ChatGPT for Global Warming
Climate change requires inter-disciplinary appproach with several topics (atm. science, oceanography, biology, etc.). ChatGPT could help with:
- Data analysis and interpretation
- Communication of complex information wo wide audience
- Decision making and recommendations
- Climate scenario generation, with alternative inputs
Limitations:
- Understanding complex concepts (scientific background, may not understand scientific correlations)
- Lack of contextual awareness
- Biases from training data
- Accountability for decision and output (ethical problems)
- Limited scope of expertise (up to date knowledge problem)
TODO: CAN WE TRAIN IT ON DAILY INFORMATION, TO KEEP IT UP TO DATE?
Machine Learning for Climate Change Research
Climate change research will require high adaptability to understand better the complete climate system.
Problem of uncertainty: more data in some instances could be useful.
Dimensionality reduction: differential equation govern how ESM behave --> complex There are three main approaches:
- non-dimensionalization of equation term: create reduced set of linked equations
- dimensional analysis: both collapse the complexity and relate different strands of data (linkage can aid reconstruction, parametrization or testing)
- statistical techniques: using empirical orthogonal functions (EOFs) to reduce the dimensionality of the data
- emergent constraints (EC): refine projections by searching across ESM ensembles for regression between modelled climate systems
How to integrate ML with these reduction techniques?
Machine Learning applications in climate
- Extreme events forecasting
- Uncertainty in climate response
- Build ecological-climate equations (interactions)
The key interaction equations are known
- Ecosystem photosynthesis, respiration and decomposition
- Operate at individual tree level
- Unknown are the equations that capture complex canopy structure and temperature-dependant variations in leaf processing. It can cause uncertainty in global carbon fluxes.