I’ve always wanted to capture this kind of cloud as a time lapse. I noticed them on my way to work this morning, and had a camera and tripod with me, so this was my chance.

These are altocumulus clouds with strong upward motion in them producing precipitation, in this case snow, falling in streaks and not reaching the ground (“virga”). They form when the upper troposphere is unstable, and warm advection (a warm air mass moving into a cool air mass) produces the uplift.

We live next to a 1,500 ft tall TV tower which frequently gets hit by lightning. We are so close, in fact, that it is difficult to get good photographs because the area just above the tower top is at an elevation angle of 70 deg. viewed from our yard, which is too steep to take pictures through a window, and for outdoor photos will cause rain to fall on the camera lens.

So, despite thousands of lightning strikes we have witnessed and heard over the years, I seldom attempt photos or video. Yesterday was one of the rare instances where the thunderstorm anvil trailing downwind of the approaching storm was sending out lightning strokes at regular, ~2 minute intervals before the rain began. This is the best time to watch the lightning for us, usually by lying down on an outdoor lounge chair and looking almost straight up.

My camera equipment was still in the car from a short trip to take photos of Comet NEOWISE the previous morning. Sensing this was a chance to capture some good video, I ran out to the car, retrieved my camera backpack and a tripod, and ran to the backyard. I set up the camera and tripod in record time, quickly adjusted the focus manually, and hit the record button.

In literally less than 1 second after pressing the record button, the best strike of the storm occurred. Here’s an animated GIF of the event… 20 video frame captures at 1/24 sec intervals (the vertical scale is exaggerated because I forced WordPress to load the full-resolution file… click on the video to see the proper perspective):

There are a lot of interesting features that I won’t go into here, except I especially like how the ionized channel breaks up into pockets of glowing air during dissipation, here’s one of the frames showing that structure:

Overview: Death certificate data, corrected for recent under-reporting, reveals a 10-20% decrease in weekly deaths compared to seasonal norms commencing in early March, 2020. This date coincides with the widespread closing of public schools. It is hypothesized that a decrease in traffic accidents is the most likely explanation for the decrease, a conclusion which would be confirmed from detailed analysis of the death certificate data.

I had previously blogged on the caution needed when analyzing the death counts from death certificate data compiled by the CDC. The most recent weeks always have under-counted totals because it takes weeks to months for all of the death certificates to trickle in and be counted. Use of the data without knowing this can lead to false conclusions about recently declining death rates. I outlined a simple method for doing a first-order correction of the data based upon the number of additional death reports in each successive week, a method which I use here.

The CDC data report weekly deaths in three age groups: less than 18 years old (“child”), 18-64, and 65 on up. The data are updated weekly, and the data online extend back to week 40 in 2015. I examined the death totals for the under-18 year old group versus the totals for the 18-and-older (combined) group. (Only those recent reports that were labeled as “100% reporting” were used, but this notation is misleading because the CDC means 100% of the locations around the country had submitted reports, not that all of the reports were complete.)

I removed the average seasonal cycle (2016-2019) from the weekly totals, which show a seasonal ~11% peak in deaths in early January for adults, and a weaker ~6% peak in children’s deaths in early June (Fig. 1).

Fig. 1. Seasonal variations (%) in deaths (all causes) for adults versus children, 2016 through 2019.

In order to corrected for under-reporting of recent deaths, I used the data from 4 successive weeks earlier this year to correct the most recent 52 weeks of data. Those 4 successive weeks yielded average week-to-week adjustments which accumulated to 16.5% under-reporting for 1 week previous to latest reported week; 10.4% at 2 weeks previous; 7.8% at 3 weeks; 6.4% at 4 weeks, dropping below 1% at 10 weeks previous, etc.

I then computed the weekly percent departures from the average seasonal cycle for the entire time period (since week 40 of 2015). The results (Fig. 2) show the unusually bad peak in seasonal flu and pneumonia deaths in 2017-18, which as expected results in a larger increase in adults that children.

Fig. 2. Weekly number of deaths as percent departures from seasonal normals, for adults versus children, plotted as a phase space diagram (successive weeks connected by a line).

Note that there is a 10-20% decrease in child deaths beginning in early March, which is when most schools in the U.S closed down. Since the most frequent cause of death in the under-18 age group is auto accidents, it makes sense that the greatly reduced traffic activity during “lockdown” led to fewer deaths.

