Guest Post By Walter Dnes
As a follow-up to my April 30th post attempting to project April global anomalies this post will attempt to project May anomalies for various global data sets, before they are released. But first, let’s look back at the April projections, and see how well they fared.
April 2016 Projections
| Data Set | Projected | Actual | Delta |
|---|---|---|---|
| HadCRUT4 | +0.929 | ||
| GISS | +1.14 | +1.11 | -0.03 |
| UAHv6 | +0.519 | +0.715 | +0.196 |
| RSS | +0.620 | +0.757 | +0.137 |
| NCEI | +1.0717 | +1.1028 | +0.0311 |
The NCEP/NCAR re-analysis is based on the “sig995” data set. The 995 millibar level is close to (and in some cases theoretically below) sea level. One would expect that the NCEP/NCAR data would more closely match the surface-based global data sets, than the satellite-based lower-troposphere data sets. Indeed, GISS and NCEI come in within approximately +/-0.03 degree of the projections, while RSS and UAH are approximately 0.14 and 0.20 off, respectively. HadCRUT4 April data is not yet in as of this writing.
May 2016 Projections
NCEP/NCAR runs 2 days behind real-time, so data from the first 29 days of May is available. The last 2 days are assumed to be the same as the 29th, when calculating the monthly mean. There is an additional complication with the May projections. The NCEP/NCAR index jumped sharply from +0.368 in September 2015 to +0.567 in October 2015. For at least the satellite data sets, if not all 5, there was a sharp discontinuity. The data on each side of the divide appear to be different populations. The data associated with NCEP/NCAR >= +0.567 has a steeper slope (“the blade of the hockey stick”) than the data associated with NCEP/NCAR <= +0.368 (“the shaft of the hockey stick”). This month, NCEP/NCAR is in the transition zone between the two populations, so one has to choose one of 2 possible values.
To avoid confusion, I’ll list my picks for May 2016 first, and which data set I used. The rationale will follow afterwards, along with the 2 data tables. This month, I’m switching over to my own calculated version of the NCEP/NCAR monthly anomalies, instead of copying numbers from Nick Stokes’ web page. As noted in my previous post, my calculations are in very close agreement to his, but you may see the occasional difference in the last (thousandths of a degree) digit.
- HadCRUT4 +0.834 (shaft)
- GISS +0.96 (blade)
- UAHv6 +0.299 (blade)
- RSS +0.414 (shaft)
- NCEI +1.0005 (shaft)
Rationale for my choices
The lines of “the shaft” and “the blade” meet somewhere in the transition zone between NCEP/NCAR = 0.368 and NCEP/NCAR = 0.567. In the transition zone, I go with the value from “the blade”, unless it crosses below “the shaft”. In other words, I go with the higher of the 2 values. The crossing point need not be the same for each of the 5 data sets. First, here are the data and results from the 7 months of data from “the blade”. Note that there are minor changes from the values given the previous month. The data sets always seem to make minor changes in the past few months.
In the case of HadCRUT4, I had no choice, because April data is not yet in. I’m assuming that the “shaft” data is either the correct choice, or close enough. Note that this is only possible in a transitional month. I.e. the current month is slightly above +0.45, so I use the “shaft” data (below +0.45).
| Month | NCEP/NCAR | HadCRUT4 | GISS | UAHv6 | RSS | NCEI |
|---|---|---|---|---|---|---|
| 2015/10 | +0.567 | +0.820 | +1.07 | +0.412 | +0.459 | +0.9877 |
| 2015/11 | +0.513 | +0.810 | +1.01 | +0.326 | +0.436 | +0.9657 |
| 2015/12 | +0.621 | +1.010 | +1.10 | +0.450 | +0.547 | +1.1221 |
| 2016/01 | +0.664 | +0.908 | +1.11 | +0.540 | +0.666 | +1.0345 |
| 2016/02 | +0.844 | +1.061 | +1.33 | +0.832 | +0.979 | +1.1944 |
| 2016/03 | +0.783 | +1.063 | +1.29 | +0.734 | +0.843 | +1.2297 |
| 2016/04 | +0.635 | +1.11 | +0.715 | +0.757 | +1.1028 | |
| Function | ||||||
| =slope() | +0.802 | +0.997 | +1.472 | +1.656 | +0.766 | |
| =intercept() | +0.412 | +0.486 | -0.401 | -0.425 | +0.584 | |
| Extrapolation | ||||||
| 2016/05 | +0.475 | +0.96 | +0.299 | +0.362 | +0.9484 |
Here are the data and calculations for “the shaft of the hockey stick”. The 12 most recent months with NCEP/NCAR <= +0.4 are used, i.e. October 2014 through September 2015.
