Climate processes

Abstract:

Estimating the effects of continuous-valued interventions from observational data is a critically important task for climate science, healthcare, and economics. Recent work focuses on designing neural network architectures and regularization functions to allow for scalable estimation of average and individual-level dose-response curves from high-dimensional, large-sample data. Such methodologies assume ignorability (observation of all confounding variables) and positivity (observation of all treatment levels for every covariate value describing a set of units), assumptions problematic in the continuous treatment regime. Scalable sensitivity and uncertainty analyses to understand the ignorance induced in causal estimates when these assumptions are relaxed are less studied. Here, we develop a continuous treatment-effect marginal sensitivity model (CMSM) and derive bounds that agree with the observed data and a researcher-defined level of hidden confounding. We introduce a scalable algorithm and uncertainty-aware deep models to derive and estimate these bounds for high-dimensional, large-sample observational data. We work in concert with climate scientists interested in the climatological impacts of human emissions on cloud properties using satellite observations from the past 15 years. This problem is known to be complicated by many unobserved confounders.

Abstract:

Abstract:

The Sahel rainfall has a close teleconnection with North Atlantic sea surface temperature (NASST) variability, which has separately been shown to be affected by aerosols. Therefore, changes in regional aerosols emission could potentially drive multidecadal Sahel rainfall variability. Here we combine ensembles of state-of-the-art global climate models (the CESM and CanESM large ensemble simulations and CMIP6 models) with observational data sets to demonstrate that anthropogenic aerosols have significantly impacted 20th-century detrended Sahel rainfall multidecadal variability through modifying NASST. We show that aerosol-induced multidecadal variations of downward solar radiative fluxes over the North Atlantic cause NASST variability during the 20th century, altering the ITCZ position and dynamically linking aerosol effects to Sahel rainfall variability. This process chain is caused by aerosol-induced changes in radiative surface fluxes rather than changes in ocean circulations. CMIP6 models further suggest that aerosol-cloud interactions modulate the inter-model uncertainty of simulated NASST and potentially the Sahel rainfall variability.

Abstract:

<jats:p>Abstract. Large computer models are ubiquitous in the Earth sciences. These models often have tens or hundreds of tuneable parameters and can take thousands of core hours to run to completion while generating terabytes of output. It is becoming common practice to develop emulators as fast approximations, or surrogates, of these models in order to explore the relationships between these inputs and outputs, understand uncertainties, and generate large ensembles datasets. While the purpose of these surrogates may differ, their development is often very similar. Here we introduce ESEm: an open-source tool providing a general workflow for emulating and validating a wide variety of models and outputs. It includes efficient routines for sampling these emulators for the purpose of uncertainty quantification and model calibration. It is built on well-established, high-performance libraries to ensure robustness, extensibility and scalability. We demonstrate the flexibility of ESEm through three case studies using ESEm to reduce parametric uncertainty in a general circulation model and explore precipitation sensitivity in a cloud-resolving model and scenario uncertainty in the CMIP6 multi-model ensemble. </jats:p>