Climate Change and Variability in the Southern Hemisphere: An Atmospheric Dynamics Perspective

Transcription

1 Climate Change and Variability in the Southern Hemisphere: An Atmospheric Dynamics Perspective Edwin P. Gerber Center for Atmosphere Ocean Science Courant Institute of Mathematical Sciences New York University *currently visiting the Max Planck Institute for Meteorology, Hamburg, Germany

2 What s been happening in the atmosphere in SOUTHERN recent decades? HEMISPHERE CLIMATE DM7 [Son et al. 21]

3 DJF Trends in zonal mean zonal wind late 2th century ECMWF [Son et al. 28; Gerber et al. 211]

4 DJF Trends in zonal mean zonal wind late 2th century ECMWF CMIP3 w/o O 3 CMIP3 w/ O 3 [Son et al. 28; Gerber et al. 211]

5 DJF Trends in zonal mean zonal wind late 2th century ECMWF CMIP3 w/o O 3 CMIP3 w/ O 3 predictions? [Son et al. 28; Gerber et al. 211]

6 Questions What are the relative roles of green house gases (GHGs) and ozone in forcing Southern Hemisphere circulation change? What is causing uncertainty in the circulation response? Can we constrain model predictions based on their representation of the observed period? Can a focus on large scale dynamics improve climate change projections?

7 Coupled Climate Models: CMIP3, plus some tentative results from CMIP5 Chemistry Climate Models (CCMs) from the CCMVal2 Project simulate interactive ozone chemistry in the stratosphere well resolved stratosphere, comparable horizontal resolution to coupled climate models, but generally forced with specified SSTs Dry Dynamical Cores primitive equation dynamics on the sphere (guts of an atmospheric model) simple Held and Suarez 1994 climate physics (no radiation, convection) Barotropic Vorticity Dynamics on the Sphere 2 dimensional flow NCAR Foothills Theatre (cast, in order of decreasing complexity) prescribed stirring to capture affect of baroclinic eddies

8 GHG warming pushes jet... 1 JULY 21 BUTLER ET AL Butler et al. 21

9 GHG warming pushes jet... 1 JULY BUTLER ET AL. JOURNAL OF CLIMATE... ozone pulls it3481 VOLUME 23 Butler et al. 21

10 Quantifying the response: Ozone critical for understanding SH trends in DJF Arblaster and Meehl 26: ensemble of forcings with a coupled model Perlwitz et al. 28: Chemistry Climate Model (CCM) study Son et al. 28: CCMs and CMIP3 coupled models Polvani et al. 211; McLandress and Shepherd 211 (detailed studies with individual GCMs)

11 Quantifying the response: Ozone critical for understanding SH trends in DJF Arblaster and Meehl 26: ensemble of forcings with a coupled model Perlwitz et al. 28: Chemistry Climate Model (CCM) study Son et al. 28: CCMs and CMIP3 coupled models Polvani et al. 211; McLandress and Shepherd 211 (detailed studies with individual GCMs) Today: a simple approach that allows us explore the response in the both CCMVal2 and CMIP3 models

12 A Simple Model of the Jet Response jet shift = ozone pull + GHG push U lat = r O3 T 3 + r GHG T GHG model simulations give us the forcings and response

13 A Simple Model of the Jet Response Global Climate Projections Chapter 1 TO3 AR4 multimodel temp. change, A1B scenario Global Climate Projections TGHG Figure 1.7. Zonal means of change in atmospheric (top) and oceanic (bottom) temperatures ( C), shown as cross sections. Values are the multi-model means for the A1B scenario for three periods (a c). Stippling denotes regions where the multi-model ensemble mean divided by the multi-model standard deviation exceeds 1. (in magnitude). Anomalies are relative to the average of the period 198 to Results for individual models can be seen in the Supplementary Material for this chapter. time period. The pattern is very similar over the three periods, consistent with the rapid adjustment of the atmosphere to the [IPCC AR4, Chp 1] ZHVWHUOLHV 5lLVlQHQ DQG ZHVWHUO\ PRPHQWXP ÀX[ LQ WKH lower stratosphere particularly in the tropics and southern mid-

