Gulf of Oman oil tanker fire viewed by NASA satellite

June 13th, 2019

One or two oil tankers near the Strait of Hormuz were hit by torpedoes or mines this morning. I checked the NASA Worldview website to see if NASA’s MODIS imager on the Terra satellite picked it up. I had to enhance the image to bring out the dark smoke against the dark ocean background:

I suspect gasoline prices are about to rise.

Electric-blue night clouds are invading the U.S.

June 11th, 2019

Over the next few weeks, mid-latitude observers might experience the best noctilucent cloud viewing of their lifetimes.

Extremely high-altitude noctilucent (night-shining) clouds viewed from Corvallis, Oregon on June 10, 2019 (Tucker Shannon, Google Pixel phone).

Observers over the northern half of the United States are reporting something they have never seen before — electric-blue noctilucent (night-shining) clouds. They are wispy in appearance, and continuously change shape. They can be seen when the sun is about 6 to 16 deg. below the horizon, so about 1 to 2 hours after sunset or before sunrise. During that time of night the sun is still shining on these clouds, but not on any normal weather-related clouds.

In the late spring every year, people at far northern latitudes have often seen these on clear or partly-cloudy evenings. But solar-minimum conditions, with few if any sunspots, are causing cooling in the extreme upper atmosphere around 80 km (~50 miles) high where the lowest atmospheric temperatures are recorded, approaching -150 deg. F (-100 deg. C). That altitude is above 99.999% of the air in the atmosphere.

Noctilucent clouds observed from the International Space Station on June 13, 2012 (NASA).

Adding to the spectacular electric blue displays is increasing atmospheric methane, which gets converted to water vapor at these altitudes, and increasing atmospheric carbon dioxide, which causes enhanced cooling of the upper atmosphere. The result is that the conditions necessary for NLC formation are extending farther south than ever before.

The wispy and undulating appearance of the clouds is due to upward-propagating gravity (air density) waves that cause temperatures to rise and fall, and the clouds form in the colder portions of those waves. Ice grows on meteor dust particles, creating a (nearly) outer space version of cirrus clouds. Time lapse photography has been used to show how the clouds change shape as the gravity waves well up through the extremely cold upper mesosphere:

2017 NOCTILUCENT CLOUD CHASING SEASON TEASER – 4K (UHD) from Night Lights Films on Vimeo.

If you miss seeing them in the next several weeks, take heart — solar minimum conditions should persist until the next NLC season arrives, making the summer of 2020 a good viewing opportunity, too.

You can see recent NLC photos from around the Northern Hemisphere, updated daily, here.

A Simple “No Greenhouse Effect” Model of Day/Night Temperatures at Different Latitudes

June 7th, 2019

Abstract: A simple time-dependent model of Earth surface temperatures over the 24 hr day/night cycle at different latitudes is presented. The model reaches energy equilibrium after 1.5 months no matter what temperature it is initialized at. It is shown that even with 1,370 W/m2 of solar flux (reduced by an assumed albedo of 0.3), temperatures at all latitudes remain very cold, even in the afternoon and in the deep tropics. Variation of the model input parameters over reasonable ranges do not change this fact. This demonstrates the importance of the atmospheric “greenhouse” effect, which increases surface temperatures well above what can be achieved with only solar heating and surface infrared loss to outer space.

As a follow-up to yesterday’s post regarding why climate scientists use ~340 W/m2 as the global average solar flux available to the climate system, here I present a model which includes how the incident solar flux (starting with the 1,370 W/m2 solar constant) varies across the Earth as a function of latitude and every 15 minutes throughout the diurnal (day/night) cycle.

I am providing this model to avoid any objections regarding how much solar energy is input into the climate system on average, how that averaging should be done (or whether it is even physically meaningful), whether the nighttime lack of any solar flux should be excluded from the averaging, whether certain assumptions constitute a “flat-Earth” mentality, etc. Instead, the model uses the actual variations of the incident solar radiation on the (assumed spherical) Earth as a function of latitude and time of day. For simplicity, equinox conditions are assumed and so there is no seasonal cycle.

This is not meant to be a realistic model of regional climate; instead, it goes beyond the global averages in the Kiehl-Trenberth energy budget diagram and shows how unrealistically cold temperatures are when you assume there is no greenhouse effect — even in the deep tropics during the afternoon. The model “evolves” the final temperatures, from any starting temperature you specify, based upon a simple energy budget equation (energy conservation) combined with an assumed surface heat capacity. Imbalances between absorbed solar energy and emitted IR energy cause a temperature change which eventually stops (in a long-term average sense) when the daily rate of emitted IR energy equals the daily absorbed solar energy.

