DOI: 10.17882/112029
title: Ocean Monitoring Indicators: Sea Surface Temperature (SST) Anomaly and Trends (1982–2025) over the Mid-Atlantic Ridge (21°N–29°N; 48°W–43.5°W)

description: Short description
This dataset provides Ocean Monitoring Indicators (OMI) for the Mid-Atlantic Ridge region around the seabed exploration area (21°N–29°N; 48°W–43.5°W). It includes three components:
Time series of monthly mean Sea Surface Temperature (SST) anomalies (1982–2025), relative to the 1991–2020 climatology, area-averaged for the region. 2025 SST anomaly map: Monthly anomalies averaged over 2025, on a 0.05°×0.05° latitude-longitude grid. SST trend map (1982–2025): Trends in °C/year, on a 0.05°×0.05° latitude-longitude grid.
SST as a Key Climate Indicator
SST is a key climate variable since it deeply contributes in regulating climate and its variability (Deser et al., 2010). SST is then essential to monitor and characterise the state of the global climate system (GCOS 2010). Long-term SST variability, from interannual to (multi-)decadal timescales, provides insight into the slow variations/changes in SST, i.e. the temperature trend (e.g., Pezzulli et al., 2005). On shorter timescales, SST anomalies serve as key indicators for extreme events, such as marine heatwaves (Hobday et al., 2018).
Data and Methodology
The monitoring of SST over the Mid-Atlantic Ridge region is based on two primary datasets: the Climate Data Record (CDR) spanning 1980 to 2021, and satellite data provided by the Copernicus Climate Change Service (C3S) from 2022 onward (Embury et al., 2024). This combined dataset, which is deemed suitable for climate applications, integrates SST observations derived from twenty infrared and two microwave radiometers. The data are made available at their native resolution, spatially averaged onto a global 0.05° latitude-longitude grid (single-sensor, with gaps), and as a daily, gap-free, merged SST analysis at the same resolution. 
This study utilizes the global Sea Surface Temperature (SST) analysis time series to derive SST indicators including:
 - SST anomalies: Monthly mean time series (1982–present), area-averaged over the Mid-Atlantic Ridge region, and computed relative to the 1991–2020 climatological baseline derived from the Climate Data Record (CDR).
 - SST trends: Long-term trends calculated over the same period.
The 1991–2020 climatological reference period was selected in accordance with World Meteorological Organization (WMO) guidelines (WMO, 2017) and aligns with the most recent practices adopted by the U.S. National Oceanic and Atmospheric Administration (NOAA) (https://wmo.int/media/news/updated-30-year-reference-period-reflects-changing-climate).
The analytical approach follows the framework described in Mulet et al. (2018) and is further detailed in Good et al. (2020). The processing pipeline comprised the following steps:
Monthly Aggregation: Daily SST analyses were averaged to generate monthly mean fields. Climatology Calculation: A 30-year climatology (1991–2020) was computed by averaging the monthly means over the reference period. Anomaly Computation: Monthly SST anomalies were derived by subtracting the climatological monthly means from the corresponding monthly SST values. Seasonal Adjustment: The time series for each grid cell was decomposed using the X11 seasonal adjustment procedure (Pezzulli et al., 2005), which isolates the seasonal, trend, and residual error components. Trend Analysis: The trend component was extracted as a filtered version of the monthly time series. The slope of the trend was estimated using the non-parametric Sen’s method (Sen, 1968), which also provides the 95% confidence interval for the slope.
SST Indicators and Trend Analysis
The analysis reveals an overall SST trend of 0.023 ± 0.002 °C/year over the period 1982–2025, which remains consistent across the region. The area-averaged SST anomaly for 2025 is +0.54 °C, with slightly higher values observed in the northern part of the study area. 
 
References
Deser, C., Alexander, M. A., Xie, S.-P., Phillips, A. S., 2010. Sea Surface Temperature Variability: Patterns and Mechanisms. Annual Review of Marine Science 2010 2:1, 115-143. https://doi.org/10.1146/annurev-marine-120408-151453
Embury, O., Merchant, C. J., Good, S. A., Rayner, N. A., Høyer, J. L., Atkinson, C., ... & Donlon, C. (2024). Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Scientific Data, 11(1), 326.
GCOS. Global Climate Observing System. 2010. Update of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (GCO-138).
Good, S.A., Kennedy, J.J, and Embury, O. Global sea surface temperature anomalies in 2018 and historical changes since 1993. In: von Schuckmann et al. 2020, Copernicus Marine Service Ocean State Report, Issue 4, Journal of Operational Oceanography, 13:sup1, S1-S172, doi: 10.1080/1755876X.2020.1785097.
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Sen, P. K., 1968. Estimates of the regression coefficient based on Kendall’s tau. J Am Statist Assoc. 63:1379–1389.
WMO, Guidelines on the Calculation of Climate Normals, 2017, WMO-No-.1203