Regional climate modeling over China with COSMO-CLM: Performance assessment and climate projections

Journal of Geophysical Research

Volumn 119, Issue 21, 2014, Pages 12,151-12,170

  Bucchignani, Edoardo ^a,b   Montesarchio, Myriam ^a,b   Cattaneo, Luigi ^a   Manzi, Mariapaola ^a   Mercogliano, Paola ^a,b  

^a CENTRO EURO MEDITERRANEO SUI CAMBIAMENTI CLIMATICI   (Italy)

^b ITALIAN AEROSPACE RESEARCH CENTER   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

CLIMATE MODELING; CLIMATE PREDICTION; PRECIPITATION (CLIMATOLOGY); REGIONAL CLIMATE; SEASONAL VARIATION; SIMULATION;

CHINA;

EID: 84913534088     PISSN: 01480227     EISSN: 21562202     Source Type: Journal    
DOI: 10.1002/2014JD022219     Document Type: Article

References (68)

1. Adam, J. C., and D. P. Lettenmaier (2003), Adjustment of global gridded precipitation for systematic bias, J. Geophys. Res., 108(D9), 4257, doi:10.1029/2002JD002499.
2. Adler, R. F., et al. (2003), The Version 2 Global Precipitation Climatology Project (GPCP) Monthly Precipitation Analysis (1979-present), J. Hydrometeorol., 4, 1147–1167.
3. Bachner, S., A. Kapala, and C. Simmer (2008), Evaluation of daily precipitation characteristics in the CLM and their sensitivity to parameterizations, Meteorol. Z., 17(4), 407–419, doi:10.1127/0941-2948/2008/0300.
4. Bellprat, O., S. Kotlarski, D. Lüthi, and C. Schär (2012), Objective calibration of regional climate models, J. Geophys. Res., 117, D23115, doi:10.1029/2012JD018262.
5. Boberg, F., and J. H. Christensen (2012), Overestimation of Mediterranean summer temperature projections due to model deficiencies, Nat. Clim. Change, 2, 433–436, doi:10.1038/NCLIMATE1454.
6. Bucchignani, E., P. Mercogliano, M. Montesarchio, A. L. Zollo, and M. Manzi (2012), Prearrangement of the COSMO-CLM Model on the Chinese region and sensitivity analysis, Tech. Rep. RP0136, CMCC. [Available at http://www.cmcc.it/wp-content/uploads/2013/05/rp013-isc-07-2012.pdf.]
7. Bucchignani, E., A. Sanna, S. Gualdi, S. Castellari, and P. Schiano (2013a), Simulation of the climate of the XX century in the Alpine space, Nat. Hazards, 67, 981–990, doi:10.1007/s11069-011-9883-8.
8. Bucchignani, E., P. Mercogliano, M. Montesarchio, M. Manzi, and A. Zollo (2013b), Performance evaluation of COSMO-CLM over Italy and climate projections for the XXI century, in Climate Change and Its Implications on Ecosystem and Society (Proceedings of I SISC Conference), Lecce, pp. 78–89, Società Italiana per le Scienze del Clima, Lecce, Italy, 23–24 September.
9. Byun, Y. H., Y. Hyun, J. S. Park, and C. Cho (2013), Recent progress and consensus of CORDEX-East Asia, in Abstract Booklet of International Conference on Regional Climate CORDEX 2013, p. 87, WCRP, Bruxelles, Belgium, 4–7 November.
10. Cattaneo, L., V. Rillo, and P. Mercogliano (2014), Clime: Analysing climate data in GIS environment, in Proceedings of II SISC Conference, Venezia, pp. 434–447, Società Italiana per le Scienze del Clima, Venezia, Italy, 29-30 Sept.
11. Chen, W., Z. Jiang, L. Li, and P. Yiou (2011), Simulation of regional climate change under the IPCC A2 scenario in southeast China, Clim. Dyn., 36, 491–507, doi:10.1007/s00382-010-0910-3.
12. Christensen, J. H., F. Boberg, O. B. Christensen, and P. Lucas-Picher (2008), On the need for bias correction of regional climate change projections of temperature and precipitation, Geophys. Res. Lett., 35, L20709, doi:10.1029/2008GL035694.
13. Dee, D., et al. (2011), The era-interim reanalysis: Configuration and performance of the data assimilation system, Q. J. R. Meteorol. Soc., 137, 553–597, doi:10.1002/qj.828.
14. Doms, G., J. Föster, E. Heise, H. J. Herzog, M. Raschendorfer, R. Schrodin, T. Reinhardt, and G. Vogel (2002), A description of the nonhydrostatic regional model LM. Part II: Physical parameterization, Tech. Rep., Deutscher Wetterdienst, Offenbach.
