TY - JOUR
T1 - RECCAP2 Future Component: Consistency and Potential for Regional Assessment to Constrain Global Projections
AU - Jones, Chris d.
AU - Ziehn, Tilo
AU - Anand, Jatin
AU - Bastos, Ana
AU - Burke, Eleanor
AU - Canadell, Josep g.
AU - Cardoso, Manoel
AU - Ernst, Yolandi
AU - Jain, Atul k.
AU - Jeong, Sujong
AU - Keller, Elizabeth d.
AU - Kondo, Masayuki
AU - Lauerwald, Ronny
AU - Lin, Tzu‐shun
AU - Murray‐tortarolo, Guillermo
AU - Nabuurs, Gert‐jan
AU - O’sullivan, Mike
AU - Poulter, Ben
AU - Qin, Xiaoyu
AU - Von randow, Celso
AU - Sanches, Marcos
AU - Schepaschenko, Dmitry
AU - Shvidenko, Anatoly
AU - Smallman, T. luke
AU - Tian, Hanqin
AU - Villalobos, Yohanna
AU - Wang, Xuhui
AU - Yun, Jeongmin
N1 - Funding Information:
We acknowledge the World Climate Research Programme, which coordinated and promoted CMIP6 through its Working Group on Coupled Modeling. We thank the various climate modeling groups for producing and making available their model output, the Earth System Grid Federation (ESGF; URL, https://esgf-node.llnl.gov/search/cmip6/). CDJ and EB were supported by the Joint UK BEIS/Defra Met Office Hadley Centre Climate Programme (GA01101) and also the European Union's Horizon 2020 research and innovation programme under Grant Agreement No 101003536 (ESM2025—ESMs for the Future). EDK was supported by funding from the Government of New Zealand under the CarbonWatch-NZ Endeavour Research Programme (#C01X1817). GMT would like to thank the Universidad Nacional Autónoma de México for their support through project: DGAPA PAPIIT IA-200722. TZ receives funding from the Australian Government under the National Environmental Science Program. MC acknowledges the support from the São Paulo Research Foundation (FAPESP, Brazil, Grants 2015/50122-0 and 2017/22269-2). HT acknowledges the support from US National Science Foundation (Grant 1903722) and Andrew Carnegie Fellow Program (award no. G-F-19-56910). CvR acknowledges support from São Paulo Research Foundation (FAPESP, Brazil, Grant 2020/15230-5) and CNPq (# 314780/2020-3). GJN was supported by the European Union's H2020 Verify: Grant agreement 776810, and H2020 Resonate: Grant agreement 101000574. SJ was supported by Korea Environment Industry & Technology Institute (KEITI) through Project for developing an observation-based GHG emissions geospatial information map, funded by Korea Ministry of Environment (RS-2023-00232066). TLS is supported by the UK National Centre for Earth Observation funded by the Natural Environment Research Council (NE/R016518/1 and NE/N018079/1). The CARDAMOM analyses made use of resources provided by the Edinburgh Compute and Data Facility (EDCF) (http://www.ecdf.ed.ac.uk). JY contribution to this research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under contract to NASA (80NM0018D0004). We thank Seetharaman Seshadri for his involvement in the analysis of the models and data products used here for the South Asia region.
