I’m glad to see this news report today, and I’ve been saying the same thing ever since the whole Attorneys General flap started:

“If Democratic attorneys general can pursue climate change skeptics for fraud, then also at risk of prosecution are climate alarmists whose predictions of global doom have failed to materialize.

The cuts both ways argument was among those raised by 13 Republican attorneys general in a letter urging their Democratic counterparts to stop using their law enforcement power against fossil fuel companies and others that challenge the climate change catastrophe narrative.

Consider carefully the legal precedent and threat to free speech, said the state prosecutors in their letter this week, headed by Alabama Attorney General Luther Strange.

If it is possible to minimize the risks of climate change, then the same goes for exaggeration, said the letter. If minimization is fraud, exaggeration is fraud.

The popular comparison of legitimate skepticism and uncertainty over the causes of climate change with hiding the risks of tobacco use is just so silly. No one can be demonstrated to have been harmed by manmade climate change, partly because there is no way to establish causation, there has been no demonstrable increase in severe weather events, etc.

Besides, can any investor in Exxon Mobil really claim they never heard of the possible risks of anthropogenic climate change? That’s all we’ve been hearing in the news for the last 30 years.

“But Dr. Spencer! It can be demonstrated that flash floods have killed more and more people in their cars over the last 150 years!” Sheesh. If you really think this is a valid argument, I can’t help you.

In fact, to the extent that recent climate change has been partly caused by humans (which I do believe…even though I cannot prove it), the positive externalities have likely outweighed the negative externalities (cold weather still kills more people than hot, crop productivity goes up with increasing CO2).

That is in addition to the fact that we have no large-scale replacements for fossil fuels yet, and to the extent we force expensive renewables on people, we make poverty worse. And poverty does kill.

Environmental groups that have pressured decision makers into bed with them on the issue should be held accountable for their deceit.

My vacation this week was interrupted this morning by some hate e-mail…apparently, the recent Peabody Coal bankruptcy produced paperwork that listed everyone that was ever paid by Peabody for anything.

As far as I can recall, I am quite sure that Peabody has paid me for two things. Neither was payment for climate research, but just for presentation of information and opinions.

First was a presentation I gave to their board of directors, maybe 2-3 years ago, for which I charged my usual speaking fee plus travel expenses to Washington D.C. As I recall, my talk was back-to-back with one by a representative from Natural Resources Defense Council.

The second instance was hearing testimony I was asked to write related to a legal case I’ve already blogged about, here. That took quite a bit of time, requiring rebuttal and surrebuttal testimonies, then travel to St. Paul, MN to testify in front of a judge. I don’t do such things for free, and I always make sure I do it on vacation time from my day job so I can’t be accused of double-dipping.

If people are that concerned about not having any financial relationships with fossil fuel interests, I suggest they stop using electricity and most of our modern conveniences. I would have accepted payment from Satanists for Sane Energy Policy for my opinions if it would help prevent energy poverty and the resulting preventable deaths.

BACKGROUND

It’s been quite a while since I’ve discussed why the diagnosis of feedbacks in the climate system (and thus climate sensitivity) from observations is biased toward high climate sensitivity. It’s a controversial topic, one which we have a few published papers on, yet one I am more firmly convinced about than any other climate research I have ever published.

I’m pretty convinced that most of our detractors on the subject don’t even know what we are talking about. The refutations against our work have been a mixture of strawman arguments, red herrings, silliness, and deception.

To put it simply, if temperature change causes a change in the top-of-atmosphere radiative balance, then you can (with some assumptions regarding time lags) diagnose feedbacks by simply regressing the radiative variations against the temperature variations. BUT if it is instead a time-varying radiative imbalance causing a surface temperature change (causation reversed), then you cannot diagnose feedbacks.

If you try, then you will usually diagnose positive feedback, even if strongly negative feedback exists. Our most complete analysis of the effect was described here.

In general, both directions of causation are operating in the climate system. People like Andy Dessler will claim that ALL radiation changes are ultimately caused by temperature change, maybe at some earlier time, and so he thinks you can diagnose feedback.

But I totally reject that…there are many reasons why (for example) clouds (and thus albedo) can change that are not caused by temperature.

And if Dr. Dessler really believes it, why does he not include a time lag in his feedback diagnoses? (It usually take time — sometimes months — for the atmospheric response to a surface temperature change to fully develop). When you do that, the diagnosed feedback parameter almost always shifts in the direction of low climate sensitivity (Dick Lindzen has also published work on this issue).

ANOTHER REASON WHY FEEDBACKS CAN BE BIASED POSITIVE

For years, I’ve been mulling another reason (other than the radiation-causing-temperature change one) for diagnosed feedbacks to be biased positive. It would occur if different sources of climate variation have different feedbacks.

