the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 23 Feb 2024
The GERB Obs4MIPs Radiative Flux Dataset: A new tool for climate model evaluation
Abstract. A new radiative flux dataset, specifically designed to enable the evaluation of the diurnal cycle in top of the atmosphere fluxes, as captured by climate and Earth-system models is presented. Observations over the period 2007–2012 made by the Geostationary Earth Radiation Budget (GERB) instrument are used to derive monthly hourly mean reflected shortwave (RSW) and outgoing longwave fluxes (OLR) on a regular 1°x1° latitude-longitude grid covering 60° N–60° S and 60° E–60° W. The impact of missing data is evaluated in detail, and a data-filling solution is implemented using estimates of the broadband fluxes from the Spinning Enhanced Visible and Infrared Imager, flying on the same Meteosat platform, scaled to the GERB observations. This relatively simple approach is shown to deliver an approximate factor of ten improvement in both the bias caused by missing data and the associated variability in the error. To demonstrate the utility of this GERB ‘obs4MIPs’ dataset, comparisons are made to radiative fluxes from two climate configurations of the Hadley Centre Global Environmental model: HadGEM3-GC3.1 and HadGEM3-GC5.0. Focusing on marine stratocumulus and deep convective cloud regimes, diurnally resolved comparisons between the model and observations highlight discrepancies between the model configurations in terms of their ability to capture the diurnal amplitude and phase of the top of atmosphere fluxes: details that cannot be diagnosed by comparisons at lower temporal resolution. For these cloud regimes the GC5.0 configuration shows improved fidelity with the observations although notable differences remain. The GERB Obs4MIPs monthly hourly TOA fluxes are available from the Centre for Environmental Data Analysis with the OLR fluxes accessible at https://doi.org/10.5285/90148d9b1f1c40f1ac40152957e25467 (Bantges et al. 2023a) and the RSW fluxes at https://doi.org/10.5285/57821b58804945deaf4cdde278563ec2 (Bantges et al. 2023b).
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RC1: 'Comment on essd-2024-4', Richard Allan, 04 Mar 2024
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Review of "The GERB Obs4MIPs Radiative Flux Dataset: A new tool for climate model evaluation" by Russell et al. 2024 ESSD
A valuable and unique geostationary satellite dataset observing the diurnal cycle of Earth's radiation budget over the Africa-Atlantic hemisphere has been reprocessed and formatted to optimise and aid exploitation by climate modelling centres. The manuscript carefully outlines the rational, decisions necessary for dealing with data gaps and issues that users of the dataset need to consider in best using the product. An illustration of its use in evaluating climate model simulations is presented. The analysis is reasonable and the work will help in promoting this useful dataset to the community.
Overall, my opinion is that a more focused descrpition of the data, highlighting strengths and weaknesses and presenting quantitative assessment of the uncertainty would improve the impact of this dataset. Although interesting, I am not sure the illustration of the dataset's use in evaluating a climate model simulation is central to a dataset description paper and would benefit from a more detailed exploration elsewhere. However, if the authors prefer this more lengthy discussion, I do not see any scientific reason to object and I consider that this work is suitable for publication with only minor modifications listed below.
1) Title - I would not describe GERB Obs4MIP as a tool? How about something like:
"The GERB Obs4MIP: a dataset for evaluating diurnal and monthly variation in top of atmosphere radiative fluxes in climate models."2) L8 - since GERB data has been used in model evaluation for 20 years it is not new. What is new is the Obs4MIP aspect ("newly reprocessed" may be more appropriate).
3) L11 "approximately" since it is not a square 60S-60N, 60W-60W domain
4) L19 ":" --> "," (or we could compromise with a ";")!
5) L20 improved fidelity, relative to GC3.1
6) L27 Since outgoing longwave is used later it may be worth stating here e.g. "emitted thermal infrared (outgoing longwave) and solar reflected..."
7) L36 also Allan et al. (2011), Examination of long-wave radiative bias in general circulation models over North Africa during May–July. Q.J.R. Meteorol. Soc., 137: 1179-1192. https://doi.org/10.1002/qj.717
8) L55 add a line pointing to the fact that the HR product is a resolution enhanced version of the GERB broadband radiative fluxes using SEVIRI narrow band measurements.
9) L63 "are" --> "were"
10) L72 why is 70 degrees zenith the cut-off and not slightly more or less?
11) L74 "proceed without prejudice" is not really clear what is meant (sounds like legal speak). Is one observation representative of a 100x100km area? Presumably there are mostly lots of values per 1x1 degree box.
12) L76 incoming solar flux from what dataset or algorithm?
