The most recent El Nino event is rapidly dying, as seen in the following plot of sea surface temperature (SST) variations averaged over the Nino3.4 region (5N to 5S, 120W to 170W) as measured by the AMSR-E instrument on NASA’s Aqua satellite during its period of record, 2 June 2002 through yesterday, 13 May 2010:

The 60-day cooling rate as of yesterday was the strongest seen yet in the 8 year period of record for the Nino3.4 region.

A similar plot of the Southern Oscillation Index (SOI) data, based upon the sea level air pressure difference between Tahiti and Darwin is consistent with the SST cooling, showing an increase in the pressure gradient across the tropical South Pacific, which portends increasing trade winds and cooling of the ocean surface:

A plot of these two time series against one another (next plot) reveals that the most recent SSTs are unusually warm for the 60-day average SOI value:

There are at least three ways to interpret this excursion from the average relationship seen in the plot. One is that longer-term warming, whether natural or anthropogenic, has raised the temperature ‘baseline’ about which the El Nino/La Nina events oscillate.

A second possibility is that we are in for continued rapid cooling in the Pacific as the SSTs fall to values more consistent with the SOI index.

A third is that the current excursion toward La Nina territory is going to reverse, and SOI values will decrease to more neutral conditions, while SSTs remain relatively high.

As is always the case, all we can do is sit back and watch.

Since I keep getting requests for the data from which I do my analyses, I’ve decided to provide the main dataset I use here, in an Excel spreadsheet. The comments at the top of the spreadsheet are pretty self-explanatory and include links to the original data. After you click on and open the file with Excel, save it to your computer so you can analyze the data.

What’s In the File, Kenneth?

From original satellite data online at 2 sources, I have calculated daily global-average anomalies (departures from the average annual cycle) in (1) total-sky emitted longwave (LW, or infrared) radiative flux; (2) total-sky reflected shortwave (SW, or solar) radiative flux, and (3) UAH tropospheric temperatures (TMT).

The original radiative flux data that I computed these anomalies from are the Terra satellite CERES Flight Model 1 (FM1) instrument-based ES4 (ERBE-like) daily global gridpoint datasets, available here. These are large files in a binary format, and are not for the weak of heart.

The original UAH TMT temperature data come from here.

All of the original data were area-averaged over the Earth for each day during the 9.5 year Terra CERES period of record, March 2000 through September 2009. An average annual cycle was computed, filtered with a +/- 10 day smoother applied every day, and then anomalies were computed by subtracting the smoothed average annual cycle values from the original data. I program these calculations in Fortran-95, put the data in an Excel spreadsheet, then do all future calculations and graphical plots in Excel.

And remember, folks…“If you torture the data long enough, it will confess.”

Edited 1:35 p.m. CDT 5/13/10: Trivia question added, at the end of the post.

Despite its relative simplicity, I continue to find myself trying to explain to experts and lay persons alike how scientists made the Great Global Warming Blunder when it comes to predictions of global warming.

On the bright side, this morning I received an e-mail from a chemist who looked at the math of the problem after reading my new book, and then came to the understanding on his own. And that’s great!

For the most part, though, the climate community continues to suffer from a mental block when it comes to the true role of clouds in global warming. All climate models now change clouds with CO2 warming in ways that amplify that warming, some by a catastrophic amount.

As my latest book describes, I contend that they have been fooled by Mother Nature, and that in fact warming alters clouds in ways that mitigate – not amplify — the small amount of direct warming caused by increasing atmospheric CO2.

The difference between clouds magnifying versus mitigating warming could be the difference between global warming being little more than an academic curiosity…or a disaster for life on Earth.

So, once again I find myself trying to explain a concept that I find the public understands better than the climate experts do: when it comes to clouds and temperature, the direction of causation really does matter.

Why Are There Fewer Clouds when it is Warm?

The “scientific consensus” has been that, because unusually warm conditions are observed to be accompanied by less cloud cover, warming obviously causes cloud cover to decrease. This would be bad news, since decreasing cloud cover in response to warming would let more sunlight in, and amplify the initial warming. That’s called positive cloud feedback.

But what they have difficulty understanding is that causation in the opposite direction (cloud changes causing temperature changes) gives the ILLUSION of positive cloud feedback. It turns out that, when less cloud cover causes warmer temperatures, the cloud feedback in response to that warming is almost totally obscured.

