Validation of the Inherent Optical Properties of Seawater in the NASA-GISS Climate Model

Paul Lerner,  Columbia University / NASA GISS,  paul.lerner@nasa.gov (Presenter)
Shabab Chowdhury,  City College of New York,  chowdh021@citymail.cuny.edu
Anastasia Romanou,  Columbia University / NASA GISS,  anastasia.romanou@columbia.edu

Inherent Optical Properties (IOPs) describe the absorption and scattering of radiation in seawater, and are critical in determining the amount of photosynthetically available radiation (PAR) for phytoplankton growth. Remote sensing from instruments such as MODIS provide information on inherent optical properties of phytoplankton and other optically active constituents of near-surface waters, but at limited spectral resolution and without considering phytoplankton community composition. The latter property will be further elucidated by utilizing hyperspectral data collected by the Ocean Color Instrument onboard the PACE satellite. Validation of phytoplankton properties estimated from hyperspectral data requires climate models that can simulate oceanic IOPs for multiple phytoplankton functional types at high spectral resolution. Importantly, such models must include a representation of the full climate system (atmosphere, land, ice, etc.) to properly test whether remote sensing retrievals can accurately predict the phytoplankton characteristics of surface waters.

In this poster, we present the validation of IOPs and oceanic PAR in the NASA-GISS climate model. Our model has 16 spectral bands in the wavelengths spanned by PAR, and simulates the absorption and backscatter properties of four phytoplankton functional types (diatoms, chlorophytes, cyanobacteria, and coccolithophores). We evaluate PAR by leveraging remote-sensing data from MODIS-AQUA and data on the vertical structure of PAR from BGC-ARGO. We also evaluate our model's IOPs using data from MODIS-AQUA as well as a compilation of in-situ IOP measurements from a variety of datasets including NOMAD, MAREDAT, and SEADATANET. The IOPs in these datasets are bulk properties that represent the integral of IOP contributions from different phytoplankton functional types. Thus to further elucidate the causes of IOP biases, we compile IOPs for different phytoplankton functional types from recent field and laboratory studies, and compare this compilation to the phytoplankton functional group IOPs currently used by the model. In future work, we plan to update the model's IOPs based on this compilation and re-assess our model's ocean radiative transfer scheme (including the distribution of PAR) based on these updates.

Poster: Poster_Lerner_3-40_77_35.pdf 

Poster Location ID: 3-40

Presentation Type: Poster

Session: Poster Session 3

Session Date: Thu (May 11) 3:00-5:00 PM

CCE Program: Other