15. Du, M., S. Kawashima, S. Yonemura, T. Yamada, X. Zhang, J. Liu, Y. Li, S. Gu, andY. Tang(2007), Temperature distribution inthe high mountain regions on the Tibetan Plateau- Measurement and simulation, in Proc. MODSIM 2007 International Congress on Modelling and Simulation, pp. 2146–2152, Modelling and Simulation Society of Australia and New Zealand, Christchurch, New Zealand. [Available at http://www.mssanz.org.au/.]
16. Elguindi, N., A. Grundstein, S. Bernardes, U. Turuncoglu, and J. Feddema (2014), Assessment of CMIP5 global model simulations and climate change projections for the 21st century using a modified Thornthwaite climate classification, Clim. Change, 122, 523–538, doi:10.1007/s10584-013-1020-0.
17. Fischer, T., C. Menz, B. Su, and T. Scholten (2013), Simulated and projected climate extremes in the Zhujiang River Basin, South China, using the regional climate model COSMO-CLM, Int. J. Climatol., 33, 2988–3001, doi:10.1002/joc.3643.
18. Frei, C., and C. Schär (1998), A precipitation climatology of the Alps from high-resolution rain-gauge observations, Int. J. Climatol., 18, 873–900, doi:10.1002/(SICI)1097-0088(19980630)18:8<873::AID-JOC255>3.0.CO;2-9.
19. Gao, X. J., Z. C. Zhao, Y. H. Ding, R. H. Huang, and F. Giorgi (2001), Climate change due to greenhouse effects in China as simulated by a regional climate model, Adv. Atmos. Sci., 18(6), 1224–1230.
20. Gao, X. J., Y. Xu, Z. C. Zhao, J. S. Pal, and F. Giorgi (2006), On the role of resolution and topography in the simulation of East Asia precipitation, Theor. Appl. Climatol., 86, 173–185, doi:10.1007/s00704-005-0214-4.
21. Gao, X. J., Y. Shi, D. F. Zhang, and F. Giorgi (2012a), Climate change in China in the 21st century as simulated by a high resolution regional climate model, Chin. Sci. Bull., 57(10), 1188–1195, doi:10.1007/s11434-011-4935-8.
22. Gao, X. J., Y. Shi, D. F. Zhang, J. Wu, F. Giorgi, Z. Ji, and Y. Wang (2012b), Uncertainties of monsoon precipitation projection over China: Results from two high resolution RCM simulations, Clim. Res., 52, 213–226, doi:10.3354/cr01084.
23. Gao, X. J., M. L. Wang, and F. Giorgi (2013), Climate change over China in the 21st Century as simulated by BCC_CSM1.1-RegCM4.0, Atmos. Oceanic Sci. Lett., 6(5), 381–386, doi:10.3878/j.issn.1674-2834.13.0029.
24. Giorgi, F., J. W. Hurrell, M. R. Marinucci, and M. Beniston (1997), Elevation signal in surface climate change: A model study, J. Clim., 10, 288–296, doi:10.1175/1520-0442(1997)010<0288:EDOTSC>2.0.CO;2.
25. Giorgi, F., J. Jones, and G. R. Asrar (2009), Addressing climate information needs at the regional level: The CORDEX framework, WMO Bull., 58(3), 175–183.
26. Gualdi, S., et al. (2013), The CIRCE simulations: Regional climate change projections with realistic representation of the Mediterranean Sea, Bull. Am. Meteorol. Soc., 94, 65–81, doi:10.1175/BAMS-D-11-00136.1.
27. Hirakuchi, H., and F. Giorgi (1995), Multiyear present-day and 2 × CO2 simulations of monsoon climate over eastern Asia and Japan with a regional climate model nested in a general circulation model, J. Geophys. Res., 100, 21,105–21,125, doi:10.1029/95JD01885.
28. Hu, Z., C. Zhangh, Q. Hu, and H. Tian (2014), Temperature changes in central Asia from 1979 to 2011 based on multiple datasets, J. Clim., 27, 1143–1167, doi:10.1175/JCLI-D-13-00064.1.
29. IPCC (2012), Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation, A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change, edited by C. B. Field et al., 582 pp., Cambridge Univ. Press, Cambridge, U. K., and New York.
30. 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, edited by T. F. Stocker et al., Cambridge Univ. Press, Cambridge, U. K., and New York.
31. IPCC (2014), Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects, Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by V. R. Barros et al., Cambridge Univ. Press, Cambridge, U. K., and New York.
