The strong El Nino has obviously caused great excitement in the warmist community. It should also cause any honest skeptic/lukewarmer to re-examine whether observations remain inconsistent with models. In today’s post, I’ll show two comparisons: 1) CMIP5 models (TAS) vs HadCRUT4; 2) CMIP5 models (TLT) vs RSS (UAH is only negligibly different). For this post, I’ve used the same scripts as I used in earlier comparisons.

Surface Temperatures (TAS)

First, here is an updated comparison of the 5-95% envelope of CMIP5 runs (TAS – surface) to most recent HadCRUT4 (black ), which, as of right now, goes only to October last year. Satellite data is available through December. Weekly Nino 3-4 data shows a slight downtick in the last half of December (see David Whitehouse here; original weekly data here). .

HadCRUT4 to October had not quite reached the transient of the CMIP5 model means, but it looks like it will. In the graphic below, I used RSS satellite data to estimate HadCRUT4 (+ signs) for December – simply adding the RSS deltas to the closing HadCRUT4 value. This slightly pierces the model mean transient. If the present El Nino is like prior El Nino’s, then we can expect a fairly sharp decline in GLB temperatures in 2016. We will see whether these levels will once again fall outside the 5-95 percentile range of CMIP5 models. My guess is that they will. To my eye, the El Nino peaks are gradually losing ground to the CMIP5 model mean: 1998 went above the envelope; the prior El Nino easily exceeded the model mean.

While a comparison of observations to a 5%-95% percentile envelope of CMIP5 models is important for orientation, the next graphic, in my opinion, is considerably more illuminating as it disaggregates the various models and focuses on the trend since 1979. In it, for models with more than one run, I’ve done boxplots of the trends, grouping singletons as one model class. On the right (orange), I’ve done a boxplot for all CMIP5 runs. All of the individual models have trends well above observations, with the CanESM2 model from Andrew Weaver’s group being a wildly overheating performer. There are now over 440 months of data and these discrepancies will not vanish with a few months of El Nino.







Lower Troposphere (TLT)

Next, here are corresponding graphics for the lower troposphere, using the RSS series preferred by warmists (data is available through December). The reference period for these comparisons is 1979-1990 (versus 1961-1990). While the El Nino peaks occur at the same time in both series, there is a dramatic difference in the trend of peaks, which decline not only relative to the model mean transient, but in absolute terms. Even December El Nino values only return the series to barely within the 5-95% envelope. About a decade ago, there was considerable controversy over the discrepancy between satellite and surface trends. Skeptics obviously have taken issue with the surface record, but it should be kept in mind that the possibility of systemic bias in the satellite data also needs to be kept in mind.

Be that as it may, there is great consistency to the corresponding comparison for surface trends in the boxplot comparison of trends for individual models against observations shown below. For all models, the model trend is more or less double the observed trend, somewhat more pronounced than for surface trends, but structurally very similar.

A Comment on Anomaly versus Temperature

The El Nino had a very dramatic impact on Toronto temperatures, which set records in December. The anomaly temperature would be a gaudy red on anomaly maps. How did we manage to survive? Experientially, it was a slightly cooler version of (say) San Francisco weather. It felt very pleasant for December, “good weather” rather than bad. The forecasters now say:

Following the extended bout of warm weather, it appears Toronto will return to more seasonable temperatures in the coming days.

“More seasonable weather” sounds good, but it means that we’re now going to see some brutal cold. Ryan Maue warns of the return of the polar vortex.

I mention our local weather because the negative impact of warmer winter weather is far from obvious to me. It did have a negative impact on high CO2 footprint local residents who, like Eric Steig, travel outside the city to ski, as the season is delayed, but a positive impact on reduced heating bills.



