GOES Imager

Oceanic Heat Content, Mixed Layer Depth and Depths of 20°C and 26°C Isotherms

jebidiah.jeffery — Thu, 17 Mar 2022 15:31:14 +0000

Oceanic Heat Content, Mixed Layer Depth and Depths of 20°C and 26°C Isotherms

Oceanic Heat Content (OHC) is the measure of the integrated vertical temperature from the sea surface to the depth of the 26°C isotherm and computed from the altimeter-derived isotherm depths in the upper ocean relative to 20°C. Global 0.25 degree grids are generated daily for OHC, mixed layer depth and depths of 20°C and 26°C isotherms for 3 ocean basins: North Atlantic, North Pacific and South Pacific

jebidiah.jeffery Thu, 03/17/2022 - 11:31

Oceanic Heat Content (OHC) is the measure of the integrated vertical temperature from the sea surface to the depth of the 26°C isotherm and computed from the altimeter-derived isotherm depths in the upper ocean relative to 20°C. Satellite data inputs for sea surface temperature (SST) are from the NOAA GeoPolar Blended sea surface temperature and for the sea surface height anomaly (SSHA) are from at least 2 satellite altimetry missions. Global 0.25 degree grids are generated daily for OHC, mixed layer depth and depths of 20°C and 26°C isotherms for 3 ocean basins: North Atlantic, North Pacific and South Pacific.

BACKGROUND

More than 90% of the warming on the Earth over the past 50 years occurred in the Ocean (Shay L.K. 2019).The heat content of the ocean is the amount of heat energy (in joules) stored within a pre-defined volume of the upper ocean. To determine the heat content value in the ocean, NOAA/NESDIS produces a daily operational suite of satellite-derived Oceanic Heat Content (OHC) products for the North Atlantic, and the North and South Pacific basins. A suite of OHC products for the Indian Ocean are planned which will contribute significantly toward global coverage. OHC is an important climate change indicator and provides a high quality climatic data record. These suites of satellite-derived OHC products are validated against over one million in-situ measurements from multiple platforms to assess biases and uncertainties. The NOAA OHC product is shown to be the best product available for Hurricane Intensity Forecasting (Meyers et. al. 2015). Daily display of these OHC products provides valuable data to address key science questions related to climate such as: 1) the extent of warming (or cooling) in the warm pools of the Atlantic and Pacific Ocean basins; 2) thermodynamic processes in the equatorial wave guides associated with eastward propagating Kelvin Waves (ENSO); and linkages to the Madden-Julian Oscillation) across the tropics. Benefits for climate studies are a new understanding of the upper ocean thermodynamics, dynamics and air-sea processes relevant to tropical cyclone intensity forecasting, climatic variability (e.g., OHC anomalies over various time scales), fisheries, and coral reef bleaching. Figure 1 are the input fields (Altimetry and SST) used to generate the OHC Products.

DEFINITION

Oceanic Heat Content (OHC) is defined as the measure of the integrated vertical temperature from the sea surface to the depth of the 26°C isotherm and computed from the altimeter-derived isotherm depths in the upper ocean relative to 20°C.

METHODOLOGY

Algorithm

OHC values are estimated using four points: 1) the sea surface temperature obtained from NOAA/NESDIS Geo Polar SST Analysis; 2) the altimeter-estimates of the 20°C isotherm within a two-layer reduced gravity scheme; 3) the depth of the 26°C isotherm from a climatological relationship between the depths of the 20°C and 26°C isotherm; and 4) flexibility in estimating other isotherm depths (e.g., 25°C) then integrate. The full algorithm description is in the Algorithm Theoretical Basis Document (ATBD) (see documentation link).

Validation

Validation is accomplished by 1) calculating OHC from various in situ data sources (Argo, XBT, PIRATA/TAO, and Air-deployed probes); 2) then compared to the satellite-derived OHC grid point that is closest to that source in time and space; 3) regression analysis and root-mean-square deviation (RMSD) statistics are used to determine an agreement between the satellite and in situ data; and the accuracy has to be within 10 percent of the in situ data (kJ cm^-2).

