Evolution of Model Systematic Errors in the Tropical Atlantic Basin from the NCEP Coupled Hindcasts

Transcription

1 Evolution of Model Systematic Errors in the Tropical Atlantic Basin from the NCEP Coupled Hindcasts Bohua Huang 1,2 and Zeng-Zhen Hu 2 1 Climate Dynamics Program School of Computational Sciences George Mason University Fairfax, Virginia 2 Center for Ocean-Land-Atmosphere Studies Institute of Global Environment and Society 4041 Powder Mill Road, #302 Calverton, Maryland huangb@cola.iges.org

2 Abstract Significant systematic errors in the tropical Atlantic Ocean are common in state-ofthe-art coupled ocean-atmosphere general circulation models. In this study, a set of ensemble hindcasts from the NCEP coupled forecast system (CFS) is used to examine the initial growth of the coupled model bias. These CFS hindcasts are nine-months integrations starting from real-time oceanic and atmospheric analyses for For each starting month, the hindcasts from fifteen different initial states form an ensemble. The monthly climatologies of ensemble hindcasts from various initial months are compared with those from the oceanic and atmospheric analyses or other observationbased datasets. The large number of integrations from a variety of initial states covering all seasons provides a good opportunity to examine how the systematic errors grow. Our analyses show that two error patterns are dominant in the hindcasts. One is the rapid warming of the sea surface temperature (SST) in the southeastern tropical Atlantic Ocean in the later half of the year. This error tends to peak in January-February with a maximum at round 2 o C in the open ocean. As a result, the errors grow much faster in this region for hindcasts starting in boreal summer and fall than in late winter and spring. The SST error is associated with an excessive model shortwave radiation reaching the sea surface, especially during the period from boreal summer to early winter. The error in the shortwave radiative flux at the sea surface is caused by the model s inability to reproduce the observed amount of low cloud cover in the southeastern ocean and its annual increase from boreal summer to late fall. This SST error is one of the factors that cause a southward bias of the intertropical convergence zone (ITCZ) in boreal winter and spring. To a lesser extent, a similar error occurs near the northeastern tropical Atlantic where the model s failure in simulating the increase of low cloud cover from boreal winter to summer leads to the erroneous warming of SST there in spring and summer. The second error pattern is the excessive deepening of the model thermocline depth to the north of the equator from the western coast toward the central ocean. This error grows fastest in boreal summer. It is forced by overly strong local anticyclonic surface wind stress curls, and is in turn related to the combined effects of the northward bias of the ITCZ in the western ocean and the weakened northeast trade winds in summer and fall. The increased wind speed near the equator cools down the warm surface water from the western equatorial ocean to the eastern coast north of the equator. The thermocline error in the northwest may delay the annual shoaling of the equatorial thermocline in the Gulf of Guinea remotely through the equatorial waveguide. The potential connection between these two major error patterns is discussed. i

3 1. Introduction A major problem of state-of-the-art coupled ocean-atmosphere general circulation models (CGCM) is their significant systematic errors in simulating the mean climatology of the tropical ocean and atmosphere, even though substantial progress has been made in many other aspects of CGCM developments and applications during the past decade. In particular, the current models still cannot reproduce realistically some prominent observational features in the eastern Pacific and Atlantic Oceans near the equator (e.g., Neelin et al., 1992; Mechoso et al., 1995; Schneider et al., 1997; Meehl and Arblaster, 1998; Davey et al., 2002; Huang et al., 2004; Wang et al., 2005; among many others). These systematic errors have profound influence on the capability of these climate models to simulate the anomalous fluctuations of the tropical climate. They develop relatively quickly in the system and usually reach larger magnitudes than the climate signals we are interested in. From the perspective of climate prediction, the systematic drift of a model away from the observations limits the models skills to predict climate variability on seasonal-to-interannual time scales. Given a certain lead, the model drift creates a significantly different background state from the observed one on which the low-frequency climatic anomalies evolve. Then, the development of a model anomaly, even if it is initiated accurately, will deviate from the corresponding observed progression because of the model bias. In this paper, we focus on the errors in the tropical Atlantic basin. Recently, Huang et al. (2004) compared the mean state of a long-term CGCM simulation in the Atlantic basin with the observations. The coupled model is composed of the COLA atmospheric GCM (Schneider et al., 2001) and the quasi-isopycnal ocean model developed by Schopf 1

