Climate indicators

Climate indicators

The climate system is complex, and a complete description of its state would require huge amounts of data. However, it is possible to keep track of its conditions through summary statistics.

There are some nice resources which give an overview of a number for climate indicators. Some examples include NASA and The Climate Reality Project.

The most common indicator is the atmospheric background CO2 concentration, the global mean temperature, the global mean sea level, and the area with snow or Arctic sea ice. Other indicators include rainfall statistics, drought indices, or other hydrological aspects. The EPA provides some examples.   

One challenge has been that the state of the hydrological cycle is not as easily summarised by one single index in the same way as the global mean temperature or the global mean sea level height. However, Giorgi et al. (2011) suggested a measure of hydro-climatic intensity (HY-INT) which is an integrated metric that captures the precipitation intensity as well as dry spell length.  

There are also global datasets of indices representing the more extreme aspects of climate called CLIMDEX, providing a list of 27 core climate extremes indices (so-called the ‘ETCCDI’ indices, referring to the ‘CCl/CLIVAR/JCOMM Expert Team on Climate Change Detection and Indices’).

In addition, there is a website hosted by the NOAA that presents various U.S. Climate Extremes Index (CEI) in an interactive way.

So there are quite a few indicators for various aspects of the climate. One question we should ask, however, is whether they capture all the important and relevant aspects of the climate. I think that they don’t, and that there are still some gaps.

Perhaps there is room for more indicators inspired by the “big picture physics”, such as the planetary energy balance and the outgoing long-wave radiation (OLR). An increased greenhouse effect means that the atmosphere becomes more opaque for infra-red radiation (IR), while the visible light that heats the surface is unaffected.

The heat loss from a planet happens through IR radiation, since space is virtually a vacuum where energy is only transmitted though electromagnetic waves. If one could see the IR light, an opaque atmosphere would make the pattern of emitted IR diffuse since only the IR from the upper levels of the atmosphere escape to space after it has been absorbed and re-emitted by the greenhouse gases (this of course depends on the wavelength of the IR and the absorption spectrum, but we can use this assumption for heat loss integrated over the whole IR spectrum).

The figure below shows the mean IR estimated from the 2-meter temperature according to Q=\sigma T_{2m}^4 (upper), the OLR measured by satellites (middle), and their difference.

Long-wave radiation estimated for surface temperatures according to Stefan-Boltzman’s law (upper), measured by satellites (middle) and the difference between the two (lower). (source code olr.R; PDF)

Hence, we would expect to see increasing differences in the spatial OLR structure compared to that of the heat emission from the surface, as the greenhouse effect is increased. One index capturing this could be the correlation between the spatial patterns in OLR and the surface IR flux over time (figure below taken from Benestad (2016)). 

Trend in pattern correlation between outgoing long-wave radiation (OLR) measured by satellites and calculated for surface temperatures. A decrease in the spatial correlation is consistent with the atmosphere more opaque in terms of IR.

Another index for the state of the climate and the hydrological cycle could be an metric for the global atmospheric overturning: how much air ascends and descends.

The vertical motion in the atmospheric plays a role in moving heat and moisture to greater heights, and influences both rain patterns and the OLR. One indicator could be variance in the vertical velocity w over space estimated over i=1,...,n grid-boxes, each with area a_i: W=\sum_i^n (a_i w_i - \overline{a w})^2/n  

Trends in global variance of vertical flow over space for three different height intervals in the atmosphere. An increase in the vertical motion of the mid-troposphere is consistent with more convection and increased heat flow through convection. It is likely that this has affected the clouds. The vertical motion w is labelled as v_z in the figure (Source: Benestad (2016),PDF)

We can estimate the atmospheric overturning W from reanalyses which provide data on the flow over a range of vertical levels and on a global scale. According to the figure above, there has been an increase in the global overturning indicator for the middle atmosphere (between 1 and 6.5 km above the surface).

The overturning indicator for the lower boundary layer, characterised by turbulence, shows a different trend to that in the middle troposphere. There is also less pronounced vertical motions in the upper part of the troposphere.

Another indicator could be the height of the region where the temperature is 254K (the 254K isotherm), which can be taken as a crude proxy for the average depth of the atmosphere from which the average heat escapes (Benestad (2016)).

A neglected indicator, which I think should be an obvious one, is the daily precipitation area A_P. This indicator has a profound meaning for the hydrological cycle and is relevant for the question of flood risk and droughts.

The mean precipitation taken over area with precipitation for any given day can be considered as the wet-day mean precipitation and provides an indicator for the mean precipitation intensity.

The mean precipitation intensity is related to the mean evaporation and is proportional to the ratio of the areas of evaporation and rainfall: \mu = (A_E/A_P)  \overline{E} 

There is a kind of a “funnel effect” since the evaporated water over a large area has to come down as precipitation over a significantly smaller area. This is a bit like the action of a funnel (see figure below) where the water moves more slowly at the top where the cross-section area is greater than at the bottom with a small cross-section.

The differences in the area of evaporation and precipitation has a similar effect as a funnel: if the mean evaporation over a large area A_E is returned a smaller A_P, then the mean intensity is amplified by the factor of A_E/A_P.

It is possible to get an estimate of the semi-global precipitation area from satellite observations (Benestad, 2018). The figure below indicates that the area with daily rain between 50S-50N has decreased by 7% since 1998, which implies that the rain has become more intense and concentrated over a smaller region.

The area between 50S and 50N (77% of Earth’s surface area) with precipitation estimated from the TRMM data. A reduction in the precipitation area implies higher mean precipitation intensity, and may be linked to changes in the atmospheric overturning presented above. (Source: Benestad (2018))

There may be other climate indicators that I have missed. Nevertheless, I hope there will be more discussions about climate indicators and more resources in the future that can offer up-to-date information about the state of the climate, based on these. Such sites could offer both graphical presentations and the actual numbers.


  1. F. Giorgi, E. Im, E. Coppola, N.S. Diffenbaugh, X.J. Gao, L. Mariotti, and Y. Shi, "Higher Hydroclimatic Intensity with Global Warming", Journal of Climate, vol. 24, pp. 5309-5324, 2011.

  2. R.E. Benestad, "A mental picture of the greenhouse effect", Theoretical and Applied Climatology, vol. 128, pp. 679-688, 2016.

  3. R.E. Benestad, "Implications of a decrease in the precipitation area for the past and the future", Environmental Research Letters, vol. 13, pp. 044022, 2018.

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