Temperature overshoot responses to ambitious forestation in an Earth System Model

Domain: earth_science
Published: 2024-09-19
Editorial outcome: minor_revision

Abstract
Despite the increasing relevance of temperature overshoot and the rather ambitious country pledges on Afforestation/Reforestation globally, the mitigation potential and the Earth system responses to large-scale non-idealized Afforestation/Reforestation patterns under a high overshoot scenario remain elusive. Here, we develop an ambitious Afforestation/Reforestation scenario by harnessing 1259 Integrated Assessment Model scenarios, restoration potential maps, and biodiversity constraints, reaching 595 Mha by 2060 and 935 Mha by 2100. We then force the Max Planck Institute’s Earth System Model with this scenario which yields a reduction of peak temperature by 0.08 oC, end-of-century temperature by 0.2 oC, and overshoot duration by 13 years. Afforestation/Reforestation in the range of country pledges globally could thus constitute a useful mitigation tool in overshoot scenarios in addition to fossil fuel emission reductions, but socio-ecological implications need to be scrutinized to avoid severe side effects.

Introduction
Reaching net-zero greenhouse gas emissions as required by the Paris Agreement’s goals will require the deployment of Carbon Dioxide Removal (CDR) methods, compensating at least for hard-to-abate emissions1–3. Pursuing a more ambitious 1.5oC (compared to pre-industrial levels) goal implies a more limited remaining emission budget, and thus rapid decarbonization, major societal transformations, and large-scale CDR application1,4. Thus, CDR deployment has been at the spotlight of scientific research recently5,6. Future scenarios generated by Integrated Assessment Models (IAMs) that keep to 1.5oC by 2100 typically exhibit excess positive emissions early on which are then sharply reduced, leading to a temperature overshoot (i.e., period during which mean global temperature exceeds intended warming level, here 1.5oC, before returning to it)7,8. This delay of early action in IAMs emerges due to the high upfront investments and near-term mitigation costs required to limit the overshoot9,10, but caution is needed since the normalization of the overshoot idea could possibly facilitate a political flexibility associated with a lack of action11. Overshoot is becoming increasingly more relevant due to incredible country pledges12,13, and concerns of political feasibility and delay of the required climate action14,15. Despite that, uncertainty still remains with regards to overshoot dynamics such as the peak temperature and overshoot duration and the associated Earth system responses and adaptation needs, which should thus be further studied9,16.

Afforestation/Reforestation (AR), which has been practiced for decades, can be a useful mitigation tool17 and combined with forest management practices constitutes virtually all (99.9%) of currently applied CDR18. In the literature a wider range of possible AR sequestration potentials has been reported, varying depending on the model used, land availability, CO2 levels, and assumptions regarding the capacity of different biomes in sequestering CO2 under a changing climate7,19,20. Apart from the potential of durably sequestering carbon (C) and mitigating warming, uncertainty remains regarding the permanence of C storage21, possible impacts of large-scale AR on local climates22, ecosystem biodiversity19, food security23, and the associated societal risks7. Still, recently the Land-Gap Report24 has estimated that long-term and net-zero country-level mitigation pledges amount to quite ambitious land-based CDR commitments, which would require 633 Mha of AR globally by 2060, in addition to 551 Mha for the restoration of degraded ecosystems. This estimation has recently been updated reaching likely up to 490 Mha of AR and up to 570 Mha of restoration25. Even though such levels of AR might seem rather high and their feasibility and associated socioeconomic risks have been questioned24, it is only through decisive action and ambitious policies that we can diverge from the high-risk climate path that we are currently following13. Hence, the mitigation potential and impacts of large-scale AR in the range of country pledges should be further assessed.

The complexity of assessing ambitious AR deployment emerges since both the changes in atmospheric CO2 following CDR application and the biogeophysical effects via altered moisture and surface energy fluxes can strongly affect the Earth system26. To capture these effects and feedbacks and thus get more reliable estimates of AR potentials and impacts, fully coupled Earth System Models (ESMs) need to be employed, which solve for the exchange of mass and energy between the land surface, the atmosphere and the ocean27. The assessment of biogeophysical effects has mostly been done for land use change, land management, and related land-cover changes (short “land use” in the following) in general. Studies investigated local or remote temperature26,28, precipitation29, and circulation changes30, caused by changes in surface roughness and albedo31, moisture recycling and net radiation22, as well as their interplay with the biogeochemical effect (cooling) of the associated decrease in atmospheric CO232.

Even though significant progress has been made in isolating and investigating such effects, studies in many cases employ fully idealized land use scenarios including massive (de)forestation globally, and cannot be thus used to confidently assess the effects of more realistic patterns33–35. This is especially the case with the multitude of results reported by deforestation studies, since it has recently been demonstrated that the responses of local climate to forest loss and gain are asymmetric36. Modeling efforts with a focus on assessing the impacts and mitigation potentials of more realistic AR scenarios have been rather limited. Some have been based on the available land use change scenarios associated with the Representative Concentration Pathway 4.5 (RCP4.5) from the Coupled Model Intercomparison Project 5 (CMIP5)37–39. In RCP4.5, however, the total ~800 Mha forest area increase by 2100 also includes a forest expansion that the models simulate in response to climate change, and typically, by experimental design (comparison is only possible between various land use scenarios), AR can be assessed only together with land transitions other than AR, including avoided deforestation, and thus it is not possible to isolate the effect of CDR39. Recently, Matthews et al.40 examined the mitigation potential of forest regrowth to 1920 levels by 2056, followed by a release of sequestered carbon during the second half of the century, and demonstrated that high AR levels, even when of temporary nature, can mitigate global temperature increase under a low overshoot scenario. However, Matthews et al.40 employed an intermediate complexity climate model, which does not fully represent all the relevant processes, and an idealized spatiotemporal AR pattern. In the ongoing Land Use Model Intercomparison Project (LUMIP)41 the land use pattern of the Shared Socioeconomic Pathway (SSP) SSP1-2.6 is considered. Even though this was intended to include substantial AR based on the marker IAM (IMAGE) scenario42, translation to ESMs has failed to reproduce that signal yielding moderate AR levels43, thus falling behind the ambitious amounts suggested by country pledges globally.

Overall, uncertainty still remains regarding the Earth system responses under temperature overshoot pathways16. Reported results show a significant variability of responses among ESMs, whose statistical significance, as well as the signal emergence from internal natural variability have not been robustly assessed, due to the lack of multiple (within and across model) ensemble members16,44. Yet, in most AR studies high future emission scenarios are used34,39. Importantly, such approaches suffer from the paradox of applying ambitious AR -implying strong coordinated climate action-, while following high-end emission scenarios-implying delayed and insufficient climate action. The normalization of the idea of coupling largely unmitigated warming scenarios with ambitious AR could connote the establishment of AR as an alternative to emission reductions, and needs to be treated with caution45. From a biophysical perspective, high-end scenarios induce an enhanced CO2 fertilization effect, thus strengthening vegetation carbon uptake, land surface fluxes, mitigation potentials and biogeochemical cooling34,38,39,41. Typically, they also yield intensified disturbances (e.g., wildfires) which could compromise the permanence of carbon stored in vegetation46. Evidently, the role of AR under scenarios with stronger emission reductions yielding a weaker vegetation fertilization, and hence less biogeochemical cooling, as well as lower disturbance rates, thus suffering less from CDR permanence overestimation, remains unclear.

To fill this gap, we investigate to what extent an ambitious constrained AR scenario in the range of total country pledges globally can mitigate a temperature overshoot emission scenario and alter its dynamics (duration, peak and end-of-century temperature), and whether biogeochemically-induced cooling can emerge from internal variability and compensate for any possible AR-induced biogeophysical warming. We first develop an AR scenario in the range of country pledges globally reaching 595 Mha of AR in 2060 and 935 Mha by 2100, and then run fully coupled simulations with the Max Planck Institute’s Earth System Model (MPI-ESM). Simulations are performed under a high-overshoot SSP5-3.4os emissions scenario which follows SSP5-8.5 until 2040, followed by rapid decarbonization, and reaching net-zero around 2070 and net-negative thereafter. To take into account the technoeconomic considerations, biogeophysical potentials, and biodiversity concerns, we harvest the recently published AR6 Scenarios Database (AR6-SD)47, restoration potential maps, and maps of human influence. Beyond these considerations we also discuss in detail the possible socioeconomic barriers and tradeoffs of such ambitious AR implementation.

Our results show that ambitious AR in the range of country pledges globally can mitigate a temperature overshoot scenario by reducing the peak and end-of-century temperature by 0.08 oC and 0.2 oC respectively, as well as the duration of the overshoot by 13 years. Overall, biogeochemical cooling due to the reduction of CO2 dominates, and at least compensates for any biogeophysically-induced warming.

Results
Afforestation/Reforestation pattern
Following AR6-SD estimates (Fig. 1, “Methods”), AR is applied at the expense of grazing land globally, while cropland areas and natural grassy and shrubby biomes remain unchanged. By considering restoration potential maps and biodiversity proxies (Fig. 2, “Methods”), the resulting AR scenario (Fig. 3) exhibits a strong increase in forest area over the 21st century, with slightly higher dynamics in the first half, reaching 595 (935) Mha of forest area increase by 2060 (2100) over current (2015) levels. This occurs at the expense of heavily managed pastures, which decrease by 472 (575) Mha and more lightly managed rangelands, which decrease by 123 (360) Mha. Latin America (256 Mha in 2100) and Africa (215 Mha) emerge as hotspots of forest area increase, followed by the Organization for Economic Cooperation and Development (OECD90) developed countries (201 Mha), Asia (162 Mha), and Eastern European countries and the reforming economies of the former Soviet Union (101 Mha). At the country level, the United States (142 Mha), Brazil (113 Mha), China (110 Mha), Russian Federation (64 Mha), and Argentina (24 Mha) carry out in total 48% of AR by 2100 (Supplementary Fig. 1). The scenario includes reforesting 514 Mha in 2100 of previously deforested areas globally, corresponding to 60% of total historical deforestation (851 Mha during 1850–2014 in MPI-ESM following the Land Use Harmonization 248 historical pattern) (Supplementary Fig. 2). The remaining 421 Mha are afforestation, which is employed by priority over the less biodiverse rangelands (Fig. 2 & Supplementary Fig. 2).

Mitigation of global temperature
The average global 2m air temperature (compared to the 1850–1900 MPI-ESM average) and the global carbon fluxes are shown in Fig. 4. Under the reference (REF) simulation (constant land use at 2015 levels, see “Methods”), temperature overshoot lasts for ~65±2 years (mean±standard deviation estimated from smoothing and bootstrapping—see “Methods”), from 2035 up to 2099. The probability that end-of-century temperature will be within 1.5°C over pre-industrial levels is 46%, with an average temperature of 1.51°C (minimum—maximum of 5-year period from ensemble members: 1.27–1.87°C—see “Methods”). In year 2058±2, a peak temperature of 2.06°C (1.89–2.29°C) is reached, which follows the year during which atmospheric carbon reaches its maximum value (~+980 GtCO2, 126 ppm in 2057 compared to 2015). Under the AR simulation, temperature overshoot lasts for ~52±2 years, from 2036 up to 2087. The probability that the 1.5°C target is returned to by the end of the century is 90%, with an average temperature of 1.31°C (1.02–1.59°C). In year 2058±2, a peak temperature of 1.98°C (1.8–2.23°C) is reached, staying below the Paris Agreement 2°C target with a probability of 48%. Peak temperature lags ~3 years after the maximum in atmospheric carbon.

Comparing the REF and AR simulations suggests that the isolated effect of AR on average global temperature yields peak temperature reduced by ~0.08 oC, overshoot duration by ~13 years, and end-of-century temperature by ~0.2 oC. The impact of AR on global temperature emerges already in ~2052±2 years, when the temperature difference with REF starts becoming statistically significant (Fig. 4a). At that point, AR has reached 495 Mha, atmospheric carbon is lower by ~110 GtCO2 (30 PgC, 14 ppm), and oceanic carbon by ~22 GtCO2 (6 PgC), due to a total increase of ~132 GtCO2 (36 PgC) in the land carbon sink (Fig. 4b). The temperature trajectory under AR is smoother, rather reaching a plateau before it starts leveling off. By examining different percentiles of global average daily temperature a similar behavior is demonstrated (Supplementary Fig. 3).

CO2 removal
The land C stock divergence of AR from REF is notable within few decades and continues until the end of the century (Fig. 4b). Over grid cells where AR is applied the land sequesters in total ~395 GtCO2 (108 PgC) more compared to REF by 2100, with less carbon being sequestered over the forested rangelands, which are typically more arid (Fig. 4c). This is the net result of ~420 GtCO2 (115 PgC) sink enhancement over 912 Mha of our AR area, and a weakening of the land C sink by ~25 GtCO2 (7 PgC) over the remaining 23 Mha, mostly in the tropics (despite the forest area increase). Overall, ~249 GtCO2 (68 PgC) correspond to the partial reversal of historical deforestation. Over grid cells where AR is not applied, a total weakening of CO2 uptake by ~13 GtCO2 (4 PgC) emerges, most prominently over the Amazon. Therefore, the net land sink increase compared to REF is ~382 GtCO2 (104 PgC) by 2100 in total, reducing the atmospheric CO2 level by ~281 GtCO2 (77 PgC, 36 ppm), and the ocean sink by ~101 GtCO2 (27 PgC).

