The Version 6.0 global average lower tropospheric temperature (LT) anomaly for June, 2021 was -0.01 deg. C, down from the May, 2021 value of +0.08 deg. C.

REMINDER: We have changed the 30-year averaging period from which we compute anomalies to 1991-2020, from the old period 1981-2010. This change does not affect the temperature trends.

The linear warming trend since January, 1979 remains at +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 (1991-2020) average for the last 18 months are:

YEAR MO GLOBE NHEM. SHEM. TROPIC USA48 ARCTIC AUST 
2020 01  0.42  0.44  0.40  0.52  0.57 -0.22  0.41
2020 02  0.59  0.74  0.45  0.63  0.17 -0.27  0.20
2020 03  0.35  0.42  0.27  0.53  0.81 -0.96 -0.04
2020 04  0.26  0.26  0.25  0.35 -0.70  0.63  0.78
2020 05  0.42  0.43  0.41  0.53  0.07  0.83 -0.20
2020 06  0.30  0.29  0.30  0.31  0.26  0.54  0.97
2020 07  0.31  0.31  0.31  0.28  0.44  0.27  0.26
2020 08  0.30  0.34  0.26  0.45  0.35  0.30  0.24
2020 09  0.40  0.41  0.39  0.29  0.69  0.24  0.64
2020 10  0.38  0.53  0.22  0.24  0.86  0.95 -0.01
2020 11  0.40  0.52  0.27  0.17  1.45  1.09  1.28
2020 12  0.15  0.08  0.22 -0.07  0.29  0.44  0.13
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 -0.65  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

Despite the near-normal global average temperatures, the USA Lower 48 temperature anomaly of +1.44 deg. C was the warmest in the 43 year satellite record, ahead of +1.15 deg. C in 1988. In contrast, the Antarctic region (poleward of 60 S latitude) experienced its 2nd coldest June (-1.25 deg. C below the 30-year baseline), behind -1.34 deg. C in June, 2017.

The full UAH Global Temperature Report, along with the LT global gridpoint anomaly image for June, 2021 should be available within the next few 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

“The magnitude of the increase is unprecedented.”

A new study published by NASA’s Norman Loeb and co-authors examines the CERES satellite instruments’ measurements of how Earth’s radiative energy budget has changed. The period they study is rather limited, 2005-2019, probably to be able to use the most extensive Argo float deep-ocean temperature data.

The study includes some rather detailed partitioning of what sunlight-reflecting and infrared-emitting processes are responsible for the changes, which is very useful. They also point out that the Pacific Decadal Oscillation (PDO) is responsible for some of what they see in the data, while anthropogenic forcings (and feedbacks from all natural and human-caused forcings) presumably account for the rest.

One of the encouraging results for NASA’s CERES Team is that the rate of increase in the accumulation of radiant energy in the climate system is the same in the satellite observations as it is when computed from in situ data, primarily the Argo float measurements of the upper half of the ocean depths. It should be noted, however, that the absolute value of the imbalance cannot be measured by the CERES satellite instruments; instead, the ocean warming is used to make a “energy-balanced” adjustment to the satellite data (which is the “EB” in the CERES EBAF dataset). Nevertheless, the CERES dataset is proving to be extremely valuable, even if its absolute accuracy is not as high as we would like in climate research.

The main problem I have is with the media reporting of these results. The animated graph in the Verge article shows a planetary energy imbalance of about 0.5 W/m2 in 2005 increasing to about 1.0 W/m2 in 2019.

First of all, the 0.5 to 1.0 W/m2 energy imbalance is much smaller than our knowledge of any of the natural energy flows in the climate system. It can be compared to the estimated natural energy flows of 235-245 W/m2 in and out of the climate system on an annual basis, approximately 1 part in 300.

Secondly, since we don’t have good global energy imbalance measurements before this period, there is no justification for the claim, “the magnitude of the increase is unprecedented.” To expect the natural energy flows in the climate system to stay stable to 1 part in 300 over thousands of years has no scientific basis, and is merely a statement of faith. We have no idea whether such changes have occurred in centuries past.

This is not to fault the CERES data. I think that NASA’s Bruce Wielicki and Norm Loeb have done a fantastic job with these satellite instruments and their detailed processing of those data.

What bothers me is the alarmist language attached to (1) such a tiny number, and (2) the likelihood that no one will bother to mention the authors attribute part of the change to a natural climate cycle, the PDO.

Executive Summary
A new paper by Santer et al. in Journal of Climate shows that observed trends during 1988-2019 in sea surface temperature [SST], tropospheric temperature [TLT and TMT], and total tropospheric water vapor [TWV] are generally inconsistent, by varying amounts, with climate model trends over the same period. The study uses ratios between observed trends in these variables to explore how well the ratios match model expectations, with the presumption that the models provide “truth” in such comparisons. Special emphasis is placed on the inconsistency between TWV moistening rates and the satellite tropospheric temperature warming rates: the total water vapor has risen faster than one would expect for the weak rate of satellite-observed tropospheric warming (but both are still less than the average climate model trends in either CMIP5 or CMIP6).

While the paper itself does not single out the tropospheric temperatures as being in error, widespread reporting of the paper used the same biased headline, for instance this from DailyMail.com: “Satellites may have been underestimating the planet’s warming for decades”. The reporting largely ignored the bulk of what was in the paper, which was much less critical of the satellite temperature trends, and which should have been more newsworthy. For example: (1) SST warming is shown in the paper to be well below climate model expectations from both CMIP5 and CMIP6, which one might expect could have been a major conclusion; (2) the possibility that the satellite-based TWV is rising too rapidly (admitted in the paper, and addressed below), and especially (3) the possibility that TWV is not a good proxy anyway for mid- and upper-tropospheric warming (discussed below).

