Carbon–concentration and carbon–climate feedbacks in CMIP6 models and their comparison to CMIP5 models

Abstract

Abstract. Results from the fully and biogeochemically coupled simulations in which CO2 increases at a rate of 1 % yr−1 (1pctCO2) from its preindustrial value are analyzed to quantify the magnitude of carbon–concentration and carbon–climate feedback parameters which measure the response of ocean and terrestrial carbon pools to changes in atmospheric CO2 concentration and the resulting change in global climate, respectively. The results are based on 11 comprehensive Earth system models from the most recent (sixth) Coupled Model Intercomparison Project (CMIP6) and compared with eight models from the fifth CMIP (CMIP5). The strength of the carbon–concentration feedback is of comparable magnitudes over land (mean ± standard deviation = 0.97 ± 0.40 PgC ppm−1) and ocean (0.79 ± 0.07 PgC ppm−1), while the carbon–climate feedback over land (−45.1 ± 50.6 PgC ∘C−1) is about 3 times larger than over ocean (−17.2 ± 5.0 PgC ∘C−1). The strength of both feedbacks is an order of magnitude more uncertain over land than over ocean as has been seen in existing studies. These values and their spread from 11 CMIP6 models have not changed significantly compared to CMIP5 models. The absolute values of feedback parameters are lower for land with models that include a representation of nitrogen cycle. The transient climate response to cumulative emissions (TCRE) from the 11 CMIP6 models considered here is 1.77 ± 0.37 ∘C EgC−1 and is similar to that found in CMIP5 models (1.63 ± 0.48 ∘C EgC−1) but with somewhat reduced model spread. The expressions for feedback parameters based on the fully and biogeochemically coupled configurations of the 1pctCO2 simulation are simplified when the small temperature change in the biogeochemically coupled simulation is ignored. Decomposition of the terms of these simplified expressions for the feedback parameters is used to gain insight into the reasons for differing responses among ocean and land carbon cycle models.

Optimality-based non-Redfield plankton–ecosystem model (OPEM v1.1) in UVic-ESCM 2.9 – Part 2: Sensitivity analysis and model calibration

“…The original temperature dependence of diazotrophs (f dia (T )) in the UVic-ESCM (and other models, e.g. Aumont et al, 2015), which we also employ for the OPEM configuration, limits both growth and N 2 fixation of diazotrophs to above 15 • C:…”

“…This model approach yields mass flux estimates with spatial and temporal variations in the elemental C : N : P stoichiometry of both inorganic nutrients and organic matter as observed in situ (Loh and Bauer, 2000;Martiny et al, 2013b). PELA-GOS (Vichi et al, 2007), the only ocean model with variable C : N : P in phytoplankton in CMIP5 (Bopp et al, 2013) and CMIP6 (Arora et al, 2020), has no diazotrophs; others either have only variable N : P (TOPAZ2, Dunne et al, 2013) or variable C : P (MARBL; Danabasoglu et al, 2020). While some of the existing models have a variable C : N : P based on the optimality-based model for phytoplankton growth (Kwiatkowski et al, 2018(Kwiatkowski et al, , 2019, optimality-based N 2 fixation or zooplankton behaviour are not included.…”

Optimality-based non-Redfield plankton–ecosystem model (OPEM v1.1) in UVic-ESCM 2.9 – Part 1: Implementation and model behaviour

“…Park et al, 2019), the carbon : nitrogen : phosphorus (C : N : P) stoichiometry of phytoplankton is still often represented by static (Redfield) ratios, entirely ignoring its highly variable nature (Klausmeier et al, 2008), which can affect model sensitivity to climate change (Kwiatkowski et al, 2018). The only model with variable C : N : P in phytoplankton in CMIP5 (Bopp et al, 2013) and CMIP6 (Arora et al, 2020) is PELAGOS (Vichi et al, 2007), which has no diazotrophs. Other models consider only variable N : P (TOPAZ2; Dunne et al, 2012) or C : P (MARBL (CESM2); Danabasoglu et al, 2020).…”

“…These formulations have shown their ability to describe ecosystem behaviour in 0-D and 1-D modelling studies (e.g. Fernández-Castro et al, 2016;Su et al, 2018), and to predict patterns of phytoplankton nutrient and light co-limitation based on satellite and in situ observations (Arteaga et al, 2014). In this contribution, we describe the implementation of our new optimality-based plankton-ecosystem model (OPEM) into a global 3-D ocean model component of an ESM of intermediate complexity.…”

Toward Determining the Spatio-Temporal Variability of Upper-Ocean Ecosystem Stoichiometry From Satellite Remote Sensing

“…The ocean carbon modeling community is beginning to respond to this development. For example, the state of the art CMIP5/6 models developed by various climate modeling teams around the world represent phytoplankton stoichiometry with varying degree of flexibility, from no flexibility (i.e., fixed C:N:P ratio) to fully flexible (e.g., Bopp et al, 2013;Arora et al, 2020).…”