Cloud Feedbacks on Climate: A Challenging Scientific Problem

1. Cloud Feedbacks on Climate: A Challenging Scientific Problem Joel Norris Scripps Institution of Oceanography Fermilab Colloquium May 12, 2010

2. 4th IPCC: Key Uncertainties • “Cloud feedbacks (particularly from low clouds) remain the largest source of uncertainty [to climate sensitivity].” • “Surface and satellite observations disagree on total and low-level cloud changes over the ocean.” • “Large uncertainties remain about how clouds might respond to global climate change.” • “Cloud feedbacks are the primary source of intermodel differences in equilibrium climate sensitivity…”

3. Why a challenging problem? • We have no fundamental theory for how global cloudiness should respond to greenhouse warming • We have no numerical models that produce sufficiently realistic simulations of global cloudiness • We have no stable system to monitor changes in global cloudiness and radiation on multidecadal time scales

4. Outline • Theory • Numerical Modeling • Observations • Marine Boundary Layer Clouds • Recent Results • Recommendations

5. Theory

6. A Simple Atmosphere reflected solar flux fraction ap transmitted surface flux fraction 1-e top of atmosphere solar flux absorbed surface flux fraction e emitted atmospheric flux emissivity e absorbed solar flux fraction 1-ap surface emitted surface flux absorbed atmospheric flux

7. A Simple Atmosphere Top of Atmosphere (1 – ap) S0 / 4 = e sTa4 + (1 – e) sTs4 Atmosphere e sTs4 = 2 e sTa4 Surface (1 – ap) S0 / 4 = sTs4 – e sTa4

8. How are Ts and e related? If emissivity e increases (more CO2) surface temperature Ts increases

9. The Simplest Climate Theory F upward radiation flux at top of atmosphere E external parameter (e.g., CO2, solar output) Ts global surface temperature no internal feedbacks

10. The Simplest Climate Theory If equilibrium (DF = 0) and zero internal feedbacks, then where Planck radiative response

11. Allow Internal Feedbacks Ik internal parameter e.g., cloud, snow/ice, water vapor, vertical temperature profile (lapse rate)

12. Allow Internal Feedbacks If equilibrium (DF = 0), then where

13. Net Feedback on Climate This can be rewritten as where sum of individual feedbacks

14. Net Feedback on Climate This can be rewritten as f > 0 positive feedback: internal response of climate system exacerbates externally forced warming f < 0 negative feedback: internal response of climate system mitigates externally forced warming

15. Climate Sensitivity Climate sensitivity l is the ratio of temperature response to external forcing high sensitivity: strong warming for a given forcing low sensitivity: weak warming for a given forcing

16. Individual Major Feedbacks • Snow/ice albedo feedback – obviously positive • Lapse rate feedback – small negative • Water vapor feedback – almost certainly positive • Cloud feedback – sign unknown, maybe positive

17. Water Vapor Feedback where q is water vapor mixing ratio (kg water vapor per kg dry air) water vapor is a greenhouse gas (the strongest), so

18. Water Vapor Feedback where r is relative humidity and qsat is saturation water vapor mixing ratio qsat rapidly increases with temperature r controlled by turbulent dynamics of the atmosphere

19. Saturation Mixing Ratio From Hartmann’s Global Physical Climatology

20. Water Vapor Feedback use values for location of maximum emission to space: r 0.4, T 250 K, qsat 1 g/kg Dq 0.1 g/kg (10% change) for either: DT  2.5 K (1% change) Dr  0.1 (25% change)

21. Water Vapor Feedback • To first order, water vapor feedback is controlled by saturation vapor dependence on temperature • Changes in relative humidity have second order influence Good understanding of dynamical control of humidity not required for basic knowledge of water vapor feedback

22. Cloud Feedback where C can represent multiple cloud characteristics reflection of solar radiation cloud greenhouse effect sign of net radiation flux depends on type of cloud

23. Cloud Radiative Effects high-level cloud reflection ~ 0 greenhouse << 0 warms the earth thick cloud reflection >> 0 greenhouse << 0 (reflection + greenhouse) ~ 0 low-level cloud reflection >> 0 greenhouse ~ 0 cools the earth

24. Comparison with CO2 • Reflection of solar radiation by all clouds: +48 W m-2 • Reduction in outgoing thermal radiation by all clouds:–31 W m-2 • Net cloud radiative effect of all clouds: +17 W m-2more radiation to space • Reduction in outgoing thermal radiation by CO2 increase since 1750 (280  380 ppm): –1.6 W m-2

