by Roy W. Spencer, John R. Christy, and William D. Braswell

(a PDF version of this post is available here. Monthly updates will use Version 6 starting with the April update.)

Abstract

Version 6 of the UAH MSU/AMSU global satellite temperature dataset is by far the most extensive revision of the procedures and computer code we have ever produced in over 25 years of global temperature monitoring. The two most significant changes from an end-user perspective are (1) a decrease in the global-average lower tropospheric (LT) temperature trend from +0.140 C/decade to +0.114 C/decade (Dec. ’78 through Mar. ’15); and (2) the geographic distribution of the LT trends, including higher spatial resolution. We describe the major changes in processing strategy, including a new method for monthly gridpoint averaging; a new multi-channel (rather than multi-angle) method for computing the lower tropospheric (LT) temperature product; and a new empirical method for diurnal drift correction. We also show results for the mid-troposphere (“MT”, from MSU2/AMSU5), tropopause (“TP”, from MSU3/AMSU7), and lower stratosphere (“LS”, from MSU4/AMSU9). The 0.026 C/decade reduction in the global LT trend is due to lesser sensitivity of the new LT to land surface skin temperature (est. 0.010 C/decade), with the remainder of the reduction (0.016 C/decade) due to the new diurnal drift adjustment, the more robust method of LT calculation, and other changes in processing procedures.

1. Introduction & Some Results

After three years of work, we have (hopefully) finished our Version 6.0 reanalysis of the global MSU/AMSU data. Many procedures have been modified or entirely reworked, and most of the software has been rewritten from scratch. (Please, before you ask a question, read the following to see if your question has already been answered.)

The MSU and AMSU instruments measure the thermal microwave emission from atmospheric oxygen in the 50-60 GHz oxygen absorption complex, and the resulting calibrated brightness temperatures (Tb) are nearly equivalent to thermometric temperature, specifically a vertically-weighted average of atmospheric temperature with the vertical weighting represented by “weighting functions”.

One might ask, Why do the satellite data have to be adjusted at all? If we had satellite instruments that (1) had rock-stable calibration, (2) lasted for many decades without any channel failures, and (3) were carried on satellites whose orbits did not change over time, then the satellite data could be processed without adjustment. But none of these things are true. Since 1979 we have had 15 satellites that lasted various lengths of time, having slightly different calibration (requiring intercalibration between satellites), some of which drifted in their calibration, slightly different channel frequencies (and thus weighting functions), and generally on satellite platforms whose orbits drift and thus observe at somewhat different local times of day in different years. All data adjustments required to correct for these changes involve decisions regarding methodology, and different methodologies will lead to somewhat different results. This is the unavoidable situation when dealing with less than perfect data.

After 25 years of producing the UAH datasets, the reasons for reprocessing are many. For example, years ago we could use certain AMSU-carrying satellites which minimized the effect of diurnal drift, which we did not explicitly correct for. That is no longer possible, and an explicit correction for diurnal drift is now necessary. The correction for diurnal drift is difficult to do well, and we have been committed to it being empirically–based, partly to provide an alternative to the RSS satellite dataset which uses a climate model for the diurnal drift adjustment.

The following plot (Fig. 1) shows the variety of satellites making up the satellite temperature record and their local solar time of observation as the satellites pass northbound across the Equator (ascending node).

Fig. 1. Local ascending node times for all satellites in our archive carrying MSU or AMSU temperature monitoring instruments. We do not use NOAA-17, Metop (failed AMSU7), NOAA-16 (excessive calibration drifts), NOAA-14 after July, 2001 (excessive calibration drift), or NOAA-9 after Feb. 1987 (failed MSU2).

Also, while the traditional methodology for the calculation of the lower tropospheric temperature product (LT) has been sufficient for global and hemispheric average calculation, it is not well suited to gridpoint trend calculations in an era when regional — rather than just global — climate change is becoming of more interest. We have devised a new method for computing LT involving a multi-channel retrieval, rather than a multi-angle retrieval.

The MSU instrument scan geometry in Fig. 2 illustrates how the old LT calculation required data from different scan positions, each of which has a different weighting function (see Fig. 2 inset). Thus, only one LT “retrieval” was possible from a scan line of data. The new method uses multiple channels to allow computation of LT from a single geographic location.

Fig. 2. MSU scan geometry, MSU2 weighting functions at different footprint positions and the basis for the old LT and new LT computation.

The LT retrieval must be done in a harmonious way with the diurnal drift adjustment, necessitating a new way of sampling and averaging the satellite data. To meet that need, we have developed a new method for computing monthly gridpoint averages from the satellite data which involves computing averages of all view angles separately as a pre-processing step. Then, quadratic functions are statistically fit to these averages as a function of Earth-incidence angle, and all further processing is based upon the functional fits rather than the raw angle-dependent averages.

