The Influence of the Mean State on the Annual Cycle and ENSO Variability: A Sensitivity Experiment of a Coupled GCM

Transcription

1 The Influence of the Mean State on the Annual Cycle and ENSO Variability: A Sensitivity Experiment of a Coupled GCM Julia V. Manganello 2 and Bohua Huang 1,2 1 Climate Dynamics Program School of Computational Sciences George Mason University Fairfax, VA Center for Ocean-Land-Atmosphere Studies 4041 Powder Mill Road, Suite 302 Calverton, MD julia@cola.iges.org June 2006

2 ABSTRACT Simulation of the annual-mean climate, seasonal and interannual variability in the tropical Pacific is compared in two versions of a coupled ocean-atmosphere general circulation model (CGCM). The component models in these two versions of the CGCM are identical: the atmospheric model is Version 2 of the Center for Ocean-Land- Atmosphere Studies (COLA) Atmospheric GCM, the oceanic GCM is a nonlinear reduced-gravity model of quasi-isopycnal layers. The only difference between them is the coupling strategy. In the first, component models are directly coupled over a nearly global domain. In the second, a prescribed empirical surface heat flux correction term based on the mean sea surface temperature (SST) bias of the first model run, is added to the prognostic SST equation. This correction term is constant in time but varies spatially, and is proportional to the annual mean local SST error. The addition of the empirical correction term eliminates most large SST errors, especially the warm bias near the southeastern coast of the Pacific and Atlantic Oceans. Preliminary analysis shows that in the southeast Pacific this climate bias could be associated with the simulation of the wrong type of cloud cover. Along with the SST, distributions of surface wind stress and precipitation are also improved in these regions. As a result, the corrected mean climate exhibits stronger asymmetry relative to the equator. Due to the improvement of the model mean state, the annual cycles of the SST and surface wind stress in the eastern equatorial Pacific become more realistic as a result of enhancement of their annual, rather than semi-annual, harmonics. The annual cycle of precipitation in the eastern Pacific is also improved due to more realistic seasonal SST variations in this region. Both runs simulate interannual variability in the tropical Pacific with some characteristics of ENSO. However, in the CGCM without flux adjustment phase locking of ENSO to the annual cycle is unrealistic; ENSO events exhibit some eastward propagation at the equator and show a double-peak feature in the east connected to two distinct pulses in the zonal wind stress in the west. In the model with flux adjustment, phase locking to the annual cycle is reproduced remarkably well, likely due to more realistic seasonal evolution of the mean state. SST anomalies exhibit a standing mode at the equator, and the development of ENSO events at the equator is also more realistic. On the other hand, the simulated interannual variability is weaker and the timescale of ENSO events is longer compared to the observations. High-latitude teleconnection patterns are also not reproduced very well. Flux correction does not lead to improvements in these aspects. 2

3 1. Introduction Assessments of recent coupled ocean-atmosphere general circulation models (CGCM) (e.g. STOIC described in Davey et al. 2002, ENSIP described in Latif et al. 2001) report certain improvements in the simulation of the mean climate and seasonal cycle over the tropical Pacific compared to earlier coupled systems performance (e.g. Mechoso et al. 1995). However, some large systematic errors of the earlier models still remain. One of the most common and serious errors is the positive sea surface temperature (SST) bias of as much as 5-6 C near the coast of South America due to a substantially weakened cold tongue. In fact, this warm bias is prevalent over the whole eastern Pacific south of equator. In the NCEP Climate Forecast System, a state-of-the-art fully coupled ocean-land-atmosphere dynamical seasonal prediction system (Saha et al. 2006), the warm bias is about 2-3 C. This level of systematic error is comparable to the climate variations the coupled system intends to predict. It is hypothesized that the existence of such biases distorts the predicted signals significantly. The above SST error is partly related to errors in the simulation of low-level stratus clouds in the region (Davey et al. 2002, Ma et al. 1996). These clouds are particularly important because they may form a positive feedback with the underlying SST: the less clouds there are, the more solar radiation reaches the ocean, SST gets warmer, atmospheric inversion diminishes, and even less clouds are formed. Warm SST near the South American coast could also be due to the under-estimation of alongshore surface winds by the component atmospheric general circulation model (AGCM), as is further discussed in Section 2. Also, it has been proposed that poorly resolved ocean eddies might be important. 3

