Source: Mantua, 2000
The essay below has been part of a back and forth email exchange for about a week. Bill has done some yeoman’s work here at coaxing some new information from existing data. Both HadCRUT and GISS data was used for the comparisons to a doubling of CO2, and what I find most interesting is that both Hadley and GISS data come out higher in for a doubling of CO2 than NCDC data, implying that the adjustments to data used in GISS and HadCRUT add something that really isn’t there.
The logarithmic plots of CO2 doubling help demonstrate why CO2 won’t cause a runaway greenhouse effect due to diminished IR returns as CO2 PPM’s increase. This is something many people don’t get to see visualized.
One of the other interesting items is the essay is about the El Nino event in 1878. Bill writes:
The 1877-78 El Nino was the biggest event on record. The anomaly peaked at +3.4C in Nov, 1877 and by Feb, 1878, global temperatures had spiked to +0.364C or nearly 0.7C above the background temperature trend of the time.
Clearly the oceans ruled the climate, and it appears they still do.
Let’s all give this a good examination, point out weaknesses, and give encouragement for Bill’s work. This is a must read. – Anthony
Adjusting Temperatures for the ENSO and the AMO
A guest post by: Bill Illis
People have noted for a long time that the effect of the El Nino Southern Oscillation (ENSO) should be accounted for and adjusted for in analyzing temperature trends. The same point has been raised for the Atlantic Multidecadal Oscillation (AMO). Until now, there has not been a robust method of doing so.
This post will outline a simple least squares regression solution to adjusting monthly temperatures for the impact of the ENSO and the AMO. There is no smoothing of the data, no plugging of the data; this is a simple mathematical calculation.
Some basic points before we continue.
– The ENSO and the AMO both affect temperatures and, hence, any reconstruction needs to use both ocean temperature indices. The AMO actually provides a greater impact on temperatures than the ENSO.
– The ENSO and the AMO impact temperatures directly and continuously on a monthly basis. Any smoothing of the data or even using annual temperature data just reduces the information which can be extracted.
– The ENSO’s impact on temperatures is lagged by 3 months while the AMO seems to be more immediate. This model uses the Nino 3.4 region anomaly since it seems to be the most indicative of the underlying El Nino and La Nina trends.
– When the ENSO and the AMO impacts are adjusted for, all that is left is the global warming signal and a white noise error.
– The ENSO and the AMO are capable of explaining almost all of the natural variation in the climate.
– We can finally answer the question of how much global warming has there been to date and how much has occurred since 1979 for example. And, yes, there has been global warming but the amount is much less than global warming models predict and the effect even seems to be slowing down since 1979.
– Unfortunately, there is not currently a good forecast model for the ENSO or AMO so this method will have to focus on current and past temperatures versus providing forecasts for the future.
And now to the good part, here is what the reconstruction looks like for the Hadley Centre’s HadCRUT3 global monthly temperature series going back to 1871 – 1,652 data points.
I will walk you through how this method was developed since it will help with understanding some of its components.
Let’s first look at the Nino 3.4 region anomaly going back to 1871 as developed by Trenberth (actually this index is smoothed but it is the least smoothed data available).
– The 1877-78 El Nino was the biggest event on record. The anomaly peaked at +3.4C in Nov, 1877 and by Feb, 1878, global temperatures had spiked to +0.364C or nearly 0.7C above the background temperature trend of the time.
– The 1997-98 El Nino produced similar results and still holds the record for the highest monthly temperature of +0.749C in Feb, 1998.
– There is a lag of about 3 months in the impact of ENSO on temperatures. Sometimes it is only 2 months, sometimes 4 months and this reconstruction uses the 3 month lag.
– Going back to 1871, there is no real trend in the Nino 3.4 anomaly which indicates it is a natural climate cycle and is not related to global warming in the sense that more El Ninos are occurring as a result of warming. This point becomes important because we need to separate the natural variation in the climate from the global warming influence.
The AMO anomaly has longer cycles than the ENSO.
– While the Nino 3.4 region can spike up to +3.4C, the AMO index rarely gets above +0.6C anomaly.
– The long cycles of the AMO matches the major climate shifts which have occurred over the last 130 years. The downswing in temperatures from 1890 to 1915, the upswing in temps from 1915 to 1945, the decline from 1946 to 1975 and the upswing in temps from 1975 to 2005.
– The AMO also has spikes during the major El Nino events of 1877-88 and 1997-98 and other spikes at different times.
– It is apparent that the major increase in temperatures during the 1997-98 El Nino was also caused by the AMO anomaly. I think this has lead some to believe the impact of ENSO is bigger than it really is and has caused people to focus too much on the ENSO.
– There is some autocorrelation between the ENSO and the AMO given these simultaneous spikes but the longer cycles of the AMO versus the short sharp swings in the ENSO means they are relatively independent.
– As well, the AMO appears to be a natural climate cycle unrelated to global warming.
When these two ocean indices are regressed against the monthly temperature record, we have a very good match.
– The coefficient for the Nino 3.4 region at 0.058 means it is capable of explaining changes in temps of as much as +/- 0.2C.
