ECMWF cloud scheme: Validation and Direction Adrian Tompkins

1. ECMWF cloud scheme: Validation and DirectionAdrian Tompkins • The MP Question: “What have ECMWF ever done for us”? • ECMWF’s minor role in Cloudnet: To provide data and await feedback…? • Due to my lack of time, this puts the data in the “slow feedback loop” Model parametrization 1. Development 2. Validation Data

2. Validation • Example: Validation of model versus Meteosat Brightness Temperatures • “Expensive” (human resources) validation for a fixed period • But what if t (validation) >> t (model cycle updates) ? • i.e. When results arrive they refer to “old” cycle Courtesy of F. Chevallier

3. Uses of ARM • ARM data has been used as a validation tool • Cloud cover, Cloud ice retrievals from radar (Janiskova) • Simulated Z (Morcrette) • Surface radiative fluxes and liquid water paths (JJM) • 2D-Var assimilation of radar data to test future cloudsat use (Bennedetti and Lopez) • SGP data used to validate new turbulence model (Neggers and Koehler) • Cases studies and “one-offs”, no routine use in model cycle development

4. Development • Development can mean “using the data to derive / develop / tune a parametrization” • e.g. Tompkins and Di Giuseppe use cloudnet data to tune and test a new SW cloud overlap parametrization for solar zenith angle effects on cloud geometry ECMWF SW albedo error with respect to a TIPA benchmark calculation using over 100 cloud scenes taken over Chilbolton

5. Development • Hogan Length-scale tuned to give correct Cloud Cover over Chilbolton, then used for 600 Palaiseau scenes as independent “test” • Experience: Data extremely easy to use • Reprocessing of ARM site data extremely welcome!!! ECMWF SW albedo error with respect to a benchmark calculation using over 600 cloud scenes taken over Palaiseau

6. Development • Can also mean a validation tool fast and efficient enough to be included in parametrization tests • ECMWF: T799 L91 medium-range “scores” • RMS, AC of Z,T,U • Parametrization Group: “climate suite” • 3 member 13 month atmosphere only T159L91 • Validation seasons against: MODIS, ISCCP, Quikscat, SSMI, TRMM, GPCP, Xie-Arkin, Da-Silva, CERES, ERBE • For parameters of: LWP, TCWV, TCC, 10m winds, rainfall, TOA radn fluxes, surface heat fluxes

7. Example ISCCP Total cloud cover :model cycle 29r1operational early 2005 Issue: Cloudnet in slower feedback loop, but independent and comprehensive validation (also over points) extremely important

8. Validation and “tuning” Model parametrization Fast validation = “tuned metric” Slow validation = “Independent” source Data “error”

9. ECMWF Validation needs: Ice! • Information from cloudnet regarding glaciated clouds is useful • e.g. First comparison of ice water content comparison with microwave limb sounder (Frank Li et al.)

10. ECMWF validation needs: Higher order moments • Information on subgridscale variability of ice, liquid and water vapour is paramount to developments of statistical cloud cover schemes • Much emphasis has been placed on this, and the Cloudnet results will be central to efforts at ECMWF…

11. ECMWF Directions, Short term • Numerics have been revised to reduce sensitivity to vertical resolution (moving from T511L60 to T799L91 soon) • Ice sedimentation now a pure advection term • Ice-to-Snow autoconversion added to model • Simple diagnostic parametrization to allow supersaturation with respect to ice • Final testing for implementation early 2006

12. ECMWF Directions, Medium term • Prognostic ice mass mixing ratio • Prognostic ice number concentration • Prognostic moments of total water, with cloud cover derived from a statistical cloud scheme • Interaction between aerosols and microphysics (GEMS) • Attention to numerics Reduction in ice water path in response to 3x dust aerosols over Africa