Toward seasonal to multi-annual marine biogeochemical prediction using GFDL's Earth System Model
Jong-Yeon Park
Princeton/GFDL
6 Apr, 1 pm, in 2155
Abstract: While physical
ocean prediction systems routinely assimilate observations and produce
seasonal to decadal forecasts, ocean biogeochemical (BGC) prediction
systems are less mature due to additional challenges. These include
insufficient global-scale BGC observations to inform model
initialization, uncertainties from both physical and BGC processes in
earth system models, and properties of BGC variables that challenge
data assimilation approaches (e.g., non-Gaussian, complex patterns of
cross-correlation). A first impediment, however, is the high BGC
sensitivity to transient momentum imbalances that arise during physical
data assimilation. In this study, we develop a strategy to robustly
integrate the GFDL’s ocean BGC model (i.e. COBALT) with the ensemble
coupled-climate data assimilation (ECDA) system used for GFDL’s
seasonal to decadal global climate predictions. The ocean and
atmosphere data constraints in the assimilation system are optimally
modified to reduce BGC biases caused by momentum imbalances while
retaining the information of observed physical states. We then
performed retrospective prediction runs by initializing the model with
the output from our ECDA run coupled with BGC model and investigated
seasonal to multi-annual prediction skills of BGC variables over 1991
to 2016. This earth system prediction system can provide skillful
global marine biogeochemistry predictions about one year in advance in
many ocean basins although forecast skill varied by region and
initialization month. We further investigated potential utility
of our earth system prediction system for marine resource
management.