Actively learning experimental designs in terrestrial climate science

While land-atmosphere exchanges of carbon, water, and energy are key to understanding changes in the Earth system, we still fundamentally lack a methodology to obtain representative estimates of these surface fluxes at the scale of a single grid cell of an Earth System Model (typically 10-100 km), let alone for a wider region.

ACTIVATE combines an observing system consisting of a swarm of drones carrying meteorological sensors and gas analyzers, mobile and stationary flux towers, as well as satellites, and fuses their observations with different land-atmosphere models using data assimilation methods.

ACTIVATE will develop an adaptive Bayesian Experimental Design framework to generate maximally informative observation strategies for expensive data collection, and adaptively reposition drone swarms during a flight as new observations become available to optimally infer surface fluxes in the landscape.

We will demonstrate the framework (i) in idealized synthetic experiments, (ii) at managed and industrial sites with known flux hotspots, and (iii) in targeted high-resolution simulations in poorly represented regions with expensive models that explicitly resolve subgrid-scale processes in Earth System models.

We will apply the ACTIVATE framework around existing observatories in vulnerable arctic regions, where the lack of strong observational constraints from state-of-the-art observing systems is particularly apparent and problematic.

ACTIVATE will produce: unprecedented observational datasets for new model developments in some of the most data-sparse regions on Earth, uncertainty-aware parameter estimates for critically unconstrained processes, and a pioneering active experimental design framework for terrestrial observing systems.

The broader vision of ACTIVATE is to develop active learning capabilities for improved data assimilation in models to elevate our understanding of land-atmosphere interactions across spatio-temporal scales.