Assessing the spatiotemporal variability of Sea Surface Temperature in Delaware Bay, USA, Using the GHRSST Data Product

Presented at the GHRSST XXIII international science team meeting, 27 June-1 July 2022, online and in-person (Barcelona). #GHRSST23

Short abstract

Studies of Sea Surface Temperature (SST) are essential to understanding the response of the bay's environmental and ecological system to a changing climate, given the imminent effects of climate change. The aim of this study is to assess the spatial and temporal variability of Sea Surface Temperature (SST) in Delaware Bay, USA during the period between 2003 and 2020. In the current study, two datasets consisting of in-situ daily SSTs from six stations operated by the National Data Buoy Center (NDBC) and a 17-year Group for High Resolution Sea Surface Temperature (GHRSST) dataset of 0.01° × 0.01° spatial resolution are employed. GHRSST data were evaluated against long-term in situ measurements using the Normalized Root-Mean-Square-Error (NRMSE), Normalized Bias (NB), Kling-Gupta Efficiency (KGE), and a comparison of the data probability distribution, revealing strong agreement between the data sets. Non-parametric trend analysis and a change point detection method were used to assess the temporal variability of daily and annual mean SST. Results revealed a statistically significant upward trend of SST series within the study area. The rate of change of the 95th percentile SST and the 5th percentile SST were computed to investigate the temporal evolution of extreme SSTs. An analysis of the correlation between streamflow temperature anomalies at the downstream of Delaware River and SST anomalies in the study area were conducted. A strong correlation was observed in the estuary outlet of the Delaware River. Teleconnections with climate indices showed that the variability in SST patterns was significantly affected by the Western Hemisphere Warm Pool (WHWP), and the North Atlantic Oscillation (NAO) indices.