the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 23 May 2022
Reconciling different approaches to quantifying land surface temperature impacts of afforestation using satellite observations
Abstract. Satellite observations have been widely used to examine afforestation effects on local surface temperature at large spatial scales. Different approaches, which lead potentially to differed definitions of the afforestation effect, have been used in previous studies. The results were used in climate model validation and were cited in climate synthesis reports, but large differences existed in these results. Such differences were simply treated as observational uncertainty, which can be an order of magnitude bigger than the signal itself. However, it remains unclear whether these differences arise from methodological differences that can be reconciled or they represent intrinsic uncertainty of land surface temperature change following afforestation. Here, we provide a synthesis of three influential approaches (one estimates the actual effect and the other two the potential effect) used in the literature and use large-scale afforestation over China as a test case to examine whether the differences in the effects stem from methodological differences. We found that the actual effect (ΔTa) often relates to incomplete afforestation over a medium resolution satellite pixel (1 km) for which LST is observed and that it increases with the fraction of the pixel actually afforested (89 % variation in ΔTa being explained). One potential effect approach quantifies the impact of quasi-full afforestation (ΔTp1), whereas the other quantifies the potential impact of full afforestation (ΔTp2) by assuming a shift from 100 % openland to 100 % forest coverage. An initial paired-samples t-test shows that ΔTa < ΔTp1 < ΔTp2 for the cooling effect of afforestation ranging from 0.07 K to 1.16 K. But when all three methods are normalized for full afforestation, the observed range in surface cooling becomes much smaller (0.79 K to 1.16 K). While potential cooling effects could indeed be realized through full afforestation, they might not always be feasible, given other environmental constraints such as the high water consumption of forests and competition for land usage. Although potential cooling effects have a value in academic studies where they can be used to establish an envelope of effects, they are misleading in a policy-making context where the actual cooling effect better represents policy ambitions.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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CC1: 'Comment on egusphere-2022-317', Chao Zhang, 13 Jun 2022
This study conducted an interesting research about three influential approaches in evaluating the climatic effects induced by afforestation over China. So far, no such studies have ever compared the three methods simultaneously and investigated the underlying mechanisms that lead to their discrepancies and more importantly, whether the discrepancies can be mitigated or reconciled. I'm happy to see that the authors filled this knowledge gap and gave us a good reference. As far as I know, in previous studies involving both the actual and potential effects (Li Yan, 2016, JGR-A, Shen Wenjuan, 2019, AFM), the two effects, characterized by LST changes (or cooling) were compariable and consistent in magnitude. As a result, their discrepancies attracted less attention. Fortunately, this research emphasized this point by applying the afforestation experiment over China. Coincidentally, I have a pending research (in prepare for subscription) in support of the result (actual effect is largely less than potential effect) in this study.
Overall, I appreciate the authors' efforts to put this question forward and gave a good demonstration.
Yet specifically, I have some comments or questions as follows:(1) The distribution of sample grids about the actual and potential effect were not shown. Maybe you can display them in Supplemental Meterials, like Peng Shushi et al., 2014, PNAS did.
(2) Line 313: Please explain why GlobeLand30 is not suitable for detecting forest change, instead of just citing Zeng et al., 2021.
(3) When computing the mixed and full potential effects, what threshold did the authors use to define a 1-km pixel as afforested pixel using the GLC data? In addition, the method to process land cover data (Globeland30) seems to be ambiguous, since Line 189 described using the majority method to aggregate 30 m to 1km, but Line 309-310 mentioned "vegetation type with area fraction > 50% for every 1km * 1km window". In my opinion, majority does not equal > 50%. For instance, one land cover type (i.e., cropland) accounts for 30% can also be designated as the dominated type as long as 30% is the largest area fraction.
(4) Line 311. What dataset did forest and openland stem from? Based on the early description, forest was only form GLC data and openland only from Globeland30. Please give a clear declaration here. Once more, it's important to clearly elucidate the criterion to define the afforested 1-km pixel when aggregating 30-m pixels. If the authors used 50% as the threshold, then the bars below 50% in Figure 6 seem to be unreasonable because pixels with afforestation fraction below 50% was not afforestation anymore. But if using a lower threshold, would the 1-km pixel stay as an afforestation pixel? Please, give an explicit and consistent explanation.
