Abstract

The Maritime Continent, with its complex system of islands and shallow seas, presents a major challenge to models, which tend to systematically underestimate the precipitation in this region. Experiments with a climate version of the Met Office model (HadAM3) show that even with a threefold increase in horizontal resolution there is no improvement in the dry bias. It is argued that the diurnal cycle over the islands and the complex circulation patterns generated by land–sea contrasts are crucial for the energy and hydrological cycles of the Maritime Continent and for determining the mean climate. It is shown that the model has substantial errors in its simulation of the diurnal cycle over the islands, which can rectify onto the seasonal mean climate.

It is further argued that deficient rainfall over the Maritime Continent could be a driver for other systematic errors, such as the excess precipitation over the western Indian Ocean. To demonstrate the sensitivity of global systematic model errors to the heating in this region, two experiments have been performed, one with the existing distribution of islands and a second where the island grid points are replaced by ocean grid points. In the absence of the islands of the Maritime Continent, the local precipitation increases by 15%, reducing the existing dry bias and bringing the model closer to observations. In response to this improved heating distribution, precipitation decreases over the west Indian Ocean and South Pacific convergence zone, reducing the systematic wet bias in these regions. This supports the hypothesis that tropical systematic errors are often related through vertical (Walker) circulations.

The extratropical response to changes in the Maritime Continent heat source is also well demonstrated by these experiments. The enhanced heating and, hence, divergent outflow generates Rossby waves, which have a significant impact on the winter circulation and surface temperatures across much of North America and the northeast Eurasian region. These changes are such as to substantially reduce model systematic error in these regions. These results reinforce the critical role played by the Maritime Continent in the global circulation. It emphasizes the need for better representation of convective organization over regions of complex land–sea terrains and the importance of considering the global context of model systematic errors in which biases in the Tropics may be a key factor.

1. Introduction

The tropical Maritime Continent has a unique environment where convective activity responds to forcing on many timescales and space scales, the net result of which is able to influence climate on the global scale. This region, so named because of the complex distribution of several large islands with elevated orography occupying the domain 10°S–20°N and 90°–150°E, also encompasses some of the warmest ocean temperatures of the world and is known as the “boiler box” of the Tropics (Ramage 1968). The Maritime Continent receives much of its rainfall from convective activity associated with localized thunderstorms. The Island Thunderstorm Experiment (ITEX; Keenan et al. 1989) and the Maritime Continent Thunderstorm Experiment (MCTEX; Keenan et al. 2000) were established in order to diagnose and model the importance of the thunderstorm-scale convection for the climate of the region.

The islands play an important part in the meteorology of the Maritime Continent. Larger-scale organization of thunderstorm activity is strongly influenced by the orography of the region as well as the sea-breeze circulations, which show strong diurnal variations. Satellite images show this diurnal variation over the islands to be a striking feature of this region (Holland and Keenan 1980). The nature of these sea-breeze circulations has been studied extensively. The convection related to sea-breeze convergence is able to aggregate into mesoscale convective complexes (MCCs) later in the day, which move offland to give the greatest precipitation during the local morning time over the oceans (Williams and Houze 1987).

The timing and magnitude of this convective activity on diurnal timescales is known to vary significantly between land and ocean regions. Using a climatology of window brightness derived from high temporally sampled satellite data, Yang and Slingo (2001) show that the surface inhomogeneity of the Maritime Continent makes for complex diurnal forcing of convection. During Northern Hemisphere winter, in particular, the diurnal amplitude of convective rainfall over the islands can be three times as great as that over the adjacent ocean. This is in response to the smaller thermal heat capacity of the land surface leading to large diurnal variations in low-level instability. However, there are further complications to the response, since the strong variability over the islands is able to propagate out over the oceans as gravity waves leading to coherent variations in the phase of the convective peak. Much of this variability may be related to the standard lifetime of convective systems that start over land, build, then move away over the oceans (Saito et al. 2001). Such an evolution suggests that the atmosphere has some memory of a particular convective disturbance at some previous time.

The Maritime Continent also exhibits an influence on larger-scale intraseasonal activity. The Madden–Julian oscillation (MJO) propagates over the region in its mature phase and is modulated by the presence of the underlying surface properties and the diurnal cycle before moving out over the West Pacific. This makes for a complex response whereby the diurnal cycle of convective activity becomes suppressed during the active phase of the MJO and enhanced during the break phase (Sui and Lau 1992). The presence of the Maritime Continent also modulates the strength and phase speed of the MJO. In particular, it is able to both weaken and split the active phase of the oscillation before it reintensifies in the South Pacific convergence zone (SPCZ; Zhu and Wang 1993).

The southern part of the Maritime Continent region is strongly influenced by the winter monsoon circulation involving significant transport of warm moist air from north of the equator that meets the trade wind flow south of the equator to generate the austral monsoon trough. The first strong MJO of the winter season usually signifies the onset of the monsoon regime (Sui and Lau 1992). The winter monsoon associated with convection over the Maritime Continent has also been shown to influence the midlatitude circulation through short-term teleconnections (Lau et al. 1983). For example, during an active phase of the monsoon the tropical and extratropical circulations vary in a coherent way, intensifying the local Hadley and Walker circulations and strengthening the east Asia subtropical jet (Chang and Lau 1982). The action of cold surges, which strengthen the Asian winter monsoon northeasterlies, also results in periods of enhanced convective activity throughout the season, particularly north of Borneo (Houze et al. 1981). These cold surges can typically last from 5 to 14 days (Zhang et al. 1997) and so may account for a large proportion of climate variability over the Maritime Continent during winter.

