UAH Global Temperature Update for July, 2022: +0.36 deg. C

August 2nd, 2022

The Version 6.0 global average lower tropospheric temperature (LT) anomaly for July, 2022 was +0.36 deg. C, up from the June, 2022 value of +0.06 deg. C.

 

The linear warming trend since January, 1979 still stands at +0.13 C/decade (+0.11 C/decade over the global-averaged oceans, and +0.18 C/decade over global-averaged land).

Various regional LT departures from the 30-year (1991-2020) average for the last 19 months are:

YEAR MO GLOBE NHEM. SHEM. TROPIC USA48 ARCTIC AUST 
2021 01 0.12 0.34 -0.09 -0.08 0.36 0.50 -0.52
2021 02 0.20 0.32 0.08 -0.14 -0.66 0.07 -0.27
2021 03 -0.01 0.13 -0.14 -0.29 0.59 -0.78 -0.79
2021 04 -0.05 0.05 -0.15 -0.28 -0.02 0.02 0.29
2021 05 0.08 0.14 0.03 0.06 -0.41 -0.04 0.02
2021 06 -0.01 0.30 -0.32 -0.14 1.44 0.63 -0.76
2021 07 0.20 0.33 0.07 0.13 0.58 0.43 0.80
2021 08 0.17 0.26 0.08 0.07 0.32 0.83 -0.02
2021 09 0.25 0.18 0.33 0.09 0.67 0.02 0.37
2021 10 0.37 0.46 0.27 0.33 0.84 0.63 0.06
2021 11 0.08 0.11 0.06 0.14 0.50 -0.43 -0.29
2021 12 0.21 0.27 0.15 0.03 1.63 0.01 -0.06
2022 01 0.03 0.06 0.00 -0.24 -0.13 0.68 0.09
2022 02 -0.00 0.01 -0.02 -0.24 -0.05 -0.31 -0.50
2022 03 0.15 0.27 0.02 -0.08 0.22 0.74 0.02
2022 04 0.26 0.35 0.18 -0.04 -0.26 0.45 0.60
2022 05 0.17 0.24 0.10 0.01 0.59 0.23 0.19
2022 06 0.06 0.07 0.04 -0.36 0.46 0.33 0.11
2022 07 0.36 0.37 0.35 0.13 0.70 0.55 0.65

The full UAH Global Temperature Report, along with the LT global gridpoint anomaly image for July, 2022 should be available within the next several days 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

Updated Atmospheric CO2 Concentration Forecast through 2050 and Beyond

July 18th, 2022

Summary

The simple CO2 budget model I introduced in 2019 is updated with the latest Mauna Loa measurements of atmospheric CO2 and with new Energy Information Administration estimates of global CO2 emissions through 2050. The model suggests that atmospheric CO2 will barely double pre-industrial levels by 2100, with a total radiative forcing of the climate system well below the most extreme scenario (RCP8.5) used in alarmist literature (and the U.S. national climate assessment), with the closest match to RCP4.5. The model also clearly shows the CO2 reducing effect of the Mt. Pinatubo eruption of 1991.

The Model

As described here, the simple CO2 budget model uses yearly sources and sinks of atmospheric CO2 to compute how much the atmospheric CO2 concentration changes from one year to the next.

The sink (removal) of “excess” atmospheric CO2 assumes that all of the biological and geophysical processes that remove CO2 from the atmosphere do so at a net rate proportional to the excess of the CO2 value above some ‘equilibrium’ value. When the model is calibrated with the yearly Mauna Loa CO2 data from 1959 through 2021, this rate of removal is 2.02% of the atmospheric excess above 294 ppm. So, for example, at the current CO2 concentration of 417 ppm, the biological and geophysical removal processes are removing 0.0202 x [417 – 294] = 2.48 ppm per year for 2022 (preliminary estimate).

The long-term source of CO2 increase is assumed to be anthropogenic. There are various estimates of yearly CO2 emissions, some from energy use alone, some including cement production and land use. I’ve used the Boden et al. (2017) and Our World in Data yearly estimates for 1750 through 2009, and EIA.gov estimates of yearly emissions growth rates from 2010 to 2050, and then assumed their 2050 growth rate is constant to 2100.

I also have included an ENSO term (El Nino and La Nina) to empirically account for CO2 rising during El Nino and decreasing during La Nina. This term amounts to 0.45 times the Multivariate Enso Index (MEI) value averaged from May of the previous year through April of the current year. For example, the latest yearly-average MEI value is -1.29 (La Nina conditions), so 0.45 x [-1.29] =  -0.58 ppm CO2 decrease in 2022 from La Nina activity. 

The model is initialized in 1750. The MEI data are included starting in 1958-59.

