1. ECMWF Radiation and Clouds: Towards McICA? 20060608 1 Towards a McICA representation of cloud-radiation interactions in the ECMWF model Radiation: J.-J. Morcrette Cloud processes: Adrian Tompkins

2. ECMWF Radiation and Clouds: Towards McICA? 20060608 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 T L 319L60 from 20010401 to 20010501 Tests with 4-month simulations at T L 95 L60 for same period  control (control)  random perturbation within Gaussian distribution (the relevant quantity x -> x (1+  *ran)   =2 CF (1-CF) applied on x = CF (random1)   =1.5 CF |HR tot | applied on x = HR (random2)   =2 CF sqrt (HR LW 2 +HR SW 2 ) applied on x = HR (random3)

3. ECMWF Radiation and Clouds: Towards McICA? 20060608 3 McICA: How does the model survive radiative noise? NH SH Tropics India Arabia E. Asia Anomaly correlation Z 1000 hPa NHSH Tropics India Arabia E.Asia Anomaly correlation Z 500 hPa T L 319 L60 31 x 10-day FCs

4. ECMWF Radiation and Clouds: Towards McICA? 20060608 4 McICA: How does the model survive radiative noise? NH SH Tropics NH SH Arabia E. Asia Arabia E. Asia India Mean error T 850 hPaMean error T 200 hPa T L 319 L60 31 x 10-day FCs

5. ECMWF Radiation and Clouds: Towards McICA? 20060608 5 McICA: Hoes does the model deal with radiative noise? Systematic perturbation: Re +1  m De +10  m T L 95 L60 starting 24-hour apart from 20010401 to 2001030 Results averaged over JJA Ref=ControlSystematic perturbation Difference Perturb-Control Student t-test

6. ECMWF Radiation and Clouds: Towards McICA? 20060608 6 McICA: Hoes does the model deal with radiative noise? Systematic perturbation: Re +0.1  m De +1  m Random perturbation: random3 Difference Perturb.-Control Difference Random-Control t-test

7. ECMWF Radiation and Clouds: Towards McICA? 20060608 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. ECMWF Radiation and Clouds: Towards McICA? 20060608 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 N c cloudy columns, (3) can be written as Correlated-k distributed absorption coefficients as in RRTM (1) (2) (3)

9. ECMWF Radiation and Clouds: Towards McICA? 20060608 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 * N c large enough, an unbiased value can be obtained using a different random cloud profile for each k-coefficient

10. ECMWF Radiation and Clouds: Towards McICA? 20060608 10 McICA: Tests with 1-D radiation code: LW North Slope of Alaska OLRSDLW Differences McICA-Ref South Great Plains

11. ECMWF Radiation and Clouds: Towards McICA? 20060608 11 McICA: Tests with 1-D radiation code: LW Trop. West Pacific: Manus Trop. West Pacific: Nauru OLR SDLW Differences McICA-Ref

12. ECMWF Radiation and Clouds: Towards McICA? 20060608 12 It does work in the LW : not yet in SW! ARM-TWP Nauru OLR SDLW Box100 McICA ARM-SGP OLR SDLW Box100 McICA

13. ECMWF Radiation and Clouds: Towards McICA? 20060608 13 ECMWF Plans: Statistical Scheme These explicitly specify the probability density function (PDF) for the total water q t (and sometimes also temperature) qtqt x qtqt PDF(q t ) qsqs Cloud cover is integral under supersaturated part of PDF Assumes no supersaturation LOTS OF ISSUES FOR IMPLEMENTATION: contact Adrian for his thoughts!!!

14. ECMWF Radiation and Clouds: Towards McICA? 20060608 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. ECMWF Radiation and Clouds: Towards McICA? 20060608 15 What do we expect? q liq Warm rain autoconversion dq liq dt Sundqvist Range of values If subcloud variability is ignored Taking variability into account Lower autoconversion if subgrid variability neglected hence expect higher mean cloud thickness

16. ECMWF Radiation and Clouds: Towards McICA? 20060608 16 Instead: Sensitivity opposite to expected effect. Dominated by ice microphysics (q 0.16 ice to snow) and accretion terms – i.e. Complex, esp. with multiphase microphysics