(5) When collecting the sample pixels, did the authors consider the impact of water pixels? As far as I know, the common method is to abandon the grids in which water pixels account for more than a fraction (5% or 10% or 15%...).
(6) Section 2.4, I wonder about the significance and necessity of using Bonferroni correction in this study. Many audience including me seem not to be familiar with this operation. The authors may give a more detailed explanation.
(7) Figure 6. When the fraction of afforestation reached (50, 60], why the mixed potential effect exceeded the full potential effect. It seems strange and no explanation about this phenomena was seen. In addition, significant linear trend can be found for actual effect (as displayed in Figure 5), but it seems that this significant trend was not found in mixed potential especiall the full potential effect. May the authors give an explanation about this?
(8) The reconcliation was reached when increasing the fraction to 100% for the actual effect. But why the fraction increase (through linear extrapolation) was only implemented for actual effect rather than both actual and mixed potential effect. It seems unfair because the author compared the 100% fraction-based actual effect with not 100% based (mixed) potential effect.
(9) What is the difference between Figure 8 and Figure A6 ? Mean values of all grids for Figure 8 and gross values of all grids for Figure A6? Do the cumulative biophysical changes only refer to delta_LE? Because the numbers in Line 586-587 corresponded to delta_LE in Figure. A6.
(10) Uncertainty about the Global Forest Cover dataset should be discussed. References can be found in recent papers published by Dr. Zeng henzhong.
(11) The reasons leading to the discrepancies between actual and potential effects were not considered and discussed thoroughly. 1) Actual effect was claculated using the LST data from two years (target and reference year), but the potential effect used the LST from the same year (2012 in this study). 2) When computing the actual effect, the control pixels were constant or stable unchanged forests, however, as for potential effect, the reference pixels were cropland or grassland pixels. 3) Even though the author adopted the same sample pixels (same locations) for the three approaches, the inherent afforestation fraction was not consistent because different criterions were adopted. Please give a detailed explanation and discussion about the above aspects.
Citation: https://doi.org/10.5194/egusphere-2022-317-CC1 -
AC1: 'Reply on CC1', Huanhuan Wang, 23 Aug 2022
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2022/egusphere-2022-317/egusphere-2022-317-AC1-supplement.pdf
-
AC1: 'Reply on CC1', Huanhuan Wang, 23 Aug 2022
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RC1: 'Comment on egusphere-2022-317', Anonymous Referee #1, 14 Jun 2022
The manuscript “Reconciling different approaches to quantifying land surface temperature impacts of afforestation using satellite observations” by Wang et al presented thoughtful analyses regarding three different types of temperature effects of forestation that appeared in the literature (potential vs actual), and trying to explain the causes of the different magnitudes. The research is a nice addition to the literature on this topic as it is helpful to clarify the interpretation of different results.
Major comments:
First, I disagree with the authors' interpretation of these results and the claim that the causes of the different estimates are unknown. On the contrary, spatial scale or fractions of forest change matters for interpreting the temperature impact, which has been considered in previous studies. Taking the influential work cited by the authors as an example:
In Alkama 2016, the fraction of forest cover change is explicitly taken into account, and the results clearly indicated that the temperature effect depended on the fraction of change.
In Li 2016, the fractional dependency has been reported: “It should be noted that the estimated impacts also depend on the thresholds used to define forest cover change, as discussed in section 2.2. The sensitivity analysis shows that a higher threshold to define forest change leads to stronger impacts on temperature.”
In Duveiller 2018, they used the temperature effect of 100% conversion to avoid the influence of fractional changes.
The strength of this work is that it explicitly addressed this question. Perhaps the authors could consider an alternative title better reflecting this point.
Second, the main finding is that the fraction of forestation (complete vs incomplete) explains the different magnitude of temperature effects. Fraction could indeed have a strong influence on the temperature signal. But it is not the only one. Other factors such as the timing of land cover change, length of the study period, and the spatial extent of forest cover change impact may also contribute. (1) Taking the timing of de-/forestation as an example, if the change happened in the different years of the two periods of 2002–2004 (t1) and 2010–2014 (t2) (L277), changes in 2002 and 2010 would produce a larger temperature change compared to changes in 2004 and 2014, depending on whether the change signals lasted full three years or just the last year. (2) More importantly, the space-for-time assumption is acceptable but it is not strictly true in reality. The adjacent two sites did not share the same climate condition (see Chen 2016). This also contributes to the different temperature effects. (3) When the spatial extent of forest change is large, the local and nonlocal temperature effect appear with heterogeneity which confounds the estimation of the local temperature. (4) The consistency between the actual and potential effect is also scale-dependent. At small scales (e.g., 10m resolution), it would be easier to achieve full change compared to large scales (1km). Therefore, the differences in fractional change alone cannot fully reconcile the observed differences.
