Chlorophyll‐based model to estimate underwater Photosynthetically Available Radiation for modeling, in‐situ and remote‐sensing applications
|Author(s)||Xing Xiaogang1, Boss Emmanuel2|
|Affiliation(s)||1 : State Key Laboratory of Satellite Ocean Environment Dynamics Second Institute of Oceanography Ministry of Natural Resources Hangzhou ,China
2 : School of Marine Sciences University of Maine Orono Maine, USA
|Source||Geophysical Research Letters (0094-8276) (American Geophysical Union (AGU)), 2021-04 , Vol. 48 , N. 7 , P. e2020GL092189 (11p.)|
|WOS© Times Cited||9|
|Keyword(s)||BGC‐, Argo, chlorophyll, deep chlorophyll maximum, diffuse attenuation coefficient, photosynthetically available radiation|
Accurate estimation of the underwater light field associated with photosynthetically available radiation (PAR) is a critical ocean parameter necessary to compute phytoplankton growth rate, net primary production (NPP), and to assess photo‐physiological response of phytoplankton, such as changes in cellular pigmentation. However, methods to estimate PAR used in many previous studies lack in accuracy, likely resulting in significant bias in light‐dependent products such as NPP derived from remote sensing, model simulations, or autonomous platforms. Here we propose and validate a chlorophyll‐based model for more accurate estimation of the subsurface PAR profile which uses chlorophyll‐a as its input. Validation is performed using 1,744 BGC‐Argo profiles of chlorophyll fluorescence that are calibrated with surface satellite‐derived chlorophyll concentration over their lifetime. The independent verification with the float’s PAR sensors confirms the accuracy of satellite chlorophyll estimate worldwide and in the Southern Ocean in particular.
Plain Language Summary
In this study, we propose a new model with which to compute the underwater light field used by phytoplankton. The model uses as its inputs the above water radiation (which can be obtained from public databases) and the subsurface distribution of chlorophyll‐a, a pigment shared by all phytoplankton that can be estimated from sensors deployed on robotic platforms and that is used by many models of ocean ecosystem. We test the model accuracy using data observed by the light sensors on autonomous profiling floats and find it to perform well. The comparison also highlights that estimates of surface chlorophyll concentrations from satellites are unbiased worldwide, in contrast to some published accounts.