Impacts of land use on climate and ecosystem productivity over the Amazon and the South American continent

The coupled dynamics of the land surface and atmosphere over the South American study area were simulated with RCA-GUESS (Smith et al 2011), a regional Earth system model that couples the Rossby Centre regional climate model RCA4 (Kjellström et al 2005, Samuelsson et al 2011) to LPJ-GUESS, an individual-based ecosystem model that combines an individual-based representation of vegetation structure and dynamics with process-based physiology and biogeochemistry (Smith et al 2001, Smith et al 2014).

RCA4 represents surface heterogeneity such as complex topography and incorporates a multi-level representation for the lowest atmospheric layers above and below the forest canopy (Samuelsson et al 2011). Open land and forest tiles of the land surface carry separate energy balances (Samuelsson et al 2006). In each tile, the soil is divided into three layers for soil moisture, with a maximum depth defined by a rooting depth prescribed for each tile. Root extraction depends on an exponential root distribution, and accounts for water uptake compensation for dry soil conditions, i.e. an adapted water uptake efficiency for each soil layer according to the changes in soil moisture availability. Soil water conditions influence vegetation surface resistances, thus controlling evapotranspiration and energy fluxes. A detailed description of the physical land surface representation can be found in Samuelsson et al (2006).

Vegetation in LPJ-GUESS is characterized as local neighborhoods (patches) of individuals belonging to different plant functional types (PFTs; table S1 available at stacks.iop.org/ERL/12/054016/mmedia), co-occurring and competing for light, space and soil resources. PFTs encapsulate the differential functional responses of potentially occurring species in terms of growth form, bioclimatic distribution, phenology, physiology and life-history characteristics. Vegetation development is affected by allometric growth, competition for light and soil resources among individuals, stochastic disturbances, establishment and mortality. The simulated vegetation structure affects land surface properties (albedo and roughness length, as well as the water vapor exchanges with the atmosphere) by returning updated forest and open land tile fractions and leaf area index (LAI) to RCA4. The coupling scheme between the vegetation and physical model components is described by Smith et al (2011).

RCA-GUESS has been shown to simulate realistic biophysical dynamics in applications to Europe (Wramneby et al 2010, Smith et al 2011), the Arctic (Zhang et al 2014) and Africa (Wu et al 2016).

RCA-GUESS was run over the South American domain of the Coordinated Regional Climate Downscaling Experiments (CORDEX, Giorgi et al 2009, Jones et al 2011) on a horizontal grid with a resolution of 0.44° × 0.44° (figure 1). In order to capture important circulation patterns, including potential shifts in the Inter-Tropical Convergence Zone, the South American Easterly trade wind and Southern Atlantic trade winds, the simulation domain was chosen to cover the whole South American continent. In order to isolate the biophysical effects of LULCC, two experiments were conducted, which are described further down. Both were forced with atmospheric fields and sea-surface temperature (SST) as lateral and lower boundary conditions derived from ECMWF re-analysis (ERA-Interim) (Berrisford et al 2009). Transient historical radiative forcing (i.e. atmospheric greenhouse gas concentrations) was used for RCA4. The vegetation sub-model LPJ-GUESS was forced at the same resolution as RCA4, set up with eleven PFTs representing the compositional diversity of South American vegetation including tropical evergreen and drought-deciduous trees, temperate evergreen and deciduous trees and C₃ and C₄ herbaceous vegetation ('grass') following (Smith et al 2014), adapted for the present study by increasing the respiration coefficient (table S1) of tropical PFTs to match the carbon-use efficiency (CUE, i.e. ratio of net to gross primary production) suggested by a recent meta-analysis of tropical forest carbon dynamics (Anderson-Teixeira et al 2016). Fire disturbance (Thonicke et al 2001) and nitrogen (N) limitation (Smith et al 2014) were included, applying N deposition following Lamarque et al (2010). Transient historical CO₂ concentration values were used to force LPJ-GUESS. The model was initialized with a two-stage spinup following Wramneby et al (2010).

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The first stage initialized the vegetation component with an uncoupled 500 year spinup driven by CRU TS3.1 (Harris et al 2014), followed by a coupled run for 1980–2009 with ERA-Interim boundary conditions. The second stage forced the vegetation component with the climate generated from the first stage's coupled period for another uncoupled 500 year spinup, in order to bring the vegetation sub-component to equilibrium before the start of the two experimental runs.

Potential Natural Vegetation (PNV) run: After the spinup, the coupled RCA-GUESS was run for 1980–2005 with ERA-Interim boundary conditions and without land use, to simulate natural vegetation conditions.

Land Use (LU) run: An LU simulation was conducted with the same setup as the PNV run, starting with the simulated PNV state at 1980. However, in this case, historical LULCC forcing was applied from the Harmonized Global Land Use database (Hurtt et al 2006), regridded to the RCA-GUESS resolution of 0.44° × 0.44°. Cropland and pasture fractions from the Hurtt dataset were aggregated to provide initial fraction for the open land tile in RCA-GUESS. Only C₃ and C₄ herbaceous PFTs were permitted to grow in the open land tile.

