{"id":441183,"date":"2026-04-24T06:13:25","date_gmt":"2026-04-24T13:13:25","guid":{"rendered":"https:\/\/climatescience.press\/?p=441183"},"modified":"2026-04-24T06:13:27","modified_gmt":"2026-04-24T13:13:27","slug":"ceres-data-exposes-the-25-year-paradox-why-ar6-climate-sensitivity-methods-fail-reality-checks","status":"publish","type":"post","link":"https:\/\/climatescience.press\/?p=441183","title":{"rendered":"CERES Data Exposes the 25-Year Paradox: Why AR6 Climate Sensitivity Methods Fail Reality Checks"},"content":{"rendered":"
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\"\"<\/figure>\n<\/div>\n\n\n

Andy May<\/strong> analyzes satellite data from the NCAR CERES EBAF<\/strong> (Clouds and the Earth’s Radiant Energy System, Energy Balanced and Filled) dataset to estimate climate sensitivity. It highlights tensions between observational data, IPCC approaches in AR5 versus AR6, and challenges in deriving ECS (Equilibrium Climate Sensitivity)<\/strong> from short-term records.<\/p>\n\n\n\n

CERES EBAF provides TOA radiative fluxes (shortwave + longwave), adjusted to align with upper ocean heat content for EEI (Earth Energy Imbalance). May focuses on the “toa_net_all” variable over oceans (using ERSST v5 SST anomalies, area-weighted).<\/p>\n\n\n\n

May argues the AR6 framework (variable ERF + adjusted feedbacks, per Sherwood et al. 2020 and critiques like Lewis 2023) mismatches real-world CERES observations when applied consistently. Conventional methods align better with lower sensitivity; AR6-style pushes values unrealistically high or requires ad-hoc adjustments.<\/p>\n\n\n\n

The article builds on May’s prior work comparing AR5 vs. AR6 and energy budget analyses. <\/strong><\/p>\n\n\n\n

It assumes CO\u2082\/greenhouse gases dominate the signal for the purpose of the exercise but notes clouds, solar, and variability as potential confounders.<\/strong><\/p>\n\n\n\n

_____________________________________________________________________________________<\/p>\n\n\n\n

ECS, EffCS, and the 25-year Paradox, What CERES tells us<\/strong><\/p>\n\n\n\n

From Andy May Petrophysicist<\/a><\/p>\n\n\n

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\"\"<\/figure>\n<\/div>\n\n\n

By Andy May<\/a><\/p>\n\n\n\n

The NCAR CERES EBAF<\/a> satellite dataset has been adjusted<\/a> to match upper ocean heat content changes. Thus, the EEI (Earth Energy Imbalance) from CERES EBAF (\u201cClouds and Earth\u2019s Radiant Energy Systems, Energy Balanced and Filled\u201d) 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\u2019t.<\/p>\n\n\n\n

Even if CERES EBAF numbers are not accurate enough to derive EEI on their own, they are still useful. I\u2019ve been going over the various CERES EBAF variables in some detail lately and noticed that one of the variables (\u201ctoa_net_all_mon\u201d) provides the total net incoming radiation at the top of the atmosphere or TOA. All energy exchanged between space and the top of the atmosphere is via radiation so this variable can be used to estimate ECS (the Equilibrium Climate Sensitivity to a doubling of CO2<\/sub>) if we assume that CO2<\/sub> and related anthropogenic greenhouse gases are the dominant factor in warming over the period studied.<\/p>\n\n\n\n

Originally, ECS was defined as the ultimate warming due to an instantaneous doubling of atmospheric CO2<\/sub> (IPCC, 1992, p. 73). ECS has always been tied to climate models and was explicitly used to compare climate models to one another (IPCC, 1992, p. xxv) & (Gregory et al., 2004). An instantaneous doubling of CO2<\/sub> is almost impossible and, in any case, waiting for hundreds or thousands of years to determine the ultimate effect at a new equilibrium state is impractical, but it was a good single-number model metric, and an easy way to rank models from hot to cold.<\/p>\n\n\n\n

However, as time went on, this convenient model metric began to be used as a predictive tool for the real world. People confused models with reality and claimed that the average model ECS told us how the world would warm. Attempts have been made to estimate ECS using real world measurements (see here<\/a> and here<\/a>), but due to the lack of adequate measurements over long enough time periods these estimates are considered inadequate. It is still difficult to estimate ECS using observations.<\/p>\n\n\n\n

