Response is italicized.

The study analyses long-term forestation effects on temperature, with a focus on non-local influences and the role of slower components of the Earth system such as the ocean. I find this work provides novel insights and is well structured, clearly highlighting the most important findings. Overall, my comments are minor and mainly aimed at clarifying the implications of the specific model used (UcVic) and how it differs from or compares with models used in previous literature.  I believe that the use of UcVic model simulations performed here and the performed analysis with a focus on the role of the ocean in the long term is relevant and provides new insights that can inform future research. Hopefully these comments can further help improve the manuscript. 

Thank you very much for taking the time to review and providing thoughtful comments.

A first comment concerns the final land-cover map after regrowth shown in Figure 1. The model includes only two forest types, shrubs and two grass types as the penultimate vegetation category. As I understand it, the land model has dynamic vegetation, meaning that land cover is established during the simulation according to the local climate. Although the global patterns appear reasonable, the distribution seems somewhat biased toward needleleaf forest over Europe and appears unable to represent the Siberian boreal forests. Could the authors add some discussion on whether the vegetation module has been evaluated, how it compares to present day land cover, and what the potential implications of these differences might be?

Yes, the vegetation module was evaluated and compared to present-day conditions in the Meissner et al. (2003) study. There has been some changes in the model code since then, but they have resulted in only minor differences in PFT distribution, so the findings from that study remain valid. Overall, the global PFT distribution is in good agreement with observations, but regional differences do exist (Meissner et al., 2003). Some of the areas with lower agreement with observations include, as you pointed out, Siberia and Northern North America (biased towards shrubs) and Europe (biased towards needleleaf trees) (Meissner et al., 2003). In addition, the North American Prairies (biased towards needleleaf trees), the African steppe (biased towards broadleaf trees), and Central America (biased towards broadleaf trees) are also in lower agreement with observations (Meissner et al., 2003).

We would expect these differences to have mixed effects on albedo and corresponding surface temperature changes. At high latitudes, particularly in snow-masking regions, an increase in needle leaf forest (Siberia) would likely result in a decrease in albedo, strengthening the non-local warming effect. On the other hand, in low to mid latitudes, a reduction in needleleaf or broadleaf forests would likely lead to an increase in albedo, weakening the non-local warming effect. Although the amplifying role of the ocean likely remains valid in these conditions, the magnitude of the amplification would be impacted, with a strengthening at high latitudes and a weakening at low to mid latitudes. In the manuscript, we will add a paragraph to that effect following the discussion of the impact of cloud feedbacks and atmospheric dynamics.

Secondly, I would like to commend the authors for the clear discussion of the potential role of cloud feedbacks, which are not represented in the model. However, several studies (a.o Portmann et al., 2022) also highlight the importance of atmospheric dynamics, which, as I understand from the methods section, are not included in the current modelling setup. I believe it would be useful to further discuss potential implications of this as has been done for the cloud feedbacks, given that atmospheric dynamics have been shown to be particularly important in the context of non-local effects.

We fully agree that atmospheric dynamics can play an important role in shaping non-local climate responses to forestation. While the current modelling framework does not include a fully dynamic atmosphere, we note that the analysis presented in Appendix B, which focuses on cloud feedbacks, implicitly incorporates any impact of atmospheric dynamics insofar as they influence cloud responses. In this sense, the diagnosed cloud feedbacks represent an integrated response that includes both thermodynamic effects and circulation-related influences on clouds. We will adjust the wording in the manuscript to clarify that the discussion section on cloud feedbacks also include such influences on clouds.

Based on the findings of Portmann et al. (2022), the impact of forestation on large-scale meridional atmospheric heat and moisture transport is very small. They further report that forestation causes only a very modest weakening of the Northern Hemispheric Hadley cell and a corresponding strengthening of the Southern Hemispheric Hadley cell during boreal winter, in agreement with the weak changes in total atmospheric heat and moisture transport. On the other hand, that study shows that the dominant contributor to changes in meridional heat transport is the ocean, primarily through a weakening of the Atlantic Meridional Overturning Circulation (AMOC) which is also observed in our simulations (Fig A10).  Given the weak impact of atmospheric dynamics on total atmospheric heat and moisture transport, we do not expect them to substantially alter our conclusions. We will add a comment to that effect in our Discussion section, following the discussion on cloud feedbacks.