Believe it, the experts have not accounted for this effect. I find it bizarre that most are not even aware it is an issue! As far as I know, I am the only one actively researching the issue.

As a result, the experts have fooled themselves into believing cloud feedbacks are positive. We have demonstrated theoretically in our new paper now accepted for publication in JGR that, even if strong negative cloud feedback exists, cloud changes causing temperature change will make it LOOK like positive cloud feedback.

And this indeed happens in the real climate system. The only time cloud feedback can be clearly seen in the real climate system is when temperature changes are caused by something other than clouds. And in those cases, we find that the net feedback is strongly negative (around 6 Watts per sq. meter of extra energy lost by the Earth per deg. C of global-average warming).

Unfortunately, those events only occur on relatively short climate time scales: 1 month or so. Whether this negative feedback also exists for long-term climate warming is less certain.

Do Climate Models Agree With Satellite Observations of Clouds and Temperature?

The fact that all the climate models which produce substantial global warming also approximate what we measure from satellites is NOT a validation of the feedbacks in those models. So far, after analyzing thousands of years of climate model runs, I have found no convincing way to validate the climate models’ long-term feedbacks with short-term (approx. 10 years or so) satellite observations. The reason is the same: all models have cloud variations causing temperature variations, which then obscures the feedback we are trying to measure.

But there’s another test that could be made. The modelers’ case would be stronger if they could demonstrate that 20 additional climate models, all with various amounts of negative – rather than positive — cloud feedback, are less consistent with our satellite observations than the current crop of models, all of which had positive cloud feedback.

I suspect they do not spend much time on that possibility. A climate model that does not produce much climate change is going to have difficult time getting continued funding for its support.

Trivia Question to Illustrate the Point: Assume continually increasing CO2 in the atmosphere is the only source of climate variability, and we experience continuous slow warming as a result. Will the outgoing longwave radiation (OLR, or infrared) being emitted by the Earth increase…or decrease…during this process?
ANSWER: If warming is the result of increasing CO2 in the atmosphere, then the outgoing longwave radiation (OLR) from the Earth will DECREASE over time. As scientists already know, it is this decrease in OLR that causes the warming in the first place. But because the climate system cannot warm instantly in response (there is a time lag due to the heat capacity of land, ocean, and atmosphere), the increased OLR from warming can never fully make up for the decrease in OLR causing the warming. That warming-induced increase represents the FEEDBACK RESPONSE. But it is forever more than offset by the FORCING from increasing CO2. Now, If we know the time-history of the forcing, it can be subtracted from the OLR to get the feedback. Indeed, this is how feedbacks are diagnosed from climate model experiments involving transient CO2 forcing. The “blunder” I talk about refers to the fact that climate researchers have not accounted for natural sources of radiative forcing (cloud variations) in their attempts to diagnose feedback in the real climate system.

Technical Note: We have found from modeling studies that if the natural cloud variations were truly random in time, the error in diagnosed feedback would be random, not biased toward positive feedback, and would average out to near zero in the long term. But in the real climate system, these cloud variations have preferred time scales….in other words, they have some degree of autocorrelation in time. When that happens, there ends up being a bias in the direction of positive feedback.

Arguably the single most important scientific issue – and unresolved question – in the global warming debate is climate sensitivity. Will increasing carbon dioxide cause warming that is so small that it can be safely ignored (low climate sensitivity)? Or will it cause a global warming Armageddon (high climate sensitivity)?

The answer depends upon the net radiative feedback: the rate at which the Earth loses extra radiant energy with warming. Climate sensitivity is mostly determined by changes in clouds and water vapor in response to the small, direct warming influence from (for instance) increasing carbon dioxide concentrations.

The net radiative feedback can be estimated from global, satellite-based measurements of natural climate variations in (1) Earth’s radiation budget, and (2) tropospheric temperatures.

These feedback estimates have been mostly constrained by the availability of the first measurement: the best calibrated radiation budget data comes from the NASA CERES instruments, with data now available for 9.5 years from the Terra satellite, and 7 years from the Aqua satellite. Both datasets now extend through September of 2009.

I’ve been slicing and dicing the data different ways, and here I will present 7 years of results for the global (60N to 60S) oceans from NASA’s Aqua satellite. The following plot shows 7 years of monthly variations in the Earth’s net radiation (reflected solar shortwave [SW] plus emitted infrared longwave [LW]) compared to similarly averaged tropospheric temperature from AMSU channel 5.

