Evidence of Sea Contamination In Antarctic Sat Data

Know your data!

It’s the first rule of any engineering project you undertake. Mistakes often happen at the base level of data collection, errors in the early stages propagate through a project once the data is accepted, the effects of which are often misunderstood or unquantified. Nobody is immune to this of course, it’s just a point I want to make. From an engineering standpoint, it means heavy verification, often multiple times until you’re sure that your answer is reasonable. In my world if I’m wrong, things don’t work and big money is flushed down the toilet. In climatology if they are wrong and it is pointed out, there is little recourse or concession of the mistake. It would just be another messed up science if the politicians and polyscienticians weren’t forcing ridiculous policies down our throats, but that’s another story.

Jeff C found a site on the NSIDC where they provide AVHRR satellite data from 1982 to 2004. This data is monthly data which means daily cloud masking and error correction have already occurred. The masking is different than Steig used in his Antarctic paper and it may have had a substantial impact but at least we have a point of reference. There are several questions to answer, are 3 pc’s enough to represent the data, are the trends similar to the presented trends, are there other problems in the data.

Honestly, I’ve spent many hours now looking at the data and while I don’t have the answers to all these questions, there will be some interesting things over the next week or two. I really need to send Christmas cards to the team for all the entertainment they provide.

Jeff C deserves enormous credit for accessing and processing the data, we did work on it together but there were big hours spent in analysis and verification. Anyway, instead of getting too far ahead of myself and into some of the incomplete but still interesting finds I’ve decided to catch my readers up.

Below is a movie of the skin surface temperature of the Antarctic. Watch the video a couple of times and you may see some interesting features.

As a second confirmation of the movie above, I plotted the standard deviation of each gridcell so everyone can see cells that change the most and I think more importantly, the least!

The edges of the antarctic satellite data are surrounded by pixels which don’t change skin temp. You can see in the movie they stay red despite the rest of the continent turning blue. These pixels appear to represent contamination of land data with ocean surface temperatures. This data is nearly exactly the same grid points as Steig used yet clearly from the video and the SD plot, ocean pixels are included as surface data. How does Steig expect the ocean to change temperature with monthly air?

The peninsula is one of the worst areas after watching the movie, would you use that portion of the grid to establish continental trends? Would you allow the high density of sites comprising that data to account for over 25% of the reconstruction? Or would you realize, these stations are nothing more than ocean temps and work instead with the inland data?

There are a few months without data and a couple more where the collection resulted in a helical star pattern, the removal of which is at this point unknown. Those who have been keeping up will realize the mountain range in the autocorrelated PC data is nowhere near the influence it appeared to be. The PC trend simply locked onto the peninsula as the dividing point, the apparent coincidence with the antarctic mountains was therefore likely just by chance.

As far as the sea data contaminating the trends, I’ve already masked and removed the data simply by the fact that these pixels rarely drop below 10C, even in the coldest months. My point being that it wasn’t very hard to find ocean pixels and subsequently mask them out of the reconstruction.

Rather than me going on about how crazy it is to allow ocean pixels in a land reconstruction, I’m guessing some of you can help me out.