Observational constraints reduce model spread but not uncertainty in global wetland methane emission estimates. Chang, K., Riley, W. J., Collier, N., McNicol, G., Fluet‐Chouinard, E., Knox, S. H., Delwiche, K. B., Jackson, R. B., Poulter, B., Saunois, M., Chandra, N., Gedney, N., Ishizawa, M., Ito, A., Joos, F., Kleinen, T., Maggi, F., McNorton, J., Melton, J. R., Miller, P., Niwa, Y., Pasut, C., Patra, P. K., Peng, C., Peng, S., Segers, A., Tian, H., Tsuruta, A., Yao, Y., Yin, Y., Zhang, W., Zhang, Z., Zhu, Q., Zhu, Q., & Zhuang, Q. Global Change Biology, 29(15):4298–4312, August, 2023.
Observational constraints reduce model spread but not uncertainty in global wetland methane emission estimates [link]Paper  doi  abstract   bibtex   
Abstract The recent rise in atmospheric methane (CH 4 ) concentrations accelerates climate change and offsets mitigation efforts. Although wetlands are the largest natural CH 4 source, estimates of global wetland CH 4 emissions vary widely among approaches taken by bottom‐up (BU) process‐based biogeochemical models and top‐down (TD) atmospheric inversion methods. Here, we integrate in situ measurements, multi‐model ensembles, and a machine learning upscaling product into the International Land Model Benchmarking system to examine the relationship between wetland CH 4 emission estimates and model performance. We find that using better‐performing models identified by observational constraints reduces the spread of wetland CH 4 emission estimates by 62% and 39% for BU‐ and TD‐based approaches, respectively. However, global BU and TD CH 4 emission estimate discrepancies increased by about 15% (from 31 to 36 TgCH 4 year −1 ) when the top 20% models were used, although we consider this result moderately uncertain given the unevenly distributed global observations. Our analyses demonstrate that model performance ranking is subject to benchmark selection due to large inter‐site variability, highlighting the importance of expanding coverage of benchmark sites to diverse environmental conditions. We encourage future development of wetland CH 4 models to move beyond static benchmarking and focus on evaluating site‐specific and ecosystem‐specific variabilities inferred from observations.
@article{chang_observational_2023,
	title = {Observational constraints reduce model spread but not uncertainty in global wetland methane emission estimates},
	volume = {29},
	issn = {1354-1013, 1365-2486},
	url = {https://onlinelibrary.wiley.com/doi/10.1111/gcb.16755},
	doi = {10.1111/gcb.16755},
	abstract = {Abstract
            
              The recent rise in atmospheric methane (CH
              4
              ) concentrations accelerates climate change and offsets mitigation efforts. Although wetlands are the largest natural CH
              4
              source, estimates of global wetland CH
              4
              emissions vary widely among approaches taken by bottom‐up (BU) process‐based biogeochemical models and top‐down (TD) atmospheric inversion methods. Here, we integrate in situ measurements, multi‐model ensembles, and a machine learning upscaling product into the International Land Model Benchmarking system to examine the relationship between wetland CH
              4
              emission estimates and model performance. We find that using better‐performing models identified by observational constraints reduces the spread of wetland CH
              4
              emission estimates by 62\% and 39\% for BU‐ and TD‐based approaches, respectively. However, global BU and TD CH
              4
              emission estimate discrepancies increased by about 15\% (from 31 to 36 TgCH
              4
              year
              −1
              ) when the top 20\% models were used, although we consider this result moderately uncertain given the unevenly distributed global observations. Our analyses demonstrate that model performance ranking is subject to benchmark selection due to large inter‐site variability, highlighting the importance of expanding coverage of benchmark sites to diverse environmental conditions. We encourage future development of wetland CH
              4
              models to move beyond static benchmarking and focus on evaluating site‐specific and ecosystem‐specific variabilities inferred from observations.},
	language = {en},
	number = {15},
	urldate = {2024-11-14},
	journal = {Global Change Biology},
	author = {Chang, Kuang‐Yu and Riley, William J. and Collier, Nathan and McNicol, Gavin and Fluet‐Chouinard, Etienne and Knox, Sara H. and Delwiche, Kyle B. and Jackson, Robert B. and Poulter, Benjamin and Saunois, Marielle and Chandra, Naveen and Gedney, Nicola and Ishizawa, Misa and Ito, Akihiko and Joos, Fortunat and Kleinen, Thomas and Maggi, Federico and McNorton, Joe and Melton, Joe R. and Miller, Paul and Niwa, Yosuke and Pasut, Chiara and Patra, Prabir K. and Peng, Changhui and Peng, Sushi and Segers, Arjo and Tian, Hanqin and Tsuruta, Aki and Yao, Yuanzhi and Yin, Yi and Zhang, Wenxin and Zhang, Zhen and Zhu, Qing and Zhu, Qiuan and Zhuang, Qianlai},
	month = aug,
	year = {2023},
	pages = {4298--4312},
}
Downloads: 0
About
Documentation
Keywords
Search
Users
Terms and Conditions of Use
Privacy Policy
Sitemap
© BibBase.org 2021