Here’s the global average sea surface temperature (SST) update from AMSR-E on NASA’s Aqua satellite, updated through yesterday, July 7, 2011:

The anomalies are relative the existing period of record, which is since June 2002.

As can be seen, the SSTs have not quite recovered from the coolness of the recent La Nina.

Something else I track is the ocean cloud water anomalies, also from AMSR-E, which I have calibrated in terms of anomalies in reflected sunlight based upon Aqua CERES data:

Why I watch this is it often predicts future SST behavior. For instance, the circled portion in 2010 shows a period of enhanced reflection of sunlight (thus reduced solar input into the ocean), and this corresponded to strong cooling of SSTs during 2010 as seen in the first graph.

So, the recent new enhancement of cloudiness (smaller circle) suggests a fall of SST in the next month or so. After that, it generally takes another month or so before ocean changes are transferred to the global oceanic atmosphere through enhanced or decreased convective overturning and precipitation.

After talking with John Christy, I decided I should further expound upon the points I made in my last post.

The issue is that the two main satellite-based records of global lower tropospheric temperature change have been diverging in the last 10 years, with the RSS version giving cooler anomalies than our (UAH) version in recent years, as shown in the following plot:
(the RSS anomalies have been re-computed to be relative to the 1981-2010 period we use in the UAH dataset)

Ten years ago, this meant that the AGW folks were claiming RSS was right and we (UAH) were wrong, since the RSS global warming trends were greater than ours.

But now the shoe is on the other foot, and the RSS linear trend since January 1998 has actually cooled slightly (-0.03 deg. C per decade) while ours has warmed slightly (almost +0.05 deg. C per decade).

John works hard at making our dataset as good as it can be, and has correctly reminded me that he and others have several peer reviewed and published papers recent years on the subject of the accuracy of the UAH dataset:

Christy, J.R. and Norris, W.B. 2006. Satellite and VIZ-radiosonde intercomparisons for diagnosis of nonclimatic influences. Journal of Atmospheric and Oceanic Technology 23: 1181-1194.

Christy, J.R., Norris, W.B., Spencer, R.W. and Hnilo, J.J. 2007. Tropospheric temperature change since 1979 from tropical radiosonde and satellite measurements. Journal of Geophysical Research 112: doi:10.1029/2005JD0068.

Christy, J.R. and Norris, W.B. 2009. Discontinuity issues with radiosondes and satellite temperatures in the Australia region 1979-2006. Journal of Atmospheric and Oceanic Technology 25: OI:10.1175/2008JTECHA1126.1.

Christy, J.; Herman, B.; Pielke, Sr., R.; Klotzbach, P.; McNider, R.; Hnilo, J.; Spencer, R.; Chase, T. et al. (2010). “What Do Observational Datasets Say About Modeled Tropospheric Temperature Trends Since 1979?”. Remote Sensing 2 (9): 2148. doi:10.3390/rs2092148

Douglass, D. and J. R. Christy, 2009: Limits on CO2 climate forcing from recent temperature data of Earth, Energy & Environment, Volume 20, Numbers 1-2, January 2009 , pp. 177-189(13) doi:10.1260/095830509787689277.

Randall, R.M. and Herman, B.M. 2008. Using limited time period trends as a means to determine attribution of discrepancies in microwave sounding unit derived tropospheric temperature time series. Journal of Geophysical Research: doi:10.1029/2007JD008864.

Bengtsson, L. and K.I.Hodges, On the Evaluation of Temperature Trends in the Tropical Troposphere, Clim. Dyn., doi 10.1007/s00382-009-0680-y, 2009.

These papers either directly or indirectly address the quality of the UAH datasets, including comparisons to the RSS datasets.

Based upon the evidence to date, it is pretty clear that (1) the UAH dataset is more accurate than RSS, and that (2) the RSS practice of using a climate model to correct for the effect of diurnal drift of the satellite orbits on the temperature measurements is what is responsible for the spurious behavior noted in the above graph.

