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Indian Ocean SST Biases in a Flexible Regional Ocean Atmosphere Land ATMOSPHERIC AND OCEANIC SCIENCE LETTERS, 2012, VOL. 5, NO. 4, 273−279 Indian Ocean SST Biases in a Flexible Regional Ocean Atmosphere Land System (FROALS) Model HAN Zhen-Yu1, 2, ZHOU Tian-Jun1, and ZOU Li-Wei1 1State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of At- mospheric Physics, Chinese Academy of Sciences, Beijing 100029, China 2Graduate University of the Chinese Academy of Sciences, Beijing 100049, China Received 23 March 2012; revised 19 April 2012; accepted 20 April 2012; published 16 July 2012 Abstract The authors examine the Indian Ocean sea interaction. Recent studies have shown that coupled re- surface temperature (SST) biases simulated by a Flexible gional climate models (CRCMs) generally improve the Regional Ocean Atmosphere Land System (FROALS) simulation (Seo et al., 2008; Ratnam et al., 2009). A model. The regional coupled model exhibits pronounced Flexible Regional Ocean Atmosphere Land System cold SST biases in a large portion of the Indian Ocean (FROALS) model was established at the State Key Labo- warm pool. Negative biases in the net surface heat fluxes ratory of Numerical Modeling for Atmospheric Sciences are evident in the model, leading to the cold biases of the and Geophysical Fluid Dynamics, Institute of Atmos- SST. Further analysis indicates that the negative biases in pheric Physics (LASG, IAP) (Zou and Zhou, 2011, 2012). the net surface heat fluxes are mainly contributed by the An evaluation of the model performance over the western biases of sensible heat and latent heat flux. Near-surface North Pacific found that the SST shows cold biases, meteorological variables that could contribute to the SST which partly stem from the overestimation of convection biases are also examined. It is found that the biases of frequency by the atmospheric model (Zou and Zhou, sensible heat and latent heat flux are caused by the colder 2011). However, the performance of this model over the and dryer near-surface air in the model. IO is unknown. The main motivation of the current study Keywords: Indian Ocean, SST biases, FROALS, evalua- is to assess the performance of the FROALS model over tion the IO. We attempt to address the following questions: (1) Citation: Han, Z.-Y., T.-J. Zhou, and L.-W. Zou, 2012: How well does the FROALS model capture the clima- Indian Ocean SST biases in a Flexible Regional Ocean tological features of the IO SST? (2) What are the major Atmosphere Land System (FROALS) model, Atmos. sources of the SST biases? Oceanic Sci. Lett., 5, 273–279. The remainder of the paper is organized as follows. A brief description of the FROALS model, its experimental 1 Introduction design, and the datasets employed in this study are de- scribed in section 2. Section 3 compares the simulation The Indian Ocean (IO) plays a crucial role in the results from FROALS with the observational data. Finally, Asian-Australian (A-A) monsoon variability due to the conclusions are given in the last section. importance of both the land-sea thermal contrast in con- trolling the strength of A-A monsoon and the sea surface 2 Model, experiment, and data description temperature (SST)-convection relationship. It is believed that the interannual variability of Indian monsoon activity 2.1 Model description depends heavily on air-sea interactions that take place The atmospheric component of FROALS is Regional during the travel of the monsoon current across the ocean Climate Model version 3 (RegCM3), which was devel- (Shukla, 1975; Webster et al., 1999; Meehl and Arblaster, oped at the Abdus Salam International Centre for Theo- 2002). The significant impacts of the IO on East Asian retical Physics (ICTP) (Pal et al., 2007). The oceanic and western North Pacific monsoon variability have also component is the Princeton Ocean Model version 2000 been identified in recent years (Li et al., 2008; Xie et al., (POM2K), which was developed at Princeton University 2009; Wu et al., 2010). (see http://www.aos.princeton.edu/WWWPUBLIC/htdocs. Partly due to their increased resolution and better oro- pom/ for details). The RegCM3 and POM2K models are graphic representation, regional climate models (RCMs) coupled through the Ocean Atmosphere Sea Ice Soil 3.0 have played active roles in regional climate studies. (OASIS3.0) coupler (Valcke, 2006). Further details about RCMs generally show better performance than AGCMs this model can be found in Zou and Zhou (2011, 2012). over the IO (e.g., Dash et al., 2006; Dobler and Ahrens, Different from the original version (Zou and Zhou, 2010; Mukhopadhyay et al., 2010; Polanski et al., 2010; 2011, 2012), a radiation method is used along the open Lucas-Picher et al., 2011). However, many RCMs still boundaries of the ocean to allow for