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  • View in gallery

    Simulation domain for the region Southeast Asia. The terrain elevation color scale ranges from 0 m for the darkest green color to >3000 m for the darkest brown color; the color increments are 0–100, 100–200, 200–500, 500–1000, 1000–1500, 1500–2000, and 2000–3000 m.

  • View in gallery

    Vegetation coverage of the simulation domain as specified by GLC2000. Cells with maximum plant coverage > 80% are converted to grassland. The color legend starts at light gray and ends at dark green with values ranging from 10% to 100% in increments of 10%.

  • View in gallery

    Time series of spatially averaged and annually averaged standardized variables of PC1 of the principal component analysis from 1985 to 2004 representing the major mode of variability of the changes due to deforestation. El Niño episodes are marked with red ovals, and La Niña events are highlighted with blue ovals. Loadings of the variables to PC1 are shown as a bar plot on the right. Highest loadings with absolute value > 0.25 are highlighted (pink shaded variables).

  • View in gallery

    Simulated difference in maximum temperature (1990–2004) between potential and actual land covers for (a) NDJFM long-term average without El Niño and (b) NDJFM composite of the four strongest El Niños (1991/92, 1994/95, 1997/98, and 2002/03). Hatched areas are regions where changes are statistically significant at the 0.05 level. (c) Long-term NDJFM difference of maximum temperature between El Niño and non–El Niño years of the actual land cover simulation. The color legend for (a),(b) starts at dark blue and ends at dark red with values ranging from −2° to 2°C in increments of 0.4°C. The color legend for (c) starts at dark blue and ends at dark red with values in ranging from −0.4° to 0.4°C in increments of 0.1°C.

  • View in gallery

    As in Fig. 4, but for maximum precipitation. The color legend for (a),(b) starts at dark red and ends at dark blue with values ranging from −25 to 25 mm in intervals of 5 mm. Grids with grey bars are regions where changes are statistically significant at the 0.05 level. The color legend for (c) starts at dark red and ends at dark blue with values ranging from −20 to 20 mm in intervals of 4 mm.

  • View in gallery

    As in Fig. 4, but for the four strongest La Niñas (1995/96, 1998/99, 1999/00, and 2000/01). The color legend for (a),(b) starts at dark blue and ends at dark red with values from −2° to 2°C in intervals of 0.4°C. The color legend for (c) starts at dark blue and ends at dark red with values from −0.6° to 0.6°C in intervals of 0.2°C.

  • View in gallery

    As in Fig. 6, but for maximum precipitation. The color legend for (a),(b) starts at dark red and ends at dark blue with values from −20 to 20 mm in intervals of 4 mm. Grids with grey bars are regions where changes are statistically significant at the 0.05 level. The color legend for (c) starts at dark red and ends at dark blue with values from −20 to 20 mm in intervals of 4 mm.

  • View in gallery

    (top) Area-averaged composite of simulated variables—(left to right) maximum temperature and latent and sensible heat flux—representing the changes due to land conversion together with (bottom) their standard deviations from 1988 to 2004. The first two years (1988 and 1989) with no change are also shown for comparison.

  • View in gallery

    Simulated maximum daily temperature difference (1990–2004) between potential and actual land covers for JJA. Hatched areas are regions where changes are statistically significant at the 0.05 level.

  • View in gallery

    As in Fig. A2, but for maximum precipitation.

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Impact of Abrupt Land Cover Changes by Tropical Deforestation on Southeast Asian Climate and Agriculture

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  • 1 Department of Geography, Climatology, Climate Dynamics and Climate Change, Justus-Liebig University Giessen, Giessen, Germany
  • | 2 Institute for Advanced Study in the Humanities (KWI), Essen, Germany
  • | 3 Institute of Meteorology and Climate Research–Troposphere Research, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany
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Abstract

