JLI First Forecasts

Image Credit: Walter Dnes

By Walter Dnes – Edited by Just The Facts

In a recent post I introduced The “January Leading Indicator“. At that time, there were some concerns expressed. The attached spreadsheet has been updated to address items 2 and 3 below.

1) The JLI algorithm is not a “forecast” per se, but rather a “zero skill baseline” that a “real forecast” has to beat in order to show skill. Given that the algorithm is a “rule-of-thumb”, rather than a complex physical model, I have no problem with that description. The whole point of the JLI post was to raise the bar for “zero skill”. Beating 50% is not enough. A “real forecast” has to beat 75% or 80%.

2) The period of data (1979 to 2013) was too short to generate statistically significant results. 1979 marks the start of the satellite data sets (UAH5.6 and RSS) so additional data is not possible for them.

The surface-based data sets go back a lot further. An initial examination shows data back to 1955 to have a reasonable correlation. Sea-surface anomalies count for more than half of the global anomaly. There were significant adjustments applied to sea surface temperatures prior to 1950, due to claimed changes in bucket type, among other reasons. In addition, the complete and accurate collection of global temperature data was not always possible during, and in the immediate aftermath of, a global war.

The updated spreadsheet now has surface data back to 1955.

3) Including the January data in the forecast, after it is available is an unfair advantage. The updated spreadsheet includes 2 additional tabs that compare the current year’s February-through-December anomalies versus the previous year’s February-through-December anomalies. The tab “count_gt11” does so where a year’s January anomaly is greater (warmer) than the previous year’s anomaly. The tab “count_lt11” does so where a year’s January anomaly is less (cooler) than the previous year’s anomaly. Here is a comparison between the 12-month (January-December) verification, versus the 11-month (February-December) verification.

1) 12-month (January-December) verification where forecast to be warmer

2) 12-month (January-December) verification where forecast to be cooler

3) 11-month (February-December) verification where forecast to be warmer

4) 11-month (February-December) verification where forecast to be cooler

The January data for UAH5.6 and RSS are in. Now for the forecasts. The numbers are from the attached updated spreadsheet.

The Qualitative Forecasts

The January 2014 UAH5.6 monthly anomaly was 0.291 versus 0.497 in January 2013. The JLI algorithm states that the 2014 annual mean should be less than the 2013 annual mean of 0.236. Since we already have the January mean, this is effectively an 11-month forecast. I.e. the mean of the 11 months February 2014 to December 2014 will be less than…

( 12 * 0.236 ) - 0.291

or less than 0.231.

On the graph at the head of this article the red “X” is the forecast anomaly for UAH.

The January 2014 RSS monthly anomaly was 0.262 versus 0.439 in January 2013. The JLI algorithm states that the 2014 annual mean should be less than the 2013 annual mean of 0.218. Since we already have the January mean, this is effectively an 11-month forecast. I.e. the mean of the 11 months February 2014 to December 2014 will be less than…

( 12 * 0.218 ) - 0.262

or less than 0.214.

On the graph below the red “X” is the forecast anomaly for RSS:

The Quantitative Forecasts

Tab “jan_and_avg_2” of the spreadsheet has some statistics in the block P1:V4, comparing the January anomalies with the annual anomalies. The UAH5.6 slope in cell T3 is 0.64062. The intercept in cell T4 is 0.01732. Applying the standard “y = mx + b” equation, we get a predicted 2014 annual anomaly of 0.64062 * 0.291 + 0.01732 = 0.204 with an unknown error margin.

Similarly, for RSS the slope in cell U3 is 0.64755 and the intercept in cell U4 is 0.03456. The predicted 2014 annual anomaly is 0.64755 * 0.262 + 0.03456 = 0.204 with an unknown error margin.

This is in contrast to the current ENSO forecast for the Nino3.4 region (shown below), which implies an El Nino event, which should result in a warmer year.

NINO 3.4 SST Anomalies Forecast

Additional Forecasts for Comparative Purposes:

Met Office 2014 Prediction

19 December 2013 – “The global average temperature in 2014 is expected to be between 0.43 C and 0.71 C above the long-term (1961-1990) average of 14.0 C, with a central estimate of 0.57 C, according to the Met Office annual global temperature forecast. Taking into account the range of uncertainty in the forecast, it is likely that 2014 will be one of the warmest ten years in the record which goes back to 1880. The forecast range and central estimate for 2014 are the same as were forecast by the Met Office for 2013.”

“* Observationally based estimates of global average temperature are an average of the three main global temperature datasets, which are compiled by the Met Office and University of East Anglia (HadCRUT4), NOAA National Climatic Data Center (NOAA NCDC) and NASA Goddard Institute of Space Studies (NASA GISS).”

Hansen et al., 2014 prediction:

“So what are the near-term prospects? El Niño depends on fickle wind anomalies for initiation, so predictions are inherently difficult, but conditions are ripe for El Niño initiation in 2014. About half of the climate models catalogued by the International Research Institute predict that the next El Ni ño will begin by summer 2014, with the other half predicting ENSO neutral conditions 21. The mean NCEP forecast 21 issued 13 January has an El Niño beginning in the summer of 2014, although a significant minority of the ensemble members predicts ENSO neutral conditions for 2014.

The strength of an El Niño, too, depends on the fickle wind anomalies at the time of initiation. We speculated 22 that the likelihood of “super El Niños, such as those in 1982 – 3 and 1997 –

8, has increased. Our rationale was that global warming increased SSTs in the Western Pacific, without yet having much 13 effect on the temperature of upwelling deep water in the Eastern Pacific (Fig. 2 above), thus allowing the possibility of a larger swing of Eastern Pacific temperature. Recent paleoclimate 23 and modeling 24 studies find evidence for an increased frequency of extreme El Niños with global warming.

Assuming that an El Niño begins in summer 2014, 2014 is likely to be warmer than 2013 and perhaps the warmest year in the instrumental record. However, given the lag between El Niño initiation and global temperature, 2015 is likely to have a temperature even higher than in 2014.”