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

2. The key question is – will we master the description of typhoons?
We have implemented the dynamical downscaling approach for E Asian marine weather.
The key question is – will we master the description of typhoons?
Simulation of 1948-today conditions
Simulation of possible future: An IPCC A1B scenario, 2

3. Reference data: “best track”
Provided by different weather services
JMA
CMA
Joint Tropical Cyclone Warning Center JTWC
Constructed on the fly
Homogeneous description of typhoon characteristics? Data base has massively changed in the course of years.

4. JMA best track statistics
2010
2010

5. Barcikowska, unpublished
IBTrACS- International Best Track Archive for Climate Stewardship merging few different best track data sets,
JTWC is converted from 1 min to 10 min.
NOAA - Blended Sea Winds - global 0.25° grid, 6 –hrly ocean surface vector winds, based on wind speed retrievals from satellites
equipped with passive SSMI (special sensor microwave imager + active scatterometers )
IFREMER - Near Real Time Blended Surface Winds ° global oceans grid, 6 –hrly ocean surface vector winds, derived from satellite data blended to ECMWF analyses)
BTD=JMA
BTD=JMA

6. First conclusions
1) “Best track” data likely affected by inhomogeneities related to changing observational and analytical procedures in the course of time.
2) Different agencies produce different “best track” data.
3) Comparison with satellite-based data analyses reveal further differences. “Best track” data may provide an overestimate.

7. All tracks in “reconstruction”
RCM simulations
CCLM regional atmospheric model
50 km grid resolution
Two different sets of parameters for turbulent latent heat flux (normal/high)
“Reconstructions” – NCEP forcing, incl. spectral nudging (800 km), 1948-today
“Scenario” – ECHAM5/MPIOM A1B1; also spectral nudging
All tracks in “reconstruction”

8. Barcikowska, unpublished

9. Note: different criteria employed9

10. Interannual variability
JMA “best tracks” 31 tracks in CLM
JMA “best tracks” 25 tracks in CLM 10

11. Second set of findings:
CCLM simulates typhoons.
Number and interannual variability in CLM similar to „best track“ data set.
Simulated typhoons too weak (increase of grid resolution to 18 km gives no significant improvement).
Sensitivity to different formulations of latent heat flux minor
Long term trends in CCLM and in „best track“ markedly different.
In CCLM, some intensification mainly since about 1980.
In JMA-„best track“, mainly weakening since about 1980. 11

15. Third set of findings:
1) Number and intensity in reconstructions increasing.
2) Number in scenario A1B slightly decreasing, while intensity almost stationary. Note – only one scenario.
3) Thus, scenarios can not be seen as “extending” description of past forward in time.
4) Possible explanations - reconstruction not homogenous (skill of NCEP re-analysis is improving), or - change in not related to main driver in scenario simulation (increasing GHG concentrations) - scenario calculation does not describe influence of elevated GHG concentration properly 15