Modeling the Troposphere

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.

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Testing the Dew-Point Anchor Hypothesis


By Andy May and Philip Mulholland

Earlier this year Philip Mulholland published the details of his Dew-Point Anchor Hypothesis or “DPAH” (Mulholland P. , 2026a). The hypothesis assumes that the Dew-Point Lifting Condensation Level (LCL), as calculated from surface conditions in the tropics, can act as an anchor for the lower and middle troposphere. This idea moves the independent variable in climate modeling from the radiative balance at the top of the atmosphere (TOA), to the cloud level inside the troposphere.

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Do Los Niños cause climatic cooling?

By Andy May

This post is available in German, courtesy of Christian Freuer, if you prefer that language. See here.

We’ve seen a lot of news stories about an upcoming El Niño, that may turn into a so-called “super” El Niño over the next year. This will affect our weather for a year or two, but what is the climatic effect of this weather feature, if any? Here we examine the history of warm ENSO events.

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The Moist-Adiabatic Theory vs. Reality

By Andy May

The moist‑adiabatic theory (Durran & Klemp, 1982) is one of the central organizing ideas in atmospheric thermodynamics, and it is used—both explicitly and implicitly—throughout modern climate models, especially in the tropics where it works best. It stems from thermodynamics and says that a rising parcel of humid air cools more slowly than a dry parcel because cooling causes condensation of water vapor which releases latent heat that warms the parcel. Adiabatic simply means that the strict theory requires, unrealistically, that no heat enters or leaves the air parcel being modeled from the environment around it, that is, the air surrounding the parcel.

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Calculating Earth’s Albedo, Part 2

By Andy May


It came up in the comments on my last post, CERES Albedo. What is the best way to compute Earth’s albedo? The CERES data is supplied as a 1° x 1° latitude/longitude grid. It is widely accepted that Earth’s global mean albedo is around 30%. The question is then: What is the best way to estimate it using the CERES satellite data? There are two basic ways. One is to use the average solar radiation arriving at the top of the atmosphere (CERES EBAF variable “solar_mon”), which is about 340.2 W/m2 and divide that into the total solar shortwave radiation (SW) leaving (reflected from) the Earth (toa_sw_all). Using these two numbers we get an average albedo of about 29%, unfortunately, this is a single-point average, and we cannot see the elements of the changes in albedo, mainly the changes in cloud and ice cover.

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CERES Albedo

By Andy May

Albedo (or Earth’s global reflectivity) in this post is defined as the amount of solar shortwave (SW) radiation that the Earth reflects into space, as measured at the top of the atmosphere or TOA, divided by the total solar radiation reaching Earth also measured at the TOA. In the CERES EBAF satellite context (Loeb et al., 2009, 2018, 2021), and using their variable names, this is toa_sw_all_mon divided by solar_mon, where “mon” means monthly and “sw” means shortwave radiation. In this post we compute yearly global latitude-area-weighted means from the monthly values for most of the illustrations to avoid seasonal effects, which are very large. As seen in figure 1, there is a distinct albedo peak that falls roughly between 2004 and 2007 and afterward the albedo falls until 2025, with a second smaller, but still dramatic peak in 2020.

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Earth Energy Imbalance: The Sun versus CO2

By Andy May

This is the text of a talk I gave on a Tom Nelson podcast. To listen to the talk and see Tom’s interview of me go here.

Some believe that CO2 and other greenhouse gas infrared emissions are as effective at increasing ocean heat content (or OHC) as solar radiation. Some even think greenhouse gas (abbreviated “GHG”) radiation is more effective than solar. There are many generally agreed points that dispute this conjecture:

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ECS, EffCS, and the 25-year Paradox, What CERES tells us

By Andy May

This is an updated version of this post; the original post had incorrect figures as a result of a bug in my R program. The conclusions remain the same, but the figures and the text have changed (May 4, 2026).

The NCAR CERES EBAF satellite dataset has been adjusted to match upper ocean heat content changes. Thus, the EEI (Earth Energy Imbalance) from CERES EBAF (“Clouds and Earth’s Radiant Energy Systems, Energy Balanced and Filled”) is not estimated directly from satellite measurements. If upper ocean heat content were known accurately over a sufficiently long period, this would be fine, but it isn’t.

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TOA EEI versus Surface Net Flux

By Andy May

Fasullo & Trenberth (2008) build a closed, observation‑based annual energy budget for Earth’s climate system, partitioned into the top of the atmosphere (TOA), atmosphere, land, and ocean. They combine satellite radiation measurements, weather reanalyses, a stand‑alone land model, and several ocean temperature products. Over the oceans, they diagnose the net surface flux as a residual of the TOA and atmospheric budgets and compare it to independently derived ocean heat content and its trend.

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Stefani on the Sun vs. CO2 as climate drivers

By Andy May


Frank Stefani, of the Helmholtz-Zentrum Dresden-Rossendorf, Institute of Fluid Dynamics, has published a very interesting new paper that compares the solar “aa” index and CO2 emissions to global SST (sea surface temperatures using the HadSST4.2 dataset) and finds a CO2 sensitivity (TCR or the “Transient Climate Response”) of 1.1 to 1.4K. This is at the low end of the IPCC TCR range of 1.2 to 2.4K (IPCC, 2021, p. 93), but quite close to the values calculated by Lewis and Curry and Nicola Scafetta (Lewis & Curry, 2018), (Scafetta, 2023), and (Lewis, 2023). Scafetta found a plausible range of TCR (versus HadSST4.2) of 1.0K to 1.2K and Lewis & Curry report a range of 0.9K to 1.7K for TCR versus HadCRUT4. The estimates are compared in Table 1.

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