A Statistical Representation of Sub-grid Cloudiness for CAM

1. A Statistical Representation of Sub-grid Cloudiness for CAM Peter Caldwell & Steve Klein (LLNL) Sungsu Park (NCAR) AMS Meeting, 1/26/11 Prepared by LLNL under Contract DE-AC52-07NA237344. UCRL: LLNL-PRES-464896

2. The Punchline: Our macrophysics scheme produces a climate very similar to CAM5. (TOA E balance changes by 0.65 W/m2) But improves model throughput by 7%! Taylor diagram showing relative standard deviation (radial distance), correlation (angle), and RMS error (distance from (1,1) ) using our macrophysics scheme versus default CAM5. * All runs at 2o resolution with * climatological SST

3. Background: The Concept Define the saturation excess s = qw – qs(T,p). If condensation/evaporation are instantaneous and the shape and moments of the s PDF are known,  saturation mixing ratio at temperature T and pressure p. liquid + vapor mixing ratio  Fig: Example PDF from ASTEX (dots) with Gaussian fit (line) and cloud fraction (shaded area).

4. Details: Define the saturation excess s = qw – qs(T,p). If condensation/evaporation are instantaneous and the shape and moments of the s PDF are known,  saturation mixing ratio at temperature T and pressure p. liquid + vapor mixing ratio  This concept is not new – Sommeria and Deardorf (1977) and Mellor (1977) proposed very similar parameterizations. Handling ice independently and subtracting it from the PDF is new and largely avoids ice supersaturation problems (but still permits inconsistencies).

5. Variance Calculation PDF width parameterization is important… and hard. • CAM5 cloud frac uses triangular PDF in qt with half-width (δ) ∝ Rhcrit from CAM4 • Currently spoof CAM5 by using triangle’s variance. • Future work=diagnostic, process-based variance. 0.8 Rhcrit Profile 400mb δ 700mb qs qt 0.79 0.89 land ocn Our initial goal is to add PDF consistency with as little simulation impact as possible

6. Macrophysics Results: Cloud Change Annual-Mean Vertically-Integrated Cloud Fraction Change (PDF-CTL) Annual and Zonal Mean Cloud Structure Change (PDF-CTL) frac change % Change • Maximum decreases occur at high latitudes • Weak SW impact at these latitudesexplains why cloud changes don’t affect other aspects of the simulation? • Changes are vertically and seasonally coherent (not shown) • Decrease also seen in convective regions • Is something happening with the ice phase? • Is our cloud fraction better? On the whole, no… but still not tuned.

7. Understanding Cloud Change PDF Run PDF ‘parasite’ off CTL run CTL Run 850 mbliquid cloud fraction (LCLOUD) from 1 mo CAM simulations. • Running my cloud fraction parameterization forced by CTL model state gives results much closer to PDF than CTL • Suggests it is the cloud fraction parameterization itself (rather than feedbacks) that causes cloudiness differences.

8. Macrophysics Conclusions: • PDF scheme induces little change to CAM5 climate but improves throughput by 7% • Cloud fraction decreases • particularly at high latitudes • associated with cloud fraction parameterization • retuning will bring into better alignment Macrophysics Goals: • Understand cloud changes • Add scheme to NCAR repository • Improve variance prediction • Better handle ice short-term goals long-term goals

9. Beyond Macrophysics: One PDF to Rule Them All CAM5 physics parameterizations make a variety of inconsistent subgrid assumptions. We aim to fix this. • Macrophysics • Microphysics • Radiation Column Generator (McICA) ✓ ✓

10. Microphysics (liquid water content) For microphysical process w/ local rate R=xqly: autoconversion, accretion, immersion freezing, contact freezing, sedimentation • CAM5: • assumes SGS ql variability follows Γdistn • for given ql, drop diameter D follows a Γdistn with params chosen to keep drop concentration constant • Gaussian PDF: • Implies ql follows a truncated Gaussian distn • Is this a better fit to obs than a gamma? • Can be implemented as a 1-D table lookup Microphysics is substeppedPDF must be updated

11. Microphysics: Results SCAM results from ARM SGP July 1995 IOP: summertime convection. Accretion: Autoconversion: note scale change! Immersion Freezing: (Contact freezing always 0) • Using old/new agreement to test for bugs • At first glance, scheme looks reasonable!

12. Radiation Subgrid Variability • CAM5 uses Monte Carlo-Independent Column Approximation (McICA): • Radiation codes typically compute fluxes as the sum of calculations for a series of spectral bands • McICA chooses a different cloud state for each band • makes summing over bands ≈ Monte Carlo integration over cloud states. • noisy for 1 timestep, but quickly damps • allows for arbitrary cloud overlap McICA in CAM5: Handles cloud fraction consistently Assumes uniform liquid water content (inconsistent) Merges convective and stratiform cloud, causing problems with overlap

13. Thanks! contact: caldwell19@llnl.gov