Simulations of the West African Monsoon with a Superparameterized Climate Model. Part I: The Seasonal Cycle

Rachel R. McCrary National Center for Atmospheric Research,* Boulder, Colorado

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David A. Randall Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado

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Cristiana Stan Department of Atmospheric, Oceanic and Earth Sciences, George Mason University, Fairfax, Virginia, and Center for Ocean–Land–Atmosphere Studies, Calverton, Maryland

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Abstract

The West African monsoon seasonal cycle is simulated with two coupled general circulation models: the Community Climate System Model (CCSM), which uses traditional convective parameterizations, and the “superparameterized” CCSM (SP-CCSM), in which the atmospheric parameterizations have been replaced with an embedded cloud-resolving model. Compared to CCSM, SP-CCSM better represents the magnitude and spatial patterns of summer monsoon precipitation over West Africa. Most importantly, the region of maximum precipitation is shifted from the Gulf of Guinea in CCSM (not realistic) to over the continent in SP-CCSM. SP-CCSM also develops its own biases—namely, excessive rainfall along the Guinean coast in summer. Biases in rainfall from both models are linked to a misrepresentation of the equatorial Atlantic cold tongue. Warm sea surface temperature (SST) biases are linked to westerly trade wind biases and convection within the intertropical convergence zone. Improved SST biases in SP-CCSM are linked to increased tropospheric warming associated with convection. A weaker-than-observed Saharan heat low is found in both models, which explains why the main band of precipitation does not penetrate as far northward as observed. The latitude–height position of the African easterly jet (AEJ) is comparable to observations in both models, but the meridional temperature and moisture gradients and the strength of the jet are too weak in SP-CCSM and too strong in CCSM. Differences in the AEJ are hypothesized to be influenced by the contrasting representation of African easterly waves in both models; no wave activity is found in CCSM, and strong waves are found in SP-CCSM.

The National Center for Atmospheric Research is sponsored by the National Science Foundation.

Corresponding author address: Rachel R. McCrary, National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO 80307. E-mail: rmccrary@ucar.edu

Abstract

The West African monsoon seasonal cycle is simulated with two coupled general circulation models: the Community Climate System Model (CCSM), which uses traditional convective parameterizations, and the “superparameterized” CCSM (SP-CCSM), in which the atmospheric parameterizations have been replaced with an embedded cloud-resolving model. Compared to CCSM, SP-CCSM better represents the magnitude and spatial patterns of summer monsoon precipitation over West Africa. Most importantly, the region of maximum precipitation is shifted from the Gulf of Guinea in CCSM (not realistic) to over the continent in SP-CCSM. SP-CCSM also develops its own biases—namely, excessive rainfall along the Guinean coast in summer. Biases in rainfall from both models are linked to a misrepresentation of the equatorial Atlantic cold tongue. Warm sea surface temperature (SST) biases are linked to westerly trade wind biases and convection within the intertropical convergence zone. Improved SST biases in SP-CCSM are linked to increased tropospheric warming associated with convection. A weaker-than-observed Saharan heat low is found in both models, which explains why the main band of precipitation does not penetrate as far northward as observed. The latitude–height position of the African easterly jet (AEJ) is comparable to observations in both models, but the meridional temperature and moisture gradients and the strength of the jet are too weak in SP-CCSM and too strong in CCSM. Differences in the AEJ are hypothesized to be influenced by the contrasting representation of African easterly waves in both models; no wave activity is found in CCSM, and strong waves are found in SP-CCSM.

The National Center for Atmospheric Research is sponsored by the National Science Foundation.

Corresponding author address: Rachel R. McCrary, National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO 80307. E-mail: rmccrary@ucar.edu

1. Introduction

Over the course of a year, West Africa experiences both a distinct seasonal shift in the prevailing winds and the seasonal cycle of winter dry conditions and summer rainy conditions that is characteristic of monsoon climates. Monsoon rains are the lifeblood of the people who live in West Africa. Their cultures and lifestyles have evolved around the cyclic nature of the monsoon rains and the growing season. West Africans are heavily reliant on climate-dependent agriculture and herding (Joiner et al. 2012). The lack of irrigation infrastructure in West Africa implies an overwhelming dependence on rainfed agriculture, so the approximately 317 million people living in West Africa today are highly vulnerable to variations in monsoon rainfall (Boko et al. 2007; Baron et al. 2005).

During the 1970s and 1980s, West Africa was impacted by a severe long-term drought (e.g., Nicholson 2000). The drought exacerbated the region’s normal interannual and intraseasonal rainfall variations. The drought had devastating agricultural, economic, and societal consequences for the region and drew attention to the overall vulnerability of people living in West Africa. As our planet warms because of increasing greenhouse gas concentrations, resulting changes in the African monsoon circulation could affect water resources in West Africa. How extreme will the changes in the climate over West Africa be during the next 50–100 years?

Unfortunately, the response of West Africa’s climate to global warming is currently highly uncertain. The models used in phases 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5) (Meehl et al. 2007; Taylor et al. 2012) show a wide variety of changes in West African rainfall (e.g., Alley et al. 2007; Cook and Vizy 2006; Roehrig et al. 2013; James and Washington 2013). The models disagree not only about the magnitude of the expected change, but also on the sign of the change. The large spread in model responses makes it difficult to develop robust adaptation strategies.

There are a number of reasons why the coupled general circulation models (CGCMs) project a broad range of potential changes to the West African monsoon (WAM). The models have different horizontal and vertical resolutions, they use different parameterizations to represent subgrid-scale processes associated with clouds and boundary layer turbulence, and they represent ocean physics and land surface processes using different approaches. The WAM is a complicated system that involves many interactions among the atmosphere, ocean, and land surface over a range of temporal and spatial scales, from propagating mesoscale convective systems to the planetary-scale circulation that drives the monsoon winds (Hall and Peyrillé 2006).

Many models are unable to accurately represent the timing, spatial patterns, and magnitude of monsoon precipitation over West Africa (e.g., Cook and Vizy 2006; Roehrig et al. 2013). CGCMs have difficulty representing the complex, multiscale interactions that are associated with the WAM that result in high spatial and temporal variability of rainfall over the Sahel (Roehrig et al. 2013). The current state of subgrid-scale parameterizations, including convection, turbulence, and microphysics, in conventional CGCMs is a major limitation for the simulation of the WAM. Poor representation of subgrid-scale processes, particularly diabatic heating associated with convection, prevents models from capturing the important feedbacks that occur between small-scale convection and the large-scale dynamics. An improved representation of subgrid-scale physical processes may permit more realistic simulations of the WAM.

The goal of this study is to investigate how the use of the superparameterized Community Atmosphere Model (SP-CAM) affects the simulation of the WAM. The SP-CAM is a version of the Community Atmosphere Model (CAM) that uses a two-dimensional cloud-resolving model (CRM) to represent cloud-related processes that are not resolved on the CAM’s coarse grid (e.g., Randall et al. 2003; Khairoutdinov and Randall 2001; Khairoutdinov et al. 2005, 2008). The concept is based on work by Grabowski and Smolarkiewicz (1999) and Grabowski (2001). The embedded CRM is often called a “superparameterization.” The CRM replaces the parameterizations of convective and stratiform clouds. The GCM provides the large-scale forcing to the CRM, and the CRM returns the physical tendencies of heating and drying back to the GCM. Radiation, cloud microphysics, and turbulence are still parameterized, but on the CRM’s grid. As with traditional parameterizations, the CRM results are assumed to be representative of the cloud processes that occur in the entire grid column, rather than an exact representation of a specific cloud field. Essentially both the embedded CRM and the traditional parameterizations have the same purpose: to determine the area-averaged heating and drying rates in the GCM grid column. In the SP-CAM large-scale clouds and some mesoscale processes are explicitly represented by the CRM, allowing more realistic representation of heating and drying rates. The superparameterization does not increase the resolution of the CAM; it is simply a parameterization based on a CRM.

Stan et al. (2010) coupled the SP-CAM with the Parallel Ocean Program (POP) ocean submodel of the Community Climate System Model (CCSM). The coupled model is called the superparameterized CCSM (SP-CCSM). They found that coupling with the ocean actually improves the simulated atmospheric circulation. Additional results from the coupled model are presented by DeMott et al. (2011, 2013).

Superparameterization has been shown to improve the simulation of many aspects of the global climate, including the Madden–Julian oscillation (MJO; Benedict and Randall 2009, 2011; Thayer-Calder and Randall 2009), the Asian monsoon (DeMott et al. 2011, 2013), El Niño–Southern Oscillation (Stan et al. 2010), precipitation intensity (DeMott et al. 2007), the diurnal cycle of rainfall over continents and oceans (Khairoutdinov et al. 2005), and the diurnal propagation of convection in the lee of the Rocky Mountains (Pritchard et al. 2011). These studies suggest that the SP-CAM performs better than traditional models because of its ability to represent the vertical structure of moisture and diabatic heating in different environments. For example, the SP-CAM captures the required increase and deepening of low-level humidity ahead of the MJO (Thayer-Calder and Randall 2009) and before intense precipitation events (DeMott et al. 2007). An important aspect of the embedded CRMs is that coupling between the boundary layer and deep convection is explicitly represented and does not require passing information between two independent parameterizations representing the boundary layer and convection separately. DeMott et al. (2013) suggest that the improved representation of the northward propagation of the Asian summer monsoon in the SP-CCSM is partly due to the ability of convection to respond to changes in the boundary layer.

Given these previous results, we anticipate that the simulation of the WAM would also be improved with superparameterization. Key aspects of the WAM, including the north–south migration of the intertropical convergence zone (ITCZ), the position and intensity of the African easterly jet (AEJ; Thorncroft and Blackburn 1999), the development and maintenance of African easterly waves (AEWs; Thorncroft et al. 2008), and the east–west propagation of diurnally forced convection (e.g., squall lines and mesoscale convective complexes; Fink and Reiner 2003) are associated with convection and coupling between convection and the large-scale circulation. In this study, we examine the impact that superparameterization and model physics has on the simulation of the WAM system. This, the first of two papers, is devoted to examining the overall simulation of the seasonal cycle of the WAM. In addition to an evaluation of the annual cycle of rainfall, we examine the low-level monsoon winds, sea surface temperatures (SSTs), the Saharan heat low, the African easterly jet, and African easterly wave activity. In a companion paper (McCrary et al. 2014, hereafter Part II), we focus on the relationship between convection and African easterly wave activity during boreal summer. Additional discussion can be found in McCrary (2012).

