# Forestation in CMIP6: wide model spread in tree cover and land carbon uptake

Sabine Egerer, David M Lawrence, Peter J Lawrence, Arthur Argles, Vivek Arora, Alina L Barbu, Ian N Harman, Paul A Miller, Thomas Raddatz, Nicolas Vuichard, et al.

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Sabine Egerer, David M Lawrence, Peter J Lawrence, Arthur Argles, Vivek Arora, et al. Forestation in CMIP6: wide model spread in tree cover and land carbon uptake. Environmental Research Letters, 2025, 20 (5), pp.054033. 10.1088/1748-9326/adc93e hal-05045800

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Forestation in CMIP6: wide model spread in tree cover and land carbon uptake

To cite this article: Sabine Egerer et al 2025 *Environ. Res. Lett.* 20 054033

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LETTER

# Forestation in CMIP6: wide model spread in tree cover and land carbon uptake

Sabine Egerer<fnref n="1" />,*, David M Lawrence<fnref n="2" />, Peter J Lawrence<fnref n="2" />, Arthur Argles<fnref n="1" />, Vivek Arora<fnref n="5" />, Alina L Barbu<fnref n="6" />, Ian N Harman<fnref n="7" />, Paul A Miller<fnref n="8" />, Thomas Raddatz<fnref n="3" />, Nicolas Vuichard<fnref n="9" />, David Wårlind<fnref n="8" />, Tilo Ziehn<fnref n="10" /> and Julia Pongratz<fnref n="1" />,<fnref n="3" />

<fnref n="1" /> Ludwig-Maximilians-Universität München, Munich, Germany

E-mail: Sabine.Egerer@lmu.de

**Keywords:** forestation, CMIP6 models, land carbon uptake, terrestrial carbon dioxide removal

Supplementary material for this article is available online

Abstract

Forestation is expected to play a significant role as a terrestrial carbon dioxide removal technology in low-emission scenarios by storing carbon in the biosphere, thereby changing the physical properties of the land surface. To represent land use change, including afforestation and reforestation (AR), Earth system models (ESM) that contribute to the Coupled Model Intercomparison Project Phase 6 (CMIP6) draw on common projected land use data from Integrated Assessment Models. The extent and spatial distribution of AR differ substantially between the CMIP6 models ranging from –197 to 363 Mha of tree cover change by 2100 in the low-emission scenario SSP1-2.6 that has the highest AR among all future scenarios. The variability in simulated tree cover distributions, in combination with different representations of the carbon cycle, causes a high uncertainty in future land carbon uptake. Here, we disentangle the input information used to represent AR by CMIP6 models and differences in the carbon uptake process to explain the variable simulated potential of carbon sequestration due to AR. We provide recommendations on how AR might be implemented more consistently in future model intercomparison studies, especially regarding more consistent tree cover input (e.g. aligning present-day tree cover, consistent transition between land use and land cover types), and carbon-cycle-related processes (e.g. nitrogen cycle and disturbances). Adoption of these recommendations would increase the relevance of ESMs in terms of providing more accurate estimates of future land carbon uptake through AR.

1. Introduction

Low-emission scenarios, in line with the Paris
Agreement to limit global warming to well below 2 °
C, rely on carbon dioxide removal (CDR) from the
atmosphere (Roe et al 2019, IPCC Working Group

III 2022b). Afforestation and reforestation (AR) is
currently the most effective measure for terrestrial
CDR with globally around 1.9 GtCO2yr⁻<fnref n="1" /> removals
averaged over 2013–2022 (Pongratz et al 2024). The
projections on future forested land and subsequent
carbon removal emerge from integrated assessment

<footnote n="1">1 Ludwig-Maximilians-Universität München, Munich, Germany</footnote>

<footnote n="2">2 National Center for Atmospheric Research, Boulder, CO, United States of America</footnote>

<footnote n="3">3 Max Planck Institute for Meteorology, Hamburg, Germany</footnote>

<footnote n="4">4 Met Office, Fitzroy Road, Exeter, United Kingdom</footnote>

<footnote n="5">5 Canadian Centre for Climate Modelling and Analysis, Victora, British Columbia, Canada</footnote>

<footnote n="6">6 Centre National de Recherches Météorologiques, CNRM, Toulouse, France</footnote>

<footnote n="7">7 CSIRO Environment, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Canberra, ACT, Australia</footnote>

<footnote n="8">8 Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden</footnote>

<footnote n="9">9 Laboratoire des Sciences du Climat et de l'Environnement, LSCE-IPSL (CEA-CNRS-UVSQ), Université Paris-Saclay, 91191 Gif-sur-Yvette, France</footnote>

