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Recent advances in regional air-sea coupled models

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Abstract

In this paper, we first briefly review the history of air-sea coupled models, and then introduce the current status and recent advances of regional air-sea coupled models. In particular, we discuss the core technical and scientific issues involved in the development of regional coupled models, including the coupling technique, lateral boundary conditions, the coupling with sea waves (ices), and data assimilation. Furthermore, we introduce the application of regional coupled models in numerical simulation and dynamical downscaling. Finally, we discuss the existing problems and future directions in the development of regional air-sea coupled models.

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References

  1. Chen K M, Jin X Z, Zhang X H. Some about the climate drift and sensitivity of CGCM (in Chinese). Acta Oceanol Sin, 1997, 19: 38–51

    Google Scholar 

  2. Manabe S, Bryan K. Climate calculation with a combined ocean-atmosphere model. J Atmos Sci, 1969, 26: 768–789

    Google Scholar 

  3. Manabe S, Bryan K, Spelman M J. A global ocean-atmosphere climate model, Part I: The atmospheric circulation. J Phys Oceanogr, 1975, 5: 3–29

    Google Scholar 

  4. Manabe S, Bryan K, Spelman M J. A global ocean-atmosphere climate model with seasonal variation for future studies of climate sensitivity. Dynam Atmos Oceans, 1979, 3: 393–426

    Google Scholar 

  5. Bryan K, Manabe S, Pacanowski R C. A global ocean-atmosphere climate model, Part II: The oceanic circulation. J Phys Oceanogr, 1975, 5: 30–46

    Google Scholar 

  6. Washington W M, Semtner A J, Meehl G A, et al. General circulation experiment with a coupled atmosphere, ocean and sea ice model. J Phys Oceanogr, 1980, 10: 1887–1908

    Google Scholar 

  7. Zhou T J, Yu Y Q, Yu R C, et al. Coupled climate system model coupler review (in Chinese). Chin J Atmos Sci, 2004, 28: 993–1007

    Google Scholar 

  8. Wang B, Zhou T J, Yu Y Q, et al. A perspective on earth system model development (in Chinese). Acta Meteorol Sin, 2008, 66: 857–869

    Google Scholar 

  9. Zeng Q C, Yuan C G, Zhang X H, et al. Documentation of IAP two-level atmospheric general circulation model. DOE/ER/60314-H1, TR044, 1989. 383

  10. Zhang X H. Dynamic framework of IAP nine-level atmospheric general circulation model. Adv Atmos Sci, 1990, 7: 66–77

    Google Scholar 

  11. Bi X Q. IAP 9 level atmospheric general circulation model and climate simulation (in Chinese). Dissertation for the Doctoral Degree. Beijing: Institute of Atmospheric Physics, Chinese Academy of Sciences, 1993

    Google Scholar 

  12. Liang X Z. Description of a nine-level grid poing general circulation model. Adv Atmos Sci, 1996, 13: 269–298

    Google Scholar 

  13. Zhang X H, Liang X Z. A numerical world ocean general circulation model. Adv Atmos Sci, 1989, 6: 43–61

    Google Scholar 

  14. Zhang X H, Bao N, Yu R C, et al. Coupling scheme experiments based on an atmospheric and an oceanic GCM. Chin J Atmos Sci, 1992, 16: 129–144

    Google Scholar 

  15. Chen K M. Improvement of IAP global coupled ocean-atmosphere general circulation model and numerical simulation of climate change induced by the enhanced greenhouse effect (in Chinese). Dissertation for the Doctoral Degree. Beijing: Institute of Atmospheric Physics, Chinese Academy of Sciences, 1994

    Google Scholar 

  16. Liu H, Jin X Z, Zhang X H, et al. A coupling experiment of an atmosphere and an ocean model with a monthly anomaly exchange scheme. Adv Atmos Sci, 1996, 13: 133–146

    Google Scholar 

  17. Wu G X, Zhang X H, Liu H, et al. LASG global ocean-atmosphere-land system model and simulation (in Chinese). Quart J Appl Meteorol, 1997, 8(Suppl): 15–28

    Google Scholar 

  18. Yu Y Q, Yu R C, Zhang X H, et al. A flexible coupled ocean-atmosphere general circulation model. Adv Atmos Sci, 2002, 19: 169–190

    Google Scholar 

  19. Zhou T J, Wang Z Z, Yu R C, et al. The climate system model FGOALS-s using LASG/IAP spectral AGCM SAMIL as its atmospheric component (in Chinese). Acta Meteorol Sin, 2005, 63: 702–715

