Abstract
This study investigates how to properly downscale the coupled general circulation model (CGCM) ensemble prediction dynamically more efficiently than conventional method. Specifically, the ensemble seasonal prediction skill of dynamically downscaled precipitation over South Korea is evaluated by comparing two experiments. The first experiment (EXP1) involves conventional ensemble forecasts. Five ensemble members (EMs) are downscaled dynamically with initial and lateral boundary conditions obtained from the outputs of five CGCM EMs. The results of each EM are averaged for ensemble prediction utilizing a simple composite method. The second experiment (EXP2) is the same as EXP1, but the initial and lateral boundary conditions are obtained by arithmetically averaging the outputs of the five CGCM EMs. Therefore, five integrations are carried out for the EXP1, but only one integration is performed for the EXP2. The results show that EXP2 simulates closer to the observed precipitation than EXP1. This improvement is attributed to the strongly simulated upper zonal wind that can influence the vertically integrated moisture flux convergence. EXP2 shows comparable or better performance in simulating the interannual variability of summer precipitation than EXP1. Unlike conventional methods, such as EXP1, EXP2 provides a prediction in a single integration, and the prediction is similar to or even better than the one obtained conventionally. Hence, EXP2 can be a powerful means to drastically reduce the prediction time by reducing the number of ensemble integration to just one.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
The CMAP precipitation reanalysis data were provided by the NOAA/OAR/ESRL Physical Sciences Laboratory (https://psl.noaa.gov/data/gridded/data.cmap.html). The ERA5 precipitation reanalysis data were obtained from the Copernicus Climate Change Service (https://doi.org/10.24381/cds.bd0915c6). The weather station precipitation data over South Korea were provided by the Korean Meteorological Administration (https://data.kma.go.kr/data/grnd/selectAsosRltmList.do?pgmNo=36).
References
Adachi SA, Tomita H (2020) Methodology of the constraint condition in dynamical downscaling for regional climate evaluation: a review. J Geophys Res Atmos 125(11):e2019JD032166. https://doi.org/10.1029/2019JD032166
Ahn JB, Hong JY, Shim KM (2016a) Agro-climate changes over Northeast Asia in RCP scenarios simulated by WRF. Int J Climatol 36(3):1278–1290. https://doi.org/10.1002/joc.4423
Ahn JB, Jo S, Suh MS, Cha DH, Lee DK, Hong SY, Min SK, Park SC, Kang HS, Shim KM (2016b) Changes of precipitation extremes over South Korea projected by the 5 RCMs under RCP scenarios. Asia Pac J Atmos Sci 52(2):223–236. https://doi.org/10.1007/s13143-016-0021-0
Ahn JB, Kim YH, Shim KM, Suh MS, Cha DH, Lee DK, Hong SY, Min SK, Park SC, Kang HS (2021) Climatic yield potential of Japonica-type rice in the Korean Peninsula under RCP scenarios using the ensemble of multi-GCM and multi-RCM chains. Int J Climatol 41(Suppl. 1):E1287–E1302. https://doi.org/10.1002/joc.6767
Ahn JB, Lee J, Im ES (2012) The reproducibility of surface air temperature over south korea using dynamical downscaling and statistical correction. J Meteor Soc Japan 90(4):493–507. https://doi.org/10.2151/jmsj.2012-404
Ahn JB, Shim KM, Jung MP, Jeong HG, Kim YH, Kim ES (2018) Predictability of temperature over South Korea in PNU CGCM and WRF hindcast. Atmosphere 28(4):479–490. https://doi.org/10.14191/Atmos.2018.28.4.479 (in Korean with English abstract)
Baek HJ, Kim MK, Kwon WT (2017) Observed short- and long-term changes in summer precipitation over South Korea and their links to large-scale circulation anomalies. Int J Climatol 37(2):972–986. https://doi.org/10.1002/joc.4753
Ban N, Schmidli J, Schär C (2014) Evaluation of the convection-resolving regional climate modeling approach in decade-long simulations. J Geophys Res Atmos 119(13):7889–7907. https://doi.org/10.1002/2014JD021478
