the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 11 Jun 2024
Ensemble estimates of global wetland methane emissions over 2000–2020
Abstract. Due to ongoing climate change, methane (CH4) emissions from vegetated wetlands are projected to increase during the 21st century, challenging climate mitigation efforts aimed at limiting global warming. However, despite reports of rising emission trends, a comprehensive evaluation and attribution of recent changes is still lacking. Here we assessed global wetland CH4 emissions from 2000 to 2020 based on an ensemble of sixteen process-based wetland models. Our results estimated global average wetland CH4 emissions at 158±24 (mean ± 1σ) Tg CH4 yr-1 for the period 2010–2020, with an average decadal increase of 6–7 Tg CH4 yr-1 compared to the decade of 2000–2009. The increases in the four latitudinal bands of 90° S–30° S, 30° S–30° N, 30° N–60° N, and 60° N–90° N were 0.1–0.2 Tg CH4 yr-1, 3.6–3.7 Tg CH4 yr-1, 1.8–2.4 Tg CH4 yr-1, and 0.6–0.8 Tg CH4 yr-1, respectively, over the two decades. The modeled CH4 sensitivities to temperature show reasonable consistency with eddy covariance-based measurements from 34 sites. Rising temperature was the primary driver of the increase, while precipitation and rising atmospheric CO2 concentrations played secondary roles with high levels of uncertainty. These modeled results suggest climate change is driving increased wetland CH4 emissions and that direct and sustained measurements are needed to monitor developments.
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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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Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2024-1584', Anonymous Referee #1, 09 Jul 2024
Review Comments for egusphere-2024-1584Title: Ensemble estimates of global wetland methane emissions over 2000-2020This is a comprehensive analysis of global inland wetland CH4 emission study that has taken a lot of efforts from many field observations and models. The final result of 158 Tg annual CH4 emission is an important number that fills the gap of current carbon cycle science. I would suggest acceptance after a minor revision.Abstract:It would be good to add the global total wetland area in Abstract.Introduction:Although a lot of references was mentioned, I didn't see a number or a range of global annual CH4 emission provided by previous studies. If such numbers exist, please try to add the information.Methods:Line 140-143: wetland extentIt would be good to add global wetland area here, or the range of wetland area from those models, or the area from GIESM2.Line 156: Ancillary data.It would be good to list a few more data items beyond climate data and soil wetness data, e.g., some soil carbon data and vegetation type. I understand those data maybe quite different among the 16 models. Just a suggestion, not a must. Also, maybe list a few CH4-related parameters that most models have in common?Results:Figure 1. Nice. But I only see delta CH4 values. Maybe the mean value of the 2000-2009 level should be added in the figure caption? What if you plot the absolute CH4 values on panel 'a'? Should they be the same curves but vary around the mean value?Figure 2 a. Visually, I guess panel 'a' can be improved with a more contrasting color scheme. It will be interesting to see the spatial variation on those low value areas. Currently they are all yellow.Line 313: Is it multiple liner regression? If so, add the word 'liner'.Figure 3 a. Panel 'a' comparing histogram (frequency) against degree/mm/ppm is unfamiliar to me. No critics here, just make sure you can explain well "The model ensemble suggests that temperature is the primary driver of the increase in eCH4 (Fig. 3a)." What if you use precipitation unit in cm, will that change anything? (I am not quite good at statistics.)Another way to explain why temperature is the primary driver of CH4 increase may be in panel 'b'. I see panel 'b' has regression trend lines, which may show delta T-CH4 relationship is significant because the slope seems bigger. Maybe precip and CO2 impacts are statistically insignificant?Line 366: There are two section '3.2'. The former is on Line 310.Conclusions:Line 416-417: "Resolving the large uncertainty in wetland areas and seasonal variation remains a high priority to refine bottom-up estimates of eCH4."Is it possible to report the wetland areas and/or seasonal variation from the 16 models?Citation: https://doi.org/
10.5194/egusphere-2024-1584-RC1 -
AC2: 'Reply on RC1', Zhen Zhang, 16 Sep 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1584/egusphere-2024-1584-AC2-supplement.pdf
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AC2: 'Reply on RC1', Zhen Zhang, 16 Sep 2024
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RC2: 'Comment on egusphere-2024-1584', Anonymous Referee #2, 16 Jul 2024
In “Ensemble estimates of global wetland methane emissions over 2000-2020,” Zhang and co-authors simulate global methane wetland emissions using 16 process-based wetland models with varying levels of complexity that are participating in the Global Carbon Project. Authors simulate wetland methane emissions for 2000-2020 and simulate the decadal changes in emissions and their large-scale drivers. The modeling ensemble shows an increase in 2010-2020 vs 2000-2010 emissions, and that temperature is the primary driver followed by precipitation and atmospheric CO2 concentration. Authors show that these changes and the drivers are generally support by inversions and observational evidence.