    Google Scholar 

  20. Yu Y Q, Zheng W P, Liu H L, et al. The LASG coupled climate system model FGCM-1.0. Chin J Geophys, 2007, 50: 1454–1455

    Google Scholar 

  21. Yu Y Q, Zheng W P, Wang B, et al. Versions g1.0 and g1.1 of the LASG/IAP Flexible Global Ocean-Atmosphere-Land System model. Adv Atmos Sci, 2011, 28: 99–117

    Google Scholar 

  22. Ding Y H, Li Q Q, Li W J, et al. Advance in seasonal dynamical prediction operation in China (in Chinese). Acta Meteorol Sin, 2004, 62: 598–612

    Google Scholar 

  23. Min J Z, Sun Z B, Zhu W J. Review of study on air-sea coupled models and coupling method (in Chinese). J Nanjing Inst Metrorol, 2000, 23: 449–458

    Google Scholar 

  24. Zebiak S E, Cane M A. A model El Nino-Southern Oscillation. Mon Weather Rev, 1987, 115: 2262–2278

    Google Scholar 

  25. Xu J X, Sun Z B, Lei Z C. A numerical experiment on the monsoon circulation using a coupled ocean-atmosphere model (in Chinese). J Nanjing Inst Metrorol, 1992, 15: 332–341

    Google Scholar 

  26. Boville B A. The NCAR climate system model version 1. BMRC Res Rep, 1998, 69: 5–7

    Google Scholar 

  27. Collins W D, Bitz C M, Blackmon M L, et al. The Community Climate System Model Version 3 (CCSM3). J Clim, 2006, 19, 2122-2143

    Google Scholar 

  28. Matsuno T, Kawamiya M, Sudo K, et al. Development of an integrated earth system model for prediction of environmental changes. In: Annual Report of the Earth Simulator Center. 2005

  29. Seo H, Miller A J, Roads J O. The scripps coupled ocean-atmosphere regional (SCOAR) model with applications in the Eastern Pacific sector. J Clim, 2007, 20: 381–402

    Google Scholar 

  30. Neelin J D, Latif M, Allaart M A F, et al. Tropical air-sea interaction in general circulation models. Clim Dyn, 1992, 7: 73–104

    Google Scholar 

  31. Hodur R M. The Naval Research Laboratory’s Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS). Mon Weather Rev, 1997, 125: 1414–1430

    Google Scholar 

  32. Knutson T R, Tulyea R E, Kurihara Y. Simulated increase of hurricane intensities in CO2-warmed climate. Science, 1998, 279: 1018–1020

    Google Scholar 

  33. Gustafsson N, Nyberg L, Omstedt A. Coupling of a high-resolution atmospheric model and an ocean model for the Baltic Sea. Mon Weather Rev, 1998, 126: 2822–2846

    Google Scholar 

  34. Hagedorn R, Lehmann A, Jacob D. A coupled high resolution atmosphere-ocean model for the BALTEX region. Meteorol Z, 2000, 9: 7–20

    Google Scholar 

  35. Xue H J, Pan Z Q, John M, et al. A 2D coupled atmosphere-ocean model study of air-sea interactions during a cold air outbreak over the Gulf Stream. Mon Weather Rev, 2000, 128: 973–996

    Google Scholar 

  36. Xue M, Droegemeier K K, Wong V. The Advanced Regional Prediction System (ARPS)—A multi-scale nonhydrostatic atmospheric simulation and prediction model. PART I: Model dynamics and verification. Meteorol Atmos Phys, 2000, 75: 161–193

    Google Scholar 

  37. Li Y, Xue H, John M B. Air-sea interactions during the passage of a winter storm over the Gulf Stream: A three-dimensional coupled atmosphere-ocean model study. J Geophys Res, 2002, 107: 3200

    Google Scholar 

  38. Döscher R, U Willén U, Jones C, et al. The development of the regional coupled ocean-atmosphere model RCAO. Boreal Environ Res, 2002, 7: 183–192

    Google Scholar 

  39. Schrum C, Hübner U, Jacob D, et al. A coupled atmosphere-iceocean model for the North Sea and Baltic Sea. Clim Dyn, 2003, 21: 131–141

    Google Scholar 

  40. Aldrian E, Sein D V, Jacob D, et al. Modeling Indonesian rainfall with a coupled regional model. Clim Dyn, 2005, 25: 1–17