Berthou S, Kendon EJ, Chan SC, Ban N, Leutwyler D, Schär C, Fosser G (2020) Pan-European climate at convection-permitting scale: a model intercomparison study. Clim Dyn 55(1):35–59. https://doi.org/10.1007/s00382-018-4114-6
Betts AK, Miller MJ (1986) A new convective adjustment scheme. Part II: single column tests using GATE wave, BOMEX, ATEX and arctic air-mass data sets. Q J R Meteorol Soc 112(473):693–709. https://doi.org/10.1002/qj.49711247308
Bowler NE, Arribas A, Mylne KR, Robertson KB, Beare SE (2008) The MOGREPS short-range ensemble prediction system. Q J R Meteorol Soc 134(632):703–722. https://doi.org/10.1002/qj.234
Bruyère CL, Done JM, Holland GJ, Fredrick S (2014) Bias corrections of global models for regional climate simulations of high-impact weather. Clim Dyn 43(7):1847–1856. https://doi.org/10.1007/s00382-013-2011-6
Buizza R (1997) Potential forecast skill of ensemble prediction and spread and skill distributions of the ECMWF ensemble prediction system. Mon Weather Rev 125(1):99–119. https://doi.org/10.1175/1520-0493(1997)125%3C0099:Pfsoep%3E2.0.Co;2
Chen F, Dudhia J (2001) Coupling an advanced land surface–hydrology model with the Penn State–NCAR MM5 modeling system. Part I: model implementation and sensitivity. Mon Weather Rev 129(4):569–585. https://doi.org/10.1175/1520-0493(2001)129%3C0569:Caalsh%3E2.0.Co;2
Cocke S, LaRow TE (2000) Seasonal predictions using a regional spectral model embedded within a coupled ocean–atmosphere model. Mon Weather Rev 128(3):689–708. https://doi.org/10.1175/1520-0493(2000)128%3C0689:Spuars%3E2.0.Co;2
Cocke S, LaRow TE, Shin DW (2007) Seasonal rainfall predictions over the southeast United States using the Florida State University nested regional spectral model. J Geophys Res Atmos 112(D04):D04106. https://doi.org/10.1029/2006JD007535
Colette A, Vautard R, Vrac M (2012) Regional climate downscaling with prior statistical correction of the global climate forcing. Geophys Res Lett 39(13). https://doi.org/10.1029/2012GL052258
De Haan LL, Kanamitsu M, De Sales F, Sun L (2015) An evaluation of the seasonal added value of downscaling over the United States using new verification measures. Theor Appl Climatol 122(1):47–57. https://doi.org/10.1007/s00704-014-1278-9
De Sales F, Xue Y (2013) Dynamic downscaling of 22-year CFS winter seasonal hindcasts with the UCLA-ETA regional climate model over the United States. Clim Dyn 41(2):255–275. https://doi.org/10.1007/s00382-012-1567-x
Dudhia J (1989) Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. J Atmos Sci 46(20):3077–3107. https://doi.org/10.1175/1520-0469(1989)046%3C3077:Nsocod%3E2.0.Co;2
Erfanian A, Wang G, Fomenko L, Yu M (2017) Ensemble-based reconstructed forcing (ERF) for regional climate modeling: attaining the performance at a fraction of cost. Geophys Res Lett 44(7):3290–3298. https://doi.org/10.1002/2017GL073053
Grell GA (1993) Prognostic evaluation of assumptions used by cumulus parameterizations. Mon Weather Rev 121(3):764–787. https://doi.org/10.1175/1520-0493(1993)121%3C0764:PEOAUB%3E2.0.CO;2
Ha KJ, Heo KY, Lee SS, Yun KS, Jhun JG (2012) Variability in the East Asian Monsoon: a review. Meteorol Appl 19(2):200–215. https://doi.org/10.1002/met.1320
Ham S, Lim AY, Kang S, Jeong H, Jeong Y (2019) A newly developed APCC SCoPS and its prediction of East Asia seasonal climate variability. Clim Dyn 52(11):6391–6410. https://doi.org/10.1007/s00382-018-4516-5
Hernández-Díaz L, Laprise R, Nikiéma O, Winger K (2017) 3-Step dynamical downscaling with empirical correction of sea-surface conditions: application to a CORDEX Africa simulation. Clim Dyn 48(7):2215–2233. https://doi.org/10.1007/s00382-016-3201-9
Hoffmann P, Katzfey JJ, McGregor JL, Thatcher M (2016) Bias and variance correction of sea surface temperatures used for dynamical downscaling. J Geophys Res Atmos 121(21):12,877–812,890. https://doi.org/10.1002/2016JD025383
