The North Atlantic heat budget: an Argo based study

Abstract

The Argo dataset is used to obtain estimates of the heat storage and heat divergence with
the aim of the assessing the usefulness of the Argo array for investigating the North
Atlantic heat budget. The accuracy of the Argo-based mixed layer heat storage varies
significantly throughout the North Atlantic. Errors are smallest, around 10-20 Wm-2 on
monthly timescales for 10° x 10° boxes, reducing to 5-10 Wm-2 on seasonal scales in the
subtropics and eastern basin. Heat storage errors over a fixed 300 m layer are higher, but
typically remain below 20 Wm-2 on seasonal timescales away from the western boundary.
The heat budget is closed (using net heat fluxes from the NCEP climatology and NOC
reanalysis) within the estimated error throughout the subtropical and eastern North Atlantic,
indicating the value of the Argo dataset in studies of this nature. However, within the
western boundary the heat budget residual typically exceeds 50 Wm-2, with the heat storage
overestimated or the heating from the net heat flux and/or advective and diffusive
divergence underestimated. Assuming that heat storage error estimates are accurate and
considering results in the literature regarding the bias in net heat flux products, it is likely
that heating from divergence is underestimated. The heating contribution from this term
may be large on scales that cannot be resolved using Argo. In the eastern and subtropical
North Atlantic, the errors in the Argo-based heat budget terms are smaller than the
uncertainty in the net heat flux products and can thus be used to provide insight into which
atmospheric dataset (the NCEP reanalysis or the NOC climatology) may be more accurate.
The NOC net heat flux is more accurate than that from NCEP throughout the year in the
subtropics and during the first half of the year in the eastern mid-latitudes.
The errors in the mixed layer heat storage are smaller than the interannual variability in
this term. Thus Argo can be used to investigate variability on this scale. While the current
Argo dataset is on the short side for studies of this nature, continued funding of the array is
expected to provide more insightful results.

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