troposphere – Climate- Science.press

Modeling the Troposphere

The authors previously modeled the lower troposphere with a moist-adiabatic approach using three free variables (surface temperature, surface dew point, and overall lapse rate).

This “DPAH” (Dewpoint Anchor Hypothesis) model reproduced tropical temperatures and dew points well up to ~250 hPa (~10.6 km) without needing greenhouse-gas concentrations or top-of-atmosphere radiation measurements.

From Andy May Petrophysicist

By Andy May and Philip Mulholland

IGRA2 radiosonde 1991–2025 means (10 hPa bins, 10° latitude slices) show systematic differences between observed mid-tropospheric temperatures, and the CMIP 5 & 6 (Coupled Model Intercomparison Project, see IPCC, 2021) ensemble means as shown by McKitrick & Christy (2020) and Po-Chedley et al. (2022). The difference between the CMIP models and the observations is most noticeable in the tropics as discussed here. While multiple reasons, such as too much weight on the warming effect of additional CO₂, anomalous cloud feedback, or bad weather balloon data, have been proposed (IPCC, 2021, p. 443), no one really knows why they are so poor at reproducing tropospheric temperature profiles.

In previous posts (see here and here) we focused on modeling the troposphere using models based on moist adiabatic theory, a methodology similar to the one used in CMIP6. Our method had three free variables, surface temperature, surface dew point, and overall lapse rate. These models did a good job of reproducing observed tropical air temperatures and dewpoint up to around 250 hPa (~10.6 km). The models did not require any other input, such as greenhouse gas levels or measurements of radiation in and out at the top of the atmosphere. But above ~250 hPa they performed poorly. This opened the possibility that the atmospheric temperature profile, in the tropics below 250 hPa was only dependent upon surface conditions; but above this altitude, a different model, with different forcings was required.

Encouraged by these results, we worked on extending the model to other latitudes and developing a new model to use higher in the troposphere. This post presents a composite model for the global troposphere, it is similar in concept, but not in detail to the composite moist adiabatic/RAE (RAE: radiative advective equilibrium) model of (Cronin & Jansen, 2016). The anchor for our model is the molar density intersection (or simply the “intersection”) discovered by Michael and Ronan Connolly (Connolly & Connolly, 2014a) & (Connolly M. , 2025). It turns out to be the easiest and most general way to divide the troposphere into a lower part that can be modeled using moist adiabatic theory and an upper part that is much more complicated. We call the upper troposphere model the frost-point model or “FPM.”

Tropospheric structure

The intersection (May, 2025) occurs below the traditional tropopause as defined by the WMO (WMO & Ashford, 1957, p. 137). The distance below the tropopause varies from 50 to over 200 hPa (hectopascals of pressure = one millibar) due to the occasional complexity of the lapse rate change from cooling with altitude in the troposphere to warming with altitude in the stratosphere. The tropopause is generally thought to be the altitude where the air temperature stops declining and begins to increase, that is, a lapse rate of zero. While the intersection is easily reproduced and precise, the WMO definition (WMO & Ashford, 1957) of the tropopause is vague and complicated (two full paragraphs) and can result in multiple tropopauses in the mid-latitudes as shown in figure 1. In the tropics the sharper “cold-point” form of the tropopause is seen (see figure 3 in May 2025 or see figures 1 & 2 here).

Figure 1. The 1991-2025 mean IGRA2 radiosonde February intersection and tropopause between 40°N and 50°N (Lat_slice = 4). Upper left, an illustration showing how the intersection is determined. The dots are 10 hPa mean pressures for the latitude band and month. Upper right shows the tropopause WMO region and how it compares to the intersection. Lower left shows the February intersection by latitude in meters. The lower right plot is a map of the intersection in meters. The mean February ITCZ is shown with a black line. The circles on the map identify the locations of the weather stations mapped. Click on the figure to see in a higher resolution. Data source: (May, 2025).

The upper left plot shows how the intersection is determined. It is the intersection of two best fit lines, an upper one above 125 hPa and a lower from 300 to 750 hPa. The fact that molar density forms a line is not surprising and follows from the ideal gas law, since molar density equals pressure divided by temperature times the gas constant, but the change in slope at the intersection is surprising. This change implies a change in state or gas composition, or both. For more details see here or Connolly & Connolly 2014a.

The Molar Density Intersection

The molar density intersection changes altitude continuously by latitude (see the lower left plot in figure 1), from month to month, and the peak altitude in the tropics moves with the Intertropical Convergence Zone (ITCZ) (May, 2025). The 1991-2025 mean IGRA2 radiosonde characteristics of the air at the intersection in February between 40 and 50°N are listed in figure 1. The abbreviations are: “rh” means relative humidity and “q” means specific humidity. Notably the air is quite dry, and the mean temperature is -51°C.

As noted above, the lower portion of the troposphere, below the intersection, can be accurately modeled using only three free variables, the surface temperature, overall lapse rate, and dew-point depression (temperature – dewpoint). We call this model the DPAH model (Dewpoint Anchor Hypothesis), and it is based on moist-adiabatic theory (May, 2025). Below the intersection, the troposphere behaves like a moist‑adiabatic system because water vapor is abundant enough to control buoyancy, lapse rate, and infrared opacity. Above the intersection, temperatures fall so low that any water vapor left is deposited on ice crystals suspended in the air, breaking the assumptions of moist‑adiabatic theory.

Even though liquid droplets can survive to −38 °C in the absence of ice nuclei, the intersection zone is far colder, around −56 °C on average. At these temperatures, the saturation mixing ratio over ice is extremely small (often below 0.05 g/kg). Any remaining water vapor is rapidly deposited onto existing ice crystals. The air is simply too cold to sustain meaningful water vapor concentrations. The mean specific humidity at the intersection is 0.03 (±0.02) g/kg and the saturation mixing ratio with respect to ice lies between 0.03 and 0.06 g/kg (Stull 2026, saturation-vapor-pressure formulae). The radiosonde mean specific humidity is 0.03±0.02g kg^-1, which coincides with the lower bound of Stull’s range, thus the actual humidity is at or below the minimum possible saturation value.

Key Measurements at the Molar Density Intersection (1991-2025 means)
Latitude Slice (- is south)	Mean Temperature °C	Mean height (m)	Mean Relative Humidity (%)	Mean Specific Humidity (g/kg)	Mean Pressure (hPa)	Mean Molar Density (mol/m³)
-70	-59.9	8,624.3	32.0	0.02	288.2	16.5
-60	-57.0	8,731.7	32.5	0.03	290.5	16.5
-50	-51.7	9,404.6	31.9	0.03	278.5	15.5
-40	-52.2	10,359.2	23.9	0.02	251.9	14.1
-30	-53.9	11,650.2	14.4	0.01	213.7	12.4
-20	-59.6	13,219.3	15.7	0.01	173.4	10.5
-10	-66.2	14,113.0	28.8	0.01	153.5	9.5
0	-67.3	14,281.1	36.1	0.01	150.1	9.4
10	-64.4	13,892.7	23.6	0.01	157.7	9.7
20	-61.3	13,453.6	23.6	0.01	168.2	10.2
30	-55.5	12,036.4	23.6	0.02	204.1	11.9
40	-51.7	10,702.0	25.5	0.03	241.4	13.6
50	-51.4	9,517.0	26.6	0.03	275.7	15.2
60	-48.9	8,753.1	37.2	0.06	306.9	16.7
70	-50.8	8,384.7	28.9	0.03	317.2	17.3
80	-51.2	8,139.2	38.3	0.05	324.3	17.7
Mean	-56.4	10,953.9	27.7	0.03	237.2	13.5
Standard Deviation	5.8	2,198.6	6.8	0.02	60.8	2.9

Table 1. Key radiosonde 1991-2025 mean measurements at the molar density intersection by latitude slice.

