By: Claire Bulgin
We can use satellites up in space to measure the surface temperature of the Earth over the land and sea. Satellites have now been making measurements for 40+ years and these data are really helpful for understanding trends in surface temperature as our climate changes. Measuring surface temperature from space is not without its challenges though, and one of the biggest of these is cloud.
So why do clouds matter? Basically, they block the view of the Earth’s surface from the satellite. If we try to measure the surface temperature and there is a cloud in the way, what we really measure is in part the temperature of the cloud. How much it affects our temperature measurement depends on how transparent it is, and how high up in the atmosphere it is.
So what do we do? We really only want to measure the temperature when the sky is clear. This means that we first screen our data for cloud, and then only use the clear-sky observations. However, this screening process is not always 100% accurate. Some clouds are difficult to spot even from space! Consider cold, white cloud over a cold, bright snow surface as in the example of Figure 1. This was the winter of 2010 where nearly the whole of the UK was covered in snow in early December. Some clouds are very difficult to pick out above the snow surface.
Figure 1: Snow and clouds over the UK on 08/12/10 in an image from the MODIS Terra satellite (NASA Earth Observatory, 2010).
So what do we need to do in those cases where screening is difficult? We need to understand what impact these clouds could have on our measured surface temperature. In a recent study, we compared a number of different cloud screening approaches against a cloud screening done manually by an expert. By looking at the differences between each cloud screening approach and the cloud screening done manually, and how these vary, we can build up a picture of how much getting the cloud screening wrong can introduce uncertainty in our measurement of land surface temperature.
Perhaps not surprisingly, we find that the uncertainty in land surface temperature is higher as the amount of clear-sky in the area we are looking at decreases. This is shown in Figure 2. The left hand plot shows that the uncertainty in land surface temperature is on average higher when only 20 % of the sky is cloud-free (2 °C) than when 90 % of the sky is cloud free (0.75 °C). This shows that near cloud edges (where a high fraction of the surface we are looking at is covered by cloud) the uncertainty in our measured surface temperature from cloud screening is higher than in areas with fewer clouds. The uncertainties are larger at night because cloud screening is more difficult without observations at visible wavelengths.
Figure 2: Left: Uncertainty in measured land surface temperature from clouds as a function of the clear-sky fraction (left). Right: The number of observations for each clear-sky fraction (Bulgin et al, 2018).
If we choose a consistent percentage of clear-sky pixels from our images, we can also assess how the uncertainty varies as a function of the underlying surface type. In this study we were able to look at five land surface types: Cropland, evergreen forest, bare-soil, shifting-sand and permanent snow and ice. We found that for a standardised clear-sky fraction of 74.2 %, uncertainties over snow and ice were largest at 1.95 °C, whilst for cropland they were much smaller, only 0.09 °C. The other surfaces had uncertainties between these two extremes: 1.2 °C for forest, 0.9 °C for bare soil and 1 °C for shifting sand (Bulgin et al, 2018).