Our concerns about the diurnal drift adjustment issue have been repeatedly passed on to RSS in recent years.

What Will the Next IPCC Report Say?

As an aside, it will be interesting to see how the next IPCC report will handle the various global temperature datasets. There have been a few recent papers that have gone through great pains to explain away the lack of long-term warming in the satellite and radiosonde data (the missing “hot spot”) by trying to infer its presence from upper tropospheric wind data (a dubious technique since geostrophic balance is a poor assumption in the tropics), or by using a few outlier radiosonde stations with poor quality control and spurious warming trends.

Call me a cynic, but I think we can expect the IPCC to simply ignore (or at most brush aside) any published evidence that does not fit the AGW template.

…or, OMG! HAS UAH BEEN BOUGHT OFF BY GREENPEACE!?

Over the last ten years or so there has been a growing inconsistency between the UAH and Remote Sensing Systems versions of the global average lower tropospheric temperature anomalies. Since I sometimes get the question why there is this discrepancy, I decided it was time to address it.

If we look at the entire 30+ year record, we see that the UAH and RSS temperature variations look very similar, with a correlation coefficient of 0.963 and linear trends which are both about +0.14 deg. C per decade:

(In the above plot I have re-computed the RSS anomalies so they are relative to the 1981-2010 average annual cycle we use; this does not affect the trends…just makes it more of an apples-to-apples comparison).

But if we examine a time series of the DIFFERENCE between the two temperature records, we see some rather interesting structure:

(Note: I have applied a 3-month smoother to the data to reduce noise).

As can be seen, in the last 10 years or so the RSS temperatures have been cooling relative to the UAH temperatures (or UAH warming relative to RSS…same thing). The discrepancy is pretty substantial…since 1998, the divergence is over 50% of the long-term temperature trends seen in both datasets.

WHY THE DIVERGENCE?

So, why the discrepancy? Well, if it was OUR (UAH) data that was cooling relative to RSS, people would accuse us of being bought off by Exxon-Mobil (I wish!…still waiting for that check..). At least that has been the history of this debate.

But now WE are the ones with “excess” warming. So where are the accusations that RSS is being bought off by Big Oil?

Hmmmm?

(It’s OK, we are used to the hypocrisy. 🙂 )

Anyway, my UAH cohort and boss John Christy, who does the detailed matching between satellites, is pretty convinced that the RSS data is undergoing spurious cooling because RSS is still using the old NOAA-15 satellite which has a decaying orbit, to which they are then applying a diurnal cycle drift correction based upon a climate model, which does not quite match reality. We have not used NOAA-15 for trend information in years…we use the NASA Aqua AMSU, since that satellite carries extra fuel to maintain a precise orbit.

Of course, this explanation is just our speculation at this point, and more work would need to be done to determine whether this is the case. The RSS folks are our friends, and we both are interested in building the best possible datasets.

But, until the discrepancy is resolved to everyone’s satisfaction, those of you who REALLY REALLY need the global temperature record to show as little warming as possible might want to consider jumping ship, and switch from the UAH to RSS dataset.

It’s OK, we’ve developed thick skin over the years. 🙂 You can always come home later.

Gee, I wonder if some of all that green money will start flowing our way now? I’m not going to hold my breath.

Post-La Nina Warming Continues
The global average lower tropospheric temperature anomaly for June, 2011 increased to +0.31 deg. C (click on the image for a LARGE version):

The Northern Hemisphere, Southern Hemisphere, and and Tropics all experienced temperature anomaly increases in June:

YR MON GLOBAL NH SH TROPICS
2011 1 -0.010 -0.055 +0.036 -0.372
2011 2 -0.020 -0.042 +0.002 -0.348
2011 3 -0.101 -0.073 -0.128 -0.342
2011 4 +0.117 +0.195 +0.039 -0.229
2011 5 +0.133 +0.145 +0.121 -0.043
2011 6 +0.314 +0.377 +0.251 +0.235

I would like to remind everyone that month-to-month changes in global-average tropospheric temperature have a large influence from fluctuations in the average rate of heat transfer from the ocean to the atmosphere. In other words, they are not of radiative origin (e.g. not from greenhouse gases). El Nino/La Nina is probably the most dramatic example of this kind of activity, but there are also “intraseasonal oscillations” in the ocean-atmosphere energy exchanges occurring on an irregular basis, too.