4 The mean bias in the Tropics has serious consequences for the quality of modelsimulated variability on various temporal-spatial scales. The eastern tropical Pacific happens to be the region where the ocean and the atmosphere are strongly coupled through multiple feedback mechanisms. Large positive SST errors in this area could cause weakening of the surface winds due to reduced SST gradients, which would in turn cause errors in precipitation, thermocline depth and other variables. All this would lead to not only a mean error but also to the development of an unrealistic annual cycle (Li and Hogan 1999). For instance, the loss of the annual mean SST asymmetry relative to the equator leads to the amplification of the semiannual component in the seasonal cycle (Li and Philander 1996). Since the interannual variability in this region is dependent on the strong coupling between the atmosphere and ocean, and the latter is sensitive to the model basic states, adequate simulation of the mean state and annual cycle is essential for the successful simulation of the El Niño - Southern Oscillation (ENSO) (Li and Hogan 1999). Results of the present study fully support this conclusion. To reduce annual mean errors in the coupled model, substantial improvements in the model physics need to be undertaken. In fact, more attention has been paid to this issue in recent years. This process is important but time-consuming, and requires the larger research community. In the meantime, current models could still be utilized to address many questions on the variability of the coupled system. For those variations that are potentially sensitive to the earth s mean climate, some interim approaches can be used to correct the model mean state empirically, such as the flux adjustment strategy (e.g. Manabe et al. 1991, Murphy 1995). Justification of this approach is based on the idea that though the model equilibrium climate could be significantly different from the 4

5 real climate, this does not necessarily imply that the model dynamics are too unrealistic for climate variability and sensitivity experiments (Sausen et al. 1988). Flux adjustment techniques generally constitute the addition of constant, sometimes seasonally varying, fluxes of heat, freshwater, often wind stress or sometimes SST to the coupling interface with the goal of constraining the climatology of the model to the observed values or the uncoupled model climatology. In this study, we intentionally keep the flux correction at a minimum and target those errors with relatively clear physical error sources. This paper compares results from an extended integration of two configurations of a CGCM. Our purpose is to demonstrate that the seasonal cycle and interannual variability simulated by this CGCM is sensitive to the mean state in the tropical ocean. The component parts of the coupled system, AGCM and oceanic general circulation model (OGCM), are identical for the two configurations. The two differ in their coupling strategy: in the first configuration component AGCM and OGCM are directly coupled over a nearly global domain. In the second configuration a prescribed empirical correction term is added to the surface heat flux into the ocean. This correction term varies spatially but is constant in time, and its magnitude is proportional to the annual mean local SST error in the first integration with fully coupled CGCM. Since this corrective surface heat flux does not vary with time, it does not generate fluctuations in the coupled system directly. It is intended to represent, albeit crudely, missing heat sources and sinks from inadequately represented physical processes like the stratus cloud- SST feedback mentioned above. The comparison focuses on the mean climate, annual cycle and tropical and extra-tropical climate variability associated with ENSO. Our goal is to demonstrate improvements in the simulation of the annual cycle and ENSO 5

6 variability, in addition to the improved mean state, as a result of heat flux correction term been added to the CGCM. In this sense our work substantiates the previous study by Li and Hogan (1999) using an independent coupled system and with a more detailed analysis. We also point out remaining deficiencies and new errors in the flux corrected system with the purpose of demonstrating the pros and cons of the strategy and recommending further improvements to the CGCM. This paper is organized as follows. Section 2 contains brief model descriptions and the details of the employed flux adjustment method. Simulation of the mean climate and the annual cycle is described in Section 3 and 4 respectively. Interannual variability in the tropical Pacific and model ENSO are analyzed in Section 5. A brief summary and discussion of main results follows in Section The component models and the flux adjustment method a. Atmospheric GCM The COLA AGCM (Version 2) is a global spectral model at T42 horizontal resolution (triangular truncation with 42 being the largest wave number resolved) corresponding to approximately 2.8 x 2.8 resolution in the Tropics, and with 18 unevenly spaced sigma levels in the vertical with higher resolution in the lower troposphere. The model has the same dynamical core as the National Center for Atmospheric Research (NCAR) Community Climate Model version 3.0 (CCM3) and a semi-lagrangian moisture transport scheme (Schneider et al. 2001). Solar radiation is calculated according to the parameterization of Lacis and Hansen (1974) as modified by Davies (1982). It includes atmospheric heating due to 6

