Suchergebnisse

Abstract. Typhoon (TY) Haiyan was one of the most intense and highly destructive tropical cyclones (TCs) to affect the Philippines. As such, it is regarded as a baseline for extreme TC hazards. Improving the simulation of such TCs will not only improve the forecasting of intense TCs but will also be essential in understanding the potential sensitivity of future intense TCs with climate change. In this study, we investigate the effects of model configuration in simulating TY Haiyan using the Weather Research Forecasting (WRF) Model. Sensitivity experiments were conducted by systematically altering the choice of cumulus schemes, surface flux options, and spectral nudging. In addition to using the European Centre for Medium-Range Weather Forecasts Reanalysis fifth-generation (ERA5) single high-resolution realization as initial and boundary conditions, we also used 4 of the 10 lower-resolution ERA5 data assimilation system (EDA) ensemble members as initial and boundary conditions. Results indicate a high level of sensitivity to cumulus schemes, with a trade-off between using Kain–Fritsch and Tiedtke schemes that have not been mentioned in past studies of TCs in the Philippines. The Tiedtke scheme simulates the track better (with a lower mean direct positional error, DPE, of 33 km), while the Kain–Fritsch scheme produces stronger intensities (by 15 hPa minimum sea level pressure). Spectral nudging also resulted in a reduction in the mean DPE by 20 km, and varying the surface flux options resulted in the improvement of the simulated maximum sustained winds by up to 10 m s−1. Simulations using the EDA members initial and boundary conditions revealed low sensitivity to the initial and boundary conditions, having less spread than the simulations using different parameterization schemes. We highlight the advantage of using an ensemble of cumulus parameterizations to take into account the uncertainty in the track and intensity of simulating intense tropical cyclones.

Abstract. Windstorms are a major natural hazard in many countries.
The objective of this study is to identify and characterize intense
windstorms during the last 4 decades in the US Northeast and determine
both the sources of cyclones responsible for these events and the manner in
which those cyclones differ from the cyclone climatology. The windstorm
detection is based on the spatial extent of locally extreme wind speeds at
100 m height from the ERA5 reanalysis database. During the top 10
windstorms, wind speeds exceed their local 99.9th percentile over
at least one-third of land-based ERA5 grid cells in this high-population-density region of the USA. Maximum sustained wind speeds at 100 m during
these windstorms range from 26 to over 43 ms−1, with wind speed return
periods exceeding 6.5 to 106 years (considering the top 5 % of grid cells
during each storm). Property damage associated with these storms, with inflation
adjusted to January 2020, ranges from USD 24 million to over USD 29 billion. Two of these windstorms are linked to decaying tropical cyclones,
three are Alberta clippers, and the remaining storms are Colorado lows. Two
of the 10 re-intensified off the east coast, leading to development of
nor'easters. These windstorms followed frequently observed cyclone tracks
but exhibit maximum intensities as measured using 700 hPa relative vorticity
and mean sea level pressure that is 5–10 times the mean values for
cyclones that followed similar tracks over this 40-year period. The
time evolution of wind speeds and concurrent precipitation for those
windstorms that occurred after the year 2000 exhibit good agreement with in
situ ground-based and remote sensing observations, plus storm damage
reports, indicating that the ERA5 reanalysis data have a high degree of
fidelity for large, damaging windstorms such as these. A larger pool of the
top 50 largest windstorms exhibit evidence of only weak serial clustering,
which is in contrast to the relatively strong serial clustering of
windstorms in Europe.

Abstract. Extratropical cyclones are the most damaging
natural hazard to affect western Europe. Serial clustering occurs when many
intense cyclones affect one specific geographic region in a short period of
time which can potentially lead to very large seasonal losses. Previous
studies have shown that intense cyclones may be more likely to cluster than
less intense cyclones. We revisit this topic using a high-resolution climate
model with the aim to determine how important clustering is for windstorm-related losses. The role of windstorm clustering is investigated using a quantifiable metric
(storm severity index, SSI) that is based on near-surface meteorological
variables (10 m wind speed) and is a good proxy for losses. The SSI is used
to convert a wind footprint into losses for individual windstorms or seasons.
918 years of a present-day ensemble of coupled climate model simulations from
the High-Resolution Global Environment Model (HiGEM) are compared to
ERA-Interim reanalysis. HiGEM is able to successfully reproduce the
wintertime North Atlantic/European circulation, and represent the large-scale
circulation associated with the serial clustering of European windstorms. We
use two measures to identify any changes in the contribution of clustering to
the seasonal windstorm loss as a function of return period. Above a return period of 3 years, the accumulated seasonal loss from HiGEM is
up to 20 % larger than the accumulated seasonal loss from a set of random
resamples of the HiGEM data. Seasonal losses are increased by
10 %–20 % relative to randomized seasonal losses at a return period
of 200 years. The contribution of the single largest event in a season to the
accumulated seasonal loss does not change with return period, generally
ranging between 25 % and 50 %. Given the realistic dynamical representation of cyclone clustering in HiGEM,
and comparable statistics to ERA-Interim, we conclude that our estimation of
clustering and its dependence on the return period will be useful for
informing the development of risk models for European windstorms,
particularly for longer return periods.