Funding Information:
We acknowledge the World Climate Research Programme, which coordinated and promoted CMIP6 through its Working Group on Coupled Modeling. We thank the various climate modeling groups for producing and making available their model output, the Earth System Grid Federation (ESGF; URL, https://esgf-node.llnl.gov/search/cmip6/ ). CDJ and EB were supported by the Joint UK BEIS/Defra Met Office Hadley Centre Climate Programme (GA01101) and also the European Union's Horizon 2020 research and innovation programme under Grant Agreement No 101003536 (ESM2025—ESMs for the Future). EDK was supported by funding from the Government of New Zealand under the CarbonWatch‐NZ Endeavour Research Programme (#C01X1817). GMT would like to thank the Universidad Nacional Autónoma de México for their support through project: DGAPA PAPIIT IA‐200722. TZ receives funding from the Australian Government under the National Environmental Science Program. MC acknowledges the support from the São Paulo Research Foundation (FAPESP, Brazil, Grants 2015/50122‐0 and 2017/22269‐2). HT acknowledges the support from US National Science Foundation (Grant 1903722) and Andrew Carnegie Fellow Program (award no. G‐F‐19‐56910). CvR acknowledges support from São Paulo Research Foundation (FAPESP, Brazil, Grant 2020/15230‐5) and CNPq (# 314780/2020‐3). GJN was supported by the European Union's H2020 Verify: Grant agreement 776810, and H2020 Resonate: Grant agreement 101000574. SJ was supported by Korea Environment Industry & Technology Institute (KEITI) through Project for developing an observation‐based GHG emissions geospatial information map, funded by Korea Ministry of Environment (RS‐2023‐00232066). TLS is supported by the UK National Centre for Earth Observation funded by the Natural Environment Research Council (NE/R016518/1 and NE/N018079/1). The CARDAMOM analyses made use of resources provided by the Edinburgh Compute and Data Facility (EDCF) ( http://www.ecdf.ed.ac.uk ). JY contribution to this research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under contract to NASA (80NM0018D0004). We thank Seetharaman Seshadri for his involvement in the analysis of the models and data products used here for the South Asia region.
Publisher Copyright:
© 2023 Crown copyright, Commonwealth of Australia and The Authors. This article is published with the permission of the Controller of HMSO and the King's Printer for Scotland. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.
PY - 2023/12/1
Y1 - 2023/12/1
N2 - Projections of future carbon sinks and stocks are important because they show how the world's ecosystems will respond to elevated CO2 and changes in climate. Moreover, they are crucial to inform policy decisions around emissions reductions to stay within the global warming levels identified by the Paris Agreement. However, Earth System Models from the 6th Coupled Model Intercomparison Project (CMIP6) show substantial spread in future projections—especially of the terrestrial carbon cycle, leading to a large uncertainty in our knowledge of any remaining carbon budget (RCB). Here we evaluate the global terrestrial carbon cycle projections on a region-by-region basis and compare the global models with regional assessments made by the REgional Carbon Cycle Assessment and Processes, Phase 2 activity. Results show that for each region, the CMIP6 multi-model mean is generally consistent with the regional assessment, but substantial cross-model spread exists. Nonetheless, all models perform well in some regions and no region is without some well performing models. This gives confidence that the CMIP6 models can be used to look at future changes in carbon stocks on a regional basis with appropriate model assessment and benchmarking. We find that most regions of the world remain cumulative net sources of CO2 between now and 2100 when considering the balance of fossil-fuels and natural sinks, even under aggressive mitigation scenarios. This paper identifies strengths and weaknesses for each model in terms of its performance over a particular region including how process representation might impact those results and sets the agenda for applying stricter constraints at regional scales to reduce the uncertainty in global projections.
AB - Projections of future carbon sinks and stocks are important because they show how the world's ecosystems will respond to elevated CO2 and changes in climate. Moreover, they are crucial to inform policy decisions around emissions reductions to stay within the global warming levels identified by the Paris Agreement. However, Earth System Models from the 6th Coupled Model Intercomparison Project (CMIP6) show substantial spread in future projections—especially of the terrestrial carbon cycle, leading to a large uncertainty in our knowledge of any remaining carbon budget (RCB). Here we evaluate the global terrestrial carbon cycle projections on a region-by-region basis and compare the global models with regional assessments made by the REgional Carbon Cycle Assessment and Processes, Phase 2 activity. Results show that for each region, the CMIP6 multi-model mean is generally consistent with the regional assessment, but substantial cross-model spread exists. Nonetheless, all models perform well in some regions and no region is without some well performing models. This gives confidence that the CMIP6 models can be used to look at future changes in carbon stocks on a regional basis with appropriate model assessment and benchmarking. We find that most regions of the world remain cumulative net sources of CO2 between now and 2100 when considering the balance of fossil-fuels and natural sinks, even under aggressive mitigation scenarios. This paper identifies strengths and weaknesses for each model in terms of its performance over a particular region including how process representation might impact those results and sets the agenda for applying stricter constraints at regional scales to reduce the uncertainty in global projections.
U2 - 10.1029/2023AV001024
DO - 10.1029/2023AV001024
M3 - Article
SN - 2576-604X
VL - 4
JO - AGU Advances
JF - AGU Advances
IS - 6
M1 - e2023AV001024
ER -