scholarly journals Interactive comment on" Statistical bias correction for climate change impact on the basin scale precipitation in Sri Lanka, Philippines, Japan and Tunisia" by Nyunt, C.T., Koike, T. and Yamamoto, A."

2016 ◽  
Author(s):  
Anonymous
Keyword(s):  
Climate Change ◽  
Sri Lanka ◽  
Bias Correction ◽  
Climate Change Impact ◽  
Statistical Bias ◽  
Basin Scale ◽  
Change Impact ◽  
Statistical Bias Correction

scholarly journals Statistical bias correction for climate change impact on the basin scale precipitation in Sri Lanka, Philippines, Japan and Tunisia

2016 ◽  
Author(s):  
Cho Thanda Nyunt ◽  
Toshio Koike ◽  
Akio Yamamoto
Keyword(s):  
Sri Lanka ◽  
Bias Correction ◽  
Water Resource Management ◽  
Climate Model ◽  
Global Climate Model ◽  
Rain Gauge ◽  
Statistical Bias ◽  
Regional Variability ◽  
Basin Scale ◽  
Statistical Bias Correction

Abstract. We introduce a 3-step statistical bias correction method to solve global climate model (GCM) bias by determining regional variability through multi-model selection. This is a generalized method to assess imperfect GCMs that are unable to simulate distinct regional climate characteristics, either spatially or temporally. More remaining local bias is eliminated using an all-inclusive statistical bias correction addressing the major shortcomings of GCM precipitation. First, the multi-GCM choice is determined according to spatial correlation (Scorr) and root mean square error (RMSE) of regional and mesoscale climate variation in a comparison with global references. After multi-GCM selection, there are three major steps in the proposed bias correction, i.e., the generalized Pareto distribution (GPD) for extreme rainfall bias correction, ranking order statistics for wet and dry day frequency errors, and a two-parameter gamma distribution for monthly normal rainfall bias correction. Best-fit GPD parameters are resolved by the RMSE, a Hill plot, and mean excess function. The capability of the method is examined by application to four catchments in diverse climate regions. These are the Kalu Ganga (Sri Lanka), Pampanga, Angat and Kaliwa (Philippines), Yoshino (Japan), and Medjerda (Tunisia). The assumption of GCM stationary error in the future is verified by calibration in 1981–2000 and validation over 1961–1980 at two observation sites. The results show a favorable outcome. Overall performance of catchments was good for bias-corrected extreme events and inter-seasonal climatology, compared with observations. However, the suggested method has no intermediate grid scale between the GCM grid and observation points, and requires a well-distributed observed rain gauge network to reproduce a reasonable rainfall distribution over a target basin. The results of holistic bias correction provide reliable, quantitative and qualitative information for basin or national scale integrated water resource management.

scholarly journals Is bias correction of Regional Climate Model (RCM) simulations possible for non-stationary conditions?

2012 ◽  
Vol 9 (11) ◽  
pp. 12765-12795 ◽  
Author(s):  
C. Teutschbein ◽  
J. Seibert
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Climate Change Impact ◽  
Climate Model ◽  
Regional Climate ◽  
Climate Conditions ◽  
Impact Studies ◽  
Split Sample ◽  
Sample Test ◽  
Change Impact

Abstract. In hydrological climate-change impact studies, Regional Climate Models (RCMs) are commonly used to transfer large-scale Global Climate Model (GCM) data to smaller scales and to provide more detailed regional information. However, there are often considerable biases in RCM simulations, which have led to the development of a number of bias correction approaches to provide more realistic climate simulations for impact studies. Bias correction procedures rely on the assumption that RCM biases do not change over time, because correction algorithms and their parameterizations are derived for current climate conditions and assumed to apply also for future climate conditions. This underlying assumption of bias stationarity is the main concern when using bias correction procedures. It is in principle not possible to test whether this assumption is actually fulfilled for future climate conditions. In this study, however, we demonstrate that it is possible to evaluate how well bias correction methods perform for conditions different from those used for calibration. For five Swedish catchments, several time series of RCM simulated precipitation and temperature were obtained from the ENSEMBLES data base and different commonly-used bias correction methods were applied. We then performed a differential split-sample test by dividing the data series into cold and warm respective dry and wet years. This enabled us to evaluate the performance of different bias correction procedures under systematically varying climate conditions. The differential split-sample test resulted in a large spread and a clear bias for some of the correction methods during validation years. More advanced correction methods such as distribution mapping performed relatively well even in the validation period, whereas simpler approaches resulted in the largest deviations and least reliable corrections for changed conditions. Therefore, we question the use of simple bias correction methods such as the widely used delta-change approach and linear scaling for RCM-based climate-change impact studies and recommend using higher-skill bias correction methods.

scholarly journals Converting Climate Change Gridded Daily Rainfall to Station Daily Rainfall—A Case Study at Zengwen Reservoir

Water ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 1516
Author(s):  
Tse-Yu Teng ◽  
Tzu-Ming Liu ◽  
Yu-Shiang Tung ◽  
Ke-Sheng Cheng
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
Bias Correction ◽  
Climate Change Impact ◽  
Daily Rainfall ◽  
Correction Method ◽  
Two Stage ◽  
Climate Change Impact Assessment ◽  
Daily Data ◽  
Change Impact