    Google Scholar 

  41. Pullen J, Doyle J D, Signell R P. Two-way air-sea coupling: A study of the Adriatic. Mon Weather Rev, 2006, 134: 1465–1483

    Google Scholar 

  42. Sasaki H, Kurihara K, Takayabu I, et al. Preliminary results from the coupled atmosphere-ocean regional climate model at the Meteorological Research Institute. J Meteorol Soc Jpn, 2006, 84: 389–403

    Google Scholar 

  43. Alves O, Wang G, Zhong A, et al. POAMA: Bureau of Meteorology operational coupled model seasonal forecast system. In: Proceedings of the ECMWF Workshop on the Role of the Upper Ocean in Medium and Extended Range Forecasting, 2002

  44. Xie L, Pietrafessa L J, Raman S. Coastal ocean-atmosphere coupling. Coastal and Eatuarine Studies: Coastal ocean prediction. Amer Geophys Union, 1999, 56: 101–123

    Google Scholar 

  45. Yan B L, Huang R H, Zhang R H. A simple tropical pacific atmosphere-ocean couple model with ENSO cycle characteristics (in Chinese). Chin J Atmos Sci, 2002, 26: 193–205

    Google Scholar 

  46. Min J Z, Sun Z B, Zeng G, et al. An air-sea global coupled model system (NIM/COAMS). A new flux reanalysis coupling scheme (in Chinese). J Nanjing Inst Metrorol, 2005, 28: 601–608

    Google Scholar 

  47. Qin Z K, Sun Z B, Lin Z H, et al. Evaluation on potential predictability of summer climate over East Asia by an air-sea coupled model (in Chinese). Clim Environ Res, 2007, 12: 426–436

    Google Scholar 

  48. Wang F, Ding Y H, Xu Y. The effects of radiative parameterization scheme on simulation of cloud and radiation in an AOGCM (in Chinese). J Appl Meteorol Sci, 2007, 18: 257–265

    Google Scholar 

  49. Lin W T, Zhang F, Lin Y H. Influences of nonlinear factors of governing equations for the atmosphere on ENSO Cycles in a coupled ocean-atmosphere model (in Chinese). Clim Environ Res, 2008, 13: 253–259

    Google Scholar 

  50. Zhang L, Ding Y H. Evaluation of extreme heavy precipitation in coupled ocean-atmosphere general circulation models (in Chinese). J Appl Meteorol Sci, 2008, 19: 760–769

    Google Scholar 

  51. Wang Z R, Jiang D Y, Bao C L. A regional coupled oceanatmosphere model and its experiments (in Chinese). Acta Oceanol Sin, 1995, 17: 50–58

    Google Scholar 

  52. Lü S H, Chen Y C, Zhu B C. A coupled ocean and atmosphere model and simulation experiment in the South China Sea Area (in Chinese). Plateau Meteorol, 2000, 19: 415–426

    Google Scholar 

  53. Ren X J. Research of the air-sea interaction in the South China Sea summer monsoon, development of a coupled regional air-sea model and its simulation of summer monsoon (in Chinese). Dissertation for the Doctoral Degree. Nanjing: Nanjing University, 2000

    Google Scholar 

  54. Ren X J, Qian Y F. Numerical simulation of oceanic elements at East Asian Coastal Oceans from May to August in 1998 by using a coupled regional ocean-atmosphere model (in Chinese). Clim Environ Res, 2000, 5: 482–4

    Google Scholar 

  55. Ren X J, Qian Y F. A coupled regional air-sea model, its performance and climate drift in simulation of the East Asian summer monsoon in 1998. Int J Climatol, 2005, 25: 679–692

    Google Scholar 

  56. Lin H J, Qian Y F, Zhang Y C, et al. A regional coupled air-ocean wave model and the simulation of the South China Sea summer monsoon in 1998. Int J Climatol, 2006, 6: 2041–2056

    Google Scholar 

  57. Wamg Z F, Qian Y F, Lin H J. Analysis of numerical simulation on extreme precipitation in China using a coupled regional oceanatmosphere model (in Chinese). Plateau Meteorol, 2008, 27: 113–121

    Google Scholar 

  58. Yao S X, Zhang Y C. Simulation of China summer precipitation with a regional air-sea coupled model (in Chinese). Acta Meteorol Sin, 2008, 66: 131–142

