1. The Response of Marine Boundary Layer Clouds to Climate Change in a Hierarchy of Models Chris Jones Department of Applied Math Advisor: Chris Bretherton Departments of Applied Math and Atmospheric Sciences VOCALS RF05, 72W, 20S

2. Overview Introduction: Marine boundary layer (MBL) clouds and climate sensitivity Idealized local case studies in a hierarchy of models The well-mixed MBL from observations Comparison of model responses to changes in CO 2 and temperature Summary of proposed future work

4. Earth’s Radiation Budget: R = Absorbed Solar Radiation – Outgoing Longwave Radiation (NASA) Marine boundary layer clouds especially important because… 1.They’re reflective at visible wavelengths MBL clouds

5. Earth’s Radiation Budget: R = Absorbed Solar Radiation – Outgoing Longwave Radiation (images courtesy of Chris Bretherton) Marine boundary layer clouds especially important because… 1.They’re reflective at visible wavelengths 2.They cover a lot of area Cloud Fraction Cloud forcing = R(clear sky) – R(all sky) Global net cloud radiative forcing ~ -20 W m -2 (Loeb et al, 2009) Compared to CO 2 ~ 2 W m -2

6. Earth’s Radiation Budget: R = Absorbed Solar Radiation – Outgoing Longwave Radiation Marine boundary layer clouds especially important because 1.They’re shiny (reflect incoming solar radiation) 2.They cover a lot of area 3.They’re hard to realistically represent in global climate models Interplay between dynamics and physics Nonlinear Turbulent Physics must be parameterized

7. Climate Change: Response to radiative forcing R = Absorbed Solar Radiation – Outgoing Longwave Radiation Cloud contribution most uncertain (next slide)

8. Cloud feedbacks dominate climate sensitivity uncertainty in GCMs Clouds dominate overall climate feedback uncertainty Clouds: - Positive feedback, - Large spread between models Bony et al. (2006)

9. Cloud feedbacks dominate climate sensitivity uncertainty in GCMs Clouds dominate overall climate feedback uncertainty Clouds: - Positive feedback, - Large spread between models Bony et al. (2006)

10. Cloud feedbacks dominate climate sensitivity uncertainty in GCMs Clouds dominate overall climate feedback uncertainty Low clouds dominate cloud feedback uncertainty Soden and Vecchi (2011) Clouds: - Positive feedback, - Large spread between models Bony et al. (2006)

11. Parameterizations of Physical Processes Make Profound Impact 3.2K climate sensitivity 4.0 K climate sensitivity (Gettelman et al., 2011) UW turbulence and shallow convection parameterizations largely responsible for increase in climate sensitivity from CAM4 to CAM5 – can our analysis help explain this? Equilibrium response to 2xCO 2

12. Objectives of This Research Use a localized, idealized column-oriented analysis of prototypical MBL cloud regimes to identify and evaluate MBL cloud-climate radiative response mechanisms Hierarchy of models: – Large eddy simulation (LES): high resolution cloud resolving model – closest we have to “observations” in local climate change simulations – Single-column model (SCM): ties results to GCM – Mixed-layer model (MLM): simplified model for interpretive purposes Seek to relate SCM back to parent GCM Scientific Relevance: Understanding mechanisms of change in GCMs is pre-requisite for constraining through observation and/or improving parameterizations. Mathematical Relevance: Investigate impacts of various parts of model formulation (e.g., subgrid parameterizations, model resolution, applied large-scale forcings); to what extent can models be used to interpret the behavior of other models?