The remainder of this paper is organized as follows: In section 2 we describe the models and observations that are used in this study; results from the simulations are compared with observations in section 3; and we conclude with a summary and discussion of the results in section 4.

2. Models, observations, and reanalysis

a. Models

We analyze the simulation of the West African monsoon in two CGCMs. The first is the standard Community Climate System Model, version 3 (CCSM3; Collins et al. 2006), which uses conventional cumulus parameterizations to represent cloud-scale processes. The second is the SP-CCSM (Stan et al. 2010), in which the traditional parameterizations have been replaced by a two-dimensional (2D) CRM in each atmospheric grid column.

Both models were run using very coarse T42 spectral resolution (with a 2.8° × 2.8° grid) for the atmosphere with a semi-Lagrangian dynamical core. The standard CCSM3 was run with 26 levels, whereas the SP-CCSM was run with 30 levels. The CRMs embedded within SP-CCSM have 32 columns oriented in the east–west direction, a horizontal grid spacing of 4 km, and 28 levels that are collocated with the 28 lowest levels of the GCM.

In the standard model, deep convection is parameterized following Zhang and McFarlane (1995), shallow convection is represented following Hack (1994), and stratiform clouds are parameterized following Sundqvist (1988).

In the SP-CCSM, the 2D CRMs replace the conventional parameterizations of moist convection, stratiform clouds, and turbulence. Cloud microphysics, turbulence, and radiation are still parameterized, but on the CRM’s fine grid. Since the CRMs used are 2D, momentum feedback from the CRM to the large scale is not included (Khairoutdinov et al. 2005). The CRMs are forced by the large-scale advection of heat, moisture, and momentum. The GCM, in turn, is modified by domain-averaged CRM tendencies of temperature, water vapor, and liquid/ice nonprecipitating water. The CRMs embedded within each GCM grid column use periodic lateral boundary conditions. The CRMs in neighboring GCM grid columns communicate only through their interactions with the GCM. For more detailed information about the embedded CRM and coupling between the GCM and the CRM please see Khairoutdinov and Randall (2001, 2003) and Khairoutdinov et al. (2005).

In both simulations, the atmospheric model is coupled to the low-resolution 3° version of the POP ocean model (Smith and Gent 2002) and the Community Land Model, version 3 (CLM3; Bonan et al. 2002). The CRMs are coupled to the land surface scheme at the GCM scale, so the individual columns of each CRM all see the same large-scale land surface tile. The ocean dynamics was initialized from rest, and the thermodynamics was initialized from climatological sea surface temperatures and salinity (Levitus et al. 1998). The land and atmosphere are initialized from prescribed SST experiments. Atmospheric CO2 and aerosol concentrations are the same as used in the control experiments from Collins et al. (2006). Both simulations are 27 years in length, with daily-mean output; the first 2 years of each run were ignored because of spinup.

In section 3a(2) of this paper, we also briefly examine the rainfall distribution that is simulated when the SP-CAM, the atmospheric component of SP-CCSM, is forced with observed SSTs. This run extends from 1986 to 2004 (Khairoutdinov et al. 2005).

Simulations that have adopted the superparameterized framework are on the order of 100 times more expensive than standard climate simulations. As we show in this paper, and as has been shown in a number of previous studies (see the introduction), explicit representation of subgrid-scale moist physics allows for an improved representation of many aspects of the climate system, especially in the tropics. The SP-CCSM serves as a useful tool to examine the coupling between large-scale dynamics and small-scale clouds.

While CCSM3 is a somewhat outdated version of the CCSM, its use in this study allows a direct comparison between the original unmodified “host” CGCM and the superparameterized version of the same model. This comparison allows us to examine how model physics influences the simulation of the WAM. The superparameterization is one way to modify model physics; a number of changes have been made to the standard CCSM3 in the past 7 years, which have also resulted in an improved simulation of the monsoon (e.g., Cook et al. 2012).

b. Observations and reanalysis

Model results are compared against a number of observation-based datasets. For precipitation we use the Tropical Rainfall Measuring Mission (TRMM) 3B42 precipitation dataset. The years 1998–2010 are used in this study, and the TRMM 3B42 dataset covers the tropics between 40°S and 40°N with a spacing of 0.25° in both latitude and longitude, and three-times-daily data (Huffman et al. 2007). To compare with the models, the TRMM data product was averaged to create both daily means and monthly climatologies.

The simulated Atlantic SST patterns are compared against the National Oceanographic and Atmospheric Administration (NOAA) optimal interpolation sea surface temperature high-resolution dataset (version 2) (Reynolds et al. 2007), which provides daily-mean SSTs for the period 1981–2010 and has a spacing of 0.25° in both latitude and longitude. These SSTs were retrieved from the Advanced Very High Resolution Radiometer (AVHRR).

Other meteorological fields such as winds, geopotential height, temperature, and humidity are from European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Re-Analysis (ERA-Interim; herein ERA-I) (Dee et al. 2011). The ERA-I data product is available at four times daily on a 1.5° grid. The data were averaged to obtain daily-mean values for comparison with the model output. The lack of consistent and spatially coherent observational soundings over West Africa poses a challenge. Reanalysis products such as ERA-I are necessary for long-term studies such as this one. As with all reanalysis products, variables such as specific humidity are strongly influenced by the model physics and so should be considered as observationally based estimates but not true observations.

Where differences between simulations and observations are shown, the observations were bilinearly interpolated onto the model grid.

3. Results

a. Precipitation and low-level winds

The mean annual cycle of rainfall over West Africa from observations and the two models is presented here in two ways: first, through spatial maps based on seasonal averages (Fig. 1); second, through time–latitude cross sections (Fig. 2). In Fig. 1, the 925-hPa wind fields are overlaid on precipitation to show the low-level wind patterns associated with the WAM.

Fig. 1.
Fig. 1.

Mean precipitation (filled contours; mm day−1) and 925-hPa winds (vectors) from (a)–(d) observations (TRMM and ERA-I), (e)–(h) SP-CCSM, and (i)–(l) CCSM3 averaged over four seasonal periods: January–March (JFM), April–June (AMJ), July–September (JAS), and October–December (OND). The thick black line delineates the zero contour of the zonal wind and is a good indicator of the monsoon airmass (Sultan and Janicot 2003). A wind reference vector of 5 m s−1 is shown to the right of the color bar.

Citation: Journal of Climate 27, 22; 10.1175/JCLI-D-13-00676.1

Fig. 2.
Fig. 2.

Latitude–time cross section of 5-day averaged seasonal cycle of precipitation (mm day−1) averaged between 10°W and 5°E from (a) TRMM, (b) SP-CCSM, and (c) CCSM3. The thick black line delineates the southernmost edge of the West African region.

Citation: Journal of Climate 27, 22; 10.1175/JCLI-D-13-00676.1

1) Observations

The observed seasonal cycle of the WAM has been well documented in previous studies (e.g., Liebmann et al. 2012; Zhang et al. 2006). Here we briefly describe the annual cycle of observed rainfall and low-level winds in order to illustrate the features that are important for comparison with the models. Precipitation over West Africa during the boreal summer is influenced by the north–south displacement of the ITCZ, which follows the annual march of the sun. During boreal winter (December–February), when rainfall near West Africa is at its weakest, the primary band of rainfall is found offshore of the Guinea Coast, just north of the equator, and extends out into the Atlantic Ocean (Fig. 1a). During this period, northeasterly winds from the African deserts push warm dry air to the southern edge of the continent. These winds converge with the southeasterly winds associated with the Santa Helena high just off the Guinea Coast (Fig. 1a). In boreal spring (March–June), the ITCZ moves onto the continent as the WAM begins (Fig. 1b). At this time, the southwesterly low-level monsoon winds advance across the equator and bring moisture onto the continent (Fig. 1b). The monsoon winds converge with the dry northerly harmattan winds just to the north of the 1-mm day−1 precipitation contour at the intertropical discontinuity (ITD; represented by the thick black line in Fig. 1b). The seasonal evolution of the WAM then goes through several transitions, involving active phases and pauses (Fig. 2) (e.g., Le Barbé et al. 2002; Sultan et al. 2003). The first is an intensification of rainfall along the Guinean coast during May and June (Fig. 2). This is typically referred to as the “preonset” phase or coastal phase of the WAM, when the ITD reaches 15°N and rainfall occurs along the coast (Sultan and Janicot 2003). The preonset phase is followed by a sudden northward jump of the ITCZ into the Sahelian region (~12°N) in late June and July (Fig. 2) (e.g., Sultan and Janicot 2003). In fall (October–December) rain rates decrease gradually, and the ITCZ follows a relatively smooth progression back to its position over the Gulf of Guinea (Fig. 1d).

The precise dynamical mechanisms that cause the observed jump in precipitation over West Africa are not yet known, but studies have linked the jump to a number of things; including an abrupt northward shift of the heat low (Ramel et al. 2006), the coincidental but unrelated timing of a decrease in rainfall along the coast due to the development of cold SSTs in the Gulf of Guinea and an increase in dynamical forcing that results in rain over the Sahel (Gu and Adler 2004), a northward shift of the AEJ and its associated wind shear (Sultan and Janicot 2003), and the development of a shallow meridional circulation that influences the position of maximum wind and moisture convergence (Hagos and Cook 2007).

Embedded within the zonally elongated rainband associated with the WAM are three distinct maxima in precipitation (Fig. 1c): one over the Ethiopian Highlands, a second near the Bight of Bonny west of Cameroon, and a third just off the west coast that extends into the Atlantic Ocean. These maxima are located near the Ethiopian Highlands, the Adamawa Highlands of Cameroon, and the Guinea Highlands, respectively; and the mountains may be the cause of the localized precipitation maxima. Diurnal heating of topography in this region is important for the development of convection, which propagates westward over West Africa (Laing et al. 2008).

