Projected changes of typhoon intensity in a regional climate model: Development of a machine learning bias correction scheme

2020 ◽  
Author(s):  
Jinkai Tan ◽  
Sheng Chen ◽  
Chia‐Ying Lee ◽  
Guangtao Dong ◽  
Wenyan Hu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Regional Climate Model ◽  
Bias Correction ◽  
Climate Model ◽  
Regional Climate ◽  
Model Development ◽  
Correction Scheme ◽  
Typhoon Intensity ◽  
Projected Changes

scholarly journals Precipitation ensembles conforming to natural variations derived from Regional Climate Model using a new bias correction scheme

2015 ◽  
Vol 12 (10) ◽  
pp. 10261-10287
Author(s):  
K. B. Kim ◽  
H.-H. Kwon ◽  
D. Han
Keyword(s):  
Regional Climate Model ◽  
Bias Correction ◽  
Climate Model ◽  
Regional Climate ◽  
Natural Variability ◽  
Ensemble Spread ◽  
Correction Scheme ◽  
Model Ensembles ◽  
Primary Advantage ◽  
Hadley Centre

Abstract. This study presents a novel bias correction scheme for Regional Climate Model (RCM) precipitation ensembles. A primary advantage of using model ensembles for climate change impact studies is that the uncertainties associated with the systematic error can be quantified through the ensemble spread. Currently, however, most of the conventional bias correction methods adjust all the ensemble members to one reference observation. As a result, the ensemble spread is degraded during bias correction. Since the observation is only one case of many possible realizations due to the climate natural variability, bias correction scheme should preserve ensemble spread within the bounds of natural variability (i.e. sampling uncertainty). To demonstrate the proposed methodology, an application to the Thorverton catchment in the southwest of England is presented. For the ensemble, 11-members from the Hadley Centre Regional Climate Model (HadRM3-PPE) Data are used and monthly bias correction has been done for the baseline time period from 1961 to 1990. In the typical conventional method, monthly mean precipitation of each of the ensemble members are nearly identical to the observation, i.e. the ensemble spread is removed. In contrast, the proposed method corrects the biases while maintain ensemble spread within the natural variability of observations.

scholarly journals Precipitation ensembles conforming to natural variations derived from a regional climate model using a new bias correction scheme

2016 ◽  
Vol 20 (5) ◽  
pp. 2019-2034 ◽  
Author(s):  
Kue Bum Kim ◽  
Hyun-Han Kwon ◽  
Dawei Han
Keyword(s):  
Regional Climate Model ◽  
Bias Correction ◽  
Climate Model ◽  
Regional Climate ◽  
Natural Variability ◽  
Ensemble Spread ◽  
Correction Scheme ◽  
Model Ensembles ◽  
Primary Advantage ◽  
Hadley Centre

Abstract. This study presents a novel bias correction scheme for regional climate model (RCM) precipitation ensembles. A primary advantage of using model ensembles for climate change impact studies is that the uncertainties associated with the systematic error can be quantified through the ensemble spread. Currently, however, most of the conventional bias correction methods adjust all the ensemble members to one reference observation. As a result, the ensemble spread is degraded during bias correction. Since the observation is only one case of many possible realizations due to the climate natural variability, a successful bias correction scheme should preserve the ensemble spread within the bounds of its natural variability (i.e. sampling uncertainty). To demonstrate a new bias correction scheme conforming to RCM precipitation ensembles, an application to the Thorverton catchment in the south-west of England is presented. For the ensemble, 11 members from the Hadley Centre Regional Climate Model (HadRM3-PPE) data are used and monthly bias correction has been done for the baseline time period from 1961 to 1990. In the typical conventional method, monthly mean precipitation of each of the ensemble members is nearly identical to the observation, i.e. the ensemble spread is removed. In contrast, the proposed method corrects the bias while maintaining the ensemble spread within the natural variability of the observations.

scholarly journals Projected Changes in Extreme Temperature and Precipitation Indices Over CORDEX-MENA Domain

Atmosphere ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 622
Author(s):  
Tugba Ozturk ◽  
F. Sibel Saygili-Araci ◽  
M. Levent Kurnaz
Keyword(s):  
Regional Climate Model ◽  
Radiative Forcing ◽  
Climate Model ◽  
Global Climate Model ◽  
Regional Climate ◽  
Climate Extreme ◽  
Pronounced Increase ◽  
Temperature And Precipitation ◽  
Projected Changes ◽  
Extreme Indices

