Simulated climate effects of Southeast Asian deforestation: Regional processes and teleconnection mechanisms

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1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 116,, doi: /2010jd015450, 2011 Simulated climate effects of Southeast Asian deforestation: Regional processes and teleconnection mechanisms Rainer Schneck 1 and Volker Mosbrugger 1 Received 6 December 2010; revised 1 March 2011; accepted 10 March 2011; published 11 June [1] A coupled atmosphere ocean model is used to examine the climatic effects of Southeast Asian deforestation. On the deforested grid cells surface temperatures rise, and precipitation is reduced after deforestation. Regional moisture convergence and convection increase and lead to strongly enhanced rainfall. The easterlies and associated oceanic surface currents are reduced in the Southeast Asian realm. As a result, the upwelling of cold ocean water weakens, and surface temperatures rise. This acts as a strong positive feedback mechanism for the enhanced regional moisture convergence. Besides the regional effects, we find strong teleconnections to the tropics and to the high latitudes of both hemispheres. The signal from Southeast Asia propagates to the extratropics with the high level winds in the form of sensible heat and potential energy. Possibly because of strong albedo sea ice feedbacks, some regions in the high latitudes amplify relatively small initial climate changes and exhibit strong remote effects. The excitation of atmospheric waves (probably Rossby waves) is the underlying mechanism that causes the signal propagation to the extratropics. Citation: Schneck, R., and V. Mosbrugger (2011), Simulated climate effects of Southeast Asian deforestation: Regional processes and teleconnection mechanisms, J. Geophys. Res., 116,, doi: /2010jd Introduction [2] During the last 20 years, forest clearance in Southeast Asia has led to a strong reduction of the regional forest expanse [Food and Agriculture Organization, 2009]. For example, for the years 2000 to 2005, the annual forest extent change rate was 1.3%. This trend of forest loss is likely to continue in most countries in Southeast Asia in the next two decades. The associated change in surface properties may influence regional and global climate strongly. Vegetation cover determines surface properties such as the leaf area index, the surface albedo and the roughness length [cf. Hagemann et al., 1999]. Furthermore, the rooting depth of vegetation strongly alters the amount of available soil water for plant transpiration [Kleidon and Heimann, 1998a, 1998b]. Therefore, the regional energy and water budgets may be affected considerably by Southeast Asian deforestation. [3] Many climate modeling studies quantify the impact of tropical deforestation on regional and global climate [e.g., Polcher and Laval, 1994a; Hahmann and Dickinson, 1997; Costa and Foley, 2000; Werth and Avissar, 2002, 2005]. Most of them agree in a strong reduction of the regional hydrological cycle, in a warming of the surface and in altering the regional circulation patterns. In general, relatively weak remote effects in the high latitudes are found 1 Senckenberg Research Institute and Natural History Museum, Biodiversity and Climate Research Centre, Frankfurt, Germany. Copyright 2011 by the American Geophysical Union /11/2010JD [e.g., Sud et al., 1996; McGuffie et al., 1995; Werth and Avissar, 2002]. [4] A particular situation of the Southeast Asian forests is their distribution within the warm West Pacific Ocean. As a consequence, deforestation in this region may lead to extraordinary strong atmosphere ocean feedbacks and could therefore impact the regional and global climate relatively strongly. Most studies focus on Amazonian and African deforestation or include Southeast Asian deforestation only as a part of total tropical deforestation [e.g., McGuffie et al., 1995; Zhang et al., 1996a, 1996b; Sud et al., 1996; Snyder et al., 2004; Findell et al., 2006; Hasler et al., 2009; Werth and Avissar, 2005]. Even though a strong influence of the ocean dynamics on the Indonesian deforestation effects could be expected, nearly all studies for this region use prescribed sea surface temperatures (SSTs) or a mixed layer ocean. [5] A first experiment with a fully coupled atmosphereocean model was undertaken by Delire et al. [2001]. They found reduced regional convection caused by weaker net radiation and evaporation over land. Consequently, less rain falls over the deforested land. Due to wind changes, equatorial upwelling in the northwest of the Indonesian islands is enhanced. As a result, the surface temperatures of the ocean are reduced and evaporation gets weaker. To investigate the potential of the oceanic feedbacks, Delire et al. [2001] performed the same deforestation experiments also with fixed SSTs. In contrast to the changes found with the dynamic ocean, the regional deep convection is enhanced and precipitation increases over the islands. 1of12

