1. Introduction
The interaction between land surface vegetation and the atmosphere is a very important element in the earth’s system. In the past, numerous studies have been performed concerning the land surface process and interactions between the land surface and the atmosphere. For example, the studies of Garratt (1993), Pitman and Zhao (2000), Hahmann and Dickinson (2001), Los et al. (2001), Pal and Eltahir (2001), and Freedman et al. (2001) investigated the mechanisms of the interactions between the land surface and the atmosphere. The studies of Betts et al. (1997), Bounoua et al. (1999), and Mabuchi et al. (2000) focused on vegetation physiology and the carbon circulation associated with vegetation activity and climate.
In particular, tropical vegetation controls the physical and biogeochemical interactions in climatically influential areas over the earth and plays important roles in the formation of regional and global climates. Nobre et al. (1991), Dickinson and Kennedy (1992), Henderson-Sellers et al. (1993), Pitman et al. (1993), Polcher and Laval (1994), Sud et al. (1996), Zhang et al. (1996a,b), Hahmann and Dickinson (1997), Xue (1997), and Clark et al. (2001) studied the impact of vegetation change in the tropical region. Lean and Rowntree (1997) reviewed numerical experiments that were concerned with the deforestation of the Amazon and obtained several results from the numerical study. In addition, there were several discrepancies among the results from the numerical studies of the Amazon deforestation.
In the present study, using a global climate model that includes a realistic land surface model, several numerical simulations were performed to investigate the impact of Asian tropical vegetation change on climate. Recently, forest removal in the Asian tropical region has been become a severe problem. Jang et al. (1996) assessed the global forest change between 1986 and 1993, using satellite data converted to terrestrial net primary productivity (NPP). It was found that the forest loss was a dominant feature in tropical region, with the most severe destruction occurring in Latin America, followed by Southeast Asia and Africa. It was observed that Indonesia, Papua New Guinea, and Burma accounted for about 10% of the world regions where the NPP had decreased by more than 800 g m−1 between 1986 and 1993. These NPP decreases were due to the degradation of tropical forest. FAO (2001) reported the annual deforestation and net forest area changes in the tropical regions during the period 1990–2000 by the remote sensing survey. The report indicated that the annual deforestation in Asia was −2.5 million ha yr−1 and the annual rate of net forest area change was −0.79% year−1. This net forest area change rate in Asia is the most severe one in the tropical areas in the world.
There have been several studies on the relationship between the land surface process and the atmosphere in the Asian monsoon region. For example, Sud and Smith (1985), Yasunari et al. (1991), Meehl (1994), Claussen (1997), Yang and Lau (1998), Tzeng and Lee (2001), Douville et al. (2001), and Douville (2002) examined this relationship. There have been, however, few numerical impact studies that targeted the influence of vegetation change in the Asian tropical region. Among the studies mentioned above, Henderson-Sellers et al. (1993) and Zhang et al. (1996a,b) examined the impact of deforestation of the moist forest in Southeast Asia. The resolution of the model used in the experiment, however, was coarse, and the period of the model integration for the experiment was short, compared with the present study.
In the present study, the control simulation was conducted under conditions of the actual vegetation, and the impact of three vegetation-change simulations were examined. The results of the impact experiments were compared with those of the control. The horizontal resolution of the model used in the present study was 1.875°, being finer than those of the models used in previous vegetation impact studies. The time periods of the control and each experimental impact integration were 30 yr, and last 20 yr of data were examined. The period of integration in this study was also longer than that in previous vegetation impact studies.
Section 2 describes the model and experimental design used in the present study. Section 3 presents several results, and the discussion is given in section 4.
2. Model description and experimental design
The atmospheric model used in the experiments is the spectral general circulation model developed by the Japan Meteorological Agency (JMA). This general circulation model has a triangular truncation at wavenumber 63 (T63) and employs hybrid vertical coordinates at 21 levels. The horizontal resolution is 1.875° (192 × 96 grid points). The basic equations adopted for the model are the primitive equations. The atmospheric prognostic variables are the temperature, specific humidity, divergence, and vorticity of the wind; the carbon dioxide concentration in each atmospheric layer; and surface pressure. The time step interval of the integration is about 20 min. The model includes shortwave and longwave radiation processes (Sugi et al. 1990; Lacis and Hansen 1974). Large-scale precipitation and convective precipitation are estimated separately, with convective precipitation calculated by the Kuo scheme (Kuo 1974). Vertical diffusion is calculated by the turbulent closure model (level 2.0) proposed by Mellor and Yamada (1974).
The Biosphere–Atmosphere Interaction Model (BAIM), a land surface model (Mabuchi et al. 1997), was integrated into this general circulation model, resulting in a climate model that can simulate the effects of vegetation on climate. The vegetation type at each model grid point was specified, and the interactions between the land surface vegetation and the atmosphere were estimated by the BAIM at each grid point.