Simple linear regression yields a net feedback factor of 5.8 Watts per sq. meter per degree C. If this was the feedback operating with global warming, then it would amount to only 0.6 deg. C of human-caused warming by late in this century. (Use of sea surface temperatures instead of tropospheric temperatures yields a value of over 11).

Since we have already experienced 0.6 deg. C in the last 100 years, it would also mean that most of our current global warmth is natural, not anthropogenic.

But, as we show in our new paper (in press) in the Journal of Geophysical Research, these feedbacks can not be estimated through simple linear regression on satellite data, which will almost always result in an underestimate of the net feedback, and thus an overestimate of climate sensitivity.

Without going into the detailed justification, we have found that the most robust method for feedback estimation is to compute the month-to-month slopes (seen as the line segments in the above graph), and sort them from the largest 1-month temperature changes to the smallest (ignoring the distinction between warming and cooling).

The following plot shows, from left to right, the cumulative average line slope from the largest temperature changes to the smaller ones. This average is seen to be close to 10 for the largest month-to-month temperature changes, then settling to a value around 6 after averaging of many months together. (Note that the full period of record is not used: only monthly temperature changes greater than 0.03 deg. C were included. Also, it is mostly coincidence that the two methods give about the same value.)

A net feedback of 6 operating on the warming caused by a doubling of atmospheric CO2 late in this century would correspond to only about 0.5 deg. C of warming. This is well below the 3.0 deg. C best estimate of the IPCC, and even below the lower limit of 1.5 deg. C of warming that the IPCC claims to be 90% certain of.

How Does this Compare to the IPCC Climate Models?

In comparison, we find that none of the 17 IPCC climate models (those that have sufficient data to do the same calculations) exhibit this level of negative feedback when similar statistics are computed from output of either their 20th Century simulations, or their increasing-CO2 simulations. Those model-based values range from around 2 to a little over 4.

These results suggest that the sensitivity of the real climate system is less than that exhibited by ANY of the IPCC climate models. This will end up being a serious problem for global warming predictions. You see, while modelers claim that the models do a reasonably good job of reproducing the average behavior of the climate system, it isn’t the average behavior we are interested in. It is how the average behavior will CHANGE.

And the above results show that not one of the IPCC climate models behaves like the real climate system does when it comes to feedbacks during interannual climate variations…and feedbacks are what determine how serious manmade global warming will be.

YR MON GLOBE NH SH TROPICS
2009 1 0.252 0.472 0.031 -0.065
2009 2 0.247 0.569 -0.074 -0.044
2009 3 0.191 0.326 0.056 -0.158
2009 4 0.162 0.310 0.013 0.012
2009 5 0.140 0.160 0.120 -0.057
2009 6 0.044 -0.011 0.100 0.112
2009 7 0.429 0.194 0.665 0.507
2009 8 0.242 0.229 0.254 0.407
2009 9 0.504 0.590 0.417 0.592
2009 10 0.361 0.335 0.387 0.381
2009 11 0.479 0.458 0.536 0.478
2009 12 0.283 0.350 0.215 0.500
2010 1 0.649 0.861 0.437 0.684
2010 2 0.603 0.725 0.482 0.792
2010 3 0.653 0.853 0.454 0.726
2010 4 0.501 0.796 0.207 0.634

The global-average lower tropospheric temperature continues warm: +0.50 deg. C for April, 2010, although it is 0.15 deg. C cooler than last month. The linear trend since 1979 is now +0.14 deg. C per decade.

Arctic temps (not shown) continued a 5-month string of much above normal temps (similar to Nov 05 to Mar 06) as the tropics showed signs of retreating from the current El Nino event. Antarctic temperatures were cooler than the long term average. Through the first 120 days of 1998 versus 2010, the average anomaly was +0.655 in 1998, and +0.602 in 2010. These values are within the margin of error in terms of their difference, so the recent global tropospheric warmth associated with the current El Nino has been about the same as that during the peak warmth of the 1997-98 El Nino.

