Dynamical and biogeochemical control on the decadal variability of ocean carbon fluxes

Abstract

Several recent observation-based studies suggest that ocean anthropogenic carbon uptake has slowed down due to the impact of anthropogenic forced climate change. However, it remains unclear whether detected changes over the recent time period can be attributed to anthropogenic climate change or rather to natural climate variability (internal plus naturally forced variability) alone. One large uncertainty arises from the lack of knowledge on ocean carbon flux natural variability at the decadal time scales. To gain more insights into decadal time scales, we have examined the internal variability of ocean carbon fluxes in a 1000 yr long preindustrial simulation performed with the Earth System Model IPSL-CM5A-LR. Our analysis shows that ocean carbon fluxes exhibit low-frequency oscillations that emerge from their year-to-year variability in the North Atlantic, the North Pacific, and the Southern Ocean. In our model, a 20 yr mode of variability in the North Atlantic air-sea carbon flux is driven by sea surface temperature variability and accounts for ~40% of the interannual regional variance. The North Pacific and the Southern Ocean carbon fluxes are also characterised by decadal to multi-decadal modes of variability (10 to 50 yr) that account for 20–40% of the interannual regional variance. These modes are driven by the vertical supply of dissolved inorganic carbon through the variability of Ekman-induced upwelling and deep-mixing events. Differences in drivers of regional modes of variability stem from the coupling between ocean dynamics variability and the ocean carbon distribution, which is set by large-scale secular ocean circulation

The ocean carbon sink – impacts, vulnerabilities and challenges

“…Global model simulations of deep-sea carbon distributions as well as other deep-sea properties are therefore often limited to a lower resolution as compared to their distributions in surface or shallow waters (e.g. Ilyina et al, 2013;Séférian et al, 2013;Tjiputra et al, 2013). Models need systematic improvement by combining them with and comparing them to observational data.…”

Observed small spatial scale and seasonal variability of the CO<sub>2</sub> system in the Southern Ocean

“…The choice of DIC over pCO 2 for this analysis was made with the aim of disentangling the contribution of dynamical processes that influences pCO 2 through DIC and SST from the thermodynamical effect that only affects pCO 2 . Furthermore, model-and observation-based studies concur with the fact that DIC is the main driver of surface pCO 2 variability in the Southern Ocean (Metzl et al, 2006;Lovenduski et al, 2008;Metzl, 2009;Brix et al, 2013;Séférian et al, 2013). Surface DIC concentrations generally vary with physical and/or biological factors, such as vertical and lateral dynamical transport or biological uptake by photosynthesis.…”

“…Undersampling biases are aggravated by the high variability this oceanic region displays over a wide range of temporal and spatial scales. A combination of various observation-and model-based methods has recently enabled a better quantification of the air-sea CO 2 flux in the Southern Ocean and its variability at a large spatial scale (e.g Gruber et al, 2009) and interannual timescale (e.g., Séférian et al, 2013). Substantial variability was captured at decadal timescales, with estimates derived from observations and models of the order of 0.05 to 0.15 PgC yr −1 (Metzl, 2009;Séférian et al, 2013;Brix et al, 2013) and attributed to seaice interactions leading to deep mixing events (Séférian et al, 2013) and the Southern Annular Mode (Brix et al, 2013;Séférian et al, 2013).…”

Natural variability in the surface ocean carbonate ion concentration

“…While hindcast studies can capture the observed chronology of these modes, they cannot capture the full spectrum of internal variability in the climate system. Long model simulations (order 1000 years) can capture multiple realizations of climate variability on decadal and multi-decadal timescales, and have shown to be useful in the study of ocean carbon cycle variability on these timescales Séférian et al, 2013Séférian et al, , 2014Keller et al, 2014;Lehner et al, 2015;Resplandy et al, 2015).…”

“…They suggest that recent anthropogenic trends in surface aragonite exceed natural variability by 30 times on regional scales, but do not focus on detectability in the observational record or on the mechanisms driving variability. Séférian et al (2013) analyze output from a fully coupled 1000-year control simulation of IPSL-CM5A-LR and describe decadal to multi-decadal variability in air-sea CO 2 flux and its driving factors in the North Atlantic, North Pacific, and the Southern Ocean. They find that a large fraction of the variance in CO 2 flux is driven by internal climate variability in the various regions, due to circulation-mediated variability in the upwelling of DIC to the surface ocean, but only very briefly discuss the implications of this for carbonate ion variability.…”