13. Case studies drawn from CGILS Intercomparison S12: Shallow Stratocumulus (Sc) Well-mixed BL S11: Transition between Sc and shallow cumulus (Cu) Onset of BL decoupling Cu rising into Sc S6: Shallow Cu Zhang et al (2010)

14. Hierarchy of models GCM (CAM5) SCM (SCAM5) LES (SAM) Image courtesy of NOAA (S6, courtesy of Peter Blossey) SCAM5 Vertical Resolution MLM

15. Large-scale advection Subsidence Tendencies due to physical processes, e.g., Precipitation Radiation and clouds Microphysics Turbulence Dynamics

16. (Stevens, 2007) Mixed-layer model equations

17. Advective cooling/drying Entrainment surface fluxes Radiation Precipitation (Stevens, 2007)

18. October 2008- November 2008 (http://www.atmos.washington.edu/~robwood/VOCALS/vocals_uw.html) How reasonable is the well-mixed assumption? Previous project studied the extent of well-mixed vs. decoupled boundary layers using aircraft data from VOCALS field experiment Classified flight legs as well- mixed or decoupled based on gradient of moisture and temperature quantities

19. Subcloud layer Cloud layer Well-mixed Decoupled Jones et al. (2011)

20. Case setup and proposed sensitivity studies Simulation setup Diurnally averaged summertime insolation Models run to steady-state Large-scale forcings specified from observations: – Horizontal divergence – Subsidence – Sea surface temperature – Wind profile CGILS sensitivity studies Control (CTL) – Mimics current climate 4xCO 2 concentration (4xCO2): – Captures “fast” adjustment Uniform +2K temp. increase: – Captures temperature- mediated response – Reduced subsidence (P2K) – Subsidence as in CTL (P2K OM0)

21. S12 Results: Cloud Fraction LES Results from CGILS intercomparison MLM Results

22. Preliminary S12 Results: Profiles SAM LES: Liquid static energyMoistureCloud liquid MLM:

23. SAM LES: Liquid static energyMoistureCloud liquid MLM: SCAM5: (L80)

24. All models exhibit similar steady-state mean sensitivities: 4xCO 2 has lower inversion, thinner cloud (positive cloud feedback) P2K deepens and thickens relative to control (negative cloud feedback) P2K OM0 thinner than P2K and slightly thinner than CTL (positive cloud feedback) Subsidence (large scale dynamics) plays dominant role in P2K response Preliminary S12 Results: Summary SAM (LES)-111-13+28 SCAM5 (SCM)-176-12+54 MLM-68-9+14 SAM (LES)+109+2-2 SCAM5 (SCM)+70+1-7 MLM+114+32-30 4xCO 2 P2K SAM (LES)-38-9+20 SCAM5 (SCM)+5-5+18 MLM-4 +8 P2K OM0

25. MLM 4xCO 2 Sensitivity Mechanism: Increased down-welling LW radiation  decreased cloud top radiative cooling (~10% decrease)  Less turbulence (i.e., less entrainment)  Lower z i  Cloud thickness decreases CTL 4xCO2 CTL 4xCO2

26. SCAM5 S12 Resolution Sensitivity Default CAM5 Resolution doesn’t sustain a cloud Higher resolution does Cloud fraction

27. Future Work – Apply MLM to interpreting other LESs involved in CGILS case study – Fully investigate SCAM5 S12 behavior What’s driving the resolution sensitivity? – Expand analysis to other locations (MLM may not apply) – Parameter-space representation with SCAM Use SST, Free troposphere lapse rate, CO 2 and/or subsidence as control parameters – Find a way to relate the local cloud response in SCAM to the sensitivity in its parent GCM

28. Questions? (MODIS satellite image)

29. Additional slides

30. Future Work (plenty to keep me busy) – Apply MLM to interpreting other LESs involved in CGILS case study (hypothesis: by tuning entrainment efficiency, can I reproduce their mean properties / sensitivities?) – Dig into roots of SCAM5 S12 sensitivity (interpret w/MLM when appropriate) What’s driving the resolution sensitivity? – Expand analysis to other locations (MLM may not apply) – Parameter-space representation with SCAM, following approach of Caldwell and Bretherton (2009) MLM study Use SST, Free troposphere lapse rate, CO 2 and/or subsidence as control parameters – Find a way to relate the local cloud response in SCAM to the sensitivity in its parent GCM

31. Additional Slides CRF, adjusted CRF, etc.

32. SCAM5 Default Resolution vs. VOCALS radar strip

33. SAM LES Equations Khairoutdinov and Randall (2003) Prognostic TKE SGS model Diagnostic cloud water, cloud ice, rain, and snow Periodic horizontal domain, surface fluxes from Monin-Obukhov similarity theory ISCCP cloud simulator Parallel (MPI)

34. The proposal (remember the proposal? This is a presentation about the proposal …) Use MLM to interpret output from other LESs (can “tune” parameterizations and entrainment closure as needed) Investigate sensitivities in each model for each location Map out primitive parameter-space representation using SCM (like CB09) Ultimately, most concerned with SCAM, b/c it connects directly to GCM – to what extent can we use this analysis to shed light on the low cloud- climate mechanisms in CAM5?