The rate of change in land C under both REF and AR mostly follows the signal of change in atmospheric C, which is driven by fossil fuel emissions (Supplementary Fig. 4). Under REF, the land C stock transits from being a sink to a source in ~2090, whereas under AR the land C remains a sink, even though with weak and ever decreasing sequestration rates, becoming a source only in 2100. This suggests that under AR increased forest cover coupled with decreased CO2 atmospheric levels compared to REF offer a ~10-year buffer before the land C stock transits to a source behavior, allowing for more C to be sequestered during this period. However, the reverse image is observed when the ocean C sink is examined (Fig. 4 & Supplementary Fig. 4). Here, not only is ocean C uptake consistently weaker under AR compared to REF, due to the lower CO2 concentration as a consequence of the CDR application, but in fact the ocean sink transits to a source behavior under AR in ~2085. Under REF the ocean C sink does not transit to a source behavior, even though by 2100 the rate of increase is almost zero. This demonstrates that the ocean tends to partly offset the efficiency of applied CDR. On average, for every additional 100 GtCO2 (27 PgC) sequestered in land under AR compared to REF 26 GtCO2 (7 PgC) less carbon is taken up by the ocean, leading to a net removal of 74 GtCO2 (20 PgC) from the atmosphere, which suggests a 74% efficiency.

Overall, the applied AR manages to sequester ~4.49 GtCO2/year more over land compared to REF on average, but eventually reduces atmospheric CO2 only by ~3.32 GtCO2/year on average, due to compensation by the ocean CO2 fluxes. By 2100, this corresponds to a ~41 GtCO2 (11 PgC) gain over land compared to REF and ~0.02°C temperature reduction for every 100 Mha of forest area increase, which also suggests a ~0.05°C temperature reduction for every 100 GtCO2 (27 PgC) additionally sequestered in land compared to REF. CDR efficiency expressed as temperature reduction per 100 GtCO2 removed from the atmosphere compared to REF increases across time, reaching ~0.07°C in 2100 (Supplementary Fig. 5). Efficiency expressed in terms of C removal per 100 Mha increases until ~2080 and then plateaus until the end of the century, reaching on average ~30 GtCO2 (8 PgC) (Supplementary Fig. 5).

AR sequestration rate defined as the rate of land C increase when AR and REF are compared shows a sharp increase to 3.9 GtCO2/year in 2030 (27 GtCO2 cumulatively), rising further to 6.3 GtCO2/year in 2050 (121 GtCO2) (Fig. 5). These high rates are sustained and strengthened until ~2065 (7.1 GtCO2/year, 222 GtCO2), before they start levelling off, reaching 1.7 GtCO2/year (382 GtCO2) in 2100 even though at this point the individual land C stocks under REF and AR have both transited to a source behavior. When it comes to the rate of decrease in atmospheric CO2 when AR and REF are compared, a removal rate of 3.39 GtCO2/year is achieved by 2030 (24 GtCO2), 5.3 GtCO2/year by 2050 (103 GtCO2), and 5.6 GtCO2/year by 2065 (185 GtCO2). By 2100, atmospheric CO2 rates reach −0.21 GtCO2/year (281 GtCO2 in total), since additively the ocean and land C sources under AR are stronger than the land C source under REF.

Changes in regional hydroclimate
Grid cell-level results (Fig. 6, Supplementary Fig. 6) show patches of cooling emerging as early as 2030, when the mean temperature pattern under AR is already statistically significantly different compared to REF (see “Methods”, and Supplementary Fig. 7). Regions with significant cooling during 2030–2050 emerge not only for mean, but also for more extreme temperature, mostly over the northern high-latitudes, and over Latin America, and central Africa (Fig. 6, top row). Such cooling becomes progressively more robust and widespread over time, and is already dominant during the period around the peak of global temperature (2050–2070) (Fig. 6, mid row), especially over Africa, Latin America, and the northern high-latitudes. The pattern of mitigation is consistent across the mean, and the high and low temperature extremes, but differs in magnitude over the northern-latitudes where low temperature extremes are reduced more strongly. Patches of warming at the sites of forestation in Eurasia and North America emerge, which are however statistically insignificant. By the end of the century, cooling is dominant globally for mean temperature (Fig. 6, bottom row). Cooling is stronger over Africa, Latin America south of the Amazon rainforest, northern Asia, and North America. A similar pattern is obtained for the extreme high temperature, albeit with a weaker temperature mitigation over the northern high-latitudes. On the contrary, mitigation for the extreme low percentile is stronger over the northern high-latitudes, and a small patch of statistically significant warming emerges in eastern Asia over sites of forestation.

Our results further suggest a wetter hydroclimate over forested regions emerging during the mid-century (Fig. 7). This is most clearly evident with increases in relative humidity and cloudiness over sites of forestation, which start emerging already from 2030 onwards, and are especially pronounced and widespread over Latin and North America, Africa, and east Asia. This is accompanied by statistically significant but less widespread patterns of precipitation and evapotranspiration increases evident over Latin and North America, Africa, and east Asia, where the hotspots of changes in cloudiness and relative humidity are reported. Over sites of forestation reduced albedo leads to an increase in surface net radiation, thus resulting in an increase in latent heat fluxes commensurate with the reported increase in evapotranspiration and in a reduction of sensible heat flux (Supplementary Fig. 8). An increase in albedo is reported over the Arctic already from the mid-century, which can be explained by the increase in sea-ice, which is however noisy at the global scale (Supplementary Fig. 9). Surface ocean pH reaches a 0.03 increase under AR compared to REF (Supplementary Fig. 9).

Methods
Model and experimental setup
We run fully coupled ESM simulations using the CMIP6 configuration of MPI-ESM (MPI-ESM-1-2.01p7-LR)114. MPI-ESM and its land surface component JSBACH have been widely applied, evaluated, and compared against observations and other models, and have been generally found to perform well for various key land surface variables115–117, and for both biogeophysical and biogeochemical effects35,118. The model is run in an emission-driven setup, which means that atmospheric CO2 concentration is not prescribed, but is rather dynamically updated. The fully coupled emission-driven setup offers a dynamic interaction of water, carbon, and energy fluxes between the land, the atmosphere, and the ocean, thus allowing for a full representation of the complex biogeophysical/biogeochemical effects and feedbacks of AR within the entire Earth system.

Our experimental setup includes an AR and a reference (REF) scenario spanning from 2015 to 2100, following SSP5-3.4os, except for land use. SSP5-3.4os reaches a total radiative forcing of 3.4 W/m2, and is an overshoot scenario that follows SSP5-8.5 emissions until 2040, followed by rapid decarbonization, thus reaching net-zero around 2070 and then net-negative (~−3.8 GtCO2/year)16,119. To fully isolate the total effects of AR on the Earth system, which is estimated as the mere difference between the AR and REF scenarios, we keep land use constant at the 2015 state under REF. Hence, we do not include any avoided deforestation, or other land use transitions, which would further complicate the accurate estimation of CDR. Towards that direction, even though increased forest area and wood biomass availability could impact the demand for wood products and hence harvest rates via market mechanisms120, we keep wood harvest amount constant at 2015 levels for both scenarios. At the same time, the amount of vegetated surface is kept constant for both scenarios, and competition among different Plant Functional Types (PFTs) over vegetated land is switched off, thus not allowing forest to expand as a response to a warming climate, in contrast to the CMIP6 model configuration. This allows for prescribing forest area in each timestep, thus being able to fully isolate the signal of AR, which is the main aim of this study. First, accounting naturally expanding vegetated land could raise a concern as to whether CDR can be claimed over land that becomes available due to our sole failure to decarbonize our economy. Second, the permanence of stored carbon over such land is in fact questionable since, assuming further climate mitigation, forest would contract again, suggesting carbon fluxes toward the atmosphere. We run in total 20 model runs; 10 ensemble members for each scenario, to allow for a robust probabilistic treatment of our results.

Probabilistic treatment
To robustly assess the global dynamics of the overshoot and its characteristics, a probabilistic framework is employed. To estimate the time of signal emergence, the overlapping coefficient (OVL)121 is employed, which is a measure of similarity between the probability density functions (pdfs) of two populations f1(x) and f2(x), and is calculated as follows:
OVL = ∫ min[f1(x), f2(x)] dx (1)

Over a temporally moving window, global mean 2m air temperature data from all ensemble members are pooled together, creating two samples, whose OVL is estimated with a gaussian kernel density function. This process is repeated by bootstrapping 1000 times, and a mean OVL is estimated. To estimate an OVL threshold below which dissimilarity between AR and REF distributions can be assumed, an autoregressive model (AR(1)) fitted to REF global temperature is used. At every temporal window, first 1000 pairs of stochastically generated temperature time series of trend and sample size equal to REF are created, for each of whom OVL is then estimated. The lower 5th percentile of the 1000 OVL values estimated from the synthetic data (coming from the same known AR(1) process) is determined as the OVL threshold, below which two data samples can be considered to come from dissimilar distributions (at the 5% significance level). The year of signal emergence is then calculated as the year when the mean OVL between REF and AR data starts becoming consistently lower than the significance OVL threshold. This process is repeated with a window length ranging from 5 to 10 years and a mean year of emergence is estimated, to make sure that the choice of window length does not bias the results.

To robustly characterize end-of-century temperature while taking into account interannual variability, the full end-of-century 5-year period (2096–2100) is considered. Similarly, to get robust estimates on peak temperature and avoid biases by likely extremely hot or cold years later or early on, the full 5-year consecutive period yielding the highest average temperature for each ensemble member is considered, and the temperature data are then pooled together. The choice of the period length does not bias the results, as shown in Supplementary Fig. 11.

To robustly capture overshoot duration, the signal need to be isolated from the noise of internal variability. Hence, a smoothing Savitzky-Golay122 filter is applied. Overshoot duration is then estimated with a varying smoothing window length, ranging from 5 to 10 years, to alleviate any bias induced by the choice of window length. This process is repeated 1000 times, where each time a random combination of 10 ensemble members with replacement are chosen. The mean overshoot duration is then estimated as the mean of the values obtained by bootstrapping. To get a clearer picture of the responses of the full spectrum of temperature variability, different percentiles of global average daily temperature are treated with the same approach.

At the grid level, the statistical significance of changes in 2m temperature (Fig. 6) or other hydroclimatic variables (Fig. 7, Supplementary Fig. 8) during different time periods is inferred at the 5% level. To do this, for each scenario yearly values from all 10 ensemble members during that period are pooled together, and a two-tailed Student’s t-test adjusted to account for lag-1 temporal autocorrelation123 is applied. To assess field significance (i.e., whether the annual average temperature field under AR is statistically significantly different from REF) the probability of at least one false positive among the multitude of grid cell-level tests is accounted for124. If a single grid cell is found to be statistically significantly different after accounting for this probability, then the global null hypothesis that a statistically significant difference between the AR and REF temperature field does not exist can be rejected125. To increase the robustness of the approach and test its sensitivity, a) temperature data at the grid cell level are pooled over a moving window with length ranging from 1 to 10 years, and b) field significance is declared when the null hypothesis is consistently rejected for a consecutive number of years ranging from 1 to 5, defining as year of emergence the starting year of that period. The results are presented in Supplementary Fig. 7.

AR scenario development
To design an ambitious constrained AR scenario following technoeconomic, environmental, and societal constraints, we leverage (a) the AR6 Scenarios Database (AR6-SD)47, (b) the restoration potential map published by Griscom et al.19 (hereafter GRS), (c) the Atlas of Forest and Landscape Restoration Opportunities (hereafter ATL)126, and (d) the Very Low and Low Human Influence maps published by Riggio et al.127 (hereafter VLHI and LHI, respectively).

All maps are regridded to the MPI-ESM resolution with a conservative remapping algorithm. For a given year and region, the AR target is first distributed across grid cells prioritized by GRS. The grid cells considered for AR are the ones where (a) grazing land is available, (b) the remaining GRS potential is greater than zero, (c) forest area has not already increased more than 10% in that year, to avoid excessive AR rates that could be institutionally challenging or incompatible with sustainable local socioeconomic changes, and (d) at least 20% forest cover has historically been or could potentially be sustained, to not use climatically unsuitable regions that would largely require management practices. The target is uniformly applied to the grid cells considered; however, the amount of AR applied over each is limited by (a–d). AR applied within each step reduces the remaining GRS potential by the same amount. This is an iterative process until either the regional target is met, or there are no remaining grid cells to be considered because (a–d) are no longer satisfied anywhere in the given region. In the latter case, the remaining AR target is filled by grid cells prioritized by ATL with a similar iterative process, and if still needed, the rest of the grid cells (satisfying (a–d)) can also be used to meet the AR yearly target. As a result, the AR pattern is constrained first by GRS (362 Mha in the resulting pattern), and then by ATL (375 Mha), but meeting the regional AR target each year can also mean increasing forest cover beyond total restoration potential (GRS and ATL combined) and reaching other grid cells (not prioritized by GRS or ATL), if needed (198 Mha). Reforestation of historically deforested sites is thus strongly prioritized in our approach, but reaching an ambitious target while preserving croplands globally constitutes afforestation (i.e., increasing tree cover beyond 1850 levels) and utilization of land not prioritized by restoration potential maps inevitable.

