Development of an Ensemble-Based Data Assimilation System with a - PowerPoint PPT Presentation

6 th WMO Symposium on Data Assimilation College Park, Maryland, U.S.A., October 10, 2013 Development of an Ensemble-Based Data Assimilation System with a Coupled Atmosphere– Ocean GCM Nobu Komori, 1  T. Enomoto, 1,2 T. Miyoshi, 1,3,4 A. Yamazaki, 1 and B. Taguchi 1 1 Earth Simulator Center (ESC), JAMSTEC, Yokohama, Japan 2 Disaster Prevention Research Institute, Kyoto University, Uji, Kyoto, Japan 3 RIKEN Advanced Institute for Computational Science, Kobe, Japan 4 Department of Atmospheric and Oceanic Science, University of Maryland, College Park, Maryland, U.S.A.  E-mail: komori@jamstec.go.jp

Outline of Talk • ALERA & ALERA2 (atmospheric reanalysis) • CFES –LETKF ensemble DA system • Motivations • Experimental settings • Preliminary results of CLERA-A • Comparison with ALERA2 • Comparison with EnOFES (ensemble ocean simulation) • Summary

ALERA AFES –LETKF experimental ensemble reanalysis (Miyoshi & Yamane, 2007, MWR ; Miyoshi et al., 2007, SOLA ) • first ( or second) application of LETKF to full AGCM • provides analysis ensemble spread as error estimates • a product of collaboration among JMA, JAMSTEC and CIS • available from http://www.jamstec.go.jp/esc/research/oreda/ products/

ALERA and ALERA2 compared ALERA ALERA2 AFES version 2.2 3.6 Resolution T159 L48 T119 L48 Ensemble size 40 63+1 Boundary conditions NOAA OISST weekly 1° NOAA OISST daily 1/4° 21x21x13 400 km/0.4 ln p Covariance localization Spread inflation 0.1 0.1 JMA NCEP Obs compiled by Enomoto et al. (2013)

ALERA and ALERA2 streams 2005 2006 2007 2008 2009 2010 2011 2012 2013 ALERA ALERA2 PALAU 2005 Poster Session MISMO stream 2008 A-p08 by T. Enomoto A-p35 by A. Yamazaki PALAU 2008 Mirai Arctic Ocean Cruise T -PARC stream 2010 Winter T -PARC Mirai Arctic Ocean Cruise VPREX 2010 CINDY 2011

CFES–LETKF Ensemble DA System

Motivations • Remove underestimation Forecast of Typhoon Sinlaku (2008) of ensemble spread near the sea surface • Improve SST–precipitation correlation • Evaluate observations including ocean buoys ➡ Replace AFES with CFES ‣ Atmospheric DA only Kunii & Miyoshi (2012, Wea. Forecasting )

Motivations • Remove underestimation Lag Corr. of Prec. and SST over Western Pacific (winter) of ensemble spread near the sea surface Obs CFSR • Improve SST–precipitation R1 R2 correlation • Evaluate observations including ocean buoys ➡ Replace AFES with CFES SST lead Prec Lag days Prec lead SST ‣ Atmospheric DA only Saha et al. (2010, BAMS )

Motivations • Remove underestimation of ensemble spread near the sea surface Effects of Ocean Buoy Observation • Improve SST–precipitation correlation • Evaluate observations including ocean buoys Enomoto et al. (2013) ➡ Replace AFES with CFES ‣ Atmospheric DA only

CFES • Coupled GCM for the ES • AFES + OFES • Komori et al. (2008) • CFES mini • AFES: T119 L48 • OFES: 0.5°x0.5° 54 levels • Coupling: every hour • Richter et al. (2010, GRL ), Taguchi et al. (2012, JC ), Bajish et al. (2013, SOLA ), Nagura et al. (2013, JGR ), Sasaki et al. (2013, JC )

Different treatment of sea ice AFES CFES Prognostic External forcing variable Concentration 0 or 1 Fractional 0.5 m at the Thickness No upper limit maximum

Forecast–Analysis Cycle AFES–LETKF ensemble DA system merge t-3 forecast t-3 analysis t-3 t-3 t-2 t-2 t-2 t-2 anal anal anal t-1 anal t-1 t-1 t-1 IC AFES restart split t LETKF IC restart t IC restart t IC restart t t+1 t+1 t+1 t+1 gues gues gues gues t+2 t+2 t+2 t+2 t+3 t+3 BC t+3 t+3 obs 6h window Hunt et al. (2007, Phisica D ); Miyoshi & Yamane (2007, MWR )

Forecast–Analysis Cycle CFES–LETKF ensemble DA system merge t-3 forecast t-3 analysis t-3 t-3 t-2 t-2 t-2 t-2 anal anal anal t-1 anal t-1 t-1 t-1 IC AFES restart split t LETKF IC restart t IC restart t IC restart t t+1 t+1 t+1 gues t+1 gues gues gues t+2 t+2 t+2 t+2 t+3 CFES t+3 BC t+3 gues t+3 gues gues obs 6h window IC OFES restart IC restart IC restart IC restart

Experimental settings • Ensemble size: 63 members + control • Atmospheric observation (PREPBUFR) only • Integration from 1 August to 30 September 2008 • Atmospheric ICs from ALERA2 • Oceanic ICs from ensemble simulation with OFES ( EnOFES ) • Single spin-up run: CORE v2 (1948–2007), ALERA2 (2008–) • Ensemble run: each member of ALERA2 (2 June 2008–)

Ensemble spread of oceanic ICs (1 Aug) SST 0.34ºC Sea-ice concentration

Comparison with ALERA2

SST ( mean , 2-month ave.) ALERA2 (BC) CLERA-A –3.6 +3.6 K

Precipitation ( mean , 2-month ave.) ALERA2 CLERA-A –4.5 +4.5 mm/day

Surface temperature ( spread , 2-month ave.) 1.7 K ALERA2 CLERA-A –0.36 +0.36 K

Diff. in surface variables (2-month ave.) T2 [K] Q2 [g/kg] mean mean –3.6 +3.6 –1.2 +1.2 spread spread –0.18 +0.18 –0.12 +0.12

Diff. in zonal-mean analysis (2-month ave.) Air T emperature [K] Specific Humidity [g/kg] ~700 hPa mean mean +1.8 +0.3 –0.3 –1.8 Surface 90ºS 90ºN spread spread +0.09 +0.06 –0.06 –0.09

Comparison with EnOFES

Ensemble spread of SST (1 Aug & 30 Sep) 1 Aug EnOFES CLERA-A 0.34ºC 30 Sep 30 Sep

Zonal-mean ensemble spread EnOFES CLERA-A 90ºN 0.17ºC SST SST 90ºS 1 Aug 30 Sep 85 W/m 2 Shortwave Flux Shortwave Flux

Ensemble spread of ocean temp. EnOFES CLERA-A 150ºW 0 m 800 m 90ºS 90ºN Eq. 30ºE 30ºE

Summary • The CFES–LETKF ensemble DA system has been constructed. • Ocean ensemble creates perturbed surface BC. • Ensemble spread in the lower troposphere is successfully increased. • Ensemble spread of SST is mainly induced by ensemble spread of shortwave flux. • Ensemble spread of ocean surface is slightly increased by atmosphere–ocean coupling. • Additional assimilation of oceanic observation is necessary to reduce model biases.

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