Variability on seasonal timescales modulates the rainfall over the Maritime Continent particularly during the warm phase of ENSO (Philander 1985). As the warmest SSTs move out into the central Pacific, the strongest convection follows and generates an anomalous longitudinal circulation, leading to suppression over the Maritime Continent. This is a particularly large effect since ENSO events typically reach a maximum in Northern Hemisphere winter when the precipitation totals usually reach a maximum over the Maritime Continent.

On the planetary scale, convection over the Maritime Continent represents a dominant heat source for the atmospheric circulation. Convection is most intense and the tropopause at its highest over this region. Upper-tropospheric divergent outflow from this convective region has been shown, in an idealized model, to be a major source of wave activity due to the generation of global rotational flow (Sardeshmukh and Hoskins 1988). Such a response establishes the stationary wave patterns clearly observed in time-mean fields. So it is clear that the Maritime Continent has an important role to play both in the variability of the tropical climate and for the global circulation as a whole.

The aim of this paper is to gain an insight into the climate of the Maritime Continent region and to highlight its role in the tropical and global mean climate. The following section assesses the skill of the Met Office Unified Model (UM) at reproducing the mean climate over the Maritime Continent region and identifies some possible shortcomings of the model. Specific errors relating to the presence of the Maritime Continent islands are identified and results from idealized sensitivity experiments, which attempt to remove these errors, are described. Further shortcomings of the UM's representation of the diurnal cycle are then highlighted. Investigation of a possible sensitivity to the details of the UM's radiation scheme is performed with a further short 1-yr integration of the model. Significant large-scale influences of changes in the Maritime Continent heat source region are revealed in a section on the global impacts; the implications of this study and conclusions are summarized in the final section.

2. Description of the model and its systematic errors

A suite of experiments using the Met Office Unified Model (UM), performed as part of the second phase of the Atmospheric Model Intercomparison Project (AMIP II; Gates 1992), has been analyzed and the performance of the UM assessed over our region of interest, the Maritime Continent. The experimental period extends over 17 years (1979–95) and the model is forced by monthly mean SSTs and sea ice. The climate version of the UM (HadAM3; Pope et al. 2000) is used at a resolution of 2.5° in latitude, 3.75° in longitude, and 19 levels in the vertical. This model version has significant changes compared to earlier versions, namely, the inclusion of momentum transports by subgrid-scale convective processes (Kershaw and Gregory 1997), an updated radiation scheme (Edwards and Slingo 1996), and a new land surface and vegetation scheme [Met Office Surface Exchange Scheme (MOSES); Cox et al. 1999]. For comparison, observational data are provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA; Gibson et al. 1997) and the Climate Prediction Center Merged Analysis of Precipitation (CMAP) rainfall climatology (Xie and Arkin 1996) covering the same analysis period as the AMIP II experiments.

In the UM, as with many climate models, the Maritime Continent exhibits a deficit of precipitation in all seasons (see the AMIP II Web site online at http://www-pcmdi.llnl.gov/amip). Figure 1 shows the precipitation and lower-tropospheric wind errors in the model during Northern Hemisphere winter [December–January–February (DJF)] and summer [June–July–August (JJA)]. During DJF precipitation deficits in excess of 2 mm day–1 cover most of the Maritime Continent north of the equator and the western part south of the equator. Only to the east and west of Guinea and just north of the Australian coast is there a significant excess of precipitation in the model.

Fig. 1.

Seasonal mean errors from a six-member standard AMIP II ensemble of the UM when compared with the Xie–Arkin climatology (precipitation) and ERA (winds): (a) DJF precipitation (mm day–1); (b) as in (a) but for JJA; (c) DJF 850-hPa wind vectors (m s–1); (d) as in (c) but for JJA. Precipitation values less the −2 mm day–1 are shaded. The zero contour is omitted

Fig. 1.

Seasonal mean errors from a six-member standard AMIP II ensemble of the UM when compared with the Xie–Arkin climatology (precipitation) and ERA (winds): (a) DJF precipitation (mm day–1); (b) as in (a) but for JJA; (c) DJF 850-hPa wind vectors (m s–1); (d) as in (c) but for JJA. Precipitation values less the −2 mm day–1 are shaded. The zero contour is omitted

In JJA the errors in precipitation appear less coherent, but in general the same pattern as in DJF occurs with a dry bias centered mostly off the coasts of the large islands. The pattern of errors over the Asian monsoon region show that the rainfall is overestimated across northern and central India as well as into the far southeast of Asia. In addition, the equatorial maximum over the Indian Ocean, which characterizes the break phase of the monsoon, is positioned too far to the west. Due to the presence of large-scale tropical vertical circulations it is possible that shortcomings over the Maritime Continent may have some role to play in the model's simulation of the monsoon during JJA also. Certain very local errors persist throughout the whole year. For instance, the negative errors over the Philippines and off the southwest coast of Sumatra suggest that some of the problems may lie in the model's response to the presence of these islands.

Associated with the errors in the seasonal mean precipitation are errors in the lower-tropospheric flow. In DJF (Fig. 1c), the zonal flow has a strong easterly bias from the West Pacific through to the Indian Ocean north of the equator. It is conceivable that the large-scale nature of this error is related to the lack of a strong organized MJO, which would have periods of significant westerly wind activity projecting onto the mean basic state. Indeed the AMIP I study by Slingo et al. (1996) showed that a poor model MJO and errors in the Maritime Continent basic state could be closely linked. Embedded within the large-scale flow bias are errors on the scale of the Maritime Continent islands. In general, the easterly bias tends to be increased on the west side of the islands and points toward problems in simulating the local island-scale circulation associated with sea-breeze effects and the diurnal cycle. The problems with simulating the coupling between the diurnal cycle and sea-breeze circulation and its implications for the mean climate of the model are addressed in a later section.