Finally, much of the previous software has been a hodgepodge of code snippets written by different scientists, run in stepwise fashion during every monthly update, some of it over 25 years old, and we wanted a single programmer to write a unified, streamlined code (approx. 9,000 lines of FORTRAN) that could be run in one execution if possible.

Before addressing details of how the new Version 6 processing is different from the old (Version 5.6) processing, let’s examine some results. First let’s look at time series (Fig. 3) of the global average lower tropospheric temperature (LT), and how it compares to the old (Version 5.6) LT:

Fig. 3. Monthly global-average temperature anomalies for the lower troposphere from Jan. 1979 through March, 2015 for both the old and new versions of LT (top), and their difference (bottom).

Note that in the early part of the record, Version 6 has somewhat faster warming than in Version 5.6, but then the latter part of the record has reduced (or even eliminated) warming, producing results closer to the behavior of the RSS satellite dataset. This is partly due to our new diurnal drift adjustment, especially for the NOAA-15 satellite. Even though our approach to that adjustment (described later) is empirical, it is interesting to see that it gives similar results to the RSS approach, which is based upon climate model calculations of the diurnal cycle in temperature.

The next plot we will examine (Fig. 4) is the gridpoint LT trends during 1979-2015. Version 6 has inherently higher spatial resolution than the Version 5 product, which had strong spatial smoothing as part of the data processing and through the nature of how LT was calculated:

Fig. 4. New LT gridpoint temperature trends, Dec. 1978 through March 2015.

The gridpoint trend map above shows how the land areas, in general, have warmed faster than the ocean areas. We obtain land and ocean trends of +0.19 and +0.08 C/decade, respectively. These are weaker than thermometer-based warming trends, e.g. +0.26 for land (from CRUTem4, 1979-2014) and +0.12 C/decade for ocean (from HadSST3, 1979-2014).

The gridpoint trends for LT in Fig. 4 are very difficult to measure accurately over land, primarily due to (1) the diurnal drift effect, which can be at least as large as any real temperature trends, and (2) how LT is computed, which in the old LT methodology required data from different view angles, and thus different geographic locations which can be from different air masses and over different surfaces (land and ocean).

As a result, users can expect that there will be differences between old and new LT trends on a regional basis. Differences are also attributable to our use of a new, more accurate land mask in Version 6. For example, going from Version 5.6 to 6.0 the Australia trend increased from +0.17 to +0.24 C/decade, but the USA48 trend decreased from +0.23 to +0.17 C/decade. The Arctic region changed from +0.43 to +0.23 C/decade. Note that trends are noisy over Greenland, Antarctica, and the Tibetan Plateau, likely due to greater sensitivity of the satellite measurements to surface emission and thus to emissivity changes over high altitude terrain; trends in these high-altitude areas are much less reliable than in other areas. Future changes, probably minor, can be expected as we refine the gridpoint diurnal drift adjustments and other aspects of our new processing strategy.

Fig. 5 illustrates the changes from v5.6 to v6.0 for a variety of regions of interest:

Fig. 5. Regional lower tropospheric (LT) temperature trends in Versions 6.0 and 5.6. “L” and “O” represent land and ocean, respectively.

Notice the trends decreased the most over the Northern Hemisphere extratropics, especially the Arctic, while tropical warming trends increased somewhat, especially over land. Near-zero trends exist in the region around Antarctica.

We want to emphasize that the land vs. ocean trends are very sensitive to how the difference in atmospheric weighting function height is handled between MSU channel 2 early in the record, and AMSU channel 5 later in the record (starting August, 1998). In brief, the lower in altitude the weighting function senses, the greater the brightness temperature difference between land and ocean, mostly because land microwave emissivity is approximately 0.90-0.95, while the ocean emissivity is only about 0.50. As a result, if the AMSU channel 5 view angle chosen to match MSU channel 2 is too low in altitude, the net effect after satellite intercalibration will be a spurious warming of land areas and spurious cooling of ocean areas (at least when intercalibration is performed with land and ocean data combined). We were careful to match the MSU and AMSU weighting function altitudes based upon radiative transfer theory, and are reasonably confident that the remaining land-vs-ocean effects in the above map are real, that is, the land areas have warmed faster than the ocean regions. This is consistent with thermometer datasets of surface temperature, although our warming trends are weaker. Given the importance of the microwave oxygen absorption theory to the land-versus-ocean trends, we hope to update that portion of our processing for a future version update.

2. Major Changes in Processing Procedures with Version 6

The following is meant to provide a general introduction to the new processing steps in Version 6, emphasizing departures from past practices, and not to provide exhaustive detail. It will likely be close to two years before a peer reviewed paper with greater detail gets published in a scientific journal.

