Abstract

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.

Highlights

  • Terrestrial carbon cycle feedbacks represent a large and highly uncertain factor governing the response of the global climate system to human greenhouse gas emissions (Gregory et al, 2009)

  • Where Ct is the total ecosystem carbon, Cl is the carbon in live vegetation biomass, and Cd is the carbon in dead pools, which consists of litter, coarse woody debris (CWD), and soil organic matter (SOM)

  • Since most terrestrial carbon models distinguish between litter and SOM, we propose at a minimum that future CMIP experiments separate out the fRh from fast-responding litter versus from slowresponding SOM, to better distinguish transient effects such as false priming from intrinsic changes to τd

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Summary

Introduction

Terrestrial carbon cycle feedbacks represent a large and highly uncertain factor governing the response of the global climate system to human greenhouse gas emissions (Gregory et al, 2009). Koven et al.: Productivity and turnover controls on carbon feedbacks where they act to enhance the greenhouse effect, while the other half has been incorporated into either the ocean or land carbon pools (Ciais et al, 2013). Through successive generations of offline and coupled carbon cycle climate model (hereinafter Earth system model, or ESM) intercomparisons, such uncertainties have remained large, for the case of terrestrial carbon feedbacks (Cramer et al, 2001; Friedlingstein et al, 2006; Sitch et al, 2008; Arora et al, 2013)

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