7 absorption of solar radiation by water vapor and ozone, where ozone concentration is specified from the zonal mean climatology. Predicted cloudiness is used to modify solar radiation due to absorption and scattering. Long wave emission from the Earth is formulated after Harshvardhan et al. (1987), which includes atmospheric heating due to absorption of terrestrial radiation by calculated water vapor, specified carbon dioxide and predicted clouds. Predicted cloud fraction and optical properties follow the scheme used in the CCM3 (Kiehl et al. 1994) as described by DeWitt and Schneider (1997). Change of phase of water in the COLA AGCM is accomplished through large scale condensation, deep convection parameterized following relaxed Arakawa-Schubert scheme of Moorthi and Suarez (1992) as implemented by DeWitt (1996), and shallow convection on subgrid scales near the surface according to the scheme of Tiedtke (1984). The effects of mixing of heat, momentum and moisture by small-scale turbulence are parameterized according to the turbulent closure scheme by Mellor and Yamada (1982), level 2.0. Horizontal diffusion is incorporated as scale-selective bi-harmonic type diffusion. There is also a parameterization of gravity wave drag (Palmer et al. 1986). There is no sponge layer near the top of the model atmosphere. Surface layer processes which include exchange of momentum, heat and moisture between the atmosphere and land or ocean, are parameterized according to bulk aerodynamic schemes. Surface transfer coefficients are calculated by an empirically determined functional fit to Monin-Obukhov similarity theory. Over ocean the formulae are given by Sato et al. (1989b) and over land by Xue et al. (1991). To improve simulation of the exchange processes between atmosphere and land, explicit formulation of the land surface vegetation and its exchanges with the atmosphere was based on Simple Biosphere model (SiB) of Sellers et al. (1986). The SiB 7

8 model was simplified by Xue et al. (1991), which became Simplified SiB model subsequently used by COLA AGCM. Mean surface orography (Fennessy et al. 1994) is used to represent surface elevation. b. Oceanic GCM The OGCM is a quasi-isopycnal model (Schopf and Loughe 1995, Yu and Schopf 1997). Under reduced-gravity treatment the deepest interface is at a mean depth of 2300 m, and coastal topography is represented. The OGCM has 14 nearly isopycnal layers in the vertical, where the topmost layer is treated as a bulk turbulent well-mixed surface layer. The OGCM is a global model spanning 70ºS to 65ºN with a horizontal resolution of 1º latitude x 1.25º longitude with the meridional resolution increased to 0.5º within 10ºS-10ºN to better resolve the equatorial wave dynamics. All of the mixing in the model is incorporated through the prescription of a cross-coordinate mass flux at the base of each layer. The entrainment at the base of the surface mixed layer is calculated based on a balance of wind stirring, penetrating radiation, release of mean kinetic energy due to shear at the base of the mixed layer, dissipation, and the increase in potential energy due to mixing, largely in accordance with Niiler and Kraus (1977). A shallowing mixed layer is dealt with by relaxing it to the Monin-Obukhov depth. Below the mixed layer, the internal shear-induced vertical mixing and diffusion, and convective overturning are parameterized through a Richardson number-dependent implicit vertical mixing scheme (Pacanowski and Philander 1981). Horizontal smoothing is accomplished with a modified Shapiro (1970) filter, which is applied to mass, temperature and momentum fields. 8

9 c. Coupling strategy and the flux adjustment method The AGCM and OGCM were initially directly coupled within 70ºS to 65ºN (the domain of the OGCM) where the AGCM-simulated surface fluxes of heat, freshwater and momentum were provided to the OGCM once daily, and the OGCM-simulated SST for the same interval was supplied to the AGCM. In this configuration the CGCM was integrated for 800 years with the initial conditions derived from the long-term uncoupled simulations of component GCMs. The output from year 101 to year 250 was used for most of the analysis. Analysis of the model simulated atmospheric and oceanic fields showed fairly large systematic errors both in the Tropics and the mid-latitudes (for more details see Section 3). Huang et al. (2004) demonstrated that at least in the tropical Atlantic these errors degraded the simulation of major patterns of interannual variability in the region. To improve the mean climate of the CGCM, a simple empirical surface heat flux correction term was added to the prognostic SST equation. It was set to be constant in time but varied spatially, and was proportional to the annual mean local SST error in the directly coupled GCM integration. The relaxation coefficient was set to 15 W/m 2 /K. In this configuration the CGCM was integrated for 100 years with initial conditions obtained from the coupled model integration. The output from year 11 to year 78 was used for the analysis. The above described heat flux correction term is shown in Fig. 1 along with the annual mean net surface heat flux into the ocean from the observational estimate and CGCM integration without flux correction (hereafter referred to as CTRL). Fig. 1 also shows the difference of the net surface heat flux between the CTRL and the CGCM 9

