I’m travelling the next few days, moderation will be spotty. After tomorrow, you may find that comments may go for 12 -18 hours or more without approval.
In the meantime, you can debunk my own “hockey stick”.
Thanks to all who have come by and participated with comments and ideas!
Best Regards,
Anthony
I don’t have a lot of time today, but I found this interesting. Commenter “Basil” has offered this for discussion. So I’m putting this up without comment on my part. See also the decadal trends table below. Have at it folks.
Click for full sized image
1979:01-1992:12
---------------------------------------------
GISS 0.000783764** (0.094C/decade)
HadCRUT 0.000460122** (0.055C/decade)
RSS_MSU 0.000498964 (0.060C/decade)
UAH_MSU 1.71035E-05 (0.002C/decade)
1993:01-2001:12
---------------------------------------------
GISS 0.00174741** (0.210C/decade)
HadCRUT 0.00147990** (0.178C/decade)
RSS_MSU 0.00221135** (0.265C/decade)
UAH_MSU 0.00217023** (0.260C/decade)
2002:01-2008:1
---------------------------------------------
GISS -0.00091450 (-0.110C/decade)
HadCRUT -0.00270338** (-0.324C/decade)
RSS_MSU -0.00208111 (-0.250C/decade)
UAH_MSU -0.00130882 (-0.157C/decade)
Just two days after sunspot 983 was reported, it has now disappeared. They just aren’t sticking around like they used to. This is yet another indication of the bottomed out solar minima we are in.
It will be very interesting to see if the cycle 24 predictions by Hathway at NASA for an even stronger cycle will materialize.
Though there does seem to be more discussion of a weak cycle 24 than a strong one as of late. Personally, I think this graph of Average Planetary magnetic index (Ap) is quite telling in the step that occurred in 2005. From the data provided by NOAA’s Space Weather Prediction Center (SWPC) you can see just how little magnetic field activity there has been. I’ve graphed it below:
click for a larger image
What is most interesting about the Geomagnetic Average Planetary Index graph above is what happened around October 2005. Notice the sharp drop in the magnetic index and the continuance at low levels.
From this story on space.com where they talk about the opposing views solar scientists have for cycle 24 they offer some opinions. NOAA Space Environment Center scientist Douglas Biesecker, who chaired the panel, said in a statement:
[...] despite the panel’s division on the Sun cycle’s intensity, all members have a high confidence that the season will begin in March 2008.
We shall soon see if they are correct, March starts this Saturday.
Nature will truly be the final arbiter of this argument.
UPDATE: Jeff C writes
I thought you might find this chart interesting. Since sunspot cycles overlap and there is no clear start/stop, the “start” of the new solar cycle is usually defined as the smoothed sunspot minimum between cycles (as opposed to the appearance of the first reversed-polarity spot). Although different definitions are sometimes used, this seems to be the most common and accepted variation.
The enclosed chart shows the transition from cycle 22 to cycle 23 back in 1996. It is interesting how the first new cycle sunspots appeared over a year before the commonly accepted May 1996 start date of the new
cycle.
I’m unsure of the cycle start date definition used by Douglas Biesecker, but if it is the commonly accepted definition, he will be way off. It will be interesting to see if they claim the appearance of a few reversed cycle sunspots count as a “start”. If so, then cycle 23 actually started back in March 1995 and is 13 years old.
Click for a larger image
As I mentioned in my post here about one of the satellite data sets (RSS) that showed a marked cooling globally in 2008, La Niña and PDO seem to be drivers of this change. Here is Joe D’Aleo’s take on it below. - Anthony
By Joseph D’Aleo, CCM ICECAP
Evidence is growing this La Niña will be a longer term event. Most similar important La Niñas are often multi year events (1949-1951,1954-1956, 1961-63, 1970-1972, 1973-1976, 1998-2001). Though the easternmost Pacific near South America has warmed at the surface as the seasonal weakening of the tropical easterlies led to weakened upwelling, it is still cold beneath. Below you can see the latest depth-section of ocean temperatures (top) and anomalies (bottom). Temperature are in degree Celsius. Note the large reservoir of subsurface anomalously cold water (up to 4 degrees C) in the eastern tropical Pacific at 50 to 100 meters.