14 Polar cap temperature trends are very weak, DM7 absent ozone forcing SON ET AL.: OZONE AND SOUTHERN HEMISPHERE CLIMAT [ Son et al. 21]

15 A Simple Model of the Jet Response jet shift = ozone pull + GHG push U lat = r O3 T 3 + r GHG T GHG two unknowns

16 A Simple Model of the Jet Response jet shift = ozone pull + GHG push U lat = r O3 T 3 + r GHG T GHG two unknowns two equations: 2th Century (196-99) Model Changes U lat (2C) = r T (2C) + r O3 3 GHG T GHG (2C) 21st Century (2-79) Model Changes U lat (21C) = r T (21C) + r O3 3 GHG T GHG (21C)

17 Regression Coefficients: Estimate of Sensitivity CCMVal2 Models strat. polar cap temp. (O 3 ) regression coefficients (deg./ K) tropical temp. (GHG) model U lat = r O3 T 3 + r GHG T GHG

18 Regression Coefficients: Estimate of Sensitivity CCMVal2 Models strat. polar cap temp. (O 3 ) regression coefficients (deg./ K) tropical temp. (GHG) model mean U lat = r O3 T 3 + r GHG T GHG

19 Regression Coefficients: Estimate of Sensitivity CCMVal2 Models strat. polar cap temp. (O 3 ) regression coefficients (deg./ K) tropical temp. (GHG) model mean U lat = r O3 T 3 + r GHG T GHG

20 Regression Coefficients: Estimate of Sensitivity CCMVal2 Models CMIP3 Models strat. polar cap temp. (O 3 ) strat. polar cap temp. (O 3 ) regression coefficients (deg./ K) tropical temp. (GHG) regression coefficients (deg./ K) tropical temp. (GHG) model mean model mean U lat = r O3 T 3 + r GHG T GHG

21 Attribution of 2 Century Climate Trends CCMVal2 Models CMIP3 Models trends (deg./decade) trends (deg./decade) total model 1 mean total model mean U lat = r O3 T 3 + r GHG T GHG

22 Attribution of 2 Century Climate Trends CCMVal2 Models CMIP3 Models trends (deg./decade) trends (deg./decade) O 3 total GHG model.8 1 mean O 3 total GHG model mean U lat = r O3 T 3 + r GHG T GHG

23 Summary of 2th Century Trends Jet Shift Temp. Forcing Sensitivity total O3 GHG jet shift (deg./decade) temperature trend (K/decade) regression coef. (deg./k) CCMVal2 CMIP3 CCMVal2 CMIP3 CCMVal2 CMIP3

24 Summary of 21st Century Trends Jet Shift Temp. Forcing Sensitivity total O3 GHG jet shift (deg./decade) temperature trend (K/decade) regression coef. (deg./k) CCMVal2 CMIP3 CCMVal2 CMIP3 CCMVal2 CMIP3

25 Uncertainty in the radiative response Jet Shift Temp. Forcing Sensitivity total O3 GHG jet shift (deg./decade) temperature trend (K/decade) regression coef. (deg./k) CCMVal2 CMIP3 CCMVal2 CMIP3 CCMVal2 CMIP3

26 Uncertainty in the circulation response Jet Shift Temp. Forcing Sensitivity total O3 GHG jet shift (deg./decade) temperature trend (K/decade) regression coef. (deg./k) CCMVal2 CMIP3 CCMVal2 CMIP3 CCMVal2 CMIP3

27 Attribution of 21st Century Climate Trends CCMVal2 Models CMIP3 Models trends (deg./decade) O 3 total GHG model trends (deg./decade) mean O 3 total GHG model mean U lat = r O3 T 3 + r GHG T GHG