Much of the important geographic detail of the Maritime Continent is not resolved at the coarse climate resolution of the model, and at first sight, it could be concluded that the errors described above might be corrected with higher horizontal resolution. A number of AMIP II experiments have been performed to address the sensitivity of model errors to horizontal resolution (Stratton 1999). Many improvements are seen in these experiments, such as a stronger midlatitude circulation due to better resolved storm tracks. However, even with a threefold increase in horizontal resolution there is no systematic improvement of the dry bias in the model (Fig. 2). The pattern of the errors in the tropical precipitation persists and, if anything, is enhanced with increasing resolution.

Fig. 2.

Annual mean precipitation error (mm day–1) from four UM AMIP II experiments with different horizontal resolution: (a) climate resolution (2.5° × 3.75°); (b) 1.5 × climate resolution (1.67° × 2.5°); (c) 2 × climate resolution (1.25° × 1.875°); (d) 3 × climate resolution (0.83° × 1.25°). Values less than −2 mm day–1 are shaded. The zero contour is omitted

Fig. 2.

Annual mean precipitation error (mm day–1) from four UM AMIP II experiments with different horizontal resolution: (a) climate resolution (2.5° × 3.75°); (b) 1.5 × climate resolution (1.67° × 2.5°); (c) 2 × climate resolution (1.25° × 1.875°); (d) 3 × climate resolution (0.83° × 1.25°). Values less than −2 mm day–1 are shaded. The zero contour is omitted

The results in Fig. 2 demonstrate that computationally achievable increases in resolution do not necessarily lead to improvements in model error, and indicate that it is probably deficiencies in the representation of the physical system that are the primary source of these errors. The fact that many models show similar problems over the Maritime Continent suggests that they all lack some key ingredient. In section 4 it is hypothesized that the diurnal cycle and the generation of land–sea breezes around this complex system of islands may be possible factors.

The precipitation errors shown in Fig. 1 represent a substantial fraction of the mean precipitation, which severely compromises the tropical heating pattern in the model (not shown). The global scale of this problem is demonstrated in section 5. In the following sections, a sensitivity experiment is described, which aims to improve the heating distribution over the Maritime Continent and thereby show the importance of an accurate simulation of the climate of the Maritime Continent for the global circulation.

3. Role of the islands in the climate of the Maritime Continent

As shown in the AMIP II experiments, the UM is unable to simulate well the mean climate over the Maritime Continent in terms of both the precipitation distribution and the circulation. It is clear, therefore, that the UM has great trouble in representing the observed impact of the islands of the region on the resolvable portion of the flow. Closer investigation over the individual islands reveals errors in their surface properties that may be having a detrimental impact on the modeled convection, possibly leading to the mean negative bias. The correct mean surface air temperature is important to the boundary layer stability, which is the closure method for the convection scheme in the UM (Gregory and Rowntree 1990). Figure 3a shows the mean surface air temperature error, when compared with the climatology of Legates and Willmott (1990), from the standard AMIP II ensemble experiments at climate resolution. Over the island grid points a cold bias predominates (see Fig. 4a for exact location of land grid points). This is in excess of 2°C over Borneo, the Philippines, and New Guinea. The problem is not simply a resolution issue, since even at 3 times climate resolution there is still a mean cold bias in surface air temperature over the islands (Fig. 3b).

Fig. 3.

Annual mean surface air temperature error (°C) for the UM AMIP II experiments over the Maritime Continent region at (a) climate resolution and (b) 3 × climate resolution

Fig. 3.

Annual mean surface air temperature error (°C) for the UM AMIP II experiments over the Maritime Continent region at (a) climate resolution and (b) 3 × climate resolution

Fig. 4.

Elevation of land grid points (m) and annual mean SST (°C) used in (a) control and (b) no islands experiments

Fig. 4.

Elevation of land grid points (m) and annual mean SST (°C) used in (a) control and (b) no islands experiments

A further hindrance to convection may be the presence of layer cloud in the lowest layer of the model, essentially a fog layer. Mean values can reach in excess of 30% over the islands (not shown). This also masks the fact that there exists a strong diurnal signal where cloud amounts in the lowest model layer can build to almost 100% cover during the night. Therefore after sunrise, insolation has to burn off this excessive low cloud before it can begin to heat the surface in order to generate low-level instability. This could conceivably be a hindrance to the correct evolution of convection, the consequences of which are discussed in the next section.

In an attempt to investigate the influence of the cold bias over the islands, and to artificially improve the climate of the region, two further AMIP II–type experiments have been carried out. One of the aims of these experiments is to identify how much of the error in the global Tropics seen in Fig. 1 can be attributed to a remote response to errors over the Maritime Continent. The first experiment is equivalent to the standard AMIP II integration for the full 17 years of the AMIP II period but with additional diagnostics. This will be referred to as the “control” experiment. The second is an identical experiment except that the land grid points composing the Maritime Continent islands have been removed and replaced by ocean grid points with SST bilinearly interpolated from the surrounding existing ocean grid points (see Fig. 4). This will be referred to as the “no islands” experiment. In fact, two realizations of the no islands experiment were performed to ensure that the results were significant.

The rationale behind the removal of the land grid points is to determine how detrimental the incorrect representation of the Maritime Continent islands may be to the generation of strong convection throughout the year. By replacing these land grid points with warmer SSTs, the cold bias noted in Fig. 3 is eliminated, which with the enhanced moisture availability from the sea surface should lead to greater convective activity. Therefore, it is hoped that the mean bias may be corrected by providing boundary forcing, which is more conducive to strong convection than in the control experiment. While this experiment is a useful excerise in determining the impacts of improving model error, it would not represent a viable solution given that any improvements would essentially be for the wrong reason.