Third, I feel the language of this manuscript should be improved and polished.
Specific comments:
L102-103 They may not assume 100$ complete ground coverage. They used the defined forest and nonforest in the paper. Of course, due to inherent scaling and the mixed pixel issue in remote sensing, the defined pixels cannot be 100% pure at a given scale. I think many studies were aware of this issue but they did not explicitly address it.
L161-162: How are the afforestation and adjacent control pixels defined?
L518: What do you mean “extensive variable”?
L549 to 551: For this fractional dependency, it has been reported in such as Li 2016
L572-573: The actual and potential effect is also scale-dependent, and so is the feasibility of full afforestation in reality. Fully afforested could be easily achieved for a small pixel 30m. And for this pixel, the potential and actual could be similar following the findings of this work. At larger scales, it is more difficult to become “fully” afforested, which leads to larger differences between potential and actual impacts. Therefore, whether “achieving the full cooling potential” is scale-dependent.
L581-583: I disagree with the authors on this. The potential effect is useful as it measures the possible outcome of full conversion or mostly afforested (depending on resolution and scale), and whether it is realized depends on the fraction of the change. One can take into account the fractional change to convert the potential effect to more reasonable estimation. At least for this reason, it is not misleading. It is about different interpretation and clarification is needed.
L602 to 605: I don’t agree this statement because both the actual and potential effects are scale dependent. Without mentioning the scale, it is incorrect.
References
Alkama, R., & Cescatti, A. (2016). Biophysical climate impacts of recent changes in global forest cover. Science, 351(6273), 600–604. https://doi.org/10.1126/science.aac8083
Chen, L., & Dirmeyer, P. A. (2016). Adapting observationally based metrics of biogeophysical feedbacks from land cover/land use change to climate modeling. Environmental Research Letters, 11(3), 034002. https://doi.org/10.1088/1748-9326/11/3/034002
Duveiller, G., Hooker, J., & Cescatti, A. (2018). The mark of vegetation change on Earth’s surface energy balance. Nature Communications, 9(2018). https://doi.org/10.1038/s41467-017-02810
Li, Y., Zhao, M., Mildrexler, D. J., Motesharrei, S., Mu, Q., Kalnay, E., … Wang, K. (2016). Potential and Actual impacts of deforestation and afforestation on land surface temperature. Journal of Geophysical Research: Atmospheres, 121(24), 14372–14386. https://doi.org/10.1002/2016JD024969
Citation: https://doi.org/10.5194/egusphere-2022-317-RC1 -
AC2: 'Reply on RC1', Huanhuan Wang, 23 Aug 2022
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2022/egusphere-2022-317/egusphere-2022-317-AC2-supplement.pdf
-
AC2: 'Reply on RC1', Huanhuan Wang, 23 Aug 2022
-
RC2: 'Comment on egusphere-2022-317', Anonymous Referee #2, 22 Jun 2022
The biophysical effects of deforestation/afforestation have drawn a lot of attention in the past few years. However, the results are not very consistent among different studies using different products and methods. The authors revealed the methodological differences among different studies and summarized them into one actual and two potential temperature effects. They also used afforestation in China as a test case to quantify the differences in biophysical effects using the three approaches and verify their hypotheses. The manuscript is well-structured, and the results are clearly represented. I would recommend the publication of this manuscript after minor revisions.
Some minor comments:
Language needs to be further polished throughout the text. Some long sentences are difficult to understand.
L30, “and that it … explained”, Not clear
In Methods, need to clarify how gridded effects were aggregated into the country mean for comparison among the three approaches, because different LC / LST data may have different coverage. How tis the overlapped region representative for the whole country?