The time-dependent model has adjustable inputs: the solar constant (1,370 W/m2); an albedo (for simplicity assumed 0.3 everywhere); the depth of the surface layer responding to solar heating (using the heat capacity of water, but soil heat capacity is similar); and, the assumed broadband infrared emissivity of the surface controlling how fast energy is lost to space as the surface warms. I set the time step to 15 minutes to resolve the diurnal cycle. The Excel model is here, and you are free to change the input parameters and see the results.

Here’s how the incident solar flux changes with time-of-day and latitude. This should not be controversial, since it is just based upon geometry. Even though I only do model calculations at latitudes of 5, 15, 25, 35, 45, 55, 65, 75, and 85 deg. (north and south), the global, 24-hr average incident solar flux is very close to simply 1,370 divided by 4, which is the ratio of the surface areas of a circle and a sphere having the same radius:

If I had done calculations for every 1 deg. of latitude, the model result would have been exceedingly close to 1,370/4.

If I assume the surface layer responding to heating is 0.1 m deep, a global albedo of 0.3, and a broadband IR emissivity of 0.98, and run the model for 46 days, the model reaches very nearly a steady-state energy equilibrium no matter what temperature I initialize it at (say, 100K or 300 K):

Note that even in the deep tropics, the average temperature is only 29 deg. F. At 45 deg. latitude, the temperature averages -11 deg. F. The diurnal temperature variations are very large, partly because the greenhouse effect in nature helps retain surface energy at night, keeping temperatures from falling too fast like it does in the model.

There is no realistic way to remove the very cold bias of the model without including an atmospheric greenhouse effect. If you object that convection has been ignored, that is a surface cooling (not warming) process, so including convection will only make matters worse. The lack of model heat transport out of the tropics, similarly, would only make the model tropical temperatures colder, not warmer, if it was included. The supposed warming caused by atmospheric pressure that some believe is an alternative theory to the GHE would cause (as Willis Eschenbach has pointed out) surface temperatures to rise, making the surface lose more energy to space than it gains from the sun, and there would no longer be energy balance, violating the 1st Law of Thermodynamics. The temperature would simply go back down again to achieve energy balance (we wouldn’t want to violate the 1st Law).

I hope this will help convince some who are still open-minded on this subject that even intense tropical sunshine cannot explain real-world tropical temperatures. The atmospheric greenhouse effect must also be included. The temperature (of anything) is not determined by the rate of energy input (say, the intensity of sunlight, or how fast your car engine burns gas); it is the result of a balance between energy gain and energy loss. The greenhouse effect reduces the rate of energy loss at the surface, thus causing higher temperatures then if it did not exist.

On the Flat Earth Rants of Joe Postma

June 4th, 2019

Willis Eschenbach and I have been defending ourselves on Facebook against Joe Postma’s claims we have “flat Earth” beliefs about the radiative energy budget of the Earth. The guy is obviously passionate, as our discussion ended with expletive-laced insults hurled my way (I suspect Willis decided the discussion wasn’t worth the effort, and withdrew before the fireworks began).

Joe advertises himself as an astrophysicist who works at the University of Calgary. I don’t know his level of education, but his claims have considerable influence on others, which is why I am addressing them here. He has numerous writings and Youtube videos on the subject of Earth’s energy budget and greenhouse effect, and the supposed errors the climate research community has made. I get emails and comments on my blog from others who invoke his claims, and so he is difficult to ignore.

Here I want to address just one of his claims (repeated by others, and the basis of his accusation I am a flat-Earther), recently described here, regarding the value of solar flux at the top of the atmosphere that is found in many simplified diagrams of the Earth’s energy budget. I will use the same two graphics used in that article, one from Harvard and one from Penn State:

Joe’s claim (as far as I can tell) is that that the solar flux value (often quoted to be around 342 W/m2) is unrealistic because it is for a flat Earth. But as an astrophysicist, he should recognize the division by 4 (“Fs(1-A)/4” and “S/4”) in the upper-left portion of both figures, which takes the solar constant at the distance of the Earth from the sun (about 1,370 W/m2) and spreads it over the spherical shape of the Earth. Thus, the 342 W/m2 value represents a spherical (not flat) Earth.

Just because someone then draws a diagram using a flat surface representing the Earth doesn’t mean the calculation is for a “flat Earth”.