To account for biodiversity protection, heavily managed pastures are replaced first within every iterative step of the distributive process, and then the more lightly managed rangelands are replaced progressively, starting from the less to the more biodiverse. To protect old-growth grassy biomes which can be of huge ecological importance and exhibit high biodiversity richness87, we characterize rangeland biodiversity by using the VLHI map to determine which rangelands can be considered as being closer to a pristine, and thus as more biodiverse state, and exclude them. Natural grassy biomes which are not characterized as grazing land are also excluded. Then, we use the LHI map to split remaining rangelands into 4 biodiversity groups, based on the level of agreement among the datasets used by Riggio et al.127. By using these maps as a proxy for biodiversity, we assume that the higher the dataset agreement is, the more likely a grid cell experiences low human influence, and hence it is more likely less heavily managed, with less infrastructure, human stress, and cattle densities, weaker land fragmentation, and thus, probably closer to its pristine state, and hence likely more biodiverse127. Even though anthropogenic disturbance is known to be a major driver of biodiversity loss128, biodiversity richness is not necessarily associated with the level of human influence129. At the same time, although abandoning pastures can promote biodiversity, in some cases this does not translate to ecosystems returning to their initial biodiverse state130. Even though we acknowledge these shortcomings, a more detailed treatment of biodiversity concerns is beyond the scope of this study, and the usage of largely available metrics of human influence is preferred here127.

Evidently, in our study no biophysical or climatic limit is applied when considering grid cells eligible for AR, other than whether forest cover has or could potentially (based on MPI-ESM estimates) be at least 20%. Stricter limitations would reduce the amount of grazing land used for AR, since grazing land is often found in semi-arid, transitional regimes (Fig. 2). Hence, increasing forest cover over such areas possibly implies the need of some form of forest management in the real world to support forest growth. Still, carbon sequestration is climate-dependent in MPI-ESM, and more arid regions typically yield lower productivity and sequestration rates (Fig. 4c), and hence afforesting more arid rangelands would not contribute to the mitigation potential in the same way as the more productive areas included in our scenario.

It should also be noted that our simulations start in 2015 and do not include historical land use transitions having occurred later than 2014. According to FAO data131, forest area has decreased by ~30 Mha from 2015 to 2021. By that time in our model AR amounts to ~5 Mha, which corresponds to ~0.5% of total AR application, thus yielding a net difference of ~35 Mha of forest area. By 2021 land C stocks are increased by ~3 GtCO2 (0.81 PgC) in the AR compared to the REF scenario, constituting 0.78% of total additional sequestration (382 GtCO2). Clearly, the bulk of forest area increase and additional C sequestration under AR occur after 2025 in our simulations, which suggest that our results remain relevant with respect to overshoot dynamics and mitigation, regardless of this discrepancy with recent historical data.

An overview of the methodology used to develop our scenario is shown in Fig. 2 and the detailed resulting pattern is presented in Fig. 3 and Supplementary Figs. 1 & 2.

To test the sensitivity of the AR scenario development to the different constraints employed, different configurations are considered. In particular, we develop scenarios where: a) only the restoration map published by Griscom et al.19 (GRS) and the Atlas of Forest and Landscape Restoration Opportunities126 (ATL) map (i.e., no explicit biodiversity consideration), b) only GRS, and biodiversity maps127, and c) no restoration or biodiversity maps are used to constrain the scenario development.

Results are shown in Supplementary Fig. 12. Even though differences arise between the configurations, the patterns share some similarity with the AR scenario employed in this study. It should be noted that pastures are an integral part of both GRS and ATL (the first being a subset of the latter). As a result, prioritizing pastures can partly compensate for not using restoration potential maps to guide AR, due to the inherent consistency between the two. At the same time, consideration of biodiversity does not affect the partitioning between rangelands and pasture that are given up, since biodiversity is only considered for rangelands. Therefore, since pastures are generally given up first in our algorithm regardless of the configuration tested, the rough AR pattern is not heavily sensitive to the different constraints employed. However, considering biodiversity affects the spatial pattern of AR, mostly since it determines the regions where rangelands can be considered closer to a pristine state, and thus excluded from AR. Even though the AR pattern by 2100 unavoidably converges to the available grazing land, the differences across time can be more pronounced as the partitioning between pastures and rangelands, and the specifics of each configuration change.

Literature estimates of sequestration potential
In Fig. 5, the AR sequestration estimates demonstrated here are compared with potentials that have been reported in previous literature. The list of 40 studies presented includes (a) a subset of the comprehensive list of studies provided by “CO2 removal.org”5,7,132 for which explicit quantitative information on AR sequestration potentials at the global scale is provided (19 studies in total)19,39,76,133–148, (b) studies more recent than 5,7,132, which are not included in the comprehensive list (16 studies)20,52,67,75,77,78,149–158, and (c) studies published prior to 5,7,132 that we identified as missing from the comprehensive list (5 studies)53,159–162. The complete list of studies including the estimates of AR sequestration potentials are listed in Supplementary Data 1.

Across the identified studies we have included only the ones for which cumulative sequestration AR potentials over a defined period, and/or instant sequestration fluxes at a defined year or period can be confidently estimated, based on the information available in each study. Studies for which sequestration potentials cannot be attributed to an increase in forest cover and mostly include other nature-based climate solutions or contain only regional estimates have been omitted. Importantly, modeling studies that are employing idealized AR patterns34,163–165 are not considered.

For studies reporting multiple sequestration potentials, we define the range of estimates as [minimum-maximum] and calculate the mean potential across all values. For studies providing a range of values, we also estimate their mean. It should be noted, however, that not all estimates are entirely independent from one another, but quite a few studies rather build upon prior knowledge provided in the literature. As a result, the pool of studies is not treated as a statistical sample of independent values, and hence descriptive statistics (e.g., mean, standard deviation) are not employed.

The AR6 Scenarios Database (AR6-SD) estimates of AR sequestration potentials is based on a subset of 300 scenarios for which: a) explicit information on AR sequestration is available, b) the three exclusion criteria defined by Prütz et al.166 are met. Therefore, this pool of available scenarios includes only a subset of the 1259 scenarios used to guide the spatiotemporal design of the AR pattern of this study.

Socioeconomic indicators
To aid in the discussion of the possible socioeconomic risks and tradeoffs associated with the implementation of the AR scenario employed in this study, several spatially explicit socioeconomic indicators are used. In particular, we use:

a. Governance indicator: The Worldwide Governance Indicators (WGI) provided by the World Bank are used167. The WGI includes country-level indicators on voice and accountability, political stability and absence of violence/terrorism, government effectiveness, regulatory quality, rule of law, and control of corruption over the period 1996–2022. Missing values are filled by taking the arithmetic mean of the specific world subregion that a country belongs to, based on the 22 geographical subregions defined in the United Nations Geoscheme by the United Nations Statistics Division. Following Andrijevic et al.168 a composite governance indicator is estimated by taking the arithmetic mean of the six WGI components for 2022. The composite indicator is found to correlate well (>0.8 correlation) with all of the underlying components.

b. Land tenure insecurity indicator: The Prindex 2020 global dataset169,170 of land tenure insecurity is used. This indicator describes land tenure and property insecurity as perceived by individuals over 140 countries, expressed as a percentage (%) of the survey’s respondents. Missing values are filled as described above.

c. Poverty indicator: The poverty headcount ratio (%) at $2.15 (2017 purchasing power parity, lineup est. of 2019) from the Global Subnational Atlas of Poverty (GSAP)171 is used. The data are regridded to the resolution of MPI-ESM with conservative remapping. Missing values are filled as described above.

d. Indigenous and community land: Indigenous and community land data from Landmark172 are used. The data are regridded to the resolution of MPI-ESM with conservative remapping. In particular, the land that is considered here consists of:

i. Indigenous land—Acknowledged by government: Documented and not documented.
ii. Indigenous land—Not acknowledged by government: Held or used with formal land claim submitted and held or used under customary tenure.
iii. Community land—Acknowledged by government: Documented and not documented.
iv. Community land—Not acknowledged by government: Held or used with formal land claim submitted and held or used under customary tenure.

e. Population density: The WorldPop population count173 is used. The data are regridded from the ~1 km resolution to the resolution of MPI-ESM by summing, and are then divided by grid cell area to obtain population density.

