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Demonstrating the bias-correction impact on regional climate model (RegCM) over the Democratic People’s Republic of Korea: Implication for temperature and precipitation

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Abstract

This study tests the nonlinear bias-correction method of regional climate model (RCM) outputs using observations from meteorological stations in the Democratic People’s Republic of Korea (DPRK). The correction technique applies to both monthly precipitation and temperature simulated by the RCM, RegCM4.7 for the period 1991–2011. The correction parameters of both monthly precipitation and temperature are investigated for different periods within the observational time to assess the stability of this correction method over time. The results show that this nonlinear bias-correction approach improves significantly the accuracy of the RCM mean monthly precipitation and temperature as well as the values of probability distribution over the study area. The bias-corrected forecast has significant improvement for both monthly precipitation and temperature from about 18.9 mm and 0.7°C of mean absolute error (MAE), and about 16 mm and 0.3°C of the root mean square error over the uncorrected ones, respectively. The correlation coefficient, skill score and the MAE skill are improved significantly over the study region. The successful performance of the bias-correction technique used in this study during the calibration and validation periods will be helped to capture more accurate current climate conditions and to predict future climate change over the DPRK.

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References

  • Acharya N, Chattopadhyay S, Mohanty U C, Dasha S K and Sahooc L N 2013 On the bias correction of general circulation model output for Indian summer monsoon; J. Meteorol. Appl. 20(3) 349–356.

    Article  Google Scholar 

  • Ajaaj A A, Mishra A K and Khan A A 2016 Comparison of BIAS correction techniques for GPCC rainfall data in semi-arid climate; Stoch. Environ. Res. Risk Assess. 30 1659–1675.

    Article  Google Scholar 

  • Argüeso D, Evans J P and Fita L 2013 Precipitation bias correction of very high resolution regional climate models; Hydrol. Earth Syst. Sci. 17 4379–4388.

    Article  Google Scholar 

  • Berg P, Feldmann H and Panitz H 2012 Bias correction of high resolution RCM data; J. Hydrol. 448–449 80–92.

    Article  Google Scholar 

  • Bordoy R and Burlando P 2013 Bias correction of regional climate model simulations in a region of complex orography; J. Appl. Meteorol. Climatol. 52 82–101.

    Article  Google Scholar 

  • Christensen J H, Boberg F, Christensen O B and LucasPicher P 2008 On the need for bias correction of regional climate change projections of temperature and precipitation; Geophys. Res. Lett. 35 L20709.

    Article  Google Scholar 

  • Dobler A and Ahrens B 2008 Precipitation by a regional climate model and bias correction in Europe and south Asia; Meteorol. Z. 17 499–509.

    Article  Google Scholar 

  • Dosio A and Paruolo P 2011 Bias correction of the ensembles high resolution climate change projections for use by impact models: Evaluation on the present climate; J. Geophys. Res. 116(117) 127–135.

    Google Scholar 

  • Durai V R and Rashmi B 2014 Location specific forecasting of maximum and minimum temperatures over India by using the statistical bias corrected output of global forecasting system; J. Earth Syst. Sci. 123(5) 1171–1195.

    Article  Google Scholar 

  • Ehret U, Zehe E, Wulfmeyer V, Warrach-Sagi K and Liebert J 2012 HESS opinions ‘Should we apply bias correction to global and regional climate model data?’; Hydrol. Earth Syst. Sci. 16 3391–3404.

    Article  Google Scholar 

  • Emanuel K A 1991 A scheme for representing cumulus convection in large-scale models; J. Atmos. Sci. 48 2313–2335.

    Article  Google Scholar 

  • Evans J P and Westra S 2012 Investigating the mechanisms of diurnal rainfall variability using a regional climate model; J. Clim. 25 7232–7247.

    Article  Google Scholar 

  • Feser F, Rockel B, von Storch H, Winterfeldt J and Zahn M 2011 Regional climate models add value to global model data: A review and selected examples; Bull. Am. Meteorol. Soc. 92 1181–1192.

    Article  Google Scholar 

  • Fiseha B M, Setegn S G, Melesse A M, Volpi E and Fiori A 2014 Impact of climate change on the hydrology of Upper Tiber river basin using bias corrected regional climate model; Water Resour. Manag. 28 1327–1343.

    Article  Google Scholar 

  • Gao X J, Shi Y and Giorgi F 2016 Comparison of convective parameterizations in RegCM4 experiments over China with CLM as the land surface model; Atmos. Ocean. Sci. Lett. 9(4) 246–254.

    Article  Google Scholar 

  • Ghosh S, Bhatla R, Mall R K, Srivastava P K and Sahai A K 2019 Aspect of ECMWF downscaled regional climate modeling in simulating Indian summer monsoon rainfall and dependencies on lateral boundary conditions; Theor. Appl. Climatol. 135 1559–1581.