The BAIM has two vegetation layers and three soil layers and predicts the temperature and stored moisture for each layer. The model uses three groups of parameters, which are the morphological (e.g., leaf area index, canopy height, leaf angle distribution), physiological (e.g., green leaf fraction, Rubisco capacity), and physical parameters (e.g., transmittance and reflectance of the leaf, soil reflectance, the hydraulic conductivity of soil). Snow on the leaves and on the ground is also simulated. In the presence of snow cover on the ground, the snow layer is divided into a maximum of three layers, with the temperature and amounts of snow and water stored in each layer predicted. Use of the BAIM can result in estimates of not only the energy fluxes, but also the carbon dioxide flux between terrestrial ecosystems and the atmosphere. The photosynthesis processes for C3 and C4 plants are adopted in the model. The canopy resistance, closely connected with the water vapor and carbon dioxide fluxes between the ecosystem and the atmosphere, is obtained from the integration of the leaf-level stomatal resistance, calculated from a consideration of the enzyme kinetics and electron transport properties of chloroplasts and ambient environmental parameters. The model can also predict the ground accumulation and melting of snow and the freezing and melting of water in the soil. Using BAIM, the energy fluxes and carbon dioxide flux between the land surface ecosystem and the atmosphere are estimated. For further details, the reader should refer to Mabuchi et al. (1997).
Figure 1 indicates the distribution of the vegetation used in the model and the experimental areas. The vegetation type of each model grid point was fundamentally derived from the Major World Ecosystem Complexes Ranked by Carbon in Live Vegetation dataset (Olson et al. 1983). This vegetation dataset has 47 types of vegetation. Fundamentally, these types of vegetation on the ground are divided into a number of groups—that is, forest, grassland, crop, shrub, taiga, savanna, wetland, semidesert, desert, tundra, and cryosphere. The actual vegetation of a given global land surface grid was classified into 1 of 12 types, including the desert and cryosphere. The shrub and wetland were classified as grassland, with the forest and taiga in east Siberia regarded as needle-leaf deciduous-forest-type vegetation. In the present experiment, crop-type vegetation was regarded as grassland vegetation. The major parameter values (e.g., leaf area index or vegetation height, etc.) for each vegetation type were derived from Dorman and Sellers (1989). Several modifications were made to these parameter values to conform to the general circulation model. The monthly values of each parameter for each vegetation type were assigned in the simulation.
To investigate the impact of vegetation changes on climate in the Asian tropical region, three experimental areas were defined (see Fig. 1): the Indian subcontinent area (IND), the Indochina peninsula area (ICP), and the Maritime Continent area (MTC). The IND, ICP, and MTC areas are mainly covered by the grassland, tropical seasonal forest, and tropical rain forest types of vegetation, respectively. The photosynthesis process for the grass of the actual vegetation in the experimental areas was assumed as the C3 type.
Prior to the vegetation-change impact experiments, a control time integration was performed. In this control integration (CN), the actual global vegetation (see Fig. 1) and climatic SST values were used. The sea surface temperatures and sea ice values were taken from the Global Sea Ice and Sea Surface Temperature dataset (GISST2.2) dataset (Rayner et al. 1996). The monthly climatic values of these data were assigned to each model ocean-area grid point. To estimate the initial values of soil water content, including the ice content in the soil and soil temperature, a 10-yr spinup calculation was carried out. Using the soil values obtained from the spinup calculation, the control integration was continued for 20 yr.
The purpose of this study is not only to examine the impact of deforestation, but to also investigate the role of vegetation in the formation of the climate through numerical simulations. The experiments were performed under conditions that the land surface vegetation was changed morphologically, physiologically, and physically. Through these experiments, the mechanisms of the interactions between the land surface vegetation and the atmosphere can be understood. And more importantly, the influence of deforestation on the climate under various conditions can be estimated more accurately. After the control integration, three vegetation-change impact experiments were performed: the bare soil (BS), C4 grass (C4), and green-less (GR) experiments. In the BS experiment, it was assumed that the vegetation on the ground was almost removed. In this simulation, the values of soil surface reflectance were set to those of the forest-type vegetation. In the C4 experiment, while the morphological and physical parameters were set as C3 grass type, the physiological parameters associated with the photosynthesis processes for C4 plants were used (see Mabuchi et al. 1997). In the GR experiment, the types of vegetation in the experimental areas were same as those in the control, but the greenness values of the vegetation in the areas were all set to zero. Namely, it was assumed that the morphological character of vegetation was not changed, but all leaves were considered dead. The purpose of the GR experiment was to purely simulate the effect of physiological activity of vegetation on climate. In the BS and C4 impact experiments, the actual vegetation types in the experimental areas (the IND, ICP, and MTC areas) were changed to a single vegetation type for each of the impact experiments. In each impact experiment, a 10-yr spinup integration was first performed, starting from the soil conditions at the end of the control run, and then the main experimental impact time integration was continued for 20 yr under the changed vegetation conditions. The results of these three 20-yr impact time integrations were compared with the results of the 20-yr control integration. In the present study, the analysis is generally performed on the 20-yr mean of the seasonal mean values for June–July–August.
