Morey, S. L., Wienders, N., Dukhovskoy, D. S., & Bourassa, M. A. (2018). Impact of Stokes Drift on Measurements of Surface Currents from Drifters and HF Radar. In American Geophysical Union (Vol. Fall Meeting).
Abstract: Concurrent measurements by surface drifters of different configurations and HF radar reveal substantial differences in estimates of the near-surface seawater velocity. On average, speeds of small ultra-thin (5 cm) drifters are significantly greater than co-located drifters with a traditional shallow drogue design, while velocity measurements from the drogued drifters closely match HF radar velocity estimates. Analysis of directional wave spectra measurements from a nearby buoy reveals that Stokes drift accounts for much of the difference between the velocity measurements from the drogued drifters and the ultra-thin drifters, except during times of wave breaking. Under wave breaking conditions, the difference between the ultra-thin drifter velocity and the drogued drifter velocity is much less than the computed Stokes drift. The results suggest that surface currents measured by more common approaches or simulated in models may underrepresent the velocity at the very surface of the ocean that is important for determining momentum and enthalpy fluxes between the ocean and atmosphere and for estimating transport of material at the ocean surface. However, simply adding an estimate of Stokes drift may also not be an appropriate method for estimating the true surface velocity from models or measurements from drogued drifters or HF radar under all sea conditions.
Bourassa, M. A., & Gille, S. (2008). U.S. CLIVAR working groups on high latitude surface fluxes. U.S. CLIVAR Variations , 6 (1), 8–11.
Bourassa, M. A., S. T. Gille, and C. A. Clayson. (2010). Surface Fluxes: Challenges for High Latitudes: Workshop report from the U.S. CLIVAR High Latitudes Surface Flux Working Group. U.S. CLIVAR Variations , 8 (1), 7,14.
Chien, C. - Y., K. Speer, and M. Bourassa. (2010). Comparison of Wind Products in the Southern Ocean. U.S. CLIVAR Variations , 8 (1), 8–10.
Baigorria, G. A., Chelliah, M., Mo, K. C., Romero, C. C., Jones, J. W., O'Brien, J. J., et al. (2010). Forecasting Cotton Yield in the Southeastern United States using Coupled Global Circulation Models. Agronomy Journal , 102 (1), 187.
Liu, M., Lin, J., Wang, Y., Sun, Y., Zheng, B., Shao, J., et al. (2018). Spatiotemporal variability of NO2 and PM2.5 over Eastern China: observational and model analyses with a novel statistical method. Atmos. Chem. Phys. , 18 (17), 12933–12952.
Abstract: Eastern China (27-41 degrees N, 110-123 degrees E) is heavily polluted by nitrogen dioxide (NO2), particulate matter with aerodynamic diameter below 2.5 mu m (PM2.5), and other air pollutants. These pollutants vary on a variety of temporal and spatial scales, with many temporal scales that are nonperiodic and nonstationary, challenging proper quantitative characterization and visualization. This study uses a newly compiled EOF-EEMD analysis visualization package to evaluate the spatiotemporal variability of ground-level NO2, PM2.5, and their associations with meteorological processes over Eastern China in fall-winter 2013. Applying the package to observed hourly pollutant data reveals a primary spatial pattern representing Eastern China synchronous variation in time, which is dominated by diurnal variability with a much weaker day-to-day signal. A secondary spatial mode, representing north-south opposing changes in time with no constant period, is characterized by wind-related dilution or a buildup of pollutants from one day to another.
We further evaluate simulations of nested GEOS-Chem v9-02 and WRF/CMAQ v5.0.1 in capturing the spatiotemporal variability of pollutants. GEOS-Chem underestimates NO2 by about 17 mu g m(-3) and PM2.5 by 35 mu g m(-3 )on average over fall-winter 2013. It reproduces the diurnal variability for both pollutants. For the day-to-day variation, GEOS-Chem reproduces the observed north-south contrasting mode for both pollutants but not the Eastern China synchronous mode (especially for NO2). The model errors are due to a first model layer too thick (about 130 m) to capture the near-surface vertical gradient, deficiencies in the nighttime nitrogen chemistry in the first layer, and missing secondary organic aerosols and anthropogenic dust. CMAQ overestimates the diurnal cycle of pollutants due to too-weak boundary layer mixing, especially in the nighttime, and overestimates NO2 by about 30 mu g m(-3) and PM2.5 by 60 mu g m(-3). For the day-to-day variability, CMAQ reproduces the observed Eastern China synchronous mode but not the north-south opposing mode of NO2. Both models capture the day-to-day variability of PM2.5 better than that of NO2. These results shed light on model improvement. The EOF-EEMD package is freely available for noncommercial uses.
Chassignet, E., Cenedese, E., & Verron, J. (2012). Buoyancy-Drivenn Flows . Cambridge University Press.
Bastola, S. (2014). Uncertainty in Climate Change Studies. In S. Shrestha, M. S. Babel, & V. P. Pandey (Eds.), Climate Change and Water Resources (pp. 81–108). CRC Press.
Stukel, M. R., & Barbeau, K. A. (2020). Investigating the Nutrient Landscape in a Coastal Upwelling Region and Its Relationship to the Biological Carbon Pump. Geophys. Res. Lett. , 47 (6), e2020GL087351.
Abstract: We investigated nutrient patterns and their relationship to vertical carbon export using results from 38 Lagrangian experiments in the California Current Ecosystem. The dominant mode of variability reflected onshore-offshore nutrient gradients. A secondary mode of variability was correlated with silica excess and dissolved iron and likely reflects regional patterns of iron-limitation. The biological carbon pump was enhanced in high nutrient and Fe-stressed regions. Patterns in the nutrient landscape proved to be better predictors of the vertical flux of sinking particles than contemporaneous measurements of net primary production. Our results suggest an important role for Fe-stressed diatoms in vertical carbon flux. They also suggest that either preferential recycling of N or non-Redfieldian nutrient uptake by diatoms may lead to high PO:NO and Si(OH):NO ratios, following export of P- and Si-enriched organic matter. Increased export following Fe-stress may partially explain inverse relationships between net primary productivity and export efficiency.
Fox-Kemper, B., Adcroft, A., Böning, C. W., Chassignet, E. P., Curchitser, E., Danabasoglu, G., et al. (2019). Challenges and Prospects in Ocean Circulation Models. Front. Mar. Sci. , 6 .
Abstract: We revisit the challenges and prospects for ocean circulation models following Griffies et al. (2010). Over the past decade, ocean circulation models evolved through improved understanding, numerics, spatial discretization, grid configurations, parameterizations, data assimilation, environmental monitoring, and process-level observations and modeling. Important large scale applications over the last decade are simulations of the Southern Ocean, the Meridional Overturning Circulation and its variability, and regional sea level change. Submesoscale variability is now routinely resolved in process models and permitted in a few global models, and submesoscale effects are parameterized in most global models. The scales where nonhydrostatic effects become important are beginning to be resolved in regional and process models. Coupling to sea ice, ice shelves, and high-resolution atmospheric models has stimulated new ideas and driven improvements in numerics. Observations have provided insight into turbulence and mixing around the globe and its consequences are assessed through perturbed physics models. Relatedly, parameterizations of the mixing and overturning processes in boundary layers and the ocean interior have improved. New diagnostics being used for evaluating models alongside present and novel observations are briefly referenced. The overall goal is summarizing new developments in ocean modeling, including how new and existing observations can be used, what modeling challenges remain, and how simulations can be used to support observations.