Longwave radiation budget at surface

The estimation of the Earth's longwave radiation budget represents a major objective of the World Climate Research Programme as demonstrated by its Global Energy and Water Cycle Experiment (GEWEX), and in particular the

Table 8.14 Model computed mean annual hemispherical (NH Northern Hemisphere, SH Southern Hemisphere) and global average: outgoing longwave radiation, OLR (1984-2004) and net incoming shortwave radiation NISR (1984-1997) at the top-of-at-mosphere, and net incoming all-wave radiation, NET. The corresponding values from, ERBE (Feb 1985 - May 1989) are also shown. The radiative fluxes are expressed in W

Table 8.14 Model computed mean annual hemispherical (NH Northern Hemisphere, SH Southern Hemisphere) and global average: outgoing longwave radiation, OLR (1984-2004) and net incoming shortwave radiation NISR (1984-1997) at the top-of-at-mosphere, and net incoming all-wave radiation, NET. The corresponding values from, ERBE (Feb 1985 - May 1989) are also shown. The radiative fluxes are expressed in W

ERBE

model

ERBE

model

ERBE

model

OLR

OLR

NISR

NISR

NET

NET

NH

235.5

240.6

234.7

241.0

-0.8

0.4

SH

234.7

237.6

245.4

239.6

10.7

2.0

Globe

235.1

239.1

240.0

240.3

4.9

1.2

GEWEX Surface Radiation Budget Project. The amount of downwelling longwave radiation reaching the surface of the Earth and the outgoing flux at TOA are indicators of the strength of the atmospheric greenhouse effect and hence they are key parameters in climate modelling.

The only reliable direct measurements of downwelling longwave flux at the surface, are those provided by well-calibrated surface instruments. Current archives of such measurements have a very limited temporal and geographical coverage. For example, downwelling longwave fluxes (DLR) reaching the surface, exist for less than 20 BSRN stations around the world and in most cases data exist only since the mid-1990s. Therefore, in order to monitor the surface downwelling longwave radiation on a global scale and over a long enough period to identify climate change impacts, one needs to rely on satellite data, in conjunction with radiative-transfer models, with validation against surface measurements. The reliability of the model DLR at the surface is primarily affected by its sensitivity to cloud cover and cloud properties, and to the vertical profiles of temperature and humidity, especially in the lower troposphere. Clouds, which are the most important determinants of the surface radiation budget, represent a major uncertainty in climate modelling (Intergovernmental Panel on Climate Change, IPCC 2001). The International Satellite Cloud Climatology Project (ISCCP) provides one of the most extensive and comprehensive global cloud climatologies currently available and also provides surface and atmospheric parameters for longwave radiation transfer.

8.8.1 Global distribution

Model long-term (1984-2004) results with spatial resolution of 2.5 degrees are given in Fig. 8.29 for January. The input data were cloud properties from ISCCP-D2, and temperature and humidity profiles from the NCEP/NCAR Reanalysis project. As expected, in January, the maxima of the DLR occur over a broad swath along the equator, mainly over tropical and subtropical oceans along the intertropical convection zone. In these regions cloud amounts, water vapour and air temperatures are high, while cloud bases are low. These maxima are shifted

90" n

90" n

150 200 250 300 350 400 450

Fig. 8.29. Model long-term (1984-2004) downwelling longwave flux (DLR) in W m-2 at the Earth's surface for January.

150 200 250 300 350 400 450

Fig. 8.29. Model long-term (1984-2004) downwelling longwave flux (DLR) in W m-2 at the Earth's surface for January.

slightly northwards in July. Minima occur, as expected, in the polar regions, with Antarctica and Greenland having the lowest values, and over an extended area in the Northern Hemisphere in winter. There are regional minima of the DLR in winter over dry desert areas (Sahara, Atacama, Kalahari, Central Australia), characterized by clear-sky conditions, as well as over high-altitude areas (Tibetan Plateau, Rocky Mountains, Andes, Greenland, Antarctica), with low cloud cover, low air temperatures, and low moisture content. Very high values of DLR are found in the West Pacific, a region strongly influenced by the El Niño effect.

8.8.2 Zonal, latitudinal and seasonal variations

8.8.2.1 Zonal-seasonal variation A lower seasonal variability in DLR is seen in the Southern Hemisphere, owing to the larger fraction of its surface covered by oceans, compared with that in the Northern Hemisphere. The seasonal variation is small in the tropics, owing to the smaller variation in atmospheric water vapour and temperature, for both hemispheres, with increasing variability at higher latitudes in the Northern Hemisphere (Fig. 8.30).