Southeast Asia (SE Asia) undergoes major and rapid land cover changes as a result of agricultural expansion. Landscape conversion results in alterations to surface fluxes of moisture, heat, and momentum and sequentially impact the boundary layer structure, cloud-cover regime, and all other aspects of local and regional weather and climate occurring also in regimes remote from the original landscape disturbance. The extent and magnitude of the anthropogenic modification effect is still uncertain. This study investigates the biogeophysical effects of large-scale deforestation on monsoon regions using an idealized deforestation simulation. The simulations are performed using the regional climate model COSMO-CLM forced with ERA-Interim data during the period 1984–2004. In the deforestation experiment, grasses in SE Asia, between 20°S and 20°N, replace areas covered by trees. Using principal component analysis, it is found that abrupt conversion from forest to grassland cover leads to major climate variability in the year of disturbance, which is 1990, over SE Asia. The persistent land modification leads to a decline in evapotranspiration and precipitation and a significant warming due to reduced latent heat flux during 1990–2004. The strongest effects are seen in the lowlands of SE Asia. Daily precipitation extremes increase during the monsoon period and ENSO, differing from the result of mean precipitation changes. Maximum temperature also increases by 2°C. The impacts of land cover change are more intense than the effects of El Niño and La Niña. In addition, results show that these land clearings can amplify the impact of the natural mode ENSO, which has a strong impact on climate conditions in SE Asia. This will likely have consequences for the agricultural output.

Additional affiliation: Justus-Liebig University Giessen, Giessen, Germany.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author e-mail: Merja H. Tölle, merja.toelle@geogr.uni-giessen.de

Abstract

Southeast Asia (SE Asia) undergoes major and rapid land cover changes as a result of agricultural expansion. Landscape conversion results in alterations to surface fluxes of moisture, heat, and momentum and sequentially impact the boundary layer structure, cloud-cover regime, and all other aspects of local and regional weather and climate occurring also in regimes remote from the original landscape disturbance. The extent and magnitude of the anthropogenic modification effect is still uncertain. This study investigates the biogeophysical effects of large-scale deforestation on monsoon regions using an idealized deforestation simulation. The simulations are performed using the regional climate model COSMO-CLM forced with ERA-Interim data during the period 1984–2004. In the deforestation experiment, grasses in SE Asia, between 20°S and 20°N, replace areas covered by trees. Using principal component analysis, it is found that abrupt conversion from forest to grassland cover leads to major climate variability in the year of disturbance, which is 1990, over SE Asia. The persistent land modification leads to a decline in evapotranspiration and precipitation and a significant warming due to reduced latent heat flux during 1990–2004. The strongest effects are seen in the lowlands of SE Asia. Daily precipitation extremes increase during the monsoon period and ENSO, differing from the result of mean precipitation changes. Maximum temperature also increases by 2°C. The impacts of land cover change are more intense than the effects of El Niño and La Niña. In addition, results show that these land clearings can amplify the impact of the natural mode ENSO, which has a strong impact on climate conditions in SE Asia. This will likely have consequences for the agricultural output.

Additional affiliation: Justus-Liebig University Giessen, Giessen, Germany.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author e-mail: Merja H. Tölle, merja.toelle@geogr.uni-giessen.de

1. Introduction

The region of Southeast Asia (SE Asia) has undergone major and rapid land cover changes as a result of agricultural expansion (Lepers et al. 2005). Most remarkable are increases in oil palm plantations and grasslands (IPCC 2014), often associated with large-scale deforestation, forest fires, and surface drainage of the land. Margono et al. (2012) reported a total forest loss of 11 × 106 ha over the Indonesian island of Sumatra between 1990 and 2010. Similar trends will likely continue in such tropical regions (Fitzherbert et al. 2008) with major consequences for ecosystems, air quality, CO2 emissions (Dislich et al. 2017), and society. The importance of vegetation cover and its change on the climate system is an indisputable fact (Oki et al. 2013).