An overview of the socioeconomic indicators overlaid with the AR pattern, and some indicative estimates of how AR is distributed across regions with various levels of governance, land tenure insecurity, poverty, perscentage of indigenous and community land, and population density are presented in Supplementary Fig. 10.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The authors investigate an ambitious but plausible AR global scenario in line with country pledges.\nand then assess to what degree a temperature overshoot could be reduced.\nThey develop an AR scenario reaching 595Mha of AR in 2060 and 935 Mha by 2100, based on an\nanormously large scenarios dbs.\nThey then run fully coupled simulations with the Max Planck Institute’s Earth System Model (MPI-\nESM).\nthe work as such is impressive, but I believe it is not novel enough for a Nature publication.\nBiogeochemical cycles or PNAS may be a better suited avenue.\nAs they also show themselves, many of such global scenarios exists and have been assessed in\nterms of carbon effects. many were also gross overestimates (Griscom et al, Bastin et al..),\nnot\ntaking into account either food production needs or biodiversity effects.\nThis is now not changed in present study. However they align the results also in terms of reducing\novershoot within the possibilities of the Max Planck Model. but e.g. perverse effects in terms of\nbiodiversity are not assessed. they still come with a very high required area 50--900 Mha.\nAs such a solid study, well written. but I think better suited for journals as mentioned above", "reviewer_2": "Summary and main comments:\nThis work discusses the effectiveness of forestation in an overshoot scenario, a storyline of future\nglobal average surface-near temperature rising above common set targets (e.g., the 1.5°C target in\nthe Paris Agreement) and subsequently falling below, typically within the 21st century. In a single-\nmodel ensemble simulation setup, the fully coupled MPI-Earth System Model is used to compute a\nreference (REF, no forestation) and a forestation (AR) climate scenario. The author’s main claim that\nsuch land cover and land use change (LULCC) can mitigate temperature overshoot in the model in\nterms of height and duration seems to be well-founded regarding presented scenario design and\nanalysis. Another ambition of the study is to apply a “plausible” AR scenario compatible with global\nscale AR pledges. This is done by combining scenarios from Integrated Assessment Models (IAMs)\nwith restoration potential and biodiversity maps. The study provides useful information on the\nmitigation potential of AR considering overshoot, however, I have some comments that should be\naddressed.\nI would appreciate a more in-depth analysis or discussion of the processes at play. The study\nmostly offers assessment from the narrow angle of global averages and sums rather than of region-\nspecific regimes of system behaviour. This is incomplete concerning regional effects and extreme\nclimate behaviour, which can be particularly relevant for the biophysical effects. Looking at\naverages might be enough to judge AR’s overall effectiveness for long-term earth system behaviour,\ne.g. concerning impacts on the cryosphere, information which partly might be readily available from\nthe performed coupled model simulation. However, a discussion of this relevance also seems to be\nlacking. If “ambitious”, i.e. global-scale forestation can mitigate modelled temperature overshoot,\nwhat does this mean for the oceanic, cryospheric, and land-bound systems? In that sense, what\nare the significant novel implications for climate science and/or climate action?\nAnother concern related to this is how time of signal emergence, peak temperature difference and\ninterannual variability are treated. Concerning peak global mean 2m T, I understand that a robust\ncomparison needs some smoothing of the interannually fluctuating behaviour of global mean T.\nHowever, I think it is important to not overlook differences in extremes induced or enhanced by\ninterannual variability. E.g., how does the running window 10th and the 90th percentile compare\nbetween AR and REF? Here I think some work either with statistical or more process analysis is\nuseful to give a less simplified picture of the Earth System behaviour. This will also help in the\nfollowing discussion, what does it mean to find the two scenarios distinguishable? Can you provide\nexamples for contexts in which 0.09°C less globally make a significant difference regionally for\ncertain processes?\nMy second comment addresses “plausible” scenario construction. As the authors mention, their\njudgment of plausibility is based on fitting the AR trajectory to globally aggregated AR pledges and\ndoing so by finding a fitting percentile in AR areal amount within the IAM scenario collection.\nAdditionally, they base the distribution of these area indications across global land on a method\nthat includes proxies for restoration potential and biodiversity. While IAMs are already in the\nprocess to include more and more constraining information to land use allocation, indeed this\noffline methodology could certainly be helpful. However, it should probably also be discussed or\nmentioned whether it could be an issue. IAMs may involve many assumptions, but based on these\nassumptions usually try to consistently follow certain modelling paradigms. Including constraints\nsuch as biodiversity as exogenous may create new inconsistencies (breaking some internal logic).\nThe estimates from MPI-ESM on forestation effectivity in terms of net carbon extraction from the\natmosphere and cooling potential are rather moderate which may allow more robust conclusions.\nHowever, neither globally aggregated area pledges nor socio-economic pathways modeled by IAMs\nguarantee plausibility or realism. While the authors mention these limitations, their criterion of\nrealism and plausibility and hence consequences for their results remain rather unclear.\nL.1: To me it seems like your title neither reflects scope nor methods of your study. I highly\nrecommend to indicate that you work entirely in the modelling world. Neither IAM projections of\nrestoration potential nor climate projections under large-scale forestation can be fully validated\nagainst observations.\nL.9-10: What about SSP1-2.6? Low warming, high AR (> 800 Mha in 2100) What about LUMIP land\nuse swap between SSP126 and SSP370? Indeed there is some avoided deforestation signal, but\nstill these efforts should not be forgotten.\nL.16-17: Linguistic error. Pledges are not a mitigation (technical) but a political tool. Also pledges\nfirstly occur in reality and can only be mimicked in the model world. Be careful not to mix\nterminology of actual climate history and planning with modeling jargon.\nL.31: “overshoot can seem attractive”: to whom (not)? In science vs. in global climate action? Here\nyou could give some context on interest in certain pathways.\nL.39: “typically considered as a useful …” If the picture is not crystal-clear, you may want to balance\nyour introduction with a source or two on contexts and voices not deeming it useful.\nL.45: “potential of sequestrating…”. I propose to insert “durably” as an important characteristic.\nL.45: introduce “carbon (C) ”\nL.51: I would suggest to use “high estimates” instead of “optimistic”\nL.87-90: Contextualize which scenarios in which regions, why. In the IAMs themselves, e.g. SSP1-\n2.6 by IMAGE has substantial (323 Mha by 2050, 834 Mha by 2100) amounts of AR although this\ndoes not directly translate into AR in the ESMs.\nL.99: rephrase “unmitigated scenarios”. Scenarios are not mitigated, global warming is.\nL.134: What is the reference forest cover data set? How was it generated, with which thresholds of\ntree cover to qualify forest?\nL.140: In my opinion the uncertainty range given should be contextualized with the ensemble size at\nthis point already.\nL.146-147: Is the trajectory really Paris-compatible when the time-series of annual global averages\nneeds is smoothed to strictly stay below?\nL.182: Could you plot efficiency over time and with uncertainty ranges, e.g. additional to Fig. S3?\nL.203-205: From when on could one take a snapshot map (1-year annual mean) of any ensemble\nmember and know whether it is REF or AR?\nL.254: Could this be due to MPI-ESM-specific response? Do expect significantly different balances\nbetween BGP and BGC responses from other modes?\nL.264-266: Well, as IAMs to date fail to include many significant processes, is it really reliable to\nlean onto their judgement of plausibility? What is your plausibility criterion?\nL.270: Great that you mention these limitations. Can you make even clearer what they mean for\nyour results and interpretations?\nL.283-284: If these barriers exist, could you at least include some indication (e.g. on the national\nlevel) where the cited studies see uncertainty for these reasons, maybe in Fig. 2, 3 and S1? This will\nenable to reader to judge upon plausibility more on their own.\nL.285-287: Could you give some context about the share of needed AR finance organized in reality\nto date? This will enable to reader to judge upon plausibility more on their own.\nL.288: recommendation to insert “in our simulations” between “feature” and “is the early…”\nL.316-318: This is a bit short-cut. Could you maybe detail the discussion you provide here spread\nout through the paragraph for the important studies you consider for comparison.\nL.332-342: What about the status of MPI-ESM among other CMIP6-models in terms of cooling\nbehaviour in SSP5-3.4os? What does this mean for the behaviour of AR vs. REF?\nL.342: “mitigated emission scenarios” to me seems a bit to far into jargon. Either reorder “under\nscenarios of strongly mitigated emissions” or rephrase.\nL.379: This information on the exact emission scenario came too late for me. I would appreciate\nsome context early on (e.g. in the paragraph l.s 109-118) to contextualize which GMT-trajectory you\nperturb with AR.\nL.383: I understand that the technically easiest adaption of SSP5-3.4os to AR is to deviate from it in\n2015. To not have a period until AR launches over which other kinds of LULCC occur. Or for\ntechnical simplicity to initiate the simulations in the same year. How does your scenario from 2015\nto 2025 differ from observed LULCC? How do you justify the relevance of numeric results like\nchanges in peak GMT, changes in overshoot duration, changes in 2100 GMT (l.s 149-151) when\nactual AR would occur later just 5 or 10 years?\nL.420: You frame interannual variability as noise. But isn’t the year-to-year evolution relevant for\nglobal scale impacts? What about the comparison of hottest vs. coldest percentiles of the\ndistribution of a running window you just used to eliminate this variability. Have you tried to look at\nhow extremes are changed by AR-induced mechanisms? What are the MPI-ESM-specific\nsignatures, what do you expect to generalize?\nL. 436: What is your regridding method? For reproducibility please indicate the algorithm.\nL.444-445: Same concerning interpolation: Linearly? Differently? For globally aggregated values or\nfor region-specific ones? Instead, did you consider to interpolate at grid-cell scale once the\ndistribution is completed? How sensitive is your method to such changes?\nL.445-446: So, does this mean that you only try to match pledged AR area globally and not\nregionally? I expect this to make your distribution differ fundamentally from the nationally pledged\none. If you can, to defend this doubt, maybe show a plot of how your distribution nationally\naggregated compares to national AR pledges. The IAM REMIND-MAgPIE to date already includes\nLULCC pledges at the national level. So it would be nice to show in the supplement how you do this\nand to compare to existing methods.\nL.451-453: How do you disaggregate/downscale? What are your decision rules and what are they\nbased on? If you use published methods, give references.\nL.460-506: Could you maybe provide line plots like in Fig. 3 but showing the sensitivity to different\nconstraints employed? E.g. fully (GRS-ATL-BioDiv) constrained vs. GRS-ATL- constrained vs. only\nGRS constrained vs. unconstrained?\nFig.1: I think this figure would just need to appear at bigger scaling with more axis details, gridded\nbackground and then could serve much more its purpose.\nFig.2: Could you maybe include a layer for regions where literature considers AR potential uncertain\nbeyond quantifiable constraints?\nFig.3: For line plot same as Fig.1. It is important that not only overall upwards vs. downwards trends\nbut e.g. comparison to actually observed trends 2015-2023. can be made. I think including a marker\nof country pledges in all the region-specific line-plots would help the reader a lot. Also the maps\nthe above-mentioned layer on national-level unquantified constraints (e.g. hatching) could help.\nFig.6: I think the two lines in the center of the plot are not that helpful. They are also the same as in\npanel of Figure 4, right? I think it could make more sense showing larger maps.\nSuppl.Fig.1: Same as Fig. 2 and 3. For all figures representing coarse-scale model data: I would\nhighly appreciate a way of plotting the values as a mesh instead of smooth colour blobs. This\nalienates the data at smaller scales. Also consider using discrete color-steps in you colormap for\nclearer comparison of different regions.\nSuppl.Fig.2,3: A third / fourth panel on efficiency (removal from atmos / land sequestration) over\ntime in either Fig. 2 or 3 could be very useful for the understanding of the carbon dynamics.\nSuppl.Fig.3: I would appreciate the service to the community with a second vertical axis for °C/PgC\n(b) and PgC /100 Mha (c). Speculatively this could increase the audience size with an intuition for\nthe numbers.\nSuppl.Fig.4: What is the added value of the bottom row? Where does it differ from Fig. 6 with more\ninformation?", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part\nof the Nature Communications initiative to facilitate training in peer review and to provide\nappropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Response to reviewers\nWhat follows is our detailed point-by-point responses to the reviewers’ comments. In several occasions where we consider that\nsome comments are related, these are grouped together, and a single response is provided to avoid repeons.\nReviewer #1 comments:\nComment:\nThe authors investigate an ambitious but plausible AR global scenario in line with country pledges. and then assess to\nwhat degree a temperature overshoot could be reduced. They develop an AR scenario reaching 595Mha of AR in 2060\nand 935 Mha by 2100, based on an anormously large scenarios dbs. They then run fully coupled simulations with the\nMax Planck Institute’s Earth System Model (MPI-ESM). The work as such is impressive, but I believe it is not novel\nenough for a Nature publication. Biogeochemical cycles or PNAS may be a better suited avenue.\nResponse:\nWe thank the reviewer for the overall posive evaluaon of our work. We understand that the percepon of novelty\ncan be quite subjecve and differ among researchers, but we are sll confident that our work is novel and can be of\nsignificant interest to the scienfic community, and is thus suitable for publicaon in Nature Communicaons.\nIn parcular, our novelty lies within the following:\na) There is a lack of Earth system modelling studies applying such ambious levels of AR under a non-idealized\nspaotemporal patern, even more so with such a setup that:\ni. technically allows for the full isolaon of the effect of AR on the Earth system\nii. does not include a high-emissions scenario that implies strong CO ferlizaon and that bears a\n2\npossible overesmaon of AR migaon potenal\nb) There is a lack of understanding the dynamics of overshoot pathways in the Earth system, which are gaining\nscienfic interest recently due to the increasing policy relevance of overshoot.