What causes the spread of model projections of ocean dynamic sea-level change in response to greenhouse gas forcing?

Abstract

Sea levels of different atmosphere–ocean general circulation models (AOGCMs) respond to climate change forcing in different ways, representing a crucial uncertainty in climate change research. We isolate the role of the ocean dynamics in setting the spatial pattern of dynamic sea-level (ζ) change by forcing several AOGCMs with prescribed identical heat, momentum (wind) and freshwater flux perturbations. This method produces a ζ projection spread comparable in magnitude to the spread that results from greenhouse gas forcing, indicating that the differences in ocean model formulation are the cause, rather than diversity in surface flux change. The heat flux change drives most of the global pattern of ζ change, while the momentum and water flux changes cause locally confined features. North Atlantic heat uptake causes large temperature and salinity driven density changes, altering local ocean transport and ζ. The spread between AOGCMs here is caused largely by differences in their regional transport adjustment, which redistributes heat that was already in the ocean prior to perturbation. The geographic details of the ζ change in the North Atlantic are diverse across models, but the underlying dynamic change is similar. In contrast, the heat absorbed by the Southern Ocean does not strongly alter the vertically coherent circulation. The Arctic ζ change is dissimilar across models, owing to differences in passive heat uptake and circulation change. Only the Arctic is strongly affected by nonlinear interactions between the three air-sea flux changes, and these are model specific.

Sea level changes mechanisms in the MPI-ESM under FAFMIP forcing conditions

“…All surface forcing perturbations are applied at the ocean surface, such that sea ice is not directly affected (Fig. 1, see also Couldrey et al 2021). However, there will be indirect effects on sea ice due to the changes of heat and freshwater in response to all the perturbations.…”

“…Gregory et al (2016) provide more details on FAFMIP and first results in terms of ensemble mean and spread of the total resulting sea level anomalies and the comparison to those resulting from individual forcing using low-resolution models. More recently, Couldrey et al (2021)showed that surface heat flux changes drive most of the sea level change pattern in the FAFMIP runs and that the spread between underlying Atmosphere-Ocean General Circulation Models (AOGCM) is caused largely by differences in their regional transport adjustment, which redistributes regional heat content differences that existed in the ocean prior to perturbation. However, the quantification of the mechanisms behind the simulated responses as a function of surface forcing agent remains unclear in previous publications.…”

“…Novel aspects of this study include (i) the analysis of an additional passive salt experiment (see Sect. 2.2), (ii) the quantification of the impact of feedbacks between individual forcing components on the solution and (iii) focusing on the highest resolution model in the ensemble previously analyzed by Couldrey et al (2021).…”

Quantifying Spread in Spatiotemporal Changes of Upper-Ocean Heat Content Estimates: An Internationally Coordinated Comparison

“…DOI 10.1175/JCLI-D-20-0603.1. Unauthenticated | Downloaded 12/13/21 02:57 AM UTC  Finally, the relevance of improving estimation of OHCA and providing full quantification of uncertainty is justified by the uses in a wide range of climate science, such as hindcast reanalyses assimilating ocean data (Storto et al 2019), ocean and/or climate model analyses investigating the mechanisms of ocean heat uptake (Couldrey et al 2020;Dias et al 2020a;Dias et al 2020b;Gregory 2000;Gregory et al 2016;Saenko et al 2021;Savita et al 2021), detection and attribution of changes to natural and anthropogenic drivers (e.g. Gleckler et al 2012), and in constraining model projections of climate and sea level change relevant for policyand decision-makers (Lyu et al 2021;Mimura 2007;Oppenheimer et al 2019).…”

High-End Scenarios of Sea-Level Rise for Coastal Risk-Averse Stakeholders

“…To produce the regional SLR of the sterodynamic contribution, we use the spatial patterns of ocean dynamic sea-level changes of the IPCC AR5 models. This relies on the assumption that the regional variability of sea-level sterodynamic projections is driven by the same mechanisms in Kopp et al (2014) and for the IPCC AR5, as suggested by Couldrey et al (2021).…”