2 Table 1. Surface Parameter Values Averaged on the Deforested Grid Cells in CLT and for the Warm Grass Biome, Used to Replace Forests in DEF a CTL Average Warm Grass Background albedo, a veg Surface roughness, z m 0.1 m Forest cover, C for Field capacity of soil, W s,max 0.65 m 0.68 m Leaf area index, LAI Vegetation cover, C veg a Values for albedo, surface roughness and forest cover are taken from Hagemann [2002] (warm grass refers to hot and mild grasses ). Values for the field capacity of soil, the LAI, and the vegetation cover are taken from Hagemann et al. [1999]. For the LAI and the vegetation cover the values are set to a state between the growing and the dormancy season. [6] Voldoire and Royer [2005] also simulate Southeast Asian deforestation with a dynamic ocean, but only as a part of total tropical deforestation. Their study investigates the deforestation effects with and without a dynamic ocean model. The climatic effects on the deforested grid cells obtained with the dynamic ocean (precipitation, evaporation and sensible heat flux) are principally the same as those of Delire et al. [2001]. In general, the deforestation effects found by Voldoire and Royer [2005] are very similar with fixed and with simulated SSTs. An exception is the precipitation change in the Indonesian realm, which reverses when fixed SSTs are used. The same is found by Delire et al. [2001]. [7] So far, existing studies with a dynamic ocean model illuminate only some aspects of the climatic changes after Southeast Asian deforestation. Teleconnections and their mechanisms are nearly unstudied. Therefore, in this work, we present results of Southeast Asian deforestation experiments performed with an atmosphere ocean coupled general circulation model (AOGCM). We intensively analyze the regional processes, but also focus on teleconnections and their mechanisms. 2. Model and Boundary Conditions [8] For our experiments we use the AOGCM COSMOS 1.0.0, developed at the Max Planck Institut für Meteorologie ( COSMOS includes ECHAM as atmosphere and MPIOM as ocean model. The coupling is done by the OASIS3 coupler. The complex spectral model ECHAM [Roeckner et al., 2003, 2004] is run in a horizontal resolution of T31 (about in grid point space). The atmosphere is represented by 19 irregularly spaced vertical layers using s coordinates. Prognostic variables and fluxes for the surface (e.g., surface temperature) are calculated with a surface energy balance equation. The numerical solution of this equation is obtained by using an implicit coupling scheme [Schulz et al., 2001]. Vegetation is represented in the model by its influence on surface properties. The influence of rooting depth on the water budget [Hagemann and Kleidon, 1999; Kleidon and Heimann, 1998a, 1998b, 1999, 2000] is considered by appropriate adaptations of the field capacities [Hagemann et al., 1999]. Vegetation effects such as the interception of rain and snow in the canopy and the stomatal control of evapotranspiration are parameterized in an idealized way. ECHAM5 was used for the projections of future climate change [Intergovernmental Panel on Climate Change, 2007] and for other studies [e.g., Giorgetta et al., 2002]. [9] MPIOM (Max Planck Institut Ocean Model) is an ocean general circulation model based on the primitive equations with representation of thermodynamic processes. The horizontal discretization of the MPI OM model is on a staggered Arakawa C grid [Arakawa and Lamb, 1977] and uses a bipolar orthogonal spherical coordinate system. In the present simulations a formal horizontal resolution of 3 is used. In the vertical, MPIOM uses a z coordinate system with 40 layers. The coarse horizontal and vertical resolution of MPIOM necessitates the use of subgrid scale parameterizations. The MPI OM model has parameterizations for the bottom boundary layer slope transport, horizontal and vertical eddy viscosity, vertical eddy diffusivity, the isopycnal diffusivity, eddy induced mixing, and convection. For further details see P. Wetzel et al. (The Max Planck Institute Global Ocean/Sea Ice Model MPI OM, 2008, available at MPIOM/DRAFT_MPIOM_TECHNICAL_REPORT.pdf) and Marsland et al. [2003]. [10] The OASIS (Ocean Atmosphere Sea ice Soil) coupler is an open source software for coupling independent general circulation models of the atmosphere and of the ocean, as well, as other climate modules like sea ice, land or hydrology [Valcke, 2006]. OASIS3 has been extensively used in the PRISM demonstration runs [Carril et al., 2005] and is currently used by approximately 15 climate modeling groups in Europe, USA, Canada, Australia, India and Brazil. [11] In the simulations, the model is restarted in the year 800 from an equilibrium