Results

The model fit to Mauna Loa CO2 data is shown in Fig. 1. Note that the largest discrepancies between model and observations are due to major volcanic eruptions, especially Mt. Pinatubo in 1991.

Fig. 1. Model versus observed CO2 concentrations at Mauna Loa, HI.

Contrary to popular perception, these eruptions actually remove CO2 from the atmosphere. This is likely due to increased photosynthesis due to a large increase in diffuse solar radiation from the sky, from sunlight scattered by volcanic aerosols, which can penetrate deeper into vegetation canopies.

When we run the model using 2021 EIA estimates of yearly CO2 emissions increases from 2010 through 2050, and then assuming the 2050 increase remains the same to 2100, the resulting atmospheric CO2 scenario is closest to the IPCC RCP4.5 scenario. The model CO2 concentration barely reaches the 2XCO2 level, a doubling of the pre-industrial CO2 level.

Fig. 2. As in Fig. 1, but extended to 2100, with the various IPCC radiative forcing scenarios used in recent IPCC reports.

Note the model is well below the RCP8.5 scenario, which is the one most often used to promote alarmist projections of sea level rise, temperature increase, etc. The weaker the future radiative forcing from increasing CO2, the weaker resulting climate change will be.

Discussion

Climate model projections depend critically upon how much atmospheric CO2 will rise in the future. That, in turn, depends upon (1) future anthropogenic emissions, and (2) how fast nature removes “excess” CO2 from the atmosphere.

A simple budget model of the atmospheric CO2 concentration very accurately matches the Mauna Loa CO2 data during 1959-2021 using yearly estimates of global anthropogenic CO2 emissions as a CO2 source, and the observed average rate of removal of CO2 by biological and physical processes, which is proportional to the “excess” of atmospheric CO2 over a baseline of 295 ppm as a sink. An empirical factor to account for El Nino and La Nina activity is also included, which mostly affects year-to-year fluctuations in CO2.

The resulting model projection produces atmospheric CO2 concentrations late this century well below the IPCC RCP8.5 scenario, and even below the RCP6.0 scenario. This suggests that the most dire climate change impacts the public hears about will not happen. Note that this likely reduction in future global warming impacts is in addition to the evidence that the climate system is not as sensitive to increasing CO2 as is claimed by the IPCC. In other words, future climate change will likely be much weaker than projected due not only to (1) lower climate sensitivity, but also (2) weaker anthropogenic forcing, and it is the combination of the two that determines the outcome.

James Webb Space Telescope 1st Image: It Blows Hubble Space Telescope Away

July 12th, 2022

There was no way to tell from yesterday’s White House press conference release of the first JWST “sea of galaxies” image whether it was any better or different from Hubble Space Telescope (HST) views of the same region.

In my opinion, this was a missed opportunity to wow the public.

But since then, amateur astronomer Nicholas Eggleston has stepped up with an overlay of the two telescopes’ images, aligned to view the same region. We all know how wonderful the HST views have been, even resolving individual stars in the distant Andromeda galaxy. Well, the James Webb Space Telescope (in addition to being infrared) has now demonstrated its resolution blows HST away. Check out the light arcs due to gravitational lensing (click on his page link so you can use the slider functionality, which probably won’t work on smart phones):

http://www.nicholaseggleston.com/JamesWebbHubble/index.htm

If you cannot see the 2 images with a slider separating them, here is an animated GIF I put together:

Comparison of first Webb Space Telescope image to Hubble Space Telescope view of the same region.

If you want higher resolution, right-click the animated GIF and download it (“Save image as…”) to your desktop. Then you can view it at full resolution.

UAH Global Temperature Update for June 2022: +0.06 deg. C

July 1st, 2022

The Version 6.0 global average lower tropospheric temperature (LT) anomaly for June, 2022 was +0.06 deg. C, down (again) from the May, 2022 value of +0.17 deg. C.

Tropical Coolness

The tropical (20N-20S) anomaly for June was -0.36 deg. C, which is the coolest monthly anomaly in over 10 years, the coolest June in 22 years, and the 9th coolest June in the 44 year satellite record.

The linear warming trend since January, 1979 still stands at +0.13 C/decade (+0.11 C/decade over the global-averaged oceans, and +0.18 C/decade over global-averaged land).

