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

Climate ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 18 ◽  
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.

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

2017 ◽  
Vol 8 (3) ◽  
pp. 889-900 ◽  
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).

scholarly journals Projecting Health Impacts of Future Temperature: A Comparison of Quantile-Mapping Bias-Correction Methods

2021 ◽  
Vol 18 (4) ◽  
pp. 1992
Author(s):  
Weijia Qian ◽  
Howard H. Chang
Keyword(s):  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Warm Season ◽  
Climate Science ◽  
Regional Climate Models ◽  
Health Impact ◽  
Quantile Mapping ◽  
Ed Visits

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.

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

Water ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 801 ◽  
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.

scholarly journals Bias correction and downscaling of snow cover fraction projections from regional climate models using remote sensing for the European Alps

2021 ◽  
Author(s):  
Michael Matiu ◽  
Florian Hanzer
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Bias Correction ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
European Alps ◽  
Quantile Mapping ◽  
Snow Cover Fraction ◽  
Delta Change

Abstract. Mountain seasonal snow cover is undergoing major changes due to global climate change. Assessments of future snow cover usually rely on physical based models, and often include post-processed meteorology. Alternatively, here we propose a direct statistical adjustment of snow cover fraction from regional climate models by using long-term remote sensing observations. We compared different bias correction routines (delta change, quantile mapping, and quantile delta mapping) and explore a downscaling based on historical observations for the Greater Alpine Region in Europe. All bias correction methods adjust for systematic biases, for example due to topographic smoothing, and reduce model spread in future projections. Averaged over the study region and whole year, snow cover fraction decreases from 12.5 % in 2000–2020 to 10.4 (8.9, 11.5; model spread) % in 2071–2100 under RCP2.6, and 6.4 (4.1, 7.8) % under RCP8.5. In addition, changes strongly depended on season and altitude. The comparison of the statistical downscaling to a high-resolution physical based model yields similar results for the altitude range covered by the climate models, but different altitudinal gradients of change above and below. We found trend-preserving bias correction methods (delta change, quantile delta mapping) more plausible for snow cover fraction than quantile mapping. Downscaling showed potential but requires further research. Since climate model and remote sensing observations are available globally, the proposed methods are potentially widely applicable, but are limited to snow cover fraction only.

scholarly journals How does bias correction of regional climate model precipitation affect modelled runoff?

2015 ◽  
Vol 19 (2) ◽  
pp. 711-728 ◽  
Author(s):  
J. Teng ◽  
N. J. Potter ◽  
F. H. S. Chiew ◽  
L. Zhang ◽  
B. Wang ◽  
...  
Keyword(s):  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Wrf Model ◽  
Regional Climate Models ◽  
Mapping Method ◽  
Future Climate Change ◽  
Runoff Generation ◽  
Simulated Precipitation

Abstract. Many studies bias correct daily precipitation from climate models to match the observed precipitation statistics, and the bias corrected data are then used for various modelling applications. This paper presents a review of recent methods used to bias correct precipitation from regional climate models (RCMs). The paper then assesses four bias correction methods applied to the weather research and forecasting (WRF) model simulated precipitation, and the follow-on impact on modelled runoff for eight catchments in southeast Australia. Overall, the best results are produced by either quantile mapping or a newly proposed two-state gamma distribution mapping method. However, the differences between the methods are small in the modelling experiments here (and as reported in the literature), mainly due to the substantial corrections required and inconsistent errors over time (non-stationarity). The errors in bias corrected precipitation are typically amplified in modelled runoff. The tested methods cannot overcome limitations of the RCM in simulating precipitation sequence, which affects runoff generation. Results further show that whereas bias correction does not seem to alter change signals in precipitation means, it can introduce additional uncertainty to change signals in high precipitation amounts and, consequently, in runoff. Future climate change impact studies need to take this into account when deciding whether to use raw or bias corrected RCM results. Nevertheless, RCMs will continue to improve and will become increasingly useful for hydrological applications as the bias in RCM simulations reduces.

scholarly journals Nonparametric quantile mapping using the response surface method – bias correction of daily precipitation

2017 ◽  
Vol 9 (3) ◽  
pp. 525-539
Author(s):  
Taeho Bong ◽  
Young-Hwan Son ◽  
Seung-Hwan Yoo ◽  
Sye-Woon Hwang
Keyword(s):  
Response Surface ◽  
Response Surface Method ◽  
Bias Correction ◽  
Climate Models ◽  
Regional Climate ◽  
Mapping Method ◽  
Quantile Mapping ◽  
The Past ◽  
Surface Method ◽  
Simulated Precipitation