32. Ju, L., H. Wang, and D. Jiang (2007), Simulation of the last glacial maximum climate over East Asia with a regional climate model nested in a general circulation model, Palaeogeogr. Palaeoclimatol. Palaeoecol., 248(3), 376–390.
33. Kanamitsu, M., et al. (2002), NCEP dynamical seasonal forecast system 2000, Bull. Am. Meteorol. Soc., 83, 1019–1037.
34. Kendon, E. J., N. M. Roberts, C. A. Senior, and M. J. Roberts (2012), Realism of rainfall in a very high-resolution regional climate model, J. Clim., 25, 5791–5806, doi:10.1175/JCLI-D-11-00562.1.
35. Kendon, E. J., N. M. Roberts, H. J. Fowler, M. J. Roberts, S. C. Chan, and C. A. Senior (2014), Heavier summer downpours with climate change revealed by weather forecast resolution model, Nat. Clim. Change, 4, 570–576, doi:10.1038/nclimate2258.
36. Kotlarski, S., et al. (2014), Regional climate modeling on European scales: A joint standard evaluation of the EURO-CORDEX RCM ensemble, Geosci. Model Dev. Discuss., 7, 217–293, doi:10.5194/gmdd-7-217-2014.
37. Kottek, M., J. Grieser, C. Beck, B. Rudolf, and F. Rubel (2006), World map of the Koppen-Geiger climate classification updated, Meteorol. Z., 15(3), 259–263, doi:10.1127/0941-2948/2006/0130.
38. Kusunoki, S., and O. Arakawa (2012), Change in the precipitation intensity of the East Asian summer monsoon projected by CMIP3 models, Clim. Dyn., 38, 2055–2072, doi:10.1007/s00382-011-1234-7.
39. Lawrence, P. J., and T. N. Chase (2007), Representing a new MODIS consistent land surface in the Community Land Model (CLM 3.0), J. Geophys. Res., 112, G01023, doi:10.1029/2006JG000168.
40. Macadam, I., A. J. Pitman, P. H. Whetton, and G. Abramowitz (2010), Ranking climate models by performance using actual values and anomalies: Implications for climate change impact assessments, Geophys. Res. Lett., 37, L16704, doi:10.1029/2010GL043877.
41. Maurer, E. P., T. Das, and D. R. Cayan (2013), Errors in climate model daily precipitation and temperature output: Time invariance and implications for bias correction, Hydrol. Earth Syst. Sci., 17, 2147–2159, doi:10.5194/hess-17-2147-2013.
42. Mitchell, T. D., and P. D. Jones (2005), An improved method of constructing a database of monthly climate observations and associated high-resolution grids, Int. J. Climatol., 25(6), 693–712, doi:10.1002/joc.1181.
43. Moss, R., et al. (2010), The next generation of scenarios for climate change research and assessment, Nature, 463, 747–756, doi:10.1038/nature08823.
44. Panitz, H. J., A. Dosio, M. Buchner, D. Luthi, and K. Keuler (2014), COSMO-CLM (CCLM) climate simulations over CORDEX-Africa domain: Analysis of the ERA-Interim driven simulations at 0.44° and 0.22° resolution, Clim. Dyn., 42, 3015–3038, doi:10.1007/s00382-013-1834-5.
45. Piao, S., et al. (2010), The impacts of climate change on water resources and agriculture in China, Nature, 467, 43–51, doi:10.1038/nature09364.
46. Qian, Y., L. Ruby Leung, S. J. Ghan, and F. Giorgi (2003), Regional climate effects of aerosols over China: Modeling and observation, Tellus, Ser. B, 55(4), 914–934.
47. Qin, P., Z. Xie, and X. Yuan (2013), Incorporating groundwater dynamics and surface/subsurface runoff mechanisms in regional climate modeling over river basins in China, Adv. Atmos. Sci., 30(4), 983–996, doi:10.1007/s00376-012-2095-7.
48. Rienecker, M. M., et al. (2011), MERRA: NASA’s Modern-Era Retrospective Analysis for Research and Applications, J. Clim., 24, 3624–3648, doi:10.1175/JCLI-D-11-00015.1.
49. Rockel, B., and B. Geyer (2008), The performance of the regional climate model CLM in different climate regions, based on the example of precipitation, Meteorol. Z., 17(4), 487–498, doi:10.1127/0941-2948/2008/0297.
50. Rockel, B., A. Will, and A. Hense (2008), The regional Climate Model COSMO-CLM (CCLM), Meteorol. Z., 17, 347–348, doi:10.1127/0941-2948/2008/0309.
