You can’t make this stuff up.  A priest, a rabbi, and a minister…no wait. A Prius, a Tesla, and an SUV…

3 car pileup - Prius, Tesla, SUV: caption your own joke

No, this isn’t a Photoshop trick. Proving truth is indeed stranger than fiction, it actually happened in Denmark on October 9th. It is likely the only accident of its kind in the world.

WUWT reader Lars Seiersen gives us the details and translation of this newspaper article that appeared in Ekstra Bladet:

Lars writes:

Dear Anthony,
I think the more hardcore part of your American readers will like this and provide a number of funny comments and interpretations:

http://ekstrabladet.dk/biler/bil_nyheder/article1243890.ece
(in Danish)

It is a weird car accidence:
Ocober 9, Northern Part of Jutland (near Skagen)

A VW Tuareg (a rare bird in DK because of the price, one of the biggest and most incorrect cars you find in DK) is stopped on a small road due to construction works.
Behind it stops a Tesla (an extremely rare bird – I have never seen one!).
Then a Toyota Prius ( also rare in DK) for some reason doesnt stop and pushes the Tesla under the Tuareg!
Nobody was seriously hurt!

By William M. Briggs, professional statistician

“J’accuse! A statistician may prove anything with his nefarious methods. He may even say a negative number is positive! You cannot trust anything he says.”

Sigh. Unfortunately, this oft-hurled charge is all too true. I and my fellow statisticians must bear its sad burden, knowing it is caused by our more zealous brethren (and sisthren). But, you know, it really isn’t their fault, for they are victims of loving not wisely but too well their own creations.

First, a fact. It is true that, based on the observed satellite data, average global temperatures since about 1998 have not continued the rough year-by-year increase that had been noticed in the decade or so before that date. The temperatures since about 1998 have increased in some years, but more often have they decreased. For example, last year was cooler than the year before last. These statements, barring unknown errors in the measurement of that data, are taken as true by everybody, even statisticians.

Th AP gave this data—concealing its source—to “several independent statisticians” who said they “found no true temperature declines over time” (link)

How can this be? Why would a statistician say that the observed cooling is not “scientifically legitimate”; and why would another state that noticing the cooling “is a case of ‘people coming at the data with preconceived notions’”?

Are these statisticians, since they are concluding the opposite of what has been observed, insane? This is impossible: statisticians are highly lucid individuals, its male members exceedingly handsome and charming. Perhaps they are rabid environmentalists who care nothing for truth? No, because none of them knew the source of the data they were analyzing. What can account for this preposterous situation!

Love. The keen pleasures of their own handiwork. That is, the adoration of lovingly crafted models.

Let me teach you to be a classical statistician. Go to your favorite climate site and download a time series picture of the satellite-derived temperature (so that we have no complications from mixing of different data sources); any will do. Here’s one from our pal Anthony Watts.

Now fetch a ruler—a straight edge—preferably one with which you have an emotional attachment. Perhaps the one your daughter used in kindergarten. The only proviso is that you must love the ruler.

Place the ruler on the temperature plot and orient it along the data so that it most pleases your eye. Grab a pencil and draw a line along its edge. Then, if you can, erase all the original temperature points so that all you are left with is the line you drew.

If a reporter calls and asks if the temperature was warmer or colder last year, do not use the original data, which of course you cannot since you erased it, but use instead your line. According to that very objective line the temperature has obviously increased. Insist on the scientificity of that line—say that according to its sophisticated inner-methodology, the pronouncement must be that the temperature has gone up! Even though, in fact, it has gone down.

Don’t laugh yet, dear ones. That analogy is too close to the truth. The only twist is that statisticians don’t use a ruler to draw their lines—some use a hockey stick. Just kidding! (Now you can laugh.) Instead, they use the mathematical equivalent of rulers and other flexible lines.

Your ruler is a model Statisticians are taught—their entire training stresses—that data isn’t data until it is modeled. Those temperatures don’t attain significance until a model can be laid over the top of them. Further, it is our credo to, in the end, ignore the data and talk solely of the model and its properties. We love models!

All this would be OK, except for one fact that is always forgotten. For any set of data, there are always an infinite number of possible models. Which is the correct one? Which indeed!

Many of these models will say the temperature has gone down, just as others will say that it has gone up. The AP statisticians used models most familiar to them; like “moving averages of about 10 years” (moving average is the most used method of replacing actual data with a model in time series); or “trend” models, which are distinct cousins to rulers.

Since we are free to choose from an infinite bag, all of our models are suspect and should not be trusted until they have proven their worth by skillfully predicting data that has not yet been seen. None of the models in the AP study have done so. Even stronger, since they said temperatures were higher when they were in fact lower, they must predict higher temperatures in the coming years, a forecast which few are making.

We are too comfortable with this old way of doing things. We really can prove anything we want with careful choice of models.

But they do say that “first ice-free summer expected to occur between 2060 and 2080″. By then there will be nobody that remembers this forecast.

Yet on the same day, bumbling Arctic explorer Pen Hadow says in a UK Telegraph interview:

“To all intents and purposes the Arctic will be ice free in a decade. I do find the implications of this happening in my lifetime quite shocking.“.

Gosh, who to believe? Somebody that fakes biotelemetry data or somebody that won’t hand over climate data for replication studies?

From a Met Office press release on October 15th

The extent of Arctic sea ice has been decreasing since the late 1970s. In 2007 it decreased dramatically in a single year, reaching an all-time low. At the time it was widely reported that this was caused by man-made climate change and that the rate of decline of summer sea ice was increasing.