– The coefficient for the AMO index at 0.51 to 0.75 indicates it is capable of explaining changes in temps of as much as +/- 0.3C to +/- 0.4C.
– The F-statistic for this regression at 222.5 means it passes a 99.9% confidence interval.
But there is a divergence between the actual temperature record and the regression model based solely on the Nino and the AMO. This is the real global warming signal.
The global warming signal (which also includes an error, UHI, poor siting and adjustments in the temperature record as demonstrated by Anthony Watts) can be now be modeled against the rise in CO2 over the period.
– Warming occurs in a logarithmic relationship to CO2 and, consequently, any model of warming should be done on the natural log of CO2.
– CO2 in this case is just a proxy for all the GHGs but since it is the biggest one and nitrous oxide is rising at the same rate, it can be used as the basis for the warming model.
This regression produces a global warming signal which is about half of that predicted by the global warming models. The F statistic at 4,308 passes a 99.9% confidence interval.
– Using the HadCRUT3 temperature series, warming works out to only 1.85C per doubling of CO2.
– The GISS reconstruction also produces 1.85C per doubling while the NCDC temperature record only produces 1.6C per doubling.
– Global warming theorists are now explaining the lack of warming to date is due to the deep oceans absorbing some of the increase (not the surface since this is already included in the temperature data). This means the global warming model prediction line should be pushed out 35 years, or 75 years or even 100s of years.
Here is a depiction of how logarithmic warming works. I’ve included these log charts because it is fundamental to how to regress for CO2 and it is a view of global warming which I believe many have not seen before.
The formula for the global warming models has been constructed by myself (I’m not even sure the modelers have this perspective on the issue) but it is the only formula which goes through the temperature figures at the start of the record (285 ppm or 280 ppm) and the 3.25C increase in temperatures for a doubling of CO2. It is curious that the global warming models are also based on CO2 or GHGs being responsible for nearly all of the 33C greenhouse effect through its impact on water vapour as well.
The divergence, however, is going to be harder to explain in just a few years since the ENSO and AMO-adjusted warming observations are tracking farther and farther away from the global warming model’s track. As the RSS satellite log warming chart will show later, temperatures have in fact moved even farther away from the models since 1979.
The global warming models formula produces temperatures which would be +10C in geologic time periods when CO2 was 3,000 ppm, for example, while this model’s log warming would result in temperatures about +5C at 3,000 ppm. This is much closer to the estimated temperature history of the planet.
This method is not perfect. The overall reconstruction produces a resulting error which is higher than one would want. The error term is roughly +/-0.2C but the it does appear to be strictly white noise. It would be better if the resulting error was less than +/- 0.2C but it appears this is unavoidable in something as complicated as the climate and in the measurement errors which exist for temperature, the ENSO and the AMO.
This is the error for the reconstruction of GISS monthly data going back to 1880.
There does not appear to be a signal remaining in the errors for another natural climate variable to impact the reconstruction. In reviewing this model, I have also reviewed the impact of the major volcanoes. All of them appear to have been caught by the ENSO and AMO indices which I imagine are influenced by volcanoes. There appears to be some room to look at a solar influence but this would be quite small. Everyone is welcome to improve on this reconstruction method by examining other variables, other indices.
Overall, this reconstruction produces an r^2 of 0.783 which is pretty good for a monthly climate model based on just three simple variables. Here is the scatterplot of the HadCRUT3 reconstruction.
This method works for all the major monthly temperature series I have tried it on.
Here is the model for the RSS satellite-based temperature series.
Since 1979, warming appears to be slowing down (after it is adjusted for the ENSO and the AMO influence.)
The model produces warming for the RSS data of just 0.046C per decade which would also imply an increase in temperature of just 0.7C for a doubling of CO2 (and there is only 0.4C more to go to that doubling level.)
Looking at how far off this warming trend is from the models can be seen in this zoom-in of the log warming chart. If you apply the same method to GISS data since 1979, it is in the same circle as the satellite observations so the different agencies do not produce much different results.
There may be some explanations for this even wider divergence since 1979.
– The regression coefficient for the AMO increases from about 0.51 in the reconstructions starting in 1880 to about 0.75 when the reconstruction starts in 1979. This is not an expected result in regression modelling.
– Since the AMO was cycling upward since 1975, the increased coefficient might just be catching a ride with that increasing trend.
– I believe a regression is a regression and we should just accept this coefficient. The F statistic for this model is 267 which would pass a 99.9% confidence interval.
– On the other hand, the warming for RSS is really at the very lowest possible end for temperatures which might be expected from increased GHGs. I would not use a formula which is lower than this for example.
– The other explanation would be that the adjustments of old temperature records by GISS and the Hadley Centre and others have artificially increased the temperature trend prior to 1979 when the satellites became available to keep them honest. The post-1979 warming formulae (not just RSS but all of them) indicate old records might have been increased by 0.3C above where they really were.
– I think these explanations are both partially correct.
