scholarly journals Comparison of Explicitly Simulated and Downscaled Tropical Cyclone Activity in a High-Resolution Global Climate Model

2010 ◽  
Vol 2 (4) ◽  
pp. n/a-n/a ◽  
Author(s):  
Kerry Emanuel ◽  
Kazuyoshi Oouchi ◽  
Masaki Satoh ◽  
Hirofumi Tomita ◽  
Yohei Yamada
Keyword(s):  
High Resolution ◽  
Tropical Cyclone ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Tropical Cyclone Activity ◽  
Cyclone Activity

Projected changes in tropical cyclone activity under future warming scenarios using a high-resolution climate model

2016 ◽  
Vol 146 (3-4) ◽  
pp. 547-560 ◽  
Author(s):  
Julio T. Bacmeister ◽  
Kevin A. Reed ◽  
Cecile Hannay ◽  
Peter Lawrence ◽  
Susan Bates ◽  
...  
Keyword(s):  
High Resolution ◽  
Tropical Cyclone ◽  
Climate Model ◽  
Tropical Cyclone Activity ◽  
Cyclone Activity ◽  
Future Warming ◽  
Projected Changes

Projections of future tropical cyclone damage with a high-resolution global climate model

2017 ◽  
Vol 146 (3-4) ◽  
pp. 575-585 ◽  
Author(s):  
A. Gettelman ◽  
D. N. Bresch ◽  
C. C. Chen ◽  
J. E. Truesdale ◽  
J. T. Bacmeister
Keyword(s):  
High Resolution ◽  
Tropical Cyclone ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model

scholarly journals High-resolution regional climate model projections of future tropical cyclone activity in the Philippines

2018 ◽  
Vol 39 (3) ◽  
pp. 1181-1194 ◽  
Author(s):  
Florian Gallo ◽  
Joseph Daron ◽  
Ian Macadam ◽  
Thelma Cinco ◽  
Marcelino Villafuerte ◽  
...  
Keyword(s):  
High Resolution ◽  
Tropical Cyclone ◽  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
The Philippines ◽  
Tropical Cyclone Activity ◽  
Cyclone Activity

scholarly journals Real World and Tropical Cyclone World. Part I: High-Resolution Climate Model Verification

2020 ◽  
Vol 33 (4) ◽  
pp. 1455-1472 ◽  
Author(s):  
S. Sharmila ◽  
K. J. E. Walsh ◽  
M. Thatcher ◽  
S. Wales ◽  
S. Utembe
Keyword(s):  
High Resolution ◽  
Tropical Cyclone ◽  
Geographical Distribution ◽  
Real World ◽  
Large Scale ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Model Verification ◽  
State Dependency

AbstractRecent global climate models with sufficient resolution and physics offer a promising approach for simulating real-world tropical cyclone (TC) statistics and their changing relationship with climate. In the first part of this study, we examine the performance of a high-resolution (~40-km horizontal grid) global climate model, the atmospheric component of the Australian Community Climate and Earth System Simulator (ACCESS) based on the Met Office Unified Model (UM8.5) Global Atmosphere (GA6.0). The atmospheric model is forced with observed sea surface temperature, and 20 years of integrations (1990–2009) are analyzed for evaluating the simulated TC statistics compared with observations. The model reproduces the observed climatology, geographical distribution, and interhemispheric asymmetry of global TC formation rates reasonably well. The annual cycle of regional TC formation rates over most basins is also well captured. However, there are some regional biases in the geographical distribution of TC formation rates. To identify the sources of these biases, a suite of model-simulated large-scale climate conditions that critically modulate TC formation rates are further evaluated, including the assessment of a multivariate genesis potential index. Results indicate that the model TC genesis biases correspond well to the inherent biases in the simulated large-scale climatic states, although the relative effects on TC genesis of some variables differs between basins. This highlights the model’s mean-state dependency in simulating accurate TC formation rates.

scholarly journals Climate SPHINX: evaluating the impact of resolution and stochastic physics parameterisations in the EC-Earth global climate model

2017 ◽  
Vol 10 (3) ◽  
pp. 1383-1402 ◽  
Author(s):  
Paolo Davini ◽  
Jost von Hardenberg ◽  
Susanna Corti ◽  
Hannah M. Christensen ◽  
Stephan Juricke ◽  
...  
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Climate Model ◽  
Global Climate ◽  
Rainfall Variability ◽  
Global Climate Model ◽  
Small Scale ◽  
Low Resolution ◽  
The Impact ◽  
Stochastic Physics