Hong JY, Ahn JB (2015) Changes of early summer precipitation in the Korean Peninsula and nearby regions based on RCP simulations. J Clim 28(9):3557–3578. https://doi.org/10.1175/jcli-d-14-00504.1
Hong SY, Lim JOJ (2006) The WRF single-moment 6-class microphysics scheme (WSM6). Asia Pac J Atmos Sci 42(2):129–151
Hong SY, Noh Y, Dudhia J (2006) A new vertical diffusion package with an explicit treatment of entrainment processes. Mon Weather Rev 134(9):2318–2341. https://doi.org/10.1175/mwr3199.1
Hunt BR, Kostelich EJ, Szunyogh I (2007) Efficient data assimilation for spatiotemporal chaos: a local ensemble transform Kalman filter. Phys D: Nonlinear Phenom 230(1):112–126. https://doi.org/10.1016/j.physd.2006.11.008
Hur J, Ahn JB (2015) Seasonal prediction of regional surface air temperature and first-flowering date over South Korea. Int J Climatol 35(15):4791–4801. https://doi.org/10.1002/joc.4323
Hur J, Ahn JB (2017) Assessment and prediction of the first-flowering dates for the major fruit trees in Korea using a multi-RCM ensemble. Int J Climatol 37(3):1603–1618. https://doi.org/10.1002/joc.4800
Im ES, Choi YW, Ahn JB (2017a) Robust intensification of hydroclimatic intensity over East Asia from multi-model ensemble regional projections. Theor Appl Climatol 129(3):1241–1254. https://doi.org/10.1007/s00704-016-1846-2
Im ES, Choi YW, Ahn JB (2017b) Worsening of heat stress due to global warming in South Korea based on multi-RCM ensemble projections. J Geophys Res Atmos 122(21):11,444–411,461. https://doi.org/10.1002/2017JD026731
Jiménez PA, Dudhia J, González-Rouco JF, Navarro J, Montávez JP, García-Bustamante E (2012) A revised scheme for the WRF surface layer formulation. Mon Weather Rev 140(3):898–918. https://doi.org/10.1175/mwr-d-11-00056.1
Kain JS (2004) The Kain–Fritsch convective parameterization: an update. J Appl Meteorol 43(1):170–181. https://doi.org/10.1175/1520-0450(2004)043%3C0170:Tkcpau%3E2.0.Co;2
Kang HS, Hong SY (2008) Sensitivity of the simulated East Asian summer monsoon climatology to four convective parameterization schemes. J Geophys Res Atmos 113(D15):D15119. https://doi.org/10.1029/2007JD009692
Kim HJ, Ahn JB (2015) Improvement in prediction of the arctic oscillation with a realistic ocean initial condition in a CGCM. J Clim 28(22):8951–8967. https://doi.org/10.1175/jcli-d-14-00457.1
Kim HM, Webster PJ, Curry JA (2012) Seasonal prediction skill of ECMWF System 4 and NCEP CFSv2 retrospective forecast for the Northern Hemisphere Winter. Clim Dyn 39(12):2957–2973. https://doi.org/10.1007/s00382-012-1364-6
Kim JY, Seo KH, Yeh SW, Kim HK, Yim SY, Lee HS, Kown M, Ham YG (2017) Analysis of characteristics for 2016 Changma rainfall. Atmosphere 27(3):277–290. https://doi.org/10.14191/Atmos.2017.27.3.277 (in Korean with English abstract)
Kim YH, Choi MJ, Shim KM, Hur J, Jo S, Ahn JB (2021) A study on the predictability of the number of days of heat and cold damages by growth stages of rice using PNU CGCM-WRF chain in South Korea. Atmosphere 31(5):577–592. https://doi.org/10.14191/Atmos.2021.31.5.577 (in Korean with English abstract)
Kim YH, Kim ES, Choi MJ, Shim KM, Ahn JB (2019) Evaluation of long-term seasonal predictability of heatwave over South Korea using PNU CGCM-WRF Chain. Atmosphere 29(5):671–687. https://doi.org/10.14191/Atmos.2019.29.5.671 (in Korean with English abstract)
Kirtman BP, Min D, Infanti JM, Kinter JL, Paolino DA, Zhang Q, van den Dool H, Saha S, Mendez MP, Becker E, Peng P, Tripp P, Huang J, DeWitt DG, Tippett MK, Barnston AG, Li S, Rosati A, Schubert SD et al (2014) The North American multimodel ensemble: phase-1 seasonal-to-interannual prediction; phase-2 toward developing intraseasonal prediction. Bull Am Meteorol Soc 95(4):585–601. https://doi.org/10.1175/bams-d-12-00050.1