Previous studies have shown that the number, intensity, and structure of simulated tropical cyclones (TCs) in climate models get closer to the observations as the horizontal resolution is increased. However, the sensitivity of tropical cyclone precipitation and moisture budget to changes in resolution has received less attention. In this study, we use the five-model ensemble from project PRIMAVERA/HighResMIP to investigate the systematic changes of the water budget of tropical cyclones in a range of horizontal resolutions from 1° to 0.25°. Our results show that, despite a large change in the distribution of TC intensity with resolution, the distribution of precipitation per TC (i.e., averaged in a 5° radial cap) does not change significantly. This result is explained by the fact that low- and high-resolution models represent equally well the large-scale balance that characterizes the moisture budget of TCs, with the radius of the moisture source extending to ~15° from the center of the TC (i.e. well beyond the TC edge). The wind profile is found to converge in the low and high resolutions for radii > 5°, resulting in a moisture flux convergence into the TC of similar magnitude at low and high resolutions. In contrast to precipitation per TC, TC intensity does increase at higher resolution and this is explained by the larger surface latent heat flux near the center of the storm, which leads to an increase in equivalent potential temperature and warmer core anomalies, although this extra latent heat represents a negligible contribution to the overall moisture budget. We discuss the complication arising from the choice of the tracking algorithm when assessing the impact of model resolution. ; This work has been funded by the European Union's Horizon 2020 programme under Grant Agreement 641727. We thank three anonymous reviewers for their constructive remarks and suggestions. We thank Kevin Reed, Helen Dacre, Chris Holloway, Nick Klingaman, Jianfeng Gu, and Ralf Toumi for fruitful discussions and suggestions. ; Peer Reviewed ; Postprint (published version)

Abstract
Potential changing climate threats in the tropical and subtropical North Pacific Ocean were assessed, using coupled ocean–atmosphere and atmosphere-only general circulation models, to explore their response to projected increasing greenhouse gas emissions. Tropical cyclone occurrence, described by frequency and intensity, near islands housing major U.S. defense installations was the primary focus. Four island regions—Guam and Kwajalein Atoll in the tropical northwestern Pacific, Okinawa in the subtropical northwestern Pacific, and Oahu in the tropical north-central Pacific—were considered, as they provide unique climate and geographical characteristics that either enhance or reduce the tropical cyclone risk. Guam experiences the most frequent and severe tropical cyclones, which often originate as weak systems close to the equator near Kwajalein and sometimes track far enough north to affect Okinawa, whereas intense storms are the least frequent around Oahu. From assessments of models that simulate well the tropical Pacific climate, it was determined that, with a projected warming climate, the number of tropical cyclones is likely to decrease for Guam and Kwajalein but remain about the same near Okinawa and Oahu; however, the maximum intensity of the strongest storms may increase in most regions. The likelihood of fewer but stronger storms will necessitate new localized assessments of the risk and vulnerabilities to tropical cyclones in the North Pacific.

