Development of adjoint-based ocean state estimation for the Amundsen and Bellingshausen seas and ice shelf cavities using MITgcm–ECCO (66j)

• Published in: Geoscientific Model Development
• Main Authors: Nakayama, Yoshihiro, Menemenlis, Dimitris, Wang, Ou, Zhang, Hong, Fenty, Ian, Nguyen, An T.
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2021
• Subjects: article; Verlagsveröffentlichung; Amundsen Sea; Antarc*; Antarctic; Antarctica; Ice Shelf; Pine Island; Pine Island Glacier; Sea ice
• Online Access: https://doi.org/10.5194/gmd-14-4909-2021; https://noa.gwlb.de/receive/cop_mods_00057676; https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00057326/gmd-14-4909-2021.pdf; https://gmd.copernicus.org/articles/14/4909/2021/gmd-14-4909-2021.pdf

The Antarctic coastal ocean impacts sea level rise, deep-ocean circulation, marine ecosystems, and the global carbon cycle. To better describe and understand these processes and their variability, it is necessary to combine the sparse available observations with the best-possible numerical descriptions of ocean circulation. In particular, high ice shelf melting rates in the Amundsen Sea have attracted many observational campaigns, and we now have some limited oceanographic data that capture seasonal and interannual variability during the past decade. One method to combine observations with numerical models that can maximize the information extracted from the sparse observations is the adjoint method, a.k.a. 4D-Var (4-dimensional variational assimilation), as developed and implemented for global ocean state estimation by the Estimating the Circulation and Climate of the Ocean (ECCO) project. Here, for the first time, we apply the adjoint-model estimation method to a regional configuration of the Amundsen and Bellingshausen seas, Antarctica, including explicit representation of sub-ice-shelf cavities. We utilize observations available during 2010–2014, including ship-based and seal-tagged CTD measurements, moorings, and satellite sea-ice concentration estimates. After 20 iterations of the adjoint-method minimization algorithm, the cost function, here defined as a sum of the weighted model–data difference, is reduced by 65 % relative to the baseline simulation by adjusting initial conditions, atmospheric forcing, and vertical diffusivity. The sea-ice and ocean components of the cost function are reduced by 59 % and 70 %, respectively. Major improvements include better representations of (1) Winter Water (WW) characteristics and (2) intrusions of modified Circumpolar Deep Water (mCDW) towards the Pine Island Glacier. Sensitivity experiments show that ∼40 % and ∼10 % of improvements in sea ice and ocean state, respectively, can be attributed to the adjustment of air temperature and wind. This study is a preliminary ...