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

2. 4 th 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 emitted surface flux absorbed surface flux fraction  solar flux absorbed solar flux fraction 1  p emitted atmospheric flux emissivity  absorbed atmospheric flux surface top of atmosphere transmitted surface flux fraction 1  reflected solar flux fraction  p

7. A Simple Atmosphere Top of Atmosphere (1 –  p ) S 0 / 4 =  T a 4 + (1 –  )  T s 4 Atmosphere  T s 4 = 2  T a 4 Surface (1 –  p ) S 0 / 4 =  T s 4 –  T a 4

8. How are T s and  related? If emissivity  increases (more CO 2 ) surface temperature T s increases

9. The Simplest Climate Theory Fupward radiation flux at top of atmosphere Eexternal parameter (e.g., CO 2, solar output) T s global surface temperature no internal feedbacks

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

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

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

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

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

15. high sensitivity: strong warming for a given forcing low sensitivity: weak warming for a given forcing Climate Sensitivity Climate sensitivity is the ratio of temperature response to external 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 water vapor is a greenhouse gas (the strongest), so where q is water vapor mixing ratio (kg water vapor per kg dry air)

18. Water Vapor Feedback where r is relative humidity and q sat is saturation water vapor mixing ratio q sat 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, q sat  1 g/kg  q  0.1 g/kg (10% change) for either:  T  2.5 K (1% change)  r  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 reflection of solar radiation where C can represent multiple cloud characteristics cloud greenhouse effect sign of net radiation flux depends on type of cloud

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

24. Comparison with CO 2 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 -2 more radiation to space Reduction in outgoing thermal radiation by CO 2 increase since 1750 (280  380 ppm): –1.6 W m -2

25. Comparison with CO 2 1.6 W m -2 (35% increase in CO 2 ) 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 is required for basic knowledge of cloud feedback

28. Numerical Modeling

29. Numerical Models Global or smaller-domain numerical models explicitly solve equations at scales above the grid resolution T,qT,q T,qT,q 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) clouds small-scale circulations 100 km 1 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 Change in cloud radiation effects due to 2 x CO 2 warming is completely inconsistent between models! figure from Ringer et al. (2006)

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

34. Numerical Models Global climate models do not correctly and consistently simulate cloudiness and its radiative effects Model climate sensitivity (warming per CO 2 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 Satellite  Cloud

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

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

40. Statistical Correction to Data before after

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

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

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

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

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

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

49. Recent Results collaborators: Amy Clement and Robert Burgman

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

51. Line- total cloud Bars- low cloud NE Pacific Decadal Variability warm sea surface temperature weak sea level pressure weak wind (corrected for artifacts) less stratocumulus cloud more ocean heating less boundary-layer cooling

52. NE Pacific Decadal Variability Basin-wide regression on NE Pacific SST time series

53. models with wrong sign r(cloud,SST) Correct sign r and robust simulation Observed r NE Pacific cloud and meteorology Is this feedback present in IPCC AR4 models? models with wrong sign r(cloud,LTS) wrong sign r(cloud,SLP) wrong sign r(cloud,  500 )

54. HadGEM1 2  CO 2 Change Observed Decadal 2  CO 2 Simulation cloud change 2  CO 2 cloud and circulation changes resemble observed decadal cloud and circulation changes

55. Circulation and Cloud Feedbacks On decadal time scales, decreased stratocumulus cloud cover is associated with warmer sea surface temperature and weaker atmospheric circulation Likely regional positive cloud feedback on decadal timescales due to solar warming of ocean and reduced cooling of atmospheric boundary layer Only one robust IPCC AR4 model reproduces correct sign for all 5 cloud-meteorological correlations This model exhibits stratocumulus decrease and weaker circulation for 2  CO 2 that resembles observed pattern

56. Conclusion Cloud feedback on climate is a challenging problem but progress is slowly being made

57. Recommendations We need a stable observational system to monitor global cloudiness and radiation on decadal time scales We need greater integration between observations, numerical modeling, and theory (inside and outside of parameterizations) We need comprehensive quantitative understanding of cloud and meteorological co-variability in observations and models We need new ideas!

58. Thank You!