This temperature reconstruction method works for all of the major temperature series over any time period chosen and for the smaller zonal components as well. There is a really nice fit to the RSS Tropics zone, for example, where the Nino coefficient increases to 0.21 as would be expected.
Unfortunately, the method does not work for smaller regional temperature series such as the US lower 48 and the Arctic where there is too much variation to produce a reasonable result.
I have included my spreadsheets which have been set up so that anyone can use them. All of the data for HadCRUT3, GISS, UAH, RSS and NCDC is included if you want to try out other series. All of the base data on a monthly basis including CO2 back to 1850, the AMO back to 1856 and the Nino 3.4 region going back to 1871 is included in the spreadsheet.
The model for monthly temperatures is “here” and for annual temperatures is “here” (note the annual reconstruction is a little less accurate than the monthly reconstruction but still works).
I have set-up a photobucket site where anyone can review these charts and others that I have constructed.
http://s463.photobucket.com/albums/qq360/Bill-illis/
So, we can now adjust temperatures for the natural variation in the climate caused by the ENSO and the AMO and this has provided a better insight into global warming. The method is not perfect, however, as the remaining error term is higher than one would want to see but it might be unavoidable in something as complicated as the climate.
I encourage everyone to try to improve on this method and/or find any errors. I expect this will have to be taken into account from now on in global warming research. It is a simple regression.
UPDATED: Zip files should download OK now.
SUPPLEMENTAL INFO NOTE: Bill has made the Excel spreadsheets with data and graphs used for this essay available to me, and for those interested in replication and further investigation, I’m making them available here on my office webserver as a single ZIP file
Downloads:
Annual Temp Anomaly Model 171K Zip file
Monthly Temp Anomaly Model 1.1M Zip file
Just click the download link above, save as zip file, then unzip to your local drive work folder.
Here is the AMO data which is updated monthly a few days after month end.
http://www.cdc.noaa.gov/Correlation/amon.us.long.data
Here is the Nino 3.4 anomaly from Trenbeth from 1871 to 2007.
ftp://ftp.cgd.ucar.edu/pub/CAS/TNI_N34/Nino34.1871.2007.txt
And here is Nino 3.4 data updated from 2007 on.
http://www.cpc.ncep.noaa.gov:80/data/indices/sstoi.indices
– Anthony
From Basil (12:16:49) :
“One thing that stands out to me, though, is your 4th figure, where you show the “warming” in the divergence between the temperature series, and the Nino+AMO only model, after ~1970.
“I wonder if the “warming” would not be as dramatic, or even noticeable, if you used a tropics only temperature series, rather than the global series. I’ve done enough temperature trend analysis to know that most of the “warming” we’ve seen in the past 2-3 decades occurred in the northern extra-tropics, and over land. ”
In some ways, see only the arctic heat up makes sense w/r to the liberals’ “rising-CO2-causing-rising-global-temperature” theory.
One of Hansen’s premises is that a 1x increase in CO2 GHG function is “forcing” a 10x increase in water vapor’s GHG function. He expects to see this effect most strongly where water vapor is limited (colder climates) and where the air itself is very dry (again, colder, arctic climates.) Where water vapor is higher (warmer areas and ocean/island areas) he doesn’t expect to see much GHG increases.
Thus, Hansen MUST show an increase in Arctic temperatures. Even though he cannot explain why only the ten Siberian thermometers are going up, when the more numerous Candian and Alaskan and Swedish temperatures are NOT rising.
Okay, here is the problem with the reconstruction and why it doesn’t work really, really well.
The Southern Hemisphere temps (as linked to by Basil) are not matched up by the ENSO or AMO indices. I tried the Antarctic oscillation linked to by evanjones and this gets one closer but it is still off by quite a bit.
Does anyone know of a good Southern Ocean Index to try?
The Southern Oscillation Index is more related to the El Nino and La Nina phenomenon rather than southern ocean temps as the name would indicate so that one doesn’t work – in fact the ENSO is negatively correlated with southern hemisphere temps which is a little strange I guess except when one considers that the ENSO develops out of the ocean circulations from the southern oceans sometimes and, in fact, lags the southern ocean temps versus leading global temperatures as it does. A little counter-intuitive but nonetheless.
Here is the problem.
http://img122.imageshack.us/img122/7797/shtempanomalybq1.png
Any ideas for a southern ocean index which matches this?
And Richard, I agree with you completely. Empirical evidence is king and should always overrule any theory. It is the basis for science and medicine and the reason why civilization has advanced in recent centuries. I don’t know why theory overrules evidence in the global warming field however.
John Philip (17:20:45)
Oh dear, the cherry-pick the 1998 El Nino has passed the decade mark.
If you add one more year, to 132 months, then only good ol’ GISSTEMP is
going up. Of course, that El Nino was followed by a La Nina as often happens,
and that forces the trend positive.
http://www.woodfortrees.org/plot/hadcrut3vgl/last:132/plot/hadcrut3vgl/last:132/trend/plot/gistemp/last:132/plot/gistemp/last:132/trend/plot/uah/last:132/plot/uah/last:132/trend/plot/rss/last:132/plot/rss/last:132/trend
Of course, the trend over the past two years can’t continue, but see http://www.woodfortrees.org/plot/hadcrut3vgl/last:132/plot/hadcrut3vgl/last:24/trend/plot/gistemp/last:132/plot/gistemp/last:24/trend/plot/uah/last:132/plot/uah/last:24/trend/plot/rss/last:132/plot/rss/last:24/trend
That period has the most recent El Nino and the PDO flip. It makes for a good cherry pick, but it does have useful information you can read between the lines.