35. Large-scale advection Subsidence Tendencies due to physical processes, e.g., Precipitation Radiation and clouds Microphysics Surface fluxes Turbulence

36. Primitive equations LES: SCAM:

37. Mixed-layer model equations

39. Mixed-layer model equations: Advection (cooling,drying) Entrainment warming/drying Latent heat flux Precipitation Radiative cooling Sensible heat flux subsidence

40. EPIC 2001 (Bretherton, et al.) Contributing Mechanisms for MBL Balance Subsidence Advection

41. Mixed-layer model: Advection (cooling,drying) Entrainment warming/drying Latent heat flux Precipitation Radiative cooling Sensible heat flux subsidence

42. Sc (top) vs. Cu (bottom) MBL structure (Stevens et al 2007; Stevens 2006)

43. MLM time series for S12

44. Relevant previous column modeling studies Caldwell and Bretherton Zhang and Bretherton …

45. Model run specifics Grid resolution – CESM 1.0 (CAM5): 1 deg = 0.9 deg x 1.25 deg x 30 levels – (i.e., ~100 km x 137 km x … [variable]) Time steps (?) Length of integration Numerics / miscellaneous

46. Outline Introduction – Climate sensitivity, feedbacks, and cloud radiative forcing – Why are low clouds important (to climate system, climate sensitivity)? – What has been done, and where does this study fit in? – Feedback flow chart (?) Proposal for this study: Localized case studies using a hierarchy of models – CGILS cases – Primitive equations – An assortment of models GCM (global models, under-resolved,…) SCM (single column of the GCM) LES (high-resolution column model – resolve largest, most energetic eddies, models subgrid) MLM (idealized reduced order model that uses – Decoupling work pepper VOCALS throughout MLM comparison with LES for S12 (and maybe SCAM?) Proposed dissertation topic

47. Outline Introduction – What is climate sensitivity and why do we care? – Why are low clouds important (to climate system, climate sensitivity)? – What has been done, and where does this study fit in? – Feedback flow chart (?) Proposal for this study – CGILS cases – Primitive equations – An assortment of models GCM (global models, under-resolved,…) SCM (single column of the GCM) LES (high-resolution column model – resolve largest, most energetic eddies, models subgrid) MLM (idealized reduced order model that uses – Decoupling work pepper VOCALS throughout MLM comparison with LES for S12 (and maybe SCAM?) Proposed dissertation topic

48. Our approach: Consensus that we need better understanding of the processes underlying low-cloud response to climate change (i.e., GCM intercomparison studies demonstrate clearly the global average low cloud response is a big uncertainty, but individual models differ in parameterizations of cloud processes, and climate-change output diverges widely between models) Use IDEALIZED LOCAL CASE STUDIES (drawn from CGILS intercomparison) to investigate cloud sensitivity in a hierarchy of models (LES, SCM, and MLM) to climate-change inspired tests, with the goals of: – Understanding mechanisms behind cloud sensitivity (i.e., do LES and SCM agree? Can this behavior be constrained by observations? Is improved parameterization, informed by LES necessary?) – Connecting these back to the GCM behavior of a given model.