As others have shown, free-tropospheric vapor (not well captured by TWV) would be the proper proxy for free-tropospheric warming, and the fact that climate models maintain constant relative humidity with altitude during warming is not based upon basic physical processes (as the authors imply), but instead upon arbitrary moistening assumptions implicit in model convective parameterizations. Observational evidence is shown that free-tropospheric humidity does not increase with tropospheric temperature as much as in the GFDL climate model. Thus, weak tropospheric warming measured by satellites could be evidence of weak water vapor feedback in the free troposphere, which in turn could explain the weaker than (model) expected surface warming. A potential reason for a high bias in TWV trends is also addressed, which is consistent with the other variables’ trend behavior.

Evidence Presented in Santer et al. (2021)
I’ve been asked by several people to comment on a new paper in Journal of Climate by Santer et al. (Using Climate Model Simulations to Constrain Observations) that has as one of its conclusions the possibility that satellite-based warming estimates of tropospheric temperature might be too low. Based upon my initial examination of the paper, I conclude that there is nothing new in the paper that would cast doubt on the modest nature of tropospheric warming trends from satellites — unless one believes climate models as proof, in which case we don’t need observations anyway.

The new study focuses on the period 1988-2019 so that total integrated water vapor retrievals over the ocean from the SSM/I and SSMIS satellite-based instruments can be used. Recent surface and tropospheric warming has indeed been accompanied by increasing water vapor in the troposphere, and the quantitative relationship between temperature and vapor is used by the authors as a guide to help determine whether the tropospheric warming rates from satellites have been unrealistically low.

Most of the pertinent conclusions in the new paper come from their Fig. 9, which I have annotated for clarity in Fig. 1, below.

Fig. 1. Adapted from Santer et al. (2021), comparison plots of tropical trends (1988-2019) in total integrated water vapor, sea surface temperature, and tropospheric temperature, in climate models versus observations. Note in (A) and (D) the sea surface temperature trends are well below the average model trends, which curiously was not part of the media-reported results. These plots show that in all four of the properties chosen for analysis (SST, TLT, TMT, and TWV) the observed trends are below the average climate model trends (the latter of which determine global policy responses to anthropogenic GHG emissions). The fact the observations fall off of the model-based regression lines is (as discussed below) due to some combination of errors in the observations and errors in the climate model assumptions.

The Problem with Using Integrated Water Vapor Increases as a Proxy for Tropospheric Warming
A central conclusion of the paper is that total integrated water vapor has been rising more rapidly than SST trends suggest, while tropospheric temperature has been rising less rapidly (assuming the models are correct that SST warming should be significantly amplified in the troposphere). This pushes the observations away from the climate model-based regression lines in Fig. 1a, 1b, and 1b.

The trouble with using TWV moistening as a proxy for tropospheric warming is that while TWV is indeed strongly coupled to SST warming, how well it is coupled to free-tropospheric (above the boundary layer) warming in nature is very uncertain. TWV is dominated by boundary layer water vapor, while it is mid- to upper-tropospheric warming (and thus in the TMT satellite measurements) which is strongly related to how much the humidity increases at these high altitudes (Po-Chedley et al., 2018).

This high-altitude region is not well represented in TWV retrievals. Satellite based retrievals of TWV use the relatively weak water vapor line near 22 GHz, and so are mainly sensitive to the water vapor in the lowest layer of the atmosphere.

Furthermore, these retrievals are dependent upon an assumptions regarding the profile shape of water vapor in the atmosphere. If global warming is accompanied by a preferential moistening of the lower troposphere (due to increased surface evaporation) and a thickening of the moist boundary layer, the exceedingly important free-tropospheric humidity increase might not be as strong as is assumed in these retrievals, which are based upon regional profile differences over different sea surface temperature regimes.

As shown by Spencer & Braswell (1997) and others, the ability of the climate system to cool to outer space is strongly dependent upon humidity changes in the upper troposphere during warming (see Fig. 2). The upper troposphere has very low levels of water vapor in both relative and absolute terms, yet these low amounts of vapor in the upper 75% of the troposphere have a dominating control on cooling to outer space.

Fig. 2. Adapted from Spencer & Braswell, 1997: The rate of humidity increases in the free troposphere (above the boundary layer) with long-term surface warming can dominate water vapor feedback, and thus free-tropospheric warming (e.g. from satellite-based TMT), as well as surface warming. The precipitation processes which govern the humidity in this region (and especially how they change with warming) are very uncertain and only crudely handled in climate models.

As indicated in Fig. 2, water vapor in the lowest levels of the troposphere is largely controlled by surface evaporation. If the surface warms, increasing evaporation moistens the boundary layer, and constant relative humidity is a pretty good rule of thumb there. But in the mid- and upper- troposphere, detrained air from precipitation systems largely determines humidity. The fraction of condensed water vapor that is removed by precipitation determines how much is left over to moisten the environment. The free-tropospheric air sinking in clear air even thousands of km away from any precipitation systems had its humidity determined when that air ascended in those precipitation systems, days to weeks before. As demonstrated by Renno, Emanuel, and Stone (1994) with a model containing an explicit atmospheric hydrologic cycle, precipitation efficiency determines whether the climate is cool or warm, through its control on the main greenhouse gas, water vapor.