Next in that article, Joe’s (mistaken) value for the solar constant is then used to compute the resulting Earth-Sun distance implied by us silly climate scientists who believe the solar constant is 342.5 W/m2 (rather than the true value of 1,370 W/m2). He gets twice the true, known value of the Earth-Sun distance, simply because he used a solar flux that was off by a factor of 4.

Now, I find it hard to believe an actual astrophysicist could make such an elementary error. I can ignore Joe’s profane personal insults, but he ends up influencing many people, and then I have to deal with their questions individually. Sometimes it’s better if I can just point them to a blog post, which is why I wrote this.

UPDATE: (June 6, 2019): Joe Postma has posted a YouTube video rebutting my article. If you listen to him from 2:30 to 2:45, Joe refuses to accept that the S=1,370 W/m2 “solar constant” energy that is intercepted by the cross-sectional area of the Earth must then get spread out, over time, over the whole (top-of-atmosphere) surface area of Earth. [This why S gets divided by 4 in global average energy budget diagrams, it’s the difference between the area of a circle and the area of a sphere with the same radius.] I am at a loss for words how he can refuse to accept something that is so obviously true — it’s simple geometry. I stand by everything I have written here.

UAH Global Temperature Update for May, 2019: +0.32 deg. C

June 3rd, 2019

The Version 6.0 global average lower tropospheric temperature (LT) anomaly for May, 2019 was +0.32 deg. C, down from the April, 2019 value of +0.44 deg. C:

Various regional LT departures from the 30-year (1981-2010) average for the last 17 months are:

2018 01 +0.29 +0.51 +0.06 -0.10 +0.70 +1.39 +0.52
2018 02 +0.25 +0.28 +0.21 +0.05 +0.99 +1.21 +0.35
2018 03 +0.28 +0.43 +0.12 +0.08 -0.19 -0.32 +0.76
2018 04 +0.21 +0.32 +0.09 -0.14 +0.06 +1.01 +0.84
2018 05 +0.16 +0.38 -0.05 +0.01 +1.90 +0.14 -0.24
2018 06 +0.20 +0.33 +0.06 +0.12 +1.11 +0.76 -0.41
2018 07 +0.30 +0.38 +0.22 +0.28 +0.41 +0.24 +1.49
2018 08 +0.18 +0.21 +0.16 +0.11 +0.02 +0.11 +0.37
2018 09 +0.13 +0.14 +0.13 +0.22 +0.89 +0.23 +0.28
2018 10 +0.20 +0.27 +0.12 +0.30 +0.20 +1.08 +0.43
2018 11 +0.26 +0.24 +0.28 +0.45 -1.16 +0.67 +0.55
2018 12 +0.25 +0.35 +0.15 +0.30 +0.25 +0.69 +1.20
2019 01 +0.38 +0.35 +0.41 +0.36 +0.53 -0.15 +1.15
2019 02 +0.37 +0.47 +0.28 +0.43 -0.02 +1.04 +0.05
2019 03 +0.34 +0.44 +0.25 +0.41 -0.55 +0.96 +0.59
2019 04 +0.44 +0.38 +0.51 +0.54 +0.50 +0.92 +0.91
2019 05 +0.32 +0.29 +0.35 +0.39 -0.61 +0.98 +0.39

The UAH LT global anomaly image for May, 2019 should be available in the next few days here.

Lower Troposphere:
Lower Stratosphere:

Recent Tornadoes are Due to Unusually Cold Weather

May 29th, 2019

I had an op-ed published at yesterday describing the reason why we have had so many tornadoes this year. The answer is the continuing cold weather stretching from Michigan through Colorado to California. A persistent cold air mass situated north and west of the usual placement of warm and humid Gulf air in the East is what is required for rotating thunderstorms to be embedded in a strong wind shear environment.

The temperature departures from normal so far this month show evidence of this cold:

In fact, in terms of departures from normal, so far this year the Northern Plains has been the “coldest place on Earth”, averaging 5-10 deg. F below normal:

The strong wind shear and warm advection provided at the “tightened” boundary between the warm and cold air masses is the usual missing ingredient in tornado formation, unlike Alexandria Ocasio-Cortez’s claim that a New Jersey tornado warning was somehow tied to global warming.

As has been pointed out elsewhere, a trend line fit to the number of strong to violent U.S. tornadoes has gone down from 60 in 1954 to 30 in 2018. In other words, the number of most damaging tornadoes has, on average, been cut in half since U.S. statistics started to be compiled:

Or, phrased another way, the last half of the 65-year U.S. tornado record had 40% fewer strong to violent tornadoes than the first half.