During the peak monsoon season of July–September (JAS), a distinct drying occurs along the Guinea Coast, where rain rates decrease as the primary rainband pushes northward onto the continent (Fig. 1c). The decrease in precipitation along the coast is thought to be influenced by the rapid development of cool SST along the equator in what is known as the Atlantic equatorial cold tongue (Fig. 3a; Nguyen et al. 2011). Cold SSTs are associated with a drier boundary layer and act to suppress convection along the coast (Thorncroft et al. 2011).

Fig. 3.
Fig. 3.

(left) Average SSTs for JAS from (a) observations, (b) SP-CCSM, and (c) CCSM3. (right) The difference between the observed SSTs and simulated SSTs for (d) SP-CCSM and (e) CCSM3.

Citation: Journal of Climate 27, 22; 10.1175/JCLI-D-13-00676.1

The interactions between the SSTs in the Gulf of Guinea and the WAM are complex. A few studies have tried to understand the feedback mechanisms linking these physical systems (Coëtlogon et al. 2010), but more work is needed. The basic picture, based on the state of the current science, is as follows. The Atlantic equatorial cold tongue develops as a result of the seasonal evolution of the winds associated with the onset and progression of the WAM (Mitchell and Wallace 1992; Vizy and Cook 2001; Caniaux et al. 2011; Nguyen et al. 2011). During spring, the trade winds help displace the thermocline in the eastern equatorial Atlantic such that the cold waters below the mixed layer are closer to the surface. As the monsoon progresses, enhanced southerly cross-equatorial flow acts to cool the equatorial Atlantic through upwelling, entrainment, and evaporation. (Foltz et al. 2003; Xie and Carton 2004). Southerly winds also transport the cold waters along the southwest coast of Africa north toward the equator.

The development of the equatorial Atlantic cold tongue is a key element of the monsoon. Cool SSTs are critical for the northward migration of rainfall onto the continent. They suppress convection along the coast and enhance convection in the Sahel. Along the coast, cool SSTs weaken the surface fluxes of sensible and latent heat and stabilize the atmosphere, suppressing convection in the coastal region. Cool SSTs also intensify the southerly cross-equatorial flow in the Gulf of Guinea and help to push the continental rainband inland (Okumura and Xie 2004).

As shown in the next section, the misrepresentation of the Atlantic cold tongue contributes to errors in simulated WAM precipitation.

2) Simulations

When compared to the standard CCSM3, the SP-CCSM better represents both the magnitude and the spatial patterns associated with West African precipitation (Figs. 1e–l and 2b,c). Most notably, the region of maximum precipitation during JAS is shifted from an incorrect placement over the Gulf of Guinea in CCSM3 (Fig. 1k) to a more realistic placement over the continent in SP-CCSM (Fig. 1g). In CCSM3, the main band of precipitation is much broader and weaker than observed (Figs. 2a,c). Maximum precipitation rates always occur over the ocean, never over the continent (Fig. 2c). Throughout all months of the year, CCSM3 exhibits a consistent southward bias in rainfall. The seasonal cycle of rainfall in CCSM3 appears to expand and contract, rather than displaying a true northward shift onto the continent. In SP-CCSM on the other hand, monsoon rains shift northward onto the continent, although the rainband is still too wide, and rainfall never truly stops over the Gulf of Guinea or along the coast (Fig. 2b). The coarse (~3°) resolution of the ocean in both models may explain why the rainband is too wide in the meridional direction. Rain rates in the SP-CCSM tend to be much larger than observed, and rainfall during the peak monsoon period does not penetrate as far northward as observed. While SP-CCSM produces clear active phases and pauses in the monsoon rains, there is nothing like the observed “jump” in precipitation from the coastal region to the Sahel. This may be due in part to the coarse resolution of the model (~3°), which can prevent sharp transitions in moisture convergence and rainfall. Also, as discussed below, it is possible that warm SST biases in the Gulf of Guinea prevent the suppression of rainfall along the coast during summer and limit the moisture transport into the Sahel. The SP-CCSM does capture the local maximum in precipitation that extends from just off the west coast into the Atlantic as well as the maximum over the Ethiopian Highlands. Precipitation rates are lower than observed in the mountains, possibly because the mountains are not well represented with low resolution. The SP-CCSM does not capture the maximum in precipitation near Cameroon, nor does it simulate the dry region that is observed along the Guinea Coast between the two coastal precipitation maxima. Rain along the coast is also consistently stronger than observed.

One possible explanation for the excessive rainfall along the Guinea Coast in SP-CCSM and over the Gulf of Guinea in CCSM3 is a misrepresentation of the development of the Atlantic equatorial cold tongue. Both SP-CCSM and CCSM3 exhibit consistent warm biases in the equatorial Atlantic, although these biases are much larger in CCSM3 (Fig. 3). Warm SST biases cause the boundary layer to be excessively moist, resulting in increased rainfall south of the observed monsoon during JAS. The misrepresentation of the Atlantic cold tongue is a common problem for CGCMs (Davey et al. 2002; Richter and Xie 2008; Patricola et al. 2012) and is often attributed to westerly equatorial trade wind biases in spring (Richter and Xie 2008; Richter et al. 2012).

As has been found in other CGCMs, the SST biases in SP-CCSM and CCSM3 appear to be linked to westerly trade wind biases (i.e., trades too weak) in spring. Figures 4a,b show the difference between average April–June (AMJ) 925-hPa winds from the models and ERA-I. While both models have large westerly biases over the equatorial Atlantic during AMJ, the bias is 2 m s−1 larger in CCSM3 (Fig. 4c). Trade winds that are too weak result in an unrealistically deep thermocline in the east Atlantic. This inhibits cold tongue development in summer (JAS), when the easterly component of the cross-equatorial low-level wind (Fig. 1) results in strong equatorial upwelling.

Fig. 4.
Fig. 4.

Differences in 925-hPa winds between (top) SP-CCSM and ERA-I, (middle) CCSM3 and ERA-I, and (bottom) SP-CCSM and CCSM3. (a)–(c) Differences in zonal wind speeds (contours) in AMJ and (d)–(f) in meridional wind speeds (contours) in JAS.

Citation: Journal of Climate 27, 22; 10.1175/JCLI-D-13-00676.1

While there are a number of hypotheses for why CGCMs misrepresent the trade winds (Chang et al. 2007; Richter and Zie 2008; Zermeno-Diaz and Zhang 2013), here we provide one possible explanation for the improvement of the trade winds in SP-CCSM that links surface winds to modified convection and enhanced diabatic heating in the ITCZ. Figure 5 shows vertical profiles of Q1, the apparent heat source or diabatic heating, averaged between 50°W–10°E and 0–15°N. The value Q1 has been estimated using the bulk formula from Lin and Johnson (1996) and represents the heating associated with convective processes. Midtropospheric heating within the northern equatorial Atlantic ITCZ is larger in SP-CCSM than CCSM3. This heating drives the zonal mean meridional circulation or the Atlantic component of the global Hadley circulation (e.g., Gill 1980; Hou and Lindzen 1992). The greater convective heat source found in SP-CCSM corresponds with a larger convective mass flux, stronger convergence at low levels, and stronger divergence aloft. Figure 6 shows the AMJ zonal mean meridional circulation averaged between 50°W and 10°E from SP-CCSM and CCSM3. Given that the overall circulation is stronger in SP-CCSM, the northerly and southerly surface convergence return flows in the northern and southern branches of the circulation are also stronger in SP-CCSM. As these surface winds travel toward the equator, they are turned to the west by the Coriolis force, resulting in stronger surface easterlies in SP-CCSM than CCSM3.

Fig. 5.
Fig. 5.

AMJ streamfunction for zonal mean meridional circulation (kg s−1) averaged between 50°W and 10°E from (a) SP-CCSM and (b) CCSM3.

Citation: Journal of Climate 27, 22; 10.1175/JCLI-D-13-00676.1

Fig. 6.
Fig. 6.

Average diabatic heating profile (K day−1) calculated for the region 50°W–10°E, 0°–15°N from SP-CCSM (red) and CCSM3 (blue). The value Q1 is estimated using Lin and Johnson (1996).

Citation: Journal of Climate 27, 22; 10.1175/JCLI-D-13-00676.1

Zermeno-Diaz and Zhang (2013) have recently shown that differences in the diabatic heating profile over the Amazon can lead to erroneous surface pressure gradients across the equatorial Atlantic and contribute to the westerly trade wind biases found in CGCMs. Analysis of the equatorial Atlantic surface pressure gradient and the heating profile over the Amazon performed on SP-CCSM and CCSM3 were inconclusive, and further analysis is beyond the scope of this study. However, a misrepresentation of rainfall over the Amazon likely contributes to the westerly trade wind biases in both models. Also based on work from Zermeno-Diaz and Zhang (2013), it is possible that the momentum flux between the lower troposphere and the boundary layer is misrepresented in both models, reducing surface wind speeds over the Atlantic. More detailed analysis is needed to fully address this idea.

The trade winds during spring help cool SSTs in the east Atlantic by displacing the thermocline. During JAS, cross-equatorial southerly flow in summer further decreases surface temperatures by increasing the vertical entrainment and upwelling of cold water in the east Atlantic and transporting cold water from the southwestern coast of Africa to the equator. This cross-equatorial flow also helps to transport moisture onto the continent. In both models, the misrepresentation of the mean position of the ITCZ (Fig. 1) and surface convergence (Figs. 4d,e) results in stronger-than-observed southerly winds over much of the equatorial Atlantic (Figs. 4d–f). In CCSM, the surface convergence during summer remains close to the equator, resulting in weaker-than-observed southerlies north of the equator, and stronger-than-observed southerlies south of the equator (Fig. 4e). In SP-CCSM, the main band of convection is positioned farther to the north than CCSM3. In both models, the northerly wind biases along the Guinea Coast indicate that moisture transport onto the continent in both models is weaker than observed. This limits their ability to represent convection accurately over land. Simulated differences in meridional wind stress likely influence equatorial and coastal upwelling. Examination of the ocean circulation could help illuminate differences between the models, but it is beyond the scope of this work.