As a reminder, two months ago we changed to Version 5.3 of our dataset, which accounts for the mismatch between the average seasonal cycle produced by the older MSU and the newer AMSU instruments. This affects the value of the individual monthly departures, but does not affect the year to year variations, and thus the overall trend remains the same as in Version 5.2. ALSO…we have added the NOAA-18 AMSU to the data processing in v5.3, which provides data since June of 2005. The local observation time of NOAA-18 (now close to 2 p.m., ascending node) is similar to that of NASA’s Aqua satellite (about 1:30 p.m.). The temperature anomalies listed above have changed somewhat as a result of adding NOAA-18.

[NOTE: These satellite measurements are not calibrated to surface thermometer data in any way, but instead use on-board redundant precision platinum resistance thermometers (PRTs) carried on the satellite radiometers. The PRT’s are individually calibrated in a laboratory before being installed in the instruments.]

While investigating cloud feedbacks over the ocean with the CERES Earth radiation budget instruments, I thought I would take a quick look to see how lower atmospheric temperature variations over land and ocean compare to each other. Part of my interest was the recent cold winter over the U.S. and Europe, which has seemed strange to some since our global-average temperatures are running quite warm lately.

The following plot shows tropospheric temperature variations over land versus ocean since mid-2002 as measured by the AMSU instrument on the Aqua satellite. I’ve restricted the averaging between 60N and 60S latitudes, which is 86.6% of the surface area of the Earth. These are daily running 31-day average anomalies (departures from the average seasonal cycle).

In the big picture, I was a little surprised to see that, on average, there is essentially no time lag between the land and ocean temperature variations. The correlation between the two curves is +0.63 at zero days time lag. I would have expected a tendency for oceanic changes to precede land changes, since we usually think of oceanic warming or cooling events driving land areas more than vice versa.

We also see that the recent cold winter over the U.S. and Europe was not reflective of global land areas, which is not that surprising since those regions represent only about 5% of the surface area of the Earth.

I have been particularly interested in the cause of the global cooling event of 2007-08, which I have circled in the plot above. I had assumed that this was primarily an oceanic phenomenon, but as can be seen, land areas were similarly affected.

The difference between the land and ocean curves is shown in the next plot, along with a second order polynomial fit to the data. There seems to be a low-frequency change in this relationship, with several years of land-warmer-than-ocean now switching to ocean-warmer-than-land. I have no obvious explanation to offer for this.

And if you are wondering just how real the temperature fluctuations shown above are, I also computed the oceanic atmospheric temperature variations (blue curve, 1st graph) from the AMSU flying on a totally different satellite — NOAA-15 — and found that the curves from Aqua and NOAA-15 were virtually indistinguishable.

[The reason why the above analysis is restricted to the period since 2002 is that Aqua is the first orbit-maintained satellite. Previous satellites had decaying orbits, which caused a change in the local observation time over the years which resulted in a long-term drift in over-land temperatures due to the strong day-night cycle in temperature.]

The plot that is included in Kevin Trenberth’s most recent post on Roger Pielke, Sr.’s blog actually proves the point I have been making: The trend in the imbalance in the Earth’s radiation budget as measured by the CERES instrument of NASA’s Terra satellite that has been building since about 2000 is primarily in the reflected solar (shortwave, or SW, or RSW) component, not the emitted infrared (longwave, or LW) component.

To demonstrate that, the following is the chart from Trenberth’s most recent post, upon which I have overlaid the 2000-2008 trend lines from MY plots of CERES data, and which we have computed from the official NASA-blessed ES-4 Edition 2 global gridpoint dataset.

The plots I provided in my previous post have greater resolution in the vertical axis.

For those who are following this mini-debate, please see that post, not Roger’s version of my post, which was a draft version of my post and was incomplete.

And, again I point out, the most recent dip in the LW curve (above) is consistent with cooling of the global average troposphere seen in our plot of AMSU5 data. UPDATE, 1:45 p.m. CDT: small correction to above figure.

Kevin Trenberth has a response over at Roger Pielke, Sr’s blog to my comments about his and John Fasullo’s recent Science Perspectives article about “missing energy” in the climate system.

Trenberth and Fasullo discuss in their original Science Perspectives article the observational evidence for missing energy being lost somewhere in the climate system, based upon satellite radiation budget measurements of the Earth which suggest that extra energy has been accumulating in the climate system for about the last 10 years, but with no appreciable warming of the upper ocean and atmosphere to accompany it as would be expected.