Expanding cultivation areas through deforestation offers the possibility of growing the agricultural output in the short term. However, from a long-term perspective those strategies make the local population even more vulnerable (IPCC 2012), owing to weather extremes as described in other parts of this paper. The term “vulnerability” is used frequently in science and politics. Therefore, it is important to define it accurately. We follow the definition of the Intergovernmental Panel on Climate Change (IPCC 2012) !because it connects natural and social sciences. IPCC (2012) defines vulnerability “as the propensity or predisposition to be adversely affected. Such predisposition constitutes an internal characteristic of the affected element. In the field of disaster risk [and in agriculture], this includes the characteristics of a person or group and their situation that influences their capacity to anticipate, cope with, resist, and recover from the adverse effects of physical events.” Further, “vulnerability is a result of diverse historical, social, economic, political, cultural, institutional, natural resource, and environmental conditions and processes” (IPCC 2012). Looking at the concept of vulnerability from an agricultural perspective it is important to consider specific indicators for affected people. The famine vulnerability analysis model (FVAM) offers such indicators for analyzing food insecurities and famines [for further information on the indicators, see Engler et al. (2013) or Engler and Werner (2015)]. With a certain adjustment some of those indicators1 are useful for agricultural studies in general.

SE Asia has a well-developed monsoon climate system because of the strong land–ocean thermal contrast and the thermodynamical effects of the Tibetan Plateau. The human populations in SE Asia depend on monsoon rainfall. In addition, they suffer from the drought and flood events related to the high variability of the monsoon climate, which is amplified by the El Niño–Southern Oscillation (ENSO) phenomenon (Kovats et al. 2003).

Several observational and modeling studies have studied the effect of land cover changes over China and Australia. Clearing of native vegetation due to agricultural activity increases is included in these studies. The impact of land modification is manifested through changes in albedo and evaporation and transpiration processes as well as partitioning of sensible, latent, and ground heat fluxes (Pielke et al. 2002). Transformation of the landscape is paralleled with a decline in mean annual rainfall (Timbal and Arblaster 2006) and a summer warming and drying (McAlpine et al. 2007).

The emerging links between land cover change and its dynamic role on Earth–atmosphere interactions and impacts on natural climate variability provide evidence to consider land cover change forcing on the natural mode of ENSO over SE Asia. Prior numerical experiments of anthropogenic land cover change focused largely on Australia (Narisma and Pitman 2003; Lawrence 2004; Pitman et al. 2004; Wardle and Smith 2004; Görgen et al. 2006; Timbal and Arblaster 2006; McAlpine et al. 2007; Deo et al. 2009), and a few studies have examined the impact of land modifications on the monsoon system over China (Fu 2003; Zhang et al. 2011; Pitman et al. 2012). Modeling studies show that tropical deforestation may lead to substantial local effects, including a reduction of about 20% in precipitation (IPCC 2014). Existing evidence about land cover change in these regions mainly stems from coarse-scale-resolution models, which can limit the effect of land-use–cover change (Gao et al. 2006; Tölle et al. 2014; Zhang et al. 2014). The potential effect of rapid deforestation superimposed on ENSO phases including changes in climate extremes over SE Asia has not been studied so far.

Thus for the first time numerical experiments considering anthropogenic land cover change over SE Asia are performed, and the following questions are addressed in this paper. What is the overall impact of a rapid and drastic (abrupt) decrease of a forested landscape converted to grassland, and what is the effect of this natural ecosystem disturbance on climate extremes during ENSO episodes?

This paper addresses the above questions by presenting numerical experiments over SE Asia for actual vegetation cover and potential maximum loss of vegetation cover using the nonhydrostatic regional climate model COSMO Model with climate limited-area modeling [COSMO-CLM (CCLM); Rockel et al. 2008] for the period 1984–2004. This is achieved by calculating changes of leaf area index, vegetation fraction, root depth, and surface roughness from actual to potential land covers and determining regional changes in surface energy, hydrological fluxes, and associated daily indices of extremes (maximum temperature and maximum precipitation) during major ENSO phases since 1990.

2. Methods

The land cover change experiment is set up over the region of SE Asia being influenced by oceanic tropical climate. The simulation domain is shown in Fig. 1. The tropical region is highly populated and characterized by large land–sea contrast and steep orography stimulating the monsoon circulation (Krishnamurti and Surgi 1987; Wang 2006). Indochina is often considered as part of the Asian summer monsoon region while rainfall over most locations in the Maritime Continent tends to reach a maximum during the Northern Hemispheric winter. This wet season is often related to the Australian summer monsoon (e.g., McBride 1987) because of the proximity of the two regions (Chang et al. 2005). Dry season rainfall anomalies are spatially coherent, strongly correlated with SST, and coupled to ENSO variations in the Pacific basin, whereas wet season rainfall tends to be uncorrelated with SST and spatially incoherent (Hendon 2003).