To isolate the effects of anthropogenic land cover change (mainly deforestation) on regional climate and vegetation, the differences between the LU and PNV simulations were analyzed as averages for the period 1996 to 2005. The first 15 years (1980–1995) are thereby excluded, to avoid transient changes in the climate following the transition between PNV and LU to dominate the analysis. Four areas are in focus in this study (figure 1). The Amazonian arc of deforestation over the Cerrado (area A, figure 1(a)) and the temperate grassland centred on northeastern Argentina (area B, figure 1(a)) are the two most intensive LULCC areas. They are in focus in our analysis of local impacts on climate. Changes in circulation and ecosystem productivity were analysed for the Amazon basin, divided into northwestern (NW) and southeastern (SE) parts (figure 1(a)). To assist the analyses of the LULCC-induced climate changes and their impacts on ecosystem productivity over the Amazon basin, we employed the Mann–Whitney U test based on the simulated yearly dry season or wet season means in the defined analysis period to indicate significant spatial changes to climate (figure 3) and ecosystem productivity (figure 5). In addition, linear partial correlation was used to analyze the factorial effects on net primary productivity (NPP) (figure 5), based on the sets of multi-year monthly LULCC-induced changes in climate drivers and NPP during the analysis period for all grid points.

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Relevant RCA-GUESS output was evaluated with observation-based datasets over the analysis period. Observed LAI was obtained from the GIMMS-AVHRR and MODIS-based LAI3g product (Zhu et al 2013). Aboveground biomass (AGB) originates from the harmonized satellite-based vegetation optical depth (VOD) data derived from passive microwave satellite sensors (Liu et al 2015). Simulated temperature and precipitation were compared with the mean and range of corresponding climate variables from four historical climate datasets: CRU TS3.21 (Harris et al 2014), CRUNCEP v5 (Wei et al 2014), Princeton V2 (Sheffield et al 2006) and WFDEI GPCC (Weedon et al 2011).

The simulated pattern of LAI is generally close to the LAI3g product (figure 1(b) and (c)). The model overestimates LAI around the southern margins of the Amazon forest area and over the Andes mountain range by up to 1.5, and underestimates LAI in southern Brazil by up to 1. The simulated pattern of AGB is also close to the observation (figures 1(e) and (f)), indicating an adequate distribution of forest presence and structure. The spatial bias patterns are generally similar to those for LAI.

Differences in simulated LAI and AGB between the two simulations (figure 1(d, g)) reflect the imposed land use pattern (figure 1(a)), but also show impacts of LULCC-induced climate change on AGB over areas of the Amazon basin where anthropogenic impacts of land cover are locally negligible, suggesting remote influences (see section 3.3).

The simulated surface temperature and precipitation generally depict seasonal dynamics and annual mean quantities comparable to the observed values (figure 2). For the simulated temperature, the LU simulation captures the observed annual mean and seasonal variation for area A and SE Amazon more accurately than the PNV simulation (figures 2(a) and (d)). For the simulated precipitation, the differences between the two simulations are minor, except for the area A, where LU simulation yields higher precipitation during January-March (figure 2(e)) with a smaller deviation from the observed annual mean. The results for Area B and NW Amazon show comparable seasonality but both underestimate the observations most of the year. Relatively large biases are found for May-September for NW Amazon (figure 2(g)), despite the high absolute values.

The impact of land use on climate, determined from the difference between the LU and the PNV simulations, was primarily caused by differences in the local properties of the natural versus LULCC-affected vegetation to account for energy and mass exchanges, and its consequences for surface atmospheric mixing, transpiration and local-to-mesoscale circulation. At the southeastern margin of the Amazon (area A), this resulted in two distinctly different patterns of LULCC-induced changes for the wet and the dry seasons (defined here as October-April and May-September, respectively; figures 2(a) and (e); figure 3). Further south (area B), where seasonal variations are characterized by temperature and not by hydrology, the changes are more consistent through the year (figures 2(b) and (f); figure 3).

During the dry season, LULCC generally imposes a warming effect over nearly the entire continent, with the most pronounced impact (up to +2.2 °C) along the southeastern edge of the Amazon basin (figure 3(a)). This warming is primarily caused by a decrease in evapotranspiration (−18 mm month⁻¹, figure S1(a)), which is determined by soil water availability (figure S1(e)) and the difference in soil water extraction of forested and grassland vegetation, although decrease in cloudiness is also relevant (figure S2(a)). The decreased evapotranspiration over the deforested land surface is partly associated with vegetation rooting depth and its influence on soil water availability. Herbaceous vegetation (simulated in the open land tile) has a shallower rooting depth than forest, limiting water access at depth and leading to an earlier soil moisture deficit compared to deep rooted forest vegetation (Gash and Nobre 1997). Differences in surface roughness amplify the pattern via impacts on turbulence and evapotranspiration. The shallower soil layer for the open land tends to dry out faster with higher surface wind speeds (figure S3(a)) under a warming than the soil layer for the forest tile, as is evident from negligible soil moisture changes and less intra-season variability for the latter, despite similar LULCC-induced warming (figure S1(g,h)).