Table 1 suggests that at the intersection, ice deposition has removed nearly all the water vapor and the air is not just cold but is water vapor exhausted. It appears that the intersection marks the altitude where supersaturation collapses because ice deposition has caught up.

Since the mean molar density in table 1 is below 15 mol/m³, it is reasonable to assume that radiative cooling dominates in the region. The collapse of water vapor concentration coincides with a shift from moist‑adiabatic to radiatively dominated behavior as discussed by (Cronin & Jansen, 2016). Here, outgoing longwave radiation to space from non-condensing greenhouse gases dominates.

At the intersection, the moist‑adiabat framework loses validity because its key ingredient—water vapor—is no longer present in meaningful quantities. We hypothesize that this transition from water vapor to ice has something to do with the molar density intersection. At the intersection and above it, we need a different model, the FPM model.

The Connolly’s hypothesized that the formation of oxygen and nitrogen multimers (Connolly & Connolly, 2014b) began to occur at the molar density intersection and that they accounted for the change in the molar density versus pressure slope by altering the molar density. For the Connolly multimerization mechanism to explain the molar density-pressure phase change, the implied multimer concentrations would necessarily produce detectable, altitude‑localized spectroscopic and radiative anomalies. These are not observed, despite extensive modern measurements of the radiation emissions of multimers and dimers. In contrast, the conventional explanation—transition from moist convective to radiatively controlled, dry stratified conditions—accounts for the change in molar density-pressure slope without requiring an unobserved layer of multimers.

Dimer (O₂-O₂and O₂-N₂) collision complexes (or CIAs, collision induced absorption complexes) occur at all pressures and not at a specific altitude. This phenomenon is well-studied (Adkins et al., 2023) & (Finkenzeller & Volkamer, 2022) because the CIAs affect radiation transfer and spectroscopy within the atmosphere. The dimers do occur in the upper atmosphere, but they have not been observed as a condensed phase or a heavy bulk gas that would change the macroscopic molar density slope.

The Connolly multimer hypothesis is certainly not disproven, just not observed in nature. Our idea (again unproven) is that the loss of free water vapor content is the cause of the intersection and acknowledge that more work is needed to determine the root cause. In any case the observed, and easily calculated, molar density intersection provides a convenient upper limit to the applicability of the moist adiabatic model or DPAH.

The modeling results

The DPAH moist-adiabatic model works very well almost everywhere up to the intersection. Above that point, our FPM model produces mixed results depending upon the latitude slice chosen. The tropics model well, as shown in figure 2.

Figure 2. Temperature, dewpoint, and lapse rates for the equator to 10°N latitude slice (“0 slice”). Model results are shown as red dashed lines and radiosonde observations are shown in blue.

The middle latitudes also model reasonably well, although the frequent double tropopauses in this region can cause problems for the FPM portion.

Figure 3. Temperature, dewpoint, and lapse rates for the 50 to 60°N latitude slice (“50 slice”). Model results are in red and radiosonde observations are shown in blue.

The only real blow up is in the North Polar Region where modeling is very difficult.

Figure 4. Temperature, dewpoint, and lapse rates for the 80 to 90°N latitude slice. Model results are in red and radiosonde observations are shown in blue. This is the worst match between the models and observations.

The critical variables are temperature, dewpoint, and lapse rate. We splice the FPM and DPAH model output at the molar density intersection and then compute R² for the combined curves. The R² values are excellent for temperature and the dewpoint in nearly every slice but mixed for the lapse rate as shown in table 2.

R² for the total column (1010 to 10 hPa)
Latitude slice	R2_T	R2_Td	R2_lapse
-70	98.9%	99.0%	90.5%
-60	99.2%	40.5%	65.7%
-50	98.4%	99.7%	55.6%
-40	98.4%	99.8%	68.1%
-30	99.7%	99.6%	78.4%
-20	99.4%	99.8%	79.1%
-10	96.9%	99.0%	66.0%
0	99.1%	99.8%	53.5%
10	97.5%	99.1%	72.6%
20	98.9%	96.9%	77.4%
30	98.3%	95.8%	89.3%
40	96.7%	94.0%	86.4%
50	98.5%	99.8%	82.1%
60	98.7%	99.2%	83.8%
70	98.3%	98.9%	84.3%
80	96.8%	98.6%	71.8%

Table 2. R² values for the full column (the models are spliced at the molar density intersection) for temperature, dewpoint, and lapse rate.

Methods

To do this work, the IGRA2 radiosonde data were quality controlled and then binned into 10 hPa bins for each 10° latitude slice. Table 3 lists each model parameter, for both models, and provides a short physical description, and ranks the parameters from most to least important based on the variance of the optimized parameter values across all runs. The table also includes the correlations (R²) between each parameter and the column-integrated diagnostics (T, Td, lapse rate), but these R² values do not affect the ranking.

The variance shown in table 3 is not the traditional variance, it is the variance in the optimized parameter values. Table 3 ranks the ten optimized parameters across the 16 latitude slices. The largest variance is 1.09×10⁸ (strato_amp); the smallest is 0.0 (latent_heat_rel). Figure 5 displays the corresponding Z-scores.¹ Z-scores greater than 2 occur at isolated latitudes (e.g., strato_amp at 20°N, td_coeff at 30°N, radiation_out at 50°N). These statistics describe the latitude dependence of the fitted coefficients; they do not establish which processes are “fundamental” or “compensatory.” The DPAH segment uses only three free parameters (T0, dpd, ELR); the FPM segment uses seven. The higher parameter count of FPM is from empirical “curve fitting;” it is not proof that these seven independent physical mechanisms operate or are the only mechanisms at work.

Table 3. The model variables, ranked by optimized parameter variance across latitude bands. The highest variance is “strato_amp” or the strength of stratospheric warming versus latitude. The weakest, or the most consistent across latitudes, is latent heat release.

As shown in table 3, the FPM and DPAH models use different variables and have no variables in common. This suggests that atmospheric cooling above and below the intersection work via completely different processes. Below, water vapor does the work; and above radiation and wind transport do most of the work. Indeed, our FPM model suggests that above the intersection heat is mostly carried away by radiation (radiation_out) and wind (env_loss).

Somewhat counterintuitively, the variables in table 3 that have low scores are important everywhere, those with high scores are only important in some regions (high variance). The special high variance variables required to get a reasonable fit are a bit of a mystery and there are too many of them. The “strato_amp” variable is there mostly to help get the shape of the tropopause; it may have little physical meaning. The “td_coeff” variable controls the shape of the dewpoint as it relates to relative humidity. The “env_loss” variable is always important and is a factor meant to compensate for the very high wind speeds in some portions of the upper atmosphere (May, 2025, fig. 12,13). While the DPAH model is grounded in only three physically meaningful variables, the FPM model is more of a curve fitting exercise. However, it is still very helpful, since it helps us visualize how the upper troposphere might work, and emphasizes how different it is from the lower troposphere. Figure 5 compares the variables listed in table 3 by their Z-score.^[1]

Figure 5. Dimensionless Z-scores for all variables listed in Table 3. Scores above zero mean the variable exceeds its global mean and scores below are smaller than the global mean.

