the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 26 Feb 2024
A rapid application emissions-to-impacts tool for scenario assessment: Probabilistic Regional Impacts from Model patterns and Emissions (PRIME)
Abstract. Climate policies evolve quickly, and new scenarios designed around these policies are used to illustrate how they impact global mean temperatures using simple climate models (or climate emulators). Simple climate models are extremely efficient although limited to showing only the global picture. Within the Intergovernmental Panel on Climate Change (IPCC) framework, there is a need to understand the regional impacts of scenarios that include the most recent science and policy decisions quickly to support government in negotiations. To address this, we present PRIME (Probabilistic Regional Impacts from Model patterns and Emissions), a new flexible probabilistic framework which aims to provide an efficient means to run new scenarios without the significant overheads of larger more complex Earth system models (ESMs). PRIME provides the capability to include the most recent models, science and scenarios to run ensemble simulations on multi-centennial timescales and include analysis of many variables that are relevant and important for impacts assessments. We use a simple climate model to provide the global temperatures to scale the patterns from a large number of the CMIP6 ESMs. These provide the inputs to a weather generator and a land-surface model, which generates an estimate of the land-surface impacts from the emissions scenarios. Here we test PRIME using known scenarios in the form of the Shared Socioeconomic Pathways (SSPs) to demonstrate that PRIME reproduces the climate response to a range of emissions scenarios, as shown in the IPCC reports. We show results for a range of scenarios including the SSP5-8.5 high emissions scenario, which was used to define the patterns; SSP1-2.6, a mitigation scenario with low emissions and SSP5-3.4-OS, an overshoot scenario. PRIME correctly represents the climate response for these known scenarios, which gives us confidence that PRIME will be useful for rapidly providing probabilistic spatially resolved information for novel climate scenarios; substantially reducing the time between the scenarios being released and being used in impacts assessments.
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Status: open (until 02 Jun 2024)
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RC1: 'Comment on egusphere-2023-2932', Anonymous Referee #1, 22 Mar 2024
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The authors propose a global downscaling system based on “pattern scaling” where the global-mean temperature is used to predict local mean values of all variables on a GCM grid, which is then used to drive an offline land model in order to calculate climate impacts. This approach is considerably cheaper than running a GCM and is proposed for generating large ensembles and testing more compete sets of scenarios while still providing regionally specific outputs.
I think this system is worth publishing but have some issues to raise that will require moderate revisions.
General Comments
- The success of the pattern scaling approach is not well tested in my opinion, mainly because most of the tests examine the change by end of the century under the RCP8.5 scenario, which is exactly the same one used to determine the pattern; and the change by end of century will dominate the variance that a max-likelihood linear fit is trying hardest to fit. Thus the success is built in by design and the agreement shown in e.g. Fig. 5 is meaningless. What is needed is an out-of-sample test such as the accuracy at mid-century, and/or for other RCP scenarios—the RCP6 or 4.5 scenarios would seem like the obvious test targets. The time series comparisons (Figs. 6,7) look OK but not great, as there are some errors mid-century that are as large as the signal. This suggests that the pattern scaling approach isn’t as accurate as we’d like.
In short, the paper needs to get rid of RCP8.5 tests and instead show tests on at least two other RCPs to give a realistic idea of out-of-sample performance. - One reason the performance isn’t always good, especially on precipitation, may be that the authors are ignoring the direct effects of CO2 on precipitation which are substantial (e.g. Bony et al. 2013). Past studies show that by combining the effects of CO2 and global-mean temperature, precipitation patterns can be well captured, but not based on temperature alone. Since the authors are already feeding CO2 and global-mean T to JULES, why not also use CO2 as a second predictor for the downscaling?
- I am very confused by the so-called “weather generator” since the text states that the same weather is used for every day of any given month (line 128). If so, that is extremely unrealistic and will produce extreme responses in the land model (since it will either rain every day of the month or not at all). This doesn’t sound like an actual weather generator. Little else is said about the weather generator except to cite Williams and Clark 2014—we should not have to look there to get basic information about what kind of weather is being inserted. If indeed the weather is being held constant for a whole month at a time and then switches to something else on the 1st of the next month, this needs to be highlighted as a significant limitation in discussing the results.
