I’ve been bummed that I can never scoop Roy Spencer on the UAH temperature anomalies. I wracked my brains trying to figure out how I can come out with some sort of “first” temperature reading of the month. I considered a number of strategies:
- Hiring a secret agent to sneak into Roy’s office, slip Roy sleeping powder right after he computes then numbers, then email me the values before Roy can post the values,
- Forcing Roy to go on vacation the first week of every month and
- Developing psychic powers.
None seemed likely to succeed.
So, I decided the only thing to do was: Post a dubious value no one believes. To make it slightly less dubious, I decided I would a) compute it from the value posted for the Channel5 AMSU values posted daily at The AMSU temperature page and b) explain that this is not the real UAH temperature. The real UAH temperature anomaly uses the AQUA satellite which doesn’t drift around so much for technical reasons that involve something called “fuel”.
So, today, I will announce that the average temperature from the Channel5 AMSU was 0.023C. This is down from the May-average Channel 5 AMSU temperature of 0.119C no one reported last month.
Here’s how it’s computed. I subtracted the 20-year average values for each day (indicated by the yellow curve below) from the 2009 values for each day (indicated in green).
This created a “Daily Channel 5 Anomaly”; I’ve plotted anomalies for a number of years below:
The black horizontal bar above represents the 30-day average for June, which despite VG’s fondest wishes that UAH goes negative this month, the related average of Channel 5 remained positive.
Time to guess the other anomalies!
Do remember: channel 5 is an imperfect approximation for this month’s UAH lower tropospheric value. However, it does correlate fairly well with that value.
I computed the difference between the UAH and monthly average Channel 5 values for all months back to 1999 and found that has a standard deviation of 0.08C. I also found during that period, the Channel 5 anomaly is 0.017C lower than the UAH anomaly.
On this basis, and a few other numbers, I’m going to be bold and predict that Roy will post a value of 0.02C this month. I will place absolutely no confidence interval on this.
I invite anyone who wants to take a shot at a guess to post one. You need to guess fast though, because unless Roy is on vacation, you know he’ll post the official values shortly!
Very hard to guess, but I’ll say .031.
How is the first 5 months of 2009 doing compared with 2008?
Surely one could just extrapolate using MCR. 😉
MRC! Damn this dsyelixa!
Lucia: Oh My god you win! just joking.. but its goin down…hahahah lets bet on next month but ill only do it 3/4 through this time…BTw i reckon your about right but Id say 0.15 because only the last 6 days went up?
Sorry meant 0.015 its pretty close even yours sorry what does it look like on your famous graphs?
While you are out looking for new and interesting things to analyze and plot up in your copious spare time, I thought there might be some interest in a look at how sea level change is doing when compared to the IPCC projections. “Some” at RC are saying that the IPCC projections are too low while at the same time Pielke Sr. has said that “Sea level has actually flattened since 2006”.
MikeN–
The average for the first five months of 2009 is warmer than for 2008.
VG–You aren’t proven wrong yet. UAH could still come in negative. The standard deviation of the difference is 0.08. Plus, in May Channel5 has been on the high side, and over the past few years it’s been on the high side. (But the trend in Channel 5 getting warm relative to UAH may not be statistically significant. I didn’t check.)
If you end up right, you get to chortle with glee!
Scnoerkelman– Part of the problem with the RC issue is that for reasons mysterious to me, The (Copenhagen) Synthesis Report chose to test the TAR and call these “the” (no-adjective) IPCC projections and also using phrases like “what we expected” without telling us when they expected that.
At least with respect to surface temperature, if they tested the AR4 instead of the TAR, we’d see the surface temperature have been below what “we” expect based on the AR4.
If you want some comparisons done, you might help out and find the places in either the TAR or the AR4 where those guys both projected what they now claim to project and told the public those were the projections to the level of detail shown in the comparison.
One of my concerns is that people should not claim to have made a “projection” unless they actually told others they thought that projection/prediction was something they anticipated would occur.