Various regional LT departures from the 30-year (1991-2020) average for the last 18 months are:

YEAR MO GLOBE NHEM. SHEM. TROPIC USA48 ARCTIC AUST 
2021 01 0.12 0.34 -0.09 -0.08 0.36 0.50 -0.52
2021 02 0.20 0.32 0.08 -0.14 -0.66 0.07 -0.27
2021 03 -0.01 0.13 -0.14 -0.29 0.59 -0.78 -0.79
2021 04 -0.05 0.05 -0.15 -0.28 -0.02 0.02 0.29
2021 05 0.08 0.14 0.03 0.06 -0.41 -0.04 0.02
2021 06 -0.01 0.30 -0.32 -0.14 1.44 0.63 -0.76
2021 07 0.20 0.33 0.07 0.13 0.58 0.43 0.80
2021 08 0.17 0.26 0.08 0.07 0.32 0.83 -0.02
2021 09 0.25 0.18 0.33 0.09 0.67 0.02 0.37
2021 10 0.37 0.46 0.27 0.33 0.84 0.63 0.06
2021 11 0.08 0.11 0.06 0.14 0.50 -0.43 -0.29
2021 12 0.21 0.27 0.15 0.03 1.63 0.01 -0.06
2022 01 0.03 0.06 0.00 -0.24 -0.13 0.68 0.09
2022 02 -0.00 0.01 -0.02 -0.24 -0.05 -0.31 -0.50
2022 03 0.15 0.27 0.02 -0.08 0.22 0.74 0.02
2022 04 0.26 0.35 0.18 -0.04 -0.26 0.45 0.60
2022 05 0.17 0.24 0.10 0.01 0.59 0.23 0.19
2022 06 0.06 0.07 0.04 -0.36 0.46 0.33 0.11

The full UAH Global Temperature Report, along with the LT global gridpoint anomaly image for June, 2022 should be available within the next several days 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

EV’s Fossil Fuel Economy No Better than ICE Vehicles

June 17th, 2022

…But the price per mile of EVs energy use is cheaper for the time being ($2 per gallon of gas equivalent)

Photo credit: Insideevs.com.

Most of the electricity generated in the U.S. continues to come from fossil fuels (61% in 2021). This is not likely to change much in the future as electricity demand is increasing faster than renewables (20% of total in 2020 and 20.1% of total in 2021) can close the gap versus fossil fuels. Given that fact, it is interesting to ask the question:

Which uses fossil fuels more efficiently, an EV or ICE (internal combustion engine) vehicle?

Most of what you will read about EVs versus ICE vehicles discuss how EVs are more efficient at converting the energy they carry into motion (e.g. here, and here , and here, and here, etc.) but this is only part of the equation. The generation, transmission, and battery storage of electricity is very inefficient compared to the refining and transport of gasoline, and those inefficiencies each year add up to more than all of the gasoline consumed in the U.S.

EV Energy Usage per Mile

The average energy consumption of an EV vehicle is about 0.35 kWh per mile. At the U.S. average electricity price of $0.145 per kWh in June 2022, and assuming the 2021 average new car fuel economy of 39 mpg, this makes the ICE-equivalent fuel price of an EV $1.98 per gallon of gasoline. With the U.S. average price of gas now over $5.00 a gallon, this by itself (ignoring the many other considerations, discussed below) makes the EV attractive for month-to-month savings on fuel purchases.

But since most of this electricity still comes from fossil fuels, we must factor in the efficiency with which electricity is generated and transmitted and stored in the EV’s battery. This is how we can answer the question, Which uses fossil fuels more efficiently, an EV or ICE (internal combustion engine) vehicle?

The generation of electricity is pretty inefficient with efficiencies ranging 33% from coal and 42% from natural gas. As we continue to transition away from coal to natural gas, I will use the 42% number. Next, at least 6.5% is lost in transmission and distribution. Finally, 12% of the electricity is lost in charging of the EV battery. Taken together, these losses add up to the 0.35 kWh per mile energy efficiency of an EV increasing to 1.0 kWh per mile in terms of fossil energy being used.

ICE Energy Usage per Mile

How does the internal combustion engine stack up against the EV in terms of efficiency of fossil fueled energy use?

A gallon of gas contains 33.7 kWh of energy. But like the generation of electricity, it takes energy to extract that gallon of gas from petroleum. However, the refining process is very energy efficient (about 90%), so it takes (33.7/0.9=) 37.44 kWh of energy to obtain that 33.7 kWh of energy is a gallon of gas. At the 39 mpg gas mileage of 2021 cars, this gives an energy economy number of 0.96 kWh per mile driven, which is just below the 1.0 kWh fossil fuel energy usage of an EV. With advertised fuel economy of 48 to 60 mpg, hybrid vehicles (which are gasoline powered) would thus have an advantage over EVs.

Other Considerations

Of course, the main reason EVs are being pushed on the American people (through subsidies and stringent CAFE standards) is the reduction in CO2 emissions that will occur, assuming more of our electricity comes from non-fossil fuel sources in the future. I personally have no interest in owning one because I want the flexibility of travelling long distances in a single day.

There is also the issue of the large amount of additional natural resources, and associated pollution, required to make millions of EV batteries.

