Polar Predictability results from the Greenland Flow Distortion Experiment Ian Renfrew Emma Irvine (U. Reading) Nina Petersen, Stephen Outten (UEA) Kent Moore (U. Toronto)

Outline Motivation Short-range NWP improvements – Case studies of tip jet & barrier winds – analyses validation Impact of Targeted Observations Conclusions

Motivation Local weather systems –tip jets, barrier winds, lee cyclones, polar lows Climate system –thermohaline circulation Medium-range weather forecasting –targeted observations

QuikSCAT climatology Moore and Renfrew 2005, J. Climate Mean wind speed for DJF

(Westerly) Tip Jets(Westerly) Tip Jets GFD: QuikSCAT climatology Moore and Renfrew 2005, J. Climate

Easterly Tip JetsEasterly Tip Jets GFD: QuikSCAT climatology Moore and Renfrew 2005, J. Climate

Barrier windsBarrier winds QuikSCAT climatology Moore and Renfrew 2005, J. Climate

Field programme: 17 Feb – 12 March 2007 Detachment: Keflavik, Iceland 62 flight hours + 9 hours (EUFAR)

B268 yellowB274 light green B269 is missingB275 purple B270 dark greenB276 red B271 orangeB277 cyan B272 magentaB278 lilac B273 pinkB279 beige B268 yellowB274 light green B269 is missingB275 purple B270 dark greenB276 red B271 orangeB277 cyan B272 magentaB278 lilac B273 pinkB279 beige B268 yellowB274 light green B269 is missingB275 purple B270 dark greenB276 red B271 orangeB277 cyan B272 magentaB278 lilac B273 pinkB279 beige B268 yellowB274 light green B269 is missingB275 purple B270 dark greenB276 red B271 orangeB277 cyan B272 magentaB278 lilac B273 pinkB279 beige

Results from case studies Accurate NWP hindcast simulations required consideration of –Model setup, grid size, levels, etc –SST –Air-sea-ice interaction –PBL scheme Examples: –Tip Jet (21 Feb 2007) –Barrier wind (1-6 March)

AVHRR Ch 1 14:35 UTC 21 February 2007

Easterly Tip Jet: 21 Feb Met Office UM km grid & 76 levels Initialised from Met Office global analyses

Easterly Tip Jet: 21 Feb Met Office UM km grid & 76 levels Initialised from Met Office global analyses Configuration changes: z 0 over marginal ice zone changed 100mm 0.5mm z 0 over sea ice changed 3mm 0.5mm OSTIA high resolution SST & sea-ice field See Outten et al. 2009,QJRMS Also Birch et al. 2009, J. Geophys. Res.

Easterly Tip Jet: 21 Feb Met Office UM km grid & 76 levels Initialised from Met Office global analyses Configuration changes: z 0 over marginal ice zone changed 100mm 0.5mm z 0 over sea ice changed 3mm 0.5mm OSTIA high resolution SST & sea-ice field

Easterly Tip Jet: 21 Feb Met Office UM km grid & 76 levels Initialised from Met Office global analyses Configuration changes: z 0 over marginal ice zone changed 100mm 0.5mm z 0 over sea ice changed 3mm 0.5mm OSTIA high resolution SST & sea-ice field Reasonably accurate simulation: 1-2 K and 2-3 m s -1 in ABL

Easterly Tip Jet: 21 Feb Met Office UM km grid & 76 levels Initialised from Met Office global analyses Configuration changes: z 0 over marginal ice zone changed 100mm 0.5mm z 0 over sea ice changed 3mm 0.5mm OSTIA high resolution SST & sea-ice field Reasonably accurate simulation: 1-2 K and 2-3 m s -1 in ABL

Barrier Flows: 2 March 2007

DS North Cross-sectionDS South Cross-section

Barrier Flows: 2 March 2007 DS South Cross-section

Barrier Flows: Temperature inversions Sharp elevated temperature inversions not in analysis or forecasts Due to SBL over Greenland? Data assimilation will smooth out?