Importantly, we do not know how precipitation efficiency changes with warming, therefore we don’t know how strong water vapor feedback is in the real climate system. We know that tropical rain systems are more efficient than higher latitude systems (as many of us know anecdotally from visiting the tropics, where even shallow clouds can produce torrential rainfall). It is entirely reasonable to expect that global warming will be accompanied by an increase in precipitation efficiency, and recent research is starting to support this view (e.g. Lutsko and Cronin, 2018). This would mean that free-tropospheric absolute (specific) humidity might not increase as much as climate models assume, leading to less surface warming (as is observed) and less tropospheric amplification of surface warming (as is observed).

Because climate models do not yet include the precipitation microphysics governing precipitation efficiency changes with warming, the models’ behavior regarding temperature versus humidity in the free troposphere should not be used as “truth” when evaluating observations.

While climate models tend to maintain constant relative humidity throughout the troposphere during warming, thus causing strong positive water vapor feedback (e.g. Soden and Held, 2006) and so resulting in strong surface warming and even stronger tropospheric warming, there are difference between models in this respect. In the CMIP5 models analyzed by Po-Chedley et al. (2018, their Fig. 1a) there is a factor of 3 variation in the lapse rate feedback across models, which is a direct measure of how much tropospheric amplification there is of surface warming (the so-called “hotspot”). That amplification is, in turn, directly related (they get r = -0.85) to how much extra water vapor is detrained into the free troposphere (also in their Fig. 1a).

What Happens To Free Tropospheric Humidity in the Real World?
In the real world, it is not clear that free-tropospheric water vapor maintains constant relative humidity with warming (which would result in strong surface warming, and even stronger tropospheric warming). We do not have good long-term measurements of free-tropospheric water vapor changes on a global basis.

Some researchers have argued that seasonal and regional relationships can be used to deduce water vapor feedback, but this seems unlikely. How the whole system changes with warming over time is not so certain.

For example, if we use satellite measurements near 183 GHz (e.g. available from the NOAA AMSU-B instruments since late 1998), which are very sensitive to upper tropospheric vapor, we find in the tropics that tropospheric temperature and humidity changes over time appear to be quite different in satellite observations versus the GFDL climate model (Fig. 3).

Fig. 3. Zonal averages of gridpoint regression coefficients between monthly anomalies in 183.3 GHz TB and TMT during 2005-2015 in observations (blue) and in two GFDL climate models (red and orange), indicating precipitation systems in the real world dry out the free troposphere with warming more than occurs in climate models, potentially reducing positive water vapor feedback and thus global warming.

More details regarding the results in Fig. 3. can be found here.

Possible Biases in Satellite-Retrieved Water Vapor Trends
While satellite retrievals of TWV are known to be quite accurate when compared to radiosondes, subtle changes in the vertical profile of water vapor during global warming can potentially cause biases in the TWV trends. The Santer et al. (2021) study mentions the possibility that the total vertically-integrated atmospheric water vapor trends provided by satellites since mid-1987 might be too high, but does not address any reasons why.

This is an issue I have been concerned about for many years because the TWV trend since 1988 (only retrievable over the ocean) has been rising faster than we would expect based upon sea surface temperature (SST) warming trends combined with the assumption of constant relative humidity throughout the depth of the troposphere (see Fig. 1a, 1b, 1c above).

How might such a retrieval bias occur? Retrieved TWV is proportional to warming of a passive microwave Tb near the weak 22.235 GHz water vapor absorption line over the radiometrically-cold (reflective) ocean surface. As such, it depends upon the temperature at which the water vapor is emitting microwave radiation.

TWV retrieval depends upon assumed shapes of the vertical profile of water vapor in the troposphere, that is, what altitudes and thus what temperatures the water vapor is emitting at. These assumed vertical profile shapes are based upon radiosonde (weather balloon) data from different regions and different seasons having different underlying sea surface temperatures. But these regionally- and seasonally-based shape variations might not reflect shape changes during warming. If the vast majority of the moistening with long-term warming occurs in the boundary layer (see Fig. 2 above, below 800 hPa pressure altitude), with maybe slight thickening of the boundary layer, but the upper troposphere experiences little moistening, then the retrieved TWV could be biased high because the extra water vapor is emitting microwave radiation from a lower (and thus warmer) altitude than is assumed by the retrieval. This will lead to a high bias in retrieved water vapor over time as the climate system warms and moistens. As the NASA AMSR-E Science Team leader, I asked the developer of the TWV retrieval algorithm about this possibility several years ago, but never received a response.

The New Santer at al. Study Ignores Radiosonde Evidence Supporting Our UAH Satellite Temperatures

As an aside, it is also worth noting that the new study does not even reference our 2018 results (Christy et al., 2018) showing that the most stable radiosonde datasets support the UAH satellite temperature trends.

Conclusion
The new study by Santer et al. does not provide convincing evidence that the satellite measurements of tropospheric temperature trends are unrealistically low, and the media reporting of their study in this regard was biased. Their conclusion (which they admit is equivocal) depends upon the belief in climate models for how upper tropospheric warming relates to increasing total tropospheric water vapor (TWV) amounts. Since TWV does not provide much sensitivity to upper tropospheric water vapor changes, and those changes largely determine how much tropospheric amplification of surface temperature trends will occur (e.g. the “tropical hotspot”), TWV cannot determine whether tropospheric temperature trends are realistic or not.