Along the Guinea Coast, differences in net surface radiation may also help explain why SST biases in SP-CCSM are weaker than in CCSM3. Figure 7 shows the difference between net surface radiation in SP-CCSM versus CCSM3. Based on Fig. 1, we know that the main band of convection during the peak monsoon season (JAS) remains over the equator in CCSM3, but moves northward onto the continent and along the Guinea Coast in SP-CCSM. Regional differences in convection have important implications for net surface radiation. As we see from Fig. 7, net surface radiation along the coast is lower in SP-CCSM than CCSM3. These differences are primarily caused by a reduction in the shortwave forcing at the surface. Less incoming radiation at the surface will influence SSTs along the coast, resulting in cooler SSTs in SP-CCSM and warmer SSTs in CCSM3. As discussed below, cooler SSTs in SP-CCSM have important implications for the north–south pressure gradient and onshore moisture flow.

Fig. 7.
Fig. 7.

Difference in net radiation (W m−2) at the surface during JAS between SP-CCSM and CCSM3. Includes both net longwave and net shortwave radiation.

Citation: Journal of Climate 27, 22; 10.1175/JCLI-D-13-00676.1

To further support the claim that warm biases in SSTs in the Gulf of Guinea are the main cause of the excessive precipitation found along the Guinea Coast in SP-CCSM, we have analyzed a simulation in which the atmospheric component of the SP-CCSM, the SP-CAM, is forced with observed SSTs. Figure 8 shows that, when the model atmosphere is forced with realistic SSTs, including a realistic Atlantic cold tongue, rainfall along the Guinea Coast is greatly reduced during JAS and is comparable to observations. Note, however, that in the case of the atmosphere-only simulation, the monsoon rains do not penetrate as far northward as observed. This may be because of an incorrect response of the southerly winds to the large-scale pressure gradient that occurs across West Africa, associated with a weakened Saharan heat low. Similar results are found in atmosphere-only simulations performed with CAM, version 3, but rain rates over West Africa are much lower than observed or in SP-CAM (not shown).

Fig. 8.
Fig. 8.

As in Fig. 1, but for the atmosphere-only component of the SP-CCSM, the SP-CAM, forced with observed SSTs.

Citation: Journal of Climate 27, 22; 10.1175/JCLI-D-13-00676.1

In the broadest sense, the WAM is the large-scale response to the temperature difference between the hot African continent and the relatively cool Atlantic Ocean during summer. The development of the Atlantic cold tongue along the equator helps to strengthen the thermal contrast between the ocean and land surface, contributing to the thermal forcing of the WAM (Fig. 3a; Okumura and Xie 2004). The thermal contrast between the continent and ocean is associated with relatively low surface pressures to the north over the Sahara and high pressures over the Atlantic. In the next section of this paper, we examine the position and intensity of the simulated Saharan heat low.

b. Saharan heat low

The Saharan heat low (SHL) is a major dynamical element of the WAM system. The SHL is critically important for both the low-level circulation in the monsoon as well as the development of the AEJ. The correct development, placement, and intensity of the seasonal mean heat low in the models is important for the overall simulation of the WAM. The observed seasonal cycle of the SHL has been examined in studies, such as Lavaysse et al. (2009).

A heat low is an area of low surface pressure and cyclonic vorticity that results from heating of the lower troposphere. Figures 9a–c show maps of mean JAS surface temperature (surface radiative temperature or skin temperature) and mean sea level pressure (MSLP) from ERA-I and both models. In ERA-I the SHL occurs in the northwest Sahara in a region where surface temperatures are high and surface pressures are low. This region corresponds with high net surface insolation and low evaporation (not shown). Both models exhibit a cold bias over the Sahara that results in higher-than-observed surface pressures (i.e., the low is not as deep in either model). This cold bias in both models is partly due to lower-than-observed net surface insolation; however, differences in surface albedo, clouds, and boundary layer dynamics may also be at play. In SP-CCSM, surface evaporation is also higher than found in ERA-I which may contribute to the cold bias. The SHL is typically characterized as a zonally elongated heat trough that is sometimes considered an extension of the planetary-scale thermal trough associated with the Asian monsoon, rather than a circular low (Wu et al. 2009). While the average heat low in ERA-I, as shown by the closed-off pressure contours in Fig. 9a, appears to be cut off from the low pressures over northeastern Africa, on a day-to-day basis, the heat low can appear more elongated (not shown). In both models, the region of warm surface temperatures and low surface pressures is more zonally coherent, and the SHL does not appear as a circular low.

Fig. 9.
Fig. 9.

Maps of JAS average surface radiative temperature (filled contours; K) and mean sea level pressure (line contours; contoured every 2 hPa from 1008 to 1016 hPa) from (a) ERA-I, (b) SP-CCSM, and (c) CCSM3. Latitude–height cross sections of (d)–(f) average temperature (K) and (g)–(i) specific humidity (g kg−1) for JAS averaged between 15°W and 10°E.

Citation: Journal of Climate 27, 22; 10.1175/JCLI-D-13-00676.1

Figures 9d–i show latitude–height cross sections of temperature and specific humidity averaged between 15°W and 10°E. Convective heating transfers energy from the surface into the atmosphere, resulting in a deep boundary layer that has a characteristic dome shape (Figs. 9d–f). The Saharan air mass is dry, compared to the monsoon air mass to the south (Figs. 9g–i). The low-level meridional temperature gradient is weaker than observed in both models because of the warm SST bias in the Gulf of Guinea and the cool bias over the Sahara. The region of maximum temperatures in CCSM3 occurs to the north of the region of maximum temperatures in SP-CCSM. Compared to ERA-I, the low-level meridional humidity gradients are weaker in SP-CCSM but stronger in CCSM3. In SP-CCSM, the region between 5° and 20°N is dryer than in ERA-I, while the area of the heat low is more moist. In CCSM3 we have the opposite case, with high surface humidity values equatorward of 20°N, where moisture is transported onto the continent but convection is limited. Over the heat low in CCSM, humidity values are lower. We will return to the discussion of humidity gradients in the next section when we discuss the AEJ.

The latitude–height cross sections in Fig. 10 are used to describe the shallow meridional circulation associated with the heat low. For more detailed explanations of this circulation, see Thorncroft et al. (2011). Figures 10a–c show latitude–height cross sections of geopotential height anomalies and meridional and vertical winds averaged between 15°W and 10°E. Anomalies of geopotential height are calculated by removing the meridionally averaged geopotential height from each pressure level. The circulation is highlighted in the remaining panels of Fig. 10, through averages of the vertical velocity field omega (in pascals per second, so that negative values denote upward motion) and averages of the meridional wind field.

Fig. 10.
Fig. 10.

Latitude–height cross sections of (a)–(c) anomalous geopotential height (contours; m) and vertical and meridional winds (vectors), (d)–(f) vertical velocity (omega; Pa s−1), and (g)–(i) the meridional wind (m s−1). Geopotential height anomalies are relative to the meridional mean geopotential height field. Values are from (top) ERA-I, (middle) SP-CCSM, and (bottom) CCSM3.

Citation: Journal of Climate 27, 22; 10.1175/JCLI-D-13-00676.1

In ERA-I and both models, the SHL is characterized by negative geopotential height anomalies centered at about 20°N and extending up to the 800-hPa level. The SHL is overlain by high pressure, identified by positive geopotential height anomalies. The so-called Saharan high is a result of the combined effects of the sinking branch of the Hadley circulation and the high that forms because of the thermal lifting of the isobaric surfaces. The result is a divergent anticyclone above the heat low. At low levels, the pressure gradient between the Gulf of Guinea and the Sahara is stronger in ERA-I than either model. This large-scale pressure gradient drives the low-level monsoon circulation and helps bring moisture onto the continent. Latent heating and the resulting low-level convergence also drive the low-level circulation on the southern side of the ITCZ (Grodsky et al. 2003; Pu and Cook 2010). In CCSM3, the onshore flow is clearly weaker in than in either ERA-I or SP-CCSM (Figs. 10g–i); this has important implications for moisture transport. In SPCCSM, the cross-equatorial flow into the ITCZ (south of 5°N) is much stronger than observed, but over land the low-level meridional winds associated with the heat low are comparable to those found in ERA-I.

The cross section of the vertical wind clearly shows the uplift associated with the main band of convection (Figs. 10d–f). This uplift is much stronger in SP-CCSM but much weaker and broader in CCSM3. Also evident in the panels is the secondary shallow circulation associated with the SHL. The low corresponds with strong vertical motion due to dry convection. As surface temperatures are hotter over the Sahara in ERA-I and the thermal low is deeper, the vertical motion associated with the heat low is also greater in ERA-I. In SP-CCSM, while the strength of the vertical motion is weaker, uplift extends farther into the troposphere. The increased vertical extent of uplift in SP-CCSM corresponds with a Saharan high that is weaker than is found in ERA-I.

The northerly return flow of this shallow circulation is also important for the monsoon dynamics. In ERA-I this return flow extends from approximately 800–400 hPa with maximum winds centered around 15°N at 700 hPa. As the air travels south it is turned to the west by the Coriolis effect and contributes to the strength and position of the African easterly jet (Thorncroft and Blackburn 1999). This return flow is represented differently in the two models. In both simulations, the shallow circulation associated with the heat low is weaker than observed, and thus the northerly return flow patterns are also weaker. In SP-CCSM the return flow is centered lower in the troposphere (~750 hPa) than in ERA-I. In CCSM, however, the northerly return flow occurs not only in the midtroposphere around 800 hPa, but also throughout the entire troposphere above the heat low. It is likely that the model biases in convection associated with the ITCZ are influencing the shallow circulation associated with the heat low and modifying the way the northerly return flow is represented. For example, in CCSM3, it appears that the northerly winds located at 20°N may be associated with inflow into the broad band of convection rather than the heat low.

In the next section, we discuss the AEJ. Simulated differences in the shallow circulation associated with the SHL as well as differences in the meridional circulation associated with the ITCZ will impact the representation of the AEJ.

c. African easterly jet

The AEJ is also an important component of the WAM system. It develops at about 600 hPa during summer because of the strong temperature and moisture gradients that exist between the cool moist ocean and the hot, dry Sahara desert. Numerical experiments have shown that the meridional gradients of moisture, temperature, and vegetation (and its influence on surface albedo) are important for the structure and maintenance of the observed AEJ (e.g., Wu et al. 2009). As demonstrated by Cook (1999) the AEJ is essentially geostrophic, so by the thermal wind relation the northward increase in lower-tropospheric temperature over West Africa induces easterly shear. Thorncroft and Blackburn (1999) show that diabatically driven meridional circulations associated with both the ITCZ and the SHL are also important for the development and maintenance of the AEJ. The AEJ is subject to both barotropic instability due to the strong horizontal wind shear and baroclinic instability associated with the strong vertical wind shear (Burpee 1972). These instabilities are important for the growth and propagation of AEWs and are discussed in section 3d (Hsieh and Cook 2007). AEWs extract energy from the AEJ, tending to weaken it.

