Japan Report National Forecasting System (MOVEMRI.COM) and Japan Working Team - Progress in T. Kuragano (GOVST) M. Kamachi (GOVPatron) Y. Fujii(OSE-TT), N. Usui (COSS-TT), S. Ishizaki (ET-OOFS), Y. Takaya, T. Toyoda, K. Sakamoto JMA/MRI

Recent Progress (1/2) MOVE/MRI.COM-G2 and -SETO systems will be in operation early OSE for Argo floats and TAO/TRITON array was done (Fujii et al., 2014). Impact of Aquarius SSS data was examined (Toyoda et al., in revision). Seawater mass variation was examined to detect steric component from altimeter data (Kuragano et al., 2014).

Recent Progress (2/2) Analysis and predicted variables have been available since August 2014 for research and commercial users through the Japan Meteorological Business Support Center. Application studies are in progress in collaboration with related organizations. –Fishery, Debris, and Radiation contamination 18 th Japan data assimilation summer school was held in August 2014 as GOV Outreach activity.

MOVE/MRI.COM-G2 for Seasonal/ENSO Forecast MRI.COM-G2 is an ocean part of the coupled model, JMA/MRI-CGCM2, for the next operational seasonal/ENSO forecast system in JMA Ocean Reanalysis was done by the assimilation system MOVE/MRI.COM-G2 System performance was evaluated from the hindcast experiments

MRI.COM v3.2 Tripolor grid and Sea Ice model - analysis and prediction in arctic area and sea ice Higher latitudinal resolution degree → degree Atmospheric forcing - JRA25/JCDAS -> JRA55 MOVE major revisions Change statistical vertical EOF modes  57 regions from 40 ones in previous version  Monthly EOF modes from annual EOF mode Bias correction scheme FGAT （ First-Guess at Adequate Time) Ocean mass correction for altimeter data T/S assimilation up to 1750 m from up to 1500 m Lines are drawn for every 10 grids Main Upgrades of System - MOVE/MRI.COM-G2 -

NINO3.4 SST prediction NINO3.4 RMSE (Init: Feb) JMA/MRI- CGCM2 JMA/MRI- CGCM1 (2009 experiment) JMA/MRI- CGCM1 (2014 experiment extended for LT 7-12 months) February and November initialized predictions show remarkable improvement: ACC keeps high beyond the Spring predictability barrier [K] NINO3.4 ACC (Init: Feb) NINO3 SST RMSE (Init: Nov) [K] NINO3 SST ACC (Init: Nov)

MOVE/MRI.COM-SETO for coastal high tide prediction MOVE-WNP 4DVAR 0.1x0.1 deg., 54 layers 10-day assimilation window To predict short-term open- ocean variation SETO Coastal Model 2km x 2km 54 layers increment from WNP 4DVAR To represent detailed impact on the coastal area Target: To predict costal high tide and rapid current caused by open ocean variation

8 Study for the unusual high tide in 2011 Obs Model SSH on 29SEP2011 ABST MI  Typhoon Typhoon   Assimilation experiment:  2km-coastal assimilative model  1 Aug – 31 Oct 2011  The Kuroshio took a nearshore path at the eastern flank of the Izu Ridge Izu Ridge Itsukushima Shrine Unusual high tide Kanto

Observatio n SSTSLA In Situ TAO/TRITONARGO Type EqEx OSE-XBO○○○ OSE-XAF○○○○○ OSE-AR2○○○○○○ OSE-AR4○○○○○○○ OSE-AR6○○○○○○○○ OSE-AR8○○○○○○○○○ OSE-XTT○○○ ○○○○ OSE-TTeq○○○○ ○○○○ REG-Exp○○○○○○○○○○ Last digit of WMO number Reference Data Observed Data Assimilated in Each OSEs OSE for ARGO and TAO/TRITON 9 simulation experiments are performed in the period of using MOVE-G. Profiles of Argo where the last digit of the WMO number is 8 or 9 are withheld in all simulations except for Reg-Exp, and used for the reference data. OSE Configuration

Increase of ACC against OSE-XBO (0-300m average)  The accuracy of TS fields is generally increased with the increase in the number of assimilated Argo profiles.  The complementary impact of TRITON on T is larger than that of Argo floats in the western tropical Pacific (OSE-XAF has higher T accuracy than OSE-XTT).  The increase of ACCs for both TS with the increase of the number of Argo assimilated is closer to linear in NINO3 (Enhanced deployment of Argo floats is desirable).