<footnote n="10">10 CSIRO Environment, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Aspendale, VIC, Australia</footnote>

<footnote>* Author to whom any correspondence should be addressed.</footnote>
models (IAM). They show a widespread increase in forest area between 0 and 1000 Mha by 2100 in the future low-emission scenario SSP1-2.6, which has the highest level of AR among all future scenarios (Popp et al 2017). IMAGE3.0, the marker IAM of SSP1-2.6, estimates around 500 Mha of AR by 2100 (van Vuuren et al 2017). Among scenarios that limit global warming to 2 °C or below, agriculture, forestry, and other land use (AFOLU), mainly AR, remove between 0.23 and 6.38 GtCO₂eq yr⁻¹ (mean 2.98 GtCO₂eq yr⁻¹) averaged between 2015 and 2100, reflecting a high uncertainty in carbon uptake (IPCC Working Group III 2022a).

In addition to IAMs, CMIP6 Earth system models (ESMs) with process-based carbon cycles are powerful tools for assessing future land cover and carbon uptake. ESMs are the only tools that interactively estimate future CDR potentials of AR as a response to changing climate, including carbon-climate feedbacks. They are the only source for estimates of the remaining carbon budget and climate projections while simulating the side effects of AR on climate (Sonntag et al 2016). However, the representation of tree cover change varies considerably between CMIP6 ESMs as land use information is transferred from IMAGE3.0 projections through the Land Use Harmonization Project (LUH2, Hurtt et al 2020) to the implementation of prescribed tree cover change in the CMIP6 models, which is further complicated by dynamic tree cover changes predicted by some CMIP6 models.

In the previous generation of models, CMIP5, which was based on the first phase of the Land Use Harmonization project LUH (Hurtt et al 2011), Di Vittorio et al (2014) showed a substantial underestimate of the change in tree cover in the AR scenario RCP4.5. The variable implementation of simulated land cover, land use, and land cover change caused large inter-model spreads in regional carbon and biogeophysical variables. Consequently, the land use datasets used in the framework of CMIP6 (LUH2) provided an Added Tree Cover variable to account for the missing reforestation area. Therefore, the spatial patterns of the differences in forest cover between LUH2 and each corresponding IAM were calculated annually for 2015–2100 (Hurtt et al 2020). Although a wide spread in the representation of AR was already highlighted in CMIP5, large model differences still exist, and most modeling groups did not utilize the Added Tree Cover variable. Reducing the variation in the model representation of AR should be a major priority for future model intercomparison studies. To understand model discrepancies in future land carbon uptake due to AR, the objectives of this study are to:

1. disentangle the input data of forested land and their translation into tree cover change (regarded as AR) in CMIP6 models

2. investigate how differences in model implementation along with model structure impact projections of land carbon uptake

3. provide recommendations for future model intercomparison studies to synchronize land-use input data and align the implementation of carbon uptake-related processes in land models

We focus on the different implementations of the AR CMIP6 model in the low-emission scenario SSP1-2.6. In this scenario, AR is a crucial element for climate mitigation. Thus, it is important to assess how AR contributes to land carbon uptake and how model uncertainties emerge.

Previous studies find a lack of consistency in the carbon of modeled soil despite improvements in representing net primary productivity (NPP) from CMIP5 to CMIP6 (Ito et al 2019, Varney et al 2022). Furthermore, CMIP6 models do not capture tropical forest carbon compared to observations, possibly because they do not adequately represent tree mortality (Koch et al 2021). The diverse response of land models is mainly due to the wide range of the CO₂ fertilization strength, the fraction of gross primary productivity (GPP) converted to NPP, and the residence times of carbon in vegetation, litter and soil across models (Arora et al 2020). We emphasize that model divergence per se is not necessarily bad, as there are significant uncertainties in how to best represent land carbon cycle processes. Divergence can also indicate progress towards improved process representation such as land-use practices, which models follow at different speeds (Pongratz et al 2018, Fisher and Koven 2020, Blyth et al 2021). However, we show here that much of the divergence arises because of distinct implementation of AR across models, a divergence that could and should be alleviated.

The paper is structured as follows. In section 2, the CMIP6 models used in this study are introduced and the analysis of land carbon uptake is described as a response to change in tree cover. In section 3, the changes in the global mean tree area and carbon of the land are shown and discussed. Land carbon uptake is analyzed relative to tree cover change. The results and shortcomings are discussed, and recommendations for future intercomparison studies are provided in section 4. Finally, the conclusions are presented in section 5.

