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

LIU Xiying

doi:10.1007/s13131-014-0518-2

Article Contents

Article Navigation > Acta Oceanologica Sinica > 2014 > 33(9): 56-67

LIU Xiying. Biases of the Arctic climate in a regional ocean-sea ice-atmosphere coupled model: an annual validation[J]. Acta Oceanologica Sinica, 2014, 33(9): 56-67. doi: 10.1007/s13131-014-0518-2

Citation:

LIU Xiying. Biases of the Arctic climate in a regional ocean-sea ice-atmosphere coupled model: an annual validation[J]. Acta Oceanologica Sinica, 2014, 33(9): 56-67. doi: 10.1007/s13131-014-0518-2

LIU Xiying. Biases of the Arctic climate in a regional ocean-sea ice-atmosphere coupled model: an annual validation[J]. Acta Oceanologica Sinica, 2014, 33(9): 56-67. doi: 10.1007/s13131-014-0518-2

Citation:

LIU Xiying. Biases of the Arctic climate in a regional ocean-sea ice-atmosphere coupled model: an annual validation[J]. Acta Oceanologica Sinica, 2014, 33(9): 56-67. doi: 10.1007/s13131-014-0518-2

PDF( 17646 KB)

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

doi: 10.1007/s13131-014-0518-2

LIU Xiying

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

Received Date: 2013-01-05
Rev Recd Date: 2014-05-20

Abstract

Abstract

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.
- Arctic climate,
- coupled model,
- numerical simulation

FullText(HTML)

References(32)

References

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

Relative Articles

Supplements(0)

Cited By

Proportional views