The SST’s showed lots of blue and yellow, indicating slight positive and negative anomalies. I have to admit I always have trouble integrating color images to estimate whether the net effect is a positive or negative anomaly. But, it is fun to look at the images, so I created an image by collecting together images from mid Oct., Nov. and Dec. 2007 and Oct. and Nov. 2008.

Here’s the image:

Figure 1: Comparison of SST's

Of course, I can’t show these without mentioning ENSO. the NOAA El Nino Status pages dated Nov. 6, 2008. starts like this:

Synopsis: ENSO-neutral conditions are expected to continue into early 2009.

ENSO-neutral conditions continued during October 2008, as equatorial sea surface temperatures (SSTs) were near-average across much of the Pacific Ocean, except for small areas of below-average SSTs in the east-central Pacific and off the coast of South America (Fig. 1). C…

And ends:

A majority of the SST forecasts indicate a continuation of ENSO-neutral conditions (-0.5°C to 0.5°C in the Niño-3.4 region) into the first half of 2009 (Fig. 5). Several dynamical models suggest the development of a La Niña during Northern Hemisphere Winter 2008-09. This outcome becomes more likely if the current MJO were to stall in a location that favors enhanced low-level easterlies and increased upwelling in the east-central and eastern Pacific. However, it is rare for La Niña to develop late in the year. Therefore, based on current atmospheric and oceanic conditions and recent trends, ENSO-neutral conditions are expected to continue into early 2009.

Sponsorship

Social Spark (SS) lets bloggers find temporary sponsors for their blogs. The sponsors pay a daily fee; the blogger in turn runs an form of… yes…. pop up ad.

It goes without saying that pop-ups are , shall we say “In your face”? (By the way, evidently, this type of popup is called a “Blog Welcome”.)

Normally, I’d immediately just say no to pop-ups. But this is one of those slightly less obnoxious kind of popups. The less obnoxious feature is that it’s designed to recognize visitors and only display once to any individual visitor. The visitor hits the go-away box, and, supposedly, never has to experience that particular pop again. (Of course, if the blogger gets a second sponsorship… well… )

The Poll

What do you think of sponsorship popups as described in this post?

• Blog Killing Obnoxious!
• Oh well…
• If a blogger I like makes $1 a day, and I really only see it once, I guess that’s ok.
• What fun! Can you make it flash, blink, and take evasive measures when I try to click the go-away box?!!
• I know someone who would pay more than $1 a day.

View Results

 Loading …

You may vote for up to two choices.

• Polls Archive

Comments

It’s always impossible to craft just the right questions in a blog poll. What I really want is feedback. (FWIW, I have not accepted the offer to run any sponsorships yet. I wanted to explain them to my readers first.)

Info about polls at Wordpress blogs

I always like to give information when I add a new blog features. This polls was created using Lester Chan’s WP-Polls plugin. I couldn’t get the “Poll” button in the “Write Post” button to display, so I also installed Exec-PHP plugin for WordPress. This let me insert the PHP Lester discussed in the “usage” section of his plugin description page. I inserted the PHP for the poll in the post, and the poll displayed.

Evidently, the poll will automatically show results when the poll closes. This is the first time I’m using it, so we’ll see.

The figure below compares the 12 month lagging average GMST to the 12 month running averaged based on the average of 29 IPCC AR4 runs that included volcanic forcing before 2000 and project past 2000 using the A1B SRES:

Figure 1: Twelve Month Running Average of GMST

The temperature for the observation and the individual runs are relative to their individual monthly averages from Jan 1970-Dec 1999. The ±95% uncertainty intervals represent the spread of all weather in all models, based on the assumption that the deviations from mean are normally distributed.

As you can see

1. Using the simple eyeball test, the IPCC AR4 simulations were in quite good agreement with observations during the period between publication of the IPCC TAR and the AR4. This was a period when analysts would have the opportunity to make decisions such as tweaking model parameters, screening model runs, deciding whether to weight model runs when finalizing the projections in the AR4.
2. Using the simple eyeball test, we see temperatures dropped publication of the document, but remain within the ±95% uncertainty intervals for all weather in all models.

It is worth noting that the IPCC projections are, evidently, based on averaging over models not runs. The uncertainty bands in figures in the IPCC AR4 are evidently 1 standard deviation for the average trend for all models rather than all possible weather in all possible models. As ±95% is roughly equal to two standard deviations, their uncertainty bands would be tighter. (I’ll eventually put together a graph using the weighting described verbally in the IPCC. I’m pondering how best to do it– no matter how I look at it, the temperature projections in my files always look much noisier than the ones in the IPCC AR4. I suspect smoothing was done. . . )

On Thursday, the United Nations Environment Program released a report. The story was picked up the AP; most news articles justifiably focus on the mostly undesirable aspects of the Asian Brown Cloud ( and brown clouds in general.)