To claim that global warming is causing more tornadoes is worse than speculative; it is directly opposite to the clear observational evidence.

Half of 21st Century Warming Due to El Nino

May 13th, 2019

A major uncertainty in figuring out how much of recent warming has been human-caused is knowing how much nature has caused. The IPCC is quite sure that nature is responsible for less than half of the warming since the mid-1900s, but politicians, activists, and various green energy pundits go even further, behaving as if warming is 100% human-caused.

The fact is we really don’t understand the causes of natural climate change on the time scale of an individual lifetime, although theories abound. For example, there is plenty of evidence that the Little Ice Age was real, and so some of the warming over the last 150 years (especially prior to 1940) was natural — but how much?

The answer makes as huge difference to energy policy. If global warming is only 50% as large as is predicted by the IPCC (which would make it only 20% of the problem portrayed by the media and politicians), then the immense cost of renewable energy can be avoided until we have new cost-competitive energy technologies.

The recently published paper Recent Global Warming as Confirmed by AIRS used 15 years of infrared satellite data to obtain a rather strong global surface warming trend of +0.24 C/decade. Objections have been made to that study by me (e.g. here) and others, not the least of which is the fact that the 2003-2017 period addressed had a record warm El Nino near the end (2015-16), which means the computed warming trend over that period is not entirely human-caused warming.

If we look at the warming over the 19-year period 2000-2018, we see the record El Nino event during 2015-16 (all monthly anomalies are relative to the 2001-2017 average seasonal cycle):

Fig. 1. 21st Century global-average temperature trends (top) averaged across all CMIP5 climate models (gray), HadCRUT4 observations (green), and UAH tropospheric temperature (purple). The Multivariate ENSO Index (MEI, bottom) shows the upward trend in El Nino activity over the same period, which causes a natural enhancement of the observed warming trend.

We also see that the average of all of the CMIP5 models’ surface temperature trend projections (in which natural variability in the many models is averaged out) has a warmer trend than the observations, despite the trend-enhancing effect of the 2015-16 El Nino event.

So, how much of an influence did that warm event have on the computed trends? The simplest way to address that is to use only the data before that event. To be somewhat objective about it, we can take the period over which there is no trend in El Nino (and La Nina) activity, which happens to be 2000 through June, 2015 (15.5 years):

Fig. 2. As in Fig. 1, but for the 15.5 year period 2000 to June 2015, which is the period over which there was no trend in El Nino and La Nina activity.

Note that the observed trend in HadCRUT4 surface temperatures is nearly cut in half compared to the CMIP5 model average warming over the same period, and the UAH tropospheric temperature trend is almost zero.

One might wonder why the UAH LT trend is so low for this period, even though in Fig. 1 it is not that far below the surface temperature observations (+0.12 C/decade versus +0.16 C/decade for the full period through 2018). So, I examined the RSS version of LT for 2000 through June 2015, which had a +0.10 C/decade trend. For a more apples-to-apples comparison, the CMIP5 surface-to-500 hPa layer average temperature averaged across all models is +0.20 C/decade, so even RSS LT (which usually has a warmer trend than UAH LT) has only one-half the warming trend as the average CMIP5 model during this period.

So, once again, we see that the observed rate of warming — when we ignore the natural fluctuations in the climate system (which, along with severe weather events dominate “climate change” news) — is only about one-half of that projected by climate models at this point in the 21st Century. This fraction is consistent with the global energy budget study of Lewis & Curry (2018) which analyzed 100 years of global temperatures and ocean heat content changes, and also found that the climate system is only about 1/2 as sensitive to increasing CO2 as climate models assume.

It will be interesting to see if the new climate model assessment (CMIP6) produces warming more in line with the observations. From what I have heard so far, this appears unlikely. If history is any guide, this means the observations will continue to need adjustments to fit the models, rather than the other way around.

The Weakness of Tropospheric Warming as Confirmed by AIRS

May 1st, 2019

I present comparisons between both the UAH and RSS global lower troposphere (LT) temperature variations and LT computed from the vertical temperature profiles retrieved from the NASA AIRS instrument flying on the Aqua satellite. This follows up on the recent newsworthy announcement by NASA researchers, Recent Global Warming as Confirmed by AIRS published in Environmental Research Letters in which it was claimed the AIRS surface skin temperature retrievals validated the GISTEMP record of surface air temperatures during 2003-2017.

