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Background on Surface Cloud Radiative Forcing: A useful climate model diagnostic

Background on Surface Cloud Radiative Forcing: A useful climate model diagnostic. “If you get the clouds right, chances are you get the feedbacks right” – John Mitchell, Chair WCRP Working Group on Coupled Modeling.

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Background on Surface Cloud Radiative Forcing: A useful climate model diagnostic

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  1. Background on Surface Cloud Radiative Forcing:A useful climate model diagnostic “If you get the clouds right, chances are you get the feedbacks right” – John Mitchell, Chair WCRP Working Group on Coupled Modeling • DOE/ARM CART sites: surface-based measurements, cloud profiling – comprehensive, expensive & a few fixed sites • Intensive ship-based field campaigns: similar to ARM but short duration – comprehensive and expensive • Buoys • Much better spatial distribution/sampling • Full annual cycle • Much more limited in measured variables “Comparing observations and climate models is a statistical game” – C. Fairall, after 3 Manhattans

  2. The simplest index of cloud effects on the surface energy budget: Focus on the Clouds • Cloud forcing is the difference in the observed mean radiative flux versus what the flux would be in the absence of clouds • A related variable that is often used is the maximum cloud forcing, which is the conditional change in the flux when a cloud is actually present: • Uses simple measurements that can be made accurately R=radiative flux; subscript x=s, solar or l, longwave 0 implies flux in the absence of clouds (model computation) f is the cloud fraction

  3. A Few Points About Surface Radiative CF(Besides the Obvious that more clouds give more CF) • Solar CF depends mostly on cloud optical thickness (→ large MCF) and incident solar flux (Tropics) • For a given cloud thickness depends only weakly on cloud base height • MCF strongly depends on cloud thickness; emphasis on deep convective clouds (record value = -280 W/m2; Sept. 22, 2001 EPIC2001) • Not highly correlated with cloud fraction on 1-day time scales • IR CF depends mostly on dryness of the atmospheric column, particularly PBL moisture. • For a given cloud thickness depends strongly on cloud base height; high clouds may have no effect because of intervening moisture emissions. Weakest in tropics. • MCF saturates for cloud thickness of about 100m • Highly correlated with cloud fraction on 1-day time scales

  4. Example From Equatorial E Pacific Cloud Forcing = Mean Measured Radiative Flux – Mean Clear Sky Radiative Flux. Thus, cloud forcing is the net effect the cloud have on the surface radiative fluxes. For IR flux it is positive (clouds warm the surface) while for solar flux it is negative (clouds cool the surface). Average surface heat fluxes for 7 cruises (blue=fall; red=spring). The left panel is for the IR cloud forcing (upper) and solar cloud forcing (lower). The right panel shows the net surface heat flux (upper) and the cloud forcing contribution to that flux (lower)

  5. Linking IR and Solar CF: The CF Phase Diagram Indian Ocean Monsoon Winter Storm Track N. Atlantic Ocean Equatorial EPac

  6. Monthly averages, Mean Annual Cycle Range – Amplitude annual cycle Slope – ‘Climate Zone’ Position – Biases in IR or Solar or cloud fraction

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