    Google Scholar 

  59. Xie K, Ren X J, Zhang Y C, et al. Evalution on simulation of summertime atmospheric water vapor transport over North China using a regional air-sea coupled model and its comparison with the regional climate model (in Chinese). Acta Meteorol Sin, 2009, 67: 1002–1012

    Google Scholar 

  60. Yu R C, Xue J S, Xu Y P. AREMS Mesoscale Numerical Prediction Model System (in Chinese). Beijing: Meteorological Press, 2004

    Google Scholar 

  61. Huang L W. The application of the mesoscale coupled LASG-MCM in the sea-atmosphere research of the Yellow and East China Sea region (in Chinese). In: Atmospheric Science and Geophysical Fluid Dynamics, State Key Laboratory of Numerical Simulation (LASG) Conference Proceedings, 2002

  62. Qi C X. The simulation research on a passing cyclone over the Yellow and East China Seas by the mesoscale air-sea coupling model MM5V3/ECOM-si (in Chinese). Dissertation for the Doctoral Degree. Hubei: Department of Traffic Information Engineering & Control, Wuhan University of Technology, 2003

    Google Scholar 

  63. Fang Y J, Zhang Y C, Tang J P, et al. A regional air-sea coupled model and its application over East Asia in the summer of 2000. Adv Atmos Sci, 2010, 27: 583–5

    Google Scholar 

  64. Li T, Zhou G Q. Preliminary results of a regional air-sea coupled model over East Asia. Chin Sci Bull, 2010, 55: 2295–2305

    Google Scholar 

  65. Sun Y M, Fei J F, Cheng X P, et al. Introduction of mesoscale air-ocean coupled model: WRF_ROMS-1.2 (in Chinese). Mar Forecasts, 2010, 27: 82–88

    Google Scholar 

  66. Jiang X P, Liu C X, Fei Z B. Development of a coupled mesoscale air-sea models (in Chinese). Guangdong Meteorol, 2009, 31: 1–3

    Google Scholar 

  67. Jiang X P, Liu C X, Qi Y Q. The simulation of typhoon Krovanh using a coupled air-sea model (in Chinese). Chin J Atmos Sci, 2009, 33: 99–108

    Google Scholar 

  68. Sausen R, Barthel K, Hasselmann K. Coupled ocean-atmosphere models with flux correction. Clim Dyn, 1988, 2: 145–163

    Google Scholar 

  69. Anthes R A, Hsie E Y, Kuo Y H. Descript ion of the PENN State/NCAR mesoscale model version 4 (MM4). NCAR Technical Note, 1987, NCAR/ TN-282+STR, 66

  70. Giorgi F, Marinucci M R. An investigation of the sensitivity of simulated precipitation to model resolution and its implications for climate studies. Mon Weather Rev, 1996, 124: 148–166

    Google Scholar 

  71. Warner T T, Peterson R A, Treadon R E. A tutorial on lateral boundary conditions as a basic and potentially serious limitation to regional numerical weather prediction. Bull Am Meteorol Soc, 1997, 78: 2599–2617

    Google Scholar 

  72. Harrison E, Elsberry R. A method for incorporating nested finite grids in the solution of systems of geophysical equations. J Atmos Sci, 1972, 29: 1235–1245

    Google Scholar 

  73. Phillips N A, Shukla J. On the strategy of combining coarse and fine grid meshes in numerical weather prediction. J Appl Meteorol, 1973, 12: 763–770

    Google Scholar 

  74. Staniforth A N, Mitchell H L. A variable-resolution finite-element technique for regional forecasting with the primitive equations. Mon Weather Rev, 1978, 106: 439–447

    Google Scholar 

  75. Hill G. Grid telescoping in numerical weather prediction. J Appl Meteorol, 1968, 7: 29–38

    Google Scholar 

  76. Wang H H, Halpern P. Experiments with a regional fine-mesh prediction model. J Appl Meteor, 1970, 9: 543–553

    Google Scholar 

  77. Kesel P G, Winninghoff F J. The fleet numerical weather central operational primitive-equation model. Mon Weather Rev, 1972, 100: 360–373

    Google Scholar 

  78. Wang J G, Wei S Y, Zhang Z Y. Study and tests on the lateral boundary conditions of nested-grid numerical prediction model in limited-area (in Chinese). Sci Meteorol Sin, 1992, 12: 371–379

    Google Scholar 

  79. Perkey D J, Kreitzberg C W. A time-dependent lateral boundary scheme for limited-area primitive equation models. Mon Weather Rev, 1976, 104: 744–755