The striking thing about figure 5 is the variation in extremes by latitude. The important variable at 20°S is latent heat release and at 20°N it is the stratospheric warming trend. At 30°N, the main desert-band latitude is the dewpoint coefficient. At 50°N it is lower than normal precipitation and extreme radiation out. Thus, in the upper troposphere FPM model region, the factors controlling heat loss vary tremendously by latitude.

In the desert belt (~30°N), “td_coeff” has a large negative Z-score, meaning the optimizer reduces dewpoint sensitivity to RH relative to the global mean. Here, the lower troposphere is already extremely dry, and the dewpoint depression is large, so the model does not need to amplify dewpoint–RH coupling. The dryness is “baked in” to the column, and “td_coeff” is correspondingly suppressed.

At 50°N, “radiation_out” has a very large positive Z‑score, indicating unusually strong radiative cooling in the upper troposphere at this latitude. This aligns with the high land/ocean ratio and relatively low humidity there. The “env_loss” variable has negative Z‑scores at 40–60°N, meaning the optimizer relies less on the wind/mixing term in these mid‑latitudes. The “precip” variable is also low at 50°N suggesting lower than normal precipitation in that latitude band. Next, we examine the models individually.

The Models

To understand how the DPAH and FPM model parameters behave across latitudes, we examined two complementary diagnostics. First, we compute the variance of each optimized parameter across latitudes, which indicates how strongly the model relies on that parameter to adjust to regional conditions. Parameters with high variance are structurally important locally because the optimization varies them widely.

Second, we compute correlations between each parameter and the R² metrics (temperature, dewpoint, lapse rate) to assess how strongly each parameter influences model skill. Separately, we compute a normalized importance measure for each parameter at each latitude, defined as the absolute deviation from its mean divided by its standard deviation. This normalized importance highlights where each parameter becomes unusually influential across latitude. Figures 6 and 7 plot this normalized importance for each model.

The importance statistics and Z scores reveal which parameters are globally stable and physically fundamental, and which ones vary strongly with latitude because they are responding to real latitude-specific atmospheric differences. The variance, as in table 3, tells us how much a specific variable changes globally, the Z-score tells where it changes and by how much, and the importance shows how much each variable influences the solution at a specific latitude slice.

Figure 6. The importance of the DPAH model variables. Red is the dewpoint depression, green is the lapse rate, and T0 is the surface temperature. The normalized importance of each is plotted.

In figure 6 we see that the optimized variables in the DPAH model travel together once normalized. All are maximal in the tropics and at the poles. They are less significant in the middle latitudes where zonal winds and storms are very important. This is the sort of pattern expected when the troposphere below the molar density intersection is very constrained by intersection height, the observed lapse rate (surface to intersection), the dewpoint depression, and moist adiabatic theory. The DPAH model is anchored by real physical constraints, so the importance factor reflects real atmospheric structure.

Surface temperature (T0) is extremely important at the poles and in the tropics. The lapse rate (ELR) is important over most latitudes, but extremely important at the poles where the intersection is very low. The dewpoint depression (dpd) peaks (positively or negatively) in regions where the humidity structure and the intersection height change quickly. It is significant that it has a peak at 20-30°S and a valley at 20-30°N. The latitudes from 20°S-30°S are dominated by oceans and those from 20-30°N by deserts.

Figure 7. The FPM model variables. It took seven variables to model the tropopsphere above the intersection, way too many to be physically meaningful.

Figure 7 tells us that the troposphere above the intersection is very complex. “strato_amp” or the rapidity of the temperature increase at the base of the stratosphere is very important at 20°N, which is where the height of the intersection drops most rapidly (see figure 1). At 20°S “latent_heat_release” is the dominant variable since this is about the latitude with maximal ocean surface. The “latent_heat_release” variable is also important at 20°N where the intersection height drops most quickly.

The “td_coeff” variable, or the dewpoint to relative humidity factor, is maximal at 30°N, near the aforementioned desert belt where the Sahara, SW USA/Northern Mexico, Gobi, etc. exist. In desert regions entrainment (or the mixing of air parcels with the surrounding air) dilutes rising plumes of moisture, ice formation aloft is reduced, and the dewpoint depression increases throughout the column, as seen in figure 7. The spike in “td_coeff” is physically significant and important.

The “p-trans” variable is the distance of the troposphere to the stratospheric transition above the intersection, it is maximal in the Antarctic and the high northern latitudes where the lower troposphere is very thin. The “env_loss” variable is also high in the polar regions; it estimates the loss of heat to the environment. This can be due to strong winds or melting ice. The “radiation_out” variable also increases slightly.

The noticeable “strato_amp” peak at 20°N, when it is a weak variable at other latitudes is a bit surprising. However, the cold point at 20°S is lower than at 20°N, and to compensate for this the optimizer may have boosted this variable.

Conclusions

DPAH is a reasonable model, based on moist adiabatic theory, that adequately describes heat transfer (or cooling) in the troposphere below the intersection. It reproduces the observed mean temperature and dew-point profiles from the surface to the molar-density intersection with the values listed in Table 2. No explicit CO₂ term appears in the DPAH equations. That absence is a property of the chosen functional form and of the 1991–2025 radiosonde means; it does not constitute a measurement of the radiative contribution of CO₂, if any.

Above the intersection the seven-parameter FPM fit is required to reach a comparable correlation. The increase in the number of free parameters is an empirical result of the optimization; it indicates that a low-parameter description of the observed upper-tropospheric means was not obtained with the functional forms tested. The radiosonde data themselves supply no direct measurement of the relative magnitudes of radiative, advective, or latent-heat fluxes at those altitudes. The result suggests that the cooling mechanism above the intersection is more complex than below it.

This is consistent with the failure of the CMIP climate models in the 300 to 100 hPa region, the region that contains the intersection. It has been proposed that this failure is due to an overemphasis on the greenhouse effect (McKitrick & Christy, 2020), (Po-Chedley et al., 2022), and (IPCC, 2021, p. 443).

Above the intersection there is almost no water vapor, so the driving force below the intersection is gone. Above the intersection CO₂ and other well-mixed greenhouse gases probably play a significant role, below the intersection these models suggest they do not.

The FPM model that we use has too many variables. This suggests that the upper troposphere may be inherently multi-process and has no single overriding physical root across all latitudes.

With so many processes at play in the upper troposphere, the FPM model became more an exercise in curve fitting than a physical model. In the early days of this project, we experimented with a more general, and less empirical Markov model for the upper troposphere. It failed, supporting the idea that a low-parameter physical model is not possible.

The polar regions are dominated by freezing, thawing, and radiation emissions to space. The Northern and Southern Hemispheres act differently because ocean surfaces dominate in the Southern Hemisphere and land surfaces in the Northern. We found it very interesting that the desert regions around 30°N were so visible in the model results and “radiation_out” was so prominent at 50°N where the land/ocean ratio is maximal.