Overall, I think this is a well written study and useful study. In my opinion, it should be accepted after addressing a few comments and questions.
Overall comments
The multiple linear regression lacks detail in how the predictors were selected, so it is unclear how robust those conclusions are. Authors choose global mean temperature, global total precipitation, and mean atmospheric CO2 concentration as the predictors, and then state that modeled eCH4 was “primarily associated” with those variables (line 340), but were those the only variables tested? In that case, did the exercise reveal anything new? Authors say that “other confounding drivers might influence eCH4 as well, such as solar radiation, wind speed, and nitrogen deposition” (line 325), but don’t explore these as predictors. Could authors provide more justification for their choice of the three main predictors? Did authors test model performance after leaving any of these predictors out, or adding any of the additional predictors they mentioned?
How do the ensemble modeling results for the 2020 surge compare with other studies that used satellite data to interpret the surge? Authors mention Peng et al. 2022. In addition, Feng et al. 2023 (https://doi.org/10.5194/acp-23-4863-2023) and Qu et al 2022 (https://doi.org/10.1088/1748-9326/ac8754) attribute the surge to emissions, largely from wetland and water sources in Africa. A note on how your results agree or not would be useful given the attention in this manuscript and in the literature on the 2020 surge.
Minor comments
Line 52-53: This seems like a strong statement. I think this has been addressed, for example in inversions and in the authors’ previous works, though perhaps not in the way it is addressed here. Consider being more specific.
Line 55-56, “with an average decadal increase…”: this sentence is a little unclear.
Line 97-98: Y Zhang et al. 2021 (https://doi.org/10.5194/acp-21-3643-2021), using GOSAT, is a useful comparison here.
Line 136-137 “different prescribed parameters”: Does this mean that each model has a different set of parameters and inputs, or that a different set of parameter values is given to each model? The current statement is vague.
Line 144: Authors mention high correlations for the temperate region and high latitudes, but what about the tropics with the most emissions? Ensemble mean agreement with GIEMS2 in that region seems important, but it is not discussed and it is hard to tell the performance of the tropics from Figure S1.
Line 205 and apparent Q10: Could authors comment on the choice ambient vs soil temperature here? Given the hysteresis effect, and evidence that methane emissions follow soil temperature rather than air temperature, soil seems the more logical choice, but I may be misunderstanding.
Line 226, “Suggesting enhanced wetland-CH4 sensitivity under climate change”: To me, authors haven’t demonstrated that the larger IAV in the second decade considered is evidence of larger sensitivity under climate change. The statement may be true, but I don’t think authors have demonstrated it, so I suggest adjusting the statement or providing more evidence.
Figure 2: Could authors add identifying markers for the regions in panels c,d to the maps?
Line 317-318, “with a range of -0.4 and 9.0 Tg…”: Is this the distribution of coefficients among all the wetland models?
Figure 3: It’s unclear what the Gaussian density distribution curves represent, could more description be added to the caption? In panel b, the dashed lines are too faint to distinguish.