Copyright [15-02-2020] American Meteorological Society (AMS). Permission to use figures, tables, and brief excerpts from this work in scientific and educational works is hereby granted provided that the source is acknowledged. Any use of material in this work that is determined to be "fair use" under Section 107 of the U.S. Copyright Act or that satisfies the conditions specified in Section 108 of the U.S. Copyright Act (17 USC §108) does not require the AMS's permission. Republication, systematic reproduction, posting in electronic form, such as on a website or in a searchable database, or other uses of this material, except as exempted by the above statement, requires written permission or a license from the AMS. All AMS journals and monograph publications are registered with the Copyright Clearance Center (http://www.copyright.com). Questions about permission to use materials for which AMS holds the copyright can also be directed to permissions@ametsoc.org. Additional details are provided in the AMS Copyright Policy statement, available on the AMS website (http://www.ametsoc.org/CopyrightInformation). ; A multimodel, multiresolution set of simulations over the period 1950–2014 using a common forcingprotocol from CMIP6 HighResMIP have been completed by six modeling groups. Analysis of tropicalcyclone performance using two different tracking algorithms suggests that enhanced resolution toward25 km typically leads to more frequent and stronger tropical cyclones, together with improvements inspatial distribution and storm structure. Both of these factors reduce typical GCM biases seen at lowerresolution. Using single ensemble members of each model, there is little evidence of systematic im-provement in interannual variability in either storm frequency or accumulated cyclone energy as comparedwith observations when resolution is increased. Changesin the relationships between large-scale drivers ofclimate variability and tropical cyclone variability in the Atlantic Ocean are also not robust to modelresolution. However, using a larger ensemble of simulations (of up to 14 members) with one model atdifferent resolutions does show evidence of increased skill at higher resolution. The ensemble mean cor-relation of Atlantic interannual tropical cyclone variability increases from;0.5 to;0.65 when resolutionincreases from 250 to 100 km. In the northwestern Pacific Ocean the skill keeps increasing with 50-kmresolution to 0.7. These calculations also suggest that more than six members are required to adequatelydistinguish the impact of resolution within the forced signal from the weather noise. ; Authors MR, JS, PLV, KH, BV, RH, AB, ES, LPC, LT, CR, RS, and DP acknowledge funding from the PRIMAVERA project, funded by the European Union's Horizon 2020 programme under Grant Agreement 641727. Author JM acknowledges funding from the Blue-Action project, funded by the European Union's Horizon 2020 programme under Grant Agreement 727852. Authors MR and JC acknowledge support from the U.K.–China Research and Innovation Partnership Fund through the Met Office Climate Science for Service Partnership (CSSP) China as part of the Newton Fund. Funding for authors PU and CZ to support use of the TempestExtremes suite was provided under NASA award NNX16AG62G and the U.S. Department of Energy Office of Science award DE-SC0016605. Many thanks are given to the editor and three anonymous reviewers for their comments, which have greatly strengthened this paper. ; Peer Reviewed ; Postprint (published version)

Abstract Future changes in tropical cyclone properties are an important component of climate change impacts and risk for many tropical and midlatitude countries. In this study we assess the performance of a multimodel ensemble of climate models, at resolutions ranging from 250 to 25 km. We use a common experimental design including both atmosphere-only and coupled simulations run over the period 1950–2050, with two tracking algorithms applied uniformly across the models. There are overall improvements in tropical cyclone frequency, spatial distribution, and intensity in models at 25 km resolution, with several of them able to represent very intense storms. Projected tropical cyclone activity by 2050 generally declines in the South Indian Ocean, while changes in other ocean basins are more uncertain and sensitive to both tracking algorithm and imposed forcings. Coupled models with smaller biases suggest a slight increase in average TC 10 m wind speeds by 2050. ; M. J. R. and J. C. acknowledge the support from the UK‐China Research and Innovation Partnership Fund through the Met Office Climate Science for Service Partnership (CSSP) China as part of the Newton Fund. M. J. R., J. S., P. L. V., K. H., B. V., R. H., A. B., E. S., L.‐ P. C., L. T., C. D. R., R. S., and D. P. acknowledge funding from the PRIMAVERA project, funded by the European Union's Horizon 2020 Framework Programme under Grant 641727. J. M. acknowledges funding from the Blue‐Action project, funded by the European Union's Horizon 2020 Framework Programme under Grant 727852. Funding for P. U. and C. Z. to support the use of the TempestExtremes suite was provided under National Aeronautics and Space Administration (NASA) Award NNX16AG62G and the U.S. Department of Energy Office of Science Award DE‐SC0016605. C. K. and Y. Y. acknowledge funding from the Environment Research and Technology Development Fund (2RF‐1701) by the Environmental Restoration and Conservation Agency (ERCA) of Japan and from the Integrated Research Program for Advancing Climate Models (TOUGOU) Grant JPMXD0717935457 by the Ministry of Education, Culture, Sports, Science and Technology (MEXT) of Japan. The CESM1.3 simulations are completed through the International Laboratory for High‐Resolution Earth System Prediction (iHESP)—a collaboration among QNLM, TAMU, and NCAR, from which D. F., Q. Z., G. D., N. R., H. W., and L. W. acknowledge funding. NCAR is a major facility sponsored by the U.S. National Science Foundation under Cooperative Agreement 1852977. The CESM1.3 simulations were performed on Frontera at the Texas Advanced Computing Center. ; Peer Reviewed ; Postprint (published version)