Februar 2003 Workshop Kopenhagen 1

Assessing the uncertainties in regional climate predictions of the 20th and 21th

century

Andreas Hense

Meteorologisches Institut

Universität Bonn

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Overview

• The problem– Climate system and climate models as random

systems• The consequences of randomness

– Estimation of randomness at various levels– Predictability of forced climate variations – Comparison of simulations with observations

• The conclusions

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The problem: the climate system as a random system

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The problem: the climate system as a random system

• Due to the high dimensionality ~ 10 32 degrees of freedom: statistical physics

• Due to the nonlinearities in the atmosphere, ocean and the interactions: dynamical systems theory

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The problem continued: climate models as random systems

• Due to high dimensionality ~ 10 8 degrees of freedom

• Due to nonlinearities in the model atmospheres, oceans and interactions

• Due to parametrized subgrid scale processes („clouds, rain, convection etc..“)

• Due to model errors

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The consequences: Estimation of randomness

• From the real climate system– one observation / realisation available

• randomness has to be modelled– e.g. assuming ergodicity, probabilities by

„counting“, frequentist‘s approach– bayesian approach, modelling by probability

densities• ... more at the end

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The consequences: Estimation of randomness

• In models by Monte Carlo simulations, sampling the uncertainties in initial conditions, parameters, models Initial conditions

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The consequences: estimation of randomness

Sampling models

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The consequences: predictability of forced climate variations

• Forced variations: Greenhouse gases, solar forcing, volcanoes

• overlaid by random variations– in models – in reality

• Forced variations > random variations ?– Predictability of the 2nd kind– In models Analysis-of-Variance– on specified space and time scales

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ECHAM3/LSG & HadCM2

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ECHAM3/LSG & HadCM2

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The Bayes Theorem

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The consequences: comparison of simulations with observations, Bayesian

Classification (Attribution)

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A Bayesian attribution experiment

• ECHAM3/LSG 1880-1979 Control• ECHAM3/LSG in 2000 Scenario• NCEP Reanalysis Data 1958-1999 Observations• Northern hemisphere area averages

– near surface (2m) Temperature– 70 hPa Temperature

• joint work with Seung-Ki Min, Heiko Paeth and Won-Tae Kwon

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Conclusions

• Inherent uncertainty in the climate system – due to the chaotic nature – strong dependance on space and time scales and

type of variable– annual temperature on a regional scale ~ 70%

predictable– annual sum of precipitation on a regional scale

~ 20%– decadal sum of precipiation ~ 70%

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Conclusion

• Uncertainty introduced by model errors are large on the regional scale

• Uncertainty introduced by randomized parametrizations not yet explored

• Despite of all uncertainties climate change signals on the global / hemispheric scale can be detected

• Uncertainty has to be quantified as additional input for impact studies, „meta-information“

• scales in space, time and variable have to be selected from the discipline