However, those who have been wondering about possible causes for the recent flattish trend in GMST would likely note this:

At the same time, the brown clouds have also helped mask the full impact of global warming by helping to cool the earth’s surface and tamp down rising temperatures by between 20 to 80 percent, the study said. That’s because some of the particles that make up the clouds reflect sunlight and cool down the air.

What? The UNEP suggest that brown clouds may be slowing the rate of temperature rise by as much as 20 to 80%? Could that mean that the AR4 best estimate of the warming trend– i.e. “about 2C/century” did not include the impact of the Asian brown cloud? And that it the true underlying trend might be lower? As in “about 0.4 C/century to 1.6 C/century”?

I guess I’ll have to read the report and see if the 80% is relative to the SRES used in the AR4. But, of course, this is what they mean, it appear the consensus may be moving toward the notion that temperatures are not currently rising at a rate of 2C/century as projected in the AR4.

Reaction at “some blogs”

Interstingly, I first learned of this reading comments at RC. Here’s an a reaction in RC blog comments (bold mine):

Hank Roberts Says:
14 novembre 2008 at 12:23 AM

Hey, you’ve got friends everywhere — another major source of error in the warming figures just hit the news. I wonder why this is being reported now?

Today’s Wall St. Journal:

Toxic Cloud Masks Warming Effects
By SHAI OSTER

BEIJING — A roughly two-mile thick cloud of soot and smog hanging over most of Asia is wrecking havoc on agriculture and health but masking the effects of global warming, a United Nations study found.

The atmospheric brown cloud made of different particles resulting mostly from burning coal is causing hundreds of thousands of deaths in Asia and billions of dollars in economic losses, the study said. But it helps reduce the impact of climate change by between 20% and 80%, said the report released Thursday by the Project Atmospheric Brown Cloud, established by the United Nations Environment Program…..

—————–
Bloomberg.com writes:

The pollution makes skies from Beijing to Tehran darker by blocking sunlight as it is absorbed by particles linked with burning fossil fuels. Guangzhou, a city in southern China, has reported a 20 percent drop in sunlight since the 1970s.

The cloud may also be contributing to shifts in weather patterns, including drought in northern China and flooding the southern region of the country as well as affecting the seasonal monsoon, the UN said. At the same time, it may be having a cooling effect on the planet as some soot and biomass material reflects sunlight, the report said. ….

Hank, in answer to your question: the reason the news about brown clouds is being reported now is the United Nations Environment Program released a report Thursday. The UNEP probably initiated this scientific study some time ago.

Other stories

For those of you interested in additional news reports, these both looked interesting Dirty Brown Clouds Impact Glaciers, Agriculture And The Monsoon andExperts say brown clouds not limited To Delhi, mercury not rising.

I suspect we’ll read more about this soon. I’ll be googling to find the UNEP report. If any of you have found it, let me know.

Figure 1: Hadley October Warmer than September.

Other changes in recent temperatures include:

1. September got colder during October, dropping from 0.376 to 0.371. In contrast at GISS, September got 0.1 C warmer during October.
2. August got colder during October, dropping from 0.385C to 0.384C. This change is insignificangt compared to the 0.11C drop in August’s temperature in the GISSTemp record.
3. July warmed up during October, rising from 0.412C to 0.414C. Recall that GISSTemp July dropped 0.1C during October.

So, if we ignore changes in the third significant figure, Hadley’s temperatures were stable after 1 month. My general impression is that’s the way Hadley’s temperatures behave. (But the point of initiating these sorts of comparisons last month was to see if my impressions were correct. People had been wondering.)

Brownie betting on GISSTemps’s October Anomaly are now closed! So, I’ll tabulate and we can see who wins.

Figure 1: October Anomaly, version 3.

http://data.giss.nasa.gov/gistemp/tabledata/GLB.Ts+dSST.txt in its various November incarnations.

So, shall we spin this as “October really was warm”, or “GISS really overestimated on that first try.”

The important question is: Can I win brownies by guessing the October anomaly when it’s published in December?

