Home > 2014, 33(9) > Biases of the Arctic climate in a regional ocean-sea ice-atmosphere coupled model: an annual validation

Citation: LIU Xiying. Biases of the Arctic climate in a regional ocean-sea ice-atmosphere coupled model: an annual validation. ACTA OCEANOLOGICA SINICA, 2014, 33(9): 56-67. doi: 10.1007/s13131-014-0518-2

2014, 33(9): 56-67. doi: 10.1007/s13131-014-0518-2

Biases of the Arctic climate in a regional ocean-sea ice-atmosphere coupled model: an annual validation

1.  College of Meteorology and Oceanography, People's Liberation Army University of Science and Technology, Nanjing 211101, China

Received Date: 2013-01-05
Accepted Date: 2014-05-20

Fund Project: The National Natural Science Foundation of China under contract No. 41276190.

The Coupling of three model components, WRF/PCE (polar climate extension version of weather research and forecasting model (WRF)), ROMS (regional ocean modeling system), and CICE (community ice code), has been implemented, and the regional atmosphere-ocean-sea ice coupled model named WRF/PCEROMS-CICE has been validated against ERA-interim reanalysis data sets for 1989. To better understand the reasons that generate model biases, the WRF/PCE-ROMS-CICE results were compared with those of its components, the WRF/PCE and the ROMS-CICE. There are cold biases in surface air temperature (SAT) over the Arctic Ocean, which contribute to the sea ice concentration (SIC) and sea surface temperature (SST) biases in the results of the WRF/PCE-ROMS-CICE. The cold SAT biases also appear in results of the atmospheric component with a mild temperature in winter and similar temperature in summer. Compared to results from the WRF/PCE, due to influences of different distributions of the SIC and the SST and inclusion of interactions of air-sea-sea ice in the WRF/PCE-ROMS-CICE, the simulated SAT has new features. These influences also lead to apparent differences at higher levels of the atmosphere, which can be thought as responses to biases in the SST and sea ice extent. There are similar atmospheric responses in feature of distribution to sea ice biases at 700 and 500 hPa, and the strength of responses weakens when the pressure decreases in January. The atmospheric responses in July reach up to 200 hPa. There are surplus sea ice extents in the Greenland Sea, the Barents Sea, the Davis Strait and the Chukchi Sea in winter and in the Beaufort Sea, the Chukchi Sea, the East Siberian Sea and the Laptev Sea in summer in the ROMS-CICE. These differences in the SIC distribution can all be explained by those in the SST distributions. These features in the simulated SST and SIC from ROMS-CICE also appear in the WRF/PCE-ROMS-CICE. It is shown that the performance of the WRF/PCE-ROMS-CICE is determined to a large extent by its components, the WRF/PCE and the ROMS-CICE.

Key words: Arctic climate , coupled model , numerical simulation

本文将区域气候模式WRF/PCE (polar climate extension version of weather research and forecasting model (WRF))、区域海洋 ROMS (regional ocean modeling system)及海冰模式CICE (community ice code)耦合起来,发展出区域海洋—海冰—大气耦合模式WRF/PCE-ROMS-CICE,并利用ERA-interim 再分析数据集1989年资料对耦合模式模拟结果进行了检验。为更好地理解耦合模式误差产生原因,对WRF/PCE-ROMS-CICE、WRF/PCE及 ROMS-CICE模拟结果进行了对比分析。WRF/PCE-ROMS-CICE 模拟结果中北冰洋表面气温(SAT)偏低,导致海冰密集度 (SIC) 及海表面温度(SST) 出现偏差。 SAT冷偏差也出现在WRF/PCE模拟结果中,但冬季误差减小。与WRF/PCE结果相比,由于受SIC 、 SST分布差异及海—冰—气相互作用机制影响,WRF/PCE-ROMS-CICE 模拟的SAT具有新特征。这些影响也导致更高层次大气特征出现明显差异,这些不同可视为对SST及SIC 差异的响应。1月,700和500 hPa 等压面上大气响应分布特征相近,但显示出响应强度随高度增加而减小特征。7月,大气响应可伸展至 200 hPa。ROMS-CICE模拟结果中,冬季格陵兰海、巴伦支海、戴维斯海峡及楚科奇海海冰偏多,夏季波弗特海、楚科奇海、东西伯利压海及拉普捷夫海海冰偏多。这些误差与SST 误差相联系。ROMS-CICE模拟结果中的SST及SIC误差特征也出现在WRF/PCE-ROMS-CICE结果中。这表明,耦合模式WRF/PCE-ROMS-CICE的性能在很大程度上由其模式分量WRF/PCE及ROMS-CICE的性能决定。


Asplin M G, Lukovich J V, Barber D G. 2009. Atmospheric forcing of the Beaufort Sea ice gyre: surface pressure climatology and sea ice motion. J Geophys Res, 114: C00A06 DOI:10.1029/2008JC005127


Briegleb B P, Bromwich D H. 1998. Polar climate simulation of the NCAR CCM3. J Climate, 11: 1270-1286


Bromwich D H, Hines K M, Bai L S. 2009. Development and testing of Polar Weather Research and Forecasting model: 2. Arctic Ocean. J Geophys Res, 114: D08122 DOI:10.1029/2008JD010300


Budgell W P. 2005. Numerical simulation of ice-ocean variability in the Barents Sea region: towards dynamical downscaling. Ocean Dynamics, 55: 370-387 DOI: 10.1007/s10236-005-0008-3


Carton J A, Giese B S. 2008. A reanalysis of ocean climate using simple ocean data assimilation (SODA). Monthly Weather Review, 136: 2999-3017


Cassano J J, Box J E, Bromwich D H, et al. 2001. Evaluation of polar MM5 simulations of Greenland's atmospheric circulation. J Geophys Res, 106(D24): 33867-33889