Fig. 1.
Fig. 1.

Simulation domain for the region Southeast Asia. The terrain elevation color scale ranges from 0 m for the darkest green color to >3000 m for the darkest brown color; the color increments are 0–100, 100–200, 200–500, 500–1000, 1000–1500, 1500–2000, and 2000–3000 m.

Citation: Journal of Climate 30, 7; 10.1175/JCLI-D-16-0131.1

The simulation with no land conversion represents the control simulation to which the land cover modification simulation is compared. The control simulation exemplifies present vegetation cover based on the global land cover map for the year 2000 database (GLC2000) provided by the European Commission Joint Research Centre (Bartholome et al. 2002). This information is based on remotely sensed data providing the maps of vegetation cover on a spatial resolution of 1 km. The actual vegetation for the period 1984–2004 differs from the GLC2000 dataset used for the control simulation. However, these differences are smaller than the differences in the potential land modifications. Therefore, we assume that these vegetation cover data can be considered as the actual vegetation cover during that time. In the simulation with modified land cover, grids of the simulation domain with maximum plant coverage greater than 80% forest are converted to grassland to illustrate interactions between land surface characteristics and climate at regional scales (Fig. 2). Here, forest is converted to grassland as a potential maximum land modification, which can be assumed as an upper bound of land cover changes. For abrupt land conversion a drastic decrease in forest cover in one year is assumed. The year 1990 is chosen for this vegetation disturbance. The modified land surface parameters are kept constant after the conversion throughout the simulation period to maximize model response. The most pronounced changes occur over Indochina, the islands of Indonesia, and the Philippines, where the forest has been replaced by grassland.

Fig. 2.
Fig. 2.

Vegetation coverage of the simulation domain as specified by GLC2000. Cells with maximum plant coverage > 80% are converted to grassland. The color legend starts at light gray and ends at dark green with values ranging from 10% to 100% in increments of 10%.

Citation: Journal of Climate 30, 7; 10.1175/JCLI-D-16-0131.1

A pair of numerical experiments is performed under the above two land cover conditions by using the evaluated nonhydrostatic regional climate model COSMO-CLM in its version COSMO4.8-CLM17 (Rockel et al. 2008). The model is based on the primitive thermo–hydrodynamic equations, which are formulated on a rotated horizontal grid (Arakawa and Lamb 1981) and a terrain following height coordinate. The land surface scheme includes a one-dimensional soil–canopy model as described in the work of Doms et al. (2007). Flux calculations for heat, moisture, and momentum are parameterized using Monin–Obukhov similarity theory. Each grid box is characterized by vegetation parameters such as leaf area index (LAI), plant coverage (PC), root depth (RD), and roughness length (z0), which are the key parameters influenced by deforestation. Thus, the reduction in plant biomass is achieved by a reduced roughness length, root depth, LAI, and vegetation coverage.

The control simulation is performed for the period 1984 until 2004 at a horizontal resolution of about 14 km. The year 1984 is excluded from the analysis owing to the spinup of soil moisture. The simulation with the changed vegetation is initiated from 1990 to 2004 using the control experiment spinup as initial conditions. The CORDEX Africa configuration (Panitz et al. 2014; Dosio et al. 2015) is adapted for the simulations over 450 × 240 grid points covering CORDEX SE Asia (see Table A1). ERA-Interim data (Dee et al. 2011) are used as initial and lateral boundary conditions to drive the model simulations.

Since lateral boundary conditions are not varied between the two simulations, changes can be attributed to land cover changes. Difference fields of potential minus actual vegetation datasets are used to represent the impact of changing vegetation, which is the focus of this study. Principal component analysis (PCA; Schlüter et al. 2008; Kutzbach 1967) is applied to quantify the changes due to land modifications. A multivariate dataset comprising a diverse set of variables connected with the hydrological and energy cycle is chosen for the PCA. The annual mean data are spatially averaged over all grid points and standardized to maximize variance. Prior to this analysis, the time series are inspected for changes (see the three simulated variables in Fig. A1 in the appendix).