As expected, “p_trans,” or the distance from the molar density intersection to the beginning of the stratosphere, is lowest in the tropics and highest in the middle latitudes where double WMO tropopauses are common. At the scale considered in this post, the DPAH model is fairly solid and shows that in the lower troposphere heat loss is dominated by water vapor processes. Above the molar density intersection, where water vapor is essentially zero, the FPM model has too many degrees of freedom and too many variables to be a proper model, but it is illuminating. It matches observations reasonably well by changing the variable emphasized from latitude to latitude in a reasonable way. Thus, while not a physically meaningful model on its own, it is a good learning tool.

The cause of the sharp molar density intersection is still a mystery. It is unlikely to be multimer formation since multimers occur at all altitudes and are very short lived (Welsh, 1962) & (Hartmann et al., 2011). The dehydration of the air by freezing is also a gradual process, but it could be the cause of the intersection or part of the cause.

This post benefited from conversations with Dr. Ronan Connolly; we are grateful for his help. He disagrees with our assessment of the multimerization theory and argues that our critiques have already been addressed in Connolly & Connolly (2014) We disagree, but interested readers are welcome to read his paper and form their own opinion.

The bibliography can be downloaded here.

Parameter value minus global parameter mean, divided by the global standard deviation. The scaled Z-scores in figure 5 can be used to compare the importance of each parameter across latitudes. A Z‑score measures how many standard deviations a parameter at a given latitude lies above or below its global mean. Thus, Z = +2 indicates the parameter is two standard deviations higher than average at that latitude, while Z = −1 indicates it is one standard deviation lower. Because Z‑scores are dimensionless, they allow direct comparison across parameters with very different physical units and magnitudes. Large positive or negative Z‑scores identify latitudes where a parameter plays an unusually strong or unusually weak role in the model solution. ↑

The Tropospheric Model R code can be downloaded here:
https://andymaypetrophysicist.com/wp-content/uploads/2026/08/Tropospheric_model_R_code.zip

You will also need the R code from May 2025 to prepare the IGRA2 data for the model. All the links you need are here:
https://andymaypetrophysicist.com/2025/12/19/the-story-behind-my-paper-on-the-itcz-and-the-hadley-circulation/

This post was just to present the results.

Volcano’s Stratospheric Plume Triggered Massive Methane Cleanup Scientists Never Expected

The January 2022 Hunga Tonga–Hunga Ha’apai underwater volcano eruption in Tonga was one of the most powerful in modern times. It injected huge amounts of water vapor, ash, sulfur dioxide, and other materials high into the stratosphere. While it had some warming effects from the water vapor (a greenhouse gas), a new study highlights an unexpected “self-cleaning” side effect involving methane.

The January 2022 Hunga Tonga–Hunga Ha’apai underwater volcano eruption in Tonga was one of the most powerful in modern times.

It injected huge amounts of water vapor, ash, sulfur dioxide, and other materials high into the stratosphere.

While it had some warming effects from the water vapor (a greenhouse gas), a new study highlights an unexpected “self-cleaning” side effect involving methane.

The eruption released significant methane (CH₄), a potent greenhouse gas, along with volcanic ash and seawater (rich in salts like chloride) and the plume mixed these materials high in the atmosphere.

Sunlight then drove chemical reactions: the salty aerosols and ash helped produce highly reactive chlorine species. These chlorine atoms reacted with methane, breaking it down into other compounds (with formaldehyde detected as a key byproduct/sign of this process).

In short, the volcano partially “cleaned up” some of its own methane emissions through this rare stratospheric chemistry. Researchers estimate it removed an amount of methane equivalent to the daily emissions of roughly two million cows.

Methane is mostly broken down in the troposphere by hydroxyl radicals (OH). This volcanic process created an additional, efficient destruction pathway in the stratosphere involving chlorine—something not previously observed or expected at this scale from a volcanic event.

Volcanoes were known to emit methane, but the idea that their ash and plume chemistry could actively remove some of it (acting like a natural scrubber) is new. The findings come from satellite observations tracking formaldehyde spikes and modeling the plume’s composition.

Context

Not a climate fix: This was a one-off event tied to the unique underwater nature of the eruption (seawater + ash + high injection). It doesn’t offset the eruption’s other climate impacts or broader human methane emissions.

Scientific interest: It demonstrates a natural methane sink mechanism that could inspire research into engineered ways to accelerate methane breakdown (e.g., for mitigating agricultural or fossil fuel emissions).

The study was recently published/ highlighted in outlets like Nature Communications or related reports, explaining the wave of headlines in May 2026.

This is a fascinating example of complex atmospheric chemistry—volcanoes can have both warming and cooling effects, plus these unexpected feedbacks. The Hunga Tonga event continues to yield new insights years later due to its scale and the quality of modern satellite monitoring.

_____________________________________________________________________________________

Satellite quantification of enhanced methane oxidation applied to the stratospheric plume following Hunga Tonga-Hunga Ha’apai eruption

This is a detailed, open-access study demonstrating a novel satellite-based method to quantify enhanced methane (CH₄) oxidation using formaldehyde (HCHO) as a proxy, applied as a proof-of-concept to the 2022 Hunga Tonga–Hunga Ha’apai (HTHH) stratospheric plume.

Core Methodology and Innovations

HCHO as tracer: Methane oxidation (primarily by OH, secondarily by Cl) produces ~1 HCHO molecule per CH₄ oxidized. HCHO is short-lived (hours in the stratosphere due to photolysis + reactions with radicals), making sustained enhancements strong evidence of ongoing production rather than direct volcanic injection.

TROPOMI strengths/limitations addressed: UV-based observations work over oceans (unlike IR CH₄ retrievals). Authors applied critical corrections for:

Stratospheric altitude (higher sensitivity → AMF correction factor ~4.85).
SO₂ spectral interference (up to +40% in one cloud initially, reduced later).
Aerosol/cloud effects (±20% uncertainty estimated).

Quantification combined direct integration (background subtraction) with correlations to co-emitted tracers (SO₂, sulfate aerosol from volcanic ash RGB, AOD). These agreed well, building confidence.

Key Quantitative Results

Peak HCHO: Up to ~12 ppb (±10%) at ~30 km in the plume (vs. typical stratospheric background <<0.1 ppb). Column enhancements up to 1.6 × 10¹⁵ molecules/cm².

Methane oxidation: 900 ± 220 Mg (metric tons) per day on Jan 16, with midday peaks ~75 ± 18 Mg/hour (60 ppb/day locally). Lower but detectable rates persisted (e.g., ~8 ppb/day on later days). Total suggests at least 330 Gg volcanic CH₄ injected into the stratosphere.

Chlorine requirement: To drive this, ongoing primary Cl production of 2–5 Gg Cl per day was needed — higher than prior model estimates for injected active Cl (~1.3 Gg total).

Persistence: Signal tracked for ≥10 days (up to Jan 25 in parts), consistent with Cl-driven chemistry tied to aerosols (not just initial injection). Correlated with ClO enhancements, O₃ depletion, and CO increases.

Proposed Mechanism

Photochemical Cl production from iron-chloride in sulfate-coated volcanic ash (enabled by submarine eruption: magma-seawater interaction provided salts + ash). Sunlight activates this even in the stratosphere, outside typical marine boundary layer contexts. This explains sustained HCHO without rapid decline. It also ties into low BrO observations.