I used two approaches, when I applied AR6\u2011style feedback diagnostics to CERES EBAF observations, the resulting implied ECS values are physically implausible\u20145.7 to 7.1\u00b0C per doubling. Using a more conventional (AR5 and earlier) approach, I get more reasonable values between 1.8 and 2.4. This relatively simple exercise reveals a fundamental mismatch between the AR6 ECS framework and real\u2011world data.<\/p>\n\n\n\n

Defining ECS<\/h2>\n\n\n\n

To understand what CERES can and cannot tell us, we need to revisit how ECS is currently defined and how the definition shifted after AR5 (IPCC, 2013). The definition of ECS used by the IPCC through AR5 probably originated with Gunnar Myhre (Myhre et al., 1998).<\/p>\n\n\n\n

Eq. 1: \u0394F2xCO2<\/sub> = \u03b1 \u2022 ln(C \/ C0<\/sub>)<\/p>\n\n\n\n

Where \u0394F2xCO2<\/sub> is the change in forcing due to a doubling of CO2<\/sub>. Myhre et al. specifies \u03b1 = 5.35 and C \/ C0 <\/sub>= 2 (a doubling of the CO2<\/sub> concentration), so <\/em>\u0394F2xCO2<\/sub> is a fixed value of 3.71 W\/m2<\/sup>. All IPCC reports, including AR6, then define ECS as:<\/p>\n\n\n\n

Eq. 2: ECS = -\u0394F2xCO2 <\/sub>\/\u03bb (IPCC, 2021, p. 993)<\/p>\n\n\n\n

Myhre\u2019s F2xCO2 <\/sub>value of 3.71 W\/m2<\/sup> is nearly identical to the value used in the IPCC AR5 report (IPCC, 2013, p. 818). The value of the net feedback parameter (\u03bb, substituted for the AR6 \u03b1) can be determined as the slope of a fitted line through the change in net radiation at the top of the atmosphere (TOA) versus global mean surface temperature. Both F2xCO2<\/sub> and \u03bb were fixed global averages before AR6 and ECS was computed from them using equation 2. The concept of ERF, a varying effective radiative forcing, was introduced in AR5, but not used, they still computed ECS with a fixed F, but in AR6 ERF was used and since it continuously varied, it could not be used to directly compute ECS without averaging it. In figure 1 I\u2019ve plotted the CERES top of the atmosphere (TOA) net incoming radiation (both longwave and shortwave) on the y axis and the ERSST v5 SST anomaly on the x axis.<\/p>\n\n\n\n

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Figure 1. The CERES EBAF global TOA net energy imbalance (toa_net_all) over the oceans in W\/m2<\/sup> versus SST in degrees C. The negative slope of the line is the net feedback in W\/m2<\/sup>\/\u00b0C or \u201c\u03bb.\u201d The confidence interval is shown in pink. The estimates of \u201cF\u201d are from (Myhre et al., 1998), (Wijngaarden & Happer, 2020) or vWH, and AR6. The ECS values are computed using equation 2.<\/figcaption><\/figure>\n\n\n\n

In the conventional scheme \u0394F2xCO2<\/sub> is defined as a positive number in the sense that doubling CO2<\/sub> increases the energy retained in the climate system, thus it is warming. The TOA net radiation (\u201cN\u201d in figure 1) from CERES is also the downward net radiation. The global net feedback parameter (\u03bb, units: W\/m2<\/sup>\/\u00b0C) is the negative of the slope of the best fit line through the CERES EBAF points plotted in figure 1 against the ERSST v5 SST anomaly on the x axis. The pink bands are the 95% confidence interval for the regression.<\/p>\n\n\n\n

Van Wijngaarden and Happer derive 3.0 W\/m2<\/sup> for \u0394F2xCO2 <\/sub>in the midlatitudes at the TOA and pointed out that the forcing changes with altitude (Wijngaarden & Happer, 2020, Table 3). AR6 \u201cassessed\u201d a value of 3.93 (IPCC, 2021, p. 993) for the period from 1750-2019 and they call it \u201cERF\u201d or effective radiative forcing. They explain how they get from the AR5 value of 3.71 to 3.93 in Table 7.4 (IPCC, 2021, p. 945). Basically, they believe that the forcing of CO2<\/sub> varies with temperature, stratospheric conditions, clouds etc. and try to compensate for these other factors. Using a CERES derived \u03bb of -1.64 from figure 1 and these three values of \u0394F2xCO2<\/sub>, equation 2 gives the ECS values listed in figure 1. They are all between 1.8 and 2.4 and quite reasonable. AR6 \u201cassesses\u201d a \u03bb of -1.16 (AR6, Table 7.10, p 978), which results in an ECS of 3.39 using equation 2.<\/p>\n\n\n\n

