Assessing distribution-based climate model bias correction methods over an alpine domain: added value and limitations

2016 ◽  
Vol 37 (5) ◽  
pp. 2633-2653 ◽  
Author(s):  
Martin A. Ivanov ◽  
Sven Kotlarski
Keyword(s):  
Bias Correction ◽  
Climate Model ◽  
Added Value ◽  
Model Bias ◽  
Climate Model Bias

Climate model bias correction for nonstationary conditions

2020 ◽  
Vol 47 (3) ◽  
pp. 326-336
Author(s):  
Mohammad Madani ◽  
Vinod Chilkoti ◽  
Tirupati Bolisetti ◽  
Rajesh Seth
Keyword(s):  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Bias Reduction ◽  
Historical Period ◽  
Monthly Precipitation ◽  
Model Bias ◽  
New Methods ◽  
Climate Model Bias

In most of the climate change impact assessment studies, climate model bias is considered to be stationary between the control and scenario periods. Few methods are found in the literature that addresses the issue of nonstationarity in correcting the bias. To overcome the shortcomings reported in these approaches, three new methods of bias correction (NBC_μ, NBC_σ, and NBC_bs) are presented. The methods are improvised versions of previous techniques relying on distribution mapping. The methods are tested using split sample approach over 50-year historical period for nine climate stations in Ontario, using six regional climate models. The average bias reduction improvement by new methods, in mean daily and monthly precipitation, was found to be 73.9%, 74.3%, and 77.4%, respectively, higher than that obtained by the previous methods (eQM 67.7% and CNCDFm_NP 64.1%). Thus, the methods are found to be more effective in accounting for nonstationarity in the model bias.

scholarly journals Seasonal Prediction of Surface Air Temperature across Vietnam Using the Regional Climate Model Version 4.2 (RegCM4.2)

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Tan Phan Van ◽  
Hiep Van Nguyen ◽  
Long Trinh Tuan ◽  
Trung Nguyen Quang ◽  
Thanh Ngo-Duc ◽  
...  
Keyword(s):  
Air Temperature ◽  
Bias Correction ◽  
Climate Model ◽  
Regional Climate ◽  
Surface Air Temperature ◽  
Seasonal Prediction ◽  
Ensemble Prediction ◽  
Model Bias ◽  
Skill Scores ◽  
Model Predictions

To investigate the ability of dynamical seasonal climate predictions for Vietnam, the RegCM4.2 is employed to perform seasonal prediction of 2 m mean (T2m), maximum (Tx), and minimum (Tn) air temperature for the period from January 2012 to November 2013 by downscaling the NCEP Climate Forecast System (CFS) data. For model bias correction, the model and observed climatology is constructed using the CFS reanalysis and observed temperatures over Vietnam for the period 1980–2010, respectively. The RegCM4.2 forecast is run four times per month from the current month up to the next six months. A model ensemble prediction initialized from the current month is computed from the mean of the four runs within the month. The results showed that, without any bias correction (CTL), the RegCM4.2 forecast has very little or no skill in both tercile and value predictions. With bias correction (BAS), model predictions show improved skill. The experiment in which the results from the BAS experiment are further successively adjusted (SUC) with model bias at one-month lead time of the previous run showed further improvement compared to CTL and BAS. Skill scores of the tercile probability forecasts were found to exceed 0.3 for most of the target months.

scholarly journals Improved Estimates of the European Winter Windstorm Climate and the Risk of Reinsurance Loss Using Climate Model Data

2010 ◽  
Vol 49 (10) ◽  
pp. 2092-2120 ◽  
Author(s):  
Paul M. Della-Marta ◽  
Mark A. Liniger ◽  
Christof Appenzeller ◽  
David N. Bresch ◽  
Pamela Köllner-Heck ◽  
...  
Keyword(s):  
Bias Correction ◽  
Climate Model ◽  
Generalized Pareto Distribution ◽  
Sampling Technique ◽  
Reanalysis Data ◽  
Added Value ◽  
Extreme Value Analysis ◽  
High Threshold ◽  
Ensemble Forecasts ◽  
Value Analysis

Abstract Current estimates of the European windstorm climate and their associated losses are often hampered by either relatively short, coarse resolution or inhomogeneous datasets. This study tries to overcome some of these shortcomings by estimating the European windstorm climate using dynamical seasonal-to-decadal (s2d) climate forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF). The current s2d models have limited predictive skill of European storminess, making the ensemble forecasts ergodic samples on which to build pseudoclimates of 310–396 yr in length. Extended winter (October–April) windstorm climatologies are created using scalar extreme wind indices considering only data above a high threshold. The method identifies up to 2363 windstorms in s2d data and up to 380 windstorms in the 40-yr ECMWF Re-Analysis (ERA-40). Classical extreme value analysis (EVA) techniques are used to determine the windstorm climatologies. Differences between the ERA-40 and s2d windstorm climatologies require the application of calibration techniques to result in meaningful comparisons. Using a combined dynamical–statistical sampling technique, the largest influence on ERA-40 return period (RP) uncertainties is the sampling variability associated with only 45 seasons of storms. However, both maximum likelihood (ML) and L-moments (LM) methods of fitting a generalized Pareto distribution result in biased parameters and biased RP at sample sizes typically obtained from 45 seasons of reanalysis data. The authors correct the bias in the ML and LM methods and find that the ML-based ERA-40 climatology overestimates the RP of windstorms with RPs between 10 and 300 yr and underestimates the RP of windstorms with RPs greater than 300 yr. A 50-yr event in ERA-40 is approximately a 40-yr event after bias correction. Biases in the LM method result in higher RPs after bias correction although they are small when compared with those of the ML method. The climatologies are linked to the Swiss Reinsurance Company (Swiss Re) European windstorm loss model. New estimates of the risk of loss are compared with those from historical and stochastically generated windstorm fields used by Swiss Re. The resulting loss-frequency relationship matches well with the two independently modeled estimates and clearly demonstrates the added value by using alternative data and methods, as proposed in this study, to estimate the RP of high RP losses.

