More on that Elusive Cause and Effect

Using the Tropics to Isolate Cause and Effect just leads to more questions.

This chart of 30 year sliding correlations with the Hot Tropics, 10S-10N points out a perturbation circa 1913 which could be due to a volcano not included in forcing estimates used for CMIP5 models runs.  The timing though is off, the perturbation leads the volcano I limited the SST regions to 60S-60N because of issues with early temperature measurements near the arctic circles.

The drop in correlation around 2005 was a bit of a surprise.  So I used the actual temperature data available for land areas, specifically the maximum annual temperature and minimum annual temperature to get a rough estimate of change in atmospheric forcing.  Lots of potential questions about how useful that might be, but variation in air temperature converted to approximate Wm-2 should produce a rough estimate of the total change in atmospheric forcing.

So I expand on that by doing the same thing with changes in sea surface temperature.

Using the same 1901-2013 baseline the energy anomaly for 40S-40N SST peaks at about 3 Wm-2 which is close to the land based forcing estimate.

I used 40S-40N for the SST version but I doubt using 50S-50N would make much difference.

Forty to 60 North is a bit more interesting, there is actually a significant difference, almost 2 Wm-2, at a couple of points.  Early in the record sould be dismissed as limited observation but since the most recent divergence is during the period of the best coverage, that part should be "real".

Forty to 60 South has the worse coverage, but should be some what reasonable after 1930.  This shows less "forcing", about 2 Wm-2, interesting, but the minimum is around 1930.

Comparing the actual temperatures recorded for the high latitude oceans shows a very interesting lag in responses.  The northern 40-60 band has a bottom from roughly 1905 to 1915 with a continuing down trend until it pops around 1985.  The SH portion starts its upward trend around 1930 with a plateau starting around 1980.  Both of these regions represent a fairly small portion of total ocean energy, but since temperature anomalies are not weighted towards energy, they could have a large impact on global mean temperature anomaly.  The northern high latitude ocean can impact more land area which tends to amplify change due to its lower specific heat capacity, so it could produce most of the combined PDO/AMO temperature variation.

Here I need to remind anyone still following that there is a huge change in the ratio of land to ocean area in the Northern Hemisphere.  The 60S-40S ocean region is nearly all ocean.  Latent heat loss from this region is much likely to return to the ocean in this region.  Water vapor from the surface would form clouds, releasing latent, but as the water falls as precipitation it would warm with the air regaining a large portion of that energy.

In the Northern Hemisphere it is much more likely that precipitation would fall on land transferring energy to that land then it would return to the oceans via river outflow with some delay.  When there is more water stored in sheds, reservoirs or ground water, the delay would be much longer.

The ratio of the 40N-60N band is 0.45 ocean, 20N-40N band 0.60 ocean and 0-20N band 0.75 ocean so the further north the more likely precipitation falls on land and is stored for some time, the further south the less likely precipitation is stored on land mass.  Above 60 degrees, in the north the stability of sea ice determines if the precipitation is stored and in the south, the Antarctic land mass would provide more consistent storage.

In a nut shell, northern hemisphere especially higher latitude, land and water use changes would have a much greater impact on climate via the hydrology cycle than in any other latitude band.  It isn't particularly easy to determine how much impact those changes actually have though without a "normal" period to be used as a reference.  It would generate lots of noise in the climate data.

 If you look at the temperature anomaly for the higher latitude bands you can see how response varies.  The 40N-60N band has less ocean area meaning a lower heat capacity, so it would have a faster response time.  These anomalies are based on the same 1901-2013 base line.

If you compare the actual temperature of the 40N-60N band with the bulk of the oceans, 40S to 40N you see about the same fast response.  Area wise, the 40N-60N is about 10% of the total area of these two and energy wise, 40N-60N provides about 8.8% of the entire area.

What all this seems to indicate that there is noise that makes any attempt to determine cause and effect pretty difficult.  The only consistent part of all these comparisons is that there has been some increase in atmospheric forcing on the order of 3 Wm-2 over the instrumental period.