    Google Scholar 

  80. Davies H C. A lateral boundary condition for multi-level prediction models. Quart J Roy Meteor Sos, 1976, 102: 405–418

    Google Scholar 

  81. Orlanski I. A simple boundary condition for unbounded hyperbolic flow. J Comput Phys, 1976, 2: 251–269

    Google Scholar 

  82. Chen Y J. The experiment of radiation boundary condition in the limited region model (in Chinese). Plateau Meteorol, 1987, 2: 111–116

    Google Scholar 

  83. Chu M, Xu Y F. Open boundary conditions in regional ocean models (in Chinese). Mar Sci, 2009, 33: 112–117

    Google Scholar 

  84. Treguier A M, Barnier B, Miranda A P, et al. An eddy-permitting model of the Atlantic circulation: Evaluating open boundary conditions. J Geophy Res, 2001, 106: 22115–22129

    Google Scholar 

  85. Marchesiello P, McWilliams J C, Shchepetkin A. Open boundary conditions for long-term integration of regional oceanic models. Ocean Model, 2001, 3: 1–20

    Google Scholar 

  86. Drennan W M, Taylor P K, Yelland M J. Parameterizing the sea surface roughness. J Phys Oceanogr, 2005, 35: 835–848

    Google Scholar 

  87. Hwang P A. Influence of wavelength on the parameterization of drag coefficient and surface roughness. J Phys Oceanogr, 2004, 60: 835–841

    Google Scholar 

  88. Hwang P A. Altimeter measurements of wind and wave modulation by the Kuroshio in the Yellow and East China Seas. J Phys Oceanogr, 2005, 61: 987–993

    Google Scholar 

  89. Hsu S A. A dynamic roughness equation and its application to wind strss determination at the air-sea interface. J Phys Oceanogr, 1974, 4: 116–120

    Google Scholar 

  90. Donelan M A, Dobson F W, Smith S D, et al. On the dependence of sea surface roughness on wave development. J Phys Oceanogr, 1993, 23: 2143–2149

    Google Scholar 

  91. Donelan M A, Drennan W M, Magnusson A K. Nonstationary analysis of the directional properties of propagating waves. J Phys Oceanogr, 1996, 26: 1901–1914

    Google Scholar 

  92. Smith S D, Anderson R J, Oost W A, et al. Sea surface wind stress and drag coefficients: The HEXOS results. Bound-Lay Meteorol, 1992, 60: 109–142

    Google Scholar 

  93. Johnson H K, Højstrup J, Vested H J, et al. On the dependence of sea surface roughness on wind waves. J Phys Oceanogr, 1998, 28: 1702–1716

    Google Scholar 

  94. Johnson H K, Vested H J, Hersbach H, et al. The coupling between wind and waves in the WAM Model. J Atmos Ocean Tech, 1999, 16: 1780–1790

    Google Scholar 

  95. Taylor P K, Yelland M J. The dependence of sea surface roughness on the height and steepness of the waves. J Phys Oceanogr, 2001, 31: 572–590

    Google Scholar 

  96. Oost W A, Komen G J, Jacobs C M J, et al. New evidence for a relation between wind stress and wave age from measurements during ASGAMAGE. Bound-Lay Meteorol, 2002, 103: 409–438

    Google Scholar 

  97. Fairall C W, Bradley E F, Hare J E, et al. Bulk parameterization of air-sea fluxes: Updates and verification for the COARE algorithm. J Clim, 2003, 16: 571–591

    Google Scholar 

  98. Drennan W M, Graber H C, Hauser D, et al. On the wave age dependence of wind stress over pure wind seas. J Geophys Res, 2003, 108: 8062

    Google Scholar 

  99. Lange B, Johnson H K, Larsen S, et al. On detection of a wave age dependency for the sea surface roughness. J Phys Oceanogr, 2004, 34: 1441–1458

    Google Scholar 

  100. Pan Y P, Sha W Y, Wang J H, et al. Development of new sea surface aerodynamic roughness parameterization scheme (in Chinese). Prog Nat Sci, 2007, 17: 1069–1077

    Google Scholar 

  101. Andreas E L. The temperature of evaporating sea spray droplets. J Atmos Sci, 1995, 52: 852–862

    Google Scholar 

  102. Andreas E L. A new sea spray generation function for wind speeds up to 32 m s−1. J Phys Oceanogr, 1998, 28: 2175–2184