I posted some comments here about my view that the missing energy does not really exist. I also pointed out that they failed to mention that the missing energy over the period since about 2000 was in the reflected sunlight component, not the emitted infrared. This now makes two “missing energy” sources…the other one being the lack of expected warming from increasing carbon dioxide concentrations, which causes a steadily increasing global radiative imbalance in the infrared.

So, Kevin’s response on Pielke Sr’s blog begins with, “I saw Roy Spencer’s comment for the first time and it is not correct”, but I see no specific refutation of any of the points I made.

To further support my comments, here are the global-average CERES ERBE-like ES-4 Edition 2 radiative flux anomalies for reflected solar (1st graph) and outgoing longwave radiation (OLR, 2nd graph) for the period 2000 through 2008…these are daily running 91-day averages:


Clearly, the long-term “trend” during 2000 through 2008 was in the reflected solar (SW), not OLR (LW).

What is important for global warming or cooling is the sum of the global SW and LW, shown in the following graph (note I have flipped the y-axis, to correspond to the sense of the plot Kevin and John Fasullo showed in their Science Perspectives article):

But rather that address my points, Kevin instead focuses on the anomalous drop in OLR around the beginning of 2008. While he makes it sound like this event is currently inexplicable, he should recognize that there is indeed a very simple explanation for it: global-average temperatures were quite low at that time, as seen in the next graph:

After all, OLR is THERMALLY emitted radiation, and so it depends upon temperature. What would be the expected OLR response to such a drop in temperature? Well, we know that the expected change in OLR resulting from a 1 deg. C decrease in global average temperature should be a drop of about 3.2 Watts per sq. meter per degree C. The above temperature plot shows a fall of about 0.4 deg. C from early 2007 to early 2008, which should then cause a reduction in OLR by about (0.4 x 3.2 ), or about 1.3 Watts per sq. meter.

And indeed, as seen in the LW plot above, there was a fall of about 1 Watt per sq. meter in the LW (OLR) during the same time. To the extent that the drop in OLR with cooling was not quite as much as might be expected could be due to a small positive feedback in high clouds and/or water vapor. These are just rough estimates, anyway…the point is, one must take into account temperature changes when diagnosing the reasons for changes in OLR. This fact is seldom mentioned.

In our new paper accepted for publication in JGR, we show that the 2007-08 cooling event Kevin Trenberth discussed was due to a temporary increase in low cloud cover, evidence of which is clearly seen in the form of a large spike in reflected sunlight in the first plot, above. There is a lead-lag relationship between the two which clearly indicates the primary direction of causation.

And, as discussed in our JGR paper, this fact makes the diagnosis of feedback from natural climate variations much more difficult than previous researchers have been led to believe.

In my new book, The Great Global Warming Blunder: How Mother Nature Fooled the World’s Top Climate Scientists, I show the results of experiments with a simple climate model that runs in an Excel spreadsheet. The model is meant to illustrate how natural monthly-to-yearly variability in global (a) cloud cover and (b) surface evaporation can affect our satellite observations of (1) temperature and (2) total radiative flux.

Those last two measurements are what are traditionally used to determine the temperature “sensitivity” of our climate system. By specifying that sensitivity (with a total feedback parameter) in the model, one can see how an analysis of simulated satellite data will yield observations that routinely suggest a more sensitive climate system (lower feedback parameter) than was actually specified in the model run.

And if our climate system generates the illusion that it is sensitive, climate modelers will develop models that are also sensitive, and the more sensitive the climate model, the more global warming it will predict from adding greenhouse gasses to the atmosphere.

Here is the model to download. It is currently set up to do a 100 50 year simulation at monthly daily time resolution. Here are two example plots from the model, run with a 50 meter deep ocean and a feedback parameter of 3 Watts per sq. meter per deg. C…but the output of the model suggests a feedback of 2.08, rather than 3:

The 4 basic inputs to the model are in large blue font, all of which are adjustable. These include (in no particular order):

1) Bulk heat capacity of the system, specified as an equivalent ocean water depth (nominally 50 meters deep).
2) Net feedback parameter (controlling the model’s temperature sensitivity to energy imbalances)
3) Radiative forcing (e.g. from natural variations in cloud cover)
4) Non-radiative forcing (from fluctuations in convective heat transfer between the surface and atmosphere)

Those last 2 heat flux forcings are driven by a random number generator. The radiative forcing also has a low-pass filter applied to the monthly random numbers, which seems to mimic the satellite observations pretty well.