When feedbacks are strongly negative, then temperature changes will be minimized, because that’s what negative feedback does — it damps temperature change.

But when feedbacks are positive, the temperature changes are allowed to grow.

So, the BIG temperature changes and their associated radiation changes during positive feedback events will dominate our observations of the climate system, while the small temperature changes during negative feedback events will be less noticeable.

The net result will be an average diagnosed feedback that is biased positive, that is, toward high climate sensitivity, because we are really only analyzing the big climate events that were allowed to grow due to positive feedbacks.

1D FORCING-FEEDBACK MODEL TEST

One can test this idea quantitatively with a simple 1D forcing-feedback energy balance model (like the one we have use in our papers, but here assuming a simple 1-layer swamp ocean 25 m deep, and a 30 day time step). If I force the ocean surface temperatures departures from an average state with a random number generator that is smoothed in time, then assume a sinusoidally varying feedback parameter between 0 and 6.4 W m-2 K-1 over a period of 28 months as the radiative response to those temperature variations, I get behavior like this:

The net feedback parameter diagnosis is then usually just the regression slope between the radiative flux variations and the temperature variations, which from the model output looks like this:

We see that the regression diagnosis of the feedback parameter is biased low. Instead of an average of 3.2 W m-2 K-1 as specified (which would be 1.2 deg. C equilibrium climate sensitivity), the diagnosis is 2.07 W m-2 K-1 (about 1.8 deg. C climate sensitivity).

If I add in some time-varying radiative forcing like we have addressed in our recent papers (e.g. this one), the bias toward high climate sensitivity is even greater (not shown here).

The above discussion is nowhere near exhaustive; I’m just trying to stimulate thought and discussion on an issue I feel very strongly about, that is: climate feedbacks diagnosed from observational data are very error-prone, with the errors most likely leading to overestimates of climate sensitivity.

NOTE: This is the fourteenth monthly update with our new Version 6.0 dataset. Differences versus the old Version 5.6 dataset are discussed here. Note we are now at “beta5” for Version 6, and the paper describing the methodology is still in peer review.

The Version 6.0 global average lower tropospheric temperature (LT) anomaly for May, 2016 is +0.55 deg. C, down 0.16 deg. C from the April value of +0.71 deg. C (click for full size version):

The global, hemispheric, and tropical LT anomalies from the 30-year (1981-2010) average for the last 17 months are:

YEAR MO GLOBE NHEM. SHEM. TROPICS
2015 01 +0.30 +0.44 +0.15 +0.13
2015 02 +0.19 +0.34 +0.04 -0.07
2015 03 +0.18 +0.28 +0.07 +0.04
2015 04 +0.09 +0.19 -0.01 +0.08
2015 05 +0.27 +0.34 +0.20 +0.27
2015 06 +0.31 +0.38 +0.25 +0.46
2015 07 +0.16 +0.29 +0.03 +0.48
2015 08 +0.25 +0.20 +0.30 +0.53
2015 09 +0.23 +0.30 +0.16 +0.55
2015 10 +0.41 +0.63 +0.20 +0.53
2015 11 +0.33 +0.44 +0.22 +0.52
2015 12 +0.45 +0.53 +0.37 +0.61
2016 01 +0.54 +0.69 +0.39 +0.84
2016 02 +0.83 +1.17 +0.50 +0.99
2016 03 +0.73 +0.94 +0.52 +1.09
2016 04 +0.71 +0.85 +0.58 +0.94
2016 05 +0.55 +0.65 +0.44 +0.72

Cooling from the weakening El Nino is now rapidly occurring as we transition toward likely La Nina conditions by mid-summer or early fall.

The “official” UAH global image for May, 2016 should be available in the next several days here.

The new Version 6 files (use the ones labeled “beta5”) should be updated soon, and are located here:

Lower Troposphere: http://vortex.nsstc.uah.edu/data/msu/v6.0beta/tlt/uahncdc_lt_6.0beta5.txt
Mid-Troposphere: http://vortex.nsstc.uah.edu/data/msu/v6.0beta/tmt/uahncdc_mt_6.0beta5.txt
Tropopause: http://vortex.nsstc.uah.edu/data/msu/v6.0beta/ttp/uahncdc_tp_6.0beta5.txt
Lower Stratosphere: http://vortex.nsstc.uah.edu/data/msu/v6.0beta/tls/uahncdc_ls_6.0beta5.txt

NOTE: This is the thirteenth monthly update with our new Version 6.0 dataset. Differences versus the old Version 5.6 dataset are discussed here. Note we are now at “beta5” for Version 6, and the paper describing the methodology is in peer review.