Lead author note:

Maarten van Herpen (Acacia Impact Innovation BV) has a related patent on satellite quantification for engineered methane removal (distinct from this natural case). This is transparently declared.

This is rigorous atmospheric chemistry detective work leveraging an exceptional event. The full paper is freely available and includes extensive supplementary data/figures.

Natural methane (CH₄) sink processes remove most emitted methane from the atmosphere, maintaining a rough balance in pre-industrial times. Methane’s atmospheric lifetime is about 9–12 years, primarily due to these sinks.

Published: Nature Communications

DOI: 10.1038/s41467-026-72191-4

Authors: Maarten M.J.W. van Herpen,
Isabelle De Smedt,
Daphne Meidan,
Alfonso Saiz-Lopez,
Matthew S. Johnson,
Thomas Röckmann &
Jos de Laat

Abstract

Methane is a powerful greenhouse gas whose atmospheric sink remains uncertain, and emerging strategies to enhance its removal will require quantification and monitoring to verify any hypothetical future methane removal. Here we present satellite quantification of enhanced atmospheric methane oxidation, based on TROPOMI observations of a short-lived intermediate in methane oxidation, HCHO. We find a large HCHO enhancement of up to 12 ppb±10% at 30 km altitude, in the plume from the Hunga Tonga-Hunga Ha’apai eruption, persisting for ten days or more, and also explaining its low BrO levels. Total methane oxidation is 900 ± 220 Mg/day, suggesting at least 330 Gg of volcanic methane was injected into the stratosphere. The observed methane oxidation requires an estimated ongoing primary production of 2-5 Gg Cl per day that appears unexplained by known mechanisms. We show that chlorine production by iron-chloride photochemistry in sulfate-coated volcanic ash is a plausible mechanism, even outside the marine boundary layer. This method of measuring methane loss using formaldehyde can be sufficiently sensitive to quantify the impact of hypothetical future enhanced atmospheric methane oxidation approaches.

Our Atmospheric Heat Engine

From Science Matters

By Ron Clutz

Climate as heat engine. A heat engine produces mechanical energy in the form of work W by absorbing an amount of heat Qin from a hot reservoir (the source) and depositing a smaller amount **Qout into a cold reservoir** (the sink). **(a) An ideal Carnot heat engine** does the job with the maximum possible efficiency. **(b) Real heat engines are irreversible**, and some work is lost via irreversible entropy production TδS. **(c) For the climate system**, the ultimate source is the Sun, with outer space acting as the sink. The work is performed internally and produces winds and ocean currents. As a result, Qin = Qout.

A previous post presented Michel Thizon’s description of gravity’s effect on the mass of air functioning as a climate thermostat. Some years ago, Dr. Murry Salby wrote in detail about the troposphere operating as a heat engine and the stratosphere as a refrigerator. This post consists of excerpts from Salby’s textbook entitled Physics of the Atmosphere and Climate. The title is a link to pdf version of the book Salby (2012). Text in italics with my bolds and added images.

A closed system that performs work through a conversion of heat that is absorbed by it is a heat engine. Conversely, a system that rejects heat through a conversion of work that is performed on it is a refrigerator. In Chap. 6, we will see that individual air parcels comprising the circulation of the troposphere behave as a heat engine. By absorbing heat at the Earth’s surface, through transfers of radiative, sensible, and latent heat, individual parcels perform network as they evolve through a thermodynamic cycle (2.13). Ultimately realized as kinetic energy, the heat absorbed maintains the circulation against frictional dissipation. It makes the circulation of the troposphere thermally driven.

In contrast, the circulation of the stratosphere behaves as a radiative refrigerator. For motion to occur, individual air parcels must have work performed on them. The kinetic energy produced is eventually converted to heat and rejected to space through LW cooling. It makes the circulation of the stratosphere mechanically driven. Gravity waves and planetary waves that propagate upward from the troposphere are dissipated in the stratosphere. Their absorption exerts an influence on the stratosphere analogous to paddle work. By forcing motion that rearranges air, it drives the stratospheric circulation out of radiative equilibrium, which results in net LW cooling to space. Salby (2012) p. 83.

**Irreversible processes in the atmosphere.** Neglecting radiative processes (not shown here), the largest sources of irreversibility in the atmosphere are those **associated with the hydrologic cycle:** evaporation, the mixing of moist and dry air, and the melt–freeze cycle (60–80% collectively), and the fallout of precipitation (5–15%). Those contributions limit the entropy generated by frictional dissipation of the winds (5–15%), which ultimately places a **limit on the work performed by the atmospheric heat engine** in generating circulations. Percentages are estimated based on global climate simulations12 and idealized high-resolution simulations.8

Changes of thermodynamic state that accompany vertical motion follow from the distribution of atmospheric mass, which is determined ultimately by gravity. In the absence of motion, Newton’s second law applied to the vertical reduces to a statement of hydrostatic equilibrium (1.16). Gravity is then balanced by the vertical pressure gradient force. This simple form of mechanical equilibrium is accurate even in the presence of motion because the acceleration of gravity is, almost invariably, much greater than vertical acceleration of individual air parcels. Only inside deep convective towers and other small-scale phenomena is vertical acceleration large enough to invalidate hydrostatic equilibrium.

Because it is such a strong body force, gravity must be treated with some care. Complications arise from the fact that the gravitational acceleration experienced by an air parcel does not act purely in the vertical. It also varies with location. According to the preceding discussion, gravity is large enough to overwhelm other contributions in the balance of vertical forces. The same holds for the balance of horizontal forces. Horizontal components of gravity that are introduced by the Earth’s rotation and other sources must be balanced by additional horizontal forces. Unrelated to air motion, those additional forces unnecessarily complicate the description of atmospheric motion. Salby (2012) p. 150.

The temperature of a dry air parcel decreases with its altitude at the dry adiabatic lapse rate. To a good approximation, the same holds for a moist air parcel under unsaturated conditions – because the trace abundance of water vapor modifies thermal properties of air only slightly. Under saturated conditions, the adiabatic description of air breaks down due to the release of latent heat that accompanies the transformation of water from one phase to another. Latent heat exchanged with the gas phase then offsets adiabatic cooling and warming, which accompany ascending and descending motion. Salby (2012) p. 162

Net heat absorption and work performed by individual air parcels make the general circulation of the troposphere behave as a heat engine, one that is driven thermally by heat transfer at its lower and upper boundaries. Work performed by individual parcels is associated with a redistribution of mass: Air that is effectively warmer and lighter at the lower boundary is exchanged with air that is effectively cooler and heavier at the upper boundary. This redistribution of mass represents a conversion of potential energy into kinetic energy. The conversion of energy maintains the general circulation against frictional dissipation. Salby (2012) p. 163

The idealized behavior just described relies on heat transfer being confined to the lower and upper boundaries of the layer, where an air parcel resides long enough for diabatic influences to become important. Between the boundaries, the time scale of motion is short. For motion that operates on longer time scales, typical of the stratosphere, the evolution of an individual air parcel is not adiabatic.