    Article  Google Scholar 

  • Giorgi F, Coppola E, Solmon F and Marriott 2012 RegCM4: Model description and preliminary tests over multiple CORDEX domains; Clim. Res. 52 7–29.

    Article  Google Scholar 

  • Grell G 1994 Prognostic evaluation of assumptions used by cumulus parameterizations; Mon. Weather Rev. 121 764–787.

    Article  Google Scholar 

  • Gudmundsson L, Bremnes J, Haugen J and Engen-Skaugen T 2012 Downscaling RCM precipitation to the station scale using statistical transformations – A comparison of methods; Hydrol. Earth Syst. Sci. 16(9) 3383–3390.

    Article  Google Scholar 

  • Haerter J O, Hagemann S, Moseley C and Piani C 2011 Climate model bias correction and the role of timescales; Hydrol. Earth Syst. Sci. 15 1065–1079.

    Article  Google Scholar 

  • Hay L E, Wilby R J L and Leavesley G H 2000 A comparison of delta change and downscaled GCM scenarios for three mountainous basins in the United States; J. Am. Water Resour. Assoc. 36 387–397.

    Article  Google Scholar 

  • Huang D L and Gao S B 2017 Impact of different reanalysis data on WRF dynamical downscaling over China; Atmos. Res. 200 25–35.

    Article  Google Scholar 

  • Hurkmans R, Terink W, Uijlenhoet R, Torfs P, Jakob D and Troch P 2010 Changes in streamflow dynamics in the Rhine basin under three high-resolution regional climate scenarios; J. Clim. 23 679–699.

    Article  Google Scholar 

  • Ines A V M and Hansen J W 2006 Bias correction of daily GCM rainfall for crop simulation studies; J. Agric. For. Meteorol. 138 44–53.

    Article  Google Scholar 

  • Jiang D B, Tian Z P and Lang X M 2016 Reliability of climate models for China through the IPCC third to fifth assessment reports; Int. J. Climatol. 36(3) 1114–1133.

    Article  Google Scholar 

  • Kiehl J T, Hack J J, Bonan G B, Boville B A, Breigleb B P, Williamson D and Rasch P 1996 Description of the NCAR community climate model (CCM3); Technical report NCAR/TN-430 + STR, National Center for Atmospheric Research.

  • Lafon T, Dadson S, Buysa G and Prudhommea C 2013 Bias correction of daily precipitation simulated by a regional climate model: A comparison of methods; Int. J. Climatol. 33 1367–1381.

    Article  Google Scholar 

  • Leander R and Buishand T 2007 Resampling of regional climate model output for the simulation of extreme river flows; J. Hydrol. 332 487–496.

    Article  Google Scholar 

  • Lenderink G, Buishand A and van Deursen W 2007 Estimates of future discharges of the river Rhine using two scenario methodologies: Direct versus delta approach; Hydrol. Earth Syst. Sci. 11 1145–1159.

    Article  Google Scholar 

  • Li W K, Guo W D, Xue Y K, Fu C B and Qiu B 2016 Sensitivity of a regional climate model to land surface parameterization schemes for east Asian summer monsoon simulation; Clim. Dyn. 47(7–8) 2293–2308.

    Article  Google Scholar 

  • Maurya R K S, Sinha P, Mohanty M R and Mohanty U C 2018 RegCM4 model sensitivity to horizontal resolution and domain size in simulating the Indian summer monsoon; Atmos. Res. 210 15–33.

    Article  Google Scholar 

  • Mishra A K and Dwivedi S 2019 Assessment of convective parameterization schemes over the Indian subcontinent using a regional climate model; Theor. Appl. Climatol. 137(3–4) 1747–1764.

    Article  Google Scholar 

  • Mishra A K, Kumar P, Dubey A K, Javed A, Saharwardi M S, Sein D V, Martyanov S and Jacob D 2021 Impact of horizontal resolution on monsoon precipitation for CORDEX-south Asia: A regional earth system model assessment; Atmos. Res. 14 105681.

    Article  Google Scholar 

  • Muerth M J, Gauvin St-Denis B, Ricard S, Velazquez J A, Schmid J, Minville M, Caya D, Chaumont D, Ludwig R and Turcotte R 2013 On the need for bias correction in regional climate scenarios to assess climate change impacts on river runoff; Hydrol. Earth Syst. Sci. 17 1189–1204.

    Article  Google Scholar 

  • Mukhopadhyay P, Taraphdar S, Goswami B N and Krishnakumar K 2010 Indian summer monsoon precipitation climatology in a high-resolution regional climate model: Impacts of convective parameterization on systematic biases; Wea. Forecast. 25 369–387.