| Month | NCEP/NCAR | HadCRUT4 | GISS | UAHv6 | RSS | NCEI |
|---|---|---|---|---|---|---|
| 2014/10 | +0.281 | +0.626 | +0.87 | +0.244 | +0.276 | +0.7808 |
| 2014/11 | +0.105 | +0.489 | +0.70 | +0.235 | +0.247 | +0.6885 |
| 2014/12 | +0.212 | +0.634 | +0.79 | +0.223 | +0.284 | +0.8306 |
| 2015/01 | +0.210 | +0.688 | +0.82 | +0.295 | +0.364 | +0.8146 |
| 2015/02 | +0.269 | +0.660 | +0.87 | +0.187 | +0.324 | +0.8843 |
| 2015/03 | +0.286 | +0.681 | +0.91 | +0.173 | +0.252 | +0.8955 |
| 2015/04 | +0.168 | +0.656 | +0.74 | +0.085 | +0.172 | +0.7775 |
| 2015/05 | +0.275 | +0.696 | +0.79 | +0.269 | +0.310 | +0.8546 |
| 2015/06 | +0.204 | +0.730 | +0.79 | +0.310 | +0.393 | +0.8772 |
| 2015/07 | +0.164 | +0.696 | +0.73 | +0.154 | +0.288 | +0.8076 |
| 2015/08 | +0.306 | +0.732 | +0.78 | +0.246 | +0.388 | +0.8734 |
| 2015/09 | +0.369 | +0.784 | +0.82 | +0.229 | +0.380 | +0.9202 |
| Function | ||||||
| =slope() | +0.678 | +0.578 | +0.163 | +0.452 | +0.702 | |
| =intercept() | +0.512 | +0.664 | +0.182 | +0.199 | +0.667 | |
| Extrapolation | ||||||
| 2016/05 | +0.475 | +0.834 | +0.94 | +0.260 | +0.414 | +1.0005 |
Very interesting to me, since I also try to follow these, but don’t always collect the data! Am I correct in understanding that you are using data only from October 2014? If so, this is a very sound technique. Projecting future values by making linear fits over longer periods can often come seriously adrift, due to step changes, which by their nature are unannounced, and are very common in temperature data. I hope for confirmation of the URLs of data sources.
Robin
> Very interesting to me, since I also try to follow these, but
> don’t always collect the data! Am I correct in understanding
> that you are using data only from October 2014?
Yes. I try to use the 12 most recent months of *APPLICABLE* data. But things are never simple. The data from October 2015 through May 2016 appears to be a separate population, with a higher slope for the line. This is what the AGW crowd calls “the blade of the hockey stick”. With the El Nino going away, we’re transitioning to the other population; “the shaft of the hockey stick”.
The latest 12 months of the lower-slope (“shaft”) population are October 2014 to September 2015. Assuming temperatures continue to fall as we head to a La Nina, next month I’ll use…
* November 2014 to September 2015
* and June 2016
for the 12-month sample. With each succeeding month, there will be one less pre-El-Nino month and one more post-El-Nino month. I wonder if a strong La Nina will have its own “blade”, sloping downwards. More complications.
Walter Dnes — what will be the values after deducting the 60-year cycle anomaly reaching positive peak in 2016 in the Sine Curve?
Dr. S. Jeevananda Reddy
Re: the data source URLs
* HadCRUT4 http://www.metoffice.gov.uk/hadobs/hadcrut4/data/current/time_series/HadCRUT.4.4.0.0.monthly_ns_avg.txt
* GISS http://data.giss.nasa.gov/gistemp/tabledata_v3/GLB.Ts+dSST.txt
* UAH http://vortex.nsstc.uah.edu/data/msu/v6.0beta/tlt/tltglhmam_6.0beta5.txt
* RSS ftp://ftp.ssmi.com/msu/monthly_time_series/rss_monthly_msu_amsu_channel_tlt_anomalies_land_and_ocean_v03_3.txt
* NCEI note that this has to be done manually
– Go to webpage… https://www.ncdc.noaa.gov/monitoring-references/faq/anomalies.php
– Click on… “Anomalies and Index Data”
– Click on the blue”Download” button. The default values “Monthly”, “Global”, “Land and Ocean”, and “CSV” are the correct values for what you want. You’ll get a text page of numbers. In your web browser, Save the page as a text file.
Many thanks, Walter. I’ll try to find some time to do a few analyses.
