Rainy Day Look at "Surface" Temperatures

This is just another version of the approximate "global" average absolute surface temperature, using the Reynold's Oiv2 for the SST "calibration" and the BEST NH and SH absolute temperature estimates for the land areas.  Since BEST is still a bit controversial and apparently offline, I used GISS land only temperatures and the Hadley Center SST3 version for the oceans.  In the background is contribution by surface.  SH land contributes less than 1 C to "Global" temperature.  NH land about 2 C to "global" temperature, the NH oceans about 6C to "global" temperature and the big Kahuna is SH oceans with nearly 7 C to global temperature.  This provides a little different "look" at what is providing what to "Global" warming.

Using a 1905 start date with the full 1905 to 2012 baseline for anomaly, this chart compares the contribution of each of the hemisphere surfaces.  In other words, this is the areal weighted anomaly.  The SH oceans provide most of the warming followed by the NH Land, NH oceans with the SH land bringing up the rear.  From this "vantage" point, the SH oceans are leading the way indicating an Oceans causation for the majority of the warming.  That could be due to a number of factors with some CO2/Land use influence, but natural factors would have to be considered a major part of the "cause" if the data is reliable.  Then I may have spreadsheet errors or using my personal bias to sway your opinion. 

The data is available with a simple Google of Berkeley Earth Surface Temperature, GISS temp and Hadley Center Sea Surface Temperature.  The Berkeley site provides land surface area, if it is up again, so you can easily replicate my results or find some glaring error purposefully made by a Climate Denying Skeptical Conservative etc. etc.  to cast doubt on the elegant theory that fossil fuel CO2 emissions by man are destroying the planet at an alarming rate.  

To Remove or Not Remove, the Seasonal Cycle

Since I believe the impact of land use, agriculture wheat mainly, is grossly underestimated, I have been looking at the seasonal cycles for clues.  Most data has the seasonal cycle removed.  BEST uses absolute temperature and just averages, so the seasonal cycle remain in the data.  Leaving the seasonal cycle in doesn't have much impact on long term trends, but it does have impact on baseline anomalies.

Using the BEST NH data from 1900 to 2011 as a baseline, this chart compares using a 1900-1929 baseline versus using a 1981-2010 satellite era baseline.  That is a significant shift. 

Winter wheat crops require a consistent spring thaw to prevent snow mold and other problems.  Farmers can spread or spray ash, dust low albedo fertilizers etc. on snow to force the melt to match the crop.  The practice is also used to help prepare fields for earlier spring planting of a variety of crops.  This comparison shows exactly what you might expect if large scale agriculture was having an impact on Northern Hemisphere Land based climate.  Removing the seasonal cycle would tend to subdue the data possibly hiding an impact that is significant on a global scale. 

More Fun with Charts

UPDATE: More stuff at the end

Since the ocean data has less conversion issues, it is definitely a lot easier to work with.  Sea level is pretty much fixed and for the satellite era we have actual surface and layers to deal with.  So on the way to hopefully bigger and better things, this post expands on the "layers" method of teasing out sensitivities to different forcing events.

This is a bit of a rehash with some minor tweaks.  The Global Reynold's Oiv2 SST data set from 1981 to 2013 with the absolute temperature value, 291.38K is used as the reference for this comparison.  Using the standard Stefan-Boltzmann relationship, that absolute temperature is converted to effective surface energy in Wm-2.  Then using the UAH data, each temperature anomaly series is adjusted to the average absolute temperature then converted to effective Wm-2.  The actual temperature of the atmospheric layers is not equal to the SST, but using this adjustment, the effective Wm-2 variation of the atmospheric layer can be related to the sea surface energy flux variations.  The Lower Stratosphere data is inverted to simplify comparison.

Globally, there is no seasonal signal in any of the data.  Averaging globally natural smooths the season "signal".  When only hemisphere data is used, the Reynold's Oiv2 data has a strong seasonal cycle which needs to be removed for simple chart comparisons.  The satellite data doesn't have a significant season signal, so that has likely been removed before the data published online.  So the Hemisphere and other comparisons have a few more steps and more places for me to screw up.

So here is what it looks like.  For the Reynold's Oiv2 SST data the average of each month for the entire period is determined, the average of the entire period subtracted from the month averages producing a seasonal signal which is subtracted from the monthly values of the entire period.  Not as complicated as it may sound, all it is is removing the season anomaly leaving the absolute value intact.  Since that procedure was performed on the SST data, the same was performed on the others though the remaining season signal was small in the UAH data.  If the Reynold's Oiv2 and UAH had the same start date, there would likely not have been any residual seasonal signal at all.  I will try to add a spreadsheet later if anyone is extremely bored.

The result is a comparison where the actual forcing changes can be compared.  Some rescaling may be required, but it provides a reasonable picture of how the Stratosphere anomaly is an amplification of surface energy flux variation.

Here is the Southern Hemisphere done the same way.  The two volcanic events are obvious so I didn't include arrows.  In the NH and SH the peak forcing based on this reference method is about 10 Wm-2.  The Global peak is slight lower, closer to 9 Wm-2.  That difference is likely due to the seasonal signal removal either in my spread sheet or in the UAH process.  It is a indication that the accuracy of this method cannot be better than 1 Wm-2, though it is likely considerable worse than that.  What is most interesting is that the forcing of the two volcanic events is about the same in this comparison where the typical forcing estimates have the 1991 Pinatubo event being much larger than the early 1980s El Chicon event.  Estimates of direct and indirect volcanic aerosols are not proving to be very accurate and this coarsely confirms that they are off a touch.  In any case, this fairly simple comparison using an absolute temperature reference instead of temperature anomaly looks like it can get within a couple of Wm-2 of actual variations in atmospheric forcing. There are of course multimillion dollar platforms to do this "better", but it is kind of neat seeing how much can be done with some of the longer term satellite data series. 