3. Results
a. Verification of the results of the model control integration
The results of the control integration are examined in this section. Figures 2 and 3 indicate the comparison of the results of the model control integration with the analysis data of the June–July–August (JJA) mean. The analysis data used in the verification is the global objective analysis data compiled by the JMA. The grid resolution of the analysis data is also 1.875°.
In Fig. 2, compared with the analysis data, the model values of sea surface pressure in the Northern Hemisphere are somewhat higher over the North Pacific Ocean and lower over the Asian continent and the North American continent. In the Southern Hemisphere, the model values are somewhat higher over Australia and the Antarctic continents, and lower over the ocean. Although the contrast of the pressure values between the model ocean and continent is clearer than those in the analysis data, the pressure distribution pattern of the model is rather consistent with that of the analysis data. In Fig. 3, examining the surface wind vectors, the differences between the model results and the analysis are relatively small.
Although figures at the 500-hPa level are not shown, the model heights at that level exhibit slightly higher values than the analysis data. The distribution patterns of the model results, however, indicate good agreement with those of the analysis data. The model wind vector patterns at 500 hPa also coincide with those of the analysis data (not shown).
Figure 4 indicates the comparison of the model precipitation with the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) data (Xie and Arkin 1997). Although the precipitation distribution pattern of the model roughly agrees with the CMAP data, the values of the model precipitation along the intertropical convergence zone (ITCZ), especially at 150°W and the surrounding area, and over the western equatorial Pacific are less than those of the CMAP data.
These differences in the atmospheric elements between the results of the model control integration and the analysis data will be considered during the examination of the results of the impact experiments.
b. Impact of vegetation change on climate
1) Changes of the surface albedo and roughness length
Table 1 lists the parameter values of transmittance and reflectance of the vegetation used in the simulation. Table 2 indicates the comparison of experimental-area mean values of the vegetation parameters in the control and each experiment. Furthermore, Table 3 indicates the comparison of experimental-area means of the calculated physical values for each simulation.
The results of the three impact experiments—BS, C4, and GR—are compared with those of the control experiment. When changing the vegetation, some physical characteristics of the land surface are altered by the physiological and morphological features of the vegetation. Among them, the surface albedo and the roughness length are clearly altered by the vegetation change.
The general feature when undergoing a change of vegetation from the actual vegetation (CN) to BS is that the surface albedo values significantly increase in the area where the actual vegetation was the forest type and significantly decrease in the area where the vegetation was grassland. In the IND area, the vegetation was mainly the grassland type. Therefore, the albedo values decrease over the entire area. In the ICP area, the albedo values increase in the areas where the vegetation was the seasonal rain forest and decrease in the areas where the vegetation was grassland. The albedo value decreases in the mean of the overall area. In the MTC area, the main vegetation was the tropical rain forest. Therefore, the albedo values increase in most places. In this study, the value of soil surface reflectance for the BS experiment was set to the value for soil in the forest (not for desert). Therefore, the albedo values decreased by the change from the grassland type to bare soil.
In the case of the vegetation change from CN to C4, while the albedo values in the area where the vegetation was grassland do not change, albedo values in the area where the vegetation was the forest type significantly increase. Therefore, the value of the area mean albedo in the IND area in the C4 experiment is almost the same as that of the CN, and those in the ICP and MTC areas are larger than that of the CN.
In the GR, the albedo values in the grassland area significantly increase, while those in the forest area significantly decrease, compared with that of the CN. Therefore, the mean value in the IND area increases, that in the MTC decreases, and that in the ICP slightly increases. In grass-type vegetation, the transmittance values of a dead leaf or stem are greater than those of a green leaf, and the reflectance values of a dead leaf or stem are also greater than those of a green leaf (see Table 1). Therefore, the grassland albedo value in the green-less experiment becomes greater than that of the control. In forest-type vegetation, although the reflectance of a dead leaf or stem for visible radiation is greater than that of a green leaf, the reflectance of a dead leaf or stem for near-infrared radiation is less than that of a green leaf. In addition, the transmittances of a dead leaf or stem are very small. Through these effects, the albedo value for forest-type vegetation in the green-less experiment decreases as a whole, compared with that in the control.
