Model input data

In order to calculate the longwave and shortwave radiation budgets using the models described in Chapters 4 and 6, various atmospheric, cloud and surface properties are required. These are listed in Table 8.1.

Table 8.1 Input data required to model the Earth's radiation budget. For clouds the data required are for each cloud type (low, middle and high-level).

Cloud properties Atmospheric properties Surface properties

Cloud amounts Scattering optical depth Absorption optical depth Cloud-top pressure Cloud-top temperature Cloud-base temperature Geometrical thickness

Temperature Specific humidity CO2, O3, CH4, N2O Aerosol optical depth, Single scattering albedo, Asymmetry parameter

Pressure (topography) Surface temperature Albedo Emissivity

Land/water/coast cover Ice/snow cover

8.2.1 Cloud radiative properties

Clouds also play a very important role in determining the Earth's radiation budget, as they cover on average about 60% of the Earth's surface. Most clouds are so optically thick in the infra-red that they emit as blackbodies. As they are located at high altitudes within the troposphere, their effective blackbody temperature is much lower than that of the Earth's surface. Hence, they absorb infra-red radiation from below, that is emitted at higher temperatures, and re-radiate it to space as cooler blackbodies with the result that they warm the planet. The low-level and middle-level clouds are sufficiently optically thick in the infra-red to be regarded usually as blackbodies. High-level clouds can be optically thin and hence they absorb and emit as non-ideal blackbodies.

Scattering and absorption of the incoming SW radiation at TOA, depends strongly on the presence and type of clouds in the atmosphere, the composition of the atmosphere (gases plus particulate matter) and the reflectivity of the Earth's surface. It is weakly dependent on the thermal structure of the atmosphere. The cloudy-sky component is subdivided into components covered by low-, middle-, and high-level clouds.

8.2.2 Cloud data sets

All of the cloud meteorological data listed in Table 8.1, except for the cloud-base temperature, are available from the International Satellite Cloud Climatology Project (ISCCP), established in 1982 as part of the World Climate Research Programme to collect and analyse satellite radiance measurements to infer the global distribution of clouds, their properties, and their diurnal, seasonal and in-terannual variations. Data collection began in July 1983 and continues to date. ISCCP currently provides the most extensive and comprehensive cloud climato-logical database that quantifies the variations of cloud properties at the global scale for over more than 20 years.

The ISCCP-D2 data set, which contains monthly data on a 2.5-degree resolution are derived from the ISCCP-DX data, which are 3-hourly and with a resolution of 30 km. There are also the ISCCP-D1 data, which are 3-hourly also on a 2.5-degree grid. Missing data in specific gridboxes may be replaced with values derived by linear interpolation between the values of the neighbouring gridboxes. Cloud-base temperature is necessary for the estimation of the longwave down-welling flux at the surface but it is not provided by satellite data. This parameter can be estimated from the cloud-top pressure and the cloud physical thickness.

ISCCP-D2 cloud properties are provided for nine cloud types that are grouped into three categories: low-level clouds having top pressures greater than or equal to 680 mbar, high-level clouds with top pressures less than 440 mbar and middle-level clouds in between. Cumulus (Cu), stratus (St) and stratocumulus (Sc) clouds are considered low-level clouds. The middle-level clouds include altocumulus (Ac), altostratus (As) and nimbostratus (Ns) clouds, while cirrus (Ci), cirrostratus (Cs) and deep-convective clouds are considered as high-level clouds. Figure 8.2 summarizes the ISCCP radiometric cloud classification. High-level clouds are treated as ice clouds, while each of the low and middle clouds are subdivided into liquid- or ice-phase clouds, resulting in 15 cloud types for which properties such as cloud cover, cloud optical thickness, cloud-top temperature and pressure, as well as liquid or ice water path are provided by ISCCP-D2. The values for the 15 independent cloud types can be appropriately averaged to yield data for low, middle and high-level clouds. LW and SW radiation transfer models

ISCCP CLOUD CLASSIFICATION

O 560

ISCCP CLOUD CLASSIFICATION

O 560

CIRRUS

CIRROSTRATUS

DEEP CONVECTION

"ALTOCUMULUS

ALTOSTRATUS

NIMBOSTRATUS-

- CUMULUS

STRATOCUMULUS

MIDDLE

1.3 3.6 9.4 23 60 379 CLOUD OPTICAL THICKNESS

1.3 3.6 9.4 23 60 379 CLOUD OPTICAL THICKNESS

flg. 8.2. The ISCCP radiometric cloud classification. Cloud-top pressure in mbar.

can use sub-type cloud properties derived from a combination of visual, infrared and near-infra-red measurements. Cloud-cover fractions for low-, middle- and high-level clouds are calculated from the sum of cover fractions for independent ISCCP-D2 cloud types, Aci, whereas the total cloud-cover fraction, Ac, is calculated from the sum of fractions for low, middle and high clouds. Figure 8.3 shows the global distribution of long-term average total cloud cover for January.