Now don't get too excite sports fans, there is still a CO2 signature in there, it just looks a lot lower that those fat tail estimates. 


More Stuff:

Fun with Data - The Land Only Greenhouse Effect

While I prefer the Lower Stratosphere Data, the "surface" temperature data and the Lower Troposphere can be fun to compare.  The GISS "surface" for land is taken at not very uniformly distributed locations at about 3 meters (6 feet) above the actual "surface" at a variety of elevations with the "average" elevation around 680 meters.  The Lower Troposphere data is "filtered" and "weighted" from approximately the "surface" to around 5000 meters in altitude.  Despite totally different challenges they provide fairly accurate, +/- 0.2 C degree temperature anomalies for their not all that well defined "surfaces".

For GISS, the "average" temperature anomaly is the daily maximum - the daily minimum divided by two.  This has become "THE" indicator of climate change.  With the satellite era, there has been some grousing that the satellite "surface" data is not the same as the land "surface" data.  They should be different, they are measuring different "surfaces".  While they are measuring different "things", the trend relationship between the two should mean something.

The only thing that changes differently between the two is the amount of CO2, H2O and "other" stuff floating around in the atmosphere.  Since GISS is at "THE" surface and UAH is near "THE" atmospheric boundary layer, the biggest differences should be clouds, aerosols and CO2 in that order.

In the Chart above I have ploted GISS NH land only, the one with the scary linear regression, UAH NH land only, the one with the not as scary linear regression and the difference.  You should notice that the chart says Energy not temperature.  I converted the temperature anomaly to approximate Energy anomaly using 11 C as the approximate "surface" temperature for both.

There is considerable uncertainty in this comparison, but the difference should roughly approximate the atmospheric changes influencing temperature.  That 3e-02 is about 0.3 Wm-2 per decade or 3 Wm-2 per century.  If 3.7 Wm-2 produces 1C of warming, the 3 would produce less, ~ 0.8 C of warming.  Not a very accurate estimate, but in the general ballpark of the "no feedback" climate sensitivity for whatever that is worth.

The common trend would likely be due to something else.  Remember there is that whole tropospheric hot spot thing that is supposed to happening that is not.  If this trend difference is meaningful, then is should be approximately equal to the ocean energy imbalance.

Since Land only seems to be the big thing, this compares the hemispheres by the land only data.  I used UAH minus GISS this time, but note the similarity in the trends.  The trends are not pronounced enough given the noise and uncertainty to be significant, but that is a pretty strong correlation between hemispheres.

Now think about the differences between comparing the land to ocean rate and the land to lower troposphere rate.  Two different frames of references with atmosphere as the conduit. 

I could do the same with the oceans, but there is such a small difference because SST has less variation that it is not of much use.  That is were the Stratosphere comparisons have a large advantage. 

As you can see in this comparison, the surface, lower and middle troposphere are extremely close together.  Too close to confidently tell some event from instrumentation noise.  The lower stratosphere amplifies the signal naturally allowing a more realistic comparison.  This by the way was adjusted to the surface energy anomaly based on the absolute temperature.  By adjust all of the data sets to the same absolute temperature, this shows a "relative" response to a variation in surface energy anomaly.  While it is a bit noisy, you may be able to make out the "approach curve" shape in the LS data.

The values are still too coarse for much precision, but it is a neat way to make better use of the available data.

How to Splice Instrumental Data to Paleo Reconstructions?

That is a pretty deep question.  Just "eyeballing" the problem, the paleo data has a variety of natural and added smoothing methods that cause real time shifts in the data and suppress some portion of the signal.  Just splicing the instrumental period to a "best fit" seems to cause a good bit of grief since there are preconceived notions involved.   So when I looked at the Oppo 2009 Indo-Pacific Warm Pool (IPWP) temperature reconstruction, which was done the way it was done to make instrumental splicing simpler, I tried to guess which direction the Author(s) were going.

The IPWP recon was assembled in decade data points with 50 year averaging of the data points.  For sea floor sediment proxies, that is a good "standard" for the natural smoothing which is the time period required to form a sediment layer.  I was not all that trilled with the decade "pseudo data points" but on second thought, it might be an elegant solution for the splicing.

To check out how it might work I calculated the centered decade averages for the Hadley Center HADCRUT4 data set using the NH, SH and 3030 or tropical zone.  The overlap period for the Oppo 2009 data and HADCRUT4 is 1855 to 1955.  I used that as the anomaly baseline period.  That is what the instrumental data would look like in order to do the splice.

Because of the different smoothing periods, the splice is off a bit.

By shifting the IPWP recon back one decade, the splice appears to be better centered.  That shifts the anomaly baseline period a touch though.

So just to be thorough, this chart shows both the time shift and new anomaly baseline period, 1855 to 1945.  That is not a bad fit in my opinion, but I am sure there is probably a better way.

Tah Dah! There is the tropical instrumental to Indo-Pacific Warm Pool Splice.  Doesn't look much like the more famous hockey sticks for some reason.  If you are a fan of ENSO and long term persistence in climate though, it may be a lot sexier.

Data: 

WDC PALEO CONTRIBUTION SERIES CITATION: 
Oppo, D.W., et al. 2009. Makassar Strait 2,000 Year Foraminiferal SST and d18Osw Reconstructions. 

IGBP PAGES/World Data Center for Paleoclimatology Data Contribution Series # 2009-089. 