28 Importance of Uncertainty in the Forcing (21st Century Trends) CCMVal2 Models CMIP3 Models trends (deg./decade) O 3 total GHG model trends (deg./decade) mean O 3 total GHG model mean U lat = r O3 T 3 + r GHG T GHG

29 Importance of Uncertainty in the Forcing (2th Century Trends) CCMVal2 Models CMIP3 Models trends (K/decade) O 3 total GHG trends (K/decade) O 3 total GHG model mean model mean U lat = r O3 T 3 + r GHG T GHG

30 Importance of Uncertainty in Circulation Sensitivity CCMVal2 Models CMIP3 Models trends (deg./decade) trends (deg./decade) O 3 total GHG model.8 1 mean O 3 total GHG model mean U lat = r O3 T 3 + r GHG T GHG

31 Importance of Uncertainty in Circulation Sensitivity CCMVal2 Models CMIP3 Models 1 1 trends (K/decade) trends (K/decade) O 3 total GHG model 4 5 mean O 3 total GHG model mean U lat = r O3 T 3 + r GHG T GHG

32 Uncertain Forcing vs. Uncertain Dynamics 1K warming of tropical upper troposphere OR cooling in polar stratosphere causes ~.2 o shift in the SH jet: jet responds to temperature gradient Variability in modeled circulation response are due to differences in radiative forcing of ozone and GHGs differences in circulation sensitivity

33 Connection between Jet Shift in A2 Scenario and 2th Century Climatological Jet 21 Century Jet Shift (degrees) Climatological Jet Position (degrees) [ Kidston and Gerber 21]

34 Connection between Jet Shift in A2 Scenario and 2th Century Climatological Jet 21 Century Jet Shift (degrees) equatorward bias position of jet in reanalyses Climatological Jet Position (degrees) [ Kidston and Gerber 21]

35 Connection between Jet Shift in A2 Scenario and 2th Century Climatological Jet 21 Century Jet Shift (degrees) equatorward bias larger jet shift position of jet in reanalyses Climatological Jet Position (degrees) [ Kidston and Gerber 21]

36 Connection between the Climatological Jet Position and Time Scales of Internal Variability Annular Mode Time Scale (days) longer time scales equatorward bias Climatological Jet Position (degrees) [ Kidston and Gerber 21]

37 The annular mode time scale Annular Mode Patterns 2 AM fast AM slow geopotential height (m) latitude (first EOFs of zonal mean Z)

38 The annular mode time scale Annular Mode Patterns Annular Mode Indices geopotential height (m) AM fast AM slow latitude (first EOFs of zonal mean Z) AM index AM index days days

39 The annular mode time scale autocorrelation Autocorrelation Functions 1 ACF fast ACF slow lag (days) AM index AM index (first EOFs of zonal mean Z) 2 2 Annular Mode Indices days days

40 The annular mode time scale autocorrelation Autocorrelation Functions 1 ACF fast ACF slow exp( t/11) exp( t/24) lag (days) (first EOFs of zonal mean Z) AM index AM index 2 2 Annular Mode Indices days days

41 What does this annular mode time scale represent? a) model w/ short time scales b) model w/ long time scales J 5 hpa geopotential height anomalies in two models D N O S A J J M A M F J latitude latitude [ Gerber et al. 21]

42 Connection between the Climatological Jet Position and Time Scales of Internal Variability Annular Mode Time Scale (days) longer time scales equatorward bias Climatological Jet Position (degrees) [ Kidston and Gerber 21]

43 Internal Variability - Jet Shift Connection 21 Century Jet Shift (degrees) longer time scale larger jet shift Annular Mode Time Scale (days) [ Kidston and Gerber 21]

44 Similar Connections in CCMVal2 Models (2th Century) equatorward bias longer time scale longer time scale larger jet shift [ Son et al. 21]