YEARLY temperature averages probably provide a better indication of the existence of radiative forcings on the climate system (whether warming or cooling). Nevertheless, we must remember that even DECADAL time scale (or longer) changes in the ocean circulation could also be involved, which can cause long-term climate change independent of any kind of greenhouse gas (or cosmic ray-induced) radiative forcing. (That last sentence has not been approved by the IPCC…but I don’t really care.)

I’m pretty excited that today (Independence Day, 2011) is the release date for my new book, Fundanomics: The Free Market, Simplified.

Our friend, Josh, did the cover art and it perfectly captures one of the book’s main messages: the greatest prosperity for ALL in a society is achieved when people are free to benefit from their good ideas.

In Chapter 1, A Tale of Two Neanderthals, Borgg and Glogg are the tribe’s firestarters, who get the idea to invent firesticks (matches). This leads to a system of trading with a neighboring tribe which has many great hunters, and as a result the inventors’ tribe never goes hungry again.

But the favored treatment the inventors receive from the tribe’s elders later leads to resentment in the tribe, and people forget how much better off they all are than before — even the poorest among them. Technology and prosperity might change, but human nature does not.

Simply put, a successful economy is just people being allowed to provide as much stuff as possible for each other that is needed and wanted. Economics-wise, everything else is details. When we allow politicians and opportunistic economists to fool us into supporting a variety of technical and murky government “fixes” for the economy, we lose sight of the fundamental motivating force which must be preserved for prosperity to exist: Liberty and the pursuit of happiness.

The main role of the government in the economy is help ensure people play fair…and then get out of the way.

I devote each chapter to a common economic myth.

For example, it’s not about money, which has no inherent value and is simply a convenient means of exchange of goods and services that is more efficient than bartering.

It’s not even about “jobs”, because it makes all the difference what is done in those jobs. Many poor countries have a much lower standard of living than ours, yet fuller employment. If we want full employment, just have half the population dig holes in the ground and the other half can fill them up again. The goal is a higher standard of living…not just “jobs”.

And the desire of some for a “more level playing field” and for “spreading the wealth around” is simply pandering to selfishness and laziness. The truth is that most of the wealth has already been spread around, in the form of a higher standard of living. If we do not allow the few talented and risk-taking people among us have at least the hope of personally benefiting in proportion to their good ideas, then economic progress stops.

The good news is that those few talented people need help, which is where most of the rest of us come in. One person with a new idea for a computer cannot design, manufacture, market, distribute, and sell millions of computers to the rest of society. They need our help, and in the process everyone benefits.

I also examine the role of various government economic programs, most of which end up hurting more than helping. A major reason why the government is so prone to failure is the lack of disincentives against failure in government service. In the real marketplace failures are not rewarded, which helps keep us on the right track to prosperity.

Even the truly needy in our country would be better off if we allowed private charitable organizations, rather than inefficient government bureaucracies to compete for the public’s donations.

I’ve been interested in basic economics for the last 25 years, but frustrated by the technical details (marginal costs, money supply, etc.) that too often scare people away from understanding the most basic forces which propel societies to ever high standards of living. Now, with our country facing tough decisions about our financial future, I decided it was time to stop yelling at the idiots on TV (and giving away all my ideas to talk show hosts) and put the material in a short — less than 100 pages — book that would be approachable by anyone.

I’ll be signing the first 500 copies. The price is $12.95 (including free shipping in the U.S.) You can see all of the chapter first pages at Fundanomics.org. I think this book would be especially valuable to homeschoolers.