    Google Scholar 

  103. Kepert J D. Comments on “The Temperature of Evaporating Sea Spray Droplets”. J Atmos Sci, 1996, 53: 1634–1641

    Google Scholar 

  104. Wu J. Concentrations of sea-spray droplets at various wind velocities: Separating productions through bubble bursting and wind tearing. J Phys Oceanogr, 2000, 30: 195–200

    Google Scholar 

  105. Meirink J F, Makin V K. The impact of sea spray evaporation in a numerical weather prediction model. J Atmos Sci, 2001, 58: 3626–3638

    Google Scholar 

  106. Wang Y Q, Kepert J D, Holland G J. The effect of sea spray evaporation on tropical cyclone boundary layer structure and intensity. Mon Weather Rev, 2001, 129: 2481–2500

    Google Scholar 

  107. Andreas E L, Emanuel K A. Effects of sea spray on tropical cyclone intensity. J Atmos Sci, 2001, 58: 3741–3751

    Google Scholar 

  108. Perrie W, Ren X J, Zhang W Q, et al. Simulation of extratropical Hurricane Gustav using a coupled atmosphere-ocean-sea spray model. Geophys Res Lett, 2004, 31: L03110

    Google Scholar 

  109. Perrie W, Zhang W Q, Andreas E L, et al. Sea spray impacts on intensifying midlatitude cyclones. J Atmos Sci, 2005, 62: 1867–1883

    Google Scholar 

  110. Zhang W Q, Perrie W, Li W B. Impacts of waves and sea spray on midlatitude storm structure and intensity. Mon Weather Rev, 2006, 134: 2418–2442

    Google Scholar 

  111. Zhang W Q, Perrie W. The influence of air-sea roughness, sea spray, and storm translation speed on waves in North Atlantic Storms. J Phys Oceanogr, 2008, 38: 817–839

    Google Scholar 

  112. Gall J S, Frank W M, Kwon Y. Effects of sea spray on tropical cyclones simulated under idealized conditions. Mon Weather Rev, 2008, 136: 1686–1705

    Google Scholar 

  113. Andreas E L, Persson P O G, Hare J E. A bulk turbulent air-sea flux algorithm for high-wind, spray conditions. J Phys Oceanogr, 2008, 38: 1581–1596

    Google Scholar 

  114. Andreas E L. Spray-mediated enthalpy flux to the atmosphere and salt flux to the ocean in high winds. J Phys Oceanogr, 2010, 40: 608–619

    Google Scholar 

  115. Lynch A H, Chapman W L, Walsh J E, et al. Development of a regional climate model of the western Arctic. J Clim, 1995, 8: 1555–1570

    Google Scholar 

  116. Hasselmann K. Ocean circulation and climate change. Tellus, 1991, 4: 82–103

    Google Scholar 

  117. Ly L N. A numerical coupled model for studying air-sea-wave interaction. Phys Fluids, 1995, 7: 2396–2406

    Google Scholar 

  118. Powers J G, Stoelinga M T. A coupled air-sea mesoscale model: Experiments in atmospheric sensitivity to marine roughness. Mon Weather Rev, 2000, 128: 208–228

    Google Scholar 

  119. Bao J W, Wilczak J M, Choi J K, et al. Numerical simulations of air-sea interactions under high wind conditions using a coupled model: A study of hurricane development. Mon Weather Rev, 2000, 128: 2190–2210

    Google Scholar 

  120. Doyle J D. Coupled atmosphere-ocean wave simulations under high wind conditions. Mon Weather Rev, 2002, 130: 3087–3099

    Google Scholar 

  121. Nagai H, Kobayashi T, Tsuduki K, et al. Coupled atmosphere, land-surface, hydrology, ocean-wave, and ocean-current models for mesoscale water and energy circulations. In: The 11th Conference on Mesoscale Processes, 2005

  122. Chen S S, Zhao W, Donelan M A, et al. The CBLAST-hurricane program and the next-generation fully coupled atmosphere-wave-ocean models for hurricane research and prediction. Bull Am Meteorol Soc, 2007, 88: 311–317

    Google Scholar 

  123. Warner J C, Armstrong B, He R Y, et al. Development of a Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST) Modeling System. Ocean Model, 2010, 35: 230–244

    Google Scholar 

  124. Huang L W, Deng J, Wen Y Q, et al. The Chinese coast mesoscale air-sea-wave coupled model system and its typhoon coupling experiments (in Chinese). In: The 6th National Dynamic Meteorology Conference Proceedings. 2005