Also see the latest CPC depicted ocean heat content in the tropical Pacific. This shows the heat content remains at near maximum deficit levels.
These suggest as the easterlies increase again, cooling will return to the east Pacific and La Niña will persist at least well into 2008. The Pacific Decadal Oscillation (PDO) has dropped strongly negative (latest value from NCEP is -1.54 STD). This decline may represent another Great Pacific Climate Shift as the PDO warm and cold phases tend last 25 to 30 years and the last change , to a warm Pacific, occurred in 1976. See more in this pdf here. If indeed the PDO shift is the real deal, we might expect more La Niñas and fewer weaker El Niños over the next few decades with a net tendency for cooling. Add to that a quieter sun and eventually a cooling Atlantic, and you have a recipe for global cooling.
However, this has its own drawbacks, La Ninas bring more drought and summer heat waves, landfalling hurricanes, large tornado outbreaks, spring floods, winter snows and cold outbreaks than their more famous counterpart, El Niño, which has dominated during the warm PDO era. A while back, Stan Changnon did an interesting analysis which I reported on recently here that suggests the era we have gone through since the late 1970s with dominant El Niños was unusually benign with more benefits than damages and will be looked on as the golden era, a modern climate optimum. Even if all this is correct, you might expect the media and enviro-alarmists ‘evidence’ we are affecting our climate to morph from warming and ice melt to the climate extremes characteristic of La Niñas.
See full pdf here.
<!– –>
NOTE: Please note that part 2 is now online, please see it here.
I recently plotted all four global temperature metrics (GISS, HadCRUT, UAH, RSS) to illustrate the magnitude of the global temperature drop we’ve seen in the last 12 months. At the end of that post, I mentioned that I’d like to get all 4 metrics plotted side-by-side for comparison, rather than individually.
Of course I have more ideas than time these days to collate such things, but sympathetic reader Earle Williams voluntarily came to my rescue by collating them and providing a nice data set for me in an Excel spreadsheet last night.
The biggest problem of course is what to do with 4 different data sets that have different time spans. The simplest answer, at least for a side by side comparison is to set their time scales to be the same. Satellite Microwave Sounder Unit (MSU) data from the University of Alabama, Huntsville (UAH), and Remote Sensing Systems (RSS) of Santa Rosa, CA only go back to 1979. So the January 1979-January 2008 period is what we’ll concentrate on for this exercise as it very nearly makes up a 30 year climate period. Yes, I know some may call this an arbitrary starting point, but it the only possible point that allows comparison between land-ocean data-sets and the satellite data-sets.
Here is the first graph, the raw anomaly data as it was published this month by all the above listed sources:
Here is the source data file for this plot and subsequent plots.
4metrics_temp_anomalies.txt
I also plotted a magnified view to show the detail of the lat 12 months with notations added to illustrate the depth of the anomaly over the past 12 months.
I was particularly impressed with the agreement of the 4 metrics during the 1998 El Niño year as well as our current 2008 La Niña year.
I also ran a smoothed plot to eliminate some of the noise and to make the trends a bit more visible. For this I used a 1 year (12 month) average.
Again there is good agreement in 1998 and in 2008. Suggesting that all 4 metrics picked up the ENSO event quite well.
The difference between these metrics is of course the source data, but more importantly, two are measured by satellite (UAH, RSS) and two are land-ocean surface temperature measurements (GISS, HadCRUT). I have been critical of the surface temperature measurements due to the number of non-compliant weather stations I’ve discovered in the United States Historical Climatology Network (USHCN) from my www.surfacestations.org project.
One of the first comments from my last post on the 4 global temperature metrics came from Jeff in Seattle who said:
Seems like GISS is the odd man out and should be discarded as an “adjustment”.
Looking at the difference in the 4 times series graphs, differences were apparent, but I didn’t have time to study it more right then. This post today is my follow up to that examination.
Over on Climate Audit, there’s been quite a bit of discussion about the global representivity of the GISS data-set due to all of the adjustments that seem to have been applied to the data at locations that don’t seem to need any adjustments to compensate for things like urban heat islands. Places like Cedarville, CA and Tingo Maria, Peru both illustrate some of the oddities with the adjustment methodology used by NASA GISS. One of the issues being discussed is the application of city nightlights (used as a measure of urbanization near the station) as a proxy for UHI adjustments to be applied to cities in the USA. Some investigation has suggested that the method may not work as well as one might expect. There’s also been the issue of whether of not stations classified as rural are truly rural.