Bill,
It would be very significant, I think, if separate out the temperature of the tropics and the temperature of the northern extratropics, and can show that the AMO is more important to the latter, and can explain some (a lot?) of the anomalous behavior in temperature trends in the northern extratropics since the 1970’s.
Basil
Basil,
I have the tropics and the northern hemisphere pretty much nailed. The tropics is driven by the ENSO (but has an AMO influence as well) and the northern hemisphere is driven mainly by the AMO (but has an ENSO influence as well.)
Which itself is a significant development I think.
But I’m really looking to pull the natural variation out of the total global temp anomaly so it appears I need something for the southern hemisphere.
The reconstruction was already very close so I don’t need a perfect correlation, just something that is reasonably close.
Just catching up with all the discussions on this thread so I could easily have missed or misunderstood something but, with regards to the “ocean warming”
Richard Courtney writes
(c) The heating from AGW of the ocean is (i) mostly direct radiant IR input that is absorbed within the top few meters of the ocean,
Shouldn’t that be millimetres – or even fractions of mm. I was under the impression that IR only penetrated the top ‘skin’ of liquid water. I’ve seen Doug Hoyt demonstrate this using transmission formula with appropriate absorption coefficients for typical IR wavelengths.
This raises the question (for me at least) as to how increased GHGs warm the ocean. Some AGWers seem to be arguing that, rather than warming from the direct downward IR effect, increased GHGs actually slow the rate of ocean cooling.
R.Sharpe
The Planck’s distribution of the excited vibrationnal levels of CO2 demands that the proportion of excited levels stays CONSTANT for a given temperature .
Consequence is that CO2 simply must radiate away what it absorbs because else we have no more LTE .
Bear with me here as I am trying to understand this, and may have to go away and read a physics text book.
However, what says that the temperature must stay CONSTANT? When the atmosphere, via CO2 increases, absorbs more energy, wouldn’t its temperature shift upwards and reach a different LTE point?
The temperature stays constant for a given concentration – that is the LTE condition .
Indeed when the CO2 concentration changes , the equilibrium temperature changes but very slightly .
Yes the atmosphere will absorb more but it will also RADIATE more . Absorption and radiation always go hand in hand .
The Planck’s distribution of the energy levels has a very low sensibility to temperatures – around the room temperature only about 5 % of the CO2 molecules are excited . At lower temperatures even less .
The main point which Phil has got wrong is that he imagines that for a given CO2 concentration CO2 only absorbs and transfers the absorbed energy by collisions to N2&O2 .
Or in other words that the CO2 doesn’t radiate in the troposphere .
Of course that is not what happens because :
a) CO2 radiates as any tropospheric spectrum shows .
b) The collisional reaction CO2* + N2 CO2 + N2* where the symbol * means high energy species (it can be either translationnal energy or vibro/rotationnal energy) is an equilibrium . From that follows that for every CO2* that transfers energy to N2 , there is an N2* that transfers energy to CO2 .
Obviously if it was not the case , the reaction would not be an equilibrium .
The constraint is that the energy distribution of both CO2 and N2 must stay constant because by definition we are in LTE and the temperature is constant .
c) From b) above follows that as the collisional reaction is an equilibrium , the CO2 molecules must radiate approximately the same energy as the one they absorb because N2 can radiate only a small amount .
This is a trivial consequence of energy conservation .
So the naive vision in which CO2 is a kind of “pump” which absorbs IR , never radiates and heats the air is simply wrong .
What CO2 absorbs is radiated away (and reabsorbed and reradiated etc) .
If the CO2 concentration changes , the processes described above don’t change but the equilibrium temperature will slightly change with negligible impact on the energy distribution .
I forgot the problems with the “arrow” symbols .
In the above collisional reaction it shoud read :
CO2* + N2 (-) CO2 + N2* where the “(” and “)” are left and right arrows .
Re Colin Aldridge (11:21:51) and Richard M (11:37:18)
Anyone seen a plausible explanation for ENSO?