Furthermore, the electrical grid will need to be expanded to provide the increase in electricity needed. This greater electricity demand, along with the high cost of wind and solar energy, might well make the fuel cost advantage of the EV disappear in the coming years.

Finally, a portion of the true price of a new EV is hidden through subsides (which the taxpayer pays for) and high CAFE fuel economy regulations, which require auto manufacturers not meeting the standard to pay companies like Tesla, a cost which is passed on to the consumer through higher prices on ICE cars and (especially) trucks.

 

The NASA TROPICS Mission: Monitoring Tropical Cyclones with a Fleet of Small Microwave Radiometers

June 12th, 2022

As early as today at noon EDT an Astra rocket will launch the first two of six small CubeSats into tropical orbits at 550 km altitude from Cape Canaveral (video coverage here). Those satellites, named the TROPICS mission, will carry microwave radiometers operating at relatively high frequencies, from 90 to 205 GHz. They will measure precipitation-size ice particles in the upper reaches of tropical storms (at 90 and 205 GHz), and provide temperature (118 GHz channels) and humidity profiles (183 GHz channels) in the environment surrounding, and inside the warm cores of those storms.

History
The use of microwave radiometers in space to observe the Earth was first proposed by German meteorologist Konrad Buettner in 1963. By the late 1960s aircraft missions were being flown by NASA and the U.S. Weather Bureau to demonstrate the technology.

In the early 1970s NASA/Goddard was the leader in the construction and flight of the first spaceborne microwave radiometers to demonstrate the measurement of precipitation and sea ice with the single-frequency ESMR instruments operating at 19.35 and 37 GHz.

Later, JPL developed the SMMR instrument that provided measurements from 6.6 to 37 GHz starting in late 1978 on the Nimbus-7 satellite. That instrument allowed me as a post-doc at UW-Madison to demonstrate the ability to measure precipitation over land by isolating the ice scattering signature at 37 GHz, which gave me my first peer-reviewed paper (a cover article in Nature) and led to several more papers describing severe thunderstorm detection and rainfall measurement. It is this ice scattering signature that will be exploited as part of the TROPICS mission.

The field of researchers in satellite passive microwave remote sensing up to the Nimbus-7 SMMR period was pretty small. It was the 1987 launch of the first SSM/I instrument on a DoD DMSP satellite that got many more researchers involved in using satellite microwave measurements. The unusual inclusion of higher-frequency 85.5 GHz channels on SSM/I exploited the ice signature that Dick Savage (UW-Madison) also documented along with Jim Weinman.

Meanwhile, the JPL-built MSU instruments were providing atmospheric temperature profile data since late 1978 in the 60 GHz oxygen absorption complex, mostly for data input to weather forecast models. Later, NOAA and NASA/GSFC developed the higher-resolution AMSU instruments, first launched in 1998, which still provide global atmospheric temperature information.

It was around 1990 that John Christy and I demonstrated that the MSU/AMSU series of temperature-profiling instruments could provide a relatively stable long-term record of global atmospheric temperatures for monitoring of climate change. While I had helped with the early planning of NASA’s TRMM satellite to monitor tropical rainfall, I chose to change my career focus from precipitation monitoring to temperature monitoring, and declined to become part of the official TRMM Team. I would still become the U.S. Science Team leader for the AMSR-E instrument (built by Japan), which like SSM/I measured a wide variety of parameters, but my time would be increasingly devoted to the global temperature monitoring effort.

The radical TROPICS approach to tropical cyclone monitoring
One of the main advantages of passive microwave measurements is the ability to see through many clouds (especially cirrus). Unfortunately, satellite passive microwave radiometry has always struggled with poor spatial resolution. The beamwidth of a diffraction-limited microwave antenna increases with the wavelength of radiation being measured, so large antennas are required to obtain high resolution on the Earth’s surface. Or, shorter wavelength (higher frequency) channels need to be utilized. The trouble with high frequencies, though, is that clouds become more opaque as the frequency increases. If you increase the frequency too high you move into the infrared, and we already have geostationary satellites providing those data on a continuous basis.

The TROPICS approach employs both a lower altitude (550 km compared to 700 to 850 km from other satellites) and higher frequencies (90 to 205 GHz) to improve spatial resolution. Microwave circuit electronics have improved in sensitivity and noise and have been reduced in size to the point that the very small and lightweight CubeSat architecture can be used. The TROPICS satellites use 3 CubeSat modules, making the satellite bus only (approximately) 4 x 4 x 12 inches in size. The mission’s Principal Investigator, William Blackwell at MIT’s Lincoln Lab has been spearheading this new, smaller microwave radiometer concept. The temperature sounding utilizes the 118 GHz oxygen absorption complex, rather than 60 GHz (as with the AMSUs) which improves spatial resolution by a factor of two.

