CIRFA seminar 25 March: Atsushi Matsuoka
The next CIRFA seminar will be presented by our colleague Atsushi Matsuoka from the University of New Hampshire.
TITLE: Ocean color remote sensing in the Arctic Ocean
WHEN: 25 March 2021, 14:00-15:00.
WHERE: Click here to join the meeting
Climate change is affecting a broad spectrum of marine, terrestrial, atmospheric, and cryospheric environments in high northern latitudes. Satellite records reveal that Arctic sea ice area and thickness have been decreasing almost over the last four decades due to global warming and ice-albedo feedback. The newly opened area is responsible for dissolution of atmospheric CO2. Depending on nutrient availability and physical conditions (e.g., mixing), primary production of the Arctic Ocean (AO) tends to increase but is likely explained by the different mechanisms over the last two decades. On land, an increase in river discharge has been observed in both North American and Siberian sides of the Arctic region particularly since the late 20th century. A significant amount of organic carbon originating from permafrost thaw is now anticipated to be delivered by river discharge into the AO. A portion of this organic carbon that was previously sequestered in the permafrost may be actively utilized by heterotrophic bacteria, which may accelerate CO2 release back to the atmosphere. Whether the AO is a sink or source of atmospheric CO2 is still not clear.
To address the role of organic matter in answering this important question, I have worked on satellite remote sensing estimates of dissolved (DOC) and particulate organic carbon (POC). In more recent collaborative work, we are integrating estimates of those concentrations into a numerical model. My research also includes investigation of a recent trend in fluxes of DOC and POC observed in major Arctic river mouths by developing a semi-analytical algorithm with known uncertainty. To examine the influence of river input on coastal marine ecosystems, I have also developed an objective algorithm for discriminating different surface water sources using remote sensing data alone. Broader application of this algorithm may lead to the discrimination of water sources in the surface layer in a variety of environments, which may be useful to improving our understanding of physical and biogeochemical processes related to each water source.