Lee JY, Kwon M, Yun KS, Min SK, Park IH, Ham YG, Jin EK, Kim JH, Seo KH, Kim W, Yim SY, Yoon JH (2017) The long-term variability of Changma in the East Asian summer monsoon system: a review and revisit. Asia Pac J Atmos Sci 53(2):257–272. https://doi.org/10.1007/s13143-017-0032-5
Lee MH, Im ES, Bae DH (2019) Impact of the spatial variability of daily precipitation on hydrological projections: a comparison of GCM- and RCM-driven cases in the Han River basin, Korea. Hydrol Process 33(16):2240–2257. https://doi.org/10.1002/hyp.13469
Lim CM, Yhang YB, Ham S (2019) Application of GCM bias correction to RCM simulations of East Asian winter climate. Atmosphere 10(7):382. https://doi.org/10.3390/atmos10070382
Lim EP, Hendon HH, Langford S, Alves O (2012) Improvements in POAMA2 for the prediction of major climate drivers and south eastern Australian rainfall. CAWCR Tech. Rep. No. 051. https://www.cawcr.gov.au/technical-reports/CTR_051.pdf
Lorenz EN (1969) The predictability of a flow which possesses many scales of motion. Tellus 21(3):289–307. https://doi.org/10.3402/tellusa.v21i3.10086
Meehl GA (1995) Global coupled general circulation models. Bull Am Meteorol Soc 76(6):951–957. https://doi.org/10.1175/1520-0477-76.6.951
Meyer JDD, Jin J (2016) Bias correction of the CCSM4 for improved regional climate modeling of the North American monsoon. Clim Dyn 46(9):2961–2976. https://doi.org/10.1007/s00382-015-2744-5
Michelangeli PA, Vrac M, Loukos H (2009) Probabilistic downscaling approaches: application to wind cumulative distribution functions. Geophys Res Lett 36(11):L11708. https://doi.org/10.1029/2009GL038401
Min YM, Kryjov VN, Oh SM (2014) Assessment of APCC multimodel ensemble prediction in seasonal climate forecasting: retrospective (1983–2003) and real-time forecasts (2008–2013). J Geophys Res Atmos 119(21):12,132–112,150. https://doi.org/10.1002/2014JD022230
Mlawer EJ, Taubman SJ, Brown PD, Iacono MJ, Clough SA (1997) Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J Geophys Res Atmos 102(D14):16663–16682. https://doi.org/10.1029/97JD00237
Molteni F, Stockdale T, Balmaseda M, Balsamo G, Buizza R, Ferranti L, Magnusson L, Mogensen K, Palmer T, Vitart F (2011) The new ECMWF seasonal forecast system (system 4). ECMWF Technical. Memorandum 656 https://www.ecmwf.int/sites/default/files/elibrary/2011/11209-new-ecmwf-seasonal-forecast-system-system-4.pdf
Peng X, Che Y, Chang J (2013) A novel approach to improve numerical weather prediction skills by using anomaly integration and historical data. J Geophys Res Atmos 118(16):8814–8826. https://doi.org/10.1002/jgrd.50682
Qiu L, Im ES, Hur J, Shim KM (2020) Added value of very high resolution climate simulations over South Korea using WRF modeling system. Clim Dyn 54(1):173–189. https://doi.org/10.1007/s00382-019-04992-x
Ratnam JV, Behera SK, Doi T, Ratna SB, Landman WA (2016) Improvements to the WRF seasonal hindcasts over south africa by bias correcting the driving SINTEX-F2v CGCM fields. J Clim 29(8):2815–2829. https://doi.org/10.1175/jcli-d-15-0435.1
Richardson DS (2000) Skill and relative economic value of the ECMWF ensemble prediction system. Q J R Meteorol Soc 126(563):649–667. https://doi.org/10.1002/qj.49712656313
Seo GY, Ahn JB (2020) Sensitivity analysis of cumulus parameterization in WRF model for simulating summer heavy rainfall in South Korea. J Clim Res 15(4):243–256. https://doi.org/10.14383/cri.2020.15.4.243 (in Korean with English abstract)
Shukla S, Lettenmaier DP (2013) Multi-RCM ensemble downscaling of NCEP CFS winter season forecasts: implications for seasonal hydrologic forecast skill. J Geophys Res Atmos 118(19):10,770–710,790. https://doi.org/10.1002/jgrd.50628
Song CY, Ahn JB (2022) Influence of Okhotsk Sea blocking on summer precipitation over South Korea. Int J Climatol 42(6):3553–3570. https://doi.org/10.1002/joc.7432
Song CY, Kim SH, Ahn JB (2021) Improvement in seasonal prediction of precipitation and drought over the United States based on regional climate model using empirical quantile mapping. Atmosphere 31(5):637–656. https://doi.org/10.14191/Atmos.2021.31.5.637 (in Korean with English abstract)
