Downscaling Tropical Cyclones from global re-analysis: Statistics of multi-decadal variability of TC activity in E Asia, VON STORCH Hans and FESER Frauke 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.

Causes of inhomogenities: Changes in –Instruments –Sampling frequencies –Measuring units –Environments (e.g. trees, buildings) –Location Representativity of near surface wind speed measurements

1.25 m/s Representativity of near surface wind speed measurements Dotted – station relocation

Representativity of near surface wind speed measurements

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 Landsea et al., 2004

“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

Dynamical downscaling, deriving regionally disaggregated descriptions from global re-analysis or global climate change scenarios.

Climate = statistics of weather The genesis of climate C s = f(C l, Φ s ) with C l = larger scale climate C s = smaller scale climate Φ s = physiographic detail at smaller scale von Storch, H., 1999: The global and regional climate system. In: H. von Storch and G. Flöser: Anthropogenic Climate Change, Springer Verlag, ISBN , 3-36  “ downscaling ”

Problem for synoptic systems solved by in N Europe, using RCM spectrally nudged to NCEP - retrospective analysis good skill with respect to statistics, but not all details are recovered. Weisse, R., H. von Storch and F. Feser, 2005: Northeast Atlantic and North Sea storminess as simulated by a regional climate model and comparison with observations. J. Climate 18, Wind speed [m/s] Wind direction [degrees] Sig. wave height [m]Mean wave direction [degrees] (Weisse and Günther. 2007) Observations – black; Hindcast - green At a platform in the S North Sea

The added value of RCM runs in hindcast mode 1.Enhanced variability of medium spatial scales 2.Better description of medium scale variations. 3.Better description of variability in topographically structured regions (e.g., coasts) 4.Additional dynamical features (e.g., Polar Lows, typhoons) 5.Spatially complete data on fine grid, needed to run ocean and wave field models

In the red marked areas, the day-to-day wind description by CLM compares better than NCEP re- analysis to QUICKSCAT satellite data Winterfeldt, 2008

Simulation of additional dynamical detail Formation of Catalina Eddy in description of Californian Climate (CaRD10) Kanamitsu, SIO, pers. comm.

Climate simulations with CLM of Polar Lows driven by the NCEP reanalysis spectral nudging about 50 km grid resolution Zahn, M., and H. von Storch, 2008: A longterm climatology of North Atlantic Polar Lows. Geophys. Res. Lett., 35, L22702, doi: /2008GL035769

Based on these findings, we believe that we can use the concept also for describing Typhoon stats variability and possible trends and constructing scenarios

We have implemented the dynamical downscaling approach for E Asian marine weather. The key question is – will we master the description of typhoons? Done: Case studies and seasons – formation of typhoons induced by large scale dynamics and NOT by initial values. Presently under examination: Continuous 6-decade simulations constrained by NCEP global re-analyses. Feser, F., and H. von Storch, 2008: Regional modelling of the western Pacific typhoon season 2004, Meteor. Z. 17 (3), DOI: / /2008/0282 Feser, F., and H. von Storch, 2008: A dynamical downscaling case study for typhoons in SE Asia using a regional climate model. Mon. Wea. Rev. 136,

50 km 16 km

Case study: Typhoon Winnie, August 1997 simulated with different set-ups

12 TCs in Season 2004 only 10 were found in CLM simulation Following Zhang et al., Meteor. Atmos. Phys.

Simulated typhoons are weaker than found in the „best track data“ – too high core pressures, too weak winds, But considerably stronger than in the driving NCEP re- analyses. The model‘s performance does not improve much by enhancing the horizontal resolution from about 50 km to about 16 km. Experiments with different convection parametrizations are presently doen with encouraging results.

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

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.

Ren, F., G. Wu, W. Dong, X. Wang, Y. Wang, W. Ai, and W. Li, 2006: Changes in tropical cyclone precipitation over China. Geophys Res. Lett. 33, L20702, doi: /2006GL in China But …

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)