Abstract
Abstract. The West Antarctic Peninsula (WAP) is a rapidly warming region, with substantial ecological and biogeochemical responses to the observed 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 modeling, 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 introduce a one-dimensional variational data assimilation planktonic ecosystem model (i.e., the WAP-1D-VAR v1.0 model) equipped with a 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 other ecosystem models that WAP-1D-VAR model was adapted from, including diagnostic sea-ice forcing and trophic interactions specific to the WAP region. We then present the results from model experiments where we assimilate 11 different data types from an example Palmer LTER growth season (October 2002–March 2003) directly related to corresponding model state variables and flows between these variables. The iterative data assimilation procedure reduces the misfits between observations and model results by 58 %, compared to before optimization, via an optimized set of 12 parameters out of a total of 72 free parameters. The optimized model results capture key WAP ecological features, such as blooms during seasonal sea-ice retreat, the lack of macronutrient limitation, and modeled variables and flows comparable to other studies in the WAP region, as well as several important ecosystem metrics. One exception is that the model slightly underestimates particle export flux, for which we discuss potential underlying reasons. The data assimilation scheme of the WAP-1D-VAR model enables 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 can be successfully employed to link the snapshots collected by the available data sets together to explain and understand the observed dynamics along the coastal WAP.
Highlights
We (1) describe the structure and schemes of the West Antarctic Peninsula (WAP)-1D-VAR model in great detail, (2) evaluate the model performance and robustness using a variety of quantitative metrics, and (3) discuss the model applicability with regard to capturing the key WAP ecological and biogeochemical features using the data from an example growth season
We include the data types directly related to corresponding model outputs, including a mix of ecosystem stocks or state variables – NO3, PO4, Chl for diatoms and cryptophytes, bacterial biomass, microzooplankton biomass, SDOC, particulate organic carbon (POC), and particulate organic nitrogen (PON), as well as carbon flows among model stocks – bulk net primary production (PP) and bacterial production (BP)
We developed the WAP-1D-VAR v1.0 model, a onedimensional variational data assimilation model specific to the coastal WAP region, evaluated the model performance and robustness using a variety of quantitative metrics, and discussed the model applicability with regard to capturing the key WAP ecological and biogeochemical features using the data from the example growth season
Summary
The West Antarctic Peninsula (WAP) has experienced significant atmospheric and surface ocean warming since the 1950s, resulting in decreased winter sea-ice duration, the retreat of glaciers, and changes in upper-ocean dynamics (Clarke et al, 2009; Cook et al, 2005; Henley et al, 2019; King, 1994; Meredith and King, 2005; Stammerjohn et al, 2008; Vaughan et al, 2003, 2006; Whitehouse et al, 2008) These climate-driven changes propagate through marine food webs by affecting physiology of individual organisms and the whole communities (Ducklow et al, 2007). We (1) describe the structure and schemes of the WAP-1D-VAR model in great detail, (2) evaluate the model performance and robustness using a variety of quantitative metrics, and (3) discuss the model applicability with regard to capturing the key WAP ecological and biogeochemical features using the data from an example growth season
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