Mesoscale Effects on Carbon Export: A Global Perspective

Abstract

Carbon export from the surface to the deep ocean is a primary control on global carbon budgets and is mediated by plankton that are sensitive to physical forcing. Earth system models generally do not resolve ocean mesoscale circulation ( scriptO(10–100) km), scales that strongly affect transport of nutrients and plankton. The role of mesoscale circulation in modulating export is evaluated by comparing global ocean simulations conducted at 1° and 0.1° horizontal resolution. Mesoscale resolution produces a small reduction in globally integrated export production (<2%); however, the impact on local export production can be large (±50%), with compensating effects in different ocean basins. With mesoscale resolution, improved representation of coastal jets block off‐shelf transport, leading to lower export in regions where shelf‐derived nutrients fuel production. Export is further reduced in these regions by resolution of mesoscale turbulence, which restricts the spatial area of production. Maximum mixed layer depths are narrower and deeper across the Subantarctic at higher resolution, driving locally stronger nutrient entrainment and enhanced summer export production. In energetic regions with seasonal blooms, such as the Subantarctic and North Pacific, internally generated mesoscale variability drives substantial interannual variation in local export production. These results suggest that biogeochemical tracer dynamics show different sensitivities to transport biases than temperature and salinity, which should be considered in the formulation and validation of physical parameterizations. Efforts to compare estimates of export production from observations and models should account for large variability in space and time expected for regions strongly affected by mesoscale circulation.

Global Impacts of Subseasonal (<60 Day) Wind Variability on Ocean Surface Stress, Buoyancy Flux, and Mixed Layer Depth

“…Therefore, the model configuration is called "mesoscale resolving" (Hallberg, 2013). The configuration of the ocean model is similar to recent high-resolution ocean/sea ice configurations of CESM in most other ways as well (e.g., Bryan and Bachman, 2015;Delman et al, 2018;DuVivier et al, 2018;Harrison et al, 2018;Johnson et al, 2016;Soares et al, 2019). For example, as in these previous versions, runoff is mapped to a broad swath of grid cells near the coast, there are no explicit overflow, tidal mixing, near-inertial mixing, submesoscale, or mesoscale parameterizations, time stepping is achieved using the time-averaged leap frog scheme with a time step of 3.6 min, subgrid-scale turbulent vertical transport of tracers and momentum is computed with the K-profile parameterization (KPP) of Large et al (1994), and subgrid-scale lateral transport is parameterized via a spatially variable biharmonic viscosity and diffusivity that scale with grid size as in Bryan and Bachman (2015).…”

“…Here, we quantify the sensitivity of the climatological annual mean, seasonal cycle, and subseasonal variance of the MLD to subseasonal winds, via their effects on ocean surface stress and buoyancy flux, at the finest resolved spatial scales (3–11 km) in a global mesoscale‐resolving ocean/sea ice numerical circulation model for the first time. Although it is well known that subseasonal winds have a significant impact on the stress, buoyancy flux, and MLD, no previous study has quantified these impacts in a global model that resolves mesoscales, which generally dominate upper‐ocean variance (Delworth et al, ; Ferrari and Wunsch, ; Kirtman et al, ; Small et al, ; Wunsch, ) and have a significant impact on the mean and seasonal cycle of the MLD (DuVivier et al, ; Hausmann et al, ; Harrison et al, ; Lee et al, ; Sein et al, ). In this modeling context, the motivating questions for this study are as follows: What fraction of the climatological mean, seasonal cycle, and subseasonal variability of the MLD is attributable to subseasonal winds, and how do these fractions vary across the globe?…”

“…In addition, mesoscale and submesoscale oceanic variability, which must also be parameterized in a 1-D model, can significantly impact the mean and seasonal cycle of the MLD in some regions, like the Subantarctic Zone of the Southern Ocean (du Plessis et al, 2017;DuVivier et al, 2018;Fox-Kemper et al, 2011;Lee et al, 2011;Li and Lee, 2017;Swart et al, 2015). Indeed, high-resolution simulations that resolve some subseasonal atmospheric variability and ocean mesoscale variability show that (among other properties; (e.g., Delworth et al, 2012;Griffies et al, 2015;Kirtman et al, 2012;Maltrud and McClean, 2005;Maltrud et al, 2008Maltrud et al, , 2010McClean et al, 2011;Small et al, 2014;Winton et al, 2014) the mean MLD depends on the horizontal grid spacing of the atmosphere and ocean model at resolutions between 2 • and 0.1 Harrison et al, 2018;Lee et al, 2011;Sein et al, 2018), which suggests that both atmospheric and oceanic mesoscale processes (which vary on subseasonal time scales) have a substantial impact on the annual mean MLD. Hence, high-resolution three-dimensional (3-D) ocean circulation models are more suitable than 1-D models for simulating the dynamics of the climatological mean, seasonal cycle, and subseasonal variability of the MLD on a global scale.…”

Effects of Eddy‐Driven Subduction on Ocean Biological Carbon Pump

“…The traditional view that the gravitational pump and mixed‐layer pump operate solely on large scales is also being reassessed. Studies have shown that upward vertical velocities associated with eddies and fronts can supply nutrients to the euphotic zone, locally promote biological production, and eventually modulate the sinking of POC (e.g., Harrison et al, ; Lévy et al, ; Mahadevan, ; McGillicuddy, ). This dynamically driven variability in the gravitational pump could be further reenforced by biological processes, such as zooplankton diel and seasonal vertical migrations, which are expected to locally add 10% to 200% to the passive sinking flux of POC (Jónasdóttir et al, ; Steinberg & Landry, ).…”

Evaluation of an Online Grid‐Coarsening Algorithm in a Global Eddy‐Admitting Ocean Biogeochemical Model

“…Recent observations have challenged this large-scale picture of the ocean by adding large fluctuations in ocean fluxes of carbon (Landschützer et al, 2015) and heat (e.g., Liu et al, 2016) at shorter timescales (season to decades). There are now robust evidences that eddies or mesoscale ocean structures also influence a number of biogeochemical processes across temporal and spatial scales (e.g., Bettencourt et al, 2015;Dufour et al, 2015;Gruber et al, 2011;Harrison et al, 2018;Lacour et al, 2017;Lévy & Martin, 2013;Mazloff et al, 2018;Munday et al, 2014;Oschlies, 2002;Resplandy et al, 2009;Resplandy et al, 2013;Sweeney et al, 2003;Villar et al, 2015).…”