The terrestrial biosphere exerts disproportionate influence on Earth's climate, making improvements in its representation key to reducing climate uncertainty. After 50 years of development, land surface models contain detailed processes of energy fluxes, photosynthesis, hydrology, C-N-P cycles, and land-use within coarse non-interacting grid cells. Remaining discrepancies in fidelity to observed carbon and water cycles appear primarily related to deficiencies in the representation of forests and human activity. These include the omission of spatial processes of disturbance, migration, adaptation, and management. Also missing is the generative process of life, evolution, which gives rise to life history strategies, trophic-metabolic networks, leaf economics, local adaptation (i.e., optimality, acclimation), and plant behaviour. Despite improvements in representing vegetation demography by utilizing emergent properties of allometric scaling, canopy geometric realism remains low. This may bias carbon and water cycles per radiative transfer and coupled processes of photosynthesis, regeneration, evapotranspiration, heterotrophic respiration, and disturbance.
We believe that physics-based botanical models, forest landscape models, and terrestrial biosphere models may soon merge into new multi-scale models. While low-dimensional representations of forests are often used to improve computational efficiency and cope with a dearth of 4-D forest observatories, deep learning may be combined with new autonomous scanning systems - proximal and/or remote - such as our proposed global tower-based '5DNet' to infer evolvable 4-D physics-based models. This includes learning multi-generation tree models with 4-D traits from image and/or laser scanning time-series. To date, 4-D ontogeny has been inferred from individual scans of mature trees, multi-plant phenological events have been tracked in real-time, and the self-similar and -organizing nature of plants has been used to efficiently compress tree models down to their generating parameters. Achieving leaf-to-global scaling may require co-processor acceleration and fusing deep learning with 3-D radiative transfer modeling to infer global surface properties. An additional focus on evolution and human activity comes as 21st century land surface models mature into general simulations of life on Earth.
This Union Symposium presents exciting work toward achieving this moonshot in Earth observation and systems modeling.
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