Finally, as noted in lines 412–416 of the manuscript, some studies using models with fully dynamic atmospheres find a non-local cooling effect in the tropics driven by atmospheric feedbacks, which include both cloud-related processes and atmospheric dynamics. As noted, such tropical non-local cooling would act to reduce the magnitude of the ocean amplification discussed here. We will clarify in the manuscript that such non-local cooling could be a result of both cloud feedbacks and atmospheric dynamics and mention that the amplifying role of the ocean likely remains valid in the presence of both cloud feedbacks and atmospheric dynamics.

In the discussion, the authors note that some effects of vegetation greening are found here that are typically not present in previous research (line 380-381). One reason given is that greening is slow to establish. Could the authors clarify what is meant by this? Only a few previous studies employ dynamic vegetation; most rely on prescribed land-cover maps for forestation scenarios. Is that what is meant here, i.e. that differences arise (partially) due to the model setup? The underlying point is the same, but being more explicit would help the reader follow the argument.

Non-local warming effects driven by ocean dynamics take multiple centuries to emerge, which, combined with the slow rate of vegetation growth at high latitudes in the UVic ESCM, leads to the long timescale required for the development of the temperature–vegetation feedback at high latitudes. This explains why other studies that either do not use a dynamic vegetation model, or use one but run simulations over shorter timescales, may not have detected an increase in non-local warming associated with the temperature–vegetation feedback. We will revise the manuscript in lines 380-381 to clarify our point, based on the above.



Lastly, the authors acknowledge that their simulations differ significantly from results presented by Boysen et al. (2020) in the deforest-glob simulations, particularly in terms of latent heat flux. I follow the argument that, because of a high bias in sensible heat flux, the total non-radiative flux is comparable, implying that the results may still be reasonably comparable. However, I wonder whether the authors have any insight into why this bias occurs. Including some explanation of what happens within the land model, or at least a hypothesis regarding the possible cause, would help readers understand and contextualize the discrepancy relative to other models.

The UVic ESCM uses the MOSES (Met Office Surface Exchange Scheme) land surface model (Meissner et al., 2003). In MOSES, canopy transpiration is tightly coupled to photosynthesis through canopy conductance, which is primarily driven by carbon demand (Cox et al., 1999). Grasses are also parameterized as being more productive than forests and therefore sustain higher leaf photosynthetic rates and leaf-level stomatal conductance (Cox et al., 1999). Moreover, the scaling of leaf stomatal conductance by Leaf Area Index saturates quickly at moderate leaf area, and the deeper roots of forests primarily influence soil moisture availability and do not impact transpiration in non-water limiting conditions (Cox et al., 1999). As a result, forests often exhibit lower canopy conductance and reduced transpiration rates than grasses, leading to lower latent heat fluxes and compensating increases in sensible heat fluxes following reforestation.

In the model description section of the manuscript, we will therefore add that the UVic ESCM uses the MOSES land surface model, followed by a brief description of this model. In the Discussion section around line 417 when discussing the effect of the decrease in the latent heat flux, we will indicate that this is a result of the tight coupling between transpiration and photosynthesis through a canopy conductance formulation that is driven by carbon demand.

References

Cox, P. M., Betts, R. A., Bunton, C. B., Essery, R. L. H., Rowntree, P. R., and Smith, J.: The impact of new land surface physics on the GCM simulation of climate and climate sensitivity, Climate Dynamics, 15, 183–203, https://doi.org/10.1007/s003820050276, 1999.

Meissner, K. J., Weaver, A. J., Matthews, H. D., and Cox, P. M.: The role of land surface dynamics in glacial inception: a study with the UVic Earth System Model, Climate Dynamics, 21, 515–537, https://doi.org/10.1007/s00382-003-0352-2, 2003.

Portmann, R., Beyerle, U., Davin, E., Fischer, E. M., De Hertog, S., and Schemm, S.: Global forestation and deforestation affect remote climate via adjusted atmosphere and ocean circulation, Nat Commun, 13, 5569, https://doi.org/10.1038/s41467-022-33279-9, 2022.

Response is italicized.

