Chapter 5 - The Oceanic Heat Budget

5.5 Global Data Sets for Fluxes

Ship and satellite data have been processed to produce global maps of fluxes. Observations from ship measurements made over the past 150 years yield maps of the long-term mean values of the fluxes, especially in the northern hemisphere. Ship data, however, are sparse in time and space, and they are being replaced more and more by satellite data.

The most useful maps are those made by combining level 3 and 4 satellite data sets with observations from ships, using numerical weather models. Let's look first at the sources of data, then at a few of the more widely used data sets.

International Comprehensive Ocean-Atmosphere Data Set
Data collected from observers on ships are the richest source of marine information. Slutz et al., (1985) describing their efforts to collect, edit, summarize, and publish all marine observations write:

Since 1854, ships of many countries have been taking regular observations of local weather, sea surface temperature, and many other characteristics near the boundary between the ocean and the atmosphere. The observations by one such ship-of-opportunity at one time and place, usually incidental to its voyage, make up a marine report. In later years fixed research vessels, buoys, and other devices have contributed data. Marine reports have been collected, often in machine-readable form, by various agencies and countries. That vast collection of data, spanning the global oceans from the mid-nineteenth century to date, is the historical ocean-atmosphere record.

These marine reports have been edited and published as the Comprehensive Ocean-Atmosphere Data Set COADS (Woodruff et al. 1987) available through the National Oceanic and Atmospheric Administration.

The ICOADS release 2.4 includes 238 million reports of marine surface conditions collected from 1784–2007 by buoys, other platform types, and by observers on merchant ships. The data set include fully quality-controlled (trimmed) reports and summaries. Each unique report contains 22 observed and derived variables, as well as flags indicating which observations were statistically trimmed or subjected to adaptive quality control. Here, statistically trimmed means outliers were removed from the data set. The summaries included in the data set give 14 statistics, such as the median and mean, for each of eight observed variables: air and sea surface temperatures, wind velocity, sea-level pressure, humidity, and cloudiness, plus 11 derived variables.

The data set consists of an easily-used data base at three principal resolutions:

1. individual reports,
2. year-month summaries of the individual reports in 2° latitude by 2° longitude boxes from 1800 to 2005 and 1° latitude by 1° longitude boxes from 1960 to 2005, and
3. decade-month summaries.

Note that data from 1784 through the early 1800s are extremely sparse–based on scattered ship voyages. See Overview and an example of temperature reports.

Duplicate reports judged inferior by a first quality control process designed by the National Climatic Data Center NCDC were eliminated or flagged, and "untrimmed" monthly and decadal summaries were computed for acceptable data within each 2° latitude by 2° longitude grid. Tighter, median-smoothed limits were used as criteria for statistical rejection of apparent outliers from the data used for separate sets of trimmed monthly and decadal summaries. Individual observations were retained in report form but flagged during this second quality control process if they fell outside 2.8 or 3.5 estimated standard-deviations about the smoothed median applicable to their 2° latitude by 2° longitude box, month, and 56-, 40-, or 30-year period (i.e., 1854-1990, 1910-1949, or 1950-1979).

The data are most useful in the northern hemisphere, especially the North Atlantic. Data are sparse in the southern hemisphere and they are not reliable south of 30°S. Gleckler and Weare (1997) analyzed the accuracy of the ICOADS data for calculating global maps and zonal averages of the fluxes from 55°N to 40°S. They found that systematic errors dominated the zonal means. Zonal averages of insolation were uncertain by about 10%, ranging from ±10 W/m² in high latitudes to ± 25W/m² in the tropics. Long wave fluxes were uncertain by about ± 7W/m². Latent heat flux uncertainties ranged from ±10 W/m² in some areas of the northern oceans to ±30 W/m² in the western tropical oceans to ±50 W/m² in western boundary currents. Sensible heat flux uncertainties tend to be around ±5-10 W/m².

Josey et al., (1999) compared averaged fluxes calculated from ICOADS with fluxes calculated from observations made by carefully calibrated instruments on some ships and buoys. They found that mean flux into the oceans, when averaged over all the seas surface had errors of ± 30W/m². Errors vary season-ally and by region, and global maps of fluxes require corrections such as those proposed by DaSilva, Young, and Levitus (1995) shown in Figure 5.7.

Satellite Data
Raw data are available from satellite projects, but we need processed data. Various levels of processed data from satellite projects are produced (Table 5.3):

The operational meteorological satellites that observe the ocean include:

1. NOAA series of polar-orbiting, meteorological satellites;
2. US Defense Meteorological Satellite Program DMSP polar-orbiting satellites, which carry the Special Sensor Microwave/Imager (SSM/I);
3. Geostationary meteorological satellites operated by NOAA (GOES), Japan (GMS) and the European Space Agency (METEOSTATS).