Specific heat capacity is a great noise filter and since this is really an energy problem, the more specific heat capacity the better.

 Sea level rise may be the go to metric to estimate "global warming" in the long run.  Scaling the Jevrejeva et al sea level rise reconstruction to tropical 25S-25N SST indicates a long term increase with a slight acceleration over the past 150 years.  As you can see it pretty well filters out the noise.  I used a 1854 to 2002 baseline for this effort which indicates "noise" aka natural variability is likely on the order of +/-0.35 C or so at least in the tropics.  One could try to make a case for Volcanic forcing or solar, but considering the Crowley and Unterman 2013 Volcanic Aerosol Optical Depth reconstruction, there are more exceptions to the normal concept of atmospheric forcing than agreements.

So using SLR as a reference, today's temperatures may seem exceptional, but Berkeley's land temperature reconstruction seems to indicate other wise.

Sea level rise also filters out the oscillations in the Oppo et al 2009 Indo-Pacific Warm Pool reconstruction.

Just like the 30N-60N SST comparison above, Northern Hemisphere reconstructions appear to have the same fast response.  This Christian and Ljungqvist reconstruction is for 30N to 90N and uses a lot of tree ring reconstruction in very high northern latitudes.  Each of these individual reconstructions have a great deal of variability and how a multi-proxy combination ends up looking depends a lot on choice of proxies and methodology.  Spliced on the end I have GISS land only in both annual and 50 year moving average to match the C&L recon.  Their 2009 version is archived at NCDC with 71 of the reconstructions in an easy to download file with actual temperatures in most cases so you can compare the relative energy contribution as a double check of their weighting.  From that you can see most of the real signal is in the lower latitudes where there is more energy per unit anomaly.  The sigma variations they use don't give you any sense of how those recons should be weighted other than a rough area.

I picked this recon btw to show how messing with smoothing has become an art for some of the less ethical.

Skeptical Science played the loess game of hockey stick enhancement which produces those too good to be true uncertainty intervals so popular with advocates.  You can easily download the data from NCDC and instrumental from Climate Explorer to make you own comparison should you think I am some kind of lying SOB.

Using the less adulterated data I believe indicates a good deal of the "cause" of the warming is buried in multi-century scale wandering and less that representative presentation of selected facts.  Focusing on energy anomaly instead of temperature anomaly might limit some of the creativity.

Using Tropical Correlation to Isolate Cause and Effect

If you have followed some of my rambling you know that I have been focusing on the tropical oceans and specifically the Indo-Pacific Warm Pool because of the Oppo et al. 2009 reconstruction which appears to be very closely correlated with "global" climate.  Areas with high correlation can be considered good "teleconnections" when trying to reconstruct a climate history.  No one location has a "perfect" teleconnection so you need to look for all the exception to the rule before other people start poking holes in you theory.  So this busy chart is hopefully a step in that direction.

This compares what I call the Hot tropics, 10S-10N with expanding ocean areas plus a NH and SH 0-30 degree band.  Areas of de-correlation should be related to perturbations like Volcanic activity or changes in ocean circulation that could be related to sea ice extent or shifting of atmospheric circulation patterns.  Toggwieler, J. R. with the GFDL mentions the "shifting" westerlies or changes in the Inter-tropical convergence zones (ITCZ) as a larger than many expect climate variable.

As should be expected, the correlation decreases as distance from the reference zone increases.  What is interesting is that the Southern Hemisphere (30S-0), the darkest green curve, and the Northern Hemisphere (0-30N) light blue curve, maintain a good correlation with the hot tropics and surprisingly well with each other.