In this study, projected changes in climate extreme indices defined by the Expert Team on Climate Change Detection and Indices were investigated over Middle East and North Africa. Changes in the daily maximum and minimum temperature- and precipitation- based extreme indices were analyzed for the end of the 21st century compared to the reference period 1971–2000 using regional climate model simulations. Regional climate model, RegCM4.4 was used to downscale two different global climate model outputs to 50 km resolution under RCP4.5 and RCP8.5 scenarios. Results generally indicate an intensification of temperature- and precipitation- based extreme indices with increasing radiative forcing. In particular, an increase in annual minimum of daily minimum temperatures is more pronounced over the northern part of Mediterranean Basin and tropics. High increase in warm nights and warm spell duration all over the region with a pronounced increase in tropics are projected for the period of 2071–2100 together with decrease or no change in cold extremes. According to the results, a decrease in total wet-day precipitation and increase in dry spells are expected for the end of the century.

scholarly journals Bias Correction, Quantile Mapping, and Downscaling: Revisiting the Inflation Issue

2013 ◽  
Vol 26 (6) ◽  
pp. 2137-2143 ◽  
Author(s):  
Douglas Maraun
Keyword(s):  
Regional Climate Model ◽  
Bias Correction ◽  
Climate Model ◽  
Daily Precipitation ◽  
Temporal Structure ◽  
Regional Climate ◽  
Quantile Mapping ◽  
Model Simulations ◽  
Scale Mismatch ◽  
Climate Model Simulations

Abstract Quantile mapping is routinely applied to correct biases of regional climate model simulations compared to observational data. If the observations are of similar resolution as the regional climate model, quantile mapping is a feasible approach. However, if the observations are of much higher resolution, quantile mapping also attempts to bridge this scale mismatch. Here, it is shown for daily precipitation that such quantile mapping–based downscaling is not feasible but introduces similar problems as inflation of perfect prognosis (“prog”) downscaling: the spatial and temporal structure of the corrected time series is misrepresented, the drizzle effect for area means is overcorrected, area-mean extremes are overestimated, and trends are affected. To overcome these problems, stochastic bias correction is required.

scholarly journals High-resolution Med-CORDEX regional climate model simulations for hydrological impact studies: a first evaluation in Morocco

2013 ◽  
Vol 10 (5) ◽  
pp. 5687-5737 ◽  
Author(s):  
Y. Tramblay ◽  
D. Ruelland ◽  
S. Somot ◽  
R. Bouaicha ◽  
E. Servat
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Regional Climate Model ◽  
Bias Correction ◽  
Hydrological Model ◽  
Climate Model ◽  
Regional Climate ◽  
Impact Studies ◽  
Hydrological Impact ◽  
Split Sample

Abstract. In the framework of the international CORDEX program, new regional climate model (RCM) simulations at high spatial resolutions are becoming available for the Mediterranean region (Med-CORDEX initiative). This study provides the first evaluation for hydrological impact studies of these high-resolution simulations. Different approaches are compared to analyze the climate change impacts on the hydrology of a catchment located in North Morocco, using a high-resolution RCM (ALADIN-Climate) from the Med-CORDEX initiative at two different spatial resolutions (50 km and 12 km) and for two different Radiative Concentration Pathway scenarios (RCP4.5 and RCP8.5). The main issues addressed in the present study are: (i) what is the impact of increased RCM resolution on present-climate hydrological simulations and on future projections? (ii) Are the bias-correction of the RCM model and the parameters of the hydrological model stationary and transferable to different climatic conditions? (iii) What is the climate and hydrological change signal based on the new Radiative Concentration Pathways scenarios (RCP4.5 and RCP8.5)? Results indicate that high resolution simulations at 12 km better reproduce the seasonal patterns, the seasonal distributions and the extreme events of precipitation. The parameters of the hydrological model, calibrated to reproduce runoff at the monthly time step over the 1984–2010 period, do not show a strong variability between dry and wet calibration periods in a differential split-sample test. However the bias correction of precipitation by quantile-matching does not give satisfactory results in validation using the same differential split-sample testing method. Therefore a quantile-perturbation method that does not rely on any stationarity assumption and produces ensembles of perturbed series of precipitation was introduced. The climate change signal under scenarios 4.5 and 8.5 indicates a decrease of respectively −30% to −57% in surface runoff for the mid-term (2041–2062), when for the same period the projections for precipitation are ranging between −15% and −19% and for temperature between +1.28°C and +1.87°C.

Sign in / Sign up

Export Citation Format

Share Document