Potential Impacts of Remotely-Sensed Ocean Surface Boundary Layer LiDAR Profiler Observations on Ocean State Estimation
Abstract
The Ocean Surface Boundary Layer (OSBL), or mixed layer, modulates air-sea exchange of heat, momentum, freshwater and trace gases, and plays crucial roles in ocean biology and acoustics. For this broad range of reasons, scientists generally agree on the importance of OSBL observations, yet data remains sparse, with negative impacts on our understanding and prediction of many phenomena. In support of the 2017 Decadal Survey's inclusion of ocean mixed layer characteristics as desired variables, we present results from a set of Observing System Simulation Experiments (OSSEs), created using a global, eddying ( 4km resolution) run of the Massachusetts Institute of Technology General Circulation Model (MITgcm) and a lower resolution state estimation model (based on the Estimating the Circulation and Climate of the Ocean, or ECCO, project models), to examine the impact of hypothetical remotely-sensed OSBL temperature profiles on an ocean state estimate. Our goal is to evaluate the science potential of a prospective space-based LiDAR ocean observing system. We use the high-resolution model to conduct a "Real Ocean" simulation, and extract mock observations from that model solution analogous to currently available observations used in real ECCO state estimates for multiple variables. Three mock state estimates were then performed using the coarser resolution model, in which upper ocean temperature profiles were applied in different ways to represent separate data scenarios: (1) no OSBL profile observations, (2) profile observations only at currently available in situ sites, and (3) observations sampled along the A-train satellite track in the presence of clouds (based on CALIPSO cloud data). We compare and contrast OSBL state variable and process budget results from these OSSE estimates and the high resolution model at sites of interest in the tropics and N. Atlantic Ocean, with particular focus on regions associated with ENSO and the SPURS/SPURS-2 field study sites, in order to isolate the impacts of the simulated OSBL profiles on the results. We also compare these results to actual data assimilation products from the Estimating the Climate and Circulation of the Ocean (ECCO) project. Results indicate that the addition of along-track profiles significantly reduces model error, even in the presence of clouds.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2018
- Bibcode:
- 2018AGUFM.B41M2905H
- Keywords:
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- 0480 Remote sensing;
- BIOGEOSCIENCESDE: 1640 Remote sensing;
- GLOBAL CHANGE