The Version 6.0 global average lower tropospheric temperature (LT) anomaly for April, 2016 is +0.71 deg. C, down slightly from the March value of +0.73 deg. C (click for full size version):

The global, hemispheric, and tropical LT anomalies from the 30-year (1981-2010) average for the last 16 months are:

YEAR MO GLOBE NHEM. SHEM. TROPICS
2015 01 +0.30 +0.44 +0.15 +0.13
2015 02 +0.19 +0.34 +0.04 -0.07
2015 03 +0.18 +0.28 +0.07 +0.04
2015 04 +0.09 +0.19 -0.01 +0.08
2015 05 +0.27 +0.34 +0.20 +0.27
2015 06 +0.31 +0.38 +0.25 +0.46
2015 07 +0.16 +0.29 +0.03 +0.48
2015 08 +0.25 +0.20 +0.30 +0.53
2015 09 +0.23 +0.30 +0.16 +0.55
2015 10 +0.41 +0.63 +0.20 +0.53
2015 11 +0.33 +0.44 +0.22 +0.52
2015 12 +0.45 +0.53 +0.37 +0.61
2016 01 +0.54 +0.69 +0.39 +0.84
2016 02 +0.83 +1.17 +0.50 +0.99
2016 03 +0.73 +0.94 +0.52 +1.09
2016 04 +0.71 +0.85 +0.58 +0.94

I expect average cooling to continue throughout the year as El Nino weakens and is replaced with La Nina, now expected by mid-summer or early fall. Nevertheless, 2016 could still end up as a record warm year in the satellite record…it all depends upon how fast the warmth from the El Nino dissipates and La Nina sets in.

The “official” UAH global image for April, 2016 should be available in the next several days here.

The new Version 6 files (use the ones labeled “beta5”) should be updated soon, and are located here:

Lower Troposphere: http://vortex.nsstc.uah.edu/data/msu/v6.0beta/tlt/uahncdc_lt_6.0beta5.txt
Mid-Troposphere: http://vortex.nsstc.uah.edu/data/msu/v6.0beta/tmt/uahncdc_mt_6.0beta5.txt
Tropopause: http://vortex.nsstc.uah.edu/data/msu/v6.0beta/ttp/uahncdc_tp_6.0beta5.txt
Lower Stratosphere: http://vortex.nsstc.uah.edu/data/msu/v6.0beta/tls/uahncdc_ls_6.0beta5.txt

I just learned through Climate Depot and Tony Heller that Bill Gray has died.

Bill was a pioneering researcher and hurricane forecaster, as well as a skeptic of the seriousness of the manmade global warming threat.

Bill’s legacy goes well back before my time…and I’m getting to be an old guy (60). He was at most of the Heartland conferences, and he always made it a point to spend time with me. In fact, he was embarrassingly effusive with his praise. He was quite a character, and very sharp. We didn’t always agree on the science, but that’s ok.

My earliest memory of Bill was my very first scientific conference, an AMS Hurricanes and Tropical Meteorology Conference in Oklahoma, maybe around 1982. The big thing then was the mesoscale modelers were all giving papers showing that their model would take a pre-existing tropical disturbance, say an African easterly wave, and turn it into a hurricane. This indeed was an achievement.

After sitting through all of these presentations, Bill Gray, who was sitting just in front of me, stood up and asked in his Jimmy Stewart-esque way, “All you modelers keep showing your models producing a hurricane out of a disturbance…but that usually doesn’t happen…where are the model results showing that a hurricane doesn’t develop?”

I will never forget the question…it was a good one.

Goodbye, Bill. You will be missed.

Ever since we started posting global temperature comparisons between models and observations there has been a recurring objection over the way the data are plotted. I really thought that the issue would go away because it’s so silly. But I’ve seen it crop up again in the last week in a series of messages between Judy Curry and Gavin Schmidt.

This issue can be summarized with the idealized temperature plots, below. The first plot shows how our critics apparently think two datasets should be plotted for comparison. The second plot is how we think the data should be plotted.

Note that no matter which one you choose, though, both plots show what is important… that the red temperature trace is warming twice as fast as the blue trace.

You might say, “but the disagreement in 2015 between the two traces is only half as big in the first plot as it is in the second plot“.

Yes, but that ignores the fact that there the other half of the disagreement has just been shifted to 1979…nothing has been gained.

It’s really the trends which are important (or, if you don’t like linear trends, pick some other metric of long-term temperature change…none of them is perfect).

We choose to plot the data relative to the same point early in the record. We usually do it relative to the average of the first 5 years of data, so that noise in both datasets has a minimum effect on aligning their starting points.

Or, to avoid that problem entirely, you can compute the trends from each dataset and simply plot all of the linear trend lines with the same starting point.