So with all of this discussion, and with this newly collated data-set handed to me today, it gave me an idea. I had never seen a histogram comparison done on all four data-sets simultaneously.
Doing so would show how well the cool and warm anomalies are distributed within the data. If there is a good balance to the distribution, one would expect that the measurement system is doing a good job of capturing the natural variance. If the distribution of the histogram is skewed significantly in either the negative or positive, it would provide clues into what bias issues might remain in the data.
Of course since we have a rising temperature trend since 1979, I would expect all 4 metrics to be more distributed on the positive side of the histogram as a given. But the real test is how well they match. All four metrics correlate well in the time series graphs above, so I would expect some correlation to be present in the histogram as well. The histograms you see below were created from the raw data from 1979-2008. No smoothing or adjustments of any kind were made to the data. The “unadjusted” data in this source data file were used: 4metrics_temp_anomalies.txt
First we have the satellite data-set from UAH:
The UAH data above looks well distributed between cool and warm anomaly. A slight warm bias, but to be expected with the positive trend since 1979.
Next we have the satellite data-set from RSS:
At first I was surprised at the agreement between UAH and RSS in the percentages of warm and cool, but then I realized that these data-sets both came from the same instrument on the spacecraft and the only difference is methodology in preparation by the two groups UAH and RSS. So it makes sense that there would be some agreement in the histograms.
Here we have the land-ocean surface data-set from HadCRUT:
Here, we see a much more lopsided distribution in the histogram. Part of this has to do with the positive trend, but other things like UHI, microsite issues with weather station placement, and adjustments to the temperature records all figure in.
Finally we have the GISS land-ocean surface data-set:
I was surprised to learn that only 5% of the GISS data-set was on the cool side of zero, while a whopping 95% was on the warm side. Even with a rising temperature trend, this seems excessive.
When the distribution of data is so lopsided, it suggests that there may be problems with it, especially since there appears to be a 50% greater distribution on the cooler side in the HadCRUT data-set.
Interestingly, like with the satellite data sets that use the same sensor on the spacecraft, both GISS and HadCRUT use many of the same temperature stations around the world. There is quite a bit of data source overlap between the two. But, to see such a difference suggests to me that in this case (unlike the satellite data) differences in preparation lead to significant differences in the final data-set.
It also suggests to me that satellite temperature data is a more representative global temperature metric than manually measured land-ocean temperature data-sets because there is a more unified and homogeneous measurement system, less potential bias, no urban heat island issues, no need of maintaining individual temperature stations, fewer final adjustments, and a much faster acquisition of the data.
One of the things that has been pointed out to me by Joe D’Aleo of ICECAP is that GISS uses a different base period than the other data-sets, The next task is to plot these with data adjusted to the same base period. That should come in a day or two.
UPDATE1: I’ve decided to make this a 3 part series, as additional interest has been generated by commenters in looking at the data in more ways. Stay tuned for parts 2 and 3 and we’ll examine this is more detail.
UPDATE2: I had mentioned that I’d be looking at this in more detail in parts 2, and 3. However it appears many have missed seeing that portion of the original post and are saying that I’ve done an incomplete job of presenting all the information. I would agree for part1, but that is what parts 2 and 3 were to be about.
Since I’m currently unable to spend more time to put parts 2 and 3 together due to travel and other obligations, I’m putting the post back on the shelf (archived) to revisit again later when I can do more work on it, including show plots for adjusted base periods.
The post will be restored then along with the next part so that people have the benefit of seeing plots and histograms done on both ways. In part 3 I’ll summarize 1 and 2.
In the meantime, poster Basil has done some work on this of interest which you can see here.
UPDATE3: Part 2 is now online, please see it here.
I found this over on Jerry Pournelle’s Chaos Manor. It seemed fitting given the discussion as of late.
Some say the world will end in fire,
Some say in ice.
From what I’ve tasted of desire
I hold with those who favor fire.