## 2. Methods

We analyze global mean changes and spatial patterns of tree cover fractions and vegetation and land carbon between 2015 and 2100 from eight CMIP6 ESMs. We focus here on the AR scenario SSP1-2.6 of ScenarioMIP within CMIP6 (Eyring et al 2016, O'Neill et al 2016). The models are detailed in appendices A1, A2, and table A1. Land carbon includes
vegetation, soil, and litter for most models and carbon from wood products for CNRM-ESM2-1, EC-Earth3-Veg, MPI-ESM1-2-LR, and UKESM1-0-LL. For UKESM1-0-LL, soil carbon contains litter carbon. In addition, five of the models also provide information on tree vegetation carbon (figure A6). We compare time series and global maps of changes in tree fraction and land carbon uptake in ESMs to LUH2 and IMAGE3.0 to understand the high uncertainty in land carbon uptake. We study regional variations in tree cover changes and land carbon uptake between the tropical, temperate, and boreal zones and analyze how climate impacts carbon uptake. We analyze the interrelation between the mean change in tree cover between 2015 and 2100, the tree cover fraction in 2015, and the mean land carbon uptake, respectively. Thereby, we analyze whether land carbon uptake results mainly from areas of a high present-day tree fraction or from newly established forests. While we focus on analyzing model disagreement of land carbon uptake due to different implementations of tree cover change, other sources of uncertainty (for example, differences in implementation of the carbon cycle, disturbances) also contribute to model differences. However, without additional sensitivity experiments, we can not quantify all these sources of uncertainty. Our method is not suitable for inferring causality. However, the causal relationship between the change in tree cover and land carbon uptake is described by photosynthetic equations and implemented in climate models. These equations relate NPP and carbon uptake with tree cover and leave area index (LAI). We account for the temporal dynamics of changes by calculating the mean change over time instead of the accumulated values in 2100.

## 3. Results

### 3.1. Change in global mean tree area and land carbon

There is a high uncertainty in the change in global mean tree cover between the models, ranging from -197 to 363 Mha in 2100 (figure 1). Note that some models prescribe tree cover change and are Dynamical Global Vegetation Models (DGVMs), which have prescribed change in natural vegetation, but tree cover can evolve dynamically. The forest cover area change simulated by the IMAGE3.0 IAM is about 500 Mha by 2100. Thus, it is higher than the change in tree cover predicted by the models, while the gain in the forest cover area of LUH2 is only about 80 Mha in 2100. The forest gain in LUH2 and the Added Tree Cover variable add up to 718 Mha of AR. LUH2 represents forest area, which is translated into tree cover fraction in the land models. The large discrepancy between forest area in LUH2 and tree cover in CESM2, which is the only model that uses the Added Tree Cover variable, arises from afforestation in LUH2 in regions with sparse forests

and low tree cover (Lawrence et al 2022, Reiner et al 2023). CanESM5 is the only model with decreasing tree cover between 2015 and 2100 because land-use change is solely based on changes in the LUH2 crop area. Since the crop area is increasing, the area of other land use types is decreasing.

There are several differences in model implementation between models that contribute to differences in tree cover and tree cover change. For example, because none of the CMIP6 models evaluated in this study have an explicit representation of rangeland, the LUH2 rangeland land use type is likely to be interpreted differently between the models as forest, pasture, shrubs, or some combination resulting in different tree cover (Loughran et al 2023). In EC-Earth3-Veg, there is a patch-destroying disturbance with a 1% annual probability. This disturbance eliminates all vegetation within a patch, converting it to litter, and forces the vegetation to regrow on bare ground, resulting in a temporarily lower tree fraction. This is one reason why EC-Earth3-Veg shows the least smooth curve in figure A2 and patchy patterns in figures 2 and A8. In contrast, the high tree fraction of UKESM1-0-LL in the boreal zone could be due to the lack of disturbance processes in the model.

The present tree area in most CMIP6 models is in agreement with satellite-based estimates, which show a global tree area between 1992 and 2000 of between 3566 and 4274 Mha (Ma et al 2020) (figure A2). The only models with a larger tree area today are CanESM5 and IPSL-CM6A-LR because shrubs are included in the tree PFTs. In contrast, EC-Earth has a much lower than observed present-day tree area. EC-Earth3-Veg is known to have a cold bias in the boreal zone, especially in Siberia, which substantially reduces the dynamically calculated tree area (Döscher et al 2022). Another reason for the low tree cover in EC-Earth3-Veg is that tree cover is determined from the tree crown area and its LAI. Deciduous trees have a tree cover of zero when they drop their leaves during winter, which influences the annual mean tree cover.