500 p 400300 200 100 0.-

400; 300 200 100

400 300 ~ 200 100

400 300 -200" 100

400; 300 200 100

400

400

300

300

200

200

100

30°-40°

100

40°-50°

400

60°-70°

400

70°-80°

400

80°-90°

300

300

300

200

200

200

100

100

100

Month

Month

Month

Month

Month

Month flG. 8.30. Model zonal mean long-term (1984-2004) seasonal variation of DLR in W m-2 at the Earth's surface for the Northern Hemisphere (solid line), and Southern Hemisphere (dotted line).

8.8.2.2 Seasonal variation In Fig. 8.31 is shown the seasonal variation of the DLR for both hemispheres and the globe. The variation for the hemispheres is approximately between 320 and 380 W m~2, or about 60 W m~2 over the year compared to about double that for the DSR. The DLR for the northern hemisphere exhibits greater variation owing to its greater land fraction that results in greater heterogeneity in atmospheric conditions, compared to the largely ocean covered southern hemisphere.

8.8.2.3 Latitudinal variation In Fig. 8.32 is shown the model global mean long-term (1984-2004) latitudinal variation of DLR in W m~2 at the Earth's surface. The DLR peaks in the tropics at about 400 W m~2 owing to higher tro-pospheric temperatures, more water vapour and the effects of warmer cloud-base temperatures. Antarctica is colder and drier then the Artic so receives substantially lower, by about 100 W m~2, DLR at the surface.

8.8.3 Long-term anomaly

The long-term trend (1984-2004) in the monthly downwelling longwave radiation anomaly, based on ISCCP-D2 2.5-degree cloud climatologies and NCEP/NCAR specific humidity and atmospheric temperature profiles, is shown in Fig. 8.33.

1 2 3 4 5 6 7 8 9 10 11 12 Month

flG. 8.31. Model global mean long-term (1984-2004) seasonal variation of DLR in W m~2 at the Earth's surface.

Latitude

Fig. 8.32. Model global mean long-term (1984-2004) latitudinal variation of DLR in W m~2 at the Earth's surface.

Latitude

Fig. 8.32. Model global mean long-term (1984-2004) latitudinal variation of DLR in W m~2 at the Earth's surface.

There appears to be no trend in the DLR in comparison with the trend in the DSR. Also shown are the results of a more detailed spectral model using NASA-Langley 1-degree resolution cloud climatologies and GEOS-1 Reanalysis humidity and temperature profiles for the available years of data (1986-1994). Both sets of model results indicate no trend in the DLR.

8.8.4 Long-term hemispherical and global means

In Table 8.15 are shown the long-term mean Northern (NH), Southern (SH) Hemisphere and global downwelling flux (W m~2), estimated by different methods. The model results of Pavlakis et al. (2004) were based on ISCCP-D2 data for the 10-year period 1984-1993. The climate models give values close to the es-

simple 2.5x2.5 (D2-NCEP) spectral 1x1 (Langley-GEOS)-

simple 2.5x2.5 (D2-NCEP) spectral 1x1 (Langley-GEOS)-

"l6984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004

Months

"l6984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004

Months flG. 8.33. Model global mean long-term (1984-2004) trend in downwelling longwave flux anomaly in W m-2 at the Earth's surface, using ISCCP-D2 2.5x2.5 degree cloud data and NASA-Langley 1.0x1.0 degree cloud data.

timate based on the GEBA ground-based data. Radiation models give a slightly higher DLR for the Northern Hemisphere compared with the Southern Hemisphere. The corresponding DLR values for the 21-year period 1984-2004 are 343.8, 341.2 and 342.5 W m~2, for NH, SH and the globe.

Table 8.15 Mean Northern (NH), Southern (SH) Hemisphere and global downwelling flux (W mT2), estimated by a selection of different methods.

Source

NH

SH

Global

Radiation-transfer models

Pavlakis et al. (2004)

343.3

341.1

342.2

Gupta et al. (1999)

351.0

344.6

347.8

Rossow and Zang(1995)

348.0

General circulation models

Wild et al. (2001)

344.0

Garratt et al. (1998)

339.0

ECMWF

339.3

339.9

339.6

GEBA&BSRN stations 345

GEBA&BSRN stations 345

8.8.5 Sensitivity analysis

8.8.5.1 Atmospheric temperature profile A sensitivity test examining the effect on the DLR to an increase (or decrease) in the air temperature by 2 K (the entire profile is moved to higher/lower temperature) shows that such an increase causes an increase of the global average DLR by 9 W m~2 (or decrease of 8.9 W m~2), as shown in Table 8.16. This effect depends on the climatic conditions, i.e. on temperature, cloud cover and water-vapour content. The weakest effect (about 2 W m~2) is observed in very cold climates with very low cloud cover (practically clear sky), while the largest effect (about 10 W m~2) occurs in hot and cloudy (mainly tropical) regions.