NOAA/NCDC Paleoclimatology Program, Boulder CO, USA.

http://www.cru.uea.ac.uk/cru/data/temperature/ 

UPDATE:

Here is another high resolution paleo reconstruction along with the Oppo 2009.
The Lake Tanganyila Lake Surface Temperature by Tierney et al. 2010.  This was a poster child for scary "unprecedented" warming.  Don't be too alarmed, the margin of error is pretty large in their reconstruction and "lake" is a buzz word for look out for land use impacts.  Tierney has a longer 66,000 year Lake Tanganyila reconstruction that only have a couple of data points over lapping this 1500 year recon.  There is a "gap" of about half a degree between the two.  As you can see, the variance, aka standard error, is larger than Oppo, but it still is possibly a useful data series.  

The Tierney 2010 data is not in consistent bins, the dates vary from about 18 years to over 30 years.  That means that the smoothing per data point would also be inconsistent.  So I arranged the Tierney data by date to the closest 10 year bin used by Oppo et al.  In order to Average, simple interpolation between dates with data was used.  In most cases only one date was missing data, but in a few there were two dates missing, so each received the same simple interpolation, not fancy spline fits and such.  This is the result with no temporal shift as above.  Even allowing for various things that could impact the accuracy of each reconstruction, there is evidence of a lead/lag relationship that is a little complex.  It looks like adding more reconstructions will smooth out most of the information producing a smoother shaft for a hockey stick.  The phase may shift between the reconstructions, but the range is remarkably consistent, that may be more important than the timing. 

Indian Ocean Warm Pool

Citations:
WDC PALEO CONTRIBUTION SERIES CITATION: Oppo, D.W., et al. 2009. Makassar Strait 2,000 Year Foraminiferal SST and d18Osw Reconstructions. IGBP PAGES/World Data Center for Paleoclimatology Data Contribution Series # 2009-089. NOAA/NCDC Paleoclimatology Program, Boulder CO, USA.

WDC PALEO CONTRIBUTION SERIES CITATION: Etheridge, D.M., et al. 2010. Law Dome Ice Core 2000-Year CO2, CH4, and N2O Data. IGBP PAGES/World Data Center for Paleoclimatology Data Contribution Series # 2010-070. NOAA/NCDC Paleoclimatology Program, Boulder CO, USA.


This is another project I will never finish.


When I play with the paleo I notice there are quite a few issues finding reliable reference "regions" since ocean currents vary and many of the "bugs" that are used as proxies tend to drift with currents and have optimum temperature zones.  Long term CO2 reconstructions are severely limited to the Antarctic ice domes because melt at other locations tend to limit time and accuracy.  The Southern Hemisphere though doesn't look like it is a very good "global" reference if land surface temperature is the metric used to determine the degree of "global" warming.  Since most of the ocean paleo reconstructions seem to indicate that there are out of phase cycles or pseudo-cycles between the hemispheres, temperature at some surface over land and at higher altitude doesn't seem to accurately indicate the actual changes in energy of the climate system.  I call this land "amplification" though it could be related to "phantoms" in the temperature records caused by the huge amount of noise in the land based temperature proxy and surface stations. 

This chart plots the running 21 year standard deviation of the BEST Monthly Maximum and Minimum temperatures by hemisphere.  Individually, there appears to be some significant issues especially in the Southern Hemisphere.  That ~1950 to 1980 increase in standard deviation just doesn't look right and the surface station cover "down under" is not the greatest.  Basing a large part of your "correlation" with CO2 forcing on noise would not be a great career move. 

Now there has definitely been "global" warming, but with individual location proxy reconstructions showing today's temperatures are not all that exceptional over the past 2000 years.  The warming from the depths of the last little ice age is some what exceptional based on the Oppo data.  I have included a "no feed back" temperature curve based on the Law Dome 2000 year CO2 reconstruction, use the full period "average" for each for alignment.    That indicates that CO2 all by itself "may" be responsible for ~0.35 C of the warming but the margin of error looks to be quite large. 

Anyway, this is just a quick post to serve as a reminder for me of things I might think about finishing. 

Tropical not so Hot Spot with Tortured Data

The Elusive Global Absolute Surface Temperature post was about estimating, well, that elusive global absolute surface temperature.  Surface temperature anomaly has been the chosen metric for illustrating the impacts of global warming for so long we are stuck with it but anomaly just doesn't cut the mustard for some comparisons.  Like above, the equ-30N curve is the calibrated to absolute temperature ERSST anomaly for that band of the northern hemisphere oceans converted to approximate energy, Wm-2.  The yellow curve is for the northern part of the NH oceans and the blue curve is the energy imbalance estimated between the two.  That could just be noise or it could mean something. 

In the Tropical Hot Spot post I tried to show how much geeky fun you could have using the UAH Satellite data to find some of the missing heat that is occasionally in the Climate Science news.  Here in blue is the tortured UAH lower stratosphere (LS) data with its new friend NH SST energy imbalance.  By roughly overlaying the two, I now have a rough impact scale.  You should notice that while there are different types of noise in each series, they share the inverted decay curve.  That should mean they are approaching a common limit.  The two black arrows point to the two recent major volcanic impacts.  With both of these series smoothed with a 25 month moving average, the exact start times are blurred or smeared, but the responses are at least similarly treated.  The rough northern hemisphere impact is in the ballpark of up to 3 Wm-2 for the oceans. There is a whole lot of uncertainty, but at least there is a rough number to work with. 

Last I checked, volcanoes where still "natural" forcing.  Now some would say that the "natural" forcing just "masked" the impact of AGW.  With more evidence that the "system" is approaching a common limit, that logic is compromised.  The story may get even more interesting. 

Tropical Hot Spot

UPDATE: Dang it! One of the Charts got lost in the upload:

"The lack of any significant warming in the tropical troposphere since the beginning of space observations in 1979 is particularly intriguing in particular as present models show a warming trend over the same time of 0.3-0.4°C in the average, figure 2. Such results, scientifically very puzzling as they are, have hardly received any media attention but instead the public has been overwhelmed in recent years by excessive reports of a rapid and threatening global warming very soon running out of control, unless the most drastic steps are taken to stop it".– Lennart Bengtsson via Climate Etc.

