Reduced Complexity Model Intercomparison Project Phase 2: Synthesizing Earth System Knowledge for Probabilistic Climate Projections. Issue 6 (4th June 2021)

• Record Type: Journal Article
• Authors: Nicholls, Z.; Meinshausen, M.; Lewis, J.; Corradi, M. Rojas; Dorheim, K.; Gasser, T.; Gieseke, R.; Hope, A. P.; Leach, N. J.; McBride, L. A.; Quilcaille, Y.; Rogelj, J.; Salawitch, R. J.; Samset, B. H.; Sandstad, M.; Shiklomanov, A.; Skeie, R. B.; Smith, C. J.; Smith, S. J.; Su, X.; Tsutsui, J.; Vega‐Westhoff, B.; Woodard, D. L.
• Is Part Of: Earth's future. Volume 9:Issue 6(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-06-04
• Subjects: climate -- model intercomparison -- probabilistic projections -- RCMIP -- reduced complexity climate model; Environmental sciences -- Periodicals; Environmental sciences; Periodicals; 550
• DOI: 10.1029/2020EF001900
• Languages: English
• ISSNs: 2328-4277

Abstract

Abstract: Over the last decades, climate science has evolved rapidly across multiple expert domains. Our best tools to capture state‐of‐the‐art knowledge in an internally self‐consistent modeling framework are the increasingly complex fully coupled Earth System Models (ESMs). However, computational limitations and the structural rigidity of ESMs mean that the full range of uncertainties across multiple domains are difficult to capture with ESMs alone. The tools of choice are instead more computationally efficient reduced complexity models (RCMs), which are structurally flexible and can span the response dynamics across a range of domain‐specific models and ESM experiments. Here we present Phase 2 of the Reduced Complexity Model Intercomparison Project (RCMIP Phase 2), the first comprehensive intercomparison of RCMs that are probabilistically calibrated with key benchmark ranges from specialized research communities. Unsurprisingly, but crucially, we find that models which have been constrained to reflect the key benchmarks better reflect the key benchmarks. Under the low‐emissions SSP1‐1.9 scenario, across the RCMs, median peak warming projections range from 1.3 to 1.7°C (relative to 1850–1900, using an observationally based historical warming estimate of 0.8°C between 1850–1900 and 1995–2014). Further developing methodologies to constrain these projection uncertainties seems paramount given the international community's goal to contain warming to below 1.5°C above preindustrial in the long‐term. Our findings suggest that users of RCMs should carefully evaluate their RCM, specifically its skill against key benchmarks and consider the need to include projections benchmarks either from ESM results or other assessments to reduce divergence in future projections. Plain Language Summary: Our best tools to capture state‐of‐the‐art knowledge are complex, fully coupled Earth System Models (ESMs). However, ESMs are expensive to run and no single ESM can easily produce responses which represent the full range of uncertainties. Instead, for some applications, computationally efficient reduced complexity climate models (RCMs) are used in a probabilistic setup. An example of these applications is estimating the likelihood that an emissions scenario will stay below a certain global‐mean temperature change. Here we present a study (referred to as the Reduced Complexity Model Intercomparison Project (RCMIP) Phase 2) which investigates the extent to which different RCMs can be probabilistically calibrated to reproduce knowledge from specialized research communities. We find that the agreement between each RCM and the benchmarks varies, although the best performing models show good agreement across the majority of benchmarks. Under a very‐low‐emissions scenario median peak warming projections range from 1.3 to 1.7°C (relative to 1850–1900, assuming historical warming of 0.8°C between 1850–1900 and 1995–2014). Investigating new ways to reduce these projection uncertainties seems paramount given the international community's goal to limit warming to below 1.5°C above preindustrial in the long‐term. Key Points: Probabilistic global‐mean temperature projections often use reduced complexity climate models (RCMs) because of their low computational cost We evaluate how well RCMs' probabilistic setups can synthesize knowledge from multiple research domains for policy relevant projections No RCM is able to capture all forcing, warming, heat uptake and carbon cycle metrics we evaluate, however some come close across a range