In the actual vegetation, the roughness length of the forest-type vegetation is generally larger than that of grass vegetation. In the experimental areas of this study, the roughness length of the tropical rain forest has the largest value, followed by that of the seasonal forest, with the grassland having the smallest value. Considering these effects, the roughness length of the CN in the mean of the MTC area has the largest value, followed by the ICP area mean, with that in the IND area having the smallest value. The roughness length of bare soil is very small and less than that of grassland. By the change of vegetation from the actual vegetation to bare soil (BS experiment), the values of roughness length in the MTC and ICP significantly decrease. Although the value of the change is small, the roughness length in the IND area also significantly decreases. In the C4 experiment, while the roughness length values in the area where the vegetation was grassland do not change, those in the area where the vegetation was the forest type significantly decrease. Consequently, the changes in the roughness length in the C4 experiment are similar to those in the BS experiment. In the GR experiment, the values of the morphological parameters for the vegetation do not change. Therefore, the values of roughness length do not change in each experiment area of the GR experiment.
2) Impact on the heat and water balances at the land surface
Impacts on the heat and water balances for each experimental area were examined. From the results of Student’s t test, the changes that appeared in each area were generally statistically significant.
In Table 3, when the vegetation changes to bare soil, the net radiation values in the areas where the actual vegetation was forest decrease because of the increased albedo values in these areas. On the other hand, the net radiation values in the areas where the actual vegetation was grassland increase because of the decreased values of the albedo. The latent heat fluxes in the experimental areas generally increase. The reason for this is that while the latent heat fluxes associated with transpiration and interception decrease, the latent heat flux due to direct evaporation from the soil surface increases. In the BS experiment, the value of soil water content of the overall experimental-area mean increases slightly. Therefore, the increase of the direct evaporation from the soil surface is related to the increase in the surface wind speed over the land in the experimental areas. This is one of the distinctive features found in these areas. Discussion concerning the change in the atmospheric circulation is given in the next section.
The change in the pattern of the sensible fluxes in the experimental areas is opposite to that of the latent heat fluxes. The soil surface temperatures in the experimental areas generally increase because of the increase in the radiation that reaches the soil surface due to the removal of the canopy. Although the changes of the soil water content vary according to locality, the mean values in the IND and ICP areas increase, while that in MTC decreases. The precipitation changes are discussed in the next section.
In the C4 experiment, the decreases in net radiation in the areas where the actual vegetation was forest are more significant, compared with the case of the vegetation change to bare soil. The reason for this is that the increased albedo values in the areas where the actual vegetation was forest are large, when the vegetation changes to C4 grass. On the other hand, the changes in the areas where the actual vegetation type was grassland are not significant.
The latent heat fluxes in the forest areas generally decrease. It is considered that one reason for this is the decrease in the net radiation in these areas, and another reason is the decrease in the roughness length. The latent heat fluxes in the IND area generally increase because of the increase in the transpiration from the leaves of vegetation. The main vegetation type assigned to the IND area as the actual vegetation is C3-type grass. Therefore, it is considered that by vegetation change to C4 grass, photosynthesis becomes more active in the IND area. This is due to the fact that C4 photosynthesis is more suitable than C3 photosynthesis in a hot and dry environment, such as the IND area. In actuality, the grasses that exist in the IND area include both the C3 type and C4 type. Therefore, the possibility exists that the change in the latent heat flux in the IND area simulated by this study is overestimated.
The sensible heat fluxes in the experimental areas generally decrease. The decreases in the sensible heat fluxes in the ICP and MTC areas are due to decreases in the net radiation, and that in the IND area to the increase in the latent heat flux.
The influences on the canopy temperature and on the soil surface temperature are somewhat complicated. The canopy temperatures in the ICP and MTC areas generally increase, and that in the IND area decreases. The soil surface temperatures in the ICP and IND areas generally decrease, and that in the MTC increases. In the IND, the latent heat fluxes associated with transpiration from the canopy leaves and direct evaporation from the soil surface increase. Therefore, both the canopy temperature and the soil surface temperature decrease. In the ICP area, the latent heat fluxes by the transpiration and the evaporation of intercepted water decrease; consequently the canopy temperature increases. The decrease of the soil surface temperature in the ICP area is due to the increase in the latent heat flux by the direct evaporation from the soil surface. In the MTC area, the increase in the canopy temperature results from the same causes as found in the ICP area. The net radiation for the total vegetation layer of the MTC decreases. The radiation absorbed by the soil surface, however, increases. As a result, the temperature of the soil surface increases.
The soil water content in the IND area generally decreases because of the increase in the latent heat flux. In the MTC area, the soil water content decreases because of the decrease in precipitation. In the ICP area, the precipitation and the latent heat flux both decrease. Consequently, the change in the soil water content in the ICP is small when compared with the control run.
The changes in the forest areas in the C4 experiment are fundamentally the same as those found in the results of the deforestation experiments of Franchito and Rao (1992) and Defries et al. (2002).