The mean response to the removal of the islands in the Maritime Continent is a net increase in precipitation over and around the region. Figure 5 shows the changes in precipitation and lower-tropospheric flow associated with the removal of the Maritime Continent islands. This figure should be compared with Fig. 1, showing the errors from observations of the original AMIP II experiments. In DJF there is an increase in precipitation off the coast of the western islands of the Maritime Continent and a decrease in the SPCZ region and north of the Australian continent, reducing the strength of the winter monsoon. These changes largely correct for the complex pattern of precipitation error seen in the standard version of the UM. Nonlocal changes in precipitation are also evident in the western Indian Ocean, which again partially correct for errors there.

Fig. 5.

Mean differences between the no islands and control experiments: (a) DJF precipitation (mm day–1); (b) as in (a) but for JJA; (c) DJF 850-hPa wind vectors (m s–1); (d) as in (c) but for JJA. Precipitation changes less than −1 mm day–1 are shaded and the zero contour is omitted

Fig. 5.

Mean differences between the no islands and control experiments: (a) DJF precipitation (mm day–1); (b) as in (a) but for JJA; (c) DJF 850-hPa wind vectors (m s–1); (d) as in (c) but for JJA. Precipitation changes less than −1 mm day–1 are shaded and the zero contour is omitted

In JJA, changes in precipitation are more dramatic and extend to a greater part of the adjacent regions north and east of the equator. Over the Maritime Continent region there is a coherent increase in precipitation correcting for the existing bias. In response to these increases there is a general reduction in precipitation on the periphery of the Maritime Continent. Over the equatorial Indian Ocean this leads to an improved, more zonally oriented precipitation distribution. To the north and northeast of the region, the changes lead to a more realistic strength for the Asian summer monsoon and the removal of the spurious extension of the precipitation pattern into the western Pacific, east of the Philippines. As in DJF the SPCZ wet bias is reduced.

Although Fig. 5 shows a consistent improvement in the precipitation field, the lower-tropospheric wind changes tend to reinforce existing errors in DJF, with enhanced easterlies in the region, particularly north of the equator. In JJA the wind changes also add to the existing errors in the west Pacific, but in the Indian Ocean and over southern Asia the changes are somewhat more favorable, consistent with the precipitation changes. The low-level flow into the Indian and Asian monsoon region is reduced and the flow is diverted more toward the Maritime Continent after crossing the equator in the western Indian Ocean. Such a change in the flow is consistent with a stationary Rossby wave response to the enhanced heating over the Maritime Continent. However, considerable errors remain, particularly in the low-level flow over the Indian subcontinent. This suggests that these errors may be due to more local problems associated with the interaction of the model physics in the monsoon region (Martin 1999).

The changes seen in the no islands experiment are significant when compared to both the standard AMIP II ensemble and the control integration. Figure 6 shows this clearly, with the mean annual variation in precipitation averaged over the Maritime Continent area lying outside the ensemble spread of the standard AMIP II integrations and being significantly different from the control experiment, particularly during northern summer. More importantly the mean annual evolution is in much closer agreement with observations than any of the standard AMIP II or control experiments.

Fig. 6.

Mean monthly variation of precipitation (mm day–1) averaged over the region 20°S–10°N and 90°–150°E for the 17 years of the standard AMIP II integrations for the control and the no islands experiments

Fig. 6.

Mean monthly variation of precipitation (mm day–1) averaged over the region 20°S–10°N and 90°–150°E for the 17 years of the standard AMIP II integrations for the control and the no islands experiments

The overall increase in precipitation over the Maritime Continent region must be accompanied by an increase in the supply of moisture. Since the island grid points are being replaced by warmer, saturated ocean grid points, one possibility is that the low-level buoyancy is increased, due to temperature and humidity effects, with the moisture supply for the increased convection being provided locally by the enhanced evaporation from the sea surface. However, this is not entirely the case. Table 1 summarizes the surface energy and moisture budgets for the Maritime Continent region. There is a net increase in precipitation in the no islands experiment, which brings the total into closer agreement with estimates from the Xie–Arkin climatology. However, local evaporation within the Maritime Continent region accounts for only a quarter of the precipitation change between the two experiments. Therefore, the remaining 75% must be provided by nonlocal moisture convergence. This also raises the issue of whether the increase in surface temperature, due to the reduction of orographic elevation to sea level, or the increase in surface moisture availability, is important for the increase in precipitation in the no islands experiment.

Table 1.

Surface energy components (W m–2) [latent heating (LH); sensible heating (SH); shortwave heating (SW); Longwave heating (LW)] and surface moisture components of precipitation and evaporation (mm day–1) averaged over the Maritime Continent region (15°S–15°N; 90°E–150°E) during the AMIP II experiment period (1979–95) for the control and no islands experiments, and the ERA/Xie–Arkin climatologies. Positive energy budget values indicate fluxes out of the surface

Surface energy components (W m–2) [latent heating (LH); sensible heating (SH); shortwave heating (SW); Longwave heating (LW)] and surface moisture components of precipitation and evaporation (mm day–1) averaged over the Maritime Continent region (15°S–15°N; 90°E–150°E) during the AMIP II experiment period (1979–95) for the control and no islands experiments, and the ERA/Xie–Arkin climatologies. Positive energy budget values indicate fluxes out of the surface
Surface energy components (W m–2) [latent heating (LH); sensible heating (SH); shortwave heating (SW); Longwave heating (LW)] and surface moisture components of precipitation and evaporation (mm day–1) averaged over the Maritime Continent region (15°S–15°N; 90°E–150°E) during the AMIP II experiment period (1979–95) for the control and no islands experiments, and the ERA/Xie–Arkin climatologies. Positive energy budget values indicate fluxes out of the surface

To address this issue a second much shorter sensitivity experiment was performed where the land grid points were retained but the orography was reduced to zero. Briefly, the results show a local and remote response that is quantitatively similar to the no islands experiment, but of much reduced magnitude. Therefore, we can conclude that the absence of an interactive land surface and its replacement by a fixed SST boundary forcing provides a much greater contribution toward the enhanced mean precipitation in the no islands experiment than the removal of the orographic forcing.

