Tropical AtlanticBiases in CCSM4

Semyon A. Grodsky, James A. Carton, and Sumant Nigam

March 17, 2011

To be submitted to the Journal of Climate

Department of Atmospheric and Oceanic Science

University of Maryland

College Park, MD20742

AbstractThis paper focuses on the tropical Atlantic biases in the control simulation of the Community Climate System Model version 4 (CCSM4). We find that local and remote biases in both, atmospheric and oceanic components of the coupled model contribute.Like in previous version, CCSM3, the atmospheric component of CCSM4 (CAM4) has abnormally high (by a few mbar) mean sea level pressure (MSLP) in the subtropical pressure highs and abnormally low MSLP in the polar lows. As a result all global scale winds are accelerated. Wind stress in the trade winds is approximately 0.05 N/m^2 (~2 m/s) stronger.In spite of anomalously strong trade winds in the north and south, the SST error in the tropical Atlantic changes sign across the Equator. This dipole-like SST error pattern suggests that errors in the coupled model climate may be further amplified by projecting on the natural meridional mode of variability inherent to the tropical Atlantic.In the northern tropics the time-mean sea surface temperature (SST) is colder than normal by 1-2°C in line with stronger northeasterly trades and local evaporation. Similar anomalous cooling of SST is present in the south. But, in distinction from the north where the cold SST error is present across the entire basin, the cold SST error in the south is limited to the west, though stronger than normal winds are basin-wide there. Despite stronger winds, the SST has a warm bias in the southeastern tropical Atlantic.By comparing higher horizontal resolution ocean simulations(0.25°x0.4°)with simulations on a CCSM4 ocean grid (1.125°x0.55°), we find that decrease in horizontal resolution (below the local Rossby radius of deformation) has significant impact on the eastern boundary currents. In particular, the currents affecting the Angola-Benguela temperature front are distorted in CCSM4. The southward Angola Current that originates at around 5°S is shifted southward while the coastal jet of the cold Benguela Current is replaced by a broad northward flow. These biases in the eastern boundary currents and their meridional heat transport result in stretching of the SST front and warming of SSTs at latitudes where cold water transported by Benguela Current is normally present. In CCSM4 the warm SST bias along the coast of Southwestern Africa (originated in the ocean component) is amplified and expanded into the open ocean via positive feedback from marine stratocumulus. This study suggests that smaller biases in coupled simulations of the tropical Atlantic may be achieved via improving the large scale pressure fields in the atmospheric component and by more accurate simulation of the coastal circulation in the ocean component. The latter needs locally enhanced horizontal resolution in the ocean boundaries.

1. Introduction

Although the climate of the tropical Atlantic Ocean is mostly seasonal, its coupled simulation still remains a problem that is evidentin notorious biases in regional winds and SST that have being present in recent generations of coupled models (Davey et al., 2002; Deser et al., 2006;Chang et al., 2007). In the Pacific sector, better representation of the deep-convection in the NCAR Community Atmosphere Model version 4 (CAM4) has lead to significant improvements in the phase, amplitude and spatial patterns of El Nino Southern Oscillation (ENSO, Neale et al., 2008). But in the Atlantic sector improvements are not that noticeable. Due to proximity of continents the Atlantic coupled air-sea system is impacted by land processes (e.g. Zeng et al., 1996, Richter and Xie, 2008). Thus errors originated in any module of the Atlantic ocean-atmosphere-land system may impact each other and grow through coupled interactions.

Several recent studies have linked the causes for the persistent tropical Atlantic biases in coupled simulations with problems in the atmospheric component. In particular, Chang et al. (2007) have found that warm SST bias in the equatorial and southeastern tropical Atlantic in CCSM3 is due to below normal zonal equatorial winds during March-April-May (MAM) thatoriginate due to deficit of rainfall (lack of diabatic heating/ colder air/ higher pressure) in the Amazon. Thebias in equatorial westerlywinds during MAM is also present in uncoupled atmospheric model component, CAM3,forced by observed SST (Atmospheric Model Intercomparison Project, AMIP, style run) suggesting that the bias is initiated in the atmospheric component. The link between equatorial zonal winds and Amazon rainfall have been demonstrated by Chang et al. (2008) who have reproduced the equatorial westerly bias in a model forced by diabatic heating bias over the Amazon. In parallel, Richter and Xie (2008) have shown that erroneously weak Atlantic Walker circulation in conjunction with deficient and excessive terrestrial precipitation over equatorial South America and Africa, respectively, are robust across coupled modelsfrom the third Coupled Model Intercomparison Project (CMIP3) and their uncoupled AMIP counterparts.The causes of biases in tropical convection over the two continents may be in boundary conditions from the land component or in the convection scheme used by atmospheric component but further studies are needed to evaluate their relative importance (Richter et al., 2010b). Using a simplified model Zeng et al. (1996) have demonstrated that Atlantic Walker circulation weakens and equatorial zonal SST gradient drops by 1C as a result of changes in land albedo and evapotraspiration due to Amazon deforestation.

