the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 24 Feb 2025
Likely breaks in cloud cover retrievals complicate attribution of the trend in the Earth Energy Imbalance
Abstract. There is a broad scientific consensus that the earth is warming due to anthropogenic emissions of greenhouse gases (GHG). Increasing GHGs decrease the outgoing longwave radiation (OLR) at the top of the atmosphere (TOA). Since climate change is driven by the Earth Energy Imbalance (EEI), it is crucial to have accurate estimates of the TOA radiative fluxes and identify the factors that drive the observed trend in EEI. In this research, we examined satellite-measured TOA radiative fluxes. In accordance with other studies we found a substantial increase in the absorbed solar radiation (ASR) and a smaller increase in OLR since 2000, which indicates that increased ASR played an important role in recent global warming. We derived a statistical model that quantifies the contribution of different factors to the observed trends in ASR and OLR. We found that the assessment of the contribution of trends in clouds is complicated due to inhomogeneities in retrieved clear-sky fluxes and the underlying cloud datasets. A formal break detection algorithm strongly suggests the existence of breaks in especially low cloud cover. The cloud effect on ASR is therefore relatively hard to estimate, but it is likely a major cause of the increase in ASR. OLR can be more accurately reproduced with cloud cover, temperature and water vapour changes, but the expected decrease due to GHG was not found. We conclude that the inhomogeneities detected in this study warrant more study as they impact the attribution of the trend in EEI.
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Status: open (until 07 Apr 2025)
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RC1: 'Comment on egusphere-2025-418', Anonymous Referee #1, 07 Mar 2025
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Overall take
The paper attempts to attribute changes in Outgoing Longwave Radiation (OLR) and Absorbed Solar Radiation (ASR), which when combined represent the Earth's Energy Imbalance (EEI) to changes in clouds, Water Vapor + Temperature, GHG, Aerosols + Surface Albedo. A statistical tool is used to diagnose whether the "residual" (GHG, Aerosols, Albedo) is heterogeneous in time, namely whether distinct periods exist where the influence changes sign or accelerates/decelerates rapidly. So-called "breaks" are found, but there is little interpretation or speculation of why they are there (it is admittedly difficult to pin down the reasons). I believe there is a serious misinterpretation of one of the critical datasets, no clear picture emerges, and the conclusion highlighted by the abstract (the role of low cloud changes) is not supported by the presentation, in my opinion. I thought the paper would discuss the challenges of putting together and reconciling long time series of disparate data, but it focuses on the well-observed post-2000 period (where we have high-quality CERES and MODIS); so one has to wonder why other, lower quality data have to be brought in. Ultimately, I think that this paper instead of shedding light on what's going on with OLR/ASR/EEI variations adds confusion with its questionable methodology and interpretation, and I thus can't recommend publication in anything close to its current form.
Below, I provide some specific comments in case the paper will be considered for publication:
Major points
-- Conclusion about low clouds Lines 9-10, but not reiterated in the conclusions. If this is a major finding of the paper any relevant results and figures can't be in the Appendix.
-- Lines 91-93. I think there's a major misunderstanding here. First of all the authors talk about cloud data in CERES SSF1deg above and then in these lines they invoke a CERES EBAF document. I think this statement draws from the DQS document p. 19, Table 7-1. What this part of the document addresses is how to convert MODIS narrowband radiances (within the CERES footprint) to broad band radiances so that a clear-sky flux at the sub-footprint level can be calculated from the clear-sky MODIS pixels. So C5 and C61 refer to radiance (L1b data) not cloud retrievals (Table 7-1 caption mentions fluxes). C5 and C61 cloud retrievals are performed by the GSFC-based Atmospheric Discipline MODIS team. The CERES project has its own cloud retrievals which are included in various CERES products. Invariably, when a retrieval algorithm changes, all the data is being reprocessed with the same algorithm: you don't use one algorithm for one part of the time series and another for another period. I think this major misinterpretation of different cloud retrievals for different periods unfortunately propagates throughout the paper.
-- Now, actual discontinuities in cloud retrievals, despite processing with the same algorithm, can happen because of other events. See for example https://atmosphere-imager.gsfc.nasa.gov/issues/cloud
-- Why was the break detection algorithm not applied to the Appendix cloud cover time series? That would've been interesting!
-- Cloud_cci, ERA5, and HIRS do not appear in sections 5 and 6. Why even include them in the paper?
-- Lines 11-12, decrease of OLR due to GHG increase. Well, there are a lot of negative trends in Table 3. Does this say something about deficiencies in the methodology? Clear-sky OLR does not have to go down if temperature goes up (the Earth warms to get rid of the excess heat).
-- There is no single plot of EEI = OLR+ASR. Wouldn't that give some clues about the quality of the datasets?
-- ISCCP is a notoriously inhomogeneous cloud dataset because of changes in the satellite fleet and instruments throughout time. But this is not mentioned.
Somewhat minor points
-- Figs. 1-4: Except for Fig. 1, pre-2000, it is hard to see what's going on. Perhaps smoothed versions (running mean) are in order? What's going on with ISCCP ASR circa 1984?
-- What's the physical meaning of the specific values of c? Are these values suitable for the anomaly values of this paper? Or is it a normalized value. They seem so arbitrary. So if I was looking at anomalies as high as 100 in the time series, how would those c values change? In general I found subsection 3.2 a bit too long and confusing. Are these details really needed? I think the reader has to take a leap of faith here and assume that the authors are using BEAST correctly.
-- Constant incoming solar assumption. Given the small magnitude of ASR anomalies, can you really make that assumption? I guess in a relative comparison of different ASR anomalies it's OK, but for absolute anomaly magnitudes, it is a bit precarious to make this assumption.
-- Lines 106-109: Why are you mentioning this? Does it matter for the analysis that follows?
-- Lines 98-99. The (radiative) tops of middle and high clouds lie between and above those levels, not the entire clouds.
-- Lines 225-226. This sentence basically says: OLRall-sky = OLRclr + (OLRall-sky - OLRclr). What is the point here? The two OLRclr's are not the same?
-- So in Fig. 5, residual = GHG. -0.27 Wm-2decade-1 is the value of the red bar, In Fig. 5b, and of the slope of th ered line in Fig. 5c, correct? In Fig. 6 residual = other. These things could be explained more clearly.
-- Lines 152-154. This sentence is unclear to me.
-- Lines 83-84. This sentence doesn't make sense.
Citation: https://doi.org/10.5194/egusphere-2025-418-RC1
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