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Near Future Projection of Extreme Temperature over CORDEX-East Asia Phase 2 Region Using the WRF Model Based on RCP Scenarios

RCP 시나리오 기반 WRF를 이용한 CORDEX-동아시아 2단계 지역의 가까운 미래 극한기온 변화 전망

  • Seo, Ga-Yeong (Division of Earth Environmental System, Pusan National University) ;
  • Choi, Yeon-Woo (Division of Earth Environmental System, Pusan National University) ;
  • Ahn, Joong-Bae (Division of Earth Environmental System, Pusan National University)
  • 서가영 (부산대학교 지구환경시스템학부) ;
  • 최연우 (부산대학교 지구환경시스템학부) ;
  • 안중배 (부산대학교 지구환경시스템학부)
  • Received : 2019.09.14
  • Accepted : 2019.12.03
  • Published : 2019.12.31

Abstract

This study evaluates the performance of Weather Research and Forecasting (WRF) model in simulating temperature over the COordinated Regional climate Downscaling EXperiment-East Asia (CORDEX-EA) Phase 2 domain for the reference period (1981~2005), and assesses the changes in temperature and its extremes in the mid-21st century (2026~2050) under global warming based on Representative Concentration Pathway (RCP) scenarios. MPI-ESM-LR forced by two RCP scenarios (RCP2.6 and RCP8.5) is used as initial and lateral boundary conditions. Overall, WRF can capture the observed features of temperature distribution reflecting local topographic characteristic, despite some disagreement between the observed and simulated patterns. Basically, WRF shows a systematic cold bias in daily mean, minimum and maximum temperature over the entire domain. According to the future projections, summer and winter mean temperatures over East Asia will significantly increase in the mid-21st century. The mean temperature rise is expected to be greater in winter than in summer. In accordance with these results, summer (winter) is projected to begin earlier (later) in the future compared to the historical period. Furthermore, a rise in extreme temperatures shows a tendency to be greater in the future. The averages of daily minimum and maximum temperatures above 90 percentiles are likely to be intensified in the high-latitude, while hot days and hot nights tend to be more frequent in the low-latitude in the mid-21st century. Especially, East Asia would be suffered from strong increases in nocturnal temperature under future global warming.