In coupled simulations, the westerly error in equatorial winds during MAM leads to abnormaldeepening of the thermocline in the east that attenuates the cold tongue inthe next season (JJA). In CCSM3 this abnormal thermocline deepening is accompanied by reversal of the Equatorial Undercurrent (EUC) and by reversal of normally westward gradient of equatorial SST (Chang et al., 2007). Thiserroneous, eastward SST gradient is further amplified and prolonged by the Bjerknes feedback and is present in all CMIP3 models (Richter and Xie, 2008) as well as in most of the non-flux-corrected coupled simulations analyzed by Davey et al. (2002).

The important role of valid equatorial wind stress for proper simulations of the equatorial SST gradient has been confirmed by Wahl et al. (2011).Richter et al. (2010b) have examined impact of the seasonal errors in equatorial winds. They have found that cold tongue SST in JJA reduces by 3 K and warm pool SST increases by 0.5 K if model-generated wind stress in MMA is substituted with observed climatology. Their results thus confirm that deficient equatorial easterly in MAM is a possible reason for the anomalously warm JJA cold tongue.

The warm SST bias present in coupled simulations in the eastern equatorial area extends in the tropical southeastern Atlantic where it is more persistent and less seasonally dependent (Stockdale et al. 2006; Chang et al., 2007; Huang et al. 2007). In fact,the largest mean SST biases develop along the eastern boundaries of subtropical gyres (Large and Danabasoglu, 2006). Many studies have suggested that wind stress along the equator influences the coastal region of southwestern Africa via Kelvin waves (Florenchie et al. 2004, Richter et al. 2010a). So that biases in wind stress originated in the atmospheric component are responsible in part for biases in the ocean response. Sensitivity experiments of Richter et al. (2010 a,b) have shown that errors in both zonal equatorial winds (remote impact via equatorial and coastal waves) and local off-equatorial along-shore winds (impact on local upwelling) contribute comparably to the warm SST biases in the southeastern tropical Atlantic.

But ocean model deficiencies also contribute.In fact, the warm SST bias is present along the southwestern coast of Africa in uncoupled ocean simulations as well(Large and Danabasoglu, 2006). Hence the local atmospheric forcing is only a part of the problem along eastern boundaries, and the representations of ocean upwelling and meridional heat transport in the ocean component areother likelycontributors.If the impact of anomalously weak upwelling is corrected by setting coastal temperature and salinity along the southeastern Atlantic boundary close to observations, the coupled model SST improves across the entire southeastern Atlantic(Large and Danabasoglu, 2006).

Warm coastal SST bias is advected into open regions of the southeastern Atlanticby wind-driven ocean currents (Large and Danabasoglu, 2006). In addition, the marine stratocumulus clouds (developing over cold SSTs) provide yet another coupled ocean-atmosphere mechanism for spreading warm SST anomalies off the coast.Marine stratocumulus clouds cover significant portion of the ocean and are particularly evident over the upwelling areas along the western coasts of continents (Zuidema et al., 2009). Due to their high reflectivity, stratocumulus clouds playimportant roles in the ocean radiation budget. Marine stratocumulus clouds affect SST not only through their radiative shading effects, but also dynamically: Long-wave cooling from the cloud tops is balanced by adiabatic warming, i.e., subsidence. The subsidence leads to near-surface divergence, and thus counter clockwise circulation in the Southern Hemisphere, i.e., to southerlies along the coast (see Nigam, 1997). Not enough clouds results in weaker upwelling favorable southerly wind along the coast and warmer SSTs. Thus, both, radiative and dynamic effects provide positive feedback on SST.