Figure 11 shows maps of the mean zonal wind speed at 600 hPa during JAS, the meridional cross sections of zonal wind averaged between 15°W and 10°E, and zonal cross sections of the zonal wind at 15°N. While the position and intensity of the AEJ varies between the different reanalysis products (primarily because of a lack of sounding data over this part of Africa), in this paper we compare the models against ERA-I. Our goal here is to understand physically why the AEJ in the models may differ from ERA-I. Similar analysis could be performed comparing against other reanalysis products, and while the model biases may be different, the physical explanations for those differences are expected to be the same.

Fig. 11.
Fig. 11.

JAS averages of (a)–(c) zonal wind at 600 hPa, (d)–(f) meridional height cross section of zonal wind averaged between 15°W and 10°E, and (g)–(i) zonal height cross section of zonal wind along 15°N for (left) ERA-I, (center) SP-CCSM, and (right) CCSM3 (m s−1).

Citation: Journal of Climate 27, 22; 10.1175/JCLI-D-13-00676.1

In ERA-I peak winds reach about 12 m s−1, are positioned at about 15°N and 600 hPa, and span the region from 30°W to 10°E. Easterly winds also extend downward to about 850 hPa, where they transition to the westerly monsoon winds. The latitudinal position of the AEJ corresponds closely with the greatest surface meridional temperature gradients (Fig. 11a). The height of the peak winds in the AEJ occurs where the meridional temperature gradient reverses sign (Fig. 11d). The elevation where the meridional temperature gradient reverses sign is determined by the different humidity profiles of southern West Africa, which is wet, and northern Africa, which is dry. This is because temperatures decrease faster with height following a dry adiabat than a moist adiabat.

Both of the models simulate the AEJ, each with its own biases. In SP-CCSM, the AEJ is weaker than observed, with maximum wind speeds of 10 m s−1. The AEJ’s mean position is slightly higher than observed, at around 550 hPa, and its zonal extent is restricted to the continent. As with observations, the latitudinal position of peak winds corresponds to the strongest meridional temperature gradients (Figs. l2a–c), and the height of the AEJ occurs where the meridional temperature gradient reverses sign (Figs. 12d–f). We expect that the slight difference in the vertical position of the AEJ core in SP-CCSM corresponds with the positive humidity bias found over North Africa and the weaker-than-observed surface meridional humidity gradient in SP-CCSM. Greater humidity values in North Africa imply that temperatures will decrease more slowly with height in SP-CCSM than ERA-I, thereby pushing the location of the temperature reversal up.

Fig. 12.
Fig. 12.

JAS averages of the (a)–(c) surface meridional temperature gradient and the (d)–(f) latitude–height cross section of the average meridional temperature gradient from 15°W to 10°E (K °lat−1). The diagonal cross in each panel marks the position of the maximum zonal winds associated with the AEJ.

Citation: Journal of Climate 27, 22; 10.1175/JCLI-D-13-00676.1

In CCSM3, the jet is stronger than observed and stronger than in SP-CCSM. The peak winds in CCSM3 reach 14 m s−1, and the zonal extent of the AEJ is much broader than observed, with strong zonal wind speeds extending out to 60°W over the Atlantic Ocean. The stronger jet may be due to weaker eddy activity; this is discussed further below.

Determining the reasons for the differences in the strengths of the AEJ between the models and ERA-I is more difficult. Figures 12a–c indicate that surface temperature gradients are comparable between all three datasets. Figures 12d–f show that in the low levels of the troposphere (<850 hPa), temperature gradients are weaker than ERA-I in SP-CCSM, and stronger than ERA-I in CCSM3. The differences in meridional baroclinicity may influence jet speed in the different products. Also, Thorncroft and Blackburn (1999) demonstrated that the position and strength of the AEJ is sensitive to the location, shape, and intensity of the thermally driven circulations associated with both the ITCZ and the SHL. It is likely that the strength of the jet is influenced by the widely different heating profiles and corresponding circulations associated with the ITCZ and the SHL in the two models. As discussed in the next section, AEWs are known to extract energy from the AEJ through barotropic and baroclinic energy conversion. Differences in observed and simulated AEW activity may also help explain the differences found in the AEJ.

d. African easterly waves

AEWs are synoptic-scale disturbances with wavelengths of 3000–6000 km, and periods of 3–6 days (Kiladis et al. 2006). They are the dominant mode of synoptic-scale atmospheric variability over West Africa during the summer (June–September) and are important for organizing precipitation over this region. While our understanding of these waves is incomplete, current theory suggests that they can be initiated by convective heating in central and eastern Africa (Berry and Thorncroft 2005; Mekonnen et al. 2006; Hsieh and Cook 2007) and propagate westward, feeding off of the barotropic–baroclinic instability associated with the AEJ (Hall et al. 2006).

Eddy kinetic energy (EKE) is considered a reliable measure of AEW activity over West Africa and is a good indicator of the location and intensity of AEWs (Ruti and Del Aqula 2010). Here we define the EKE per unit mass by applying a 2–6-day bandpass filter to both the meridional and zonal winds (e.g., Leroux et al. 2010). Figure 13 shows the July–September mean EKE fields from ERA-I, SP-CCSM, and CCSM. In ERA-I, AEW activity occurs over West Africa west of 10°E. Peak AEW activity occurs just off the coast at approximately 12°N. Much like the AEJ, placement of peak AEW activity differs between the different reanalysis products, but they all show qualitatively similar results.

Fig. 13.
Fig. 13.

JAS 2–6-day bandpass-filtered average EKE (m2 s−2) from (a) ERA-Interim, (b) SP-CCSM, and (c) CCSM. This is a measure of AEW activity.

Citation: Journal of Climate 27, 22; 10.1175/JCLI-D-13-00676.1

AEWs are overly active in SP-CCSM and too weak in CCSM. AEW activity in SP-CCSM extends from approximately 30°E to 40°W and from 0° to 25°N, with peak activity centered over the region where precipitation is largest. The large differences between the observed and simulated AEW activity are investigated further in Part II. Given the importance of convection for AEW development and maintenance (Thorncroft et al. 2008), we can anticipate here that the differences in the representation of convection play an important role in the wave dynamics found in the two models. It is also possible, however, that the differences in the 3D structure of the AEJ described in section 3c may influence the stability of the jet and the ability of AEWs to form (Leroux and Hall 2009).

4. Summary and discussion

The use of a superparameterization in the CCSM3 improves the overall representation of the seasonal cycle of the West African monsoon. The mean position of the WAM precipitation maximum is shifted from an unrealistic placement over the Gulf of Guinea in CCSM3 to a more realistic continental location in SP-CCSM. Average precipitation rates are also closer to observed in SP-CCSM. The SP-CCSM has its own biases, however. Rainfall rates are too high over the Gulf of Guinea, and large positive precipitation biases are found along the southern coast in between the Guinea Highlands and Cameroon. Anomalously warm conditions in the Gulf of Guinea and the lack of development of the equatorial Atlantic cold tongue are consistent with large biases in the simulated zonal and meridional wind fields, which lead to the precipitation biases found in both models. A prescribed SST experiment with the atmospheric component of the SP-CCSM suggests that more realistic Atlantic SSTs would improve the simulated spatial distribution of rainfall over West Africa during the monsoon season.

The equatorial Atlantic cold tongue is a critical component of the WAM system. The coupling between SSTs, low-level monsoon winds, and convection is complex and is an area where most CGCMs struggle. Our results suggest that improving the vertical structure of diabatic heating within the ITCZ strengthens the zonal mean meridional circulation over the Atlantic, reducing trade wind biases and SST biases in the equatorial Atlantic.

In both SP-CCSM and CCSM3, the main band of precipitation does not penetrate as far northward as observed during JAS. While there is a clear connection with SSTs, biases in the mean position and intensity of the SHL may also account for the reduced cross-continental flow found in the two models. Both models simulate a weaker-than-observed heat low during the peak monsoon season because of cool biases in surface temperatures over the Sahara. The biases over the Sahara result in a weaker-than-observed north–south pressure gradient across West Africa and prevent the monsoon winds from pushing as far northward as they should. Biases in the heat low may also explain why there is no simulated “jump” in monsoon rains in the SP-CCSM. Although not explicitly addressed in this paper, the land surface scheme in CLM3 determines the radiative and moisture characteristics of the surface, greatly influencing surface temperatures over the Sahara. Simulations performed with an improved land surface parameterization may represent the SHL with increased fidelity.

Our results show that the mean position of the AEJ is similar to that observed in both models and that differences in the location correspond with differences in the surface temperature and moisture gradients. We also found that the AEJ is weaker than observed in SP-CCSM and stronger than observed in CCSM. While the differences in the diabatically forced circulations associated with the ITCZ and the SHL are likely to influence the strength of the AEJ, we also hypothesize that the weak AEJ in SP-CCSM is due in part to the overly active easterly wave activity simulated by the model. AEWs extract energy from the AEJ and smooth the north–south gradients in temperature and moisture gradients, both factors which will tend to weaken the AEJ. By comparison, the lack of AEW activity in CCSM3 may contribute to the overly strong AEJ found in this model.

One of the more interesting results of this paper is the sharp contrast between the complete lack of AEW activity in the CCSM3 and the overly strong waves found in the SP-CCSM with the same resolution. In a companion paper (Part II) we explore in more detail the differences between the simulated waves and waves found in reanalysis. Part II also investigates the role that convection plays in the development of AEWs over West Africa in the simulations.

Other aspects of the West African monsoon system that were not examined in this paper, but would be interesting to study within the context of the SP-CCSM, are the diurnal cycle of convection over West Africa and both the intraseasonal and interannaual variability of rainfall over the region.

