1 / 22

TAWEPI Thorpex Arctic Weather and Environmental Prediction Initiative summary of

TAWEPI Thorpex Arctic Weather and Environmental Prediction Initiative summary of modelling & data assimilation activities. Ayrton Zadra Meteorological Research Division Environment Canada on behalf of TAWEPI’s team of researchers.

medea
Télécharger la présentation

TAWEPI Thorpex Arctic Weather and Environmental Prediction Initiative summary of

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. TAWEPI Thorpex Arctic Weather and Environmental Prediction Initiative summary of modelling & data assimilation activities Ayrton Zadra Meteorological Research Division Environment Canada on behalf of TAWEPI’s team of researchers Arctic System Model Workshop III – July 16th, 2009 – UQAM, Montreal, Quebec

  2. Stephane Belair (PI) Yi-Ching Chung (ongoing) Greg Flato (PI) Youyu Lu (PI) Nadja Steiner (done) Paul Vaillancourt (PI) Jason Milbrandt Frederick Chosson (ongoing) Saroja Polavarapu (PI) Mateusz Reszka (done) Jocelyn Mailhot (PI) Louis Garand (PI) Sylvain Heilliette Ovidiu Pancrati (ongoing) Mark Buehner (PI) Ayrton Zadra (PI) Ahmed Mahidjiba (done) TAWEPI projects & team Primary objective: develop & validate Polar- GEM, an experimental regional Numerical Weather Prediction model* over the Arctic RPN CCCma modelling snow over sea-ice sea-ice modelling modelling polar clouds stratospheric analyses singular vector sensitivity studies validation & assimilation of polar-orbiting satellite data ARMA ___________________________________________________________ http://collaboration.cmc.ec.gc.ca/science/rpn/tawepi/en/index.html _______________________ * for 1- to 2-day forecasts

  3. Collaboration: Status of extended regional model at CMC* • Polar extension of CMC’s regional NWP model • global, rotated, variable-resolution • lat-lon grid • core: 15-km resolution • 58 hybrid vertical levels, top 10 hPa • timestep: 7.5 min • 4 runs per day: • - 00Z & 12Z runs: 48h forecasts • - 06Z & 18Z runs: 54h forecasts • Implementation • implemented in March 2009; • has become the CMC’s • operational regional model • extension into the middle • atmosphere (raising of model lid • from 10 hPa to 0.1 hPa) in Jun • 2009 Figure: Grid of CMC’s new regional model (Note: Only every 5 grid-point is shown) _____________________________________________________________________________ * Project partly funded by IPY-LIEP. Grid parameters kindly provided by A. Patoine (CMC).

  4. Collaboration: Status of extended regional model at CMC* • A continental • GEM-LAM version • for the CMC’s • regional NWP model • project led by L. Fillion • (RPN) • may become operational • in the next 12 months Figure: Grid of REGLAM15 (in red) and CMC’s currently operational regional model (in blue) ________________________________________ * Image kindly provided by E. Lapalme (CMC).

  5. sublimation suspension saltation wind Sea ice Subproject 1: Coupling blowing snow, snow and iceY.-C. Chung, S. Bélair, J. Mailhot For more details, see poster “The impact of blowing snow on Arctic sea ice and snow: results from an improved sea-ice / snow / blowing snow coupled system”, by Y.-C. Chung et al., in session 21-J (Tue Jul 21). 1-D, blowing snow model, PIEKTUK (Déry, 2001) • Goal Examine the effect of blowing snow on the simulation of snow and sea ice in the Arctic Ocean. • Blowing snow effect • saltation and suspension • sublimation from blowing snow - 32% of snowfall on the Arctic coast of Alaska (Benson et al. 1982) - 37-85 mm of snow water equivalent (SWE) over Canadian Arctic tundra (Essery et al.1999) • accumulation and erosion Blowing snow on the ice shelf edge near Rampen (72S, 16W), Antarctica, from Dr. R. Bintanja. Institute for Marine and Atmospheric Research Utrecht (IMAU), Utrecht University, the Netherlands snow 1-D, multi-layer snow model SNTHERM (Jordan, 1991) ocean Multi-layer, thermodynamic sea ice model from Meteorological Service of Canada (MSC) operational forecasting system run 1-D, offline model

  6. Fig 1 - Observed wind speed versus PIEKTUK-simulated blowing snow sublimation 1- Sublimation due to blowing snow: • The accumulated sublimation is significant during SHEBA year (total of 56mm SWE) Fig 2 - Simulated evolution of snow grain size [mm] in snowpack 2- Impact of blowing snow on snow and ice properties: • Blowing snow improves the estimates of snow depth and temperature at snow/ice interface • Blowing snow has a small impact on ice thickness and snow structure (e.g. grain size and density)

