The central role of clouds in ENSO variability
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1 The central role of clouds in ENSO variability Gaby Rädel Max- Planck- Ins-tute for Meteorology, Hamburg with: Thorsten Mauritsen, Bjorn Stevens, Daniela Matei ENSO Workshop Australia, Sydney, 4 6 February 2015
2 LiCoS project within large German MiKliP programme on decadal predictability aims to inves-gate to what extent meteorological versus atmospheric chemical factors limit climate predictability on -mes scales of years to decades in par-cular: role of clouds / cloud feedbacks in regional predictability via impact on large scale climate pauerns Cloud feedback processes is the largest source of uncertainty concerning climate sensi-vity in global climate models (Vial et al. 2013)
3 ENSO major player in global climate variability with global impact understand what are the underlying processes that drive variability, amplitude, onset of ENSO in GCMs and in par-cular MPI- ESM in par-cular: role of atmosphere feedbacks and especially clouds being source of largest uncertainty in GCMs
4 Atmospheric feedbacks like posi-ve Bjerknes- and nega-ve surface heat flux- FB are known to play an important role in ENSO descrip-ons in GCMs (e.g. Lloyd et al, (2012) and Guilyardi et al. (2012)) obs CMIP3 CMIP5 However large spread of these feedbacks among CMIP3/CMIP5 models, and eventual agreement with re- analysis par-ally due to cancella-on of errors
5 From M. Collins et al, Nature Geoscience, 2010, The impact of global warming on the tropical Pacific ocean and El Niño: Cloud feedbacks and their link to the two large- scale circula-on regimes that operate in the east Pacific (subsidence and convec-ve) remain a large uncertainty in CGCMs probably driving a large frac-on of the ENSO errors in the control climate condi-ons of present- day CGCMS
6 Locking cloud feedbacks in MPI- ESM- LR One way to study impact of cloud feedbacks on e.g. ENSO variability: compare standard CMIP5 picontrol MPI- ESM run to special MPI- ESM run with disabled cloud feedbacks (T. Mauritsen et al, Climate Dynamics (2013)) In a first model run cloud variables (IWC, LWC, cover) are wriuen out and saved for every -me step New: In a second model run these variables are read in by radia-on module for a randomly chosen year to avoid imposed low- frequency variability resul-ng from clouds due to scenes auto- correla-ng in -me fluxes calculated for cloud proper-es that don t correspond to clouds produced by model... - > disable cloud feedback without changing climate substan-ally 250 years have been simulated under these condi-ons with MPI- ESM- LR; - > 200 years used for following analysis
7 Time series of Niño3.4 index control (CMIP5) T [K] strength (std dev): 0.83 K locked cloud FB non- interac-ve clouds 0.44 K
8 Power spectrum for Niño3.4 index clouds radia-ve effect has impact on ENSO variability
9 Composite analysis for El Niño select only strong El Niño events: monthly means of ENSO3.4 index > 1 std dev Niño3.4 index / surface flux anomalies control locked FB Wm- 2 Wm- 2 Wm- 2 Wm- 2 LW SW lead/lag -me [months]
10 Composite analysis for El Niño Niño3.4 index fluxes at TOA anomalies control locked cloud FB Wm- 2 Wm- 2 LW Wm- 2 Wm- 2 SW lead/lag -me [months]
11 Possible enhanced high- level cloudiness associated with deep convec-on over warm anomalies of the El Niño conditions: ENSO3.4 region reduce Reduced radiative atmospheric LW cooling locally cooling leads to atmospheric ascending mo-on and anomalous convergence of low- level winds. moist sta-c energy is transported towards the SST anomaly in a posi-ve feedback loop enhancing its strength and persistence Equator Enhanced circulation Weakened easterlies Thermocline Warmer SST s results in weakened easterly surface winds - > Bjerknes Feedback
12 Supported by Eastern- central Pacific OLR ENSO3.4 index (Chiodi & Harrison, JC, 2010)
13 ENSO3.4 composites analysis horizontal surface winds [m/s] anomalies for strong El Niño events (DJF) (monthly mean of ENSO3.4 index > 1 σ) difference: control non- interac-ve clouds
14 Nino3.4 composites analysis P- E [mm/day] anomalies for strong El Niño events (DJF) (monthly mean of Nino3.4 index > 1 σ) stronger latent heat convergence with cloud feedback
15 Niño3.4 composites analysis P- E [mm/day] for strong El Niño events (DJF) (monthly mean of Niño3.4 index > 1 σ) difference: control non- interac-ve clouds latent heat convergence in tropics also seen for total values of (P- E) (mean climate state not subtracted) Signal strongest for Niño4 winds + P- E consistent with proposed explana-on à
16 Possible enhanced high- level cloudiness associated with deep convec-on over warm anomalies of the El Niño conditions: ENSO3.4 region reduce Reduced radiative atmospheric LW cooling locally cooling leads to atmospheric ascending mo-on and anomalous convergence of low- level winds. Enhanced circulation moist sta-c energy is transported towards the SST anomaly in a posi-ve feedback loop enhancing its strength and persistence Equator Weakened easterlies Thermocline Warmer SST s results in weakened easterly surface winds - > Bjerknes Feedback
17 ENSO3.4 in CMIP5 Can a correla-on between cloud feedback strength and ENSO strength be established in other CMIP5 models? Compute LW feedback as regression of - OLR and Nino3.4 index LW FB = - Δ OLR / Δ ENSO3.4 for 41 models and observa-ons: 30 years reconstructed data based on CERES ( ) (R.Allan et al., GRL, 2014) Compute Niño3.4 index for 144 years of observa-ons ( ) HadlSST (Rayner et al, JGR, 2003)
18 ENSO3.4 in CMIP5 Can a correla-on between cloud feedback strength and ENSO strength be established in other CMIP5 models? CMIP5 models observa-ons MPI- ESM- LR control non- interac-ve clouds error bars on indicate sta-s-cal uncertainty on observa-ons For all models: LW feedbacks are posi-v ENSO strength bigger than for run with non- interac-ve clouds
19 Conclusions in MPI- ESM- LR cloud feedbacks are responsible for about half of the ENSO variability proposed explana-on: enhanced high- level cloudiness over warm anomalies of the ENSO3.4 region reduce atmospheric longwave cooling locally, change low level winds and transport moist sta-c energy towards the SST anomaly in a posi-ve feedback loop, enhancing its strength and persistence. - > posi-ve LW feedback mechanism is best understood from TOA perspec-ve Indica-on that mechanism is responsible for part of ENSO variability also in other CMIP5 models
20 addi-onal slides
21 Composite analysis for El Niño Niño3.4 index / LW fluxes at TOA anomalies Nino4 Nino3.4 Nino3 control K Wm- 2 Wm- 2 non- interac-ve clouds
22 Regressions against ENSO3.4 index control LW at TOA locked cloud feedback Wm - 2 K - 1 red = warm T s associated with less outgoing radia-on Wm - 2 K - 1 SW flux at TOA Wm - 2 K - 1 Wm - 2 K - 1
23 DJF
24 Regression of surface SW flux against SST ENSO3 rc = 3.0 rc= ENSO4 rc= - 5.6
25 DJF Precipita-on Indian Ocean (60-90 ) JJA
26 ENSO3.4 composites analysis horizontal surface winds [m/s] for strong El Niño events (DJF) (monthly mean of ENSO3.4 index > 1 σ) difference: control non- interac-ve clouds
27 Locking cloud feedbacks in MPI- ESM- LR global mean surface temperature locked FB picontrol (CMIP5)
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