Seawater temperature is a critical factor in the functioning of both marine and terrestrial ecosystems, as well as human activities that rely on the ocean. Sea surface temperature (SST) is the temperature of the uppermost layer of seawater, typically the top few meters. This layer is in direct contact with the atmosphere, and its temperature is influenced by a variety of factors, including solar radiation, wind, and currents. Monitoring changes in SST is essential for understanding and mitigating the impacts of climate change and other environmental stressors. Changes in global SST can also impact climate regimes over land through teleconnections, such as the well known El Niño and La Niña events.
The NOAA Extended Reconstructed Sea Surface Temperature (ERSST) dataset is an important product for studying long-term changes in the SST. It is widely used in climate research, weather forecasting, and other environmental applications.
A Long-Term Record
SST is a key indicator related to climate change because it describes conditions at the boundary between the atmosphere and the oceans, which is where the transfer of energy between the two takes place. As the oceans absorb more heat from the atmosphere, SST is expected to increase. Changes in SST can affect circulation patterns and ecosystems in the ocean and also influence global climate through the transfer of energy back into the atmosphere.
The ERSST dataset is a global monthly sea surface temperature analysis derived from the International Comprehensive Ocean Atmosphere Dataset and recent autonomous observation platforms such as Argo floats. ERSST can describe global trends in SST from 1854 to the present. Trends are based on in situ (taken by an on-site instrument) measurements of water temperature worldwide. When paired with appropriate screening criteria and bias correction algorithms, in situ data provide a reliable long-term record, or climatology, of temperature. ERSST is provided on a global ocean 2°x2° grid.
Tracking Anomalies
Because of the long-term nature of ERSST, the dataset also provides global SST anomalies computed monthly with respect to a 1971-2000 monthly climatology. It can also be adjusted for other climatological base time periods (e.g., 1991-2020). The ability to track anomalies makes ERSST a valuable tool for El Niño monitoring and forecasting.
El Niño events occur when there is a sustained warming of the SST in the central and eastern equatorial Pacific (the opposite phase is called La Niña). The ERSST dataset provides information on historical SST patterns in the region, which can be used to identify El Niño-like patterns and provide insight into the likelihood of an El Niño event occurring in the future. The ERSST dataset allows scientists to identify the presence of the warming up of the “cold tongue of water” in the central and eastern equatorial Pacific; this warming is a key characteristic of El Niño events. By tracking the warming of this normally cold tongue of water over time, scientists can make more accurate predictions about the onset and strength of an El Niño event. These forecasts can help governments, businesses, and individuals to better prepare for the impacts of El Niño, such as droughts, floods, and changes in agricultural productivity.
ERSST is also integrated with land surface air temperature from the Global Historical Climatology Network-Monthly dataset to create integrated surface temperature analyses in NCEI’s NOAA Global Surface Temperature (NOAA GlobalTemp) product. This product is provided in the monthly and annual Global Climate Report.
In addition to this centennial scale (1854 onward) monthly 2°x2° grid SST product, NCEI also produces another higher-resolution (i.e., daily 0.25°x0.25° grid) SST product for the satellite era (1981 onward in this case) by blending in situ observations used in ERSST together with satellite observed SSTs, resulting in the Daily Optimum Interpolation SST (DOISST) product.