(NOTE REGARDING COMMENTS, BELOW: In response to a comment that it was ironic for a scientist whose research is 100% funded by the U.S. Government to be against wasteful government spending, my statement that “I view my job a little like a legislator” has caused quite a stir, especially over at ThinkProgress. This was a rather poor analogy…my point was that a federally-funded person like myself can be against excess government spending, just as some federally-funded legislators are, that’s all. I did not mean to imply I wanted to be a de facto legislator. The context of the full comment, below, should have made that clear.

And, once again, ThinkProgress reveals the hypocrisy of those who think its OK for Al Gore to play a climate scientist, or NASA’s James Hansen to actively campaign for Malthusian energy policy changes and for presidential candidates – in violation of the Hatch Act, as NASA employees are told during their annual ethics training classes.)

NOTE: I am making available the Excel spreadsheet with the simple forcing-feedback-diffusion model so people can experiment with it. The spreadsheet is fairly self-explanatory. THE DIFFUSION COEFFICIENTS CANNOT BE VARIED TOO DRASTICALLY SINCE, WITH A MONTHLY TIME STEP, THE MODEL WILL CREATE UNSTABLE TEMPERATURE OSCILLATIONS. This is a common problem with numerical integration, which could be eliminated by reducing the time step, but I wanted to keep the model file size manageable:
simple-forcing-feedback-ocean-heat-diffusion-model-v1.0
FOLLOWUP NOTE: The above spreadsheet has an error in the equations, which does not change the conclusions, but affects the physical consistency of the calculations. The heat capacity used for water is 10 times too low, and the diffusion coefficients are also 10x too low. Those errors cancel out. I will post a new spreadsheet when I get back to the office, as I am on travel now.

NASA’s James Hansen is probably right about this point: the importance of ocean heat storage to a better understanding of how sensitive the climate system is to our greenhouse gas emissions. The more efficient the oceans are at storing excess heat during warming, the slower will be the surface temperature response of the climate system to an imposed energy imbalance.

Unfortunately, the uncertainties over the rate at which vertical mixing takes place in the ocean allows climate modelers to dismiss a lack of recent warming by simply asserting that the deep oceans must somehow be absorbing the extra heat. Think Trenberth’s “missing heat“. (For a discussion of the complex processes involved in ocean mixing see here.)

Well, maybe what is really missing is the IPCC’s willingness to admit the climate system is simply not as sensitive to our greenhouse gas emissions as they claim it is. Maybe the missing heat is missing because it does not really exist.

This is where we can learn from the 40+ year record of deep ocean temperature changes. Even the 2007 IPCC report admitted the oceans have warmed more slowly at depth than the climate models can explain.

Here I will show quantitatively with a simple forcing-feedback-diffusion model that recent ocean warming is actually consistent with a climate sensitivity which is so low that the IPCC considers it very unlikely.

I will also show how disingenuous the IPCC 2007 report was in presenting the ocean warming evidence to support its view that anthropogenic global warming will be a serious problem.

The Need for Ocean Layers in a Climate Model

Ten years ago Dick Lindzen used a simple climate model to look into the issue of whether the rate of ocean warming at 500 meters depth might help us better understand how sensitive the climate system is. He concluded that it is the surface temperature that is most important, not what happens deeper down in the ocean.

Conceptually, the line of reasoning here would be that the deep ocean can’t warm unless the surface warms first, and the rate of surface warming is largely controlled by atmospheric feedbacks.

In contrast, Roger Pielke, Sr. has always maintained that the heat content of the ocean is what we should be monitoring, not the surface temperature, to better understand how sensitive the climate system is to various forcings.

I WILL say I firmly believe that the surface temperature is THE MOST important temperature in the climate system. This is because (1) the surface is where most sunlight is absorbed, (2) the atmosphere is then convectively coupled to the surface, and (3) the surface and atmosphere together are the ONLY way for the Earth to radiatively cool to space in the face of continuous solar heating.

But, as we will see, the detailed profile of recent warming with depth in the ocean does appear to have additional information about climate sensitivity that is not apparent from surface warming alone.