49. Proposal: use a hierarchy of models to investigate low cloud response to climate perturbations Local analysis: – Focus on 3 regions used in CGILS intercomparison study representing 3 low cloud regimes with idealized large scale forcings – Use 3 types of column models to investigate cloud sensitivity to a variety of perturbations: Ultimate goal: Connect these back to GCM

50. Subcloud legs drizzle Profiles Surface layer Cloud layer Well-mixed Decoupled

51. C-130 flight path (grey) Cloud base (lidar-derived) LCL (“well-mixed cloud base”) Radar reflectivity (drizzle proxy) (courtesy of Rob Wood) We use vertical profiles and subcloud level legs

52. Inversion Jumps Inversion base Inversion “top”

53. Use REx C-130 profiles to calculate jumps/decoupling, adjacent subcloud legs to calculate cloud fraction. Restrict to flights before 10:00 LT in left panel. κ > 0.4 often (but not always) goes with broken cloud. For κ < 0.5 there is no obvious correlation of κ and decoupling. POC and non-POC distributions overlap Blue = well-mixed Red = decoupled Hollow = POC Dash = Lock (2009) LES results

55. Shiny clouds MODIS Visible Image

56. Marine Boundary Layer (MBL) clouds:

57. CGILS Cases (focus on S12 this talk) S12: Shallow Stratocumulus (Sc) Well-mixed BL => mixed-layer model appropriate Focus of remainder of this talk S11: Transition between Sc and shallow cumulus (Cu) Onset of BL decoupling Cu rising into Sc S6: Shallow Cu

58. Mixed-layer model equations horizontal advection Entrainment surface fluxes Radiation Precipitation

59. Marine Boundary Layer (MBL) Clouds (Infrared satellite image, courtesy of Rob Wood)

60. Marine Boundary Layer (MBL) Clouds NASA MODIS Satellite Image

61. Questions?

63. Marine boundary layer clouds: 1.Reflect incoming solar radiation 2.Cover a large fraction of the surface

64. MODIS visible satellite image Reflective

65. Clouds in climate models - change in low cloud amount for 2  CO 2 from Stephens (2005) GFDL CCM model number

66. Subcloud layer Cloud layer Well-mixed Decoupled Approximately 30% of profiles in VOCALS-REx were well-mixed (blue)

67. Climate Change: Response to radiative forcing R = Absorbed Solar Radiation – Outgoing Longwave Radiation Radiative forcing (e.g., increased CO 2 ) Cloud contribution most uncertain

68. Earth’s Radiation Budget: R = Absorbed Solar Radiation – Outgoing Longwave Radiation (Infrared satellite image, courtesy of Rob Wood) Marine boundary layer clouds especially important because… 1.They’re reflective at visible wavelengths 2.They cover a lot of area

69. Climate Change: Response to radiative forcing R = Absorbed Solar Radiation – Outgoing Longwave Radiation Radiative forcing (e.g., increased CO 2 )

70. Cloud feedbacks dominate climate sensitivity uncertainty in GCMs Clouds dominate overall climate feedback uncertainty Low clouds dominate cloud feedback uncertainty Clouds: - Positive feedback, - Large spread between models Bony et al. (2006)Soden and Vecchi (2011)

71. Earth’s Radiation Budget: R = Absorbed Solar Radiation – Outgoing Longwave Radiation (NASA) Marine boundary layer clouds especially important because... MBL clouds

72. IPCC (2007)

73. The Models LES (high resolution): System for Atmospheric Model (SAM) – High resolution cloud resolving model – Largest, most energetic eddies resolved – Subgrid-scale turbulence is modeled – The closest we have to “observations” for climate change simulations – Parallel effort by Peter Blossey and Chris Bretherton for CGILS LES intercomparision SCM (single column of global model): SCAM5 (CAM5 GCM, operating in single column mode) – Single grid column from the GCM – Approximately 1 degree horizontal resolution, 30 vertical levels – Parameterize subgrid physical processes MLM (idealized, interpretive model): – Idealized reduced order model applicable in Sc region (S12) when MBL remains “well-mixed” – When applicable, good for diagnosing / interpreting sensitivities in other models

74. Earth’s Radiation Budget: R = Absorbed Solar Radiation – Outgoing Longwave Radiation (NASA MODIS visible satellite image in Eastern Pacific) Marine boundary layer clouds especially important because… 1.They’re reflective at visible wavelengths