To analyze the influence of land cover change in SE Asia during different phases of ENSO, differences in patterns of maximum daily temperature and maximum daily precipitation amount for the winter monsoon months November to March (NDJFM) are compared during the four strongest El Niño and La Niña episodes since 1990. The results of the differences during Northern Hemispheric summer months can be assessed from the appendix. Permutation tests (von Storch and Zwiers 1999) are applied to the results to determine the statistical significance. Here, the maximum values of daily maximum temperature and maximum 1-day precipitation are used as climate extremes (Heim 2015; Hartmann et al. 2013).

It is not the aim of our study to compare the modeled results of COSMO-CLM with observations. For that, we refer to the work of Wang et al. (2013), who described and evaluated the simulations of the East Asian monsoon region. They showed that the RCM COSMO-CLM is able to predict monsoon features even on small scales and bears resemblance to climatological means. In addition, other RCMs were employed over similar regions and proved the ability to model climate variability (Walsh and McGregor 1997; Park et al. 2016). Fu et al. (2015) have evaluated several aspects of RCMs connected with the Asian monsoon. Dobler and Ahrens (2008) received accurate results for Indian summer monsoon studies.

3. Results and discussion

a. Impact of abrupt land cover change

Principal component analysis is performed over a multivariate dataset comprising a diverse set of variables connected with the hydrological and energy cycle (see the variables and their loadings in Fig. 3). Prior to this analysis the annual mean time series of the variables are spatially averaged over all grid points of the domain. The focus of the present study is the major mode of variability of the changes due to land modifications. Therefore, the first principal component (PC1) of standardized changes between forest and grassland of the time series from 1985 to 2004 along with the loadings of the variables is shown in Fig. 3. PC1 illustrates the major mode of variability of the changes due to abrupt deforestation. The loadings reveal which variables account for most of the changes shown in PC1. To demonstrate the abrupt shift in land cover characteristics the time series of PC1 starts at 1985, representing no change until 1989. After that changes result from land modifications until 2004.

Fig. 3.
Fig. 3.

Time series of spatially averaged and annually averaged standardized variables of PC1 of the principal component analysis from 1985 to 2004 representing the major mode of variability of the changes due to deforestation. El Niño episodes are marked with red ovals, and La Niña events are highlighted with blue ovals. Loadings of the variables to PC1 are shown as a bar plot on the right. Highest loadings with absolute value > 0.25 are highlighted (pink shaded variables).

Citation: Journal of Climate 30, 7; 10.1175/JCLI-D-16-0131.1

PC1 accounts for 75% of the variability seen in the dataset. PC2 (not shown) accounts for 13% of the main variation and PC3 (not shown) for 5%. The first five PCs model 99% of the total impact. The changed surface properties immediately affect the environment with greatest magnitude (PC1 < −4) in the year of the disturbance (1990). With that the system is switched to a different climate state, which is seen in the persistently reversed sign of PC1 from 1990 to 2004. The variability of El Niño episodes reaches a peak (marked with red ovals in Fig. 3), and for La Niña events (marked with blue ovals in Fig. 3) the values of PC1 are smaller.

The highest loadings for PC1 (>0.25 in absolute value) of the variables are highlighted. The variables associated with the surface energy and water balance (temperatures, net longwave radiation, latent and sensible heat flux, evaporation, and precipitation) show the strongest contribution to the pattern seen in PC1 (see the loadings in Fig. 3 and Table 1). The product of the prefix of the loading and the PC1 reveals the direction of the change. A reduction in total precipitation (positive loading sign and negative PC1 sign) and increases in temperature (negative loading sign and negative PC1 sign) are associated with an increase in planetary boundary layer height and sensible heat fluxes and a decrease in latent heat fluxes and net longwave radiation. Wardle and Smith (2004) found a modeled temperature increase due to decreased albedo, which can be confirmed for our study. Reduced vegetation fraction and a marked reduction in the leaf area index contribute to a decrease in latent heat flux, while sensible heat flux increases. The direct changes in surface roughness due to the reduction of forest vegetation increases the strength of surface winds by reducing the aerodynamic drag (Lawrence 2004), while changes in leaf area index, root depth, and plant coverage modify evaporation, latent and sensible heat fluxes, and planetary boundary layer properties (Sellers 1992). Thus, the conversion of forested landscape to grassland results in a warmer climate seen in the increase of mean annual, minimum, and maximum temperatures. In contrast to Zhang et al. (2014), who reported little change in minimum temperature due to land clearing, this analysis demonstrates remarkable increases in this variable. Zhang et al. (2014)’s estimates are from small-scale land clearings (hectares), and our study assumes a large-scale land conversion, which might explain the differences. As temperature increases so does the process of evaporation. As the vegetation fraction is decreased, evaporation from the soil is increased. More evaporation does not necessarily lead to more precipitation owing to boundary layer processes.