state and then integrated for further 100 years. The simulations run with an atmospheric CO 2 concentration of 360 ppm. For the methane concentration the preindustrial value 805 ppb of the year 1860 is used. The control run is referred to as CTL. Southeast Asian deforestation is included in the boundary conditions of the deforestation run (referred to as DEF) by adapting the surface properties in Southeast Asia to a Warm Grass biome (Table 1). Figure 1 shows the grid points where these surface parameter changes are applied. The results are averaged over the last 20 years of the simulations. For precipitation, 2 m temperature and geopotential height we use a Student s t test to quantify the statistical significance of the anomaly patterns. The level of significance is set to p = Results 3.1. Regional Changes [12] On the deforested grid cells the leaf area index (LAI) and roughness length decline. As a result, the averaged evapotranspiration on these grid cells decreases by 367 mm/yr ( 32%, see Table 2). This reduces specific humidity directly over the grid cells but not in the air above the 850 hpa level. The average precipitation on the deforested grid cells decreases only by 154 mm/yr ( 11%). This decrease is relatively small (only about 1/3) as compared to the strong decrease of evapotranspiration on the same gird cells. Thus, a strong intensification of moisture convergence (+213 mm/yr, +118%) largely compensates the evapotranspiration reduction. 2of12

3 Figure 1. Deforested grid cells in Southeast Asia. The square illustrates the area in the text referred to as the Southeast Asian region. [13] The climatic changes on the isolated grid cells do not represent the climate changes of the whole region (20 S to 20 N and 90 E to 50 E, see square in Figure 1). The evapotranspiration of the whole region remains nearly constant because evaporation over the West Pacific ocean increases as a response to the warming of the surface water (explained later). While evapotranspiration remains nearly constant, the regional precipitation even increases by +132 mm/yr (+5%) after deforestation (Figure 2). On the one hand, the regional increase of precipitation is a result of rising air moisture (Figure 3a). On the other hand, the precipitation increase is caused by a stronger convection between 110 E and 150 E (Figure 4). Both mechanisms are connected to each other, because the convection leads to the influx of wet air from the surrounding ocean areas and thus leads to the increase of air moisture. The proximity to the surrounding ocean and its unlimited water supply enables this increase of moisture convergence. The intensified condensation causes rising air temperatures over Southeast Asia (Figure 3b). [14] Averaged over the deforested grid cells shortwave atmospheric forcing increases by +5 W/m 2 because of reduced cloud cover and the associated transmittance of shortwave radiation. The overall incoming radiation at the surface increases by +2%. Despite this rise in incoming radiation, the net surface radiation still decreases by 9% because of the intensified albedo and the increase of outgoing longwave radiation at the surface. Averaged over the whole Southeast Asian region the cloud cover increases. The weaker incoming shortwave radiation at the regional surface is compensated by an increase of incoming longwave radiation (due to rising air temperature and more outgoing longwave radiation from the surface reflected by the atmosphere). The overall incoming radiation at the regional surface remains unchanged. The net surface radiation for the whole region decreases only by 1% as compared to the control simulation. [15] Even when net surface radiation is reduced on the deforested grid points, the 2 m temperatures rise in consequence of strongly reduced evaporative cooling (Figure 5). The 2 m temperatures over the surrounding oceans also increase, especially in the eastern part of the region. In this area, the net surface radiation decreases and the intensified evaporation supports a cooling of the surface. Nevertheless, the temperature increases because the trade winds are reduced (Figure 6) and weaken the upwelling of cold ocean water. The ocean circulation is crucial for the regional deforestation effects, since it enables a warming of the surface, which causes convection and associated with this strong convergence of air and moisture Remote Changes [16] After Southeast Asian deforestation rainfall in the Indian Ocean as well as in the central Pacific Ocean (around 170 E) is reduced. Both reductions are caused by a shift toward descending air over these regions. While the convection is reduced over the Indian Ocean, the downward large scale motion over the equatorial central Pacific is intensified. For the Indian Ocean this is clearly visible in Figure 4b. Over the Pacific Ocean the stronger descent of air occurs