  125. Wen Y Q, Huang L W, Yu S S, et al. Atmosphere-ocean-wave model coupling system based on distributed computing environment (in Chinese). J Wuhan Univ Technol, 2006, 28: 125–127

    Google Scholar 

  126. Wen Y Q, Huang L W, Deng J, et al. Framework of distributed coupled atmosphere-ocean-wave modeling system. Adv Atmos Sci, 2006, 23: 442–448

    Google Scholar 

  127. Zhang J F, Huang L W, Wen Y Q, et al. Implementation of a distributed atmosphere-wave-ocean coupling model: Typhoon wave simulation in the China Seas. In: International Symposium on Distributed Computing and Applications for Business, Engineering and Sciences. 2006

  128. Zhang J F, Huang L W, Wen Y Q, et al. A distributed coupled atmosphere-wave-ocean model for typhoon wave numerical simulation. Int J Comput Math, 2008, doi: 10.1080/ 00207160802047632

  129. Guan H, Zhou L, Shi Y, et al. Using Jason_1 data to validate the effectiveness of the regional air-sea coupled model simulation of the typhoon waves (in Chinese). Mar Forecasts, 2009, 26: 84–93

    Google Scholar 

  130. Le Dimet F X, Talagrand O. Variational algorithms for analysis and assimilation of meteorological observations: Theoretical aspects. Tellus, 1986, 38: 97–110

    Google Scholar 

  131. Derber J C. Variational four-dimensional analysis using quasigeostrophic constraints. Mon Weather Rev, 1987, 115: 998–1008

    Google Scholar 

  132. Talagrand O, Courtier P. Variational assimilation of meteorological observations with the adjoint vorticity equation. Part I: Theory. Q J R Meteorol Soc, 1987, 113: 1311–1328

    Google Scholar 

  133. Courtier P, Thepaut J N, Hollingsworth A. A strategy for operational implementation of 4D-Var, using an incremental approach. Q J R Meteorol Soc, 1994, 120: 1367–1388

    Google Scholar 

  134. Peng S Q, Zou X L. Assimilation of ground-based GPS zenith total delay and rain gauge precipitation observations using 4D-Var and their impact on short-range QPF. J Meteorol Soc Jpn, 2004, 82: 491–506

    Google Scholar 

  135. Peng S Q, Xie L A. Effect of determining initial conditions by four-dimensional variational data assimilation on storm surge forecasting. Ocean Model, 2006, 14: 1–18

    Google Scholar 

  136. Huang X Y, Coauthors. Four-dimensional variational data assimilation for WRF: Formulation and preliminary results. Mon Weather Rev, 2009, 137: 299–314

    Google Scholar 

  137. Evensen G. Sequential data assimilation with a nonlinear quasigeostrophic model using Monte Carlo methods to forecast error statistics. J Geophys Res, 1994, 99: 10143–10162

    Google Scholar 

  138. Evensen G. The ensemble Kalman filter: Theoretical formulation and practical implementation. Ocean Dyn, 2003, 53: 343–367

    Google Scholar 

  139. Houtekamer P L, Mitchell H L. Data assimilation using an ensemble Kalman filter technique. Mon Wea Rev, 1998, 126: 796–811

    Google Scholar 

  140. Houtekamer P L, Mitchel H L. Ensemble Kalman filtering. Q J R Meteorol Soc, 2005, 131: 3269–3289

    Google Scholar 

  141. Anderson J L. An ensemble adjustment Kalman filter for data assimilation. Mon Weather Rev, 2001, 129: 2884–2903

    Google Scholar 

  142. Hamill T M. Ensemble-based atmospheric data assimilation. In: Palmer T N, Hagedorn R, eds. Predictability of Weather and Climate Cambridge: Cambridge Press, 2006. 124–156

  143. Meng Z Y, Zhang F Q. Tests of an ensemble Kalman filter for mesoscale and regional-scale data assimilation. Part III: Comparison with 3DVAR in a real-data case study. Mon Weather Rev, 2008, 136: 522–540

    Google Scholar 

  144. Torn R D. Performance of a mesoscale ensemble Kalman filter (EnKF) during the NOAA high-resolution hurricane test. Mon Weather Rev, 2010, 138: 4375–4392

    Google Scholar 

  145. Chen D, Zebiak S E, Busalacchi A J, et al. An improved procedure for El Nino forecasting: Implications for predictability. Science, 1995, 269: 1699–1702