With respect to the TAR, I’m have a dim recollection that the RAPER paper (used to specify the tunings in the model used in the TAR) said that sea level predictions using the “simple model” from RAPER 2001 were poor, so this was already known when the projections were made. (I’ll look for that.) In consequence, I think the TAR may not actually ‘project’ sea level with any confidence. In reality, I suspect all the sea level chart confirms is that “they” thought they couldn’t project sea level back then and they are confirming they can’t. But I could be wrong on that, so it would take me a while to figure out what to report about those figures.
Dr. Spencer always carefully points out that his daily AMSU plots use a different satellite than his monthly UAH anomaly results.
My question is why doesn’t he use AQUA for the daily plots also?
Molon– I don’t know why he doesn’t use AQUA for the daily plots. I can imagine a zillion reasons ranging from purely bureaucratic to technical. The only way to know is to ask.
Good stuff lucia.
While we can never be sure what kind of corrections need to be applied to this dataset, there is some interesting analysis which can be done with the different channels etc.
My UAH temp model says 0.064C for June without changing the AMO number for June. (The model was off by 0.015C in May).
So… you’re guessing 0.064 C? That’s the warmest so far!
OFF THREAD I was casting about, surfing the net, trying to get a sense of what this thing “parameterization” (and optimization thereof) is all about and I happened upon something called “stochastic inversion”. That’s stochastic inversion as in: “More importantly, they were able to demonstrate that Bayesian stochastic inversion using multiple VFSA was one to two orders of magnitude more efficient than the Metropolis/Gibbs sampler”
How am I ever going to get to the bottom of what that is all about? I was impressed though because that man using stochastic inversion made what I though was a subtle remark, viz: “but it is important to remember that when models are tuned toward the mean of what is observed, they may no longer be fully representative of the realistic range of probable outcomes.”
Any comments?
lucia, #15601
The model is based on the ENSO and AMO and CO2 influences, so I’m just quoting what it says for June. Sometimes it is a little high for a few months, then it is low for a few months so I’ve learned not to use last month’s actual temp number to predict the changes but just stick with the model says.
There is a 3 month lag between the Nino 3.4 anomaly and its impact on temps. 3 months ago, the Nino 3.4 anomaly went from -0.65C to -0.48C so there is a very slight warming effect from that.
There is another dataset one can use for guessing the anomalies.
http://www.cpc.ncep.noaa.gov/products/stratosphere/strat-trop/
At 600 MB, equivalent to channel 5, Northern hemisphere mid-latitudes were cooler than average.
http://www.cpc.ncep.noaa.gov/products/stratosphere/strat-trop/gif_files/time_pres_TEMP_ANOM_AMJ_NH_2009.gif
Equatorial regions were cooler than average.
http://www.cpc.ncep.noaa.gov/products/stratosphere/strat-trop/gif_files/time_pres_TEMP_ANOM_AMJ_EQ_2009.gif
And Southern Hemisphere mid-latitudes were warmer than average.
http://www.cpc.ncep.noaa.gov/products/stratosphere/strat-trop/gif_files/time_pres_TEMP_ANOM_AMJ_SH_2009.gif
Hank
Well.. I can explain in terms of simpler problems.
Say you were trying to figure out heat gain in a multistory building which, among other things, has double paned windows. You aren’t going to model heat transfer through the double paned window in great detail; you will model that using a simple model, which contains a some parameters. One of these parameters might be the ‘effective’ resistance to conductive heat losses. (Oddly, the greatest uncertainty in computing this requires analyzing heat transfer due to convection inside the double panes and on either side of the panes. But, anyway, you parameterize that with an equation that has an “effective conductivity” or “effective resistance” — the inverse of the effective conductivity.)
When doing a heat transfer problem through a double paned window, you will look up published correlations for heat transfer through double paned windows, which will specify a) a functional form and b) a value for a parameter. You will use this parameter.
If you dig deeper into the literature, and get the paper cited in the handbook providing the correlation for heat transfer from vertical surface, you will discover that the experimentalists may have reported some value of uncertainty around their best value. In fact, you are likely to discover the uncertainty around the best estimate is 50%. (Not making this up.)
