Changes in forest cover have a strong effect on climate through the
alteration of surface biogeophysical and biogeochemical properties that
affect energy, water and carbon exchange with the atmosphere. To quantify
biogeophysical and biogeochemical effects of deforestation in a consistent
setup, nine Earth system models (ESMs) carried out an idealized experiment in the
framework of the Coupled Model Intercomparison Project, phase 6 (CMIP6).
Starting from their pre-industrial state, models linearly replace
Forests cover about
Biogeophysical effects of forest cover changes can be studied by using different model setups. As oceans cover most of the planet they dominate the response of the global temperature to any changes in boundary conditions. Experiments with interactive oceans and sea ice (Brovkin et al., 2009; Davin and de Noblet-Ducoudré, 2010) as well as with slab oceans (Laguë et al., 2019) have shown a global response of changes in climate in response to changes in forest cover. The sea-ice–albedo feedback amplifies the response to a given external change, especially for boreal deforestation (Bala et al., 2007). Global effects of tropical deforestation are less certain, with effects of reduced water vapour generally leading to cooling of the atmospheric column (Ganopolski et al., 2001), while remote effects on atmospheric circulation are difficult to track (Lorenz et al., 2016). For example, teleconnections between tropical deforestation and precipitation over temperate North America could operate via the propagation of Rossby waves (Medvigy et al., 2013). An experimental setup with atmosphere-only models in which sea surface temperatures (SSTs) are prescribed allows us to increase the signal-to-noise ratio of models' response to deforestation. In coupled atmosphere–ocean simulations, the cooling of the land surface via enhanced albedo cools and dries the whole troposphere, which in turn transfers this signal via reduced longwave radiation further to the ocean. With prescribed SSTs the mediating effect of the ocean on the land temperatures is missing, resulting in overestimated tropical warming and underestimated boreal cooling over deforested areas (Davin and de Noblet-Ducoudré, 2010).This setup assumes that the effect of large-scale circulation changes is small and can be ignored. Climatic effects of historical land use and land cover changes (LULCCs) studied in this setup show substantial differences among global climate models due to differences in land surface schemes and their implementation of changes in land cover to represent deforestation (Boisier et al., 2012; de Noblet-Ducoudré et al., 2012; Pitman et al., 2009).
Ideally, biogeophysical effects of deforestation are studied using a set of transient coupled simulations by comparing experiments with and without deforestation (Brovkin et al., 2013; Lawrence et al., 2012). These studies require dedicated model experiments that are computationally costly. A less expensive approach is based on the idea of analysing differences in response of neighbouring pairs of model grid cells that are deforested to different extents in the same numerical experiment (e.g. Kumar et al., 2013; Lejeune et al., 2018). This approach is well suited for post-processing results from existing experiments. It is also applied for analysis of remotely sensed data with pairs of grid cells that are affected differently by land cover changes (Alkama and Cescatti, 2016; Duveiller et al., 2018b; Li et al., 2015). Analysis of remote sensed data or any other analysis based on comparing grid cells with different vegetation cover under a similar climate, e.g. upscaled analysis of local fluxes (Bright et al., 2017), leads to different interpretation of the effects of deforestation when compared to results from fully coupled model simulations. Typically, observation-based studies find a global warming in response to deforestation opposed to model simulations in which a global cooling dominates. Winckler et al. (2019a) showed that the reason for this discrepancy lies in the analyses of observation-based effects of deforestation, which eliminate the non-local effects that propagate signals outside the location of deforestation by advection or changes in atmospheric circulation and constitute mostly a cooling for deforestation. Chen and Dirmeyer (2020) confirmed for temperature extremes that accounting for atmospheric feedbacks could reconcile observations and model simulations.