Successful simulation of marine stratocumulus clouds still remains a challenge. Maximum stratocumulus cover develops when SST is coldest, in particular in austral spring in the Namibian and Peruvian stratus regions. This link to SST leads to positive feedback (warm SST – less stratocumulus clouds) in coupled ocean-atmosphere models (Mechoso et al., 1995; de Szoeke et al., 2010) that amplifies magnitudes and spatial coverage of warm anomalies originated along the western coasts.Once anomalously warm SST off the southwestern coast of Africa is in place, the reduced low-clouds over the warm anomaly force SST warming in a larger area thus spreading the SST anomaly in the direction of mean southeasterly winds (Huang and Hu, 2007).

Sea surface salinity (SSS) can also affect SST and air-sea interactions through its impact on the upper ocean stratification and barrier layers (e.g. Breugem et al., 2008). Tropical SSS biases in coupled modelsare generally attributed to errors in precipitation. In CCSM3 the fresh SSS bias is the largest south of the equator (in excess of 1.5 psu) due to the southward shift of the ITCZ and the “double” ITCZ (Large and Danabasoglu, 2006).Meridional shift of rainfall has significant impact on the tropical rivers discharge in the Atlantic sector. In particular, the Congo dischargein CCSM3 more than doubles the climatological discharge of Large and Yeager (2009). An excessive river plume produced by anomalously strong Congo dischargeaffects barrier layers (BLs) in the southeastern tropical Atlantic. BLs lead to SST warming (e.g. Breugem et al., 2008) by suppressing vertical heat exchange between thermocline and the mixed layer and thus provide a positive feedback on already warm SST bias in the region. Similarly in the north tropical Atlantic, the absence of BLs (that are normally present in this area due to the Amazon freshwater transport in the surface layers and the subduction of high haline water from the subtropical SSS maximum) results in anomalous deepening and entrainment cooling of the winter mixed layer that again provide a positive feedback on already cold SST bias in the region (Balaguru et al., 2010). Surface salt flux, , is replaced in POP by where is the global mean sea surface salinity. Underestimation of the surface salt fluxin the high salinity pools produces lower salinity there, that in turn leads to weaker barrier layers on the equatorward flanks of subtropical salinity maxima.

2. Model and Data

This research focuses on the tropical Atlantic biases in the Community Climate System Model version 4 (CCSM4).The CCSM4 is a coupled climate model composed of four separate models simultaneously simulating the earth's atmosphere, ocean, land surface and sea-ice, and one central coupler component[1].The CCSM4 data used in this study are the output data of the 1300 yr control model integration (archived as b40.1850.track1.1deg.006). This run is forced by historical ozone, solar, volcanic, green house gases, carbon, and sulfur dioxide/trioxide. Our analysis focuses on data for 97 year period (model years 863-959). A sensitivity examination has been carried out to ensure that the climatology of this particular period is similar to that of other periods. Our focus is on the performance of atmospheric and oceanic model components. Fluxes exchanged among component models were not adjusted during this simulation.

The atmosphere component of CCSM4, Community Atmosphere Model, version 4 (CAM4); employs an improved deep convection scheme (in comparison with CAM3 of Collins et al. 2006) by inclusion of convective momentum transport and a dilution approximation for the calculation of convective available potential energy (Neale et al., 2008). It is run on a 26 vertical levels, 1.25° longitude x 1° latitude grid. To separate errors originated in the atmospheric component from those in the coupled system we also use a stand alone CAM4 data simulated on the same grid and forced by observed SST (CAM4/AMIP, f40.1979_amip.track1.1deg.001)

The ocean model component of CCSM4 has been updated to the Parallel Ocean Program version 2 (POP2) of the Los Alamos National Laboratory (Smith et al., 2010). Among otherimprovements the POP2implements a simplified version of the near-boundary eddy flux parameterization of Ferrari et al., (2008), vertically-varying thickness and isopycnal diffusivity coefficients (Danabasoglu and Marshall, 2007),modified anisotropic horizontal viscosity coefficients with much lower magnitudes than in CCSM3 (Jochum et al., 2008); and modified K-Profile Parameterization that uses horizontally-varying background vertical diffusivity and viscosity coefficients (Jochum, 2009). The number of vertical levels has been increased from 40 levels in CCSM3 to 60 levels in CCSM4. It is run on a displaced pole grid with average horizontal resolution of 1.125°-longitude x 0.55°-latitude in midlatitudes. To separate errors originated in the ocean component from those in the coupled system we also use a stand alone POP2/NYF simulation run on the same grid and forced by repeating Normal Year Forcing (NYF) fluxes of Large and Yeager (2009), (c40.t62x1.verif.01).