Furthermore, there is some evidence that the TWV trends are themselves biased high, which the study authors admit is one possible explanation for the trend relationships they have calculated.

The existing observations as presented in the Santer et al. study are largely consistent with the view that global warming is proceeding at a significantly lower rate that is predicted by the latest climate models, and that much of the disagreement between models and observations can be traced to improper assumptions in those models.

Specifically:

1) SST warming has been considerably less that the models predict, especially in the tropics

2) Tropospheric amplification of the surface warming has been weak or non-existent, suggesting weaker positive water vapor feedback in nature than in models

3) Weak water vapor feedback, in turn, helps explain weak SST warming (see [1]).

4) Recent published research (and preliminary evidence shown in Fig. 3, above) support the view that climate model water vapor feedback is too strong, and so current models should not be used to validate observations in this regard.

5) Satellite-based total water vapor trends cannot be used to infer water vapor feedback because they are probably biased high due to vertical profile assumptions and because they probably do not reflect how free-tropospheric water vapor has changed with warming, which has a large impact on water vapor feedback.

REFERENCES

Christy, J. R., R. W. Spencer, W. D. Braswell, and R. Junod, 2018: Examination of space-based bulk atmospheric temperatures used in climate research.
Intl. J. Rem. Sens., DOI:https://doi.org/10.1080/01431161.2018.1444293

Lutsko, N. J. and T. W. Cronin, 2018: Increase in precipitation efficiency with surface warming in radiative-convective equilibrium. J. of Adv. Model. Earth Sys., DOI:https://doi.org/10.1029/2018MS001482.

Po-Chedley, S., K. C. Armour, C. M. Bitz, M. D. Zelinka, B. D. Santer, and Q. Fu, 2018: Sources of intermodel spread in the lapse rate and water vapor feedbacks. J. Climate, DOI:https://doi.org/10.1175/JCLI-D-17-0674.1.

Renno, N. O., K. A. Emanuel, and P. H. Stone, 1994: Radiative-convective model with an explicit hydrologic cycle: 1. Formulation and sensitivity to model parameters, J. Geophys. Res. – Atmos., DOI:https://doi.org/10.1029/94JD00020.

Santer, B. D., S. Po-Chedley, C. Mears, J. C. Fyfe, N. Gillett, Q. Fu, J. F. Painter, S. Solomon, A. K. Steiner, F. J. Wentz, M. D. Zelinka, and C.-Z. Zou, 2021: Using climate model simulations to constrain observations. J. Climate, DOI:https://doi.org/10.1175/JCLI-D-20-0768.1

Soden, B. J., and I. M. Held, 2006: An assessment of climate feedbacks in coupled ocean–atmosphere models. J. Climate, DOI:https://doi.org/10.1175/JCLI3799.1.

Spencer, R.W., and W.D. Braswell, 1997: How dry is the tropical free troposphere? Implications for global warming theory. Bull. Amer. Meteor. Soc., 78, 1097-1106.

The Version 6.0 global average lower tropospheric temperature (LT) anomaly for May, 2021 was +0.08 deg. C, up from the April, 2021 value of -0.05 deg. C.

REMINDER: We have changed the 30-year averaging period from which we compute anomalies to 1991-2020, from the old period 1981-2010. This change does not affect the temperature trends.

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

YEAR MO GLOBE NHEM. SHEM. TROPIC USA48 ARCTIC AUST 
2020 01  0.42  0.44  0.41  0.52  0.57 -0.22  0.41
2020 02  0.59  0.74  0.45  0.63  0.17 -0.27  0.20
2020 03  0.35  0.42  0.28  0.53  0.81 -0.96 -0.04
2020 04  0.26  0.26  0.25  0.35 -0.70  0.63  0.78
2020 05  0.42  0.43  0.41  0.53  0.07  0.83 -0.20
2020 06  0.30  0.29  0.30  0.31  0.26  0.54  0.97
2020 07  0.31  0.31  0.31  0.28  0.44  0.27  0.26
2020 08  0.30  0.34  0.26  0.45  0.35  0.30  0.24
2020 09  0.40  0.41  0.39  0.29  0.69  0.24  0.64
2020 10  0.38  0.53  0.22  0.24  0.86  0.95 -0.01
2020 11  0.40  0.52  0.27  0.17  1.45  1.09  1.28
2020 12  0.15  0.08  0.22 -0.07  0.29  0.44  0.13
2021 01  0.12  0.34 -0.09 -0.08  0.36  0.49 -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

The full UAH Global Temperature Report, along with the LT global gridpoint anomaly image for May, 2021 should be available within the next few 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

The Version 6.0 global average lower tropospheric temperature (LT) anomaly for April, 2021 was -0.05 deg. C, down from the March, 2021 value of -0.01 deg. C.

REMINDER: We have changed the 30-year averaging period from which we compute anomalies to 1991-2020, from the old period 1981-2010. This change does not affect the temperature trends.

The global cooling impact of the current La Nina is being fully realized now in global tropospheric temperatures.