\nc) The spaotemporal AR patern employed here is informed by considering a variety of technoeconomic,\nsocietal, and environmental consideraons, and a multude of >1,200 IAM-generated scenarios.\nd) Most studies suffer by the lack of ensemble-members when it comes to variables that normally have strong\ninterannual variability, and robust conclusions cannot be drawn with respect to the signal. As a result, more\noen than not, robust inference with respect to temperature migaon cannot be made. Therefore, we\novertook the computaonally heavy task of creang a very big ensemble-size (20 runs in total) that allows for\nrobust probabilisc treatment and inference.\ne) In the revised version of the manuscript, the applied AR scenario is complemented by a thorough discussion\non the associated socioeconomic barriers based on quanfied socioeconomic indicators, which is not typical\nfor an Earth system modelling study.\nComment:\nAs they also show themselves, many of such global scenarios exists and have been assessed in terms of carbon effects.\nmany were also gross overestimates (Griscom et al, Bastin et al..), not taking into account either food production needs\nor biodiversity effects.\nThis is now not changed in present study. However they align the results also in terms of reducing overshoot within the\npossibilities of the Max Planck Model. but e.g. perverse effects in terms of biodiversity are not assessed. they still come\nwith a very high required area 50--900 Mha.\nAs such a solid study, well written. but I think better suited for journals as mentioned above.\nResponse:\nWe thank the reviewer for their comment.\nThe reviewer argues that food producon needs are not taken into account in our study, similar to other gross\noveresmates. We clarify here that even though we do not directly use food demand and producon data to generate\nour scenario, such consideraons are implicitly included since we are harvesng the Integrated Assessment Model\n(IAM) database, and the Griscom restoraon map. We comment on that in Lines [403-407]. We refer to the food\nsecurity concerns in Lines [47 & 443-447].\nIt should also be noted that we do not claim in our work that biodiversity is not affected by large-scale AR applicaon.\nNor do we claim that such ambious AR does not possibly come with severe consequences, about which stakeholders,\npolicy-makers, and sciensts should definitely be informed. In fact we carefully discuss the tradeoffs that such an\napplicaon can have not only with respect to biodiversity, but also with respect to other societal factors (secon\n“Contextualizing ambious Afforestaon/Reforestaon”). In this secon, we also very clearly arculate that the\npresent scenario is not meant to serve as a proposed patern that should be followed in Lines [399-403].\nThe goal of this study has not been to directly quanfy ecological disrupon and posive or negave impacts to\nbiodiversity under our AR scenario, as this would require an enre separate work ( e.g., Trisos et al., 2020), nor is it\nclaimed in the manuscript that under our AR scenario biodiversity is fully protected. Our declared goal has been\nreducing the biodiversity impacts of AR applicaon by appropriately priorizing specific biomes over others within\ngiven technoeconomic consideraons. To be able to assess the Earth system impacts of ambious forestaon in the\nrange of country pledges, our study augments the exisng IAM scenario informaon by allocang its AR informaon\nspaally while avoiding the most severe biodiversity impacts. As we implement AR while preserving croplands globally,\nsuch an allocaon implies afforestaon of (semi-)natural ecosystems and thus destrucon of habitats. We thus propose\nan allocaon that reduces the negave impacts in our AR scenario by:\na) Following restoraon potenal maps\nb) Excluding natural grasslands and shrublands\nc) Excluding grazing lands that can be considered closer to a prisne state (while acknowledging the possible\ncaveats of using Human Influence maps as a proxy for biodiversity in Lines [641-659])\nd) Replacing grazing land starng from the least biodiverse first\nReviewer #2 comments:\nComment:\nThis work discusses the effectiveness of forestation in an overshoot scenario, a storyline of future\nglobal average surface-near temperature rising above common set targets (e.g., the 1.5°C target in\nthe Paris Agreement) and subsequently falling below, typically within the 21st century. In a single-model\nensemble simulation setup, the fully coupled MPI-Earth System Model is used to compute a\nreference (REF, no forestation) and a forestation (AR) climate\nscenario. The author’s main claim that such land cover and land use change (LULCC) can mitigate temperature\novershoot in the model in terms of height and duration seems to be well-founded regarding presented scenario design\nand analysis. Another ambition of the study is to apply a “plausible” AR scenario compatible with global\nscale AR\npledges. This is done by combining scenarios from Integrated Assessment Models (IAMs) with restoration potential\nand\nbiodiversity maps. The study provides useful information on the mitigation potential of AR considering overshoot,\nhowever, I have some comments that should be addressed.\nResponse:\nWe thank the reviewer for their overall posive evaluaon of our work.\nComment:\nI would appreciate a more in-depth analysis or discussion of the processes at play. The study\nmostly offers assessment from the narrow angle of global averages and sums rather than of region-specific regimes of\nsystem behaviour. This is\nincomplete concerning regional effects and extreme climate behaviour, which can be particularly relevant for the\nbiophysical effects. Looking at\naverages might be enough to judge AR’s overall effectiveness for long-term earth system behaviour,\ne.g. concerning impacts on the cryosphere, information which partly might be readily available from\nthe performed coupled model simulation. However, a discussion of this relevance also seems to be\nlacking. If “ambitious”,\ni.e. global-scale forestation can mitigate modelled temperature overshoot,\nwhat does this mean for the oceanic, cryospheric, and land-bound systems? In that sense, what\nare the significant novel implications for climate science and/or climate action?\nResponse:\nWe thank the reviewer for their comment. Following their recommendaons, we have significantly extended our\nanalysis to provide a more detailed overview of the Earth system responses under AR.\nIn parcular, we have extended our analysis by including the changes in hydroclimate (Figure R 1), focusing on changes\nin precipitaon, evapotranspiraon, cloudiness levels and relave humidity, and discussed the regional paterns\nidenfying hotspots of changes. Overall, AR leads to a weter hydroclimate over regions of forestaon as evident by\nincreases in precipitaon, and evapotranspiraon, cloudiness, and relave humidity. To provide a more complete\npicture we also include in our analysis changes in net radiaon, latent and sensible heat fluxes, and albedo (Figure R\n2). Finally, we further report on changes in sea ice extent globally and over the Arcc, and changes in global average\nsea surface pH (Figure R 3).\nFigure R 1 has now been added as a main figure in our manuscript (Fig. 7). Figure R 2 & 3 have now been added in the\nSupplementary material (Supplementary Figure 8 & 9). The results and their regional characteriscs are presented and\ndiscussed in Lines [227-238, 310-319]\nFigure R 1: Spatiotemporal pattern of changes in hydroclimate: From left to right, the differences in precipitation (mm/day),\nevapotranspiration\n(mm/day), cloud cover fraction, and relative humidity (%) between AR and REF simulations during 2030-2050 (top),\nthe period around peak\nwarming (2050-2070) (mid), and end-of-century (2090-2100) (bottom) are shown. A negative value indicates a reduction in the AR scenario. Dots\nindicate regions where the difference is statistically insignificant at the 5% level, after correcting for lag-1 temporal\nautocorrelation (Zwiers & von\nStorch, 1995).\nFigure R 2: Spatiotemporal pattern of changes in radiation and heat fluxes: From left to right, the differences in surface net radiation (W/m2),\nlatent and sensible heat fluxes (W/m2), and albedo between AR and REF simulations during 2030-2050 (top), the period around peak warming\n(2050-2070) (mid), and end-of-century (2090-2100) (bottom) are shown. A negative value indicates a reduction in the AR scenario. Dots indicate\nregions where the difference is statistically insignificant at the 5% level, after correcting for lag-1 temporal autocorrelation (Zwiers & von Storch,\n1995).\nFigure R 3: Top row: From left to right, changes in sea ice fraction over the Arctic between AR and REF simulations during 2030-2050 , the period\naround peak warming (2050-2070), and end-of-century (2090-2100) are shown. Bottom row: From left to right, changes in global minimum,\naverage, and maximum sea ice extent (107 km2) for AR (green) and REF (blue) are shown, with the thick lines representing the ensemble mean for\neach scenario.\nComment:\nAnother concern related to this is how time of signal emergence, peak temperature difference and\ninterannual variability are treated. Concerning peak global mean 2m T, I understand that a robust\ncomparison needs some smoothing of the interannually fluctuating behaviour of global mean T. However, I think it is important to not overlook\ndifferences in extremes induced or enhanced by\ninterannual variability. E.g., how does the running window 10th and\nthe 90th percentile compare between AR and REF? Here I think some work either with statistical or more process analysis is\nuseful to give a less simplified picture of the Earth System behaviour.\nL.420: You frame interannual variability as noise. But isn’t the year-to-year evolution relevant for global scale impacts?\nWhat about the comparison of hottest vs. coldest percentiles of the distribution of a running window you just used to\neliminate this variability. Have you tried to look at how extremes are changed by AR-induced mechanisms? What are\nthe MPI-ESM-specific signatures, what do you expect to generalize?\nL.146-147: Is the trajectory really Paris-compatible when the time-series of annual global averages needs is smoothed\nto strictly stay below?\nResponse:\nWe thank the reviewer for their comments.\nAt first, we should clarify that in the old version of the manuscript the me of signal emergence was not esmated by\nsmoothing out interannual variability. Therefore, this has not changed in the revised version.\nTo address the remaining issues raised and to avoid any biases induced by the user and/or confusion to the reader, the\ninformaon on peak and end-of-century temperature is now not esmated based on the smoothing and bootstrapping\nscheme employed in the previous version of the manuscript any more. In the revised version of the manuscript, this\ntemperature informaon is now rather directly taken from the unsmoothed temperature meseries of each ensemble\nmember, and uncertainty is expressed in terms of minimum-maximum values obtained from the different members.\nTo robustly capture end-of-century temperature and take into account interannual variability suggesng that the 1.5oC\ntarget might be occasionally exceeded, we consider not only 2100, but the pooled data of full 5-year period 2096-2100\nacross the ensemble members. Similarly, to get robust esmates on peak temperature and avoid biases by likely\nextremely hot years later or early on, the full 5-year consecuve period yielding the highest average temperature for\neach ensemble member is considered and the data are then pooled together. We have tested the sensivity of our\nresults to the chosen period length, as shown in Figure R 4 showing a negligible bias. This is now clarified in the\nmethodology secon in Lines [540-545]. Figure R 4 has been added to the Supplementary Material (Supplementary\nFig. 11).\nFigure R 4: The effect of the choice of period length (x axis) in terms of estimating peak and end-of-century temperature mitigation between AR\nand REF is shown.\nWhen it comes to esmang the temporal characteriscs of the overshoot we choose to keep the\nsmoothing/bootstrapping technique, since a smoothed meseries allows for a more straighorward idenficaon of\novershoot onset and offset ming.\nFollowing the reviewer’s suggeson, we have also extended our analysis to different percenles of temperature both\nat the gridded level and averaged globally. In parcular, in the revised Figure 6 of the manuscript, maps of the 10th (low\ntemperature extreme) and 90th (high temperature extreme) percenles of temperature at the gridded level are now\nshown (Figure R 5).\nAddionally, we present the 5th, 10th, 25th, 75th, 90th, and 95th percenle of globally averaged daily temperature in\nSupplementary Figure 3 (Figure R 6). In a given year and for every ensemble member, the Xth percenle of temperature\nis obtained from the globally averaged daily temperature meseries. For all these percenles of globally averaged daily\ntemperature we treat the temperature meseries with the same stascal treatment presented in the Methods\nsecon, to esmate the me of emergence of the signal.\nOur results both at the gridded and global level show a robust migaon of temperature across all percenles of the\ntemperature distribuon, and not only when it comes to the mean. These results are presented and discussed in Lines\n[162-163, 210-226, 257-259, 320-335].\nFigure R 5: Spatiotemporal pattern of temperature change: The in 10th percentile (low temperature extremes, left), mean (mid), and 90th\npercentile (high temperature extremes, right) of 2m air temperature between AR and REF simulations during 2030-2050 (top), the period around\npeak warming (2050-2070) (mid), and end-of-century (2090-2100) are shown. A negative difference (blue color) indicates that temperature is\nlower in the AR scenario. Dots indicate regions where the difference is statistically insignificant at the 5% level, after correcting for lag-1 temporal\nautocorrelation (Zwiers & von Storch, 1995).\nFigure R 6: The panels show different percentiles of 2m air temperature (difference compared to pre-industrial era, expressed here as the 1850-\n1900 average) for the REF (blue color) and AR (green color) scenarios. The thick lines represent the ensemble mean for each scenario. The bright\ngreen shaded region indicates that there is no statistically significant difference between the two scenarios, while the purple shading suggests\nthat a statistically significant difference exists (see Methods). The percentile of temperature used is noted at the title of each panel. In a given\nyear and for every ensemble member, the Xth percentile of temperature is obtained from the globally averaged daily temperature timeseries.\nComment:\nL.140: In my opinion the uncertainty range given should be contextualized with the ensemble size at this point already.\nResponse:\nFollowing the reviewer’s suggeson, uncertainty range is now contextualized with the ensemble size, by reporng the\nminimum and maximum values obtained from the different ensemble members.\nComment:\nThis will also help in the following discussion, what does it mean to find the two scenarios distinguishable? Can you\nprovide examples for contexts in which 0.09°C less globally make a significant difference regionally for certain\nprocesses?\nResponse:\nTo demonstrate the extent to which reducing the overshoot affects the Earth system, our extended analysis including\nchanges in hydroclimate, energy fluxes, and the ocean (as described above) is presented separately for 2030-2050, the\nperiod around peak warming (2050-2070), and the end-of-century (2090-2100) (Figure R 5/ Fig. 6 of the main\nmanuscript).