1 / 23

Increasing Global Cloud Convection Projection Reliability with High-Resolution Modeling

Explore advancements in global cloud and convection projection reliability using high-resolution modeling for sub-seasonal variability like tropical cyclones and the Madden-Julian Oscillation. Learn about the impact of resolution on simulation quality and research focus evolution over the past decade.

greenawalt
Download Presentation

Increasing Global Cloud Convection Projection Reliability with High-Resolution Modeling

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. S2S contribution with a global non-hydrostatic model Quick review of research and proposalKazuyoshi Oouchi (JAMSTEC) S2S meeting, February 14, 2014 NOAA Center for weather and climate prediction Thanks to: NICAM team@JAMSTEC, Univ of Tokyo and AICS/RIKEN

  2. Research characterization in the past 10 years GOAL: to increase understanding and reliability of projection of global cloud/convection Sub-seasonal Variability ・　TC, MJO, ・ monsoon-related phenomena S2S focus upgrade of supercomputer Earth Simulator(2002~) K-computer(2012-)

  3. Short History & model characteristics (1/2) • Key feature of GCRM : • resolve clouds, let them self-develop in the model • resolve what type of clouds/convection ? • - element of hierarchy – cumulus convection, • mesoscale convection, cloud cluster • - multi-scale interaction • => How they are essential to “S2S predictability” • supercomputers • resources determine reliability ! • (ensemble number, resolution, integration period …) • available: Earth Simulator2,3 & K-computer

  4. Short History & model characteristics (2/2) • Typical horizontal resolutions : • 14, 7, 3.5 km, • … 870m(Miyamoto et al.2013 GRL) • Integration periods : • 1, 3, 5 months x [a few years] • naturally focused on sub-seasonal time range • Milestone sub-seasonal experiments • - boreal winter experiments (2006 MJO) • - boreal summer experiments (2004 MJO) • - AMIP run (1979-, 30years)

  5. 28km 14km 14km 28km Impacts ot resolusion 7km 3.5km 7km 3.5km 1.7km 1.7km 870m 870m 6UTC, 25 Aug. 2012 by Y. Miyamoto, H. Tomita (AICS,RIKEN)

  6. Impacts of resolution (Tropical Cyclone Bolaven) 14km 7km 3.5km 1.7km 870m • The eye is clearer at the • resolutions higher than • 7-km • “structure” and • accurate intensity • discussion by Y. Miyamoto (AICS,RIKEN)

  7. ES-era: MJO case studies previously demonstrated its potential to produce “good looking” MJOs MJO cloud clusters observed from satellite and reproduced by NICAM Miura et al. (2007,Science) Plan views of CMT displayed relative to center of MJO Miyakawa et al. (2012.JAS)

  8. K-era : further increasing the resolution … • First-ever subkilometer global simulation: dx=870m (Miyamoto et al. 2013 GRL) • Multi-ensembles: MJO case sweep ensemble • Multi year experiments: 30 years AMIP-like experiment cf. 8 summers: Athena project (Kinter et al.,2012,BAMS)

  9. a MJO sweep experiment (by Tomiki Miyakawa) Assigning initial dates Criteria: ・2003 - 2012, Winter cases (October – March) ・Average amplitude of phases 2 – 5 ≧ 1 in RMM diagram (Wheeler and Hendon 2004) http://www.bom.gov.au/climate/mjo/graphics/rmm.74toRealtime.txt ⇒　19 cases Initial dates: ・The first day the MJO enters Phase 2 ・The first day the MJO enters phases 1 and 8, if traceable. ⇒　54initial dates RMM diagram of CINDY/DYNAMO MJO cases. (NOAA-OLR + NCEP reanalysis) Initial dates used for simulations are marked by orange circles (10, 15, 20 Oct and 13, 17, 21 Nov). http://www.bom.gov.au/climate/mjo/

