EESS talk on "On the potential predictability of vital details of climate change"
Event details
| Date | 20.03.2018 |
| Hour | 12:15 › 13:15 |
| Speaker | Dr Simone Fatichi, Hydrology, Institute of Environmental Engineering, ETH Zurich |
| Location | |
| Category | Conferences - Seminars |
Abstract:
Decision makers and stakeholders are usually concerned about climate change projections at local spatial scales and fine temporal resolutions. This contrasts with the reliability of climate models, which is typically higher at the global and regional scales, Therefore, there is a demand for advanced methodologies that offer the capability of transferring predictions of climate models and relative uncertainty to scales commensurate with practical applications (e.g., few square kilometres and sub-daily scale). A stochastic downscaling technique that makes use of an hourly weather generator (AWE-GEN) and of a Bayesian methodology to weight realizations from an ensemble of climate models is used to generate local scale meteorological time series of plausible “futures”. The methodology is designed to partition three main sources of uncertainty: uncertainty due to climate models (model epistemic uncertainty), anthropogenic forcings (scenario uncertainty), and internal climate variability (stochastic uncertainty). For air temperature, the magnitude of the different uncertainty sources is comparable for mid-of-the-century projections, while scenario uncertainty dominates at large lead-times. The dominant source of uncertainty for changes in precipitation mean and extremes is internal climate variability, which leaves a limited room for uncertainty reduction. However, the inference is not necessarily discouraging because the uncertainty in precipitation due to historic climate variability is already covering a large fraction of the total uncertainty for the projected future.
Short biography:
Simone Fatichi is Research Associate and Lecturer at the Institute of Environmental Engineering at ETH Zurich since 2011. He received his BSc and MSc (cum laude) in Earth and Environmental Engineering at the University of Firenze (Italy) and he owns an International PhD title joint between the T.U. Braunschweig (Germany) and University of Firenze (Italy). His areas of expertise are soil-water-plant interactions, biogeosciences, hydrology, and analysis of climate change effects. His research covers a variety of topics and techniques including distributed ecohydrologic modeling, ecosystem modeling, modeling of processes of plant physiology and soil biogeochemistry, methods of stochastic hydrometeorology, and downscaling techniques to study climate change impacts. Recent research efforts have been devoted to the understanding of global change and its implications on water and soil resources, ecosystems, and the carbon cycle. He was recipient of the Torricelli award in 2014.
Decision makers and stakeholders are usually concerned about climate change projections at local spatial scales and fine temporal resolutions. This contrasts with the reliability of climate models, which is typically higher at the global and regional scales, Therefore, there is a demand for advanced methodologies that offer the capability of transferring predictions of climate models and relative uncertainty to scales commensurate with practical applications (e.g., few square kilometres and sub-daily scale). A stochastic downscaling technique that makes use of an hourly weather generator (AWE-GEN) and of a Bayesian methodology to weight realizations from an ensemble of climate models is used to generate local scale meteorological time series of plausible “futures”. The methodology is designed to partition three main sources of uncertainty: uncertainty due to climate models (model epistemic uncertainty), anthropogenic forcings (scenario uncertainty), and internal climate variability (stochastic uncertainty). For air temperature, the magnitude of the different uncertainty sources is comparable for mid-of-the-century projections, while scenario uncertainty dominates at large lead-times. The dominant source of uncertainty for changes in precipitation mean and extremes is internal climate variability, which leaves a limited room for uncertainty reduction. However, the inference is not necessarily discouraging because the uncertainty in precipitation due to historic climate variability is already covering a large fraction of the total uncertainty for the projected future.
Short biography:
Simone Fatichi is Research Associate and Lecturer at the Institute of Environmental Engineering at ETH Zurich since 2011. He received his BSc and MSc (cum laude) in Earth and Environmental Engineering at the University of Firenze (Italy) and he owns an International PhD title joint between the T.U. Braunschweig (Germany) and University of Firenze (Italy). His areas of expertise are soil-water-plant interactions, biogeosciences, hydrology, and analysis of climate change effects. His research covers a variety of topics and techniques including distributed ecohydrologic modeling, ecosystem modeling, modeling of processes of plant physiology and soil biogeochemistry, methods of stochastic hydrometeorology, and downscaling techniques to study climate change impacts. Recent research efforts have been devoted to the understanding of global change and its implications on water and soil resources, ecosystems, and the carbon cycle. He was recipient of the Torricelli award in 2014.
Practical information
- General public
- Free
- This event is internal
Organizer
- EESS - IIE
Contact
- Dr Paolo Benettin & Prof. Andrea Rinaldo, ECHO