Modelling of Arctic sea ice by the Met Office Hadley Centre climate model shows that ice invariably recovers from extreme events, and that the long-term trend of reduction is robust — with the first ice-free summer expected to occur between 2060 and 2080. It is unlikely that the Arctic will experience ice-free summers by 2020.

Analysis of the 2007 summer sea-ice minimum has subsequently shown that this was due, in part, to unusual weather patterns. Arctic weather systems are highly variable year-on-year and the prevailing winds can enhance, or oppose, the southward flow of ice into the Atlantic. Consequently, the sea ice has not declined every year, but has shown considerable variability — both in extent and thickness.

The high variability has made it difficult to attribute the observed trend to man-made emissions of greenhouse gases, although there is now enough data to detect a human signal in the 30-year trend. The trend and observed variability, including the minimum extent observed in 2007, is consistent with climate modelling from the Met Office.

About half of the climate models involved in the Intergovernmental Panel on Climate Change fourth assessment report, show that ice declines in steps — failing to recover from extreme years. The observed temporary recovery from the 2007 minimum in 2008 and 2009 indicates that the Arctic ice has not yet reached a tipping point, if such exists. We expect Arctic ice to continue to decline in line with increasing global temperatures. If the rate of global temperature rise increases then so will the rate of Arctic sea-ice decline.

h/t to WUWT reader Patrick Davis

I’m off this weekend – talk quietly and politely amongst yourselves. Don’t make me come back here.

If you have something worth posting on the front page, flag a moderator.  In the meantime I have a couple of stories that will post using the WordPress scheduler. – Anthony

From World Climate Report: Antarctic Ice Melt at Lowest Levels in Satellite Era

Where are the headlines? Where are the press releases? Where is all the attention?

The ice melt across during the Antarctic summer (October-January) of 2008-2009 was the lowest ever recorded in the satellite history.

Such was the finding reported last week by Marco Tedesco and Andrew Monaghan in the journal Geophysical Research Letters:

A 30-year minimum Antarctic snowmelt record occurred during austral summer 2008–2009 according to spaceborne microwave observations for 1980–2009. Strong positive phases of both the El-Niño Southern Oscillation (ENSO) and the Southern Hemisphere Annular Mode (SAM) were recorded during the months leading up to and including the 2008–2009 melt season.

Figure 1. Standardized values of the Antarctic snow melt index (October-January) from 1980-2009 (adapted from Tedesco and Monaghan, 2009).

The silence surrounding this publication was deafening.

It would seem that with oft-stoked fears of a disastrous sea level rise coming this century any news that perhaps some signs may not be pointing to its imminent arrival would be greeted by a huge sigh of relief from all inhabitants of earth (not only the low-lying ones, but also the high-living ones, respectively under threat from rising seas or rising energy costs).

But not a peep.

But such is not always the case—or rather, such is not ever the case when ice melt is pushing the other end of the record scale.

For instance, below is a collection of NASA stories highlighting record high amounts of melting (or in most cases, simply higher than normal amounts in some regions) across Greenland in each of the past 3 years, as ascertained by Marco Tedesco (the lead author of the latest report on Antarctica):

NASA Researcher Finds Days of Snow Melting on the Rise in Greenland

“In 2006, Greenland experienced more days of melting snow and at higher altitudes than average over the past 18 years, according to a new NASA-funded project using satellite observations….”

NASA Finds Greenland Snow Melting Hit Record High in High Places

“A new NASA-supported study reports that 2007 marked an overall rise in the melting trend over the entire Greenland ice sheet and, remarkably, melting in high-altitude areas was greater than ever at 150 percent more than average. In fact, the amount of snow that has melted this year over Greenland is the equivalent of more than twice the surface size of the U.S…”

Melting on the Greenland Ice Cap, 2008

“The northern fringes of Greenland’s ice sheet experienced extreme melting in 2008, according to NASA scientist Marco Tedesco and his colleagues.”

And lest you think that perhaps NASA hasn’t had any data on ice melt across Antarctica in past years, we give you this one:

NASA Researchers Find Snowmelt in Antarctica Creeping Inland

“On the world’s coldest continent of Antarctica, the landscape is so vast and varied that only satellites can fully capture the extent of changes in the snow melting across its valleys, mountains, glaciers and ice shelves. In a new NASA study, researchers [including Marco Tedesco] using 20 years of data from space-based sensors have confirmed that Antarctic snow is melting farther inland from the coast over time, melting at higher altitudes than ever and increasingly melting on Antarctica’s largest ice shelf.”

But this time around, nothing, nada, zippo from NASA when their ice melt go-to guy Marco Tedesco reports that Antarctica has set a record for the lack of surface ice melt (even more interestingly coming on the heels of a near-record low ice-melt year last summer).

So, seriously, NASA, what gives? If ice melt is an important enough topic to warrant annual updates of the goings-on across Greenland, it is not important enough to elucidate the history and recent behavior across Antarctica?

(These are not meant as rhetorical questions)

Reference

Tedesco M., and A. J. Monaghan, 2009. An updated Antarctic melt record through 2009 and its linkages to high-latitude and tropical climate variability. Geophysical Research Letters, 36, L18502, doi:10.1029/2009GL039186.

August 2009 Global Temperature Update: +0.23 deg. C

Dr. Roy Spencer September 4th, 2009

Spurious SST Warming Revisited

Dr. Roy Spencer August 31st, 2009

My previous post described what I called “smoking gun” evidence of a spurious drift in the NOAA sea surface temperature (SST) product when compared to SSTs from the TRMM satellite Microwave Imager (TMI). The drift seemed to be mostly confined to 2001, almost a ’step’ jump. The moored buoy validation statistics of the TMI sea surface temperatures from Frank Wentz’s web site (SSMI.com) suggested that the TMI SSTs had good long-term stability.

