Lucky Number 0.07
Posted by Jeff Id on October 11, 2009
We’re getting closer to publication on the Antarctic trends. There are still a few details being worked out so this is not the final result, however thousands of people read here so it’s important to provide an update of the results and a comparison of the similarities and difference from Steig 08.
From the abstract of Steig et al.
Here we show that significant warming extends well beyond the Antarctic Peninsula to cover most of West Antarctica, an area of warming much larger than previously reported. West Antarctic warming exceeds 0.1 6C per decade over the past 50 years, and is strongest in winter and spring.
West Antarctica warmed between 1957 and 2006 at a rate of 0.1760 +/-.06 C per decade (95% confidence interval). Thus, the area of warming is much larger than the region of the Antarctic Peninsula. The peninsula warming averages 0.1160 C per decade. We also find significant warming in East Antarctica at 0.1060 C per decade (1957–2006).
Confidence intervals were edited out.
The main revelation in the paper was reported to be an increased warming of the West Antarctic as compared to other results. However, most of us skeptics are more interested in the continental trend. From the area weighted reconstruction which is based on surface stations within the Antarctic continent the trend we find is 0.064 C/Decade. The trend distribution looks like this:
The method is simple and is a target pattern for the trend magnitude and distribution. The deep red of the peninsula section at the left is colored in the + 0.3 to 0.7 C/Decade range and is populated by over a dozen surface stations. In Steig et al only 3 pc’s were used to describe the continent, the low pc count necessarily results in the blending of surface station information around the continent. It doesn’t necessarily result in an incorrect average trend however. In this case though, there is a high concentration of surface stations in the peninsula region who’s information is blended across the continenet. I believe I’ve shown that reasonably well in a few posts including this post here. I need to mention that NicL did an excellent post which contradicts that conclusion. While I found Nic’s results accurate, I don’t interpret his method the same way. His post is here.
The point of this rehashing of the discussion occurs because a long time ago, back in February JeffC provided a regridded form of data based on hexagon gridcells with the purpose of reducing the effects of oversampling a single region of the antarctic. Since these results used the PC’s of Steig et al right from his published AVHRR data and Matlab RegEM, it represents a good comparison to the original results and no changes in understanding since that time have had an effect on the result. Please excuse the crappy old style graphic, note the trend in the figure is 0.07 Deg C/Decade:
Interesting that we achieved such a close match to the offset surface station trend with only 3 pc’s by simply making sure the peninsula information wasn’t recopied over and over prior to regression.
Below is another reconstruction by Jeff C, also back in February. This one used manned stations to infill surface stations. Steig et al reported a trend of 0.138 C/Decade for the AWS reconstruction but again simple regridding to insure that high density areas didn’t overweight their import in the reconstruction resulted in a trend of approximately 0.07 C/Decade.
It’s clear from these examples that the trends are sensitive to the density of stations in low PC RegEM. This is pretty obvious conceptually at this point, but may be confusing if you’re not familiar with our work here. What’s more is that I believe it’s strong evidence supporting my station weighting conclusions that the peninsula trends are driving the trend over Nic’s version.
Below is the latest emulation of Steig et al by Ryan. Ryan figured out that in using correlation scaled satellite data the resolved PC’s are a match to Steig and replication is now near (but not quite) perfect. Many of us have seen the Antarctic temperatures from Steig et al and their bright red colors, however when the results are plotted on the same scale used in the surface stations above the story changes a bit.
Note that the trend is actually higher than Fig 1, but the blending of the trend across the continent is pretty clear. It’s also clear that the Peninsula warming is heavily understated compared to the oversampled surface station information in the peninsula. If you haven’t followed the whole thing this next graph might not make sense bit it is a plot of the fitted data only rather than a combined plot of AVHRR and fit data. The S08 results used raw satellite data from 1982 onward instead of displaying the fit results. In the end this resulted in a further distortion of trends as the satellite data is unbelievably noisy due to cloud contamination and had an anomalously high trend in comparison to the surface station data. I think it was done because RegEM doesn’t make it easy to get the full timeframe of the reconstruciton but it is one of the problems with S08.
Note the change mutes the trends considerably, however the pre-satellite data portion of the reconstruction dominates the trend.
We’re still discussing the final version of the improved reconstruction for publication, however we’re down to about a tenth of a degree difference between any method we choose. Two of the several methods are presented here, one was an eigen weighted EM reconstruction which weights surface stations according to their eigenvalues. It’s basically an iteration improvement in the unweighted EM version which changes the final result slightly. Most of the difference occurs because of the inclusion of what we believe is the correct number of PC’s. You cannot include this many PC’s in the RegEM version without overfitting so this represents a significant improvement over the RegEM method. Also, these methods have basis in previously published literature, nobody’s inventing the (magic) wheel.
You can see that the high PC count has localized the peninsula data much better. This is important IMHO as my view is that this information drove the high trend of Steig et al, at least in the pre-1982 portion of the reconstruction.
In addition Ryan has employed an improved method using Regularized Lease Squares on the left singular vector (the spatial distribution ) from a principal components analysis to fit the surface station data. This isolates and improves the response to the effects of known large discrepancies in the satellite trend information created by several factors, including steps between satellites, cloud noise and satellite temperature drift.
A table summarizing the various trends by region is shown below. There is a lot of detail here.
|Steig et al||Emulation||Regridded Steig||Area Weighted||Eigenweighted||RLS|
|Ross ice shelf||0.219||0.24||0.009||0.127|
Note that the AWS reconstruction trend of 0.069 was not included as it is not a satellite reconstruciton, however it matches the many methods we have tried and the four on the table right well. There is a large difference in trend between S08’s three PC version and the various other reconstructions. Over the past 9 months we have verified through several different methods that the actual continental trend of the Antarctic temperature is very close to 0.07 C/Decade from 1957 – 2007. People want a confidence interval for that so I’ll say it’s +/-0.1 ish so the trend is not significant. However, please don’t misunderstand the meaning of significance. The trend is real to a high degree of certainty, significance just determines the likelihood that the trend is caused by shorter term signals. The assumptions about significance in this case of normal distribution of weather and signal are probably not terribly accurate but it gives you an idea of the magnitude of high frequency signals on this data.
The 1982 onward trends are equally important however, and they have a slightly different look from Steig et al.
And of course, Steig et al.
The point is that first Steig et al overstates the trend post 1982 by what is very likely a statistically significant amount in comparison to weather noise, however it is certainly a significant amount in comparison to the measurement accuracy. In my opinion it’s likely out of whack from the measurement accuracy by several sigma.
However despite the mathematical errors and trend blending, the warming of the west Antarctic region (considered the lower left) does indeed seem to be real.