EUCLIPSE Toulouse meeting April 2012

Roel Neggers

Process-level evaluation at selected grid-Process-level evaluation at selected grid-points:points:

Constraining a system of interacting Constraining a system of interacting parameterizations through multiple parameterizations through multiple

parameter evaluation at Cabauwparameter evaluation at Cabauw

Process-level evaluation

We have built good experience at (idealized) single-case level (e.g. We have built good experience at (idealized) single-case level (e.g. GCSS), and can demonstrate successes in model improvement.GCSS), and can demonstrate successes in model improvement.

However, possible shortcomings:However, possible shortcomings:

* Cases might not represent actual climate; parameterizations might * Cases might not represent actual climate; parameterizations might get tuned to rare situationsget tuned to rare situations

* Cases might not represent those situations that are most * Cases might not represent those situations that are most troublesome in GCMs;troublesome in GCMs;

* The use of relevant observational data has been somewhat limited.* The use of relevant observational data has been somewhat limited.

These arguments motivate a move towards more comprehensive, These arguments motivate a move towards more comprehensive, statistically significant approach in model evaluation, in statistically significant approach in model evaluation, in combination with a more efficient use of observational datasets.combination with a more efficient use of observational datasets.

The “testbed” idea

1) SCM and GCM evaluation for long periods of time at permanent 1) SCM and GCM evaluation for long periods of time at permanent meteorological sites (e.g. ARM, CloudNet)meteorological sites (e.g. ARM, CloudNet)

Emphasis: fast physics (boundary-layer, soil)Emphasis: fast physics (boundary-layer, soil)

2) Use a 2) Use a multiple-parameter approachmultiple-parameter approach in the evaluation in the evaluation (“CloudNet+”)(“CloudNet+”)

Constrain the system of interacting parameterizations at multiple Constrain the system of interacting parameterizations at multiple points with key measurementspoints with key measurements

Goals:Goals: * Try and identify compensating errors in interaction between low * Try and identify compensating errors in interaction between low

clouds & radiative transfer (e.g. too bright – too few)clouds & radiative transfer (e.g. too bright – too few)

* Trace their impact through the coupled BL – soil system * Trace their impact through the coupled BL – soil system (heat & moisture budgets)(heat & moisture budgets)

Neggers et al, BAMS, in press, 2012

Short example

The motivation: problems with a new BL scheme in IFSThe motivation: problems with a new BL scheme in IFS

Too little cloud cover at noonToo little cloud cover at noon

Associated 2m T warm bias over landAssociated 2m T warm bias over land

Can long-term SCM evaluation at Cabauw provide some insight?Can long-term SCM evaluation at Cabauw provide some insight?

1. less PBL clouds

2. larger SW down

3. larger H

4. low level warming

SW

Hypothesis

Long-term SCM evaluation at Cabauw (2007-2010)

Obs vs Model scatterplots of monthly meansObs vs Model scatterplots of monthly means RACMO SCMRACMO SCM: : Control (red) and new (blue) schemeControl (red) and new (blue) scheme RACMO 3D in forecast mode (grey)RACMO 3D in forecast mode (grey)

8-point check

Expanding to multiple independently-measured parameters that reflect the Expanding to multiple independently-measured parameters that reflect the impact mechanism as illustrated beforeimpact mechanism as illustrated before

Model performance – Taylor diagram

Reproduction of observed pattern in variation of monthly-meanReproduction of observed pattern in variation of monthly-mean

Correlation between model differences

Tracing impacts

Following degrading correlations in coupled BL – soil systemFollowing degrading correlations in coupled BL – soil system

Zooming in

Identifying regimes

Cabauw cloud-scenes on 8 days with biggest model differenceCabauw cloud-scenes on 8 days with biggest model difference

Conditional sampling

Make a list of shallow cu daysMake a list of shallow cu days

Criterion:Criterion:1.1. positive surface buoyancy fluxpositive surface buoyancy flux2.2. LCL below BL topLCL below BL top3.3. Total cloud cover < 50%Total cloud cover < 50%

Evaluation against LES

New set of 4 parameters New set of 4 parameters reflecting cloud vertical reflecting cloud vertical structure in the boundary-structure in the boundary-layerlayer

LES (x) vs SCM (y), daily valuesLES (x) vs SCM (y), daily values

Evaluated for the time-range Evaluated for the time-range 10-14 UTC to capture diurnal 10-14 UTC to capture diurnal variationvariation

One month of DALES (June 2008)One month of DALES (June 2008)

zzbasebase

cfcfmaxmax

zzcf maxcf max

overlap ratiooverlap ratio

controlcontrol newnew

Bar-chart

Monthly-mean bias for June 2008Monthly-mean bias for June 2008

Impact of including a new cloud overlap function

Multi-year bias for 2007-2010Multi-year bias for 2007-2010Green: including SGS overlap for cumuliform clouds Green: including SGS overlap for cumuliform clouds (Neggers et al, JGR, 2011)(Neggers et al, JGR, 2011)

Conclusions

Multiple parameter evaluation was performed against multi-year Multiple parameter evaluation was performed against multi-year Cabauw dataCabauw data

RACMO physics was subjected to a 12-point check reflecting the cloud RACMO physics was subjected to a 12-point check reflecting the cloud structure, radiative budget and heat budget of coupled boundary-structure, radiative budget and heat budget of coupled boundary-layer soil systemlayer soil system

This revealed the existence of a compensating error between the This revealed the existence of a compensating error between the representation of cloud vertical structure and cloud overlap in the representation of cloud vertical structure and cloud overlap in the cumuliform boundary layercumuliform boundary layer