45 What can simple models of the large scale dynamics tell us about these relationships? barotropic vorticity and primitive equation dynamics What set s the time scale of the annular mode? Explore Fluctuation-Dissipation Theory Why is the the Southern Hemisphere such a challenge for models? Test bed for understanding mechanisms

46 Barotropic Vorticity Dynamics 2D zonal momentum: u t + u u x + v u y fv = φ x ru take zonal mean, and express eddy momentum flux divergence as a vorticity flux dissipation u t = v ζ ru ( is zonal mean, deviation therefrom)

47 Barotropic Vorticity Dynamics the linearized vorticity eqn. ζ t + u ζ x + γv = F ζ + D ζ meridional gradient of abs. vorticity

48 Barotropic Vorticity Dynamics the linearized vorticity eqn. divide by ϒ, multiply by ζ, take zonal mean to get pseudomomentum eqn. M = ζ2 γ ζ t + u ζ x + γv = F ζ + D ζ meridional gradient of abs. vorticity M t + v ζ = 1 γ (ζ F ζ + ζ D ζ )

49 Barotropic Vorticity Dynamics the linearized vorticity eqn. divide by ϒ, multiply by ζ, take zonal mean to get pseudomomentum eqn. M = ζ2 γ ζ t + u ζ x + γv = F ζ + D ζ meridional gradient of abs. vorticity M t + v ζ = 1 γ (ζ F ζ + ζ D ζ ) Lastly, combine w/ the momentum equation: u t + M t = ru + 1 γ (ζ F ζ + ζ D ζ ) stirring damping

50 Barotropic Vorticity Dynamics Rossby wave activity emitted stir midlatitudes (equator)

51 Barotropic Vorticity Dynamics waves break/damp Rossby wave activity emitted stir midlatitudes waves break/damp (equator)

52 Barotropic Vorticity Dynamics waves break/damp Rossby wave activity emitted momentum fluxed into source region stir midlatitudes waves break/damp (equator)

53 Barotropic Vorticity Dynamics on the Sphere random perturbations to the vorticity forced in synoptic wave numbers in midlatitude band; perturbations have no net vorticity [Vallis et al. 24]

54 Model Results *# 4 +,-.-/1 )# (# $# '#!# &# ζ D ζ ζ F ζ "# %# 5,:;.895.<<1718=1 # 4!!!" # "! $ %2,3 & [Vallis et al. 24]

55 Model Results *" )" (" '" 21, ,-.-/1!" &" %" $" #" " 5!! "! #" 234 [Vallis et al. 24]

56 Model Results *" )" (" '" 21,657.64,66/+, ,-.-/1!" &" %" $" #" " 5!! "! #" 234 [Vallis et al. 24]

57 Zeroth Order Model for AM Persistence variations in zonal mean driven by variations in vorticity fluxes project u anomalies onto AM, approx. forcing as white noise: looks like Ornstein-Uhlenbeck process (discrete analog AR-1) dx dt = Ẇ rx Anomalies of zonal mean winds are the integral of the vorticity fluxes created by baroclinic eddies, hence will have redder spectrum of variability! [Vallis et al. 24]

58 Zeroth Order Model for AM Persistence variations in zonal mean driven by variations in vorticity fluxes project u anomalies onto AM, approx. forcing as white noise: looks like Ornstein-Uhlenbeck process (discrete analog AR-1) dx dt = Ẇ rx Anomalies of zonal mean winds are the integral of the vorticity fluxes created by baroclinic eddies, hence will have redder spectrum of variability! Friction will bound variability on low frequencies. [Vallis et al. 24]

59 Zeroth Order Model for AM Peristence decrease the damping, increase the time scales non-linear interactions limit time scales ) ()2738 &!'%!'$!'#!'" strong damping weak damping /275578* 5!& +9+: 5!& +9+&" 5!& +9+": 6;<,!2=:'# 6;<,!2=#'$ 6;<,!2=$'>! +! " # $ % &! ()*+,-)./ [Vallis et al. 24] +

