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A Statistical Cloud Scheme for CAM

A Statistical Cloud Scheme for CAM. Informal Thoughts Oct 27, 2009. The Basic Idea:. Within each GCM grid cell, derive the PDF of saturation excess ( = s):. PDF of s=q t – q s (T). -2 -1 0 1 2.

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A Statistical Cloud Scheme for CAM

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  1. A Statistical Cloud Scheme for CAM Informal Thoughts Oct 27, 2009

  2. The Basic Idea: Within each GCM grid cell, derive the PDF of saturation excess (=s): PDF of s=qt – qs(T) -2 -1 0 1 2 Distance from Saturation ( s=qt – qs(T); g/kg )

  3. Not New • HadGEM, ECHAM, CCMA etc do/have used this approach operationally… • Even CAM has had PDF schemes implemented • Ben Johnson – modified beta distn, ~2005 • Sungsu Park – triangular distn ~2008

  4. Issues: • How to handle ice? (new) • What PDF to use? (old) • Connection to convective scheme (old/new?) • Include T variations? (old) • Condensation/heating circularity issue? (old) • How to compute PDF moments? (old/new?) • Connection to radiation scheme (old?) • Connection to microphysical SGS assumptions • Implementation

  5. How to Handle Ice? (Part 1) • Ice equilibration is slow  <qi> requires a prognostic equation. • can start with CAM’s current ice treatment. • Steve suggests relaxing <qi> towards the all-ice PDF equilibrium. • If <qi> is solved prognostically, integrating over the PDF should be used to define PDF parameters in order to maintain mass/cloud fraction consistency. • Otherwise a prognostic eq for the qiPDF could be made? (sounds hard!)

  6. How to handle ice? (Part 2) Formally, For cold clouds, the probability P(qi) depends not just on T,qt, but also on the presence of ice. How to define this? at what RH does ice start existing? liquid equilibrates fast, so for warm clouds this term would be qt-qs(T,p). Ice equilibrates slower, so qi<qt-qsi(T,p)… How to define this? Answers to these questions are needed for a credible cold-cloud PDF. • use aircraft measurements of simultaneous T, qt,qi, ql? • Currently using existing machinery for ice and formulating our qlPDF using qw=qt – qiinstead of qt. • Incorporating ice in our PDF is an important future goal.

  7. Thoughts on ice?

  8. PDF Choice: bimodal due to mesoscalevar-iability Gaussian typical for well- mixed layers • Obs PDFs can be complicated. • Specifying complex PDFs takes many moments. • Cldfrac and <ql>are relatively insensitive to PDF shape. • We choose a symmetric, unimodal PDF (Gaussian or Triangular) negskewness due to entrainment events pos skewness due to warm, moist updrafts From Larson et al. (2001; JAS): examples of s=qt – qs(T,p) PDFs from 50 km aircraft legs.

  9. A Symmetric PDF “Feature”: • For any symmetric PDF, cldfrac = 50% when <RH> = 1. This causes models to underpredictcldfrac (Wilson et al. 2008, QJRMS). • UKMO invoked subgrid vertical variability to try to fix this. Cldfrac = 0.5 RH=1

  10. Links to Convection • PDF skewness is mainly due to convection • convection→pos. skewness→increasedcldfrac… • Neggers(2009; JAS) describes a double-Gaussian PDF with 1 stratiform hump and the other coming from a mass-flux convection scheme. • Someday expand our PDF to handle strat + convective clds in this way?

  11. Do T Variations Matter? • Some schemes ignore T variations. Bad because: • T’ non-negligible in obs. • Cirrus due to grav waves (T’). • Integrating the joint PDF is a hassle. Instead, note: • qw, T only appear as so use the PDF of s instead. s=qw-qs(T,p) From Zhu and Zuidema (2008; GRL): qt(top) andl(bottom) contributions to saturation from a variety of LES runs.

  12. Recap: The Plan • Short term: apply a Gaussian (or triangular) PDF for qw, T using the s distn to reduce dimensionality. Leave ice calculations as-is. • box model done for Gaussian (Peter), CAM qw-only version done for triangular (Sungsu). • Long term: • revert to qt, include ice phase. • include convective clouds in our PDF?

  13. PDF Comments/Questions?

  14. Condensation/Heating Circularity <ql> computed from Gaussian distn for a variety of <qt>. Tl approx in blue, exact in red. Is this approximation good enough?

  15. Computing PDF Moments: • Tomkins (2008) notes that qw and s param-eterizations are functionally equivalent: T’ only helps when it affects PDF shape. • Prognostic moment equations are complicated and hard to make physically based. • Plan: • Start with variance  <qt> (has prob noted above). • Get turb component of variance from UW PBL scheme) and add parameterized mesoscale variance • Try to stick to diagnostic variance contributions… I need to think more about this…

  16. Connection to Radiation • Does McICA draw stratiform/convective profiles with probability  the area of each within the cell? • Is in-cloud heterogeneity from our PDF applied consistently in McICA? • Is radiation overlap handled appropriately? I haven’t thought about these questions at all.

  17. Connection to Microphysics: • Currently, MG has ql~ (<ql>-1, <ql>2). For a process with rate F(ql)=x qly,this yields a cell-mean rate of <F(ql)>= (y) F(<ql>). • Our qwPDF assumption results in a different qlPDF: • For Gaussian PDF, will need 2d table lookup(?) • Does our PDF interfere with the assumed w PDF used for cloud droplet nucleation? P(ql)=P(qw– qs) for qw > qs else P(ql)=0 P(ql) qw:0 5 qs 10 ql:0……………………………….0 10-qs

  18. Implementation in CAM: • My vision: • Compute <ql> and cldfracwherever needed and <s> or the PDF width has changed. • no separate mmacro? • how often do these quantities need to be updated? • Compute PDF width in a separate routine for easy future development and because shape probably doesn’t change as often as <s>. • Interact w/ ice as currently: start mmicro w/ ice nucleation, then compute <ql> and apply Bergeron…

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