Explaining the Observed Heating Profile of the Ocean

The following picture is worth a thousand words….but I will try to use fewer than that. First, it is partly a reworking of Fig. 9.15 of the IPCC 2007 report, showing the substantial discrepancy between observed global-average warming of the oceans to 700 meters depth (red curve), and warming estimated from the PCM1 climate model (solid green curve) for the 40-year period ending (I believe) around 2005 (click for the large version).

The green dashed line is my simple model simulation of the PCM1 model’s 40-year warming profile, where I used the GISS yearly global climate forcings to force temperature changes in the 30-layer model, where all layers have adjustable diffusion coefficients.

To get this match to the PCM1 model results, I specified the known PCM1 model net feedback parameter (1.8 Watts per sq. meter per degree), and then adjusted the diffusion coefficients between the simple model’s 50-meter thick layers extending from the surface to a depth of 1,500 meters until I got good agreement between my simple model and the PCM1 model results.

As you can see, my model fit (green dashed line) to the PCM1 model results (green solid line) is pretty good. (Some small amount of warming occurs all the way to the 1,500 m bottom of the model, although it is extremely weak). This demonstrates that the simple model can basically replicate the behavior of the much more complex PCM1 model, albeit for only global-average results.

Next, after I got the simple model to mimic the PCM1 model, then I tried to explain the observations (red) curve by adjusting (1) the model sensitivity (assumed feedback parameter), and (2) the diffusion coefficients, until the model explained the actual observed warming profile. Interestingly, the diffusion coefficients only needed to be changed for the top three ocean layers (down to 200 m depth, which would be about the bottom of the themocline.) The rest of the diffusion coefficients remained the same as in the PCM1-matching simulation.

The result is the blue curve. Significantly, the simple model required a feedback parameter equivalent to a climate sensitivity of only 1.3 deg. C in response to a doubling of CO2. This is well below the range of warming the IPCC claims is most likely (2.5 to 4 deg. C).

What It Means

The bottom line is that 40 years of warming of the 0-700 meter ocean layer has been so modest that, even if we assume it was caused by the GISS forcings (which Hansen believes will eventually cause strong warming) , it corresponds to low climate sensitivity anyway.

In other words, the oceans have not warmed enough to support the IPCC’s predictions of future warming.

The problem in the IPCC models seems to be that they mix excess heat too rapidly from the mixed layer into the deep ocean. This allows the models to retain high climate sensitivity, while limiting the amount of surface warming they produce to match the observed warming to date.

Voila! The models can thus “explain” the surface temperature record AND STILL predict strong warming for the future.

Even though the model I use is admittedly simple, this does not really matter because, in the global average, long-term temperature change is only a function of 3 basic processes:

(1) the strength of the forcing (imposed energy imbalance on the climate system, due to whatever);

(2) the strength of the climate system’s resistance to that forcing (net feedback, which determines climate sensitivity); and,

(3) the rate of ocean mixing (which affects surface temperature, which affects the rate of energy loss to space through feedback processes).

Everything else is details.

How the IPCC Cheated

In the process of this little study I learned that the IPCC’s 2007 presentation of the ocean warming data was, at best, disingenuous. Here’s the original PCM1 panel of Fig. 9.15 from the 2007 IPCC report, from which I took numbers to re-plot on the figure, above:

With this figure, the IPCC was cleverly able to make it LOOK like there was general agreement between their climate models (green shaded area) and observations (red curve), with no less than four ploys:

1) They chose a climate model (PCM1) that is the 2nd LEAST sensitive of the twenty-something climate models they survey. PCM1 produces even less warming than the IPCC’s official projected range of warming from a doubling of CO2.

2) For the PCM1 model results, they presented a rather broad range of warming (green shaded area), meant to represent natural climate variations about the average warming produced by the model. In this way, they were able to get the weak observed warming to better overlap with the model produced warming, suggesting agreement.

3) They omitted the 0 deg. (no temperature change) vertical line from the figure, the presence of which would have visually revealed the significant discrepancy between the PCM1 model results and the observations.