Data are also available from instruments on experimental satellites such as:

1. Nimbus-7, Earth Radiation Budget Instruments;
2. Earth Radiation Budget Satellite, Earth Radiation Budget Experiment;
3. The European Space Agency's ERS-1 & 2;
4. The Japanese Advanced Earth Observing System (ADEOS) and Midori;
5. Quikscat,
6. The Eaeth-Observing System satellites Terra, Aqua, and Envisat,
7. The Tropical Rainfall Measuring Satellite (TRMM); and
8. Topex/Poseidon and its replacement Jason-1.

Satellite data are collected, processed, and archived by government organizations. Archived data are further processed to produce useful flux data sets.

International Satellite Cloud Climatology Project
The International Satellite Cloud Climatology Project is an ambitious project to collect observations of clouds made by dozens of meteorological satellites from 1985 to 1995, to calibrate the the satellite data, to calculate cloud cover using carefully verified techniques, and to calculate surface insolation (Rossow and Schiffer, 1991). The clouds were observed with visible-light instruments on polar-orbiting and geostationary satellites.

Global Precipitation Climatology Project
This project uses three sources of data to calculate rain rate (Huffman, et al., 1995, 1997):

1. Infrared observations of the height of cumulus clouds from goes satellites. The basic idea is that the more rain produced by cumulus clouds, the higher the cloud top, and the colder the top appears in the infrared. Thus rain rate at the base of the clouds is related to infrared temperature.
2. Measurements by rain gauges on islands and land.
3. Radio-frequency emissions from from water droplets in the atmosphere observed by the SSM/I.

Accuracy is about 1 mm/day. Data from the project are available on a 2.5° latitude by 2.5° longitude grid from July 1987 to December 1995 from the Global Land Ocean Precipitation Analysis at the NASA Goddard Space Flight Center.

Xie and Arkin (1997) produced a 17-year data set based on seven types of satellite and rain-gauge data combined with the output from the NCEP/NCAR reanalyzed data from numerical weather models. The data set has the same spatial and temporal resolution as the Huffman data set.

Reanalyzed Data From Numerical Weather Models
Surface heat fluxes have been calculated from weather data using numerical weather models by various reanalysis projects described in §4.4. The fluxes are consistent with atmospheric dynamics, they are global, they are calculated every six hours, and they are available for many years on a uniform grid. For example, the NCEP/NCAR reanalysis, available on a CD-ROM, include daily averages of wind stress, sensible and latent heat fluxes, net long and short wave fluxes, near-surface temperature, and precipitation.

Accuracy of Calculated Fluxes
Recent studies of the accuracy of fluxes computed by numerical weather models and reanalysis project suggest:

1. Heat fluxes from the NCEP and ECMWF reanalyses have similar global average values, but the fluxes have important regional differences. Fluxes from the Goddard Earth Observing System reanalysis are much less accurate (Taylor, 2000: 258).
2. The fluxes are biased because they were calculated using numerical models optimized to produce accurate weather forecasts. The time-mean values of the fluxes may not be as accurate as the time-mean values calculated directly from ship observations.
3. The simulation of boundary-layer clouds is a significant source of error in calculated fluxes. The poor vertical resolution of the numerical models does not adequately resolve the low-level cloud structure (Taylor, 2001).
4. The fluxes have zonal means that differ significantly from the same zonal means calculated from ICOADS (International Comprehensive Ocean-Atmosphere Data Set) data. The differences can exceed 40 W/m².
5. The atmospheric models do not require that the net heat flux averaged over time and Earth's surface be zero. The ECMWF data set averaged over fifteen
   years gives a net flux of 3.7 W/m² into the ocean. The NCEP reanalysis gives a net flux of 5.8 W/m² out of the ocean (Taylor, 2000: 206). ICOADS data give a net flux of 16 W/m² into the ocean (figure 5.7).

Thus reanalyzed fluxes are most useful for forcing climate models needing actual heat fluxes and wind stress. ICOADS data are most useful for calculating time-mean fluxes except perhaps in the southern hemisphere. Overall, Taylor (2000) notes that there are no ideal data sets, all have significant and unknown errors.

Output From Numerical Weather Models
Some projects require flux data a few hours after after observations are collected. The surface analysis from numerical weather models is a good source for this type of data.

chapter contents