HIGH

MIDDLE

The ISCCP-D2 provides cloud scattering optical depth, t®, only for the visible wavelength of A = 0.6 ¡m and at the infra-red wavelength of 11 ¡m through a conversion. For the computation of the cloud SW transmissivity and reflectivity in the near-infrared, a radiation transfer model also requires cloud absorption optical depth, rca and t® data in the near-infra-red. The near-infra-red values can be derived from the ratios t® (near-infra-red)/T®(UV-visible) and Tca (near-infra-red)/T®(UV-visible) that result either from Mie computations (Chapter 6) using the standard ISCCP liquid-droplet spectrum, with a specific gamma distribution function having an effective radius, reff of 10 ¡m and an effective variance of 0.15 ¡m, or from existing parameterizations for liquid and ice clouds. The standard ISCCP-D2 ice-cloud model assumes a random fractal crystal shape and a 2-power-law size distribution from 20 to 50 ¡m, with an effective radius of 30 ¡m and an effective variance of 0.1 ¡m. Typically, values of Tca(near-infra-red)=0.08TC(UV-visible) and Tca(near-infra-red)=0.03T,®(UV-visible) can be used in a model for the liquid and ice clouds, respectively.

Earth Surface Models
flg. 8.3. Long-term average total cloud cover for January based on ISCCP-D2 data.

8.2.3 Water vapour and temperature profiles

The vertical temperature and humidity profiles (including surface pressure) are available from three different reanalyses projects: (i) NCEP/NCAR (National Center for Environmental Prediction/National Center for Atmospheric Research) Reanalysis project; (ii) ECMWF (European Centre for Medium-Range Weather Forecasts Reanalysis: ERA 15 and 40); and (iii) GEOS-1 Reanalysis (Goddard Earth Observing System). Each data set can be used separately, after averaging and remapping them to match the spatial resolution required. The different data sets for the temperature and humidity profiles of the atmosphere have significant differences, which are subsequently reflected in model fluxes.

Air temperature plays an important role particularly in determining the longwave fluxes. For example, a comparison of the global distribution of the difference between the mean temperature of the lowest 100 mbar of the atmosphere given by GEOS and that given by NCEP/NCAR, for the month of January, shows that the largest differences, reaching values of 6 K (with NCEP/NCAR giving the higher values), occur over land, particularly in extended regions of the Northern Hemisphere in winter (North America, Siberia, Antarctica). Over oceans the differences are smaller, of the order of 1 K. The discrepancies be-

□ 1 2 3 4 5

flG. 8.4. Long-term (1984-2004) average global distribution of precipitable water vapour (g cm~2) for January, based on NCEP/NCAR data.

tween NCEP/NCAR and ECMWF are generally less pronounced. Again, best agreement is found over oceans, while differences of up to 4 K can be found over land, especially in high-altitude regions (Andes, Greenland, Tibetan plateau, Antarctica). An intercomparison of the water-vapour content in the lower 100 mbar of the atmosphere as given by the different data sets give differences that are typically within 25% over much of the globe, although there are extended regions in the Northern Hemisphere, mostly above 40° (North America and Asia), where there are much larger differences, that exceed 60%. In Fig. 8.4 is shown the global distribution of precipitable water vapour (g cm~2) for January 1988, based on NCEP/NCAR data.

8.2.4 Other greenhouse gases

Monthly 2.5-degree latitude-longitude pixel data of the total O3 column abundance (in Dobson units), are available from the Television Infrared Observational Satellite (TIROS) Operational Vertical Sounder (TOVS), archived in the ISCCP-D2 package. For CO2 a fixed total atmospheric amount can be taken, equal to 0.57 g cm~2, corresponding to 365 (1998 value) parts per million by

fiG. 8.5. Long-term (1984-1997) global distribution of surface albedo for January. The effects of Fresnel reflection at low solar elevation angles are shown by zonal bands for the Northern Hemisphere winter, where the Arctic has polar night.

fiG. 8.5. Long-term (1984-1997) global distribution of surface albedo for January. The effects of Fresnel reflection at low solar elevation angles are shown by zonal bands for the Northern Hemisphere winter, where the Arctic has polar night.

volume (ppmv). Similarly, one can set the amounts for CH4, and N2 O equal to 1.7 and 0.3 ppmv, corresponding to 9.0 and 4.6x10~4 g cm~2, respectively. We note that the methane and nitrous oxide mixing ratio is not constant up to high altitudes as for CO2, so that the amounts in g cm~2 are slightly lower than expected for a constant mixing ratio in the atmosphere.