But 2001 was also the year that the TRMM satellite was boosted into a higher orbit, which concerned me. I asked Frank about the effect of this event on the TMI SSTs (which also come from his web site). Frank couldn’t remember the details, but said he spent quite a bit of time correcting for the altitude change on the retrieved SSTs since the microwave emission of the sea surface depends upon the TMI instrument’s view angle with respect to the local vertical.

I know from our many years of work together on the AMSR-E Science Team that Frank is indeed a careful researcher, yet it seemed like more than a coincidence that the TMI and NOAA sea surface temperatures diverged during the same year as the orbit boost. So, I went back to see what might have caused the problem. I went back and thought about the different ways in which one can compute area averages from satellite data.

To make a long story short, because the orbit boost caused the TMI to be able to “see” to slightly higher latitudes, the way in which individual latitude bands are handled has a significant impact on the resulting temperature anomalies that are computed over time. The previous results I presented were for the 40N to 40S latitude band, which is nominally what the TMI instrument sees today. But before 2001, the latitudinal extent was slightly smaller than it was after 2001.

As shown in the following figure, if I restrict the latitude range to 38N to 38S, which was always covered during the entire TRMM mission, I find that the divergence between the TMI and NOAA average SST measurements essentially disappears.

Click for larger image

Even though I was processing the NOAA and TMI datasets in the same manner, I should NOT have been. This is because there were not as many gridpoints over cooler SST regions going into the ‘global’ averages before the satellite altitude boost as after the boost. So, for example, one must be very careful in computing a latitude band average, say from 39N to 40N, to make sure that there has been no long-term change in the sampling of that band.

Based upon the above comparisons, I would now say there is no statistically significant difference in the SST trends since 1998 between TMI, the NOAA ERSSTv3b product, and the HadSST2 product. And it does look like July 2009 might well have experienced a warmer SST anomaly than July 1998, as was originally claimed by NOAA. (Remember, TMI can not see all of the global oceans, just equatorward of about 40 deg. N and S latitude.)

In the bottom panel of the above figure, I also have a comparison between the TMI and AMSR-E sea surface temperatures, which are available only since June of 2002 from the Aqua satellite. As can be seen, there is no evidence of a calibration (or sampling) drift in that comparison either.

So, what’s the moral of this story? Always question your results…even after finding the obvious errors. And maybe I should eliminate the term ’smoking gun evidence’ from any results I describe in the future.

Oh…and don’t believe everything you read on the internet.

From NASA’s hurricane/tropical cyclone page

NASA's MODIS instrument on the Aqua satellite captured Typhoon Vamco on August 24 at 0255 UTC (August 23 at 10:55 p.m. EDT). Credit: NASA/MODIS Rapid Response Team

Typhoon Vamco is being as stubborn in its quest to live in the Pacific Ocean as Bill is in the Atlantic Ocean this past week, and NASA satellite data confirmed that the large storm has a huge eye, about 45 miles in diameter!

NASA’s Aqua satellite flew over Typhoon Vamco early today, Monday, August 24, and infrared imagery from Aqua’s Atmospheric Infrared Sounder (AIRS) instrument clearly showed Vamco’s 45-mile in diameter eye. Around the huge eye, AIRS showed Vamco’s cold high thunderstorm cloud temperatures were colder than minus 63 Fahrenheit. That’s an indication that the storm is still strong, and it is still a category one typhoon.

Also on NASA’s Aqua satellite, the Moderate Imaging Spectroradiometer (MODIS) instrument captured a stunning look at Typhoon Vamco’s clouds at 0255 UTC (August 23 at 10:55 p.m. EDT).

On August 24, Vamco was 990 miles northwest of Wake Island, near 32.1 north and 155.0 east. It was moving north near 12 mph and had sustained winds near 85 knots (97 mph) creating 25 foot-high waves in the open ocean. Typhoon Vamco is maintaining strength and will start to weaken later as upper-level winds start to batter the storm.

Vamco is currently in the Northern West Pacific Ocean and will begin changing to an extra-tropical storm tomorrow while moving into the North Central Pacific Ocean. The extreme west Aleutian Islands will see Vamco as a non-tropical low by Wednesday (today).

The Aleutian Islands are a chain of 300 small volcanic islands in the Northern Pacific Ocean that extend about 1,200 miles west from the Alaska Peninsula toward the Kamchatka Peninsula. They are the westernmost part of the United States.

> View larger image
Aqua’s Atmospheric Infrared Sounder (AIRS) instrument captured Vamco’s frigid cloud temperatures at the same time. The image revealed Vamco’s huge eye 45 miles in diameter, and indicated Vamco’s high thunderstorm cloud temperatures were colder than minus 63 Fahrenheit.
Credit: NASA/JPL, Ed Olsen

Hot off the press from Dr. Roy Spencer. After being essentially zero last month, we have a jump to .410°C in July. This was not unexpected, as a El Nino has been developing.

July 2009 Global Temperature Update: +0.41 deg. C

August 5th, 2009

Guest post by John Goetz

The GISStemp Step 1 code combines “scribal records” (multiple temperature records collected at presumably the same station) into a single, continuous record. There are multiple detailed posts on Climate Audit (including this one) that describe the Step 1 process, known affectionately as The Bias Method.

On the surface seems like a reasonable concept, and in reading HL87 the description of the algorithm makes complete sense. In simple terms, HL87 says that:

1. The longest available record is compared with the next longest record, and the period of overlap between the two records is identified.
2. The average temperature during the period of overlap is calculated for each station.
3. The difference between the average temperature for the longer station and shorter station is calculated, and that difference (a bias) is added to all temperatures of the shorter station to bias it – bringing it in line with the longer station.
4. The two records can now be combined as one, and the process repeats for additional records.