This is a portion of a paragraph copied from a post on Climate Etc. (linked above).  The Tropical Troposphere "hot spot" is a warming that should be due to the increase in CO2 concentration.  It would be a signature or fingerprint of the effects of the CO2 and other Well Mixed Greenhouse Gases (WMGHG).  Not being evident in the satellite data is a bit of an issue.  Since it is one of the "fingerprints", its existence would be verification of the GHE theory.  However, its not being there is not "proof" that the GHE theory is not valid.

That may sound like double talk, but with as many factors that can impact climate, the missing tropical troposphere heat does deserve a bit more attention.

First, the hot spot is projected in the models.  The models make quite a few simplifications in order for the programs to run more quickly.  If the models included every detail, there would be little information available because "runs" could take years.  If the simplifications work, great.  If they don't, you should figure out why, fix the problem and start over.  Of course, the data could also be wrong.  Suspect data was the first choice of the modelers and suspect models was the first choice of the data gathers.  Since I could not possibly care less about the models, I looked at the satellite data in a number of ways to estimate its usefulness. 

The satellite data is impressively useful.  I am not going to quibble about the absolute accuracy, but the data is much better than I would have ever expected, so the satellite era is my choice of baseline.

This chart is the UAH Tropical Lower Stratosphere with the normalization and inversion torture explained in a previous post.  The Stratosphere is likely the best reference we have for climate since is responds quickly and naturally smooths responses.  Smooth is not a term many might use for the Stratosphere data, but if you consider all the gravity waves and vortices constantly happening the stratosphere, that is very smooth data in comparison.  There are four natural events marked, two Volcanoes and two El Nino Southern Oscillation events.  In the Tropics, all of these events lag the initiation of the natural events.  The valley following Pinatubo which erupted in 1991 is close to 1993, the 1998/99 Super El Nino valley is close to 2000.  There is about a two year lag with some variability.  Part of that lag is due to the centered 25 month smoothing, but the monthly data is there for comparison.

 This chart is the Northern Hemisphere stratosphere tortured the same way.  Both of the Volcanos show clearly with a little less lag, but the ENSO events are not as easy to pick out.  The Vocanic events had more impact on the NH stratosphere than the ENSO events.

With this Southern Hemisphere tortured Lower Stratosphere Troposphere you can more clearly see ENSO event cycles and with the exception of the land plot, volcano impact is a little harder to pick out.  The Antarctic is included in the SH land area and tends to amplify the land response.  .

Here is the Southern Hemisphere Stratosphere with the two Volcanic black arrows still plain to see, but the ENSO events are nearly gone. 

UPDATE:

 Since I had to edit anyway, this is the NH Lower Troposphere showing how smoothed the impacts are in the LT Oceans versus the LS.  That should be an indication of internal ocean heat transfer stabilizing the impact of the Volcanic events.  Notice how the ocean temps vary more with the ENSO events. 

Globally, the volcanoes show up nicely but the ENSO events are smoothed pretty much out of the picture like in the SH chart. 

Now here is the chart for the "hot spot".  There is a major debate over whether the UAH tropics are accurate, but based on this normalized comparison, it would be hard to pick out any significant difference between the lower and mid-troposphere data.  Again, there is more stratospheric sensitivity to volcanic and about the same ENSO response though with slightly different timing.

With this chart of the Northern Hemisphere oceans, there is some mid-tropospheric warming, but this is not the tropics, just part of the tropics combined with the rest of the Northern Hemisphere oceans.  The black volcanic and blue ENSO arrows show the events.  From this look, the volcanic events had a longer term impact than the ENSO events.  Because of timing, the Super El-Nino is given credit for the  "step" warming in the Northern Hemisphere.  Judging intensity using the yellow Stratosphere curve, the "step" appears to be more likely due to volcanic temperature depression prior to the Super El-Nino.  Because of the Lag in surface response following the Volcanoes, it is likely the total impact or "forcing" of the volcanic aerosols on the northern hemisphere was under estimated and the Super El-Nino impact over-estimated.  This particular period could be 70% or more, "natural" warming "noise".  With heat being transferred from the tropical and southern oceans to "restore" energy lost during the volcanic events, the only "signature" of the GHE is the predicted, just lost in the natural noise, tropospheric warming.

The satellite data is appearing to show exactly what it should, a series of natural events hiding a lower than estimated GHE impact.  Time series can be tricky though.  Still, this looks like the missing tropical troposphere "warm spot".

I hope this wasn't too boring, but it is a little hard to follow without a number of steps.  While I will save it for later,  there may be a way to estimate more accurately the percentages of natural and anthropogenic impacts on the oceans.  There is a fairly accurate absolute SST and the Northern Hemisphere oceans have a better long term instrumental record, so that might be a fun puzzle for a windy or rainy day. 

CO2 Balance between the Atmosphere and Oceans

Without getting into much detail, gases have a saturation vapor pressure that varies with temperature.  To have a saturation vapor pressure there has to be a liquid or solid present with the gas.  If the concentration of the gas cannot vary, i.e. a balloon full of gas that is warm enough not to condense will continue to have just as may gas molecules as liquid/solid molecules if they don't react with the material that the balloon is made out of.  If you happen to have a butane lighter, you know the flame stays about the same as long as there is liquid butane sloshing around inside the lighter.  There is some variation in the flame based on the temperature of the lighter, but as long as there is liquid that can change to gas, it lights.

CO2 is a gas that can be absorbed in water.  Depending on the temperature of the water and the air, the amount of CO2 in each will vary.  There is a lot more CO2 in the water than the air, so the temperature of the water is a stronger control variable. 