    Article  Google Scholar 

  • Nadeem I and Herbert F 2016 Sensitivity studies of high-resolution RegCM3 simulations of precipitation over the European Alps: The effect of lateral boundary conditions and domain size; Theor. Appl. Climatol. 126(3–4) 617–630.

    Article  Google Scholar 

  • Piani C, Haerter J and Coppola E 2010 Statistical bias correction for daily precipitation in regional climate models over Europe; Theor. Appl. Climatol. 99 187–192.

    Article  Google Scholar 

  • Portoghese I, Bruno E, Guyennon N and Iacobellis V 2011 Stochastic bias-correction of daily rainfall scenarios for hydrological applications; Nat. Haz. Earth Syst. Sci. 11 2497–2509.

    Article  Google Scholar 

  • Saha S K, Halder S, Suryachandra Rao A and Goswami B N 2012 Modulation of ISOs by land-atmosphere feedback and contribution to the interannual variability of the Indian summer monsoon; J. Geophys. Res. Atmos. 117 1–14.

    Google Scholar 

  • Tariku T B and Gan T Y 2018 Sensitivity of the weather research and forecasting model to parameterization schemes for regional climate of Nile river basin; Clim. Dyn. 50(11–12) 4231–4247.

    Article  Google Scholar 

  • Terink W, Hurkmans R T W L, Torfs P J J F and Uijlenhoet R 2010 Evaluation of a bias correction method applied to downscaled precipitation and temperature reanalysis data for the Rhine basin; Hydrol. Earth Sys. Sci. 14 687–703.

    Article  Google Scholar 

  • Teutschbein C and Seibert J 2012 Bias correction of regional climate model simulations for hydrological climate-change impact studies: Review and evaluation of different methods; J. Hydrol. 456–457 12–29.

    Article  Google Scholar 

  • Themeßl M J, Gobiet A and Heinrich G 2012 Empirical-statistical downscaling and error correction of regional climate models and its impact on the climate change signal; Clim. Change 112 449–468.

    Article  Google Scholar 

  • Themeßl M J, Gobiet A and Leuprecht A 2011 Empirical-statistical downscaling and error correction of daily precipitation from regional climate models; Int. J. Climatol. 31 1530–1544.

    Article  Google Scholar 

  • Wood A W, Leung L R, Sridhar V and Lettenmaier D P 2004 Hydrologic implications of dynamical and statistical approaches to downscale climate model outputs; Clim. Change 62 189–216.

    Article  Google Scholar 

  • Xu J, Koldunov N, Remedio A R C, Sein D V, Zhi X, Jiang X, Xu M, Zhu X, Fraedrich K and Jacob D 2018 On the role of horizontal resolution over the Tibetan plateau in the REMO regional climate model; Clim. Dyn. 51(11–12) 4525–4542.

    Article  Google Scholar 

  • Yang H, Jiang Z H and Li L 2016 Biases and improvements in three dynamical downscaling climate simulations over China; Clim. Dyn. 47(9–10) 3235–3251.

    Article  Google Scholar 

  • Yang H, Jiang Z H and Li Z X 2017 Applicability of a quantile–quantile (Q–Q) bias-correction method for climate dynamical downscaling at Beijing station; Acta Meteorol. Sin. 75(3) 460–470 (in Chinese).

    Google Scholar 

  • Yu E T, Sun J Q, Chen H P and Xiang W L 2015 Evaluation of a high-resolution historical simulation over China: Climatology and extremes; Clim. Dyn. 45(7) 2013–2031.

    Article  Google Scholar 

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Acknowledgements

We acknowledge HMBK (Hydro-Meteorological Bureau of the DPRK), for providing the observed daily precipitation data of the study region. We thank the reviewers for their useful comments and suggestions to improve the manuscript.

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Authors and Affiliations

  1. Faculty of Global Environmental Science, Institute for Numerical Weather Prediction, Kim Il Sung University, Pyongyang, Taesong District, Democratic People’s Republic of Korea

    Kum-Ryong Jo & Ki-Song Pak

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  1. Kum-Ryong Jo
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  2. Ki-Song Pak
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Contributions

K-RJ: Conceptualisation, resources, writing original draft, review and editing. K-SP: Data analysis and preparation of maps.

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Correspondence to Kum-Ryong Jo.

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Communicated by Parthasarathi Mukhopadhyay

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Jo, KR., Pak, KS. Demonstrating the bias-correction impact on regional climate model (RegCM) over the Democratic People’s Republic of Korea: Implication for temperature and precipitation. J Earth Syst Sci 131, 100 (2022). https://doi.org/10.1007/s12040-022-01836-x

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  • DOI: https://doi.org/10.1007/s12040-022-01836-x

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