\nComment:\nMy second comment addresses “plausible” scenario construction. As the authors mention, their judgment of plausibility\nis based on fitting the AR trajectory to globally aggregated AR pledges and doing so by finding a fitting percentile in AR\nareal amount within the IAM scenario collection. Additionally, they base the distribution of these area indications across\nglobal land on a method that includes proxies for restoration potential and biodiversity. While IAMs are already in the\nprocess to include more and more constraining information to land use allocation, indeed this\noffline methodology could certainly be helpful. However, it should probably also be discussed or\nmentioned whether it could be an issue. IAMs may involve many assumptions, but based on these\nassumptions usually try to consistently follow certain modelling paradigms. Including constraints\nsuch as biodiversity as exogenous may create new inconsistencies (breaking some internal logic).\nThe estimates from MPI-ESM on forestation effectivity in terms of net carbon extraction from the\natmosphere and cooling potential are rather moderate which may allow more robust conclusions.\nHowever, neither globally aggregated area pledges nor socio-economic pathways modeled by IAMs\nguarantee plausibility or realism. While the authors mention these limitations, their criterion of\nrealism and plausibility and hence consequences for their results remain rather unclear.\nL.264-266: Well, as IAMs to date fail to include many significant processes, is it really reliable to\nlean onto their judgement of plausibility? What is your plausibility criterion?\nL.270: Great that you mention these limitations. Can you make even clearer what they mean for\nyour results and interpretations?\nResponse:\nWe thank the reviewer for their comment on the plausibility of our scenario.\nIt should be noted that the usage of the term “spaotemporally plausible” was meant to highlight the contrast of our\nscenario with the fully-idealized spaotemporal paterns employed in many Earth system modelling studies (e.g., cases\nwhere AR is applied over all gridcells, or over an enre latudinal band such as the tropics, and/or all AR is applied at\none instance in me etc.), that do not include any technoeconomical, biodiversity, or other consideraon across me\nnor space, and it thus remains unclear to what extent the Earth system responses reported therein remain relevant for\nreal-world applicaons.\nOn the contrary, our AR scenario is constrained by a multude of factors either implicitly - to the extent that they are\nconsidered in IAMs - or explicitly - in the form of biodiversity and restoraon potenal maps. Our scenario could thus\nresemble future applicaon across space and me to the extent that:\na) 55% of the forest area increase is reforestaon of historically (from 1850 onwards) deforested areas. We argue\nthat one can reasonably assume that a big part of forestaon globally in the future would include reforesng\npreviously deforested land mainly because:\ni. This land can naturally support the growth of forest from a biophysical perspecve. Reforestaon\nwould thus require less effort and probably accumulate more carbon, thus being more efficient both\nfinancially and migaon-wise.\nii. Afforestaon would likely jeopardize biodiverse grassland ecosystems which should be protected – as\nis the case in our study.\nWe comment on this in Lines [414-416]\nb) The temporal evoluon of the global AR target and its disaggregaon across the different world economic\nregions follows technoeconomic constraints.\nc) The global AR target is in the range of country pledges.\n“Plausibility” in our case does not suggest that the employed scenario faces no or less hurdles with respect to\nimplementaon (e.g., due to issues related to governance, land insecurity, public acceptance etc), that would\nconstute it “more feasible” than others. In fact - as the reviewer also highlights - we commented on feasibility\nconcerns and discussed such limitaons in Lines [51-52, 271-287] of the old manuscript.\nIn the revised version of the manuscript we also discuss the polical feasibility of seng such an ambious AR target,\nas reflected by the credibility rangs of the countries undertaking the AR applicaon. In parcular, we quanfy the\npercentage of AR across the countries with the higher pledges (Dooley et al., 2022; Self et al., 2023), and also across\nthe countries with the higher AR in our scenario, and demonstrate how all of these countries are countries of either\nlow or very low confidence rangs on their net-zero policies, as esmated by Rogelj et al. (2023). Country-level data\nare presented in Figure R 7, which has now been added to the Supplementary Material (Supplementary Fig. 1).\nThe above are discussed in Lines [417-428].\nFigure R 7: Country level AR data: The bar plots show country level data of land-based mitigation pledges for AR and ecosystem restoration (Dooley\net al. 2022; Self et al. 2023), and the amount of AR achieved by 2060 and 2100 in the scenario employed in this study. The three plots show (top\nleft) the 15 countries with the higher amount of AR reached in 2060 in our scenario, (top right) the 15 countries with the higher AR pledge based\non the Land-Gap report estimates (Dooley et al. 2022; Self et al. 2023), and (bottom left) the 15 countries with the higher amount of AR reached\nin 2100 in our scenario. For these quantities, the cumulative share (%) with increasing number of countries sorted in descending order is shown\n(bottom right).\nAddionally, to allow the readers to judge upon the plausibility of such a scenario on their own, we present a new\nassessment of the developed AR scenario through the lens of social indicators and the associated tradeoffs (Figure R\n8). In parcular, we employ: a) a composite indicator of governance, b) an indicator of land tenure insecurity, c) a\npoverty indicator, d) a map of indigenous and community lands, and e) a map of populaon density. Detailed\ninformaon on the data and methodology used is presented in the Supplementary Methods (secon “Socioeconomic\nindicators”). All of these socioeconomic factors are indicave of and associated with an array of possible barriers to\nimplementaon of AR, threats to the permanence of newly planted forest, and societal consequences and tradeoffs.\nWe quanfy how the developed AR patern spaally aligns with these indicators (e.g., how much of AR is applied over\nregions with high poverty) and what that could suggest in terms of possible consequences, barriers to implementaon,\nand threats to permanence. This detailed assessment is presented in Lines [441-469], while Figure R 8 has been added\nto the Supplementary Material (Supplementary Fig. 10).\nFigure R 8: The maps indicate: a) indigenous and community land expressed as a fraction of land per gridcell (Dubertret & Alden Wily, 2015), b)\nland tenure insecurity expressed as the percentage (%) of people perceiving their land or property to be insecure (Feyertag et al., 2020; Prindex,\n2020), c) governance expressed with a composite governance indicator (Kaufmann & Kraay, 2023), d) poverty indicator expressed as the\npercentage (%) of people below the $2.15 threshold (World Bank, 2023), and e) population density (people/km2) (WorldPop, 2018). The hatching\nshows the AR area in the employed scenario. The bar plots show the cumulative AR area (Mha) across different bins of the various socioeconomic\nindicators.\nAs the reviewer also acknowledges, in the old version of the manuscript we had clarified that our scenario can be\nconsidered “plausible” only to the extent that IAMs can be treated as such, and also openly discussed IAM weaknesses\n(in Lines [264-270]). However, despite these limitaons, the IAMs sll remain the main tool at hand when it comes to\ntrying to contextualize the technical, social, and economic developments in the world, and developing constrained\nspaotemporal scenarios that go beyond fully-idealized setups. We comment on that in Lines [410-413] of the revised\nversion of the manuscript.\nNevertheless, even though the term “plausibility” - which was used to characterize our scenario - can have have a wide\nvariety of meanings (Jewell & Cherp, 2020), we acknowledge that it might possibly be interpreted by some readers as\nreflecng some underlying normave judgement on feasibility, and thus possibly implying the absence of real-life\nbarriers to financing and implementaon of such an ambious project. Having clarified all that, and despite all\nlimitaons have been transparently discussed in detail and expanded in the revised version of the manuscript, to avoid\nany confusion we now characterize our scenario as “constrained” throughout the text.\nTo increase visibility, all these issues are now discussed under the new secon “Contextualizing ambious\nAfforestaon/Reforestaon”, that follows the Discussion secon. As a result, some of the paragraphs of the Discussion\nsecon of the old version of the manuscript have been moved to the new secon, while the remaining paragraphs\nhave been reordered accordingly to maintain consistency throughout the text.\nIt should also definitely be acknowledged that pooling mulple AR6-SD scenarios together inevitably breaks the\ninternal consistency found within single IAM scenarios. At the same me, disaggregang the regional AR6-SD\ninformaon to the gridcell level based on restoraon potenal and biodiversity maps, as well as not implemenng land\nuse transions other than AR, breaks the internal IAM logic with respect to land use dynamics. Further, selecng an\nemission trajectory (SSP5-3.4os) independently of the land use patern employed can also introduce inconsistencies.\nFor example, achieving net-negave emissions in SSP5-3.4os is heavily based on carbon capture and storage (CCS)\n(Melnikova et al., 2021), which suggests that addional land pressure would be exerted by the need for large-scale\nbioenergy CCS applicaon, thus increasing compeon with AR over land. It is thus clear that the offline approach\nemployed in our study, introduces some inconsistencies.\nHowever, despite the methodology employed here breaks internal IAM logic, a single IAM scenario has not been used,\nand a methodology tailored to cover the specific needs of this study has been developed instead. In parcular, only by\nnot allowing for land use transions other than AR to occur, are we able to fully isolate the signal of AR on the Earth\nsystem - which is key to our study. This choice inevitably introduces inconsistencies, however it is not arbitrary, but is\nrather supported by the data shown in Fig. 1 of the main manuscript, that show that in IAMs ambious AR occurs at\nthe expense of grazing lands, with croplands remaining roughly unchanged. This general IAM logic when it comes to\nambious AR is thus preserved in our scenario. At the same me, to strengthen our approach and preserve the general\nspaal features of AR reflecng regional-level dynamics and market mechanisms as realized in IAMs, we have\naddionally ulized regional AR6-SD data as a guide to the spaal disaggregaon process. It should also be highlighted\nthat pooling mulple AR6-SD scenarios together eliminates the bias of selecng a single IAM scenario, which would\nmean selecng a single combinaon of IAM, SSP, and the configuraon and assumpons therein, which would likely\nalso raise quesons. It is important to also note that inconsistencies are inevitably introduced even when a single IAM\nscenario is run with an ESM, due to differences in spaal resoluon and land use paterns, and discrepancies in the\nrepresentaon of the biosphere and the carbon cycle (Cheng et al., 2024; Melnikova et al., 2022).\nThe above are acknowledged and discussed in Lines [385-397].\nComment:\nL.1: To me it seems like your title neither reflects scope nor methods of your study. I highly recommend to indicate that\nyou work entirely in the modelling world. Neither IAM projections of restoration potential nor climate projections under\nlarge-scale forestation can be fully validated against observations.\nResponse:\nThe tle of the manuscript has been changed according to the reviewer’s recommendaons and is now “Temperature\novershoot responses to ambious forestaon in an Earth System Model”.\nComment:\nL.16-17: Linguistic error. Pledges are not a mitigation (technical) but a political tool. Also pledges firstly occur in reality\nand can only be mimicked in the model world. Be careful not to mix terminology of actual climate history and planning\nwith modeling jargon.\nResponse:\nThis has been rephrased in the abstract.\nComment:\nL.31: “overshoot can seem attractive”: to whom (not)? In science vs. in global climate action? Here you could give some\ncontext on interest in certain pathways.\nResponse:\nThe whole paragraph has been rephrased and reorganized.\nComment:\nL.39: “typically considered as a useful …” If the picture is not crystal-clear, you may want to balance your introduction\nwith a source or two on contexts and voices not deeming it useful.\nResponse:\nIn the old version of the manuscript, following this sentence we menoned the possible societal, biodiversity, and food\nsecurity risks in Lines [44-48]. Later on in Lines [51-52] we menoned that the feasibility and associated socioeconomic\nrisks of ambious AR have been quesoned. Therefore, to our understanding the old version of the manuscript already\noffered a quite balanced introducon without trying to present AR as a panacea.\nNevertheless, to avoid misinterpretaon we have a) rephrased from “typically considered as a useful” to “can be a\nuseful”, and b) added the concerns over permanence in the list of possible risks in Lines [44-48].\nComment:\nL.45: “potential of sequestrating…”. I propose to insert “durably” as an important characteristic.\nResponse:\nThis has been changed according to the reviewer’s recommendaons.\nComment:\nL.45: introduce “carbon (C) ”\nResponse:\nThis has been changed according to the reviewer’s recommendaons.\nComment:\nL.51: I would suggest to use “high estimates” instead of “optimistic”\nResponse:\nThis has been changed according to the reviewer’s recommendaons.\nComment:\nL.9-10: What about SSP1-2.6? Low warming, high AR (> 800 Mha in 2100) What about LUMIP land use swap between\nSSP126 and SSP370? Indeed there is some avoided deforestation signal, but still these efforts should not be forgotten.\nL.87-90: Contextualize which scenarios in which regions, why. In the IAMs themselves, e.g. SSP1-2.6 by IMAGE has\nsubstantial (323 Mha by 2050, 834 Mha by 2100) amounts of AR although this does not directly translate into AR in the\nESMs.\nResponse:\nWe thank the reviewer for these two comments. We group these and address them together since both refer to AR\nunder SSP1-2.6.\nAs the reviewer rightly points out, there is a known mismatch between what the marker IAM intended and what\neventually was implemented in ESMs. However, based on data available on the SSP database and the AR6 scenario\ndatabase we have not been able to validate the numbers of AR suggested by the reviewer (Figure R 9) and thus have\nnot included them in the revised version of the manuscript. In parcular, based on these data (Figure R 9) the total\nforest area change is 259 Mha and 516 Mha in 2050 and 2100 respecvely. Further, this reflects the net foret area\nchange including both AR and likely deforestaon, so maybe to some extent this could jusfy this discrepancy. However,\nit is not possible to isolate the amount of AR from neither the databases nor the publicaon associated with IMAGE-\nSSP1-2.6 (Doelman et al., 2018).\nWe would deeply appreciate it if the reviewer could redirect us to these data, if they are available.