NASA Climatology Interdisciplinary Data Collection

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[Starry Globe] ------------------------------------------------------------------------ Earth is not a jigsaw puzzle with separate domains of land, sea, and atmosphere. After centuries of study we have learned that Earth's components and processes do not function as separate phenomena, but as a set of interrelated systems. We must abandon old practices of studying discrete elements of our complex planet and apply our efforts to the evolving approach called Earth Systems Science. The approach recognizes that the so-called "discrete elements" of Earth's behavior are really interdependent phenomena whose interactions can best be understood from simultaneous, multidisciplinary studies. (Vincent Salomonson, director, Earth Sciences Directorate, NASA Goddard Space Flight Center, in the Goddard News, April 1995) ------------------------------------------------------------------------ Readme Contents Introduction List of Included Data Sets and Their Formats Alphabetical List of Physical Parameters Brief Summary of Contents of Each Data Set References Appendex Organization of CIDC CD-ROM set [rule] Introduction To facilitate the use of integrated, multiyear data sets related to the Global Change Program, the Distributed Active Archive Center (DAAC), at the NASA Goddard Space Flight Center, compiled a consistent summary of 25 important data sets. Select data from the atmospheric, oceanic, and land use sciences were placed on a common global map grid. Most are on a uniform spatial and temporal scale (1 x 1 degree, monthly), but a few of our data sets do have larger meshes (2 x 2 degrees, etc.). Measurements not presented in map form include the total solar irradiances, carbon dioxide station data, the Southern Oscillation Index and angular distribution models (to aid in the interpretation of measured radiances). Only in recent years have some of these data products been upgraded to a level suitable for interannual investigations. In making the data set selections, we considered such factors as the advice of senior scientists in various fields, the number of years available, the quality of the data sets and the reformatting effort required. These data products come from many sources, but when two or more similar product sets are available we have a preference, in this collection, for those archived at the Goddard DAAC or produced at Goddard. This climate data collection is planned to meet the needs of interdisciplinary scientists for research and for university undergraduate and graduate level class room applications. This is a dynamic collection, and specific products will be added, extended, or replaced as new data become available. We are interested in the suggestions and comments of our data users concerning the results of their investigations, data and documentation problems encountered while using a data set, and new products they would like added to this collection. This data collection contains approximately 70 different physical climate parameters distributed among the 25 different data sets listed in section 2. The full name, and an abbreviated name, are given for each data set. In a second table the format, spatial resolution, and spatial & temporal coverage of the data sets are summarized. The physical parameters are listed alphabetically in Section 3 together with the abbreviated name(s) of the associated data set(s). In Section 4, a brief description of each data set is given. This includes a parameter list, the time period covered, the source of the data set and a principle reference. Each data set is accompanied by a detailed Readme User's Guide which describes the background of the data set, the data format, and lists several references dealing with the formation, validation and scientific uses of the data set. Data users are urged to read the User's Guide before analyzing the data. The making of accurate measurements is an exacting science. It requires excellent instruments which have to be carefully maintained and calibrated. For satellite observations the emphasis is on excellent instruments and adjustments to the calibration equations as the instruments and the observing conditions change. This is because it is difficult or often impossible to adjust or replace specific satellite instruments. Satellite instruments observe the Earth's surface through the Earth's atmosphere which is continuously changing. Because of the many problems, observing teams are normally set up to derive specific physical parameters such as precipitation or atmospheric ozone from the satellite measurements. Over time these teams develop algorithm changes which better interpret instrument changes and the variable observing conditions. An example in our collection is the Nimbus-7 Total Ozone Mapping Spectrometer (TOMS) data set. These monthly mean maps of total column atmospheric ozone (1978-1993) come from version-7 of the production algorithm. It is common for the data teams to produce at least two or three algorithm versions. Both the data teams and other scientists spend a good deal of time examining the accuracy of the various climate data sets. Rossow et al. (1993) examine the differences between the International Satellite Cloud Climatology Program (ISCCP) C- algorithm cloud products and those from some other well known cloud data sets. Rossow et al. (1996) describe why the new ISCCP D-algorithm was adopted to replace the original C-algorithm. The Earth's surface radiation budget is hard to observe; this makes it difficult to validate the various computed surface radiation budgets. There are considerable uncertainties concerning the effects of clouds on the surface radiation budget. These are