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An Introduction to Atmospheric and Oceanographic Datasets


9. ANCILLARY DATASETS


Some datasets contain information which is not meteorological or oceanographic but influence or interact with the climate system. Topography (sometimes called orography), vegetation soil type, wetlands, albedo, hydrological information (e.g., streamflow and runoff) and solar data fit into these categories.

Topography

Topography alters the flow of the atmosphere. The lee sides (i.e., the downwind side) of large mountain ranges are areas where cyclones often develop or where weak disturbances intensify (i.e., cyclogenesis). In addition, topography can act as a barrier that prevents cold or warm air from penetrating certain geographic regions. For example, the Indian Peninsula is protected from bitter cold Siberian air masses during the winter season, while during summer, the topography can cause considerable variability in precipitation amounts over very short distances. Regions where winds have a component blowing toward the mountains can result in large amounts of precipitation due to orographic lifting.

The ocean bottom topography is also very significant in oceanography. The continental slopes and shelves that adjoin the continents form barriers for oceanic currents. Some eastward flowing ocean currents develop into strong western boundary currents (e.g., the Gulf Stream, and Kuroshio Current) along the continental slopes. Mid-ocean ridges form the largest relief features on earth and strongly influence deep water circulation and the distribution of deep ocean water properties. At shallow levels there are sills and constrictions that isolate deeper water in fjords and enclosed seas (e.g., the Mediterranean Sea, Gulf of Mexico, and Sea of Japan). Topographic features like these greatly control the water exchanged with the open ocean and determine many characteristics of the enclosed sea. In some cases, the water that spills over the sills is traceable over much of the open ocean. For example, the Straits of Gibraltar are very shallow (the Gibraltar Sill) and restrict the flow of the very saline waters of the Mediterranean Sea into the Atlantic. The saline water is more dense and would normally flow out along the bottom, but the sill retards this flow to the extent that when the water can finally spill over the sill, it is traceable over much of the Atlantic Ocean because of its relatively high temperature and salinity.

Topographic datasets are available in a range of grid resolutions. Some are coarse (e.g., 2.5 degree) while others are of high resolution (e.g., 30 seconds). Users should be aware that those with coarser resolutions generally represent a spatial average while the high resolution grids represent point values. In some cases, however, ``high-resolution'' datasets are actually interpolated from those with lower resolutions. The highest resolution datasets are generally available for specific regions of the globe (e.g., North America) because of the large volume of data.

Land-Surface and Runoff Data

Vegetation, soil characteristics (e.g., texture, color, wetness) and streamflow/runoff are important components of the climate system. Both vegetation and soil are important factors in the global radiation budget. For example, the color of vegetation and soil directly contribute to the surface albedo (i.e., the ratio of radiation reflected by the surface to that incident upon it) which is central to radiation budget studies. Vegetation and other land-surface processes are major factors in the global carbon budget and biogeochemical cycles. Land-surface hydrology (e.g., river/stream flow and runoff) is important both from a local and a global perspective. These can influence crops/vegetation, soil wetness and irrigation.

Land-surface datasets contain much information. Some examples include: vegetation classifications and indexes, major world ecosystems, biomass, hydrological information and soil characteristics. These datasets are difficult to evaluate and each has strengths and weaknesses. Some are derived from atlas data and, given the rapidly changing world in which we live, may not be applicable on a local basis. Some may have detailed soil types while others use very broad definitions.

Solar Data

Solar data are necessary for radiation budgets and it is possible that even small variations in energy output by the sun may influence the climate. Sunspot data have been available for centuries. One of the best known geophysical cycles is the solar sunspot cycle. This is a quasi-periodic variation with a period of about 11 years. Although satellite data have indicated that the variation of solar energy at the top of the atmosphere is small (about 0.1%, but much larger in certain wavelengths such as UV), chemical reactions such as the creation of ozone in the upper atmosphere can be very sensitive to these variations.


Table 9.1
Topography
NCAR
ID
SourceGridArea
ds750.0Scripps1 degreeglobal
ds750.1RAND1 degreeglobal
ds754.0U.S. Navy10' global
ds755.0 U.S.A.F.1 degree, 30', 5' global
ds756.1DMA30 secU.S.
ds757.0NMC2.5 degree + spectral global
ds759.1NGDC5' global
ds768.0Cogley/Briggs1 degree global

Table 9.2
Soil, Vegetation and Albedo
NCAR
ID
SourceGrid
(degree)
Type
ds207.0RAND4 - 5 albedo
ds676.0 NESS 2.5 albedo
ds765.0 Matthews-GISS1 vegetation
cultivation intensity
seasonal albedo
ds765.5 Matthews-GISS1 wetlands+vegetation
% inundation
methane emission
soil
ds766.0 Argonne 1/4, 1/6 NA land usage
(Landsat)
ds767.0 Wilson, H-S1 vegetation/color
soil
ds768.0 Cogley/Briggs1 vegetation/color
soil/pollen
ds769.0 Olson0.5 ecosystems
(veg by carbon den)
ds770.0 Staub-Rosenw 1 soil
(GISS+FAO)1 sfc slope


Table 9.3
Datasets with Solar Information
NCAR
ID
Max
Period
Description
ds503.0 1952-76 NCDC SOLMET TD9724 Solar + Sfc Obs, daily
ds504.0 NCDC TD9734 Typical Meteorological Year, Solar + Sfc
ds565.1 1891-1984 CDIAC U.S. Historical Sunshine Obs
ds730.0 1978-83 Campbell's Earth Radia Budget Exper (ERBE)
ds732.0 1984-86 Barkstrom's (ERBE)
ds733.0 1978-87 NIMBUS 7 ERBE Matrix Data, daily 1978Nov-1987Nov
ds832.0 1932-81 NOAA WDCA Magnetic Indices + Sunspots
ds834.0 1610-1993 Sunspots, 1610-1986 (from Clark + NGDC)


Table 9.4
Datasets with River Runoff Data
NCAR
ID
Max
Period
Description
ds550.1  USGS River Flow Data
ds552.0 1800-1972 UNESCO Flow Rates of Selected World Rivers, monthly
ds553.01880-1985 Russia monthly (varies by station)
ds768.0  streamflow

Topography
Land-Surface and Runoff Data
Solar Data

An Introduction to Atmospheric and Oceanographic Datasets
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