Such small satellites can be launched in batches with smaller rockets, which reduces cost. It also aligns with NASA’s overriding interest in satellite technology advancement. With six of these satellites in a low-inclination (30 deg) orbit, quasi-hourly coverage (on average) of tropical cyclones is anticipated.

What will forecasters do with the data?
Many years ago I visited both the National Hurricane Center (Florida) and the Joint Typhoon Warning Center (Hawaii) to promote the use of passive microwave measurements of tropical storms. Since then, several researchers (e.g. Chris Velden and Mark DeMaria) have worked tirelessly with these centers to establish procedures for passive microwave monitoring of tropical cyclones.

I suspect the measurements will mostly be used to better isolate where a tropical depression or storm might be forming since the microwave measurements penetrate most cirrus cloud cover and can better reveal the low-level swirl of clouds marking the circulation center. Regarding the monitoring of a tropical cyclone’s warm core (what causes the intense low pressure at the surface) it isn’t until the storm comes close to hurricane intensity (65 knot maximum sustain surface winds) that the warm core can be reliably measured by TROPICS satellites. Also, the 27 km (best case) spatial resolution for the 118 GHz temperature sounding channels is still a little too coarse to avoid precipitation contamination (a cold signature) of the warm core signature that typifies tropical cyclones. Nevertheless, I’m sure researchers will find clever ways to isolate the warm core signature, just as we did in Spencer et al. (2001) using AMSU satellite data at 50 km resolution.

There will no doubt be some new capabilities that emerge as data are gathered from these satellites. For example, a large hurricane with frequent coverage by TROPICS satellites might reveal rapid deepening of the storm through a stronger warm core signature. In the absence of Hurricane Hunter flights into the storms, storm intensity is still largely based upon weather satellite visible and infrared cloud features, which is a rather indirect (but surprisingly accurate) technique that has a long history. TROPICS offers the chance to have a more physics-based measurement of hurricane intensity than through cloud appearance alone. Also, since data will continuously be collected over the entire tropical latitude belt, a wide variety of other applications will arise.

Let’s hope the Astra launch (maybe today) will be successful. So far, there have only been two successful Astra launches out of eight attempts (one rather dramatic failure is shown below). Fingers crossed.

UAH Global Temperature Update for May, 2022: +0.17 deg. C

June 1st, 2022

The Version 6.0 global average lower tropospheric temperature (LT) anomaly for May, 2022 was +0.17 deg. C, down from the April, 2022 value of +0.26 deg. C.

The linear warming trend since January, 1979 still stands at +0.13 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 (1991-2020) average for the last 17 months are:

YEAR MO GLOBE NHEM. SHEM. TROPIC USA48 ARCTIC AUST 
2021 01 0.12 0.34 -0.09 -0.08 0.36 0.50 -0.52
2021 02 0.20 0.31 0.08 -0.14 -0.66 0.07 -0.27
2021 03 -0.01 0.12 -0.14 -0.29 0.59 -0.78 -0.79
2021 04 -0.05 0.05 -0.15 -0.28 -0.02 0.02 0.29
2021 05 0.08 0.14 0.03 0.06 -0.41 -0.04 0.02
2021 06 -0.01 0.30 -0.32 -0.14 1.44 0.63 -0.76
2021 07 0.20 0.33 0.07 0.13 0.58 0.43 0.80
2021 08 0.17 0.26 0.08 0.07 0.32 0.83 -0.02
2021 09 0.25 0.18 0.33 0.09 0.67 0.02 0.37
2021 10 0.37 0.46 0.27 0.33 0.84 0.63 0.06
2021 11 0.08 0.11 0.06 0.14 0.50 -0.43 -0.29
2021 12 0.21 0.27 0.15 0.03 1.63 0.01 -0.06
2022 01 0.03 0.06 0.00 -0.24 -0.13 0.68 0.09
2022 02 -0.00 0.01 -0.02 -0.24 -0.05 -0.31 -0.50
2022 03 0.15 0.27 0.02 -0.08 0.22 0.74 0.02
2022 04 0.26 0.35 0.18 -0.04 -0.26 0.45 0.60
2022 05 0.17 0.24 0.10 0.01 0.59 0.22 0.19

The full UAH Global Temperature Report, along with the LT global gridpoint anomaly image for May, 2022 should be available within the next several days 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

UAH Global Temperature Update for April, 2022: +0.26 deg. C

May 2nd, 2022

The Version 6.0 global average lower tropospheric temperature (LT) anomaly for April, 2022 was +0.26 deg. C, up from the March, 2022 value of +0.15 deg. C.

