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Climate model and scenario selection

guidance_ensembleSelection.Rmd

Colin Mahony ()
Research Climatologist
British Columbia Ministry of Forests

Model selection

Wherever possible, climate change studies will benefit from the use of projections from multiple models to assess modeling uncertainties. There is broad scientific agreement that an ensemble of at least eight independent climate models are required to represent modeling uncertainties about climate change outcomes over large regions (Pierce et al. 2009, McSweeney et al. 2014, Cannon 2015, Wilcke and Bärring 2016). However, small ensembles of 3-5 GCMs may be adequate for studies that are limited to a small area or a single time of year. If a single representative projection of long-term climate trends is desired, a multi-GCM ensemble mean is likely more reliable than any single GCM projection (Pierce et al. 2009).

All of the 13 models provided in climr are individually useful and provide a good representation of the larger CMIP6 ensemble (Mahony et al. 2022). However, like the full CMIP6 ensemble they should be viewed as an arbitrary collection of models that don’t necessarily provide a reliable representation of climate change uncertainty, as explained by NASA’s Gavin Schmidt. In Mahony et al. (2022) we recommend 8 models for ensemble analysis. This 8-model ensemble is more consistent with the IPCC’s assessment of climate sensitivity than the full 13-model climr ensemble, and excludes a model with problematic spatial artefacts in BC Coast Mountains. The recommended 8-model ensemble is: ACCESS-ESM1.5, CNRM-ESM2-1, EC-Earth3, GFDL-ESM4, GISS-E2-1-G, MIROC6, MPI-ESM1.2-HR, and MRI-ESM2.0.

Scenario selection

CMIP6 climate projections follow scenarios of future greenhouse gas emissions called Shared Socioeconomic Pathways (SSPs). climr includes projections for the four major SSP scenarios: SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. SSP1-2.6 assumes strong emissions reductions (mitigation) roughly consistent with the goals of the Paris Climate Accords to limit global warming to 2oC above pre-industrial temperatures. SSP2-4.5 assumes moderate mitigation roughly consistent with current emissions policies and economic trends. SSP3-7.0 is representative of a broader range of “baseline” scenarios that assume the absence of mitigation policies, and is associated with linear increase in the rate of greenhouse gas emissions. SSP5-8.5 is at the high end of the baseline scenarios, representing rapid expansion of greenhouse gas emissions over the next several decades and end-of-century emissions more than three times higher than current emissions (Riahi et al. 2017).

Collectively, SSP1-2.6, SSP2-4.5, and SSP3-7.0 provide a reasonable representation of optimistic, neutral, and pessimistic outlooks on global emissions policies and socioeconomic development. Where possible, I recommend using all three scenarios to represent scenario uncertainty in climate change projections. SSP2-4.5 alone is sufficient for studies focused on the near future (the 2021-2040 period) since there is only minor differentiation between the three recommended emissions scenarios in this period relative to differences between climate models. SSP5-8.5 should be used with caution in impacts and adaptation research. The emissions pathway described by SSP5-8.5 is extremely unlikely based on constraints to the supply and demand for high-carbon energy sources and current trends in energy economics and policy (Hausfather and Peters 2020), though SSP5-8.5 greenhouse gas concentrations may be plausible, if unlikely, due to carbon cycle feedbacks.

Time period selection

Instead of the traditional 30-year definition of climate normals, climr provides normals for a set of five 20-year periods for the 21st Century: 2001-2020, 2021-2040, and so on. These shorter 20-year periods are more appropriate to representing the rapidly changing climates of this century. The practice of summarizing climate in 20-year periods is consistent with recent IPCC reports. The 2001-2020 period provides the opportunity for direct comparison of model simulations vs. observations, which can give important context to interpretations of future projections. climr normals for the 2001-2020 period are calculated from the historical model runs for the years 2001-2014 and the SSP scenario runs for the years 2015-2020.

When interpreting projections for the near future, it is important to recognize that individual GCM projections are not predictions. GCM runs used for climate change projections are initiated in the 1850s and are not directly constrained by observed climate conditions. Consequently, GCM projections are essentially as uncertain for next year as they are for 20 years into the future. Decadal climate prediction, which is analogous to weather prediction for timescales of 1-10 years, is an emerging but not yet operational science that may help to reduce the uncertainty of near-term projections (Boer et al. 2016). In the meantime, it is considered best practice to use an ensemble of climate projections, such as the 8-model ensemble recommended here, for near-term regional climate change studies (Brekke et al. 2008, Knutti 2008, Pierce et al. 2009). Given that the recent observed climate may differ substantially from the ensemble mean, and may even be outside the ensemble range, climate change adaptation decisions for the near-term (1-10 years) should carefully consider recent observed trends in addition to climate model simulations.

References

Boer, G. J., D. M. Smith, C. Cassou, et al. 2016. The Decadal Climate Prediction Project (DCPP) contribution to CMIP6. Geoscientific Model Development 9:3751–3777.

Brekke, L. D., M. D. Dettinger, E. P. Maurer, and M. Anderson. 2008. Significance of model credibility in estimating climate projection distributions for regional hydroclimatological risk assessments. Climatic Change 89:371–394.

Burgess, M., J. Ritchie, J. Shapland, and R. Pielke. 2021. IPCC baseline scenarios over-project CO2 emissions and economic growth. Environmental Research Letters 16:014016

Cannon, A. J. 2015. Selecting GCM scenarios that span the range of changes in a multimodel ensemble: Application to CMIP5 climate extremes indices. Journal of Climate 28:1260–1267.

Hausfather, Z., and G. P. Peters. 2020. Emissions - the “business as usual” story is misleading. Nature 577:618–620.

Knutti, R. 2008. Should we believe model predictions of future climate change? Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 366:4647–4664.

McSweeney, C. F., R. G. Jones, R. W. Lee, and D. P. Rowell. 2014. Selecting CMIP5 GCMs for downscaling over multiple regions. Climate Dynamics 44:3237–3260.

Pierce, D. W., T. P. Barnett, B. D. Santer, and P. J. Gleckler. 2009. Selecting global climate models for regional climate change studies. Proceedings of the National Academy of Sciences of the United States of America 106:8441–8446.

Riahi et al. 2017. The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Global Environmental Change 42:153–168.

Wilcke, R. A. I., and L. Bärring. 2016. Selecting regional climate scenarios for impact modelling studies. Environmental Modelling and Software 78:191–201.