between 15 N and 15 S and is flanked by regions where the air ascent increases. Since the values in Figure 4b are averaged between 20 N and 20 S the air descent seems weak. India and China are also strongly connected with the changes in Southeast Asia. The surface temperatures in Table 2. Area Averages Over the Deforested Grid Cells ( Deforested ) and the Southeast Asian Region ( Regional ) a CTL Deforested DEF Deforested DEF CTL Deforested CTL Regional DEF Regional DEF CTL Regional Evapotranspiration ( 32%) ( 1%) Precipitation ( 11%) (+5%) Moisture convergence (+118%) (+16%) 2 m temperature (+3%) (+1%) Net shortwave radiation at the top of the atmosphere ( 3%) ( 1%) Shortwave atmospheric forcing (SAF) ( 2%) (+1%) Incoming surface radiation (+2%) (0%) Surface net radiative energy ( 9%) ( 1%) Longwave surface cloud forcing ( 9%) (0%) Shortwave surface cloud forcing ( 10%) (+4%) Net surface cloud forcing ( 10%) (+4%) a See Figure 1 for the areas. Hydrological values are in mm/yr, radiation values are in W/m 2, and temperatures are in C. The percentages of change as compared to the corresponding control simulation are given in parentheses. 3of12

4 Figure 2. Precipitation changes of DEF CTL in mm/yr. The contour line interval is 100 mm/yr; the contour line for 0 mm/yr is not plotted. Colored shading illustrates significant positive (blue) and significant negative (brown) changes with a Student s t test (p = 0.05). Figure 3. Regional changes of DEF CTL. Vertical view in zonal direction. Values are averaged between the longitudes 90 E and 150 E (Southeast Asian region). Grey shading illustrates negative values. (a) Specific humidity in g/kg. (b) Air temperature in C. 4of12

5 Figure 4. Tropical winds in m/s. Vertical view from the south. Atmosphere levels in pressure coordinates (hpa). The vertical component of the winds is scaled by the same factor as the factor by which the vertical extent of the atmosphere is exaggerated as compared to its horizontal extent. The arrow length therefore represents only the wind speed of horizontal winds. The colored shaded boxes illustrate the percentage of grid boxes filled with landmass. All values are interpolated between the latitudes 20 S and 20 N. (a) CTL. (b) DEF CTL. 5of12

6 Figure 5. The 2 m temperature of DEF CTL in C. The contour line interval is 0.25 C; the contour line for 0 C is not plotted. Colored shading illustrates significant positive (red) and significant negative (blue) changes with a Student s t test (p = 0.05). these areas rise as a response of an increase of net surface radiation (mainly in China because of a reduced cloud cover) and a reduction of evapotranspiration (mainly in India). [17] The most interesting remote effects occur in the high latitudes. In northern Asia the 2 m temperatures are strongly reduced. This cooling is not a direct result of meridional heat flux changes (discussed later), but established by three regional processes: (1) reduced radiative absorption at the surface, (2) blocked heat flux through increased sea ice cover, and (3) stronger winds from the north. Two changes cause the reduced radiative absorption at the surface: the increase of the surface albedo due to growing snow and sea ice cover (Figure 7) and the reduction in Surface Shortwave Cloud forcing due to increased cloud cover. Thus, the remote cooling in northern Asia is finally caused by wind changes, the decrease of atmospheric moisture and the increases of cloud, snow and sea ice cover. Except for the wind changes, all these changes are initially caused by atmospheric cooling and then amplify the cooling effect. However, it is unclear what causes the initial cooling triggering the additional cooling feedbacks. In principal atmospheric transport changes can only affect air moisture and air temperature directly. The northern Asian moisture reduction does not cause the rising cloud and snow cover. Instead, cloud and snow cover increase indirectly through atmospheric cooling. Thus, the only possibility for the moisture reduction to initiate a cooling process is the reduced water vapor greenhouse effect, which could then start an increase of cloud and snow cover. However, this seems unlikely, since the moisture reduction itself acts against a cloud and snow cover rise. Therefore, it is probable, that the atmospheric transport changes trigger the remote cooling directly by reducing the air temperatures. The intensified north winds support this assumption (compare Figure 6). [18] In the Southern Hemisphere, the pressure gradient between the subtropical high and the subpolar low is intensified. As a consequence the westerlies get stronger (Figure 6). The temperature changes in the high southern latitudes are mainly caused by an increase (decrease) of the sea ice cover, which increases (decreases) the albedo and also decreases (increases) the sensible heat flux