The problem: bias in filtered & unfiltered KE (contours) and mean wind (vectors)

Post on 29-Jan-2016

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The problem: bias in filtered & unfiltered KE (contours) and mean wind (vectors). Unfiltered (KE). Filtered (EKE). Excessively zonal mean wind extending into E urope, made worse at higher resolution. Excessive EKE in stormtrack exit, made worse at higher resolution. - PowerPoint PPT Presentation

Transcript of The problem: bias in filtered & unfiltered KE (contours) and mean wind (vectors)

The problem: bias in filtered & unfiltered KE (contours) and mean wind (vectors)

Unfiltered (KE) Filtered (EKE)

Excessively zonal mean wind extending into Europe, made worse at higher resolution. Excessive EKE in stormtrack exit, made worse at higher resolution.

Changing to MYJ and Zhang McFarlane

Unfiltered KE (contours) and mean wind (vectors)

New physics results in underestimation of KE at low resolution, corrected (overcorrected?) at higher resolution. Mean wind is also no longer excessively zonal. Comparing 20cur to 120 cur (below) shows that track gains more of a SW – NE tilt at higher resolution with these physics.

Changing to MYJ and Zhang McFarlane

Filtered EKE (contours) and mean wind (vectors)

Excessive EKE remains at 20 km. I did the CFSR comparison and it looks the same. Therefore, is definitely a bias that increases with resolution and not something that was being ‘missed’ due to the coarse NCEP (or FNL) resolution. Damn.

What if you forget to turn on the SST?

Unfiltered KE (contours) and mean wind (vectors)

sst_update=0 sst_update=1

Sensitivity to CP and PBL scheme choice >> sensitivity to sst_update

What if you forget to turn on the SST?

Filtered EKE (contours) and mean wind (vectors)

sst_update=0 sst_update=1

Sensitivity to CP and PBL scheme choice >> sensitivity to sst_update