stable, long-term show low skill in simulating precipitation over the oce- integrations. We require that the normal barotropic veloci- anic regions, partly due to the absence of two-way air-sea ties satisfy the following radiation condition, according to Flather (1976): Corresponding author: ZHOU Tian-Jun, [email protected] Ub = Uobs + (c/H)(ηb−1 − ηobs), (1) 274 ATMOSPHERIC AND OCEANIC SCIENCE LETTERS VOL. 5 where subscripts b and b−1 denote the boundary point and Therefore, we use the mean temperature of the upper 5 m the interior point closest to b, respectively; obs denotes (T5m) to represent the SST for both model outputs and the observation; U denotes the normal barotropic velocity; observations. The observed SST used in the evaluation is 1/2 c = (gH) , the local shallow water wave speed; H de- the monthly mean T5m from SODA. 2) Because there are notes the water depth; and η denotes the surface elevation. significant uncertainties in the surface heat flux and radia- Because the fresh water flux is not included in the cou- tion estimates, two products of the monthly mean surface pling process, to reduce unrealistic salinity changes in the turbulent fluxes, near-surface meteorological variables mixed layer, a weak relaxation of salinity to the monthly (Objectively Analyzed air-sea Fluxes (OAFlux) (Yu and climatology in the upper five layers of the model is ap- Weller, 2007) and Goddard Satellite-Based Surface Tur- plied. bulent Fluxes (GSSTF) 2c (Chou et al., 2003)) and sur- face radiation (International Satellite Cloud Climatology 2.2 Experimental design and observational data Project-Flux Data (ISCCP-FD) (Zhang et al., 2004) and In this study, the FROALS model was set up over a Surface Radiation Budget (SRB) 3.0 (Gupta et al., 2006)) broad IO domain from 20°S to 40°N, 40°E to 120°E. The are used to evaluate the model outputs. Tibet Plateau is included to avoid steep orography along Here, we deduct the shortwave penetration into the the boundary regions. The horizontal resolution of the subsurface (deeper than 5 m) for both model outputs and RegCM3 is 60 km, and there are 18 vertical levels. observations. The deduction factor is 0.296, according to RegCM3 is initialized on 1 January 1995 using the Na- a simple subsurface heating parameterization (Paulson tional Center for Environmental Prediction-National Cen- and Simpson, 1977), ter for Atmospheric Research (NCEP-NCAR) reanalysis SW =SW (RR⋅+− e−−5/aa12 (1 )e 5/ ) , (2) dataset (Kalnay et al., 1996). The boundary conditions of pen 0 the RegCM3 are derived from NCEP-NCAR reanalysis where R (=0.62) is a separation constant, and a1 (=0.6 m) and updated every six hours. The horizontal resolution of and a2 (=20.0 m) are the attenuation length scales; SWpen the POM2K is 0.5°, and there are 30 vertical levels (see denotes the shortwave penetration into the subsurface; Table 1). The level thickness varies non-uniformly, with a and SW0 denotes the shortwave radiation at the surface. higher resolution in the upper ocean and a lower resolu- Then, the ensemble observation of surface turbulent tion in the deep ocean. The minimum and maximum fluxes (surface radiations) is the arithmetic average value depths for the model are 5 m and 5000 m, respectively. of OAFlux and GSSTF (ISCCP and SRB). Finally, these The initial conditions of the POM2K are derived from observed fields and model outputs are all remapped onto a Simple Ocean Data Assimilation (SODA) 2.1.4 monthly uniform 0.5×0.5 horizontal grid. data (Carton and Giese, 2008). The climatological ocean current and surface elevation from SODA are also em- 3 Results ployed for the oceanic boundary conditions. The air-sea coupling frequency is three hours. After a spin-up of three The spatial distribution of annual mean SST biases in years, a consecutive 10-year simulation from 1998 to the IO from FROALS along with the SST from SODA 2007 is performed. In the following analysis, the model’s are shown in Figs. 1a−b. The observed SST is character- monthly climatology is constructed based on the 10-year ized by a warm pool (>28°C) to the north of 10°S, except model outputs. for the northwestern Arabian Sea, and shows a strong To assess the model performance, the following obser- negative meridional gradient to the south of 10°S. Cold vational products are used: 1) In ocean model outputs, the biases in the SST are evident in the board regions of the SST is defined as the mean temperature over the first warm pool, with the greatest values in the Bay of Bengal layer of several meters thick, which may not be consistent (BoB), where the biases are larger than 1.5°C. Warm bi- with some observational products (Donlon et al., 2002). ases are found over the western Arabian Sea, the western Table 1 Values of sigma coordinates used in the ocean model.
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