The Antarctic is Getting Some Scientific Love

There is a new paper out on the Antarctic climate change.  The paper by Orsi, A. J, Cornuelle, D. B. and Severinghaus, J. P., Little Ice Age cold interval in West Antarctica: Evidence from borehole temperature at the West Antarctic Ice Sheet (WAIS) Divide, has and interesting chart.

The ice core data is compared to the Steig et al, 2009 temperature fabrication and a cloud masked satellite data series.  The match with Steig et al is generally poor with the exception of the more modern period which was not an issue in Steig et al. and a better but not all that outstanding match with the cloud masked satellite data.  Finding a reliable and long Antarctic temperature series is not all that easy.  It is a rough place which is rough on people and equipment.  So since I like being helpful, I tried a little trickery on the Amundsen-Scot temperature record downloaded from GISStemp.


The trickery is pretty simple.  Amundsen-Scot is very cold year around.  So temperature anomaly may not be all that meaningful.  Energy anomaly might be more meaningful.  Since the source of the ice was water somewhere in the area at or near freezing, I converted the Amundsen-Scot temperatures to Watts, determined the anomaly in Watts, added that anomaly to the energy at o degrees C (273.15K degrees) and converted that to temperature anomaly.


Why?  Because it was raining and I was playing with my spreadsheet.  Actually, the oxygen isotope ratio in the core would relate to the temperature of the water forming the ice more than to the local temperature.  Since Orsi and gang used the 1986 approximate satellite record start, here is my little contribution with a 1986 start.

While the scale I use doesn't look that menacing, the slope of the regressions are in the ballpark of Orsi and gang.  Note that the orange curve slope is a good bit less than the blue curve slope.  A smaller change in temperature at 0 C can produce a larger impact on a area at -45C.  So there has been considerable warming at Amundsen-Scot since 1986.  There would be even a greater rate of warming starting in 1995 when the stratosphere temperature shift occurred and even more starting in 1999 following the super El Nino.  


What about starting at the beginning of the Amundsen-Scot records?

There is minor warming for the full record which would be a better match for their reconstruction.  Imagine that?


Now if Orsi and gang really want to wow the masses, they could compare sea ice variation with their reconstruction and find out where that ice came from.  


Early I said that the oxygen isotope ration in the ice core would relate more to the energy of the water that formed the ice, so why is there a pretty good match with the Amundsen-Scot surface temperature?  That snow is delivers a good portion of the energy that reaches Amundsen-Scot, especially in the winter. 


UPDATE:  I redid the Amudsen-Scot with a 60 month centered moving average and rescaled to overly the Orsi et al. chart.

Note that the time scale is shifted back about four years.  That may be a product of my smoothing, but more likely a product of the nature of the Antarctic.

Just Modulatin'

When I threw together the deviation from mean of the Mauna Loa above I used the full average monthly variation which includes the rising trend in CO2 concentration. Nothing good or bad about that, just should be noted. Also the right Y-axis is for the deviation from mean.

This is the deviation from mean using the full Mauna Loa record with the trend in CO2 removed. This is just the monthly change as provide by the NOAA minus the average monthly change. This is on the same Y-axis to show the magnitude of the deviation. Comparing the two may be useful to see what impact the increase in CO2 concentration has on some of the variability in climate.

Just in case you were wondering, here is the average Mauna Loa change with the tropical ocean and northern extent land for comparison. Note that the temperature scale is on the left and the CO2 scale on the right.

Notes on Stuff

I love messy charts. Showing the short term changes are a reminder of how chaotic things can be. I nice milquetoast smoothed chart is for pansies. Show me the nasty!

This is real nasty. It is the average CO2 monthly change measured at Mauna Loa, the actual monthly change and the deviation from the mean (average) monthly change. Annually, the CO2 measured varies a few parts per million at Mauna Loa. I just took the period from 1979 to 2011 and found the average variation for each month, subtracted the actual value for each month and viola! deviation from the average or mean. Lots of noise, but there are periods of less and more variation.