In the GR experiment, the net radiation values in the area where the vegetation type was grassland significantly decrease as a result of an increase in the albedo. In the forest area of the ICP, although the albedo value decreases, the net radiation value does not change because of a decrease in the downward shortwave radiation. Although no figures are shown, the decrease in the downward shortwave radiation in that area is due to the increase in low-level clouds. In the MTC, precipitation significantly increases and the downward shortwave radiation decreases. Therefore, although the value of the albedo decreases, the net radiation decreases.
The latent heat fluxes in the experimental areas generally decrease because of the decrease in the transpiration from the leaves of vegetation. In the grassland areas, the direct evaporation from the soil surface increases. Therefore, the magnitude of the decrease in the latent heat flux in the grassland is smaller than that in the forest areas. The sensible heat fluxes in the experimental areas increase, especially in the forest areas, resulting from the decrease in transpiration. The temperatures of both the canopy and soil surface in the experimental areas generally increase, especially in the forest areas, as a result of the decrease in the latent heat flux.
The values of the soil water content in the southern part of the IND, and in the MTC, increase because of the increase of precipitation and the decrease in the latent heat flux. The soil water content in the southwestern part of the ICP decreases because of the decrease in precipitation. In the northwestern part of the IND, the soil water content increases as a result of the increase in precipitation. In the other experimental areas, the change in soil water content is not clear as the result of decreases in both the precipitation and the latent heat flux. Consequently, in each experimental-area mean, the soil water values in the MTC and IND areas significantly increase, and the change of that in the ICP is small.
3) Impact on the atmospheric circulation
The changes in the land surface vegetation lead to changes in the atmospheric circulation. In this section, the impacts of the three vegetation changes on the atmospheric circulation are discussed.
Although the figure is not shown, the global-scale distribution patterns of the divergent/convergent wind by the model control calculation are as follows. In the JJA season, at the lower atmospheric level, a core convergence area exists over the southeastern part of Asia, and divergence areas are found over the eastern part of the North Pacific and over the North Atlantic. In the Southern Hemisphere, divergence areas exist along the zone of 30°S latitude. On the other hand, at the upper atmospheric level, a core divergence area exists over the southwestern part of the North Pacific, and a core convergence area is found over the low latitudes of the South Atlantic. These distribution patterns of circulation by the model generally agree with observed data (not shown).
Figures 5 –10 show the comparisons of the JJA mean atmospheric circulations simulated by the impact experiments with those of the control integration (impact–control). Figures 5 and 6 indicate the differences between BS and CN (BS–CN), Figs. 7 and 8 those between C4 and CN (C4–CN), and Figs. 9 and 10 those between GR and CN (GR–CN). Furthermore, the comparisons of the JJA mean precipitation of the impact experiments with that of the control integration (impact–control) are indicated in Figs. 11 –13.
In Fig. 5 (BS–CN), although areas of statistically significant differences are distributed globally, the strengthening of the Asian summer monsoon winds over the experimental areas is a direct effect of the vegetation change in these areas. The strengthening of the winds is due to the decrease in the roughness length by the vegetation change in the experimental areas (see Table 3). These difference patterns also exist over the Indochina peninsula and the Maritime Continent at the 850-hPa level (figures are not shown). The strengthening of the monsoon winds induces the strengthening of the convergence at the lower atmospheric level over the southern part of China, and the weakening of those over the western coast of India, the western coast of the Indochina peninsula, and over the islands of the Maritime Continent (upper panel in Fig. 6).
At the 250-hPa level (lower panel in Fig. 6), the areas where the divergence is strengthened spread from China to the Middle East and are related to the strengthening of the low-level convergence. Although the figures are not shown, ascending anomalies exist at the 500-hPa level over the low-level areas of stronger convergence. These changes in the atmospheric circulation induce changes in precipitation.
In Fig. 11, the values of precipitation significantly increase over the areas from southeastern India to the area around the Philippine Islands. On the other hand, precipitation significantly decreases on the western coast of India, the western coast of the Indochina peninsula, and over the islands of the Maritime Continent.
In Figs. 7 and 8 (C4–CN), although the strengthening of the Asian summer monsoon winds in the C4 experiment is less than that in the BS case, the anomaly patterns of the atmospheric circulation are fundamentally the same as those in Figs. 5 and 6. In this case, the strengthening of the monsoon winds occurs only at the surface level. As in the BS experiment, the values of roughness length in the experimental areas in the C4 experiment also decrease compared with those of the control run. The effect of the change in the roughness length on the wind field in the C4 experiment is less than that in the BS experiment, since the magnitude of the decrease in the roughness length in the C4 experiment is smaller than that in the BS experiment. The change in the pattern of precipitation in the C4 case is also fundamentally the same as that in the BS case (Fig. 12). The magnitudes of the decreases in the precipitation on the western coast of the Indochina peninsula and the islands of the Maritime Continent are larger than those in the BS case. The reasons for these phenomena are considered as follows. The albedo values in these areas significantly increase in the C4 case. The net radiation, the latent heat flux, and the sensible heat flux all decrease. These factors all lead to the local convective activity’s being suppressed.