Abstract. The Climate SPHINX (Stochastic Physics HIgh resolutioN eXperiments) project is a comprehensive set of ensemble simulations aimed at evaluating the sensitivity of present and future climate to model resolution and stochastic parameterisation. The EC-Earth Earth system model is used to explore the impact of stochastic physics in a large ensemble of 30-year climate integrations at five different atmospheric horizontal resolutions (from 125 up to 16 km). The project includes more than 120 simulations in both a historical scenario (1979–2008) and a climate change projection (2039–2068), together with coupled transient runs (1850–2100). A total of 20.4 million core hours have been used, made available from a single year grant from PRACE (the Partnership for Advanced Computing in Europe), and close to 1.5 PB of output data have been produced on SuperMUC IBM Petascale System at the Leibniz Supercomputing Centre (LRZ) in Garching, Germany. About 140 TB of post-processed data are stored on the CINECA supercomputing centre archives and are freely accessible to the community thanks to an EUDAT data pilot project. This paper presents the technical and scientific set-up of the experiments, including the details on the forcing used for the simulations performed, defining the SPHINX v1.0 protocol. In addition, an overview of preliminary results is given. An improvement in the simulation of Euro-Atlantic atmospheric blocking following resolution increase is observed. It is also shown that including stochastic parameterisation in the low-resolution runs helps to improve some aspects of the tropical climate – specifically the Madden–Julian Oscillation and the tropical rainfall variability. These findings show the importance of representing the impact of small-scale processes on the large-scale climate variability either explicitly (with high-resolution simulations) or stochastically (in low-resolution simulations).

scholarly journals Future Changes in Tropical Cyclone Activity in High‐Resolution Large‐Ensemble Simulations

2017 ◽  
Vol 44 (19) ◽  
pp. 9910-9917 ◽  
Author(s):  
Kohei Yoshida ◽  
Masato Sugi ◽  
Ryo Mizuta ◽  
Hiroyuki Murakami ◽  
Masayoshi Ishii
Keyword(s):  
High Resolution ◽  
Tropical Cyclone ◽  
Tropical Cyclone Activity ◽  
Cyclone Activity ◽  
Large Ensemble ◽  
Ensemble Simulations

scholarly journals BASIC EXAMINATION OF FUTURE CHANGE OF TROPICAL CYCLONE AND STORM SURGE PROPERTIES UNDER CLIMATE CHANGE

2012 ◽  
Vol 1 (33) ◽  
pp. 23
Author(s):  
Sota Nakajo ◽  
Nobuhito Mori ◽  
Tomohiro Yasuda ◽  
Hajime Mase
Keyword(s):  
Tropical Cyclone ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Temporal Correlation ◽  
Future Change ◽  
Ensemble Averaging ◽  
Global Climate Changes ◽  
Correlation Algorithm ◽  
Future Prediction

Recently high-resolution Global Climate Model (GCM) shows that global climate changes may cause the future change of the Tropical Cyclone (TC) characteristics, such as frequency, developing process and intensity. However, there are two difficulties for assessment of future TC disaster, one is uncertainty of future prediction in GCM, and another is shortage of sample TC data. In this paper, we estimated future changes of TC properties and reduced uncertainty by ensemble averaging of multi-GCM prediction results, and generated many synthetic TC data with Global Stochastic Tropical Cyclone Model (GSTCM). In addition, GSTCM which have empirical temporal correlation algorithm was improved for the reproducibility of arrival TC statistics by cluster analysis of TC data. This upgrade could pave the way to local future prediction of TC disaster.

scholarly journals Tracking Scheme Dependence of Simulated Tropical Cyclone Response to Idealized Climate Simulations

2014 ◽  
Vol 27 (24) ◽  
pp. 9197-9213 ◽  
Author(s):  
Michael Horn ◽  
Kevin Walsh ◽  
Ming Zhao ◽  
Suzana J. Camargo ◽  
Enrico Scoccimarro ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
Tropical Cyclones ◽  
General Circulation ◽  
Climate Model ◽  
Circulation Model ◽  
General Circulation Models ◽  
Tropical Cyclone Activity ◽  
Model Data ◽  
Cyclone Activity ◽  
Sensitivity Testing

Abstract Future tropical cyclone activity is a topic of great scientific and societal interest. In the absence of a climate theory of tropical cyclogenesis, general circulation models are the primary tool available for investigating the issue. However, the identification of tropical cyclones in model data at moderate resolution is complex, and numerous schemes have been developed for their detection. The influence of different tracking schemes on detected tropical cyclone activity and responses in the Hurricane Working Group experiments is examined herein. These are idealized atmospheric general circulation model experiments aimed at determining and distinguishing the effects of increased sea surface temperature and other increased CO2 effects on tropical cyclone activity. Two tracking schemes are applied to these data and the tracks provided by each modeling group are analyzed. The results herein indicate moderate agreement between the different tracking methods, with some models and experiments showing better agreement across schemes than others. When comparing responses between experiments, it is found that much of the disagreement between schemes is due to differences in duration, wind speed, and formation-latitude thresholds. After homogenization in these thresholds, agreement between different tracking methods is improved. However, much disagreement remains, accountable for by more fundamental differences between the tracking schemes. The results indicate that sensitivity testing and selection of objective thresholds are the key factors in obtaining meaningful, reproducible results when tracking tropical cyclones in climate model data at these resolutions, but that more fundamental differences between tracking methods can also have a significant impact on the responses in activity detected.

scholarly journals The role of serial European windstorm clustering for extreme seasonal losses as determined from multi-centennial simulations of high-resolution global climate model data

2018 ◽  
Vol 18 (11) ◽  
pp. 2991-3006 ◽  
Author(s):  
Matthew D. K. Priestley ◽  
Helen F. Dacre ◽  
Len C. Shaffrey ◽  
Kevin I. Hodges ◽  
Joaquim G. Pinto
Keyword(s):  
High Resolution ◽  
Return Period ◽  
Large Scale ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Geographic Region ◽  
Near Surface ◽  
Short Period

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.

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