In looking at numerous stations with multiple records, more often than not the temperatures during the period of overlap are identical, so one would expect the bias to be zero. However, we often see a slight bias existing in the GISS results for such stations, and over the course of combining multiple records, that bias can be several tenths of a degree.

This was one of Steve McIntyre’s many puzzles, and we eventually figured out why we were getting bias when two records with identical overlap periods were combined: GISStemp estimates the averages during the overlap period.

GISStemp does not take the monthly data during the overlap period and simply average it. Instead, it calculates seasonal averages from monthly averages (for example, winter is Dec-Jan-Feb), and then it calculates annual averages from the four seasonal averages. If a single monthly average is missing, the seasonal average is estimated. This estimate is based on historical data found in the individual scribal record. If two records are missing the same data point (say, March 1989), but one record covers 1900 – 1990 and the other 1987 – 2009, they will each produce a different estimate for March, 1989.  All other data points might match during the period of overlap, but a bias will be introduced nonetheless.

The GISS algorithm forces at least one estimation to always occur. The records used begin with January data, but the winter season includes the previous December. That December datapoint is always missing from the first year of a scribal record, which means the first winter season and first annual temperature in each scribal record is estimated. Thus, if two stations overlap from January 1987 through December 1990 (a common occurance), and all overlapping temperatures are identical, a bias will be applied because the 1987 annual temperature for the newer record will be estimated.

Obviously, the bias could go either way: it could warm or cool the older records. With a large enough sample size, one would expect the average bias to be near zero. So what does the average bias really look like? Using the GISStemp logs from June, 2009, the average bias on a yearly basis across 7006 scribal records was:

Guest post by John Goetz

#17 - Selinsgrove, PA (in 2003)

The GISS temperature record, with its various adjustments, estimations, and re-estimations, has drawn my attention since I first became interested in the methods used to measure a global temperature. In particular, I have wondered how the current global average can even be compared with that of 1987, which was produced using between six and seven times more stations than today. Commenter George E. Smith noted accurately that it is a “simple failure to observe the standard laws of sampled data systems.” GISS presents so many puzzles in this area, it is difficult to know where to begin.

My recent post on the June, 2009 temperature found that the vast majority of temperatures were taken from airports and urban stations. This would cause some concern if the urban heat island (UHI) effect were not accounted for in those stations. GISS does attempt to filter out UHI from urban stations by using “nearby” rural stations – “nearby” meaning anything within 1000 KM. No attempt is made to filter UHI from airports not strictly listed as urban.

If stations from far, far away can be used to filter UHI, then it stands to reason some stations may be used multiple times as filters for multiple urban stations. I thought it would be amusing to list which stations were used the most to adjust for UHI. Fortunately, NASA prints that data in the PApars.statn.use.GHCN.CL.1000.20 log file.

The results were as I expected – amusing. Here are the top ten, ranked in order of the number of urban stations they help adjust:

Unfortunately, having three of the top four stations located at airports was the the sort of thing I expected.

Looking a little further, it turns out all of the top 100 stations are in either the US or Canada, and none of those 100 stations have reported data since 2007. (By the way, #100 is itself used 147 times.) Several of the top-100 stations have been surveyed by surfacestations.org volunteers who have documented siting issues, such as the following:

• Mohonk Lake, N.Y. (197 times) – much too close to ground, shading issues, nearby building
• Falls Village, Conn. (193 times) – near building and parking lot
• Cornwall, Vt. (187 times) – near building
• Northfield, Vt. (187 times) – near driveway, building
• Enosburg Falls, Vt. (180 times) – adjacent to driveway, nearby building.
• Greenwood, Del. (171 times) – sited on concrete platform
• Logan, Iowa (164 times) – near building, concrete slabs
• Block Island, R.I. (150 times) – adjacent to parking lot and aircraft parking area.

The current state of a rural station, however, is an insufficient criterion for deciding to use it to adjust the history of one or more other urban stations. The rural station’s history must be considered as well, with equipment record and location changes being two of the most important considerations.

Take for example good ‘ole Crawfordsville, which came in at #23, having been used 219 times. As discussed here, Crawfordsville’s station lives happily on a farm, and does seem to enjoy life in the country. However, up until 16 years ago the station lived in the middle of Crawfordsville, spending over 100 years at Wabash College and at the town’s power plant.

Guest post by John Goetz

As noted in the previous post, GISS has released their monthly global temperature summary for June, 2009. This month’s whopping anomaly of 0.63C is once again much higher than that of RSS, UAH, and even NOAA, which is the source of the GISS temperature data. Not only is the anomaly higher than the other metrics, but it is trending in the opposite direction.

Temperature data from 1079 stations worldwide contributed to the analysis, 134 of them being located in the 50 US states. Data from essentially the same few stations have been used for the past twenty-four months. Many, many hundreds of stations that have historically been included in the record and still collect data today continue to be ignored by GISS in global temperature calculations.

Once again, the bulk of temperatures comprising the present-day worldwide GISS average come from airports – in this case 554 airports, according to the NOAA metadata from the V2 station inventory. In the US, the ratio of airports to total stations continues to run very high, with 121 out of the 134 reporting stations being located at airports.

Why worry about airports? Aside from recent posts on this site documenting problems with airport ASOS equipment in the US, WUWT has also documented a number of equipment siting problems, notably the typical close proximity of the equipment to a tarmac heat sink. Airports can introduce a mini-UHI effect where one would otherwise not be found.

The NOAA metadata is not entirely accurate, and several stations located at airports are not noted as such. Some examples include Londrina and Brasilia in Brazil, Ely / Yelland in Nevada, and Broome in Austrailia. Those stations were easy to find because they had “airport” (or some variant) in the station name. A check of coordinates using Google Earth confirmed the airport locations.