The data I use are the AIRS Version 6 monthly average gridpoint retrievals covering September 2002 through March 2019 (16.6 years, NASA registration required). To compute LT from the AIRS profiles I have taken into account the somewhat different vertical profiles of sensitivity in the UAH and RSS LT weighting functions, as well as the different southern extent of the “global” domains (UAH extends to 82.5 deg. S, while RSS is to 70 deg. S) in the global averages.

First up is the comparison of UAH LT versus LT computed from the AIRS profiles:

Fig. 1. Global (82.5N-82.5S) lower tropospheric temperature variations (deg. C) about the 2003-2018 mean annual cycle for UAH Version 6 LT and Version 6 LT computed from the NASA AIRS temperature profiles over the same region.

Note that El Nino and La Nina variations dominate this short period of record, and much of the warming trend (+0.15 C/decade) is due to this activity. The agreement is very good, with nearly identical trends in UAH and AIRS and an explained variance of 88.9%.

The agreement is rather remarkable given that the AIRS is an infrared instrument with much more serious cloud contamination effects than the UAH LT which is totally microwave-based from the Advanced Microwave Sounding Units (AMSUs) flying on 5 different satellites during this period. The AIRS cloud effects are removed in processing through a “cloud clearing” algorithm when scattered clouds are present, but temperatures in the lower troposphere cannot be measured in extensive cloud regions.

Next let’s look at a similar comparison for the RSS LT product. It also shows very good agreement with AIRS (87.3% explained variance), but with a somewhat greater trend compared to AIRS (by 0.07 C/decade). Again, the record is rather short (16.6 years) and so this trend difference should not be assumed to apply to the whole 40-year satellite record of RSS LT:

Fig. 2. Global (82.5N-70.0S) lower tropospheric temperature variations (deg. C) about the 2003-2018 mean annual cycle for RSS Version 4 LT and Version 4 LT computed from the NASA AIRS temperature profiles over the same region.

Again, the LT layers measured by UAH and RSS are somewhat different. The UAH LT layer is deeper, and so it picks up more of the enhanced warming in the 250-300 mb layer, as seen in the vertical profile of AIRS global temperature trends:

Fig. 3. AIRS global temperature trends (gray) as a function of height, during the period Sept. 2002 through March 2019. The solid point at the bottom is the surface skin temperature, while the open circle is the surface air temperature. Also shown are the different weighting profiles of the UAH and RSS “lower tropospheric” (LT) products which are applied to the AIRS temperature profile to obtain LT estimates from AIRS.

Given the rather high level of agreement between the microwave and infrared measures of global-average tropospheric temperatures, I see no reason why the AIRS data should not be used as a way to do periodic checks on the UAH and RSS LT global temperature variations.

Finally, since J. Susskind and Gavin Schmidt have proclaimed AIRS as confirming the GISTEMP record of substantial surface warming (“Recent Global Warming as Confirmed by AIRS”), I am similarly going to proclaim Fig. 1 as evidence that AIRS also validates the UAH LT record of only modest tropospheric warming.

So there.

UAH Global Temperature Update for April, 2019: +0.44 deg. C.

May 1st, 2019

The Version 6.0 global average lower tropospheric temperature (LT) anomaly for April, 2019 was +0.44 deg. C, up from the March, 2019 value of +0.34 deg. C:

Various regional LT departures from the 30-year (1981-2010) average for the last 16 months are:

2018 01 +0.29 +0.51 +0.06 -0.10 +0.70 +1.39 +0.52
2018 02 +0.25 +0.28 +0.21 +0.05 +0.99 +1.21 +0.35
2018 03 +0.28 +0.43 +0.12 +0.08 -0.19 -0.32 +0.76
2018 04 +0.21 +0.32 +0.10 -0.14 +0.06 +1.01 +0.84
2018 05 +0.16 +0.38 -0.05 +0.01 +1.90 +0.13 -0.24
2018 06 +0.20 +0.33 +0.06 +0.12 +1.10 +0.76 -0.41
2018 07 +0.30 +0.37 +0.22 +0.28 +0.41 +0.24 +1.49
2018 08 +0.18 +0.21 +0.16 +0.11 +0.02 +0.10 +0.37
2018 09 +0.13 +0.14 +0.13 +0.22 +0.89 +0.22 +0.28
2018 10 +0.20 +0.27 +0.12 +0.30 +0.20 +1.08 +0.43
2018 11 +0.26 +0.24 +0.28 +0.45 -1.16 +0.67 +0.55
2018 12 +0.25 +0.35 +0.15 +0.30 +0.24 +0.69 +1.21
2019 01 +0.38 +0.35 +0.41 +0.36 +0.53 -0.15 +1.15
2019 02 +0.37 +0.47 +0.28 +0.43 -0.02 +1.04 +0.06
2019 03 +0.34 +0.44 +0.25 +0.41 -0.55 +0.96 +0.59
2019 04 +0.44 +0.38 +0.51 +0.54 +0.50 +0.92 +0.91

The UAH LT global anomaly image for April, 2019 should be available in the next few days here.

