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Dependence of the evolution of carbon dynamics in the northern permafrost region on the trajectory of climate change.

A David McGuireDavid M LawrenceCharles KovenJoy S CleinEleanor BurkeGuangsheng ChenElchin JafarovAndrew H MacDougallSergey MarchenkoDmitry NicolskyShushi PengAnnette RinkePhilippe CiaisIsabelle GouttevinDaniel J HayesDuoying JiGerhard KrinnerJohn C MooreVladimir RomanovskyChristina SchädelKevin SchaeferEdward A G SchuurQianlai Zhuang
Published in: Proceedings of the National Academy of Sciences of the United States of America (2018)
We conducted a model-based assessment of changes in permafrost area and carbon storage for simulations driven by RCP4.5 and RCP8.5 projections between 2010 and 2299 for the northern permafrost region. All models simulating carbon represented soil with depth, a critical structural feature needed to represent the permafrost carbon-climate feedback, but that is not a universal feature of all climate models. Between 2010 and 2299, simulations indicated losses of permafrost between 3 and 5 million km2 for the RCP4.5 climate and between 6 and 16 million km2 for the RCP8.5 climate. For the RCP4.5 projection, cumulative change in soil carbon varied between 66-Pg C (1015-g carbon) loss to 70-Pg C gain. For the RCP8.5 projection, losses in soil carbon varied between 74 and 652 Pg C (mean loss, 341 Pg C). For the RCP4.5 projection, gains in vegetation carbon were largely responsible for the overall projected net gains in ecosystem carbon by 2299 (8- to 244-Pg C gains). In contrast, for the RCP8.5 projection, gains in vegetation carbon were not great enough to compensate for the losses of carbon projected by four of the five models; changes in ecosystem carbon ranged from a 641-Pg C loss to a 167-Pg C gain (mean, 208-Pg C loss). The models indicate that substantial net losses of ecosystem carbon would not occur until after 2100. This assessment suggests that effective mitigation efforts during the remainder of this century could attenuate the negative consequences of the permafrost carbon-climate feedback.
Keyphrases
  • climate change
  • human health
  • magnetic resonance imaging
  • risk assessment
  • deep learning