Environment, Climate, and Earth Science

1. Environment, Climate, and Earth Science Forrest M. Hoffman and Monty Vesselinov, Co-Leads

2. Grand Challenges in Earth Science • FE: Double hydrocarbon extraction efficiency from unconventional reservoirs and diminish environmental impacts • GTO: Discover and exploit hidden geothermal resources • CCUS (Carbon capture utilization and storage): Maximize carbon storage efficiency while managing subsurface pressures, and maximizing hydrocarbon recovery (site-specific and operational parameters can impact carbon storage efficiency by an order of magnitude) • BER: Will the tropics remain a carbon sink under climate change?

3. Grand Challenges in Earth Science • BER: Is there increased wood density in the warming Arctic, and will it increase rapidly enough to counteract loss of permafrost carbon? What’s the balance of carbon release between CO2 and CH4? • BER: Can we reduce uncertainty in aerosol and cloud processes to improve climate prediction? • BER: Can we accurately predict glacier loss and sea level rise? • BER: How will watershed hydrology and nutrient/solute/metal biogeochemistry change with climate? Role of hot-spots/hot-moments? • BER: How can we use models to predict fluxes across interfaces, including terrestrial-aquatic interfaces?

4. Grand Challenges in Earth Science • Can we improve localization and yield forecast for sustainable energy production with more accurate climate models? • Can we improve bioenergy crop yield forecasting? (Using, e.g. historical yield, satellite and in situ sensor data). • Can we improve earthquake, tsunami, wildfire, and/or --geothermal energy-- volcanic eruption prediction? (Using, e.g. seismic) • Can we improve urban growth models for short-, medium-, and long-term predictions?

5. Common Themes • Data are disparate or diverse, missing, too short, or at the wrong spatial or temporal scale • Big data challenges vs. small data challenges, and their connections • Detailed (physics, chemistry, biology) theory is uncertain; new hypotheses and algorithms could be applied • Model evaluation and benchmarking can characterize biases to constrain future projections • Emergent constraints can be useful to constrain future projections based on contemporary observations of states or variability

6. Paths Forward • Models: Surrogate, hybrid AI/process, subgrid scale • Data: Reduction, spatial/temporal extrapolation, subgrid scale • Forecasting: Short- to medium-range (info theory), extreme events, stochasticity

7. Environment, Climate, and Earth Science Summary for Day 2 Discussion

8. Grand Challenges in Earth Science (1/3) • Subsurface characterization and simulation • FE: How can we double hydrocarbon extraction efficiency from unconventional reservoirs and diminish environmental impacts? • GTO: How do we optimally discover and exploit hidden geothermal resources? • CCUS: How do we maximize carbon storage efficiency? • BER: Can we couple continental-scale surface and subsurface interactions?

9. Grand Challenges in Earth Science (2/3) • Predictive understanding of terrestrial ecosystem responses to environmental change and Earth system feedbacks • BER: Will the tropics remain a carbon sink under environmental change? • BER: Is wood density increasing in the Arctic and to what degree will it counteract the loss of permafrost carbon? • BER: How will watershed hydrology and nutrient biogeochemistry change as a result? • BER: What are the Earth system feedbacks (water, energy, carbon, nutrients)?

10. Grand Challenges in Earth Science (3/3) • Human impacts and environmental sustainability • BER: Can we provide realistic, high resolution predictions of weather and climate at urban scales through direct simulation and/or downscaling? • BER: Can we accurately predict glacier loss, sea level rise, assess risk and vulnerability? • BETO: Can we improve bioenergy crop yield forecasting and resulting energy production? • BER: Can we predict water availability and quality under environmental change for human impacts, agriculture, and energy production and use?

11. Common Themes from Earth Science Challenges • Data are disparate or diverse, missing, too short, or at the wrong spatial or temporal scale • Big data challenges vs. small data challenges, and their connections • Detailed (physics, chemistry, biology) theory is uncertain; new hypotheses and algorithms could be applied • Model evaluation and benchmarking can characterize biases to constrain future projections • Emergent constraints can be useful to constrain future projections based on contemporary observations of states or variability

12. Potential Paths Forward in Earth Science • Models: Surrogate models, hybrid AI/process models, subgrid scale parameterizations • Data: Reduction, spatial/temporal extrapolation, subgrid scale characterization, model–data integration • Forecasting: Short- to medium-range (info theory), extreme events vs. mean state, stochasticity, parameter/structural uncertainty and sensitivity