Table 1.

Relative contribution as a percentage (%) of each variable to the first principal component.

Table 1.

b. Impact of land cover change on ENSO episodes

Daily maximun temperature and maximum precipitation amount are selected for further analysis since these variables showed one of the strongest weights to the PC1 and are important factors for ecosystem functioning.

We compared maximum temperature patterns during four El Niño phases from 1990 to 2004 for the winter monsoon months November to March (NDJFM) to analyze the influence of land cover change on extreme warming events in SE Asia during episodes of ENSO. The dominant mesoscale features of the SE Asian summer monsoon may be less pronounced during the winter monsoon period (Krishnamurti and Surgi 1987; Wang 2006). The SE Asian rainfall is uncorrelated with SSTs during the wet season because of air–sea feedbacks (Haylock and McBride 2001; Hendon 2003). Hence, the influence of land use changes may become more apparent during the winter monsoon season. We refer to the appendix for the analysis of the summer monsoon period.

Figures 4a,b compare the differences in maximum temperature between deforested land and actual land covers (experiment with land cover change minus control with actual land cover) using the long-term NDJFM average excluding El Niño years and a composite of four El Niño events. Figure 4c additionally shows the long-term NDJFM difference between the El Niño and non–El Niño years of the control simulation with actual land cover to present the effects of ENSO. Results of the long-term difference of maximum temperature during NDJFM in Fig. 4a from 1990 to 2004 indicate a consistent warming of up to 2°C over land areas with the largest statistically significant warming: the Philippines, south of Vietnam and Thailand, Malaysia, and Cambodia, as well as over the Indonesian islands of Sumatra and Borneo, which are all regions of major land cover change in the simulation. The strongest effects are seen in the lowlands of these countries. This warming is additionally enhanced during El Niño years (see Fig. 4b), showing more grid points of warming and increased values. Without land cover changes El Niño years show alternating patterns of warming and cooling over the simulation domain with less magnitude in warming (maximum increase in Tmax of 0.4°C; see Fig. 4c).

Fig. 4.
Fig. 4.

Simulated difference in maximum temperature (1990–2004) between potential and actual land covers for (a) NDJFM long-term average without El Niño and (b) NDJFM composite of the four strongest El Niños (1991/92, 1994/95, 1997/98, and 2002/03). Hatched areas are regions where changes are statistically significant at the 0.05 level. (c) Long-term NDJFM difference of maximum temperature between El Niño and non–El Niño years of the actual land cover simulation. The color legend for (a),(b) starts at dark blue and ends at dark red with values ranging from −2° to 2°C in increments of 0.4°C. The color legend for (c) starts at dark blue and ends at dark red with values in ranging from −0.4° to 0.4°C in increments of 0.1°C.

Citation: Journal of Climate 30, 7; 10.1175/JCLI-D-16-0131.1

Comparisons of modeled results of deforested and actual land cover characteristics show alternating maximum precipitation patterns with statistically significant increases of extreme precipitation in a band stretching from eastern Sumatra to Borneo (Fig. 5a). These increases of maximum precipitation are greater in magnitude during El Niño episodes over the same area (Fig. 5b). However, compared to temperature, the area with significant changes is small. Meanwhile, El Niño years without land cover change present an overall decrease in maximum precipitation with a different precipitation pattern (Fig. 5c), reflecting the influence of ENSO on this region (Chang et al. 2004).