Acknowledgments

We thank the reviewers for their helpful and constructive comments, which helped improved this work greatly. This work has been supported by the National Science Foundation Science and Technology Center for Multiscale Modeling of Atmospheric Processes, managed by Colorado State University under cooperative agreement ATM-0425247. Cristiana Stan was also supported by the NSF Grant AGS-1211848. We acknowledge the support of the Computational and Information Systems Laboratory at NCAR for providing computer time for this work.

REFERENCES

  • Alley, B., and Coauthors, 2007: Summary for policymakers. Climate Change 2007: The Physical Science Basis, S. Solomon et al., Eds., Cambridge University Press, 1–18. [Available online at http://www.ipcc.ch/pdf/assessment-report/ar4/wg1/ar4-wg1-spm.pdf.]

    • Search Google Scholar
    • Export Citation
  • Baron, C., B. Sultan, M. Balme, B. Sarr, S. Traore, T. Lebel, S. Janicot, and M. Dingkuhn, 2005: From GCM grid cell to agricultural plot: Scale issues affecting modelling of climate impact. Philos. Trans. Roy. Soc. London, B360, 20952108, doi:10.1098/rstb.2005.1741.

    • Search Google Scholar
    • Export Citation
  • Benedict, J. J., and D. A. Randall, 2009: Structure of the Madden–Julian oscillation in the superparameterized CAM. J. Atmos. Sci., 66, 32773296, doi:10.1175/2009JAS3030.1.

    • Search Google Scholar
    • Export Citation
  • Benedict, J. J., and D. A. Randall, 2011: Impacts of idealized air–sea coupling on Madden–Julian oscillation structure in the superparameterized CAM. J. Atmos. Sci., 68, 19902008, doi:10.1175/JAS-D-11-04.1.

    • Search Google Scholar
    • Export Citation
  • Berry, G., and C. D. Thorncroft, 2005: Case study of an intense African easterly wave. Mon. Wea. Rev., 133, 752766, doi:10.1175/MWR2884.1.

    • Search Google Scholar
    • Export Citation
  • Boko, M., and Coauthors, 2007: Africa. Climate Change 2007: Impacts, Adaptation and Vulnerability, M.L. Parry et al., Eds., Cambridge University Press, 433–467. [Available online at http://www.ipcc.ch/pdf/assessment-report/ar4/wg2/ar4-wg2-chapter9.pdf.]

  • Bonan, G. B., K. W. Oleson, M. Vertenstein, S. Levis, X. Zeng, Y. Dai, R. E. Dickinson, and Z.-L. Yang, 2002: The land surface climatology of the community land model coupled to the NCAR community climate model. J. Climate, 15, 31233149, doi:10.1175/1520-0442(2002)015<3123:TLSCOT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Burpee, R. W., 1972: The origin and structure of easterly waves in the lower troposphere of North Africa. J. Atmos. Sci., 29, 7790, doi:10.1175/1520-0469(1972)029<0077:TOASOE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Caniaux, G., H. Giordani, J.-L. Redelsperger, F. Guichard, E. Key, and M. Wade, 2011: Coupling between the Atlantic cold tongue and the West African monsoon in boreal spring and summer. J. Geophys. Res., 116, C04003, doi:10.1029/2010JC006570.

    • Search Google Scholar
    • Export Citation
  • Chang, C.-Y., J. A. Carton, S. A. Grodsky, and S. Nigam, 2007: Seasonal climate of the tropical Atlantic sector in the NCAR Community Climate System Model 3: Error structure and probable causes of errors. J. Climate, 20, 10531070, doi:10.1175/JCLI4047.1.

    • Search Google Scholar
    • Export Citation
  • Coëtlogon, G., S. Janicot, and A. Lazar, 2010: Intraseasonal variability of the ocean–atmosphere coupling in the Gulf of Guinea during boreal spring and summer. Quart. J. Roy. Meteor. Soc.,136, 426–441. doi:10.1002/qj.554.

  • Collins, W. D., and Coauthors, 2006: The Community Climate System Model version 3 (CCSM3). J. Climate, 19, 21222143, doi:10.1175/JCLI3761.1.

    • Search Google Scholar
    • Export Citation
  • Cook, K. H., 1999: Generation of the African easterly jet and its role in determining West African precipitation. J. Climate, 12, 11651184, doi:10.1175/1520-0442(1999)012<1165:GOTAEJ>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Cook, K. H., and E. K. Vizy, 2006: Coupled model simulations of the West African monsoon system: Twentieth- and twenty-first-century simulations. J. Climate, 19, 36813703, doi:10.1175/JCLI3814.1.

    • Search Google Scholar
    • Export Citation
  • Cook, K. H., G. A. Meehl, and J. M. Arblaster, 2012: Monsoon regimes and processes in CCSM4. Part II: African and American monsoon systems. J. Climate, 25, 26092621, doi:10.1175/JCLI-D-11-00185.1.

    • Search Google Scholar
    • Export Citation
  • Davey, M., and Coauthors, 2002: STOIC: A study of coupled model climatology and variability in tropical ocean regions. Climate Dyn., 18, 403420, doi:10.1007/s00382-001-0188-6.

    • Search Google Scholar
    • Export Citation
  • Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553597, doi:10.1002/qj.828.

    • Search Google Scholar
    • Export Citation
  • DeMott, C. A., D. A. Randall, and M. Khairoutdinov, 2007: Convective precipitation variability as a tool for general circulation model analysis. J. Climate, 20, 91112, doi:10.1175/JCLI3991.1.

    • Search Google Scholar
    • Export Citation
  • DeMott, C. A., C. Stan, D. A. Randall, J. L. Kinter III, and M. Khairoutdinov, 2011: The Asian monsoon in the superparameterized CCSM and its relationship to tropical wave activity. J. Climate, 24, 51345156, doi:10.1175/2011JCLI4202.1.

    • Search Google Scholar
    • Export Citation
  • DeMott, C. A., C. Stan, and D. A. Randall, 2013: Northward propagation mechanisms of the Boreal summer intraseasonal oscillation in the ERA-Interim and SP-CCSM. J. Climate, 26, 19731992, doi:10.1175/JCLI-D-12-00191.1.

    • Search Google Scholar
    • Export Citation
  • Fink, A. H., and A. Reiner, 2003: Spatiotemporal variability of the relation between African Easterly Waves and West African Squall Lines in 1998 and 1999. J. Geophys. Res., 108, 4332, doi:10.1029/2002JD002816.

    • Search Google Scholar
    • Export Citation
  • Foltz, G. R., S. A. Grodsky, J. A. Carton, and M. J. McPhaden, 2003: Seasonal mixed layer heat budget of the tropical Atlantic Ocean. J. Geophys. Res., 108, 3146, doi:10.1029/2002JC001584.

    • Search Google Scholar
    • Export Citation
  • Gill, A. E., 1980: Some simple solutions for heat-induced tropical circulation. Quart. J. Roy Meteor. Soc., 106, 447462, doi:10.1002/qj.49710644905.

    • Search Google Scholar
    • Export Citation
  • Grabowski, W. W., 2001: Coupling cloud processes with the large-scale dynamics using the cloud-resolving convection parameterization (CRCP). J. Atmos. Sci., 58, 978997, doi:10.1175/1520-0469(2001)058<0978:CCPWTL>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Grabowski, W. W., and P. K. Smolarkiewicz, 1999: CRCP: A Cloud Resolving Convection Parameterization for modeling the tropical convecting atmosphere. Physica D, 133, 171178, doi:10.1016/S0167-2789(99)00104-9.

    • Search Google Scholar
    • Export Citation
  • Grodsky, S. A., J. A. Carton, and S. Nigam, 2003: Near surface westerly wind jet in the Atlantic ITCZ. Geophys. Res. Lett., 30, 2009, doi:10.1029/2003GL017867.

    • Search Google Scholar
    • Export Citation
  • Gu, G., and R. F. Adler, 2004: Seasonal evolution and variability associated with the West African monsoon system. J. Climate, 17, 33643377, doi:10.1175/1520-0442(2004)017<3364:SEAVAW>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hack, J. J., 1994: Parameterization of moist convection in the National Center for Atmospheric Research community climate model (CCM2). J. Geophys. Res., 99, 55515568, doi:10.1029/93JD03478.

    • Search Google Scholar
    • Export Citation
  • Hagos, S. M., and K. H. Cook, 2007: Dynamics of the West African monsoon jump. J. Climate, 20, 52645284, doi:10.1175/2007JCLI1533.1.

  • Hall, N. M. J., and P. Peyrillé, 2006: Dynamics of the West African monsoon. J. Phys. IV France, 139, 8199, doi:10.1051/jp4:2006139007.

    • Search Google Scholar
    • Export Citation
  • Hall, N. M. J., G. N. Kiladis, and C. D. Thorncroft, 2006: Three-dimensional structure and dynamics of African easterly waves. Part II: Dynamical modes. J. Atmos. Sci., 63, 22312245, doi:10.1175/JAS3742.1.

    • Search Google Scholar
    • Export Citation
  • Hou, A. Y., and R. S. Lindzen, 1992: The influence of concentrated heating on the Hadley circulation. J. Atmos. Sci., 49, 12331241, doi:10.1175/1520-0469(1992)049<1233:TIOCHO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hsieh, J.-S., and K. H. Cook, 2007: A study of the energetics of African easterly waves using a regional climate model. J. Atmos. Sci., 64, 421440, doi:10.1175/JAS3851.1.

    • Search Google Scholar
    • Export Citation
  • Huffman, G. J., and Coauthors, 2007: The TRMM Multisatellite Precipitation Analysis: Quasi-global, multiyear, combined-sensor precipitation estimates at fine scale. J. Hydrometeor., 8, 3855, doi:10.1175/JHM560.1.

    • Search Google Scholar
    • Export Citation
  • James, R., and R. Washington, 2013: Changes in African temperature and precipitation associated with degrees of global warming. Climatic Change, 117, 859872, doi:10.1007/s10584-012-0581-7.

    • Search Google Scholar
    • Export Citation
  • Joiner, E., D. Kennedo, and J. Sampson, 2012: Vulnerability to climate change in West Africa: Adaptive capacity in the regional context. Climate Change and African Political Stability Student Working Paper 4, 43 pp.

  • Khairoutdinov, M. F., and D. A. Randall, 2001: A cloud resolving model as a cloud parameterization in the NCAR Community Climate System Model: Preliminary results. Geophys. Res. Lett., 28, 36173620, doi:10.1029/2001GL013552.