Let’s examine the metadata a little further, shall we?

NOAA says that 345 of the stations it passes on to GISS are rural and presumably free of UHI influence. Fifteen of those stations are located in the US. However, only 201 of those rural stations are not located at an airport, and therefore presumably free of UHI effects (including tarmac heat sinks). In the US, only one of the fifteen stations is listed as both rural, and not located at an airport: Ely / Yelland in Nevada.

Doh!!! As noted above, that station is located at an airport – confirmed not just by Google Earth, but also by NOAA’s NCDC website as well! This means that all of the US temperatures – including those for Alaska and Hawaii – were collected from either an airport (the bulk of the data) or an urban location.

As for the rest of the world, some of the stations listed as being rural and not at an airport have metadata indicating they are located in an area of “dim” or “bright” lights. Filtering those out, we find a total of 128 stations that are rural, not at an airport, and “dark”.

Why are “dark” stations important? Recall that GISS uses dark stations to adjust for UHI in the urban stations. With only 128 dark stations available, none being in the US, it would seem this is an impossible task.

Fortunately, GISS adjustment rules allow old data to be used in adjusting new data. The older “non-reporting” rural weather stations continue to adjust reporting urban stations, even though the most recent two years of overlap is missing.

Thankfully, the algorithms are robust enough to calculate adjustments to the 100th of a degree even when data is missing.

I was surprised to learn today, that one of the most prominent newspapers in the USA, the Orange County Register in the Los Angeles area, carried an editorial of which my work was the subject. It is quite a turnaround from the brush off I got last year by their Science Dude blogger who wrote a story on the warming of Santa Ana, CA.

By the way here is what the official NOAA weather station for Santa Ana looks like, note the a/c heat exchanger exhausts:

Santa Ana Station looking North.  Click for a larger image

The editorial about my work was published in the OC Register on Monday, June 1st. I’ve reposted it below.

Editorial: Cooling down with global-warming data

U.S. and world temperature records are compromised by monitoring station errors.

You have to wonder- what were these guys thinking? The only media visual they could have chosen that would send a worse message of forecast certainty was a dart board…or maybe something else?

MIT's "wheel of climate" - image courtesy Donna Coveney/MIT

From Popular Science:

The Greenhouse Gamble: Ronald Prinn, director of MIT’s Center for Global Change Science, and his group have revised their model that shows how much hotter the Earth’s climate will get in this century without substantial policy change. Standing with the group’s “roulette wheel” are, from left to right, Mort Webster, professor in the Engineering Systems Division; Adam Schlosser, principal research scientist at the Center for Global Change Science; Prinn, the TEPCO Professor of Atmospheric Chemistry; and Sergey Paltsev, principal research scientist, MIT Energy Initiative.

Popular Science writes:

It’s time to call your bookie, because the line on global warming is in. A new paper from MIT breaks down the odds of different outcomes from global warming, based on whether governments take action now or later. And if you’re taking that action, bet on “government getting involved” to beat the spread, as last week an important climate change bill made it out committee in the House of Representatives.

The bill, named the American Clean Energy and Security Act, would institute a cap-and-trade program, and reduce carbon emissions by 17 percent over fifteen years. The plan also calls for increased research into alternative energy, and provides $750 billion in subsidies to consumers to help offset the increase in energy cost the bill would cause.

See the compete article here

With that kind of cash payout, and since an MIT odds calculating machine is involved in making the modeling forecasts over 400 model runs, maybe this would be a more appropriate prop for the MIT photo op:

OR…. There are Increases in Trend with Each Update While The Causes of Downward Biases Are Deleted

Guest Essay by Bob Tisdale:

In the recent WUWT post Something hinky this way comes: NCDC data starts diverging from GISS, the differences between GISS and NCDC global temperature anomaly data was discussed. I commented that the GISS and NCDC global surface temperature anomaly data relied on two different SST datasets.

NCDC has their own SST anomaly dataset for their global surface temperature product, and they calculate anomalies against the base years of 1901 to 2000. GISS has used the NCDC OI.v2 SST anomaly data since December 1981, and before that they had used the Hadley Centre’s HADSST data. GISS then splices the two datasets together. This post does not discuss the HADSST data, but delves into the differences between the multiple NCDC SST anomaly datasets, one of which is used by GISS.

GRAPHS OF GLOBAL OI.v2 (USED BY GISS) and “NCDC Ocean” SST ANOMALY DATA

I have not been able to find GISS SST anomaly data as a separate dataset, so for a short-term comparison, I’ll use their source, the OI.v2 SST anomaly data available through the NOAA NOMADS system. Unfortunately, the OI.v2 SST data uses a third climatology for their anomalies (with base years of 1971-2000), but don’t let that concern you. It just makes for an unusual comparative graph.

Figure 1 is a short-term comparison (November 1981 to April 2009) of the OI.v2 Global SST anomaly data (used by GISS) and the NCDC’s “Global Ocean Temperature”. The NCDC data is available toward the bottom of the NCDC Global Surface Temperature Anomalies webpage here:
http://www.ncdc.noaa.gov/oa/climate/research/anomalies/index.php
Specifically:
ftp://ftp.ncdc.noaa.gov/pub/data/anomalies/monthly.ocean.90S.90N.df_1901-2000mean.dat
http://i41.tinypic.com/sec4kh.jpg
Figure 1

The two datasets appear to track one another, and the obvious difference, the shift in the data, is a result of the different base years. But if we subtract the OI.v2 SST data from the NCDC “Global Ocean” SST anomaly data, we can see that one dataset rose more than the other since November 1981. Refer to Figure 2. The NCDC “Global Ocean” SST anomaly dataset rose at a greater rate than the OI.v2 SST anomaly data that’s used by GISS. This would bias the NCDC global surface temperature upward over this time span, or bias the GISS data down, depending on your point of view.
http://i39.tinypic.com/qzlsvo.jpg
Figure 2

So to conclude this section of this post, part of the difference between the GISS and NCDC global surface temperatures discussed in WUWT post Something hinky this way comes: NCDC data starts diverging from GISS results from the use of different SST anomaly datasets.