\nFigure R 9: Global forest area under the SSP1-2.6 scenario based on the IMAGE IAM presented in Mha (left axis) and Mha compared to 2010 as a\nreference (right axis). Data are extracted from AR6 scenario database.\nIt is due to the above consideraons why in the older version of the manuscript we commented that AR is not ambious\nenough in LUMIP and other previous studies that were listed, and thus we stated in the abstract that “studies typically\napply only moderate levels or idealized AR paterns and under high-emission scenarios”. Nevertheless, we understand\nthe reviewer’s concern that this could possibly be misinterpreted, and therefore: a) we clarify this further in Lines [89-\n93], and b) we have completely rephrased the abstract to avoid confusion. Results based on the LUMIP study of\nLoughran et al., (2023) which was published following the inial submission of our manuscript are also presented in\nLines [284-288].\nComment:\nL.99: rephrase “unmitigated scenarios”. Scenarios are not mitigated, global warming is.\nResponse:\nThis has been changed according to the reviewer’s recommendaons.\nComment:\nL.134: What is the reference forest cover data set? How was it generated, with which thresholds of tree cover to qualify\nforest?\nResponse:\nWe understand that the term “forest cover” might be confusing. We have changed to “tree cover” instead to avoid\nconfusions.\nComment:\nL.182: Could you plot efficiency over time and with uncertainty ranges, e.g. additional to Fig. S3?\nResponse:\nThe Supplementary Figure (od version: 3, new version: 5) has been changed following the reviewer’s\nrecommendaons. In parcular, efficiency expressed as atmospheric removal / land sequestraon (%) is now ploted.\nIn addion, we have added uncertainty ranges to all quanes in both Supplementary Figures 4 and 5.\nComment:\nL.203-205: From when on could one take a snapshot map (1-year annual mean) of any ensemble member and know\nwhether it is REF or AR?\nResponse:\nWe thank the reviewer for their comment. To infer upon field significance (i.e. whether the annual average temperature\nfield under AR is stascally significantly different from REF), we first apply at the gridcell level the same test stasc\nthat we applied at globally averaged temperature (see Methods), and then account for the probability of at least one\nfalse posive among a multude of tests (equal to the number of gridcells in our case), called the familywise error rate\n(Cortés et al., 2020). If a single gridcell is found to be stascally significantly different aer accounng for this\nprobability, then we can reject the global null hypothesis that a stascally significant difference between the AR and\nREF field does not exist.\nTo increase the robustness of our approach we have performed a sensivity analysis by: a) pooling temperature data\nat the gridcell level over a moving window with length ranging from 1 (1-year snapshot) to 10 years, and b)\ncharacterizing field significance when the null hypothesis is consistently rejected for a consecuve number of years\nranging from 1 to 5, defining as year of emergence the starng year of that period.\nWe tested the sensivity of these results by using a variety of commonly used methods including the method by\nBonferroni (1936), Walker (1914), Hochberg (1988), Holm (1979), Benjamini and Hochberg (1995), and Benjamini and\nYekueli (2001), yielding similar results.\nResults are presented in Figure R 5. Given the highly variable nature of temperature paterns and the ensemble size,\nfield significance at the 5% significance level does not emerge when 1-year snapshots are examined. Field significance\nhowever starts emerging as we increase the moving window length (Figure R 10). The results are uncertain for small\nmoving window lengths, but converge to ~2030 as the sample size increases.\nThis result is now included in the manuscript in Lines [210-212], and the methodology is explained in the Methods\nsecon in Lines [556-566]. Figure R 10 has been added to the Supplementary Material (Supplementary Fig. 7)\nFigure R 10: Year of emergence of field significance (y axis) using the Bonferroni method (Bonferroni, 1936), depending on the moving window\nlength chosen (x axis), and the number of consecutive years rejecting the null hypothesis needed to declare significance (color). Missing (not\nplotted) values suggest that a signal has not emerged. Dots are slightly nudged across the vertical axis to aid interpretation.\nComment:\nL.254: Could this be due to MPI-ESM-specific response? Due expect significantly different balances between BGP and\nBGC responses from other modes?\nResponse:\nTo our knowledge, there is no study that has robustly addressed this research queson. For example, based on the\nLUMIP esm-ssp585-ssp126Lu simulaons Loughran et al. (2023) reported no significant temperature migaon\nglobally (Lines [284-288]). As a result, the biogeochemical cooling therein is too weak to offer any insight to the relave\nstrength of biogeophysically-induced warming and biogeochemical cooling at the local level.\nTherefore, the extent to which this model behavior can be consistent across models remains to be assessed. This is\nfurther clarified in Line [335].\nComment:\nL.283-284: If these barriers exist, could you at least include some indication (e.g. on the national level) where the cited\nstudies see uncertainty for these reasons, maybe in Fig. 2, 3 and S1? This will enable to reader to judge upon plausibility\nmore on their own.\nResponse:\nIn the updated version of the manuscript, a more detailed quanfied post-assessment of our scenario based on several\nsocioeconomic factors including land tenure insecurity is discussed in the new secon “Contextualizing ambious\nAfforestaon/Reforestaon” and is presented in Supplementary Fig. 10 (as described in our responses above and\nshown inFigure R 8).\nComment:\nL.285-287: Could you give some context about the share of needed AR finance organized in reality to date? This will\nenable to reader to judge upon plausibility more on their own.\nResponse:\nAs the reviewer rightly suggests, unl this day lack of both public and private finance has been one of the key barriers\nto meeng global restoraon needs, and scaling-up funding would be a big challenge that would require the\nimplementaon of new green finance mechanisms and regulaons and subsidies for restoraon (Löfqvist et al., 2023).\nThis is now discussed in Lines [438-440].\nComment:\nL.288: recommendation to insert “in our simulations” between “feature” and “is the early…”\nResponse:\nThis has been changed according to the reviewer’s recommendaons.\nComment:\nL.316-318: This is a bit short-cut. Could you maybe detail the discussion you provide here spread out through the\nparagraph for the important studies you consider for comparison.\nResponse:\nThis paragraph has now been refined and we comment on each of the selected studies in more detail.\nComment:\nL.332-342: What about the status of MPI-ESM among other CMIP6-models in terms of cooling behaviour in SSP5-3.4os?\nWhat does this mean for the behaviour of AR vs. REF?\nResponse:\nWe thank the reviewer for their comment.\nBoth peak and end-of-century temperature under REF are at the lower range of the CMIP6 SSP5-3.4os mul-model\nranges (not including MPI-ESM) of 2-4.35oC and 1.39-3.47oC respecvely, as recently reported by Asaadi et al. (2024),\nsuggesng that MPI-ESM is among the models with the stronger cooling behavior as a response to negave emissions.\nHowever, direct comparisons of the results reported here with those of Asaadi et al. (2024) cannot be made with\nconfidence, since in our study land-use under REF is constant, even though emissions follows the emission trajectory\nof SSP5-3.4os. It should also be noted that the ranges reported therein are not based on mulple ensemble members.\nDespite the sensivity of MPI-ESM to negave emissions not having been been directly compared against other models\n(Asaadi et al., 2024; Melnikova et al., 2021) and uncertainty remaining, it is the addional sequestraon under AR and\nthe consequent feedbacks that determine temperature migaon compared to REF. Therefore, it is rather the feedback\nstrength within MPI-ESM that would play the key role.\nThis is now discussed in Lines [289-309]\nComment:\nL.342: “mitigated emission scenarios” to me seems a bit to far into jargon. Either reorder “under scenarios of strongly\nmitigated emissions” or rephrase.\nResponse:\nThis has been changed according to the reviewer’s recommendaons.\nComment:\nL.379: This information on the exact emission scenario came too late for me. I would appreciate some context early on\n(e.g. in the paragraph l.s 109-118) to contextualize which GMT-trajectory you perturb with AR.\nResponse:\nThis has been added in the Introducon (Lines [118-120]).\nComment:\nL.383: I understand that the technically easiest adaption of SSP5-3.4os to AR is to deviate from it in 2015. To not have\na period until AR launches over which other kinds of LULCC occur. Or for technical simplicity to initiate the simulations\nin the same year. How does your scenario from 2015 to 2025 differ from observed LULCC? How do you justify the\nrelevance of numeric results like changes in peak GMT, changes in overshoot duration, changes in 2100 GMT (l.s 149-\n151) when actual AR would occur later just 5 or 10 years?\nResponse:\nWe thank the reviewer for their comment. As they rightly assume, branching off from 2015 is the technically easiest\nway to adapt SSP5-3.4os. According to FAO data, forest area has decreased by 29.96 Mha from 2015 to 2021. By that\nme, AR amounts to ~5 Mha, which corresponds to 0.5% of total AR applicaon in our scenario (935 Mha), thus yielding\na net difference of ~35 Mha of forest area with historical data. Most importantly however, by 2021 land C stocks are\nincreased by 0.81 PgC in the AR compared to the REF scenario, constung 0.78% of total addional sequestraon.\nThese numbers demonstrate that the bulk of forest area increase and addional C sequestraon under AR occur aer\n2025 in our simulaons, which suggests that our results remain relevant with respect to overshoot dynamics and\nmigaon, regardless of this discrepancy with recent historical data.\nWe discuss the above in Lines [669-677].\nComment:\nL.445-446: So, does this mean that you only try to match pledged AR area globally and not regionally? I expect this to\nmake your distribution differ fundamentally from the nationally pledged one. If you can, to defend this doubt, maybe\nshow a plot of how your distribution nationally aggregated compares to national AR pledges. The IAM REMIND-MAgPIE\nto date already includes LULCC pledges at the national level. So it would be nice to show in the supplement how you do\nthis and to compare to existing methods.\nResponse:\nAt the me of wring of the inial manuscript version, a complete list of naonal-level data and the corresponding\nland requirements were not available in the Land-Gap report, or elsewhere to our knowledge. As a result, only globally\nand not regionally/naonally pledged area was considered, as the reviewer rightly points out. The fact that the pledges\nare considered at the global level is now clarified in mulple occasions throughout the text.\nOnly aer the submission of our manuscript, naonal AR country pledges have been included in an update of the Land-\nGap report (Self et al., 2023). It should be noted that in this recent update of the Land-Gap report total country pledges\nby 2060 amount up to 490 Mha (Self et al., 2023), which is 22.5% less compared to the previous esmate of 633 Mha\n(Dooley et al., 2022). This new esmate is now clearly reported throughout all text and figures.\nHowever, there are sll not available esmates for all countries, especially when it comes to Lan America, Africa, and\nAsia (Dooley et al., 2022; Self et al., 2023), which would force us to not apply AR over these countries, if gridcell level\nallocaon were to be consistent with pledges at the country-level. More importantly however, it should be noted that\nthe pledge from Saudi Arabia accounts for 42% of the total AR target globally, and can only be met through internaonal\noffsets in addion to domesc land-based CDR (Self et al., 2023). Since it is unclear where this AR will be applied, this\nuncertainty suggests that if AR in the range of country pledges is to be applied, then our spaal patern should not\nstrictly be defined by the narrow region/country boundaries, and therefore the disaggregaon of the global target has\nbeen based on regional AR6-SD esmates. At the same me, employing the AR6-SD allows for an explicit treatment of\nthe temporal evoluon of AR across the century based on technoeconomic consideraons, instead of simply\ninterpolang pledges up to 2060, and arbitrarily extrapolang thereaer.\nWe comment on this in Lines [594-602].\nNevertheless, we sll show a comparison of country-level AR in our scenario with the data from Land-Gap Report 2023\nupdate in Supplementary Figure 1 (Figure R 7), and show and discuss the characteriscs of country pledges, and how\nthey compare to the country-level AR in our scenario in Lines [134-136,419-426].\nComment:\nL.451-453: How do you disaggregate/downscale? What are your decision rules and what are they based on? If you use\npublished methods, give references.\nResponse:\nWe thank the reviewer for their comment. This was indeed not clarified in the older version of the manuscript.\nWe treat regional data in a similar way as the global-level data. For every region we pool available scenarios together,\nand select the 90th percenle of the pooled regional yearly forest area change. Given that a different number of\nscenarios is available at the regional (1,124) compared to the global level (1,259), and that we do not select a single\nscenario, but rather choose a percenle, the sum of all regional 90th percenles might not be equal to the 90th\npercenle of global esmates. As a result, the regional esmates are rescaled so that their sum matches the yearly\nglobal target (preserving their relave magnitude with respect to their sum).\nThis is now clarified in Lines [606-612].\nComment:\nL. 436: What is your regridding method? For reproducibility please indicate the algorithm.\nResponse:\nThe use of the conservave remapping algorithm is now menoned in the text.\nComment:\nL.460-506: Could you maybe provide line plots like in Fig. 3 but showing the sensitivity to different constraints\nemployed? E.g. fully (GRS-ATL-BioDiv) constrained vs. GRS-ATL- constrained vs. only GRS constrained vs. unconstrained?\nResponse:\nTo test the sensivity of the AR scenario development algorithm to the different constraints employed, we re-run the\nalgorithm with different configuraons. In parcular, we develop scenarios where: a) only GRS and ATL restoraon\nmaps (i.e., no explicit biodiversity consideraon), b) only GRS, and biodiversity maps c) no restoraon or biodiversity\nmaps are used to constrain the scenario development. Results are shown in Figure R 11.\nEven though differences arise between the configuraons, the paterns share some similarity with the AR scenario\nemployed in this study. It should be noted that pastures are an integral part of both GRS and ATL (the first being a\nsubset of the later). As a result, priorizing pastures can partly compensate for not using restoraon potenal maps\nto guide AR, due to the inherent consistency between the two. At the same me, consideraon of biodiversity does\nnot affect the paroning between rangelands and pastures that are given up, since biodiversity is only considered for\nrangelands. Therefore, since pastures are generally given up first in our algorithm regardless of the configuraon\ntested, the rough AR patern is not heavily sensive to the different constraints employed. However, considering\nbiodiversity affects the spaal patern of AR mostly since it determines the regions where rangelands can be considered\ncloser to a prisne state, and thus excluded from AR. Even though the AR patern by 2100 unavoidably converges to\nthe available grazing land, the differences across me can be more pronounced as the paroning between pastures\nand rangelands, and the specifics of each configuraon change.