Try http://climatechange1.wordpress.com/2008/11/21/the-enso-driver/
All the discussion of carbon dioxide and its supposed effect on surface temperature should be tempered by observation of the reaction of ozone to the strong seasonal increase in radiation from the Earth each northern summer. This is due to the distribution of land and sea with 40% of the northern hemisphere land by comparison with only 20% in the southern hemisphere. A mid year burst of OLR (continents heat the atmosphere and cloud cover declines with relative humidity) produces an increase in temperature at the tropopause (100hPa) in the southern tropics of something like 4-5°C in August every year. The pattern of seasonal change of temperature at 150hPa or 200hPa (peaking in April – May in the southern tropics) is unaffected. Conclusion: there is no effective transfer of energy from the heated tropopause down into the atmosphere immediately below. Convection cancels the effect of down welling radiation. Full exposition at: http://climatechange1.wordpress.com/2008/11/08/earth-laboratory-tests-the-greenhouse-theory-once-a-year-every-year-and-finds-it-wanting-every-time/
The convection dynamic in the troposphere where temperature falls with elevation is very strong. ‘Tropos’ is Greek for turning. It is less vigorous in the stratosphere (‘stratos’ = layered) where temperature increases with altitude. Strangely, the strong effect of OLR on atmospheric temperature via ozone falls away long short of peak ozone concentration at 30hPa. I suggest that this is due to increasing ease of emission to space as atmospheric density declines with elevation. Thus the pattern of evolution of temperature within the year reveals the relative strength of the forces that are in operation. Here is the observational evidence of the irrelevance of greenhouse theory in the real world of atmospheric dynamics.
Bill, congratulations on what appears to be, from a non statistician’s point of view, an advance in attributing effect to cause. However, I want to point out that all of the oceans acquire heat in tropical zones and emit more energy than they receive from the sun, pole wards of 40° of latitude. The North Atlantic has a much larger swing in temperature than the other oceans but this may have something to do with the ratio between the relatively small surface area of the Atlantic in relation to the large cloud free tropical zone where the solar energy is absorbed. Additionally, the relatively closed circulation of this ocean due to the particular arrangement of the coast of Brazil in relation to the push of the warmed waters means that little of the surface circulation of warmed water is lost to the southern hemisphere where the water volume is vast and temperatures depressed by the presence of Antarctica and its constant all season downdraft of air at minus 80°C or thereabouts. So, the AMO expresses the evolution of global temperature with a vigor that is not seen in the Pacific with its vast southern component.
The tropical oceans absorb energy from the sun. A useful index of the amount of energy received might be the temperature of the water at 0-10°N latitude where the warmest waters lie. However since, after a certain point, the energy received by the ocean is resolved via evaporation rather than increase in surface temperature and much of that energy is released as latent heat at 850hPa, a useful index could be temperature at 850hPa (over the ocean) between the equator and 10°N. That might come close to expressing the the power of the solar driver that works via cloud cover change in the ozone rich high pressure zones of subsiding, relatively cloud free air in the tropics. Despite there being little low altitude cloud there is a strong flux in cirrus above 500hPa depending upon local temperature. To see this mechanism in action on an hourly basis in the Pacific east of South America see Fulldisk Satellite Image from GOES8 at http://www.intelliweather.com/imagesuite_specialty.htm This blog provides a permanent reference point to current images in the header above.
There is a key assumption here, i.e.
The ENSO and the AMO are capable of explaining almost all of the natural variation in the climate. and
The coefficient for the Nino 3.4 region at 0.058 means it is capable of explaining changes in temps of as much as +/- 0.2C.
The coefficient for the AMO index at 0.51 to 0.75 indicates it is capable of explaining changes in temps of as much as +/- 0.3C to +/- 0.4C.
That is, the essay assumes that correlation demonstrates causality. This is a logical fallacy, without considering the underlying physics it is impossible to conclude whether the oscillations are driving the temperature or the oscillations are modified by the changing temperatures, As Trenberth and Hoar (2001) found
Both the recent trend for more ENSO events since 1976 and the prolonged 1990-1995 ENSO event are unexpected given the previous record, with a probability of occurrence about once in 2,000 years. This opens up the possibility that the ENSO changes may be partly caused by the observed increases in greenhouse gases.
If this is the case then simply subtracting the ENSO index from the observed temperatures will not give the ‘real’ residual global warming signal.
From an eyeball of the first chart it seems the good correlation starts to break down in the second half of the 20th century as GHG warming becomes dominant. The model attempts to plug this gap with a formula based on the multiple of the natural log of CO2 plus an arbitrary constant, which gives a reasonable match. The expected global warming from theory is given as:
When you add up all the forcings from all the GHGs and other feedbacks – the estimated forcing per doubling is 4.33 w/m^2 * 0.75C /w/m^2 = 3.25C per doubling.
In effect I am just regressing to find out how much forcing change there has really been to date (assuming forcings result in 0.75C per watt per metre although it really doesn’t matter what the number is). In effect, I am skipping all these steps and just regressing for the actual temperature response.
And this is expressed as the formula:
The warming models are really based on (using the anomaly C basis rather than Kelvin):
— 4.7 * ln(280) – 26.9 = -0.42C
— 4.7 * ln(560) – 26.9 = +2.84C
and the change is +3.25C per doubling
The regression returns
— 2.7 * ln(280) – 15.8 = -0.42C
— 2.7 * ln(560) – 15.8 = +1.44C
and the change is +1.85C per doubling
In other words the ‘regression’ simply uses lower values for the multiplier and constant.
This is not legitimate, the 0.75C/W/m2 figure is an expression of the climate sensitivity, that is the expected temperature rise produced by a given forcing. However the IPCC define climate sensitivity as
the equilibrium change in the annual mean global surface
temperature following a doubling of the atmospheric equivalent
carbon dioxide concentration.