L275-277, afforestation from GFC is not consistent with the inventory data, so can the results based on GFC be considered as the real biophysical effects of afforestation in China? I think this key message is important for policy makers.
L391, that’s what I meant, the afforestation area is much smaller than the national inventory.
Fig. 4, better to show the latitudes on the left axis of (a)
Fig. 5, did you consider the spatial distribution of each bin? Whether the regions with higher Faff happen to be in the tropics with larger cooling effects?
Fig. 8, I guess the differences for changes in seasonal fluxes would be much larger between the partial and full coverage of each pixel, especially in the snowing regions in winter.
L742, should be Nature Communications
Citation: https://doi.org/10.5194/egusphere-2022-317-RC2 -
AC3: 'Reply on RC2', Huanhuan Wang, 23 Aug 2022
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2022/egusphere-2022-317/egusphere-2022-317-AC3-supplement.pdf
-
AC3: 'Reply on RC2', Huanhuan Wang, 23 Aug 2022
Interactive discussion
Status: closed
-
CC1: 'Comment on egusphere-2022-317', Chao Zhang, 13 Jun 2022
This study conducted an interesting research about three influential approaches in evaluating the climatic effects induced by afforestation over China. So far, no such studies have ever compared the three methods simultaneously and investigated the underlying mechanisms that lead to their discrepancies and more importantly, whether the discrepancies can be mitigated or reconciled. I'm happy to see that the authors filled this knowledge gap and gave us a good reference. As far as I know, in previous studies involving both the actual and potential effects (Li Yan, 2016, JGR-A, Shen Wenjuan, 2019, AFM), the two effects, characterized by LST changes (or cooling) were compariable and consistent in magnitude. As a result, their discrepancies attracted less attention. Fortunately, this research emphasized this point by applying the afforestation experiment over China. Coincidentally, I have a pending research (in prepare for subscription) in support of the result (actual effect is largely less than potential effect) in this study.
Overall, I appreciate the authors' efforts to put this question forward and gave a good demonstration.
Yet specifically, I have some comments or questions as follows:(1) The distribution of sample grids about the actual and potential effect were not shown. Maybe you can display them in Supplemental Meterials, like Peng Shushi et al., 2014, PNAS did.
(2) Line 313: Please explain why GlobeLand30 is not suitable for detecting forest change, instead of just citing Zeng et al., 2021.
(3) When computing the mixed and full potential effects, what threshold did the authors use to define a 1-km pixel as afforested pixel using the GLC data? In addition, the method to process land cover data (Globeland30) seems to be ambiguous, since Line 189 described using the majority method to aggregate 30 m to 1km, but Line 309-310 mentioned "vegetation type with area fraction > 50% for every 1km * 1km window". In my opinion, majority does not equal > 50%. For instance, one land cover type (i.e., cropland) accounts for 30% can also be designated as the dominated type as long as 30% is the largest area fraction.
(4) Line 311. What dataset did forest and openland stem from? Based on the early description, forest was only form GLC data and openland only from Globeland30. Please give a clear declaration here. Once more, it's important to clearly elucidate the criterion to define the afforested 1-km pixel when aggregating 30-m pixels. If the authors used 50% as the threshold, then the bars below 50% in Figure 6 seem to be unreasonable because pixels with afforestation fraction below 50% was not afforestation anymore. But if using a lower threshold, would the 1-km pixel stay as an afforestation pixel? Please, give an explicit and consistent explanation.
(5) When collecting the sample pixels, did the authors consider the impact of water pixels? As far as I know, the common method is to abandon the grids in which water pixels account for more than a fraction (5% or 10% or 15%...).
(6) Section 2.4, I wonder about the significance and necessity of using Bonferroni correction in this study. Many audience including me seem not to be familiar with this operation. The authors may give a more detailed explanation.
(7) Figure 6. When the fraction of afforestation reached (50, 60], why the mixed potential effect exceeded the full potential effect. It seems strange and no explanation about this phenomena was seen. In addition, significant linear trend can be found for actual effect (as displayed in Figure 5), but it seems that this significant trend was not found in mixed potential especiall the full potential effect. May the authors give an explanation about this?
(8) The reconcliation was reached when increasing the fraction to 100% for the actual effect. But why the fraction increase (through linear extrapolation) was only implemented for actual effect rather than both actual and mixed potential effect. It seems unfair because the author compared the 100% fraction-based actual effect with not 100% based (mixed) potential effect.
