Towards predicting climate system changes and diagnosing feedbacks from observations Gabi Hegerl, GeoSciences, U Edinburgh Thanks to: Reto Knutti, Simone Morak, Susan Solomon, Xuebin Zhang, Francis Zwiers Photo credits: Tagesschau/NCDC

Estimating climate feedbacks and predicting future changes Modelling approach: Model as well as possible based on mechanisms Inverse / top-down approach: Diagnosing responses and with it feedbacks from observed changes –How to do it –Findings –Interpretation

Needed 1. Observations y with well-estimated uncertainties 2. Estimate of climate variability (to assess which observed changes can be explained without forcing); (observations; or climate models checked against observed / palaeo reconstructed long-term variability). 3. Fingerprints for external forcing X=(x i ),i=1..n ; models of any complexity that is appropriate for problem

Transient climate response relates directly to observed attributable warming Estimated warming at the time of CO 2 doubling in response to a 1% per year increase in CO 2 Separate the greenhouse gas fingerprint from response to natural forcings and response to other anthropogenic forcing (aerosol direct and indirect, ozone trop. And strat.) Estimate scaling factors a i u,v: noise residual

Attributable warming…. Scaling factors for greenhouse gas, other anthropogenic and natural fingerprints Translated into estimate of attributable warming

Fig 9.21 => overall estimate based on rescaling diverse individual model-based estimates Figure: from Hegerl et al., 2007 after Stott et al Yields an estimate of transient climate response

Equilibrium climate sensitivity Does not relate in a simple way to observed warming rate, but needs estimate of ocean heat uptake with uncertainty; Example: last millennium Comparison of several reconstructions with amplitude uncertainties (dotted) with energy balance model simulation Hegerl et al., 2006

Estimating ECS Run EBM with > 1000 model simulations, varying equilibrium climate sensitivity, effective ocean diffusivity, and aerosol forcing Estimate likelyhood that residual between reconstruction and range of EBM simulations is indistinguishable from best fit residual Var(Res-res min ) ~ F(k,l) (after Forest et al., 2001) Account for uncertainties: Calibration uncertainty of reconstruction Data noise and internal variability Uncertainty in magnitude of past solar and volcanic forcing

Result: 20 th century forward modeling most other results are top-down (asking what model parameters yield simulations consistent with data) Knutti and Hegerl, 2008 remaining uncertainties ranging from large to small`

Conclusions for large scales Top down/inverse approaches indicate consistent estimates as forward modelling, but larger uncertainties Similar approaches can be applied to constrain carbon cycle sensitivity (Frank et al., 2010) And have been applied to estimate aerosol effects on temperature yielding recently ~ consistent estimates Regional changes and their effect on feedbacks, for example, through vegetation, are another matter… Depend on seasonal changes in temperature distribution, and precipitation

Change in temperature distribution Eastern North America : change in vegetation? (Portmann et al., PNAS 2009) similarly: Eastern Asia – aerosols? (from Morak) TN90 Expected from Tmin Expected from Tmax

Regional: circulation can be important Gillett et al., NGEO, 2008 => attributable human influence SAM congruent residual model observ ation

Fig. SPM-6 IPCC SPM Feedbacks will depend on precipitation change Mechanism: Clausius Clapeyron => wetter when warmer Longwave forcing suppresses some of the response Dynamics and circulation have major influence Allen and Ingram, 2002 Equ. 2xCO2 models Transient change

Estimate from observations Santer et al., 2010: detectable changes in water vapour Zhang et al., 2007: detectable changes in land precipitation

Fingerprint detection and attribution study Detectable signal, but larger than simulated! (scaling > 0 but also >1!)

Similar problems may occur in response to shortwave forcing Photo: NASAafter Trenberth et al., GRL, 2007 Similarly, attributable change in Arctic precipitation (Min et al., 2009) is significantly larger than simulated

Shortwave Geoengineering: We should be very worried, and not trust model simulated impacts! (Hegerl and Solomon, 2009) Global land precipitation: Observed vs models (5-yr smoothed) (from Hegerl et al., 2007; adapted from Lambert et al., 2005)

Conclusions Changes in temperature distribution may point at missing processes on regional scale Precipitation shows a detectable human influence, but the observed changes are larger than simulated! Errors in models (missing / erroneous feedbacks), forcings, observations or all of this? Missing local processes and feedbacks as well as problems in precipitation changes will affect simulations of earth system feedbacks Top down estimates provide important evaluation of modelled feedbacks, and point at problems for regional changes and precipitation