This is the same 30 year sliding correlation between those two regions.  The roughly 1913 volcano shows as a small spike and the major climate shifts (red) seem to be where they would be expected except for around 2005 there is an indication of some other shift.  That shift is in the warmer direction, but pretty short term so far.  The main reason I am doing this is to see how well the sliding correlation window aligns with perturbations and that one is a bit of a surprise.  The other black arrow appears to be volcanic related but Pinatubo in 1991 is missing.  Pinatubo may have had equal hemispheric impact so it wouldn't show up in this correlation.  Somewhat surprisingly, Pinatubo doesn't really stand out in the other correlations.  It may have been more of an atmospheric thing than an ocean thing.

In any case these correlations are interesting to me.  I had expected the shifts to stand out and there to be a long term improvement in correlation, but not the major shift following the 1998 Super El Nino.  However, that shift seems to be related to surface winds, shifting westerlies, agreeing with Toggwieler, who might be a good addition to a lot of folks reading lists.  What wind data there is though just indicates an increase from a low around 1930-1940 without much variation.

Land surface temperature though gets most of the press.  Since land and ocean should be closely coupled, there should be a fairly high correlation between land and ocean temperatures.  Using the same main ocean bands I substituted the BEST global land for the Sstv4 (10S-10N) original reference to get this group of correlations.

This is a bit more interesting.  The same 1913 volcanic perturbation results in the lowest correlation then the correlation increases with time.  That to me would indicate either long term persistent recovery from past cooling and/or issues with the temperature data sets.   BEST uses kriging though and with most older long term stations being coastal, long term persistence should have a greater potential, with the exception of recent higher latitude station made practical with modern equipment.

In any case, there is a perturbation, ~1913, apparently due to the largest volcano of the 20th century, that was not include in model forcing estimates that has an obvious impact on temperatures.

This chart compares the CMIP5 model mean ocean temperature (tos) for the 50S-50N with the Crowley and Unterman 2013 estimated volcanic forcing (scaled to tos) to show the obvious difference in estimated forcing/impact.  This uses just the Northern Hemisphere which had the largest volcanic forcing.

The average volcanic forcing is a better match for CMIP5, but the ~1913 difference is still obvious.


"Forcing" though is not as simple as it may seem.  "Atmospheric" forcing is the focus of the models and the huge heat capacity of the oceans can "make-up" for negative atmospheric forcing and buffer positive forcing.  Satellite and ARGO era measurements make up for the limitations of "surface" temperature now but that doesn't help in determining if there is long term persistence.

With actual temperatures you can get a rough estimate of the change in atmospheric forcing.  You can't attribute the change very easily, but you can estimate the magnitude of the change.  Since the Oceans and atmosphere de-correlate fairly often, this might provided some insight but is going to be controversial.  This chart is based on seasonal (not actual Tmax and Tmin used to estimate Tave) Tmax and Tmin values for the regions in the legend using hadcrut Ts which is in C degrees.  CRUTs isn't intended to be a "global" temperature product but it does try to capture maximum and minimum values for determining dates of first frost and such.  The change in energy associated with Tmax and Tmin is surprisingly consistent for these regions.  I excluded the highest latitudes because that is were CRUTs accuracy seems to suffers most as should be expected.  According to this, there wasn't much change in atmospheric forcing until 1985 which is a problem period due to changes in surface temperature instrumentation.  In any case, this seems to indicate that current atmospheric forcing is about 4 Wm-2 above the 1901 to 2013 average.


While some might not like the CRUTs, Berkeley has an actual temperature estimate with about the same results.

The Northern Hemisphere is where most of the land is of course.

The Southern Hemisphere indicates less land forcing since BEST includes the Antarctic which may not be all that useful due to its high altitude and ultra cold temperatures.  The advantage of CRUTs was being able to mask regions which is difficult to do with BEST considering an absolute temperature.  All these used the same 1900 to 2013 mean and seasonal Tmax and Tmin not the actual Tmax and Tmin used to estimate Tave.  Note that the legend on the last two chart have BEST gbl instead of NH and SH as indicated in the chart titles which are correct.

The actual Tmin is currently being questioned due to variation the the nocturnal boundary layer as subject for another day.