4 and Louhe (1995) that is named Poseidon. They found that the main error of this CGCM is characterized by a substantially weakened cold tongue with warm sea surface temperature (SST) errors extending from the southeastern boundary to the equator. The center of the SST errors is located around 15 o S near the Angola coast with its magnitude larger than 3 o C. Associated with this SST error are weakened southeast trade winds from the equator to around 15 o S and to the west of the maximum SST error. Another major systematic problem of the model in this region is an unrealistic annual migration of the intertropical convergence zone (ITCZ), which moves too far to the south in boreal spring and produces substantial precipitation over the artificial warm water around 5 o S-10 o S. The persistence of this unrealistic rain belt creates the so-called double ITCZ structure in boreal summer, which has been noticed for some time in the CGCM simulated Pacific climate (e.g., Schneider et al., 1997). The precipitation error also contributes to the weakening of the equatorial easterlies. As a result, the model mean zonal gradients of the SST and thermocline depth along the equator are much weaker than the observed. It is generally believed that the errors of the cold tongue and the ITCZ are connected. The SST error found in this particular model seems to reflect a general problem of current CGCMs. Examining 23 state-of-the-art CGCMs, Davey et al. (2002) found that most of these models produce the mean zonal SST gradient that is opposite to the observed one along the equator in the Atlantic Ocean, which is probably a manifestation of the excessively warm SST simulated in the eastern tropical Atlantic by these models. More recently, Wang et al. (2005) showed substantial warm SST bias in both the southeastern Pacific and Atlantic Oceans simulated by the coupled ocean-atmosphere forecast system (CFS) model at the National Center for Environment Predictions 2

5 (NCEP). The magnitude and structure of the SST errors in the tropical Atlantic Ocean is similar to that shown in Huang et al. (2004). The significant differences between the CGCM simulated and observed climatologies are potentially associated with several processes. Huang et al. (2004) found that the SST errors in the COLA-Poseidon CGCM could be caused by inadequate coastal upwelling due to weak model alongshore winds (Huang and Schneider 1995; Schneider et al., 1997). They also speculated that these SST errors might be linked to the southward bias in the model simulated ITCZ position because unrealistic deep convections over the southern Atlantic in the model weaken the southeast trade winds near the equator and further increase the SST there. Carton et al. (2005) found that the warm SST errors in the CFS model are mainly sustained by excessive surface heat flux, especially the shortwave radiative flux into the ocean. This is consistent with findings from some previous studies, which have linked the CGCM SST errors near the eastern Pacific to insufficient model stratus cloud decks over cold waters near the southeastern coast (e.g., Philander et al., 1996; Ma et al., 1996; Yu and Mechoso 1999; Gordon et al., 2000; Gudgel et al., 2001; Bretherton et al., 2004). Analyzing long-term CFS simulations, Xie et al. (2005) pointed out the significance of low cloud to the tropical Atlantic SST errors in boreal winter. However, it is not yet clear how this error is related to the biased ITCZ position that seems to also occur in these models. Although the analyses of the long-term mean model climatology provide us much useful information about the operation of the key processes in the model and their deviations from the reality, the error sources (i.e., the key model deficiency that triggers the errors, if there is one) may still not be fully identified from such an analysis. The 3

6 character of the initial errors in the model might have been changed substantially during their growth because complicated feedbacks and nonlinear interactions of many processes all play roles in producing the saturated errors of finite amplitude. As a result, it is hard to infer the processes of the error development and the key factors from the error patterns of the quasi-stationary model state. For instance, the double ITCZ and its associated wind field can be explained as either the reason or the result of the large SST errors over and to the south of the equator in the eastern Pacific. Different balances of the surface flux exchanges from different CGCMs in their states of equilibrium may partly reflect the different feedbacks involved in each model to balance the initial errors. Therefore, a complementary approach to diagnosing the mean states from long-term CGCM simulations is to examine the error evolution of the model from its initial states close to observations. In this aspect, the operational forecast models, such as the CFS, have a definite advantage because they have been used to make predictions from observed initial conditions. In this paper, we examine the climate drift derived from a set of the experimental hindcasts for conducted in NCEP using the CFS model (Saha et al., 2005). These hindcasts are nine-months integrations starting from initial conditions based on the observational states for every month of the 23 years. From each ensemble forecast, integrations from fifteen initial conditions at different dates are made. The hindcasts provide an ideal dataset for this kind of study because the large number of integrations from the variety of initial states gives us a better chance to examine how the initial errors grow, including its dependence on seasons. From the perspective of prediction, it makes sense to examine the gradually drifting basic state on which the evolving climatic anomalies develop. Recently, Peng et al. (2004) analyzed the climate 4

7 drift of the CFS and its dependence on the lead-time of the hindcasts with emphasis on the extratropical region. This paper is structured as following: The CFS model and the hindcast data are described in Section 2. The evolution of the systematic errors of the SST and depth of 20 o C isotherm (D20) from the ensemble mean hindcasts is described in Section 3. Section 4 presents some results from the corresponding atmospheric fields to understand the pattern and seasonality of the SST and D20 errors. A summary of the results is given in Section Data and analysis method The CFS model, the hindcast dataset, and the method of our analysis are briefly described in this section. For a detailed description of the CFS model and the design of the hindcast experiments, the reader is referred to the paper by Saha et al. (2005). The atmospheric component of CFS is a lower resolution version of the Global Forecast System, which has been the operational global weather prediction model at NCEP since Horizontally, the spectral model has a triangular truncation of the spherical harmonic functions at the 62 nd zonal waves (T62). The vertical discretization of the model is accomplished with finite differencing using 64 σ levels. The sub-scale physical parameterizations have been improved from the version of the model used for the NCEP/NCAR reanalysis (Kalney et al., 1996), as described in Saha et al. (2005). The oceanic component is the Geophysical Fluid Dynamics Laboratory Modular Ocean Model (Version 3, Pacanowski and Griffies 1998). Its domain extends to the world oceans from 74 o S to 64 o N. The model horizontal grid is 1 o x1 o poleward of 30 o S and 30 o N 5