  7. Sundqvist et al. Single Moment (1989) CCCMARAD Li and Barker (2005) Milbrandt and Yau Single and Double Moment (2005) Subproject 2 [ P. Vaillancourt (PI),J. Milbrandt, F. Chosson] Polar-GEM clouds Motivation Especially in Arctic, clouds are poorly represented in NWP and RCM, e.g. • too low cloud cover and condensed water • too high albedo, too low absorptivity • water phase change (mixed-phase cloud) • multi-layered cloud systems • Arctic haze or “clear sky” ice precipitation… Objective Improve representation of clouds & precipitation and surface radiative energy budget in GEM, specially over the Arctic. GEM “GLOBAL" Polar-GEM “LAM" Boundary conditions MICROPHYSICAL SCHEMES RADIATIVE TRANSFER SCHEME

  8. Simulator of sat. obs. Ongoing work: • Improve consistency/link between microphysical and radiative schemes in GEM (e.g. effective size, shape, type of hydrometeors and their optical properties) • Assess 2-moment microphysical scheme in POLAR-GEM and compare with the other schemes using: • - AIRS (IR) satellite data (subproject 5) and CO2-slicing simulator • - CALIPSO (lidar), CLOUDSAT (radar) satellite data and COSP simulator COSP = CFMIP Observational Simulator Package CFMIP: Cloud Feedback Model Intercomparison Project COSP simulates the signal that satellites (e.g. CloudSat) would see in a model-generated world. Initiative of the UK Met Office,LMD/IPSL (Paris), LLNL, Colorado State Univ. and Univ. of Washington (USA). CLOUD FRACTION GEM CLOUD FRACTION SIMULATED CLOUD FRACTION OBSERVED AIRS AIRS CALIPSO Polar-GEM COSP CLOUDSAT

  9. Canadian Centre for Climate Modelling and Analysis Centre canadien de la modélisation et de l'analyse climatique Subproject 3: Sea-ice modellingN. Steiner, G. Flato, Y. Lu • Implement & expand latest version of Los Alamos CICE model (used in several GCMs and US Navy's ice-ocean forecast model)‏ • Apply & test model in various settings (operational sea-ice, ocean & atmosphere forecasting – couple to Polar-GEM/GEM-LAM, coupled climate studies)‏ • Develop a Canadian community sea-ice model to be used in climate mode (GCM,RCM) and forecast mode (weather, sea-ice)‏ Photo: N. Steiner

  10. Canadian Centre for Climate Modelling and Analysis Centre canadien de la modélisation et de l'analyse climatique Status (Due to hiring issues and funding cuts, work on subproject 3 has been significantly slowed down). • Global CICE4.0 installed on CCCma machines as standalone sea-ice model. • Adjustments to CCCma grid & format have been performed. • Model currently tested with climatological daily forcing from a 20 year GCM run (atmosphere) and monthly Polar Science Center Hydrographic Climatology (PHC) (ocean). • An option to run as a regional model is now included in CICE4.0 and will be attempted for the MSC (Meteorological Service of Canada) RCM grid as soon as global model testing is complete

  11. TAWEPI subproject 4: Sensitivity studies in the Arctic using singular vectors A. Mahidjiba, M. Buehner, A. Zadra SV calculation Optimization Time Interval (OTI): 48h Norms: Initial: Total Energy (TE) over the globe Final: TE over Arctic region (60ºN≤ lat ≤ 85º N) Vertical domain for final-time norm: 0.1044 ≤ η≤ 1 (from ~100hPa to surface) GEM 3.3.0 model resolution: for SVs calculation: 240x120x58 for NL integration: 800x600x58 Number of SVs: 15 Analysis: CMC operational 4D-Var high resol. • Objectives • quantify fraction of forecast error explained by errors in initial conditions • examine sensitivity of weather forecast over the Arctic due to analysis errors • Singular Vectors (SV) • patterns of greatest instability in initial uncertainty • in early stages of development, error growth is governed by linear dynamics • Methodology • daily calculation of SVs for IPY period • compute combinations of SVs that best explain forecast error

  12. Summer 2007(11 Jul to 20 Sep 2007) Time+vertical average of the total energy (J/m^2) of the 48-h forecast error 80N 60N 40N 180E 90W 0 90E 0

  13. Summer 2007(11 Jul to 20 Sep 2007) Time+vertical average of the total energy (J/m^2) of the forecast-error projection on SVs 80N 60N 40N 180E 90W 0 90E 0

  14. Summer 2007(11 Jul to 20 Sep 2007) Time+vertical average of the total energy (J/m^2) of SV projection at initial time (pseudo-inverse) 80N 60N 40N 180E 90W 0 90E 0