Of course, the same kind of reduction would be expected in the adult age category, but it is completely overwhelmed in Fig. 2 by the large increase due to COVID-19 deaths, which peaked in mid-April. Since there have been very few COVID-19 deaths in children we more clearly see the reduction in that age group. In absolute terms, a 15% reduction in childhood deaths equates to about 85 children per week. 

For the last 10 years I have consulted for grain growing interests, providing information about past and potential future trends in growing season weather that might impact crop yields. Their primary interest is the U.S. corn belt, particularly the 12 Midwest states (Iowa, Illinois, Indiana, Ohio, Kansas, Nebraska, Missouri, Oklahoma, the Dakotas, Minnesota, and Michigan) which produce most of the U.S. corn and soybean crop.

Contrary to popular perception, the U.S. Midwest has seen little long-term summer warming. For precipitation, the slight drying predicted by climate models in response to human greenhouse gas emissions has not occurred; if anything, precipitation has increased. Corn yield trends continue on a technologically-driven upward trajectory, totally obscuring any potential negative impact of “climate change”.

What Period of Time Should We Examine to Test Global Warming Claims?

Based upon the observations, “global warming” did not really begin until the late 1970s. Prior to that time, anthropogenic greenhouse gas emissions had not yet increased by much at all, and natural climate variability dominated the observational record (and some say it still does).

Furthermore, uncertainties regarding the cooling effects of sulfate aerosol pollution make any model predictions before the 1970s-80s suspect since modelers simply adjusted the aerosol cooling effect in their models to match the temperature observations, which showed little if any warming before that time which could be reasonably attributed to greenhouse gas emissions.

This is why I am emphasizing the last 50 years (1970-2019)…this is the period during which we should have seen the strongest warming, and as greenhouse gas emissions continue to increase, it is the period of most interest to help determine just how much faith we should put into model predictions for changes in national energy policies. In other words, quantitative testing of greenhouse warming theory should be during a period when the signal of that warming is expected to be the greatest.

50 Years of Predictions vs. Observations

Now that the new CMIP6 climate model experiment data are becoming available, we can begin to get some idea of how those models are shaping up against observations and the previous (CMIP5) model predictions. The following analysis includes the available model out put at the KNMI Climate Explorer website. The temperature observations come from the statewide data at NOAA’s Climate at a Glance website.

For the Midwest U.S. in the summer (June-July-August) we see that there has been almost no statistically significant warming in the last 50 years, whereas the CMIP6 models appear to be producing even more warming than the CMIP5 models did.

Fifty years (1970-2019) of U.S. corn belt summer (JJA) warming since 1970 from observations (blue); the previous CMIP5 climate models (42 model avg., green); and the new CMIP6 climate models (13 model avg., red). The three time series have been vertically aligned so their trend lines coincide in the first year (1970), which is the most meaningful way to quantify the long-term warming since 1970.

The observed 50-year trend is only 0.086 C/decade (barely significant at the 1-sigma level), while the CMIP5 average model trend is 4X as large at 0.343 C/decade, and the CMIP6 trend is 5.7X as large at 0.495 C/decade. While the CMIP6 trend will change somewhat as more models are added, it is consistent with the report that the CMIP6 models are producing more average warming than their CMIP5 predecessors.

I am showing the average of the available models rather than individual models, because it is the average of the models which guides the UN IPCC reports and thus energy policy. It is disingenuous for some to claim that “not all IPCC models disagree with the observations”, as if that is some sort of vindication of all the models. It is not. If there are one or two models that agree the best with observations, why isn’t the IPCC just using those to write its reports? Hmmm?

What I find particularly troubling is that the climate modelers are increasingly deaf to what observations tell us. How can the CMIP5 models (let alone the newer CMIP6 models) be used to guide U.S. energy policy when there is such a huge discrepancy between the models and the observations?

I realize this is just one season (summer) in one region (the U.S. Midwest), but it is immensely important. The U.S. is the world leader in production of corn (which is used for feed, food, and fuel) and behind only Brazil in soybean production. Blatantly false claims (e.g. here) of observed change in Midwest climate have fed the popular opinion that U.S. crops are already feeling the negative effects of human-caused climate change, despite the facts.

This is just one example of many that the news media have been complicit in the destruction of rational climate debate, which is now extending to outright censoring of alternative climate views on not only social media, but also in mainstream news sources like Forbes which disappeared environmentalist Michael Shellenberger’s op-ed in which he confessed he no longer believes in a “climate crisis”.

The Version 6.0 global average lower tropospheric temperature (LT) anomaly for June, 2020 was +0.43 deg. C, down from the May, 2020 value of +0.54 deg. C.