WHAT’S THE DIFFERENCE BETWEEN THE TWO DATASETS?

The use of satellite data appears to have an impact.

NOAA describes the Optimum Interpolation (OI.v2) SST anomaly data (used by GISS) as, “The optimum interpolation (OI) sea surface temperature (SST) analysis is produced weekly on a one-degree grid. The analysis uses in situ and satellite SST’s plus SST’s simulated by sea-ice cover.” The in situ data is from buoy and ship measurements. The full description of the OI.v2 data is here:
http://www.cdc.noaa.gov/data/gridded/data.noaa.oisst.v2.html

The NCDC identifies the “Global Ocean Temperature” dataset as SR05 in its Global Surface Temperature Anomalies webpage:
http://www.ncdc.noaa.gov/oa/climate/research/anomalies/index.php#sr05

Linked to the webpage is a paper by Smith et al (2005) “New surface temperature analyses for climate monitoring” GEOPHYSICAL RESEARCH LETTERS, VOL. 32, L14712, doi:10.1029/2005GL023402, 2005.
http://www.ncdc.noaa.gov/oa/climate/research/Smith-comparison.pdf

On page 2, Smith et al describe the SR05 data as, “The SR05 SST is based on the International Comprehensive Ocean Atmosphere Data Set (ICOADS [Woodruff et al., 1998]). It uses different, though similar, historical bias adjustments to account for the change from bucket measurements to engine intake SSTs [Smith and Reynolds, 2002]. In addition, SR05 is based on in situ data.”

It appears, from that quote and the rest of the paper, the SR05 SST dataset does NOT use satellite data. This is consistent with NCDC’s other long-term SST datasets. They also abstain from satellite data.

COMPARISON OF SR05 TO THE NCDC’s OTHER TWO SST ANOMALY DATSETS

In addition to the SR05 SST data, the NCDC also has two other long-term SST datasets called Extended Reconstructed SST (ERSST) data. The ERSST.v2 (Version 2) data was introduced in 2004 with the Smith and Reynolds (2004) paper Improved Extended Reconstruction of SST (1854-1997), Journal of Climate, 17, 2466-2477. Many of my early Smith and Reynolds SST Posts used ERSST.v2 data through the NOAA NOMADS system. Unfortunately, ERSST.v2 data is no longer available through that NOAA system, so the latest ERSST.v2 global SST anomaly data from NOMADS I have on file runs through October 2008.

The ERSST.v2 data was updated with ERSST.v3 data. In my opinion, it provides the most detailed analysis of high latitude SST in the Southern Hemisphere (the Southern Ocean). The ERSST.v3 data was introduced last year with the Smith et al (2008) paper: Improvements to NOAA’s Historical Merged Land-Ocean Surface Temperature Analysis (1880-2006), Journal of Climate,21, 2283-2296. The NCDC updated it with their ERSST.v3b version later in 2008, but more on that later. A limited number of datasets (based on latitude) for the ERSST.v3b data are available from NCDC (though it is available on a user-selected coordinate basis through the KNMI Climate Explorer website, as is ERSST.v2 data).

I have found no source of SR05 SST anomaly data, other than the Global, Northern Hemisphere, and Southern Hemisphere “Ocean Temperature” datasets linked to the Global Surface Temperature webpage.

Figures 3 and 4 are long-term comparisons (1880 to “present”) of the “NCDC Global Ocean” (SR05) SST anomaly data to the ERSST.v2 and to the ERSST.v3b SST anomalies. Based on the linear trends, the “NCDC Global Ocean” (SR05) data resides between the older ERSST.v2 and the more recent ERSST.v3b data.
http://i40.tinypic.com/am84ma.jpg
Figure 3
########
http://i43.tinypic.com/2u9pwk6.jpg
Figure 4

But note that the trend increases with each SST dataset improvement.

THE ERSST.v3 DATASET ONCE USED SATELLITE DATA

In “Improvements to NOAA’s Historical Merged Land-Ocean Surface Temperature Analysis (1880-2006)”, Smith et al note the use of satellite data for ERSST.v3 data in their abstract, “Beginning in 1985, improvements are due to the inclusion of bias-adjusted satellite data.” That’s a positive description. They further proclaim, “Of the improvements, the two that have the greatest influence on global averages are better tuning of the reconstruction method and inclusion of bias adjusted satellite data since 1985.” In fact there is a whole subsection in the paper about the satellite adjustments.

WHY THEN DID THE NCDC DELETE THE SATELLITE DATA IN THE MOST RECENT VERSION, ERSST.v3b?

Reynolds, Smith, and Liu write in a November 14, 2008 attachment to their main ERSST.v3b webpage, “In the ERSST version 3 on this web page WE HAVE REMOVED SATELLITE DATA from ERSST and the merged product. The addition of satellite data caused problems for many of our users. Although, the satellite data were corrected with respect to the in situ data as described in reprint, there was a residual cold bias that remained as shown in Figure 4 there. The bias was strongest in the middle and high latitude Southern Hemisphere where in situ data are sparse. THE RESIDAL BIAS LED TO A MODEST DECREASE IN THE GLOBAL WARMING TREND AND MODIFIED GLOBAL ANNUAL TEMPERATURE RANKINGS.” [Emphasis added.]

