the Air Vent

Because the world needs another opinion

Sea Ice Copenhagen Update

Posted by Jeff Id on December 9, 2009

A special post for those who would save us from the global warming. The regulars already know we do these sea ice plots from the NSIDC gridded satellite data.

Figure 1 - Barely Significant

Sea ice – extent full satellite record, barely significant trend WRT high frequency weather noise.

Global sea ice area is even closer.

Figure 2- Area Plot, On the edge of significance

Cherry pick - 1979 to present - 30 years of non-significant sea ice trend

This one is interesting because it shows that without 1978 the past 30 years have not shown a statistically significant decline in sea ice. What makes it even better is that in 1978 there are only 2 months of data starting from October 26. So the above graph which shows a less than 95% confidence that the decline is not ‘weather’ noise is only missing the first 2 months.

Figure 5, really really not a significant trend

So of course we should plot the stats for all the years, just to make sure I’m not cherry picking.

Figure 6, Statistical Significance of Global Sea Ice Trend by Year. Blue and Red line - Upper and Lower Bounds. Black Line - Sea Ice Trend from Year to Present

Global Sea ice trend by year only (barely) crosses 95% significance when the first two months of satellite data is included for the entire record.

Just for fun, from the uber-Scientific and obviously a-political Copenhagen Diagnosis report we learn that ‘scientists’ can only see Arctic Sea ice.

Rapid Arctic sea-ice decline: Summer-time melting of Arctic sea-ice has accelerated far beyond the expectations of climate
models. The area of summertime sea-ice melt during 2007-2009 was about 40% less than the average prediction from IPCC
AR4 climate models.

Below is the Arctic Sea ice anomaly.

Figure 7, Arctic Ice Area Anomaly - Significant Negative Trend

Figure 8, Antarctic Ice Area Anomaly - Insignificant growth WRT weather

So the question is, is sea ice declining – the easy answer is clearly yes. However, the more nuanced answer is yes but it appears globally to be an insignificant amount of melt which could be due to short term variations caused by high frequency weather patterns. What’s more is that we only have 30 years of data here to work from and there is substantial evidence that this ice has melted in the past and the whole trend could quite easily be part of a much longer trend as shown in an awesome post on arctic history by Tony Brown.

I do a special plot of Global ice anomaly at tAV where I add back in the average ice area on the earth. It’s important for people to visualize because it gives a slightly different impression than the ‘scientists’ desperately want you to see. As I say often here, don’t take my word for it, make your own mind up. — I say it for lurkers though, most blog commenter’s have little trouble with expressing opinion right?

Figure 9, Global Sea Ice Doom - Anomaly offset by average area.

Just to make the effects of variation of weather on Sea Ice and Polar bears (which there are more of today) below is a new and much improved video on global Sea Ice. The resolution is better, both poles are shown at the same time and I’ve brought it up to date for this year.

As my 3 YO said , smaller, smaller ,smaller, bigger, bigger, bigger, smaller ,smaller smaller, bigger big…..

The R code for this is a bit sloppy, but you’re welcome to it – email me on the right.


24 Responses to “Sea Ice Copenhagen Update”

  1. Jeff Id said

    It took 2 days for this dual processor laptop to calculate the video.

  2. Kenneth Fritsch said

    Jeff ID, I think that your exercise should make clear for those who perhaps have trouble differentiating between marketing/propogandist and scientific approaches. I like the fact that you do it with a light touch.

    I guess if I were into a paternalistic government view (I am not) that these claims by the consensus need to be followed with a warning that they are a part of an advertising and/or marketing program to promote immediate mitigation of the supposed bad effects of AGW.

  3. hswiseman said

    Jeff, please stop confusing me with all these facts!

    It is amazing to watch the AGW spokesmodels refuse to discuss, debate or try to explain facts and the obvious implications of facts. When I first started following the issue about 2 years ago, I could not believe the poor data quality and all the unequivocal claims being made using these data sets. I didn’t know the half of it. Give credit where credit is due, Mike Mann has done a masterful job of ruling with an ironhand and pulling off the complete hijack of climate science by the proxybuilders. Mann brooks no dissent either internally or externally. At least he is consistent in this respect.

  4. Hey Jeff

    Nice job on the graphs and graphix. Wow, 2 day rendering job!

    I’m an fold art, (and getting older and *artier every day).
    But I don’t understand why the anomaly graphs have Degrees C as the y-axis label.

    Also, I wish I could see or understand the ‘95%’ confidence thing by looking at the graphs. I believe you but I am not just seeing the message in the graphs.

