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Abstract: Ocean eddies produce strong vertical heat flux (VHF) in the upper ocean, exerting profound influences on the climate and ecosystem. Currently, mooring array provides a standard way to estimate the eddy-induced VHF (EVHF) based on the adiabatic potential density equation. Apart from the validity of adiabatic assumption, it remains unclear to what extent the estimated EVHF at a single location within a limited time period is representative of its climatological mean value. In this study, we analyzed the above issue by systematically evaluating the variability of EVHF simulated by a 1-km ocean model configured over the Kuroshio Extension. It is found that the EVHF at a single location exhibits pronounced variability. Even averaged over one year that is comparable to the current maintenance capacity of mooring array, the EVHF still deviates significantly from its climatological mean value. For more than 49% of locations in our computational domain (31°–40°N, 149°–166°E), the discrepancy between the one-year mean EVHF and its climatological mean value at the peaking depth is larger than the climatological mean itself. The mesoscale eddies play a dominant role in the variability of EVHF but contribute little to the climatological mean EVHF; the opposite is true for submesoscale eddies. Our findings indicate that nested mooring array allowing for isolating the effects of submesoscale eddies will be useful to obtain climatological mean EVHF.
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Figure 1. Surface vertical relative vorticity
$ {\zeta } $ normalized by local inertical frequency f on December 1, 2004. The black box denotes the selected area.
Figure 2. Vertical profiles of regional averaged annual mean (black), winter-half-year (October to March) mean (blue) and summer-half-year (April to September) mean (red) EVHF. Dash-dotted lines represent for four specific depths: 53 m, 180 m, 308 m and 554 m.
Figure 3. Horizontal distributions of
$ \overline{\mathrm{E}\mathrm{V}\mathrm{H}\mathrm{F}} $ (a),$ {\left\langle{\overline{\mathrm{E}\mathrm{V}\mathrm{H}\mathrm{F}}}\right\rangle}_{\mathrm{h}} $ (b) and${{\log}_{10}{\text{γ}} }_{{\rm{h}}}$ (c) at four chosen depths (53 m, 180 m, 308 m and 554 m).
Figure 4. Horizontal distributions of log10
${\text{γ}}_{{\rm{w}}}$ (a) and log10${\text{γ}}_{{\rm{s}}}$ (b) at four chosen depths (53 m, 180 m, 308 m and 554 m).
Figure 5. Horizontal distributions of
${\overline{{\mathrm{E}\mathrm{V}\mathrm{H}\mathrm{F}}^{{\rm{sub}}}}}$ (a),${\left\langle{\overline{{\mathrm{E}\mathrm{V}\mathrm{H}\mathrm{F}}^{{\rm{sub}}}}}\right\rangle}_{\mathrm{h}}$ (b),${{{\rm{log}}}_{10}({\text{γ}} }_{{\rm{h}}})$ (c) and${\left\langle{\overline{{\mathrm{E}\mathrm{V}\mathrm{H}\mathrm{F}}^{{\rm{sub}}}}}\right\rangle}_{\mathrm{h}}\;/\;{\left\langle{\overline{\mathrm{E}\mathrm{V}\mathrm{H}\mathrm{F}}}\right\rangle}_{\mathrm{h}}$ (d) at four chosen depths (53 m, 180 m, 308 m and 554 m) with subscript sub denotes anomalies from the 50 km×50 km running means, and e–h are as in a–d but for defining submesoscale anomalies by the perturbations from forth-order Butterworth filter with 14-d cut-off frequency. -
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