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Towards a McICA representation of cloud-radiation interactions in the ECMWF model

Towards a McICA representation of cloud-radiation interactions in the ECMWF model. Radiation: J.-J. Morcrette Cloud processes: Adrian Tompkins. McICA. In long seasonal runs and high-resolution 10-day forecasts How do the model survive noise in radiative heating rate?

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Towards a McICA representation of cloud-radiation interactions in the ECMWF model

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  1. Towards a McICA representation of cloud-radiation interactions in the ECMWF model Radiation: J.-J. Morcrette Cloud processes: Adrian Tompkins

  2. McICA • In long seasonal runs and high-resolution 10-day forecasts • How do the model survive noise in radiative heating rate? • How do the model survive noise in layer cloud fraction? • Tests with 31x10-day FC at TL319L60 from 20010401 to 20010501 • Tests with 4-month simulations at TL95 L60 for same period • control (control) • random perturbation within Gaussian distribution (the relevant quantity x -> x (1+s*ran) • s=2 CF (1-CF) applied on x = CF (random1) • s=1.5 CF |HRtot| applied on x = HR (random2) • s=2 CF sqrt (HRLW2+HRSW2) applied on x = HR (random3)

  3. McICA: How does the model survive radiative noise? SH NH SH NH Anomaly correlation Z 500 hPa Anomaly correlation Z 1000 hPa India India Tropics Tropics TL319 L60 31 x 10-day FCs E.Asia E. Asia Arabia Arabia

  4. McICA: How does the model survive radiative noise? SH SH NH NH Mean error T 850 hPa Mean error T 200 hPa Tropics India Tropics India TL319 L60 31 x 10-day FCs Arabia E. Asia E. Asia Arabia

  5. McICA: Hoes does the model deal with radiative noise? Systematic perturbation: Re +1 mm De +10 mm TL95 L60 starting 24-hour apart from 20010401 to 2001030 Results averaged over JJA Difference Perturb-Control Student t-test Ref=Control Systematic perturbation

  6. McICA: Hoes does the model deal with radiative noise? Systematic perturbation: Re +0.1 mm De +1 mm Difference Perturb.-Control t-test Random perturbation: random3 Difference Random-Control t-test

  7. McICA: How does the model survive radiative noise? • For each variable, • first column is difference • Second is area with difference significant at > 95% level • Third is area with difference significant at > 97.5 % level • No particular problem in either forecast or long run mode • The McICA approach can then be used (Pincus et al., 2004, JGR)

  8. What is McICA? • Monte-Carlo Independent Column Approximation • The CKD approach for 1-D PPH columns is • The ICA approach for domain averages is (ICA: Independent Column Approx.) • Combining (1) and (2) gives • Assuming clear- and cloudy-sky columns of gas, and if there are Nc cloudy columns, (3) can be written as Correlated-k distributed absorption coefficients as in RRTM (1) (2) (3)

  9. What is McICA? • Which can be simplified to • The hypothesis is that can be given by • In which case, it follows (see Barker’s May 2002 presentation) that The model is unbiased in the ICA sense, so for T=K * Nc large enough, an unbiased value can be obtained using a different random cloud profile for each k-coefficient

  10. McICA: Tests with 1-D radiation code: LW North Slope of Alaska South Great Plains OLR SDLW Differences McICA-Ref

  11. McICA: Tests with 1-D radiation code: LW Trop. West Pacific: Manus Trop. West Pacific: Nauru Differences McICA-Ref SDLW OLR

  12. It does work in the LW : not yet in SW! SDLW SDLW ARM-SGP OLR ARM-TWP Nauru OLR Box100 Box100 McICA McICA

  13. qt x PDF(qt) qt qs ECMWF Plans: Statistical Scheme These explicitly specify the probability density function (PDF) for the total water qt (and sometimes also temperature) Assumes no supersaturation Cloud cover is integral under supersaturated part of PDF LOTS OF ISSUES FOR IMPLEMENTATION: contact Adrian for his thoughts!!!

  14. Can use PDF information consistently in other schemes: Radiation, microphysics… Example of use (with Rob Pincus): Use “cloud generator” to split cloudy column into many subcolumns to investigate effect of subgrid variability on ECMWF microphysics

  15. dqliq dt What do we expect? Warm rain autoconversion Sundqvist Taking variability into account If subcloud variability is ignored qliq Range of values Lower autoconversion if subgrid variability neglected hence expect higher mean cloud thickness

  16. Instead: Sensitivity opposite to expected effect. Dominated by ice microphysics (q0.16 ice to snow) and accretion terms – i.e. Complex, esp. with multiphase microphysics

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