The global uptake of vegetation carbon ranges from 44 to 133 GtC in 2100, and the global uptake of land carbon from 61 to 254 GtC between land models (figure 1). In comparison, the global uptake of land carbon in IMAGE3.0 for SSP1-2.6 by 2100 is 76-80 GtC<fnref n="11" />. Most models that use prescribed vegetation cover (CanESM5, CESM2, CNRM-ESM2-1, and IPSL-CM6A-LR) project a higher increase in land and vegetation carbon uptake, whereas models using dynamic vegetation (EC-Earth3-Veg, MPI-ESM1-2-LR, and UKESM1-0-LL) have a lower carbon uptake despite a relatively higher increase in tree area. The only exception is ACCESS-ESM1.5, which has one of the smallest changes in vegetation and land carbon, possibly because it is the only model with phosphorus

<footnote n="11">¹¹ IAM scenario explorer database (https://data.ene.iiasa.ac.at)</footnote>
<figure><img src="image_5.png" /><figcaption>**Figure 1.** Change in global mean tree area for CMIP6 Earth system models (ESMs; top left), vegetation carbon (bottom left), and land carbon (bottom right) of CMIP6 models compared to 2015. CMIP6 models that use dynamic vegetation are marked with (D). In addition, the changes in forest area for LUH2 (with and without Added Tree Cover) and IMAGE3.0 and the change in tree area for the model mean are given (top right)—note the different scales compared to the ESMs. For IMAGE3.0 we also show the accumulated carbon sequestration from land use from the AR6 database (https://data.ece.iiasa.ac.at/ar6).</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>**Figure 2.** Change in tree cover fraction between 2015 and 2100 (% land surface cover).</figcaption></figure>
limitation (table A1) (Ziehn et al 2021). CanESM5 has one of the highest increases in vegetation and land carbon despite a loss in tree cover due to its high absolute tree cover (figure A2). Furthermore, in CanESM5, a much higher fraction of GPP is converted to NPP (Arora et al 2020) compared to other models. The three models without nitrogen limitation (CanESM5, CNRM-ESM2-1, and IPSL-CM6A-LR) project the highest vegetation and land carbon uptake. CESM2 exhibits one of the largest increases in tree cover and also projects one of the largest changes in vegetation and land carbon stocks in existing and newly planted forests, due to reduced nutrient constraints on photosynthesis in the 21st century and a relatively strong CO2 fertilization (Fisher et al 2019). Despite having one of the highest increases in tree cover, EC-Earth3-Veg projects medium vegetation and land carbon gain, partly due to its much lower absolute tree cover compared to other models. Changes in vegetation carbon agree reasonably well with changes in land carbon. Five models provide information on tree vegetation carbon (figure A6). In four of them, the vegetation carbon gain in trees exceeds the overall vegetation carbon gain (not shown), which means that non-tree PFTs must be losing carbon. For IPSL-CM6A-LR, the tree vegetation carbon and the total vegetation carbon gains are similar. Fire events can dynamically reduce the carbon density in affected areas for CESM2, CNRM-ESM2-1, EC-Earth3-Veg, and MPI-ESM1-2-LR.

## 3.2. Regional variations in tree cover changes and land carbon uptake

The spatial distribution of tree cover change is similar to LUH2 in models with prescribed land cover change (CanESM5, CESM2, CNRM-ESM2-1 and IPSL-CM6A-LR) with AR hotspots in the eastern US, Europe and Southeast Asia (figure 2). CESM2 uses the Added Tree Cover variable of LUH2 with outstanding AR in the mid-USA, Europe, Southeast Africa, the Sahel, Southeast Asia, and South American grasslands. Despite the relatively high increase in tree cover the carbon gain is low in most added-tree-cover areas (figure A7). CNRM-ESM2-1 uses LUH2 land use information for crops and grasses, whereas forest fractions are adjusted to the remaining space within a grid cell. Hence, the high increase in tree cover in Eastern China and Europe compared to LUH2 (figure 2) is due to lower crop and grass fractions.

In the three dynamic vegetation models (EC-Earth3-Veg, MPI-ESM1-2-LR, UKESM1-0-LL), the tree cover fraction decreases in the Amazon basin. The tree cover fraction increases stronger at higher latitudes for MPI-ESM1-2-LR, UKESM1-0-LL, and partially for EC-Earth3-Veg compared to the other models due to the warmer climate. MPI-ESM1-2-LR and CESM2 show a high change in tree cover in the

Sahel. The added trees in high latitudes (MPI-ESM1-2-LR and UKESM1-0-LL) and in the Sahel (MPI-ESM1-2-LR and CESM2) result in low carbon uptake (figure A7). Areas with high carbon gain can be found in the Amazon basin, the Congo basin and Indonesia (that is, regions with high current tree cover) for CESM2, MPI-ESM1-2-LR, and EC-Earth3-Veg, and high northern latitudes for almost all models (figure A8). In ACCESS-ESM1.5, CanESM5 and UKESM1-0-LL, we find a decrease in carbon from land and vegetation in tropical zones (especially in the Amazon and Congo basins), which is likely due to a warming and drying trend in these regions (Schroeter et al 2024). In addition, ACCESS-ESM1.5 has a warm bias in these regions and a dry bias in parts of the Amazon (Ziehn et al 2020).