For water at 0 C degrees, the saturation vapor pressure of CO2 is about 3.4 g (CO2 gas) per kilogram of H2O (liquid).  At 4C degrees, the saturation vapor pressure 3.0 g (CO2 gas) per kilogram of H2O (liquid).  A 4 C change in the water temperature would produce a 10 to 13 percent change in the amount of CO2 the water could hold. 

Since I tend to run on about the changes made with the Drake Passage opening, I am not going to lose another opportunity to run on about the Drake Passage opening.  That opening cooled the southern hemisphere at the expense of northern hemisphere warming with an approximate net decrease in global average temperature of 3 C degrees. That happened because the Antarctic Circumpolar Current effectively isolated the Antarctic thermally from the rest of the world.  Because of the more efficient mixing of the oceans allowed by the 100 to 150 million cubic meters or water per second continuously circulating that wasn't circulating before, some things changed.  One was the "Average" temperature of the Southern Oceans which make up the majority of the global oceans.  Since colder water can hold more CO2 and colder water sinks to some deeper depth in the oceans, the relationship between atmospheric CO2 and ocean CO2 changed.  It is really pretty simple physics.

Since the "Average" temperature of the colder surface water in the NH is about 3C warmer than the colder surface water in the SH, there are different rates of CO2 uptake and out flow from the oceans.  About 10 to 13 percent difference in fact, if the oceans and atmosphere are in some true equilibrium.  If the oceans and atmosphere are not in an equilibrium state, then the temperature changes the rate of uptake or out flow of CO2.  There is other stuff going on, pressure is a factor, so the deeper the cold water sinks with its CO2 captured at the surface, the longer it can hold onto that CO2.  Then since CO2 forms a weak acid in water it can react with the bases in the water either strongly, like rock or shells or weakly like various carbonate, or carbonites or whatever happens at different times in the Marine Carbonate Chemistry cycles.  You can make a career out of that stuff all by itself.

If the average temperature alone can make a 10 to 13 percent change in the CO2 "balance", it would seem likely that the other cycles would respond when concentration near their "sweet spot".   Which when you consider that the minimum temperature of the sinking cold sea water is closer to -2C degrees, if you have enough time on your hands you could "explain" a natural variation of CO2 on the order of +/- 50 percent. 

There are a lot of smart folks working on these potential impacts and I am not one of them.  I just want to bring the temperature portion of the puzzle to the fore front for a moment.  If the average temperature of the oceans were to change 4C degrees, the total change in carbon dioxide would be on the order of 1 x 10^20 grams.  That is one serious butt load of carbon dioxide.  If it could change that much, it probably would have already.   That implies that the Carbon Cycle is much more interesting than just temperature relationships.

Mankind is one of the biological components of the Carbon Cycle.  We do have some impact, we just are not really smart enough to know how much, at least yet. 

Artfully Torturing Data

This "Torturing Data" series was inspired by a rather arrogant commenter on one of the blogs.  This commenter takes the BEST Land only data fits a CO2 forcing curve to it and calls it "science".  I am not a scientist.  I was an engineer.  As an engineer, my job was to make things work or figure out why they did not work.  Engineers are in general more critical than scientists and also more optimistic in general, at least in my experience.  To a good engineer, there are no problems, only opportunities.  To become a good engineer, you likely have to learn common sense first, then get fancy.

This could be considered artfully tortured data.  While that NOAA website bills the data as Extended Reconstructed Sea Surface Temperature Data, this is actually land and ocean monthly data.  All the torture required was adjusting the anomaly to the 1981-2010 satellite era baseline and normalizing based on the satellite era standard deviation.  The most current data should be the most accurate after all.  In the fore ground in orange is the 60S to 30 S combination SST and Land Surface temperature data.

I have done the same thing with the GISS regional annual data in the past to locate the most reasonable starting point.  The less noise the better.  The southern extratropical band sans the polar region is the most stable data.  Even though the data coverage is poor in comparison to the northern hemisphere, the thermal mass of the predominately ocean region of the Earth is rock solid stable.  That makes it a good reference and a good place to start.

Since the standard deviation of this region is so small, normalizing tends to seriously amplify the changes.  This region with the Antarctic Circumpolar Current (ACC) has huge heat exchange potential.  As I have mentioned before, the ACC moves approximately 100 to 150 million cubic meters of salt water per second.  That is about 3 to 6 Gulf Stream Current equivalents.  A very minor change in the ACC would have 3 to 6 times the impact of a similar minor change in the Gulf Stream current.

This is an overlay of the Hadley Climate Research Unit version 4 with the tortured ERSST which is actually land and ocean, but not all that much land.  There is a reasonable but not outstanding correlation, just "eyeballing" anyway.  Notice though the scale on the right side.  Somewhere in the ballpark of 1.0 C +/- a touch.  That is about +/- 0.5 C in nearly 200 years.  Since the HADCRU4 data is not perfect, that really is about +/- 0.5 C around the 160 year mean with a margin of error of about +/- 0.2 C degrees.

Now, some how scientists have taken this +/- 0.5C with a +/- 0.2 C margin of error and turned it into 3C of "Global Warming" due nearly entirely to CO2 introduced by mankind and completely discounted that tiny wiggle in a region with 3 to 6 equivalent Gulf Stream currents. 

By itself, a doubling of CO2 can produce about 4Wm-2 of additional atmospheric resistance which could produce 1 C of warming.  All the additional "projected" warming requires some help with feed backs.  Until recently, the possibility of longer term recovery or other natural increases in "global" temperature which would also be amplified by the Atmospheric resistance, have been minimized if not completely ignored. 

While this little band of Earth with its 3 to 6 Gulf Stream currents worth of energy has varied about +/-0.4 C +/- about 0.125 C allowing for standard error over the past 100 years. 