As well as a change in the hydrological cycle, Table 1 also shows that the surface shortwave radiation increases despite the enhanced convective activity. This is because the diurnal cycle of cloud in the lowest model layer is eliminated over the island grid points. Table 1 suggests that although the desired effects of increased precipitation and some improved aspects of the climatology have been achieved, this has been at the expense of a deterioration in the surface energy budget with the caveat that the ERA fluxes are themselves not always reliable.

The no islands experiment has demonstrated that inadequate treatment of the islands of the Maritime Continent in global climate models may be responsible for the deficiencies in precipitation in this region and hence to errors in the tropical heating distribution throughout the warm pool region of the west Pacific and Indian Oceans. Despite the unrealistic nature of the no islands experiment, the improved rainfall climatology over the Maritime Continent in this integration has proved key to answering questions relating to the global effects of errors in the atmospheric heat source of the Maritime Continent, as discussed in section 5. It is clear that the meteorology of the islands is a key factor in determining the heat and moisture budgets. In the next section a particular aspect of that meteorology, the diurnal cycle, will be examined.

4. Role of the diurnal cycle in the Maritime Continent

On shorter timescales the characteristics of the diurnal cycle, its phase and amplitude, are important for the organization of precipitation in the Tropics. The study of Yang and Slingo (2001) reveals that the UM has low skill in reproducing the observed diurnal variation of rainfall over the Maritime Continent, and indeed over all tropical land areas. Throughout the convectively active Tropics, the model systematically maximizes precipitation too early during the day. Over land the peak occurs predominantly near local noon, too soon after the solar maximum, while over the ocean the peak is around local midnight.

More specifically, over the Maritime Continent region the model demonstrates particularly poor performance (cf. Figs. 7a,b). In the satellite observations, convection is seen to maximize in the late evening over the islands, whereas the model shows a maximum near local noon. This implies that the life cycle of convection in the model, from initial buoyancy excess of air parcels, through shallow cumulus and cumulus congestus, to organized thunderstorms, is much too short. Strong convection develops too soon during the day, which then cuts off the solar radiation to the surface, leading to time-average surface temperatures that are consistently lower than observations over all the Maritime Continent islands (Fig. 3). Such shortcomings of the model may not be surprising, since the observed diurnal cycle over the Maritime Continent islands is known to involve subtle interactions with small horizontal scale (tens of kilometers) sea-breeze circulations (Keenan et al. 2000), as well as a smooth transition through a multistage convective life cycle (Saito et al. 2001).

Fig. 7.

Local time of the maximum in the diurnal cycle of precipitation over the Maritime Continent: (a) mean of two winters derived from the global window brightness temperature of the Cloud Archive User Service (CLAUS; see Yang and Slingo 2001); (b) a single winter from the control experiment

Fig. 7.

Local time of the maximum in the diurnal cycle of precipitation over the Maritime Continent: (a) mean of two winters derived from the global window brightness temperature of the Cloud Archive User Service (CLAUS; see Yang and Slingo 2001); (b) a single winter from the control experiment

As well as the problems noted above, other shortcomings in the simulated diurnal cycle have been highlighted, most acutely over land and just off the coasts where the diurnal cycle accounts for a large proportion of the precipitation variability. As an example, Fig. 8 demonstrates the contrasting characteristics in the diurnal cycle averaged separately over land and ocean areas within the Maritime Continent. Here the diurnal cycle is calculated using the first three diurnal harmonics derived from instantaneous data at three hourly intervals.

Fig. 8.

Mean diurnal cycle derived from one year of a 3-hourly sampled integration of the control experiment: (a) convective heating (°C day–1) over the land grid points of the Maritime Continent; (b) convective heating (°C day–1) over the ocean grid points adjacent to the Maritime Continent; (c) as in (a) [and (d) as in (b)] but for convective moistening (g kg–1 day–1); (e) as in (a) [and (f) as in (b)] but for total cloud cover fraction

Fig. 8.

Mean diurnal cycle derived from one year of a 3-hourly sampled integration of the control experiment: (a) convective heating (°C day–1) over the land grid points of the Maritime Continent; (b) convective heating (°C day–1) over the ocean grid points adjacent to the Maritime Continent; (c) as in (a) [and (d) as in (b)] but for convective moistening (g kg–1 day–1); (e) as in (a) [and (f) as in (b)] but for total cloud cover fraction

The convective heating in Figs. 8a,b shows that, averaged over the land grid points, there is a strong maximum just before noon in the midtroposphere. What is also evident is the dominance of deep convection in the model, with no apparent buildup of convection through the morning. Over ocean grid points, the weaker amplitude shows that the diurnal cycle is a less important component of precipitation variability. The convective moistening in Figs. 8c,d reveals that the action of the convection is predominantly to dry the atmosphere, implying that the convection rapidly develops to precipitating convection in the model without the moistening, preconditioning phase associated with cumulus congestus. Idealized experiments with an aquaplanet version of the UM have confirmed that the drying of the atmosphere by precipitating convection is an overdominant process in the model (Inness et al. 2001), in that case with implications for the intraseasonal organization of convection associated with the MJO.