In figure 3, the mean land carbon uptake between 2015 and 2100 is shown as a function of the mean annual temperature and precipitation averaged between 2015 and 2100. The highest increase in land carbon occurs under very moist and hot conditions, corresponding to the tropical zone for CESM2 and MPI-ESM1-2-LR. For CNRM-ESM2-1, IPSL-CM6a-LR and UKESM1-0-LL, land carbon uptake is still higher than $2 \text{ kgC m}^{-2}$ for temperatures below $0 \text{ }^{\circ}\text{C}$ corresponding to the boreal zone. The modest increase in temperature due to climate change favors vegetation growth, hence litter fall. The modest increase in precipitation together with the weak permafrost thaw result in very wet conditions that limit organic matter decomposition, and hence litter decomposition. As a result, land carbon increases. For CanESM5 and EC-Earth3-Veg, there is carbon loss for almost all temperature and precipitation regimes.

## 3.3. Land carbon uptake relative to tree cover change

In this section, we analyze the relationship between tree cover change and land carbon uptake. As pointed out before, the causal relation follows from photosynthetic equations that are implemented in climate models. Thus, it is sufficient to study the correlation between both variables. Furthermore, we explore whether carbon uptake is mainly caused by an increase in tree cover change or due to an already high present-day tree cover. In the first case, carbon gain is mainly attributed to the higher density of forest compared to other types of land use. In the latter case, we expect a high carbon gain due to the $CO_2$ fertilization effect in the established forest. In figure 4, the mean change in land carbon between 2015 and 2100 contrasts with the mean change in tree fraction. The highest land carbon uptake occurs due to a higher tree cover in 2015 (for CanESM5, CESM2, CNRM-ESM2-1, EC-Earth3-Veg, and IKPS-CM6-LR with high R\_Tree\_2015) or a high increase in tree cover fraction from a lower tree fraction in
<figure><img src="image_7.png" /><figcaption>**Figure 3.** Mean change in land carbon (kgC m⁻²) between 2015 and 2100 as a function of mean annual temperature and annual precipitation averaged between 2015 and 2100.</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>**Figure 4.** Mean change in tree fraction between 2015 and 2100 compared to 2015 (%) versus mean change in land carbon between 2015 and 2100 (kg C m⁻²) compared to 2015. Colors indicate the tree fraction (%) in 2015. Each dot represents one grid cell. On the upper left side, the Pearson correlation coefficient between the tree fraction in 2015 and the mean change in land carbon (RTree_2015) as well as the mean change in tree fraction and the mean change in land carbon (R_dTree) are shown.</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>**Figure 5.** Distribution of land area by mean change in land carbon for all years compared to 2015. The red bars represent grid cells with a tree fraction >80% in 2015 and a small mean change in tree cover between 2015 and 2100 (<5%). The green bars represent grid cells with a large change in tree fraction between 2015 and 2100 (>10%). Mean (M) and standard deviation (SD) are calculated and mean values are marked as dashed lines.</figcaption></figure>

2015 (for ACCESS-ESM1-5 and UKESM1-0-LL with high R_dTree). For ACCESS-ESM1.5, we find a strong carbon release for grid cells with a constant and high tree fraction. For CESM2, CNRM-ESM2-1, and MPI-ESM1-2-LR, we find low carbon uptake in grid cells with a high increase in tree fraction, which might be due to unsuitable AR locations related to the Added Tree Cover variable (CESM2) and dynamic vegetation (MPI-ESM1-2-LR) (figure A7). In fact, the dynamic vegetation module distributes the PFTs according to their bioclimatic limits. However, especially in the Sahel, MPI-ESM1-2-LR overestimates tree cover because tree mortality after heat stress is not explicitly resolved, tree replacement through C4 grasses is inhibited, and soil moisture is unrealistically high in the Sahel (Raddatz et al 2007). Surprisingly, there is a high carbon uptake in grid cells with low tree cover in 2015 and a low tree cover gain in UKESM1-0-LL likely due to a high increase in soil and litter carbon (figure A9).