from the ocean (not shown) Teleconnection Mechanisms [19] In the following we investigate how the signal from Southeast Asia propagates to the remote regions in the high latitudes. Even when the oceanic circulation strongly affects the regional climate in Southeast Asia, we found no hints for an oceanic contribution to the signal propagation to the extratropical regions. [20] The meridional circulation of the Northern Hemisphere gets somewhat stronger after deforestation (Figure 8). The equatorial ascent of air is intensified leading to a stronger descending branch of the northern Hadley cell. The latitudinal extent of the Hadley cell is not affected. The Northern Hemisphere Ferrel and Polar cell are also intensified. The zonally averaged heat flux changes are weak and give no clear hints for the meridional signal propagation from the tropics to the high latitudes (not shown). The geographical distribution of the heat flux changes is shown in Figure 9. Even when air moisture changes are strong in 6of12

7 Figure 6. Wind and surface pressure of DEF CTL. Pressure (shaded) is given in hpa. Winds are plotted for the 850 hpa level. Arrow length is equivalent to wind speed. The reference arrow represents 2 m/s. Arrows for wind speeds less than 0.2 m/s are not shown. the tropics, the potential and sensible heat flux changes are much stronger than the latent heat flux changes. Changes in the sensible heat flux show essentially the same pattern as those of the potential heat flux, but they are generally stronger. In the vertical domain, the potential heat flux changes are strongest between the 400 and 150 hpa level (not shown). The sensible heat flux changes are strongest between the 400 and 300 hpa level. The comparison with high and low level wind changes as well as further calculations shows that these heat flux changes are nearly exclusively caused by wind changes and not by changes in the geopotential height or by changes in the amount of sensible and latent heat. However, the explanation and illustration of this statement would exceed the volume of this paper and is therefore omitted. Overall, the signal propagation from the tropics into the extratropics is realized mainly by potential and sensible heat fluxes and by highlevel wind changes. There is no individual pathway or region of strongly pronounced heat flux changes. Instead, heat flux changes occur globally. [21] The underlying mechanisms leading to the wind changes after Southeast Asian deforestation is still unclear. Zhang et al. [1996b] show that tropical deforestation can cause waves that propagate from the tropics into the high latitudes. These waves can be identified by large scale changes of the geopotential height in the middle and high latitudes. After Southeast Asian deforestation, the geopotential height exhibits wave trains in the midlatitudes that are similar to those found by Zhang et al. [1996b] for tropical deforestation (Figure 10). These geopotential height changes are associated with wind changes. Positive changes of the geopotential are associated with anticyclonic flow changes in both hemispheres. Inversely, negative geopotential height changes are associated with cyclonic wind changes. Since wind changes cause the heat flux changes, it is likely that the teleconnections can be traced back to the excitation of the global wave trains after Southeast Asia deforestation. As mentioned above for the heat flux changes, no individual pathway of these wave trains exists. Instead, the wave patterns are a global system response and initiate small climate changes in the whole middle and high latitudes. Downstream, these initial climate changes may be amplified in sensitive regions and can cause strong remote effects. Initial temperature changes in the Southern Hemisphere, for example, may be amplified by the sea ice albedo feedback. Northern Asia may be more sensitive than Europe because the local feedback mechanisms (described above) are not suppressed by the Gulf Stream in this region. 4. Summary and Conclusions [22] Our sensitivity study examines the climatic effects of Southeast Asian deforestation with a fully coupled AOGCM. 7of12