The new Version 6 files should also be updated at that time, and are located here:

Lower Troposphere:
Lower Stratosphere:

NASA AIRS: 80% of U.S. Warming has been at Night

April 30th, 2019

I have previously addressed the NASA study that concluded the AIRS satellite temperatures “verified global warming trends“. The AIRS is an infrared temperature sounding instrument on the NASA Aqua satellite, providing data since late 2002 (over 16 years). All results in that study, and presented here, are based upon infrared measurements alone, with no microwave temperature sounder data being used in these products.

That reported study addressed only the surface “skin” temperature measurements, but the AIRS is also used to retrieve temperature profiles throughout the troposphere and stratosphere — that’s 99.9% of the total mass of the atmosphere.

Since AIRS data are also used to retrieve a 2 meter temperature (the traditional surface air temperature measurement height), I was curious why that wasn’t used instead of the surface skin temperature. Also, AIRS allows me to compare to our UAH tropospheric deep-layer temperature products.

So, I downloaded the entire archive of monthly average AIRS temperature retrievals on a 1 deg. lat/lon grid (85 GB of data). I’ve been analyzing those data over various regions (global, tropical, land, ocean). While there are a lot of interesting results I could show, today I’m going to focus just on the United States.

Because the Aqua satellite observes at nominal local times of 1:30 a.m. and 1:30 p.m., this allows separation of data into “day” and “night”. It is well known that recent warming of surface air temperatures (both in the U.S. and globally) has been stronger at night than during the day, but the AIRS data shows just how dramatic the day-night difference is… keeping in mind this is only the most recent 16.6 years (since September 2002):

AIRS temperature trend profiles averaged over the contiguous United States, Sept. 2002 through March 2019. Gray represents an average of day and night. Trends are based upon monthly departures from the average seasonal cycle during 2003-2018. The UAH LT temperature trend (and it’s approximate vertical extent) is in violet, and NOAA surface air temperature trends (Tmax, Tmin, Tavg) are indicated by triangles. The open circles are the T2m retrievals, which appear to be less trustworthy than the Tskin retrievals.

The AIRS surface skin temperature trend at night (1:30 a.m.) is a whopping +0.57 C/decade, while the daytime (1:30 p.m.) trend is only +0.15 C/decade. This is a bigger diurnal difference than indicated by the NOAA Tmax and Tmin trends (triangles in the above plot). Admittedly, 1:30 a.m. and 1:30 pm are not when the lowest and highest temperatures of the day occur, but I wouldn’t expect as large a difference in trends as is seen here, at least at night.

Furthermore, these day-night differences extend up through the lower troposphere, to higher than 850 mb (about 5,000 ft altitude), even showing up at 700 mb (about 12,000 ft. altitude).

This behavior also shows up in globally-averaged land areas, and reverses over the ocean (but with a much weaker day-night difference). I will report on this at some point in the future.

If real, these large day-night differences in temperature trends is fascinating behavior. My first suspicion is that it has something to do with a change in moist convection and cloud activity during warming. For instance more clouds would reduce daytime warming but increase nighttime warming. But I looked at the seasonal variations in these signatures and (unexpectedly) the day-night difference is greatest in winter (DJF) when there is the least convective activity and weakest in summer (JJA) when there is the most convective activity.

One possibility is that there is a problem with the AIRS temperature retrievals (now at Version 6). But it seems unlikely that this problem would extend through such a large depth of the lower troposphere. I can’t think of any reason why there would be such a large bias between day and night retrievals when it can be seen in the above figure that there is essentially no difference from the 500 mb level upward.

It should be kept in mind that the lower tropospheric and surface temperatures can only be measured by AIRS in the absence of clouds (or in between clouds). I have no idea how much of an effect this sampling bias would have on the results.

Finally, note how well the AIRS low- to mid-troposphere temperature trends match the bulk trend in our UAH LT product. I will be examining this further for larger areas as well.