Abstract

In this study, the relation between two approaches to assess the ocean predictability on interannual to decadal time scales is investigated. The first pragmatic approach consists of sampling the initial condition uncertainty and assess the predictability through the divergence of this ensemble in time. The second approach is provided by a theoretical framework to determine error growth by estimating optimal linear growing modes. In this paper, it is shown that under the assumption of linearized dynamics and normal distributions of the uncertainty, the exact quantitative spread of ensemble can be determined from the theoretical framework. This spread is at least an order of magnitude less expensive to compute than the approximate solution given by the pragmatic approach. This result is applied to a state-of-the-art Ocean General Circulation Model to assess the predictability in the North Atlantic of four typical oceanic metrics: the strength of the Atlantic Meridional Overturning Circulation (AMOC), the intensity of its heat transport, the two-dimensional spatially-averaged Sea Surface Temperature (SST) over the North Atlantic, and the three-dimensional spatially-averaged temperature in the North Atlantic. For all tested metrics, except for SST, sim 75% of the total uncertainty on interannual time scales can be attributed to oceanic initial condition uncertainty rather than atmospheric stochastic forcing. The theoretical method also provide the sensitivity pattern to the initial condition uncertainty, allowing for targeted measurements to improve the skill of the prediction. It is suggested that a relatively small fleet of several autonomous underwater vehicles can reduce the uncertainty in AMOC strength prediction by 70% for 1–5 years lead times.

Highlights

  • Anthropogenic global warming has changed Earth’s climate over the last century (IPCC 2007, 2013) In this context, there is an ever increasing societal pressure to predict climate changes from local to global scales and from seasonal to centennial time scales

  • We have focused on the North Atlantic to assess the predictability of four ocean metrics: the Atlantic Meridional Overturning Circulation (AMOC) intensity (MVT at 50◦N and above 1500 m depth), the intensity of its heat transport (MHT at 25◦N), the spatially-averaged Sea Surface Temperature (SST) over the North Atlantic, and the spatial and depth averaged North Atlantic ocean temperature

  • Following the study of Chang et al (2004), we have developed an exact expression of the ocean predictability for given metrics under 3 main assumptions

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Summary

Introduction

Anthropogenic global warming has changed Earth’s climate over the last century (IPCC 2007, 2013) In this context, there is an ever increasing societal pressure to predict (as accurately as possible) climate changes from local to global scales and from seasonal to centennial time scales. Despite good qualitative progress in assessing the interannual to decadal predictability of the North Atlantic climate variability, the quantitative results still substantially differ between studies This disagreements might be caused by the model uncertainty that has been showed to dominate on decadal time scale (Hawkins and Sutton 2009). This allowed the study of a wide range of climatically relevant problems of the North Atlantic from idealized ocean models (Sévellec et al 2007) to state-of-the-art ocean general circulation models (Sévellec and Fedorov 2017) Through this new formulation of GSA, changes are not restricted to non-normal growths and are comparable to the pragmatic ensemble approach based on perturbing initial conditions. Dynamical attribution of oceanic prediction uncertainty in the North Atlantic: application

Propagating errors in a linear framework
Ensemble spread and predictability
Application to an idealized stochastic model
Experimental set‐up
Error growth attribution
Method
Applicability to in situ measurements
Findings
Discussion and conclusion
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