Constraining estimates of climate sensitivity with present-day observations

1. Constraining estimates of climate sensitivity with present-day observations Daniel 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

2. Simplifying the problem Two ensemble: • capturing parametric uncertainty -> perturbed parameter ensemble -> focus on clouds • encompassing structural uncertainty -> CMIP3 ensemble Now we use the simple ensemble as an analogy to the complex ensemble as the complex ensemble is used as an analogy to nature.

3. 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, because Mainly cloud parameters are varied Change in the cloud radiative effect is driving the spread in climate sensitivity. Broad error matrices have to deal with compensating errors.

4. Similarities between the ensembles

5. Skill vs Climate Sensitivity

6. Parametric sensitivity

7. Targeted metric (tropical oceans)

8. PDF of climate sensitivity Stddev Mean 0.56 3.41 0.38 3.80

9. Breaks for a more complex ensemble

10. 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.

11. Skill metric Present-day distribution of: Short wave cloud radiative effect Long wave cloud radiative effect Cloud cover Precipitation Root-mean-square error of monthly means over the seasonal cycle Errors are standardized and aggregated Targeted metric is justified, because Mainly cloud parameters are varied Change in the cloud radiative effect is driving the spread in climate sensitivity. Broad error matrices have to deal with compensating errors.

12. Motivation Present-day performance Climate Sensitivity 1.5 – 4.5 1.5 – 4.5 2.0 – 4.5 (3.0) Reichler&Kim, 2008 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. Skill measure RMS for cloud radiative effects (sw, lw), precipitation and cloud cover according to two observational data sets (climatologies) for each ‘model’

14. Model ranking

15. Climate Sensitivity

16. 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.