And people wonder why engineers are so skeptical of climate scientists. 

Torturing Data

nor·mal·ize

 [ náwrm'l z ]   

1. make something normal: to make something normal or return something to normal, or become or return to normal
2. make something or somebody conform: to make something or somebody conform to a standard
3. heat steel: to heat steel above a specific temperature and then cool it in order to reduce internal stress.  

 A time series can be Normalized by dividing the elements of the time series by the standard deviation of some period of the time series.  The chart above is for the University of Alabama -Huntsville (UAH)  Microwave Sounding Unit (MSU), Lower Troposphere (LT), Mid-Troposphere (MT) and Lower Stratosphere (LS) temperature anomaly data for the Northern Hemisphere (NH) land only, region.  The LS has a (-) minus sign in front meaning it is inverted. 

By forcing the data for the three layers of the atmosphere into a "normal" form, you can more easily compare the data.  The first rule of time series analysis should be look at your data.  Normalizing allows a closer look.

The LS data is inverted because its response is inverse to the response of the LT and MT data.  There is a similarity, but there are differences.

cor·re·la·tion

 [ kàwrə láysh'n ]   

1. mutual or complementary relationship: a relationship in which two or more things are mutual or complementary, or one thing is caused by another
2. act of correlating: the act of correlating, or the condition of being correlated
3. relatedness of variables: the degree to which two or more variables are related and change together

-LS to LT correlation, 0.424,  _LS to MT correlation 0.204, LT to MT correlation 0.894

Perfect correlation is 1 or 100%.  LT to MT correlation is very strong, -LS to LT is weak but may be significant depending on the number of points in the time series, -LS to MT correlation SUCKS.  That is a technical Redneck engineering term.  Any decent spread sheet program will have a correlation function.  For these I used the OpenOffice, CORREL()

This chart compares the Southern Hemisphere (SH) Ocean inverted LS with NH LT and MT for land only.  There is a small difference in the "eyeballed" correlation.  According to OpenOffice, the (-LS) to LT correlation is 0.472, the (-LS) to MT correlation is 0.338.  The 0.338 sucks less than 0.204 in the first chart even though the SH Ocean LS is half a world away.

By shifting the SH Oceans (-LS) forward by 12 months, the correlations improves to 0.558 and 0.404 respectively which in both cases suck significantly less.  Some might consider that the SH oceans LS temperature might indicate that something has more influence on land surface and mid troposphere temperatures than CO2 forcing during this portion of time known as the satellite era.  Since warming in the SH appears to lead warming in NH land regions, that could be natural variability or CO2 forcing uses a time shifting mechanism.

The data is available at NOAA- National Climatic Data Center under Upper Atmospheric Temperature Data.  There is another upper atmospheric temperature data set commonly used, RSS, but unfortunately RSS doesn't provide as many regional options.

In any case, feel free to torture data, er... look into different ways of looking at the data.  :)

More Rehashing of the Obvious OHC and the Stratosphere

The oceans of the world have a huge thermal capacity.  Just like any capacitor, the oceans will have a response curve associated with changes in that thermal capacity.  The shape of the charge curve does not have to be the same as the discharge curve and in fact rarely is the same.  You can short a battery and it will discharge much quicker than you can charge the battery unless you have an infinite power supply in your tool box.  If you happen to look at the coefficient of heat exchange between air and water, you will find that the rough "average" ratio is 100:1 water to air.  It is easier to discharge heat energy stored in water to air than it is to discharge energy stored in air to water.  So "charging" the world's oceans with heat stored in the atmosphere is not a quick process. 

We have temperature data for the Stratosphere which has a much lower heat capacity than the oceans and lower atmosphere.  That makes the Stratosphere a good "Wattmeter".  The energy transferred through the Stratosphere would vary proportionally with the temperature T raised to the 4th power.  Because of different energy absorption spectra, that is not an exact relationship, but close enough to be useful.

There is an inverse relationship that has to be understood between the ocean heat capacity and the Stratospheric temperatures.  As the oceans collect energy, there is less energy flowing through the Stratosphere, so the temperatures in the Stratosphere would cool.  When the oceans release energy, there is more energy flow through the Stratosphere, so the Stratosphere temperature would increase.  Unfortunately, the resistance to energy flow in the Stratosphere can vary with composition, but still, the relationship holds well enough to be useful. 

The chart above is the Stratosphere temperature anomalies for the indicated regions normalized by dividing by the standard deviation and invert to correlate with ocean heat capacity. 

The two black arrows are responses to volcanic eruptions.  In both cases, the oceans released energy to compensate for the impact of the volcanic aerosols.  The Blue arrow is related to the 1998 El Nino.  The tropical oceans lost heat to the atmosphere and the northern and southern extratropical regions gained heat, the Red arrow.  Since the total heat capacity of the tropical oceans is greater than the combined extratropical oceans, there was a net loss in total ocean heat capacity. 

The black curve is the approach curve of the ocean heat capacity using the Stratospheric "Wattmeter".  This indicates that the rate of Ocean Heat uptake is slowing.  The Stratospheric "Wattmeter" is not by any stretch of the imagination "ideal", but it can be useful. 

So if your geeky friends show you a reconstruction of ocean heat uptake with some exponential rise intended to intimidate, just mention the Stratospheric "Wattmeter" to them.  They will have obviously neglected this piece of information since it either goes against their agenda or they are clueless. 

Ocean Heat Content versus Stratospheric Temperatures

Since the world's oceans are the black body source and the Stratosphere is near the effective gray body "shell" it only make sense that the Stratospheric temperatures would make a good proxy for ocean heat content.  The Stratospheric temperature change is inversely proportional though, so in the chart above i have inverted the 25 month moving average of the UAH NH and SH Ocean stratospheric temperature series.  The 0-700 meters ocean heat content (OHC) in 10^22 Joule units is from NOAA National Oceanographic Data Center.  I just tweaked the Open Office scaling a touch to over lay the two sets of data.  The fit is not perfect, then the OHC data is pieced together for most of the time period.