The link for that quote is here:
http://www.ncdc.noaa.gov/oa/climate/research/sst/papers/merged-product-v3.pdf

Note that the “merged product” referenced above is their ERSST.v3b-based land plus sea surface temperature data.

Figure 5 illustrates the difference between the ERSST.v3b and ERSST.v3 global SST anomaly data (ERSST.v3 data MINUS ERSST.v3b data). The “dip” after 1985 would appear to be the satellite bias.
http://i43.tinypic.com/6yfx8h.jpg
Figure 5

Hmmm. It looks as though, if you’re a SST data producer, downward biases are bad, but increases in trend with each update are good.

SOURCES

The ERSST.v3b SST anomaly data is available through the NCDC’s ERSST.v3 webpage:
http://www.ncdc.noaa.gov/oa/climate/research/sst/ersstv3.php
Link to the available datasets:
ftp://eclipse.ncdc.noaa.gov/pub/ersstv3b/pdo
I used this dataset for this post:
ftp://eclipse.ncdc.noaa.gov/pub/ersstv3b/pdo/aravg.mon.ocean.90S.90N.asc

The NCDC’s “Global Ocean Temperature” dataset is available through:
http://www.ncdc.noaa.gov/oa/climate/research/anomalies/index.php
Specifically:
ftp://ftp.ncdc.noaa.gov/pub/data/anomalies/monthly.ocean.90S.90N.df_1901-2000mean.dat

ERSST.v2 data are no longer available through the NOAA NOMADS System. I relied on ERSST.v2 global SST anomaly data from my files for this post. I also used the ERSST.v3 I also had on file for the comparison to the ERSST.v3b data.

The OI.v2 data is available through the NOAA NOMADS system:
http://nomad3.ncep.noaa.gov/cgi-bin/pdisp_sst.sh?lite

Well, it took me a week to get it, and finally here it is. Last Friday, May 9th as you may recall NOAA’s Space Weather Prediction Center had a teleconference which I was not invited to listen in on. But, Doug Biesecker kindly provided the audio.

This press teleconference coincided with SWPC’s announcement of going with the lower Solar cycle 24 prediction curve.

You can listen to the MP3 audio of the teleconference here

Your media player or MP3 player should open automatically.

UPDATE: Admittedly, the SWPC audio is a bit rough. WUWT reader John Sayers has run the MP3 through some audio processing to improve the quality. His improved version is here.

The quote of interest in their new update (from Spaceweather.com)  is:

A new active period of Earth-threatening solar storms will be the weakest since 1928 and its peak is still four years away, after a slow start last December, predicts an international panel of experts led by NOAA’s Space Weather Prediction Center.

Here is the new sunspot prediction graph, and combined it with the previous prediction as an overlay, which I have presented below:

click for larger image – note this is an overlay done by WUWT

Here is the “press release” as feature story from spaceweather.com

http://www.spaceweather.com/headlines/y2009/08may_noaaprediction.htm

May 8, 2009: A new active period of Earth-threatening solar storms will be the weakest since 1928 and its peak is still four years away, after a slow start last December, predicts an international panel of experts led by NOAA’s Space Weather Prediction Center. Even so, Earth could get hit by a devastating solar storm at any time, with potential damages from the most severe level of storm exceeding $1 trillion. NASA funds the prediction panel.

Solar storms are eruptions of energy and matter that escape from the sun and may head toward Earth, where even a weak storm can damage satellites and power grids, disrupting communications, the electric power supply and GPS. A single strong blast of solar wind can threaten national security, transportation, financial services and other essential functions.

The panel predicts the upcoming Solar Cycle 24 will peak in May 2013 with 90 sunspots per day, averaged over a month. If the prediction proves true, Solar Cycle 24 will be the weakest cycle since number 16, which peaked at 78 daily sunspots in 1928, and ninth weakest since the 1750s, when numbered cycles began.

The most common measure of a solar cycle’s intensity is the number of sunspots—Earth-sized blotches on the sun marking areas of heightened magnetic activity. The more sunspots there are, the more likely it is that solar storms will occur, but a major storm can occur at any time.

“As with hurricanes, whether a cycle is active or weak refers to the number of storms, but everyone needs to remember it only takes one powerful storm to cause expensive problems,” said NOAA scientist Doug Biesecker, who chairs the panel. “The strongest solar storm on record occurred in 1859 during another below-average cycle similar to the one we are predicting.”

The 1859 storm shorted out telegraph wires, causing fires in North America and Europe, sent readings of Earth’s magnetic field soaring, and produced northern lights so bright that people read newspapers by their light.

A recent report by the National Academy of Sciences found that if a storm that severe occurred today, it could cause $1-2 trillion in damages the first year and require four to ten years for recovery, compared to $80-125 billion that resulted from Hurricane Katrina.

The panel also predicted that the lowest sunspot number between
cycles—or solar minimum—occurred in December 2008, marking the end of Cycle 23 and the start of Cycle 24. If the December prediction holds up, at 12 years and seven months Solar Cycle 23 will be the longest since 1823 and the third longest since 1755. Solar cycles span 11 years on average, from minimum to minimum.

An unusually long, deep lull in sunspots led the panel to revise its 2007 prediction that the next cycle of solar storms would start in March 2008 and peak in late 2011 or mid-2012. The persistence of a quiet sun since the last prediction has led the panel to a consensus that the next cycle will be “moderately weak.”

NOAA’s Space Weather Prediction Center (SWPC) is the nation’s first alert of solar activity and its effects on Earth. The Center’s space weather experts issue outlooks for the next 11-year solar cycle and warn of storms occurring on the Sun that could impact Earth. SWPC is also the world warning agency for the International Space Environment Service, a consortium of 12 member nations.