60 Dynamical core experiments: interaction between the stirring and the flow zonal mean zonal wind, u 1 pressure (hpa) latitude [Vallis et al. 24]

61 Dynamical core experiments: interaction between the stirring and the flow u and the annular mode 1 5 pressure (hpa) latitude [Vallis et al. 24]

62 Experiment #1: Vary surface friction pressure u, kf = 4/3 day latitude [Gerber and Vallis 27]

63 Experiment #1: Vary surface friction pressure u, kf = 1 day latitude [Gerber and Vallis 27]

64 Experiment #1: Vary surface friction pressure u, kf = 2/3 day latitude [Gerber and Vallis 27]

65 Experiment #1: Vary surface friction 1.8 AM Autocorrelation Functions strong damping weak damping k f =.75 k f =1 4/3 1 2/3 k f =1.5 autocorrelation lag (days) [Gerber and Vallis 27]

66 Experiment #1: Vary surface friction 1.8 AM Autocorrelation Functions strong damping weak damping k f =.75 k f =1 4/3 1 2/3 k f =1.5 autocorrelation.6.4 stronger damping, greater persistence! lag (days) [Robinson 1996; Gerber and Vallis 27]

67 Experiment #1: Vary surface friction Jet Latitude - Time Scale Connection 1 AM time scale (days) longer time scale vary kf (momentum damping) equatorward bias position of surface jet (degrees) [Gerber and Vallis 27]

68 Experiment #2: Vary thermal damping time scale Jet Latitude - Time Scale Connection 1 AM time scale (days) longer time scale vary kf (momentum damping) vary ka (thermal damping) equatorward bias position of surface jet (degrees) [Gerber and Vallis 27]

69 Experiment #3: Equator-Pole Temperature Gradient Jet Latitude - Time Scale Connection 1 AM time scale (days) longer time scale vary kf (momentum damping) vary ka (thermal damping) vary ΔTeq (temp. gradient) equatorward bias position of surface jet (degrees) [Gerber and Vallis 27]

70 Summary of GCM Experiments equilibrium temperature gradient thermal damping momentum damping T eq k a k f AM time scale jet moves equatorward

71 Summary of GCM Experiments equilibrium temperature gradient thermal damping momentum damping T eq k a k f AM time scale jet moves equatorward Implications: 1) AM time scale is distinct from the imposed time scales, rather a product of eddy-mean flow interactions 2) processes that set jet location also set AM time scale

72 First Order Model for AM Persistence variation in annular mode index z(t) dz dt = Ẇ z τ + bz stochastic eddy forcing frictional damping positive feedback btw jet and eddies [After Lorenz and Hartmann 21]

73 First Order Model for AM Persistence variation in annular mode index z(t) dz dt = Ẇ z τ + bz stochastic eddy forcing frictional damping positive feedback btw jet and eddies Net effect of feedback is new dz, τ 2 =(τ 1 b) 1 time scale τ 2 dt = Ẇ z τ 2 [After Lorenz and Hartmann 21]

74 Mechanisms Persistence of the jet: Surface friction regenerates baroclinicity, while upper level momentum fluxes maintain jet (Robinson 1996, 26; Gerber and Vallis 27) Mean flow channels the eddy fluxes (Lorenz and Hartmann 21, 23) Jet Shift: Shifts in critical lines / changes in eddy phase speed (Chen and Held 27) Upper level winds shape eddy momentum fluxes (Simpson et al. 21 Temperature gradients directly modulate baroclinic eddies (Riviere 211, Butler et al. 212) Poleward propagation of jet anomalies (Feldstein 1998)

75 Fluctuation-Dissipation Theory Impact of longer time scale on response to forcing shading: first EOF of u from control integration, negative and positive 2 pressure latitude [After Ring and Plumb 28]

76 Fluctuation-Dissipation Theory Impact of longer time scale on response to forcing apply a torque shading: first EOF of u from control integration, negative and positive that projects 2 onto the EOF pressure latitude [After Ring and Plumb 28]