I guess I could try to be more precise, but since there is quite a bit of uncertainty in both sets, that would likely be a waste of time.  As general fits go, this is good enough to amaze your geeky friends

Ocean Land Temperature Differential

Since there is a discussion on what "Fixed" lapse rate really means, I thought I would break out this comparison.  In orange is the temperature differential between the southern hemisphere oceans and land mass.  There is less than 50 million kilometers squared of land in the southern hemisphere and nearly 15 million of that is covered with 2000 meters of ice.  The southern hemisphere is water dominate.

In blue is the northern hemisphere oceans and land temperature differential.  There is about 100 million kilometers squared of land in the northern hemisphere and a much small fraction is covered by ice of any depth.  It should be pretty obvious that the two hemispheres respond differently to all forcing.  Assuming that the "Global" temperature will respond uniformly to all forcing would appear to be a fantasy over time period less than thousands of years.

If you would like to estimate how much impact there will be with increased CO2, it would seem to be wise to consider the MAJORITY of the surface of the Earth.

Using CO2, both directly measured at Mauna Loa and estimated based on fossil fuel land use to 1800 AD, this chart indicates that with the exception of the northern hemisphere land, the "sensitivity" to CO2 forcing is approximately 1.6C per doubling using my Redneck CO2 Tracer simple "fit".  That "fit" does not include longer term change in climate.  Since prior to the modern instrumental era it is rumored that there was a little ice age, some portion of that "sensitivity" could be due to a long term "persistent" increase in ocean heat capacity.

The real question should be how much and how long has there been long term persistence since regional paleo indicates that there has been throughout history.  "Globally" the surface temperature may be stable, but mankind lives mainly in the northern hemisphere. 

Sea level rise appears to indicate that there is considerable long term persistence.  Unfortunately, with statistical massaging you can find anything you would like.  Selecting an anomaly baseline that is too short or on an upslope/downslope skews your results to that period.  If you select an anomaly baseline that is too long, you lose most of the information.  When you compare too short with too long you get fruit salad.  This chart uses the full global mean sea level data with an anomaly baseline from 1880 to 2006, the full length of the CMAR data set then it is "normalized" by dividing by the standard deviation for that baseline period.  Using the same procedure on the ERSST data and Hadley Center CRU4 Global data set, this is what you get.  Sea level, SST and Global Mean Temperature Anomaly all indicate that there is long term persistence. 

Of course if I select an different shorter baseline, the results vary considerably.

Here by using the 1955 to present baseline I get a nice hockey stick.  The hockey stick market is cornered, so I doubt that I can make any money trying to scare the little kiddies of the world with this, though it is a great method to use for Cli-Fi fiction.  This post outlines the methods and should provide any links I have missed.

There is a wealth of real science published that deals with the long term ocean driven climate pseudo-cycles which I find much more fascinating and believable than the Cli-fi hockey sticks. To each his own though. 

Adjusting the CO2 Tracer Signal

CO2 is one of the few atmospheric forcing components we have a reasonable handle on.  A doubling of CO2 will produce about 3.7Wm-2 of atmospheric forcing, which based on a temperature 255K degrees (240Wm-2) a temperature increase of approximately 1 C degree.  If you live in an area that has an average temperature of -18C degrees (255K), then you can expect about one degree of warming per doubling of CO2.  Anything more or less requires help in the form of amplifying or dampening feedbacks.

Since zero C is the freezing point of water, that other greenhouse gas, if you live in an area that has an average annual temperature of zero C can can expect a little bit more warming due to CO2.  As temperature and water vapor increase, the impact of CO2 decreases.  This is the reason for the polar amplification often mentioned and the less than expected warming over the oceans and in the tropics.  If you don't want to take my word for it, you can find a number of sources, but that is not the main point of this post.

The Carbon Dioxide Information Analysis Center (CDIAC) has a ton of information on CO2.  There have been direct measurements since 1959 at Mauna Loa in Hawaii, but longer term CO2 measurements are generally dismissed for some reason or another.  Since Fossil fuel use is the culprit that is supposed to cause the CO2 increase, there are good estimates of fossil fuel use going back to 1880 available at CDIAC plus some data of land use contributions to atmospheric CO2.  Using that data I have piece together an approximation of Anthropogenic contributions to CO2.

The typical formula used to calculate CO2 impact is T=5.25*ln(Cf/Ci) where T is temperature in C or K, Cf is the final CO2 concentration and Ci is the initial CO2 concentration.  Ci is generally assumed to be 280 ppmv based on various paleo exidence, primarily ice cores.  Other paleo proxies for CO2 indicate that Ci could be closer to 300 ppmv and there is some uncertainty.

This is what my little comparison of CO2 forcing extended back to 1880 looks like.  The CO2 cocentration from 1959 to present is just the Mauna Loa.  From 1880 to 1959 I used the ratio of the CDIAC fossil fuel plus land use to extrapolate CO2 concentration.  That required an exponential factor (Cf/Ci)^.075 to fit the 1959 CO2 to an approximate preindustrial 280 ppm.

To "fit" the ln(Cf/Ci) to the ERSST data required one main adjustment, I had to use 2.25 instead of the 5.25 multiplier.  I also adjusted the preindustrial concentration to 285 instead of 280 ppm to tweak the fit.   Since pre-industrial is assumed to be normal, I used a 1880 to 1900 baseline for the temperature anomaly.  You can see how the Northern Hemisphere land which has plenty of areas between -18 and 0 C warmed better than the SH and ocean temperature data.  Generally, the more alarmist of the CO2 aficionados will use the most noisy NH land data in an effort to make their case that CO2 is a danger to the world.  CO2 may be a danger, but it appears the danger is a good bit overly stated.

