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1. The ECCO‐Darwin Data‐Assimilative Global Ocean Biogeochemistry Model: Estimates of Seasonal to Multidecadal Surface Ocean p CO ₂ and Air‐Sea CO ₂ Flux

   https://doi.org/10.1029/2019MS001888  

   Carroll, D. ; Menemenlis, D. ; Adkins, J. F. ; Bowman, K. W. ; Brix, H. ; Dutkiewicz, S. ; Fenty, I. ; Gierach, M. M. ; Hill, C. ; Jahn, O. ; et al ( October 2020 , Journal of Advances in Modeling Earth Systems)

   Quantifying variability in the ocean carbon sink remains problematic due to sparse observations and spatiotemporal variability in surface oceanpCO₂. To address this challenge, we have updated and improved ECCO‐Darwin, a global ocean biogeochemistry model that assimilates both physical and biogeochemical observations. The model consists of an adjoint‐based ocean circulation estimate from the Estimating the Circulation and Climate of the Ocean (ECCO) consortium and an ecosystem model developed by the Massachusetts Institute of Technology Darwin Project. In addition to the data‐constrained ECCO physics, a Green's function approach is used to optimize the biogeochemistry by adjusting initial conditions and six biogeochemical parameters. Over seasonal to multidecadal timescales (1995–2017), ECCO‐Darwin exhibits broad‐scale consistency with observed surface oceanpCO₂and air‐sea CO₂flux reconstructions in most biomes, particularly in the subtropical and equatorial regions. The largest differences between CO₂uptake occur in subpolar seasonally stratified biomes, where ECCO‐Darwin results in stronger winter uptake. Compared to the Global Carbon Project OBMs, ECCO‐Darwin has a time‐mean global ocean CO₂sink (2.47 ± 0.50 Pg C year⁻¹) and interannual variability that are more consistent with interpolation‐based products. Compared to interpolation‐based methods, ECCO‐Darwin is less sensitive to sparse and irregularly sampled observations. Thus, ECCO‐Darwin provides a basis for identifying and predicting the consequences of natural and anthropogenicmore »perturbations to the ocean carbon cycle, as well as the climate‐related sensitivity of marine ecosystems. Our study further highlights the importance of physically consistent, property‐conserving reconstructions, as are provided by ECCO, for ocean biogeochemistry studies.

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2. Constraining the Surface Flux of Sea Spray Particles From the Southern Ocean

   https://doi.org/10.1029/2019JD032026  

   Hartery, S. ; Toohey, D. ; Revell, L. ; Sellegri, K. ; Kuma, P. ; Harvey, M. ; McDonald, A. J. ( February 2020 , Journal of Geophysical Research: Atmospheres)

   Modeling the shortwave radiation balance over the Southern Ocean region remains a challenge for Earth system models. To investigate whether this is related to the representation of aerosol‐cloud interactions, we compared measurements of the total number concentration of sea spray‐generated particles within the Southern Ocean region to model predictions thereof. Measurements were conducted from a container laboratory aboard the R/VTangaroathroughout an austral summer voyage to the Ross Sea. We used source‐receptor modeling to calculate the sensitivity of our measurements to upwind surface fluxes. From this approach, we could constrain empirical parameterizations of sea spray surface flux based on surface wind speed and sea surface temperature. A newly tuned parameterization for the flux of sea spray particles based on the near‐surface wind speed is presented. Comparisons to existing model parameterizations revealed that present model parameterizations led to overestimations of sea spray concentrations. In contrast to previous studies, we found that including sea surface temperature as an explanatory variable did not substantially improve model‐measurement agreement. To test whether or not the parameterization may be applicable globally, we conducted a regression analysis using a database of in situ whitecap measurements. We found that the key fitting parameter within this regression agreed wellmore »with the parameterization of sea spray flux. Finally, we compared calculations from the best model of surface flux to boundary layer measurements collected onboard an aircraft throughout the Southern Ocean Clouds, Radiation, Aerosol Transport Experimental Study (SOCRATES), finding good agreement overall.