Radiative transfer is the primary diabatic influence outside the boundary layer and cloud. It is characterized by cooling rates of order 1 K day−1 in the troposphere (see Fig. 8.24). Cooling rates as large as 10 K day−1 occur in the stratosphere and near cloud (Fig. 9.36). (2012) p. 164

Unlike the troposphere, buoyancy in the stratosphere opposes vertical motion because, invariably, warm (high-θ) air overlies cool (low-θ) air. To exchange effectively-heavier air at lower levels with effectively-lighter air at upper levels, work must be performed against the opposition of buoyancy. The rearrangement of mass represents a conversion of kinetic energy (that of the waves driving the motion) into potential energy. Manifest in temperature, the potential energy is dissipated thermally through LW emission to space. (2012) p. 168

See Also

Fearless Physics from Dr. Salby

In reading the textbook, I found two main reasons why Salby is skeptical of AGW (anthropogenic global warming) alarm. This knowledgeable book is an antidote to myopic and lop-sided understandings of our climate system.

CO2 Alarm is Myopic: Claiming CO2 causes dangerous global warming is too simplistic. CO2 is but one factor among many other forces and processes interacting to make weather and climate.

Myopia is a failure of perception by focusing on one near thing to the exclusion of the other realities present, thus missing the big picture. For example: “Not seeing the forest for the trees.” AKA “tunnel vision.”

2. CO2 Alarm is Lopsided: CO2 forcing is too small to have the overblown effect claimed for it. Other factors are orders of magnitude larger than the potential of CO2 to influence the climate system.

Lop-sided refers to a failure in judging values, whereby someone lacking in sense of proportion, places great weight on a factor which actually has a minor influence compared to other forces. For example: “Making a mountain out of a mole hill.”

Gravity-induced Atmospheric Thermostat

The temperature of the atmosphere near the Earth’s surface is influenced by
its invariable atmospheric mass, solar irradiation and the greenhouse effect.

From Science Matters

By Ron Clutz

Michel Thizon published in 2024 a paper explaining why earth’s always variable climate is constrained within a narrow range. Influence of Adiabatic Gravitational Compression of Atmospheric Mass on the Temperature of the Troposphere. Excerpts in italics with my bolds and added images

ABSTRACT

The temperature that the Earth’s surface would have without the greenhouse effect, with an atmosphere completely transparent to infrared radiation, or even without an atmosphere at all, is generally estimated at -18°C. The greenhouse effect is estimated to induce a warming of 33°C to justify the surface temperature of +15°C.

To explain this discrepancy, we examine, with the ideal gas law, to which the Earth’s atmosphere obeys with its normal conditions of pressure and temperature, the role that the adiabatic compression of the atmospheric mass subjected to gravity can play. The dimensional analysis of the ideal gas law demonstrates that compression of the atmosphere produces energy, which can be calculated in Joules.

The temperature of the atmosphere near the Earth’s surface is influenced by
its invariable atmospheric mass, solar irradiation and the greenhouse effect.

This calls into question the commonly established Earth’s energy budgets which consider almost exclusively radiative effects, and which deduce a back radiation attributed to the greenhouse effect which is abnormally high.

Earth temperature without atmosphere or greenhouse effects

Goody et al. estimated the solar energy available to heat, both directly and indirectly, the earth and its atmosphere at an average of 224 W/m-2 [1]. Applying the Stefan-Boltzmann law they assumed that the Earth radiates as a perfect black body in the infrared band at a temperature of 255.5 K (or min 17.6°C) for the effective emission temperature [2]. These authors noted that this temperature is lower than the average temperature of the Earth’s surface and indicated that much of the radiation to space must come from the atmosphere rather than from the surface. Goody et al. arbitrarily assigned a value of 1 to the emissivity ε for the calculation, while Jacquemoud assigned a value of 0.98 [3].

According to Hansen, a solar irradiance of 1367 W/m-2 or generally accepted today 1361 W/m-2, but varying with solar fluctuations, leads to a surface temperature of 255 K (or min 18°C), which induces a greenhouse effect of +33°C [4] Cotton reported that the emission temperature is -19°C and the earth temperature is +14°C, which corresponds to a global greenhouse effect of +33°C [5]. The global greenhouse effect is also estimated at +33°C [6-8]
.
Logically, at -18°C the surface of the earth without an atmosphere or with an atmosphere totally transparent to longwave radiation and that plays no physical role, without any greenhouse effect, should be entirely frozen and covered with frost over its entire surface. This would result in a high Albedo which could be on the order of 0.5 to 0.9 instead of an albedo of 0.30 or 0.29 generally accepted in its current state. In this situation, instead of the solar energy absorbed by the surface reaching approximately 160 to 168 W/m-2 (Figure 1) this energy could be on the order of 70 W/m-2 [9-11]. The Stefan-Boltzmann formula yields a potential surface temperature of approximately -85°C [2]. Note that at these temperatures the water vapor pressure above ice is infinitesimal and could only generate an infinitesimal greenhouse effect. However, according to Nikolov et al., the effects linked to the atmosphere would bring approximately 90°C and not 33°C to the surface at a temperature of 15°C [12,13]. This would suggest that the global natural effect of atmosphere could be on the order of 90°C rather than the 33°C of the traditional purely radiative approach as reported by almost all the authors.

Global mean energy budget of the Earth

Many authors have endeavored to establish an overall assessment of the energy flows to which the earth is subjected to justify the surface temperature in an essentially radiative system. The Intergovernmental Panel on Climate Change (IPCC) itself places great emphasis on this in each of its reports. The Figure 1 summarizes the values and differences obtained while Table 1 summarizes the main authors who evaluated this earth assessment over a period of approximatively twenty years.

**Figure 1. Range of nine energy balances (minimum/maximum according to the authors).**

**Table 1. Global energy balance of the Earth according to the authors.**

The dispersion and imprecision of the results do not allow the effect on surface temperature to be deduced with sufficient accuracy. These budgets must be improved as noted by Lupo et al. [22]

Effect of atmospheric pressure

Few authors have mentioned the role that an atmospheric mass subject to gravity could play in temperature. We can nevertheless cite Leroux [23] Jelbring [24], and Chilingar [25] but these authors evoke a potential role of atmospheric pressure on a qualitative level without seeking to calculate and quantify the effects, probably given the difficulty of integrating the atmosphere as a whole. Nikolov et al. clarify the role of atmospheric pressure for several planets through a complex semiempirical iterative approach [11]

Dimensional analysis of the ideal gas law PV=nRT

The ideal gas law PV=nRT is one of the most fundamental laws of physics and applies entirely to the lower troposphere under its usual conditions of pressure and temperature. This universally accepted law, established in 1834 by Émile Clapeyron, has been perfectly stable for nearly 200 years, which is the case for very few physical laws.

P is the pressure (Pa);
V is the volume of the gas (m3);
n is the quantity of material (mol);
T is the absolute temperature (K);
R is the universal constant of ideal gases (8.314 J K−1 mol−1);

Dimensional analysis leads to:
R=PV/nT i.e., J K−1 mol−1=Pa.m3 K−1 mol−1, Hence J=Pa.m3=energy

The volume of air multiplied by the pressure to which it is subjected is considered energy (Joules). The atmosphere is heated by compression due to the gravitational field to which it is subjected. Isolated in space, the Earth can only exchange energy with space by radiation, but the atmospheric mass cannot radiate spontaneously since its homonuclear constituents O2, N2, and Ar are passive and cannot radiate.