discussed by Li et al. (1997), Wielicki et al. (1995) and Cess et al. (1995). In analyzing climate parameters, it is important to consider how accurate each is. In many cases the accuracy can only be estimated. We have attempted to include only well documented, state of the art data sets in this Interdiscipline Data Collection. However we leave it to each user to examine the user's guides and listed references before deciding how accurate specific data sets are. All of the many climate parameters interact to produce regional and global climate regimes. If climate is to be understood and predicted, then these various interactions must be examined and modeled. Global Data Assimilation Systems (GDAS) are used to model the atmospheric climate system. Geophysical equations of atmospheric motion, programmed into a General Circulation Model (GCM), are used to blend selected observational measurements with climate data and coefficients to produce self consistent models of the dynamic atmosphere. This ensures that the thermodynamical structure of the atmosphere in each region of the globe is physically consistent in space and time with that of other regions and also with the large-scale circulation of the atmosphere. These models do not perfectly mirror the physical world. They do best with the parameters that are directly assimilated such as winds, pressures and specific humidity. They do less well with parameters such as precipitation, cloudiness, and surface energy fluxes which are strongly influenced by the physical parameterization of the model and the level of sophistication in its analysis techniques. Our Interdiscipline Data Collection includes 26 parameters subsetted from the reanalyzed assimilated data set produced by the Data Assimilation Office (DAO) at the Goddard Space Flight Center (Schubert et al, 1993). This is a program to reanalyze recent climate data records to produce a consistent picture of the climate for the past decade or so. Schubert et al. (1995) discuss some of the strong and weak points of this data set. Problems can arise from deficiencies in the input data, in the assimilation algorithm and with the GCM itself. Cess et al. (1990 & 1993) compare a number of GCMs and some of their deficiencies. Trenberth and Guillemot (1995) examine the global atmospheric moisture budget as seen in the DAO and two competing reanalysis data sets. These are the European Climate Model for Medium Range Weather Forecasts (ECMWF) model and the National Centers for Environmental Prediction (NCEP) model data sets. Boney et al. (1997) compared the DAO and NCEP hydrology and radiation budgets with each other and with several independent measurement and calculation data sets. They used a number of the data sets included in this collection. These include the Atmospheric Soundings (TOVS), Clouds (ISSCP), Surface Radiation Budget (NASA/Langley), Sea Surface Temperature (NOAA/NCEP), Atmospheric Precipitable Water (SSMI), and the Global Land and Ocean Precipitation Analysis (GPCP) data sets. Details concerning these data sets are given in Section 4. Boney et al. (1997) noted some large differences and some deficiencies in the hydrological and radiative fields of the DAO and NCEP reanalyses but found that these did not affect the large-scale dynamics too strongly. "Thus, in investigating the behavior of large-scale atmospheric circulations , the choice of a particular set of reanalyses may not be too critical". However when using parameters such as the surface net heat flux the biases and specificities of each set of reanalyses should be recognized. In general these several comparison papers tend to agree that programs to derive specific parameter types (precipitation, clouds, surface radiation budget, etc.) give more accurate results for these specific parameters than do the DAO and other reanalysis data assimilation programs which attempt to produce all or most of them. In the data assimilation programs, all these physical parameters interact with one another and at times the uncertainties and errors can be compounded. Boney et al. (1997) conclude: "Regardless, these reanalyses provide, at the present time, a unique and extraordinary global dataset for research purposes: to better understand the physical and dynamical processes that govern the stability, variability, and evolution of our climate." The global climate program is quite dynamic both in terms of developing improved measurement techniques as well as keeping the observations up to date. Therefore the data sets in this collection are periodically updated. As indicated in Section 3, a number of the physical parameters in our collection appear in two or more data sets. In making a choice as to which parameter version to use please read carefully the data set User's Guides and the pertinent references. The Goddard DAAC is interested in receiving your comments concerning your experiences with the Goddard DAAC in general and particularly with the Interdisciplinary Data Collection. Comments concerning the data products themselves can also be sent directly to the science team chair persons or data producers identified in the dataset READMEs. Comments concerning the Interdisciplinary Data Collection should be directed to, H. Lee Kyle Code 902.2, NASA Goddard Space Flight Center Greenbelt, MD 20771 Phone: Voice 301-614-5352; Fax: 301-614-5268 Email: lkyle@eosdata.gsfc.nasa.gov ------------------------------------------------------------------------ II. List of Included Data Sets and Their Formats The included data sets are grouped into seven categories in Table 1. These categories are based partial on the physical parameters involved and partially on the procedures used to calculate the parameters. The same physical parameter, such as precipitation, may