http://www.smh.com.au/environment/weather/weather-2014-australias-third-hottest-year-on-record-20150106-12ignf.html

But then I’m sure you already knew that.

The Aussie Bureau of meteorolgy was saying we we in boarderline El Nino conditions until recenty when they switched to neutral but its been a very long time since it rained at all ,let alone rained properly here and its going to be forty degrees celsius (104 Fahrenheit) this weekend according to the forecast. When you work outside as I do, you tend to notice that.

Of course once could also address El Nino and no El Nino years separately.

I think the 10-year trend is probably more reliable than the 30-year trend. And the reason for that is the existence of decadal-scale variability in the temperature series.

I would have thought short term variation (e.g. the La Ninas in 2011 and 2012) would degrade the reliability of using 10 year trends more than using 30 year trends.

I didn’t include autocorrelation when I ran it for every year. I did a correction for AR(1) autocorrelation on a couple of sample years. For the trend starting in 1981, the StdErr increases slightly from 0.09 to 0.10. For the trend starting in 2001, the StdErr increass from 0.06 to 0.08. The error in prediction from the shorter trends is still smaller than the error from the longer trends.

One could do a more rigorous model of autocorrelation (ARMA or whatever). If we were talking about monthly data, it would obviously be important. But there’s not a ton of autocorrelation in the annual data.

Please note that I’m not suggesting using short trends to draw conclusions about 21st century warming or climate sensitivity or whatever. I’m not one of “those” people. But for predicting this year’s temperature measurement, I think the 10-year trend is probably more reliable than the 30-year trend. And the reason for that is the existence of decadal-scale variability in the temperature series.

For start dates in the 1990s the standard error drops off

I don’t understand how that happens. Do you include autocorrelation?

OK, I am going to contradict myself again, and flip back to saying I do *not* expect a new record in 2015.

First, looking at the real-world situation, it’s not clear that the long-awaited El Nino is going to do much. The ENSO forecast is pretty quiet right now.

So that leaves the statistics, which we’ve been obsessing over in this thread. Looking at this further, I don’t think the 30-to-40-year linear trend that Chris is referring to is in fact the best basis for predicting 2015.

Shorter-term trends actually do a better job of prediction in this case. Trends starting in the 1970s and 1980s have a standard error of about 0.09. For start dates in the 1990s this drops off, and from 2001 onward the standard error of prediction is around 0.06. In addition to having a lower standard error, the trends for these shorter time periods predict a lower 2015 value, one that could set a new record but probably won’t.

Another way of looking at this is with LOWESS models. LOWESS models with an alpha corresponding to less than 30 years incorporate some of the decadal-scale variability that we’ve seen in recent years, and predict a somewhat lower value (non-record-setting) for 2015. Only models with longer timescales predict that 2015 will exceed 2014’s value, and those models don’t really capture the observed pattern of decadal variability.

So I will go back to my previous stance. For the specific purpose of predicting *next year’s temperature* I think that methods focusing on shorter time scales are more appropriate than extrapolating a linear model from the 1970s … and those methods suggest that 2015 is somewhat more likely to come in below 2014 rather than above.

Having now flip-flopped twice on this, I am now back at my original opinion. Without knowing what ENSO will do over the next 11 months, my a priori expectation is that 2015 will be close to, but slightly lower than, 2014.

Chris, my comment “not very robust argument” was specifically referring to the statement “2014 was actually below the trend or ‘mean'”. And that statement really *isn’t* a very robust claim.

Whether 2014 is above, on, or below the linear trend line depends on what year you choose to start the trend. Out of approx. 130 possible start years, the one you chose (1974) just happens to be the one year that makes 2014 as far as possible below the trend line, and it’s still not very far. I’m sure that was just chance, but … when your argument involves picking the most advantageous starting point out of 130 years, I think it’s fair to question its robustness. Needless to say there are many more possible start-years that would put 2014 above the trend rather than below.

But that’s because the long term time evolution of global mean temperature is nonlinear.

Now it’s true that if we want to ignore that nonlinearity and project 2015’s temperature via a linear model, then a lot of reasonable choices for a model would have 2014 very close to the trend line itself. And under those circumstances, the odds are (slightly) in favor of a new record in 2015. That’s the bigger picture here and I basically agree with it now.

But the “non-robustness” remark was in response to the idea that 2014 was not merely “on” but actually “below” the trendline. The accuracy of that statement is dependent on the start year. And, ironically, it’s also strongly affected by the existence of the 1998 El Nino. So, again, not very robust.