This compares a portion of the deviation from mean to the TIM TSI solar measurements from SORCE. This is not the solar data I wanted, but the best I have at the moment. If you squint real hard you can see a slight inverse correlation. Lower solar versus greater deviation from mean. This really should be the CO2 deviation from mean versus Solar UV. Why? Because UV creates ozone, ozone reacts with ice crystals and hydroxil (I think that is right) can react with CO2. Real chemistry was never my forte, so I will leave that vague. So what?

I did the same deviation or variation from mean with the UAH mid troposphere temperature data. This chart is the northern extent land and ocean minus the southern extent land and ocean. The extent have a push me pull you thing going on annually. By subtracting the two, it gives a bigger picture of the change in the monthly variation. The break is at 1995 with the trends lead in and out. I use 1995 because I believe that is a climate shift matching the stratospheric shift from cooling to neutral. The UAH mid troposphere data is questionable for the portion around 1983, which would increase the magnitude of the shift if UAH is in error. That shift is I believe due to the Ocean Heat Content (OHC) approaching a pseudo equilibrium for current conditions. Less uptake of heat would create a greater cooling flux which would create greater atmospheric temperature variability. This is all a crazy theory at the moment. It will require a much more serious effort to flesh out, but it does tend to jive with the southern hemisphere temperature reconstructions I have seen, the change in Antarctic sea ice area variation and the change in Artic sea ice variation. There are so many failed climate theories that I am not too enthusiastic about this one, but some pieces are fitting a little better.

That Neglected Southern Hemisphere

The NOAA Paleo Website has a huge selection of the various data attempting to reconstruct past climate. The Southern Hemisphere tends to be somewhat neglected. So I have been playing around with a couple of tree ring proxy reconstructions, Neukom, R., et al. 2010. Southern South America Multiproxy 1100 Year Temperature Reconstructions. IGBP PAGES/World Data Center for Paleoclimatology and another, Cook, E.R., D'Arrigo, R.D., and Buckley, B.M., 1998, Tasmania Temperature Reconstruction. International Tree-Ring Data Bank. IGBP PAGES/World Data Center-A for Paleoclimatology Data Contribution Series #98-040. NOAA/NGDC Paleoclimatology Program, Boulder CO, USA. The Cook et al. reconstruction had data that averaged 15 which I assumed to be degrees C, which gave me an idea to illustrate one of the issue with global averaging of temperature. While there may be a range of temperatures for a region, the range of energy flux associated with those temperatures would be quite different. So I converted the Tasmania reconstruction to degrees K and then to Watts per meter squared that may be associated with that range of temperature. Then I did the same thing with the Southern South America(SSA) reconstruction which was in temperature anomaly. In order to align the mean value for the common period, 900 AD to 1991 AD, I has to adjust the average temperature of the SSA to 288.2 K degrees. Then I averaged the two reconstruction only for there common period. Just to highlight the common points, I added an 11 year moving average. Then I included the Goddard Institute for Space Studies 24S to 44S latitude regional surface temperature average converted to approximate Watts per meter squared. I just eyeballed the fit of the GISS data which as shown, has an average temperature of 288.28K degrees. This post is just to provide proper citation to the authors of the reconstruction and GISS for the temperature data.

Changes in Tropospheric Heat Flow

I have done this a couple of times but misplaced the work. Since there is more data, it is probably a good idea to update the charts anyway. Above is the UAH atmospheric layer differentials for the lower US 48. I used that because I live in the lower 48 and have a more complete data set for UAH. RSS data should be pretty close. Around 1994 to 95 there appears to me to be a climate shift. Since the lower stratosphere has much less thermal mass than the troposphere, changes would be amplified in the lower stratosphere. By using the differentials, lower troposphere (TLT) minus mid troposphere (TMT) and the TMT minus the lower Stratosphere (TLS) the charts focus on the change in the energy flow relationship between layers. The linear regression equations show the value of the slope for the period up to 1995 and after 1995, where I believe the shift occurred.

This plot is for the global land area with the same plot set up and data set.

This is for the global ocean area using the same stuff. There appears to be a change around 1995.

Northern Extent land, same change.

Tropics land, not as huge a change, but a change.