The mean diurnal evolution of the total cloud fraction is shown in Figs. 8e,f. First, the diurnal cycle of cloud fraction in the lowest model layer is very marked over land, ranging from 0% at the time of the convective maximum to an islands-wide maximum of 70% a couple of hours prior to sunrise. This undoubtedly has an impact on the evolution of convection since clouds will delay the time at which insolation can start to heat the land surface. As already noted, it also affects the overall energy budget of the island grid points.

It is clear from the above results that the model has serious difficulties in simulating the diurnal cycle over the islands of the Maritime Continent and it is important to investigate whether this failing has implications for the simulated mean climate of the region. In common with many climate models (e.g., ECMWF; Morcrette 2002), the UM does not perform a full radiation calculation at every time step. For reasons of computational cost, the standard practice is to perform a full radiation calculation every 3 h or six model time steps. At intermediate time steps, for the purpose of the radiation calculation, the cloud amounts and surface and atmospheric temperatures remain unchanged, although the solar fluxes and heating rates are scaled by the correct zenith angle. This means, first, that the longwave fluxes cannot respond to changes in solar forcing, and second, that there is a time lag in the cloud forcing, particularly for the surface fluxes.

It is possible that this approximation may be important for the evolution of the diurnal cycle. For example, a strong surface energy imbalance may occur at the start and end of the solar day, when the insolation is changing rapidly but the atmospheric and surface properties are only updated every 3 h. The combination of the shortwave and longwave approximations could contribute to a too-rapid increase in surface temperature and the generation of strong lower-tropospheric buoyancy excess too early in the day. This could lead to a maximum in convection over the tropical islands also too early in the day, which would in turn cut off the solar radiation and limit the net energy input to the land surface (Yang and Slingo 2001).

To investigate the effect of such approximations in the model's radiation calculation, a sensitivity experiment was performed with a full radiation calculation carried out every time step, hereafter referred to as the “full radiation” experiment. Due to the high computational cost, the integration was run for only 15 months, with the final 12 months retained from March 1979–February 1980 to provide data for each season. Figure 9a shows the mean diurnal evolution of surface air temperature in the control and full radiation experiments. Although the results from the full radiation experiment show a less rapid rise in surface temperature early in the day, changes in the diurnal cycle of convective rainfall (Fig. 9b) are slight. There is a systematic increase in precipitation of between 1 and 2 mm day–1 between 0600 and 1800 local time but no significant shift in the phase of the maximum precipitation. There does, however, appear to be a shift in the phase of the maximum outgoing longwave radiation (OLR) by about 2 h from 1100 to 1300 local time (Fig. 9c). This can be related to an increase in the terminal detrainment from the enhanced convection (Fig. 9d), evident in the increase in upper-tropospheric cloud of almost 10% over the land grid points (Fig. 9f). Over the ocean grid points of the Maritime Continent, the characteristics of the diurnal cycle are mainly unchanged in the full radiation experiment.

Fig. 9.

Comparison of the mean diurnal cycle over the Maritime Continent land grid points from 12-month integrations of the control and full radiation experiments: (a) surface air temperature (°C) using 3-hourly radiation time steps (solid line) and 0.5-hourly radiation time steps (dashed line); (b) as in (a) but for precipitation (mm day–1); (c) as in (a) but for OLR (W m–2); (d) change in the mean diurnal cycle of convective heating (°C day–1), 0.5-hourly radiation time steps minus 3-hourly radiation time steps; (e) as in (d) but for convective moistening (g kg–1 day–1); (f) as in (d) but for total cloud cover fraction

Fig. 9.

Comparison of the mean diurnal cycle over the Maritime Continent land grid points from 12-month integrations of the control and full radiation experiments: (a) surface air temperature (°C) using 3-hourly radiation time steps (solid line) and 0.5-hourly radiation time steps (dashed line); (b) as in (a) but for precipitation (mm day–1); (c) as in (a) but for OLR (W m–2); (d) change in the mean diurnal cycle of convective heating (°C day–1), 0.5-hourly radiation time steps minus 3-hourly radiation time steps; (e) as in (d) but for convective moistening (g kg–1 day–1); (f) as in (d) but for total cloud cover fraction

Although an accurate treatment of the diurnal cycle in the radiation calculation has not had a major impact on the phase of the diurnal cycle, nevertheless the changes in strength of the diurnal cycle appear to affect the mean climate of the model. Figure 10a shows the precipitation difference between the full radiation and control experiments. In general, over the islands there is a net increase in precipitation of around 1 mm day–1, consistent with the 1–2 mm day–1 increase seen during the daytime in the diurnal cycle. In response to these localized changes in heating there are larger-scale changes in the circulation. These lead to a general enhancement of precipitation on and south of the equator, coupled with a decrease north of the equator. They go some way to correcting for the geographical distribution of the systematic precipitation errors shown in Fig. 1, although, unlike the no islands experiment, the changes are not significant in the context of the ensemble of AMIP II integrations described earlier (see Fig. 6).

Fig. 10.

Change in the mean climate over the Maritime Continent from the full radiation experiment compared with the control integration: (a) precipitation (mm day–1); (b) OLR (W m–2)

Fig. 10.