be found in two or more categories, and in several data sets. Following the name of each data set, an abbreviated name appears in parenthesis. This abbreviation is used to identify the data set in the Physical Parameters Table 3. It also frequently appears in the names of the data files. Table 1: Included Data Sets Atmospheric Dynamics & 1. Assimilation Atmospheric Dynamics Atmospheric Soundings Subset, DAO (assim) 2. Atmospheric Soundings, TOVS, (tovs) 1. Outgoing Longwave Radiant Flux, ERBE (erbe) 2. Total Solar Irradiance (solarirrad) 3. Clouds, ISCCP C2 (isccpc2) Radiation and Clouds 4. New Clouds, ISCCP D2 (isccpd2) 5. Surface Solar Irradiance, NASA/GISS (srfsolar) 6. Surface Radiation Budget, NASA/Langley (srb) 1. Vegetation Index, AVHRR NDVI (ndvi) The Biosphere 2. Ocean Pigment Concentration, CZCS (czcs) 3. Global Land Cover, ISLSCP (vegmap) Variable Atmospheric 1. Ozone, Nimbus-7 TOMS (tomsn7) Constituents 2. Greenhouse Gases, CDIAC (cdiacgrnh) 1. Sea Surface Temperature, NOAA/NCEP (ncepsst) 2. Temperature Deviations, U. East Anglia Measured Surface (ueatemp) Temperatures & Pressures 3. Southern Oscillation Index, U. East Anglia (ueasoi) 4. Global Temperatures Deviations, NASA/GISS (gisstemp) 1. Atmospheric Precipitable Water, SSMI (pwssmi) 2. Snow Depth, SMMR (smmrsnw) 3. Sea Ice Concentration, SMMR & SSMI (seaice) Hydrology 4. Global Rain Gauge Analysis, GPCC (gpgauge) 5. Global Land and Ocean Precipitation Analysis, GPCP (gpcmb) 6. Soil Characteristics, FAO (soilchar) 7. Monsoon Rain, SMMR (msnrain) Remote Sensing Science Angular Radiation Distribution Models, ERBE (erbeadm) Data Set Characteristics Most of the data is in IEEE 4 byte floating point format but some small files are in ascii. World grid data start in the North and at 180-degrees west and progresses to the east and then southward. In most cases the data values are grid square averages. However in the Assimilated Atmospheric Dynamics data set the values refer to the grid points. The data sets are listed below by data categories and not alphabetically--see start of Section 2. The full data set names and abbreviations are given in Table 1. Table 2: Data Set Formats and Spatial & Temporal Coverage Data Set Resolution File Coverage (abbreviation) (degrees) Format Fill Value Size Spatial Temporal (Bytes) Atmospheric Dynamics 65520 & Global (assim) 2x2 IEEE -999.9 524160 3/80-11/93 Atmospheric Soundings 259200 Global (tovs) 1x1 IEEE -999.9 to 1/85-12/92 1814400 Outgoing Longwave Global (ERBE) 1x1 IEEE -999.9 259200 1/86-12/88 Solar Irradiance ASCII -9.9 to 400 to Solar Disc (solarirrad) N/A Tables -9999.999 123000 11/78-12/96 Clouds ISCCP-C2 Global (isccpc2) 1x1 IEEE -999.9 259200 7/83-6/91 Clouds ISCCP-D2 Global (isccpd2) 1x1 IEEE -999.9 259200 1/86-1/87;1/90-12/92 Surface solar Global irradiance (sfrsolar) 1x1 IEEE -999.99 259200 7/83-6/91 Surface Radiation Global Budget (srb) 1x1 IEEE -999.9 259200 7/83-6/91 Vegetation Index data Global (ndvi) 1x1 IEEE gap=-99.999 259200 7/81-8/94 water=-9.999 Ocean Pigment data gap=-99.0 Global (czcs) 1x1 IEEE land,ice=-999.9 259200 11/78/6/86 Land Cover (vegmap) 1x1 IEEE none 259200 Global Ozone, Toms (tomsn7) 1x1 IEEE -999.9 259200 Global 11/78- 4/93 Greenhouse Gases varies: -99.9, 280 to Station (cdiacgrnh) Station Data IEEE -999.9,-999.99 38000 168000 b.p.-6/94 Sea Surface Global Ocean Temperature (ncepsst) 1x1 IEEE -99.999 259200 11/81- 7/97 Temperature 5x5, IEEE & -999.0 varies 1851-1996 Deviations (ueatemp) hemispheres& ASCII -99.9 10368 to 1856-1996 global Tables 1244160 Temperature ASCII Global Deviations (gisstemp) Global Tables none 16000 1/1866-9/1997 S. Oscillation Index ASCII Global (ueasoi) Station Data Tables none ~13000 1866-1994 Precipitable Water Global Ocean (pwssmi) 1x1 IEEE -999.9 259200 8/87/-11/91 data gap = -999.9; Snow Depth (smmrsnw) 1x1 IEEE permanent ice = 259200 Global Land 254 10/78-8/87 water =-99.0 Top latitudes (84-90 North) =-999.9 Sea Ice (seaice) 1x1 IEEE land=-9999.0 259200 Global Ocean oceans where no 10/78-12/96 data available =-1 Rain Gauge Data Global, chiefly Land (gpgauge) 1x1 IEEE -999.99 259200 1/86-6/97 Land & Ocean Global Precipitation(gpcmb) 1x1 IEEE -99.99 259200 7/87-6/97; except Dec'87 land=-9.0 Ocean Monsoon Rain land (30.5N,29.5E) (msnrain) 1x1 IEEE contaminated 417244 - (30.5S,200.5E) =-99.0 10/78-8/87 Soil Characteristics Global (soilchar) 1x1 IEEE -999.0 259200 Land only Angular Radiance varies Distribution Models 12 Scene ASCII -999.9 204 to 12 Global (erbeadm) Types Tables 3430 Scene Types III. Alphabetical list of Physical Parameters The included physical parameters are listed alphabetically in the following table together with the abbreviated names of the data set(s) in which they appear. A list of the data set names and abbreviations are given in Table 1. In most cases when the same parameter appears in more than one data set, some what different algorithms were used to derive the parameter. Table 3: Physical Parameter List Parameter Units Data Sets Notes at 7 pressure levels, Cloud Fractions by tovs, Levels: unitless Isccpd2 at 3 levels (low, mid & high) Cloud Fractions by Type: Day time cloud cover Low(cumulus, at 3 levels (Low, mid stratocumulus, & & high.) stratus) % Isccpd2 (Low & mid level Mid(altocumulus, clouds have liquid altostratus & and ice nimbostratus) subcategories. High High(cirrus, clouds assumed ice.) cirrostratus, deep convective) assim,tovs, assim are calculated, Cloud fraction, Total % isccpd2, isccpc2,srb Others observed srb same as isccpc2 Cloud Optical for mean day time Thickness unitless isccpc2 & Isccpd2 clouds Cloud Top Pressure mb tovs,isccpc2, for mean total cloud Isccpd2 Cloud Top Temperature K tovs,isccpc2, for mean total cloud Isccpd2 Cloud Mean Water Path g/m2 Isccpd2 total mean day time cloud Chlorophyll in the mg/m3 Ocean czcs from Ocean Color Evaporation from Surface mm/day assim .. Geopotential Height m assim at 8 pressure levels Greenhouse Gas, CH4 ppb cdiacgrnh station & ice core data Greenhouse Gas, CO2 ppm cdiacgrnh station & ice core data Greenhouse Gas, N2O ppb cdiacgrnh station Greenhouse Gas, Air from glacier ice Temperature Variation C cdiacgrnh cores Humidity, specific (sphu) g/kg assim at 8 pressure levels Humidity, specific (sphu) kg/kg assim at 2 meters Humidity,sphu Fluxes (m/s)(g/kg) assim vertically averaged (u & v) winds x sphu Ice/cloud % isccpd2 .. Ice/snow % isccpd2, assim see also 'snow' Ice, sea % seaice .. 