Robin
https://www.ncdc.noaa.gov/cag/time-series/global/globe/land_ocean/p12/12/1880-2016.csv
That’s all…
Thanks Bindidon @ June 3, 2016 at 2:13 pm
wget is much simpler
I suspect that May in the UK will show as particularly cold – current temps are around 5 degC below normal and have been for many days …. and this will continue into the first week or so of June
Temperature’s in N.Ireland are at least 1 degree above average.
Yes, but early May had a notable warm spell.
CET is expected to come out at 12.5 or 12.6C, just above the ’81 to ’10 ave of 11.7C.
https://www.netweather.tv/forum/topic/85425-may-2016-cet-forecasts/?page=8
Yes May CET was warm, but not exceptional, will rank top 60 or so in full 1659 set (~360 years).
yet north and south of the cet area was much colder. this warming always seems to conveniently appear in certain areas.
None of the usual 50-odd reporting stations reported a below average May in the UK.
Compared with the 1981-2010 average, anomalies were all between +0.2 and +1.4, and overall it was the warmest May for 8 years.
North and south of the CET area it was also above average. For example, Herstmonceaux on the south coast was +1.4, and Edinburgh was +0.4.
There were no dramatic regional differences, and no suggestion that the commonly-quoted Central England Temperature (CET) was somehow chosen to be warmer than the rest of the country. The final figure of 12.54 C was 0.9 degrees above the 1981-2010 average
Sure, Old England! But we are talking here about global temperatures, I guess 🙂
Maybe I should have a go at it since by an ‘incredible coincidence’ I’ve got SC24max to the first decimal place correct (0.1% accuracy, that must be a record) some 10 years ahead of the event, independent verification is welcome (details in the link). However, the problem is with the climate science that the past temperature data are more uncertain than the future ones. /sarc
Yep, the future is certain , it’s only that past which changes. 😉
” the problem is with the climate science that the past temperature data are more uncertain than the future ones”
+1
The problem is…your prediction is better fit than your input data, for instance 1975. Normally you would have a very good hind cast and miss by a mile in the prediction, so you’ve got it backwards somehow /sarc
Vuc that was brilliant!
Made my morning enjoyable. You are quite right. The future we know with some certainty. The Past is highly uncertain.
To put this in perspective, are your guesses any closer than say just using the previous months figure?
Month NCEP/NCAR HadCRUT4 GISS UAHv6 RSS NOAA/NCEI
2016/03 +0.783 +1.063 +1.28 +0.734 +0.842 +1.2181
NCEI is marginally closer , the others you would have done better to keep last months value as a guess.
I don’t think there is much predictive skill in your method.
“I don’t think there is much predictive skill in your method.”
It gets surface temperatures mostly right. It isn’t really predictive; the temperatures have already happened. It predicts what people will say about them, based on past correlations with a now known measure of surface temperature. So it correlates well with other surface measures, as one would hope. And it correlates poorly with temperatures high in the troposphere. As the April post showed, if you attempt a correlation of NCEP/NCAR with TLT, there are two very different fits depending whether El Nino or not, while surface correlates with both. That is because one is a measurement correlation (of the same thing), which the other depends on whether the measurand behaves physically in the same way – often it doesn’t.
I expect May 2016 values for UAH and RSS to be close to or slightly below those of May 1998 (which were .64 for UAH v6, .667 for RSS), or exceeding the above projections for May 2016 by about the same margins as was the case for April 2016 (resulting in UAH v6 being .495, RSS being .531).
yes, I would say that overlaying 1998 would be a more credible way of projecting coming months.
UAH is now in, at .55.
It looks like maybe I should stick to surface data sets for predictions, or re-do my algorithm for the satellite data sets. The problem is that the sig995 data (995 mb pressure level) is apples-to-apples for surface temps, but apples-to-oranges for lower troposphere satellite data.
I noticed that the May 2016 value for UAH of 0.55 was close to the 0.527 that you predicted for last month. Is it possible that when going down from a strong El Nino that the satellites lag expectations by 1 month?
“I should stick to surface data sets for predictions”
I think so. NCEP/NCAR is measuring surface temperature, and you are correlating with other ways of measuring the same thing. With TLT, you are perturbed by its different actual behaviour, in addition to measurement issues.
Based on ch06 here:
https://ghrc.nsstc.nasa.gov/amsutemps/amsutemps.pl
I expect UAH and RSS to be very close to the 2010 values which were 0.414 and 0.526 respectively.
P.S. Congratulations on nailing Hadcrut4 in April which finally came in at 0.926 versus a projection of 0.929!