In Fig. 9 (GR–CN), the pattern of change in the atmospheric circulation differs from those of the other experiments. In the GR experiment, the Asian summer monsoon winds become somewhat weaker than those in the control. The same anomaly patterns of wind exist at the 850-hPa level and also remain over the Indochina peninsula at the 700-hPa level. At the lower atmospheric level, the convergence over the southern part of India and over the islands of the Maritime Continent strengthens (upper panel in Fig. 10). At the upper atmospheric level, the divergence over these areas also strengthens (lower panel in Fig. 10).
In Fig. 13, the precipitation increases over the areas of stronger convergence at the lower atmospheric level and decreases over the surrounding areas. The precipitation anomaly pattern in the GR experiment is the opposite of those found in the BS and C4 experiments. The reason for this precipitation anomaly is considered as follows. In the GR experiment, the roughness lengths in the experimental areas do not change. Therefore, the effects due to changes in the roughness length on the wind field, such as in the BS or C4 experiments, do not occur. On the other hand, the temperatures of the canopy and the soil surface both increase and the sensible heat flux increases as a result of the decrease in the latent heat flux by the transpiration from the leaves of vegetation, especially in the forest areas. The islands of the Maritime Continent are surrounded by the ocean and have an abundant supply of water vapor. Under these conditions, the low-level convergence strengthens over the Maritime Continent islands, and convective precipitation over these areas increases.
4. Summary and discussion
Using a global climate model that includes a realistic land surface model, several numerical simulations were performed to investigate the impact of Asian tropical vegetation changes on the climate. The control simulation, under conditions of the actual vegetation, and three vegetation-change impact experiment simulations were performed, with the results of the impact experiments compared with those of the control.
In the BS experiment, the vegetation was changed from the actual vegetation to bare soil. The changes of the albedo and roughness length in the forest areas are characteristics common to all of the previous deforestation experiments. In the present study, the value of soil surface reflectance for the BS experiment was set to the value for soil in the forest (not for desert). Therefore, the albedo values decreased by the change from the grassland type to bare soil. If the bare soil is considered as a kind of desert, the albedo values would increase by this change. The general results of previous experiments can be summarized as decreases in evaporation and precipitation and an increase in the surface air temperature. The results of the present study are, however, somewhat different from the general results of the previous studies. The latent heat fluxes generally increase because of the increase in the direct evaporation from the soil surface. This is one of the distinctive features in the present simulation. The strengthening of the winds, because of the decreased roughness length by the vegetation change, induced changes in the atmospheric convective circulations, ultimately resulting in changes in convective precipitation over these areas.
In the C4 experiment, the vegetation was changed form the actual vegetation to C4 grassland. Among the three experiments of the present study, the assumption in the C4 experiment is the most similar assumption to those of the other deforestation experiments, although the C4 photosynthesis process was assumed in the present study. In particular, Henderson-Sellers et al. (1993) examined the impact of vegetation change in Southeast Asia and found that during the wet season (July), the surface temperatures significantly decreased over the Indochina peninsula and the island of Borneo, and the evaporation decreased over the islands of Borneo and New Guinea and the Indochina peninsula. Concerning precipitation, however, there was no great change in the basic pattern of rainfall, and few of the changes were statistically significant. In their experiment, the impact of vegetation change in Southeast Asia on the atmospheric circulation and moisture convergence were also small, and the changes were not identified. Zhang et al. (1996a) also discussed the seasonal variation of impacts of deforestation over Southeast Asia. It was concluded that the evapotranspiration and the net radiation indicated statistically significant decreases, but the precipitation changes were not statistically significant. In the present study, the results differ somewhat from these previous studies.
In the results of the present C4 experiment, the latent heat flux in the forest areas generally significantly decreased. If the distribution of C4 grass becomes broad because of C3 grass destruction in the tropical region, the possibility exists that the latent heat flux may increase as a result of the increase in transpiration by C4 grass. Although the strengthening of the winds of the Asian summer monsoon in the C4 experiment was not as strong as that in the BS case, the anomaly patterns of the atmospheric circulations were fundamentally the same as those in the BS case. There were a number of statistically significant changes in the distribution of precipitation. The pattern of the change in precipitation in the C4 case was fundamentally the same as that in the BS case. The magnitudes of the decreases in precipitation along the western coast of the Indochina peninsula, and over the islands of the Maritime Continent, were greater than those in the BS case. In the forest areas, the latent heat flux and precipitation decreased and the surface temperature increased, especially over the islands of the Maritime Continent.