Southern extent land, more change than the tropics less than the northern extent. Since Blogger is having issues with tables and paragraphs again,

Here is a screen capture table of the regressions for each and the changes for each. Pinatubo erupted around 1991, so some think that the change may be related to volcanic activity. I don't think so. I am inclined to believe the oceans have reached or are reaching, a point of pseudo equilibrium. That would change the relationship between the lower troposphere and the stratosphere. When the oceans are warming from a long cooling period, the atmospheric or greenhouse effect would be stronger, holding more heat energy. As the oceans heat uptake decreases, they would release more heat resulting in reduced atmospheric effect and a reduction in the rate of cooling of the stratosphere. Enough heat loss would result in a warming of the stratosphere. The atmosphere appears to be reaching a control point, so only now can the impact of CO2 be reasonable estimated.

De-Trending UAH

The University of Alabama, Huntsville Microwave Sounding Unit (UAH MSU) atmospheric temperature data is supposedly the gold standard for global mean temperature. Since I am playing with Open Office trying to get it to do some time series stuff, I detrended the Northern Extent, Tropics and Southern Extent data sets.

The method I used is cludgy, but appears to work fairly well. I add the maximum trend or slope of the individual data series to the start of the series and incrementally decrease the the addition per data point to the end of the series. In this case, there are 399 data points, the first has the slope times 399/399 added to the first and the slope time 0/399 to the final point. This increases the mean of the series by the slope, which I subtract to return the plot to zero. Using the linear regression and the mean value functions in the charting program, the regression after de-trending is equal to the mean which is equal to zero as a check. Obviously, the complexity of the system dynamics would not allow perfect removal, but a good portion of the internal variability would be reduced by subtracting the "average" from each series.

Here is the results of subtracting the "average" from each series and removing the de-trending. The final slope of the Tropics and Southern extent is 0.007 degrees C per year or 0.07 degrees C per decade. This should be the slope of response to continuous forcing change. The slope of the Northern Extent in this case is greater, 0.026 C per year or .26 degrees C per decade. This should be due to water vapor enhancement in the high northern latitudes, amplification from the oceans thermohaline current and land use change amplification of CO2 or other continuous forcing.

In this plot I change the start of the comparison to 1995. There is a minor change in slope that is unlikely to be statistically significant. This is not a proof of the method, but tends to lend some credibility since there is a much larger change in slope using the raw data with the internal variability included. Since there is some speculation that cooling started circa 2000 here is that plot.

In this case there is a statistically significant change in the Tropics and Southern Extent with no significant change in the Northern extent. The length of the series is only 11 years, so there cannot be a great deal of confidence in the significance, but it is slightly over 50% likely to be a shift in the climate, if the method is valid. Solar forcing is the only likely cause, so there may be more solar impact than generally thought in the global oceans which make up the majority of the Tropical and Southern extent surface.

Just to be complete, this is from 2002. This is much too short to be of any significance, but it is comforting to see there is no drastic changes from the 2000 start as far as the methodology is concerned. I am not going to attempt to derive some validation of the method at this time. In the future I may use the same procedure for other regions comparing land to oceans which may help either discredit the method or help determine the degree of land use impact. What will happen, I don't know, but it should be a reasonable way to remove most of the internal variability without trying to specifically target the individual causes. Then again, it could be a waste of time. Initially though, it appears to agree rather well with the Douglass and Christy "Limits on CO2 Climate Forcing from Recent Temperature Data of Earth" results published in 2009 with the addition of a little hint of solar impact. Note: I am sure this method has probably been used before, but I just developed what I am doing here on my own. If anyone has a link to the original development, if it is indeed a valid method, let me know. UPDATE: http://phys.org/news/2012-05-satellite-global-climate-closer.html There will be some adjustments to UAH which will be interesting since while it should increase the overall slope is should also increase the lower slope from 2000. I will revise this when the new data is available. UPDATE 5/9: Since the death of UAH MSU seems to have been announced in the climate world, I thought I would check if the reports are accurate.

Using the same method on RSS, the results are seriously different. Since I had the spread sheet for the UAH detrend minus common signal, I plugged in the RSS tropics and extents using the UAH common trend. This should highlight where the two sets differ.