Line 411-413, “Furthermore…eCh4”: The meaning of this sentence is unclear.
Lines 418-421: The MLR analysis seems to show a lower relative importance of the CO2 fertilization effect. Could authors reconcile the MLR analysis with the factorial analysis on this point?
Citation: https://doi.org/10.5194/egusphere-2024-1584-RC2 -
AC1: 'Reply on RC2', Zhen Zhang, 16 Sep 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1584/egusphere-2024-1584-AC1-supplement.pdf
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AC3: 'Reply on RC2', Zhen Zhang, 16 Sep 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1584/egusphere-2024-1584-AC3-supplement.pdf
-
AC1: 'Reply on RC2', Zhen Zhang, 16 Sep 2024
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2024-1584', Anonymous Referee #1, 09 Jul 2024
Review Comments for egusphere-2024-1584Title: Ensemble estimates of global wetland methane emissions over 2000-2020This is a comprehensive analysis of global inland wetland CH4 emission study that has taken a lot of efforts from many field observations and models. The final result of 158 Tg annual CH4 emission is an important number that fills the gap of current carbon cycle science. I would suggest acceptance after a minor revision.Abstract:It would be good to add the global total wetland area in Abstract.Introduction:Although a lot of references was mentioned, I didn't see a number or a range of global annual CH4 emission provided by previous studies. If such numbers exist, please try to add the information.Methods:Line 140-143: wetland extentIt would be good to add global wetland area here, or the range of wetland area from those models, or the area from GIESM2.Line 156: Ancillary data.It would be good to list a few more data items beyond climate data and soil wetness data, e.g., some soil carbon data and vegetation type. I understand those data maybe quite different among the 16 models. Just a suggestion, not a must. Also, maybe list a few CH4-related parameters that most models have in common?Results:Figure 1. Nice. But I only see delta CH4 values. Maybe the mean value of the 2000-2009 level should be added in the figure caption? What if you plot the absolute CH4 values on panel 'a'? Should they be the same curves but vary around the mean value?Figure 2 a. Visually, I guess panel 'a' can be improved with a more contrasting color scheme. It will be interesting to see the spatial variation on those low value areas. Currently they are all yellow.Line 313: Is it multiple liner regression? If so, add the word 'liner'.Figure 3 a. Panel 'a' comparing histogram (frequency) against degree/mm/ppm is unfamiliar to me. No critics here, just make sure you can explain well "The model ensemble suggests that temperature is the primary driver of the increase in eCH4 (Fig. 3a)." What if you use precipitation unit in cm, will that change anything? (I am not quite good at statistics.)Another way to explain why temperature is the primary driver of CH4 increase may be in panel 'b'. I see panel 'b' has regression trend lines, which may show delta T-CH4 relationship is significant because the slope seems bigger. Maybe precip and CO2 impacts are statistically insignificant?Line 366: There are two section '3.2'. The former is on Line 310.Conclusions:Line 416-417: "Resolving the large uncertainty in wetland areas and seasonal variation remains a high priority to refine bottom-up estimates of eCH4."Is it possible to report the wetland areas and/or seasonal variation from the 16 models?Citation: https://doi.org/
10.5194/egusphere-2024-1584-RC1 -
AC2: 'Reply on RC1', Zhen Zhang, 16 Sep 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1584/egusphere-2024-1584-AC2-supplement.pdf
-
AC2: 'Reply on RC1', Zhen Zhang, 16 Sep 2024
-
RC2: 'Comment on egusphere-2024-1584', Anonymous Referee #2, 16 Jul 2024
In “Ensemble estimates of global wetland methane emissions over 2000-2020,” Zhang and co-authors simulate global methane wetland emissions using 16 process-based wetland models with varying levels of complexity that are participating in the Global Carbon Project. Authors simulate wetland methane emissions for 2000-2020 and simulate the decadal changes in emissions and their large-scale drivers. The modeling ensemble shows an increase in 2010-2020 vs 2000-2010 emissions, and that temperature is the primary driver followed by precipitation and atmospheric CO2 concentration. Authors show that these changes and the drivers are generally support by inversions and observational evidence.