The manuscript by Banville et al. is a well-written paper that addresses the issue of dynamic ocean feedback in simulations of the biogeophysical impacts of afforestation. In their manuscript, they discuss in detail the mechanisms of amplification of forest change by the ocean and compare the local and non-local effects of partial reforestation. Based on a series of numerical experiments using the UVIC model, they concluded that the dynamic ocean amplifies warming from reforestation compared to prescribed SSTs.

Thank you very much for taking the time to review and providing thoughtful comments.

Major comments

The results of their analysis are strongly influenced by the well-known limitation of the UVIC model due to non-interactive cloud cover. The authors acknowledge this limitation, but the discussion of the impact of fixed cloud cover could be more detailed. As we know from many studies (e.g. the recent study by de Vrese et al., 2024), interactive clouds play an important role in the feedbacks between land surface and climate. Clouds influence planetary albedo, and assuming that clouds do not change would alter the impact of changes in the hydrological cycle on climate.

We agree that interactive clouds play an important role in the feedbacks between land surface and climate. We noted that forestation tends to increase low-level cloud coverage locally, most strongly in the Tropics, partially counteracting the decrease in albedo (Cerasoli et al., 2021; Duveiller et al., 2021; Hua et al., 2023; Luo et al., 2024). However, the non-local effects on clouds are more uncertain, but most models point towards a decrease in non-local cloudiness in mid to high latitudes (De Hertog et al., 2023; Hua et al., 2023).

To facilitate comparison with models with a fully dynamic atmosphere, we included a comparison of the surface energy fluxes between the UVic ESCM and CMIP6 models based on a deforestation experiment within the Land Use Model Intercomparison Project (LUMIP) in Appendix B. Based on this comparison, we concluded that consideration of cloud feedbacks in our simulations would likely result in a decrease in net shortwave radiation in the Tropics and an increase in net shortwave radiation at high latitudes (Fig. B2d). Moreover, we indicated that including cloud feedbacks would also change the strength of the water vapor feedback and hence the incoming longwave radiation, weakening it in the Tropics and strengthening it at high latitudes (Fig. B2e).

Finally, we noted that most models incorporating cloud dynamics show a global warming response to forestation driven by a strong non-local warming effect at high latitudes, a similar result to our study (Boysen et al., 2020; De Hertog et al., 2023; Liu et al., 2023). However, we commented that many models find a non-local cooling effect in the Tropics driven by atmospheric feedbacks, yet this effect is generally weaker and less widespread than the warming at high latitudes (De Hertog et al., 2023; Liu et al., 2023). We concluded that, although it is likely that the amplifying role of the ocean found in this study remains valid in the presence of cloud feedbacks, a tropical non-local cooling would reduce the magnitude of this amplification.

In light of your comment, we will add, prior to our comparison of the energy fluxes between the UVic ESCM and the CMIP6 models, that cloud feedbacks also impact the hydrological cycle and the strength and geographic distribution of surface evaporative fluxes (de Vrese et al., 2024). The impact of this change on surface temperature is already addressed through our discussion of the impact of the cloud feedbacks on the water vapor feedback previously mentioned.

In contrast to many other studies on changes in forest cover, reforestation in the UVIC experiments leads to a reduction in latent heat flux on land. This is likely due to parameterization of evapotranspiration. How does transpiration depend on the parameters of the plant functional types in the land surface model (e.g. LAI, root depth)? Could the authors provide more details about their land surface model?

This is a very similar comment to the first reviewer, for which we responded as follows:

The UVic ESCM uses the MOSES (Met Office Surface Exchange Scheme) land surface model (Meissner et al., 2003). In MOSES, canopy transpiration is tightly coupled to photosynthesis through canopy conductance, which is primarily driven by carbon demand (Cox et al., 1999). Grasses are also parameterized as being more productive than forests and therefore sustain higher leaf photosynthetic rates and leaf-level stomatal conductance (Cox et al., 1999). Moreover, the scaling of leaf stomatal conductance by Leaf Area Index saturates quickly at moderate leaf area, and the deeper roots of forests primarily influence soil moisture availability and do not impact transpiration in non-water limiting conditions (Cox et al., 1999). As a result, forests often exhibit lower canopy conductance and reduced transpiration rates than grasses, leading to lower latent heat fluxes and compensating increases in sensible heat fluxes following reforestation.