Keywords

References

  1. Ahn, J.-B., and Coauthors, 2016: Changes of precipitation extremes over South Korea projected by the 5 RCMs under RCP scenarios. Asia-Pac. J. Atmos. Sci., 52, 223-236, doi:10.1007/s13143-016-0021-0.
  2. Ahn, J.-B., Y.-W. Choi, and S. Jo, 2018: Evaluation of reproduced precipitation by WRF in the region of CORDEX-East Asia phase 2. Atmosphere, 28, 85-97, doi:10.14191/Atmos.2018.28.1.085 (in Korean with English abstract).
  3. Betts, A. K., and M. J. Miller, 1986: A new convective adjustment scheme. Part II: Single column tests using GATE wave, BOMEX, ATEX and arctic air‐mass data sets. Q. J. Roy. Meteor. Soc., 112, 693-709. https://doi.org/10.1002/qj.49711247308
  4. Bretherton, C. S., M. Widmann, V. P. Dymnikov, J. M. Wallace, and I. Blade, 1999: The effective number of spatial degrees of freedom of a time-varying field. J. Climate, 12, 1990-2009. https://doi.org/10.1175/1520-0442(1999)012<1990:TENOSD>2.0.CO;2
  5. Cha, D.-H., and D.-K. Lee, 2009: Reduction of systematic errors in regional climate simulations of the summer monsoon over East Asia and the western North Pacific by applying the spectral nudging technique. J. Geophys. Res., 114, D14108, doi:10.1029/2008JD011176.
  6. Cha, D.-H., and Coauthors, 2016: Future changes in summer precipitation in regional climate simulations over the Korean Peninsula forced by multi-RCP scenarios of HadGEM2-AO. Asia-Pac. J. Atmos. Sci., 52, 139-149, doi:10.1007/s13143-016-0015-y.
  7. Chen, F., and J. Dudhia, 2001: Coupling an advanced land surface-hydrology model with the Penn State-NCAR MM5 modeling system. Part II: preliminary model validation. Mon. Wea. Rev., 129, 587-604. https://doi.org/10.1175/1520-0493(2001)129<0587:CAALSH>2.0.CO;2
  8. Choi, S.-J., D.-K. Lee, and S.-G. Oh, 2012: Regional climate simulations over East-Asia by using SNURCM and WRF forced by HadGEM2-AO. J. Kor. Earth Sci. Soc., 32, 750-760, doi:10.5467/JKESS.2011.32.7.750 (in Korean with English abstract).
  9. Choi, Y.-W., and J.-B. Ahn, 2017: Impact of cumulus parameterization schemes on the regional climate simulation for the domain of CORDEX-East Asia phase 2 using WRF model. Atmosphere, 27, 105-118 (in Korean with English abstract). https://doi.org/10.14191/Atmos.2017.27.1.105
  10. Choi, Y.-W., J.-B. Ahn, M.-S. Suh, D.-H. Cha, D.-K. Lee, S.-Y. Hong, S.-K. Min, S.-C. Park, and H.-S. Kang, 2016: Future changes in drought characteristics over South Korea using multi regional climate models with the standardized precipitation index. Asia-Pac. J. Atmos. Sci., 52, 209-222, doi:10.1007/s13143-016-0020-1.
  11. Collins, W. D., J. K. Hackney, and D. P. Edwards, 2002: An updated parameterization for infrared emission and absorption by water vapor in the National Center for Atmospheric Research Community Atmosphere Model. J. Geophys. Res., 107, ACL17-1-ACL17-20.
  12. Dasari, H. P., R. Salgado, J. Perdigao, and V. S. Challa, 2014: A regional climate simulation study using WRF-ARW model over Europe and evaluation for extreme temperature weather events. Int. J. Atmos. Sci., 2014, 704079, doi:10.1155/2014/704079.
  13. Della-Marta, P. M., M. R. Haylock, J. Luterbacher, and H. Wanner, 2007: Doubled length of western European summer heat waves since 1880. J. Geophys. Res., 112, D15103, doi:10.1029/2007JD008510.
  14. Deng, H., Y. Chen, X. Shi, W. Li, H. Wang, S. Zhang, and G. Fang, 2014: Dynamics of temperature and precipitation extremes and their spatial variation in the arid region of northwest China. Atmos. Res., 138, 346-355, doi:10.1016/j.atmosres.2013.12.001.
  15. Fischer, E. M., and C. Schar, 2010: Consistent geographical patterns of changes in high-impact European heatwaves. Nature Geosci., 3, 398-403, doi:10.1038/ngeo866.