SSS impact on MOVE/MRI.COM Impact of Aquarius SSS data on MOVE/MRI.COM-G2 are examined for future operational use of SSS Aquarius official release level 3 SSS standard mapped image daily data v2.0 CTL Exp.: assimilation run for T/S profile and SLA data ASA Exp.: SSS data are additionally assimilated

Impacts in the North Pacific Increase of subsurface salinity and temperature especially in winter Strengthen of mixing at surface layer, which is consistent with in-situ data based analysis Impacts in the North Pacific Increase of subsurface salinity and temperature especially in winter Strengthen of mixing at surface layer, which is consistent with in-situ data based analysis S 100 m T 100 m PV 100 m MLD CTL MLD ASA In-situ based Analysis

Ocean mass correction for SLA SL variation caused by ocean mass variation is examined to steric height component from altimetric SLA. Local mass variation as a response to seasonal water flux, wind stress and surface pressure variation is examined using a barotropic global ocean model. The results are well consistent with seasonal mean variation of altimeteric SLA minus steric height. The results are adopted for the correction of SLA data in MOVE/MRI.COM-G2. Altimetric SL Kuragano & Kamachi (2000) Steric SL Ishii and Kimoto (2009) mass Amplitude of SL variation by mass Phase of SL variation by mass

Cost function: Ocean mass correction for SLA Model steric height Altimetric SLA Seasonal mass-related SSH Trend of global mean mass-related SSH:2mm/yr Assimilation including mass correction terms Results of global mean SSH variation by applying mass correction

18 th Data Assimilation Summer School Aug. 2014, in Mutsu, Aomori Prefecture As an Outreach of GOV It continues from 1997 under the support of Japan Marine Science Foundation & JAMSTEC. 3 days course of fundamental lectures, practices and applications Participants BBQ Party Lectures

Tsujino et al. (2010) MOVE-G2 Tri-polar grid Toyoda et al. (2013) OGCM, sea-ice model MOVE/MRI.COM-G2, tri-polar coordinate Thermodynamics based on Mellor and Kantha (1989) (1-layer) Dynamics (and other processes) based on Los Alamos sea-ice model (CICE; Hunke and Ducowicz, 2002) (Elastic-Viscous-Plastic rheology) Five thickness categories are set in a grid (0.6, 1.4, 2.4, 3.6 m) Albedo of sea Ice/snow is defined by near-IR and visible radiations: the albedo takes smaller value when ice melts in consideration with existence of melt ponds.

RMSE differences and RMSDs in the Eq. Section  Impacts are commonly large around the thermocline in the NINO3 region.  There is a similarity among the three impacts depicted above. (The distributions of the impacts are not determined by the property of the observations.)  The distribution of RMSDs between OSEs averaged for upper 300 m are basically similar to the distribution of the corresponding RMSE differences. (The correction by DA generally improves the accuracy.) TT impacts (intrinsic) TT impacts Argo impacts

Upgrade of ocean model - MRI.COM v3.2 - Tripolor grid and Sea Ice model analysis and prediction in arctic area and sea ice Latitudinal resolution degree → degree Atmospheric forcing JRA25/JCDAS -> JRA55 Lines are drawn for every 10 grids

Upgrade ocean data assimilation system - MOVE/MRI.COM-G2 - 3DVAR for T/S Incremental Analysis Update （ IAU ） with 10-day assimilation window Model prediction for Sea Ice. Observation data Temperature and salinity : WOD2009+GTSPP SST: COBE-SST for the region 45S-45N SSH from 1992 Major revisions Change statistical vertical EOF modes 57 regions from 40 ones in previous version Monthly EOF modes from annual EOF mode Bias correction scheme Ocean mass correction in assimilating altimeter data FGAT （ First-Guess at Adequate Time) Assimilation up to 1750m from up to 1500m

MOVE/MRI.COM-SETO for coastal high tide prediction Kuroshio pass variation is one of the causes of coastal high tide. Short-term variation of the Kuroshio pass should be analyzed/predicted and its detailed impact on coastal SL should be represented. Incremental 4DVAR system has been developed in MRI, and is in preparation for operation in the JMA HQs.