Biogeochemical effects of deforestation are mainly quantified as losses of carbon storage in vegetation biomass and soils, but there can also be contributions from changes in the budgets of other greenhouse gases such as methane. As less above-ground carbon is stored in boreal ecosystems than in the tropics, boreal deforestation leads to less carbon losses per unit area than tropical deforestation. Carbon losses depend also on what replaces the forest, cropland or grassland, and the post-deforestation land management practices such as fertilization and irrigation.
The Land Use Model Intercomparison Project (LUMIP;
Lawrence et al., 2016) provides a unique opportunity
to compare the sensitivity to deforestation for Earth system models (ESMs) participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6; Eyring et al., 2016).
This study focuses on the idealized global deforestation experiment
(
Here, we analyse the response to this idealized deforestation scenario in nine ESMs participating in CMIP6. We first focus on the biogeophysical effects, which manifest at local and non-local scales. This is underlined by in-depth analyses including the temporal development of climate responses including time of emergence (ToE), a new metric, fraction of emergence (FoE), and land–atmosphere coupling strength (surface energy balance, SEB). Next, we analyse the changes in land carbon pools due to deforestation and provide insights into different model formulations. These results provide insight into LULCC processes that affect climate and their representation in the state-of-the-art models, but also have important implications for areas experiencing rapid deforestation today.
The
In combination with the corresponding
Nine ESMs carried out the
Due to their model structure, some ESMs had to diverge from the simulation
protocol as described hereafter. MIROC does not simulate a specific forest
fraction and instead implemented the replacement of primary to secondary
natural vegetation, which allows for regrowth of forests. EC-Earth
implemented the deforestation by introducing primary to secondary land use
transitions on the forested natural land area and switched off the dynamic
tree establishment in the newly generated secondary land areas. In UKESM
deforestation is implemented in a way that woody vegetation comprising
trees and shrubs is converted to agricultural grassland. Dynamic vegetation
processes continued to allow the trees and shrubs to compete for space in
the remaining natural part of the grid cell, but they only allowed C
While most models provided one realization of the experiment, IPSL and CESM2 conducted three ensemble members and MPI seven. Further, MPI and MIROC continued the simulation for 70 years and CanESM for 10 years beyond the required 30 years after the end of deforestation.
All spatial plots presented in this study show the running mean centred over
the last 30 years of the simulation for climate variables (year 50 to year 79) and over the last 10 years for carbon variables (year 70 to year 79),
thereby representing conditions at the end of the required stabilization
period. Accordingly, the first 30 years for climate and 10 years for carbon
variables from the piControl simulation after branching off the
The surface energy balance (SEB) decomposition approach is used to infer the
contribution of changes in energy fluxes to changes in the surface
temperature (
We use the concept of time of emergence (ToE) to assess in which year the
signal of near-surface 2 m temperature (
The transient climate response to cumulative carbon emissions (TCRE,
Gillett et al., 2013) identifies the amount
of warming (
We retrieved deforestation patterns (Fig. 1) by taking the difference of
forest fraction between the
Deforestation fractions
Deforestation of the top 30 % grid cells with regard to their forested
fraction in the
Changes in the mean state of near-surface temperature (
The reconstructed potential forest cover is estimated to be 48.68 Mkm
The analysis of biogeophysical effects of deforestation is split into sections on global and regional changes in the mean state by the end of the simulation period and the temporal evolution of the primary energy quantities.