Strictly speaking, if both F and \u03bb vary independently, as claimed by AR6, it is very hard, if not impossible, to determine ECS from them. Equation 2 only works when F2xCO2 <\/sub>is a fixed number, which is presumably why AR6 changed the ECS calculation and changed \u201cF\u201d to \u201cERF\u201d or \u201ceffective radiative forcing\u201d (IPCC, 2021, pp. 959, 1005). For a description of the new AR6 ECS calculation see (Sherwood et al., 2020) and for a critique of the method see (Lewis, 2023). They also introduce an \u201cEffective Equilibrium climate sensitivity,\u201d which is still is the surface temperature response to a doubling of CO2<\/sub>, but can be different from ECS. \u201cECS\u201d has become very bewildering.<\/p>\n\n\n\n

The global mean ERSST v5 global sea surface temperature anomaly is related to ocean heat content or OHC, which is used to tune the CERES EBAF TOA net radiation measurement plotted on the y axis in figure 1. Thus, the two numbers plotted are not completely independent of one another. Both means are area-weighted by latitude and only values from populated ERSST v5 cells are used, thus land (~29%<\/a> of the Earth) is ignored in this study. The CERES grid is a 1\u00b0x1\u00b0 latitude\/longitude grid, but it was aggregated to match the 2\u00b0x2\u00b0 ERSST grid. A plot of the slopes, that is the change in downward total radiation flux from the TOA per the change in SST, for each 2\u00b0x2\u00b0 grid cell, is shown in figure 2.<\/p>\n\n\n\n

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Figure 2. A plot of the change in TOA net downward radiation divided by the change in ERSST v5 SST from 2001 to 2025. This is the negative of the net feedback. Red means more positive net downwelling change in radiation at the TOA per SST degree and blue means less.<\/figcaption><\/figure>\n\n\n\n

Since \u0394F2xCO2 <\/sub>in equation 2 is fixed, for any given value, ECS is a function of \u03bb (the slope mapped in figure 2 is -\u03bb). Figure 2 shows that both \u03bb and ECS vary a lot with location, AR6 does a \u201cspatial pattern effect\u201d analysis to try and use these anomalies to determine both ERF (effective radiative forcing) and \u03bb (IPCC, 2021, Section 7.3, page 941) & (Gregory et al., 2004). Normally, these studies involve comparing model results to observations spatially over the oceans because land observations are usually much more erratic (relative to model results) than ocean observations (IPCC, 2021, p. 942), although the eastern Pacific off South America is always a problem since it is cooling in recent years and the models predicted significant warming (IPCC, 2021, p. 990).<\/p>\n\n\n\n

AR6 does consider observations. They study model-observation differences to evaluate model quality and to determine effective radiative forcing and \u03bb. The large areal variability shown in figure 2 is problematic and may invalidate global \u03bb estimates (including mine) over short periods of time. It suggests internal variability dominates the signal, at least over the period from 2001-2025, this is reinforced by known long-term ocean oscillations like the AMO<\/a>. Such long-term internal variability, if not taken into account, may invalidate the AR6 pattern-effect methodology.<\/p>\n\n\n\n

The Gregory Plot<\/h2>\n\n\n\n

Gregory et al. first described the new model-based \u201ceffective\u201d forcing and climate sensitivity ideas used extensively in AR6 (Gregory et al., 2004). Gregory et al. devised a plot, since dubbed the \u201cGregory plot,\u201d to evaluate model results. The plot is of modeled surface temperature versus the modeled change in downward flux where the intercept reflects ERF and the slope reflects the net feedback. I was curious what it would look like with real data, as opposed to model output. The plot is shown with CERES EBAF data and ERSST v5 SST data in figure 3.<\/p>\n\n\n\n

Gregory et al. allows \u201c\u0394F2xCO2<\/sub>\u201d or \u201cF\u201d to vary, it is still positive downward and here taken as Forster\u2019s \u201cBest ERF\u201d (Forster et al., 2023). The standard Gregory equation is:<\/p>\n\n\n\n

Eq. 3: <\/em>N = F \u2013 \u03bb\u0394T<\/p>\n\n\n\n

Where:<\/p>\n\n\n\n