There is a swoop curve, so the differences are near the start and end. For the start, early in the satellite program, is not all that exciting to see less than stellar performance. It's a learning curve thing ya know. The end though is a bit unusual, by now things should have been improving. So what's up? Notice that the RSS detrended has very pronounced change in the orange, tropics plot. The blue, northern extent plot is subdued. Since these are plotted with the average of the three detrended, the most subdued produced the strongest common signal. So the Northern extent would be driving climate according to the RSS data. While that may sound nifty, the ocean heat content is no piker in the climate game. The tropics trend is negative for the RSS with detrended variation removed, which goes counter to what I would expect. So I may have screwed up or there may be some unintentional bias in the RSS data. So I will go back to check to see how bad I screwed up, but I really suspect the RSS calibration is not consistent with the satellite changes. UAH is also likely to have issues, but it looks like any errors they may have made were consistent, which would mean the data is still useful. Found a issue in the spread sheet so the chart above is updated. Since the Tropics are mainly the issue.

This chart shows the RSS tropics in green with the average of the tropics and extents, detrended, removed. There is no change to the slope, but there may some interesting things.

That is the same plot using the UAH data. In the UAH there is a difference in the slope between the raw and the raw minus the detrend average. There is virtually no difference in the RSS tropics data. Which one makes more sense? Hard to say, but the no extent and so extent have larger changes in RSS. As a note, the GISS surface data is very similar to the RSS when I use the same procedure. So similar, that RSS may have fudged their calibration a touch to more closely match GISS. Now that would generate a little buzz in the remote sensing community :) Still, UAH may be high early but RSS looks low late. Time will tell.

More on the Atmospheric R-value

The R-value used to rate the insulation quality of construction materials and clothing is pretty simple. R=delta T/delta Q where T is the temperature, Q is the heat flow and delta is the difference of rate of change in either. If you have a room you want to be at 72 degrees with an outside air temperature of 32 degrees and only what to use 12.0 MBTH, then R=40/12=3.33 minimum R-value for the space. 3.33 is about the R value of a 3/4" airspace with some of that radiant reflective barrier stuff added. Not a very high R-value, but that was just an example. The atmosphere can have an R-value from one point to another. Since there are seldom dead air spaces, it is pretty low. It is like a house with no walls or roof, pretty hard to air condition. Adding greenhouse gases is supposed to increase the R-value of the atmosphere. If there were dead air spaces, the GHGs would do a fine job. Without dead air spaces, it is s touch more difficult to figure out how good a job they will do. Part of the R-value calculation is delta T. Since we have pretty good temperature data for the atmosphere at differing altitudes, we can get part of the R-value information.

That plot is for the University of Alabama, Huntsville, middle troposphere data minus the lower stratosphere data for the Northern and Southern extents. Adding CO2, CH4 and other gases and particles while change the temperature relationship between these atmospheric layers. As you can see, the differential for both is increasing with time. Since the delta T part of the equation between these two layers is increasing, the R-value would be increasing if the delta Q part remained the same or decreased. For global warming theory, delta Q should decrease as delta T increases.

This plot is just for the Northern Extent from 1995 with the estimated R-value included in yellow. I based the flux on a surface temperature of 273K (zero C degrees or 32 degrees F) and the differential based on 255K degrees. This is not an actual value of R but should give a trend. In this case the R estimate decreases very slightly. There would not be a radical change in the R-value, but it should be increasing. Of course these are only two regions with large assumptions made, but it could be an indication that the R-value is not linear with altitude. Which is what I suspect.

Making Data Dance

The Microwave Sounding Unit (MSU) data appears to be pretty reliable. While the fits are far from perfect, some indication of the CO2 forcing change, Solar forcing change and even changes in mean sea level can be teased out of the data. That should mean that even more can be coaxed out of all the noise.

That is a chart of the UAH southern extent, tropics and northern extent lower stratosphere data from 1994. I chose 1994 because there appears to be a general shift from cooling to neutral in the global stratosphere temperatures. The stratosphere should cool as the lower troposphere warms. In the lower troposphere, the 1998 super El Nino was a huge temperature event. It is not so huge in the troposphere. In the chart above, 2003 appears to be the largest stratospheric temperature event. That gave me an idea, possibly not the smartest idea I have ever had, but one that may be worth pursuing.