  10. Model Configuration No observation after initial date is given to the model: “forecast” mode simulation

  11. CINDY/DYNAMO MJO cases CINDY/DYNAMO MJO cases Case 1 NOAA NICAM Case 2 Longitude-time sections (Hovmellor diagrams) of the two MJO cases. Colors are daily OLR averaged between 5S – 5N. Resolutions of NICAM data are lowered to equal NOAA-OLR data. Runs initialized at Phase 1 are shown. RMM diagrams for two MJO cases of CINDY/DYNAMO. Black: NOAA-OLR + ERA-interim, (case1: 50 days, case 2 : 48 days) Red: NICAM (40 days), initialized at Phase 8 Blue: NICAM (40 days), initialized at Phase 1 Purple: NICAM (40 days), initialized at Phase 2

  12. CINDY/DYNAMO case 2 10S – 10N , 40E – 20W OLR NICAM NOAA 20 Nov 30 Nov 10 Dec 90 140 190 240 290 (W/m2) “Maritime Continent Prediction Barrier” problem (Vitart et al. 2007, Weaver et al. 2011, Fu et al. 2011) is not seen Precip TRMM NICAM 20 Nov 30 Nov 10 Dec

  13. Bivariate correlation with regards of lead time (Lin et al. 2008, Gottschalck et al. 2010) COR phase 1 start phase 2 start phase 8 start all (54 cases) days COR > 0.6 for 26 – 28 days

  14. Summary of MJO research Case sweep ensemble simulation of winter MJOs through 2003 – 2012 is performed. CINDY/DYNAMO case 2 is included, which showed coherent eastward propagating convective envelope. MJO forecast skill measured by bivariate correlation ⇒ about 27 days. Mean rainfall anomaly patterns that accompany MJOs appear to be well produced.

  15. Proposal for contribution (1/2) Make the most of the GCRM benefits … Process study for understanding predictability 1 scale interaction related to cloud organization => gray zone problem - what processes are parameterizable or not - any insights from GCRM (non-hydrostatic) model ? 2 sub-seasonal typical extreme event - how the GCRM has strength in expressing sub-seasonal phenomena (MJO, ISV, tropical cyclone …) 3 atmosphere-ocean coupling (will start within a few years) - how much coupling is essential to sub-seasonal phenomena in terms of energetics, structure and predictabilities - with the GCRM framework ?

  16. Proposal for contribution (2/2) Data sharing with existing data & coordinated experiments what type of GCRM experiment & data would be beneficial for S2S participating groups and community ? • Specific case study of sub-seasonal phenomena • Sensitivity study for understanding predictability • (e.g., physical schemes) • Coordinated experiment (upon requests) • Data sharing for inter-comparison • ▶hindcast - AMIP run (1979-2010) at 14-km • ▶forecast – experimental forecast run will start within • the next five years for uncoupled mode, and for • coupled mode afterwards (unit, 1-6 months) • [for] process study & inter-comparison • [not for] routine predictability simulations • (trade-off between ensemble size and resolution)

  17. Thank you !

  18. Supplementary slides

  19. NICAM: Nonhydrostatic Icosahedral Atmospheric Model • Development since 2000 Tomita and Satoh (2005, Fluid Dyn. Res.) Satoh et al. (2008, J. Comp. Phys.) • First global dx=3.5km run in 2004 using the Earth Simulator (JAMSTEC) Tomita et al.(2005, Geophys. Res. Lett.) Miura et al.(2007, Science) • International collaborations Athena project (2009-10): COLA, NICS, ECMWF, JAMSTEC, Univ. of Tokyo G8-call ICOMEX (2011-): Germany, UK, France, US, Japan • K-computer studies (10PF; Kobe,Riken, since 2012)

  20. MJO phase composited rainfall anomaly GPCP - 1dd NICAM

  21. Tuning NSW6 • An unrealistic feature with NSW6 • A larger amount of cloud ice remaining in the upper troposphere. • A higher stability in the upper troposphere. • This possibly prohibited smaller-scale sporadic convection and enhanced stronger organized system localized near forcing. • Tuning for • MJO speed & organization • TC initiation • Energy balance (OLR) • More than 150 runs until now.

  22. a CINDY MJO – pilot run (by Hiroaki Miura) • Settings very close to those of Miura et al. (2007) and Miura et al. (2009). • The Earth Simulator • Initial: October 16, 2011 • Duration: 40 days • Initial data: interpolated from NCEP ds083.2 • Microphysics: Grabowski (1998) OLR: NOAA OLR: NICAM (14-km mesh)

More Related