25. Comparison with CO2 1.6 W m-2 (35% increase in CO2) equal to either: • 3% change in the reflection of solar radiation by clouds • 5% change in the reduction of outgoing thermal radiation by clouds • 9% change in net effect of clouds on radiation

26. Cloud Response to Temperature clouds exist where r ≥ 1, absent where r < 1 r controlled by turbulent dynamics of the atmosphere

27. Cloud Feedback • Changes in clouds on the order of 1% can have major impacts on Earth’s radiation budget • Radiative impacts of different cloud types can have opposite sign • Changes in relative humidity have first order influence Good understanding of dynamical control of humidity isrequired for basic knowledge of cloud feedback

28. Numerical Modeling

29. T,q T,q Numerical Models Global or smaller-domain numerical models explicitly solve equations at scales above the grid resolution winds solar radiation thermal radiation temperature moisture

30. Numerical Models Processes at scales below the grid resolution must be parameterized (approximated in terms of grid-scale values) 1 km clouds small-scale circulations 100 km

31. Numerical Models • Ideally, sub-grid turbulence should be homogeneous, isotropic, and cascade downscale to viscous dissipation • Turbulence with these characteristics typically occurs only at scales less than 10-100 meters • Global climate models must parameterize turbulence that is inhomogeneous, non-isotropic, and non-linear • Cloud parameterizations do not represent the underlying processes with sufficient accuracy

32. Cloud Feedbacks in Models figure from Ringer et al. (2006) Change in cloud radiation effects due to 2 x CO2 warming is completely inconsistent between models!

33. Simulated Cloud Change for 2CO2 Courtesy of Brian Soden Models predict different signs of cloud change

34. Numerical Models • Global climate models do not correctly and consistently simulate cloudiness and its radiative effects • Model climate sensitivity (warming per CO2 increase) depends most on what is understood least (cloud parameterizations)

35. Observations

36. Cloud Observations • Surface visual observations of clouds have had a consistent (?) identification procedure since 1950 • Semi-standardized observations of clouds from weather satellites are available since the early 1980s • Observing systems are designed for monitoring weather, not climate – no built-in long-term stability!

37. Surface and SatelliteCloud

38. Satellite Cloud Record Low-level cloudiness is the largest contributor to the apparent artifact in total amount (not shown).

39. Satellite Cloud Record Low-level cloudiness is the largest contributor to the apparent artifact in total amount (not shown).

40. Statistical Correction to Data after before

41. Cloud Observations • Surface and satellite cloud records are dominated by spurious variability • Observational uncertainty is much larger than the magnitude of significant radiative impacts on climate • Statistical correction of data can provide more realistic regional variability • Very precise after-the-fact calibration must be applied to satellite observations to produce a climate-ready dataset

42. Marine Boundary Layer Clouds

43. Low-Level Cloud and Net Radiation Hartmann et al. 1992 Cloud with tops below 680 mb (less than 3 km) Low-level clouds and especially marine stratocumulus cool the planet (solar reflection by clouds greater than greenhouse effect of clouds)

44. Subtropical Marine Boundary Layer dry free troposphere Td T temperature inversion 50+ m cloud layer moist boundary layer 500 to 2000 m subcloud layer sea surface

45. Subtropical Marine Boundary Layer dry free troposphere ws < 0 subsidence subsidence  entrainment temperature inversion entrainment we cloud layer moist boundary layer divergence subcloud layer ws= 0 sea surface

46. entrainment drying + drizzle  surface moistening entrainment + surface warming radiative + advective cooling  Subtropical Marine Boundary Layer dry free troposphere subsidence temperature inversion entrainment radiative cooling drying and heating cloud layer moist boundary layer divergence drizzle loss subcloud layer advection from midlatitudes moistening and heating sea surface

47. buoyancy generation entrainment + dissipation  Subtropical Marine Boundary Layer dry free troposphere subsidence temperature inversion entrainment radiative cooling negative buoyancy drying and heating cloud layer moist boundary layer convection and turbulent mixing divergence drizzle loss subcloud layer advection from midlatitudes positive buoyancy moistening and heating sea surface

48. Boundary Layer Structure and Clouds Stratocumulus Cu-under-Sc Cumulus qt qe qt qe qt qe inversion cloud layer stable layer surface layer surface well-mixed boundary layer cloud layer decoupled from surface layer conditionally unstable boundary layer

49. Recent Resultscollaborators: Amy Clement and Robert Burgman

50. NE Pacific Decadal Variability Does a cloud feedback promote decadal variability in sea surface temperature and circulation?