I am puzzled about something. If you go to:
https://ghrc.nsstc.nasa.gov/amsutemps/amsutemps.pl
and click on ch06 and compare 2010 with 2016 for the month of May, then the area above the lines and below the lines are approximately equal. So I predicted that May 2016 would be very close to May 2010.
For UAH, May 2016 was 0.545 but May 2010 was 0.414.
For RSS, May 2016 was 0.525 and May 2010 was also 0.525!
Does anyone have any idea why UAH was so different than RSS?
Dear Walter (Dnes),
Surely you have something better to do.
Neither you nor anyone else can accurately forecast “temperatures” so whatever your “projections” may be, they shall only ever be academic.
It seems such a waste of, well, everything.
Regards,
WL
They are not even forecasts.
They are guesses at the result of the various calculations being done right now on last month’s temperature, which will all be released in a couple of days.
Compare them to exit polls in elections. Sure, we will know the results in hours, but people are still very interested in knowing results before the official count is done. CNN may make projections based on exit polls the second a poll closes in a state. As for the “couple of days”, that applies to satellites, but Hadcrut for April was not released until June!
Ridiculous
Sorry I suppose the actuals could have been negative and then the delta would have been much higher. I shouldn’t have said ridiculous, sorry.
Even so; I still believe that you were right the first time. It is all rather ridiculous.
Over 99.95% of the atmosphere is comprised of N2, O2, and AR which are essentially transparent to LWIR radiation. It stands to reason then that 99.95% of the LWIR radiating from the surface of the earth passes through the atmosphere while never contacting a CO2/GHG molecule. Not that it matters because in the 400 to 1,000,000 rare and remote chance LWIR contacts a CO2 molecule with an effective emissivity across the spectrum being less than 0.01 (Nasif Nahle et. al.) there is negligible emission/absorption. Ergo, the magical perpetual downwelling/upwelling energy loop required for GHE theory does not exist.
The perpetual GHG/GHE energy loop without added work violates the second law. The perpetual GHG/GHE energy loop as shown on Trenberth et. al. 2011 Figure 10 and WE’s steel greenhouse requires a spontaneous creation/duplication of the energy flowing in the system, violating the first law: energy cannot be created or destroyed. The perpetual GHG/GHE energy loop without added work violates the second law. The perpetual GHG/GHE energy loop moves energy from a cold sink to a warm source without adding work, violating the third law. Ergo, the magical perpetual down welling/upwelling energy loop required for GHE theory does not exist.
The GHE theory was conjured to explain a confounding anomaly. The S-B equation for an ideal or near ideal black body with a net incoming energy of 240 W/m^2 calculates a surface temperature of -18 C, 33 C less than the observed surface temperature of 15 C. What could explain this warming? Thus was born the GHE theory. However, the assumption that the earth’s surface is an ideal or even near ideal black body, i.e. 90% emissivity, is in error. If the effective emissivity’s across the spectrum for the polar ice caps, oceans and land surfaces are 0.0, .58 (sea-surface-emissivity-lge-sidran-1981), and .90 respectively the effective emissivity for the earth would be about .61 and the S-B equation with 240 W/m^2 would calculate 15 C. Ergo, the GHE theory addresses a problem that does not exist.
Point 1
Point 2
Point 3
Result! (for us Wheel Dealer fans.)
GHE theory – You’re FIRED!!!
Nicholas
Your atmospheric composition DRY. You must redo that explanation with water vapour included in the mental model because it dominates any GHG effect, if there is much of one.
Convection in the atmosphere is akin to conduction in an insulating blanket. The insulating effect of of dry blanket or atmosphere is real and impressive. We use it all the time.
The insulating effect of a wet atmosphere or wet insulation is much less. Thus the big error in CAGW is the idea that we should estimate the effect of CO2 on a dry atmosphere, because we don’t have one.
The GHG effect is real. The tropical thunderstorm effect is real and much larger, and many times larger than AG CO2’s effect. In fact AG CO2 is so ineffectual at making a difference to the naturally varying background temperature that the human influence is undetectable, in spit of vain imaginings.
The GHG effect is real.
==============
The GHG effect is calculated to be 33C. The center of mass of the atmosphere is 5.5km. The lapse rate due to gravity and condensation of water is 6.5C/km.
convection = 5.5km X 6.5C/km = 32.5C due to gravity and condensation of water.
The GHG effect must then be 33C – 32.5C = 0.5C
So if you double the GHG effect by doubling the CO2, you are going to get another 0.5C of warming. This will raise the temp of the earth from 15C to 15.5C (59F to 60F).
Whoopee we are all going to be a bit more comfortable, as for most people 60F is more comfortable than 59F. Otherwise why heat your house to 70F?