The addition of new satellite data requires new validation of the product.

Visual images of the OHC product suite are created on a daily basis.

Short Names

Satellite derived Ocean Heat Content

Temporal Coverage

Daily

Product Families

Ocean Heat Content

ATBD: Shay, L. K., J. K. Brewster, E. Maturi, P. C.,Meyers and C. McCaskill, Algorithm Theoretical Basis Document for Satellite-dervied Oceanic Heat Content Product, Version 3.1, June 2015

Shay, L.K., G.J. Goni and P.G. Black.(2000) Effects of a warm oceanic feature on Hurricane Opal., Monthly Weather Review, 125(5), 1366-1383.
De Maria, M. , M. Mainelli , L.K. Shay, J.A. Knaff, and J. Kaplan. (2005) Further improvements to the statistical hurricane intensity prediction scheme (SHIPS). Weather and Forecasting, 20(4), 531-543.
Ali, M. M., P.S.V. Jagadeesh and Sarika Jain (2007a). Effects of Eddies on Bay of Bengal Cyclone Intensity, EOS, Vol. 88, p 93, 95.
Halliwell GR, Shay LK, Jacob SD, Smedstad OM, Uhlhorn EW (2008) Improving Ocean Model Initialization for Coupled Tropical Cyclone Forecast Models Using GODAE Nowcasts. Monthly Weather Review, 136(7): 2576-2591.
Mainelli M, De Maria M, Shay LK, Goni G (2008) Application of Oceanic Heat Content Estimation to Operational Forecasting of Recent Atlantic Category 5 Hurricanes. Weather and Forecasting 23(1): 3-16.
Shay LK and Uhlhorn EW (2008) Loop Current Response to Hurricanes Isidore and Lili. Monthly Weather Review 136(9): 3248
Jaimes, B. and L. K. Shay. (2009) Mixed layer cooling in mesoscale eddies during Katrina and Rita. Monthly Weather Review. 137(12), 4188-4207.
Hallliwell, G., L. K. Shay, J. Brewster, and W. Teague, (2011) Evaluation and sensitivity analysis of an ocean model to hurricane Ivan in the northern Gulf of Mexico. Monthly Weather Review. 139(3), 921-945.
Shay, L. K., and J. Brewster (2010). Eastern Pacific oceanic heat content estimation for hurricane forecasting. Monthly Weather Review . 138, 2110-2131.
Shay, L. K., P. C. Meyers and J. K. Brewster, (2012) Development and analysis of the Systematically Merged Atlantic Region Temperature and Salinity (SMARTS) climatology for ocean heat content estimates. J. Atmos and Oceanogr. Tech. (In Preparation)
Meyers, P. C., L. K. Shay, and J. K. Brewster, (2013) Development and analysis of the Systematically Merged Atlantic Region Temperature and Salinity (SMARTS) climatology for ocean heat content estimates. J. Atmos and Oceanogr. Tech. (Submitted)

Measurements

Depth of 20° and 26° Isotherms

Ocean Heat Content

Ocean Mixed-Layer Depth

Sea Surface Height Anomalies

Sea Surface Temperature - Geostationary

Sea Surface Temperature - Polar-orbiting

Processing Levels

Level 4

Spatial Coverage

Global

Latency Groups

24+ hours (Delayed)