The ~1913 volcanic perturbation shows up most strongly in the BEST NH Tmin energy approximation and BEST SH seems to indicate a continuous long term persistent recovery starting prior to that perturbation.   Remember all these used a 1901 to 2013 baseline.

Using the SSTv4 50S-50N temperature which had the best combined correlation with land temperatures I can also estimate forcing using the Crowley and Unterman 2013 Volcanic combined with the BEST interpolation of CO2.  I used a 1.6 C "sensitivity" estimate for CO2 forcing as a reference to all forcing aligning with the peak values.  This assumes that the evident cooling was natural/volcanic.  The volcanic forcing does not line up well with temperature (in this case converted to energy) response.  Assuming all the natural/volcanic cooling has completely recovered, the current atmospheric forcing is about 2.5 Wm-2 greater than it was in circa 1880.

Using the same baseline for comparison with "known" forcing equaling about 2 Wm-2 and SST for the 50S-50N region starting below the 1901-2013 mean, about half of the forcing indicated using the Land temperature data could be recovery from a long term persistent cool period just as indicated in the Oppo et al. 2009 Indo-Pacific Warm Pool reconstruction.

I am positive that this rambling look at various estimates of changes in atmospheric forcing isn't going to sway any believers, but it might inspire some to redo this in a more easily followed manner with more precise citations and proper jargon.  From my perspective, not including the more recent volcanic forcing estimates and not focusing on ocean and regional response has lead many modelers down a garden path.  It might be time to review some of the previous work of others, like Herbert Lamb.

Surface Temperature Product Issues

I have discussed this in numerous posts but thought I might as well consolidate some of that here again.  All of the land "surface" temperature products produce a "global" result that is very consistent with each other using different data sets and methods.  That is a great validation of the "surface" temperature.   Locally though there are very obvious differences in results for small areas that are "unique" in some way.

  Iceland is one example.  This chart compares Berkeley Earth with the Cowtan and Way kriging methods using Climate Explorer masking for the data.  I included the monthly data in the background and a 27 month 3 stage cascade  filter in the fore ground.  Berkeley produces a "country" product while Cowtan and Way doesn't, so I used Climate Explorer to hopefully produce an apples to apples comparison.  There is a fairly obvious difference in the results in Iceland for the two methods.

On a "global" scale the difference is meaningless, but if you happen to like maintaining Iceland's temperature record as pristine as possible, you might be concerned.  Any type of interpolation will result in some smoothing of data and very strong or "unique" data points will tend to be smeared across a larger area that normal "average" data points.  Kriging is a more "unbiased" way of interpolation, but that doesn't mean it is a perfect form of interpolation.

Iceland has the energy versus temperature anomaly issue I harp on in spades.  If you were kriging gold deposits you would want to include some indication of yield along with existence for any sample point.  If Iceland were the "mother lode", no other point would ever compare to Iceland.  So if you interpolate/krige a location north of Iceland that has a huge rise in winter temperature from say -36 to -26 C degrees with a southern area that has a small decrease in temperature from +15 to +14 C degrees, you would think Iceland warmed significantly.

The energy per degree change for the northern location would be about 3.2 Wm-2 and the energy for the one degree change for the southern location would be 5.4 Wm-2 excluding any latent energy change.  Energy wise, the southern location should be given greater weight during the interpolation.  For this particular case temperature anomaly would result in a 4.5 C change while energy anomaly excluding latent would result in a 3 C change.  So you need to include absolute temperature (or energy) when determining what a "peer" for Iceland should be for interpolation.

Since you don't have accurate latent data, you will never reach a perfect interpolation method, but including "sensible" energy will at least improve "pair" or "peer" selection if you want to produce a reasonable "local" product.  Once different methods start reducing "local" differences, there would be some progress made toward a consistent treatment of the energy that temperature anomaly is supposed to represent.  Then possibly "local" and "micro-site" impacts might be discernible.