the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Monte Carlo Drift Correction – Quantifying the Drift Uncertainty of Global Climate Models
Benjamin S. Grandey
Zhi Yang Koh
Dhrubajyoti Samanta
Benjamin P. Horton
Justin Dauwels
Lock Yue Chew
Abstract. Global climate models are susceptible to drift, causing spurious trends in output variables. Drift is often corrected using data from a control simulation. However, internal climate variability within the control simulation introduces uncertainty to the drift correction process. To quantify this drift uncertainty, we develop a probabilistic technique: Monte Carlo drift correction (MCDC). MCDC involves random sampling of the control time series. We apply MCDC to an ensemble of global climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6). We find that drift correction partially addresses a problem related to drift: energy non-conservation. Nevertheless, the energy balance of several models remains suspect. We quantify the drift uncertainty of global quantities associated with energy balance and thermal expansion of the ocean. When correcting drift in a cumulatively-integrated energy flux, we find that it is preferable to integrate the flux before correcting the trend: an alternative method would be to correct the bias before integrating the flux, but this alternative method amplifies the drift uncertainty by up to an order of magnitude. We find that drift uncertainty is often smaller than other sources of uncertainty: for thermosteric sea-level rise projections for the 2090s, ensemble-mean drift uncertainty (9 mm) is an order of magnitude smaller than scenario uncertainty (138 mm) and model uncertainty (98 mm). However, drift uncertainty may dominate time series that have weak trends: for historical thermosteric sea-level rise since the 1850s, ensemble-mean drift uncertainty is 15 mm, which is of comparable magnitude to the impact of omitting volcanic forcing in control simulations. Therefore, drift uncertainty may influence comparisons between historical simulations and observation-based estimates of thermosteric sea-level rise. When evaluating and analysing global climate model data that are susceptible to drift, researchers should consider drift uncertainty.
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Benjamin S. Grandey et al.
Status: open (until 07 May 2023)
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RC1: 'Comment on egusphere-2022-1515', Damien Irving, 16 Mar 2023
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# General comments
In general, I think this manuscript makes a valuable contribution to the literature. It introduces a concept - internal drift uncertainty arising from internal climate variability within model control simulations - that is typically overlooked by authors working with climate model variables that are prone to drift (i.e. those influenced by the deep ocean). The main result - that drift uncertainty can be relatively large in comparison to forced trends in historical simulations - is important and the authors put forward a useful method (Monte Carlo Drift Correction) for quantifying/checking the size of drift uncertainty.
Some other minor results are also interesting and well worth documenting:
1. The results the authors present regarding the how the fraction of excess energy absorbed by the ocean and expansion efficiency of heat behaves in control simulations before and after drift correction also adds a little to the existing literature on energy conservation in CMIP models (Hobbs et al 2016; Irving et al 2021).
2. The authors point out that it is preferable to integrate fluxes before correcting the trend (as opposed to correcting the bias before integrating the flux) which is something other papers do (e.g. Irving et al 2019; https://doi.org/10.1029/2019GL082015) but don't necessarily explain why they make that methodological choice.
# Specific comments
The authors acknowledge in the manuscript (line 415 and elsewhere) that a limitation of their study is that they ignore branch time metadata, which could be used to reduce uncertainty by allowing for higher order polynomials to be fitted. I agree with the authors that in some cases the branch time metadata is either not available or incorrect, but more often than not branch time metadata is available/correct and where it isn't it can usually be estimated. For instance, Irving et al (2019; https://doi.org/10.1029/2019GL082015) analyse an ensemble of CMIP models and say the following: "We obtained a drift estimate by fitting a cubic polynomial to the full control time series... The time period in the control simulation that parallels the forced simulation was then identified using the branch time information provided in the file metadata, so that the correct segment of the cubic polynomial could be subtracted from the forced simulation. For models with erroneous metadata, the branch time was estimated via visual inspection of the globally integrated OHC timeseries."
I strongly encourage the authors to follow the lead of Irving et al (2019) by attempting to verify model branch times by plotting a variable such as globally integrated OHC. Since essentially all models have a fairly large drift in globally integrated OHC, if you plot the control and forced experiment time series (using branch time information to line up the respective time axes) it's usually pretty easy to see if the first value of the forced experiment does in fact branch off the control experiment at the time the metadata says it does. If it doesn't, it's usually pretty easy to approximately esimate where the branch point actually is. Following this procedure I'd be surprised if there were many models for which the branch time couldn't be verified as correct or sufficiently estimated. This would allow the authors to overcome some of the main limitations of their study.
# Technical corrections
Irving et al "2020" is quoted throughout the paper but the actual publication year of that paper is 2021: https://doi.org/10.1175/JCLI-D-20-0281.1
Citation: https://doi.org/10.5194/egusphere-2022-1515-RC1
Benjamin S. Grandey et al.
Data sets
d22a-mcdc: Analysis Code for "Monte Carlo Drift Correction – Quantifying the Drift Uncertainty of Global Climate Models" Benjamin S. Grandey https://doi.org/10.5281/zenodo.7488335
Benjamin S. Grandey et al.
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