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

YEAR MO GLOBE NHEM. SHEM. TROPIC USA48 ARCTIC AUST 
2020 01  0.42  0.44  0.41  0.52  0.57 -0.22  0.41
2020 02  0.59  0.74  0.45  0.63  0.17 -0.27  0.20
2020 03  0.35  0.42  0.28  0.53  0.81 -0.96 -0.04
2020 04  0.26  0.26  0.25  0.35 -0.70  0.63  0.78
2020 05  0.42  0.43  0.41  0.53  0.07  0.83 -0.20
2020 06  0.30  0.29  0.30  0.31  0.26  0.54  0.97
2020 07  0.31  0.31  0.31  0.28  0.44  0.26  0.26
2020 08  0.30  0.34  0.26  0.45  0.35  0.30  0.25
2020 09  0.40  0.41  0.39  0.29  0.69  0.24  0.64
2020 10  0.38  0.53  0.22  0.24  0.86  0.95 -0.01
2020 11  0.40  0.52  0.27  0.17  1.45  1.09  1.28
2020 12  0.15  0.08  0.22 -0.07  0.29  0.43  0.13
2021 01  0.12  0.34 -0.09 -0.08  0.36  0.49 -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

The full UAH Global Temperature Report, along with the LT global gridpoint anomaly image for April, 2021 should be available within the next few 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

The claim by the Biden Administration that climate change has placed us in a moment of “profound crisis” ignores the fact that the energy policy changes being promoted are based upon computer model simulations which have produced average warming rates at least DOUBLE those observed in the last 40+ years.

Just about every climate claim made by politicians, and even many vocal scientists, has been either an exaggeration or a lie.

While it is easy for detractors of what I will show to claim I am in the scientific minority (true), or that I am a climate denier (not true; I do not deny some level of human-caused warming), the fact is that the “official” observations in recent decades are in disagreement with the “official” climate models being promoted for the purposes of implementing expensive, economically-damaging, and poverty-worsening energy policies.

Global Ocean Temperatures are Warming at Only ~50% the Rate of Climate Model Projections

Today’s example comes from global-average sea surface temperatures. The oceans provide our best gauge of how fast extra energy is accumulating in the climate system. Since John Christy and I are working on a project that explains global ocean temperatures since the late 1800s with a 1D climate model, I thought I would show you just how the observations are comparing to climate models simulations.

The plot below (Fig. 1) shows the monthly global (60N-60S) average ocean surface temperature variations since 1979 for 68 model simulations from 13 different climate models. The 42 years of observations we now have since 1979 (bold black line) shows that warming is occurring much more slowly than the average climate model says it should have.

Fig. 1. 68 CMIP6 climate model simulations of global average sea surface temperature (relative to the 5 year average, 1979-1983), and compared to observations from the ERSSTv5 dataset.

In terms of the linear temperature trends since 1979, Fig. 2 shows that 2 of the top-cited ocean temperature datasets have warming trends near the bottom of the range of climate model simulations.

Fig. 2. Linear temperature trends, 1979-2020, for the various model and observational datasets in Fig. 1, plus the HadSST3 observational record.

Deep Ocean Warming Could Be Mostly Natural

A related issue is how much the deep oceans are warming. As I have mentioned before, the (inarguable) energy imbalance associated with deep-ocean warming in recent decades is only about 1 part (less than 1 Watt per sq. m) in 300 of the natural energy flows in the climate system.

This is a very tiny energy imbalance in the climate system. We know NONE of the natural energy flows to that level of accuracy.

What that means is that global warming could be mostly natural, and we would not even know it.

I’m not claiming that is the case. I am merely pointing out the level of faith that is involved in the adjustments made to climate models, which necessarily produce warming due to increasing CO2 because those models simply assume that there is no other source of warming.

Yes, more CO2 must produce some warming. But the amount of warming makes all the difference to global energy policies.

Seldom is the public ever informed of these glaring discrepancies between basic science and what politicians and pop-scientists tell us.

Why does it matter?

It matters because there is no Climate Crisis. There is no Climate Emergency.

Yes, irregular warming is occurring. Yes, it is at least partly due to human greenhouse gas emissions. But seldom are the benefits of a somewhat warmer climate system mentioned, or the benefits of more CO2 in the atmosphere (which is required for life on Earth to exist).

But if we waste trillions of dollars (that’s just here in the U.S. — meanwhile, China will always do what is in the best interests of China) then that is trillions of dollars not available for the real necessities of life.

Prosperity will suffer, and for no good reason.

The Version 6.0 global average lower tropospheric temperature (LT) anomaly for March, 2021 was -0.01 deg. C, down substantially from the February, 2021 value of +0.20 deg. C.

REMINDER: We have changed the 30-year averaging period from which we compute anomalies to 1991-2020, from the old period 1981-2010. This change does not affect the temperature trends.

Right on time, the maximum impact from the current La Nina is finally being felt on global tropospheric temperatures. The global average oceanic tropospheric temperature anomaly is -0.07 deg. C, the lowest since November 2013. The tropical (20N-20S) departure from average (-0.29 deg. C) is the coolest since June of 2012. Australia is the coolest (-0.79 deg. C) since August 2014.

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

YEAR MO GLOBE NHEM. SHEM. TROPIC USA48 ARCTIC AUST 
2020 01  0.42  0.44  0.41  0.52  0.57 -0.22  0.41
2020 02  0.59  0.74  0.45  0.63  0.17 -0.27  0.20
2020 03  0.35  0.42  0.28  0.53  0.81 -0.96 -0.04
2020 04  0.26  0.26  0.25  0.35 -0.70  0.63  0.78
2020 05  0.42  0.43  0.41  0.53  0.07  0.83 -0.20
2020 06  0.30  0.29  0.30  0.31  0.26  0.54  0.97
2020 07  0.31  0.31  0.31  0.28  0.44  0.26  0.26
2020 08  0.30  0.34  0.26  0.45  0.35  0.30  0.25
2020 09  0.40  0.41  0.39  0.29  0.69  0.24  0.64
2020 10  0.38  0.53  0.22  0.24  0.86  0.95 -0.01
2020 11  0.40  0.52  0.27  0.17  1.45  1.09  1.28
2020 12  0.15  0.08  0.22 -0.07  0.29  0.43  0.13
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 -0.79 -0.79

The full UAH Global Temperature Report, along with the LT global gridpoint anomaly image for March, 2021 should be available within the next few 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

The Version 6.0 global average lower tropospheric temperature (LT) anomaly for February, 2021 was +0.20 deg. C, up from the January, 2021 value of +0.12 deg. C.