With improvements in data quality and technology, the statistical downscaling data of General Circulation Models (GCMs) for climate change impact assessment have been refined from monthly data to daily data, which has greatly promoted the data application level. However, there are differences between GCM downscaling daily data and rainfall station data. If GCM data are directly used for hydrology and water resources assessment, the differences in total amount and rainfall intensity will be revealed and may affect the estimates of the total amount of water resources and water supply capacity. This research proposes a two-stage bias correction method for GCM data and establishes a mechanism for converting grid data to station data. Five GCMs were selected from 33 GCMs, which were ranked by rainfall simulation performance from a baseline period in Taiwan. The watershed of the Zengwen Reservoir in southern Taiwan was selected as the study area for comparison of the three different bias correction methods. The results reveal that the method with the wet-day threshold optimized by objective function with observation rainfall wet days had the best result. Error was greatly reduced in the hydrology model simulation with two-stage bias correction. The results show that the two-stage bias correction method proposed in this study can be used as an advanced method of data pre-processing in climate change impact assessment, which could improve the quality and broaden the extent of GCM daily data. Additionally, GCM ranking can be used by researchers in climate change assessment to understand the suitability of each GCM in Taiwan.

scholarly journals HESS Opinions "Should we apply bias correction to global and regional climate model data?"

2012 ◽  
Vol 16 (9) ◽  
pp. 3391-3404 ◽  
Author(s):  
U. Ehret ◽  
E. Zehe ◽  
V. Wulfmeyer ◽  
K. Warrach-Sagi ◽  
J. Liebert
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Climate Change Impact ◽  
Climate Model ◽  
End Users ◽  
Model Output ◽  
Uncertainty Range ◽  
Impact Studies ◽  
Regional Circulation ◽  
Change Impact

Abstract. Despite considerable progress in recent years, output of both global and regional circulation models is still afflicted with biases to a degree that precludes its direct use, especially in climate change impact studies. This is well known, and to overcome this problem, bias correction (BC; i.e. the correction of model output towards observations in a post-processing step) has now become a standard procedure in climate change impact studies. In this paper we argue that BC is currently often used in an invalid way: it is added to the GCM/RCM model chain without sufficient proof that the consistency of the latter (i.e. the agreement between model dynamics/model output and our judgement) as well as the generality of its applicability increases. BC methods often impair the advantages of circulation models by altering spatiotemporal field consistency, relations among variables and by violating conservation principles. Currently used BC methods largely neglect feedback mechanisms, and it is unclear whether they are time-invariant under climate change conditions. Applying BC increases agreement of climate model output with observations in hindcasts and hence narrows the uncertainty range of simulations and predictions without, however, providing a satisfactory physical justification. This is in most cases not transparent to the end user. We argue that this hides rather than reduces uncertainty, which may lead to avoidable forejudging of end users and decision makers. We present here a brief overview of state-of-the-art bias correction methods, discuss the related assumptions and implications, draw conclusions on the validity of bias correction and propose ways to cope with biased output of circulation models in the short term and how to reduce the bias in the long term. The most promising strategy for improved future global and regional circulation model simulations is the increase in model resolution to the convection-permitting scale in combination with ensemble predictions based on sophisticated approaches for ensemble perturbation. With this article, we advocate communicating the entire uncertainty range associated with climate change predictions openly and hope to stimulate a lively discussion on bias correction among the atmospheric and hydrological community and end users of climate change impact studies.

scholarly journals The effect of empirical-statistical correction of intensity-dependent model errors on the temperature climate change signal

2015 ◽  
Vol 19 (10) ◽  
pp. 4055-4066 ◽  
Author(s):  
A. Gobiet ◽  
M. Suklitsch ◽  
G. Heinrich
Keyword(s):  
Climate Change ◽  
Model Uncertainty ◽  
Bias Correction ◽  
Climate Change Signal ◽  
Model Errors ◽  
Statistical Bias ◽  
Data Set ◽  
Dependent Model ◽  
Temperature Climate ◽  
Statistical Bias Correction

Abstract. This study discusses the effect of empirical-statistical bias correction methods like quantile mapping (QM) on the temperature change signals of climate simulations. We show that QM regionally alters the mean temperature climate change signal (CCS) derived from the ENSEMBLES multi-model data set by up to 15 %. Such modification is currently strongly discussed and is often regarded as deficiency of bias correction methods. However, an analytical analysis reveals that this modification corresponds to the effect of intensity-dependent model errors on the CCS. Such errors cause, if uncorrected, biases in the CCS. QM removes these intensity-dependent errors and can therefore potentially lead to an improved CCS. A similar analysis as for the multi-model mean CCS has been conducted for the variance of CCSs in the multi-model ensemble. It shows that this indicator for model uncertainty is artificially inflated by intensity-dependent model errors. Therefore, QM also has the potential to serve as an empirical constraint on model uncertainty in climate projections. However, any improvement of simulated CCSs by empirical-statistical bias correction methods can only be realized if the model error characteristics are sufficiently time-invariant.

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