    • Search Google Scholar
    • Export Citation
  • Khairoutdinov, M. F., and D. A. Randall, 2003: Cloud-resolving modeling of the ARM summer 1997 IOP: Model formulation, results, uncertainties, and sensitivities. J. Atmos. Sci., 60, 607625, doi:10.1175/1520-0469(2003)060<0607:CRMOTA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Khairoutdinov, M. F., D. A. Randall, and C. DeMott, 2005: Simulations of the atmospheric general circulation using a cloud-resolving model as a superparameterization of physical processes. J. Atmos. Sci., 62, 21362154, doi:10.1175/JAS3453.1.

    • Search Google Scholar
    • Export Citation
  • Khairoutdinov, M. F., C. DeMott, and D. A. Randall, 2008: Evaluation of the simulated interannual and subseasonal variability in an AMIP-style simulation using the CSU Multiscale Modeling Framework. J. Climate, 21, 413431, doi:10.1175/2007JCLI1630.1.

    • Search Google Scholar
    • Export Citation
  • Kiladis, G. N., C. D. Thorncroft, and N. M. J. Hall, 2006: Three-dimensional structure and dynamics of African easterly waves. Part I: Observations. J. Atmos. Sci., 63, 22122230, doi:10.1175/JAS3741.1.

    • Search Google Scholar
    • Export Citation
  • Laing, A. G., R. Carbone, V. Levizzani, and J. Tuttle, 2008: The propagation and diurnal cycles of deep convection in northern tropical Africa. Quart. J. Roy. Meteor. Soc.,134, 93–109, doi:10.1002/qj.194.

  • Lavaysse, C., C. Flamant, S. Janicot, D. J. Parker, J.-P. Lafore, B. Sultan, and J. Pelon, 2009: Seasonal evolution of the West African heat low: A climatological perspective. Climate Dyn., 33, 313330, doi:10.1007/s00382-009-0553-4.

    • Search Google Scholar
    • Export Citation
  • Le Barbé, L., T. Lebel, and D. Tapsoba, 2002: Rainfall variability in West Africa during the years 1950–90. J. Climate, 15, 187202, doi:10.1175/1520-0442(2002)015<0187:RVIWAD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Leroux, S., and N. M. J. Hall, 2009: On the relationship between African easterly waves and the African easterly jet. J. Atmos. Sci., 66, 23032316, doi:10.1175/2009JAS2988.1.

    • Search Google Scholar
    • Export Citation
  • Leroux, S., N. M. J. Hall, and G. N. Kiladis, 2010: Climatological study of transient–mean-flow interactions over West Africa. Quart. J. Roy. Meteor. Soc., 136, 397410, doi:10.1002/qj.474.

    • Search Google Scholar
    • Export Citation
  • Levitus, S., and Coauthors, 1998: Introduction. World Ocean Database 1998, Vol. 1, NOAA Atlas NESDIS 18, 346 pp.

  • Liebmann, B., I. Bladé, G. N. Kiladis, L. M. V. Carvalho, G. Senay, D. Allured, S. Leroux, and C. Funk, 2012: Seasonality of African precipitation from 1996 to 2009. J. Climate, 25, 43044322, doi:10.1175/JCLI-D-11-00157.1.

    • Search Google Scholar
    • Export Citation
  • Lin, X., and R. H. Johnson, 1996: Kinematic and thermodynamic characteristics of the flow over the western Pacific warm pool during TOGA COARE. J. Atmos. Sci., 53, 695715, doi:10.1175/1520-0469(1996)053<0695:KATCOT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • McCrary, R. R., 2012: Seasonal, synoptic, and intraseasonal variability of the West African monsoon. Ph.D. dissertation, Colorado State University, 157 pp. [Available online at http://kiwi.atmos.colostate.edu/rr/groupPIX/rachel/McCrary_Rachel.pdf.]

  • McCrary, R. R., D. A. Randall, and C. Stan, 2014: Simulations of the West African monsoon with a superparameterized climate model. Part II: African easterly waves. J. Climate, 27, 83238341, doi:10.1175/JCLI-D-13-00677.1.

  • Meehl, G. A., C. Covey, T. Delworth, M. Latif, B. McAvaney, J. F. B. Mitchell, R. J. Stouffer, and K. E. Taylor, 2007: The WCRP CMIP3 multimodel dataset: A new era in climate change research. Bull. Amer. Meteor. Soc., 88, 13831394, doi:10.1175/BAMS-88-9-1383.

    • Search Google Scholar
    • Export Citation
  • Mekonnen, A., C. D. Thorncroft, and A. R. Aiyyer, 2006: Analysis of convection and its association with African easterly waves. J. Climate, 19, 54055421, doi:10.1175/JCLI3920.1.

    • Search Google Scholar
    • Export Citation
  • Mitchell, T., and J. M. Wallace, 1992: The annual cycle in equatorial convection and sea surface temperature. J. Climate, 5, 11401156, doi:10.1175/1520-0442(1992)005<1140:TACIEC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Nguyen, H., C. D. Thorncroft, and C. Zhang, 2011: Guinean coastal rainfall of the West African Monsoon. Quart. J. Roy. Meteor. Soc., 137,18281840, doi:10.1002/qj.867.

    • Search Google Scholar
    • Export Citation
  • Nicholson, S. E., 2000: The nature of rainfall variability over Africa on time scales of decades to millenia. Global Planet. Change, 26, 137158, doi:10.1016/S0921-8181(00)00040-0.

    • Search Google Scholar
    • Export Citation
  • Okumura, Y., and S.-P. Xie, 2004: Interaction of the Atlantic equatorial cold tongue and the African monsoon. J. Climate, 17, 35893602, doi:10.1175/1520-0442(2004)017<3589:IOTAEC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Patricola, C. M., M. Li, Z. Xu, P. Chang, R. Saravanan, and J.-S. Hsieh, 2012: An investigation of tropical Atlantic bias in a high-resolution coupled regional climate model. Climate Dyn., 39, 24432463, doi:10.1007/s00382-012-1320-5.

    • Search Google Scholar
    • Export Citation
  • Pritchard, M. S., M. W. Moncrieff, and R. C. J. Somerville, 2011: Orogenic propagating precipitation systems over the United States in a global climate model with embedded explicit convection. J. Atmos. Sci., 68, 18211840, doi:10.1175/2011JAS3699.1.

    • Search Google Scholar
    • Export Citation
  • Pu, B., and K. H. Cook, 2010: Dynamics of the West African westerly jet. J. Climate, 23, 62636276, doi:10.1175/2010JCLI3648.1.

  • Ramel R., H. Gallée, and C. Messager, 2006: On the northward shift of the West African monsoon. Climate Dyn., 26, 429440, doi:10.1007/s00382-005-0093-5.

    • Search Google Scholar
    • Export Citation
  • Randall, D., M. Khairoutdinov, A. Arakawa, and W. Grabowski, 2003: Breaking the cloud parameterization deadlock. Bull. Amer. Meteor. Soc., 84,15471564, doi:10.1175/BAMS-84-11-1547.

    • Search Google Scholar
    • Export Citation
  • Reynolds, R. W., T. M. Smith, C. Liu, D. B. Chelton, K. S. Casey, and M. G. Schlax, 2007: Daily high-resolution-blended analyses for sea surface temperature. J. Climate, 20, 54735496, doi:10.1175/2007JCLI1824.1.

    • Search Google Scholar
    • Export Citation
  • Richter, I., and S.-P. Xie, 2008: On the origin of equatorial Atlantic biases in coupled general circulation models. Climate Dyn., 31, 587598, doi:10.1007/s00382-008-0364-z.

    • Search Google Scholar
    • Export Citation
  • Richter, I., S.-P. Xie, A. T. Wittenberg, and Y. Masumoto, 2012: Tropical Atlantic biases and their relation to surface wind stress and terrestrial precipitation. Climate Dyn., 38, 9851001, doi:10.1007/s00382-011-1038-9.

    • Search Google Scholar
    • Export Citation
  • Roehrig, R., D. Bouniol, F. Guichard, F. Hourdin, and J.-L. Redelsperger, 2013: The present and future of the West African monsoon: A process-oriented assessment of CMIP5 simulations along the AMMA transect. J. Climate, 26, 64716505, doi:10.1175/JCLI-D-12-00505.1.

    • Search Google Scholar
    • Export Citation
  • Ruti, P. M., and A. Dell’Aquila, 2010: The twentieth century African easterly waves in reanalysis systems and IPCC simulations, from intra-seasonal to inter-annual variability. Climate Dyn., 35, 10991117, doi:10.1007/s00382-010-0894-z.

    • Search Google Scholar
    • Export Citation
  • Smith, R., and P. Gent, Eds., 2002: Reference manual for the Parallel Ocean Program (POP): Ocean component of the Community Climate System Model (CCSM2.0 and 3.0). Los Alamos National Laboratory Tech. Rep. LAUR-02-2484, 75 pp. [Available online at http://www.cesm.ucar.edu/models/ccsm3.0/pop/doc/manual.pdf.]

  • Stan, C., M. Khairoutdinov, C. A. DeMott, V. Krishnamurthy, D. M. Straus, D. A. Randall, J. L. Kinter III, and J. Shukla, 2010: An ocean–atmosphere climate simulation with an embedded cloud resolving model. Geophys. Res. Lett., 37, L01702, doi:10.1029/2009GL040822.

    • Search Google Scholar
    • Export Citation
  • Sultan, B., and S. Janicot, 2003: The West African monsoon dynamics. Part II: The “preonset” and “onset” of the summer monsoon. J. Climate., 16, 34073427, doi:10.1175/1520-0442(2003)016,3407:TWAMDP.2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Sultan, B., S. Janicot, and A. Diedhiou, 2003: The West African monsoon dynamics. Part I: Documentation of intraseasonal variability. J. Climate, 16, 33893406, doi:10.1175/1520-0442(2003)016<3389:TWAMDP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Sundqvist, H., 1988: Parameterization of condensation and associated clouds in models for weather prediction and general circulation simulation. Physically-Based Modelling and Simulation of Climate and Climate Change, M. E. Schlesinger, Ed., Kluwer Academic, 433–461.

