Drivers of upper ocean heat content extremes around New Zealand revealed by Adjoint Sensitivity Analysis

Abstract

Marine heatwaves can have devastating ecological and economic impacts and understanding what drives their onset is crucial to achieving improved prediction. A key knowledge gap exists around the subsurface structure and temporal evolution of MHW events in continental shelf regions, where impacts are most significant. Here, we use a realistic, high-resolution ocean model to identify marine heatwaves using upper ocean heat content (UOHC) as a diagnostic metric. We show that, embedded in the inter-annual variability of UOHC across the Tasman Sea, regional UOHC around New Zealand varies at short temporal and spatial scales associated with local circulation which drives the onset of extreme events with median duration of 5–20 days. Then, using a novel application of Adjoint Sensitivity Analysis, we diagnose the regional drivers of extreme UOHC events and their 3-dimensional structure. We compute the sensitivity of UOHC to changes in the ocean state and atmospheric forcing over the onset of MHW events using ensembles of between 34 and 64 MHW events across 4 contrasting regions over a 25-year period. The results reveal that changes in regional UOHC on short (5-day) timescales are largely driven by local ocean circulation rather than surface heat fluxes. Where the circulation is dominated by boundary currents, advection of temperature in the mixed layer dominates the onset of extreme UOHC events. Higher magnitude MHW events are typically associated with shallower mixed layer and thermocline depths, with higher sensitivity to temperature changes in the upper 50–80 m. On the west coast, where boundary currents are weak, UOHC extremes are sensitive to density changes in the upper 1,000 m and likely caused by downwelling winds. Our results highlight the importance of understanding the different temporal and spatial scales of UOHC variability. Understanding the local circulation associated with heat content extremes is an important step toward accurate MHW predictability in economically significant shelf seas.