\nThis sensivity analysis is now presented in the Supplementary Material under the secon “Sensivity of AR scenario\ndevelopment”, while Figure R 11 has also been added in the Supplementary Material (Supplementary Figure 12).\nComment:\nL.444-445: Same concerning interpolation: Linearly? Differently? For globally aggregated values or for region-specific\nones? Instead, did you consider to interpolate at grid-cell scale once the distribution is completed? How sensitive is your\nmethod to such changes?\nResponse:\nInterpolaon of the 5-year AR6-SD data to yearly both at the global and regional level is performed with a piecewise\ncubic hermite interpolang polynomial algorithm. This is now clarified in Lines [611-612].\nTo test the sensivity of our method to the temporal interpolaon, we have developed a scenario where the 5-year\nIAM data are not interpolated at the yearly level. Instead, the cumulave 5-year AR target is rather directly distributed\nwith the algorithm at the gridcell-level every five years. Aer that, the AR within the gridcell can be temporally\ninterpolated (e.g., linearly). The results presented in Figure R 12 show that not interpolang the 5-year IAM values\nyields litle differences in the AR patern compared to the employed AR scenario both at 2070 and 2100. These\ndifferences can be explained by the 10% threshold posed to the yearly forest fracon increase over a gridcell, which\nbecomes more important when the full 5-year AR target is distributed at once by the algorithm. However, when the\nmaximum yearly cover fracon increase over a gridcell is increased to 30%, the differences to the AR scenario are\npraccally eliminated.\nWe refrained from including this sensivity analysis in the Supplementary Material to keep the material concise.\nFigure R 11: The sensitivity of the AR scenario development algorithm to the different constraints employed is shown for configurations where the\nscenario is: a) constrained only by GRS and ATL (GRS+ATL), b) constrained only by GRS and biodiversity (GRS+BIO), and c) unconstrained. Left: Line\nplots show the changes in pasture (top) and rangeland (bottom) for the different scenarios, where also the scenario employed in this study is\nshown (noted as “AR scenario”). Maps: The maps show the difference in forest fraction between the different configurations tested here and the\nAR scenario employed in this study in 2070 and 2100.\nFigure R 12: The sensitivity of the AR scenario development algorithm to the temporal interpolation is shown for configurations where: a) 5-year\nIAM data are not interpolated at the yearly level, but instead the cumulative 5-year AR target is rather directly distributed with the algorithm at\nthe gridcell-level every five years (“Not interpolated”), and b) same as (a), but with increasing the maximum yearly increase in cover fraction\nallowed from 10% to 30% (“Not interpolated increased yearly capacity”). Left: Line plots show the changes in pasture (top) and rangeland (bottom)\nfor the different scenarios, where also the scenario employed in this study is shown (noted as “AR scenario”). Maps: The maps show the difference\nin forest fraction between the different configurations tested here and the AR scenario employed in this study in 2070 and 2100.\nComment:\nFig.1: I think this figure would just need to appear at bigger scaling with more axis details, gridded background and\nthen could serve much more its purpose.\nResponse:\nThe figure has been refined.\nComment:\nFig.2: Could you maybe include a layer for regions where literature considers AR potential uncertain beyond quantifiable\nconstraints?\nResponse:\nSpaal informaon on various socioeconomic indicators is now presented in Supplementary Fig. 10 (Figure R 7, and as\ndescribed in our responses above), with the AR spaal patern overlaid with hatching to ease interpretaon. Aggregate\nAR values across various ranges of the socioeconomic indicators are also shown with bar plots and discussed in Lines\n[447- 469].\nComment:\nFig.3: For line plot same as Fig.1. It is important that not only overall upwards vs. downwards trends\nbut e.g. comparison to actually observed trends 2015-2023. can be made. I think including a marker of country pledges in all the region-specific line-plots would help the reader a lot. Also the maps the above-mentioned layer on national-level unquantified constraints (e.g. hatching) could help.\nResponse:\nWe comment on the observed trends in Lines [669- 677]. Given the substanally lower amounts, we refrain from\nplong observed trend, to not make the figure overwhelmed with subsidiary informaon.\nComment:\nFig.6: I think the two lines in the center of the plot are not that helpful. They are also the same as in panel of Figure 4, right? I think it could make more sense showing larger maps.\nResponse:\nFig. 6 has now been revised, the lines have been removed, and now the mean, 10th (low temperature extreem) and\n90th (high temperature extreme) percenles of temperature for 2030-2050, 2050-2070 (period around peak warming),\nand 2090-2100 (end of century) are shown.\nComment:\nSuppl.Fig.1: Same as Fig. 2 and 3. For all figures representing coarse-scale model data: I would highly appreciate a way of plotting the values as a mesh instead of smooth colour blobs. This alienates the data at smaller scales. Also consider using discrete color-steps in you colormap for clearer comparison of different regions.\nResponse:\nAcross all figures containing maps, the style of plong has now changed to a mesh, and the colormaps used are now\nmore discrete.\nComment:\nSuppl.Fig.2,3: A third / fourth panel on efficiency (removal from atmos / land sequestration) over time in either Fig. 2 or 3 could be very useful for the understanding of the carbon dynamics.\nResponse:\nEfficiency expressed as “atmospheric removal / land sequestraon” has been added, following the reviewer’s\nrecommendaons.\nComment:\nSuppl.Fig.3: I would appreciate the service to the community with a second vertical axis for °C/PgC (b) and PgC /100 Mha (c). Speculatively this could increase the audience size with an intuition for the numbers.\nResponse:\nThis has been changed according to the reviewer’s recommendaons.\nComment:\nSuppl.Fig.4: What is the added value of the bottom row? Where does it differ from Fig. 6 with more information?\nResponse:\nThe reviewer is right in their remark that botom-row maps presented the same informaon as Fig. 6, and were only\npresented to provide context for the upper- and middle- row maps of the Supplementary Figure, without one needing\nto consult with Fig. 6 at the same me. We understand that this might cause confusion, so this has now been removed.\nReviewer #3 comments:\nComment:\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.\nResponse:\nWe thank the Early Career Researcher for their contribuon to this review.\nReferenes\nAsaadi, A., Schwinger, J., Lee, H., Tjiputra, J., Arora, V., Séférian, R., Liddicoat, S., Hajima, T., Santana-Falcón, Y., & Jones,\nC. D. (2024). 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Worldwide Governance Indicators, 2023 Update (www.govindicators.org) [dataset].\nhtps://www.worldbank.org/en/publicaon/worldwide-governance-indicators\nLöfqvist, S., Garret, R. D., & Ghazoul, J. (2023). Incenves and barriers to private finance for forest and landscape\nrestoraon. Nature Ecology & Evolution, 7(5), 707–715. htps://doi.org/10.1038/s41559-023-02037-5\nLoughran, T. F., Ziehn, T., Law, R., Canadell, J. G., Pongratz, J., Liddicoat, S., Hajima, T., Ito, A., Lawrence, D. M., & Arora,\nV. K. (2023). Limited Migaon Potenal of Forestaon Under a High Emissions Scenario: Results From Mul-\nModel and Single Model Ensembles. Journal of Geophysical Research: Biogeosciences, 128(12),\ne2023JG007605. htps://doi.org/10.1029/2023JG007605\nMelnikova, I., Boucher, O., Cadule, P., Ciais, P., Gasser, T., Quilcaille, Y., Shiogama, H., Tachiiri, K., Yokohata, T., & Tanaka,\nK. (2021). Carbon Cycle Response to Temperature Overshoot Beyond 2°C: An Analysis of CMIP6 Models. Earth’s\nFuture, 9(5), e2020EF001967. htps://doi.org/10.1029/2020EF001967\nPrindex. (2020). Prindex 2020 global dataset [dataset]. htps://www.prindex.net/data/\nRogelj, J., Fransen, T., den Elzen, M. G. J., Lamboll, R. D., Schumer, C., Kuramochi, T., Hans, F., Mooldijk, S., & Portugal-\nPereira, J. (2023). Credibility gap in net-zero climate targets leaves world at high risk. Science, 380(6649), 1014–\n1016. htps://doi.org/10.1126/science.adg6248\nSelf, A., Burdon, R., Lewis, J., Riggs, P., & Dooley, K. (2023). The Land Gap Report: 2023 Update.\nhtps://www.landgap.org/\nTrisos, C. H., Merow, C., & Pigot, A. L. (2020). The projected ming of abrupt ecological disrupon from climate change.\nNature, 580(7804), 496–501. htps://doi.org/10.1038/s41586-020-2189-9\nWalker, G. T. (1914). Correlation in Seasonal Variations of Weather, III: On the Criterion for the Reality of Relationships\nOr Periodicities. Meteorological Office.\nWorld Bank. (2023). Geospatial Poverty Portal. World Bank Group [dataset]. htps://pip.worldbank.org/home\nWorldPop. (2018). WorldPop (www.worldpop.org—School of Geography and Environmental Science, University of\nSouthampton; Department of Geography and Geosciences, University of Louisville; Departement de\nGeographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN),\nColumbia University) [dataset]. www.worldpop.org\nZwiers, F. W., & von Storch, H. (1995). Taking Serial Correlaon into Account in Tests of the Mean. Journal of Climate,\n8(2), 336–351. htps://doi.org/10.1175/1520-0442(1995)008<0336:TSCIAI>2.0.CO;2"}, {"round": 2, "reviewer_comments": {"reviewer_2": "Summary and comments:\nThis work, an update to “Ambitious forestation can mitigate temperature overshoot”, shows\nsignificant improvement, in particular a broader analysis, a deeper evaluation of the forcing\nscenario, and more careful reflections and discussions on the method and results. These changes\naddress not only the concerns raised by me, but - it seems like - also the ones brought up by the\nfirst referee. Now, the manuscript presents nicely the novelty of this work, gives an outstanding\nintroduction and contextualizes the findings in a useful manner within a separate section. I\nrecommend the manuscript for publication after some minor adjustments.\nLine-by-line comments:\nAbstract: The title has been adapted to make clear that the results are based on one ESM. In the\nabstract this should be concretized. I suggest to modify the sentence (line 14-15) to make clear that\nthe results are based on the MPI model (by explicitly mentioning the name of the model).\nIntroduction: This is a very good introduction now. Many aspects introduced. Outstanding\ncompilation.\nL. 20: I think it should say “net-zero greehouse gas emissions”\nL.123: Here at the end of the introduction, mentioning your section 4 could help the reader.\nL.305-307: Here, not only TCR but also ZEC and findings from the flat10 experiments could give\nhelpful context on how MPI-ESM behaves within an ensemble of ESMs.\nConclusion: Informative paragraph. Either in the discussion or here, I recommend to collect and\nsummarize dispersedly mentioned efforts needed beyond this study to further evaluate the\neffectiveness of forestation as strategy to contribute to mitigate global warming.\nL. 573-575: I would remove the word “robustly” and mention explicitly the model used. For\nexample: “Our results based on MPI-ESM …”\nL 576-578: The biochemical cooling is contrasted against “local warming”. There should probably\nbe a mentioning of biophysical effects. Otherwise it is contrasting “biochemical” against “local”.\nOne of them referring to processes and the other to a spatial scale. In addition, the sentence seems\nto be slightly overstated considering that there still seem to be significantly warmer grid cells in\nFigure 6.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part\nof the Nature Communications initiative to facilitate training in peer review and to provide\nappropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Response to reviewers\nWhat follows is our detailed point-by-point responses to the reviewers’ comments.\nReviewer #2 comments:\nComment:\nSummary and comments:\nThis work, an update to “Ambitious forestation can mitigate temperature overshoot”, shows significant\nimprovement, in particular a broader analysis, a deeper evaluation of the forcing scenario, and more\ncareful reflections and discussions on the method and results. These changes address not only the\nconcerns raised by me, but - it seems like - also the ones brought up by the first referee. Now, the\nmanuscript presents nicely the novelty of this work, gives an outstanding introduction and\ncontextualizes the findings in a useful manner within a separate section. I recommend the manuscript\nfor publication after some minor adjustments.\nIntroduction: This is a very good introduction now. Many aspects introduced. Outstanding compilation.\nResponse:\nWe thank the reviewer for their overall posive evaluaon of the revised version of our manuscript.\nComment:\nLine-by-line comments:\nAbstract: The title has been adapted to make clear that the results are based on one ESM. In the abstract\nthis should be concretized. I suggest to modify the sentence (line 14-15) to make clear that the results\nare based on the MPI model (by explicitly mentioning the name of the model).\nResponse:\nThis has been changed according to the reviewer’s recommendaons.\nComment:\nL. 20: I think it should say “net-zero greehouse gas emissions”\nResponse:\nThis has been corrected.\nComment:\nL.123: Here at the end of the introduction, mentioning your section 4 could help the reader.\nResponse:\nThe discussion on socioeconomic tradeoffs is now menoned in the introducon (Lines 113-114).\nComment:\nL.305-307: Here, not only TCR but also ZEC and findings from the flat10 experiments could give helpful\ncontext on how MPI-ESM behaves within an ensemble of ESMs.\nResponse:\nFollowing the reviewers’ suggesons, we now comment on the behaviour of MPI-ESM compared to\nother models based on the ZECMIP simulaons (Lines 283-285).\nComment:\nConclusion: Informative paragraph. Either in the discussion or here, I recommend to collect and\nsummarize dispersedly mentioned efforts needed beyond this study to further evaluate the\neffectiveness of forestation as strategy to contribute to mitigate global warming.\nResponse:\nChanges have been made in the Conclusion according to the reviewer’s recommendaons (Lines 453-\n462).\nComment:\nL. 5473-5475: I would remove the word “robustly” and mention explicitly the model used. For example:\n“Our results based on MPI-ESM …”\nResponse:\nThis has been changed according to the reviewer’s recommendaons (Line 448).\nComment:\nL 5476-5478: The biochemical cooling is contrasted against “local warming”. There should probably be\na menoning of biophysical effects. Otherwise it is contrasng “biochemical” against “local”. One of\nthem referring to processes and the other to a spaal scale. In addion, the sentence seems to be\nslightly overstated considering that there sll seem to be significantly warmer grid cells in Figure 6.\nResponse:\nThis sentence has been clarified (Lines 450-453).\nReviewer #3 comments:\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part\nof the Nature Communications initiative to facilitate training in peer review and to provide appropriate\nrecognition for Early Career Researchers who co-review manuscripts.\nResponse:\nWe thank the Early Career Researcher for their contribuon to this review."}]

Decision letter