But if it had to perish twice,
I think I know enough of hate
To say that for destruction ice
Is also great
And would suffice.- Robert Frost
The Dr. Roger Pielke Sr. weblog today includes a letter from Dr. Joanne Simpson, recently retired. He calls her “among the most preeminent scientists of the last 100 years”. It seems that she really spoke her mind on the subject of climate models and the problems of the changing measurement environment around climate monitoring stations.
The full letter is here on that weblog.
Excerpt:
Since I am no longer affiliated with any organization nor receive any funding, I can speak quite frankly. [...] The main basis of the claim that man’s release of greenhouse gases is the cause of the warming is based almost entirely upon climate models.
We all know the frailty of models concerning the air-surface system. We only need to watch the weather forecasts. [...] The term “global warming” itself is very vague. Where and what scales of response are measurable? One distinguished scientist has shown that many aspects of climate change are regional, some of the most harmful caused by changes in human land use.
No one seems to have properly factored in population growth and land use, particularly in tropical and coastal areas.
[...] But as a scientist I remain skeptical. I decided to keep quiet in this controversy until I had a positive contribution to make. […] Both sides (of climate debate) are now hurling personal epithets at each other, a very bad development in Earth sciences.
I agree, enough of this sniping.
Witness the cordial exchange I have with Atmoz, a graduate student at the University of Arizona in Tucson. We see things differently, each of us has made some good analyses and each of us has made some mistakes, but we don’t insult each other over it.
Though I do wish he and others would remove the cloaks of anonymity. Science has never been advanced by an anonymous person, there’s always a real person with a name at the center of discovery and progress.
Calling cycle 24, calling cycle 24……where are you?
The SIDC in Belgium just issed an end to their “all quiet alert”
The first new sunspot in weeks has emerged today. The spot that has emerged is small and on the equator, so it appears that it is a cycle 23 spot rather than one from the cycle 24 that is gave one spot on January 8th, signaling a start of cycle 24, but has given no cycle 24 type spots since.
Based on what we know about the sun, a cycle 24 spot would be reverse polarity to cycle 23 spots and high latitude. The longer cycle 24 continues to delay producing its spots heightens the concern that we may be in for a longer inactive period on the sun, such as a Dalton type minimum.
A thought occurred to me. Given that all of the sunspots seen recently during our solar minimum are very small, I wonder if they could be resolved at all with the primitive equipment available during periods like the Maunder Minimum? Today we have satellites and advanced solar telescopes with hydrogen spectra filters that are available to amateurs, so catching any sunspot, even if small, is now easy. In fact this sunspot was was first noted by an amateur observer, Howard Eskildsen, in Ocala, FL, showing that amateurs still have a role in science.
It makes me wonder if an extended minimum really isn’t an absence of sunspots altogether, but just an absence of larger easily observable sunspots. It is possible that primitive equipment of the period could not easily resolve smaller sunspots.
The survey project continues to move forward, even in these cold and snowy winter months. I’m pleased to announce that we have just passed the 500 mark for surveyed stations. Now with 41.1% of the network surveyed comprising 502 stations surveyed so far, that leaves 719 to go out of 1221 stations nationwide.
Some stations have recently become catalysts for larger investigations, such as the station in Lampasas TX, done by Julie K. Stacy which has brought out questions from a number of other bloggers. This prompted a review of stations previously surveyed, such as Cedarville, CA, which then prompted a larger investigation in the satellite city nightlights methodology used by NASA GISS. A whole new avenue of exploration has now opened up not just for US stations, but worldwide thanks to new features of Google Earth.
You never know where curiosity and serendipity will lead you. Thanks to Atmoz for starting the ball rolling. I also want to thank Barry Wise and Gary Boden, our early volunteers, whose help on this project has been indispensable.
Recently, this project got a significant endorsement from Dr. Roger Pielke of the University of Colorado in Boulder in his weblog. I and all the volunteers appreciate the recognition.
Here is the latest breakdown of USHCN stations that have been surveyed, and their site quality ratings:
We could really use some help this spring and summer in the following states:
Kansas, Nebraska, Arkansas, Alabama, Illinois, Idaho, Kentucky, Tennessee, Missouri, Mississippi, North Dakota, South Dakota, Oklahoma, Texas.
