LEAC
Learning About Cloud Brightening under Risk and Uncertainty: Whether, When and How to do Field Experiments
- Prof. Dr. Johannes Quaas // Leipzig University // PI
johannes.quaas@uni-leipzig.de - Prof. Dr. Martin Quaas // Kiel University (CAU) // PI
quaas@economics.uni-kiel.de - Dr. Wilfried Rickels // CAU
- Aswathy Nair // Leipzig University
Background and Aim
No consensus has been achieved in science, society and politics even about the question whether in-depth research in the form of field experiments on Climate Engineering should be conducted. This projects aims at theoretical clarification of this question without actually doing experiments.
Cloud seeding:
- Climate Engineering by injection of aerosol which would serve as cloud condensation nuclei and thus increase cloud brightness
- May enable field experiments which are scalable in intensity as well as spatial and temporal extent.
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Approach
1. Quantification of the uncertainty of the radiative forcing by cloud seeding.
2. Estimate how this uncertainty could be reduced depending on intensity as well as spatiotemporal extent of a possible field experiment.
3. Characterisation of an optimal climate policy for given uncertainties and different social risk- and time preferences.
4. Characterisation of the optimal learning by field experiments for different social risk- and time preferences.
Methods
The project will apply or develop
- Satellite data
- A global aerosol-climate model (ECHAM6-HAM2)
- An integrated assessment model for climate system and economy (IAM),
extended by
- Effectiveness and cost of Climate Engineering by cloud seeding
- Bayesian learning on probability distributions of Climate Engineering damages
- Hyperbolic time preferences