4) They made the ocean depth scale nonlinear, which disproportionally emphasized the agreement in the relatively shallow mixed layer of the ocean, while downplaying the rather large discrepancy deeper down. But there is NO physical reason to make the ocean depth scale nonlinear; the total heat carrying capacity of the ocean varies linearly with depth, not non-linearly.

Conclusion

It appears that the vertical profile of ocean warming could be a key ingredient in getting a better idea of how sensitive the climate system is to our greenhouse gas emissions. The results here suggests the warming has been considerably weaker than what would be expected for a sensitive climate system.

The sensitivity number I estimate — 1.3 deg. C — arguably puts future warming in the realm of “eh, who cares?”

It will be interesting to see how the next IPCC report, now in the early stages of preparation, explains away the increasing discrepancies between their climate models and the observations. Since IPCC outcomes are ultimately driven by desired governmental policies and politicians, rather than science, I’m sure the wordsmithing (and figuresmithing) will be artfully done.

IMPORTANT NOTE: The above model simulation does not account for the possibility that some of recent warming could have been due to one or more natural processes, in which case the diagnosed climate sensitivity would be even lower.

Little Change from Last Month
The global average lower tropospheric temperature anomaly for May, 2011 was just about the same as last month: up slightly to +0.13 deg. C (click on the image for a LARGE version):

Note the tropics continue to warm as La Nina fades:

YR MON GLOBAL NH SH TROPICS
2011 1 -0.010 -0.055 0.036 -0.372
2011 2 -0.020 -0.042 0.002 -0.348
2011 3 -0.101 -0.073 -0.128 -0.342
2011 4 0.117 0.195 0.039 -0.229
2011 5 0.131 0.143 0.120 -0.044

I have also updated the global sea surface temperature (SST) anomalies computed from AMSR-E through yesterday, June 8 6 (note that the base period is different, so the zero line is different than for the lower tropospheric temperature plot above):

I’ve been getting quite a few e-mails about the data on the UAH/NASA Discover website, which gives daily global average deep-layer average temperatures from the AMSU instruments on the NOAA-15 satellite and NASA’s Aqua satellite (as well as sea surface temperatures from the AMSR-E instrument on Aqua).

I have been advising users that, of all the AMSU channels listed there, to trust only the “ch. 5” (mid-tropospheric) temperatures, since all of the other channels at the Discover website are from the NOAA-15 satellite, which has diurnal drift issues. Hopefully later this week we will transition those other channels from NOAA-15 AMSU to Aqua AMSU, so there will be no long-term drift issues from changes in the satellite observation time. The downside will be that all of the data will only be available since Aqua data started flowing, in mid-2002. (Again, the sea surface temperature variations are very accurate, and come from a completely different instrument using different methods).

Since there is considerable interest in the subject of “global cooling”, I thought I would give a preview of some of the Aqua AMSU data for the Northern Hemisphere mid-latitudes, which are shown in the following plot…these are daily running 31-day averages, area averaged over the 30N to 60N latitude band (click on the image for the HUGE version):

There are several observations that can be made from the above plot:

1) there has been no significant long-term LINEAR trend in any of the channels;

2) the mid-troposphere and low-troposphere channels show warming early in the record, then cooling late in the record, as indicated by the 2nd order polynomial curved fits shown;

3) Late summer 2010 was, indeed, quite warm in the NH mid-latitudes.

Note that, in general, ch. 3 should not be relied upon to infer temperature changes, due to it’s sensitivity to a variety of non-temperature effects from the surface. This is especially true over the ocean, where low clouds produce an anomalous warm signature against the radiometrically cold ocean background (I will show an example later).

But since we are looking at a primarily land-covered latitude band, we only need to be concerned with a different non-temperature effect: snowcover. The particularly strong late-period cooling seen in the channel 3 data is likely the result of volume scattering by enhanced snow cover in 2010 and 2011. This is a known issue with passive microwave measurements, and is the basis for snow cover, snow depth, and snow water equivalent retrievals which are used by NASA and NOAA, primarily from the AMSR-E instrument.