16 surface types Land cover classes code vegmap (water, vegetation, etc.) NDVI unitless ndvi normalized difference vegetation index Ozone, total Dobson tomsn7 From TOMS on Nimbus-7 Precipitable Water, atmospheric water total cm assim, tovs,pwssmi vapor Precipitable Water,for tovs: 5 layers atmospheric layers cm tovs, isccpd2 isccpd2: 2 layers in gpgauge units are assim, tovs, mm/month; in msnrain Precipitation mm/day gpgauge, gpcmb, microns/hr. msnrain climatology included in msnrain. Precipitation measurement error mm/day gpcmb .. Pressure, Boundary level depth hPa assim .. Pressure, Cloud top .. .. see cloud top pressure Pressure, Sea level mb or hPa assim .. Pressure (P), Surface mb or hPa assim, tovs,isccpd2 .. based on sea level Pressure, Southern pressure difference oscillation index unitless ueasoi between Tahiti and Darwin, Australia Radiation, Solar at mean Earth to Sun irradiance W/m^2 solarirrad distance, Measured Radiation, terrestrial at surface: srb has both clear LW downward W/m^2 srb and all-sky downward SW downward W/m^2 srb,srfsolar LW & SW Radiation,Surface,net: LW, SW W/m^2 assim, srb .. Radiation, terrestrial at top of atmosphere: LW, cloud forcing W/m^2 tovs all sky - clear sky LW, outgoing W/m^2 assim,tovs,erbe in erbe it is SW,incoming W/m^2 assim measured SW, outgoing W/m^2 assims downward SW reflected SW Snow, depth cm smmrsnow see also : ice, snow From the Food and Soil, average slope degrees soilchar Agricultural Organization (FAO) of the UN Soil, profile depth cm soilchar .. Soil, Texture code soilchar .. Soil, Type code soilchar .. Surface reflectance fraction isccpc2, isccpd2 for clear sky conditions Surface roughness m assim .. Surface stress velocity m/s assim .. Surface type code assim land,water,ice vegmap vegetation type Temperature, cloud top .. .. see:cloud,temperature Temperature 11/81-7/97(only deviations, C ncepsst, ocean) global maps ueatemp 1851-1996 Temperature deviations, C ueatemp 1856-1996 mean global gisstemp 1866-1996 Temperature, at different pressure K assim at 8 pressure levels levels Temperature,mean for Temperature means for layers K tovs 4 atmospheric layers Temperature, near surface air K assim, isccpd2 .. isccpc2 & isccpd2 are Temperature,surface assim,tovs,isccpc2, for clear skies; skin K isccpd2,ncepsst ncepsst is sea surface only & includes climatology Winds, surface speed m/s assim .. Winds (u&v), at different pressure K assim at 8 atmospheric levels pressure levels Anisotropic SW ERBE broad spectral reflection & LW unitless erbeadm band, top of the emission factors atmosphere, scene dependent models Scene dependent correlation of LW & SW unitless erbeadm for the ERBE scene radiances types Mean scene dependent for the 12 ERBE albedo unitless erbeadm global scene types Mean scene dependent for season and daytime LW W/m^2 erbeadm latitude band Mean LW flux for the ERBE scene difference (day-night) W/m^2 erbeadm types Scene dependent standard deviations of W/m^2 erbeadm for the ERBE scene LW & SW radiances types IV. Brief Summary of Contents of Each Data Set (Dataset summaries: Source, Parameter list, and Period Covered) Summaries of over 20 data sets are grouped into seven categories. The grouping is influenced partially by the types of physical parameters invalid and partially by the way that they are processed. Because of this the same physical parameter may appear in several data sets and in more than one category. When this occurs different algorithms have normally been used to produce the parameter. The included data sets, source, time period covered, their parameters and an abbreviated data set name are listed below. The abbreviated name frequently forms part of the file name in the data files. Detailed science, reference and format information about each data set can be found in its Readme User's Guide. ------------------------------------------------------------------------ Atmospheric Dynamics & Atmospheric Soundings Name: Assimilation Atmospheric Dynamics Subset, DAO (abbreviation: assim). This is a 26 parameter subset. Source: The Data Assimilation Office (DAO) at NASA/Goddard Space Flight Center Reference: Schubert et al. (1993) Area Covered: Global Period: March 1980-November 1993 Parameters: Upper air variables include vertical profiles of u &v winds, geopotential height, temperature, and specific humidity at 8 pressure levels (1000, 950, 900, 850, 700, 500, 300 200 mb) Variables related to moisture include surface evaporation, precipitation, total precipitable water above the surface, and vertically integrated moisture fluxes Variables related to the radiative energy budget include incident solar radiation at the top of the atmosphere, and net longwave and net shortwave radiative fluxes at the top and bottom of the atmosphere, and total cloud fraction Diagnostic variables related to the surface and the boundary layer characteristics include: ground temperature, surface pressure, sea level pressure, surface type, temperature and specific humidity at 2 meters, surface wind speed, surface friction velocity, surface roughness and planetary boundary layer Notes: These data were produced by the Goddard Data Assimilation Office (DAO) using the Version 1 Goddard Earth Observing System (GEOS-1). The original data were on a 2 x 2.5 degree latitude-longitude grid that started at map coordinates (90S, 180W). In this collection the monthly data are reformatted to a 2 x 2 degree latitude-longitude world grid that starts at (90N, 180W) and runs eastward and southward to latitude 90S. Thus a total of 16,380 grid points span the globe. The values given are calculated from the input data and refer to the grid points. In most of the other data sets grid square means are given. ------------------------------------------------------ Name: Atmospheric Soundings, TOVS (abbreviation: tovs) This is an 11 parameter subset. Source: The Sounder Research Team of the Laboratory for Atmospheres, NASA/Goddard Space Flight Center Reference: Susskind et al. (1997) Area covered: Global Period: 1985-1992 Parameters: Upper air: mean temperature in 4 layers(*), precipitable water at 5 layers (**), cloud fractions for seven layers(***), top of the atmosphere outgoing longwave radiation, longwave cloud forcing (clear sky - mean), total cloud fraction, mean cloud top temperature and pressure, surface skin temperature, total precipitation, and surface pressure. Notes: These products are from the TIROS Operational Vertical Sounder (TOVS) Pathfinder A program. The monthly data are on a 1 x 1 degree world grid that starts at the N Pole and the Date line. The original data started at the S Pole. (*)The atmospheric temperature layers are: Surface to 500, 500-300, 300-100, and 100-30 mb. (**) Integrated precipitable water is given above the levels: the surface, 850, 700, 500, and 300 mb. (***)Cloud fractions are given between: Surface-800, 800-680, 680-560, 560-440, 440-310, 310-180, above 180 mb. ------------------------------------------------------------------------ Radiation and Clouds Name: Outgoing Longwave Radiant Flux, ERBE (abbreviation: erbe) Source: The Earth's Radiation Budget Experiment (ERBE) Team Reference: Barkstrom et al. (1989) Area covered: Global Period: 1985-1988 Parameter: Outgoing longwave radiant flux at the top of the atmosphere Notes: We plan to add other ERBE products soon. This is the scanner combined satellites S4 product regridded from the original 2.5 x 2.5 degrees to a 1 x 1 degree world grid. ------------------------------------------------------ Name: Clouds, ISCCP C2 products (abbreviation: isccpc2) Source: The International Satellite Cloud Climatology Program (ISCCP) production team at the Goddard Institute for Space Studies (GISS). Reference: Rossow and Garder (1993) Area covered: Global Period: July 1983 - June 1991 Parameters ( a six parameter subset): Monthly mean diurnal cloud fraction, cloud top pressure and temperature, mean daytime cloud optical thickness, surface reflectance, and surface temperature. Notes: The ISCCP C1 (daily) and C2 (monthly) products were originally produced on a (275x275 km^2) equal area world grid. At the equator this is equivalent to (2.5 degrees latitude by 2.5 degrees longitude) grid squares. In our data collection the products have been regridded to a 1-degree by 1-degree world grid. The ISCCP Team is in the process of reprocess the data to produce the improved ISCCP new D version cloud products. When the reprocessing is completed the ISCCP C version cloud products will be withdrawn from this site. -------------------------------------------------------- Name: Clouds, ISCCP D2 (new version) products (abbreviation: isccpd2) Source: The International Satellite Cloud Climatology Program (ISCCP) production team at the Goddard Institute for Space Studies (GISS). Reference: Rossow et al. (1996) Area cover: Global Period: Jan'86-Jan'87, Jan'90-Dec'92 Parameters (a 36 parameter subset): Low, mid & high altitude IR only determined cloud fractions with associated cloud top pressures and temperatures; cloud fractions for 9 daytime bispectral (visual plus IR channels used in algorithm) cloud types with ice and water cloud differentiation; monthly mean diurnal cloud fraction, cloud top pressure and temperature, mean daytime cloud optical thickness, surface reflectance, and surface temperature. Additional parameters are the mean: ice/snow cover, surface pressure, near-surface air temperature and the integrated precipitable water for the layers (1000-680 mb) and (680- 310 mb); these are input parameters used as an aid in estimating the cloud fractions. Notes: The monthly mean data are presented on 1x1 degree latitude-longitude world grids that starts at (89.5N, 179.5W) and runs eastward and southward to latitude 89.5 S. The original ISCCP D1 (daily) and D2 (monthly mean) products were calculated on an approximately equal area world grid (275x275 km^2) which is equivalent to a 2.5x2.5 degree latitude-longitude grid at the equator. When the reprocessing is completed the ISCCP C version cloud products will be with drawn from this site. ----------------------------------------------------- Name: Total Solar Irradiance (abbreviation; solar) Source: These are the daily and monthly means from the Nimbus-7 ERB (Hoyt et al., 1992), ACRIM I & II (Willson 1994), and the ERBS/ERBE (Lee 1995) measurement programs. Area covered: The Solar disk Period: total period is November 16, 1978 - December 31, 1996. The period for the four component data sets varies. Parameter: The total Solar irradiance Notes: All measurements are converted to the mean Earth/Sun distance. This is the incoming solar energy outside of the Earth's atmosphere. ----------------------------------------------------- Name: Surface Solar Irradiance derived by NASA/GISS (abbreviation: srfsolar) Source: Produced at the Goddard Institute for Space Studies (GISS) Reference: Bishop