Six of the nine models simulate a statistically significant decrease in
global near-surface air temperature in response to large-scale
deforestation. Results of statistically significant changes (Table 1) range
from
Spatial patterns of near-surface air temperature (
The global net decrease in air temperature is dominated by the changes over
the oceans and in the Arctic (Fig. 2). Using the surface energy balance (SEB)
decomposition approach, we can analyse the contribution of varying energy
fluxes to the change in surface temperature (
Zonally averaged surface energy balance (SEB) decomposition
component-wise for every model after deforestation (averaged over year 50 to
year 79) including only areas of deforestation (
In the mid- to high northern latitudes all models simulate an increase in
albedo in response to deforestation, which induces a cooling (Fig. 3a). This
increase in albedo mainly originates from the reduction in snow-masking
effect of forests allowing for a denser and longer lasting snow cover
towards summer over grasslands that replace forests. Some models even
simulate non-local effects: in CESM2, BCC and EC-Earth this effect is
carried beyond the geographical regions of deforestation, and in CanESM and
UKESM the geographical extent of the cooling is amplified due to a positive
sea-ice–albedo feedback over the Arctic Ocean (see Fig. S5). Longwave
radiation is reduced across all models northward of 35
All models simulate reduction in available energy (due to reduced net
shortwave and incoming longwave radiation) over areas of temperate
(50 to 23
Relationship between near-surface temperature changes (
The global-scale deforestation-induced cooling is only offset over tropical
forests. Here, most models (with the exception of EC-Earth and UKESM) show a
warming over
In CESM2, in the equatorial tropics, evaporation increases (Fig. S8). This
unintuitive response may be due to the fact that C
Comparing
The SEB approach applied here neglects the ground heat flux on longer
averaging periods, subsurface heat storage or changing emissivity. However,
inferring the difference between modelled and analytically determined
In four out of nine models that simulate tropical warming and
temperate/boreal cooling in response to deforestation the switch in sign of
Overall, the local response over deforested areas is a reduction of
available energy (net shortwave plus downwelling longwave radiation, Fig. S4b) across all models at higher latitudes (by
Previous studies on the temperature effects of large-scale or historical deforestation have shown that the locally induced changes in albedo after boreal deforestation are almost balanced by concurrent changing turbulent heat fluxes. However, the increased boreal albedo can also induce a non-local cooling over land and oceans via advection of cooler and dryer air (Chen and Dirmeyer, 2020; Davin and de Noblet-Ducoudré, 2010; Winckler et al., 2019a). Like in other multi-model studies on the biogeophysical effects of deforestation, it is difficult to separate local and non-local effects without further separation experiments. However, we also find a mean cooling across all models globally and locally over the areas of deforestation. Only MPI, IPSL and BCC simulate weaker non-local cooling effects, thus almost balancing global mean temperature effects of tropical warming and boreal cooling.
Still a key question is how models simulate the impact of deforestation on the turbulent heat fluxes (de Noblet-Ducoudré et al., 2012; Pitman et al., 2009), depending not only on the plant-physiological behaviour (e.g. stomatal conductance, growing seasons, leaf area index) but also on parameterizations of surface roughness and the soil hydrology schemes.
The sensitivity of the models to the imposed deforestation signal by the end
of the simulation period can be quantified in terms of the temperature
change (
In the temperate and boreal regions, UKESM, CanESM and EC-Earth show
temperature changes of more than
However, FS not only reflects local but also the superimposed non-local
effects caused by feedback mechanisms. In the tropical region where
non-local effects are smaller, we still see some differences in intensity.
In particular, CESM2 and BCC and to a smaller degree MPI reveal areas of
stronger sensitivity to deforestation in the tropics than elsewhere (up to
8, 6 and 4
To draw more broad conclusions, FS is averaged for every 10 % increase in
Previous studies have argued that the local temperature response to complete deforestation is stronger the smaller the initial forest cover was, and thus non-linear (Li et al., 2016; Pitman and Lorenz, 2016; Winckler et al., 2017).
Only CESM2 and IPSL seem to produce the suggested non-linear, saturating behaviour over tropical regions where non-local effects are smaller (see Fig. 2), and a clear linear behaviour cannot be found with any of the models.
However, drawing conclusions on the (non-) linearity is difficult. In our
setup,
At a higher level of spatial precision including climate and ecozones, results like the ones presented here could be used to generate lookup tables for climate responses of each model to a given level of deforestation. These would provide computationally inexpensive tools to draw fast conclusions on the climate effects of deforestation in, for example, future land use scenarios. However, in some models the responses show a non-linear behaviour not only to local coupling mechanisms but also due to climate feedbacks acting at the global scale. This superimposed, non-local signal should be isolated for models with strong Arctic amplifications (here CanESM, CNRM, UKESM, CESM2 and EC-Earth) to derive local climate responses. In addition, it would be preferable to use results from longer simulation periods once the models have equilibrated for such lookup tables.