This is because you start a race at the beginning….not in the middle. How much warming we have seen over a certain period of time (say, since 1979) is relative to the start of that time period…not the middle of the period.

That this issue continues to be a point of contention, quite frankly, astonishes me. All I can think of is that the defenders of the climate models cannot allow any significant criticisms of the models to survive, even when the model shortcomings are staring everyone in the face.

On April Fools day (April 1), there was a report of snow on the tropical island of Guadeloupe on the night of March 31, 2016. Guadeloupe is an island in the northern Lesser Antilles. The news report even mentions a light dusting on the ground.

I’m calling BS on this report.

It claimed to not be an April Fool’s joke.

Well, the movie Fargo was also supposed to be a “true story”.

If people there really saw something, it wasn’t “snow”.

The high temperature at Le Raizet Airport was 83 deg F, and the low was 72, on March 31.

The elevation of the town (St. Claude) where the snow was reported was, at most, 3000 ft. That elevation could have been 10 deg. F colder….maybe 15 deg. F under unusual circumstances. So, we’re talking maybe the upper 50s deg. F at the absolute coldest. Not cold enough for snow, unless under exceptionally low humidity (which would never occur at this location).

There wasn’t a deep, cold air layer sufficient to cause such an event. It would have to have been an amazingly cold air mass, which penetrated deeply into the tropics. To a latitude of 16 deg. N.

So, I’m calling BS on the Guadeloupe snow report.

Exactly 9 years ago this week, the eastern U.S. was plunged into below-freezing weather after an unusually warm March just got things growing. The result was about $2 billion in agricultural losses across 16 states.

Similar to that Easter 2007 event, the current forecast for late this week has temperatures into the lower 20s as far south as South Carolina; both Saturday and Sunday morning should see below-freezing temperatures into Alabama and Georgia (forecast graphics courtesy of Weatherbell.com):

The state with the greatest losses in 2007 was Georgia, with about $400 million in damage to blueberries, peaches, pecans, and livestock grasses.

NOTE: This is the twelfth monthly update with our new Version 6.0 dataset. Differences versus the old Version 5.6 dataset are discussed here. Note we are now at “beta5” for Version 6, and the paper describing the methodology is in peer review.

The Version 6.0 global average lower tropospheric temperature (LT) anomaly for March, 2016 is +0.73 deg. C, down a little from the February record-setting value of +0.83 deg. C (click for full size version). This makes March 2016 the warmest March in the satellite record (since 1979), and statistically tied with April 1998 for the second warmest month.

The global, hemispheric, and tropical LT anomalies from the 30-year (1981-2010) average for the last 15 months are:

YEAR MO GLOBE NHEM. SHEM. TROPICS
2015 01 +0.30 +0.44 +0.15 +0.13
2015 02 +0.19 +0.34 +0.04 -0.07
2015 03 +0.18 +0.28 +0.07 +0.04
2015 04 +0.09 +0.19 -0.01 +0.08
2015 05 +0.27 +0.34 +0.20 +0.27
2015 06 +0.31 +0.38 +0.25 +0.46
2015 07 +0.16 +0.29 +0.03 +0.48
2015 08 +0.25 +0.20 +0.30 +0.53
2015 09 +0.23 +0.30 +0.16 +0.55
2015 10 +0.41 +0.63 +0.20 +0.53
2015 11 +0.33 +0.44 +0.22 +0.52
2015 12 +0.45 +0.53 +0.37 +0.61
2016 01 +0.54 +0.69 +0.39 +0.84
2016 02 +0.83 +1.17 +0.50 +0.99
2016 03 +0.73 +0.94 +0.52 +1.09

I suspect that February and March represent peak El Nino warmth in the lower troposphere, and the rest of the year will see cooling. Whether 2016 ends up being a record warm year will depend upon just how fast global temperatures fall as La Nina approaches, now forecast for late summer or early fall.

The “official” UAH global image for March, 2016 should be available in the next several days here.

The new Version 6 files (use the ones labeled “beta5”) should be updated soon, and are located here:

Lower Troposphere: http://vortex.nsstc.uah.edu/data/msu/v6.0beta/tlt/uahncdc_lt_6.0beta5.txt
Mid-Troposphere: http://vortex.nsstc.uah.edu/data/msu/v6.0beta/tmt/uahncdc_mt_6.0beta5.txt
Tropopause: http://vortex.nsstc.uah.edu/data/msu/v6.0beta/ttp/uahncdc_tp_6.0beta5.txt
Lower Stratosphere: http://vortex.nsstc.uah.edu/data/msu/v6.0beta/tls/uahncdc_ls_6.0beta5.txt