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3. Quantifying Dynamical Proxy Potential Through Shared Adjustment Physics in the North Atlantic

   https://doi.org/10.1029/2020JC016112  

   Loose, N. ; Heimbach, P. ; Pillar, H. R. ; Nisancioglu, K. H. ( August 2020 , Journal of Geophysical Research: Oceans)

   Oceanic quantities of interest (QoIs), for example, ocean heat content or transports, are often inaccessible to direct observation, due to the high cost of instrument deployment and logistical challenges. Therefore, oceanographers seek proxies for undersampled or unobserved QoIs. Conventionally, proxy potential is assessed via statistical correlations, which measure covariability without establishing causality. This paper introduces an alternative method: quantifying dynamical proxy potential. Using an adjoint model, this method unambiguously identifies the physical origins of covariability. A North Atlantic case study illustrates our method within the ECCO (Estimating the Circulation and Climate of the Ocean) state estimation framework. We find that wind forcing along the eastern and northern boundaries of the Atlantic drives a basin‐wide response in North Atlantic circulation and temperature. Due to these large‐scale teleconnections, a single subsurface temperature observation in the Irminger Sea informs heat transport across the remote Iceland‐Scotland ridge (ISR), with a dynamical proxy potential of 19%. Dynamical proxy potential allows two equivalent interpretations: Irminger Sea subsurface temperature (i) shares 19% of its adjustment physics with ISR heat transport and (ii) reduces the uncertainty in ISR heat transport by 19% (independent of the measured temperature value), if the Irminger Sea observation is added without noisemore »to the ECCO state estimate. With its two interpretations, dynamical proxy potential is simultaneously rooted in (i) ocean dynamics and (ii) uncertainty quantification and optimal observing system design, the latter being an emerging branch in computational science. The new method may therefore foster dynamics‐based, quantitative ocean observing system design in the coming years.

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4. Leveraging Uncertainty Quantification to Design Ocean Climate Observing Systems

   https://doi.org/10.1029/2020MS002386  

   Loose, Nora ; Heimbach, Patrick ( April 2021 , Journal of Advances in Modeling Earth Systems)

   Ocean observations are expensive and difficult to collect. Designing effective ocean observing systems therefore warrants deliberate, quantitative strategies. We leverage adjoint modeling and Hessian uncertainty quantification (UQ) within the ECCO (Estimating the Circulation and Climate of the Ocean) framework to explore a new design strategy for ocean climate observing systems. Within this context, an observing system is optimal if it minimizes uncertainty in a set of investigator‐defined quantities of interest (QoIs), such as oceanic transports or other key climate indices. We show that Hessian UQ unifies three design concepts. (1) An observing system reduces uncertainty in a target QoI most effectively when it is sensitive to the same dynamical controls as the QoI. The dynamical controls are exposed by the Hessian eigenvector patterns of the model‐data misfit function. (2) Orthogonality of the Hessian eigenvectors rigorously accounts for redundancy between distinct members of the observing system. (3) The Hessian eigenvalues determine the overall effectiveness of the observing system, and are controlled by the sensitivity‐to‐noise ratio of the observational assets (analogous to the statistical signal‐to‐noise ratio). We illustrate Hessian UQ and its three underlying concepts in a North Atlantic case study. Sea surface temperature observations inform mainly local air‐sea fluxes. Inmore »contrast, subsurface temperature observations reduce uncertainty over basin‐wide scales, and can therefore inform transport QoIs at great distances. This research provides insight into the design of effective observing systems that maximally inform the target QoIs, while being complementary to the existing observational database.

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5. Winter Vertical Diffusion Rates in the Arctic Ocean, Estimated From ⁷ Be Measurements and Dissipation Rate Profiles

   https://doi.org/10.1029/2022JC019197  

   Schulz, K. ; Kadko, D. ; Mohrholz, V. ; Stephens, M. ; Fer, I. ( February 2023 , Journal of Geophysical Research: Oceans)

   Ocean turbulent mixing is a key process affecting the uptake and redistribution of heat, carbon, nutrients, oxygen and other dissolved gasses. Vertical turbulent diffusivity sets the rates of water mass transformations and ocean mixing, and is intrinsically an average quantity over process time scales. Estimates based on microstructure profiling, however, are typically obtained as averages over individual profiles. How representative such averaged diffusivities are, remains unexplored in the quiescent Arctic Ocean. Here, we compare upper ocean vertical diffusivities in winter, derived from the⁷Be tracer‐based approach to those estimated from direct turbulence measurements during the year‐long Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, 2019–2020. We found that diffusivity estimates from both methods agree within their respective measurement uncertainties. Diffusivity estimates obtained from dissipation rate profiles are sensitive to the averaging method applied, and the processing and analysis of similar data sets must take this sensitivity into account. Our findings indicate low characteristic diffusivities around 10⁻⁶ m² s⁻¹and correspondingly low vertical heat fluxes.