  16. Giorgetta, M. A., and Coauthors, 2013: Climate and carbon cycle changes from 1850 to 2100 in MPI-ESM simulations for the Coupled Model Intercomparison Project phase 5. J. Adv. Model. Earth Sy., 5, 572-597, doi:10.1002/jame.20038.
  17. Griffiths, G. M., and Coauthors, 2005: Change in mean temperature as a predictor of extreme temperature change in the Asia-Pacific region. Int. J. Climatol., 25, 1301-1330. https://doi.org/10.1002/joc.1194
  18. Gu, H., Z. Yu, J. Wang, G. Wang, T. Yang, Q. Ju, C. Yang, F. Xu, and C. Fan, 2015: Assessing CMIP5 genera l circulation model simulations of precipitation and temperature over China. Int. J. Climatol., 35, 2431-2440, doi:10.1002/joc.4152.
  19. Harris, I., P. D. Jones, T. J. Osborn, and D. H. Lister, 2014: Updated high-resolution grids of monthly climatic observations - the CRU TS3.10 Dataset. Int. J. Climatol., 34, 623-642, doi:10.1002/joc.3711.
  20. Heikkila, U., A. Sandvik, and A. Sorteberg, 2011: Dynamical downscaling of ERA-40 in complex terrain using the WRF regional climate model. Climate Dyn., 37, 1551-1564, doi:10.1007/s00382-010-0928-6.
  21. Hong, J.-Y., and J.-B. Ahn, 2015: Changes of early summer precipitation in the Korean Peninsula and nearby regions based on RCP simulations. J. Climate, 28, 3557-3578, doi:10.1175/JCLI-D-14-00504.1.
  22. Hong, S.-Y., J. Dudhia, and S.-H. Chen, 2004: A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation. Mon. Wea. Rev., 132, 103-120. https://doi.org/10.1175/1520-0493(2004)132<0103:ARATIM>2.0.CO;2
  23. Hong, S.-Y., Y. Noh, and J. Dudhia, 2006: A new vertical diffusion package with an explicit treatment of entrainment processes. Mon. Wea. Rev., 134, 2318-2341. https://doi.org/10.1175/MWR3199.1
  24. Im, E.-S., J.-B. Ahn, and S.-R. Jo, 2015: Regional climate projection over South Korea simulated by the HadGEM2-AO and WRF model chain under RCP emission scenarios. Climate Res., 63, 249-266, doi:10.3354/cr01292.
  25. Im, E.-S., Y.-W. Choi, and J.-B. Ahn, 2017a: Worsening of heat stress due to global warming in South Korea based on multi-RCM ensemble projections. J. Geophys. Res., 122, 11444-11461, doi:10.1002/2017JD026731.
  26. Im, E.-S., Y.-W. Choi, and J.-B. Ahn, 2017b: Robust intensification of hydroclimatic intensity over East Asia from multi-model ensemble regional projections. Theor. Appl. Climatol., 129, 1241-1254, doi:10.1007/s00704-016-1846-2.
  27. Im, E.-S., N.-X. Thanh, Y.-H. Kim, and J.-B. Ahn, 2019: 2018 summer extreme temperatures in South Korea and their intensification under 3$^{\circ}C$ global warming. Environ. Res. Lett., 14, 094020, doi:10.1088/1748-9326/ab3b8f.
  28. IPCC, 2013: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of The Intergovernmental Panel on Climate Change. T. F. Stocker et al. Eds., Cambridge University Press. 1535 pp.
  29. IPCC, 2018: Global warming of 1.5$^{\circ}C$, An IPCC special report on the impacts of global warming of 1.5$^{\circ}C$ above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty. V. Masson-Delmotte et al. Eds., World Meteorological Organization, 616 pp.
  30. Janjic, Z. I., 1994: The step-mountain eta coordinate model: further developments of the convection, viscous sublayer, and turbulence closure schemes. Mon. Wea. Rev., 122, 927-945. https://doi.org/10.1175/1520-0493(1994)122<0927:TSMECM>2.0.CO;2
  31. Katz, R. W., and B. G. Brown, 1992: Extreme events in a changing climate: Variability is more important than averages. Climatic Change, 21, 289-302. https://doi.org/10.1007/BF00139728
  32. Kim, G., D.-H. Cha, and C. Park, 2018: Estimation of uncertainties and projection for bias-corrected outputs of multi-regional climate models. J. Climate Res., 13, 263-273, doi:10.14383/cri.2018.13.4.263 (in Korean with English abstract).
  33. KMA, 2018: Projection of fine resolution future climate change over Korean Peninsula and East Asia based on RCP Scenarios. Korea Meteorological Administration, 945 pp (in Korean).