10 integration with flux correction (hereafter referred to as HFA). It is interesting to note that the addition of the correction term does not alter significantly the net heat balance at the sea surface. Instead, as we will see in Section 3, these additional heat fluxes are effective in changing the SST and surface winds, which also modifies the other components of the surface fluxes. The annual mean SST error that makes up the heat flux correction term is presented in Fig. 2e. To calculate this error the observed SST data was taken from U.S. Climate Prediction Center s (CPC) SST dataset for (Smith et al. 1996). Values of the heat flux correction larger than ±50 W/m 2 (uncertainty of the observations) are observed in the eastern tropical Pacific, North Pacific and North Atlantic and in the Southern Ocean. Possible sources of these errors and the effectiveness of flux adjustment method in reducing them are further discussed in the following section. 3. Mean climate a. Sea Surface Temperature One of the main benchmarks of a CGCM performance, as well as a quantity of great interest, is the SST. Fig. 2 shows long-term mean SST distributions from the CTRL and HFA integrations (Figs. 2b and 2c respectively), and the corresponding SST biases relative to the CPC SST dataset for (Figs. 2e and 2f). Fig. 2b contains common deficiencies reported in Mechoso et al. (1995) and Davey et al. (2002): equatorial symmetry is quite strong, the cold tongue is too narrow and equatorially confined both in the Pacific and the Atlantic, the western Pacific warm pool is too restricted in the meridional direction. In the eastern Pacific and Atlantic the largest errors 10

11 are associated with the widespread positive SST bias (up to 6ºC in the Pacific and 3ºC in the Atlantic) that is strongest off the west coasts of South America and Africa. In the northern oceans, the SST gradient seems adequate over the Kuroshio Current but is much too weak in the Gulf Stream region. On the other hand, over large areas of the North Pacific and North Atlantic (north of 40ºN) the gradients are overestimated resulting in SSTs colder by 3-4ºC (Fig. 2e). In the Southern Ocean SSTs are overestimated in the entire latitude belt. Errors in the Southern Ocean are possibly due to the lack of an adequate sea ice model. As shown below, flux adjustment is not useful in the North Pacific: SST is in better agreement with the observations when heat flux adjustment is restricted only to the Tropics (30ºS to 30ºN). In the North Atlantic large errors are possibly due to poorly resolved Gulf Stream and the presence of an artificial boundary that cuts off currents beyond 65ºN. Errors in the eastern tropical Pacific and Atlantic likely originate from under-representation of coastal processes. However, the large spatial extent of the warm regions indicates that coupled feedbacks that could amplify the initial error and spread it around the basin could be important as well. It is known that under-estimated alongshore surface winds due to insufficient resolution of the component AGCM could lead to weak coastal upwelling and hence warm SST near the South American coast (Huang and Schneider 1995). Once a positive SST anomaly is established it could drive anomalous convergence of surface winds, whereby the initial anomaly could amplify through e.g. evaporation-wind feedback (Xie 1994). Another important positive feedback mechanism is related to deficiencies in simulating low-level stratus clouds off the coasts of Peru and Angola (Mechoso et al. 1995). Underestimation of these clouds leads to stronger short- 11