The linear warming trend since January, 1979 is +0.14 C/decade (+0.12 C/decade over the global-averaged oceans, and +0.18 C/decade over global-averaged land).

Various regional LT departures from the 30-year (1981-2010) average for the last 18 months are:

 YEAR MO GLOBE NHEM. SHEM. TROPIC USA48 ARCTIC AUST 
 2019 01 +0.38 +0.35 +0.41 +0.36 +0.53 -0.14 +1.15
 2019 02 +0.37 +0.47 +0.28 +0.43 -0.02 +1.05 +0.05
 2019 03 +0.34 +0.44 +0.25 +0.41 -0.55 +0.97 +0.58
 2019 04 +0.44 +0.38 +0.51 +0.54 +0.49 +0.93 +0.91
 2019 05 +0.32 +0.29 +0.35 +0.39 -0.61 +0.99 +0.38
 2019 06 +0.47 +0.42 +0.52 +0.64 -0.64 +0.91 +0.35
 2019 07 +0.38 +0.33 +0.44 +0.45 +0.11 +0.34 +0.87
 2019 08 +0.39 +0.38 +0.39 +0.42 +0.17 +0.44 +0.23
 2019 09 +0.61 +0.64 +0.59 +0.60 +1.14 +0.75 +0.57
 2019 10 +0.46 +0.64 +0.27 +0.30 -0.03 +1.00 +0.49
 2019 11 +0.55 +0.56 +0.54 +0.55 +0.21 +0.56 +0.38
 2019 12 +0.56 +0.61 +0.50 +0.58 +0.92 +0.66 +0.94
 2020 01 +0.56 +0.60 +0.53 +0.61 +0.73 +0.12 +0.66
 2020 02 +0.76 +0.96 +0.55 +0.76 +0.38 +0.02 +0.30
 2020 03 +0.48 +0.61 +0.34 +0.63 +1.09 -0.72 +0.16
 2020 04 +0.38 +0.43 +0.34 +0.45 -0.59 +1.03 +0.97
 2020 05 +0.54 +0.60 +0.49 +0.66 +0.17 +1.15 -0.15
 2020 06 +0.43 +0.45 +0.41 +0.46 +0.38 +0.80 +1.20

The UAH LT global gridpoint anomaly image for June, 2020 should be available within the next week here.

The global and regional monthly anomalies for the various atmospheric layers we monitor should be available in the next few days at the following locations:

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

Those who defend climate model predictions often produce plots of observed surface temperature compared to the models which show very good agreement. Setting aside the debate over the continuing adjustments to the surface temperature record which produce ever-increasing warming trends, let’s look at how the most recent (CMIP6) models are doing compared to the latest version of the observations (however good those are).

First, I’d like to explain how some authors get such good agreement between the models and observations. Here are the two “techniques” they use that most annoy me.

1. They look at long periods of time, say the last 100+ years. This improves the apparent agreement because most of that period was before there was substantial forcing of the climate system by increasing CO2.
2. They plot anomalies about a common reference period, but do not show trend lines. Or, if they show trends lines, they do not start them at the same point at the beginning of the record. When you do this, the discrepancy between models and observations is split in half, with the discrepancy in the latter half of the record having the opposite sign of the discrepancy in the early part of the record. They say, “See? The observed temperatures in the last few decades nearly match the models!”

In the following plot (which will be included in a report I am doing for the Global Warming Policy Foundation) I avoid both of those problems. During the period of strongest greenhouse gas forcing (since 1979), the latest CMIP6 models reveal 50% more net surface warming from 1979 up to April 2020 (+1.08 deg. C) than do the observations (+0.72 deg. C).

Note I have accounted for the trends being somewhat nonlinear, using a 2nd order polynomial fit to all three time series. Next, I have adjusted the CMIP time series vertically so that their polynomial fit lines are coaligned with the observations in 1979. I believe this is the most honest and meaningful way to intercompare the warming trends in different datasets.

As others have noted, it appears the CMIP6 models are producing even more warming than the CMIP5 models did… although the KNMI Climate Explorer website (from which all of the data was downloaded) has only 13 models archived so far.

The Mauna Loa atmospheric CO2 concentration data continue to show no reduction in the rate of rise due to the recent global economic slowdown. This demonstrates how difficult it is to reduce global CO2 emissions without causing a major disruption to the global economy and exacerbation of poverty.

After removal of the strong seasonal cycle in Mauna Loa CO2 data, and a first order estimate of the CO2 influence of El Nino and La Nina activity (ENSO), the May 2020 update shows no indication of a reduction in the rate of rise in the last few months, when the reduction in economic activity should have shown up.

