Downscaling Tropical Cyclones from global re-analysis:

Statistics of multi-decadal variability of TC activity in E

Asia, 1948-2007 VON STORCH Hans and FESER Frauke

1Institute for Coastal Research, GKSS Research Center, Germany

2clisap-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/s

Max: 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., 2008. Normalized Hurricane Damages in the United States: 1900-2005. 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 climateCs = f(Cl, Φs)

with Cl = larger scale climate

Cs = smaller scale climate

Φs = 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 3-540-65033-4, 3-

36

“downscaling”

Problem for synoptic systems solved by CoastDat@GKSS in N Europe, using RCM spectrally nudged to NCEP

- retrospective analysis 1958-2007- 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 1958-2001 and comparison with observations. J. Climate 18, 465-479

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 scales2.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 1948-2007 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:10.1029/2008GL035769

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), 1-10. DOI: 10.1127/0941-2948/2008/0282Feser, 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, 1806-1815

50 km

16 km

Case study: Typhoon Winnie, August 1997 simulated with different

set-ups

12 TCs in Seaso

n 2004only 10

were found in

CLM simulationFollowing Zhang et

al., 2007. 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 1948-2007using CLM with 0.5º grid resolutionand NCEP/NCAR reanalysis

Spectral nudging of scales larger than about 800 km.

All „best tracks“, 1951-2007

1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010

0

10

20

30

40

E A sian t r opical cyclones as given byC LM dow nscaling

JM A best track data

Note: different criteria employed

36 “best tracks26 tracks in CLM

16 “best tracks” 16 tracks in CLM

Interannual variability

1960 1980 2000

- 15

- 10

- 5

0

5

10

15A nnual T C countB ias CL M vs B est T r ack

CLM - Best T r ack11 year r unning mean

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 1980.5)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:10.1029/2006GL027951

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 195307 (NINA)

1953-08-08 06:00 1953-08-19 00:00

JMA Best Track

JMA Best Track

Largest drop in core pressure August 11-12

65 hPa in 6 hours

Typhoon 195307 (NINA)

1953-08-08 06:00 1953-08-19 00:00

Typhoon 195313 (TESS)

1953-09-18 00:00 1953-09-27 18:00

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 ConclusionsDynamical 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)