The linear warming trend since January, 1979 still stands at +0.13 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 (1991-2020) average for the last 16 months are:

YEAR MO GLOBE NHEM. SHEM. TROPIC USA48 ARCTIC AUST 
2021 01 0.12 0.34 -0.09 -0.08 0.36 0.49 -0.52
2021 02 0.20 0.32 0.08 -0.14 -0.66 0.07 -0.27
2021 03 -0.01 0.12 -0.14 -0.29 0.59 -0.78 -0.79
2021 04 -0.05 0.05 -0.15 -0.29 -0.02 0.02 0.29
2021 05 0.08 0.14 0.03 0.06 -0.41 -0.04 0.02
2021 06 -0.01 0.30 -0.32 -0.14 1.44 0.63 -0.76
2021 07 0.20 0.33 0.07 0.13 0.58 0.43 0.80
2021 08 0.17 0.26 0.08 0.07 0.32 0.83 -0.02
2021 09 0.25 0.18 0.33 0.09 0.67 0.02 0.37
2021 10 0.37 0.46 0.27 0.33 0.84 0.63 0.06
2021 11 0.08 0.11 0.06 0.14 0.50 -0.43 -0.29
2021 12 0.21 0.27 0.15 0.03 1.62 0.01 -0.06
2022 01 0.03 0.06 0.00 -0.24 -0.13 0.68 0.09
2022 02 -0.01 0.01 -0.02 -0.24 -0.05 -0.31 -0.50
2022 03 0.15 0.27 0.02 -0.08 0.21 0.74 0.02
2022 04 0.26 0.35 0.18 -0.04 -0.26 0.45 0.60

The full UAH Global Temperature Report, along with the LT global gridpoint anomaly image for April, 2022 should be available within the next several days 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

Explaining Mauna Loa CO2 Increases with Anthropogenic and Natural Influences

April 9th, 2022

SUMMARY

The proper way of looking for causal relationships between time series data (e.g. between atmospheric CO2 and temperature) is discussed. While statistical analysis alone is unlikely to provide “proof” of causation, use of the ‘master equation’ is shown to avoid common pitfalls. Correlation analysis of natural and anthropogenic forcings with year-on-year changes in Mauna Loa CO2 suggest a role for increasing global temperature at least partially explaining observed changes in CO2, but purely statistical analysis cannot tie down the magnitude. One statistically-based model using anthropogenic and natural forcings suggests ~15% of the rise in CO2 being due to natural factors, with an excellent match between model and observations for the COVID-19 related downturn in global economic activity in 2020.

Introduction

The record of atmospheric CO2 concentration at Mauna Loa, Hawaii since 1959 is the longest continuous record we have of actual (not inferred) atmospheric CO2 concentrations. I’ve visited the laboratory where the measurements are taken and received a tour of the facility and explanation of their procedures.

The geographic location is quite good for getting a yearly estimate of global CO2 concentrations because it is largely removed from local anthropogenic sources, and at a high enough altitude that substantial mixing during air mass transport has occurred, smoothing out sudden changes due to, say, transport downwind of the large emissions sources in China. The measurements are nearly continuous and procedures have been developed to exclude data which is considered to be influenced by local anthropogenic or volcanic processes.

Most researchers consider the steady rise in Mauna Loa CO2 since 1959 to be entirely due to anthropogenic greenhouse gas emissions, mostly from the burning of fossil fuels. I won’t go into the evidence for an anthropogenic origin here (e.g. the decrease in atmospheric oxygen, and changes in atmospheric carbon isotopes over time). Instead, I will address evidence for some portion of the CO2 increase being natural in origin. I will be using empirical data analysis for this. The results will not be definitive; I’m mostly trying to show how difficult it is to determine cause-and-effect from the available statistical data analysis alone.

Inferring Causation from the “Master Equation”

Many processes in physics can be addressed with some form of the “master equation“, which is a simple differential equation with the time derivative of one (dependent) variable being related to some combination of other (independent) variables that are believed to cause changes in the dependent variable. This equation form is widely used to describe the time rate of change of many physical processes, such as is done in weather forecast models and climate models.

In the case of the Mauna Loa CO2 data, Fig. 1 shows the difference between the raw data (Fig. 1a) and the more physically-relevant year-to-year changes in CO2 (Fig. 1b).

Fig. 1. Mauna Loa CO2 data, 1959-2021, show as (a) yearly average values, and (b) year-on year changes in those values (dCO2/dt).

If one believes that year-to-year changes in atmospheric CO2 are only due to anthropogenic inputs, then we can write:

dCO2/dt ~ Anthro(t),

which simply means that the year-to-year changes in CO2 (dCO2/dt, Fig. 1b) are a function of (due to) yearly anthropogenic emissions over time (Anthro(t)). In this case, year-on-year changes in Mauna Loa CO2 should be highly correlated with yearly estimates of anthropogenic emissions. The actual relationship, however, is clearly not that simple, as seen in Fig. 2, where the anthropogenic emissions curve is much smoother than the Mauna Loa data.