13) Figure 3 - the colour bar seems to bear no resemblance to the maps. Also this figure could be designed in a way to maximise the size of the panels (or reduce the size of the plot and all the dead space)
14) L187 "exiting" --> "existing"
15) L200 this paragraph could be reduced to the first line plus "(compare Figures 5 and 3)".
16) L205 these lines are difficult to understand. Can they be written more clearly? There is a lot of detail in this section that may be unnecessary for users of the dataset so another option is to more briefly note issues and refer to prior papers if the reader is interested.
17) Figures 6/7 - could these be designed to fit better on the page and maximise the size of the panels? Does the line at 25oS relate to a dead or damaged pixel?
18) L251 - of course it is feasible to fill missing GERB data with GERB like - there is no need to hypothesise, just quantify the expected error and assess whether this is tolerable for the designed usage of the dataset. A key line to emphasise is that the strategy is to "fill missing GERB fluxes with the corresponding GERB-like fluxes, adjusted by the GERB/GERB-like ratio calculated at the monthly hourly mean temporal and
1⁰ spatial scale." and now the associated errors are quantified.19) L259 1 degree "latitude/longitude" (throughout)
20) Figure 8 - make text bigger. It's not obvious what the use of this is since the decision to scale GERB-like with the ratio seems obvious (and the mean bias of about zero is by design)
21) L270 - the improvement in agreement between GERB and GERB-like after essentially removing the bias is obvious by design. This sentence can be removed. Section 2.5 seems to be the main analysis following the rational for the approach, which could be more concisely presented as the method
22) Figure 9 - it's not clear to me what the benefit of showing all the unfilled results as well as all the filled results. If the idea is to compare Figure 9 and Figure 4 then an unfilled vs filled line could be presented on the same figure. Are all times lumped together? In this case massive errors due to missing parts of the diurnal cycle will be introduced won't it (which would obviously not be considered in practice)?
23) L306 AMIP needs to be explained
24) Section 3.1 - although important and well described, this is a bit of a gear change from describing the dataset. Is all this information necessary?
25) Figure 10/11 are very qualitative and it is rather difficult to link colours on the bar to values in the plots. A plot of differences would be more informative. Why are coincident model years not uesed (CMIP6 amip simulations usually end in 2014)? I am not sure the Figure 12 results are very relevant to the GERB data description; a reference could suffice. Figure 13/14 seem much more relevant - a legend to denote model simulation is needed and perhaps a thick mean (or median) model value would be useful. Showing albedo may reveal the diurnal cycle of stratocumulus better than RSW (which is more dependent on the insolation). Figure 14 - if the idea is to compare the model versions, it would be more useful to have the two mean lines (and perhaps shading for range) in the same plot.
26) L352 - it could be mentioned that these issues are not fully solved in higher resolution simulations e.g. Watters et al. (2021) J. Clim doi: 10.1175/JCLI-D-20-0966.1.
27) Table 1 - how much is positional/regime error and how much is cloud property error? How have biases changed since earlier analysis (e.g. for the NWP version comparison in Allan et al. (2007) QJRMetS clouds were instead too bright due to too much water)?
28) L442 the model seems to overestimate OLR for both months - does cirrus outflow or water vapour contribute?
29) L475 - it is still not clear what this factor of 10 reduction in uncertainty means. Is this just for simplistic averaging, where parts of the diurnal cycle are missing and which would not be undertaken in a serious analysis? Or is it referring to missing days?
30) L478 Some clear statements about product uncertainty and it's recommended use could strengthen the conclusions
31) L479 some illustrative comparison with model simulations are useful for introducing the dataset though I think the work presented here is more deserving of a separate, more detailed investigation.
32) Acknowledgements - the work of the GERB team could be mentioned in the acknowledgments and more specifically the contribution to initial discussions of the strategy for dealing with sunglint led by Jo Futyan and the terminator led by me could be stated.
Richard Allan
Citation: https://doi.org/10.5194/essd-2024-4-RC1
Data sets
Monthly-mean diurnal cycle of top of atmosphere outgoing longwave radiation from the GERB instrument (GERB-HR-ED01-1-1 rlut 1hrCM), v20231221 R. J. Bantges, J. E. Russell, and H. E. Brindley https://dx.doi.org/10.5285/90148d9b1f1c40f1ac40152957e25467
Monthly-mean diurnal cycle of top of atmosphere outgoing shortwave radiation from the GERB instrument (GERB-HR-ED01-1-1 rsut 1hrCM), v20231221 R. J. Bantges, J. E. Russell, and H. E. Brindley https://dx.doi.org/10.5285/57821b58804945deaf4cdde278563ec2
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