In the present study, a GR experiment was also performed. From this experiment, the effects of the physiological elements of the vegetation could be examined. When setting the greenness values of the vegetation to zero, the albedo values in the grassland area increased, and those in the forest area decreased, compared with those of the control run. The morphological parameter values of the vegetation were not changed. Therefore, the roughness length values were not changed. The latent heat fluxes in the experimental areas generally decreased due to the decreased transpiration from the leaves of vegetation. The sensible heat flux in the experimental areas increased, especially in forest areas, because of the decreased transpiration. The temperatures of both the canopy and the soil surface in the experimental areas generally increased as a result of the decreased latent heat flux.
The pattern of the change in the atmospheric circulation differed from those of the BS and C4 experiments. In the GR experiment, the winds of the Asian summer monsoon became somewhat weaker than those in the control run. At the lower atmospheric level, the convergence areas over southern India and over the islands of the Maritime Continent were strengthened. Precipitation increased over these areas and decreased in the areas surrounding the stronger convergence. The anomaly pattern of precipitation in the GR experiment was the opposite of those in the BS and the C4 experiments.
Sud and Smith (1985) examined the influence of local land surface processes on the Indian monsoon. One of the numerical experiments included the case of no evapotranspiration from the land surface. In the results, there was very little change in the rainfall because of the enhanced moisture convergence, produced as a consequence of the increased sensible heating over land largely compensating for the lack of evapotranspiration. For the month of July, the moisture supply for precipitation over India was advected from the nearby Indian Ocean. Without evapotranspiration, the increased PBL heating by the sensible heat flux promotes this process by producing a thermal low. Polcher (1995) studied the relation between land surface process changes and variations in the frequency of convective events and indicated that the highest sensitivity was found for the sensible heat flux, and its increase leads to deeper convective events. Although the design and the results of the present GR experiment somewhat differ from those of previous studies, the mechanism of the precipitation increase over the islands of the Maritime Continent in the present GR experiment was consistent with those of Sud and Smith (1985) and Polcher (1995).
In Fig. 4, the values of model precipitation along the ITCZ, especially at 150°W and the surrounding area, and over the western equatorial Pacific were somewhat less than those of the CMAP data. It is difficult to exactly estimate the effect of these precipitation differences between the model and the CMAP data on the results of the impact experiments. The model could, however, reproduce the potential pattern of precipitation in the tropical area, and it was also possible that the model could reproduce the mechanism for the precipitation in that area. Therefore, it is considered that the results of the impact experiments indicate the mechanisms of the potential effect of vegetation changes on the atmospheric circulations.
The parameter values used in the present study were common values that were usually used in the general circulation model. The soil conditions in the vegetation change were determined by a vegetation parameter. The results of the model control integration had a number of discrepancies compared with the analysis data. Although the limitations described above exist in the experiment, the model results can be explained by consistent physical mechanisms, and a qualitative analysis of the impact of vegetation changes on the climate can be performed.
The morphological, physiological, and physical changes of the land surface vegetation in the Asian tropical region certainly induced significant climatic changes in these and surrounding areas. In summary, from the results of the BS and C4 experiments, the decreased roughness lengths, and from the GR experiment, the decreased latent heat flux, exerted strong influences on the horizontal and convective circulations of the atmosphere. Consequently, the distribution of precipitation changed. Other energy and water balances at the land surface were also influenced by the changes in vegetation, and the induced changes are generally statistically significant.
It was considered that the influences of vegetation changes in the Asian tropical region were more complicated than those in the Amazon. The effects of the vegetation changes in the Asian tropical region had spatially different features. One reason for this was that the Asian tropical region is strongly influenced by the Asian monsoon circulation. Another reason was that the land–sea distribution and the distribution of vegetation in the Asian tropical region are not as simple when compared with a tropical rain forest, as in the Amazon.
The Asian monsoon wind patterns clearly change with the season. In the present paper, the analysis of the June–July–August case was discussed as Part I. In Part II (Mabuchi et al. 2005) the analysis deals with the December–January–February mean.
Acknowledgments
The authors wish to express their thanks to Prof. Takehisa Oikawa of the University of Tsukuba, Prof. Tatsuo Sweda of Ehime University, Dr. Susumu Yamamoto, and Dr. Nobuko Saigusa of the National Institute of Advanced Industrial Science and Technology for many helpful suggestions and discussion. Special thanks are extended to Prof. Tetsuzo Yasunari of Nagoya University and Prof. Masahiro Amano of Waseda University for helpful suggestions. Two anonymous reviewers provided helpful comments for improving the quality of this paper. This research was partially supported by the Funds for the Promotion of Surveys and Research in Earth Science and Technologies and Ocean Development of the Science and Technology Agency and was also supported by the Grants-in-Aid for Scientific Research 14208062 of the Ministry of Education, Culture, Sports, Science and Technology. The model computations were performed on the HITACH S3800 and SR8000 computers.