If you think that you can help with this project by surveying a station near you, please visit the www.surfacestations.org website and sign up. We’ll provide instructions and help on locating stations in need of surveying.
You may also wish to consider signing up for the national flower and foiliage survey to help track climate change which is prominently mentioned on Dr. Roger Pielke’s weblog. You can double your fun!
Last week, I broke the story of a press release issued by NOAA where they publish an opinion smashing any link between hurricanes and global warming saying that “There is nothing in the U.S. hurricane damage record that indicates global warming has caused a significant increase in destruction along our coasts.”
Many readers may recall that Al Gore used hurricanes prominently in An Inconvenient Truth, and mentions hurricane Katrina specifically. Gore claims that increased hurricane activity is caused by global warming.
Last week, when the NOAA press release came out smashing any link between hurricanes and global warming, I wrote to my local newspaper editor, David Little, and said to him “Do you care to bet that AP and Reuters won’t run this story?” He responded: “I hope they do, it seems newsworthy to me.”
Well here is is, 4 days later, not a peep.
A Google search of news stories for “NOAA increased hurricane” (keywords of the press release) reveals a tiny handful of stories about the press release. Could you imagine though if the story said the reverse? What if NOAA claimed they had established a definitive link between global warming and hurricanes. Oh my, the humanity of it all! Gloom, doom, death, destruction, angst, and demands for action on Kyoto. If it bleeds it leads. Compare to all the stories still circulating about hurricane Katrina and global warming.
Here is another story about a point from Gore’s AIT hit parade; Mount Kilimanjaro. Mr. Gore asserted that the disappearance of snow on Mount Kilimanjaro in East Africa was expressly attributable to global warming; “Within the decade, there will be no more snows of Kilimanjaro.” That was in 2005 in his movie An Inconvenient Truth.
Deforestation seems to be causing Mount Kilimanjaro’s shrinking glacier. Researchers think deforestation of the mountain’s foothills is the most likely culprit. Without the forests’ evapotranspiration of humidity into the air, previously moisture-laden winds blowing across those forests now blow drier. The summit, no longer replenished with water from those winds, started shrinking. Studies show the ice is evaporating through a process called sublimation. You can witness this effect at home, have you ever noticed that ice cubes left in your freezer tend to shrink with time?
Last year, a British Court ruled Gore’s point about Kilimanjaro not to be true.
So when a news story crossed my desk today that said: “Mount Kilimanjaro: On Africa’s roof, still crowned with snow” I had to wonder, will we see this one covered in the main stream media? Or maybe those beacons of truth over at Real Climate will make a note of it?
Don’t hold your breath. But, at least the New York Times travel section covered it. It seems more of a touristy thing to have snow on Kilimanjaro than a scientific issue of truth I suppose.
UPDATE: Kate over at SDA created a collage over time showing the snow of Mt. Kilimanjaro:
Uh oh…
You can click on the “animate this image link” under the photo to set it in motion. This photo from last night, the animated loop is current. More at www.kpay.com and click on weather link.
Local Forecast – click graphic to get one for your city, 40-50+ mph winds expected Saturday night, if you have hatches, batten them!
From a NOAA press release that came out to me via email just minutes ago:
A team of scientists have found that the economic damages from hurricanes have increased in the U.S. over time due to greater population, infrastructure, and wealth on the U.S. coastlines, and not to any spike in the number or intensity of hurricanes.
“We found that although some decades were quieter and less damaging in the U.S. and others had more land-falling hurricanes and more damage, the economic costs of land-falling hurricanes have steadily increased over time,” said Chris Landsea, one of the researchers as well as the science and operations officer at NOAA’s National Hurricane Center in Miami. “There is nothing in the U.S. hurricane damage record that indicates global warming has caused a significant increase in destruction along our coasts.”
On the Web:
NOAA National Hurricane Center: http://www.hurricanes.gov
Link to paper:
http://sciencepolicy.colorado.edu/admin/publication_files/resource-2476-2008.02.pdf
UPDATE: URL to paper as originally posted above was missing a period, works now, try again if you missed it before.
Well that pretty much says it all don’t you think? Will Gore revise AIT now?
January 2008 was an exceptional month for our planet, with a significant cooling, especially since January 2007 started out well above normal.
