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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.

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  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.

Why extremes are expected to change with a global warming

Filed under: — rasmus @ 5 September 2017

Joanna Walters links extreme weather events with climate change in a recent article in the Guardian, however, some  reservations have been expressed about such links in past discussions.

For example, we discussed the connection between single storms and global warming in the post Hurricanes and Global Warming – Is there a connection?, the World Meteorological Organization (WMO) has issued a statement, and Mike has recently explained the connection in the Guardian.

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Observations, Reanalyses and the Elusive Absolute Global Mean Temperature

One of the most common questions that arises from analyses of the global surface temperature data sets is why they are almost always plotted as anomalies and not as absolute temperatures.

There are two very basic answers: First, looking at changes in data gets rid of biases at individual stations that don’t change in time (such as station location), and second, for surface temperatures at least, the correlation scale for anomalies is much larger (100’s km) than for absolute temperatures. The combination of these factors means it’s much easier to interpolate anomalies and estimate the global mean, than it would be if you were averaging absolute temperatures. This was explained many years ago (and again here).

Of course, the absolute temperature does matter in many situations (the freezing point of ice, emitted radiation, convection, health and ecosystem impacts, etc.) and so it’s worth calculating as well – even at the global scale. However, and this is important, because of the biases and the difficulty in interpolating, the estimates of the global mean absolute temperature are not as accurate as the year to year changes.

This means we need to very careful in combining these two analyses – and unfortunately, historically, we haven’t been and that is a continuing problem.

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Predicting annual temperatures a year ahead

Filed under: — gavin @ 16 September 2016

I have a post at Nate Silver’s 538 site on how we can predict annual surface temperature anomalies based on El Niño and persistence – including a (by now unsurprising) prediction for a new record in 2016 and a slightly cooler, but still very warm, 2017.

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Do regional climate models add value compared to global models?

2016-05-20 10.08.17

Global climate models (GCM) are designed to simulate earth’s climate over the entire planet, but they have a limitation when it comes to describing local details due to heavy computational demands. There is a nice TED talk by Gavin that explains how climate models work.

We need to apply downscaling to compute the local details. Downscaling may be done through empirical-statistical downscaling (ESD) or regional climate models (RCMs) with a much finer grid. Both take the crude (low-resolution) solution provided by the GCMs and include finer topographical details (boundary conditions) to calculate more detailed information. However, does more details translate to a better representation of the world?

The question of “added value” was an important topic at the International Conference on Regional Climate conference hosted by CORDEX of the World Climate Research Programme (WCRP). The take-home message was mixed on whether RCMs provide a better description of local climatic conditions than the coarser GCMs.

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