In the model description section of the manuscript, we will therefore add that the UVic ESCM uses the MOSES land surface model, followed by a brief description of this model. In the Discussion section around line 417 when discussing the effect of the decrease in the latent heat flux, we will indicate that this is a result of the tight coupling between transpiration and photosynthesis through a stomatal conductance formulation that is driven by carbon demand.

Minor comments

Is the dynamic vegetation model (TRIFFID) active in the reforestation simulations? If so, how does it enable reforestation in the areas that were not previously deforested? I guess, this is a limitation on how much forest can regrow, especially in the boreal zone.

In our experiments, first, we performed a global deforestation simulation. Then, we performed reforestation simulations where we allowed forest to regrow on a subset of the grid cells (50% or 25%, based on Table 1). TRIFFID is indeed active in the reforestation simulations. We will clarify this on line 127 of the manuscript. Non-forest vegetation (i.e. grasses) can continue to grow in areas that remain deforested based on climate conditions.

As a result of the non-local warming effect, we see an increase in temperature both in areas that remained deforested and in areas subject to reforestation, particularly at high latitudes, which leads to further growth in vegetation in those areas due to the temperature-vegetation feedback. In the areas that remained deforested, the additional growth, measured through an increase in Leaf Area Index, is for grasses, not forests, whereas it is for forests in areas subject to reforestation. We will clarify in lines 269-271 of the manuscript, that the increase in Leaf Area Index is for grasses in areas that remained deforested and for forests in areas subject to reforestation.

In addition to simulating reforestation, the authors also carry out simulations of deforestation. This leads to global cooling (Fig. A1). After 500 years of free forest dynamics, the forest cover returns to the initial state, indicating that there is no multi-stability of tree cover. These experiments are similar to the deforestation and free dynamics experiments with MPI-ESM (Brovkin et al., 2009), with which it is worth comparing.

Thank you for this. In our discussion of forest regrowth in lines 382-385, we will add that the absence of alternative stable states is similar to what was found in the Brovkin et al. (2009) study using MPI-ESM and the 500-year timeframe to reach the Reforested State is also comparable.

Fig.1 Why is the boreal forest in northern Eurasia not growing back, is this due to the active TRIFFID model?

This area is biased towards shrubs when the Reforested State is reached. See the first comment for the first reviewer explaining the differences in the distribution of PFT between the vegetation module that we used and present-day conditions.

Fig. A4: The local effects are negligible. Why is this the case?

Local effects on surface air temperature are negligible due to the UVic ESCM’s definition of surface air temperature and its single-layer energy-moisture balance model of the atmosphere. Surface air temperature in the UVic ESCM is defined as the sea level air temperature determined from the vertically-integrated atmospheric energy balance equation (lines 166-168). Due to the single-layer energy-moisture balance model of the atmosphere, heat is efficiently transported between neighboring grid cells via advection and diffusion resulting in no significant differences in surface air temperature between grid cells that remain deforested and adjacent grid cells that are reforested. As a result, changes in surface air temperature fall primarily under non-local effects. This is different from other Earth System Models with multiple atmospheric layers and atmospheric dynamics, where non-negligible local effects have been observed. This is why we have focused on surface temperature in the main text to compare local and non-local effects, and only included surface air temperature as a comparison in the Appendix. We will clarify this process in lines 166-168 to indicate why changes in surface air temperature fall primarily under non-local effects.

Fig. A9: What determines the time scale of reforestation? Why are the changes in forest cover in the tropics so small?

The time scale of reforestation is determined by the TRIFFID model and the changes in climate resulting from land cover changes, such as the non-local effects driven by ocean dynamics that are slow to fully emerge. The temperature–vegetation feedback operates through a warming-induced increase in total leaf area index, which reduces surface albedo and enhances net incoming shortwave radiation, thereby amplifying surface warming. This feedback is particularly strong at high latitudes, where the non-local warming driven by ocean dynamics is most pronounced. As a result, both the temperature–vegetation feedback and the associated changes in forest cover are stronger at high latitudes than in the Tropics.

In lines 269-271, we will clarify that this feedback is primarily visible at high latitudes due to the stronger temperature increases at those latitudes. We will also indicate in our Discussion section in lines 380-381 that the long timescale required for the development of the temperature–vegetation feedback at high latitudes is due to the non-local warming effects driven by ocean dynamics that take multiple centuries to fully emerge combined with the slow rate of vegetation growth at high latitudes in the UVic ESCM.