The key point being that there is thermal inertia in the climate system and so the observed temperatures are only expected to match the modelled temperatures once equilibrium is reached (this is a convenient construct, in fact equilibrium is never reached, but it is a useful concept), Hansen et al (2005) finds :
Evidence from Earth’s history and climate models suggests that climate sensitivity is 0.75° ± 0.25°C per W/m2, implying that 25 to 50 years are needed for Earth’s surface temperature to reach 60% of its equilibrium response.
In other words in the half century following a change in forcing as the climate system is still responding, it is unsurprising that a simple logarithmic model with reduced coefficients gives a reasonable match to observed temperatures, but it is not valid simply to extrapolate this forward.
Some of the forcing goes to increasing the ocean heat content, this figure from the same paper shows how the models’ estimate of the increased OHC in the top 750m compare with observations. Any model that assumes all the extra forcing goes purely to increasing surface temperatures must explain the origin of this extra heat.
As explained above the statement …
As well, the AMO appears to be a natural climate cycle unrelated to global warming. is incorrect because the dataset chosen had already had the trend removed, the trend is described as
It is exactly the same data I used before except it has very slight trend in it, about 0.002C per year or about 0.02C per decade GW impact potential.
Actually slightly over 0.025C/ decade. Given that the data cover 15 decades, the difference over the dataset is approx 0.375C, equivalent to more than 50% of the 20th century warming. The fact that a change of this magnitude has an apparently minor impact on the model tells us something very interesting about its robustness.
Let us take a brief look at what the IPCC models which include the ocean physics and feedbacks actually projected for recent decades. The figures from the TAR are here. Taking Scenario A2 as a reasonable midrange choice and a good match to the actual forcings trajectory the temperatures were projected to increase by 0.35C from the baseline year of 1990 to 2010, a linear trend of 0.175C/decade. The actual trend in the monthly HADCrut dataset over the period since January 1990 to 3dp is 0.176C/decade.
Not bad.
John Finn:
Thankyou for correcting me. You say:
“Richard Courtney writes
(c) The heating from AGW of the ocean is (i) mostly direct radiant IR input that is absorbed within the top few meters of the ocean,”
Shouldn’t that be millimetres – or even fractions of mm. I was under the impression that IR only penetrated the top ’skin’ of liquid water. I’ve seen Doug Hoyt demonstrate this using transmission formula with appropriate absorption coefficients for typical IR wavelengths.”
Yes, of course you are right.
My error was to say “direct radiant IR input” when I should have said “direct radiant IR and visible input”. Visible wavelengths penetrate to tens of meters before being absorbed, and they provide a significant energy input (i.e. heating) to the ocean surface layer.
Again, thankyou for pointing out my mistake.
Richard
Bill Illis:
You ask;
“But I’m really looking to pull the natural variation out of the total global temp anomaly so it appears I need something for the southern hemisphere.”
OK, I understand what you want and I cannot provide real help. However, there is a possibility that – I think – warrants mention if – as I hope – you publish your work.
I remind that I wrote above:
“Other natural cycles may also be affecting the trend, and the method is not applicable to cycles with lower frequency than the time series. Such a very low frequency oscillation does seem to exist. There is an apparent ~900 year oscillation that caused the Roman Warm Period (RWP), then the Dark Age Cool Period (DACP), then the Medieval Warm Period (MWP), then the Little Ice Age (LIA), and the present warm period (PWP). ”
And I later wrote above:
“Additionally, the water that went into deep ocean ~800 years ago is returning to the surface now. This could be affecting ocean surface layer pH, ocean surface layer temperature, and – therefore – atmospheric carbon dioxide concentration (mostly by the pH change). It could be expected to affect the magnitude of the residual trend detected in the analysis provided by Bill Illis. ”
In other words, there is an apparent natural cycle with too great a frequency to be analysed by your method that could be expected to provide warming which was stored in the oceans during the MWP and is now being returned to the atmosphere.
Indeed, this possibility is proclaimed by those who advocate “AGW in the pipeline”.
However, as I also said;
“The Second Law of Thermodynamics says that the atmospheric temperature rise induced by return to the surface of that AGW stored in deep ocean cannot be more than the temperature rise that put it into the ocean. Therefore, it threatens “our children’s children” (in ~800 years time) with no more AGW than we experienced in the twentieth century. And that warming is so small that we cannot discern it from natural variation.”
Therefore, this postulated ‘warming from the MWP’ cannot provide a temperature rise greater than the temperature rise from the Dark Age Cool Period (DACP) to the Medieval Warm Period (MWP). But, conservatively it could be assumed to have provided 0.2 deg.C to observed recent Southern Hemisphere temperature rise.
I repeat that I know this fails to answer your need, but I do think it merits at least a footnote in the publication I hope you will provide.
Richard
Actually, although IR only penetrates less than 1 mm an important part of the mechanism by which GHG forcing heats the ocean is IR absorbtion in the surface skin layer which reduces the temperature gradient across the layer, causing more of the heat from sunlight absorbion to be retained in the water below.