During the brou-ha-ha regarding the “Does Siberia get cold in winter? ” glitch in the October GISSTemp anomaly, John Mashey suggested the obvious QA problem at GISSTemp could be solved by increasing funding at GISS. Gavin estimated that GISStemp is allocated 0.25FTE of NASA staff time, and described a peculiarly odd, unnecessarily labor intensive approach to QA which would cost $500,000 to implement.

Some might wonder: Is the budget for GISSTemp large enough? Is GISSTemp underfunded? Would more money solve the problem?

Are those questions rhetorical?

Of course. Now, let’s check out some products at http://data.giss.nasa.gov and think about whether these the cost of providing these features should take precedence over efforts to ensure massive amounts of September data aren’t used compute the October temperature anomalies.

When considering cost, remember that at government entities, costs must cover staff time involved proposing a product, deciding to fund it, implementing the software, having editorial staff check the product to make sure appropriate disclaimers, and logos are applied, and do any number of things required to make sure a publicly accessible web page is meets all the requirements for a laboratory web page.

Now, here are a few GISSTemp products available at http://data.giss.nasa.gov

1. 

   (Click to visit NASA)

   Animated maps of 5 and 10 year averaged surface temperature anomalies from 1881-2003. (Available in both Quictime and MP4.)

   Kewl! Great for PR!

   Cost compared to adding an if/then/else check to the GISSTemp script to set flags if October temperature are identical to Septembers? Who knows!

2. A GUI to permit climate blog addicts to create nifty color maps.

   Kewl!

   We all know the best use of this too: Commenters at WUWT, Climate Audit and pretty much everywhere pull down menus to create maps like the ones shown below:

   

   From Climate Audit

   Are these maps “kewl”? Yes, these maps are “kewl”. Maps like this hepled . “Chris” at WUWT and “JohnS” at Climate Audit identify the “October is still September” glitch that affected the GISS data.

   How much does this cost compared to adding an “if/then/else” clause to the GISSTemp calculation to flag the numerous unprecedented anomaly outliers that existed in the incorrect first try at the October anomalie computation? Who knows?

3. GUIs to permit the entire planet to create zonal means vs month graphs.

   I’d say Kewl… ehrmm…?

   Maybe some researcher somewhere testing something is downloading gridded data from PCMDI, feeding the data into a script to massage it into some post-processed data product, printing out a color map and comparing their data to this particular color map.

   What’s the cost benefit relative to including a teensie-beensie bit of obvious QA in the scripts that compute the anomalies that are later massaged and transformed into this graph?

4. Graphical displays of annual averages for station data.

   Far be it from me to suggest that instant access to graphics illustrating the possibly incorrect annual average temperature in Essej should be a low priority. After all, graphics on the fly are cool.

   However, it was the text data available by clicking links that permitted Chris and JohnS to identify the “September=October” issue in the underlying data. More important: They bother to check.

5. Other GUIS for other products: Seasonal Cycle vs. Time, Time series of Zonal Means and of course various graphs and plots like the one shown to the right.

   No doubt, children across American are learning something from that funky graph even as we speak. Also, it may help some researcher somewhere do something.

   Who is to say the cost of that graph would have been better spent on a brain-storming session to develop QA algorithms for GISSTemp ?

What do you think of the list?

I can tell you what I think of the list above. I think GISS devotes a not insignificant number of hours a week to develop and post window dressing for GISSTemp.

I also suspect Steve was half right when he said this:

Re: Steve McIntyre (#36),
They get a lot of publicity out of data that they spend only 0.2 man-years preparing. I’m sure that some people responsible for funding presume that this takes more of their budget than it seems to.

GISS does get lots of publicity for the 0.2 (or 0.25) man-years spent on GISSTemp. I also think GISS is spending money on GISSTemp and will continue to do so. If taxpayers are lucky, the money to improve QA for GISS temp will come from their current window dressing budget.

So, now I find myself posting the values I showed Nov. 10 and the newer values today. The only real change from Nov 10 is the October value, which decreased precipitously: it dropped 0.2C.

To provide context for this magnitude of drop, I plotted the Jan 2001-Oct 2008 GISS Land/Ocean anomalies from both the Nov 10 and the Nov 12 updates. These are shown below:

Figure 1: GISS Temp Through October 2008.

The Nov 10 and 12 data are indistinguishable except for the October anomaly. The 0.78C, which would have been a record for October is circled in green; the 0.58 C is circled in blue. It’s easy to see the 0.2 C is substantial in light of the overall scatter.

As I noted in an early post, Siberia was thought to be warm– just not burning hot as in the first GISS update. Due to warmish conditions overall, October anomaly was high enough to start making the GISSTemp Jan 2001-now trend rise relative to the value in September, though the trend remains slightly negative.