January 2008 capped a 12 month period of global temperature drops on all of the major well respected indicators. I have reported in the past two weeks that HadCRUT, RSS, UAH, and GISS global temperature sets all show sharp drops in the last year.
Also see the recent post on what the last 10 years looks like with the same four metrics – 3 of four show a flat trendline.
Here are the 4 major temperature metrics compared top to bottom, with the most recently released at the top:
The purpose of this summary is to make it easy for everyone to compare the last 4 postings I’ve made on this subject.
I realize that not all the graphs are of the same scale, so my next task will be to run a combined graphic of all the data-sets on identical amplitude and time scales to show the agreements or differences such a graph would illustrate.
UPDATE: that comparison has been done here
Here is a quick comparison and average of ∆T for all metrics shown above:
| Source: | Global ∆T °C |
| HadCRUT |
- 0.595 |
| GISS | - 0.750 |
| UAH | - 0.588 |
| RSS | - 0.629 |
| Average: | - 0.6405°C |
For all four metrics the global average ∆T for January 2007 to January 2008 is: – 0.6405°C
This represents an average between the two lower troposphere satellite metrics (RSS and UAH) and the two land-ocean metrics (GISS and HadCRUT). While some may argue that they are not compatible data-sets, since they are derived by different methods (Satellite -Microwave Sounder Unit and direct surface temperature measurements) I would argue that the average of these four metrics is a measure of temperature, nearest where we live, the surface and near surface atmosphere.
UPDATE AND CAVEAT:
The website DailyTech has an article citing this blog entry as a reference, and their story got picked up by the Drudge report, resulting in a wide distribution. In the DailyTech article there is a paragraph:
“Anthony Watts compiled the results of all the sources. The total amount of cooling ranges from 0.65C up to 0.75C — a value large enough to erase nearly all the global warming recorded over the past 100 years. All in one year time. For all sources, it’s the single fastest temperature change ever recorded, either up or down.”
I wish to state for the record, that this statement is not mine: “–a value large enough to erase nearly all the global warming recorded over the past 100 years”
There has been no “erasure”. This is an anomaly with a large magnitude, and it coincides with other anecdotal weather evidence. It is curious, it is unusual, it is large, it is unexpected, but it does not “erase” anything. I suggested a correction to DailyTech and they have graciously complied.
UPDATE #2 see this post from Dr. John R. Christy on the issue.
UPDATE #3 see the post on what the last 10 years looks like with the same four metrics – 3 of four show a flat trendline.
The global surface temperature anomaly data from the UK Hadley Climate Research Unit (Temp anomaly is plotted below) has just been released, and it shows a significant drop in the global temperature anomaly in January 2008, to just 0.034°C, just slightly above zero.
This caps a full year of temperature drop from HadCRUT’s January 2007 value of 0.632°C
above data is HadCRUT3 column 2 which can be found here
description of the HadCRUT3 data file columns is here
The ∆T for the past 12 months is minus 0.595°C which is in line with other respected global temperature metrics that I have reported on in the past two weeks. RSS, UAH, and GISS global temperature sets all show sharp drops in the last year. We are in an extended solar minimum, we have a shift in the Pacific Decadal Oscillation to a cold state, and we are seeing arctic ice extents setting new records and rebounding from the summer melt.While weather is defined as such variability, the fact that so many things are in agreement on a global scale in such a short time span of one year should give us all pause for consideration.
UPDATE: see all 4 global temperature indexes compared in this next post
Two big things will/may happen in space Wednesday. A total lunar eclipse visible over much of the United States that evening, and a possible attempt by the NAVY to shoot down the defunct spy satellite USA193.
On February 14th, the US military announced that they have plans to shoot the USA193 satellite down with a missile, “to reduce the danger to human beings”. This gives a new twist to the story.
Official sources say there is about 450 kg of hydrazine fuel (a very toxic rocket fuel) on board, and expect 1100 kg (about one ton) of debris of the satellite itself might reach earth’s surface intact.