8 Figure 7. Snowfall and sea ice cover of DEF CTL. Contour lines illustrate the snowfall in mm/yr. The colored grid boxes show increasing sea ice cover in % of total coverage. We investigated the regional climatic changes as well as teleconnections and their mechanisms in detail. The climate changes in the Southeast Asian region exhibit strong interdependencies. There are two dominant processes determining these changes. First, the positive feedback between intensified convection associated with reduced easterlies and the warming of the ocean which further enhances convection. It should be highlighted that a dynamic ocean model is mandatory to depict this feedback. Second, the extraordinary strong positive feedback between the air ascent and intensified condensation. Both feedback mechanisms are only possible because the small scale Southeast Asian forests are distributed within the West Pacific. Strong radiation changes are connected to the alterations of the regional hydrological cycle. Nevertheless, radiation changes are of minor importance for the establishment of the regional climate change. [23] In contrast to the present results, the modeling studies of Werth and Avissar [2005] and Avissar and Werth [2005] show a reduction of precipitation in the Indonesian region. Both studies use prescribed SSTs. McGuffie et al. [1995] uses a mixed layer ocean and finds a nearly unchanged precipitation for the Indonesian realm after tropical deforestation. The regional moisture convergence is also increased in their study but surface temperatures are reduced. Zhang et al. [1996a] perform nearly the same modeling experiments as McGuffie et al. [1995] with a higher resolution and longer integration times. In their study, precipitation on the grid cells decreases with 8% after deforestation. The present study shows roughly the same amount for deforested grid cells ( 11%). The surface air temperatures given by Zhang et al. [1996a] are reduced as well as regional moisture convergence. Our results contradict those of the earlier study. Polcher and Laval [1994b] also find decreasing precipitation and moisture convergence for Indonesia after tropical deforestation. Tropical deforestation in the study of Sud et al. [1996] results in increasing precipitation in the western Indonesian realm and decreasing precipitation in the eastern Indonesian realm. The same pattern is found for moisture convergence. In the present study the opposite pattern is found. However, none of these earlier studies includes a dynamic ocean model and thus cannot represent the feedback mechanisms that lead to an increase of moisture convergence and precipitation. [24] The two studies in literature including a dynamic ocean for Indonesian deforestation [Delire et al., 2001; Voldoire and Royer, 2005] principally agree in the climatic effects on the deforested grid cells: precipitation and evaporation decrease and the sensible heat flux increases. In the present study, the same is found for the evaporation and the sensible heat flux. This is probably due to the fact that these changes are directly connected to the included surface parameter changes for deforestation. However, in the present study the opposite effect is found for precipitation. In the work by Delire et al. [2001] but also in the work by 8of12

9 Figure 8. Zonal wind averages in mm/s. Vertical view from the east. The vertical component of the winds is scaled by the same factor as the factor by which the vertical extent of the atmosphere is exaggerated as compared to its horizontal extent. The arrow length therefore represents only the wind speed of horizontal winds. (a) CLT. The reference arrow represents 3 m/s. (b) DEF CTL. The reference arrow represents 0.3 m/s. 9of12

10 Figure 9. Northward eddy heat flux of DEF CTL in PW. Values are vertically integrated over the atmosphere with respect to level thickness and orography. (a) Potential heat flux. (b) Sensible heat flux. (c) Latent heat flux. (d) Total heat flux. Voldoire and Royer [2005] (only summer and winter results are presented) intensified easterlies lead to a cooling of the ocean surface. Furthermore, Delire et al. [2001] find reduced regional convection and enhanced equatorial upwelling. Thus, their results show essentially the same mechanisms that we found, but in opposite direction. In each case, the studies agree in a strongly influence of the ocean circulation on climate changes. We conclude that modeling studies for Southeast Asian deforestation without a dynamic ocean model are not able to depict realistic climate changes. [25] Southeast Asian deforestation leads to strong remote effects in the high latitudes. Till now, there is no published study showing a similar teleconnection of tropical deforestation to the high latitudes. In northern Asia a large scale temperature decline occurs. The high latitudes of the Southern Hemisphere show wave patterns of temperature change. The analysis of the heat fluxes show that the signal propagation is mainly caused by the high level wind changes and potential and sensible heat fluxes. The latent heat fluxes are generally much weaker. Furthermore, there is no single pathway for the signal from Southeast Asia to the extratropics. Instead, the heat flux changes occur globally and in wave patterns. The geopotential height pattern also indicates a wave mechanism for the signal propagation. [26] Equatorial thermal forcing can emit waves that cause significant remote response of the geopotential at high latitudes [Webster, 1981]. The response in the high latitudes is found to be stronger