Because you are trying to solve a bigger problem, which will include parameterizations for many things, even though you know the answer your program spits out will differ if you modify the value of the parameter 50%, you aren’t going to explore the uncertainty associated with the possible range of values of every parameterization you used in your model.
So, you will not discover the full range of uncertainty of the response of your high-rise to all possible weather or all possible internal uses of the building.
In climate science, similar things are bound to occur.
The additional complications in all modeling problems (and especially in non-linear problems) are that you can’t assume that on average, the errors on choice of individual parameters will balance out and b) the functional form of the simplified formulation for something (like heat transfer) may not work for any value of parameters. (This happens in Computational Fluid Dynamics all the time. Earlier models and evencurrent ones, often use “gradient transport” simplifications to the effect of sub-grid motions on the transport of mass momentum and energy. These are known to break down in many flow conditions.)
So, not only is the full uncertainty associated with the range of parameterizations unlikely to be explored, the even greater uncertainty associated with the vast possible array of sub-grid models are not fully explored.
All are partially explored. (But this is true in all science, engineering, or even general research.)
“The real UAH temperature anomaly uses the AQUA satellite which doesn’t drift around so much for technical reasons that involve something called “fuelâ€.”
😆 Aerospace engineer…very amused.
Gee… I thought AGW was a global phenomenom….LOL
Lucia
If you want to provide critically important leading climate indicators, I see three primary drivers to global temperature:
1) Effective Solar input
TSI * (1- albedo) with albedo obtained(from cloud area and density.
2) Global optical depth of BOTH H2O and CO2
and
3) Ocean oscillation: PDO + AMO
Using his HARTCODE, Ferenc Miskolczi has calculated Earth’s actual optical depth τA, including variations in H2O, CO2 and temperature. He found that it increased linearly at only 8.08 ppm / year with a mean of 1.869 over those six decades from 1948-2008!
See: for publications by Miskolczi and Zagoni. http://miskolczi.webs.com/
1) Recommend working with Miskolczi and his HARTCODE to post historic and monthly track of the Global Optical Depth.
No one else has calculated or posted Global Optical Depth to my knowledge. That will give a strong direct input to projecting temperature.
See NOAA’s Reanalysis data:
http://www.cdc.noaa.gov/cgi-bin/data/timeseries/timeseries1.pl
Obtaining effective H2O and CO2 from satellites and their respective optical depths would be even more impressive.
2) If you can obtain global albedo from satellite data and plot TSI*(1-Albedo), that would be an equally critical parameter.
(As a refinement, you could plot Galactic Cosmic Rays as an parameter modulating low level clouds and albedo.)
3) The third would be to report PDO+AMO which a number of researchers have correlated with global climate.
Compare parameters and graphs shown at: Solar Cycle 24 http://www.solarcycle24.com
Happy hunting.
David–
I don’t entirely understand Miscolski. That said, my impression is that he never tried to predict monthly fluctuations, did he? I don’t know where I would get monthly measurements of optical depths or even CO2 and H20, and especially don’t know how I could get them before the temperatures are published. This would make it very difficult to use either to predict a monthly temperature.
Lucia
After his figure 19, see Miskolczi’s (2009) graphs:
“Trends in the NOAA 61 year series” where he shows annual data for Temperature, H2O column amount, and CO2 column amount.
Below that he shows:
“Trends in Flux Optical Depth”
As data is available, I am suggesting that it might be possible to calculate monthly “Flux Optical Depth”.
NOAA nominally provides monthly values for Air Temperature, and specific humidity. (Though it would take some digging to trace raw data to delivered parameters.) CO2 is available from Mauna Loa.
With those parameters, Miskolczi calculates the flux optical depth with his very high resolution HARTCODE.
(I don’t think this depends on the planetary greenhouse theory he developed. The moisture and CO2 lapse rate could be included for refinement.)
There are also new satellites providing current detailed H2O and CO2 with altitude and location. (Apparently the
Orbiting Carbon Observatory didn’t make it into orbit.)
It would be worth a megabuck grant and several PhD theses to evaluate those for a more accurate global flux optical depth.