From Wikipedia: Hydrazine is highly toxic and dangerously unstable, especially in the anhydrous form. Symptoms of acute exposure to high levels of hydrazine in humans may include irritation of the eyes, nose, and throat, dizziness, headache, nausea, pulmonary edema, seizures, coma, and it can also damage the liver, kidneys, and central nervous system. The liquid is corrosive and may produce dermatitis from skin contact in humans and animals. Effects to the lungs, liver, spleen, and thyroid have been reported in animals chronically exposed to hydrazine via inhalation. Increased incidences of lung, nasal cavity, and liver tumors have been observed in rodents exposed to hydrazine.
The plan is to intercept the satellite using one or more SM-3 intercept missiles fired from naval vessels in the North Pacific. The SM-3 missiles need to be modified for this task as they normally target object at lower altitude on a ballistic trajectory instead of a true orbit.
At left, a launch of an SM-3 from the Navy AEGIS cruiser USS Lake Erie (CG 70) on April 27th, 2007 in first dual missle test of its kind.
On Feb 18th Ted Molczan has drawn attention to a NOTAM, (NOtice To Air Men) issued by the FAA, that might point to a possible ASAT shoot-down attempt on USA-193 on Feb 20th, at 3:30GMT/UTC (Feb 21st by UTC) which would be about 7:30PM Pacific Standard Time Wednesday evening.
Here is the NOTAM:
PHZH HONOLULU CONTROL FACILITY02/062 (A0038/08) – AIRSPACE CARF NR. 90 ON EVELYN STATIONARY RESERVATION WITHINAN AREA BNDD BY 3145N 17012W 2824N 16642W 2352N 16317W 1909N 16129W 1241N 16129W1239N 16532W 1842N 17057W 2031N 17230W 2703N 17206W SFC-UNL. 21 FEB 02:30 2008UNTIL 21 FEB 05:00 2008. CREATED: 18 FEB 12:51 2008
The NOTAM excludes an area just west of Hawaii over which USA 193 will pass near the time above (see below map, showing USA 193’s approximate position at 7:30 PM PST Wednesday evening, Feb 20th (Feb 21st 3:30 UTC):
(click map to enlarge)
This would put the shoot-down right in the middle of the Wednesday lunar eclipse.
If they do attempt it then, they’ll certainly have a lot of eyes, cameras, and telescopes out with the lunar eclipse, so it will either be a spectacular success (with lots of pictures) or a spectacular failure (with lots of pictures).
(h/t) SatTrackCam Leiden
Ultimate175 writes:
Anthony, I enjoy perusing your site most days, and I’ve learned a lot. Any subject that carries this much political, social, and/or cultural baggage deserves all the skepticism it gets. Count me a skeptic. I was wondering, being rather new to this issue, if you could compile and post an “acronym definition” page on your site. There are so many, and it’s hard for a newbie to keep all the organizations, measurement devices, networks, etc. straight. Just a suggestion.
Done! I had been considering this for some time, and Evan Jones gave me a head-start. Your request made so much sense that it seemed the right time to do this. I added some relevant to this blog plus many more that came to me courtesy of Evan’s post.
See the Glossary link on the toolbar under the cloud header picture. There’s a comment form to suggest additions or changes. Please use it – Anthony
My first 100K unique visits month, and the month isn’t even over yet!
Stay tuned, more interesting topics coming your way…
While looking at information about the NASA GISS methodology of using night time DOD satellite photos to count street lights around weather stations to use as a way of assigning an “urban bias”, I came across this night time satellite photo:
It occurs to me that North Korea may have some of the most “rural” weather stations in any Northern Hemisphere country. In contrast, South Korea looks like it would not be such a good place to measure temperature. Fortunately, the DMZ keeps any errant civilization or industrialization bias from creeping across the border.
I do see one light on in North Korea, I wonder who that belongs to?
I got to thinking from discussions with Steve Mosher and others at Climate Audit about just what sort of adjustments might be made to a place with a good record and little in the way of station moves, and that had changed little in 100 years.
One place came to mind that I’ve visited; Cedarville, CA
It has it’s issues, such as the Stevenson Screen being encroached upon by things being built around it, like a new concrete pad for the Forest Service office.
Click for larger image and other photos from surfacestations.org
But as the town goes, it has changed very little in 100 years. There is no Interstate highway nearby, its off essentially in the middle of nowhere by itself, a self contained agrarian community, mostly hay farmers. The small main street has many of the original buildings from 100+ years ago:
This is the sort of view (below) you can see to the east of the town, open land as far as the eye can see. It is definitely rural.
