EESS talk on "Success stories and challenges in simulating future climate"
Event details
| Date | 30.04.2019 |
| Hour | 12:15 › 13:00 |
| Speaker | Dr Reto Knutti, professor, Climate Physics Group, Institute for Atmospheric and Climate Science, Dept. of Environmental Systems Science, ETHZ - is Associate vice president for sustainability of ETH Zurich and the president of ProClim, the Forum for Climate and Global Change of the Swiss Academies. The activities in research and teaching of the Climate Physics group are directed towards understanding changes in the global climate system caused by the growing emissions of anthropogenic greenhouse gases. The main goal is to understand the 20th century climate change, to quantify the human contribution to it, and to use that information to improve projections into the future and to quantify the projection uncertainties arising from uncertainties in scenarios, climate feedbacks and the carbon cycle. |
| Location | |
| Category | Conferences - Seminars |
Abstract:
As our understanding improves, more observations become available, and computational capacity increases, climate models continue to increase in complexity to synthesize all that knowledge. They have become the standard tool for predicting future climate change, and the hope is that as more and more processes are considered at greater realism and higher resolution, the models will converge to reality. But are they really, how do we know, and indeed should they? What is the purpose of current global climate models? Are they built to understand processes, to quantify past changes, or to predict the future, and do all of those require the same models?
There are many success stories in climate modeling, but open questions remain. What is the purpose of these models? How do we quantify uncertainty? Climate projections are often summarized as multi model means, assuming that the average of models is better than a single model. Yet averaging models is problematic, because the models are not independent and share biases and code, and the models may not span the full uncertainty range. A seemingly obvious step is to select individual models based on how well they simulate the past and present climate. But metrics of model performance and model weighting is a thorny issue. The lack of verification of the actual climate projections means that we do not know, or cannot agree on which metrics are most relevant to identify a good model.
An overview of the performance and limitations of current climate models is given, along with projections to 2100, with a focus on recent coupled model intercomparisons, and a discussion of major challenges in interpreting the results. Model agreement with observations continues to improve, but uncertainty in climate projections is difficult to quantify, and has not decreased significantly in the past few years, partly as a result of irreducible climate variability. Progress in model evaluation as well as statistical methods to interpret and combine model projections is urgently needed, in particular as more models of different quality and higher complexity, including perturbed physics ensembles and ensembles with structurally different models become available
As our understanding improves, more observations become available, and computational capacity increases, climate models continue to increase in complexity to synthesize all that knowledge. They have become the standard tool for predicting future climate change, and the hope is that as more and more processes are considered at greater realism and higher resolution, the models will converge to reality. But are they really, how do we know, and indeed should they? What is the purpose of current global climate models? Are they built to understand processes, to quantify past changes, or to predict the future, and do all of those require the same models?
There are many success stories in climate modeling, but open questions remain. What is the purpose of these models? How do we quantify uncertainty? Climate projections are often summarized as multi model means, assuming that the average of models is better than a single model. Yet averaging models is problematic, because the models are not independent and share biases and code, and the models may not span the full uncertainty range. A seemingly obvious step is to select individual models based on how well they simulate the past and present climate. But metrics of model performance and model weighting is a thorny issue. The lack of verification of the actual climate projections means that we do not know, or cannot agree on which metrics are most relevant to identify a good model.
An overview of the performance and limitations of current climate models is given, along with projections to 2100, with a focus on recent coupled model intercomparisons, and a discussion of major challenges in interpreting the results. Model agreement with observations continues to improve, but uncertainty in climate projections is difficult to quantify, and has not decreased significantly in the past few years, partly as a result of irreducible climate variability. Progress in model evaluation as well as statistical methods to interpret and combine model projections is urgently needed, in particular as more models of different quality and higher complexity, including perturbed physics ensembles and ensembles with structurally different models become available
Practical information
- General public
- Free
- This event is internal
Organizer
- EESS - IIE
Contact
- Dr Hendrik Huwald and Prof. Michael Lehning, CRYOS