Dee D P, Uppala S M, Simmons A J, et al. 2011. The ERA-interim reanalysis: configuration and performance of the data assimilation system. Quart J R Meteorol Soc,137: 553-597


Di Lorenzo E. 2003. Seasonal dynamics of the surface circulation in the southern California Current System. Deep-Sea Res: Part II, 50: 2371-2388


Dinniman M S, Klinck J M, Smith W O Jr. 2003. Cross shelf exchange in a model of the Ross Sea circulation and biogeochemistry. Deep-Sea Res: Part II, 50: 3103-3120


Dorn W, Dethloff K, Rinke A. 2012. Limitations of a coupled regional climate model in the reproduction of the observed Arctic sea-ice retreat. The Cryosphere, 6: 985-998


Doscher R, Koenigk T. 2013. Arctic rapid sea ice loss events in regional coupled climate scenario experiments. Ocean Sci, 9: 217-248 DOI:10.5194/os-9-217-2013


Doscher R,Wyser K, Meier H E M, et al. 2010. Quantifying Arctic contributions to climate predictability in a regional coupled ocean-ice-atmosphere model. Climate Dyn, 34:1157-1176 DOI 10.1007/s00382-009-0567-y


Holton J R. 2004. An Introduction to Dynamic Meteorology. 4th ed. New York: Academic Press, 70-73


Hunke E C, Lipscomb W H. 2010. CICE: the Los Alamos sea ice model, documentation and software User's Manual Version 4.1. T-3 Fluid Dynamics Group, Los Alamos National Laboratory, Tech Rep LA-CC-06-012. Los Alamos: Los Alamos National Laboratory


Kauker F, Gerdes R, Karcher M, et al. 2003. Variability of Arctic and north Atlantic sea ice: a combined analysis of model results and observations from 1978 to 2001. J Geophys Res, 108: 3182 DOI:10.1029/2002JC001573 Liu X Y, Liu H L, Li W, et al. 2008. Numerical simulation of atmosphereocean-sea ice interaction during interannual cycle in high northern latitudes. Acta Meteorologica Sinica, 22: 119-128


Liu X Y, Zhang X H, Yu R C, et al. 2005. Experiments of sea ice simulation. Journal of Hydrodynamics, 17:686-692


Liu X Y, Zhang X H, Yu R C, et al. 2007. Fine-resolution simulation of surface current and sea ice in the Arctic Mediterranean Seas. Chinese Journal of Oceanology and Limnology, 25:132-138


Liu X Y. 2010. Implementation of a sea ice-ocean coupled model in form of coupler component. Computer Engineering and Applications (in Chinese), 46: 24-27


Liu X Y. 2011. Numerical simulations of sea ice with different advection schemes. Journal of Hydrodynamics, 23:372-378


Liu X Y, Zhao J H, Xia H S, et al. 2013. Temperature biases in modeled polar climate and adoption of physical parameterization schemes. Advances in Polar Sciences, 23: 30-40


Marchesiello P, McWilliams J C, Shchepetkin A. 2003. Equilibrium structure and dynamics of the California Current System. J Phys Oceanogr, 33: 753-783


Peliz A, Dubert J, Haidvogel D B, et al. 2003. Generation and unstable evolution of a density-driven Eastern Poleward Current: the Iberian Poleward Current. J Geophys Res, 108: 3268 DOI:10.1029/2002JC001443 Proshutinsky A, Aksenov Y, Gerdes R, et al. 2011. Recent advances in Arctic Ocean studies employing models from the Arctic Ocean Model Intercomparison Project. Oceanography, 24:102-113


Shchepetkin A F, McWilliams J C. 2005. The regional ocean modeling system: a split-explicit, free-surface, topography following coordinates ocean model. Ocean Modelling, 9: 347-404


Simmons A, Uppara S, Dee D, et al. 2006. ERA-interim: new ECMWF reanalysis products from1989 onwards. ECMWF Newsletter,(110): 25-35


Skamarock W, Dudhia J, Gill D O, et al. 2008. A Description of the Advanced Research WRF version 3. NCAR Technical Note TN-475+STR. Boulder: NCAR


Slonosky V C, Mysak L A, Derome J. 1997. Linking Arctic sea ice and atmospheric circulation anomalies on interannual and decadal time scales. Atmosphere-Ocean, 35: 333-366


Smith D, Dukowicz J K, Malone R C. 1992. Parallel ocean general circulation modeling. Physica D, 60: 38-61


Tjernstrom M, Zagar M, Svensson G, et al. 2004. Modelling the Arctic boundary layer: an evaluation of six ARCMIP regional-scale models using data from the SHEBA project. Boundary-Layer Meteorology, 117: 337-381


Vihma T, Tisler P, Uotila P. 2012. Atmospheric forcing on the drift of Arctic sea ice in 1989-2009. Geophys Res Lett, 39: L02501 DOI:10.1029/2011GL050118


Warner J C, Sherwood C R, Arango H G, et al. 2005. Performance of four turbulence closure methods implemented using a generic length scale method. Ocean Modelling, 8: 81-113


Wilkin J L, Arango H G, Haidvogel D B, et al. 2005. A regional ocean modeling system for the long-term ecosystem observatory. J Geophys Res, 110: C06S91 DOI:10.1029/2003JC002218


Wu B Y, Wang J, Walsh J E. 2006. Dipole anomaly in the winter Arctic atmosphere and its association with sea ice motion. J Climate, 19: 210-225

  • PDF Downloads()
  • Abstract Views()
  • HTML Views()

Figures And Tables

Biases of the Arctic climate in a regional ocean-sea ice-atmosphere coupled model: an annual validation

LIU Xiying