8 with the meridional resolution increased gradually to 1/3 o between 10 o S and 10 o N. The model has 40 levels vertically with 27 of them in the upper 400 meters. The parameterizations of the sub-scale mixing and diffusive processes are also documented in Saha et al. (2005). The oceanic and atmospheric components are coupled on a daily basis without flux adjustment or correction. The hindcasts covers all calendar months from 1981 to For the hindcast experiments, the CFS is initiated by instantaneous atmospheric and oceanic analyses from the NCEP Reanalysis II (Kanamitsu et al., 2002) and the Global Ocean Data Assimilation (GODAS, Behringer and Xue 2004) respectively. With a given starting month (lead month 0 or LM0), 15 prediction runs of nine months length are conducted with oceanatmosphere initial conditions. According to Saha et al. (2005), ocean initial states are chosen as the three GODAS analyses at 11 th and 21 st of lead month 0 (LM0) and 1 st of lead month 1 (LM1). The atmospheric initial states are taken from the corresponding atmospheric fields from NCEP reanalysis II. In addition, each of the three oceanic states is also paired with other four atmospheric states, namely those within two days before and after at a daily interval to form a five member local cluster. Therefore, there are in total 15 ocean-atmosphere initial conditions spread from the 9 th of LM0 to the 3 rd of LM1, from which the integrations of nine months length are conducted. An ensemble mean forecast can then be formed from lead month 0 to 8 by averaging the monthly means from the corresponding months of all members of this ensemble. The datasets of these monthly mean predictions from the 15 individual members as well as their ensemble mean are available from the NCEP website (or anonymous ftp) at 6

9 A mean monthly climatology can be derived as the average of all ensemble mean predictions starting from the same calendar month. Such an average is a time series of nine consecutive months that starts from the given calendar month and evolves with the lead months of the predictions. It represents the mean evolution of the CFS model from the observed initial states at that calendar month. In fact, for all different starting calendar months, twelve separate mean monthly climatologies can be constructed. These separately constructed mean monthly climatologies are different not just in their starting calendar months. In fact, they characterize the mean evolution of the model from initial states at different months. Therefore, the corresponding seasonal means from these different climatologies can be different if the model s climate drifting rate is seasonally dependent. In this sense, the mean monthly climatology of the hindcasts is a function of both lead-month and starting calendar month. We can also construct the observed monthly climatologies using the NCEP Reanalysis II and GODAS data in the same way as we have done for the hindcasts. These observed climatologies should, in contrast, be stationary. i.e., they should be nearly the same at the same calendar month except that different climatologies cover different periods. This is because the climate drifts in the oceanic and atmospheric analyses produced by their data assimilation systems are negligible. As a result, we can define the bias of the hindcasts as the mean monthly climatology of the hindcasts minus the corresponding one of the observations. It is clear that the bias is a function of both the lead and calendar month. In the next two sections, these sequences of bias will be examined. Given the fact that the ensemble hindcasts are composed of integrations starting from three consecutive clusters of initial conditions separated at a near-pentad-day interval, the 7

10 averages formed from different clusters within the ensemble for a specific lead month may have systematic differences with each other and with the ensemble mean. The earlier cluster of initial states has more time to drift than the later ones and should be in a more advanced stage of drift in a specific lead month. However, these differences, associated with a maximum integration time of around 20 days, should still be smaller than those from ensemble means for different months. Therefore, they are negligible if we are interested in difference of biases from initial states at different seasons. The surface heat fluxes, cloud, and precipitation from the hindcasts are compared with climatologies from other independent datasets, especially these based on satellite measurements, because the atmospheric reanalysis is less reliable for these quantities. The atmosphere shortwave radiative fluxes at the surface have been produced globally from several satellite measurements for July December 1998 on a 2.5 o latitude x 2.5 o longitude grid (Whitlock et al., 1995). The surface latent and sensible heat fluxes are obtained from the version 2 of the Goddard Satellite-based Surface Turbulent Fluxes (GSSTF2) based mainly on the measurements of the Special Sensor Microwave/Imager (SSM/I) on board several satellites. The data are available from July 1987 to December 2000 over the global ocean on a 1 o x1 o grid (Chou et al., 2003). The high and low cloud cover is obtained from the International Satellite Cloud Climatology Project (ISCCP), which collects and analyzes satellite radiance measurements to infer the global distribution of clouds properties (Rossow and Dueñas. 2004). The dataset covers the period from July 1983 to June The precipitation fields are based on a combination of satellite retrievals, in situ rain gauge stations, and atmospheric model products, analyzed by the Climate Prediction Center. This dataset spans from January 1979 to 8

