GFDL’s global non-hydrostatic modeling system for multi-time-scale tropical cyclone simulations and predictions Shian-Jiann Lin with contributions from: M. Zhao, I. Held, and G. Vecchi NOAA/Geophysical Fluid Dynamics Laboratory Princeton, NJ, USA Workshop on Retrospective Simulation and Analysis of Changing SE Asian High-Resolution Typhoon Wind and Wave Statistics March 12, 2009

Outline The prototype GFDL global cloud-resolving model (aka, HiRam) Model validation: basic climate state & simulated tropical cyclone climatology with the C180 (~50 km) HiRam (Zhao et al. 2009) Skill of the seasonal predictions Deterministic forecasts with the C360 (~25 km) HiRam Initialization: NCEP analysis (large-scale) with 4D vortex-breeding (small-scale) Preliminarily results: 5-day forecasts for HFIP (Hurricane Forecast Improvement Project ) 10-day forecast

The GFDL High-Resolution Atmosphere Model (HiRam) is the same as the GFDL AM2.1 used in the IPCC AR4 Except the following: Non-hydrostatic Cubed-sphere finite-volume dynamical core. 6-category bulk cloud microphysics (based mostly on Lin et al. 1984) The deep convective parameterization scheme (Relaxed Arakawa-Schubert) is replaced by a non-precipitating shallow convection scheme (based on Bretherton et al. 2004) Surface fluxes modified for high-wind situation over ocean (Moon et al. 2007)

Climate Model inter-comparisons: GFDL finite-volume models vs. other IPCC AR4 models

Observed cyclone tracks: 1981-2005 Simulated tracks: 1981-2005 (C180 model) One realization

Seasonal cycle of hurricanes (1981-2005) (red: 4-member ensemble)

Inter-annual cycle/trend (1981-2005) Number of West Pacific Typhoons Number of East Pacific hurricanes Number of North Atlantic hurricanes Reds: observed Blue: model (4 realizations) Model-obs correlation ~ 0.83

Skill of CSU (statistical) seasonal hurricane forecasts Suzana J. Camargo, Anthony G. Barnston, Philip J. Klotzbach and Christopher W. Landsea, 2007

GFDL Seasonal (Jul-Dec) hurricane predictions 1985-2005 5-member C180 (~50 km) model ensemble SST: climatology plus persistent anomaly from June of the forecast year N. Atlantic Correlation ~ 0.66 E. Pacific Correlation ~ 0.63

GFDL global models vs. CSU (statistical) hurricane predictions for 1999-2007

Hurricanes: intensity vs Hurricanes: intensity vs. resolution Central pressure – surface wind correlation (1981-2005) C90 C180 Cat 4-5 C360

NCEP/GFS TC initialization “Remove” (by filters) the vortex from the first-guess “Relocate” the vortex to the observed position Main issue: Initial vortex (if existed) location is correct but the intensity is typically very weak

Regional GFDL hurricane model initialization “Remove” (by filters) the vortex from the NCEP analysis “Grow” a balanced vortex offline by an axis-symmetrical model (to achieve internal dynamical, thermodynamic, and micro-physics balance). “Insert” the fully developed and balanced vortex into the NCEP background Main issues: The large-scale “background” is not in dynamical or thermodynamic balance with the vortex – possibly causing degraded track forecasts Multiple vortices do not interact with themselves nor the environment Being 3D (time discontinuous), vortex information does not propagate to the next forecast time.

A simple 4D data assimilation for tropical cyclone prediction: Large-scale nudging (using NCEP T382L64 gridded analysis) + storm-scale 4D (time continuous) vortex-breeding 00Z 06Z 12Z 18Z Assimilation forecast

Re-Assimilation of hurricane Katrina into NCEP analysis GFDL c360 model is capable of reproducing all storms in the IBTrac data with the re-assimilation procedure

Katrina forecasts 2005 operational models GFDL C360 HiRam www.weatherundergroud.com 00Z 08/25 GFDL C360 HiRam 00Z 08/26

Intensity forecast: NCEP/GFS forecasts GFDL HiRam forecasts

Summary: For seasonal hurricane prediction to be skillful, the model must be able to simulate a credible tropical-cyclone climatology, including inter-annual variability & seasonal cycle A 25-km global model can be skillful in hurricane intensity forecasts.

A simple 4D assimilation for hurricane prediction (continued): “storm-scale vortex breeding” Interpolation in time the NHC “best tracks” (latitude, longitude; slp) Construction of two radial (Gaussian) SLP distributions based on the observed center pressure and model environment pressure (blue area); radial distance determined iteratively. If the model’s SLP is outside the two bounds, dry air mass is instantly “transported” from(into) the inner area (red) to the outer ring (green area) Large-scale nudging towards NCEP analysis is gradually masked out (from green to the red region)

The Finite-Volume (FV) Cubed Sphere dynamical core Key features in the Cubed-Sphere dynamical core Quasi-uniform resolution over the globe; self-consistent global-regional nesting & Adaptive Mesh Refinement capability (to be implemented) Vertically Lagrangian control-volume discretization (for both hydrostatic & non-hydrostatic) A Lagrangian (stable for large CFL number) Riemann Solver for sound waves; non-reflective upper boundary condition Tracer transport is strictly 2D and uses a vertically dependent time stepping, which dramatically enhanced the computational efficiency if many tracers are required (e.g., chemistry, carbon cycle, and cloud microphysics) Highly scalable: super linear scaling to ~10,000 CPUs at global cloud-resolving resolutions.

SLP: Katrina 1st US landfall (~ 4 days before 2nd landfall)

Hurricane Ike (2008) 10-day forecast: 00Z 20080906-20080915