  • Taylor, K. E., R. J. Stouffer, and G. A. Meehl, 2012: An overview of CMIP5 and the experiment design. Bull. Amer. Meteor. Soc., 93, 485498, doi:10.1175/BAMS-D-11-00094.1.

    • Search Google Scholar
    • Export Citation
  • Thayer-Calder, K., and D. A. Randall, 2009: The role of convective moistening in the Madden–Julian oscillation. J. Atmos. Sci., 66, 32973312, doi:10.1175/2009JAS3081.1.

    • Search Google Scholar
    • Export Citation
  • Thorncroft, C. D., and M. Blackburn, 1999: Maintenance of the African easterly jet. Quart. J. Roy. Meteor. Soc., 125, 763786, doi:10.1002/qj.49712555502.

    • Search Google Scholar
    • Export Citation
  • Thorncroft, C. D., N. M. J. Hall, and G. N. Kiladis, 2008: Three-dimensional structure and dynamics of African easterly waves. Part III: Genesis. J. Atmos. Sci., 65, 35963607, doi:10.1175/2008JAS2575.1.

    • Search Google Scholar
    • Export Citation
  • Thorncroft, C. D., H. Nguyen, C. Zhang, and P. Peyrillé, 2011: Annual cycle of the West African monsoon: Regional circulations and associated water vapour transport. Quart. J. Roy. Meteor. Soc.,137, 129147, doi:10.1002/qj.728.

    • Search Google Scholar
    • Export Citation
  • Vizy, E. K., and K. H. Cook, 2001: Mechanisms by which Gulf of Guinea and eastern North Atlantic sea surface temperatures can influence African rainfall. J. Climate, 14, 795821, doi:10.1175/1520-0442(2001)014<0795:MBWGOG>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Wu, M.-L. C., O. Reale, S. D. Schubert, M. J. Suarez, R. D. Koster, and P. J. Pegion, 2009: African easterly jet: Structure and maintenance. J. Climate, 22, 44594480, doi:10.1175/2009JCLI2584.1.

    • Search Google Scholar
    • Export Citation
  • Xie, S.-P., and J. A. Carton, 2004: Tropical Atlantic variability: Patterns, mechanisms, and impacts. Earth’s Climate: The Ocean-Atmosphere Interaction, Geophys. Monogr., Vol. 147, Amer. Geophys. Union, 121–142.

  • Zermeño-Diaz, D. M., and C. Zhang, 2013: Possible root causes of surface westerly biases over the equatorial Atlantic in global climate models. J. Climate, 26, 81548168, doi:10.1175/JCLI-D-12-00226.1.

    • Search Google Scholar
    • Export Citation
  • Zhang, C., P. Woodworth, and G. Gu, 2006: The seasonal cycle in the lower troposphere over West Africa from sounding observations. Quart. J. Roy. Meteor. Soc., 132, 25592582, doi:10.1256/qj.06.23.

    • Search Google Scholar
    • Export Citation
  • Zhang, G. J., and N. A. McFarlane, 1995: Role of convective scale momentum transport in climate simulation. J. Geophys. Res., 100, 14171426, doi:10.1029/94JD02519.

    • Search Google Scholar
    • Export Citation
Save
  • Alley, B., and Coauthors, 2007: Summary for policymakers. Climate Change 2007: The Physical Science Basis, S. Solomon et al., Eds., Cambridge University Press, 1–18. [Available online at http://www.ipcc.ch/pdf/assessment-report/ar4/wg1/ar4-wg1-spm.pdf.]

    • Search Google Scholar
    • Export Citation
  • Baron, C., B. Sultan, M. Balme, B. Sarr, S. Traore, T. Lebel, S. Janicot, and M. Dingkuhn, 2005: From GCM grid cell to agricultural plot: Scale issues affecting modelling of climate impact. Philos. Trans. Roy. Soc. London, B360, 20952108, doi:10.1098/rstb.2005.1741.

    • Search Google Scholar
    • Export Citation
  • Benedict, J. J., and D. A. Randall, 2009: Structure of the Madden–Julian oscillation in the superparameterized CAM. J. Atmos. Sci., 66, 32773296, doi:10.1175/2009JAS3030.1.

    • Search Google Scholar
    • Export Citation
  • Benedict, J. J., and D. A. Randall, 2011: Impacts of idealized air–sea coupling on Madden–Julian oscillation structure in the superparameterized CAM. J. Atmos. Sci., 68, 19902008, doi:10.1175/JAS-D-11-04.1.

    • Search Google Scholar
    • Export Citation
  • Berry, G., and C. D. Thorncroft, 2005: Case study of an intense African easterly wave. Mon. Wea. Rev., 133, 752766, doi:10.1175/MWR2884.1.

    • Search Google Scholar
    • Export Citation
  • Boko, M., and Coauthors, 2007: Africa. Climate Change 2007: Impacts, Adaptation and Vulnerability, M.L. Parry et al., Eds., Cambridge University Press, 433–467. [Available online at http://www.ipcc.ch/pdf/assessment-report/ar4/wg2/ar4-wg2-chapter9.pdf.]

  • Bonan, G. B., K. W. Oleson, M. Vertenstein, S. Levis, X. Zeng, Y. Dai, R. E. Dickinson, and Z.-L. Yang, 2002: The land surface climatology of the community land model coupled to the NCAR community climate model. J. Climate, 15, 31233149, doi:10.1175/1520-0442(2002)015<3123:TLSCOT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Burpee, R. W., 1972: The origin and structure of easterly waves in the lower troposphere of North Africa. J. Atmos. Sci., 29, 7790, doi:10.1175/1520-0469(1972)029<0077:TOASOE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Caniaux, G., H. Giordani, J.-L. Redelsperger, F. Guichard, E. Key, and M. Wade, 2011: Coupling between the Atlantic cold tongue and the West African monsoon in boreal spring and summer. J. Geophys. Res., 116, C04003, doi:10.1029/2010JC006570.

    • Search Google Scholar
    • Export Citation
  • Chang, C.-Y., J. A. Carton, S. A. Grodsky, and S. Nigam, 2007: Seasonal climate of the tropical Atlantic sector in the NCAR Community Climate System Model 3: Error structure and probable causes of errors. J. Climate, 20, 10531070, doi:10.1175/JCLI4047.1.

    • Search Google Scholar
    • Export Citation
  • Coëtlogon, G., S. Janicot, and A. Lazar, 2010: Intraseasonal variability of the ocean–atmosphere coupling in the Gulf of Guinea during boreal spring and summer. Quart. J. Roy. Meteor. Soc.,136, 426–441. doi:10.1002/qj.554.

  • Collins, W. D., and Coauthors, 2006: The Community Climate System Model version 3 (CCSM3). J. Climate, 19, 21222143, doi:10.1175/JCLI3761.1.

    • Search Google Scholar
    • Export Citation
  • Cook, K. H., 1999: Generation of the African easterly jet and its role in determining West African precipitation. J. Climate, 12, 11651184, doi:10.1175/1520-0442(1999)012<1165:GOTAEJ>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Cook, K. H., and E. K. Vizy, 2006: Coupled model simulations of the West African monsoon system: Twentieth- and twenty-first-century simulations. J. Climate, 19, 36813703, doi:10.1175/JCLI3814.1.

    • Search Google Scholar
    • Export Citation
  • Cook, K. H., G. A. Meehl, and J. M. Arblaster, 2012: Monsoon regimes and processes in CCSM4. Part II: African and American monsoon systems. J. Climate, 25, 26092621, doi:10.1175/JCLI-D-11-00185.1.

    • Search Google Scholar
    • Export Citation
  • Davey, M., and Coauthors, 2002: STOIC: A study of coupled model climatology and variability in tropical ocean regions. Climate Dyn., 18, 403420, doi:10.1007/s00382-001-0188-6.

    • Search Google Scholar
    • Export Citation
  • Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553597, doi:10.1002/qj.828.

    • Search Google Scholar
    • Export Citation
  • DeMott, C. A., D. A. Randall, and M. Khairoutdinov, 2007: Convective precipitation variability as a tool for general circulation model analysis. J. Climate, 20, 91112, doi:10.1175/JCLI3991.1.

    • Search Google Scholar
    • Export Citation
  • DeMott, C. A., C. Stan, D. A. Randall, J. L. Kinter III, and M. Khairoutdinov, 2011: The Asian monsoon in the superparameterized CCSM and its relationship to tropical wave activity. J. Climate, 24, 51345156, doi:10.1175/2011JCLI4202.1.

    • Search Google Scholar
    • Export Citation
  • DeMott, C. A., C. Stan, and D. A. Randall, 2013: Northward propagation mechanisms of the Boreal summer intraseasonal oscillation in the ERA-Interim and SP-CCSM. J. Climate, 26, 19731992, doi:10.1175/JCLI-D-12-00191.1.

    • Search Google Scholar
    • Export Citation
  • Fink, A. H., and A. Reiner, 2003: Spatiotemporal variability of the relation between African Easterly Waves and West African Squall Lines in 1998 and 1999. J. Geophys. Res., 108, 4332, doi:10.1029/2002JD002816.

    • Search Google Scholar
    • Export Citation
  • Foltz, G. R., S. A. Grodsky, J. A. Carton, and M. J. McPhaden, 2003: Seasonal mixed layer heat budget of the tropical Atlantic Ocean. J. Geophys. Res., 108, 3146, doi:10.1029/2002JC001584.

    • Search Google Scholar
    • Export Citation
  • Gill, A. E., 1980: Some simple solutions for heat-induced tropical circulation. Quart. J. Roy Meteor. Soc., 106, 447462, doi:10.1002/qj.49710644905.

    • Search Google Scholar
    • Export Citation
  • Grabowski, W. W., 2001: Coupling cloud processes with the large-scale dynamics using the cloud-resolving convection parameterization (CRCP). J. Atmos. Sci., 58, 978997, doi:10.1175/1520-0469(2001)058<0978:CCPWTL>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Grabowski, W. W., and P. K. Smolarkiewicz, 1999: CRCP: A Cloud Resolving Convection Parameterization for modeling the tropical convecting atmosphere. Physica D, 133, 171178, doi:10.1016/S0167-2789(99)00104-9.

    • Search Google Scholar
    • Export Citation
  • Grodsky, S. A.,