Downscaling Tropical Cyclones from global re-analysis: Statistics of multi-decadal variability of TC activity in E Asia, Hans von Storch and Frauke Feser 1 Institute for Coastal Research, GKSS Research Center, Germany 2 clisap-Klimacampus, University of Hamburg, Germany

Estimating the changing risk related to typhoons – Problems and issues: Inhomogeinity of observational evidence (higher accuracy, more details in recent times). Data about typhoon-related damages are affected by changing socio-economic regional usage and conditions. Change may be related to interannual and interdecadal variability or to systematic climate change.

Example of inhomogeneities in quality controlled wind records, mainly related to changes in the local environment.

Analysis using additional aircraft reports. Analysis using all available surface observations Max: 52 m/sMax: 20 m/s Example: Inhomogeneity of analyses of hurricanes – Erin, in September 2001

“Great Miami”, 1926, Florida, Alamaba – damages of 2005 usage - in 2005 money: 139 b$ Katrina, 2005: 81 b$ Pielke, Jr., R.A., Gratz, J., Landsea, C.W., Collins, D., Saunders, M., and Musulin, R., Normalized Hurricane Damages in the United States: Natural Hazards Review The increase in damages related to extreme weather conditions is massive – but is it because the weather is getting worse? Losses from Atlantic Hurricanes Hardly

Any assessment of how weather patterns have changed in recent decades requires long and homogeneous time series. Local time series representing wind speeds are usually not homogeneous, even for a few decades (sensitivity to instrumentation and surrounding). Homogeneous description of variability of meso- scale storms in recent decades has also not been achieved. Model-based “reconstructions” may help

Case studies of downscaling TCs in East Asia using spectrally nudged Regional Climate Models Frauke Feser and Hans von Storch Institute for Coastal Research, GKSS Research Centre Geesthacht, Germany

Global model: NCEP Reanalysis ○ x ○ Regional model : REMO 0.5 ○ x 0.5 ○ Sponge zone 8 grid points Spectral nudging of large scales Successfully implemented for Western Europe and NE Atlantic climate. Homogeneous, realistic description of the past atmospheric state. Studies of waves, water levels, currents and storm surges were computed using the high-resolution atmospheric data set. Downscaling approach

Downscaling SE Asian marine weather Application of this dynamical downscaling approach for SE Asia. Description of typhoons? Case studies and analysis of several typhoons of one typhoon season – formation of typhoons induced by large-scale dynamics and not by initial values (RCM run in climate mode). Feser, F. and H. von Storch, 2007: A dynamical downscaling case study for typhoons in SE Asia using a regional climate model. Mon. Wea. Rev., 136 (5), Feser, F. and H. von Storch, 2008: Regional modelling of the western Pacific typhoon season 2004 Meteorolog. Z., 17 (4),

Model areas for both regional CLM simulations. Model area of the 0.5° simulation Simulation area for the double nested 0.165° CLM run Analysis of the typhoon season 2004

Model Orography NCEP 1,875 degrees ~210 km x 210 km CCLM 0.5 degrees ~ 53 km x 53 km CCLM degrees ~18 km x 18 km

A case study: Simulating typhoon Winnie (August 1997) with regional atmospheric model CLM Sensitivity to spectral nudging: 5 runs initiated at or close to 1. August 1997 with spectral nudging and 5 simulations without.

Conventional RCM-simulations with spectral nudging NCEP

Dates refer to time of initialization of simulation Effect of spectral nudging and different initial states CLM runs on 0.5º grid

Effect of horizontal grid configuration: 1.875º → 0.5º and double nesting 1.875º → 0.5º → 0.165º

Effect of horizontal grid configuration: double nesting 1.875º → 0.5º → 0.165º and direct 1.875º → 0.165º

Core pressure development

Wind speed development

Overview for all 12 analysed tracks of the typhoon season 2004.

Dianmu Songda SLP 10m Wind Speed

Great circle distance (km) between JMA best track data and NCEP reanalyses, 0.5° CLM simulation, and 0.165° CLM simulation Dianmu Songda

RMSE of SLP and 10m wind speed as well as great circle distance between JMA best track data and NCEP, CLM 0.5°, and CLM 0.165°. Dianmu Songda

Min. pressure, max. pressure change within 6 hours, max. 10m wind speed, and max. 10m wind speed change within 6 hours Dianmu Songda

Brier Skill Score between JMA best track data and NCEP, CLM 0.5°, and CLM 0.165° SLP 10m Wind Speed > 0 : CLM closer to best track 0 : CLM and NCEP equally close to best track < 0 NCEP closer to best track

Precipitation rate (mm/h) CLM 0.5° CLM 0.165° Tokage Megi

 Ensemble simulations with nudging: smaller internal ensemble variability and closer to observations than standard simulations.  RCM core pressure values and near-surface wind speed values closer to best track data than forcing reanalyses.  The highest spatial resolution (0.165°) shows largest added value for precipitation patterns and SLP compared to 0.5° simulation.  Location of typhoon track already well represented by reanalyses, an added value was found for core pressure and wind speed development as well as for precipitation patterns.  Long-term atmospheric hindcast was computed to serve as a basis to assess interannual variability and long-term trends.

Complete simulation of using CLM with 0.5º grid resolution and NCEP/NCAR reanalysis Spectral nudging of scales larger than about 800 km. All „best tracks“,

Note: different criteria employed

36 “best tracks 26 tracks in CLM 16 “best tracks” 16 tracks in CLM Interannual variability

Annual statistics of minimum pressure along tracks

hourly Annual statistics of maximum pressure falls along tracks

Findings so far: 1)CLM-simulated typhoons too weak. 2)Number and interannual variability in CLM similar to „best track“ data set. 3)Long term trends in CLM and in in „best track“ markedly different. 4)In CLM, intensification since about )In „best track“, weakening since about 1980.

1953

Typhoon Season 1953 JMA Best Track (black lines) – 23 typhoons, CLM (colored lines) 28 typhoons Several typhoons of the Best Track data show very large drops in core pressure – Are they realistic?

JMA Best Track Typhoon (NINA) : :00

JMA Best Track Largest drop in core pressure August hPa in 6 hours Typhoon (NINA) : :00

Typhoon (TESS) : :00 Largest drop in BT core pressure: 93 hPa in 6 hours

The CLM shows much smaller drops in core pressure - probably too small in most cases. The most extreme pressure falls described in the “best track” data set took place over the open ocean, where no satellite data was available in 1953 – how was it observed? Later years show overall less extreme pressure 6-hourly drops. Use caution when using earlier “best track” years.

Overall Conclusions Dynamical Downscaling of NCEP reanalysis with regional climate models returns typhoon climatology better than NCEP, even if cyclones are still too weak. Results concerning change -Strong year-to-year variability -Little decadal variability -No overall trend in numbers -Trends in intensities opposite in CLM and in “best track”. -“Best track” suffer likely from severe inhomogeneities in the early years (e.g., 1953)

Annual max m/s -Upward trends in minimum pressure and max wind since 1990s in homogeneous CLM data set. -Too early to consider an association with global warming.

Informal Typhoon Project