Image above from the Surprise Valley website
Here is a Google earth view. The weather station is at the far north end of town.
According to US census data:
As of the census of 2000, there were 849 people, 381 households, and 249 families residing in the ZCTA of 96104. The population density was 3.2 per sq mi. There were 457 housing units at an average density of 1.6/sq mi.
There are only 3 missing years all the way back to 1894, so it seemed like a good candidate. 1894 is a partial record, 1915 and 1957 are also partials with not enough data to complete the yearly average.
So this seemed like a really good candidate to test for what adjustments GISS might make to it.
I plotted the GISS provided USHCN data and the homogenized data from the GISTEMP website and the graph for that is shown below:
click for a larger image
Note the missing year at 1957, 1915 should have not plotted also, but for some reason my program insists on doing so.
But the important thing is that once again, GISS has made the past colder and the present is unchanged. Even more odd, the far past prior to 1900 is adjusted upwards, warmer.
Yet it is the recent past to present when the most change has occurred in Cedarville, such as the addition of a concrete pad to the Forest Service building, etc. If anything, pre 1900 data should likely be colder because exposure of thermometers was not standardized until after the US Weather Bureau was formed in 1892.
They seem backwards. Why does GISS do this? We’ll find the answer.
My post How not to measure temperature, part 51 was also cross posted over at Climate Audit, and has created quite a stir when Atmoz, who is at the University of Arizona, tried to demonstrate that the temperature spike shown in the GISS data at Lampasas, TX, was not due to the relocation next to a building and asphalt parking lot, but rather some problem with GISS algorithm to do homogeneity adjustment to the data.
Steve McIntyre had doubts and posted a tongue in cheek rebuttal where he blamed the problem on UFA’s (Unidentified Faulty Algorithms). It seems reasonable given the fact that Arizona is already the center of surface measurement weirdness given the parking lot weather station operated by the Atmospheric Science Department of the University of Arizona.
Enter serendipity. Warren Meyers’ son Nicolas, has been actively surveying Arizona stations for his school science project. My inbox had a new station from him today, Miami, AZ. So I decided to take a look at it.
As is typical when an MMTS sensor gets installed by NOAA/NWS to replace the traditional Stevenson Screen, it got closer to human habitation, and in this case, a LOT closer. Too close I’d say:
click for full sized and additional images at surfacestations.org database
So I though I’d take a look at the raw GISS temperature plot for Miami, AZ to see if the move would show a spike, it did:
From NCDC’s MMS database, they have a map showing station moves. This is at the Magma Copper Mine in Arizona, and the station used to be further away from the administration buildings near the pit:
Seeing a similar scenario to what occurred in Lampasas, TX, where a rural station was moved from a cooler location to a much warmer one, I decided to do the same sort of comparison on the GISS temperature plots as I did before:
RAW GISS DATA:
HOMOGENIZED GISS DATA:
Note that I changed the color to red using a hue shift to prepare for the next step, to see the original GISS data, click on the image.
HOMGENIZED GISS DATA OVERLAID ON RAW DATA:
Notice that after the GISS homogeneity adjustment, the past temperatures go down, with the present acting as a hinge point, thus making the slope of the temperature trend rise. The new slope is purely artificial, and appears to be an artifact of data adjustment by NASA GISS on this rural station. This is the second instance of this happening, the first being seen in the GISS Lamapasas, TX data adjustment for homogeneity.
In both cases, the abnormal spike coinciding with a station move near the present time remains in the record, and that is what the homogeneity adjustment is supposed to catch and remove as I understand it.
In a comment on the subject, Steve Mosher offers an explanation:
In Hansen 2001 Hansen says he uses nightlights to determine
if a station is Rural in the US and population everywhere else.
Miles city population is less than 10K which makes it rural,
BUT, nightlights ( satellite imagery taken in 1995)
indicates a brightness factor for Miles of 26! effectively making it urban.
I concur. There appears to be a flaw in the GISS nightlight methodology and adjustment algorithm. I look forward to seeing GISS investigate, and if this problem is indeed verified, a dataset correction.
Or if you like, fling funds
to help keep the www.surfacestations.org project going.
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