Sensitivity of climate simulations to low-level cloud feedbacks
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1 Sensitivity of climate simulations to low-level cloud feedbacks C. Roberto Mechoso 1, Timothy Myers 1 and Mike DeFlorio 2 1 U. California, Los Angeles, USA 2 NASA/Caltech Jet Propulsion Laboratory, USA OUTLINE 1. Role of low-level cloud feedbacks on the amplification in the tropics of a cooling perturbation in the Southern Ocean. 2. Do CMIP that more successfully capture the strength of low-level cloud feedback similarly produce more realistic patterns of climate variability? (PDO, AMO, AMM Mechoso et al., GRL 216; Myers et al. Clim Dyn 217a, b
2 SSTs are lowered over the Southern Ocean Colder SSTs
3 Colder SSTs are advected to the Scu regions Colder oceanic advection into Scu regions by subtropical gyres Colder SSTs
4 The SCu response may amplify in the tropics the perturbation over the Southern Ocean. Scu response Colder oceanic advection into Scu regions?? Colder SSTs
5 A tale of two CGCMs with the same cooling over SO: Impacts on net radiation at TOA are large, model dependent, and consistent with changes in low level clouds NorESM UCLA CGCM 6N Net radiationucla TOA CGCM UCLA CGCM Stratocumulus Clouds 6N 4N 4N 3N 1N 3N 1S EQ 1S EQ CTRL 1N 3N EQ 1N EQ 1N 3S 1S 3S 1S 4S 3S 4S 3S 6S 4S 6W 6S 6W EXPT-CTRL NorESM Low-Level Clouds 6N 4N 4N 3N 6N Net radiation TOA NorESM 6N 6E 12E 6E 12E Cloud Amount 18 12W 18 12W 6S 6W 4S6W 6S 6W 6W N 4N 4N 3N 3N 1N 1N EQ EQ 1S 1S 3S 3S 4S 4S 6S 6W 6E 12E E 6E E 18 12E 18 12W 6W 12E 18 12W 6W (W m 12W 6W (% Cloud Amount 6S 6W 6W 12W E (%
6 In the idealized experiments, subsidence increases in southern tropics, more so in the UCLA CGCM EXPT-CTRL CTRL ω UCLA CGCM Vertical Velocity 6S 4S 3S 1S EQ 1N 3N 4N 6N ω CGCM NorESM Vertical Velocity (Pa/s 6S 4S 3S 1S EQ 1N 3N 4N 6N ω ESM 6S 4S 3S 1S EQ 1N 3N 4N 6N ω 6S 4S 3S 1S EQ 1N 3N 4N 6N (Pa/s Top: Control Bottom: Experiment-Control Also, the ITCZ is enhanced in northern tropics
7 In the two CGCMs, from the monthly anomalies we look at the slope of regression of net SW/TOA on SST over the major stratocumulus regions over the global oceans UCLA CGCM: Twice the observed SW/SST (W m K ISCCP obs CERES obs UCLA CGCM NorESM CCSM4 NorESM: One-half the observed Errorbars denote 9% confidence bounds, taking into account temporal and spatial autocorrelation ISCCP: (198 2/CERES ( ERA-Interim SST
8 Low cloud-sst feedbacks and biases in the simulation of the climate variability Regression of detrended SW CRE monthly anomalies averaged over subtropical stratiform cloud regions onto similarly averaged SST anomalies (ω 7 >1 hpa/day. The results are regionally dependent, corresponding to ocean basins where the climate modes occur. SW/SST reflects relationship of clouds to SST, strength of temperature inversion, any other cloud-controlling factor correlated to SST
9 Summertime SST variability (JJA Observed a SW CRE (W m SW CRE (W m σ b SST (K 6 JJA a SW SW CRE CRE (W (W m σ North Pacific (PDO pattern SST (K σ b SST (K JJA a SW CRE (W m North Atlantic (AMO pattern SST (K σ b SST (K -8-4 Regressions of detrended JJA anomalies onto SST anomalies averaged over b SST (K subtropical 6 NE Pacific and NE Atlantic..Results are similar to the first EOF of monthlymean variability (Myers et al. 217a, Clim. Dyn
10 Slope of regression of monthly anomalies of SW CRE/TOA on SST CMIP models: California Stratus SW/SST (W m K GISS-E2-R GISS-E2-H MIROC-ESM ISCCP obs CERES obs merged obs. 9% CI MIROC MPI-ESM-LR IPSL-CMA-LR CanESM2 GFDL-ESM2M GFDL-ESM2G CSIRO-Mk3-6- HadGEM2-ES NorESM1-M CCSM4 ACCESS1-3 CESM1-CAM FGOALS-g2 ACCESS1- MRI-CGCM3 IPSL-CMB-LR CMIP: Historical runs (1976 2
11 a SW CRE (W mfeedback Neg./null feedback Positive 6 a SW CRE (W m 3 c SW CRE (W m c SW CRE m(w a SW(W CRE m Amplification of northern oceans summertime SST variability in CMIP ensemble via positive cloud feedback feedback models Neg./null feedback b SST (K PDO pattern positive 3 feedback -8. models SST (K d 6 SST (Kb Positive feedback. 2. c SW CRE (W m d SST (K d S 28.2 (K SST Myers, Mechoso and DeFlorio, 217a, Clim. Dyn d SST (K b- SST (K- d6 SST (K Pos Positive Neg./null feedback Positive feedback feedback.2 SW CRE (W m SW CRE (W m SST (K -. (W m a SW CRE (W m d SST (K c SW CRE (W m b SST (K c SW CRE b SST (K 2. PositiveNeg./null feedbackfeedback d SST (K SST (K SW(WCRE (W m SST (K c SW m b SST (K (K d SSTb (KSST a SW CRE (W m c SW C (K d SST CRE Positive feedback Neg./null feedback c SW CRE (WCRE m (W a SW m b SST (K SW CRE (W m a SW CRE (W m b SST (K Neg./null feedback SW CRE (W m neutral AMO pattern