REMINDER: We have changed the 30-year averaging period from which we compute anomalies to 1991-2020, from the old period 1981-2010. This change does not affect the temperature trends.

The linear warming trend since January, 1979 remains at +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 (1991-2020) average for the last 14 months are:

YEAR MO GLOBE NHEM. SHEM. TROPIC USA48 ARCTIC AUST 
2020 01  0.42  0.44  0.41  0.52  0.57 -0.22  0.41
2020 02  0.59  0.74  0.45  0.63  0.17 -0.27  0.20
2020 03  0.35  0.42  0.28  0.53  0.81 -0.96 -0.04
2020 04  0.26  0.26  0.25  0.35 -0.70  0.63  0.78
2020 05  0.42  0.43  0.41  0.53  0.07  0.83 -0.20
2020 06  0.30  0.29  0.30  0.31  0.26  0.54  0.97
2020 07  0.31  0.31  0.31  0.28  0.44  0.26  0.26
2020 08  0.30  0.34  0.26  0.45  0.35  0.30  0.25
2020 09  0.40  0.41  0.39  0.29  0.69  0.24  0.64
2020 10  0.38  0.53  0.22  0.24  0.86  0.95 -0.01
2020 11  0.40  0.52  0.27  0.17  1.45  1.09  1.28
2020 12  0.15  0.08  0.22 -0.07  0.29  0.43  0.13
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

The full UAH Global Temperature Report, along with the LT global gridpoint anomaly image for February, 2021 should be available within the next few 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

As most you you know by now, Rush Limbaugh’s death from cancer was announced this morning. I suspected he would work right up to the end, and we would learn of his death when we least expected it. That was just Rush.

I don’t know when I started listening to him. I suspect it wasn’t long after his radio show became nationally syndicated in 1988. Like many of his life-long listeners, Rush was able to articulate things we were feeling at the time, but could not express very well.

As a tribute, I thought I would share some personal anecdotes about the man. There are so many things that his detractors get wrong.

It’s been over 10 years since I called into the show to talk about global warming. I wanted to support his views at the time. It was late in the 3-hour show that day, and he liked what I was saying, and asked if I could continue the conversation the next day.

They investigated my background overnight, and the next day he was excited to have an actual climate scientist on his side. That night we had a long e-mail conversation talking about how similar our backgrounds were growing up.

Within days he was calling me the “Chief Climatologist of the EIB Network”. An unpaid position, but he knew that mentioning my name on the radio was plenty payment enough; it led to many speaking opportunities in the years that followed. He provided me with his “super-secret” email address, and that’s how we would correspond from then on.

He immediately suggested I write my first book, and when it came out he plugged it on the show quite a few times. Within a couple weeks, his influence got the book on the NYT bestsellers list. When I told him the news, he had a typically funny response, “Watch out, Oprah!”

Over the last 10 years, he has always read my emails to him, and responded when appropriate. I could usually tell when it was something he would use on the air (and it was usually not related to climate). It took years before I got used to the idea that he was actually interested in what I had to say.

Not long after all this started, my family and I were visiting my daughter who was in law school in Miami, and Rush found out I was in the area. He invited us over to his house in Palm Beach on a Saturday, where his extended Missouri family was visiting for an annual sports weekend for a Missouri football game. Rush was a very gracious host, and his family and relatives are very friendly. He showed me around his palatial estate, showed me how his new cochlear implant worked, and gave me a tour of his climate-controlled cigar room. I was struck by how “average” of a guy he was on a personal level.

But my favorite memory of that visit was of David Limbaugh and my daughter (the law student) having a discussion about law while standing around the pool table. Rush was listening in (he would stroll from room to room to make sure all of his guests were being taken care of).

I was marveling at the whole experience: here was my daughter discussing law with David Limbaugh while Rush listened. I will never forget the surreal feeling I had in that moment.

He then entered the conversation (I don’t recall the specific subject) to explain about how the Bush administration had sent people down to Palm Beach more than once to change his mind on some issue. But he wouldn’t budge.

But that was Rush. He wasn’t a ‘political’ animal in the usual sense. He had specific conservative principles, and if the current Republican president violated them, Rush would not hesitate to call them on it.

Rush was the same person, on the air and off the air.

In the intervening years I would have hundreds of discussions with Rush, usually not on climate-related issues. I always marveled at his boundless energy… he always took time to find out what I wanted to say to him. Several times he would remember things I told him that I had forgotten I had told him!. Once I asked him, “How do you remember so much stuff?”. His silly answer was, “It’s the booze”.

Rush had a a unique combination of talents that probably won’t come together again. In addition to his unabashed conservativism, he could articulate those principles in a way that resonated with his listeners. He had a quick mind, perfect timing on the radio, a great radio voice, and he knew how to run a business. He had a great sense of humor; many of Paul Shanklin’s parody songs came from Rush’s ideas, and one even came from me, and one from my wife. I also gave him some advice on how to make the show better (something that I told him was confusing for listeners), which he actually took and implemented.

