Yoichi Ishikawa 1, Toshiyuki Awaji 1,2, Teiji In 3, Satoshi Nakada 2, Tsuyoshi Wakamatsu 1, Yoshimasa Hiyoshi 1, Yuji Sasaki 1 1 DrC, JAMSTEC 2 Kyoto University 3 Japan Marine Science Foundation Development of an incremental 4D-VAR system for ocean model downscaling

Introduction 4DVAR data assimilation system with Eddy-Resolving OGCM have been successfully developed (e.g. Ishikawa et al., 2009) Strong Western Boundary Currents, meso-scale eddies, strong flows through narrow channels. Estimate initial conditions with 1month assimilation window

Introduction Eddy resolving/permitting OGCM with 1/6x1/8 resolution limitation of computational resources limitation of available observation data Resolution is not enough for detailed processes for eddy activities, detachment, junction, deformation, etc. detailed processes associated with narrow channel, Tsushima strait, Tsugaru strait. Higher resolution is required but cannot execute. Down scaling approach is often adopted.

Introduction Downscaling approach is very effective to obtain high- resolution data set. Initial & boundary conditions are realistic because they are taken from reanalysis dataset. However, the quality of downscaled dataset is not guaranteed different physical processes, different topography, different parameterization There differences sometimes leads serious biases downscaling dataset To obtain realistic high-resolution dataset, data assimilation and downscaling systems are integrated. make reanalysis dataset suitable for downscaling.

Kyoto Univ. Ocean General Circulation Model σ -z hybrid vertical coordinate Takano-Onishi scheme (Ishizaki and Motoi, 1999) Equation of Motion Equation of Tracer Mixed layer sheme based on turbulence closure(Noh, 2005) Isopycnal diffusion and eddy parameterization (Gent and McWillams, 1990; Griffies, 1998) 3rd-Order advection scheme (Hasumi, 2000) OGCM & data assimilation system is based on Ishikawa et al., 2009.

Configuration of system 1/6x1/8 deg. Parent model 1/18x1/24 deg child model

Observation data Sea surface temperature :OSTIA (Operational Sea Surface Temperature and Sea Ice Analysis) by NCOF, 1/20deg. Sea surface height : Ssalto/Ducacus grided absolute dynamic topography by AVISO, 1/3 deg. In-situ data : GTSPP (global temperature-salinity profile program) XBT and CTD data by NOAA/NODC.

Variational adjoint method Cost function : constraint for observational data and intial guess of control variables Control variables : initial conditions of model variables Gradient descent method :Popular scheme (Fujii and Kamachi, 2003), which can utilize non-diagonal part of the error covariance matrix for initial guess. This method is modified in this study for combining downscaling system

Assimilation & downscaling Low resolution Parent model : High resolution child model Classical framework High resolution data assimilation in future High resolution model

Assimilation & downscaling Low resolution Parent model : High resolution child model new approach in this study

Incremental approach Make new formulation using increment: parent model: Child model: Outer Loop: Inner Loop: Approximate: Bias (Constant in Inner Loop):

Calculation Procedure 1. forecast Parent & Child model 2. calculate bias 3. optimized initial condition 4. forecast Parent & Child model

Experimental setting Assimilation period: 28day observation data are averaged every 1day Start from Jan currently, 1 year integration Compare new approach with classical downscaling

Snapshot of SST Apr. 1 st, 2011 Classical DownscalingNew incremental 4DVAR Observation data Reduce warm biases appears in classical Downscaling

RMSD with observation of SST Classical DownscalingNew incremental 4DVAR

Time series of RMSD of SST Seasonal change of RMSD is due to the change of mixed layer depth. Summer: thin mixed layer & heat flux is effective Winter: thick mixed layer & advection is effective Classical Downscaling New incremental 4DVAR

Vertical profile of RMSD Classical Downscaling New incremental 4DVAR

SST and surface velocity Classical DownscalingNew incremental 4DVAR

Temperature at 100m depth Classical DownscalingNew incremental 4DVAR

Velocity at 100m Classical DownscalingNew incremental 4DVAR

Tsushima strait (child model) Classical DownscalingNew incremental 4DVAR

Tsushima strait (parent model) Classical DownscalingNew incremental 4DVAR

Tsugaru strait (child model) Classical DownscalingNew incremental 4DVAR

Tsugaru strait (parent model) Classical DownscalingNew incremental 4DVAR

Along 41N Classical DownscalingNew incremental 4DVAR

Along 40.5N Classical DownscalingNew incremental 4DVAR

Summary To obtain high resolution analysis, incremental approach is introduced in 4DVAR system, considering the biases in downscaling. Associating strong flows through the narrow channel, significant improvement can be recognized. Topographic effect and nonlinear behavior is important. Configuration of Inner-Outer loop will be examined for better estimation.