We are interested whether a high increase in land carbon is due to an already high tree fraction (>80%) in 2015, which may react positively to environmental changes (for example, CO₂ fertilization), or due to a high mean increase in tree fraction (>10%) between 2015 and 2100 (figure 5). Shortcomings in the representation of carbon processes lead to model differences in land carbon uptake due to the high fraction

of present-day trees (Blyth et al 2021). Differences in carbon uptake through newly established forests emerge because forest regrowth is not captured well by most models, possibly due to a lack of forest demographics (Fisher et al 2018). We find a higher mean land carbon uptake due to a high increase in tree fraction in ACCESS-ESM1.5, CNRM-ESM2-1, IPSL-CM6A-LR and UKESM1-0-LL. These models accumulate land carbon mainly in the temperate and boreal zones (figure A4) and most of them have a high R_dTree in figure 4. In contrast, for CESM2 and MPI-ESM1-2-LR, the high increase in land carbon in regions with a high tree fraction in 2015 (high R_dTree in figure 4) indicates high CO₂ fertilization. These areas are mainly in the tropics (figure A4). Areas with a high increase in tree cover in CESM2 can have either a high increase in land carbon uptake or almost no land carbon uptake. The latter regions are therefore unsuitable areas for AR, at least within the climate simulated in CESM2, as shown in figure A7. For CanESM5, there are hardly any grid cells with a high increase in tree cover fraction due to the high absolute tree cover in 2015 (figure A2). Land carbon increases strongly in grid cells with high tree cover in 2015, which are located mainly in the tropical and temperate zones (figure A4). In contrast, the low tree cover in 2015 for EC-Earth3-Veg results from a cold bias in the boreal zone and because deciduous trees
have a tree cover of zero when they drop their leaves
during winter (figure A2).

4. Discussion

We find a wide model variability in global tree cover
change and land carbon uptake among the CMIP6
models for the low-emission scenario SSP1-2.6 pro-
jecting the highest forestation among all scenarios.
The change in tree cover depends strongly on (1)
the representation of present-day tree cover, (2) the
translation of land use from LUH2 into model-
specific PFTs (and thereby from forest cover to tree
cover), (3) the implementation of prescribed land
use changes or dynamic vegetation-based changes in
PFT fractions, and (4) the use or not of the Added
Tree Cover variable provided by LUH2. A signific-
ant source of uncertainty originates from the trans-
lation of forest areas into tree cover fractions. First,
the definition of the crown cover fraction for forests
differs (Pongratz et al 2021). For example, Griscom
et al (2017) use a crown cover fraction of 25%.
Instead, the FAO uses a threshold of 10% (FAO 2020).
Second, trees are not always located within forests,
e.g. 29% of the tree cover has been found beyond
the areas previously classified as tree cover in contin-
ental Africa (Reiner et al 2023). Furthermore, CMIP6
models differ strongly in the proportion of carbon
uptake from existing forests and AR after 2015 due to
regional differences in the distribution of AR and the
strength of CO2 fertilization. Simulations with gross
land use transitions produce emissions that are 15%–
40% higher than net transitions (Stocker et al 2014,
Wilkenskjeld et al 2014, Hansis et al 2015, Hartung
et al 2021). Furthermore, including a nitrogen and
phosphorus cycle and natural disturbances due to
wildfires and wind-throw might limit carbon uptake
(Arora et al 2020, Ziehn et al 2021).

These findings have strong implications for
assessing the future global terrestrial carbon dioxide
removal (tCDR) potential. Currently, AR contributes
almost all CDR and will play a major role in CDR
in the future (Smith et al 2024). Current estimates
of annual global CDR through AR are approximately
0.49 GtC (0.32–0.61 GtC; full range across bookkeep-
ing models), averaged over 2013–2022, based on the
latest Global Carbon Budget estimates (Pongratz et al
2024). The conventional CDR potential, mainly AR,
reaches 1.23 GtC per year in 2035 and 1.55 GtC per
year in 2050, cumulating to 40.9 GtC at the point of
net zero CO2. Across IPCC Sixth Assessment Report
scenarios limiting global warming to 2 °C (>67%)
or below, CO2 removal from AFOLU (mainly AR)
reaches 68 (5.4–113) GtC between 2020 and 2100,
showing a high uncertainty between IAM assess-
ments. For IMAGE3.0, the marker IAM for SSP1-
2.6 scenario, carbon sequestration from land use

cumulates to 86 GtC until 2100. Among CMIP6
models, land carbon removal ranges between 61–
254 GtC between 2015 and 2100 (figure 1). Although
IMAGE3.0 uses a simple process-based DGVM to
assess the carbon cycle, it does not explicitly consider
carbon-climate feedbacks and ignores the response
of ecosystems to environmental changes. Thus, it
underestimates carbon productivity compared to
ESMs. An advantage of CMIP6 ESMs over IAMs
in providing more accurate estimates is that ESMs
rely on a consistent land use pattern of AR. However,
the implementation of land use again happens in
a model-specific way. We find that the distribution
of prescribed tree cover based on IAMs/LUH2 dif-
fers substantially from those of dynamic vegetation
models. The former is determined by human-made
economic decisions and climate policies (e.g. increas-
ing protection levels for carbon-intensive ecosystems,
reforestation of degraded or deforested areas) (van
Vuuren et al 2017). In contrast, the latter is determ-
ined by climatic and soil conditions.

