Evaluating forecasts of the evolution of the cloudy boundary layer using radar and lidar observation...
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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

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

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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

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

Models Used


Different mixing schemes

Different mixing schemes

Local mixing scheme (e.g. Meteo France)

Longwave cooling

  • Define Richardson Number:

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

Height (z)

dqv/dz<0

Virtual potential temp. (qv)

Eddy diffusivity (Km)

(strength of the mixing)

  • 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)


Different mixing schemes1

Different mixing schemes

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

  • 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)

Longwave cooling

Height (z)

Virtual potential temp. (qv)

Eddy diffusivity (Km)

(strength of the mixing)

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


Different mixing schemes2

Different mixing schemes

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

  • Model carries an explicit variable for TKE

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

Longwave cooling

TKE generated

Height (z)

TKE transported downwards by turbulence itself

dqv/dz<0

dqv/dz>0

TKE destroyed

Virtual potential temp. (qv)


Observed radar and lidar

Observed Radar And Lidar

Figures from cloud-net.org


Cloud values compared

Cloud values compared

Observed

Cloud

Fraction

  • Cloud Existence

  • Cloud Top

  • Cloud Base

  • Cloud Thickness

  • Liquid Water Path

ECMWF

Model

Cloud

Fraction


Composite over diurnal cycle

Composite over diurnal cycle


Liquid water composite

Liquid Water Composite


Biases and random errors

Biases and random errors

  • Worst two models in terms of bias and random error

  • Tendency for all models to place cloud too low


Conclusions

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


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