    Google Scholar 

  146. Chen D, Cane M A, Zebiak S E, et al. The impact of sea level data assimilation on the Lamont model prediction of the 1997/98 El Niño. Geophys Res Lett, 1998, 25: 2837–2840

    Google Scholar 

  147. Ballabrera-Poy J, Busalacchi A J, Murtugudde R. Application of a reduced-order kalman filter to initialize a coupled atmosphere-ocean model: Impact on the prediction of El Niño. J Clim, 2001, 14: 1720–1737

    Google Scholar 

  148. Zhang S, Harrison M J, Rosati A, et al. System design and evaluation of coupled ensemble data assimilation for global oceanic climate studies. Mon Weather Rev, 2007, 135: 3541–3564

    Google Scholar 

  149. Zhang S, Rosati A, Harrison M J. Detection of multidecadal oceanic variability by ocean data assimilation in the context of a “perfect” coupled model. J Geophys Res, 2009, 114: C12018

    Google Scholar 

  150. Zhang S, Rosati A. An inflated ensemble filter for ocean data assimilation with a biased coupled GCM. Mon Weather Rev, 2010, 138: 3905–3931

    Google Scholar 

  151. Bender M A, Ginis I, Kurihara Y. Numerical simulations of tropical cyclone-ocean interaction with a high-resolution coupled model. J Geophys Res, 1993, 98: 23245–23263

    Google Scholar 

  152. Bender M A, Ginis I. Real-case simulations of hurricane-ocean interaction using a high-resolution coupled model: Effects on hurricane intensity. Mon Weather Rev, 2000, 128: 917–946

    Google Scholar 

  153. Bender M A, Ginis I, Tuleya R E, et al. The operational GFDL coupled hurricane-ocean prediction system and a summary of its performance. Mon Weather Rev, 2007, 135: 3965–3989

    Google Scholar 

  154. Loglisci N, Qian M W, Rachev N, et al. Development of an atmosphere-ocean coupled model and its application over the Adriatic Seaduring a severe weather event of bora wind. J Geophys Res, 2004, 109: D01102

    Google Scholar 

  155. Huang B, Schopf P S, Shukla J. Intrinsic ocean-atmosphere variability of the tropical Atlantic Ocean. J Clim, 2004, 17: 2058–2077

    Google Scholar 

  156. Xie S P, Miyama T, Wang Y, et al. A regional ocean-atmosphere model for Eastern Pacific climate: Toward reducing tropical biases. J Clim, 2007, 20: 1504–1522

    Google Scholar 

  157. Mikolajewicz U, D V Sein D V, Jacob D, et al. Simulating Arctic sea ice variability with a coupled regional atmosphere-ocean-sea ice model. Meteorol Z, 2005, 14: 793–800

    Google Scholar 

  158. Knutson T R, Tuleya R E. Impact of CO2-induce dwarming on simulated hurricane intensity and precipitation: Sensitivity to the choice of climate model and convective parameterization. J Clim, 2004, 17: 3477–3495

    Google Scholar 

  159. Emanuel K. Climate and tropical cyclone activity: A new model downscaling approach. J Clim, 2006, 19: 4797–4802

    Google Scholar 

  160. Bender M A, Knutson T R, Tuleya R E, et al. Modeled impact of anthropogenic warming on the frequency of intense Atlantic hurricanes. Science, 2010, 327: 454–458

    Google Scholar 

  161. Raisanen J, Hansson U, Ullerstig A, et al. European climate in the late 21st century: Regional simulations with two driving global models and two forcing scenarios. Clim Dyn, 2004, 22: 13–31

    Google Scholar 

  162. Meier H E M. Baltic Sea climate in the late twenty-first century: A dynamical downscaling approach using two global models and two emission scenarios. Clim Dyn, 2006, 27: 39–68

    Google Scholar 

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Authors and Affiliations

  1. State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, 510301, China

    ShiQiu Peng, DuanLing Liu & YiNeng Li

  2. Department of Atmospheric Sciences, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    DuanLing Liu & ZhaoBo Sun

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  1. ShiQiu Peng
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  2. DuanLing Liu
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  3. ZhaoBo Sun
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  4. YiNeng Li
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Correspondence to ShiQiu Peng.

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Peng, S., Liu, D., Sun, Z. et al. Recent advances in regional air-sea coupled models. Sci. China Earth Sci. 55, 1391–1405 (2012). https://doi.org/10.1007/s11430-012-4386-3

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