Bashmachnikov, I. L., Fedorov, A. M., Vesman, A. V., Belonenko, T. V., & Dukhovskoy, D. S. (2019). Thermohaline convection in the subpolar seas of the North Atlantic from satellite and in situ observations. Part 2: indices of intensity of deep convection.16 (1), 191–201.
Abstract: Variation in locations of the maximum development of deep convection in the subpolar seas, taking into account their small dimensions, represent difficulty in identifying its interannual variability from usually sparse in situ data. In this work, the interannual variability of the maximum convection depth, is obtained using one of the most complete datasets ARMOR, which combines in situ and satellite data. The convection depths, derived from ARMOR, are used for testing the efficiency of two indices of convection intensity: (1) sea-level anomalies from satellite altimetry and (2) the integral water density in the areas of the most frequent development of deep convection. The first index, capturing some details, shows low correlations with the interannual variability of the deep convection intensity. The second index shows high correlation with the deep convection intensity in the Greenland, Irminger and Labrador seas. Asynchronous variations in the deep convection intensity in the Labrador-Irminger seas and in the Greenland Sea are obtained. In the Labrador and in the Irminger seas, the quasi-seven-year variations in the convection intensity are identified.
Bastola, S., & Misra, V. (2015). Seasonal hydrological and nutrient loading forecasts for watersheds over the Southeastern United States. Environmental Modelling & Software , 73 , 90–102.
Coles, V. J., Stukel, M. R., Brooks, M. T., Burd, A., Crump, B. C., Moran, M. A., et al. (2017). Ocean biogeochemistry modeled with emergent trait-based genomics. Science , 358 (6367), 1149–1154.
Abstract: Marine ecosystem models have advanced to incorporate metabolic pathways discovered with genomic sequencing, but direct comparisons between models and “omics” data are lacking. We developed a model that directly simulates metagenomes and metatranscriptomes for comparison with observations. Model microbes were randomly assigned genes for specialized functions, and communities of 68 species were simulated in the Atlantic Ocean. Unfit organisms were replaced, and the model self-organized to develop community genomes and transcriptomes. Emergent communities from simulations that were initialized with different cohorts of randomly generated microbes all produced realistic vertical and horizontal ocean nutrient, genome, and transcriptome gradients. Thus, the library of gene functions available to the community, rather than the distribution of functions among specific organisms, drove community assembly and biogeochemical gradients in the model ocean.
Downes, S. M., Farneti, R., Uotila, P., Griffies, S. M., Marsland, S. J., Bailey, D., et al. (2015). An assessment of Southern Ocean water masses and sea ice during 1988-2007 in a suite of interannual CORE-II simulations. Ocean Modelling , 94 , 67–94.
Dukhovskoy, D. S., Myers, P. G., Platov, G., Timmermans, M. - L., Curry, B., Proshutinsky, A., et al. (2016). Greenland freshwater pathways in the sub-Arctic Seas from model experiments with passive tracers. J. Geophys. Res. Oceans , 121 (1), 877–907.
Dukhovskoy, D. S., Yashayaev, I., Proshutinsky, A., Bamber, J. L., Bashmachnikov, I. L., Chassignet, E. P., et al. (2019). Role of Greenland Freshwater Anomaly in the Recent Freshening of the Subpolar North Atlantic. J. Geophys. Res. Oceans , 124 (5), 3333–3360.
Abstract: The cumulative Greenland freshwater flux anomaly has exceeded 5000 km3 since the 1990s. The volume of this surplus fresh water is expected to cause substantial freshening in the North Atlantic. Analysis of hydrographic observations in the subpolar seas reveal freshening signals in the 2010s. The sources of this freshening are yet to be determined. In this study, the relationship between the surplus Greenland freshwater flux and this freshening is tested by analyzing the propagation of the Greenland freshwater anomaly and its impact on salinity in the subpolar North Atlantic based on observational data and numerical experiments with and without the Greenland runoff. A passive tracer is continuously released during the simulations at freshwater sources along the coast of Greenland to track the Greenland freshwater anomaly. Tracer budget analysis shows that 44% of the volume of the Greenland freshwater anomaly is retained in the subpolar North Atlantic by the end of the simulation. This volume is sufficient to cause strong freshening in the subpolar seas if it stays in the upper 50�100 m. However, in the model the anomaly is mixed down to several hundred meters of the water column resulting in smaller magnitudes of freshening compared to the observations. Therefore, the simulations suggest that the accelerated Greenland melting would not be sufficient to cause the observed freshening in the subpolar seas and other sources of fresh water have contributed to the freshening. Impacts on salinity in the subpolar seas of the freshwater transport through Fram Strait and precipitation are discussed.