The results presented so far do not take into account whether the models
have reached equilibrium by the end of the simulation period. Globally,
UKESM, CNRM, CanESM and EC-Earth simulate a linear response to the
deforestation signal with CanESM, EC-Earth and CNRM showing a continuing
downward trend after the end of deforestation, while UKESM stabilizes over
Time series of temperature
The temporal evolution of
For models that provided several ensemble members of the deforestation
experiment (MPI, IPSL and CESM2) we calculated the time of emergence (ToE).
In the tropics, near-surface temperature changes emerge over the regions of
strongest deforestation before the end of the first 50 years of the
simulation (Fig. 6). Interestingly, the signal propagates from the centre of
deforestation to the edges in the tropical zone. In the central tropics, the
signal becomes robust (that is, exceeds the signal-to-noise ratio (SNR) of 2) with up to 20 % to 35 % of deforestation still left (Fig. S12). This
hints at the advection of temperature changes towards the centre of
deforested area due to non-local effects. In boreal zones, CESM2, and to a
lesser degree in MPI, demonstrates signals propagating westwards starting
from the boreal east coasts with about 30 % and 10 % of deforestation,
respectively, still left (Fig. S12). The main attributors here are the
westerly winds that carry the modified air by deforestation from the west to
the east coast of the continent where the signal is therefore strongest and
emerges earlier. In CESM2, Arctic amplification further amplifies this
process (see Sect. 3.2.1), leading also to responses outside
The results of the ToE analysis have to be treated with caution since only a
few ensemble members were available, and thus uncertainty remains high.
However, following up on the earlier analysis (Sect. 3.2.1), the observed
patterns make sense from a causal perspective. After 30 years, all three
models demonstrate a propagation of signals from the centre to the edges of
Our results have important implications for ongoing land cover changes and
climate policies. Between 2001 to 2018, 3.61 Mkm
The global net effect of precipitation changes over
Spatial patterns of precipitation responses averaged over year 50 to year 79. Only statistically significant changes shown. Contours depict the areas of deforestation (Fig. 1).
Over time, MPI, UKESM and IPSL simulate linear responses to the deforestation signal with only MPI showing global stabilization after ending forest removal (Fig. 5b). Other models exhibit longer time periods (CanESM and MIROC) of continuing positive or negative changes depending on the region. For example, over North America (not shown), CanESM simulates a downward and MIROC an upward trend in precipitation.
The strongest global mean reduction of moisture transfer to the atmosphere
via evapotranspiration (Fig. S8) and resulting precipitation over
Over tropical
Global deforestation affects precipitation by altering circulation patterns and by changing the moisture inputs from the surface to the atmosphere. The SEB analysis (Sect. 3.2.1) demonstrated how new plant types govern the land–atmosphere interaction via turbulent heat fluxes. In most cases we could infer a causal link between changes in turbulent heat fluxes, longwave radiation linked to cloud cover and precipitation, which is in line with previous studies (e.g. Akkermans et al., 2014; Lejeune et al., 2015; Spracklen et al., 2012). While most models simulate moisture decreases as less productive and evapotranspirative grasses replace trees, some models simulate local increases due to advected moisture (e.g. BCC) or favourable parameterizations of grasses (e.g. CESM2 and EC-Earth).
Land carbon (cLand, the sum of vegetation, soil and litter carbon) losses
range from
Changes in carbon cycle pools over time smoothed by a 10-year
moving average. Note that for CanESM and BCC only
For all models but MIROC, it is mainly the changes in vegetation carbon
(
In UKESM dynamic vegetation adjustments in the remaining natural parts of
the deforested grid cells drive the continuing decline and weak recovery of
In MIROC, because the vegetation type was fixed as woody types in the
deforestation, forest recovery started soon after the deforestation process.