Change in the mean climate over the Maritime Continent from the full radiation experiment compared with the control integration: (a) precipitation (mm day–1); (b) OLR (W m–2)

The full radiation experiment has enabled two important conclusions to be drawn. The first is that errors in the phase of the diurnal cycle over land are related to more fundamental errors in the physical processes of the model, such as the evolution of the convection field, as discussed by Yang and Slingo (2001). Second, it has demonstrated that even quite small but systematic changes to the diurnal cycle can rectify onto the mean climate, suggesting that significant improvements to the diurnal cycle over the Maritime Continent could have the potential to make a major impact on the model's large-scale systematic errors.

5. Global effects of systematic errors over the Maritime Continent

In both the no islands and full radiation experiments, there is an enhancement of the heat source over the Maritime Continent region. On seasonal to interannual timescales this will have an impact away from this region through the generation of Rossby waves. Therefore, it is appropriate to assess the global effects of this enhanced heating, and to determine the possible impacts on model error if improvements in the mean climate of the Maritime Continent were achieved. The opposite side of this argument is that we can also assess where the model may be in error remotely, because of the errors associated with the Maritime Continent heat source.

As identified in the analysis of the standard AMIP II experiments, the model underestimates the tropical convective heat source over the Maritime Continent, compared to observations, while creating local maxima over the west Indian Ocean and west Pacific. The increased deep convection in the no islands experiment enhances the divergent outflow and, depending on the ambient conditions, has the potential to influence remote areas of the globe through planetary wave propagation, as proposed by Sardeshmukh and Hoskins (1988). Figure 11 shows that this is certainly the case. The change in the tropical heat source in the no islands experiment has significant remote effects, which give rise to large-scale surface temperature increases over western Russia and Scandinavia, as well as to other significant changes over North America. The extratropical surface temperature changes in Figs. 11a,b are a consequence of changes in the global circulation, which can be described by the 500-hPa geopotential height anomalies in Figs. 11c,d, respectively. Only the winter hemisphere is shown in each case since it is in this season that the ambient conditions are most conducive to tropical–extratropical interaction.

Fig. 11.

Seasonal mean differences averaged for 1979–95 between the no islands and the control experiments: (a) DJF Northern Hemisphere surface air temperature (°C); (b) JJA Southern Hemisphere surface air temperature (°C); (c) DJF Northern Hemisphere 500-hPa geopotential height (m); (d) JJA Southern Hemisphere 500-hPa geopotential height (m)

Fig. 11.

Seasonal mean differences averaged for 1979–95 between the no islands and the control experiments: (a) DJF Northern Hemisphere surface air temperature (°C); (b) JJA Southern Hemisphere surface air temperature (°C); (c) DJF Northern Hemisphere 500-hPa geopotential height (m); (d) JJA Southern Hemisphere 500-hPa geopotential height (m)

The changes in surface temperature and 500-hPa geopotential height can be compared with the UM's systematic errors in these fields, as shown in Fig. 12. This reveals the importance of an improved representation of the Maritime Continent heat source. In DJF the Northern Hemisphere is mainly dominated by a cold bias in surface temperature, although over North America the model has a warm bias over Canada and a cold bias over the United States and Mexico. This general large-scale pattern of systematic errors is substantially corrected in the no islands experiment by changes of 0.5°–1°C (Fig. 11a). Similarly over Scandinavia and northeast Russia, the systematic cold bias of over 5°C is partially corrected by the changes in the no islands experiment with an increase in surface temperature of over 2°C. Errors in Southern Hemisphere surface temperature during JJA are complicated by the problems with model sea ice on the periphery of the Antarctic continent (Fig. 12b).

Fig. 12.

Seasonal mean errors in the standard AMIP II ensemble experiments: (a) DJF Northern Hemisphere surface air temperature (°C); (b) JJA Southern Hemisphere surface air temperature (°C); (c) DJF Northern Hemisphere 500-hPa geopotential height (m); (d) JJA Southern Hemisphere 500-hPa geopotential height (m)

Fig. 12.

Seasonal mean errors in the standard AMIP II ensemble experiments: (a) DJF Northern Hemisphere surface air temperature (°C); (b) JJA Southern Hemisphere surface air temperature (°C); (c) DJF Northern Hemisphere 500-hPa geopotential height (m); (d) JJA Southern Hemisphere 500-hPa geopotential height (m)

The 500-hPa height changes during both DJF and JJA (Figs. 11c,d) in the no islands experiment lead to more obvious improvements in the model's systematic errors (Figs. 12c,d). Again the effects are wide ranging, with substantial changes in the stationary waves over the Euro–Atlantic sector. The potential for heating anomalies over the warm pool to influence the Euro–Atlantic sector is consistent with the response of the global circulation to La Niña. The pattern of 500-hPa geopotential height anomalies over the Euro-Atlantic sector in Fig. 11c is reminiscent of that associated with the 1998/99 La Niña. This was also characterized by enhanced convection over the Maritime Continent and west Pacific regions (Dong et al. 2000), similar to the changes in the heating pattern between the control and no islands experiments.

In JJA, the 500-hPa geopotential height anomalies over the southern oceans are clearly linked to an equivalent barotropic planetary wave response emanating from the enhanced convection over the Maritime Continent region. However, in DJF the wave propagation patterns are less coherent and, if anything, the surface temperature anomalies over western Russia appear to be the result of upstream development from the Maritime Continent. During DJF, although there is a net increase in precipitation over the Maritime Continent, the change in the heating pattern is less coherent spatially. This leads to a smaller-scale increase in upper-level divergence over the region, which does not excite the coherent wave activity seen in JJA, where there is a larger and more expansive region of enhanced upper-level divergence.

