Andrew Barrett, Robin Hogan and Ewan O’Connor Submitted to Geophys. Res. Lett.

Evaluating forecasts of the evolution of the cloudy boundary layer using

radar and lidar observations

Andrew Barrett, Robin Hogan and Ewan O’Connor

Submitted to Geophys. Res. Lett.

Introduction

• Stratocumulus interacts strongly with radiation– Important for forecasting surface temperature– A key uncertainty in climate prediction

• Very difficult to forecast because of many factors:– Surface sensible and latent heat fluxes: to first order, sensible heat

flux grows the boundary layer while latent heat flux moistens it– Turbulent mixing, which transports heat, moisture and momentum

vertically– Entrainment rate at cloud top– Drizzle rate, which depletes the cloud of liquid water

• Use Chilbolton observations to evaluate the diurnal evolution of stratocumulus in six models

Models Used

Institute Model Horizontal resolution

(km)

Vertical levels

(BL, <3km)

Grid-box depth at 1km (m)

Met Office Mesoscale 12 38 (12) 277

Met Office Global 60 38 (12) 277

ECMWF Integrated Forecast 39 60 (16) 235

Météo France ARPEGE 24 41 (14) 227

Royal Netherlands Meteorological Institute (KNMI)

Regional Atmospheric

Climate Model (RACMO)

18 40 (14) 235

Swedish Meteorological and

Hydrological Institute (SMHI)

Rossby Centre Regional

Atmospheric Model (RCA)

44 24 (8) 358

Longwave cooling

Different mixing schemes

Virtual potential temp. (v)

Heig

ht (z

)

dv/dz<0

Eddy diffusivity (Km)(strength of the mixing)

Local mixing scheme (e.g. Meteo France)

2shear

v

v

dgdzRi

• Local schemes known to produce boundary layers that are too shallow, moist and cold, because they don’t entrain enough dry, warm air from above (Beljaars and Betts 1992)

• Define Richardson Number:

• Eddy diffusivity is a function of Ri and is usually zero for Ri>0.25

Longwave cooling

Different mixing schemes

• Use a “test parcel” to locate the unstable regions of the atmosphere

• Eddy diffusivity is positive over this region with a strength determined by the cloud-top cooling rate (Lock 1998)Virtual potential temp. (v)

Heig

ht (z

)

Eddy diffusivity (Km)(strength of the mixing)

Non-local mixing scheme (e.g. Met Office, ECMWF, RACMO)

• Entrainment velocity we is the rate of conversion of free-troposphere air to boundary-layer air, and is parameterized explicitly

Longwave cooling

Different mixing schemes

• Model carries an explicit variable for TKE

• Eddy diffusivity parameterized as Km~TKE1/2, where is a typical eddy size

Virtual potential temp. (v)

Heig

ht (z

)

Prognostic turbulent kinetic energy (TKE) scheme (e.g. SMHI-RCA)

dv/dz<0

TKE shear production buoyancy production transport dissipationddz

dv/dz>0

TKE generated

TKE destroyed

TKE transported downwards by turbulence itself

Observed Radar And Lidar

Figures from cloud-net.org

Cloud values compared

• Cloud Existence• Cloud Top• Cloud Base

• Cloud Thickness• Liquid Water Path

ObservedCloudFraction

ECMWFModelCloudFraction

Composite over diurnal cycle

Liquid Water Composite

Biases and random errorsModel Cloud Top

(m) Cloud Base

(m) Cloud

Thickness (m)

Met Office Mesoscale -213 ± 370 -190 ± 307 -22 ± 429

Met Office Global -270 ± 416 -287 ± 365 +18 ± 485

ECMWF -126 ± 378 -84 ± 354 -42 ± 416

Météo France -567 ± 415 -326 ± 366 -241 ± 443

KNMI – RACMO 63 ± 432 -115 ± 357 178 ± 495

SMHI-RCA -46 ± 778 -387 ± 436 341 ± 823 Worst two models in terms of bias and random error

• Tendency for all models to place cloud too low

Conclusions

• Met Office Mes best at placing clouds at right time• Met Office, ECMWF & RACMO best diurnal cycle

– All use non-local mixing with explicit entrainment– Met Office and ECMWF clouds too low by 1 model level– RACMO height good: ECMWF physics but higher res.

• Meteo-France clouds too low and thin– Local mixing scheme underestimates growth

• SMHI-RCA clouds too thick and evolve little through the day– Only model to use prognostic turbulent kinetic energy