11 December 2004 (Xie and Arkin 1996). The monthly climatologies of these observational datasets are used in the following sections. 3. SST and thermocline biases In this section, we examine the evolutions of the model SST and thermocline biases in the tropical Atlantic Ocean. The thermocline is represented by the depth of the 20 o C isotherm (D20). Figure 1 shows the mean annual cycle of the SST and D20 from the NCEP GODAS analysis. The GODAS SST monthly climatology (the panels on the left-hand side in Fig.1) is very similar to those derived in previous studies based on statistical objective analyses of the in situ measurements such as that of Servain and Legler (1986). It is characterized by two cold tongues off the African coast in the northern and southern oceans respectively, which are separated by a belt of warm surface water in low-latitudes from the South American to the North African coast. Following the seasonal increase of the trade winds, each cold tongue is enhanced in its winter while expanding equatorward. In particular, an equatorial cold tongue develops in May and June (Fig.1e) and peaks in August (Fig.1g). Accompanying the annual evolution of the equatorial cold tongue, the warm water belt to its north is farthest north in late boreal summer and early fall (Figs.1i and 1g) and closest to the equator from late boreal winter to spring (Figs.1a and 1c). The mean annual cycle of the GODAS D20 is consistent with the patterns of the surface dynamic height constructed by Merle and Arnault (1985) based on in situ observations and the upper ocean temperature fields compiled by Meyer et al. (1998). The D20 is in general deeper in the western ocean (right-hand side panels in Fig.1). The 9

12 deepest ridges are located near 20 o S and 20 o N, which are the centers of the subtropical gyres with the westward flowing North and South Equatorial Currents on their equatorial flanks. Within the tropics, the annual cycle is concentrated in two regions (Merle and Arnault 1985). In the equatorial zone, the thermocline shoals in the eastern ocean in boreal summer (Figs.1f and 1h), which strengthens the surface cold tongue. A secondary shoaling occurs in the Gulf of Guinea during late fall (Fig.1l). To the north of the equator, the thermocline dome near the North African coast penetrates into the open ocean between 5 o N and 10 o N from May to September (Figs.1f, 1h, and 1j), which strengthens the North Equatorial Counter Current (NECC, Garzoli and Katz 1983). In the following subsections, both the SST and D20 systematic errors of the hindcasts are examined on the background of the observed climatology. 3.1) SST error Figures 2 and 3 show the nine-month SST error fields of the ensemble mean hindcasts from the initial conditions with LM0 in March and June respectively for all years of Both show similar spatial patterns of the SST bias. In general, major warm errors are developed away from the equator in both hemispheres, which are centered in the eastern ocean. Between the warm errors, a cold error belt extends from the western boundary to the northern African coast. The warm errors appear near the eastern boundary in both hemispheres immediately after the hindcasts started (Figs.2a and 3a) possibly because weak model alongshore winds do not produce adequate coastal upwelling (Huang and Schneider 1995). However, the warm SST error grows much faster in the southeastern ocean than in the northeast and reaches larger magnitudes in the southeast at the ends of both sets of hidncasts. This 10

13 faster growth occurs mostly in the latter half of the year. In the March case (Fig.2), the error magnitude at the center of the warm SST bias near the eastern boundary around 20 o S is largely unchanged during the first five months of the integration from March to July (Figs.2a-2e). During the last four months of the hindcast (Figs.2f-2i), however, the error grows much more quickly to the east of 10 o W and north of 20 o S, where it is doubled from below 1 o C to around 2 o C during these four months (Figs.2f-2i). The fastest growing errors are in the open ocean away from the coast and closer to the equator than the coastal error center. The hindcasts with LM0 in June also show this error increase starting from August, just three months after the hindcasts start (Fig.3c). Here the errors continue to grow in the next five months until January (Figs.3d-3h) when the maximum SST error is above 2 o C (Fig.3h). The faster growth of the southeastern SST errors in the latter half of the year from both sets of hindcasts suggests that the systematic error is more dependent on season than on the length of integration. To further demonstrate this property, Fig.4a presents the evolution of the SST bias averaged in the area within 3 o S-18 o S and 10 o W-10 o E for hindcasts starting from different months. Each segment of the curves represents the evolution of the SST error starting from its initial month (LM0) and ending nine months later. The figure shows that all curves from initial conditions with LM0 from March to November peak at or asymptotically approach the maximum value of 1.8 o C in January. The hindcast with LM0 in December also peaks in January but its maximum value is below 1.4 o C. The curves with LM0 in January and February both have their local maxima at LM1. 11

