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01:10Propagation of Uncertainty from Systematic Error
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01:50Global Climate Change
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00:59Propagation of Uncertainty from Random Error
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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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01:05Estimation of the Physical Quantities
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Reduced Complexity Model Intercomparison Project Phase 2: Synthesizing Earth System Knowledge for Probabilistic
Z Nicholls1,2, M Meinshausen1,2,3, J Lewis1
1Australian-German Climate & Energy College University of Melbourne Parkville VIC Australia.
Earth'S Future
|July 5, 2021
Summary
Reduced complexity models (RCMs) are crucial for climate projections. Phase 2 of RCMIP shows that RCMs constrained by benchmarks better reflect climate realities, aiding efforts to limit global warming.
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Area of Science:
- Climate Science
- Earth System Modeling
- Climate Change Research
Background:
- Climate science has advanced significantly, with Earth System Models (ESMs) as key tools.
- ESMs face computational limits and structural rigidity, hindering the capture of diverse uncertainties.
- Reduced Complexity Models (RCMs) offer computational efficiency and flexibility for spanning various climate dynamics.
Purpose of the Study:
- To present Phase 2 of the Reduced Complexity Model Intercomparison Project (RCMIP Phase 2).
- To conduct the first comprehensive intercomparison of probabilistically calibrated RCMs.
- To assess RCM performance against key benchmark ranges from specialized research communities.
Main Methods:
- Probabilistic calibration of RCMs using benchmark ranges.
- Intercomparison of multiple RCMs under the SSP1-1.9 low-emissions scenario.
- Analysis of peak warming projections based on observational historical warming estimates.
Main Results:
- RCMs constrained by benchmarks demonstrate improved fidelity to those benchmarks.
- Median peak warming projections across RCMs under SSP1-1.9 range from 1.3 to 1.7°C.
- Historical warming estimate between 1850-1900 and 1995-2014 was 0.8°C.
Conclusions:
- Constraining RCMs with benchmarks is crucial for reliable climate projections.
- Further development of methodologies to constrain projection uncertainties is vital for meeting climate goals.
- Users of RCMs must evaluate model skill against benchmarks and consider incorporating diverse projection sources.