than the geopotential changes at the thermal source. Other studies support this wave mechanism that can propagate tropical heat forcing to the high latitudes [Simmons, 1982; Hoskins and Karoly, 1981]. Simmons [1982] argues that these waves could be Rossby waves. Zhang et al. [1996b] and Werth and Avissar [2002] find similar geopotential height changes as the present study. Both argue that these changes are caused by a Rossby wave propagation mechanism. Therefore, we assume that the observed wave patterns in the present study are Rossby waves. [27] All remote effects occur in regions that are sensitive to small initial changes. The remote changes in the high latitudes of the Southern Hemisphere are amplified by the sea ice albedo feedback mechanism. The remote changes in Asia are amplified by the sea ice and snow albedo effect and through the property of clouds to reflect shortwave radiation. Therefore we suggest that climate changes in the remote areas do not primarily occur because these regions are strongly connected to Southeast Asia, but because these regions exhibit strong feedback mechanisms that amplify 10 of 12

11 Figure 10. Geopotential height and winds of the 550 hpa level of DEF CTL. Arrow length is equivalent to wind speed. The reference arrow represents 2 m/s. Arrows for wind speeds less than 0.4 m/s are not shown. Contour lines show geopotential height changes in gpm. Grey shading illustrates significant positive (dark gray) and significant negative (light gray) changes with a Student s t test (p = 0.05). small initial changes. To test this assumption, we are preparing further modeling studies which focus on Amazonian and African deforestation. [28] Acknowledgments. We gratefully acknowledge the comments of our two anonymous reviewers and Dieter Uhl, which helped to improve our manuscript. Thanks also to Arne Micheels for his programming support. This work was supported by the federal state Hessen (Germany) within the LOEWE initiative. References Arakawa, A., and V. R. Lamb (1977), Computational design of the basic dynamical processes of the UCLA general circulation model, Methods Comput. Phys., 17, Avissar, R., and D. Werth (2005), Global hydroclimatological teleconnections resulting from tropical deforestation, J. Hydrometeorol., 6, , doi: /jhm Carril, A. F., R. Budich, J. Cole, G. De Martino, M. E. Demory, R. Doscher, P. G. Fogli, E. Guilyardi, U. Hansson, and M. Kastowsky (2005), The PRISM demonstration runs, PRISM Rep. Ser., 14, Program for Integr. Earth Syst. Model., Reading, U. K. [Available at org/publications/reports/report14.pdf.] Costa, M. H., and J. A. Foley (2000), Combined effects of deforestation and doubled atmospheric CO 2 concentrations on the climate of Amazonia, J. Clim., 13, 18 34, doi: / (2000)013<0018: CEODAD>2.0.CO;2. Delire, C., P. Behling, M. T. Coe, J. A. Foley, R. Jacob, J. Kutzbach, Z. Liu, and S. Vavrus (2001), Simulated response of the atmosphere ocean system to deforestation in the Indonesian Archipelago, Geophys. Res. Lett., 28, , doi: /2000gl Findell, K. L., T. R. Knutson, and P. C. D. Milly (2006), Weak simulated extratropical responses to complete tropical deforestation, J. Clim., 19, , doi: /jcli Food and Agriculture Organization (2009), State of the world s forests 2009, report, Rome. Giorgetta, M. A., E. Manzini, and E. Roeckner (2002), Forcing of the quasi biennial oscillation from a broad spectrum of atmospheric waves, Geophys. Res. Lett., 29(8), 1245, doi: /2002gl Hagemann, S. (2002), An improved land surface parameter dataset for global and regional climate models, Rep. 336, 28 pp., Max Planck Inst. für Meteorol., Hamburg, Germany. Hagemann, S., and A. Kleidon (1999), The influence of rooting depth on the simulated hydrological cycle of a GCM, Phys. Chem. Earth, 24, Hagemann, S., M. Botzet, L. Dümenil, and B. Machenhauer (1999), Derivation of global GCM boundary conditions from 1 km land use satellite data, Rep. 289, 34 pp., Max Planck Inst. für Meteorol., Hamburg, Germany. Hahmann, A. N., and R. E. Dickinson (1997), RCCM2 BATS model over tropical South America: Applications to tropical deforestation, J. Clim., 10, , doi: / (1997)010<1944: RBMOTS>2.0.CO;2. Hasler, N., D. Werth, and R. Avissar (2009), Effects of tropical deforestation on global hydroclimate: A multimodel ensemble analysis, J. Clim., 22, , doi: /2008jcli Hoskins, B. J., and D. J. Karoly (1981), The steady linear response of a spherical atmosphere to thermal and orographic forcing, J. Atmos. Sci., 38, , doi: / (1981)038<1179:tslroa>2.0. CO;2. Intergovernmental Panel on Climate Change (2007), Climate Change 2007: The Physical Science Basis Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, edited by S. Solomon et al., Cambridge Univ. Press, Cambridge, U. K. [Available at report/ar4/wg1/ ar4 wg1 frontmatter.pdf.] 11 of 12

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