scholarly journals Uncertainties in Projected Rainfall over Brazil: The Role of Climate Model, Bias Correction and Emission Scenario

2020 ◽  
Author(s):  
Carolina de Moura ◽  
Jan Seibert ◽  
Miriam Mine
Keyword(s):  
Bias Correction ◽  
Climate Model ◽  
Emission Scenario ◽  
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 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

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

scholarly journals Impacts of bias nonstationarity of climate model outputs on hydrological simulations

2020 ◽  
Vol 51 (5) ◽  
pp. 925-941
Author(s):  
Yu Hui ◽  
Yuni Xu ◽  
Jie Chen ◽  
Chong-Yu Xu ◽  
Hua Chen
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Time Windows ◽  
Natural Climate Variability ◽  
Change Impact ◽  
Natural Climate ◽  
Precipitation And Temperature

Abstract Bias correction methods are based on the assumption of bias stationarity of climate model outputs. However, this assumption may not be valid, because of the natural climate variability. This study investigates the impacts of bias nonstationarity of climate models simulated precipitation and temperature on hydrological climate change impact studies. The bias nonstationarity is determined as the range of difference in bias over multiple historical periods with no anthropogenic climate change for four different time windows. The role of bias nonstationarity in future climate change is assessed using the signal-to-noise ratio as a criterion. The results show that biases of climate models simulated monthly and annual precipitation and temperature vary with time, especially for short time windows. The bias nonstationarity of precipitation plays a great role in future precipitation change, while the role of temperature bias is not important. The bias nonstationarity of climate model outputs is amplified when driving a hydrological model for hydrological simulations. The increase in the length of time window can mitigate the impacts of bias nonstationarity for streamflow projections. Thus, a long time period is suggested to be used to calibrate a bias correction method for hydrological climate change impact studies to reduce the influence of natural climate variability.

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