John Phillips says – “In other words the ‘regression’ simply uses lower values for the multiplier and constant (for the warming reconstruction). ”
No, I’m saying they ARE lower so far. Not simply uses, they are lower.
I can perhaps adjust the global warming model’s log formula to include a “Time” component which is missing so far as follows:
— 2.7 * ln(560) – 15.8 + (Another up to 1,000 years of additional not-really-well-defined-or-explained warming in the pipeline) = 3.25C
So far, the pipeline has some leaks in it.
—-
And I had read the paper you linked to earlier about the AMO when it was discussed on realclimate a week or so ago.
I note the abstract states the AMO is a natural climate cycle independent of global warming.
“The results imply the AMO is a genuine quasi-periodic cycle of internal climate variability persisting for many centuries, and is related to
variability in the oceanic thermohaline circulation (THC). This relationship suggests we can attempt to reconstruct past THC changes, and we infer an increase in THC strength over the last 25 years. Potential predictability
associated with the mode implies natural THC and AMO decreases over the next few decades independent of anthropogenic climate change.”
And they did forecast (so far correctly) that it would weaken in the next decades.
Actually, although IR only penetrates less than 1 mm an important part of the mechanism by which GHG forcing heats the ocean is IR absorbtion in the surface skin layer which reduces the temperature gradient across the layer, causing more of the heat from sunlight absorbion to be retained in the water below.
Wouldn’t the IR “heated” skin just evaporate?
John Philip asserts:
“Actually, although IR only penetrates less than 1 mm an important part of the mechanism by which GHG forcing heats the ocean is IR absorbtion in the surface skin layer which reduces the temperature gradient across the layer, causing more of the heat from sunlight absorbion to be retained in the water below.”
Really? Prove it.
The assertion assumes little mixing of the ocean surface layer. Indeed, it assumes a “surface skin layer” that is “less than 1 mm” thick and is independent of the underlying water. But the entire surface layer is turbulent and it is very turbulent near the surface.
While the possibility of the assertion cannot be rejected, it is so implausible as to be worthy of rejection until supporting evidence is provided.
As a postscript, I add that I spent the first 3 years of this decade living on a boat in an attempt to quantify energy interactions at sea surface. My endeavour was defeated – so the project was a failure – by effects of surface ripples (n.b. ripples, not waves) that I do not think had been previously detected. Hence, having wasted those three years of my life, I am extremely sceptical of any simplistic assertions concerning energy interactions at sea surface.
Richard
Richard,
“While the possibility of the assertion cannot be rejected, it is so implausible as to be worthy of rejection until supporting evidence is provided.”
It seems that in climate science, any assertion supporting AGW is immediadely accepted as truth. The effect is then added into the models, and if actual data is found to disagree with the models, then the data is “corrected”.
Mike
Jeff Id, re feedbacks … published just last month, Dessler et al found …
Between 2003 and 2008, the global-average surface temperature of the Earth varied by 0.6C. We analyze here the response of tropospheric water vapor to these variations. Height-resolved measurements of specific humidity (q) and relative humidity (RH) are obtained from NASA’s satelliteborne Atmospheric Infrared Sounder (AIRS). Over most of the troposphere, q increased with increasing global-average
surface temperature, although some regions showed the opposite response. RH increased in some regions and decreased in others, with the global average remaining nearly constant at most altitudes. The water-vapor feedback implied by these observations is strongly positive, with an average magnitude of lq = 2.04 W/m2/K, similar to that simulated by climate models. The magnitude is similar to that obtained if the atmosphere maintained constant RH everywhere
and concluded …
The existence of a strong and positive water-vapor feedback means that projected business-as-usual greenhouse gas emissions over the next century are virtually guaranteed to produce warming of several degrees Celsius. The only way that will not happen is if a strong, negative, and currently unknown feedback is discovered somewhere in our climate system.
(My bold).
cheers,
JP
Thanks John Philips for linking to a public version of the Dessler paper.
I note Dessler had published a study earlier which indicated water vapour response was not keeping up with climate model’s predictions (but this study did not use all of the troposphere).
In this paper (funded by NASA – GISS I presume), water vapour response was measured across the whole troposphere as temps declined from DJF 2007 to DJF 2008 (due to the La Nina and the AMO as I have been saying all along – temps declined by 0.4C, very close to the predictions of the reconstruction).
In the very lower troposphere, relative humidity decreased by 1.5% (percentage points) and it increased in the very upper troposphere by 1.5% (the middle was constant).
Give there is much more water vapour in the very lower troposphere than in the top, the study really found there was a decline in the overall weighted-average relative humidity as temperatures declined.
So how does that support global warming? Relative humidity is supposed to stay more-or-less constant.
To be fair, the models do produce results similar to this when temps are increasing, but not when they are decreasing.
These results imply a runaway greenhouse or runaway ice planet. The models would never work if there a decline in relative humidity as temps fell and an increase in relative humidity as temps increase.