14 Consistent with what we have pointed out from Figs.2 and 3, the slope of the curve with LM0 in March (the back curve) increases significantly after July, indicating a much faster error growth rate. The convergence of curves in January shows that the hindcasts starting from later months (July and after) have to have much faster error growth rate in their first few months to catch up with those hindcasts starting from the earlier months. The differences of initial growth rates with seasons can be seen very clearly from the starting points of the different curves, which indicate the errors derived during LM0 of the hindcasts. If we use the level 0.4 o C as a subjective standard, the initial error growth is relatively small from March to July but large from August to February. The largest initial error growth occurs in November and December, which is around 0.9 o C. A local minimum appears in April for all curves starting with LM0 from August to February, showing that errors there actually decrease from winter to spring for some hindcasts. For those curves that are near 1.8 o C in January, their minimum values are also close to each other at around 1.2 o C, indicating a nearly constant decay rate during this period. For the case of LM0 in January, the minimum in April (0.6 o C) is smaller than the SST bias for the month of LM0 (0.8 o C). It is interesting that growth rate is also consistent after the minimum point from April to July, as shown by the nearly parallel increases of the curves in this interval. Unlike the latter half, the error growth rate is quite uniform in the earlier half of a calendar year. To a lesser extent, the warm SST systematic bias in the northern ocean also shows seasonality. The error grows faster from June to September, mainly in a region between 10 o N-20 o N away from the African coast (Figs. 2d-2g and Figs.3a-d), reaching 0.75 o C at their peaks. This seasonal dependence is verified for all hindcasts by the growth of an 12

15 index of the averaged SST (not shown). Nearly simultaneous with the warming in the north, the equatorial ocean cools down from 0.5 o C to 1.0 o C within a belt stretching from the southwestern boundary across the Atlantic Ocean to the North African coast, which occurs in both sets of the hindcasts (Figs.3 and 4) and is also verifiable by other hindcasts not shown here. This cooling weakens the model warm water belt during boreal summer and fall. 3.2) D20 error In some situation, transient D20 errors may be generated by a mismatch between the oceanic initial states and surface winds. This seems the case during the first two months (March and April) of the hindcast from March, which shows an excessive shoaling of the D20 in the eastern equatorial ocean, accompanied by deepened thermocline in the west with two off-equatorial centers nearly symmetric on either side of the equator (Figs.5a and 5b). Associated cold SST errors appear in the equatorial ocean (Figs.2a-2c). However, this transient process does not cause permanent bias in the coupled system. More permanent systematic error of the model thermocline depth is established in the northwestern part of the tropical Atlantic (Figs.5 and 6). In the March case, the Atlantic region is dominated by a D20 error of more than 25 meters in the northwestern ocean between 5 o N and 10 o N from May to August (Figs.5c-5f). This error grows quickly and almost simultaneously with the establishment of the surface cold bias while the SST is warmed up further north. Once established, the thermocline bias persists throughout the time of hindcast, ranging from 20 to 30 meters (Figs.5d-5i). The D20 errors from the June hindcasts (Fig.6) largely mirror those of the March ones in their corresponding Calendar month (instead of lead month). In this case, the D20 error is established more quickly in 13

16 July and August (Figs.6b and 6c) although the bias is small in the first month (Fig.6a). This seasonal dependence of thermocline error growth is also quantified by the averaged D20 error in an area within 5 o N-10 o N and 30 o W-50 o W (Fig.4b). For any hindcasts, no matter when it starts, it is clear that the largest error increase usually occurs during May- July, with a peak error of more than 20 meters in August. Once formed, the subsurface bias leaks warm water to the east along the equator in May and June (Figs.5c-5d) and again in September and November (Figs.5g-5i), which seem to have significant consequence. In GODAS, the depth of equatorial thermocline (Fig.7a) has a dominant annual cycle in the west, which is deepest (above 150 meters) in September and the shallowest (around 120 meters) in May. In the east, however, there is a stronger semi-annual component of the thermocline fluctuation with the major shoaling in July (30-35 meters) and a secondary shoaling in December (40-45 meters). This semiannual cycle in the east is seen in the independent satellite altimeter measurement from TOPEX/POSEIDON. In the hindcasts, the equatorial surge of warm water in boreal spring delays the shoaling of the thermocline from April to June, as seen in Figs.7b, 7c, and 7e. The hindcasts also underestimate the second peak in December, as can be seen from Figs.7b-7d. As a result, the semiannual tendency in the Gulf of Guinea has been significantly weakened in the hindcasts. Through the eastward surges of the warm water, the annual cycle in the northwestern Atlantic seems to exert a too strong influence on the eastern equatorial ocean, thereby undermining the semiannual signals there. Overall, our analysis suggests that two distinct systematic errors occur in the tropical Atlantic Ocean. Both of them are seasonally dependent. One is the fast increase of warm SST errors in the southeastern tropical ocean occurring from August to January. The 14

17 other is characterized by the unrealistically deep model thermocline in the northwestern Atlantic Ocean off the South American coast, which may be related to the SST errors in the northern and equatorial ocean. However, the surface warming in the southeast is not directly related to the local D20 errors, which is relatively small to the south of the equator from June to October when the surface warming is fastest. 4. Atmospheric biases In this section, we examine the systematic errors in the atmospheric component of CFS. The examination is confined to those fields that are directly related to the SST and D20 errors discussed in the last section. They include surface momentum and heat fluxes into the ocean, cloudiness, and precipitation. The latter two suggest possible sources of the major errors in this region. Since our discussion above shows that the growth of the SST and D20 systematic error is largely dependent on seasons, this part of the discussion will mainly be presented with seasonally averaged field for succinctness while faster growth in some specific months will be pointed out in the text. Moreover, since the above analyses also show that the hindcasts starting from June (Figs.3 and 6) are representative of the major error growth period from boreal summer and fall, our discussion hereafter will concentrate on these hindcasts. 4.1 Surface fluxes Figure 8 shows the climatological seasonal mean surface wind stresses and their curls from the NCEP reanalysis 2 and the CFS hindcasts with LM0 in June as well as the errors of the hindcasts. The divergence of the wind stress is also shown as a proxy of ITCZ (Fig.9), which is characterized by a belt of convergence near the equator that separates 15

