Controls on terrestrial carbon feedbacks by productivity versus turnover in the CMIP5 Earth System Models

Abstract. To better understand sources of uncertainty in projections of terrestrial carbon cycle feedbacks, we present an approach to separate the controls on modeled carbon changes. We separate carbon changes into four categories using a linearized, equilibrium approach: those arising from changed inputs (productivity-driven changes), and outputs (turnover-driven changes), of both the live and dead carbon pools. Using Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations for five models, we find that changes to the live pools are primarily explained by productivity-driven changes, with only one model showing large compensating changes to live carbon turnover times. For dead carbon pools, the situation is more complex as all models predict a large reduction in turnover times in response to increases in productivity. This response arises from the common representation of a broad spectrum of decomposition turnover times via a multi-pool approach, in which flux-weighted turnover times are faster than mass-weighted turnover times. This leads to a shift in the distribution of carbon among dead pools in response to changes in inputs, and therefore a transient but long-lived reduction in turnover times. Since this behavior, a reduction in inferred turnover times resulting from an increase in inputs, is superficially similar to priming processes, but occurring without the mechanisms responsible for priming, we call the phenomenon "false priming", and show that it masks much of the intrinsic changes to dead carbon turnover times as a result of changing climate. These patterns hold across the fully coupled, biogeochemically coupled, and radiatively coupled 1 % yr−1 increasing CO2 experiments. We disaggregate inter-model uncertainty in the globally integrated equilibrium carbon responses to initial turnover times, initial productivity, fractional changes in turnover, and fractional changes in productivity. For both the live and dead carbon pools, inter-model spread in carbon changes arising from initial conditions is dominated by model disagreement on turnover times, whereas inter-model spread in carbon changes from fractional changes to these terms is dominated by model disagreement on changes to productivity in response to both warming and CO2 fertilization. However, the lack of changing turnover time control on carbon responses, for both live and dead carbon pools, in response to the imposed forcings may arise from a common lack of process representation behind changing turnover times (e.g., allocation and mortality for live carbon; permafrost, microbial dynamics, and mineral stabilization for dead carbon), rather than a true estimate of the importance of these processes.

[1]  Peter M. Cox,et al.  Description of the "TRIFFID" Dynamic Global Vegetation Model , 2001 .

[2]  Pierre Friedlingstein,et al.  Uncertainties in CMIP5 Climate Projections due to Carbon Cycle Feedbacks , 2014 .

[3]  R. Schnur,et al.  Climate-carbon cycle feedback analysis: Results from the C , 2006 .

[4]  Yadvinder Malhi,et al.  Confronting model predictions of carbon fluxes with measurements of Amazon forests subjected to experimental drought. , 2013, The New phytologist.

[5]  Dipankar Dwivedi,et al.  Long residence times of rapidly decomposable soil organic matter: application of a multi-phase, multi-component, and vertically resolved model (BAMS1) to soil carbon dynamics , 2014 .

[6]  Alessandro Anav,et al.  Evaluation of Land Surface Models in Reproducing Satellite Derived Leaf Area Index over the High-Latitude Northern Hemisphere. Part II: Earth System Models , 2013, Remote. Sens..

[7]  I. C. Prentice,et al.  A dynamic global vegetation model for studies of the coupled atmosphere‐biosphere system , 2005 .

[8]  Jeffrey M. Warren,et al.  CO2 enhancement of forest productivity constrained by limited nitrogen availability , 2010, Proceedings of the National Academy of Sciences.

[9]  R. Matear,et al.  Nitrogen and phosphorous limitations significantly reduce future allowable CO2 emissions , 2014 .

[10]  J. Randerson,et al.  Changes in soil organic carbon storage predicted by Earth system models during the 21st century , 2013 .

[11]  K. Paustian,et al.  Stabilization mechanisms of soil organic matter: Implications for C-saturation of soils , 2002, Plant and Soil.

[12]  F. Woodward,et al.  Carbon residence time dominates uncertainty in terrestrial vegetation responses to future climate and atmospheric CO2 , 2013, Proceedings of the National Academy of Sciences.

[13]  Y. Malhi,et al.  The allocation of ecosystem net primary productivity in tropical forests , 2011, Philosophical Transactions of the Royal Society B: Biological Sciences.

[14]  J. Randerson,et al.  Causes of variation in soil carbon simulations from CMIP5 Earth system models and comparison with observations , 2012 .

[15]  J. Houghton,et al.  Climate Change 2013 - The Physical Science Basis: Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change , 2014 .

[16]  Atul K. Jain,et al.  Where does the carbon go? A model–data intercomparison of vegetation carbon allocation and turnover processes at two temperate forest free-air CO2 enrichment sites , 2014, The New phytologist.

[17]  Victor Brovkin,et al.  Global biogeophysical interactions between forest and climate , 2009 .

[18]  J. Randerson,et al.  Causes and implications of persistent atmospheric carbon dioxide biases in Earth System Models , 2013 .

