Author: nc56@duke.edu (Page 1 of 4)

Prospective undergraduate students, graduate students, and postdoctoral fellows:

We are seeking motivated scientists to join our lab. Please contact Nicolas Cassar for available positions.
A list of funding support (Duke and external) for graduate and postdoctoral studies can be found here. See also the database provided by the Institute for Broadening Participation (IBP).

Contact information here.

Gittings, J. A., Dall’Olmo, Tang, W., G., Llort, J., Jebri, F., Livanou, E., Nencioli, F.,  Darmaraki, S., Theodorou, I., Brewin, R. J., Srokoz, M., Cassar, N., Raitsos, D. 2024. An exceptional phytoplankton bloom in the Southeast Magascar Sea driven by African dust deposition. PNAS Nexus, https://doi.org/10.1093/pnasnexus/pgae386.

See blog on NSOE Lab of the Month:

https://nicholas.duke.edu/news/nsoe-lab-month-cassar-lab

New production (NP) and net community production (NCP) measurements are often used as estimates of carbon export potential from the mixed layer of the ocean, an important process in the regulation of global climate. Diverse methods can be used to measure NP and NCP, from research vessels, autonomous platforms, and remote sensing, each with its own set of benefits and uncertainties. The various methods are rarely applied simultaneously in a single location, limiting our ability for direct comparisons of the resulting measurements. In this study, we evaluated NP and NCP from thirteen independent datasets collected via in situ, in vitro, and satellite-based methods near Ocean Station Papa during the 2018 Northeast Pacific field campaign of the NASA project EXport Processes in the Ocean from RemoTe Sensing (EXPORTS). Altogether, the datasets indicate that carbon export potential was relatively low (median daily averages between −5.1 and 12.6 mmol C m⁻² d⁻¹), with most measurements indicating slight net autotrophy in the region. This result is consistent with NCP estimates based on satellite measurements of sea surface temperature and chlorophyll a. We explored possible causes of discrepancies among methods, including differences in assumptions about stoichiometry, vertical integration, total volume sampled, and the spatiotemporal extent considered. Results of a generalized additive mixed model indicate that the spatial variation across platforms can explain much of the difference among methods. Once spatial variation and temporal autocorrelation are considered, a variety of methods can provide consistent estimates of NP and NCP, leveraging the strengths of each approach.

See Niebergall, A., Traylor, S. Huang, Y., Feen, M., Meyer, M. G., McNair, H. M., Nicholson, D., Fassbender, A. J., Omand, M. M., Marchetti, A., Menden-Deuer, S., Tang, W., Gong, W., Tortell, P., Hamme, R., Cassar, N. 2023. Evaluation of new and net community production estimates by multiple ship-based and autonomous observations in the Northeast Pacific Ocean. Elementa: Science of the Anthropocene (2023) 11 (1): 00107.

The number of carbon dioxide (CO₂) molecules per unit surface area per unit time that enter the ocean surface from the atmosphere is quantified by the air-sea CO₂ flux (F). These CO₂ molecules impact many chemical and biological properties within the ocean. Yet, the direct controls on how many molecules can possibly be exchanged between the atmosphere and the ocean surface depend on several environmental factors such as wind speed at some reference height, the amount of CO₂ molecules in the atmosphere and in the water (or their imbalance ∆pCO₂), the wave height, and sea surface temperature. These environmental factors vary on many time scales such as daily, monthly, seasonal, annual, inter-annual, and decadal. The work demonstrates that the CO₂ gas exchange is dominated by the wind effect on subseasonal time scales, while on longer time scales, the ∆pCO₂ term, closely related to the variability of both atmospheric and oceanic CO₂, emerges as a leading driver.

See:

Gu, Y., Katul, G. G., Cassar, N. 2023. Multiscale temporal variability of the global air-sea CO₂ flux anomaly. JGR Biogeosciences, https://doi.org/10.1029/2022JG006934.

Want to know what polar work feels like? Read Perrin’s riveting blogs !

The importance of dissolved Fe (dFe) in regulating ocean primary production and the carbon cycle is well established. However, the large-scale distribution and temporal dynamics of dFe remain poorly constrained in part due to incomplete observational coverage. In this study, we use a compilation of published dFe observations (n=32,344) with paired environmental predictors from contemporaneous satellite observations and reanalysis products to build a data-driven surface-to-seafloor dFe climatology with 1°×1° resolution using three machine-learning approaches (random forest, supper vector machine and artificial neural network). Among the three approaches, random forest achieves the highest accuracy with overall R² and root mean standard error of 0.8 and 0.3 nmol L^-1, respectively. Using this data-driven climatology, we explore the possible mechanisms governing the dFe distribution at various depth horizons using statistical metrics such as Pearson correlation coefficients and the rank of predictors importance in the model construction. Our results are consistent with the critical role of aeolian iron supply in enriching surface dFe in the low latitude regions and suggest a far-reaching impact of this source at depth. Away from the surface layer, the strong correlation between dFe and apparent oxygen utilization implies that a combination of regeneration, scavenging and large-scale ocean circulation are controlling the interior distribution of dFe, with hydrothermal inputs important in some regions. Finally, our data-driven dFe climatology can be used as an alternative reference to evaluate the performance of ocean biogeochemical models. Overall, the new global scale climatology of dFe achieved in our study is an important step toward improved representation of dFe in the contemporary ocean and may also be used to guide future sampling strategies.

