Reto Knutti Institute for Atmospheric and Climate Science ...Conclusions and challenges Climate...
Transcript of Reto Knutti Institute for Atmospheric and Climate Science ...Conclusions and challenges Climate...
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Is CESM the best Earth System Model?
Reto KnuttiInstitute for Atmospheric and Climate ScienceETH Zurich, [email protected]
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Is CESM the best Earth System Model?
Yes
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Is CESM the best Earth System Model?
YesNo
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Is CESM the best Earth System Model?
YesNo
Stupid question
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Is CESM the best Earth System Model?
• What is the purpose, and is the model adequate for that purpose?
• What means “best” anyway?• What is the evidence that a model is doing the right thing?• Why are predictions uncertain?• How well can we quantify uncertainty?• How do we combine evidence from different models and
observations?• Why is it so hard, and are we making progress?
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How do we measuremodel performance?
Gleckler et al., JGR 2008
Performance metricMeasure of agreement between model and observation
Model quality metricMeasure designed to infer the skill of a model for a specific purpose
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My model is betterthan your model
Reichler and Kim, BAMS 2008
Model performanceBetter Worse
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Confidence in climate modelsWhat is the evidence?
Physicalprinciples
Reproduceclimate
Reproducetrends
Processes
Weather Past climate Robustness
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Distance to observations (tas/pr)Is CESM the best model?
Masson and Knutti, GRL 2011, Knutti, Masson and Gettelman, in preparation
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Distance to observations (tas/pr)Is CESM the best model?
Knutti, Masson and Gettelman, in preparation
CMIP5
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Climate model genealogyModels are not independent
Dissimilarity for surface temperature and precipitation
Knutti, Masson and Gettelman, in preparation
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Climate model genealogyModels are not independent
Knutti, Masson and Gettelman, in preparation
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Climate model genealogyModels are not independent
Masson and Knutti, GRL 2011
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Climate model genealogyDistance from CESM1(CAM5)
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Climate model genealogyDistance from CESM1(CAM5)
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CCSM4 to CESM1(CAM5)Tracing model development
Knutti, Masson and Gettelman, in preparation
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CCSM4 to CESM1(CAM5)Tracing model development
Knutti, Masson and Gettelman, in preparation
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ConclusionsMetrics and model quality
• An infinite number of metrics can be defined.• Many metrics are dependent.• Observation datasets and uncertainty matters.• The concept of a “best model” is ill-defined.• There may be a best model for a particular purpose, where
“best” measured in a specific way. But determining that is hard.
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ConclusionsWhere CESM certainly stands out
• Free availability• Documentation• Portability• Support• Community involvement• Tutorials
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Relating model performance to projections
Knutti, Phil Trans Roy Soc 2008
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How do we relatemodel performance to projections?
We don’t.
IPCC AR4, Fig. SPM7
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Present day temperature and warmingdo not correlate across models
Knutti et al., J. Climate 2010
Correlation of Dec-Feb temperatureto projected warming
R<-0.4R> 0.4
o-o-o Model- - - - Random
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Some observable climate indicesdo correlate with future warming
Huber et al., J. Climate 2011
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Some observable climate indicesdo correlate with future warming
Huber et al., J. Climate 2011
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Some relationshipsare not stable in different ensembles
Masson and Knutti, J. Climate, submitted
Summer to winter interannual variability
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Some relationshipsare not stable in different ensembles
Masson and Knutti, J. Climate, submitted
Summer to winter interannual variability
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Model spread in an ensemble of opportunitynot always useful as a measure of uncertainty
IPCC 2007
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Assumptions in the weightingmatter for the result
Tebaldi and Knutti, Phil Trans Roy Soc, 2007
Surface warming South East AsiaDec-Feb 2080-2099, A1B scenario
Surface warming (°C)
Pro
babi
lity
dens
ity TebaldiGreeneFurrerRaw models
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Arctic sea ice declineFaster than forecast?
Andy Mahoney, NSIDC
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Arctic sea icedecreases in all models, but less than observed
Stroeve et al., GRL 2007, Mahlstein and Knutti, 2012, JGR
ModelsMeanObservations
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Arctic sea iceis approximately linearly related to temperature
Mahlstein and Knutti, 2012, JGR
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Arctic sea iceA calibrated prediction
Mahlstein and Knutti, 2012, JGR
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How is the weather tomorrow?Combining lines of evidence
?
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Next stepsWhere are we going?
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Are we making progress?The end of model democracy?
www.ipcc.unibe.ch
• “There should be no minimum performance criteria for entry into the CMIP multi-model database.”
• “Researchers may select a subset of models for a particular analysis but should document the reasons why.”
• “IPCC assessments should consider the large amount of scientific work on CMIP3, in particular in cases where lack of time prevents an in depth analysis of CMIP5.”
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Are we making progress?Projections are robust, uncertainties remain
Old New
Knutti and Sedlacek, submitted
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Are we making progress?June-August precipitation projections
New Old
Knutti and Sedlacek, submitted
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Limits of predictabilityWarmest and coolest of 40 realizations
Deser, Knutti, Solomon and Phillips in press
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Limits of predictabilityWettest and driest of 40 realizations
Deser, Knutti, Solomon and Phillips in press
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Are we making progress?Yes and no. And why it is hard.
• Open system, complex, non-linear• Interaction from seconds to millennia, from micrometers to
thousands of kilometers• Dimensionality• Natural variability• Limited and uncertain observations• Model discrepancy• Expensive models, petabytes of data• Calibration problem in a high-dimensional space• Out of sample prediction, no proper verification
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Establishing confidence in a predictionWhy do you believe the weather forecast?
Tuesday: Sunny, with a high near 70. Breezy, with a west southwest wind 9 to 18 mph, with gusts as high as 29 mph
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Establishing confidence in a predictionLack of verification
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Establishing confidence in a predictionLack of verification
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Conclusionsand challenges
Climate models have reached a remarkable level of maturity, but:• Model sampling is neither systematic nor random.• We use a collection of ‘best guesses’ not designed to span any
uncertainty range, and not independent.• Models are getting “better” at things we observe, but model
spread is not reduced for projections.• Model performance varies but we don’t know how to translate
into weights. But we discard old models.• Combining model may help but can create unphysical results.• No independent verification of the prediction.• There is no best model without defining a purpose.