8.8.5.2 Specific humidity When the specific humidity is increased (or decreased) by 25%, in each atmospheric layer for each latitudinal-longitudinal grid-cell, the result is a global increase in the DLR of 6.2 W m~2 (or decrease by 8.3 W m~2) on average, with differences ranging from 1 to 14 W m~2. Regions with originally low water-vapour content are the least affected. The response of the DLR to water-vapour changes clearly depends also on temperature, with the colder regions the least affected, and on low and middle cloud cover, since, when cloud is present, water vapour thermal emission that reaches the surface comes from a much smaller atmospheric column. The largest sensitivity of the DLR on water vapour is encountered in the dry zones of the subtropics and the Eastern Pacific ocean. For tropical regions with large water-vapour content, larger than approximately 3 g cm~2, the DLR sensitivity becomes saturated, as the infra-red emissivity of the water-vapour layer increases towards unity.

8.8.5.3 Cloud cover Satellite cloud-cover uncertainties can be quite large as thresholds in temperature and reflectivity are used to identify clouds from satellite measurements. As a result, the fractional cloud cover can differ greatly among various satellite data sets depending upon the threshold values used. The sensitivity of the DLR to possible errors in the cloud data is given in Table 8.16. When the total cloud amount is changed by ±30% of its value for each pixel the global mean value of the DLR at the surface changes by about ±10 W m~2. If just the low cloud amount is changed by ±30%, the effect on the global average DLR is about ±6 W m~2, while if the middle cloud amount is changed by ±30%, the effect on the DLR is less but comparable to the low cloud case (about ±4 W m~2). For the high cloud cover the effect is, expectedly, much less significant (±0.8 W m~2). Close examination of the effect on a regional basis shows that the difference in the DLR ranges from 0 to 25 W m~2, depending on the original (low and middle) cloud cover, water-vapour content and temperature. For the same amount of cloud cover, the smaller the amount of water vapour in the lower atmosphere, the higher the increase in the DLR caused by the increase in cloud cover. The warmest regions, in which the mean temperature of the lower part of the atmosphere is larger than 290 K, show the lowest sensitivity to the cloud cover increase. This is due to the fact that in these same regions generally the water vapour-content of the atmosphere is very high, which in conjunction with high atmospheric temperatures leads to a significant contribution to the DLR under clear-sky conditions. On the other hand, cold regions (mean T <270 K)

with low water-vapour content are the most affected by an increase in cloud cover. This is an important result, given that the most severe discrepancies in cloud cover between different data sets occur in cold climates (Pavlakis et al. 2004).

8.8.5.4 Cloud overlap scheme One known limitation of the ISCCP-D2 data set is the assumption that the clouds are classified into non-overlapping layers. From the satellite point of view, if there are low-level clouds under optically thick middle-level clouds, they will not be observed. This fact leads to a systematic underestimation in low-level cloud amount, and consequently in DLR at the surface. To examine the possible effect that this assumption has on the DLR, two cloud-overlap schemes, based on random and maximum overlap, can be employed. The random-overlap scheme assigns lower cloud amounts under higher clouds, based on cloud cover in areas where the satellite's view to the lower cloud is not obscured. The maximum-overlap scheme assumes that a lower cloud always exists under the higher cloud. Overall, the random overlap assumption seems to be the preferable procedure. The maximum effect on the DLR reaches about 10 W m~2, for the random-overlap scheme and is observed in areas where originally both low and middle cloud cover is significantly high.

Table 8.16 Changes in mean global downwelling flux DLR, for January, for corresponding ± changes in key climatic parameters, based on model runs with ISCCP climatological data. (Pavlakis et al. 2004)

Parameter

DLR difference (W m-2)

Precipitable water Air temperature Surface temperature Total cloud cover Low-cloud cover Middle-cloud cover High-cloud cover Cloud-base height Clouds Clouds

Random cloud-overlap scheme +1.2

Maximum cloud-overlap scheme +3.2

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