2.1 LT Calculation
We have fundamentally changed the calculation of the lower tropospheric temperature product, LT, from a multi-angle method to a multi-channel method. The main reason we changed methods for LT calculation is the old view angle method had unacceptably large errors at the gridpoint level. While the errors cancel for global averages on a monthly time scale, on a regional or gridpoint basis they can be large. The errors arise because the different view angles necessary to calculate a single LT “retrieval” sample different geographic locations, for instance radiometrically colder ocean and warmer land (see Fig. 2, above).

This would not present as big a problem if the data from the different regions were simply averaged together, but instead they are differenced. The problem is further magnified (literally) because the old LT required a weighted difference between view angles (and thus regions) with large weights (+4, -3 for the MSUs), which amplified any regional Tb differences. Compounded with the need to do diurnal drift adjustments, which can vary substantially from land to ocean, the problems with the old LT were deemed to be too large to continue the old LT calculation methodology.

So, instead of the past method of calculating LT as a weighted difference between different view angles of MSU2 (or AMSU5), we are now calculating it as a weighted difference between MSU channels 2, 3, and 4 (or AMSU channels 5, 7, and 9) at a constant Earth incidence angle. This has the very important advantage that all satellite data necessary for the LT retrieval come from the same location.

This required a correction for calibration drifts in MSU channel 3, especially during 1980-81, which was the original stated reason why a multi-channel retrieval method was not implemented over 20 years ago. That correction is made based upon regression of global monthly anomalies of MSU3/AMSU7 data against MSU2/AMSU5 and MSU4/AMSU9 during 1982 through 1993 (a 12 year period exhibiting two large volcanic eruptions with differential responses in the different altitude channels). We then apply the resulting regression relationship to the entire 1979-2015 period to estimate MSU3 (AMSU7) from MSU2,4 (AMSU5,9), and compare it to the raw intercalibrated global MSU3/AMSU7 time series. A difference time series of the regression estimated and the observed MSU3/AMSU7 time series is fitted with a piecewise linear estimator to give a time series of adjustment which are then applied to the MSU3/AMSU7 monthly anomaly fields. The resulting corrections cause a few hundredths of a degree per decade increase in the MSU3/AMSU7 trend (1979-2014), which ends up being very close to zero.

The following graph (Fig. 6) shows the resulting time series of LT, MT (mid-troposphere, from MSU2/AMSU5), TP (our new “tropopause level” product, from MSU3/AMSU7) and LS (lower stratosphere, from MSU4/AMSU9):

Fig. 6. Monthly global-average temperature variations for the lower troposphere, mid-troposphere, tropopause level, and lower stratosphere, 1979 through March 2015.

The LT computation is a linear combination of MSU2,3,4 or AMSU5,7,9 (aka MT,TP, LS):

LT = 1.538*MT -0.548*TP +0.01*LS

As seen in Fig. 7, the new multi-channel LT weighting function is located somewhat higher in altitude than the old LT weighting function. But if global radiosonde trend profile shapes (dashed line in Fig. 7) are to be believed, the net difference between old and new LT trends should be small, less than 0.01 C/decade. This is because slightly greater sensitivity of the new LT to stratospheric cooling is cancelled by even greater sensitivity to enhanced upper tropospheric warming.

Fig. 7. MSU/AMSU weighting functions which define the sensitivity of the various channels to temperature at different altitudes. Also shown is the vertical profile of the average trends from two radiosonde datasets during 1979-2014, and the weighting function-sampled trends that would result from hypothetical satellite measurements of those radiosonde trends.

Specifically, we see from Fig. 7 that application of the old and new LT weighting functions to the radiosonde trend profiles (average of the RAOBCORE and RATPAC trend profiles, 1979-2014) leads to almost identical trends (+0.11 C/decade) between the new and old LT. These trends are a good match to our new satellite-based LT trend, +0.114 C/decade.

The new LT weighting function is less sensitive to direct thermal emission by the land surface (17% for the new LT versus 27% for the old LT), and we calculate that a portion (0.01 C/decade) of the reduction in the global LT trend is due to less sensitivity to the enhanced warming of global average land areas. The same effect does not occur over the ocean because all of these channels’ microwave frequencies are not directly sensitive to changes in SST since ocean microwave emissivity decreases with increasing SST in such a way that the two effects cancel. This effect likely also causes a slight enhancement of the land-vs-ocean trend differences. Thus, over ocean the satellite measures a true atmosphere-only temperature trend, but over land it is mostly atmospheric with a small (17%, on average) direct influence from the surface. One might argue that a resulting advantage of the new LT is lesser sensitivity to long-term changes in land surface microwave emissivity, which are largely unknown.

The rest of the reduction in the LT trend between Versions 6.0 and 5.6 (-0.016 C/decade) is believed to be partly due to a more robust method of LT calculation, and the new diurnal drift adjustment procedure, described later. It is well within our previously stated estimated error bars on the global temperature trend (+/- 0.040 C/decade).