  15. Summer 2007(11 Jul to 20 Sep 2007) (c) (a) (b) (a) Total energy (J/m2) of pseudo-inverse, (b) of its propagationwith TLM at 48h, and (c) 48-h forecast error over the Arctic, vertically integrated and averaged over the period of 11 July to 20 September 2007. Fraction of forecast error explained by SVs Growth rate given by SV1 over the Arctic for the summer Average = 362 Average = 17.6%

  16. Winter 2007-2008(21 Dec 2007 to 20 Mar 2008) (c) (a) (b) (a) Total energy (J/m2) of pseudo-inverse, (b) of its propagationwith TLM at 48h, and (c) 48-h forecast error over the Arctic, vertically integrated and averaged over the period of 21 December 2007 to 20 March 2008. Fraction of forecast error explained by SVs Growth rate given by SV1 over the Arctic for the autumn Average = 522 Average = 15%

  17. TAWEPI subproject 5: Hyperspectral IR assimilation in cloudy atmospheres: global and IPY applications O. Pancrati, L. Garand, S. Heilliette • Background • AIRS radiances assimilated operationally • (since June 2008) • - 87 channels • - radiances not sensitive to lower clouds • assimilated • Therefore need to validate cloud • height/cover determination for • improved quality control • By extension, interest in validating trial • fields of cloud parameters and more • generally cloudy radiance spectra to infer • model deficiencies • Specific problems found in • Arctic/Antarctic region linked to cloud • parameter determination. Validation with • independent data needed (MODIS, • Calipso, MISR datasets) • Also: Using model output combined with • calculated cloudy radiances allows to • validate cloud parameter retrieval method • for climate studies (notably minimize • systematic retrieval biases on height) AIRS Satellite (image taken from NASA Airs Satellite Homepage) AIRS = Atmospheric Infrared Sounder MODIS = Moderate Resolution Imaging Spectroradiometer CALIPSO = Cloud-Aerosol Lidar and Infrared Pathfinder MISR = Multi-angle Imaging SpectroRadiometer

  18. Cloud height/amount from CO2-slicing technique CO2 slicing: 12 estimates of cloud height from as many channels coupled with a reference profile peaking near the surface. Mean of valid estimates used. Observed CTP (CO2-slicing) Direct model output CTP Calculated CTP (CO2-slicing) – 6h forecast _____________________________________________ For more details, see poster “Validation of model cloud parameters using AIRS radiances” by O. Pancrati et al., session 21-J (Tue, Jul 21). Note: Good overall agreement; differences most notable for low clouds west of continents.

  19. Focus on Arctic areas (July 2008): Cloud parameters comparison with independent data sources Note: The differences depend on input data and retrieval methodology AIRS (CO2-slicing) AIRS (official product) MODIS Cloud Top Pressure Cloud Fraction Source: AIRS science team Source: MODIS science team

  20. TAWEPI subproject 6: GEM-BACH Stratospheric Analyses for IPYM. Reszka, J. DeGrandpré, A. Robichaud, C. Charette, M. Roch, S. Polavarapu • Main results to date • Dynamics and chemistry analyses for March 1, 2007 – Feb 28, 2009 have been generated and provided to SPARC IPY database (NetCDF) • Dynamics fields are produced using Canadian Meteorological Centre's 3D-Var global assimilation scheme and GEM forecast model • Chemistry fields are produced using an online stratospheric chemistry package (Belgian Institute for Space Aeronomy) • Data set is being used to study several processes, including • fine structure of polar temperatures during 2007/2008 stratospheric sudden warming • trace gas distribution as compared with spectrometer measurements • deep stratospheric intrusions as revealed by the ozone field • Current activities • Documentation is in preparation For access, see http://www.sparc.sunysb.edu/html/user_ipy.html

  21. Comparison of trace-gas measurements from a Fourier transform spectrometer with GEM-BACH IPY analyses at Eureka, Nunavut – Mar 01 to Oct 30, 2007 R. Batchelor (U. of Toronto) O3 N2O • Total (and partial) columns derived from spectrometer data (blue) and analyses (red) are in very good agreement for most gases measured (e.g. O3, N2O, HCl, HNO3) • ClONO2 and HF columns exhibit a bias, but variability is captured quite well • CO and CH4 less satisfactory (probably due to lack of tropospheric chemistry) • See e.g. Batchelor et al., CSPARC Workshop presentation (Toronto, 2008) • See also talk by Rebecca Batchelor, “Characterizing the spring-time Arctic stratosphere during IPY”, J03 – IPY symposium

  22. Thank you… … and thanks to all TAWEPI investigators and collaborators www.ec.gc.ca

More Related