I had previously explained why the slowdown would likely not be large enough to affect measured atmospheric CO2 levels compared to natural variations in global sources and sinks of CO2. I calculated that the Energy Information Administration-estimated 11% reductions in CO2 emissions during 2020 would have to be four times larger to stop the rise of atmospheric CO2 over 2019 values (assuming no substantial natural variations in CO2 sources and sinks).

The Version 6.0 global average lower tropospheric temperature (LT) anomaly for May, 2020 was +0.54 deg. C, up from the April, 2020 value of +0.38 deg. C.

The linear warming trend since January, 1979 is +0.14 C/decade (+0.12 C/decade over the global-averaged oceans, and +0.18 C/decade over global-averaged land).

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

 YEAR MO GLOBE NHEM. SHEM. TROPIC USA48 ARCTIC AUST 
 2019 01 +0.38 +0.35 +0.41 +0.36 +0.53 -0.14 +1.15
 2019 02 +0.37 +0.47 +0.28 +0.43 -0.02 +1.05 +0.05
 2019 03 +0.34 +0.44 +0.25 +0.41 -0.55 +0.97 +0.59
 2019 04 +0.44 +0.38 +0.51 +0.54 +0.49 +0.92 +0.91
 2019 05 +0.32 +0.29 +0.35 +0.40 -0.61 +0.98 +0.38
 2019 06 +0.47 +0.42 +0.52 +0.64 -0.64 +0.91 +0.35
 2019 07 +0.38 +0.33 +0.44 +0.45 +0.10 +0.33 +0.87
 2019 08 +0.39 +0.38 +0.39 +0.42 +0.17 +0.44 +0.24
 2019 09 +0.61 +0.64 +0.59 +0.60 +1.14 +0.75 +0.57
 2019 10 +0.46 +0.64 +0.28 +0.31 -0.03 +0.99 +0.50
 2019 11 +0.55 +0.56 +0.54 +0.55 +0.21 +0.56 +0.38
 2019 12 +0.56 +0.61 +0.50 +0.58 +0.92 +0.66 +0.94
 2020 01 +0.56 +0.60 +0.53 +0.62 +0.73 +0.12 +0.66
 2020 02 +0.76 +0.96 +0.55 +0.76 +0.38 +0.02 +0.30
 2020 03 +0.48 +0.61 +0.34 +0.63 +1.09 -0.72 +0.17
 2020 04 +0.38 +0.43 +0.34 +0.45 -0.59 +1.03 +0.97
 2020 05 +0.54 +0.60 +0.49 +0.66 +0.17 +1.15 -0.15

The UAH LT global gridpoint anomaly image for May, 2020 should be available within the next week here.

The global and regional monthly anomalies for the various atmospheric layers we monitor should be available in the next few days at the following locations:

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

Noctilucent clouds (NLCs) are often visible in the extreme upper atmosphere (80-85 km altitude) well after sunset in the late spring and summer at high northern latitudes. They form from ice condensing on meteoric dust at extremely cold temperatures (below 150 Kelvin or -190 deg. F). The cold temperatures are due to adiabatic cooling from dynamic uplift combined with little ozone to absorb the sun’s ultraviolet radiation.

Noctilucent clouds on May 31, 2020 (Alan Tough, Scotland)

With current solar minimum conditions reducing solar heating by ultraviolet radiation, and slowly increasing CO2 in the atmosphere causing enhanced infrared cooling to outer space, record low temperature are occurring. This has extended the southernmost viewing opportunity for NLCs from the usual ~60 deg. N latitude to as low as 35N latitude last year, with NLC sightings near Los Angeles and Las Vegas in 2019.

This recent article by Dr. Tony Phillips suggested that 2020 is shaping up to be another good NLC viewing season. An earlier article by Tony included this nice plot of April-May temperatures at 80 N latitude from this year compared to previous years, showing the general cooling trend during the current solar minimum conditions.

Data plot courtesy of Dr. Lynn Harvey, U. of Colorado.

We downloaded some more recent Aura satellite Microwave Limb Sounder data, and plotted it as a function of latitude. Note how fast temperatures dropped in only 6 days… 1-2 deg. C per day depending upon latitude.

Upper mesospheric temperatures at 83 km altitude on two different days in May, 2020 as a function of latitude. Lines are 2nd order polynomial fits to the data, providing average temperatures as a function of latitude.

The 141 K average temperature on May 27 (Day 148) at 80N latitude appears to be a new record low if we compare it to the data in the previous plot by Dr. Harvey.