2.2 Monthly Averaging Methodology
In order to compute gridpoint values of LT, we must first compute gridpoint averages of the three channels used to compute LT. We have a new methodology for computing monthly gridpoint averages from MSU channels 2, 3, 4 (AMSU ch. 5, 7, 9) which is based upon initially computing monthly gridpoint averages from all channels’ view angles separately: 6 view angles from 11 footprints of MSU, or 15 view angles from 30 footprints of AMSU, which are separately averaged in 2.5 deg. lat/lon bins during the month.

The resulting monthly Tb gridpoint averages for each of the three channels are then fitted as a function of Earth incidence angle with a second order polynomial. The Tb for any desired Earth incidence angle is then estimated from the fitted curve, rather than from the raw view-angle averages.

An example of this fit is shown in Fig. 8, for AMSU channel 5 for a single gridpoint for a single month from a single satellite (NOAA-15):

Fig. 8. Example of how monthly gridpoint averages of AMSU ch. 5 Tb from separate footprints are fitted as a function of Earth incidence angle so Tb can be estimated from the smooth functional fit to the data.

This new averaging procedure has the following advantages:

1) All of the different view angle Tb measurements are included in the optimum estimation of the Tb at the desired Earth incidence angle, reducing sampling noise.

2) The resulting average calculation for a gridpoint location is based only upon data from that location, a new feature that avoids sampling noise inherent in the old calculation of LT from geographically different areas.

3) The orbit altitude decay effect (which has been large only for calculation of the old LT), as well as different satellites’ altitudes, is automatically handled since we use routine satellite ephemeris updates to calculate Earth incidence angles, which are the new basis for Tb estimation, not footprint positions per se.

4) Working from monthly grids of separate view angle averages allows rapid reprocessing of all of the data from 1979 forward, allowing us to efficiently test the use of different nominal view angles for the products, matching of the MSU and AMSU view angles, changes in diurnal drift estimation, etc.

The nominal footprint position we use for all MSU channels (see Fig. 2) is footprint position 4 and 8 (Earth incidence angle 21.59 deg), rather than nadir position 6; and nominal footprint position 6.33 and 24.66 for AMSU channel 5 (Earth incidence angle 34.99 deg.); and Earth incidence angles 13.18 deg. for AMSU7 and 36.31 deg for AMSU9. The choice of MSU prints 4,8 is because the resulting sampling at those footprint positions gives approximately 28 measurements evenly distributed in longitude around the Earth twice a day, rather than only 14 samples if the nadir position (MSU print #6, or AMSU print #15,16) was used as the reference. We find this greatly reduces sampling noise in the middle latitudes caused by coincidental phasing of moving weather systems with the satellite orbital sampling patterns.

Nevertheless, a few months in the record still exhibit mid-latitude striping patterns (especially over the southern oceans) when the precession of satellite orbits combined with warm and cold air mass movements happen to lead to non-random sampling patterns, even with as many as three satellites operating. So, we apply a +/- 2 gridpoint smoother in the east-west direction for the monthly gridded anomaly grid fields, which is applied over land and ocean separately to prevent “bleeding” of signals between land and ocean.

2.3 Diurnal Drift Calculation
As the 1:30 satellites drift to later local observation times (an indirect result of orbit decay), the MSU2 (AMSU5) Tb tend to cool, especially over land in certain seasons, due to the day-night cycle in temperature. As the 7:30 satellites drift to earlier observation times, the Tb tend to warm for the same reason. These average relationships change at very high latitudes because the ascending and descending satellite passage times converge — while they are ~12 hours apart at the equator, they approach the same local time at high latitudes.

These diurnal drift effects are empirically quantified at the gridpoint level by comparing NOAA-15 (a drifting 7:30 satellite) to Aqua (a non-drifting satellite), and by comparing NOAA-19 against NOAA-18 during 2009-2014, when NOAA-18 was drifting rapidly and NOAA-19 had no net drift. The resulting estimates of change in Tb as a function of local observation time are quite noisy at the gridpoint level, and so require some form of spatial smoothing. Since they also depend upon terrain altitude and the dryness of the region (deserts have stronger diurnal cycles in temperature than do rain forests), a regression is performed within each 2.5 deg. latitude band between the gridpoint diurnal drift coefficients and terrain altitude as well as average rainfall (1981-2010) for that calendar month, then that relationship is applied back onto the gridpoint average rainfall and terrain elevation within the latitude band. Over ocean, where diurnal drift effects are small, the gridpoint drift coefficients are replaced with the corresponding ocean zonal band averages of those gridpoint drift coefficients.

Fig. 9 shows an example of the diurnal drift coefficients (in deg. C per hour of ascending node time drift) used for MSU ch. 2 at nominal footprint 4 (and for AMSU ch. 5, a nominal footprint position between #6 and #7) for the month of June:

Fig. 9. Example diurnal drift coefficients (deg. C/hr) for MSU2/AMSU5 for the month of June for adjustment of the afternoon (“1:30”) satellites.