Although the mechanism for the remote, extratropical response in DJF is unclear, it is certainly the case that these are significant changes. The same patterns were reproduced in both no islands integrations, and the anomalies were statistically different from the intraensemble variability of the climate in the standard AMIP II experiments. Such changes to the global circulation raise two points. First, they show that it is vital that the hydrological cycle over the Maritime Continent is simulated accurately since the effects of correcting the local heat source are indeed global. Also, if tropical changes in heat sources can have such dramatic effects on the surface temperatures of the continents of the mid-latitudes and high latitudes, then they serve as a warning when trying to correct locally for biases in the model. For example, over western Eurasia, the significant cold bias of up to 8°C has been attributed to problems with freezing of soil moisture. However, it has been shown here that even a modest improvement in the heat source over the Maritime Continent can account for up to 2°C of this bias.

6. Discussion and conclusions

This paper has investigated the errors in the simulation of the climate of the Maritime Continent in the Met Office Unified Model. The lack of precipitation over the region is an ubiquitous feature of the model results during all seasons, as shown in standard AMIP II experiments. These errors persist even in AMIP II integrations at much higher resolution, implying that, at these resolutions, deficiencies in the representation of the physical system are primarily responsible.

The importance of the Maritime Continent climate has been demonstrated using AMIP II sensitivity experiments. By removing the island grid points of the region and replacing them with oceanic grid points, some aspects of the mean climate are improved. Precipitation increases to be closer to observed amounts and the flow patterns over the Indian Ocean during the Asian monsoon season are improved, such that the excessive circulation strength is reduced. However, significant errors remain. In particular the excessive lower-tropospheric easterlies in the west Pacific have been made worse by the removal of the island grid points.

Following the results of Slingo et al. (1996), which suggested that the MJO was sensitive to the mean climate in the warm pool region, it was hypothesized that the no islands experiment might lead to an improved MJO. Diagnosis of the MJO shows no significant change in this experiment suggesting that, like the diurnal cycle, it is more fundamentally dependent on aspects of the model's formulation, such as the vertical resolution (e.g., Inness et al. 2001), interaction with the ocean surface (Inness and Slingo 2003), and the physical parametrizations.

The influence of the Maritime Continent has been shown to be global. A robust teleconnection pattern is seen, particularly in the Southern Hemisphere winter season, which is consistent with an equivalent barotropic Rossby wave response to an enhanced tropospheric heat source located over the Maritime Continent. Such a response raises important questions concerning the methodology for tackling systematic errors in climate models. It is possible that model errors in a particular region, for example, Eurasia, may be attributed to local problems such as soil moisture freezing (e.g., Viterbo et al. 1999). However, given that improvements over the Maritime Continent have been shown to lead to significant changes in remote locations, then care has to be taken when addressing systematic errors based entirely on local processes.

Although no final solution to the systematic errors in the model's tropical heating distribution has been presented here, this paper has highlighted the importance of the diurnal cycle for the climate of the Maritime Continent. Recent analysis of high-resolution satellite data by Yang and Slingo (2001) has demonstrated the coherent propagation of convection away from the islands, indicative of gravity waves. In particular, coastal regions with a large dry bias are strongly collocated with regions where these propagating diurnal signals are absent in the model. It is hypothesized that capturing or better representing the effects of these diurnally forced gravity waves and land–sea breezes may lead to improvements in the mean precipitation field in two ways. First, they may increase the wind variability near the surface leading to enhanced surface fluxes, particularly of moisture. Second, they may act as triggers for convection.

It is clear that models with coarse-grid domains are incapable of capturing the smaller-scale land–sea breeze circulations and diurnal variability, which are likely to be key processes in the regions of largest dry bias around the Maritime Continent in the UM. At the same time, experiments that have addressed island-scale convection at smaller scales (e.g., Saito et al. 2001) do not attempt to reproduce the large-scale organization in the off-coastal regions by the diurnal forcing initiated over land, essentially the interaction of convection with the larger scales. Therefore, to understand relevant processes an intermediate regional modeling approach is needed. This requires the domain of the Maritime Continent to be at fine enough resolution to capture the diurnal variability and sea breezes initiated over the island regions, but with a domain large enough to simulate the large-scale forcing and organization. Such a modeling activity is currently being undertaken with the goal of developing a parameterization of the subgrid mesoscale organization in the context of a fractional tiling of coastal grid cells, which takes into account the different surface fluxes from land and sea.

Much work is still needed to improve the subgrid-scale organization aspect of convective parameterization in course-grid-scale general circulation models, both through collaborative programs aimed at better understanding the physics of convection [e.g., European Project on Cloud Systems in Climate Models (EUROCS), online at http://www.cnrm.meteo.fr/gcss/EUROCS/EUROCS.html] and the use of novel techniques such as grid-box cloud resolving model sampling (e.g., Grabowski 2001). In the intermediate term the use of a parameterization that is able to represent the known dominant organization processes related to sea breezes is considered to be the best option for providing improvements to the simulation of the Maritime Continent climate.

Acknowledgments

The authors acknowledge many useful contributions from the CGAM Tropical Group and the much appreciated input from three anonymous reviewers. Richard Neale is supported by the Met Office through grant Met 1b/2601. J. Slingo acknowledges support through the Natural Environment Research Council (NERC)–funded UK Universities' Global Atmospheric Modeling Programme.

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Footnotes

*

Current affiliation: NOAA–CIRES Climate Diagnostics Center, Boulder, Colorado

Corresponding author address: Dr. Richard Neale, NOAA–CIRES Climate Diagnostics Center, R/CDC1, 325 Broadway, Boulder, CO 80305-3328. Email: rneale@cdc.noaa.gov