- The probabilistic framework being used is not clear from the description. Any probabilities will depend on the priors for example, which are not stated, and on what observations the probabilities are conditioned on which is also not stated. There are also some confusing statements in the text (see detailed comments below). This needs to be clarified if the intention is for this tool to be used for probability estimation. It looks like the pdfs are traced to an ensemble calculated by WGI of AR6 but still the assumptions should be stated here.
Detailed/Technical Comments
line 183: by constraining future projections here, do you mean constraining equilibrium climate sensitivity? This is not the same as constraining RCP projections directly (which depend on factors other than ECS, most importantly historical forcings).
line 196: this is stated a bit confusingly—I assume the CO2 is a forcer to the land model, not to the meteorology (which depends only on global-mean temperature).
line 200-202: I don’t understand why in your ensemble, CO2 and ECS would be correlated. I think this is because the important elements noted in Major Point 4 are missing. Even if you are conditioning on historical warming I don’t see why a higher future CO2 would imply a lower ECS. This would only make sense if you were targeting a specific warming, but that isn’t stated clearly and you are showing a spread of possible warmings for any given RCP, as occurs in standard GCM simulations where a prior is placed (implicitly and usually independently) on both ECS and carbon cycle parameters and this implies a posterior distribution of temperature at any future time. There are a number of past studies that obtain pdfs of future warming conditional on historical warming using EMICs, and this study should follow a similar approach; most of them use the GCM ECS distribution as a prior but some use observationally-constrained priors on ECS.
Fig. 3: y axis or caption needs to identify at what time the CO2 concentration is determined.
Fig. 4: upper right figure panel needs to specify what humidity it is (specific humidity, according to the text).
line 220-222: I think what this text means is that you are correlating the decadal means of the predicted vs. CMIP variables—please state clearly. I would not say the correlations are very good for precipitation, wind etc., since much of the map is around .4 or less which means only 20% of the variance is captured by the emulator.
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Bony S; Bellon G; Klocke D; Sherwood S; Fermepin S; Denvil S, 2013, 'Robust direct effect of carbon dioxide on tropical circulation and regional precipitation', Nature Geoscience, 6, pp. 447 - 451, http://dx.doi.org/10.1038/ngeo1799
Citation: https://doi.org/10.5194/egusphere-2023-2932-RC1 - The success of the pattern scaling approach is not well tested in my opinion, mainly because most of the tests examine the change by end of the century under the RCP8.5 scenario, which is exactly the same one used to determine the pattern; and the change by end of century will dominate the variance that a max-likelihood linear fit is trying hardest to fit. Thus the success is built in by design and the agreement shown in e.g. Fig. 5 is meaningless. What is needed is an out-of-sample test such as the accuracy at mid-century, and/or for other RCP scenarios—the RCP6 or 4.5 scenarios would seem like the obvious test targets. The time series comparisons (Figs. 6,7) look OK but not great, as there are some errors mid-century that are as large as the signal. This suggests that the pattern scaling approach isn’t as accurate as we’d like.
Data sets
FaIR: Calibration data for FaIR v1.6.2 is available from zenodo Chris Smith https://doi.org/10.5281/zenodo.6601980
ESMValTool Climate patterns code Greg Munday, Eleanor Burke, and Chris Huntingford https://zenodo.org/records/10635588
Temps and CO2 concentrations for running PRIME from FaIRv1.6.4 Camilla Mathison and Chris Smith https://zenodo.org/records/10524337
JULES output from PRIME version 1 Eleanor Burke and Camilla Mathison https://doi.org/10.5281/zenodo.10634291
Model code and software
FaIR v1.6.2 Chris Smith https://doi.org/10.5281/zenodo.4465032
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