The reason why the drift coefficients change sign at high northern latitudes is a combination of early sunrise time in June, late sunset time, and the fact that the ascending and descending orbit satellite observations at high latitudes approach the same time, instead of being 12 hours apart as they are at the equator.

We also compute and apply diurnal drift coefficients for MSU channels 3 and 4 (AMSU channels 7 and 9), but the drifts and resulting adjustments are very small.

3. Final Comments

This should be considered a “beta” release of Version 6.0, and we await users’ comments to see whether there are any obvious remaining problems in the dataset. In any event, we are confident that the new Version 6.0 dataset as it currently stands is more accurate and useful than the Version 5.6 dataset.

The new LT trend of +0.114 C/decade (1979-2014) is 0.026 C/decade lower than the previous trend of +0.140 C/decade, but about 0.010 C/decade of that difference is due to lesser sensitivity of the new LT weighting function to direct surface emission by the land surface, which surface thermometer data suggests is warming more rapidly than the deep troposphere. The remaining 0.016 C/decade difference between the old and new LT product trends is mostly due to the new diurnal drift adjustment procedure and is well within our previously stated range of uncertainty for this product’s trend calculation (+/- 0.040 C/decade).

We have performed some calculations of the sensitivity of the final product to various assumptions in the processing, and find it to be fairly robust. Most importantly, through sensitivity experiments we find it is difficult to obtain a global LT trend substantially greater than +0.114 C/decade without making assumptions that cannot be easily justified.

The new Version 6 files are located here:

Lower Troposphere: http://vortex.nsstc.uah.edu/data/msu/v6.0beta/tlt
Mid-Troposphere: http://vortex.nsstc.uah.edu/data/msu/v6.0beta/tmt
Tropopause: http://vortex.nsstc.uah.edu/data/msu/v6.0beta/ttp
Lower Stratosphere: http://vortex.nsstc.uah.edu/data/msu/v6.0beta/tls

The explosive double eruption of Calbuco Volcano in Chile late yesterday afternoon and last night, combined with mostly clear skies, allowed some spectacular photos and videos, even from a drone.

Here’s one of the prettiest photos of the first eruption, which occurred before sunset:

The two mature eruption plumes as seen by the GOES weather satellite show plume top temperatures so cold that the stratosphere was surely penetrated, a necessary (but not sufficient) condition for effects on global climate (arrows indiate volcano location, satellite imagery courtesy of CIRA at ColoState):

From low Earth orbit, this was how the area around the snow-capped volcano looked just hours before the eruption yesterday from NASA’s Terra satellite, and then again this morning. Note the large region where the snow-capped mountains and the valleys are covered in volcanic ash:

In flagrant disregard for humanity’s attempts to clean up air pollution, the Chilean volcano Calbuco erupted violently on Earth Day 2015, pumping millions of tons of hazardous materials into the pristine South American atmosphere.

Calbuco Volcano eruption on 22 April, 2015 (photo: AFP).

The eruption terrorized local residents, one of whom had just returned from an Earth Day protest tearfully exclaiming, “¿Por qué está haciendo la Tierra esto a nosotros? Nos plantamos un árbol!”

The eruption continued into the night last night, producing an angry display of toxic, asthma-causing emissions and lightning:

The media has been buzzing about an unusual photo taken on Long Island this morning, from a commuter train at the Glen Cove station, of what appeared to be a quadruple rainbow. Being a meteorologist and having a website that answers questions like “What Causes Rainbows?”, I have more than a passing interest in this.

I’ll admit, I had never seen such a thing before, captured by Amanda Curtis and posted on Twitter:

While at first I figured this was a hoax of some sort, there were multiple reports and photos of this event, and I found even more photos (but not many) on the internet of similar quadruple rainbows, e.g. this one from Reddit.com:

Now, most people have seen double rainbows, which are basically concentric segments of two halos opposite the sun, when the sun is low in the sky, created by refraction of sunlight through raindrops.

What is unique about these sightings is that there are TWO sets of double halos…but with different centers, which means (in effect) from “two suns”.

So, what could cause this? Well, the two suns on Tatooine from the original Star Wars movie would do it:

OR…it can be caused by reflection of the sun off a water body providing a second “sun”, which then provides the additional point source of light for the second set of rainbows.

Note that the two examples above were close to water bodies (Long Island in the first photo, and then the unknown coastal location in the second).

I did a quick search online and could not find a similar explanation, but it is the only immediately obvious one that I can envision. Maybe readers here have more information.

Overheard in a local forest…

Molly Maple: “Hey, Heath, you gonna participate in Earth Day activities this year?”

Heath Hickory: “I don’t know, Molly, I’m not really into that whole Earth Day thing. What’s it about?”