I think the study just shows there is still a lot of variability in relative humidity that we do not understand yet.
For a discussion of the ‘ocean skin’ issue please see my article here:
http://co2sceptics.com/news.php?id=1645
I would add that natural swings in the power of the atmospheric greenhouse effect are likely to occur routinely as a result of natural changes in the total amount of water vapour in the atmosphere.
As the Earth warms for whatever natural reason the atmosphere will hold more water vapour and if it cools for whatever natural reason then the atmosphere will hold less water vapour.
Either way the natural water vapour variations will dwarf by orders of magnitude any changes in the power of the greenhouse effect that could be attributed to human CO2.
Furthermore those natural water vapour swings have never caused us to cross a tipping point so why should a tiny variation induced by any effect from human CO2 do so ?
…it is so implausible as to be worthy of rejection until supporting evidence is provided.
This guy (who seems to understand a thing or two about ocean surface thermodynamics, judging by his publication record) did the research and it was published in
Linking thermal skin gradients at the sea-surface to the radiative coupling of the atmosphere and ocean: a mechanism for heating of the oceans by atmospheric greenhouse gases
From the abstract … The heat flux to the atmosphere is achieved though conduction though the skin layer of the ocean, within which a temperature gradient exists, so that the interfacial temperature of the ocean is cooler than the bulk temperature below. The thickness of the conductive skin layer is of comparable size to the emission (and absorption) depth of infrared radiation in water. The differences in the skin SST and the subsurface bulk temperature are typically a few tenths of a degree, an amount that is important in terms of attempting to detect oceanic warming caused by climate change. Given that the ocean absorbs the infrared radiation emitted by the atmosphere, including by greenhouse gases, within the radiative skin layer, concern has been expressed about how the increasing levels of greenhouse gases can heat the ocean. However, the skin temperature gradient is believed to be responsive to the intensity of the incident infrared radiation at the surface, and this modulates the heat flow from ocean to atmosphere. Empirical evidence to support this hypothesis will be presented, based on measurements taken at sea using the Marine-Atmospheric Emitted Radiance Interferometer (M-AERI).
Cheers,
This is a great analysis. My only issue with it is the apparent assumption of no cross biases for unevaluated parameters.
For example, suggesting that the remaining trend may have some small, but basically minimal room for influence on the temps is a bit mesleading. It may well be true that there is little additional explanation in the temperature variations or trends in addition to what’s been looked at simply because ENSO and AMO are proxy measures for these otehr things, but that is different from suggesting a lack of solar influence. To the extent that the ENSO and AMO occur precisely from other influences means that they are really a proxy measure for something else. It would be similar to saying that age as an insurance rating parameter isn’t really the risk factor, but it is a proxy for experience.
This doesn’t necessarily change the analysis at all, but it may shift the question. Does the sun influence the El Nino and/or AMO cycle? And how much? Is the relative magnitude of El Nino affected by GHGs?
I am also curious as to why the PDO is not considered. Even with a good fit, I’m just uncomfortable with the assumption that global temps so easily boils down to three parameters. I personally suspect that some of the attribution currently given to GHGs in the analysis may even yet be overstated if other things were considered.
It may seem like I’m being too critical, I suppose. I don’t mean to be. All in all, I find it to be a very intriguing study.
[snip] John stop that, nothing was claimed – Anthony
Moderator – Fair enough, however FYI the first line of the article linked to and authored by Stephen Wilde states
Stephen Wilde has been a Fellow of the Royal Meteorological Society since 1968. however I counted half a dozen factual errors in the first few paras of the piece, which I thought odd for someone claiming to be a FRMetS.
The RMS lists those entitled to use the FRMetS title on its website
http://www.rmets.org/about/people/fellows.php
There is no Stephen Wilde listed. It also lists the requirements …
Becoming a Fellow normally requires a formal qualification (eg. a first degree in a science subject and/or post-graduate degree or an NVQ in a relevant discipline) and at least five years of professional experience within or directly related to meteorology. Exceptionally, long experience and performance at a high professional level, suitably attested by peer review, can replace the requirement for a formal academic or vocational qualification. MSc or PhD study in a relevant subject counts as one or two years experience respectively.
which contrasts with Mr Wilde’s profile which states he runs a Law firm and follows Meteorolgy in his spare time
http://co2sceptics.com/contributors.php
Now there may be some innocent explanation, misunderstanding or administrative error, but given the recent post about Karl’s phantom doctorate perhaps we could ask Mr Wilde to explain, as it appears to be a primae facie case of someone impersonating a meteorologist
😉
REPLY: My initial issue was with claims (or lack thereof) made in comments, however it appears that you aren’t the first one to notice this regarding articles outside of this blog.
See
http://petesplace-peter.blogspot.com/2008/05/myth-of-man-caused-global-warming.html
I agree that he is not on the list. But let’s hear why. Perhaps there is a valid explanation.
Mr. Wilde, what say you?
Richard S Courtney says:
Doesn’t that then provide a mechanism for the transfer of energy (heat) from the surface layer to lower layers, and thus reduce the opportunity for its removal via evaporation?