Preprints
https://doi.org/10.5194/gmd-2022-152
https://doi.org/10.5194/gmd-2022-152
Submitted as: methods for assessment of models
07 Jun 2022
Submitted as: methods for assessment of models | 07 Jun 2022
Status: this preprint is currently under review for the journal GMD.

Gulf Stream and interior western boundary volume transport as key regions to constrain the future North Atlantic Carbon Uptake

Nadine Goris1, Klaus Johannsen2, and Jerry Tjiputra1 Nadine Goris et al.
  • 1NORCE Norwegian Research Centre and Bjerknes Centre for Climate Research, Bergen, Norway
  • 2NORCE Norwegian Research Centre, Bergen, Norway

Abstract. As one of the major carbon sinks in the global ocean, the North Atlantic is a key player in mediating the ongoing global warming. However, projections of the North Atlantic carbon sink in a high-CO2 future are highly uncertain due to greatly varying model results. A previous study analysed an ensemble of 11 CMIP5-models and identified two indicators of contemporary model behavior that are highly correlated with a model´s projected future carbon-uptake in the North Atlantic: (i) the high latitude winter pCO2sea-anomaly, which is tightly linked to winter mixing and nutrient supply and (ii) the fraction of the anthropogenically altered carbon-inventory stored below 1000 m depth, indicating the efficiency of dissolved inorganic carbon transport into and within the deep ocean. Both relationships build so-called emergent constraints, where observed contemporary indicators can be used to improve future North Atlantic carbon sink estimates. 

In this study, we apply a genetic algorithm to optimize these emergent relationships by constraining the spatial extent of the indicators, i.e. to identify key regions that maximise the cross-correlations between the indicators and the future carbon uptake. We pre-define the shape of the desired regions as (i) rectangles and ellipses of different sizes for the first surface-2D indicator and (ii) cuboids and ellipsoids of different volumes for the second water column-3D indicator. Independent on shape and size, the genetic algorithm persistently identifies the Gulf Stream region as optimal for the first indicator as well as the pathway of the broad interior southward volume transport for the second indicator. This is further confirmed with high correlations between the North Atlantic future carbon uptake and volume transport values extracted for the central latitudes and depths of these optimal regions. Though the importance of volume transport for the carbon uptake is well known, our results go beyond traditional knowledge and identify which depth-ranges and latitudes of this volume transport are consistently of importance across the multi-model ensemble. Our study shows that regional optimisations of emergent constraint can isolate key drivers responsible for multi-model spread and furthermore provide information on where observations are most crucial to constrain future projections. Moreover, a comparison of the model performance in the identified key regions and the large-scale North Atlantic indicates that models whose mean values are in good agreement with observations within one key area do not necessarily perform well when looking at another key area. This hampers the applicability of emergent constraints and highlights the need to additionally evaluate spatial model features.

Nadine Goris et al.

Status: open (until 04 Aug 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CEC1: 'Comment on gmd-2022-152', Juan Antonio Añel, 16 Jun 2022 reply

Nadine Goris et al.

Nadine Goris et al.

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Short summary
Projections of the North Atlantic carbon sink in a high-CO2 future are highly uncertain. A new study identifies key regions that dynamically explain the model spread of the future North Atlantic carbon uptake. The authors find that the upper and interior ocean volume transport at 26 N are key for a correct estimate of the North Atlantic carbon uptake. However, results indicate that models rarely perform well for both indicators, making it difficult to constrain the model uncertainty.