[19]  J. Lamarque,et al.  The HadGEM2-ES implementation of CMIP5 centennial simulations , 2011 .

[20]  C. Koven,et al.  Toward improved model structures for analyzing priming: potential pitfalls of using bulk turnover time , 2015, Global change biology.

[21]  K. Lindsay,et al.  Evolution of carbon sinks in a changing climate. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[22]  Jerry F. Franklin,et al.  Causes and implications of the correlation between forest productivity and tree mortality rates , 2011 .

[23]  Maurizio Santoro,et al.  Global covariation of carbon turnover times with climate in terrestrial ecosystems , 2014, Nature.

[24]  P. Friedlingstein,et al.  Toward an allocation scheme for global terrestrial carbon models , 1999 .

[25]  A. Weaver,et al.  Primary productivity control of simulated carbon cycle–climate feedbacks , 2005 .

[26]  Christopher B. Field,et al.  Forest biomass allometry in global land surface models , 2011 .

[27]  Jianyang Xia,et al.  Traceable components of terrestrial carbon storage capacity in biogeochemical models , 2013, Global change biology.

[28]  J. Chambers,et al.  Observed allocations of productivity and biomass, and turnover times in tropical forests are not accurately represented in CMIP5 Earth system models , 2015 .

[29]  Jens Kattge,et al.  Will the tropical land biosphere dominate the climate–carbon cycle feedback during the twenty-first century? , 2007 .

[30]  Karl E. Taylor,et al.  An overview of CMIP5 and the experiment design , 2012 .

[31]  F. Woodward,et al.  Global response of terrestrial ecosystem structure and function to CO2 and climate change: results from six dynamic global vegetation models , 2001 .

[32]  D. Manning,et al.  Persistence of soil organic matter as an ecosystem property , 2011, Nature.

[33]  K. Denman,et al.  Carbon emission limits required to satisfy future representative concentration pathways of greenhouse gases , 2011 .

[34]  I. C. Prentice,et al.  Evaluation of the terrestrial carbon cycle, future plant geography and climate‐carbon cycle feedbacks using five Dynamic Global Vegetation Models (DGVMs) , 2008 .

[35]  P. Ciais,et al.  Mortality as a key driver of the spatial distribution of aboveground biomass in Amazonian forest: results from a dynamic vegetation model , 2010 .

[36]  O. Phillips,et al.  The global relationship between forest productivity and biomass , 2007 .

[37]  J. Terborgh,et al.  The above‐ground coarse wood productivity of 104 Neotropical forest plots , 2004 .

[38]  G. Bonan,et al.  Evaluating soil biogeochemistry parameterizations in Earth system models with observations , 2014 .

[39]  J. Lawton,et al.  Earth System Science , 2001, Science.

[40]  R. N. Juárez Observed allocations of productivity and biomass, and turnover times in tropical forests are not accurately represented in CMIP5 Earth system models , 2015 .

[41]  Yadvinder Malhi,et al.  The productivity, metabolism and carbon cycle of tropical forest vegetation , 2012 .

[42]  J. Terborgh,et al.  Long-term decline of the Amazon carbon sink , 2015, Nature.

[43]  K.,et al.  Carbon–Concentration and Carbon–Climate Feedbacks in CMIP5 Earth System Models , 2012 .

[44]  Benjamin Smith,et al.  Importance of vegetation dynamics for future terrestrial carbon cycling , 2015 .

[45]  V. Brovkin,et al.  Representation of natural and anthropogenic land cover change in MPI‐ESM , 2013 .

[46]  P. Friedlingstein,et al.  What determines the magnitude of carbon cycle‐climate feedbacks? , 2007 .

[47]  P. Ciais,et al.  Permafrost carbon-climate feedbacks accelerate global warming , 2011, Proceedings of the National Academy of Sciences.

[48]  John A. Taylor,et al.  Sources and Sinks of Atmospheric CO2 , 1992 .

[49]  P. Cox,et al.  Uncertainty in climate’carbon-cycle projections associated with the sensitivity of soil respiration to temperature , 2003 .

[50]  Peter E. Thornton,et al.  Influence of carbon‐nitrogen cycle coupling on land model response to CO2 fertilization and climate variability , 2007 .

[51]  Yiqi Luo,et al.  Faster Decomposition Under Increased Atmospheric CO2 Limits Soil Carbon Storage , 2014, Science.

[52]  J. Gregory,et al.  Quantifying Carbon Cycle Feedbacks , 2009 .

[53]  R. Ceulemans,et al.  Forest response to elevated CO2 is conserved across a broad range of productivity. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[54]  R. Charlson,et al.  1 - Introduction: Biogeochemical Cycles as Fundamental Constructs for Studying Earth System Science and Global Change , 2000 .

[55]  William R. Wieder,et al.  Global soil carbon projections are improved by modelling microbial processes , 2013 .