51. Roeckner, E., et al. (2003), The atmospheric general circulation model ECHAM5. Part I: Model description, Rep. 349, Max-Planck-Institut fur Meteorologie, Hamburg, Germany.
52. Scoccimarro, E., S. Gualdi, A. Bellucci, A. Sanna, P. Fogli, E. Manzini, M. Vichi, P. Oddo, and A. Navarra (2011), Effects of tropical cyclones on ocean heat transport in a high resolution coupled general circulation model, J. Clim., 24, 4368–4384, doi:10.1175/2011JCLI4104.1.
53. Stanelle, T., B. Vogel, H. Vogel, D. Baumer, and C. Kottmeier (2010), Feedback between dust particles and atmospheric processes over West Africa during dust episodes in March 2006 and June 2007, Atmos. Chem. Phys., 10, 10,771–10,788, doi:10.5194/acp-10-10771-2010.
54. Steppeler, J., G. Doms, U. Schättler, H. W. Bitzer, A. Gassmann, U. Damrath, and G. Gregoric (2003), Meso-gamma scale forecasts using the nonhydrostatic model LM, Meteorol. Atmos. Phys., 82, 75–96, doi:10.1007/s00703-001-0592-9.
55. 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. Clim., 23, 3187–3208, doi:10.1175/JCLI-D-12-00321.1.
56. Sun, Q., C. Miao, Q. Duan, D. Kong, A. Ye, Z. Di, and W. Gong (2014), Would the ‘real’ observed dataset stand up? A critical examination of eight observed gridded climate datasets for China, Environ. Res. Lett., 9, 015001, doi:10.1088/1748-9326/9/1/015001.
57. Tanre, D., J. F. Geleyn, and J. M. Slingo (1984), First results of the introduction of an advanced aerosol-radiation interaction in the ECMWF low resolution global model, in Aerosols and their Climatic Effects, edited by H. Gerber and A. Deepak, pp. 133–177, Hampton, Va.
58. Tegen, I., A. A. Lacis, and I. Fung (1996), The influence of mineral aerosols from disturbed soils on the global radiation budget, Nature, 380, 419–422, doi:10.1038/380419a0.
59. Wang, D., C. Menz, T. Simon, C. Simmer, and C. Ohlwein (2013), Regional dynamical downscaling with CCLM over East Asia, Meteorol. Atmos. Phys., 121, 39–53, doi:10.1007/s00703-013-0250-z.
60. White paper: China’s policies and actions on climate change, The State Council Information Office (2012). [Available at http://www.china.org.cn/government/news/2008-10/29/content_16681689.htm.]
61. Xu, Y., and C.-H. Xu (2012), Preliminary assessment of simulations of climate changes over China by CMIP5 multi-models, Atmos. Oceanic Sci. Lett., 5(6), 489–494.
62. Xu, Y., X. J. Gao, Y. Shen, C. H. Xu, Y. Shi, and F. Giorgi (2009), A daily temperature dataset over China and its application in validating a RCM simulation, Adv. Atmos. Sci., 26(4), 763–772.
63. Yatagai, A., K. Kamiguchi, O. Arakawa, A. Hamada, N. Yasutomi, and A. Kitoh (2012), APHRODITE: Constructing a long-term daily gridded precipitation dataset for Asia based on a dense network of rain gauges, Bull. Am. Meteorol. Soc., 93, 1401–1415, doi:10.1175/BAMS-D-11-00122.1.
64. Yu, E. T., H. J. Wang, and J. Q. Sun (2010), A quick report on a dynamical downscaling simulation over China using the nested model, Atmos. Oceanic Sci. Lett., 3(6), 325–329.
65. Zhang, D. F., X. J. Gao, and L. C. Ouyang (2008), Simulation of present climate over China by a regional climate model, J. Trop. Meteorol., 14(1), 19–23.
66. Zou, H., J. Zhu, L. Zhou, P. Li, and S. Ma (2014), Validation and application of reanalysis temperature data over the Tibetan plateau, J. Meteorol. Res., 28(1), 139–149.
67. Zou, L., and T. Zhou (2013), Near future (2016-40) summer precipitation changes over China as projected by a regional climate model (RCM) under the RCP8.5 emissions scenario: Comparison between RCM downscaling and the driving GCM, Adv. Atmos. Sci., 30(3), 806–818, doi:10.1007/s00376-013-2209-x.
68. Zubler, E. M., U. Lohmann, D. Lüthi, and C. Schär (2011), Intercomparison of aerosol climatologies for use in a regional climate model over Europe, Geophys. Res. Lett., 38, L15705, doi:10.1029/2011GL048081.