantile Mapping ◽

Mapping Bias

This study uses the quantile mapping bias correction (QMBC) method to correct the bias in five regional climate models (RCMs) from the latest output of the Rossby Center Climate Regional Model (RCA4) over Kenya. The outputs were validated using various scalar metrics such as root-mean-square difference (RMSD), mean absolute error (MAE), and mean bias. The study found that the QMBC algorithm demonstrates varying performance among the models in the study domain. The results show that most of the models exhibit reasonable improvement after corrections at seasonal and annual timescales. Specifically, the European Community Earth-System (EC-EARTH) and Commonwealth Scientific and Industrial Research Organization (CSIRO) models depict remarkable improvement as compared to other models. On the contrary, the Institute Pierre Simon Laplace Model CM5A-MR (IPSL-CM5A-MR) model shows little improvement across the rainfall seasons (i.e., March–May (MAM) and October–December (OND)). The projections forced with bias-corrected historical simulations tallied observed values demonstrate satisfactory simulations as compared to the uncorrected RCMs output models. This study has demonstrated that using QMBC on outputs from RCA4 is an important intermediate step to improve climate data before performing any regional impact analysis. The corrected models may be used in projections of drought and flood extreme events over the study area.

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