The earth’s surface is warmer, and the atmosphere cannot cool down on contact with it. The compression is thus adiabatic. The greenhouse gases contained in the atmosphere at low levels, mainly H2O and CO2, are capable of radiating at long wavelengths but do not interact radiatively with O2 and N2; additionally, they are under the influence of permanent terrestrial infrared radiation, which they are capable of absorbing, and which is generated continuously from the solar energy received by the Earth’s surface.

The process includes the upward expansion, toward vacuum of the agitated molecules whose kinetic energy decreases and therefore the pressure, which causes cooling with altitude. It is not due to a decrease in gravity which decreases by less than 3/1000 at a 10 km altitude but of a struggle between gravity and the suction of the vacuum, until the equilibrium which defines an adiabatic thermal gradient. Gravity nevertheless prevents air molecules from escaping into space. Only some H2 molecules can reach the release speed.

RESULTS HEATING of the atmosphere in °K by adiabatic compression

**Table 2. Data for an air layer 100 m thick. The left part is from U.S. Standard Atmosphere, according to The EngineeringToolBox [26]**

As a tight approximation, for 100 m of atmospheric thickness
Altitude 0 m

PV=(10.13 × 104 Pa) (5.101 × 1016 m3)=5.167 × 1021 J
Volumetric heat capacity of air C=1256 J m−3 K−1 (at 0 m, 15°C)
For 5.101 × 1016 m3 of air; +1°K requires 1256 × 5.101 × 1016 J=6.41 × 1019 J
5.167 × 1021 J/6.41 × 1019 J=80.7
+80.7 K overheating due to pressure

Note: With an air layer of 200 m the precision is lower and leads to an overheating of 80.6 K

Gravity compression results, to the Earth’s surface, in 80.7°C of natural greenhouse energy equivalence, which means that to reach 15°C the initial temperature without atmosphere would be -65.7°C, very different from the -18°C admitted by radiative approaches for an inactive atmosphere.

Direct application of the ideal gas law T=PV/nR

Altitude 0 m T=(10.13 × 104 × 5.10 × 1016)/(2.165 × 1018 × 8.314)=287.1 K (+14.0°C)
Altitude 5,000 m T=254.9 K (-18.2°C)
Altitude 10,000 m T=222.4 K (-50.7°C)
Altitude 15,000 m T=215.3 K (-57.8°C)

The standard thermal gradient from 0 to 10 km is -6.49°C/km. The ideal gas law explains phenomena linked to temperatures up to 10,000 m in altitude. Beyond that, the results diverge, and other factors and phenomena are involved, like ozone and UV influence.

CONCLUSION

The temperature on the surface of the earth is mainly determined by the action of gravity on the atmospheric mass, which is an immutable fact on a scale of millennia. Climatic variations are the result of lesser phenomena. The solar influence is felt during the day by the direct radiation received, mainly when the sun is at its zenith, and the balance is modified by direct thermal exchanges between the sunny surface and the air in contact. The earth’s surface and the upper layers of the atmosphere radiate permanently towards space by emitting infrared radiation day and night, thus restoring the overall balance.

Surface infrared radiation is probably less intercepted in the lower troposphere by greenhouse molecules than is usually thought, thus explaining the surface temperature. However, there is an atmospheric dynamic, in particular through the water cycle, by evaporation-condensation, but whose overall energy balance is zero. Air mass movements and convection contribute to the overall dynamics, mainly due to the rotation of the Earth and the alternations between the presence and absence of solar radiation.

Astronomical fluctuations in sunshine, surface phenomena such as ocean currents, El Niño or La Niña phenomena, extreme weather phenomena or even volcanic eruptions, as well as other factors that are probably poorly characterized, lead to variations in surface temperature that nevertheless remain relatively damped due to the stabilizing effect of the invariable atmospheric mass subject to gravity.

See Also

Planetary Warming: Back to Basics

New Study Finds An Extremely Low CO2 Climate Sensitivity For The Arctic And Antarctic

The phenomenon of increasing the concentration of greenhouse gases (water vapor, carbon dioxide, methane) is assumed to lead to sharply rising temperatures in polar regions, or “polar amplification.” As it turns out, it doesn’t.

From NoTricksZone

By Kenneth Richard

Per a new radiative forcing model (Notholt et al., 2024), increasing CO2 from pre-industrial to present levels (278 to 400 ppm) results in a slight cooling effect for all of Antarctica (-0.01°C), reinforcing the “negative greenhouse effect” conclusion of previous studies.

The increased CO2 concentration does result in a warming for the Arctic in their model, but only 0.42°C for the 122 ppm increase. This implies CO2 cannot be responsible for the bulk of modern Arctic warming.

It was also determined that for Antarctica, “doubling CH4 leads to a cooling of almost the whole troposphere.”

The negative greenhouse effect of water vapor – Earth’s main greenhouse gas – dominates over CO2 with regard to climate impacts in polar regions.

The authors suggest their results provide an explanation for the “lack of warming seen in Antarctica throughout the last decades.”

Geophysical-Research-Letters-2024-Notholt-Infrared-Radiative-Effects-of-Increasing-CO2-and-CH4-on-the-Atmosphere-in Herunterladen

40 years of expert failure: New NOAA STAR satellite temperatures only show half the warming that climate models do

An all new reanalysis of the STAR satellite data finds markedly lower temperature trends for the last 40 years. The big deal about this is that this third dataset suddenly supports the original UAH satellite data, not the other RSS system, and not the “surface thermometers” sitting near hot tarmacs and absolutely not the climate models.

From JoNova

By Jo Nova h/t Cohenite

New NOAA STAR Satellite system gets a major correction and suddenly agrees with UAH satellites, not RSS

The warming trend in the troposphere was only half of what the expert models predicted. From the paper:

Santer et al. (2021) reported that the multi-model averages for the TTT trends from CMIP5 and CMIP6 were 0.28–0.29 K/decade during 1979–2019. The total TTT trend found in this study was only one-half of the climate model simulations during the same period.

The authors admit that this has strong implications for the models, and supports a paper by skeptics Ross McKitrick and John Christy: Ross McKitrick replied in the Financial Post:

An important new study on climate change came out recently…

Zou’s team notes that their findings “have strong implications for trends in climate model simulations and other observations” because the atmosphere has warmed at half the average rate predicted by climate models over the same period. They also note that their findings are “consistent with conclusions in McKitrick and Christy (2020),” namely that climate models have a pervasive global warming bias.

Zou et al from NOAA is a bit of a blockbuster. Certainly a surprise. Skeptics have long favoured the original UAH satellite data set that consistently showed far less warming than climate models and for good reasons (see below) — especially because UAH agreed with 28 million weather balloons. A second satellite set called RSS gradually showed warmer trends than UAH did. A third satellite set was collected by NOAA and called STAR and it agreed with RSS — at least until a few weeks ago. Now suddenly it’s been re-worked thoroughly and the trend is almost identical to the UAH one.

As Roger Pielke Jnr says “if true — this is very scientifically important”:

He quotes Zou et al who are agreeing that their results support the skeptics

This is consistent with conclusions in McKitrick and Christy (2020) for a slightly shorter period (1979–2014). Possible reasons for the observation-model differences in trends may include climate model biases in responding to external forcings (McKitrick & Christy, 2020), deficiencies in the post-millennium external forcings used in model simulations (Santer, Fyfe, et al., 2017), phase mismatch in natural internal climate variability (Po-Chedley et al., 2021; Suárez-Gutiérrez et al., 2017), and possible residual errors in satellite data sets.