Note that the polynomial fits I have applied to the data are only meant to show what happened during this period…I am not suggesting they have any forecast value for the future. I will let others discuss that. Nevertheless, I think the data are useful for getting some idea of what has happened over the last decade in the region where most people live. I think the bottom line from a global warming perspective is that there has been no obvious sign of warming in the last 9 years.

The Arctic Since 2002

Here are the results for the Arctic region, 60N to 85N, but without channel 7 which has a large stratospheric contamination in Arctic air masses:

The tropospheric temperatures show no trend, while the channel 3 data shows an upward trend. In the Arctic, the channel 3 data now also become sensitive to sea ice, which causes a warm signature compared to the cold ocean background. If you don’t believe ice can cause a “warm” signal, check out today’s NOAA-15 channel 3 imagery, which shows warm signatures in the Arctic ocean and in the sea ice area surrounding Antarctica:

This is because microwave radiometers measure brightness temperature, which is a product of the temperature of an object and its microwave emissivity (1 minus its reflectivity).

To make things even more difficult in the interpretation of ch. 3, more snow cover on sea ice will cause an anomalously cold signal, rather than warm signal, just as it does over land — unless the snow is melting, in which case it switches to an anomalously warm signal. The point is that it is difficult to interpret what the upward trend in the Arctic channel 3 data is due to. Stratifying the data by season would probably give some insight.

For now, though, I think the tropospheric (AMSU ch. 5) data are pretty clear: there are no signs of warming in the last nine years in those regions where the strongest warming in the last 30 to 40 years has occurred, that is, in the Northern Hemisphere mid- and high-latitudes. And, there might even be signs of recent cooling over the last few years in the mid-latitudes, but whether this will persist is anyone’s guess.

This is a continuation of the theme of my last 2 blog posts, dealing with the fact that a greater number of strong to violent tornadoes occur in unusually COOL years, not warm years. As a quick review, the following plot clearly shows this:

This refutes the claim of a few of the global-warming-causes-everything pundits who assume that more tornadoes MUST be Gaia’s way of punishing us for providing her with more plant food (carbon dioxide).

There are 2 main reasons why stronger tornadoes are usually associated with unseasonably cool conditions, and why there has been a decrease in strong tornadoes during a period of average warming:

1) The missing ingredient for tornado formation is not a lack of warm moist air, but a lack of synoptic (large) scale wind shear.

2) At least until recently, the positive phase of the Pacific Decadal Oscillation (PDO) which has predominated since the late 1970’s has suppressed strong tornado activity.

Let’s look at these two issues.

THE MISSING INGREDIENT: WHY 99% OF THUNDERSTORMS DO NOT PRODUCE TORNADOES
I’ve been trying to think of why some might assume a warmer climate would produce more tornadoes, and I suspect the main reason is that tornadoes are produced by thunderstorms, and thunderstorms require warm, moist air for fuel. Ergo, warming should lead to more tornadoes.

But even here in the U.S., which is Tornado Central for the world, 99 out of 100 thunderstorms do NOT produce tornadoes. Why is this?

The missing ingredient is wind shear, specifically an increase in wind speed with height, and a change in wind direction with height.

So what causes this type of wind shear? It occurs in advance of low pressure areas that form along the boundary between warm and cool air masses. So, anything that increases the frequency of these conditions could lead to more tornadoes.

The classic tornado situation involves a longwave low pressure trough over the western U.S., caused by an unusually cool atmosphere in the lowest 5-10 miles of the atmosphere. This has remained true for this year’s enhanced tornado activity. I suspect the persisting mountain snowpack from a very snowy winter has played a role in this.

The following plot of the U.S. shows correlation between the annual number of strong (F3) to violent (F5) warm-season (March through August) tornadoes and regional temperature departures from normal.

First, we see in ALL regions a negative correlation between temperature and the total number of strong to violent tornadoes in the U.S.