(9) What is the difference between Figure 8 and Figure A6 ? Mean values of all grids for Figure 8 and gross values of all grids for Figure A6? Do the cumulative biophysical changes only refer to delta_LE? Because the numbers in Line 586-587 corresponded to delta_LE in Figure. A6.
(10) Uncertainty about the Global Forest Cover dataset should be discussed. References can be found in recent papers published by Dr. Zeng henzhong.
(11) The reasons leading to the discrepancies between actual and potential effects were not considered and discussed thoroughly. 1) Actual effect was claculated using the LST data from two years (target and reference year), but the potential effect used the LST from the same year (2012 in this study). 2) When computing the actual effect, the control pixels were constant or stable unchanged forests, however, as for potential effect, the reference pixels were cropland or grassland pixels. 3) Even though the author adopted the same sample pixels (same locations) for the three approaches, the inherent afforestation fraction was not consistent because different criterions were adopted. Please give a detailed explanation and discussion about the above aspects.
Citation: https://doi.org/10.5194/egusphere-2022-317-CC1 -
AC1: 'Reply on CC1', Huanhuan Wang, 23 Aug 2022
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2022/egusphere-2022-317/egusphere-2022-317-AC1-supplement.pdf
-
AC1: 'Reply on CC1', Huanhuan Wang, 23 Aug 2022
-
RC1: 'Comment on egusphere-2022-317', Anonymous Referee #1, 14 Jun 2022
The manuscript “Reconciling different approaches to quantifying land surface temperature impacts of afforestation using satellite observations” by Wang et al presented thoughtful analyses regarding three different types of temperature effects of forestation that appeared in the literature (potential vs actual), and trying to explain the causes of the different magnitudes. The research is a nice addition to the literature on this topic as it is helpful to clarify the interpretation of different results.
Major comments:
First, I disagree with the authors' interpretation of these results and the claim that the causes of the different estimates are unknown. On the contrary, spatial scale or fractions of forest change matters for interpreting the temperature impact, which has been considered in previous studies. Taking the influential work cited by the authors as an example:
In Alkama 2016, the fraction of forest cover change is explicitly taken into account, and the results clearly indicated that the temperature effect depended on the fraction of change.
In Li 2016, the fractional dependency has been reported: “It should be noted that the estimated impacts also depend on the thresholds used to define forest cover change, as discussed in section 2.2. The sensitivity analysis shows that a higher threshold to define forest change leads to stronger impacts on temperature.”
In Duveiller 2018, they used the temperature effect of 100% conversion to avoid the influence of fractional changes.
The strength of this work is that it explicitly addressed this question. Perhaps the authors could consider an alternative title better reflecting this point.
Second, the main finding is that the fraction of forestation (complete vs incomplete) explains the different magnitude of temperature effects. Fraction could indeed have a strong influence on the temperature signal. But it is not the only one. Other factors such as the timing of land cover change, length of the study period, and the spatial extent of forest cover change impact may also contribute. (1) Taking the timing of de-/forestation as an example, if the change happened in the different years of the two periods of 2002–2004 (t1) and 2010–2014 (t2) (L277), changes in 2002 and 2010 would produce a larger temperature change compared to changes in 2004 and 2014, depending on whether the change signals lasted full three years or just the last year. (2) More importantly, the space-for-time assumption is acceptable but it is not strictly true in reality. The adjacent two sites did not share the same climate condition (see Chen 2016). This also contributes to the different temperature effects. (3) When the spatial extent of forest change is large, the local and nonlocal temperature effect appear with heterogeneity which confounds the estimation of the local temperature. (4) The consistency between the actual and potential effect is also scale-dependent. At small scales (e.g., 10m resolution), it would be easier to achieve full change compared to large scales (1km). Therefore, the differences in fractional change alone cannot fully reconcile the observed differences.
Third, I feel the language of this manuscript should be improved and polished.
Specific comments:
L102-103 They may not assume 100$ complete ground coverage. They used the defined forest and nonforest in the paper. Of course, due to inherent scaling and the mixed pixel issue in remote sensing, the defined pixels cannot be 100% pure at a given scale. I think many studies were aware of this issue but they did not explicitly address it.
