Medium-range prediction in the polar regions: current status and future challenges
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1 Medium-range prediction in the polar regions: current status and future challenges Sarah Keeley Marine Prediction Section Linus Magnusson, Peter Bauer, Patricia de Rosnay, Steffen Tietsche, Thomas Haiden and others..
2 Polar Region Forecasts (Day 6) General error reduction over the past decade Improvements in model and data assimilation systems Higher resolution does help to reduce some of the errors in the forecast. Bauer et al. 2014
3 Skill of forecast systems Over the last decade there has been an increased consistency in the analysis of the polar region. BUT still differences especially in the sea ice and snow covered regions. Bauer et al. 2014
4 And the surface? Spread in the analysis of T2m throughout the seasons in regions of sea ice and snow except summer when the ice surface temperature is closer to that of melting. Jung et al Bauer et al Winter spread error relationship Free troposphere good match Nearer the surface model error and spread don t match - errors in analysis/representative issues for SYNOP obsevations.
5 Diagnosing cause of model error Use analysis and short forecast to understand model errors. Breakdown of model tendencies in comparison to the analysis increment which corrects them Slides from L. Magnusson
6 Examples of uncertainty in the model and observations Model in background shading and observations within the rings. Cloud cover winter Cloud cover summer Figure courtesy of Thomas Haiden
7 Comparing models with observations Sea ice thickness observations from altimeters Thickness derived from freeboard IceSAT CryoSat 2010 present IceSAT2 from 2017 IceSAT Difference ORAP5 Zuo et al., Clim. Dyn. (2015) Tietsche et al., Clim. Dyn.(2015) Slides from S. Tietsche
8 New observations and new reanalysis tools Maps of thin sea ice during freeze-up SMOSIce observations 11 Nov 2012 ORAP5 ocean reanalysis Thin new ice reliably detected, reasonably well simulated Slides from S. Tietsche
9 Baffin Bay Laptev Sea Time series at fixed location Slides from S. Tietsche
10 Maps of thin sea ice during late winter SMOSIce observations 22 Mar 2013 ORAP5 ocean reanalysis Modelled thickness much larger than SMOSIce (polynias, uncertain snow cover,...) Slides from S. Tietsche
11 Snow in the ECMWF IFS Snow Model. Liq. Water. Density. Albedo. Fraction Snow Obs and DA. OI. 4km IMS. Obs preproc/qc. IMS latency/acquisition Dutra et al., JHM 2010 de Rosnay et al., Res Memo 2010, 2011 Brun et al., Snow Watch Additional in situ obs. New BUFR template. WMO/SnowWatch action. IMS data assimilation. obs error revision de Rosnay et al., Surv. Geophys 2014 Ongoing. BUFR SYNOP. Snow COST action. Snow Watch. MultiLayer model Future: RT modelling de Rosnay et., ECMWF Newsletter 143, Spring 2015 Slides from P. de Rosnay
12 Snow analysis: Forecast impact Impact on snow October 2012 to April 2013 (using 251 independent observations) Impact on atmospheric forecasts October 2012 to April 2013 (RMSE new-old) de Rosnay et al., ECMWF Newsletter 143, Spring 2015 Slides from P. de Rosnay
13 Changes to our forecast system 2016 atmos resolution upgrade: 41r1 41r2 from linear (L) grid to cubic octahedral (Co) grid Grid res HRES LegA ENS LegB/45d 4DVAR Inner Loops 1 st 2 nd 3 rd EDA loops Outer 1 st 2 nd 128 km TL255 TL255 TL255 TL159 TL191 TL159 TL km TL319 TL319 TL399 TL km TL639 TCo km TL1279 TCo639 TCo639 9 km TCo1279 Ocean model Upgrade in 2016 (NEMO): from 1.0 o /42 lev to 0.25 o /75 lev Add dynamic sea ice model
14 Impact of dynamic ice model in medium range SEA ICE Control Northern Extratropics: Z500 Forecast Skill (weeks 1-4) 80 cases- all seasons The vertical bars represent the 95% level of confidence
15 Impact of dynamic ice model in medium range Change in ice cover in 6 days Period of rapid freeze up Average turbulent flux into atmosphere over first 6 days >50 W/m2 in some areas
16 Autumn and Spring only SEA ICE Control
17 Impact of sea ice changes (Z 0 ) example of use of campaign data z 0 In CY41R1: Elvidge et al Cd over sea ice forecast from , t+6,12,18,24 MPS contributions to CY41R1 rcimin=2% z 0 = max(z 0 (ci), 0.001) Charnock from WAM Slides from J. Bidlot
18 Summary Current status: Forecasts at medium range timescale have skill out to about day 7 in terms of large scale flow Limited observations make it difficult to diagnose model error sources; challenges remain with representation of the boundary layer, clouds and snow and ice covered surfaces. Observation error large due to assumptions made about the state of the system Future Challenges/ Opportunites: Model provides tools for diagnosis and process understanding Confronting models with observations ground truth Observational campaigns Reanalysis tools available that perform well in polar regions Higher resolution medium range coupled forecast systems running in YOPP.
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