8.2.5 Surface properties

ISCCP-D2 provides surface-type fractions such as land, water, ice and snow cover. Land-surface-type information is also provided by the International Satellite Land Surface Climatology Project (ISLSCP). Fifteen different surface types are included in this data set. Reflectances for water and ice can be computed as described in Chapter 6.

8.2.5.1 Surface albedo The land-surface reflectance, Rg can be computed based on surface-type information and corresponding reflectances. Figure 8.5 shows the global distribution of shortwave albedo.

8.2.5.2 Surface emissivity Computation of the thermal infra-red fluxes requires the total surface emissivity, eg. In the calculation of eg we need to consider the following four types of surface: land, ocean, snow, and ice (frozen ocean). The

Table 8.2 Thermal infra-red emissivity for various surfaces. (Houghton 1985)

Surface Emissivity

Water 0.92-0.96

Sand 0.89-0.90 Grass 0.90 Forest 0.90

surface emissivity can then be computed from

£g = /landel + /oceanic. + /snow es + /iceei, (8.1)

where / is the fraction of the Earth's surface covered by each type of emitting surface. The various fractions / change with the season and location. Typical values for the emissivities are given in Table 8.2 for various surfaces.

8.2.5.3 Surface topography A topography scheme is needed in a model, and this can be obtained using surface-pressure data given either by the NCEP or from ECMWF Global Reanalysis Projects gridded in 2.5x 2.5 degree pixels. Consideration of topography is important for regions with high altitudes, such as the Tibetan plateau, Antarctica, Rocky mountains or the Andes mountains, for the correct computation of layer and total atmospheric amounts of the gases considered, as well as the correct extent of the Rayleigh-scattering layer and the appropriate computation of the mean humidity of the aerosol layer.

8.2.6 Aerosol particles

The aerosol particles perturb the radiation field sufficiently, especially SW radiation, to warrant their consideration in the estimation of the SW radiation budget. The radiative effect of aerosols at TO A, within the atmosphere, and at the surface, can be computed by a SW model, using a modified two-stream approximation or the modified Delta-Eddington method (see §6.7) allowing for scattering and absorption in the UV-visible and near-infra-red. Such models require aerosol optical properties such as aerosol extinction optical thickness (AOT), single scattering albedo (w), and asymmetry parameter (g). Climato-logical aerosol data can be derived from the Global Aerosol Data Set (GADS). In GADS, aerosols are described as internal plus external mixtures of 10 main aerosol components, which are representative of the atmosphere and characterized through their size-distribution and refractive index, depending on wavelength. Data of AOT, waer, and gaer, are provided by GADS at 61 wavelengths from 0.25 to 40 pm, 27 of which lie in the SW range, for 8 values of the relative humidity (0, 50, 70, 80, 90, 95, 98, and 99%). Given the strong dependence of

Aerosol Optical Thickness at 500mm

Fig. 8.6. The global mean (2000-2005) distribution of the visible (0.50 ¡m) aerosol optical thickness for the month of January, from MODIS based on the Terra satellite data. The main aerosol source regions are clearly seen, white areas correspond to either polar night or missing data.

Fig. 8.6. The global mean (2000-2005) distribution of the visible (0.50 ¡m) aerosol optical thickness for the month of January, from MODIS based on the Terra satellite data. The main aerosol source regions are clearly seen, white areas correspond to either polar night or missing data.

aerosol optical properties on ambient relative humidity, the original GADS properties need to be computed for actual relative humidity values within the aerosol layer. Subsequently, the computed values of aerosol optical properties can be averaged within the UV-visible and near-infra-red spectral intervals, weighted by the spectral distribution of the incoming solar flux.

Real-time satellite data are becoming available, for example there are available the MODIS data from the Terra and Aqua satellites. In Fig. 8.6, we show the global distribution of the visible (0.5 ¡m) aerosol optical thickness for the month of January, from the MODIS (2000-2005) data set, based on the Terra satellite data.

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