David–
The specific humidity for June isn’t available yet. So, Miscolski and figured out how to compute Miscolski’s flux optical depth, I couldn’t calculate it for June yet. I suspect Roy will post his temperature anomaly before the data to compute “flux optical depth” are available. So, whether Miscolski is right or wrong, this isn’t going to work as a leading indicator for monthly temperature anomalies because I can’t get the data to compute it until after the thing we want to predict is posted.
The moving average flux optical depth gives you a leading indicator of the solar absorption which affects the temperature SLOPE. This would help you project the temperature trend and thus the monthly temperature anomaly. The actual result could be refined/corrected the following month.
Note:
2.7 Terabytes of data per month should give you plenty to play with!
More importantly, showing the trends in H2O optical depth, the CO2 optical depth and the Total optical depth should clearly show the relative impacts and the amplification factor.
Terabytes of data? I don’t want to deal with terabytes of data just to try to predict next months anomaly. Someone else is going to have to come up with that indicator.
My SWAG for UAH is -.03
I guess -0.32 so (maybe) I can claim its actually warming up faster than predicted.
I’ve been trying for several months to get a correlation between one or more of the UAH dailies, and the UAH monthly. I finally gave up on that. I have had some success “predicting” Hadley+GISS, using a two-step process involving RSS monthly and UAH “near surface” daily ch04. Here are the steps…
1) Calculate a raw projection based on the last 12 months of data. *THIS IS NOT MY JUNE FORECAST*. For June, I have…
Hadley temp 0.385; slope 0.407
GISS temp 0.40; slope 0.602
UAH temp -0.087; slope 0.997
RSS temp 0.113; slope 0.922
2) Wait for RSS to come out, and make my June forecasts as follows…
delta = RSS(June) – 0.113
Hadley = 0.385 + delta * 0.407 / 0.922
GISS = 0.40 + delta * 0.602 / 0.922
I don’t bother with UAH monthly, because it seems all over the place. Anyhow, Roy publishes it on his blog at http://www.drroyspencer.com/category/blogarticle/ before updating the monthly temperature.
From Climate Research News:
Cosmic Ray Decreases Affect Atmospheric Aerosols and Clouds
Svensmark, H., T. Bondo, and J. Svensmark (2009),
Cosmic ray decreases affect atmospheric aerosols and clouds,
Geophys. Res. Lett., doi:10.1029/2009GL038429, in press.
(accepted 17 June 2009)
IF IF…AH temp -0.087; slope 0.997 in which case i WOULD win LOL not likely though….
My self-coded I Ching does UAH forecasts when it’s in the mood: +0.010C
Your all wrong-It’s Zero on the money:
http://www.drroyspencer.com/2009/07/june-2009-global-temperature-anomaly-update-000-deg-c/
Didn’t quite go negative but you can’t get much closer?
Not unless you’re in calculus!
http://en.wikipedia.org/wiki/Infintesimal
Lucia thanks for your reply. I have been mulling my way through it. It took a day to dawn on me – Oh, this is the overworn greenhouse metaphor fleshed out as it should be in the skyscraper age. But aside from that, when you say, “will specify a) a functional form and b) a value for a parameter,” does a “functional form” mean something expressed like a typical formula I might see in a high school physics textbook (or college textbook if it were expressed with differentials)?
And the next natural question, when architects and engineers design a skyscraper. How often do they get the size of their air conditioning units wrong? Judging from news reports regarding the State of Illinois building in Chicago – It can happen.
Lucia, another quick followup. Are parameters akin to the factors of Raymond Cattell’s “factor analysis”? My formal education in statistics ended when I changed majors from psychology in my sophomore year at college.
An interesting first report from the Channel 5 Action News Team.
Incidentally, is Roy a verb in “predict that Roy a value”?
That’s a good analogy. The difference is that in your high school physics texts the authors focus on physical processes that are very well understood.
I’m not sure, but I doubt it! (I’m not familiar with Raynold Cattell’s “factor analysis” but,mostly, the parameters in AOGCMs’ don’t come from factor analyses of anything.