Perhaps the heat flow dominant equation should be Q = U * A * dT and not σ * ε * A * T^4. An insulating blanket slows the rate of heat flow, it does not “absorb” it and it most certainly does not reflect or re-emit it. If U, conductance (inverse R-value) goes down for a given Q, dT must increase and for a given cold sink the hot source temperature must rise. Certainly water vapor, especially its latent properties, dominates the atmospheric heat engine. That is not news. The S-B equation affect is negligible w/ 400 ppm and .01 emissivity, i.e. zero down welling back radiation. What happens in the system stays in the system and the GHG/GHE down/up welling loop zeroes out, can be erased from the balance, and makes zero difference at ToA.
btw Trenberth et. al. 2011 Figure 10 shows cooling at ToA, not warming.
Atmospheric Moisture Transports from Ocean to Land and Global Energy Flows in Reanalyses
“The GHG effect is real. The tropical thunderstorm effect is real and much larger, and many times larger than AG CO2’s effect. In fact AG CO2 is so ineffectual at making a difference to the naturally varying background temperature that the human influence is undetectable, in spit of vain imaginings.”
Same thing in my words. Pls refer to my other postings on WUWT, WriterBeat & LinkedIn..
Disputing this GHG loop concept is not denying the greenhouse effect/principle/process. A greenhouse operator can increase thermal mass by installing boxes of rocks, trombe walls, black painted plastic tubes and barrels full of water or eutectic salts, aka the oceans. If it gets too hot the operator can pull down reflective shades reducing the incoming heat, aka albedo which is more than just clouds. BTW IPCC AR5 credits clouds with -20 W/m^2 of RF and that’s a lot more cooling than CO2’s 2 W/m^2 of heating. The operator can turn on misting water sprays and evaporative cooling to reduce the air temperature and raise relative humidity, aka storms, rain, snow, etc. Both IPCC and Trenberth (same as IPCC) admit they really don’t understand the water vapor cycle, clouds, etc. very well. IPCC AR5 in TS.6 and Trenberth in the papers mentioned earlier.
The following is based on Figure 10 of “Atmospheric Moisture Transports from Ocean to Land and Global Energy Flows in Reanalyses” Trenberth et. al. 2011 and in particular the 333 W/m^2 GHG/GHE S-B radiation perpetual heat loop.
341 W/m^2 arrive at ToA.
102 W/m^2 are promptly reflected by the albedo which includes clouds, ice, ocean, vegetation and the ground and do not participate in the 333 W/m^2 GHG/GHE perpetual heat loop.
239 W/m^2 travel on beyond the albedo.
78 W/m^2 are absorbed by the atmosphere, i.e. clouds, water vapor, etc. and do not participate in the 333 W/m^2 GHG/GHE S-B radiation perpetual heat loop.
161 W/m^2 travel on past the atmosphere to strike the surface.
0.9 W/m^2 are absorbed by the surface and do not participate in the 333 W/m^ GHG/GHE S-B radiation perpetual heat loop.
160 W/m^2 are partitioned thus.
17 W/m^2 leave the surface as thermal convection and do not participate in the 333 W/m^ GHG/GHE S-B radiation perpetual heat loop.
80 W/m^2 leave the surface as evapotranspiration and do not participate in the 333 W/m^ GHG/GHE S-B radiation perpetual heat loop.
63 W/m^2 leave the surface of the earth as S-B radiation.
40 W/m^2 travel through the atmospheric window and do not participate in the 333 W/m^ GHG/GHE S-B radiation perpetual heat loop.
23 W/m^2 leave the surface per S-B radiation, but travel through the GHG/GHE level and do not participate in the 333 W/m^ GHG/GHE S-B radiation perpetual heat loop.
All of the energy (power flux) is accounted for without including the GHG/GHE S-B radiation perpetual heat loop.
So what is the origin of the energy which feeds the 333 W/m^2 GHG/GHE S-B radiation perpetual heat loop?
Not that it matters because simply erasing the GHG/GHE loop makes absolutely no difference in the overall and ToA/albedo/OLR balance.
Relatively minor fluctuations in the albedo reflection and ocean absorption will raise or lower the atmospheric & surface temperatures per Q = U * A * dT without resorting to some magic unicorn’s GHG/GHE S-B up/down welling “back” radiation perpetual heat loop.
Nicholas Schroeder June 1, 2016 at 7:54 am
Over 99.95% of the atmosphere is comprised of N2, O2, and AR which are essentially transparent to LWIR radiation. It stands to reason then that 99.95% of the LWIR radiating from the surface of the earth passes through the atmosphere while never contacting a CO2/GHG molecule.