But the most important talent that distinguished Rush from the pack of radio personalities who sought to emulate his success was that he was genuinely kind to his callers, even if they disagreed with him. He let them speak. He praised them when there was merit to the points they were making, even if it seemed to be a stretch to praise them. Every liberal viewpoint that was called into the show was used as a teachable moment.

We are sorry we lost you so early, Rush.

Well done.

SUMMARY: The Urban Heat Island (UHI) is shown to have affected U.S. temperature trends in the official NOAA 1,218-station USHCN dataset. I argue that, based upon the importance of quality temperature trend calculations to national energy policy, a new dataset not dependent upon the USHCN Tmax/Tmin observations is required. I find that regression analysis applied to the ISD hourly weather data (mostly from airports)  between many stations’ temperature trends and local population density (as a UHI proxy) can be used to remove the average spurious warming trend component due to UHI. Use of the hourly station data provides a mostly USHCN-independent measure of the U.S. warming trend, without the need for uncertain time-of-observation adjustments. The resulting 311-station average U.S. trend (1973-2020), after removal of the UHI-related spurios trend component, is about +0.13 deg. C/decade, which is only 50%  the USHCN trend of +0.26 C/decade. Regard station data quality, variability among the raw USHCN station trends is 60% greater than among the trends computed from the hourly data, suggesting the USHCN raw data are of a poorer quality. It is recommended that an de-urbanization of trends should be applied to the hourly data (mostly from airports) to achieve a more accurate record of temperature trends in land regions like the U.S. that have a sufficient number of temperature data to make the UHI-vs-trend correction.

The Urban Heat Island: Average vs. Trend Effects

In the last 50 years (1970-2020) the population of the U.S. has increased by a whopping 58%. More people means more infrastructure, more energy consumption (and waste heat production), and even if the population did not increase, our increasing standard of living leads to a variety of increases in manufacturing and consumption, with more businesses, parking lots, air conditioning, etc.

As T.R. Oke showed in 1973 (and many others since), the UHI has a substantial effect on the surface temperatures in populated regions, up to several degrees C. The extra warmth comes from both waste heat and replacements of cooler vegetated surfaces with impervious and easily heated hard surfaces. The effects can occur on many spatial scales: a heat pump placed too close to the thermometer (a microclimate effect) or a large city with outward-spreading suburbs (a mesoscale effect).

In the last 20 years (2000 to 2020) the increase in population has been largely in the urban areas, with no average increase in rural areas. Fig. 1 shows this for 311 hourly weather station locations that have relatively complete weather data since 1973.

Fig. 1. U.S. population increases around hourly weather stations have been in the more populated areas (except for mostly densely populated ones), with no increase in rural areas.

This might argue for only using rural data for temperature trend monitoring. The downside is that there are relatively few station locations which have population densities less than, say, 20 persons per sq. km., and so the coverage of the United States would be pretty sparse.

What would be nice is that if the UHI effect could be removed on a regional basis based upon how the average warming trends increase with population density. (Again, this is not removal of the average difference in temperature between rural and urban areas, but the removal of spurious temperature trends due to UHI effects).

But does such a relationship even exist?

UHI Effects on the USHCN Temperature Trends (1973-2020)

The most-cited surface temperature dataset for monitoring global warming trends in the U.S. is the U.S. Historical Climatology Network (USHCN). The dataset has a fixed set of 1,218 stations which have records extending back over 100 years. Because most of the stations’ data consist of daily maximum and minimum temperatures (Tmax and Tmin) measured at a single time daily, and that time of observation (TOBs) changed around 1960 from the late afternoon to the early morning (discussion here), there was a TOBs-related temperature bias that occurred, which is somewhat uncertain in magnitude but still must be adjusted for.

NOAA makes available both the raw unadjusted, and adjusted (TOBs & spatial ‘homogenization’) data. The following plot (Fig. 2) shows how both of the datasets’ station temperature trends are correlated with the population density, which should not be the case if UHI effects have been removed from the trends.

Fig.2. USHCN station temperature trends are correlated with population density, which should not be the case if the Urban Heat Island effect on trends has been removed.

Any UHI effect on temperature trends would be difficult to remove through NOAA’s homogenization procedure alone. This is because, if all stations in a small area, both urban and rural, are spuriously warming from UHI effects, then that signal would not be removed because it is also what is expected for global warming. ‘Homogenization’ adjustments can theoretically make the rural and urban trends look the same, but that does not mean the UHI effect has been removed.

Instead, one must examine the data in a manner like that in Fig. 2, which reveals that even the adjusted USHCN data (red dots) still have about a 30% overestimate of U.S. station-average trends (1973-2020) if we extrapolate a regression relationship (red dashed line, 2nd order polynomial fit) to zero population density. Such an analysis, however, requires many stations (thus large areas) to measure the average effect. It is not clear just how many stations are required to obtain a robust signal. The greater the number of stations needed, the larger the regional area required.

U.S. Hourly Temperature Data as an Alternative to USHCN

There are many weather stations in the U.S. which are (mostly) not included in the USHCN set of 1,218 stations. These are the operational  hourly weather stations operated by NWS, FAA, and other agencies, and which provide most of the data the National Weather Service reports to you. The data are included in the multi-agency Integrated Surface Database (ISD) archive.