12 wave radiative flux into the ocean and consequently too warm SST. It is beyond the scope of this paper to analyze in detail what deficiencies in the CGCM lead to large SST errors described above. However, some analysis and discussion presented below shows that the flux correction is useful to ease the consequences of these potential errors. As anticipated, incorporation of heat flux adjustment eliminates major SST errors in the whole tropical belt of the global ocean (remaining errors are about ±1ºC) (Fig. 2f), with the exception of the areas off the west coasts of North America and North Africa where errors are about 2-3ºC. However, the flux adjustment reverses the sign of SST errors in the extra-tropics of the North Pacific and North Atlantic: SSTs in these regions become on average warmer by about 3-4ºC. Examination of seasonal mean SST error maps in both CGCM integrations (Fig. 10) reveals that in winter and spring the flux adjustment does a good job in reducing the cold errors in these regions (Figs. 10e and 10f) with slight over-compensation. On the other hand, during summer and fall the largest errors are in the western parts of the basins (more so in the North Pacific than the North Atlantic). Apparently the flux adjustment based on the annual mean SST error overshoots in the east during these two seasons and creates large positive errors there. We also suspect that the SST errors in the mid-latitudes are not totally generated by local surface fluxes, but are influenced by the subtropical gyre and the western boundary currents. To test these assumptions an additional CGCM integration was performed with the flux adjustment restricted only to the Tropics (30ºS to 30ºN) (HFA_Trop hereafter). The resultant mean SST distribution and its errors are shown in Figs 2d and 2g respectively (data from years 11 to 83 was used for the analysis). SST values are significantly improved in the North Pacific. Additional analysis demonstrated that error 12

13 correction in the tropics enhances meridional transport significantly. On average, in the western Pacific between the equator and 45ºN, northward vertically integrated oceanic heat transport is stronger in the HFA_Trop simulation compared to the CTRL simulation. This change is primarily due to the enhancement of the northward flow, especially between 30 and 40ºN (not shown), and appears to be related to the northward shift of the anti-cyclonic subtropical surface flow. Somewhat surprisingly, there is not much change in the North Atlantic compared to the CTRL simulation, except that SST errors are shifted more to the north. Based on these results, we speculate that the SST errors in the northern oceans are not caused by errors in the surface fluxes. On the other hand, we find little effect of the extra-tropical flux correction on the tropics. Since we mainly concentrate on the tropical oceans, in the rest of the paper results from the CGCM integration with global flux correction are presented, unless otherwise noted. b. Surface Wind Stress A major benefit of the heat flux correction is the improvement of other variables through their dynamical connection. Annual mean surface wind stress distributions from the CTRL and HFA integrations are shown in Figs. 3a and 3b respectively along with the errors relative to the surface wind stress data from the National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) reanalysis data (NCEP hereafter) for (Figs 3c and 3d). The largest errors in the Tropics correspond to the anomalous convergence in the eastern Pacific and Atlantic, which is co-located with the largest positive SST errors in these regions and is likely generated by these errors. In addition, the resultant surface wind errors preclude cross- 13

14 equatorial flow thus establishing strong equatorial symmetry of the mean state in these areas (see Figs. 2b, 3a and 4b). It is quite conceivable that the wind errors amplify the SST errors through reduced upwelling and positive feedbacks, like evaporation-wind feedback. Thus, the resulting wind-sst errors are partly a coupled phenomenon. Therefore it is not surprising that elimination of this SST bias through flux adjustment significantly reduces the associated erroneous surface wind stress (Fig. 3d). However, the HFA run still shows weak trade winds in all oceans south of equator, even at the eastern boundary of the Indian Ocean, with error patterns similar to the CTRL run. This suggests that poor simulation of these winds is at least partly due to other distinct model deficiencies. In the northern mid-latitudes the largest errors amount to stronger westerlies in the North Pacific largely due to a cold temperature bias in the high latitudes of the component AGCM. Global flux adjustment reduces these errors to a certain extent. On the other hand, as is the case with SST, flux adjustment introduces additional erroneous patterns in both Pacific and Atlantic: easterlies are reduced across the basins between about 10ºN and 15ºN; northerly alongshore flow at the eastern boundary in the North Pacific and North Atlantic is much weaker compared to the observations. Positive SST errors in these regions in the HFA run are consistent with the reduced upwelling associated with this weak northerly flow. c. Precipitation Annual mean precipitation maps (Figs. 4b and 4c) reveal moderate improvement due to flux adjustment. The CTRL experiment shows a double Inter-tropical 14

