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Constraining estimates of climate sensitivity with present-day

observationsDaniel Klocke*,1, Robert Pincus2, Johannes Quaas1

1Max Planck Institute for Meteorology*International Max Planck Research School on Earth System Modelling

2University of Colorado/NOAA Earth System Research Lab

EUCLIPSE kick-off 28.09.2010

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Simplifying the problem

Two ensemble:

1.capturing parametric uncertainty-> perturbed parameter ensemble-> focus on clouds

2. encompassing structural uncertainty-> CMIP3 ensemble

Now we use the simple ensemble as an analogy to the complexensemble as the complex ensemble is used as an analogy tonature.

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Perturbed parameter ensemble

ECHAM5 (T31L19)

•500 one year simulation•Each with perturbed parameters•Parameters are varied simultaneously•Prescribed SST + sea ice cover

-> Evaluate the models Pincus et al., 2008

Targeted metric is justified, because1.Mainly cloud parameters are varied2.Change in the cloud radiative effect is driving the spread in climate sensitivity.3.Broad error matrices have to deal with compensating errors.

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Similarities between the ensembles

5/11

Skill vs Climate Sensitivity

6/11

Parametric sensitivity

7/11

Targeted metric (tropical oceans)

8/11

Stddev Mean

0.563.410.383.80

PDF of climate sensitivity

9/11

Breaks for a more complex ensemble

10/11

Conclusions and implications

We reproduced the diversity in some important measures of the CMIP3 ensemble.

• with a surprisingly simple parametric sensitivity-> interpreting the results from the CMIP3 ensemble as the full range of uncertainty is unfounded.

Weighting by simple skill measures or excluding models on thresholds does not put higher constraints on climate sensitivity.

For a simpler ensemble we were able to identify a well-targeted metric which related climate sensitivity to an observable in the present-day climate.

• Weighting by this measure narrowed the PDF of climate sensitivity.

The fact that this did not generalize to a more complex ensemble, we find no basis, that one could generalize from the complex ensemble to the real world, even if one finds a relation.

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Targeted metric is justified, because1.Mainly cloud parameters are varied2.Change in the cloud radiative effect is driving the spread in climate sensitivity.3.Broad error matrices have to deal with compensating errors.

Skill metric

Present-day distribution of:Short wave cloud radiative effectLong wave cloud radiative effectCloud coverPrecipitation

Root-mean-square error of monthly means over the seasonal cycle

Errors are standardized and aggregated

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Motivation

Reichler&Kim, 2008

1.5 – 4.5

1.5 – 4.5

2.0 – 4.5 (3.0)

ClimateSensitivity

Present-dayperformance

The approach: making models better to narrow the answers did not work so far.

Alternative approach: Weighting models by the likelihood that it is correct. (Murphy et al. 2004)

13/11

RMS for cloud radiative effects (sw, lw), precipitation and cloud cover according to two observational data sets (climatologies) for each ‘model’

Skill measure

14/11

Model ranking

15/11

Climate Sensitivity

16/11

What is coming

Ensemble Kalman filter data assimilation (DART) to initialize the model with something close to reality and use the assimilation increments as skill measures.

Does the error in the mean climate state manifest itself already in the first few time steps?

Can we use data assimilation to estimate (some) parameters?

Or at least narrow the range for the parameters, get some objective justification for parameter choices.