The data archive is quite large, since it has (up to) hourly resolution data (higher with ‘special’ observations during changing weather) and many weather variables (temperature, dewpoint, wind, air pressure, precipitation) for many thousands of stations around the world. Many of the stations (at least in the U.S.) are at airports.

In the U.S., most of these measurements and their reporting are automated now, with the AWOS and ASOS systems.

This map shows all of the stations in the archive, although many of these will not have full records for whatever decades of time are of interest.

Fig. 3. Locations of ISD surface weather data quality-controlled and stored at NOAA.

The advantage of these data, at least in the United States, is that the equipment is maintained on a regular basis. When I worked summers at a National Weather Service office in Michigan, there was a full-time ‘met-tech’ who maintained and adjusted all of the weather-measuring equipment. 

Since the observations are taken (nominally) at the top of the hour, there is no uncertain TOBs adjustment necessary as with the USHCN daily Tmax/Tmin data.

The average population density environment is markedly different between the ISD (‘hourly’) stations and the USHCN stations, as is shown in Fig. 4.

Fig. 4. The dependence of U.S. weather station population density on averaging area is markedly different between 1,218 USHCN and 311 high-quality ISD (‘hourly’) stations, mainly due to the measurement of the hourly data at “uninhabited” airports to support aviation safety.

In Fig. 4 we see that the population density in the immediate vicinity of the ISD stations averages only 100 people in the immediate 1 sq. km area since no one ‘lives’ at the airport, but then increases substantially with averaging area since airports exist to serve population centers.

In contrast, the USHCN stations have their highest population density right in the vicinity of the weather station (over 400 persons in the first sq. km), which then drops off with distance away from the station location.

How such differences affect the magnitude of UHI-dependent spurious warming trends is unknown at this point.

UHI Effects on the Hourly Temperature Data

I have analyzed the U.S. ISD data for the lower-48 states for the period 1973-2020. (Why 1973? Because many of the early records were on paper, and at hourly time resolution, that represents a lot of manual digitizing. Apparently, 1973 is as far back as many of those stations data were digitized and archived).

To begin with, I am averaging only the 00 UTC and 12 UTC temperatures (approximately the times of maximum and minimum temperatures in the United States). I required those twice-daily measurements to be reported on at least 20 days in order for a month to be considered for inclusion, and then at least 10 of 12 months from a station to have good data for a year of that station’s data to be stored.

Then, for temperature trend analysis, I required that 90% of the years 1973-2020 to have data, including the first 2 years (1973, 1974) and the last 2 years (2019-2020), since end years can have large effects on trend calculations.

The resulting 311 stations have an 8.7% commonality with the 1,218 USHCN stations. That is, only 8.7% of the (mostly-airport) stations are also included in the 1,218-station USHCN database, so the two datasets are mostly (but not entirely) independent.

I then plotted the Fig. 2 equivalent for the ISD stations (Fig. 5).

Fig. 5. As in Fig. 2, but for the ISD (mostly airport) station trends for the average of daily 00 and 12 UTC temperatures. Where the regression lines intercept the zero population axis is an estimate of the U.S. temperature trend during 1973-2020 with spurious UHI trend effects removed.

We can see for the linear fit to the data, extrapolation of the line to zero population density gives a 311-station average warming trend of +0.13 deg. C/decade.

Significantly, this is only 50% of the USHCN 1,218-station official TOBs-adjusted, homogenized average trend of +0.26 C/decade.

It is also significant that this 50% reduction in the official U.S temperature trend is very close to what Anthony Watts and co-workers obtained in their 2015 analysis using the very best-sited USHCN stations.

I also include the polynomial fit in Fig. 5, since my use of the fourth root of the population density is not meant to perfectly capture the nonlinearity of the UHI effect, and some nonlinearity can be expected to remain. In that case, the extrapolated warming trend at zero population density is close to zero. But for the purpose of the current discussion, I will conservatively use the linear fit in Fig. 5. (The logarithm of the population density is sometimes also used, but is not well behaved as the population approaches zero.)

Evidence that the raw ISD station trends are of higher quality than those from UHCN is in the standard deviation of those trends:

Std. Dev. of 1,218 USHCN (raw) trends = +0.205 deg. C/decade

Std. Dev. of 311 ISD (‘hourly’) trends = +0.128 deg. C/decade

Thus, the variation in the USHCN raw trends is 60% greater than the variation in the hourly station trends, suggesting the airport trends have fewer time-changing spurious temperature influences than do the USHCN station trends.

Conclusions

For the period 1973-2020:

1. The USHCN homogenized data still have spurious warming influences related to urban heat island (UHI) effects. This has exaggerated the global warming trend for the U.S. as a whole. The magnitude of that spurious component is uncertain due to the black-box nature of the ‘homogenization’ procedure applied to the raw data.
2. An alternative analysis of U.S. temperature trends from a mostly independent dataset from airports suggests that the U.S. UHI-adjusted average warming trend (+0.13 deg. C/decade) might be only 50% of the official USHCN station-average trend (+0.26 deg. C/decade).
3. The raw USHCN trends have 60% more variability than the raw airport trends, suggesting higher quality of the routinely maintained airport weather data.

Future Work

This is an extension of work I started about 8 years ago, but never finished. John Christy and I are discussing using results based upon this methodology to make a new U.S. surface temperature dataset which would be updated monthly.

I have only outlined the very basics above. One can perform similar calculations in sub-regions (I find the western U.S. results to be similar to the eastern U.S. results). Also, the results would probably have a seasonal dependence in which case that should be calculated by calendar month.

Of course, the methodology could also be applied to other countries.