Fig. 2. Mauna Loa year-on-year observed changes in CO2 versus estimate of global anthropogenic emissions.

Therefore, there are clearly natural processes at work in addition to the anthropogenic source. Also note those natural fluctuations are much bigger than the ~6% reduction in emissions between 2019 and 2020 due to the COVID-19 economic slowdown, a point that was emphasized in a recent study that claimed satellite CO2 observations combined with a global model of CO2 transports was able to identify the small reduction in CO2 emissions.

So, if you think there are also natural causes of year-to-year changes in CO2, you could write,

dCO2/dt ~ Anthro(t) + Natural(t),

which would approximate what carbon cycle modelers use, since it is known that El Nino and La Nina (as well as other natural modes of climate variability) also impact yearly changes in CO2 concentrations.

Or, if you think year-on-year changes are due to only sea surface temperature, you can write,

dCO2/dt ~ SST(i),

and you can then correlate year-on-year changes in CO2 to a dataset of yearly average SST.

Or, if you think causation is in the opposite direction, with changes in CO2 causing year-on-year changes in SST, you can write,

dSST/dt ~ CO2(t),

in which case you can correlate the year-on-year changes in SST with CO2 concentrations.

In addition to the master equation having a basis in physical processes, it avoids the problem of linear trends in two datasets being mistakenly attributed to a cause-and-effect relationship. Any time series of data that has just a linear trend is perfectly correlated with every other time series having just a linear trend, and yet that perfect correlation tells us nothing about causation.

But when we use the time derivative of the data, it is only the fluctuations from a linear trend that are correlated with another variable, giving some hope of inferring causation. If you question that statement, imagine that Mauna Loa CO2 has been rising at exactly 2 ppm per year, every year (instead of the variations seen in Fig. 1b). This would produce a linear trend, with no deviations from that trend. But in that case the year-on-year changes are all 2 ppm/year, and since there is no variation in those data, they cannot be correlated with anything, because there is no variance to be explained. Thus, using the master equation we avoid inferring cause-and-effect from linear trends in datasets.

Now, this data manipulation doesn’t guarantee we can infer causation, because with a limited set of data (63 years in the case of Mauna Loa CO2 data), you can expect to get some non-zero correlation even when no causal relationship exists. Using the ‘master equation’ just puts us a step closer to inferring causation.

Correlation of dCO2/dt with Various Potential Forcings

Lag correlations of the dCO2/dt data in Fig. 1b with estimates of global anthropogenic CO2 emissions, and with a variety of natural climate indicies, are shown in Fig. 3.

Fig. 3. Lag correlations of Mauna Loa dCO2/dt with various other datasets: Global anthropogenic emissions, tropical sea surface temperature (ERSST), global average surface temperature (HadCRUT4), the Atlantic Multi-decadal Oscillation (AMO), the Indian Ocean Dipole (IOD), the Multivariate ENSO Index (MEI), Mauna Loa atmospheric transmission (mostly major volcanoes),the Pacific Decadal Oscillation (PDO), and the North Atlantic Oscillation (NAO).

The first thing we notice is that the highest correlation is achieved with the surface temperature datasets, (tropical SST or global land+ocean HadCRUT4). This suggests at least some role for increasing surface temperatures causing increasing CO2, especially since if I turn the causation around (correlate dSST/dt with CO2), I get a very low correlation, 0.05.

Next we see that the yearly estimates of global anthropogenic CO2 emissions is also highly correlated with dCO2/dt. You might wonder, if the IPCC is correct and all of the CO2 increase has been due to anthropogenic emissions, why doesn’t it have the highest correlation? The answer could be as simple as noise in the data, especially considering the emissions estimates from China (the largest emitter) are quite uncertain.

The role of major volcanic eruptions in the Mauna Loa CO2 record is of considerable interest. When the atmospheric transmission of sunlight is reduced from a major volcanic eruption (El Chichon in 1983, and especially Pinatubo in 1991), the effect on atmospheric CO2 is to reduce the rate of rise. This is believed to be the result of scattered, diffuse sky radiation penetrating deeper into vegetation canopies and causing enhanced photosynthesis and thus a reduction in atmospheric CO2.

Regression Models of Mauna Loa CO2

At this point we can choose whatever forcing terms in Fig. 3 we want, and do a linear regression against dCO2/dt to get a statistical model of the Mauna Loa CO2 record.

For example, if I use only the anthropogenic term, the regression model is:

dCO2/dt = 0.491*Anthro(t) + 0.181,

with 57.8% explained variance.