18 the regions of the anticyclones. In Fig.8, the ITCZ location generally corresponds to the cyclonic (positive) curls of the wind stress. During boreal summer (June-July-August, JJA), the northeast and southeast trade winds converge into the ITCZ around 5 o N-10 o N (Figs.8a and 8b) while the divergence (Figs.9a and 9b) and anticyclonic curl (negative, Figs.8a and 8b) of the wind stress dominate the subtropics in both hemispheres. Both the cyclonic curl and convergence of the model surface wind stress are located further to the north in comparison with the observations, indicating a slight northward bias of its ITCZ position. This bias is also discernable from the stronger model cross-equatorial winds in Fig.8c. The largest error of the model wind stress is over the tropical northwestern ocean off the South America coast, where the overly strong cross-equatorial model southeast trade winds and the weak northeast trade winds further north form the center of the negative wind curls at 5 o N and 40 o W-50 o W, along with a weaker positive center further north (Fig.8c). This error of anticyclonic wind curl forces the local deepening of D20 during these three months (Figs.6a-6c). In the monthly maps (not shown), the development of the D20 and the wind stress curl are more closely linked by the Sverdrup relation. During September-October-November (SON), the model northeast trade winds are weakened and become more zonal, resulting in a broader region with weaker anticyclonic curl (Figs.8d and 8e) and divergence (Figs.9d and 9e) in the subtropics. The errors of the wind stress curl are also enhanced and extend eastward (Fig.8f), forcing a further deepening and eastward expansion of the D20 (Figs.4d-4f). These developments of the wind errors are likely a response to the excessive warming of the sea surface in the northern tropical ocean in late summer (Fig.3c) and early fall (Figs.3d-3e). 16

19 By December-Janaury-February (DJF), the observed ITCZ migrates toward the equator (Figs.8h and 9h) and the northeast trade winds move southward correspondingly. In this season, however, the model surface winds are largely zonal from 10 o S to 10 o N with little convergence near the equator (Figs.8g and 9g). As a result, the model ITCZ is not clearly defined in the divergence field. This lack of meridional component in the model can be seen clearly in the wind error map (Fig.8i). The wind curl error also shows a negative center near the equator and two positive centers on both sides. The negative curl is responsible for the deepening of D20 near the equator in the west in January (Fig.6h) and February (Fig.6i). The error map of the divergence shows that, apart from the larger model divergence near the equator, the model wind is more convergent than the observed near 10 o N and 5 o S-10 o S (Fig.9i). As we will show later on, this error pattern of the surface wind is consistent with the model precipitation errors. Figure 10 compares the net shortwave radiative surface fluxes from the ERBE satellite climatology and the CFS hindcasts starting from June. Qualitatively, the observed annual cycle has been simulated reasonably well by CFS in the central and the western ocean and over the continents (left and middle columns, Fig.10). The major errors, however, are located near the eastern coasts: The model shows the maxima of the net fluxes there, especially in SON (Fig.10d) and DJF (Fig.10g). In the observations, however, a minimum is already seen near the southeastern boundary during JJA (Fig.10b) and most clear during SON (Fig.10e) while large fluxes into the ocean are shifted westward. In fact, the minimum in the east persists into DJF (Fig.10h). As a result, a substantial amount of excessive shortwave radiation reaches the model ocean in the southeast (Figs.10c, 10f, and 10i). This excess is centered at 5 o S-15 o S and 0 o -10 o E and 17

20 peaks in SON at close to 100Wm -2 (Fig.10f). Its location matches that of the increasing warm SST error from August to February (e.g., Figs.3c-3i) described in detail in the last section. The peak of SST error in February lags that of the shortwave radiation (October, not shown) by about four months. This result confirms the finding of Carton et al. (2005) based on a long-term CFS simulation. To a lesser extent, a similar situation occurs off the North African coast in JJA (Fig.10c). The center of excess heat flux occurs at 15 o N and 20 o W with a maximum value of 30Wm -2. This possibly explains the relatively mild surface warming in this region from June to September. The other significant feature in this season is the deficit near the South America coast and the western ocean. In DJF, the model error near the equator shows a pattern of surplus to the north of the equator with deficits on both sides (Fig.10i), which apparently reflects the diffused ITCZ discussed above (Figs.8i and 9i). Other components of the surface heat budget generally counter the excessive solar radiation in the southeast. In particular, the model evaporative heat loss at the surface is 80-90Wm -2 more than that of the GSSTF2 climatology in this region (Figs.11f and 11i). This is likely a response to the increased SST there because the local surface wind speed is actually reduced (Figs.8f and 8i). The model s net long wave radiation heat loss at the sea surface is also larger than that from dasilva et al. (1994) climatology (not shown). The difference in the sensible heat fluxes between the model and GSSTF2 is quite small over the basin compared to errors in the other components, because the total magnitude of the sensible heat flux is generally small in the tropical oceans. The other major region of model bias in latent heat flux is the excessive heat loss to the north of the equator (Figs.11c, 11f, and 11i) that is likely related to the biased model ITCZ and contributes to 18