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Distribution of the original vegetation used in the model and the experimental regions. The vegetation is indicated by the color legend below the figure. The experimental regions are defined by the boxed areas, and are the Indian subcontinent area (IND), the Indochina peninsula area (ICP), and the Maritime Continent area (MTC).
Citation: Journal of Climate 18, 3; 10.1175/JCLI-3273.1
Comparison of the JJA mean sea surface pressure (hPa) calculated by the model control integration with that of the analysis data. (top) The analysis data and (bottom) the model results. Values greater than 1015 hPa are shaded.
Citation: Journal of Climate 18, 3; 10.1175/JCLI-3273.1
The same as in Fig. 2, except for the surface wind vectors.
Citation: Journal of Climate 18, 3; 10.1175/JCLI-3273.1
Comparison of the JJA mean precipitation (mm day−1) calculated by the model control integration with that of the CMAP data. (top) The CMAP data and (bottom) the model results. Values greater than 6 mm day−1 are shaded.
Citation: Journal of Climate 18, 3; 10.1175/JCLI-3273.1
Comparison of the JJA mean results of the impact experiments with those of the control integration (impact–control). The differences in the surface wind vectors between BS and CN are indicated. The areas where the Student’s t test values indicate statistically significant differences (at the 95% level) are shaded.
Citation: Journal of Climate 18, 3; 10.1175/JCLI-3273.1
The same as in Fig. 5, except for the velocity potential (10 6 m 2 s –1) and the divergence/convergence of wind vectors. The results at the (top) surface and (bottom) 250-hPa level.
Citation: Journal of Climate 18, 3; 10.1175/JCLI-3273.1
The same as in Fig. 5, except for the differences between C4 and CN.
Citation: Journal of Climate 18, 3; 10.1175/JCLI-3273.1
The same as in Fig. 6, except for the differences between C4 and CN.
Citation: Journal of Climate 18, 3; 10.1175/JCLI-3273.1
The same as in Fig. 5, except for the differences between GR and CN.
Citation: Journal of Climate 18, 3; 10.1175/JCLI-3273.1
The same as in Fig. 6, except for the differences between GR and CN.
Citation: Journal of Climate 18, 3; 10.1175/JCLI-3273.1
Comparison of the JJA mean values of precipitation of the impact experiments with those of the control integration (impact–control). The differences between BS and CN are indicated. The results for the experimental areas and the surrounding areas are indicated. The colors toward red indicate relatively large values. The areas where the Student’s t test values indicate statistically significant differences (at the 95% level) are hatched.
Citation: Journal of Climate 18, 3; 10.1175/JCLI-3273.1
The same as in Fig. 11, except for the differences between C4 and CN.
Citation: Journal of Climate 18, 3; 10.1175/JCLI-3273.1
The same as in Fig. 11, except for the differences between GR and CN.
Citation: Journal of Climate 18, 3; 10.1175/JCLI-3273.1
The values of the vegetation parameters used in the simulations. Here the parameter values are for grassland (Grass), tropical rain forest (Rain-f.), broadleaf deciduous forest (Seas.-f.), and bare soil (B-soil). The values are also for transmittance of green leaf (G-Tran), transmittance of dead leaf or stem (D-Tran), reflectance of green leaf (G-Ref), reflectance of dead leaf or stem (D-Ref), soil surface reflectance (S-Ref), visible radiation (VIS), and near-infrared radiation (NIR).
Comparison of the values of the vegetation parameters in the experimental area. Values are JJA means and are listed for the IND, ICP, and MTC area means. Numbers in parentheses are gridpoint numbers located in each experimental area. The values are also for leaf area index (LAI), green leaf fraction (Grn), and vegetation height (V-H) (m). The experiments are the CN, BS, C4, and GR. The differences of the vegetation parameter values from the CN are also indicated (Diff).
Comparisons of the calculated physical values at the land surface in the experimental areas. Values are JJA means and are listed for the IND, ICP, and MTC area means. Numbers in parentheses are gridpoint numbers located in each experimental area. The labels are also for the surface albedo (ALB), the roughness length (Z0) (m), the net radiation (RNET) (MJ m−2 day−1), the latent heat flux (E) (MJ m−2 day−1), the sensible heat flux (H) (MJ m−2 day−1), the canopy temperature (TC) (°C), the soil surface temperature (TG) (°C), the soil water content (WA) (cm), and the precipitation (P) (mm day−1). The experiments are the CN, BS, C4, and GR. The difference values from the CN are also indicated (Diff).
