Case 2: The Role of Anomalies in Climate Science

1. What is a temperature anomaly?

   The term temperature anomaly means a departure from a reference value or long-term average. A positive anomaly indicates that the observed temperature was warmer than the reference value, while a negative anomaly indicates that the observed temperature was cooler than the reference value.

2. Why use temperature anomalies (departure from average) and not absolute temperature measurements?

   Absolute estimates of global average surface temperature are difficult to compile for several reasons. Some regions have few temperature measurement stations (e.g., the Sahara Desert) and interpolation must be made over large, data-sparse regions. In mountainous areas, most observations come from the inhabited valleys, so the effect of elevation on a region's average temperature must be considered as well. For example, a summer month over an area may be cooler than average, both at a mountain top and in a nearby valley, but the absolute temperatures will be quite different at the two locations. The use of anomalies in this case will show that temperatures for both locations were below average.

   Using reference values computed on smaller [more local] scales over the same time period establishes a baseline from which anomalies are calculated. This effectively normalizes the data so they can be compared and combined to more accurately represent temperature patterns with respect to what is normal for different places within a region.

   For these reasons, large-area summaries incorporate anomalies, not the temperature itself. Anomalies more accurately describe climate variability over larger areas than absolute temperatures do, and they give a frame of reference that allows more meaningful comparisons between locations and more accurate calculations of temperature trends.

Some issues about anomalies

1. Suppose we have observed data using one reference period, and model data using another. How do we compare the two?

2.. What if we are looking at variability, not just means? For example, the variance in an ensemble of models is often used to estimate the uncertainty in the models (whether that is a reasonable thing to do is a different discussion). How are estimates of variability affected by looking at anomalies instead of raw data? Does it change if we use a longer (or shorter) reference period?

3. What if we want to compare distributions? Say of data and model output? What kind of reference periods should one use?

4. Global average temperatures over the latest 15 years or so may not have increased as rapidly as the climate projections are expecting. Is this unusual? Does it depend on which reference period is used? Which temperature series is used?

The final product for this project is a poster. Format 70 x 100 cm. They can be printed at

http://www.chalmers.se/en/about-chalmers/organisation/administration-and-services/Pages/service-department.aspx

The department will pay for the printing; we will have an open poster session, and the posters will be displayed in some visible places for ome time to come.

The groups stay the same as for case 1, except that Kazutoshi is added to group 1 and Tuomas to group 3.

Data sets

Global annual mean temperature (text files)
    GISS3
    HadCRUT4
Climate model output from 45 models, 4 RCPs, 1850-2300 (NetCDF data)
Reading  NetCDF data into R
Reading NetCDF data into MATLAB
QQ- and shift plot function in R
Reading the climate model data, an example
Some notes on empirical plots
Some statistical tests

A paper by Martin Tingley on the danger of anomaly calculations.

Posters

Group 1
Group 2
Group 3