15 Convergence Zone (ITCZ) both in the Pacific and the Atlantic oceans. Compared to rainfall estimates from the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP hereafter) by Xie and Arkin (1996) this distribution results in large positive rainfall errors in the eastern Pacific and western Atlantic south of equator and in the central Pacific north of equator, and negative errors in-between (Fig. 4d). In the HFA integration this double ITCZ is not present in the Pacific, although there is erroneous rainfall minimum at the equator in the west. Elimination of large precipitation errors in the eastern Pacific in this integration enhances the more realistic asymmetric distribution (Fig. 4c). The flux adjustment also increases the amount of precipitation within the tropical ocean and improves the simulation of the South Pacific Convergence Zone (SPCZ). However, in the west, north of equator, precipitation is overestimated in the HFA run, and the SPCZ is still too zonal and extends too far to the east compared with the observations (Fig. 4e). In the Atlantic the erroneous ITCZ in the Southern Hemisphere is also reduced. In both model integrations there is generally too little precipitation over the Maritime Continent and South-East Asia and too much rainfall in the southern and eastern Indian Ocean. In fact, flux adjustment tends to push the center of precipitation further westward in the Indian Ocean away from the Sumatra coast. Precipitation is also underestimated over the Amazon basin and overestimated over the Andes Mountains by both runs. The latter errors were also reported by Kirtman et al. (2002) in their CGCM and Anomaly Coupled GCM using the same AGCM but a different OGCM than in this paper: this comparison indicates that the model errors discussed here largely originate 15

16 from the atmospheric component. Positive rainfall errors in the eastern North Pacific and North Atlantic are somewhat increased with the incorporation of flux correction. d. Heat Content Model ocean heat content (defined as the vertically averaged temperature in the upper 250 m) and its errors relative to the one from the COLA Ocean Data Assimilation system (ODA) are shown in Fig. 5. The COLA ODA produces monthly averaged nearglobal ocean analyses for the period of using a two-dimensional variational minimization scheme (Derber and Rosati 1989) to combine temperature observations with an OGCM. (A more detailed description of the ODA system, the OGCM employed and the observational data used in the assimilation are given in Huang and Kinter (2002)). Compared with the observations, the HFA run produces a more realistic pattern of the mean heat content in the tropical Pacific, including the shape and orientation of the subtropical thermocline domes in both hemispheres (Fig. 5c). This result suggests better simulation of the subtropical gyres due to improved curls of the surface wind stress. On the other hand, the HFA integration is still too warm in the eastern Pacific and too cold in the western Pacific, as shown by the error field in Fig. 5c. In the Atlantic the largest heat content errors are along 10ºN and 10ºS and are enhanced in the integration with flux adjustment. e. Sea Surface Temperature, Zonal Wind Stress and Heat Content at the Equator Equatorial annual mean SSTs (averaged between 2ºS and 2ºN) are shown in Fig. 7 (top panel). In contrast with the reviews of Mechoso et al. (1995) and Davey et al. 16

17 (2002) a strong cold SST bias is not observed in this particular CGCM. However, as in other CGCMs the simulated SST gradient is too weak due to cold SSTs in the western Pacific as well as the warm bias in the central and eastern parts of the basin. In the HFA integration equatorial SSTs are fairly similar to the observations over most of the Pacific, except around 100ºW where they are colder by about 1ºC. In both integrations SSTs are severely overestimated near the South American coast. The SST gradient in the equatorial Atlantic is quite well simulated both in the CTRL and HFA integrations. While it is slightly too flat in the former experiment but realistic in the latter one, the SSTs in the HFA simulation are colder by about ºC over the whole equatorial Atlantic. This result differs from simulations of some other CGCMs in this region. According to the review of Davey et al. (2002), the long-term mean SST gradient in the central equatorial Atlantic is of opposite sign to observations in the vast majority of models without flux adjustment. Flux-adjusted models generally simulate the sign but not the magnitude of the gradient. As is the case in the Pacific, SSTs near the coast of Africa are severely overestimated in both model integrations. Consistent with the results of Davey et al. (2002) both CGCM integrations have a cold bias in the equatorial Indian Ocean. In the central part of the basin the cold bias is weaker in the HFA simulation. In the CTRL integration negative heat content errors along the equator in the Pacific (Fig. 7, bottom panel) correspond to a shallow and flat thermocline, which is also shown in the depth of a 20ºC isotherm (Fig. 6b). This thermocline error is related to the weak simulated zonal wind stress along the equator (Fig. 7, middle panel). The HFA run shows improved simulation of the thermocline depth (Fig. 6c) including the significant 17