For those curious what happened to the anomalies from June-September, I took snapshots of the recent values. GISS Temps first attempt at an October update is on top; the September values are below them. At the very bottom, I included GISS’s second October update.

Figure 2: GISS's Second Try at the October Anomaly.

1. June’s anomalie rose from 0.29 C to 0.32 C.
2. July’s anomaly dropped from 0.53C to 0.52 C.
3. August’s anomaly dropped from 0.50 C to 0.39 C.
4. Septembers anomaly rose from 0.49 C to 0.50 C.
5. The current October anomaly is 0.58C down from 0.78C reported on Nov 10.

I suspect the October anomaly is still unstable. The Brownie bet to guess the October anomaly to be reported in December is still on!

# B.D. Says:
12 November 2008 at 9:46 AM

You would have been far better off simply saying that you found an error, are working to correct it, and the new data will be posted at that point. End of story. Instead, while complaining about much ado about nothing, you actually make much ado by: 1) incorrectly placing the blame on NOAA instead of your own processing algorithm. You can accurately model the atmosphere into the future but you can’t detect that a +13C anomaly might be a red flag? Once you take a product and use it to produce a new product, YOU take responsibility for that new product. And 2) ludicrously blaming “temperature observers” for heavily wanting to find something wrong. There are nutcases on both sides of the AGW issue, but legitimate temperature observers want the data to be correct, whether is pro- or anti- AGW. Maybe the “auditors” made your molehile into Mt. Washington, but your blame-deflection game elevated it to Mt. Everest.

[Response: I’m finding this continued tone of mock outrage a little tiresome. The errors are in the file ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/v2/v2.mean.Z, not in the GISTEMP code (and by the way, the GISTEMP effort has nothing to do with me personally). The processing algorithm worked fine. Multi-degree anomalies are not that unusual (checks are made for unphysical outliers which wasn’t the case here). I daresay they’ll put in more checks now to deal with this specific issue, but you can’t check for all possible kinds of corrupted data in the input files ahead of time. Science works because people check things - against expectations, against alternate records, against other sources of data - this is what occurred here. - gavin]

Yes. Gavin does seem hellbent on turning the molehill into an even bigger mountain. If he keeps this up, maybe the mountain can turn into a volcanic eruption of Krakatoa like proportions which would then lower the GMST. . .

But, back to the comment.

I wonder what sorts of outliers Gavin considers “unphysical”? What does Gavin mean by, “The processing algorithm worked fine?”

It may have worked as designed. But the design must not catch even the most egregious data glitches. Based on other factoids Gavin posted, before GISS yanked the October data and their October anomaly report, as many as 10% of the stations used to compute the October anomaly had held over temperature values since September.

Here’s a news flash: Station’s don’t move around. The glitches affected stations in Siberia (among other places.) Multi-degree outliers in, for example, Turuhansk are highly unusual.

I downloaded the full set of Turuhansk monthly temperatures since 1881:

1. Fraction of observed anomalies that are larger than 10C: 0.4%
2. Fraction of observedanomalies less than -10C: 1.4%
3. October 2008 Turuhansk anomaly pre-correction: 13.53 C.
4. Fraction of observed with absolute values greater than 13 C: 0.00%. (That is none out of 1480 observations.)
5. Temperature change from Sept 2008 to Oct 2008, pre-correction: 0C. Full range of temperature changes from Sept-October: -2.8C to -19.1C. Note: A change of 0C has never happened.

In short, the data processed by GISS was chock-ful of unprecedented events.

Had these unprecedented events occurred, that would have been real news.

As it happened the files contained errors. These errors could have been caught with quite rudimentary QA tests, involving a few “if/then/else” test comparing the data in the files to readily available statistics describing the range of anomalies and temperature changes associated with a particular station or a particular region of the earth.

If a data record is showing numerous totally unprecedented temperature anomalies along with numerous unprecedented 0.0C temperature changes from one month to the next, any halfway decent script ought to catch that.

BTW: Does anyone have any clue why Gavin thinks the tone is mock outrage? The tone I hear is amazement and laughter!

===
As I was getting ready to publish, I got a note from SteveM. He also appears to perplexed by the idea the algorithm worked fine.

It’s been confirmed: GISS.NASA.GOV is down and has been for hours.

Update:

Steve reminded me of this song: Say My Name.

I know you say that I am assuming things
Something’s going down that’s the way it seems
Shouldn’t be the reason why you’re acting strange
….

But… Steve… isn’t your name “many”?