4.1. Shortcomings in representing forestation in CMIP6 models

A key limitation of the CMIP6 models is the absence
of any representation of forest plantations. In reality,
AR will at least partially rely on forest plantations with
fast-growing species such as eucalyptus and poplar
with potentially higher yields than natural forests (e.g.
Sedjo 1999, Wang et al 2013, Stolarski and Stachowicz
2023). The CMIP6 models do not represent these fast-
growing forest plantations. However, because forest
plantations are harvested regularly and thus carbon
is released into the atmosphere (Lewis et al 2019), it
depends on the lifetime of wood products whether
they effectively sequester more carbon compared to
natural forests.

Another limitation is that most models use pre-
scribed maps of net land use change instead of
gross land use transitions, and they do not con-
sider primary and secondary forest changes separ-
ately. Consequently, in grid cells where deforestation
and AR exist, the specific impacts of deforestation and
AR are not separately accounted for.

In climate models, carbon gains from elevated
CO2 tend to offset any temperature- or drought-
induced carbon losses (Koch and Kaplan 2022). Some
models include wildfires (table A1) and wind-throw,
however, other potential large-scale forest damages
(e.g. by invasive species, pests, insect outbreaks) are
not represented in ESMs. Fisher et al (2018) discuss
the inclusion of forest demography in ESMs which
only EC-Earth-Veg represents among the models in
this study. A general drawback of many modeling
groups is that new developments in the land-surface
models are not always implemented in the corre-
ponding CMIP6 version (Fisher et al 2018, Pongratz
<table><caption>**Table 1.** Recommendations for future model intercomparison studies including an explanation and example to each item.</caption><thead><tr><th></th><th>Explanation</th><th>Example</th></tr></thead><tbody><tr><td>Land-use input data</td><td>1. Use consistent land-use input data<br>2. Revise Added Tree Cover variable, hence adapt dynamic vegetation<br>3. Standard present-day land cover data set to be used by all models</td><td>consistent and easy to implement accounting of forestation in the LUH product<br>prescribe trees only where they can grow and sequester carbon<br>e.g. derived from satellite data</td></tr><tr><td>Land models</td><td>4. Consistent transition between LUH2 land-use types and PFT fractions in land models<br>5. Consistent scaling of tree cover fraction to obtain realistic tree cover in land models<br>6. Consistent translation from forest area to tree cover fraction in land models<br>7. Model improvements</td><td>e.g. transition from LUH2 rangeland to forests/grasses/pasture<br>e.g. when using additional satellite data<br>e.g. following Lawrence et al (2022)<br>include nitrogen cycle, disturbances, forest management and demography, tree mortality</td></tr><tr><td>Output data</td><td>8. Expand the list of compulsory variables to be available from all models with a better description of how they should be reported</td><td>PFT-specific land and vegetation carbon stocks</td></tr><tr><td>Documentation</td><td>9. More detailed documentation on the methodology used to derive land-use land cover forcing data</td><td>translation of LUH2 forest into land model tree cover</td></tr><tr><td>Testing</td><td>10. Inclusion of additional simulations to explore the dependence on the interpretation of the LUH2 forcing within each model</td><td>e.g. as part of the Land Use Model Intercomparison Project (LUMIP)</td></tr></tbody></table>

et al 2018, Blyth et al 2021). For example, new versions including forest demography were developed in JSBACH4 (Nabel et al 2019) and CABLE-POP (Haverd et al 2018), which have not been adopted in the corresponding CMIP model branch.

The accessibility of PFT-specific data limits the analysis of carbon uptake. Total land carbon also includes carbon gain/loss in areas covered by vegetation other than forests (grasses, shrubs, crops, and pastures). Therefore, we investigate to what extent total land carbon uptake is a good indicator of carbon gain by AR. We compared the sum of vegetation, litter, and soil carbon of trees, representing the total land carbon, to the total land carbon (figure A3). For three models (CNRM-ESM2-1, IPSL-CM6A-LR, MPI-ESM1-2-LR), information on vegetation, litter, and soil carbon of trees are available. For CNRM-ESM2-1 and MPI-ESM1-2-LR, the carbon gain in trees is higher due to carbon loss in other PFTs. For IPSL-CM6A-LR, both variables agree.