In this chart I plotted the mid-troposphere temperature divided by the lower stratosphere temperature for both the northern extent and the southern extent, again using the UAH data. By dividing by the lower stratosphere temperature, spikes should show when the monthly temperature approaches the mean anomaly. Depending on the sign of each, there would be positive ore negative spikes. It both data sets were fluctuation around their mean, the spikes would be evenly distributed, there would be more noise. In the chart above, there is much less noise than I expected. There was also more interesting noise than I expected. Since the mean for the data sets are based on the 1981 to 2010 period, most of the record, I would have expect no spikes at all prior to the 1994 to 95 leveling off of the stratospheric cooling with lot more noisy spikes after 1994 similar to the Sothern extent Orange curve. Since the Northern extent has more surface warming, I would have expected it to have more noise early in the series and less later, not fairly well distributed as it appears to be. That may mean that is could be possible to adjust the base line periods for the two data sets to establish similar noise patterns. That would give some indication of the relationship of "average" for each set with common events. With similar patterns, it could be possible to "slide" the relative timing of each series to isolate lag times.

Here, by adding 0.25C to the Northern extent lower stratosphere average, I was able to get the 1998 spike, the tropics 1998 spike is based on the 1978 to 1998 average of the tropical stratosphere and the Southern extent get a large negative spike with -0.02 used to adjust the average. I need a more logical method to adjust for optimum correlation, but there seems to be some potential here. I also am trying the differential of the lower troposphere and the lower stratosphere to compare changes in the emissivity of the atmosphere. Also a fairly crude method, but interesting.

This is the differential temperature for the entire UAH series.

And this is the temperature differential from 1995. The slopes of the regressions are much closer since 1995. With the short data series this will be a major challenge, but the average slope that best compares to Greenhouse gas forcing may be found by removing solar forcing change. It possible, then I may be able to estimate the land use impact. A lot of ifs and mays here, but the nearly double slope of the northern extent is fairly close to what I would expect for land use impact amplified by CO2 forcing. This is all probably a glorious waste of time. Still, there appears to be an outside chance of teasing out some useful information. This is more of a personal note than a real post.

It's Moving!

The change in the altitude of a parcel of air does whacky things. If you have ever looked at a thunder cloud you have seen the anvil top. That is where high velocity air is moving over the cloud, shearing off the top of the cloud. That is a big deal for guys like me living in hurricane country. I want to know better whether I need to plan on visiting relatives out of state or not. Low shear winds mean bigger storms which means Kansas here I come! With thunder storms, the greater the shear, the weaker the storm. Warmer rising air creates a downdraft of colder air which feeds the storm circulation. If the warmer air displaces colder air further down the road, there is less local energy to push more air into the base of the storm. Radiantly, the same thing happens. If the air above the warmer air is stationary, both masses approach the same temperature so there is radiant feedback allowing the warm air to cool slower. If the air above the warmer air is at a fixed temperature, the warmer air would continue to cool at the same rate and never be able to warm the layer above it. Try to visualize a three dimensional model with the surface, one sphere and the tropopause an outer concentric sphere. Now adjust the shape of the outer sphere so that it contours with the average tropopause temperature, say -60 degrees centigrade. It would have a large bulge at the equator and hug the surface as it approaches the poles. At one pole, it would actually intersect the inner surface sphere, leaving that pole open to the stratospheric temperatures. Now insert a sphere in the middle at the average temperature between the surface and outer spheres. Allow the surface sphere to remain fixed, rotate the middle sphere at some velocity near the average velocity of the surface wind speed and the outer sphere to rotate at the average velocity of the jet stream. You now have a basic radiant model of the coupled Earth atmosphere system. To tweak the model, include hexagonal shapes turning the spheres into "Bucky" ball geodesic shapes. Allow the middle sphere shape to rotate in simulation of the atmospheric vorticies. Now you have a model that may be able to do some good. For some reason Blogger is not allowing paragraphs, sorry.