Only true if our atmosphere is one molecule thick!
Exactly … there’s the small matter of path-length.
But let’s not let science (in this case the Beer-Lambert Law) get in the way of rubbishing climate science eh?
Let’s just do nonsensical and ignorant arithmetic instead.
After all most on here will buy it unthinking.
It really is sad the way warmista trolls have to paint with such a broad brush.
It’s almost as if they know that they can’t argue the actual data so they have to try and make disagreement with their religion personal.
Congratulations on being a sky-dragon slayer. Your mum will be proud I’m sure. Anthony less so.
That’s one more person than is proud of you.
If I have done the math correctly, I get an average distance of .0034 micrometers of space between each carbon dioxide molecule in every cubic meter of atmosphere. All 3 different IR wavelengths it is tuned to make it resonate are longer than this; so these wavelengths should fill have a very high chance of hitting and saturating each molecule. After it is saturated with equipartition of energy, it will be able to transfer that energy over to the molecules it collides with, or it will spontaneously emit the photon of IR that it absorbed. these energized carbon dioxide molecules stand a good chance of hitting Nitrogen molecules which end up with more translation kinetic energy. It is the collision with water that I imagine has the best outcome to rid the heat of the energy carbon dioxide transfers to it. For the water must condense at some time, and when it does, it loses all that energy, and radiates it all back, in all directions. Due to the fact that there is about a 53% chance of the photon going up and out back to space, as opposed to returning toward the earth below, the photons will likely refract toward space and be gone. As we heat more and more water, taking surface heat with it, the effect gets compounded and the earth’s thermostat remains well regulated within a fraction of a degree. As water vapor molecules get bombarded by excited carbon dioxide molecules and faster moving nitrogen molecules, the water vapor can become excited in its vibrational (IR) and rotational modes (microwave). Now the water can spontaneously emit IR and microwave photons. Some of these can be reabsorbed and some can escape; however, don’t forget those photons that are going to escape when water vapor turns to liquid or even ice crystals is some clouds. Talk about negative feedback loops.
Update… HadCRUT4 anomaly for April came in today at +0.926 versus my +0.929 extrapolation. That gives me more confidence in the extrapolations going forward.
From today’s WSJ:http://www.wsj.com/articles/trump-makes-sense-on-energy-1464733991
Our site has the NCEP CFSR temp initialization every 6 hours! May against their 35 year record was a record warm one, which makes 7 out of the last 8 ( I believe it was plus .425, it will back up shortly as the monthly has to recycle. June is the 1st month since September not to open on the first day higher than the monthly record, and with continued cooling it looks like it will not break the record
You can see it all here. I believe its not behind the paywall
http://models.weatherbell.com/temperature.php
I’ve been following the daily and monthly NCEP CFSR/CFSV2 posted by the University of Maine (UM) Climate Change Institute (CCI). Based on final daily estimates posted for May 1-18 and preliminary May 19-31 estimates, I get a monthly global temperature anomaly of 0.44C referenced to 1981-2010. This estimate will probably change slightly when UM CCI posts final daily estimates through the end of May, and may change slightly again when they post the final monthly estimate (they have posted final monthly estimates through April 2016 but they sometimes go several months before posting the latest monthly estimates).
The trend graph for the 21st Century so far (since 2001) continues to show a downward slope, despite the recent spike that is now dropping rapidly:
More monthly graphs here:
https://oz4caster.wordpress.com/monthly-trends/
But on a longer time scale…..
Yes, and if this average rise of 0.0011C per month over the 37 years from 1979 to 2016 continues for 100 years, the increase would be a paltry 1.3C with probably more net benefits than detriments. However, trend graphs in climate do not have very good predictive powers because climate, like weather, constantly fluctuates and sometimes in chaotic and unpredictable ways. So I doubt that the indicated trend will be very accurate at predicting more than about 10 to 20 percent beyond the end of the range, or about 4 to 8 years. I also doubt that our infant and very restricted Global Climate Models can perform any better. Where is a climate model that accurately predicted the current El Niño? If they can’t predict El Niños, what make us think they can predict even longer trends?
And on a yet longer time scale humanity may need as much CO2 effect as possible to delay the next glacial cycle that could start at any time. I doubt even the wildest claimed maximal hypothetical CO2 effect will be able to stop the next glacial cycle. Here is a paleo climatological persistence forecast:
Notice how trolls lie.
He takes the coldest year in the last 100 years, and compares it to the warmest year in the last 35.
