Climate change with an iris-effect. Thorsten Mauritsen and Bjorn Stevens Max Planck Institute for Meteorology, Hamburg

Transcription

1 Climate change with an iris-effect! Thorsten Mauritsen and Bjorn Stevens Max Planck Institute for Meteorology, Hamburg

2 AR4 Temperature anomaly ( C) w.r.t } AR4 CMIP3 } Observations FAR SAR TAR B1 A

3 AR4 Temperature anomaly ( C) w.r.t } AR4 CMIP3 } Observations FAR SAR TAR B1 A

4 AR4 Temperature anomaly ( C) w.r.t } AR4 CMIP3 } Observations FAR SAR TAR B1 A

5 AR4 Temperature anomaly ( C) w.r.t } AR4 CMIP3 } Observations FAR SAR TAR B1 A

6 Climate sensitivity: Recent warming (~2K) (e.g. Otto et al. 2013) vs. Model-based (3-5K) (Clement et al. 2009, Fasullo and Trenberth 2012, Sherwood et al. 2014) Models underestimate Hydrological sensitivity: by about a factor 2 (Zhang et al. 2007, Wentz et al. 2007, Durack et al. 2012, Ren et al. 2013) Tropospheric warming: Models warm too much in upper troposphere (Thorne et al. 2011, Po-Chedley and Fu 2012)

7 Strong OLR Weak OLR Iris-expansion Dry and Clear Rising tropopause pa Moist and Cloudy Radiative cooling Latent heating

8 Strong OLR Weak OLR An Iris-effect: C p (T s )=C o (1 + I e ) T s T o, Iris-expansion Dry and Clear Rising tropopause pa Moist and Cloudy Radiative cooling Latent heating ECHAM6, T63L47 Coupled to mixed-layer ocean 2xCO2 forcing Partial radiative perturbations (PRP) feedback analysis

9 a) b) Equilibrium climate sensitivity (K) ECHAM6 I e= 0.2 I e= 0.5 I = 1.0 e / / / Iris-effect only CMIP5 ensemble Lindzen et al. (2001) -2 Feedback (Wm /K) Water Vapour Water Vapour + Lapse-rate Cloud feedback: { LW SW Net Lapse-rate -2 Total feedback (Wm /K) CMIP5 subset, Vial et al. (2013)

10 Longwave cloud feedback: Shortwave cloud feedback: Net cloud feedback:

11 Hydrological sensitivity is controlled by atmospheric energy budget: Strong OLR Weak OLR Iris-expansion Dry and Clear Rising tropopause pa Moist and Cloudy Radiative cooling Latent heating

12 a) b) Global mean precipitation change (%) ECHAM6 I e= 0.2 I e= 0.5 I = 1.0 e 4 %/K 3 %/K 2 %/K Equilibrium climate sensitivity (K) -2 Change in atmospheric heating (Wm /K) Lapse-rate Water vapour Clouds Sensible heat flux

13 Po-Chedley and Fu (2012)

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15 Mechanism?

16 Muller and Held (2012)

17 Nilsson and Emanuel (1999)

18 Warmer atmosphere is more prone to aggregate Emanuel et al. (2013)

19 More aggregated Tobin et al. (2012)

20 A negative feedback loop (longwave) Forcing SST increase Reduced anvils and tropospheric drying Enhanced aggregation

21 We have implemented a representation of an iris-effect in ECHAM6:! Climate sensitivity is only lowered from 2.8 to K not to 1 K as suggested earlier due to natural compensation from lapse-rate and shortwave cloud feedbacks! Hydrological sensitivity increases, in order to sustain the enhanced atmospheric cooling, to values higher than that of any other model! Troposphere warms less than a moist adiabat with an iris-effect! The results show that an iris-effect, for instance caused by unrepresented convective aggregation, could be a missing link between models and observations, thus deserving further attention

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24 AR4 Temperature anomaly ( C) w.r.t } AR4 } 1. CMIP3 Forcing Observations 2. Ocean heat uptake 3. Climate sensitivity FAR SAR TAR B1 A