Molly: “Well, it recognizes that the world is now dangerously overcrowded. Did you know there are now about 1 trillion of us? Experts at United Forests say we are now at the carrying capacity of the Earth. Each year billions of us die just due to overcrowding and competition for natural resources.”

Heath: “So? What can I do about it? I’m only one tree. “

Molly: “Well, we could start by not producing so many seedlings. Also, do you ever think about all of the vines, flowers, and shrubs that we displace? They have rights, too, you know.”

Heath: “I don’t know, Molly. Don’t you think that trees have a right to take what we want? I mean…survival of the fittest and all.”

Molly: “Really, Heath? Really? That sounds kinda biophobic to me.”

Heath: “See Molly, that’s why I don’t like to get involved with things like Earth Day. Everyone is so judgmental, trying to tell all the other trees how to live. Just let me live my life as nature intended.”

Molly: “OK, Heath, but you’ll understand someday, when trees have used up all of the natural resources. The experts at the UF all agree that our continued use of atmospheric carbon dioxide could cause mass starvation by the end of this century.”

Heath: “Yeah, well, they’ve been saying scary stuff like that since I was knee-high to a shrub, over 100 years ago. And too many trees like you believe it. So, go ahead and participate in Earth Day. Have fun.”

Molly: “OK, well, I just thought I’d ask. (under her breath) Stupid pin-oak.

Heath: (under his breath) “Hard-headed maple.”

Updated with model forecast made Thursday morning, April 16.

A strong spring storm developing over the Northern Rockies will bring heavy snow and strong winds to portions of Wyoming and Colorado over the next few days, with the heaviest snows expected on Friday. Winter storm watches and warnings are starting to be issued, with more modest snow totals expected in Montana, Idaho, Utah, and New Mexico.

Predicted total snowfall amounts from the GFS model reach up to 5 ft by Sunday morning in the mountains (graphic courtesy of Weatherbell.com):

The cold air mass is expected to then shift east, with reinforcing cold air coming south through Canada, bringing a chance of Spring snows to the upper Midwest, including Chicago, by late next week.

There’s something about the greenhouse effect /sky radiation / downwelling infrared / back radiation issue that keeps drawing me back to the subject.

I guess it’s the number of people who don’t believe the so-called greenhouse effect exists (I still get e-mails from them, even today), combined with the difficulty of convincing them that their everyday experience is consistent with its existence.

I’ve used a handheld IR thermometer to directly measure its effect (the temperature of the surface of a thermopile in the device increases as you scan from pointing straight up in a clear sky to pointing at an angle…voila! Downwelling sky radiation changing surface temperature!). But, no, that’s not enough.

I’ve also tried to explain that the temperature of the Earth and its atmosphere (or anything else, for that matter) is a function of rates of energy gain and energy loss, not of just how much solar radiation is absorbed. I’ve argued this using the analogy of insulation in a house…even though the insulation does not add “new energy” (just as the atmospheric greenhouse effect doesn’t add new energy to the Earth system,) it does make the house warmer in winter by reducing the rate of energy loss to its colder surroundings.

But, no, that’s not accepted since, you know, house insulation works by conduction, not by radiation. Sigh.

So, just for laughs, here’s another demonstration, involving a simple model of the cooling of the soil at night.

At night the soil cools by loss of infrared radiation. The Stefan-Boltzmann equation lets us estimate the rate at which IR energy is being lost based upon surface temperature and emissivity, and simply dividing that by the product of the soil depth and soil bulk heat capacity gives us the rate at which the soil layer temperature will fall. Basic physics and thermodynamics.

From that we can make a simple time-dependent model to calculate the change in temperature throughout the night. This simple spreadsheet model I’ve provided here will allow you to change assumed parameters to see how to get a realistic temperature decline over 12 nighttime hours. What you will find is that the temperature falls to unrealistically cold levels unless you assume a large downwelling energy flux from the sky into the soil (also adjustable in the model).

If you are wondering, “what about cooling of the atmosphere in contact with the ground?”, well just make the soil layer deeper…it turns out that 0.2 meters of soil is equivalent in bulk heat capacity to about 200 m of atmosphere.

The adjustable parameters (in red in the spreadsheet) are soil depth (0.2 m is typical for day-night temperature changes), the soil heat capacity (2.5 is typical, water is 4.18), the IR emissivity (0.90-0.95 would be typical), the downwelling sky radiation intensity (0 for all you sky dragon slayers [SDSs] out there, 250-350 for the rest of us), and the starting temperature (300 K is about 80 deg. F).

For example, for a 0.2 m moist soil layer (about 8 inches thick), starting at 80 deg. F, the rate of energy loss over 12 hours is enough to cool that soil layer down to 25 deg. F….IF you don’t assume any downwelling IR from the sky (the SDS-recommended setting):

But, if you assume the Trenberthian global-average value of 330 W/m2 for downwelling sky radiation, the soil cools from 80 to about 67 deg. F, a much more realistic value:

In reality, the soil surface cools faster that does the deeper layers, but I didn’t want to complicate things with a multi-level model (which I don’t have time to work on anyway). This is just a simple, bulk model calculation meant to illustrate how extremely cold it would get at night without downwelling IR from the sky (aka, the “greenhouse effect”) reducing the net rate of energy loss to outer space.