L161-162: How are the afforestation and adjacent control pixels defined?
L518: What do you mean “extensive variable”?
L549 to 551: For this fractional dependency, it has been reported in such as Li 2016
L572-573: The actual and potential effect is also scale-dependent, and so is the feasibility of full afforestation in reality. Fully afforested could be easily achieved for a small pixel 30m. And for this pixel, the potential and actual could be similar following the findings of this work. At larger scales, it is more difficult to become “fully” afforested, which leads to larger differences between potential and actual impacts. Therefore, whether “achieving the full cooling potential” is scale-dependent.
L581-583: I disagree with the authors on this. The potential effect is useful as it measures the possible outcome of full conversion or mostly afforested (depending on resolution and scale), and whether it is realized depends on the fraction of the change. One can take into account the fractional change to convert the potential effect to more reasonable estimation. At least for this reason, it is not misleading. It is about different interpretation and clarification is needed.
L602 to 605: I don’t agree this statement because both the actual and potential effects are scale dependent. Without mentioning the scale, it is incorrect.
References
Alkama, R., & Cescatti, A. (2016). Biophysical climate impacts of recent changes in global forest cover. Science, 351(6273), 600–604. https://doi.org/10.1126/science.aac8083
Chen, L., & Dirmeyer, P. A. (2016). Adapting observationally based metrics of biogeophysical feedbacks from land cover/land use change to climate modeling. Environmental Research Letters, 11(3), 034002. https://doi.org/10.1088/1748-9326/11/3/034002
Duveiller, G., Hooker, J., & Cescatti, A. (2018). The mark of vegetation change on Earth’s surface energy balance. Nature Communications, 9(2018). https://doi.org/10.1038/s41467-017-02810
Li, Y., Zhao, M., Mildrexler, D. J., Motesharrei, S., Mu, Q., Kalnay, E., … Wang, K. (2016). Potential and Actual impacts of deforestation and afforestation on land surface temperature. Journal of Geophysical Research: Atmospheres, 121(24), 14372–14386. https://doi.org/10.1002/2016JD024969
Citation: https://doi.org/10.5194/egusphere-2022-317-RC1 -
AC2: 'Reply on RC1', Huanhuan Wang, 23 Aug 2022
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2022/egusphere-2022-317/egusphere-2022-317-AC2-supplement.pdf
-
AC2: 'Reply on RC1', Huanhuan Wang, 23 Aug 2022
-
RC2: 'Comment on egusphere-2022-317', Anonymous Referee #2, 22 Jun 2022
The biophysical effects of deforestation/afforestation have drawn a lot of attention in the past few years. However, the results are not very consistent among different studies using different products and methods. The authors revealed the methodological differences among different studies and summarized them into one actual and two potential temperature effects. They also used afforestation in China as a test case to quantify the differences in biophysical effects using the three approaches and verify their hypotheses. The manuscript is well-structured, and the results are clearly represented. I would recommend the publication of this manuscript after minor revisions.
Some minor comments:
Language needs to be further polished throughout the text. Some long sentences are difficult to understand.
L30, “and that it … explained”, Not clear
In Methods, need to clarify how gridded effects were aggregated into the country mean for comparison among the three approaches, because different LC / LST data may have different coverage. How tis the overlapped region representative for the whole country?
L275-277, afforestation from GFC is not consistent with the inventory data, so can the results based on GFC be considered as the real biophysical effects of afforestation in China? I think this key message is important for policy makers.
L391, that’s what I meant, the afforestation area is much smaller than the national inventory.
Fig. 4, better to show the latitudes on the left axis of (a)
Fig. 5, did you consider the spatial distribution of each bin? Whether the regions with higher Faff happen to be in the tropics with larger cooling effects?
Fig. 8, I guess the differences for changes in seasonal fluxes would be much larger between the partial and full coverage of each pixel, especially in the snowing regions in winter.
L742, should be Nature Communications
Citation: https://doi.org/10.5194/egusphere-2022-317-RC2 -
AC3: 'Reply on RC2', Huanhuan Wang, 23 Aug 2022
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2022/egusphere-2022-317/egusphere-2022-317-AC3-supplement.pdf
-
AC3: 'Reply on RC2', Huanhuan Wang, 23 Aug 2022
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Huanhuan Wang
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