Overall, I think this is a well written study and useful study. In my opinion, it should be accepted after addressing a few comments and questions.
Overall comments
The multiple linear regression lacks detail in how the predictors were selected, so it is unclear how robust those conclusions are. Authors choose global mean temperature, global total precipitation, and mean atmospheric CO2 concentration as the predictors, and then state that modeled eCH4 was “primarily associated” with those variables (line 340), but were those the only variables tested? In that case, did the exercise reveal anything new? Authors say that “other confounding drivers might influence eCH4 as well, such as solar radiation, wind speed, and nitrogen deposition” (line 325), but don’t explore these as predictors. Could authors provide more justification for their choice of the three main predictors? Did authors test model performance after leaving any of these predictors out, or adding any of the additional predictors they mentioned?
How do the ensemble modeling results for the 2020 surge compare with other studies that used satellite data to interpret the surge? Authors mention Peng et al. 2022. In addition, Feng et al. 2023 (https://doi.org/10.5194/acp-23-4863-2023) and Qu et al 2022 (https://doi.org/10.1088/1748-9326/ac8754) attribute the surge to emissions, largely from wetland and water sources in Africa. A note on how your results agree or not would be useful given the attention in this manuscript and in the literature on the 2020 surge.
Minor comments
Line 52-53: This seems like a strong statement. I think this has been addressed, for example in inversions and in the authors’ previous works, though perhaps not in the way it is addressed here. Consider being more specific.
Line 55-56, “with an average decadal increase…”: this sentence is a little unclear.
Line 97-98: Y Zhang et al. 2021 (https://doi.org/10.5194/acp-21-3643-2021), using GOSAT, is a useful comparison here.
Line 136-137 “different prescribed parameters”: Does this mean that each model has a different set of parameters and inputs, or that a different set of parameter values is given to each model? The current statement is vague.
Line 144: Authors mention high correlations for the temperate region and high latitudes, but what about the tropics with the most emissions? Ensemble mean agreement with GIEMS2 in that region seems important, but it is not discussed and it is hard to tell the performance of the tropics from Figure S1.
Line 205 and apparent Q10: Could authors comment on the choice ambient vs soil temperature here? Given the hysteresis effect, and evidence that methane emissions follow soil temperature rather than air temperature, soil seems the more logical choice, but I may be misunderstanding.
Line 226, “Suggesting enhanced wetland-CH4 sensitivity under climate change”: To me, authors haven’t demonstrated that the larger IAV in the second decade considered is evidence of larger sensitivity under climate change. The statement may be true, but I don’t think authors have demonstrated it, so I suggest adjusting the statement or providing more evidence.
Figure 2: Could authors add identifying markers for the regions in panels c,d to the maps?
Line 317-318, “with a range of -0.4 and 9.0 Tg…”: Is this the distribution of coefficients among all the wetland models?
Figure 3: It’s unclear what the Gaussian density distribution curves represent, could more description be added to the caption? In panel b, the dashed lines are too faint to distinguish.
Line 411-413, “Furthermore…eCh4”: The meaning of this sentence is unclear.
Lines 418-421: The MLR analysis seems to show a lower relative importance of the CO2 fertilization effect. Could authors reconcile the MLR analysis with the factorial analysis on this point?
Citation: https://doi.org/10.5194/egusphere-2024-1584-RC2 -
AC1: 'Reply on RC2', Zhen Zhang, 16 Sep 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1584/egusphere-2024-1584-AC1-supplement.pdf
-
AC3: 'Reply on RC2', Zhen Zhang, 16 Sep 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1584/egusphere-2024-1584-AC3-supplement.pdf
-
AC1: 'Reply on RC2', Zhen Zhang, 16 Sep 2024
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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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