Latency Details

~36 h

Spatial Resolution Groups

2km+

Spatial Resolution Details

0.25 degree gridded

Platforms

Instruments

Data Providers

NOAA

NESDIS

OSPO

Data Tool Links

CoastWatch Tools

CoastWatch Data Portal

OSPO Tools

Satellite Ocean Heat Content Suite

Sample Filenames

ohc_naQG3_2012_215.nc, ohc_npQG3_2012_215.nc, ohc_spQG3_2012_215.nc

HTTPS

https://coastwatch.noaa.gov/pub/socd2/coastwatch/ocean_heat

THREDDS

https://coastwatch.noaa.gov/thredds/socd/coastwatch/catalog_coastwatch_ohc.html

Seascape Pelagic Habitat Classification

jebidiah.jeffery — Thu, 23 Dec 2021 17:21:24 +0000

Seascape Pelagic Habitat Classification

MBON Seascapes identifies spatially explicit water masses with particular biogeochemical features using a model and satellite-derived measurements. The seascape product is generated as monthly and 8-day composites at 5 km spatial resolution

jebidiah.jeffery Thu, 12/23/2021 - 12:21

The US and global Marine Biodiversity Observation Network (MBON) partnered with the US Integrated Ocean Observation System (IOOS), NOAA/OAR/AOML and NOAA/NESDIS/STAR to develop and routinely generate this Seascape Pelagic Habitat Classification product (MBON Seascapes) and make it available on CoastWatch. Derived from dynamic fields of satellite and modelled data, MBON Seascapes are classified and used as a biogeographical framework to describe dynamic, changing ocean habitats for MBON and other applications. MBON Seascapes provide information about the quality and extent of different oceanographic habitats or features and can be used to assess and predict the different planktonic and fisheries communities that reside within seascapes. Current MBON Seascapes products include monthly and 8-day time steps at 5 km resolution. High resolution (1 km) case studies are planned on a case by case basis through cooperation with US and global MBON partners.

Table of Seascape ID number definitions. SST, sea surface temperature; SSS sea surface salinity; ADT, absolute dynamic topography; ICE, Ice; CDOM, chromophoric dissolved organic material; CHLA, chlorophyll-a; NFLH, normalized fluorescent line height; NaN, not a number.

Table of Seascape ID number definitions

Short Names

seascapes

Temporal Coverage

Monthly and 8-Day

Product Families

Seascape Classification

Montes Enrique, Djurhuus Anni, Muller-Karger Frank E., Otis Daniel, Kelble Christopher R., Kavanaugh Maria T., 2020. Dynamic Satellite Seascapes as a Biogeographic Framework for Understanding Phytoplankton Assemblages in the Florida Keys National Marine Sanctuary, United States. Frontiers in Marine Science, 7, https://doi.org/10.3389/fmars.2020.00575
Kavanaugh, M.T., Church, M.J., Davis, C.O., Karl, D.M., Letelier, R.M. and Doney, S.C., 2018. ALOHA from the Edge: Reconciling three decades of in situ Eulerian observations and geographic variability in the North Pacific Subtropical Gyre. Frontiers in Marine Science, 5, p.130.
Muller-Karger, F., Digna Rueda-Roa, Francisco Chavez, Maria Kavanaugh, and Mitchell A. Roffer. 2017. Megaregions Among the Large Marine Ecosystems of the Americas. Environmental Development. http://dx.doi.org/10.1016/j.envdev.2017.01.005.
Kavanaugh, M.T., Oliver, M J., Chavez, F. P., Letelier, R.M., Muller-Karger, F.E., Doney, S.C. 2016. Seascapes as a new vernacular for ocean monitoring, management and conservation. ICES Journal of Marine Science. doi:10.1093/icesjms/fsw086.
Kavanaugh, M.T., Hales, B., Saraceno, M., Spitz, Y.H., White, A.E. and Letelier, R.M., 2014. Hierarchical and dynamic seascapes: A quantitative framework for scaling pelagic biogeochemistry and ecology. Progress in Oceanography, 120, pp.291-304.
Muller-Karger, F.E., M.T. Kavanaugh, E. Montes, W.M. Balch, M. Breitbart, F.P. Chavez, S.C. Doney, E.M. Johns, R.M. Letelier, M.W. Lomas, H.M. Sosik, and A.E. White. 2014. A framework for a marine biodiversity observing network within changing continental shelf seascapes. Oceanography 27(2):18–23, http://dx.doi.org/10.5670/oceanog.2014.56.