12 Slope of regression of monthly anomalies of SW CRE/TOA on SST SW/SST (W m K ISCCP obs CERES obs GISS-E2-R GISS-E2-H NorESM1-M CCSM4-3 CMIP models: Namibian Stratus FGOALS-g2 MIROC MPI-ESM-LR GFDL-ESM2M IPSL-CMA-LR CanESM2 MIROC-ESM ACCESS1- CSIRO-Mk3-6- CESM1-CAM HadGEM2-ES GFDL-ESM2G ACCESS1-3 merged obs. 9% CI IPSL-CMB-LR MRI-CGCM3 CMIP: Historical runs (1976 2
13 Tropical Atlantic interhemispheric variability in springtime: AMM pattern Observation SLP (hpa σ Neutral feedback Positive feedback f regression of MAM (a SST, (b SW CRE at TOA, (c low-level CF, and models models hpa horizontal wind anomalies onto the normalized time SST(hPa (K σσ SLP (hpa σdifference SLP rth-south interhemispheric in MAM SST anomalies averaged pical NE and SE Atlantic based on observations provided by ISCCP, e ERA-Interim reanalysis. The time series of anomalies are created here uent observational results by merging together anomalies from the separate 2114 records, respectively. X s indicate statistical significance at ence level using a two-tailed t test, taking into account temporal. Black wind vectors indicate statistical significance of either the zonal or mponent of the wind. The regions over which SST is averaged to create the 4 shown as black rectangles. Myers et al.,(b 217b 6 Fig. 3 Mean of slopes of regression of MAM (a SW CRE at TOA, SST, Clim. and (cdyn. SLP
14 4 Boundary layer cloud dipole associated 2 with AMM evident in CALIPSO cloud fraction anomaly -3 composites latitude 18 Atlantic zonal z mean CALIPSO CF anomaly (% when N-S SST index > 1 std. dev 22 CALIPSO CF CF anomaly (% (% when when spatial N-S N-S SST SST index average index < < std. std. dev dev NE Atlantic warmer than SE Atlantic altitude (km Cloud frac. (% altitude (km altitude (km altitude (km % when d. dev latitude 18 CALIPSO CF anomaly (% when N-S SST index < std. dev CALIPSO CF anomaly latitude (% when index > Cloud frac. (% NE Atlantic SE Atlantic.. NE Atlantic SE Atlantic CA NE Atlantic cooler than SE Atlantic 2 3 altitude (km CALIPSO -3 CF anomaly 1 latitude (% 2 when 3 index 4 > 1 CALIPSO CF anomaly (% when index > 1 NE Atlantic NE Atlantic altitude (km altitude (km CALIPSO -3 CF anomaly (% 1 when 2 3 index 4 < CALIPSO Cloud CF anomaly frac. when index < NE (% Atlantic NE Atlantic
15 Tropical Atlantic interhemispheric variability in espringtime: SST(K b SST (K AMM pattern Neutral feedback -3Positive -3 - feedback models-4 models Neutral feebackneutral feeback Positive feedback Positive fee c SLP (hpa (W m σ a SW CRE (W m SW CRE SW CRE (W m SW CRE (W m (W d SW CRE (W m a-8 SW CREd m (hpa d SW CRE(W m b SW CRE (W m -8-4SLP -4 3 Observation SST (K σ Figures b SST (K-3 b SST (K SST σ (KSST b (K b SST (K c SLP (hpa (hpa c SLP m/s f SLP (hpa (hpa s -.4 f SLP m/s.7 m/s.3 c SLP c SLP (hpa (hpa Myers (K σ SST e e SST (K SST (K s e SST (K-3 e SST (K si m/s.7 m/s feeback Positive fe Neutral feebackcneutral Positive feedback SLP (hpa f SLP (hpa SW CRE SW CRE 3 dsw CRE a SW CRE (W3ma (W m (W3md (W m.7 m/s.7 m/s f SLP f SLP (hpa (hpa et al., 217b Clim. Dyn
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17 Summary Observations show that a positive low cloud feedback is a common feature of coupled modes of atm./ocean variability, including the AMO, PDO, and AMM. Cloud pattern mimics that in SST and inv. strength. The simulation of this feedback affects the amplitude of modes of variability in climate models in stratiform cloud regions. Corroborative evidence from idealized simulations. Currentwork. Sensitivitydependence onmodelbiases.
18 EXTRA SLIDES
19 SW CRE/SST Biases in CMIP Models SST bias (K SW CRE bias (W m Baja Peru Canary Namibia Australia
20 Models and Experiments UCLA CGCM: UCLA AGCM7.1 (2.x2x29 layers coupled to MIT OGCM (1x1/3-tox46 levels NorESM: CAM4 (2.x1.9x23 layers coupled to MICOM (2x1x3 layers Control: 2-year long period from long climate run Experiment: 2-year long period restarted from Control; perturbation introduced after 18 years. Perturbation: Time-varying solar radiation incident at TOA between -3 lat and -6 lat is multiplied by 1+A sin(lat+3(lat+6, A=1. Mean perturbation value in SH is 8.14 W m.
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22 SST biases averaged in CMIP models Wang, Zhang, Lee, Wu, and Mechoso (214, Nature CC Hypothesis: CGCMs with more energy flux into the atmosphere in the southern hemisphere than in the northern hemisphere tend to have a stronger double ITCZ bias (Hwang and Frierson, PNAS 213
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