    This fine work deserves a clearer presentation of the messages.
    Thanks for all your efforts,
    TL

  5. Jeff Id said

    A 95% confidence interval is presented as the trend plus/minus the interval in the text of the graphs. It’s a common nomenclature in science.

    So if you have a trend of 0.5 +/- 1 it isn’t considered statistically significant.

  6. Jeff, great post. If a picture is worth a thousand words, then the time lapse video speaks volumes. I think the video is a great update to the work you did previously. There is probably a way for me to tell where to find the video on YouTube, but if you have the link handy, that would be great (I’d like to give folks the YouTube link separately).

    Just a couple of questions/comments:

    – You switch back and forth between extent/area several times (several graphs are extent, several are area; the video appears to be area). It might be good to choose one and stick with it, with an explanation as to why you prefer it, or, alternatively, to show both in every case. Does it change the picture much using one as opposed to the other (my understanding is that there might be a slight difference, but perhaps not significant)?

    – Based on the data you have, is it typically the case that the last couple months of the year are higher than the rest of the year? If so, then the last two months of 1978 could be skewing higher, even if that year was normal. I’ve looked at the Global Sea Ice Area on Cryosphere Today and it isn’t 100% clear whether the year marks are centering marks or beginning of year marks. I think they are beginning of year marks, in which case the high would historically be around September/October, so perhaps the last two months of 1978 are now skewing it higher.

    More importantly for calculating a proper trend, given the large variation in Sea Ice Area during the course of a year (I’m eyeballing it around 5-7M +- sq.km.), it seems to me the only rational way to derive a trend is to do a rolling 12-month analysis. Thus, if your last data is November 2009, you would need to exclude November 1978 and include December 1978; next month when your last data includes December 2009, you should exclude December 1978 and start with January 1979. If this is not done, we would be mis-stating the trend, based simply on having included too many November’s or December’s, for example.

    Thanks again for all your hard work. Keep it up.

    Eric

  7. OK, thanks, my eyes glazed over by the time I got to the decimal point.

    Are there really 9 digits of accuracy? Wow, science has come a long way since I hung up my slide rule….

    I’m still unclear on why the words “(Deg C)” appear on some of the graphs, and I can’t use them to argue with anyone until I can explain that part also.

    Jeff, I think this is a very important message and I am only asking you to clarify it to make it easier to convey to a ~CNN-level audience. i.e., like an ‘elevator-pitch’ or as we used to say ‘Make your case in 25 words or less’.
    I’m still deeply impressed by a 48hour rendering job…

    Probably more important to embrace that 3YO than answer my confusion.
    TIA
    TL

  8. Jeff Id said

    #7 Sorry been busy today. Deg C is the scale of the anomaly. Anomaly is averaged by month data in C so the scale center is expected to shift to zero. I apologize for leaving too many sigfig. Two days was my limit of effort.

    As to the CNN style audience, this is normally a science blog full of engineers, biologists and scientists of varying degrees of understanding – many with more than my own. I try to post in between most times so people who can think spend some time and figure it out. Will lower class downtown folks so easily fooled by the MSM figure it out? Not much chance.

    #6 Eric,

    Area and extent are calculated from the data in the video. Area uses sea ice concentration X pixel size. Extent uses all pixels greater than 15% concentration. They are basically the same thing in the end although I prefer the extra information of an Area plot, the most common plots are extent.

    As far as the accuracy of the trend, all values are calculated by annual anomaly across the entire dataset. A daily anomaly curve is generated and this curve is subtracted from the sinusoidesque variations in sea ice. Extra months on either end will have little effect.

  9. Joel Heinrich said

    wow, a 2,000,000 °C anomaly. Maybe Gore really was right with his temperatures…🙂

    It really should be km², as it is an area anomaly and not a temperature anomaly.

  10. Jeff Id said

    #9 s@#%!!

  11. jstults said

    Why do you fit an AR model rather than just doing a seasonal adjustment (a month factor w/ 12 levels, a season factor w/ 4 levels)? My naive take would be that all the Octobers would be similar, all the Junes, etc. This would let you correct for the effect that #6 mentioned about the last two months of 1978 being higher at the start of your series.

    Way to whale away on that laptop btw!

  12. Jeff Id said

    #7, Sorry I didn’t catch on about the axes titles. The answer as to why DegC is- Jeff ‘Id’ is short for idiot.

  13. Jeff Id said

    #11, I’m not sure I understand. The anomaly is calculated by a day of the year type average. i.e average for all day 1,2,3,4,…. 365. The data is divided by this number. I don’t think the first two months require correction or have any real effect, unless I’m missing something again. I’m just amazed with all the hype that sea ice isn’t dropping significantly.

