GC14A-07 – Nonlinear Interactions between Climate and Atmospheric Carbon Dioxide Drivers of Terrestrial and Marine Carbon Cycle Changes


Forrest M. Hoffman (forrest at climatemodeling dot org)
Oak Ridge National Laboratory
James Tremper Randerson
University of California Irvine
J. Keith Moore
University of California Irvine
Michael Goulden
University of California Irvine
Weiwei Fu
University of California Irvine
Charles Koven
Lawrence Berkeley National Laboratory
Abigail L. S. Swann
University of Washington
Natalie M. Mahowald
Cornell University
Keith Lindsay
National Center for Atmospheric Research
Ernesto Muñoz
National Center for Atmospheric Research


Carbon Feedbacks in Earth’s Climate System: Using Ocean and Land Variability to Diagnose Critical Carbon Cycle Processes I
Monday, December 11, 2017 17:30–17:45
New Orleans Ernest N. Morial Convention Center – 260–262


Quantifying interactions between global biogeochemical cycles and the Earth system is important for predicting future atmospheric composition and informing energy policy. We applied a feedback analysis framework to three sets of Historical (1850–2005), Representative Concentration Pathway 8.5 (2006–2100), and its extension (2101–2300) simulations from the Community Earth System Model version 1.0 (CESM1(BGC)) to quantify drivers of terrestrial and ocean responses of carbon uptake. In the biogeochemically coupled simulation (BGC), the effects of CO2 fertilization and nitrogen deposition influenced marine and terrestrial carbon cycling. In the radiatively coupled simulation (RAD), the effects of rising temperature and circulation changes due to radiative forcing from CO2, other greenhouse gases, and aerosols were the sole drivers of carbon cycle changes. In the third, fully coupled simulation (FC), both the biogeochemical and radiative coupling effects acted simultaneously. We found that climate–carbon sensitivities derived from RAD simulations produced a net ocean carbon storage climate sensitivity that was weaker and a net land carbon storage climate sensitivity that was stronger than those diagnosed from the FC and BGC simulations. For the ocean, this nonlinearity was associated with warming-induced weakening of ocean circulation and mixing that limited exchange of dissolved inorganic carbon between surface and deeper water masses. For the land, this nonlinearity was associated with strong gains in gross primary production in the FC simulation, driven by enhancements in the hydrological cycle and increased nutrient availability. We developed and applied a nonlinearity metric to rank model responses and driver variables. The climate–carbon cycle feedback gain at 2300 was 42% higher when estimated from climate–carbon sensitivities derived from the difference between FC and BGC than when derived from RAD. We re-analyzed other CMIP5 model results to quantify the effects of such nonlinearities on their projected climate–carbon cycle feedback gains.

Plain Language Summary

Plants and animals living on land and in the ocean are affected by changes in climate, and their responses to such changes subsequently affect the climate itself through changes in energy, carbon, and water exchanges with the atmosphere. We analyzed long-term climate simulations from a global Earth system model (ESM) called the Community Earth System Model version 1.0 (CESM1(BGC)) to quantify the land and ocean ecosystem responses of carbon uptake due to the expected rise in atmospheric carbon dioxide and resulting air temperature increases. Through multiple ESM simulations, we isolated the effects of rising carbon dioxide on air temperature and on changes in land and ocean ecosystems, and compared the sum of these changes to those from a simulation that experienced both effects. We found that these effects in isolation do not add up to the sum of both effects operating simultaneously. The nonlinearity for the ocean was due to weakening ocean circulation and mixing and for the land was due to strong gains in plant production driven by increasing precipitation and nutrient availability. We re-analyzed other model results to similarly quantify the strength of these nonlinearities and the resulting differences in climate–carbon cycle feedback gains.

Forrest M. Hoffman (forrest at climatemodeling dot org)