et al. (1994) Area covered: Global Period: July 1983-June 1991 Parameter: down welling surface solar irradiance. Notes: The surface solar irradiance presented here is from Version 2 of the Bishop and Rossow surface solar irradiance algorithm. The original data was on a 2.5x2.5 degree grid. We have interpolated this to a 1x1 degree grid starting at (89.5N, 179.5W) and runs eastward and southward. Bishop and Rossow have started production on their version 3 products. These will be made available at this site sometime in the future. ------------------------------------------------------ Name: The Surface Radiation Budget as derived by NASA/Langley (abbreviation: srb) Source: The Surface Radiation Budget (SRB) Team at the NASA/Langley Research Center. Reference: Darnell et al. (1996) Area covered: Global Period: July 1983-June 1991) Parameters: All-sky surface downward SW & LW and net SW & LW fluxes; clear-sky downward SW & LW fluxes, and cloud fraction. Notes: The Staylor SW and the Gupta LW algorithms were used to calculate the parameters on a global grid of 6596 equal area (275x275 km^2) regions (Darnell et al., 1996). Here this has been regridded to a 1x1 degree world grid starting at (89.5N, 179.5W) and runs eastward and southward. The primary input data for their computations came from the ISCCP C-version cloud data set. The team plans to reprocess the data set and to extend it through 1995. ------------------------------------------------------------------------ The Biosphere Name: Ocean Pigment Concentration from the CZCS measurements (abbreviation: czcs) Source: The Nimbus-7 Coastal Zone Color Scanner (CZCS) Team. The data were produced at the NASA/Goddard Space Flight Center. Reference: Feldman et al. (1989) Area Covered: Global oceans Period: November 1978-June 1986) Parameters: Pigment concentration (an indication of the abundance of ocean chlorophyll) Monthly fields, A 12-month climatology and a 7.5-year climatology are available. Notes: The data are on a 1 x 1 degree world grid. ------------------------------------------------------ Name: Vegetation Index, AVHRR NDVI (abbreviation: ndvi) Source: The NOAA/NASA AVHRR Pathfinder Land Team Reference: Townshend (1994) Area covered: Global, land only Period: July 1981-August 1994 Parameters: Normalized Difference Vegetation Index (NDVI) Notes: This parameter indicates the greenness of the land cover. These data were produced by the NOAA/NASA Pathfinder Land program to reprocess Advanced Very High Resolution Radiometer (AVHRR) measurements. The 1x1 degree latitude/longitude monthly climate data presented here were produced from 8 km x 8 km 10-day composted NDVI products. ------------------------------------------------------ Name:Global Land Cover Classifications. from (ISLSCP) (abbreviation: vegmap) Source: University of Maryland at College Park Reference: DeFries and Townshend (1994) Area covered: Global, Land Only period: invariant Parameters: Global Land Cover Classifications. Notes: Land cover is described in terms of 13 vegetation types plus water, ice and bare desert soil. The data set was derived from vegetation index (NDVI) data collected in 1987. The data sets is also available on the ISLSCP Initiative I CD-ROM set. ------------------------------------------------------ Variable Atmospheric Constituents Name: Total Ozone derived from the Nimbus-7 TOMS (abbreviation: tomsn7) Source: The Ozone Processing Team (OPT) of the Atmospheric Chemistry & Dynamics Branch (Code 916) at the Goddard Space Flight Center. Reference: McPeters et al. (1996) Area covered: Global Period: November 1978-April 1993 Parameters: Total column ozone derived from the Total Ozone Mapping Spectrometer (TOMS) on the Nimbus-7 satellite. Notes: Monthly means are presented on a 1x1 degrees world grid. These means are from the 7th and final algorithm version. ----------------------------------------------------- Name: Greenhouse Gases, CDIAC (abbreviation: gnhgas) Source : This is a subset of station and ice core data obtained from the Carbon Dioxide Information Analysis Center (CDIAC). Reference: This is a collection of several data sets --see Readme User's Guide for the references. Area covered: 36 stations S. Pole to Alert (Northwest Territories, Canada) and 21 shipboard measurement sites. Period: 160000 before present to June 1994, but this varies somewhat with the parameter Parameters (4): Carbon dioxide, methane and nitrous oxide, and near ice atmospheric temperature variations. Notes: The historical data, including the temperature variations, are obtained from ice cores. Direct atmospheric measurements started in recent years. In the sum, the increases in the minor greenhouse gases are as significant to Greenhouse warming as the increase in CO2 (Houghton et al. 1995). ----------------------------------------------------- Measured Surface Temperatures & Pressures Name: Sea Surface Temperature, NOAA/NCEP (abbreviation: ncepsst) Source: The National Centers for Environmental Prediction (NCEP). Reference: Reynolds and Smith (1994), Area covered: Global, ocean only Period: 1981- July 1997. Parameters: Monthly mean sea surface temperature, sea surface temperature anomalies, and a climatology for each of the 12 calendar months. Notes: The products were produced and are presented on a 1-degree latitude by 1-degree longitude world grid. ----------------------------------------------------- Name: Temperature Deviations from the U. of East Anglia (abbreviation: ueatemp) Source: The data was derived by the Climate Research Unit at the University of East Anglia. Reference: Jones et al. (1997) Area covered: Global, land and ocean Period: 1851-1996 Parameters: Monthly mean surface temperature anomalies, monthly and annual hemispherical and global anomalies and the