The land-use induced warming described above dominates the continental-scale changes (figure 3(a), but land use also results in a higher albedo of land use areas compared to forest, which generally leads to a decrease in surface energy absorption (figure S4). In our experiments, the albedo cooling effect is generally smaller than the warming from reduced evapotranspiration, except for the southern part of South America (figure 3(a)), where albedo changes dominate the temperature change, resulting in a net cooling effect.

An increase in land surface temperature changes the temperature profile in the planetary boundary layer (PBL) and results in a larger vertical temperature gradient that promotes convection. However, increased temperature leads to neither marked changes in atmospheric humidity (not shown) nor changes in moisture flux convergence (MFC) (figure 4(a)), due to the constraining influence of the decreased soil water content (figure S1(e)). As a result, changes in precipitation are negligible (figure 3(c)). A warmer climate with little change in precipitation induced by land use in the dry season is consistent with previous GCM (e.g. Costa and Pires 2010) and RCM studies (e.g. Correia et al 2008), and is also evident from satellite-based observations (Negri et al 2004). Negri et al (2004) analyzed infrared satellite data of August and September over southwestern Brazil and found that deforested areas generally have much higher temperature, exceeeding that of adjacent forest by up to +5 °C in the middle of the day, while increases of cloudiness and rainfall occurrence are limited.

For the Amazon basin, the dry season does not show distinct changes in precipitation or temperature, but cloud cover changes occur, with contrasting patterns between the NW and SE Amazon basin (figure S2(a)). This is likely caused by changes in circulation as seen in the southward low-level wind advection from the Amazon basin (figure S3(c,e)) which is related to soil moisture-induced changes in temperature contrast between the Amazon basin and the deforested area. The decrease in mean sea level pressure (figure S3(e)) during the dry season enhances the pressure contrast between the Atlantic and the Amazon basin, resulting in the strengthening of the Atlantic trade winds over the northern edge of the Amazon basin, continuing further south towards the Southern Amazon deforestation area (area A, figure S3(e)). As a result, while the moisture flux is reduced over the NW Amazon, it increases over the SE part.

During the wet season, LULCC continues to impose a warming effect over the deforested areas, but with a slightly lower warming than during the dry season over the tropics (area A) and a somewhat higher warming over the subtropics (area B) (figure 3(b)). For the LULCC area over the tropics, the generally high soil moisture content allows for evaporative cooling and reduces the temperature contrast between the forest and LULCC areas. The increase in evapotranspiration (up to 22 mm month⁻¹) for area A is not only temperature-driven, but also associated with enhanced surface wind speeds (figure S3(b)) related to LULCC-induced changes in surface roughness. This in turn promotes vertical mixing and a larger moisture flux convergence (figure 4(b)). Contrasting behaviour is found for the LULCC area over the subtropics, where the generally low soil moisture content becomes even lower, resulting in decreased evaporation and strengthened warming compared to the tropics.

For area B, an increase in temperature during the wet season does not result in a more vigorous hydrological cycle, as it is constrained by the low soil moisture content. Instead, evapotranspiration is reduced (figure S1(b)) and the MFC and precipitation decrease (figure 3(d), figure 4(b)).

The LULCC-induced changes in temperature, precipitation and cloudiness affect the functioning of plants and ecosystems, potentially inducing changes in the composition and structure of the natural vegetation as well. Our simulations exhibit changes in NPP that differ by season. The most significant changes are found for the dry season (figure 5(a)): An increase in NPP over the deforested area A, and contrasting responses for the NW Amazon (increased NPP) and SE Amazon (decreased NPP). For the wet season (figure 5(b)), no such pattern is evident.

Area A presents a year-round increase in natural vegetation productivity, which is larger for the dry season than for the wet season. During the dry season, the reduced cloudiness (figure S2(a)) increases photosynthetically active radiation (PAR), which promotes photosynthesis and increases productivity. During the wet season, the effects of increased precipitation were dampened by increased cloudiness, so NPP changes are small.

For the Amazon basin, two contrasting responses of NPP were found for the NW and SE Amazon. During May-July (dry season) in NW Amazon, PAR effects dominate the response: the partial correlation coefficient of NPP with PAR increases to +0.78 (figure 5(c)), reflecting a strong positive dependency of productivity on radiation uptake, and the PAR differences for these months are large in the NW Amazon (figure S5(f-h)), while the temperature effects are weaker and control by soil water content is minor. The increase in PAR over NW Amazon with increased NPP coincides with a local decrease in cloudiness (figure S2(a)) while temperature generally remains around 24 °C (not shown). At the end of the dry season (August-September) for SE Amazon, the importance of temperature and soil water content increase. The negative effect of temperature on NPP strengthens and peaks in September (R decreases up to −0.6, figure 5(d)). The SE Amazon becomes less productive (up to −10% changes in NPP for September, figure S5(e)) when local temperatures increase from 26 °C to 27.5 °C. During the wet season over the Amazon basin, changes in NPP become more heterogeneous (figure 5(b)) and are strongly controlled by both PAR and temperature (figure 5(c)), with PAR as the dominant driver through the year.

For area B, changes in natural vegetation NPP are minor, primarily because of the low fraction of natural vegetation in this area, but also because precipitation changes are small.