As a result, the magnitude of
CESM2 shows a steep decline in cLand, which is mainly caused by the initial
vegetation loss and enhanced fire activity (carbon emissions by fire,
Spatial patterns of
Changes in carbon fluxes over time smoothed by a 10-year moving
average. GPP and NPP are the gross and net primary productivity,
respectively; rh is the heterotrophic respiration; NEP is the net ecosystem
productivity; fFire denotes the emissions by fire; and NBP denotes the net biome
productivity. NBP is based on year-to-year variations in
The slight continuing downward trend of
EC-Earth and CNRM seem to behave similarly in terms of
In EC-Earth, increases in cLitter and cSoil are partly caused by the
deforestation itself since portions of root and, against protocol, leaves
and wood biomass are left on-site for decay. In addition, reductions in
autotrophic respiration of grasses more than compensate for GPP losses due to
deforestation, leading to a positive
IPSL shows the smallest response in land carbon, which is dominated by cVeg changes and hardly by any changes in soil or litter carbon pools (Fig. 8). The exception is in central Africa, where the higher NPP of grasses increases the litter flux affecting mainly the long-term soil carbon pool (not shown).
In BCC, tropical changes dominate the global average with the highest
observed
From CanESM, we only investigate
The net biome productivity (
Land carbon changes emerge as a signal within the first 10 years in most
places (Fig. S18). MPI and IPSL show more distinct patterns at the outermost
edges of deforestation, where 30 years pass before ToE occurs. In IPSL and
CESM2, many patches outside
Although forest removal was implemented in a similar way across models, the
trajectory and spatial patterns of carbon changes differ strongly. The major
part of the land carbon model spread stems from the removal of vegetation
carbon based on the differences in initial forest distribution and carbon
densities. A similar divergence across multiple models but of lower
magnitude was already found for a previous study investigating the effect of
future land use and land cover changes on the carbon cycle in CMIP5 models
(Brovkin
et al., 2013). The changes in
The loss of land carbon follows a similar trajectory at the global scale, with only vegetation recovery (MIROC, UKESM) and grass parameterization (CESM2) causing non-linearities. We find that not only whether fire is represented (MPI, CESM2, EC-Earth) or not can have substantial effects on the NBP and thus overall carbon losses but especially how it is implemented. In CESM2 fire is used as a deforestation tool, while it only depends on litter fluxes in MPI and EC-Earth. In the latter model, fire activity decreases with the expansion of grassland opposed to the other two models. To narrow down the sign and magnitude of fire emissions thus needs further consolidation by incorporating observational data. The protocol allowed models to simulate dynamic vegetation processes outside the deforestation area based on the assumption that the time horizon of the experiment was too short for climate change effects to affect the remaining woody vegetation (Lawrence et al., 2016). UKESM disproves this assumption since forest cover continuously declined in the remaining part of the grid cell. The separation of land carbon pools by land cover type would have therefore been advantageous. Across all models that witness a declining or constant fast soil carbon pool with the onset of deforestation (CESM2, IPSL, MIROC), the fate of below-ground plant materials (roots) remains unclear considering that root biomass is about one-fifth of the above-ground biomass (Lewis et al., 2019).
The analysis of CMIP5 models revealed that substantial uncertainty in model responses was due to implementation differences (i.e. land use patterns, Boysen et al., 2014; Brovkin et al., 2013). Having a very simple experimental protocol of replacing trees with grasses, we now show that the underlying processes themselves also explain large parts of the model spread. Strong or weak model responses may originate from including or not representing certain processes explicitly, e.g. fire activity or soil biochemistry. Our analyses also highlighted the relevance of the comparative response of different vegetation types. While most evaluation is done for total land carbon stocks and fluxes, assessment of land use change requires adequate representation of individual land use/cover types at each location relative to each other. This highlights the need for improving the process understanding of soil carbon dynamics (e.g. Chen et al., 2015; Don et al., 2011; Giardina et al., 2014; Luo et al., 2017), fluxes (Atkin et al., 2015; Huntingford et al., 2017) and biomass carbon stocks (Erb et al., 2017) using observations and field experiments.