Stensrud DJ, Bao JW, Warner TT (2000) Using initial condition and model physics perturbations in short-range ensemble simulations of mesoscale convective systems. Mon Weather Rev 128(7):2077–2107 (https://doi.org/10.1175/1520-0493(2000)128%3C2077:UICAMP%3E2.0.CO;2)
Stensrud DJ, Brooks HE, Du J, Tracton MS, Rogers E (1999) Using ensembles for short-range forecasting. Mon Weather Rev 127(4):433–446. https://doi.org/10.1175/1520-0493(1999)127%3C0433:Uefsrf%3E2.0.Co;2
Sun J, Ahn JB (2015) Dynamical seasonal predictability of the Arctic Oscillation using a CGCM. Int J Climatol 35(7):1342–1353. https://doi.org/10.1002/joc.4060
Wei M, Toth Z, Wobus R, Zhu Y (2008) Initial perturbations based on the ensemble transform (ET) technique in the NCEP global operational forecast system. Tellus A: Dyn Meteorol Oceanogr 60(1):62–79. https://doi.org/10.1111/j.1600-0870.2007.00273.x
Wei M, Toth Z, Wobus R, Zhu Y, Bishop CH, Wang X (2006) Ensemble transform Kalman filter-based ensemble perturbations in an operational global prediction system at NCEP. Tellus A: Dyn Meteorol Oceanogr 58(1):28–44. https://doi.org/10.1111/j.1600-0870.2006.00159.x
Xie P, Arkin PA (1997) Global precipitation: a 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs. Bull Am Meteorol Soc 78(11):2539–2558 (https://doi.org/10.1175/1520-0477(1997)078<2539:Gpayma>2.0.Co;2)
Xu Z, Han Y, Yang Z (2019) Dynamical downscaling of regional climate: a review of methods and limitations. Sci China Earth Sci 62(2):365–375. https://doi.org/10.1007/s11430-018-9261-5
Xu Z, Yang ZL (2012) An improved dynamical downscaling method with GCM bias corrections and its validation with 30 years of climate simulations. J Clim 25(18):6271–6286. https://doi.org/10.1175/jcli-d-12-00005.1
Yoon JH, Ruby Leung L, Correia J Jr (2012) Comparison of dynamically and statistically downscaled seasonal climate forecasts for the cold season over the United States. J Geophys Res Atmos 117(D21):D21109. https://doi.org/10.1029/2012JD017650
Yoshimura K, Kanamitsu M (2013) Incremental Correction for the Dynamical Downscaling of Ensemble Mean Atmospheric Fields. Mon Weather Rev 141(9):3087–3101. https://doi.org/10.1175/mwr-d-12-00271.1
Yun Y, Liu C, Luo Y, Liang X, Huang L, Chen F, Rasmmusen R (2020) Convection-permitting regional climate simulation of warm-season precipitation over Eastern China. Clim Dyn 54(3):1469–1489. https://doi.org/10.1007/s00382-019-05070-y
Acknowledgements
The authors thank three anonymous reviewers and editors for their valuable comments and suggestions.
Code availability
All figures were produced using National Center for Atmospheric Research Command Language (NCL) version 6.6.2 (https://www.ncl.ucar.edu/). All the NCL scripts used in this study are available from the corresponding author upon reasonable request.
Funding
This work was carried out with the support of "Cooperative Research Program for Agriculture Science and Technology Development (Project No. PJ01489102)" Rural Development Administration, Republic of Korea.
Author information
Authors and Affiliations
Contributions
Joong-Bae Ahn designed the study and revised the manuscript writing. Chan-Yeong Song analyzed the data and wrote the manuscript. All authors contributed to the manuscript review and editing.
Corresponding author
Ethics declarations
Ethics approval/declarations
Not applicable.
Consent to participate
Not applicable.
Consent for publication
Not applicable.
Conflict of interest
The authors declare no competing interests.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Song, CY., Ahn, JB. Dynamical downscaling using CGCM ensemble average: an application to seasonal prediction for summer precipitation over South Korea. Theor Appl Climatol 152, 757–772 (2023). https://doi.org/10.1007/s00704-023-04404-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00704-023-04404-5