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.
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.
Chassignet, E., Cenedese, E., & Verron, J. (2012).
Buoyancy-Drivenn Flows. Cambridge University Press.
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.
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.
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.
Bourassa, M. A., & Gille, S. (2008). U.S. CLIVAR working groups on high latitude surface fluxes.
U.S. CLIVAR Variations, 6(1), 8–11.
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.
Armstrong, E. M., Bourassa, M. A., Cram, T., Elya, J. L., Greguska, F. R., III, Huang, T., et al. (2018). An information technology foundation for fostering interdisciplinary oceanographic research and analysis. In
American Geophysical Union (Vol. Fall Meeting).
Abstract: Before complex analysis of oceanographic or any earth science data can occur, it must be placed in the proper domain of computing and software resources. In the past this was nearly always the scientist's personal computer or institutional computer servers. The problem with this approach is that it is necessary to bring the data products directly to these compute resources leading to large data transfers and storage requirements especially for high volume satellite or model datasets. In this presentation we will present a new technological solution under development and implementation at the NASA Jet Propulsion Laboratory for conducting oceanographic and related research based on satellite data and other sources. Fundamentally, our approach for satellite resources is to tile (partition) the data inputs into cloud-optimized and computation friendly databases that allow distributed computing resources to perform on demand and server-side computation and data analytics. This technology, known as NEXUS, has already been implemented in several existing NASA data portals to support oceanographic, sea-level, and gravity data time series analysis with capabilities to output time-average maps, correlation maps, Hovmöller plots, climatological averages and more. A further extension of this technology will integrate ocean in situ observations, event-based data discovery (e.g., natural disasters), data quality screening and additional capabilities. This particular activity is an open source project known as the Apache Science Data Analytics Platform (SDAP) (https://sdap.apache.org), and colloquially as OceanWorks, and is funded by the NASA AIST program. It harmonizes data, tools and computational resources for the researcher allowing them to focus on research results and hypothesis testing, and not be concerned with security, data preparation and management. We will present a few oceanographic and interdisciplinary use cases demonstrating the capabilities for characterizing regional sea-level rise, sea surface temperature anomalies, and ocean hurricane responses.