scholarly journals Climate model bias correction and the role of timescales

2010 ◽  
Vol 7 (5) ◽  
pp. 7863-7898 ◽  
Author(s):  
J. O. Haerter ◽  
S. Hagemann ◽  
C. Moseley ◽  
C. Piani
Keyword(s):  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Correction Method ◽  
Statistical Properties ◽  
Statistical Bias ◽  
Negative Effects ◽  
Climate Model Bias ◽  
Corrected Data ◽  
Statistical Bias Correction

Abstract. It is well known that output from climate models cannot be used to force hydrological simulations without some form of preprocessing to remove the existing biases. In principle, statistical bias correction methodologies act on model output so the statistical properties of the corrected data match those of the observations. However the improvements to the statistical properties of the data are limited to the specific time scale of the fluctuations that are considered. For example, a statistical bias correction methodology for mean daily values might be detrimental to monthly statistics. Also, in applying bias corrections derived from present day to scenario simulations, an assumption is made of persistence of the bias over the largest timescales. We examine the effects of mixing fluctuations on different time scales and suggest an improved statistical methodology, referred to here as a cascade bias correction method, that eliminates, or greatly reduces, the negative effects.

Plausible effect of climate model bias on abrupt climate change simulations in Atlantic sector

2011 ◽  
Vol 58 (17-18) ◽  
pp. 1904-1913 ◽  
Author(s):  
Xiuquan Wan ◽  
Ping Chang ◽  
Charles S. Jackson ◽  
Link Ji ◽  
Mingkui Li
Keyword(s):  
Climate Change ◽  
Climate Model ◽  
Abrupt Climate Change ◽  
Model Bias ◽  
Atlantic Sector ◽  
Climate Model Bias

Comparing the Lasso Predictor-Selection and Regression Method with Classical Approaches of Precipitation Bias Adjustment in Decadal Climate Predictions

2020 ◽  
Vol 148 (10) ◽  
pp. 4339-4351
Author(s):  
Jingmin Li ◽  
Felix Pollinger ◽  
Heiko Paeth
Keyword(s):  
Bias Correction ◽  
Large Scale ◽  
Transfer Functions ◽  
Classical Model ◽  
Phase Relationship ◽  
Regression Method ◽  
Added Value ◽  
Model Bias ◽  
Shrinkage Factor ◽  
Bias Adjustment

AbstractIn this study, we investigate the technical application of the regularized regression method Lasso for identifying systematic biases in decadal precipitation predictions from a high-resolution regional climate model (CCLM) for Europe. The Lasso approach is quite novel in climatological research. We apply Lasso to observed precipitation and a large number of predictors related to precipitation derived from a training simulation, and transfer the trained Lasso regression model to a virtual forecast simulation for testing. Derived predictors from the model include local predictors at a given grid box and EOF predictors that describe large-scale patterns of variability for the same simulated variables. A major added value of the Lasso function is the variation of the so-called shrinkage factor and its ability in eliminating irrelevant predictors and avoiding overfitting. Among 18 different settings, an optimal shrinkage factor is identified that indicates a robust relationship between predictand and predictors. It turned out that large-scale patterns as represented by the EOF predictors outperform local predictors. The bias adjustment using the Lasso approach mainly improves the seasonal cycle of the precipitation prediction and, hence, improves the phase relationship and reduces the root-mean-square error between model prediction and observations. Another goal of the study pertains to the comparison of the Lasso performance with classical model output statistics and with a bivariate bias correction approach. In fact, Lasso is characterized by a similar and regionally higher skill than classical approaches of model bias correction. In addition, it is computationally less expensive. Therefore, we see a large potential for the application of the Lasso algorithm in a wider range of climatological applications when it comes to regression-based statistical transfer functions in statistical downscaling and model bias adjustment.

scholarly journals Adjusting climate model bias for agricultural impact assessment: How to cut the mustard

2019 ◽  
Vol 13 ◽  
pp. 65-69 ◽  
Author(s):  
S. Galmarini ◽  
A.J. Cannon ◽  
A. Ceglar ◽  
O.B. Christensen ◽  
N. de Noblet-Ducoudré ◽  
...  
Keyword(s):  
Impact Assessment ◽  
Climate Model ◽  
Model Bias ◽  
Climate Model Bias ◽  
Agricultural Impact
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