As the world economy becomes more reliant on satellite-based communications and interlinked power grids, interest in solar activity has grown dramatically. In 2008 alone, SWPC acquired 1,700 new subscription customers for warnings, alerts, reports, and other products. Among the new customers are emergency managers, airlines, state transportation departments, oil companies, and nuclear power stations. SWPC’s customers reside in 150 countries.

“Our customer growth reflects today’s reality that all sectors of society are highly dependent on advanced, space-based technologies,” said SWPC director Tom Bogdan. “Today every hiccup from the sun aimed at Earth has potential consequences.”

---

From the Saudi Gazette

In one of the rare occasions, Saudis enjoy the snowfall in Al-Baha city south-west of Riyadh, Tuesday. Torrential rains pouring down on Al-Baha accompanied by gusty winds were accompanied by snow capping the mountains and covering the valley areas and the forests of Al-Zaraeb and Khayrah.

The last report we had like this in mid January said that the snow and cold was the “worst in 30 years“. In January, snow isn’t unexpected in Saudi Arabia, it has happened before. But in May?

While the report says “snow”, the possibility exists that it could also be small hail from thunderstorms.

Looks like it is warming up fast though. The forecast calls for 93 degrees. Just remember, weather is not climate.

Many have inquired in comments what’s happened to the ICECAP website. My query to Joe D’Aleo was answered this morning, looks like servicide.

I noticed the outage yesterday, AM.  I had emailed and spoke to the hosting customer at [my] hosting support yesterday morning and they said server with ICECAP had hardware failure and they were working in it. I was traveling giving a talk in Chicago yesterday and assumed/hoped it was back up. Found out last evening still out with no estimate for return. I was about to upgrade to a dedicated server with them.

…

I will talk to manager when he comes in this AM. It was not a DOS [attack] it appears. I am home today so I can stay on their case. Usually the outages last minutes with reboot all that has been needed They assure me they back up everything. God forbid if not. I had 3450 stories stored.

From the :

Fox Glacier is one of the world's climate change indicators.

Research by three New Zealand scientists may have solved the mystery of why glaciers behave differently in the Northern and Southern Hemispheres.

Geologist David Barrell of GNS Science, Victoria University geomorphologist Andrew Mackintosh and glaciologist Trevor Chinn of the Alpine and Polar Processes Consultancy have helped provide definitive dating for changes in glacier behaviour.

They were part of a team of nine scientists, led by Joerg Schaefer of the Lamont-Doherty Earth Observatory at Columbia University in New York, who used an isotope-dating technique to get very precise ages for glacial deposits near Mt Cook.

They measured the build-up of beryllium-10 isotopes in surface rocks bombarded by cosmic rays to pinpoint dates when glaciers in the Southern Alps started to recede. The technology is expected to be widely applied to precisely date other glaciers around the world.

Glaciers are sensitive indicators of climate changes, usually advancing when it cools and retreating when it warms.

The first direct confirmation of differences in glacier behaviour between the Northern and Southern Hemispheres, the new work topples theories based on climate in the Northern Hemisphere changing in tandem with the climate in the Southern Hemisphere.

The research argues that at times the climate in both hemispheres evolved in sync and at other times it evolved differently in different parts of the world.

Dr Barrell said their research presented “new data of novel high precision”, though the team has so far chosen not to roll out wider interpretations too quickly.

He said much of it reinforced work done 30 years ago by Canterbury University researcher Professor Colin Burrows, who used NZ glacier data to highlight some of the similarities and differences between northern and southern records over the past 12,000 years.

The paper published in Science magazine yesterday showed the Mt Cook glaciers advanced to their maximum length 6500 years ago, and have been smaller ever since.

But glaciers in the Swiss Alps advanced to their maximum only in the past 700 years – during the Northern Hemisphere’s “Little Ice Age”, which ended about 1860.

During some warm periods in Europe, glaciers were advancing in New Zealand. At other times, glaciers were well advanced in both areas.

In a commentary which accompanied the research, Greg Balco, from the Berkeley Geochronology Centre in California, said the conclusion that glacier advances in the Northern and Southern Hemispheres were not synchronised was “unexpected”.

Dr Barrell said the paper presented only the first instalment of the dating work, and more would be revealed at an international workshop on past climates to be held at Te Papa on May 15.

“The New Zealand findings point to the importance of regional shifts in wind directions and sea surface temperatures,” he said.

Regional weather patterns such as the El Nino Southern Oscillation were superimposed on the global climate trends reflected in the behaviour of glaciers.

- NZPA

(h/t to Leon Broznya)

Guest post by Steve Goddard

An Arctic Blizzard

As reported here two weeks ago, April 30 is the last safe date to recover explorers from the Arctic.  The people who rescued Pen Hadow from the Arctic in May, 2003 said this :

“People are at risk – the ice breaks and it shouldn’t really happen. No one should expect to be picked up from there later than 30 April … Going to the Pole this time of the year is a bit stupid and you put a lot of people’s lives at risk.”

In today’s Catlin update they say:

“The ice team are positioned in the path of a huge storm. Pen reported this morning that winds are picking up, and the Ops team can see that over the next 36 hours the team will experience blizzard conditions with winds of up to 40 knots and a strong possibility of heavy snowfall.”
By the time the storm is over, they will have reached what has been described by the experts as the last safe date for recovery, both for them and for the rescuers.  They are still 542 km from the pole, which is 82 days away at their average rate of travel so far.
What is the sensible thing for them to do when the delayed Twin Otter plane (hopefully) arrives later in the week?