Pielke Jnr: Read that again, especially the last sentence. [They] are suggesting the possibility that either observations of global warming are flawed, that climate models are flawed, or maybe both. These possibilities would — if true — be very scientifically important.

Here’s now different the satellite datasets were in 2018

UAH was called the outlier, but all the other datasets used similar methods to RSS. They all continued to use a satellite called NOAA-14 even though its calibration was drifting. They also used similar strategies for diurnal drift adjustments. In Roy Spencer’s words “ Thus, NOAA and UW are, to a first approximation, slightly altered versions of the RSS dataset.”

Figure 2. A comparison of warming trends from 1979 to 2015 for the radiosonde data and the three global satellites. from Roy Spencer (Christy et al 2018)

Ross McKitrick explains the history of these differing satellite temperature sets. This is a battle that’s been waging for thirty years.

Ross McKitrick:The important climate study you won’t hear about

Special to Financial Post

[John] Christy and his co-author, Roy Spencer, invented the original method of deriving temperatures from microwave radiation measurements collected by NOAA satellites in orbit since 1979. Their achievement earned them numerous accolades, but also attracted controversy because their satellite record didn’t show any warming. About 20 years ago scientists at Remote Sensing Systems in California found a small error in their algorithm that, once corrected, did yield a warming trend.

Christy and Spencer incorporated the RSS correction, but the two teams subsequently differed on other questions, such as how to correct for the positional drift of the satellites, which changes the time of day when instruments take their readings over each location. The RSS team used a climate model to develop the correction while the UAH team used an empirical method, leading to slightly different results. Another question was how to merge records when one satellite is taken out of service and replaced by another. Incorrect splicing can introduce spurious warming or cooling.

The climate modelers have been seeking some kind of endorsement of their models from satellites for decades, but the gap has grown wider instead of narrowing. STAR was a lifeline for the modelers, now it’s not:

In the end the two series were similar but RSS has consistently exhibited more warming than UAH. Then a little more than a decade ago, the group at NOAA headed by Zou produced a new data product called STAR (Satellite Applications and Research). They used the same underlying microwave retrievals but produced a temperature record showing much more warming than either UAH or RSS, as well as all the weather balloon records. It came close to validating the climate models, although in my paper with Christy we included the STAR data in the satellite average and the models still ran too hot. Nonetheless it was possible to point to the coolest of the models and compare them to the STAR data and find a match, which was a lifeline for those arguing that climate models are within the uncertainty range of the data.

Satellite show the models are wrong:

This below is the graph in McKitrick and Christy (2020) that the new STAR analysis essentially supports. The best tropospherein the world are running too hot, predicting a lot of warming that isn’t happening.

They overdo the warming in the lower troposphere and in the mid-troposphere in the tropics and all over the globe. After 40 years and massive emissions of CO2 the models are wrong. The modelers don’t know what drives the climate.

From 2017: Five reasons UAH is different (better) to the RSS global temperature estimates

I explained why UAH was scientifically much more respectable than RSS

Five reasons UAH is different to RSS

UAH agrees with millions of calibrated weather balloons released around the world. RSS now agrees more with surface data from equipment placed near airports, concrete, airconditioners and which is itself wildly adjusted.
In the latest adjustments UAH uses empirical comparisons from satellites that aren’t affected by diurnal drift to estimate the errors of those that are. RSS starts with model estimates instead.
Two particular satellites disagree with each other (NOAA-14 and 15). The UAH team remove the one they think is incorrect. RSS keeps both inconsistent measurements.
Diurnal drift probably created artificial warming in the RSS set prior to 2002, but created artificial cooling after that. The new version of RSS keeps the warming error before 2002, but fixes the error after then. The upshot is a warmer overall trend.
UAH uses a more advanced method with three channels. RSS is still using the original method Roy Spencer and JohnChristy developed with only one channel (which is viewed from three angles).

The latest corrections by Zou 2023 were intricate and involved. This is just one eye candy graph of many (below). But we get some idea of how many satellites were involved in the last 40 years. In this case the adjustments were different over land and ocean for each satellite.

Figure 5: Inter-satellite difference time series for satellite pairs between those from TIROS-N to RTMT after the frequency adjustment for (a) over the global ocean and (b) over the global land. Zou et al

It’s a testament to the work of John Christy and Roy Spencer that the new detailed corrections suggest they were probably mostly right all along — despite the immense pressure on them to “find” results and adjustments like the other teams.

It’s a credit to Zou et al and the team at NOAA for publishing what appears to be conclusion that they might have preferred to ignore. Though they do suggest that the warming has accellerated lately:

Remarkably, the total tropospheric trends during the latest half period were nearly doubled the earlier half period over the global ocean.

But there is more discussion from Ross McKitrick at Judith Curry’s site on why the claimed “recent acceleration” in this paper is short term, not significant and dependent on picking the right break point.

In sum, based on a preliminary analysis the new NOAA data do not support a claim that warming in the troposphere has undergone a statistically-significant change in trend. The Global and Tropical TTT series show no support for the claim. The Global MT series appears to show support but only if the break data is placed in a specific interval in the early part of the last decade, and more recently the tests do not support acceleration. Finally, all of these results are biased towards finding evidence of a trend break due to the treatment of g. Robust critical values could be generated, which I might get to someday if no one else does it first.

REFERENCES

Christy, J. R., Spencer R.W., Braswell W.D. & Junod, R. (2018) Examination of space-based bulk atmospheric temperatures used in climate research, International Journal of Remote Sensing, 39:11, 3580-3607, DOI: 10.1080/01431161.2018.1444293 [ResearchGate]

McKitrick, R., & Christy, J. R. (2020). Pervasive warming bias in CMIP6 tropospheric layers. Earth and Space Science, 7(9), e2020EA001281. https://doi.org/10.1029/2020ea001281

Zou, C. Z., Xu, H., Hao, X., & Liu, Q. (2023). Mid‐Tropospheric Layer Temperature Record Derived from Satellite Microwave Sounder Observations with Backward Merging Approach. Journal of Geophysical Research: Atmospheres, e2022JD037472.

Satellite image: By NASA

Tag: troposphere

Modeling the Troposphere

Tropospheric structure

The Molar Density Intersection

The modeling results

Methods

The Models

Conclusions

Like this:

Volcano’s Stratospheric Plume Triggered Massive Methane Cleanup Scientists Never Expected

Abstract

Like this:

Our Atmospheric Heat Engine

Like this:

Gravity-induced Atmospheric Thermostat

Global mean energy budget of the Earth

Effect of atmospheric pressure

RESULTS HEATING of the atmosphere in °K by adiabatic compression

Like this:

New Study Finds An Extremely Low CO2 Climate Sensitivity For The Arctic And Antarctic

Like this:

40 years of expert failure: New NOAA STAR satellite temperatures only show half the warming that climate models do

New NOAA STAR Satellite system gets a major correction and suddenly agrees with UAH satellites, not RSS

As Roger Pielke Jnr says “if true — this is very scientifically important”:

Here’s now different the satellite datasets were in 2018

Ross McKitrick:The important climate study you won’t hear about

Satellite show the models are wrong:

Five reasons UAH is different to RSS

REFERENCES

Like this: