Submitted as: development and technical paper 04 Feb 2021

Submitted as: development and technical paper | 04 Feb 2021

Review status: this preprint is currently under review for the journal GMD.

WAP-1D-VAR v1.0: Development and Evaluation of a One-Dimensional Variational Data Assimilation Model for the Marine Ecosystem Along the West Antarctic Peninsula

Hyewon Heather Kim1,2, Ya-Wei Luo3, Hugh W. Ducklow4, Oscar M. Schofield5, Deborah K. Steinberg6, and Scott C. Doney1 Hyewon Heather Kim et al.
  • 1Woods Hole Oceanographic Institution, Woods Hole, MA 02543, United States
  • 2University of Virginia, Charlottesville, VA 22904, United States
  • 3Xiamen University, Xiamen, Fujian 361102, China
  • 4Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY 10964, United States
  • 5Rutgers University, New Brunswick, NJ 80901, United States
  • 6Virginia Institute of Marine Science, Gloucester Point, VA 23062, United States

Abstract. The West Antarctic Peninsula (WAP) is a rapidly warming region, with substantial ecological and biogeochemical responses to climate change and variability for the past decades, revealed by multi-decadal observations from the Palmer Antarctica Long-Term Ecological Research (LTER) program. The wealth of these long-term observations provides an important resource for ecosystem modelling, but there has been a lack of focus on the development of numerical models that simulate time-evolving plankton dynamics over the Austral growth season along the coastal WAP. Here we developed a one-dimensional, data assimilation planktonic ecosystem model (i.e., the WAP-1D-VAR model v1.0) equipped with a variational adjoint and model parameter optimization scheme. We first demonstrate the modified and newly added model schemes to the pre-existing food-web and biogeochemical components of the WAP-1D-VAR model, including diagnostic sea-ice forcing and trophic interactions specific to the WAP region. We then conducted model experiments by assimilating eleven different data types from an example Palmer LTER growth season (October 2002–March 2003) directly related to corresponding model state variables and intercompartmental flows. The iterative, data assimilation procedure reduced by 80 % the misfits between observations and model results, compared to before optimization, via an optimized set of 14 parameters out of total 72 free parameters. The optimized model results captured key WAP ecological features, such as blooms during seasonal sea-ice retreat, the lack of macronutrient limitation, and comparable values of the assimilated and non-assimilated model state variables and flows to other studies, as well as several important ecosystem metrics. One exception was slightly underestimated particle export flux, for which we discuss fully potential underlying reasons. The data assimilation scheme of the WAP-1D-VAR model enabled the available observational data to constrain previously poorly understood processes, including the partitioning of primary production by different phytoplankton groups, the optimal chlorophyll to carbon ratio of the WAP phytoplankton community, and the partitioning of dissolved organic carbon pools with different lability. The WAP-1D-VAR model was successfully employed to glue the snapshots from a range of the available data sets together to explain and understand the observed dynamics along the coastal WAP.

Hyewon Heather Kim et al.

Status: open (until 01 Apr 2021)

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Hyewon Heather Kim et al.

Model code and software

WAP-1D-VAR v1.0: A One-Dimensional Variational Data Assimilation Model for the West Antarctic Peninsula H. Heather Kim, Ya-Wei Luo, Hugh W. Ducklow, Oscar M. Schofield, Deborah K. Steinberg, and Scott C. Doney

Hyewon Heather Kim et al.


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Short summary
The West Antarctic Peninsula (WAP) is a rapidly warming region, revealed by multi-decadal observations. Despite the region being data-rich, there is a lack of focus on ecosystem model development. Here, we introduce a data assimilation ecosystem model for the WAP region. Experiments by assimilating data from an example growth season capture key WAP features. This study enables us to glue the snapshots from available data sets together to explain the observations in the WAP.