This study aims to identify the drivers of differences in tree cover fraction and land carbon uptake among CMIP6 models. However, without additional sensitivity experiments, it is not possible to quantify the sources of differences in carbon uptake caused by the model-specific representation of the carbon cycle.

Not all variables are available for all models in the CMIP6 database, which restricts the number of models in our study.

## 4.2. Recommendations for future intercomparison studies

From our results, we infer several recommendations for future model intercomparison studies and general future model development assuming sufficiently good land use input data in table 1.

Model improvements towards a more consistent representation of tree carbon uptake include forest management and forest demographics (Fisher et al 2018). Traditional land models lack the representation of woody demography required to simulate age effects on growth and mortality that lead to very high biomass accumulation rates in young forests compared to old-growth stands (Purves et al 2008, Poorter et al 2016). A step forward might be to improve temperature- and drought-induced tree mortality (Koch and Kaplan 2022) in DGVMs, e.g. by improving plant hydraulics (Bonan et al 2014, Fisher et al 2018). A prerequisite therefore is a realistic representation of droughts in climate models.

Several of these points might be difficult to implement given the structural differences between
the models, especially for those ESMs with embed-
ded DGVMs. However, we would like to highlight
that different model implementations are not only
a burden but also a strength towards better process
understanding.

5. Conclusion

Forestation currently has the highest carbon storage among terrestrial CDR options and will likely contribute substantially to CDR in the future (Smith *et al* 2024). Inferring accurate estimates on future carbon uptake through forests from climate models is key to assess future CDR potentials. In contrast to IAMs, ESMs estimate future CDR potentials of AR as a response to changing climate (e.g. Loughran *et al* 2023, Moustakis *et al* 2024). We evaluate forestation in CMIP6 models in the low-emission scenario SSP1.2-6, which has the highest forestation among all scenarios. We find that the representation and area of AR differ substantially compared to the present-day global land cover among the models. These differences stem mainly from the translations of land use change, the representation of present-day tree cover, the implementation of the carbon cycle, and the inclusion of dynamic vegetation. Process-based models used in CMIP6, in addition to current IPCC estimates from IAMs, could provide valuable quantitative estimates of terrestrial CDR potential, including an explicit treatment of climate and carbon cycle feedbacks, if the procedure of translating land use information was better synchronized among models in future model intercomparison studies. Future work might go beyond assessing carbon cycle changes due to AR but also include hydrologic cycle impacts of AR.

Data availability statement

CMIP6 model data are available at https://esgf-node.lnl.gov/search/cmip6/.

Code availability statement

Preprocessing and postprocessing scripts are available
at https://doi.org/10.5281/zenodo.14592242.

Acknowledgments

We thank Amali Amali for providing information
on CMIP6 models by sending a questionnaire to
the modeling groups. S E was supported by the
German Federal Ministry for Research and Education
(BMBF) through the research project STEPSEC,
Grant Number: 01LS2102A and by the LMU
Postdoc Support Fund. I H and T Z acknowledge

support from Australia's National Environment
Science Program—Climate Systems Hub (https://
nesp2climate.com.au/). P M and D W was sup-
ported by the Strategic Research Area MERGE
(Modeling the Regional and Global Earth System—
www.merge.lu.se) and by the European Union's
Horizon Europe research and innovation program
through the research project RESCUE (Grant No.
101056939). The EC-Earth3-Veg computations, data
handling, and storage were enabled by resources
provided by the National Academic Infrastructure
for Supercomputing in Sweden (NAISS) and the
Swedish National Infrastructure for Computing
(SNIC) at Tetralith, NSC, Linköping University, and
SWESTORE/dCache, partially funded by the Swedish
Research Council through Grant Agreement Nos.
2022-06725 and 2018-05973. D M L and P J L and
the CESM project are supported primarily by the U.S.
National Science Foundation (NSF). This material
is based upon work supported by the NSF National
Center for Atmospheric Research, which is a major
facility sponsored by the NSF under Cooperative
Agreement No. 1852977.

Conflicts of interest

All authors declare that they have no conflicts of
interest.

ORCID iDs

Sabine Egerer https://orcid.org/0000-0002-7202-578X

Alina L Barbu https://orcid.org/0009-0008-2774-8912

Ian N Harman https://orcid.org/0000-0002-5690-0484

Thomas Raddatz https://orcid.org/0009-0002-0265-8095

Tilo Ziehn https://orcid.org/0000-0001-9873-9775

Julia Pongratz https://orcid.org/0000-0003-0372-3960

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