Fig. A11/A12: Are the global temperature changes scalable with the change in forest cover (25 vs. 50 % change)? In other words, is the effect of reforestation on climate a linear function of forest cover?

In those figures, we are focusing on the non-local effects. We discuss in lines 425-428 that the committed non-local warming driven by ocean dynamics appears to be approximately proportional to the area of reforestation based on our 25% and 50% reforestation simulations, but there may be a reforestation area threshold under which the committed non-local warming may be negligible. We would need more data points to make the claim that the non-local effects are directly scalable with changes in forest cover, which is beyond the scope of this manuscript.

References

Boysen, L. R., Brovkin, V., Pongratz, J., Lawrence, D. M., Lawrence, P., Vuichard, N., Peylin, P., Liddicoat, S., Hajima, T., Zhang, Y., Rocher, M., Delire, C., Séférian, R., Arora, V. K., Nieradzik, L., Anthoni, P., Thiery, W., Laguë, M. M., Lawrence, D., and Lo, M.-H.: Global climate response to idealized deforestation in CMIP6 models, Biogeosciences, 17, 5615–5638, https://doi.org/10.5194/bg-17-5615-2020, 2020.

Brovkin, V., Raddatz, T., Reick, C. H., Claussen, M., and Gayler, V.: Global biogeophysical interactions between forest and climate, Geophysical Research Letters, 36, https://doi.org/10.1029/2009GL037543, 2009.

Cerasoli, S., Yin, J., and Porporato, A.: Cloud cooling effects of afforestation and reforestation at midlatitudes, Proc. Natl. Acad. Sci. U.S.A., 118, e2026241118, https://doi.org/10.1073/pnas.2026241118, 2021.

Cox, P. M., Betts, R. A., Bunton, C. B., Essery, R. L. H., Rowntree, P. R., and Smith, J.: The impact of new land surface physics on the GCM simulation of climate and climate sensitivity, Climate Dynamics, 15, 183–203, https://doi.org/10.1007/s003820050276, 1999.

De Hertog, S. J., Havermann, F., Vanderkelen, I., Guo, S., Luo, F., Manola, I., Coumou, D., Davin, E. L., Duveiller, G., Lejeune, Q., Pongratz, J., Schleussner, C.-F., Seneviratne, S. I., and Thiery, W.: The biogeophysical effects of idealized land cover and land management changes in Earth system models, Earth System Dynamics, 14, 629–667, https://doi.org/10.5194/esd-14-629-2023, 2023.

Duveiller, G., Filipponi, F., Ceglar, A., Bojanowski, J., Alkama, R., and Cescatti, A.: Revealing the widespread potential of forests to increase low level cloud cover, Nat Commun, 12, 4337, https://doi.org/10.1038/s41467-021-24551-5, 2021.

Hua, W., Zhou, L., Dai, A., Chen, H., and Liu, Y.: Important non-local effects of deforestation on cloud cover changes in CMIP6 models, Environ. Res. Lett., 18, 094047, https://doi.org/10.1088/1748-9326/acf232, 2023.

Liu, S., Hua, W., Zhou, L., Chen, H., Yu, M., Li, X., and Cui, Y.: Local and Non‐Local Biophysical Impacts of Deforestation on Global Temperature During Boreal Summer: CMIP6‐LUMIP Multimodel Analysis, JGR Atmospheres, 128, e2022JD038229, https://doi.org/10.1029/2022JD038229, 2023.

Luo, H., Quaas, J., and Han, Y.: Decreased cloud cover partially offsets the cooling effects of surface albedo change due to deforestation, Nat Commun, 15, 7345, https://doi.org/10.1038/s41467-024-51783-y, 2024.

Meissner, K. J., Weaver, A. J., Matthews, H. D., and Cox, P. M.: The role of land surface dynamics in glacial inception: a study with the UVic Earth System Model, Climate Dynamics, 21, 515–537, https://doi.org/10.1007/s00382-003-0352-2, 2003.

de Vrese, P., Stacke, T., Gayler, V., and Brovkin, V.: Permafrost Cloud Feedback May Amplify Climate Change, Geophysical Research Letters, 51, e2024GL109034, https://doi.org/10.1029/2024GL109034, 2024.