21 the cooling of the model warm water belt. Somewhat surprisingly, the model also shows excessive evaporative heat loss in the northern tropical Atlantic in JJA (Fig.11a) and SON (Fig.11d) even though its northeast trades are apparently weak. Overall, we find that the major errors in the SST and D20 can be linked to the corresponding errors in the surface momentum and heat fluxes. These include the excessive shortwave radiation into the ocean in the southeastern ocean, the negative wind curl error in the northwestern tropical ocean, and the diffused ITCZ in boreal winter. 4.2 Cloud and precipitation As discussed in Section 1, previous studies have pointed out the role played by stratus and stratocumulus clouds on the solar radiation reaching the ground. In examining the southeastern Atlantic region in DJF, Xie et al. (2005) suggested that the lack of diurnal cycle off the coastal region is a major problem in a long CFS simulation. Our analysis also finds that the coupled model generated low cloud does not catch some major features of the observed mean climatology and its annual cycle in this region. According to the ISCCP measurements, low clouds in the tropical Atlantic (the lefthand panels in Fig.12) are mainly located over the cold water off the eastern boundary of the ocean and in-land near the coast of the South America. Although the cloud cover is quite substantial in the southeast throughout the year, it starts to increase in late boreal spring and early summer (Fig.12a), peaks in fall (Fig.12c), decays in DJF (Fig.12e), and maintains a relatively low value in MAM (Fig.12g). The cloud cover in the northeastern tropical Atlantic is generally lower, but it also follows a distinct annual cycle, with cloudiness increasing from boreal winter and peaking in the summer. 19

22 The distribution of low cloud is not adequately simulated by CFS (the right-hand panels in Fig.12). The model cannot reproduce the larger cloud cover over the cold water off the eastern boundary in both hemispheres. Instead, the regions of the coastal and equatorial cold tongues are generally cloudless. The substantial low cloud cover in the subtropics is shifted westward into the open ocean. Seasonally, the amount of cloud cover peaks too early in either hemisphere in JJA in the south (Fig.12b) and MAM in the north (not shown). This pattern of cloud distribution in the model does not change with hindcasts starting from different seasons. The lack of low cloud cover in the eastern part of the ocean largely explains the excessive shortwave radiation reaching the sea surface that in turn causes the seasonally dependent warm SST bias there. Another difference of the model from the observations is that a substantial amount of low cloud is generated within the ITCZ and in the continent of South America where deep convection and high cloud is active (Figs.12 and 13). The stronger overlapping between the high and low cloud covers in the model implies an interference of the shallow and deep convection there in all seasons, an effect that may not be realistic. On the other hand, the model simulates the high cloud cover much more successfully (Fig.13). For both the model and the observations, the high cloud cover over the ocean is largely associated with the ITCZ and the South Atlantic Convergence Zone. The distribution of the high cloud cover also corresponds closely to that of the major precipitation (Fig.14). For both the high cloud cover and precipitation, the model convection within the ITCZ is too strong in JJA and SON. Moreover, as shown in the surface wind stress maps, the ITCZ is biased northward in the northwestern ocean in SON (Figs.13d and 14d). The largest error, however, is the tendency to split the 20

23 precipitation in the ITCZ regions into two bands in boreal winter (Figs.13f and 14f). The locations of these bands coincide with those of the excessive surface convergence (Figs.9g and 9i). 5. Summary and discussion A set of ensemble hindcasts from the NCEP coupled forecast system (CFS) is used to examine the development of the coupled model bias. These CFS hindcasts are ninemonths integrations starting from oceanic and atmospheric analyses for For each starting month, the hindcasts are conducted from fifteen disturbed initial states to form an ensemble. In this study, we compare monthly and seasonal climatologies based on all ensemble hindcasts for a given initial month within the 23 years with those from the oceanic and atmospheric analyses or other observation-based datasets. The large number of integrations from a variety of initial states covering all seasons provides a good opportunity for us to examine how the model systematic errors grow. Our analysis shows that seasonally dependent processes dominate the growth of the major ocean-atmospheric errors throughout the hindcasts. In particular, two major error patterns are most significant in the oceanic fields. One is a rapid warming of the SST in the southeastern tropical Atlantic Ocean in the later half of the year, generally peaking in January-February. The initial error grows much faster in this region for hindcasts starting in boreal summer and fall than in late winter and spring. This large error growth rate during the latter half of the year is directly associated with the excessive shortwave radiation reaching the sea surface in the model, especially from boreal summer to early winter. This in turn arises from the model s inability to reproduce the observed the higher 21