Abstract Currently, regional climate models are widely used to provide projections of how climate may change locally. However, they sometimes have a spatial resolution that is too coarse to provide an appropriate resolution for the local scale. In this paper, a new nonparametric quantile mapping method based on the response surface method was proposed to perform an efficient and robust bias correction. The proposed method was applied to correct the bias of the simulated precipitation for the period of 1976–2005, and the performance and uncertainty were subsequently assessed. As a result, the proposed method was effectively able to reduce the biases of the entire distribution range, and to predict new extreme precipitation. The future precipitation based on representative concentration pathways of RCP 4.5 and 8.5 were bias corrected using the proposed method, and the impacts of the climate scenarios were compared. It was found that the average annual precipitations increased compared to the past for both scenarios, and they tended to increase over time in the three studied areas. The uncertainty of future precipitation was slightly higher than in the past observation period.

scholarly journals Comparing Bias Correction Methods Used in Downscaling Precipitation and Temperature from Regional Climate Models: A Case Study from the Kaidu River Basin in Western China

Water ◽  
2018 ◽  
Vol 10 (8) ◽  
pp. 1046 ◽  
Author(s):  
Min Luo ◽  
Tie Liu ◽  
Fanhao Meng ◽  
Yongchao Duan ◽  
Amaury Frankl ◽  
...  
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Bias Correction ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Hydrological Modelling ◽  
Western China ◽  
Kaidu River ◽  
Precipitation And Temperature

The systemic biases of Regional Climate Models (RCMs) impede their application in regional hydrological climate-change effects analysis and lead to errors. As a consequence, bias correction has become a necessary prerequisite for the study of climate change. This paper compares the performance of available bias correction methods that focus on the performance of precipitation and temperature projections. The hydrological effects of these correction methods are evaluated by the modelled discharges of the Kaidu River Basin. The results show that all used methods improve the performance of the original RCM precipitation and temperature simulations across a number of levels. The corrected results obtained by precipitation correction methods demonstrate larger diversities than those produced by the temperature correction methods. The performance of hydrological modelling is highly influenced by the choice of precipitation correction methods. Furthermore, no substantial differences can be identified from the results of the temperature-corrected methods. The biases from input data are often greater from the works of hydrological modelling. The suitability of these approaches depends upon the regional context and the RCM model, while their application procedure and a number of results can be adapted from region to region.

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

2020 ◽  
Author(s):  
Brian Ayugi ◽  
Guirong Tan ◽  
Rouyun Niu ◽  
Hassen Babaousmail ◽  
Moses Ojara ◽  
...  
Keyword(s):  
East Africa ◽  
Bias Correction ◽  
Impact Analysis ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Intermediate Step ◽  
Quantile Mapping ◽  
Mapping Bias

Accurate assessment and projections of extreme climate events requires the use of climate datasets with no or minimal error. This study uses quantile mapping bias correction (QMBC) method to correct the bias of five Regional Climate Models (RCMs) from the latest output of Rossby Climate Model Center (RCA4) over Kenya, East Africa. 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 demonstrate varying performance among the models in the study domain. The results show that most of the models exhibit significant 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 exemplary improvement as compared to other models. On the contrary, the Institute Pierre Simon Laplace Model CM5A-MR (IPSL-CM5A-MR) model show little improvement across various timescales (i.e. March-April-May (MAM) and October-November-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 prior to performing any regional impact analysis. The corrected models can be used for projections of drought and flood extreme events over the study area. This study analysis is crucial from the sustainable planning for adaptation and mitigation of climate change and disaster risk reduction perspective.

scholarly journals How does bias correction of RCM precipitation affect modelled runoff?

2014 ◽  
Vol 11 (9) ◽  
pp. 10683-10724
Author(s):  
J. Teng ◽  
N. J. Potter ◽  
F. H. S. Chiew ◽  
L. Zhang ◽  
J. Vaze ◽  
...  
Keyword(s):  
Bias Correction ◽  
Climate Models ◽  
Regional Climate ◽  
Wrf Model ◽  
Regional Climate Models ◽  
Mapping Method ◽  
Future Climate Change ◽  
Runoff Generation ◽  
Simulated Precipitation ◽  
The Difference

Abstract. Many studies bias correct daily precipitation from climate models to match the observed precipitation statistics, and the bias corrected data are then used for various modelling applications. This paper presents a review of recent methods used to bias correct precipitation from regional climate models (RCMs). The paper then assesses four bias correction methods applied to the weather research and forecasting (WRF) model simulated precipitation, and the follow-on impact on modelled runoff for eight catchments in southeast Australia. Overall, the best results are produced by either quantile mapping or a newly proposed two-state gamma distribution mapping method. However, the difference between the tested methods is small in the modelling experiments here (and as reported in the literature), mainly because of the substantial corrections required and inconsistent errors over time (non-stationarity). The errors remaining in bias corrected precipitation are typically amplified in modelled runoff. The tested methods cannot overcome limitation of RCM in simulating precipitation sequence, which affects runoff generation. Results further show that whereas bias correction does not seem to alter change signals in precipitation means, it can introduce additional uncertainty to change signals in high precipitation amounts and, consequently, in runoff. Future climate change impact studies need to take this into account when deciding whether to use raw or bias corrected RCM results. Nevertheless, RCMs will continue to improve and will become increasingly useful for hydrological applications as the bias in RCM simulations reduces.

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