Of course, downwelling IR from the sky is going on 24-7-365, acting to keep daytime temperatures warmer than they would otherwise be, too.

Warmer daytime + warmer nighttime = Warmer Earth.

Now, as I’ve mentioned before, as much as 75% of this big, bad greenhouse effect is “short-circuited” by convective heat loss by the surface, which is almost entirely a daytime phenomenon over land (nighttime surface temperatures quickly cool the near-surface air to make it convectively stable). (And, just for completeness, a greenhouse atmosphere is colder in the upper layers than it would otherwise be.) My point is that, just because the “greenhouse effect” exists doesn’t mean that our 1-2% enhancement of it with carbon dioxide is going to cause anything bad to happen. There are natural cooling mechanisms in place for the lower atmosphere. The stratosphere, though, probably will cool, as we have seen in satellite data.

But denying the existence of downwelling IR radiation from the sky (which is measured continuously at many sites around the world) is, in my opinion, a losing strategy.

Now, let the silliness begin….

Storm chasers captured over 6 minutes of amazing footage of the EF3 or EF4 tornado that killed one person and caused extensive damage in Fairdale, Illinois (northeast of Rochelle, southeast of Rockford) yesterday. This is one of the best tornado videos I have ever seen.

For those hearing this was a global warming-fueled tornado, here’s my post from 4 years ago explaining why more tornado activity typically requires an unseasonably cool airmass.

If warm, moist air was the missing ingredient, the tropics would be filled with tornadoes.

And if you are wondering, “Well, couldn’t more warm, moist air next to a cool air mass enhance tornado formation?” Well, yes, except the equator-to-pole temperature contrast has decreased during warming, not increased. In other words, the cool air masses have warmed more than the warm airmasses, reducing the available energy for these types of storms.

It’s just weather, folks. Global warming didn’t cause your tornado, and it didn’t cause your daughter’s asthma.

That tireless ecological zealot over at The Guardian, Dana Nuccitelli, took the opportunity of our 25th anniversary of satellite-based global temperature monitoring to rip us a new one.

Comparing John Christy and me to “scientists who disputed the links between smoking and cancer”, Dana once again demonstrates his dedication to the highest standards of journalism.

Well done, Grauniad.

I prefer to compare us to Barry Marshall and Robin Warren, who rejected the scientific consensus that peptic ulcers were due to too much stress or spicy food. While they eventually received the Nobel Prize after years of ridicule and scorn from the medical research community, we have no illusions that we will ever be credited for our long-standing position that global warming fears have been overblown. I’m sure the UN’s IPCC will find a way to take credit for that, and get another Peace Prize for it.

(I wonder if Marshall and Warren were being paid off by the spicy food lobby?)

The “97% of all climate scientists agree“ meme that Dana bitterly clings to has been thoroughly discredited…. as if scientific consensus on something so poorly understood as climate change (or stomach ulcers 15 years ago?) really means anything, anyway.

To prove that Dana should probably avoid trying to interpret simple graphs, let’s examine this chart he so likes, which allegedly shows that our (UAH) global temperature dataset has been continually adjusted for errors over the years, resulting in an increasing warming trend:

Now, setting aside the fact that (1) we actually do adjust for obvious, demonstrable errors as soon as they have been found (unlike the IPCC climate modelers who continue to promote demonstrably wrong models), and (2) RSS gets about the same (relatively benign) warming trend as we do, let’s examine some other popular temperature datasets in the same manner as the above graph:

Looks a lot like Dana’s plot, doesn’t it?

Do you want to know why? Is it really because all those other temperature dataset providers were also busily correcting mistakes in their data, too?

No, it’s largely because as the years go by, the global temperature trend changes, silly.

About the only thing Dana got reasonably correct is his article’s tag line, “John Christy and Roy Spencer are pro-fossil fuel and anti-scientific consensus.”

You’re damn right we are. But not because we are paid to say it, which we aren’t. (What are you paid to say for The Guardian, Dana?)

We are pro-fossil fuel because there are no large scale replacements available, wind and solar are too expensive, and you can’t just cut fossil fuel use without causing immense human suffering. Yes, I’ve talked to some of the top economists about it.

And indeed we are “anti-scientific consensus” because the consensus (which mostly just follows the average of the IPCC climate models) has been demonstrated to be wrong.

Finally, if Dana objects to me tiring of being called a “global warming denier” (with the obvious Holocaust connotations) for the last seven eight years and fighting back, read this and then tell me where I am wrong.

I just found out that Ron Bailey at Reason.com published an article a few days ago entitled, “What Evidence Would Persuade You That Man-Made Climate Change Is Real?”