To explore impacts of the horizontal resolution of the ocean model component, the CCSM4 data are compared to the Simple Ocean Data Assimilation (SODA) of Carton and Giese (2008) with atmosphericforcing from the Twentieth Century Reanalysis Project version 2 of Compo et al. (2011). SODA employs the same ocean model (POP2) but is run on higher horizontal resolution grid with an average resolution of 0.25°x0.4°x40 levels with 10-m spacing near the surface. In the assimilation (SODA 2.2.4)[2], a forecast produced by ocean model is corrected by contemporaneous observations and is forced by. A parallel run, SODA_simul (SODA 2.2.0), is run on the same grid and is forced with identical surface boundary conditions, but without data assimilation.

For each variable the model biases are evaluated for each calendar month as the difference between model and observed climatology. A number of observation data are used for comparisons with the CCSM4 simulations. In choosing observations we require (if possible) a minimum 10 year records in order to estimate a stable seasonal cycle.Our SST data is the Reynolds et al. (2002) optimal interpolation version 2 analysis spanning late 1981 - onward. Observed 10m neutral winds are available from the QuikSCAT scatterometer (e.g.Liu, 2002) during mid-1999-2009. For shortwave radiation (SWR) we rely on retrievals from the Moderate Resolution Imaging Spectro-radiometer (Pinker et al., 2009), which is available on a 1°x1° grid since mid-2002. Latent heat flux is based on the recent update of Institut Francais pour la Recherche et l’Exploitation de la Mer (IFREMER) weekly satellite-based turbulent fluxes of Bentamy et al. (2003, 2008) spanning 1992-2008. Precipitation is provided by the Climate Prediction Center Merged Analysis of Precipitation (CMAP) of Xie and Arkin (1997), which covers the period 1979 -present. Mean sea level pressure (MSLP) is the EuropeanCenter for Medium Range Weather Forecasts ERA-40 reanalysis (Uppala et al., 2005) monthly means spanning the period from 1958 through 2001. In-situ measurements from the PIRATA moorings in the tropical Atlantic (Bourles et al.,2008) are also used for comparisons.

3. Results

The presentation of the results is organized in the following way. In the first part of this section we address errors in the large scale atmospheric circulation and compare them with errors in the tropical-subtropical Atlantic SST. We will see that wind errors have mostly symmetric pattern (trade winds are accelerated north and south of the Equator) while the SST errors resemble a pattern of the Atlantic meridional mode (cold north and warm south).This dipole-like SST error pattern suggests in turn that errors in the coupled model climate may be further amplified by projecting on the natural mode of variability inherent to the Atlantic (Chang et al., 2007). We next examine the reasons for the dipole-like pattern of SST errors and its link with deficiencies in the atmospheric and oceanic components of the coupled model.

Mean sea level pressure and surface winds. Over the off-equatorial latitudes, the excessive subtropical high pressure systems circle the globe (Fig. 1). The anomalous pressure pattern originates from imperfections in the atmospheric module that is evident by apparent similarity of the time mean MSLP bias in CCSM4 and its atmospheric module forced by observed SST(CAM4/AMIP, compare Figs. 1a, 1b). Errors in the pressure fields are inherited by CAM4 from previous versions of the atmospheric module.In particular, similar pattern of anomalously strong subtropical high pressure systems is present in CCSM3 and its atmospheric module (CAM3/AMIP) (Figs. 1c, 1d). The CCSM4 subtropical high systems have larger errors in the Atlantic sector where MSLP high reaches 4-5 mbar above the normal in the north and south. In spite of these errors, the MSLP biases in CCSM4 have been improved in comparison with CCSM3. This improvement is particularly evident in the North Atlantic where the region of above normal MSLP has smaller spatial extension and is lower in magnitude (comp. Figs. 1a, 1c).Improvements in MSLP are noticeable in the Northern Hemisphere while they are less evident in the Southern Hemisphere. Moreover, the MSLP bias in the South Atlanticin CCSM4 and CAM4/AMIP is stronger than in corresponding version 3 runs. Anomalously high MSLP in subtropical high systems along with anomalously low MSLP in the polar low systems result in anomalously strong meridional pressure gradient (Fig. 1), thus the middle-high latitudeszonal winds.