In this study we introduce a pair of idealized tracers to quantify how changes in physical advection and mixing under climate change affect the nutrient supply, new production, and particulate export rates. The low cost and simplicity of these tracers allows us to explore the sensitivity of the model biogeochemistry, and in particular its response to a changing physical environment, to the choice of model parameters. Using CESM2.1 with active ocean and ice only, at nominal one-degree resolution, under initial conditions and forcing representative of 2000 and 2100, our idealized nutrient and particulate are within the spread of nitrate and export from CMIP5 models. The simple form of the tracers allows us to identify the physical controls on the changing rates of supply, production, and export throughout the year, which together form the different seasonal cycles. We find that the ocean basins with the largest changes in the seasonal cycle over the 21st century are the North Atlantic, the Arctic, and the eastern tropical Pacific. We present results comparing the controls across basins, focusing on shifts in the timing of deepening mixed layers and maximum production rate in the northern North Atlantic through the Arctic, and changes in the spatial and temporal patterns of vertical advective exchange in the tropics and subtropics of the Pacific and Indian Oceans. In both cases we discuss how much these changes depend on the biogeochemical model parameter values.

In this study we introduce a pair of idealized tracers to quantify how changes in physical advection and mixing under climate change affect the nutrient supply, new production, and particulate export rates. The low cost and simplicity of these tracers allows us to explore the sensitivity of the model biogeochemistry, and in particular its response to a changing physical environment, to the choice of model parameters. Using CESM2.1 with active ocean and ice only, at nominal one-degree resolution, under initial conditions and forcing representative of 2000 and 2100, our idealized nutrient and particulate are within the spread of nitrate and export from CMIP5 models. The simple form of the tracers allows us to identify the physical controls on the changing rates of supply, production, and export throughout the year, which together form the different seasonal cycles. We find that the ocean basins with the largest changes in the seasonal cycle over the 21st century are the North Atlantic, the Arctic, and the eastern tropical Pacific. We present results comparing the controls across basins, focusing on shifts in the timing of deepening mixed layers and maximum production rate in the northern North Atlantic through the Arctic, and changes in the spatial and temporal patterns of vertical advective exchange in the tropics and subtropics of the Pacific and Indian Oceans. In both cases we discuss how much these changes depend on the biogeochemical model parameter values.

Global warming may modify submesoscale activity in the ocean through changes in the mixed layer depth and lateral buoyancy gradients. As a case study we consider a region in the Northeast Atlantic under present and future climate conditions, using a time-slice method and global and nested regional ocean models. The high resolution regional model reproduces the strong seasonal cycle in submesoscale activity observed under present-day conditions. In the future, with a reduction in the mixed layer depth, there is a substantial reduction in submesoscale activity and an associated decrease in kinetic energy at the mesoscale. The vertical buoyancy flux induced by submesoscale activity is reduced by a factor of 2. When submesoscale activity is suppressed, by increasing the parameterized lateral mixing in the model, the climate change induces a larger reduction in winter mixed layer depths while there is less of a change in kinetic energy at the mesoscale. A scaling for the vertical buoyancy flux proposed by Fox-Kemper et.\ al.\, based on the properties of mixed layer instability (MLI), is found to capture much of the seasonal and future changes to the flux in terms of regional averages as well as the spatial structure, although it over predicts the reduction in the flux in the winter months. The vertical buoyancy flux when the mixed layer is relatively shallow is significantly greater than that given by the scaling based on MLI, suggesting during these times other processes (besides MLI) may dominate submesoscale buoyancy fluxes.

The ocean mixed layer plays an important role in subseasonal climate dynamics because it can exchange large amounts of heat with the atmosphere, and it evolves significantly on subseasonal timescales. Estimation of the subseasonal variability of the ocean mixed layer is therefore important for subseasonal to seasonal prediction and analysis. The increasing coverage of in-situ Argo ocean profile data allows for greater analysis of the aseasonal ocean mixed layer depth (MLD) variability on subseasonal and interannual timescales; however, current sampling rates are not yet sufficient to fully resolve subseasonal MLD variability. Other products, including gridded MLD estimates, require optimal interpolation, a process that often ignores information from other oceanic variables. We demonstrate how satellite observations of sea surface temperature, salinity, and height facilitate MLD estimation in a pilot study of two regions: the mid-latitude southern Indian and the eastern equatorial Pacific Oceans. We construct multiple machine learning architectures to produce weekly 1/2 degree gridded MLD anomaly fields (relative to a monthly climatology) with uncertainty estimates. We test multiple traditional and probabilistic machine learning techniques to compare both accuracy and probabilistic calibration. We find that incorporating sea surface data through a machine learning model improves the performance of MLD estimation over traditional optimal interpolation in terms of both mean prediction error and uncertainty calibration. These preliminary results provide a promising first step to greater understanding of aseasonal MLD phenomena and the relationship between the MLD and sea surface variables. Extensions to this work include global and temporal analyses of MLD.