Well as long as they are going to use satellite readings of SST as part of their data collection, they will always be warmer than insitu surface measurements. The readings of the satellite are off by .6K. .329 would be a closer anomaly and true of what we are really observing here on the surface. For instance, in Tucson, AZ the temperature never broke 100 deg. F for the whole month of May. We have been very cool and comfortable here in San Bernardino, CA. What we are experiencing in this wide geologically large southwest region is not indicative of a .929 anomaly.
Our Inland Southwest temperature monitor shows May 2016 finishing -1.7 F below the climate average:
http://www.atmos.washington.edu/marka/crn.2016/sw/201605.sw.txt
This is based on 13 Climate Reference Network sites…more here:
http://www.atmos.washington.edu/marka/crn.2016/sw/
We also have a California temperature monitor which shows May 2016 finishing -0.4 F below normal:
http://www.atmos.washington.edu/marka/crn.2016/ca/
And the USA48 temperature monitor shows May 2016 also at -0.4 F below the climate average. This was the first month since July 2015 to finish below the climate average:
http://www.atmos.washington.edu/marka/crn.2016/usa48/histrec.usa48.txt
Rudd
1. These anomalies are global. Thus, though you are right to suspect a sound difference between measurements at surface and those made at about 4 km, a fact remains: local temps differ from global ones.
2. Moreover, Walter Dnes unfortunately publishes anomalies “as is” instead of first normalizing them to a common baseline. If you do this for example wrt UAH’s baseline (1981-2010), HadCRUT4’s anomaly (.929, calculated wrt 1961-1990) moves down to .635 °C. For GISS, baselined 1951-1980, the difference is even higher (.428 °C).
3. Many commenters complain about NOAA and NASA having allegedly “manipulated” their temperature datasets, with the effect of making 1934 becoming no longer the warmest year ever on record. This is a misunderstanding: 1934 is, in the world wide ranking, at position… 49.
Bindidon wrote: “3. Many commenters complain about NOAA and NASA having allegedly “manipulated” their temperature datasets, with the effect of making 1934 becoming no longer the warmest year ever on record. This is a misunderstanding: 1934 is, in the world wide ranking, at position… 49.”
Not according to the Climate Change Gurus. They said 1934 (according to the Climategate emails), was the hottest in the “world wide ranking”; hotter than 1998.
Your “49” ranking must derive from the surface temperature record after the Climate Change Gurus bastardized it, and turned it from a “long-term” cooling trend into a warming trend.
So a “green crusader” has no problems travelling around the world for publicity stunts. His only concern is whether or not such a trip infringes gift rules,
From: Bob Inglis
To: Edward W. Maibach
Cc: Alex Bozmoski
Subject: Re: op-ed + a needlessly self-inflicted headache
Date: Monday, September 21, 2015 5:38:18 PM
( The last paragraph of his comment)
The Greenland idea is blindingly brilliant for sure. It’s the kind of thing that I suggested to Prince Charles–the he invite folks like George Will to accompany him to Antarctica, hosting the group at the Kiwi station there. (The prince said he’d consider it, minding that he not get too far into policy and politics.) The challenge—which perhaps can be overcome–with members of Congress is fitting such a trip into the gift rules.
Onward!
Mods: Please delete. I posted this in the wrong article; arrgh.
I was looking at a paper published in 1953 that discussed the construction and application of an IR detector for measurement of back radiation. Observed data and theoretical comparisons were tabulated. W/m^2 varied considerably over the test periods. Night time: ave: 280.5 W/m^2 +31.4 / -74. Day time: ave: 363.8 W/m^2 +55.6 / -41.4. Still trying to get my head around the concept of “back” radiation. Reviewed materials at Marohasy and Science of Doom and would like to pose a poser.
There is a difference between energy, heat, work and power. Heat, work and power are energy in motion, flux, in transition, moving from source to sink. Energy content/temperature is measured by IR instruments, heat flow, work or power are not. Temperatures are indicators of energy content, not heat flow. Per thermo laws heat flows from high temperature/energy to low temperature/energy. “Back” radiation is a measure of temperature/energy content, not heat flow.
Heat/work is energy transferred between objects/systems of two different temperatures, flow is higher to lower. An object cannot contain heat, it can only contain energy. See ESA 21.
Q heat flow = U heat conductance * Area * dT (hot -cold) Works for atmosphere, too.
https://esa21.kennesaw.edu/activities/rfactor/rfactor.pdf
If I cover the opening of a freezer w/ an IR transparent plate and measure the temperature/energy w/ an IR instrument I will see and measure the “back” radiation/temperature/energy level, but we all know which way the heat is flowing.