percent of the hemisphere or globe reporting. Notes: Departures of the surface air temperatures from the 1961-1990 reference period as determined by the Climate Research Unit (CRU) of the University of East Anglia on a 5 x 5 degree world grid. Some data gaps occur particularly in Equatorial and Southern Hemispheric regions. In the 1850s only a few regions were reporting. ----------------------------------------------------- Name: Global Temperature Deviations derived by NASA/GISS (abbreviation: gisstemp) Source: This data set was constructed by the Surface Air Temperature Study Group at the Goddard Institute for Space Studies (GISS). Reference: Hansen and Lebedeff (1987) Area covered: Mean global values are given Period: January 1866 - September 1997 Parameters: This subset contains only monthly and annual global mean temperature deviations. Notes: Temperature deviations from the reference period mean, 1951-1980, are given. The GISS study group first determines regional deviations and then finds the global averages. Our subset contains only the global averages. ----------------------------------------------------- Name: Southern Oscillation Index (SOI) from the U. of E. Anglia (abbreviation: ueasoi). Source: Data produced by the Climate Research Unit (CRU) at the University of East Anglia. Reference: Ropelewski and Jones (1987) Area covered: station data Period: 1866-1994 Parameters: Normalized pressure difference (Tahiti minus Darwin) ------------------------------------------------------------------------ Hydrology Name: Atmospheric Total Precipitable Water derived from SSM/I measurements (abbreviation: pwssmi) Source: The original data products were produced by Remote Sensing Systems, Santa Rosa, CA, using an algorithm by Frank Wentz. Reference: Wentz (1992) Area covered: Global, Oceans only Period: August-November 1987 and February 1988-November 1991) Parameters: Monthly mean total precipitable water Notes: Microwave measurements of total atmospheric water vapor on a 1 x 1 degree world grid obtained from the Special Sensor Microwave/Imager (SSM/I) on Defense Meteorological Satellite Program satellites. ---------------------------------------------------- Name:Snow Depth from SMMR (abbreviation: snowsmmr) Source: NASA/Goddard Space Flight Center Reference: Chang et al. (1987) Area cover: Global, land only Period: October 1978 - August 1987 Parameters: Monthly mean snow depth on 1x1 degree world. Notes: The snow depth was derived on a 0.5x0.5 degree latitude/longitude world grid from measurements made by the Scanning Multichannel Microwave Radiometer (SMMR) on the Nimbus-7 Satellite. We have averaged the original data to a 1x1 degree world grid for compatibility with the other data sets in our collection. ---------------------------------------------------- Name: Sea ice concentration, SMMR & SSMI (abbreviation: seaice). Source: The Oceans and Ice Branch at NASA/Goddard Space Flight Center Reference: Cavalieri et al. (1997) Area covered: Global, oceans only Period: October 1978 - December 1996 Parameters: Sea ice concentration expressed as percent x 10. Notes: The original Sea ice dataset was on a polar stereographic projection with grid elements of approximately 25 x 25 km. It is here resampled to a 1x1 degree grid. Sea ice concentration data is obtained from the brightness temperature measured by the Scanning Multichannel Microwave Radiometer (SMMR) on the Nimbus-7 Satellite (october 1978- August, 1987), series of Special Sensor Microwave/Imager SSMI F8 (September 1987-December 1991), SSMI F11 (January 1992- September 1995),and SSMI F13 (October 1995-December 1996)on the Defense Meteorological Satellite Program (DMSP) ----------------------------------------------------- Name: Global Rain Gauge Analysis data, GPCC (abbreviation: gpgauge) Source: The Global Precipitation Climatology Center (GPCC) Reference: Rudolf et al. (1994) Area covered: Land plus a few ocean regions Period: January 1986 - June 1997 Parameters: Surface precipitation plus three statistical parameters Notes: Monthly mean precipitation for 1x1 degree grid areas are estimated from the objective analysis of rain gauge measurements acquired from about 6700 stations ----------------------------------------------------- Name: Global Land and Ocean Precipitation Analysis, GPCP (abbreviation: gpcp). Source: The original data products were produced by the science investigators Dr. George Huffman and Dr. Robert Adler of Laboratory of Atmospheres, NASA Goddard Space Flight Center, under the auspices of the Global Precipitation Climatology Project (GPCP) Reference: Huffman et al.(1997) Area covered: Global Period: July 1987 - June 1997 , except December 1987 Parameters: Surface precipitation and measurement error estimate Notes: The original GPCP dataset was on a 2.5x2.5 degree grid. It is here resampled to a 1x1 degree grid. The analysis program combines satellite observations with precipitation gauge measurements to yield global, land and ocean, precipitation estimates. -------------------------------------------------- Name: Monsoon Rain from SMMR Measurements (abbreviation: msnrain). Source: Space Science and Engineering Center, University of Wisconsin at Madison Reference: Hinton et al. (1992) Area covered: Tropical ocean in the region: 30.5S to 30.5N latitude and 29.5E to 200.5E longitude. Period: October 1978 - August 1987 Parameters: Monthly and Annual rainfall rates, Harmonic analysis Annual and Semiannual Amplitudes and Phases of rainfall rates. The phases indicate the time of the year when the annual and semiannual rain rates are maximum. Notes: The rain rates were derived from measurements by the Scanning Multichannel Microwave Radiometer (SMMR) on the Nimbus-7 Satellite. ----------------------------------------------------- Name: FAO soil data, (abbreviation: faosoil) Source: This data set was developed from the Food and Agriculture Organization (FAO) Soil Map of the World. Reference: See Notes. Area covered: Global, land only Period: invariant Parameter: Soil texture, depth, slope and type Notes: Climate modelers need information on the water holding capacity of global soils. Various researchers derived the parameters; soil texture, depth, slope and type, putting them in a 1 degree x 1 degree grid that can be used in global modeling. The soil texture and soil type data are based on the work by Zobler (1986). Soil profile depth data was derived by Web et al. (1993). The average topographical slope was derived from data sets constructed, from the FAO soil map, by the Science and Applications Branch of the EROS Data Center in Sioux Falls, South Dakota. ------------------------------------------------------------------------ Remote Sensing Science Name: Radiation Angular distribution models (ADMs) for ERBE (abbreviation: erbeadm) Source: The Earth Radiation Budget Experiment (ERBE) Team Reference: Suttles et al. (1988 &1989) Area covered: Models for 12 mean global scene types Period: invariant Parameters (eight): Normalized anisotropic shortwave reflectance factor, Standard deviation of corresponding reflected radiances, Mean scene directional albedo, Correlation of longwave and shortwave radiances, Normalized anisotropic longwave emission factor, Standard deviation of emitted radiances, Mean emitted daytime fluxes, Mean day minus night flux differences Notes: This data set was developed as an aid in converting broad spectral band shortwave and longwave scanner observed radiances into top-of-the-atmosphere fluxes. For this purpose it is assumed that there are twelve global scene types: Clear ocean, land, snow, desert, land-ocean mix; Partly cloudy over ocean, land or desert, land-ocean mix; Mostly cloudy over ocean, land or desert, land-ocean mix; overcast. The shortwave anisotropic factors are presented in a three dimensional matrix which has ten solar zenith angle (0 to 90 degrees), seven viewing angle (0 to 90 degrees), and eight azimuth angle ( 0 to 180 degrees) rows. In the azimuth angle, symmetry is assumed about the principle plane. The mean albedo is given for the ten solar zenith angle bins. The longwave emission anisotropic factors are presented for four seasons, ten latitude bands (N to S Pole), and seven viewing angle bins (0 to 90 degrees). The mean scene longwave daytime flux and (day-night)flux difference are given for four seasons and ten latitude bands. ------------------------------------------------------------------------ References Barkstrom, B. R., E. Harrison, G. Smith, R. Green, J. Kibler, R. Cess, and the ERBE Science Team, 1989. Earth Radiation Budget Experiment (ERBE) archival and April 1985 results, Bull. Amer. Meteor. Soc., 70:1254-1262. Bishop, J. K. B., J. McLaren, Z. Garraffo, and W. B. 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Osborn, and K.R. Briffa 1997: Estimating sampling errors in large-scale temperature averages, J. Climate, 10, 2548-2568. Lee, R. B., III, M. A. Gibson, R. S. Wilson, and S. Thomas, 1995. Long-term total solar irradiance variability during sunspot cycle 22, J. Geophys. Res., 100:1667-1675. Li, Z., L. Moreau, and A. Arking, 1997: On solar energy disposition: a perspective from observation and modeling, Bull. Amer. Meteor. Soc., 78, 53-70. McPeters, R.D., P.K. Bhartia, A.J. Krueger, J. R. Herman, B. Schlesinger, C.G. Wellemeyer, C. J. Seftor, G. Jaross, S.L. Taylor, T. Swissler, O. Torres, G. Labow, W. Byerly, and R.P. Cebula, 1996. Nimbus-7 Total Ozone Mapping Spectrometer (TOMS) Data Products User's Guide. NASA Reference Publication 1384. Reynolds, R. W. and T. M. Smith, 1994: Improved global sea surface temperature analyses. J. Climate, 7, 929-948. Ropelewski, C. F., and P. D. Jones, 1987. An extension of the Tahiti-Darwin Southern Oscillation Index, Mon. Wea. Rev., 115:2161-2165. 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Stowe, 1989: Angular radiation models for the Earth-atmosphere system: Vol. II: Longwave radiation, NASA Ref. Publ. RP-1184, Vol. II, 87 pp. Townshend, J. R. G., 1994. Global data sets for land applications from the Advanced Very High Resolution Radiometer, International Journal of Remote Sensing, 15:3319- 3332. Trenberth, K. E., and C. J. Guillemot, 1995: Evaluation of the global atmospheric moisture budget as seen from analysis, J. Climate, 8, 2255-2272. Webb, R.S., C.E. Rosenzweig, and E.R. Levine, 1993. Specifying land surface characteristics in general circulation models: soil profile data set and derived water-holding capacities, Global Biogeochemical Cycles, 7:97-108. Wentz, F. J., 1992. Measurement of oceanic wind vector using satellite microwave radiometers, IEEE Transactions on Geoscience and Remote Sensing, 30:960-972. Wielicki, B. A., R. D. Cess, M. D. King, D. A. Randall, and E. F. Harrison, 1995: Mission to Planet Earth: role of clouds and radiation in climate, Bull. Amer. Meteor. Soc. , 76:2125-2153 Willson, R. C., 1994. Irradiance observations of SMM, Spacelab 1, UARS, and Atlas experiments, in The Sun as a Variable Star, edited by J. M. Pap, C Frohlich, H. S. Hudson and S. K. Solanki, Cambridge University Press, Cambridge, England, 54-62. Zobler, L., 1986. A world soil file for global climate modeling. NASA Tech. Memo. 87802, NASA, 33pp. ------------------------------------------------------------------------ [NASA] [GSFC] [Goddard DAAC] [cidc site] NASA Goddard GDAAC CIDC Last update:Tue Dec 9 17:01:54 EST 1997 Page Author: H. Lee Kyle -- lkyle@ame.gsfc.nasa.gov Web Curator: Daniel Ziskin -- ziskin@daac.gsfc.nasa.gov NASA official: Paul Chan, DAAC Manager -- chan@daac.gsfc.nasa.gov