scholarly journals Assessing climate change projections in the Volta Basin using the CORDEX-Africa climate simulations and statistical bias-correction

2022 ◽  
pp. 100439
Author(s):  
Kofi A. Yeboah ◽  
Komlavi Akpoti ◽  
Amos T. Kabo-bah ◽  
Eric A. Ofosu ◽  
Ebenezer K. Siabi ◽  
...  
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Statistical Bias ◽  
Climate Change Projections ◽  
Volta Basin ◽  
Climate Simulations ◽  
Statistical Bias Correction

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.

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

2015 ◽  
Vol 12 (6) ◽  
pp. 5671-5701 ◽  
Author(s):  
A. Gobiet ◽  
M. Suklitsch ◽  
G. Heinrich
Keyword(s):  
Climate Change ◽  
Model Uncertainty ◽  
Bias Correction ◽  
Climate Change Signal ◽  
Climate Projections ◽  
Model Errors ◽  
Statistical Bias ◽  
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 change signals of climate simulations. We show that QM regionally alters the mean temperature climate change signal (CCS) derived from the ENSEMBLES multi-model dataset 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 has also 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.

Bias correction of extreme values of high resolution climate simulations for risk analysis.

2021 ◽  
Author(s):  
luis Augusto sanabria ◽  
Xuerong Qin ◽  
Jin Li ◽  
Robert Peter Cechet
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Extreme Values ◽  
The Body ◽  
Future Climate ◽  
Return Periods ◽  
Climate Conditions ◽  
Mitigation And Adaptation ◽  
Climate Simulations ◽  
The Impact

Abstract Most climatic models show that climate change affects natural perils' frequency and severity. Quantifying the impact of future climate conditions on natural hazard is essential for mitigation and adaptation planning. One crucial factor to consider when using climate simulations projections is the inherent systematic differences (bias) of the modelled data compared with observations. This bias can originate from the modelling process, the techniques used for downscaling of results, and the ensembles' intrinsic variability. Analysis of climate simulations has shown that the biases associated with these data types can be significant. Hence, it is often necessary to correct the bias before the data can be reliably used for further analysis. Natural perils are often associated with extreme climatic conditions. Analysing trends in the tail end of distributions are already complicated because noise is much more prominent than that in the mean climate. The bias of the simulations can introduce significant errors in practical applications. In this paper, we present a methodology for bias correction of climate simulated data. The technique corrects the bias in both the body and the tail of the distribution (extreme values). As an illustration, maps of the 50 and 100-year Return Period of climate simulated Forest Fire Danger Index (FFDI) in Australia are presented and compared against the corresponding observation-based maps. The results show that the algorithm can substantially improve the calculation of simulation-based Return Periods. Forthcoming work will focus on the impact of climate change on these Return Periods considering future climate conditions.

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