So “back” radiation indicates/measures energy content / temperature level and says nothing about heat flow in either magnitude or direction.
Open for R&C.
Thanks,
You have stated everything here correctly. Perhaps the confusion is in properly applying all the concepts to other events going on in the atmosphere that are causing the back radiation. Exothermic events such as water vapor condensing into liquid or actually undergoing deposition into ice crystals, can and do happen high in the troposphere. When these events occur, the result is a release of photons in every direction. There is a chance these photons will escape into space; however the ones that are directed downward, and enter the aperture of your back-scatter radiation sensor, will cause it to detect the radiation. There should be no back-scatter when the humidity throughout the troposphere is low, which would result in no clouds being formed.
Hope that helps. It gets just as confusing for me as most people.
As for the differences between energy, heat, work, and power, well……that’s a pretty deep subject. Let me hit on it. As early as elementary science class, we learn that energy has the ability to do work. Heat energy that can be transferred, via radiation, conduction, and convection and this transfer of energy from a hot object to a cold object will stop when both objects reach the same temperature. The rate of the energy transfer will depend on several factors; but simply put, the rate energy is transferred can be referred to as power. Work is a force applied through a distance. So now energy has the ability to do work. The amount of work done per unit time is known as power. We can do work on a system by several ways. In thermodynamics work is done when an enclosed gas expands or contracts. When the gas is allowed to adiabatically contract the pressure must increase and the temperature will rise. When this process occurs work must be done on the gas; in this case gravity does work on the gas. This can occur when an air mass the was previously lifted by convection on one latitude of earth, now moves high to a cooler latitude and falls down to earth again. Sensors should be able to pick up this back radiation as well. This process is known as adiabatic heating. Of course the reverse process of adiabatic cooling must have occurred, probably at a lower latitude toward the equator, where the gas was heated by the surface of the earth. As the gas was heated, it rose, and expanded, and cooled. The gas did work against gravity. The rate at which it did this work could be expressed as power. The back-scatter sensors of the thermal detector don’t pick up much if the air has a low relative humidity. (at the equator, this would be rare, unless it is happening over hot/dry land) Once the gas reached its destination higher in the troposphere, maybe all the way to the top, depending on local conditions, the gas is transported across toward a higher North or lower South latitude. Winds in the upper atmosphere do this. Since the gas is being transported sideways, no work is being done against or with gravity; the only work being done is to move the mass against friction of other air molecules. When the mass gets to a location where the rate of rising air is predominately low, it can now fall. As it falls, it compresses, gravity does work on it, it heats up, and the surface heats. I would imagine that this heat would be able to be detected in a thermal sensor. I would appreciate any feedback on this explanation.
In the thread I posted about gravity doing work, maybe I should have said that the rising molecules at the equator reached a position of high gravitational potential, and when this potential is allowed to move to a low gravitational potential, work is done. Basically work is a force applied through a distance, the force is the mass of the molecules by 9.8m/s^2. This acceleration near earth’s surface is acceleration due to gravity (g). This distance that this force is applied is what ever height in the troposphere that the mass of air molecules was lifted to.
WordPress doesn’t allow deeply nested threads, so I have to start a new one to reply to this question.
Short answer… I don’t know. By the way; I don’t have a university degree, or the math background to answer the question. My highest post-secondary qualification is in computer-programming from Vancouver Community College many years ago. I assume that you would need to detrend the data first and look for a 60 year cycle. I assume that you’re referring to the PDO?
The longest period of record is HadCRUT4, starting in 1850. They show very strong peaks in…
* late 1877 to early 1878
* late 1943 through 1945
* late 1997 to mid 1998
* late 2015 to early 2016
The last 2 warm episodes were El Ninos. The other 2 probably qualify as well. The first 3 episodes were approximately 60 years apart. The latest one does not seem to fit. If you assume a slightly longer period, then the 1997 to mid 1998 episode doesn’t fit. I don’t know if there is a regular cycle. Yes, temperatures go up and down, but “cycle-seeking” is often a futile excercise.
Walter,
I thought I should check my integration method to see if that resolved the small discrepancy in our results. I posted about it here. Improving it made a very small difference, not enough to explain. My guess is that it’s a leap year issue. These are fiddly, with no very certain answer, and do have effects of about that order.
I tried answering that post with some details of my calculation methods, but my reply disappeared when I hit “Preview”. Check your contact email for the reply I wanted to post. I take some shortcuts, so any discrepancies are probably resolved in your favour.
Walter,
Sorry about the difficulty. Thanks for the email – I have responded.