Harris, R., Pollman, C., Hutchinson, D., Landing, W., Axelrad, D., Morey, S. L., et al. (2012). A screening model analysis of mercury sources, fate and bioaccumulation in the Gulf of Mexico. Environ Res , 119 , 53–63.
Abstract: A mass balance model of mercury (Hg) cycling and bioaccumulation was applied to the Gulf of Mexico (Gulf), coupled with outputs from hydrodynamic and atmospheric Hg deposition models. The dominant overall source of Hg to the Gulf is the Atlantic Ocean. Gulf waters do not mix fully however, resulting in predicted spatial differences in the relative importance of external Hg sources to Hg levels in water, sediments and biota. Direct atmospheric Hg deposition, riverine inputs, and Atlantic inputs were each predicted to be the most important source of Hg to at least one of the modeled regions in the Gulf. While incomplete, mixing of Gulf waters is predicted to be sufficient that fish Hg levels in any given location are affected by Hg entering other regions of the Gulf. This suggests that a Gulf-wide approach is warranted to reduce Hg loading and elevated Hg concentrations currently observed in some fish species. Basic data to characterize Hg concentrations and cycling in the Gulf are lacking but needed to adequately understand the relationship between Hg sources and fish Hg concentrations.
Harris, R., Pollman, C., Landing, W., Evans, D., Axelrad, D., Hutchinson, D., et al. (2012). Mercury in the Gulf of Mexico: sources to receptors. Environ Res , 119 , 42–52.
Abstract: Gulf of Mexico (Gulf) fisheries account for 41% of the U.S. marine recreational fish catch and 16% of the nation's marine commercial fish landings. Mercury (Hg) concentrations are elevated in some fish species in the Gulf, including king mackerel, sharks, and tilefish. All five Gulf states have fish consumption advisories based on Hg. Per-capita fish consumption in the Gulf region is elevated compared to the U.S. national average, and recreational fishers in the region have a potential for greater MeHg exposure due to higher levels of fish consumption. Atmospheric wet Hg deposition is estimated to be higher in the Gulf region compared to most other areas in the U.S., but the largest source of Hg to the Gulf as a whole is the Atlantic Ocean (>90%) via large flows associated with the Loop Current. Redistribution of atmospheric, Atlantic and terrestrial Hg inputs to the Gulf occurs via large scale water circulation patterns, and further work is needed to refine estimates of the relative importance of these Hg sources in terms of contributing to fish Hg levels in different regions of the Gulf. Measurements are needed to better quantify external loads, in-situ concentrations, and fluxes of total Hg and methylmercury in the water column, sediments, and food web.
Hoffman, R. N., Privé, N., & Bourassa, M. (2017). Comments on “Reanalyses and Observations: What's the Difference?”. Bull. Amer. Meteor. Soc. , 98 (11), 2455–2459.
Abstract: Are there important differences between reanalysis data and familiar observations and measurements? If so, what are they? This essay evaluates four possible answers that relate to: the role of inference, reliance on forecasts, the need to solve an ill-posed inverse problem, and understanding of errors and uncertainties. The last of these is argued to be most significant. The importance of characterizing uncertainties associated with results—whether those results are observations or measurements, analyses or reanalyses, or forecasts—is emphasized.
Ilicak, M., Drange, H., Wang, Q., Gerdes, R., Aksenov, Y., Bailey, D., et al. (2016). An assessment of the Arctic Ocean in a suite of interannual CORE-II simulations. Part III: Hydrography and fluxes. Ocean Modelling , 100 , 141–161.
