T-MEDNet observation network: a ground truth for ultra-high resolution satellite SST in the Mediterranean nearshore and coastal ocean

Short abstract

Since nearly two decades, the T-MEDNet initiative (www.t-mednet.org) aims to develop an operative and cost-effective climate change observation network in Mediterranean coastal ecosystems based on collaborative approaches. In T-MEDNet, seawater temperature is sampled continuously at hourly-frequency using data loggers (accuracy ±0.21°C) deployed by divers at standard depths, every 5m from the surface down to 40m depth. To date, the temperature network gathers 23 Marine Protected Areas and 17 Research Institutions from eight Mediterranean countries, from Gibraltar straight to the West, to the Eastern Levantine Sea. Sustained observations are conducted in 80 coastal sites, among which 40 with near surface sampling, across spatial and temporal scales (multi-decadal for the longest). The high-frequency multi-year time series are managed in a centralized way and regularly updated, resulting in a harmonized and quality-checked database of over 20 million in situ samples.

T-MEDNet observations have proved key for a range of studies including the validation of gridded satellite sea surface temperature (SST) products, the analysis of coastal dynamics, decadal warming trends, and the assessment of marine heatwaves ecological impacts. Due to its unique features, T-MEDNet observation network can be considered as a ground truth for satellite SST in the Mediterranean nearshore and coastal ocean, with strong potentialities for the validation of, then joint use with, ultra-high resolution SST (<100m) from the TRISHNA and SENTINEL missions. In that sense T-MEDNet is seeking for support and cooperation at all levels for building satellite-in situ synergies to guide evidence-based climate change coastal adaptation strategies.

Following multiple users’ requests for a reduced L3S-LEO temporal resolution and data size, with increased coverage and improved performance metrics, this presentation discusses the newly produced L3S-LEO-D product, created by collating the L3S-LEO AM/PM Day/Night products into a single daily SST. The main challenges in creating the L3S-LEO-D SST are systematic biases between individual L3S-LEO SSTs due to diurnal warming, and movement of SST features over the course of the 24-hour collation window. In the ACSPO L3S-LEO-D algorithm, the inter-satellite biases due to diurnal warming are mitigated by debiasing/harmonising individual L3S-LEO products to night-time L3S-LEO-PM using a statistical model based on modelled wind speed and mean solar insolation. After diurnal debiasing is applied, individual L3S-LEO SSTs are combined using an iterative method, focusing on preservation of features and minimization of residual cloud leakages.