See our paper for more info:

Huang, Y. Tagliabue, A., Cassar, N. 2022. Data-driven modeling of dissolved iron in the global ocean. Frontiers in Marine Science, https://doi.org/10.3389/fmars.2022.837183.

See our paper in Nature.

Droughts and climate-change-driven warming are leading to more frequent and intense wildfires. We use satellite and autonomous biogeochemical Argo float data to evaluate the effect of 2019–2020 Australian wildfire aerosol deposition on phytoplankton productivity. We find anomalously widespread phytoplankton blooms from December 2019 to March 2020 in the Southern Ocean downwind of Australia. Aerosol samples originating from the Australian wildfires contained a high iron content and atmospheric trajectories show that these aerosols were likely to be transported to the bloom regions, suggesting that the blooms resulted from the fertilization of the iron-limited waters of the Southern Ocean. Climate models project more frequent and severe wildfires in many regions^1,2,3. A greater appreciation of the links between wildfires, pyrogenic aerosols¹³, nutrient cycling and marine photosynthesis could improve our understanding of the contemporary and glacial–interglacial cycling of atmospheric CO₂ and the global climate system.

See Press coverage:

See Duke Press Release

See also original publication in Nature Communications

Since the middle of the past century, the Western Antarctic Peninsula has warmed rapidly with a significant loss of sea ice but the impacts on plankton biodiversity and carbon cycling remain an open question. Here, using a 5-year dataset of eukaryotic plankton DNA metabarcoding, we assess changes in biodiversity and net community production in this region. Our results show that sea-ice extent is a dominant factor influencing eukaryotic plankton community composition, biodiversity, and net community production. Species richness and evenness decline with an increase in sea surface temperature (SST). In regions with low SST and shallow mixed layers, the community was dominated by a diverse assemblage of diatoms and dinoflagellates. Conversely, less diverse plankton assemblages were observed in waters with higher SST and/or deep mixed layers when sea ice extent was lower. A genetic programming machine-learning model explained up to 80% of the net community production variability at the Western Antarctic Peninsula. Among the biological explanatory variables, the sea-ice environment associated plankton assemblage is the best predictor of net community production. We conclude that eukaryotic plankton diversity and carbon cycling at the Western Antarctic Peninsula are strongly linked to sea-ice conditions.

See Press coverage:

See full article for more information

The significance of the water-side gas transfer velocity for air–sea CO₂ gas exchange (k) and its non-linear dependence on wind speed (U) is well accepted. What remains a subject of inquiry are biases associated with the form of the non-linear relation linking k to U (hereafter labeled as f(U), where f(.) stands for an arbitrary function of U), the distributional properties of U (treated as a random variable) along with other external factors influencing k, and the time-averaging period used to determine k from U. To address the latter issue, a Taylor series expansion is applied to separate f(U) into a term derived from time-averaging wind speed (labeled as ⟨U⟩, where ⟨.⟩ indicates averaging over a monthly time scale) as currently employed in climate models and additive bias corrections that vary with the statistics of U. The method was explored for nine widely used f(U) parameterizations based on remotely-sensed 6-hourly global wind products at 10 m above the sea-surface. The bias in k of monthly estimates compared to the reference 6-hourly product was shown to be mainly associated with wind variability captured by the standard deviation σσU around ⟨U⟩ or, more preferably, a dimensionless coefficient of variation Iu= σσU/⟨U⟩. The proposed correction outperforms previous methodologies that adjusted k when using ⟨U⟩ only. An unexpected outcome was that upon setting I2u = 0.15 to correct biases when using monthly wind speed averages, the new model produced superior results at the global and regional scale compared to prior correction methodologies. Finally, an equation relating I2u to the time-averaging interval (spanning from 6 h to a month) is presented to enable other sub-monthly averaging periods to be used. While the focus here is on CO₂, the theoretical tactic employed can be applied to other slightly soluble gases. As monthly and climatological wind data are often used in climate models for gas transfer estimates, the proposed approach provides a robust scheme that can be readily implemented in current climate models