In June 2011 we presented a monthly ex ante decadal forecast for global mean temperature for the period until October 2017. This forecast is based on a global system model developed by our Insights self-organizing modeling app, and it has been published at Climate Etc. in October 2011.

The model describes a non-linear dynamic system of the atmosphere for decadal forecasting consisting of 5 drivers: Ozone concentration, aerosol index, radiative cloud fraction, and global mean temperature as endogenous variables and sun activity as exogenous variable of the system. The model was built in May 2011 from observational data from October 1988 till April 2011 of up to 1000 input variables with time lags of up to 120 months, which is a typical input space dimension for complex dynamic systems modeling.

This post is an updated validation of the initial ex ante prediction for May 2011 to October 2017 (78 months) of this system model by a comparison of observed temperatures (black/withe square dots; HADCRUT3) vs predicted temperatures (red lines). Both model and ex ante prediction have not been changed since their publication. However, the HADCRUT3 dataset, a joint data product of the UK Met Office Hadley Centre and the Climate Research Unit at the University of East Anglia, which was used for model building is no longer supported by the providers. Instead, a new version, HADCRUT4, is maintained now, whose values differ from the previous version ones. In order to keep our system prediction up-to-date, we now have to transform HADCRUT4 into HADCRUT3 values, which introduces minor deviations from the original HADCRUT3 data, however.

As of December 2017, the prediction accuracy of the most likely prediction (solid red line) of the Insights model is 57%. The accuracy relative to the prediction range (pink area) is 93% (fig. 1).

Fig. 1: Ex ante forecast (most likely (red), high, low (pink); May 2011 - October 2017) of the system model (as of April 2011) vs observed values (black and white square dots; HADCRUT3) from May 2011 to December 2017. Since June 2014 HADCRUT3 is not maintained anymore and, therefore, has to be derived from HADCRUT4 data for compatibility reasons.

The high temperatures from October 2015 to August 2016 are attributed to a weather anomaly of the Pacific Ocean called El Niño, which has been of special intensity this time. El Niño is an irregularly occurring heat event in the central and east-central equatorial Pacific based on complex ocean-atmosphere interactions, which is not fully understood yet and which is affecting the coastal regions of South America, California, and Asia, directly, but also has effects globally.

In comparison, the expensive General Circulation Models (GCMs) which the IPCC AR4 and AR5 projections are based on and which rely on atmospheric CO2 as major climate driver (and which are long-term trend models for 100 years, to be fair), show a prediction accuracy of just 31% for the time period 2007 (the year of their publication) till today and 29% for the same time period as the Insights system model (fig. 2).

Fig. 2: Ex ante most likely forecast (red; May 2011 - October 2017) of the self-organized system model (as of April 2011) vs observed values (black and white square dots; HADCRUT3) from May 2011 to December 2017 vs IPCC A1B projection (yellow; until October 2017) vs CO2 concentration (light gray; until October 2017). Since June 2014 HADCRUT3 is not maintained anymore and, therefore, has to be derived from HADCRUT4 data for compatibility reasons.

The HADCRUT3/4 as well as the GISS (NASA Goddard Institute for Space Studies) datasets use station based temperature observations, exclusively. Currently, about 2000 ground stations are contributing to these data, and they cover only about 75% of the geographic surface of the earth. Also, these stations are not equally distributed over the globe, which is why complex interpolation methods are used to generate the final HADCRUT/GISS datasets of a regular grid structure of 5°x5° spatial resolution, which introduce considerable uncertainty in the provided temperature data.

In contrast, there are satellite based temperature observation data of the lower troposphere of which the RSS (Remote Sensing Systems) and UAH (University of Alabama in Huntsville) datasets are the most prominent ones. They show higher spatial resolution, cover over 98% of the earth's surface, and they do not use interpolation techniques for spatial grid correction (though they have to apply calibration methods for consistency of the datasets), which improves data quality and reliability.

A comparison of the ex ante system prediction with these observed satellite data (average of RSS and UAH) shows an even higher forecasting accuracy of currently 59% and almost no bias of the most likely prediction, with the exception of the extreme El Niño weather event in 2015/16 (fig. 3). Also, it is obvious that the moderate IPCC A1B forecast is above the observed temperature data most of the time, and it overestimated the temperature development over the past 10 years, except for few months of the El Niños of 2009/10 and 2015/16.

Fig. 3: Ex ante most likely forecast (red; May 2011 - October 2017) of the self-organized system model vs observed satellite data (black and white square dots; average of RSS and UAH) from May 2011 to December 2017 vs IPCC A1B projection (yellow; until October 2017). It shows a forecasting accuracy of currently 59%.

In 2013 the UK Met Office started to publish their decadal forecast based on yearly data, which they both model and forecast update and correct every year. This forecast can be found here.