Fig. 5.
Fig. 5.

As in Fig. 4, but for maximum precipitation. The color legend for (a),(b) starts at dark red and ends at dark blue with values ranging from −25 to 25 mm in intervals of 5 mm. Grids with grey bars are regions where changes are statistically significant at the 0.05 level. The color legend for (c) starts at dark red and ends at dark blue with values ranging from −20 to 20 mm in intervals of 4 mm.

Citation: Journal of Climate 30, 7; 10.1175/JCLI-D-16-0131.1

The influence of land cover change during La Niña phases is represented in Fig. 6 for maximum daily temperature and in Fig. 7 for maximum daily precipitation. The differences in maximum temperature between deforested and actual land covers without La Niña episodes are shown in Fig. 6a and during four major La Niña events in Fig. 6b. Maximum temperature is significantly increased owing to land clearings and is enhanced during the cold phases of ENSO. The warming due to land cover change is much stronger (up to 2.4°C) than the cooling effect (down to −0.6°C) during La Niña years, as seen in Figs. 6c and 6b.

Fig. 6.
Fig. 6.

As in Fig. 4, but for the four strongest La Niñas (1995/96, 1998/99, 1999/00, and 2000/01). The color legend for (a),(b) starts at dark blue and ends at dark red with values from −2° to 2°C in intervals of 0.4°C. The color legend for (c) starts at dark blue and ends at dark red with values from −0.6° to 0.6°C in intervals of 0.2°C.

Citation: Journal of Climate 30, 7; 10.1175/JCLI-D-16-0131.1

Fig. 7.
Fig. 7.

As in Fig. 6, but for maximum precipitation. The color legend for (a),(b) starts at dark red and ends at dark blue with values from −20 to 20 mm in intervals of 4 mm. Grids with grey bars are regions where changes are statistically significant at the 0.05 level. The color legend for (c) starts at dark red and ends at dark blue with values from −20 to 20 mm in intervals of 4 mm.

Citation: Journal of Climate 30, 7; 10.1175/JCLI-D-16-0131.1

Figure 7c clearly demonstrates the influence of La Niña, showing wetter conditions over SE Asia. Maximum precipitation changes are similar in magnitude (up to 16 mm) for land cover change and La Niña conditions (see Fig. 7). However, significant positive and negative changes in maximum precipitation are fragmented over the entire simulation domain for the land cover change scenarios (Figs. 7a,b). Here, a significant increase in maximum precipitation is apparent in the coastal region of Sumatra and over Borneo during La Niña and non–La Niña years, whereas decreases in maximum precipitation are significant over the Philippines, northwest of Sumatra, and Vietnam. Therefore, land cover change may enhance or even decrease wetter conditions during La Niña episodes.

c. Impact of weather extremes on agriculture

As described in this paper, weather extremes—especially temperature extremes—increase over deforested areas. Also, heavy rains (as shown in Fig. 5) and extremes in winds (see Fig. 3) lead to greater damage in these disturbed areas. Because of high-speed winds, which can irreversibly damage a harvest, the cropping industry experiences greater monetary losses than the livestock, forestry, and fishing industries (Sivakumar et al. 2005).

All of these aspects lead to a higher socioeconomic vulnerability of the affected farmers. Specific indicators can underline this socioeconomic vulnerability, which Engler (2012a,b) and Engler et al. (2013) already developed for the case of vulnerability to undernutrition (see Table 2). Several of these indicators are directly useable or convertible to the case study of SE Asia. Further, it is not meaningful in this case to look at individual indicators but to look at developing chains in the case of heavy rains or wind extremes over deforested areas. We will portray a brief example.

Table 2.

Prefamine vulnerability indicators (Engler et al. 2013) for (a) social and (b) environmental vulnerability.

Table 2.

First, productivity becomes much more variable than before as a result of the diverse weather conditions (Rosenzweig et al. 2000). Second, crop damages will increase because of the extremes (EPA 2015). Third, the willingness to change the cultivation methods of farmers, to adapt to different and more extreme conditions, is too often too low (Ding et al. 2009). Fourth, the soil quality will probably diminish. All of these facts frequently lead to a higher use of fertilizers, which might solve some problems in the short term but further reduces the soil quality in the long run and leads to more plant diseases (California Climate and Agriculture Network 2012).