I’ve spent some time with Ron, and he is a very sharp guy. That’s why I’m a little disappointed that he would publish this mixture of straw man arguments and uncritical thinking. He is the only deep thinker I know of who switched from being a skeptic about the causes of global warming to a believer…an epiphany which occurred in 2005, according to the article. (Hmmm…I wonder if he was fooled by all those major hurricanes that hit the U.S. that year? It’s now almost 10 years later, and we haven’t had one since.)

The first problem I have is with his premise: that skeptics believe humans have no role in climate change. I don’t know of any serious skeptics who hold such a view. Now, maybe he is addressing people who deny any human involvement in global warming. His article is vague, and maybe he can clarify his intent for us.

The second problem I have is with Ron’s list of a variety of evidences of global-average warming, which (again) no skeptic worth their salt disputes. The science dispute is over how much of the warming is manmade versus natural. Like too many others, Ron conflates climate change with human-caused climate change, which are not the same thing.

Regarding his list, he seems to believe they are independent evidences of manmade warming. Wrong. To the extent warming occurs, even if it is entirely natural, warming would occur in the atmosphere and deep ocean; it would cause an increase in atmospheric water vapor, as well as precipitation; the warming would be stronger in the upper troposphere than the lower troposphere; and stronger over land than over the ocean.

These things would all occur together anyway, no matter the cause of the warming, Ron. And causation is, indeed, the question which science so far cannot answer.

Since climate models cannot even hindcast what has happened (let alone forecast), they clearly have no handle on multi-decadal temperature changes brought about by natural effects. There does not even need to be any forcing — e.g. the sun, volcanoes, etc. — in order for climate to change because the ocean-atmosphere system is nonlinear and dynamical — in a word, chaotic. It can change all by itself. For example, it this plot we see that global warming (and cooling) has been the rule, not the exception, for the last 2,000 years:

If the above graph is anywhere close to what has actually happened, then most centuries have experienced global warming or cooling. So, what would it take to predict global warming (along with all of the correlated changes, listed above) due to any cause you’d like to invoke?

Basically, all it takes is a flip of a coin.

If you wonder, how could so many scientists be wrong? Well, the medical community had millions of peptic ulcers to study, and it took decades (and two brave Australian researchers) to finally convince them of a bacterial basis for ulcers.

The Earth, in contrast, is a single subject, and we can’t easily or quickly test its response to various treatments. It would be very easy for group think to dominate the scientific community, especially when continued funding depends on a continued climate crisis.

A third problem I have with Ron’s article: he uses “falsifiable predictions” of the future as evidence supporting his case! Really? Well, we’ve already had abundant predictions of what would happen by now from modelers like James Hansen that have turned out to be wrong. There you go, skeptics have real falsified predictions they can point to….not predictions of what might happen 10 years from now.

Why didn’t he mention the recent increase in papers (our APJAS paper included) with evidence that recent warming has been partly natural? Yes, as he says, natural variability can work both ways (warming OR cooling), but the available evidence is that recent warming was partly due to nature. Significantly, that warming occurred during the period when climate modelers developed their models, and since they assumed all warming was manmade, they had to increase the models’ sensitivity. Now, they are between a rock and a hard place, continuing to publish overly-sensitivity models they know are wrong (based upon both surface AND deep ocean warming rates).

Such a travesty would never be allowed in a hard discipline that uses physics, like engineering. An impartial judge with any cajones wouldn’t even allow climate models as evidence in a court of law since they have, so far, largely failed in their predictions.

Does Ron know that the deep-ocean warming since the 1950s is so weak (if it is even real from an observational error perspective) that it represents only a 1 part in 1,000 imbalance between incoming and outgoing energy? Do we really believe that chaotic changes in the circulations of the ocean and atmosphere cannot create such a tiny imbalance? The above graph (along with historical evidence of the Little Ice Age and Medieval Warm Period) suggests such small changes are fairly routine.

So, Ron asks what evidence would convince me that recent warm was manmade? Well, how about knowledge of how much natural climate change would have occurred without human influence? Then we would know the remainder was human-induced. Except, there is no way to know the natural component. If there was, climate models would have reasonably replicated the temperature changes since the 1950s rather than looking like a bowl of spaghetti.

And since I already believe manmade climate change (to some non-zero extent) is real, maybe I can ask Ron: What evidence would persuade you that your original question is nothing more than a straw man argument?

I hate to impute motives, but I really have to wonder if he is succumbing to peer pressure, since believing anything that smacks of denial-ism is really frowned upon in the intellectual circles I’m sure Ron is part of.

Yes, I believe that adding CO2 to the atmosphere has probably caused some warming, even though there is no way to prove it. Just how much human-caused warming is the question. And, as Ron correctly notes, it says nothing about what if anything should (or even can) be done about it anyway.