I will try to keep track of these temperatures as the NLC season progresses in the coming weeks. So far, NLC sightings have been mostly in northern Europe and the UK.

Summary: Atmospheric levels of carbon dioxide (CO2) continue to increase with no sign of the global economic slowdown in response to the spread of COVID-19. This is because the estimated reductions in CO2 emissions (around -11% globally during 2020) is too small a reduction to be noticed against a background of large natural variability. The reduction in economic activity would have to be 4 times larger than 11% to halt the rise in atmospheric CO2.

Changes in the atmospheric reservoir of CO2 occur when there is an imbalance between surface sources and sinks of CO2. While the global land and ocean areas emit approximately 30 times as much CO2 into the atmosphere as humans produce from burning of fossil fuels, they also absorb about an equal amount of CO2. This is the global carbon cycle, driven mostly by biological activity.

There are variations in the natural carbon cycle, such as during El Nino (more CO2 accumulation in the atmosphere) and La Nina (more CO2 removed from the atmosphere). Greater wildfire activity releases more CO2, while major volcanic eruptions (paradoxically) lead to greater photosynthesis from more diffuse sunlight and extra removal of CO2 from the air. The most dramatic variations are seasonal, as the land-dominated Northern Hemisphere experiences an annual cycle of vegetation growth (CO2 removal) and decay (CO2 release).

The increase in atmospheric CO2 observed since the 1950s is most likely dominated by anthropogenic CO2 emissions, which are twice as large as that needed to explain the observed rise. As I have shown before, a simple CO2 budget model driven by (1) estimates of global yearly anthropogenic CO2 emissions, (2) El Nino and La Nina activity, and (3) a CO2 removal rate that is proportional to how much “extra” CO2 is in the atmosphere compared to a “preferred baseline” CO2 level, yields an excellent fit to yearly CO2 observations at Mauna Loa, Hawaii.

Fig. 1. Yearly Mauna Loa, HI CO2 observations since 1959 (red) versus a simple CO2 budget model (blue).

But those are yearly measurements, and we are now interested in whether the recent global economic slowdown is showing up in the monthly Mauna Loa CO2 data. If we remove the large seasonal variations (driven by the seasonal growth and decay of Northern Hemisphere vegetation), we see no evidence of the economic slowdown through April, 2020.

Fig. 2. Monthly CO2 data since 2015 from Mauna Loa, HI after the average seasonal cycle is statistically removed.

As can be seen in Fig. 2, there are some pretty large month-to-month jumps and dips around the long-term increase (represented by the dotted line). These are probably natural variations due to fluctuations in the average seasonal variations in vegetation growth and decay, wildfire activity, and El Nino and La Nina activity (which are imperfectly removed in the solid blue line in Fig. 2). Variations in economic activity might also be involved in these fluctuations.

The point is that given the large month-to-month variations in natural CO2 sources and sinks seen in Fig. 2, it would be difficult to see a downturn in the anthropogenic source of CO2 unless it was very large (say, over 50%) and prolonged (say over a year or longer).

Instead, the U.S. Energy Information Administration (EIA) estimates that the global economic slowdown this year due to the spread of the novel coronavirus will amount to only about an 11% reduction in global CO2 emissions. This is simply too small of a decrease in CO2 emissions to show up against a background of considerable monthly and yearly natural variability in the atmospheric CO2 budget.

That relatively small 11% reduction also illustrates how dependent humanity is on energy, since the economic disruption is leading to U.S. unemployment rates not seen since the Great Depression of the 1930s. Everything that humans do requires access to abundant and affordable energy, and even the current economic downturn is not enough to substantially reduce global CO2 emissions.

ADDENDUM: How much of a decrease in CO2 emissions would be required to stop the atmospheric rise in CO2?

An interesting aspect of the observed rise of atmospheric CO2 is that it indicates the greater the CO2 concentration, the faster the “extra” CO2 is removed by biological activity. The observed annual rate of removal is 2.3% of the excess above a baseline of 295 ppm. The greater the “excess”, the faster the rate of removal.

Because of this rapid rate of removal, the anthropogenic CO2 emissions do not have to go to zero to stop the observed rise in atmospheric CO2. Using my simple model (blue line in Fig. 1, above), I find that a 43% reduction in anthropogenic CO2 emissions in 2020 would — in the absence of natural fluctuations in the carbon cycle — lead to a halt in the observed rise of atmospheric CO2 in 2020 over 2019 levels. This is about 4 times larger than the EIA estimate of an 11% reduction in CO2 emissions for the year 2020.