TY - JOUR
T1 - Remote Estimation of Surface pCOsub2/sub on the West Florida Shelf
AU - Chen, Shuangling
AU - Hu, Chuanmin
AU - Byrne, Robert H.
AU - Robbins, Lisa L.
AU - Yang, Bo
PY - 2016/1/1
Y1 - 2016/1/1
N2 - Surface p CO 2 data from the West Florida Shelf (WFS) have been collected during 25 cruise surveys between 2003 and 2012. The data were scaled up using remote sensing measurements of surface water properties in order to provide a more nearly synoptic map of p CO 2 spatial distributions and describe their temporal variations. This investigation involved extensive tests of various model forms through parsimony and Principal Component Analysis, which led to the development of a multi-variable empirical surface p CO 2 model based on concurrent MODIS (Moderate Resolution Imaging Spectroradiometer) estimates of surface chlorophyll a concentrations (CHL, mg m −3 ), diffuse light attenuation at 490 nm (Kd_Lee, m −1 ), and sea surface temperature (SST, °C). Validation using an independent dataset showed a p CO 2 Root Mean Square Error (RMSE) of <12 µatm and a 0.88 coefficient of determination (R 2 ) for measured and model-predicted p CO 2 ranging from 300 to 550 µatm. The model was more sensitive to SST than to CHL and Kd_Lee, with a 1 °C change in SST leading to a ~16 µatm change in the predicted p CO 2 . Application of the model to the entire WFS MODIS time series between 2002 and 2014 showed clear seasonality, with maxima (~450 µatm) in summer and minima (~350 µatm) in winter. The seasonality was positively correlated to SST (high in summer and low in winter) and negatively correlated to CHL and Kd_Lee (high in winter and low in summer). Inter-annual variations of p CO 2 were consistent with inter-annual variations of SST, CHL, and Kd_Lee. These results suggest that surface water p CO 2 of the WFS can be estimated, with known uncertainties, from remote sensing. However, while the general approach of empirical regression may work for waters from other areas of the Gulf of Mexico, model coefficients need to be empirically determined in a similar fashion.
AB - Surface p CO 2 data from the West Florida Shelf (WFS) have been collected during 25 cruise surveys between 2003 and 2012. The data were scaled up using remote sensing measurements of surface water properties in order to provide a more nearly synoptic map of p CO 2 spatial distributions and describe their temporal variations. This investigation involved extensive tests of various model forms through parsimony and Principal Component Analysis, which led to the development of a multi-variable empirical surface p CO 2 model based on concurrent MODIS (Moderate Resolution Imaging Spectroradiometer) estimates of surface chlorophyll a concentrations (CHL, mg m −3 ), diffuse light attenuation at 490 nm (Kd_Lee, m −1 ), and sea surface temperature (SST, °C). Validation using an independent dataset showed a p CO 2 Root Mean Square Error (RMSE) of <12 µatm and a 0.88 coefficient of determination (R 2 ) for measured and model-predicted p CO 2 ranging from 300 to 550 µatm. The model was more sensitive to SST than to CHL and Kd_Lee, with a 1 °C change in SST leading to a ~16 µatm change in the predicted p CO 2 . Application of the model to the entire WFS MODIS time series between 2002 and 2014 showed clear seasonality, with maxima (~450 µatm) in summer and minima (~350 µatm) in winter. The seasonality was positively correlated to SST (high in summer and low in winter) and negatively correlated to CHL and Kd_Lee (high in winter and low in summer). Inter-annual variations of p CO 2 were consistent with inter-annual variations of SST, CHL, and Kd_Lee. These results suggest that surface water p CO 2 of the WFS can be estimated, with known uncertainties, from remote sensing. However, while the general approach of empirical regression may work for waters from other areas of the Gulf of Mexico, model coefficients need to be empirically determined in a similar fashion.
KW - Surface pCO2
KW - Satellite remote sensing
KW - MODIS
KW - Chlorophyll
KW - SST
KW - Kd
KW - WFS
UR - https://digitalcommons.usf.edu/msc_facpub/1784
UR - https://doi.org/10.1016/j.csr.2016.09.004
U2 - 10.1016/j.csr.2016.09.004
DO - 10.1016/j.csr.2016.09.004
M3 - Article
VL - 128
JO - Continental Shelf Research
JF - Continental Shelf Research
ER -