    Actual sea ice values rather than anomaly values are near sinusoidal in each hemisphere and an odd pattern for global values. These are anomaly and already corrected for seasonal variation.

    The AR model is a lag 1 autocorrelation estimate used to correct the DOF in determining the trend significance. I’ll copy the code in a moment- I have to switch computers but the laptop is on cause I had to unscrew every single graph.

  14. Jeff Id said

    ### Get trend
    fm=lm(window(dat, st, en)~I(time(window(dat, st, en))))

    ### Initialize variables
    N=length(window(dat, st, en))
    I=seq(1:N)/frequency(dat)
    rmI=ssq(I-mean(I))

    ### Calculate sum of squared errors
    SSE=ssq(fm[[2]])/(N-2)

    ### Calculate OLS standard errors
    seOLS=sqrt(SSE/rmI)

    ### Calculate two-tailed 95% CI
    ci95=seOLS*1.964

    ### Get lag-1 ACF
    resids=residuals(lm(window(dat, st, en)~seq(1:N)))
    r1=acf(resids, lag.max=1, plot=FALSE)$acf[[2]]

    ### Calculate CIs with Quenouille (Santer) adjustment for autocorrelation
    Q=(1-r1)/(1+r1)
    ciQ=ci95/sqrt(Q)

    return(c(fm$coef[2],ciQ))

  15. jstults said

    Jeff said:

    #11, I’m not sure I understand.
    […]
    These are anomaly and already corrected for seasonal variation.

    I’m pretty sure I misunderstood; someone has already fit a seasonal model to the sea ice data and the anomaly are just the delta from that (right?).

    Is using an AR-1 model common practice for this data set, or did you find a significant autocorrelation yourself? Have you checked the partial autocorrelation for any other significant ones? Adding significant ones to the model will tighten up the intervals.

  16. Jeff Id said

    #15, I wasn’t that thorough about it but because the data is daily and ice is so massive, the lag 1 was 0.997 – 0.998.

  17. Hey Jeff,
    I put a link to this page over at Gatewaypundit,
    in a post about the Goracle.

    My summary;
    …of the roughly 20,000,000 square kM (average) of ice on the earth, the trend line is -35,000 sq kM per year.
    So, that is less than 0.2% trend, which only barely reaches 95% significance if all available data is included.

    I used more than 25 words, obviously.

    Nice work, and timely as well.
    Negative perspiration on the label glitch.
    It doesn’t change the fine work of the actual calculations.
    Good day, sir.
    TL

  18. DeWitt Payne said

    Jeff Id,

    I’m a little surprised that the lag 1 autocorrelation for the anomalies is so high. Is the Quenouille (Santer) adjustment for autocorrelation still valid at values that high?

  19. Jeff Id said

    #18, I haven’t proven it through monte-carlo style analysis. That might be a worthwhile project.

  20. […] The false and alarmist claims that the ice caps are melting and sea levels are rising. According to National Geographic, the ice caps are melting on Mars. Arctic data cast doubt on climate change theory. Sea Ice Plots. […]

  21. […] Link to Jeff Id´s article: https://noconsensus.wordpress.com/2009/12/09/sea-ice-copenhagen-update/ […]

  22. Chris1958 said

    Just subscribing

  23. R. de Haan said

    Great job Jeff.
    It’s really incredible how the AGW crowd continues to turn mosquito’s into big blue elephant and pink crocodiles.
    I love the time lapse of Global Sea Ice Extent.
    Where do you find the time.

  24. tonyb said

    Jeff

    That was a very nice piece. Good to see such clear graphs and a lucid explanation. I see Frank has taken up your theme over at WUWT.

    Thanks for your referencing my previous article on the Arctic.

    I have gathered all the information together for my ‘Historic variations in Sea ice Part two’ which covers the extensive melting of the arctic that took place over the period from around 1918-1940. This one has the benefit of contemporary news reports and also Pathe News reels to back it up. Our grandparents watched the news reels in cinemas to find out the latest exploits of celebrities who went to the arctic to look at the ‘unprecedented’ melting.

    It is depressing that recent history is ignored, which makes putting over longer term episiodes prior to living memory highly problematic.

    I guess that computer models have taken over from observations and the historical record is seen as a poor substitute for those.

    The release of ships logs may enable more information to be garnered about arctic voyages in the 1918-1940 period so my follow up article has been delayed accordingl;y. Have just finished- but yet to edit- an artcle on historic sea level variation. This is to fill in the gap left by AR4 whose contributors clearly believed that sea levels only started rising in 1900. Or perhaps they didn’t like the message that sea levels have been higher than current levels twice in our recent historic past?

    Once again, congratulations on an excellent post.

    Tonyb

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