This is basically the regional breakdown of the U.S.-average relationship shown in the first plot above, which demonstrates that the increased frequency of strong tornadoes with cooler, not warmer, temperatures. This is no doubt an indirect effect, mirroring the more frequent intrusion of unseasonably cool air masses, which cause the wind shear conditions more conducive to tornado formation.

Secondly, we see that the relationship is the strongest over the western U.S. This pattern is consistent with a longwave low pressure trough over the western U.S., which favors more tornado formation for the reasons outlined above.

So, the next question is, what favors unseasonably cool weather over the West during tornado season? This is where the Pacific Decadal Oscillation (PDO) comes in.

THE TORNADO-PDO CONNECTION

The following plot shows that the positive phase of the PDO, which predominated roughly from the late 1970s up until just a few years ago, was also a period of depressed strong tornado activity in the U.S. Conversely, more strong tornadoes seem to be associated with the negative phase of the PDO.

The reason why this is the case appears to be that the negative phase of the PDO also favors longwave low pressure troughs over the U.S. The following regional correlation plot between the negative of the PDO and temperature shows this quite clearly, with a very cool West and somewhat warm South and Southeast:

Of course, the negative PDO/more tornadoes connection will not necessarily hold every year, just as the La Nina/more hurricanes connection does not hold every year.

But the major point here is that it is NOT the lack of warm air that inhibits tornado formation during tornado season…it is the lack of sufficient wind shear to cause thunderstorms to rotate. And it is the persistence of unseasonably cool air, over most of the U.S., that produces these wind shear conditions.

After the catastrophic death toll from the Joplin, MO tornado, which now stands at 117, we are no doubt in for more claims — dutifully amplified by the news media — that ‘climate change’ must somehow be at least partly responsible for this Spring’s wild weather.

And, to some extent, I’m inclined to agree. That is, if they are talking about the natural cooling effects of La Nina and the tendency for tornado outbreaks to be associated with cooler (not warmer) climate conditions.

But I suspect that’s just the opposite of the message they will be preaching.

So, let’s look at 2 statistics: 1) the number of strong to violent tornadoes, and 2) the deadliest tornadoes in U.S. history

THE DOWNTURN IN STRONG TORNADOES WITH WARMING
The bottom panel of following graphic shows what most meteorologists already know: there has been a downward trend in strong (F3) to violent (F5) tornadoes in the U.S. since statistics began in the 1950s. As seen in the top panel, this has also been a period of general warming. For those statistics buffs, the correlation coefficient is -0.31. Obviously, the conclusion should be that warming causes fewer strong tornadoes, not more. (Or, maybe a lack of tornadoes causes global warming!)

Even when I de-trend the data, the remaining year-to-year variability still has a negative correlation: -0.17, so the conclusion is the same for the long-term trend AND the year-to-year variations in strong tornado activity.

So how does anyone get away with claiming that global warming is contributing to more tornadoes?

Well, maybe because there has indeed been an UPward trend in total reported tornado sightings in the U.S. during the same period of time. But this is only because (as I mention in my first book, Climate Confusion) there are so many more people now spread across the fruited plain, with so many video cameras, and now so many Doppler radars are measuring the wind rotation associated with tornadoes, that it is difficult for any to go unnoticed by at least someone.

THE DOWNTURN IN TORNADO DEATHS
Also, if you hear any news reports that more deaths due to tornadoes are due to global warming, this is also a bogus claim. Here’s a little quiz for you:

QUESTION: Out of the 25 deadliest tornadoes in U.S history, how many would you guess have occurred in the last 50 years (since 1960)?

…wait for it….

ANSWER: Up until a month ago, NONE of them.

On April 27, one of the Alabama tornadoes made the bottom of the list at #25, but now has been knocked back off the list because Sunday’s tornado in Joplin will be ranked #8.

So, to the extent that you hear reports of ANYONE connecting tornadoes to global warming, I think it proves only one thing: The belief that global warming is causing most of the world’s ills is so pervasive that the facts simply do not matter anymore.