In addition to these 4 inputs, one can also “turn on” carbon dioxide forcing, which will lead to a long-term warming trend in the model at a rate that depends mainly upon the specified feedback parameter and ocean depth.

NOTES:
(1) After running the model many times, eventually the memory cache used by Excel gets filled up (I think), and garbage numbers start to appear. Just close out Excel and re-open it to fix this.
(2) A new model run is automatically made any time ANY entry in the spreadsheet is changed, including when you do a file “Save”. So if you want to show someone the results of a specific model run, you are going to have to copy and “special paste” the values somewhere else, and then make a new graphs from those.

A recent short article by Kevin Trenberth and John Fasullo discussed the fact that our satellites that monitor (1) the total amount of sunlight absorbed by the Earth, and (2) the total infrared (IR) energy given off by the Earth, have suggested that these flows of energy in and out of the Earth’s climate system have been increasingly out of balance in the last 10 years, with an increase in absorbed energy by as much as 1 Watt per sq. meter.

Even though this 1 Watt per sq. meter is small compared to the average flows of energy — which are estimated to be somewhere around 235 to 240 Watts per sq. meter — it represents a substantial heating effect.

The problem is that the oceans have not been warming in response to this imbalance. Trenberth and Fasullo seem to lean toward the possibility that this heat is “missing” somewhere, maybe temporarily trapped in the deep ocean. Roger Pielke, Sr., has voiced his opinion that the heat could not have magically avoided the ocean temperature sensors, both in space and floating around the world’s oceans, which monitor ocean surface and upper layer temperatures.

Since I’ve received a number of requests to give my opinion, I decided I would weigh in on the subject. While I agree that there is a mystery here, there are a few points and opinions I’d like to share.

1) THE MISSING ENERGY IS IN THE SOLAR, NOT THE INFRARED
Trenberth and Fasullo don’t highlight the fact that the “missing” energy is not in the infrared, which is where manmade global warming allegedly originates, but in the reflected solar component. The infrared component has essentially no trend between March 2000 and December 2007 (the last CERES Earth radiation budget data I have analyzed).

This suggests a small decrease in low or mid-level cloud cover, letting more sunlight in. The fact that the extra energy is not showing up as a temperature increase in the ocean makes me suspect the measurements themselves. If there is a problem with the Earth radiation budget measurements, then obviously there is no missing energy.

2) MAYBE THE DISCREPANCY WAS ACTUALLY BEFORE 2000
Trenberth and Fasullo correctly point out that the absolute accuracy of these radiation budget instruments is not good enough to measure very small radiation imbalances…just the CHANGE in that imbalance over time. Well then maybe it was the period BEFORE 2000 where there was an imbalance, with extra energy being lost by the Earth, but no cooling, and NOW the solar and infrared flows are once again in balance. Just a thought.

3) OCEAN TEMPERATURES ARE MUCH EASIER TO MEASURE THAN THE EARTH’S RADIATION BUDGET
Trenberth and Fasullo briefly acknowledge that there might be measurement errors involved here, and I would argue that this is much more likely in the Earth radiation budget measurements than in the ocean temperature measurements. The amount of solar energy the Earth absorbs is particularly difficult to measure because a monitoring satellite is only a single point in space, whereas the total amount of sunlight being reflected off clouds goes in all different directions.

Because of this complication, many detailed calculations must be made by the dataset developers to estimate the energy flows at all angles, based upon years of accumulated statistics with radiation budget instruments that measure some of the clouds at different angles. I think the dataset developers are doing the best they can with the available information, but what we are asking the data to reveal to us is a very small signal.

4) “YOU’VE LOST ANOTHER SUBMARINE”?
We have already been dealing with some missing global warming in the last 10 to 30 years, since 95% of the climate models suggest our carbon dioxide emissions should have caused more global warming than what has been observed — and that is due to an infrared effect. Now, we are told that there is missing SOLAR energy, too?

This reminds me of the 1990 movie, The Hunt for Red October. After an entire movie dealing with a missing experimental Soviet submarine, the end of the movie shows the Soviet Ambassador asking the U.S. to help find…what!?…ANOTHER missing submarine? It was a funny line.

I’m sorry, but at some point we need to ask whether all of this missing warming and energy are missing because they really do not exist. This is Roger Pielke, Sr.’s opinion, and at this point it is mine as well. Only time will tell.