  34. KMA, 2019: 2018 Abnormal Climate Report. Korea Meteorological Administration, 198 pp (in Korean).
  35. Lee, D., and Coauthors, 2017: Thermodynamic and dynamic contributions to future changes in summer precipitation over Northeast Asia and Korea: a multi-RCM study. Climate Dyn., 49, 4121-4139, doi:10.1007/s00382-017-3566-4.
  36. Lee, D., C. Park, Y.-H. Kim, and S.-K. Min, 2016: Evaluation of the COSMO-CLM for East Asia climate simulations : sensitivity to spectral nudging. J. Climate Res., 11, 69-85, doi:10.14383/cri.2016.11.1.69 (in Korean with English abstract).
  37. Lee, J.-W., S.-Y. Hong, E.-C. Chang, M.-S. Suh, and H.-S. Kang, 2014: Assessment of future climate change over East Asia due to the RCP scenarios downscaled by GRIMs-RMP. Climate Dyn., 42, 733-747, doi: 10.1007/s00382-013-1841-6.
  38. Moberg, A., and P. D. Jones, 2005: Trends in indices for extremes in daily temperature and precipitation in central and western Europe, 1901-99. Int. J. Climatol., 25, 1149-1171. https://doi.org/10.1002/joc.1163
  39. NIMS, 2018: Climate changes of Korean Peninsula over 100 years. National Institute of Meteorological Sciences, 31 pp (in Korean).
  40. Oh, S.-G., J.-H. Park, S.-H. Lee, and M.-S. Suh, 2014: Assessment of the RegCM4 over East Asia and future precipitation change adapted to the RCP scenarios. J. Geophys. Res., 119, 2913-2927, doi:10.1002/2013JD020693.
  41. Ozturk, T., M. T. Turp, M. Turkea, and M. L. Kurnaz, 2017: Projected changes in temperature and precipitation climatology of Central Asia CORDEX Region 8 by using RegCM4.3.5. Atmos. Res., 183, 296-307, doi:10.1016/j.atmosres.2016.09.008.
  42. Park, B.-J., Y.-H. Kim, S.-K. Min, M.-K. Kim, Y. Choi, K.-O. Boo, and S. Shim, 2017: Long-term warming trends in Korea and contribution of urbanization: an updated assessment. J. Geophys. Res., 122, 10637-10654, doi:10.1002/2017JD027167.
  43. Park, C., and Coauthors, 2016: Evaluation of multiple regional climate models for summer climate extremes over East Asia. Climate Dyn., 46, 2469-2486, doi: 10.1007/s00382-015-2713-z.
  44. Park, J.-H., S.-G. Oh, and M.-S. Suh, 2013: Impacts of boundary conditions on the precipitation simulation of RegCM4 in the CORDEX East Asia domain. J. Geophys. Res., 118, 1652-1667, doi:10.1002/jgrd.50159.
  45. Pingale, S. M., D. Khare, M. K. Jat, and J. Adamowski, 2014: Spatial and temporal trends of mean and extreme rainfall and temperature for the 33 urban centers of the arid and semi-arid state of Rajasthan, India. Atmos. Res., 138, 73-90, doi:10.1016/j.atmosres.2013.10.024.
  46. Su, F., X. Duan, D. Chen, Z. Hao, and L. Cuo, 2013: Evaluation of the global climate models in the CMIP5 over the Tibetan Plateau. J. Climate, 26, 3187-3208, doi:10.1175/JCLI-D-12-00321.1.
  47. Suh, M.-S., and Coauthors, 2016: Projections of high resolution climate changes for South Korea using multiple-regional climate models based on four RCP scenarios. Part 1: surface air temperature. Asia-Pac. J. Atmos. Sci., 52, 151-169, doi:10.1007/s13143-016-0017-9.
  48. Tang, J., S. Wang, X. Niu, P. Hui, P. Zong, and X. Wang, 2017: Impact of spectral nudging on regional climate simulation over CORDEX East Asia using WRF. Climate Dyn., 48, 2339-2357, doi:10.1007/s00382-016-3208-2.
  49. Taylor, K. E., R. J. Stouffer, and G. A. Meehl, 2012: An overview of CMIP5 and the experiment design. Bull. Amer. Meteor. Soc., 93, 485-498, doi:10.1175/BAMSD-11-00094.1.
  50. Torma, C., F. Giorgi, and E. Coppola, 2015: Added value of regional climate modeling over areas characterized by complex terrain-precipitation over the Alps. J. Geophys. Res., 120, 3957-3972, doi:10.1002/2014JD022781.
  51. von Storch, H., H. Langenberg, and F. Feser, 2000: A spectral nudging technique for dynamical downscaling purposes. Mon. Wea. Rev., 128, 3664-3673. https://doi.org/10.1175/1520-0493(2000)128<3664:ASNTFD>2.0.CO;2