In SE Asia those aspects have heavily influenced the agricultural output and will influence it in the future (Anderson et al. 2009; EPA 2015).

4. Conclusions

Our findings show that abrupt conversion of forest to grassland leads to a persistent new climate state of the system with major variability after the year of disturbance in 1990 over SE Asia. The land modification results in an overall warming, a reduction of precipitation, and changed precipitation patterns over the simulation domain.

While the overall temperature increases and precipitation amount decreases in the mean, a pronounced consistent increase in extremes (maximum daily temperature and maximum daily precipitation) is apparent during the winter monsoon period (NDJFM).

The impacts of land cover change are more intense than the effect of El Niño and La Niña. In addition, results show that these land clearings can amplify the impact of the natural mode ENSO, which has a strong impact on climate conditions in SE Asia.

The present study marks the role of land use and land cover changes in affecting not only local temperatures but also regional temperature extremes, which in turn affect large-scale precipitation and its extremes. Hence, the replacement of forest to grassland is likely to contribute to more severe droughts and increased demand for water, and to landslides.

As a next step, RCM simulations based on more realistic land use and land cover change scenarios would be valuable to further deepen our understanding of land–atmosphere feedbacks on climate variability over SE Asia.

The simulations highlight the need to account for land use and land cover changes in models and the analysis on the local scale to unravel their uncertainties and to improve the projection of future climate change and extremes. More and longer simulation studies are needed to confirm our results.

It is concluded that changes in land cover (e.g., deforestation) over Indochina and the maritime continent will likely amplify the effect of ENSO along with associated rainfall, temperature, and their extremes under climate change. This will likely have consequences for the agricultural output.

Acknowledgments

The author thanks David Hellwig and the COSMO-CLM community. This is part of related research in the project CRC990 Ecological and Socioeconomic Functions of Tropical Lowland Rainforest Transformation Systems (EFForTS) under the support of the German Research Foundation (DFG). Computational resources were made available by the German Climate Computing Center (DKRZ) through support from the German Federal Ministry of Education and Research (BMBF).

APPENDIX

Model Configuration and Results

The main configuration of the model is presented in Table A1.

Table A1.

Configuration of the model COSMO-CLM.

Table A1.

Figure A1 shows spatially averaged over all grid points annual mean and standard deviation time series of maximum temperature and sensible and latent heat flux for the time period 1988 to 2004. The upper panel presents the mean and the lower panel the standard deviation of the variables. The figure shows major changes of the variables (after 1990) due to land cover changes.

Fig. A1.
Fig. A1.

(top) Area-averaged composite of simulated variables—(left to right) maximum temperature and latent and sensible heat flux—representing the changes due to land conversion together with (bottom) their standard deviations from 1988 to 2004. The first two years (1988 and 1989) with no change are also shown for comparison.

Citation: Journal of Climate 30, 7; 10.1175/JCLI-D-16-0131.1

Figure A2 shows the influence of land cover change during the summer monsoon period on maximum daily temperature for 1990 to 2004. Significant increases in extreme temperature occur over most of the maritime continent.

Fig. A2.
Fig. A2.

Simulated maximum daily temperature difference (1990–2004) between potential and actual land covers for JJA. Hatched areas are regions where changes are statistically significant at the 0.05 level.

Citation: Journal of Climate 30, 7; 10.1175/JCLI-D-16-0131.1

Figure A3 shows the differences in patterns of maximum daily precipitation due to land cover change during the summer monsoon period for 1990 to 2004. Significant increases of extreme precipitation appear over the islands Sumatra and Borneo.

Fig. A3.
Fig. A3.

As in Fig. A2, but for maximum precipitation.

Citation: Journal of Climate 30, 7; 10.1175/JCLI-D-16-0131.1

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1

Examples of these indicators are agricultural diversification, agricultural techniques, and farming history and mean temperature and precipitation during the growth period of the staple crop.

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