COPE

Climate model projections of 21st century warming are uncertain and some climate models predict very high warming rates over the next few decades. Recent research has attempted to narrow the uncertainty range of future warming based on historical temperature trends. However, key climate system uncertainties limit our confidence in such observations-based constraints: (i) the magnitude and phase of multidecadal internal variability, (ii) anthropogenic and volcanic aerosol forcing, and (iii) potential model biases in representing the climatological base state, the strength of pattern effects, unforced variability or forced response patterns.

Here, we propose to develop robust constraints on near-future projections of climate warming via integrating climate model simulations with observations using distributionally robust statistical and machine learning (StatML) methods. Previous work within the SDSC-funded DASH has led to an improved understanding and attribution of the past climate record, from identifying historical forced vs. internal climate variability, separation of specific forced responses, and towards employing the concept of distributional robustness to multidecadal variability, thus reducing climate model spread.

We propose to leverage these developments for constraining near-term future warming projections, and projections of future temperature and precipitation at the regional scale via dynamical adjustment. A key challenge is to incorporate the concept of distributional robustness in climate science, with the potential to achieve robustness towards model structural uncertainty that will facilitate the transfer across climate models and towards observations.

Overall, the project will narrow uncertainties in near-term future climate, and advance methods using distributional robustness that are likely to transfer to many fields within Earth system science where imperfect process-based models are used and combined with observational data.