77 Fluctuation-Dissipation Theory Impact of longer time scale on response to forcing apply a torque shading: first EOF of u from control integration, negative and positive that projects 2 onto the EOF pressure contours: response of model to the torque, u forced - u control (negative dashed) latitude [After Ring and Plumb 28]

78 [Gerber, Voronin, and Polvani 28] Model with greater persistence is more sensitive to external forcing 5 projection of response 5 τ = 33 days m=89 m=17 NH, L2 NH, L4 SH, L2 SH, L projection of forcing

79 Challenge to Model the Southern Hemisphere +,-.!/ :1.-;2:<8= *" $" )" #" ("!" '" &" %" " - 4-:8<77.6A<!" #" $".>?313@A3?7-6.7B.A:6?A.-5A:23.46-;C= -

80 Challenge to Model the Southern Hemisphere Zonal Asymmetries Limit Feedback AM e folding timescale (days) no asymmetry land sea contrast equilibrium temperature gradient (K)

81 Challenge to Model the Southern Hemisphere Zonal Asymmetries Limit Feedback AM e folding timescale (days) no asymmetry land sea contrast 1 m topography equilibrium temperature gradient (K)

82 Challenge to Model the Southern Hemisphere Zonal Asymmetries Limit Feedback AM e folding timescale (days) no asymmetry land sea contrast 1 m topography 2 m topography equilibrium temperature gradient (K)

83 Challenge to Model the Southern Hemisphere Zonal Asymmetries Limit Feedback AM e folding timescale (days) no asymmetry land sea contrast 1 m topography 2 m topography LSC + 2 m topo. In toy NH model, feedback dead equilibrium temperature gradient (K)

84 Insight from simpler models of large scale dynamics Annular modes exhibit a distinct time scale, a product of eddy mean-flow interactions Eddy-mean flow interactions that sets the position of the extratropical jet also set the time scales of variability (or vice versa) Models with enhanced persistence are more sensitive to external forcing, consistent with the fluctuation-dissipation theorem Zonal asymmetries reduce eddy-mean flow interactions: SH is a greater challenge for models to simulate

85 DJF Trends in zonal mean zonal wind late 2th century ECMWF CMIP3 w/o O 3 CMIP3 w/ O 3 [Son et al. 28; Gerber et al. 211]

86 DJF Trends in zonal mean zonal wind late 2th century ECMWF CMIP3 w/o O 3 CMIP3 w/ O 3 A Paradox: Models overestimate AM time scales, [Son et al. 28; Gerber et al. 211] but their 2th century circulation response is too weak!

87 Conclusions Southern Hemisphere Jet is pushed poleward by GHG induced tropical warming and pulled poleward by ozone induce cooling. Too date, ozone has dominated DJF signal. Uncertainty stems from variance in the radiative response to anthropogenic forcing and the dynamical response to thermal anomalies A model s 2th century climatology may be linked to the sensitivity of it s circulation: models with equatorward bias in jet and long time scales are more sensitive to external forcing These biases in jet position and time scale arise out of the large scale dynamics; there are still open issues in state-of-the-art climate models!

88 Epilogue... Preliminary calculations suggest that (some) CMIP5 model s have similar biases! ECMWF NAM Time Scales SAM Time Scales a) NAM time scale, reanalysis (days) b) SAM time scale, reanalysis (days) J A S O N D J F M A M J J A S O N D J F M A M J CCMVal2 CMIP5 c) NAM time scale, REF B1 (days) 1 d) SAM time scale, REF B1 (days) J A S O N D J F M A M J J A S O N D J F M A M J a) e) "Full" NAM time NAM scale, time scale, REF B2 CMIP5 (days) f) b) SAM "Full" time SAM scale, time REF B2 scale, CMIP5 (days) J A S O N D J F M A M J J A S O N D J F M A M J (days)