Let’s look at what those regression terms mean. On average, the yearly increase in Mauna Loa CO2 equals 49.1% of total global emissions (in ppm/yr) plus a regression constant of 0.181 ppm/yr. If the model was perfect (only global anthropogenic emissions cause the CO2 rise, and we know those yearly emissions exactly, and Mauna Loa CO2 is a perfect estimate of global CO2), the regression constant of 0.181 would be 0.00. Instead, the anthro emissions estimates do not perfectly capture the rise in atmospheric CO2, and so a 0.181 ppm/yr “fudge factor” is in effect included each year by the regression to account for the imperfections in the model. It isn’t known how much of the model ‘imperfection’ is due to missing source terms (e.g. El Nino and La Nina or SST) versus noise in the data.

By using additional terms in the regression, we can get a better fit to the Mauna Loa data. For example, I chose a regression model that includes four terms, instead of one: Anthro, MEI, IOD, and Mauna Loa atmospheric transmission. In that case I can improve the regression model explained variance from 57.8% to 82.3%. The result is shown in Fig. 4.

Fig. 4. Yearly Mauna Loa CO2 observations versus a 4-term regression model based upon anthropogenic and natural forcing terms.

In this case, the only substantial deviations of the model from observations is due to the El Chichon and Pinatubo volcanoes, since the Pinatubo event caused a much larger reduction in atmospheric CO2 than did El Chichon, despite the volcanoes producing very similar reductions in solar transmission measurements at Mauna Loa.

In this case, the role of anthropogenic emissions is reduced by 15% from the anthro-only regression model. This suggests (but does not prove) a limited role for natural factors contributing to increasing CO2 concentrations.

The model match to observations during the COVID-19 year of 2020 is very close, with only a 0.02 ppm difference between model and observations, compared to the 0.24 ppm estimated reduction in total anthropogenic emissions from 2019 to 2020.

Conclusions

The Mauna Loa CO2 data need to be converted to year-to-year changes before being empirically compared to other variables to ferret out possible causal mechanisms. This in effect uses the ‘master equation’ (a time differential equation) which is the basis of many physically-based treatments of physical systems. It, in effect, removes the linear trend in the dependent variable from the correlation analysis, and trends by themselves have no utility in determining cause-versus-effect from purely statistical analyses.

When the CO2 data are analyzed in this way, the greatest correlations are found with global (or tropical) surface temperature changes and estimated yearly anthropogenic emissions. Curiously, reversing the direction of causation between surface temperature and CO2 (yearly changes in SST [dSST/dt] being caused by increasing CO2) yields a very low correlation.

Using a regression model that has one anthropogenic source term and three natural forcing terms, a high level of agreement between model and observations is found, including during the COVID-19 year of 2020 when global CO2 emissions were reduced by about 6%.

UAH Global Temperature Update for March, 2022: +0.15 deg. C

April 2nd, 2022

The Version 6.0 global average lower tropospheric temperature (LT) anomaly for March, 2022 was +0.15 deg. C, up from the February, 2022 value of -0.01 deg. C.

The linear warming trend since January, 1979 still stands at +0.13 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 (1991-2020) average for the last 15 months are:

YEAR MO GLOBE NHEM. SHEM. TROPIC USA48 ARCTIC AUST 
2021 01 0.12 0.34 -0.09 -0.08 0.36 0.49 -0.52
2021 02 0.20 0.32 0.08 -0.14 -0.66 0.07 -0.27
2021 03 -0.01 0.12 -0.14 -0.29 0.59 -0.78 -0.79
2021 04 -0.05 0.05 -0.15 -0.29 -0.02 0.02 0.29
2021 05 0.08 0.14 0.03 0.06 -0.41 -0.04 0.02
2021 06 -0.01 0.30 -0.32 -0.14 1.44 0.63 -0.76
2021 07 0.20 0.33 0.07 0.13 0.58 0.43 0.80
2021 08 0.17 0.26 0.08 0.07 0.32 0.83 -0.02
2021 09 0.25 0.18 0.33 0.09 0.67 0.02 0.37
2021 10 0.37 0.46 0.27 0.33 0.84 0.63 0.06
2021 11 0.08 0.11 0.06 0.14 0.50 -0.43 -0.29
2021 12 0.21 0.27 0.15 0.03 1.62 0.01 -0.06
2022 01 0.03 0.06 0.00 -0.24 -0.13 0.68 0.09
2022 02 -0.01 0.01 -0.02 -0.24 -0.05 -0.31 -0.50
2022 03 0.15 0.27 0.02 -0.08 0.21 0.74 0.02

The full UAH Global Temperature Report, along with the LT global gridpoint anomaly image for March, 2022 should be available within the next several days 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