Now for the interesting part,  the 2.25 versus 5.25 is a pretty big difference.  A little less than half of the expected value.  Instead of 3C per doubling the value would be 1.55 C per doubling.  This seems to be the more popular current estimated range for transient climate sensitivity to a doubling of CO2.

Please note that this estimate did not consider any recovery from past ice ages and assumes that 1880 to 1900 is "average". Allowing to amplification due to recovery from a little ice age should reduce the "sensitivity" to 2XCO2 since there would be less of that -18 to 0 C land to warm up. 

Just thought I would share. 

UPDATE:

If you are a fan of the higher sensitivity,

 You can see that the 5.25 multiplier can be useful.  The correlation is not as good, but it does make a nice visual. 

The Elusive Global Surface Temperature

Have I found it?  Likely not.  Since the average altitude of the land mass is 680 meters above sea level and sea level likes to change, there is likely not a meaningful global surface temperature that everyone could agree with.  I personally would like a better estimate of some surface temperature, so I made my own using NOAA's Extended Reconstructed Sea Surface Temperature and the Berkeley Earth Surface Temperature project data to date.  BEST is putting together a combined Land and Ocean data set, but I just could not wait.

Using the Reynold's Optimally Interpolated SST data version 2, so hopefully this version is actually optimum with a baseline from November 1981 to March 2013 I "adjusted" the ERSST data from 1880 to present for 90S to the equator and for the equator to 90N.  Of course as you approach the 90s, data quality gets worse.  I haven't bothered with error bars, but I came up with some numbers.  I did the same thing with the ER land data using a 1951 to 1980 baseline that matched the BEST absolute temperature estimate for the hemisphere land masses.

Tah Dah!  It will probably be obsolete by some time tomorrow, but there it is.  That puts the "average" absolute temperature at about 15.25C with the average SST at about 17.85 C degrees.  Depending on what you would like for your "average" period to be, you can fudge one way or the other.  Since the Land estimate uses the BEST Tave estimated absolute value, this is a mix of some average at some altitude for land with the approximate actual SST instead of some temperature 5 meters or so below the surface.  The numbers in the ledgend are million kilometers square that I used, more precise values are likely available.

The reason I did this nonsense is for this;

This is the meridional differential temperature using the Redneck Physics absolute hemispherical land and ocean absolute approximate average temperature.  For the period 1880 to 2013, the "average" differential temperature between the hemispheres is about 1.1 C degrees which is based on a mean surface temperature of ~15.25 C degrees.  If I smooth out some of that noise, the range is about 0.8 to 1.35 C degrees.  There is a nice swoop or hump from 1880 to 1980 which is the ~ 100 year pseudo cycle I have been trying to isolate a little more accurately.

The reason that is so interesting is that the radiant forcing down or "back radiation" at the upper troposphere to turbopause is the same for both hemispheres.  Corriolis effect tend to isolate the hemispheres somewhat in the atmospheric circulation but forces ocean heat flow north from the Antarctic Circumpolar Current.  There is mechanical pump action messing with the oh so sickeningly sweet ideal radiant "greenhouse effect".

You may notice, if you are a savvy climate fan, that the end swoop starting at about 1985 just happens to correspond with the shift in "global" diurnal temperature trends. 

I just wanted to get this posted since the BEST combined Land and Ocean is supposedly due out any day now and I figure this might be interesting to compare with their results. 

Land Temperature and Ocean Heat Content and Baseline Selection

This is one of those in progress posts.  Since there was such a good correlation between North Atlantic Sea Surface temperatures and "Global" land surface temperatures, I was curious how well the Ocean Heat Content (OHC) data agreed with the land surface temperature data.  The data is the Climate Research Unit CRUtemp4 data (global) and the 0-700 meter OHC data from NOAA.

The data is massaged a bit with a common baseline anomaly from 1955 to 2012 to match the OHC data period, then normalized by dividing by the standard deviation of the baseline period. As you can see there is a very good fit between the CRUtemp4 data and the North Atlantic OHC data.  Near the end, about 2005, there appears to be a divergence between CRU4 and Global OHC, but that is a pretty short period and it includes the ARGO data.  Time will tell if that is real or not.

Pretty spiffy eh?  Well, since I used the full length of the OHC data for the anomaly period, that pretty much has to fit.  I have beat the data into submission.

Here I have added the CMAR Sea Level Rise data using the same baseline, almost, 1955 to 2006 so you can see how well I can whip data.  If you extent the trend lines for each of the series the OHC and GMSL will have about the same trend while the CRU4 series will have a much different trend.  That appears to make Global Mean Land Surface Temperature the odd data out using this anomaly baseline. 

But is it Land Surface Temperature or just the Hemispheres since most of the land surface is in the Northern Hemisphere and has a lot of influence from the North Atlantic and the Gulf Stream?

Here is the Hadley Center SST version 3 with the GMSL with the same massaging using 1880 to 2006 for the baseline period.  Using the GMSL as a reference, in this case it appears that the Northern Hemisphere has much larger variations around the mean sea level "mean". 

So let's replace the NH SST with the "Global Land Surface Temperature".  This is using the same 1880 to 2006 baseline for anomaly with the same normalization.  Temperature of the Southern Oceans, Sea Level Rise and Global land surface temperature seem to make some sense with the MSL "mean".   Base on this plot, temperatures and sea level have been rising together since at least 1880 with some "wandering" of both the SST and GLST.  I could select a different baseline period and get radically different results.

Finding the right "reference" to avoid chasing wild geese should be priority one in paleo climate reconstructions.