Similarly to
The sensitivity of land carbon changes with regard to the deforestation fraction in a grid cell across latitudinal climate zones (Figs. S22 and S23) depends on the initial biomass carbon, soil carbon dynamics, the characteristics of the replacing vegetation and probably even climate.
Most models, except for MIROC and IPSL, show an almost linear decrease in
FS in the boreal and temperate region, although the magnitudes vary strongly
(Figs. S22 and S23). On average, models decrease cLand by 4.1 and 4.9 kg m
Although climatic changes affect the carbon cycle negatively via droughts or
positively via favourable warming (see also Fig. 9), the main contribution
comes from the deforestation itself as also the temporal analysis revealed.
Therefore, the carbon response to
Carbon emissions from deforestation in the real world act as a greenhouse
gas with a potential warming effect. In absence of varying CO
The estimate of
Nine Earth system models carried out the LUMIP global deforestation
experiment (
The biogeophysical effect on mean global near-surface temperatures (
While the biogeochemical effects of large-scale deforestation on total land
carbon (
Non-linear responses with time underline the importance of accounting for amplifying non-local effects, showing for example that the changes in temperature or GPP propagate from the centre to the edges of deforestation in the tropics. The detection of robust climate signals may take decades or require more than 30 %–50 % of a grid cell's forest cover to be removed – a very long time (or large area affected) for climate policies to act. Though these results were found to be causally plausible, they have to be treated with caution due to lack of a sufficiently large ensemble.
The
Biogeophysical and biogeochemical model responses differ due to the varying characteristics of the replacing grass (CESM2, EC-Earth) or regrowing vegetation (MIROC and UKESM), soil parameters and dynamics, and altered land–atmosphere coupling responsible – for example, the partitioning of available energy into turbulent fluxes or the moisture transfer. Not only the distribution of initial forest cover but also the inherent carbon stocks differ widely and thus their losses differ as well. Soil carbon and physiological dynamics of trees versus grasses and their dependence on climate need better understanding through incorporating observations and field studies to constrain fluxes like heterotrophic respiration, GPP or autotrophic plant respiration.
Future analyses of the
This study provides the first unified multi-model comparison of large-scale deforestation effects on climate and the carbon cycle. By reducing uncertainties from the land cover change implementation itself we showed that the remaining model spread largely stems from model parameterizations and process representation of trees and grasses, which could be improved by incorporating observational data.
Primary data and scripts used in the analysis and other supplementary
information that may be useful in reproducing the author's work are archived
by the Max Planck Institute for Meteorology and can be obtained at
The supplement related to this article is available online at:
LRB designed the study, performed all analyses including scripting and plotting, and wrote the manuscript. VB wrote the introduction and the abstract together with LRB. All co-authors contributed with editing the manuscript and giving suggestions on the analyses.
The authors declare that they have no conflict of interest.
Lena R. Boysen, Victor Brovkin and Julia Pongratz thank Thomas Raddatz and Veronika Gayler for setting up the deforestation
maps used in MPI and for managing the publication of the simulations. Spencer Liddicoat thanks Eddy Robertson for creating the land use ancillaries and for helpful discussions.
This paper contributes to the Land Use Model Intercomparison Project (LUMIP,
Lena R. Boysen and Victor Brovkin, Matthias Rocher, Christine Delire and Roland Séférian
received funding from the H2020 CRESCENDO project (grant agreement no. 641816). Lena R. Boysen and Julia Pongratz received funding from the DFG priority program SPP 1689 CE-Land
This paper was edited by Alexey V. Eliseev and reviewed by David Lapola and two anonymous referees.