Barrier Flows: Summary Synoptic situation controls wind speed maxima Barrier effect doubles wind speed UM simulations ok –Following sea-ice & SST changes –But fail to capture sharp temp. inversion See Petersen, Renfrew & Moore, 2009 QJRMS

Comparison of aircraft-based surface-layer observations over Denmark Strait and the Irminger Sea with meteorological analyses and QuikSCAT winds I. A. Renfrew, G. N. Petersen, D. A. J. Sproson, G. W. K. Moore, H. Adiwidjaja, S. Zhang, and R. North (2009, QJRMS)

Focus on ECMWF Analyses Underestimates U 10 at highest wind speeds.

ECMWF deg has a T 2m bias of -0.7 K –T511 has no bias.

Some ABL temperature discrepancies due to SST –At time 1 deg SST –Now OSTIA

Some ABL temperature discrepancies due to SST –At time 0.5 deg SST and sea ice fields –Now OSTIA

RH 2m well modelled

Surface turbulent fluxes are well-modelled, but scatter and biases result in relatively large rms errors.

ECMWF surface layer comparison ECMWF model does not capture the highest wind speeds observed, despite an operational resolution of T799 and archived data at T511/N400 ( 40 km). This suggests mesoscale atmospheric flow features are being smoothed out in some way (see Chelton et al. 2006). At T511/N400, the model produces good estimates for the surface- layer temperature and humidities, despite a large scatter in the SST. But at lower archived resolution (1.125 deg) a bias of 0.7 K in T 2m is introduced. The ECMWF surface turbulent fluxes correspond reasonably well with the observations

Targeted Observations in GFDex: 4 flights dropsondes per flight Dropsondes sent to GTS and assimilated into operational 12Z forecasts

Targeted Observations in GFDex: 4 flights dropsondes per flight Dropsondes sent to GTS and assimilated into operational 12Z forecasts Analysing the results via hindcast experiments: Met Office UM 6.1, 24km grid 4D-VAR data assimilation scheme, 48km grid North-Atlantic European domain Control – routine obs. only Targeted – routine obs. + targeted obs. (dropsondes) See Irvine et al. 2009, QJRMS – general results of all cases

Impact of dropsonde data on Greenland coast ORIGINAL DATASET (targeted sondes) MODIFIED DATASET (Replaced sondes on Greenland coast with sondes in Denmark Strait)

Forecast impact with modified dataset (no sondes on Greenland coast) dashed line = targeted sondes, solid line = all sondes dotted line = MODIFIED DATASET (no sondes near Greenland)

Why do the dropsondes on the Greenland coast degrade the forecast? Sonde data is spread along terrain-following model levels – up steep orography See Irvine et al (2010) MWR, in press 38 X Analysis increment in v resulting from assimilation of targeted sonde X = sonde location

Conclusions To simulate the high winds associated with polar mesoscale weather systems, a model resolution of order 10 km is necessary but is not sufficient; as appropriate ABL, surface layer and surface flux parameterizations are also crucial. An accurate prescription of the SST & sea ice is essential. In regions relatively close to the sea-ice edge, the current generation of NWP models still have problems in accurately simulating ABL temperature and humidity. Global analyses products dont appear able to capture highest wind speeds (e.g. U 10 in ECMWF). Targeted observations programme had modest impact on forecasts (5-10%); also highlighted problems with soundings near steep and high orography.

Case studies –Obs & dynamics of an easterly tip jet –Obs & modelling of a Greenland lee cyclone –Barrier flows & wakes around Greenland Climatological studies –A climatology of westerly tip jets Targeted Observations –Impact assessment in collaboration with Met Office Air-sea interaction: –Turbulent flux observations –Comparison of obs & NWP models –High-resolution ocean simulations

Stochastic-dynamic example Stochastic KE Backscatter scheme (Shutts 2005) upscale influence of deep convection in mesoscale convective systems and the statistical uncertainty of orographic drag representations Improves systematic error in the tropics & extratropics (Shutts, 2005, Berner et al. 2008) km

Potential solution: Reject sonde data below 850hPa? Green line shows an increase in forecast improvement when dropsondes near Greenland have data below 850hPa rejected

Comparison of model data and sonde data near Greenland 43 NW SE