24 amount of low cloud cover in the southeastern ocean and its annual increase from boreal summer to late fall. To a lesser extent, a similar situation occurs near the northeastern tropical Atlantic where the model fails to reproduce the observed low cloud cover increases from boreal winter to summer. The lack of cloud cover in the model here also leads to excessive shortwave radiation at the sea surface and the warming of the SST during boreal spring and summer. The other major error is the excessive deepening of the model thermocline depth to the north of the equator, which extends from the western coast toward the east. This deepening also grows most rapidly in boreal summer and is forced by the unrealistically strong local anticyclonic wind stress curl. The wind stress error is in turn related to the combined effects of the biased ITCZ position in the western ocean and weakened northeast trade winds in summer and fall. The biased ITCZ is associated with stronger northward winds. The increased wind speed enhances the local evaporative heat loss and contributes to the cooling of the warm surface water from the western equatorial to the eastern coast north of the equator. The subsurface warm water stored in the excessively deep northwestern thermocline spills over to the equatorial zone during the seasonal transitions in May-June and September-November and propagates to the eastern coast, thereby deepening the thermocline in the eastern equatorial and coastal region. These eastward surges of warm waters are likely equatorial Kelvin waves forced by off-equatorial factors because the model zonal winds are persistently stronger than the observed along the equator to the west of 25 o W. In this way, the dominant annual cycle seems to exert too strong an 22

25 influence on the eastern ocean through the equatorial wave-guide, thereby undermining the semiannual component in this region that is significant in the observations. The two major errors are possibly connected to each other. For instance, the weakened northeast trade winds in boreal summer and fall are probably related to the warmer SST in the northern tropical Atlantic Ocean, which is forced by excessive shortwave radiation into the ocean due to the lack of cloud cover. Similarly, the significant bias of the ITCZ into the southern ocean in DJF, which is the most serous error in the simulated rainfall pattern (Wang et al., 2005; Saha et al., 2005; Xie et al., 2005), may also be attributed to the rapid warming of the SST in the southeast, and the associated weakening of the southeast trade winds to the north of 10 o S. We would like to point out that the SST errors in the south might not be the sole cause of the ITCZ bias in this season. The cooling of the equatorial SST in the west, as well as the model s failure to establish the convection center in-land near the South American coast close to the equator in boreal winter and spring (Figs.14e and 14f), may be as important. It is also possible that the persistent cold bias in the surface warm water near the equator makes deep convection not sustainable there. The equatorial cold SST bias may be caused by the northward ITCZ bias in earlier seasons. On the other hand, the SST error in the southeast is very likely to enhance the error pattern. Circumstantial evidence to support this assumption may be provided by the hindcasts of the mean March precipitation with increasing lead month (Fig.15). March is chosen because it is the time when the SST error growth is slow so that newly generated warm SST errors in the southeastern ocean may not be accounted as the major factor at least in the short term. 23

26 The observed precipitation in March (Fig.15a) is dominated by rainfall over the continent of South America that is linked to the ITCZ near the equator across the ocean. The center of the precipitation is at the coast near the equator. These features are reasonably well simulated in LM0 (March, Fig.15b) although there is already sign that the rainfall is increasing further south over the ocean. With increasing lead months (Figs.15c-15f), the southern branch of the precipitation grows stronger than the northern branch as the hindcasts pass through the previous fall and winter seasons when the excessive surface heat flux is largest. The situation seems to be stabilized with hindcasts of five and six months lead starting from September (Fig.15g) and August (Fig.15h). By this time, the southern branch has become dominant and the northern one is weak. The ITCZ degradation is probably associated with the accumulation of the SST errors in this period. Since the tropical Atlantic Ocean is dominated by a strong annual cycle, the systematic bias in simulating the annual cycle may have profound effects on its ability to simulate the interannual variability. Huang (2004), Huang et al. (2004), and Huang and Shukla (2005) have shown that the SST bias in the southeastern tropical Atlantic undermines the simulation of the interannual fluctuations in this region. For climate predictions on seasonal to interannual scales, the growing systematic bias may also be a serious block for a successful forecast. At present, we are evaluating the skill of the CFS SST hindcasts in the tropical Atlantic Ocean statistically on a season-by-season basis and connecting it to the model seasonal mean state. We expect that the more rapid development of the model bias in boreal fall and winter will have a significant effect on its predictive skill in subsequent months. 24

27 Acknowledgments We would like to thank the Environmental Modeling Center at NCEP for generously providing the CFS hindcast dataset, which makes this study possible. We would also like to thank Drs. J. Shukla and J.L. Kinter III for their support of this research and Dr. D. Straus for his valuable comments and suggestions on the manuscript. We are grateful to Drs. J. Zhou, S. Yang, and V. Misra for useful discussions. The financial support is provided by NOAA s CLIVAR Atlantic Program (NA04OAR ). 25

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