18 deepening of the thermocline in the central and western equatorial Pacific. This improvement is consistent with the enhanced zonal wind stress along the equator (Fig. 7, middle panel). However, the simulated easterlies are still too weak in the central equatorial Pacific, and the zonal wind changes to westerly, as opposed to weak easterly, in the western equatorial Pacific. Consequently, the warm pool in the western equatorial Pacific is shifted to the east by about 20 degrees. Zonal winds, thermocline slope and SSTs are strongly coupled in this region: easterly trade winds along the equator maintain east-west thermocline slope and equatorial upwelling that together produce the SST gradient between the warm pool to the west and the equatorial cold tongue to the east. The SST gradient in turn drives easterly trade winds along the equator. This overall improvement therefore signifies that air-sea interaction in the equatorial Pacific in the CGCM with flux adjustment is more realistic. f. Short-Wave Radiative Flux and Total Cloud Cover Analysis of short-wave radiative flux and total cloud cover was performed primarily to determine whether the mean positive SST bias in the southeast Pacific is at least partly due to the errors in the simulation of clouds in this region. Long-term means of the total cloud cover, short-wave radiative flux into the ocean and SST from the CGCM integrations and observations are shown in Fig. 8. Observed total cloud cover and short-wave radiative flux are obtained from the International Satellite Cloud Climatology Project (ISCCP) dataset for (Rossow and Dueñas 2004) and the Earth Radiation Budget Experiment (ERBE) satellite data for respectively. In the southeast Pacific off the western coast of South America the total cloud cover is 18

19 strongly underestimated in both CGCM integrations compared to the observations, with slightly better values in the HFA simulation. Correspondingly, in the CTRL integration net short-wave radiative flux into the ocean over the same region is overestimated (Fig. 8e), which is consistent with higher than observed SSTs in this area (Fig. 8h). On the other hand, in the HFA integration short-wave radiative flux over this region is much smaller (Fig. 8f), which is somewhat surprising considering that the total cloud cover in the southeast Pacific is quite similar in both model integrations. To get better insight into the nature of cloud cover errors, the annual cycle of the total cloud cover averaged over 30ºS-10ºS and 90ºW-75ºW was computed and compared to the one of SST over the same area (Fig. 9). Although the annual cycles of SST in this region are quite realistic in both model integrations, the annual cycles of cloud cover are out of phase with the observed one. Observed total cloud cover in this region has a maximum during October-November corresponding to the minimum in SST. This indicates that the dominant cloud cover is low-level stratus since it is largest during the season of strongest static stability, which is related to the minimum in SST (Klein and Hartmann 1993). On the other hand, the model cloud cover is largest during February- April corresponding to the maximum in SST signifying that the wrong cloud type is simulated, most probably cumulus. In the CTRL simulation this is due to strong northsouth seasonal migration of the ITCZ in the eastern Pacific, as is further shown in Section 4. It must be noted that in the HFA integration there is a tendency of the total cloud cover to increase during fall, which suggests that during spring the dominant cloud cover type is cumulus, but that in the fall stratus clouds dominate. 19

20 4. Annual Cycle A major benefit of better annual mean state is the improvement of the annual cycle. The mean annual cycle of the SST at the equator (with annual mean removed) from CGCM integrations and CPC dataset for is shown in Fig. 11. In the Pacific the largest observed seasonal SST variations occur in the east. The dominant time scale of these variations is clearly annual with a warm phase peaking in March-April, corresponding to the seasonal minimum in the strength of the equatorial easterlies, and a cold phase reaching its maximum in September-October. These annual SST signals exhibit westward propagation. In the west the dominant time scale is semi-annual, since the Sun crosses equator twice. The main deficiencies in the simulation of the seasonal cycle of the SST in the CTRL integration are clearly related to the overestimation of its semi-annual harmonic: the cold tongue appears too early, close to the equatorial minimum in solar insolation (summer solstice), terminates too early, extends too far to the west and is much stronger compared to the observations (Fig. 11b). These seasonal variations extend too close to the coast in the east. In the west there is an additional minimum in February-April not present in the observations. The above errors are essentially the same as reported by Mechoso et al. (1995). Implementation of the annual mean flux adjustment has markedly improved the simulation of the seasonal cycle of the SST in the equatorial Pacific (Fig. 11c). The dominant harmonic in the east is clearly annual, and the erroneous minimum in the west is largely reduced. There are practically no local maxima near the coast. The timing of the cold tongue has improved with the maximum now centered on September, as in observations. The warm phase is now stronger than the cold one, and it appears, peaks 20