The Science of William M. Gray: His Contributions to the Knowledge of Tropical Meteorology and Tropical Cyclones

2017 ◽  
Vol 98 (11) ◽  
pp. 2311-2336 ◽  
Author(s):  
Philip J. Klotzbach ◽  
Johnny C. L. Chan ◽  
Patrick J. Fitzpatrick ◽  
William M. Frank ◽  
Christopher W. Landsea ◽  
...  
Keyword(s):  
Climate Change ◽  
Tropical Cyclone ◽  
Tropical Cyclones ◽  
Conceptual Development ◽  
Tropical Meteorology ◽  
Seasonal Forecasting ◽  
Seasonal Forecasts ◽  
Theoretical Concepts ◽  
Flight Data ◽  
Scientific Legacy

Abstract Advances in knowledge in tropical meteorological research are discussed in the context of contributions made by Professor William M. Gray. Gray pioneered the compositing approach to observational tropical meteorology through assembling of global radiosonde datasets and tropical cyclone research flight data. In the 1970s, he made fundamental contributions to knowledge of convective–larger-scale interactions. Throughout his career, he wrote seminal papers on tropical cyclone structure, cyclogenesis, motion, and seasonal forecasts. His conceptual development of a seasonal genesis parameter also laid an important framework for both seasonal forecasting as well as climate change studies on tropical cyclones. His work was a blend of both observationally based studies and the development of theoretical concepts. This paper reviews the progress in knowledge in the areas where Dr. Gray provided his largest contributions and describes the scientific legacy of Gray’s contributions to tropical meteorology.

Assessment of Météo-France current seasonal forecasting system S7 and outlook on the upcoming S8

2021 ◽  
Author(s):  
Jean-François Guérémy ◽  
Clotilde Dubois ◽  
Christian Viel ◽  
Laurent Dorel ◽  
Constantin Ardilouze ◽  
...  
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Climate Model ◽  
Coupled System ◽  
Seasonal Forecasting ◽  
Coupled Climate Model ◽  
Seasonal Forecasts ◽  
Forecasting System ◽  
Initialization Strategy ◽  
The Eu

<p>In the framework of the EU <span>C</span><span>opernicus</span> <span>Climate Change Service </span><span>(</span>C3S) program, a new coupled system has been developed at Météo-France (MF) to carry out seasonal forecasts at a 7-month range. This system (called S7) is in operation in real time since October 2019. S7 is based upon the MF coupled climate model CNRM-CM6 used for CMIP6 simulations, in its high resolution configuration: ARPEGE-Climat (Tl359-0.5° l91, including different tuning choices for the physics), NEMO 3.6 (0.25° l75) and the OASIS coupler. The aim of this presentation is twofold.</p><p>First, an assessment of S7 performance will be presented in terms of biases, and both deterministic and probabilistic predictability scores. A comparison with the earlier MF system and the current ECMWF system will be shown.</p><p>Second, incremental updates from S7 to S8, to be in operation in June 2021, will be presented and assessed versus S7. The upgrade includes a larger atmospheric resolution from l91 to l137, together with a coupled initialization strategy to replace the earlier independent atmospheric and oceanic initialization.</p>

scholarly journals Climate and Tropical Cyclone Activity: A New Model Downscaling Approach

2006 ◽  
Vol 19 (19) ◽  
pp. 4797-4802 ◽  
Author(s):  
Kerry Emanuel
Keyword(s):  
Climate Change ◽  
Tropical Cyclone ◽  
Tropical Cyclones ◽  
Power Dissipation ◽  
Climate Model ◽  
Global Climate ◽  
Mixed Layer Depth ◽  
Ocean Mixed Layer ◽  
Layer Depth ◽  
Ocean Mixed Layer Depth

Abstract While there is a pressing need to understand and predict the response of tropical cyclones to climate change, global climate models are at present too coarse to resolve tropical cyclones to the extent necessary to simulate their intensity, and their ability to simulate genesis is questionable. For these reasons, a “downscaling” approach to modeling the effect of climate change on tropical cyclones is desirable. Here a new approach to downscaling is introduced that consists of generating a large set of synthetic storm tracks whose statistics are consistent with the large-scale general circulation of the climate model, and then running a deterministic, coupled tropical cyclone model along each track, with atmospheric and upper-ocean thermodynamic conditions taken from the global climate model. As a first step in this direction, this paper explores the sensitivity of the intensity of a large sample of tropical cyclones to changes in potential intensity, shear, and ocean mixed layer depth, fixing other variables, including the space–time probability distribution of storm genesis. It is shown that a 10% increase in potential intensity leads to a 65% increase in the “power dissipation index,” a measure of the total amount of mechanical energy generated by tropical cyclones over their life spans. This is consistent with the observed increase of power dissipation over the past 50 yr. Storms are somewhat less influenced by equivalent fractional changes in environmental wind shear or ocean mixed layer depth.

Seasonal forecasting of tropical cyclone activity in the Australian and the South Pacific Ocean regions

2015 ◽  
Vol 1 (1) ◽  
Author(s):  
J.S. Wijnands ◽  
G. Qian ◽  
K.L. Shelton ◽  
R.J.B. Fawcett ◽  
J.C.L. Chan ◽  
...  
Keyword(s):  
Pacific Ocean ◽  
Tropical Cyclone ◽  
Southern Oscillation ◽  
South Pacific ◽  
Seasonal Forecasting ◽  
Support Vector ◽  
Seasonal Forecasts ◽  
The South ◽  
Explanatory Variables ◽  
South Pacific Ocean

AbstractThe Australian Bureau of Meteorology (Bureau) issues operational tropical cyclone (TC) seasonal forecasts for the Australian region (AR) and the South Pacific Ocean (SPO) and subregions therein. The forecasts are issued in October, ahead of the Southern Hemisphere TC season (November to April). Improvement of operational TC seasonal forecasts can lead to more accurate warnings for coastal communities to prepare for TC hazards. This study investigates the use of support vector regression (SVR) models, exploring new explanatory variables and non-linear relationships between them, the use of model averaging, and lastly the integration of forecast intervals based on a bias-corrected and accelerated non-parametric bootstrap. Hindcasting analyses show that the SVR model outperforms several benchmark methods. Analysis of the generated models shows that the Dipole Mode Index, 5VAR index and the Southern Oscillation Index are the most frequently selected as explanatory variables for TC seasonal forecasting in all regions. The usage of ENSOrelated covariates implies that definitions of regions and subregions may have to be updated to achieve optimal forecasting performance. Overall, the new SVR methodology is an improvement over the current linear discriminant analysis models and has the potential to increase accuracy of TC seasonal forecasts in the AR and SPO.

scholarly journals Coastal flooding will disproportionately impact people on river deltas

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Douglas A. Edmonds ◽  
Rebecca L. Caldwell ◽  
Eduardo S. Brondizio ◽  
Sacha M. O. Siani
Keyword(s):  
Climate Change ◽  
Tropical Cyclone ◽  
Tropical Cyclones ◽  
Coastal Flooding ◽  
Tropical Cyclone Activity ◽  
Cyclone Activity ◽  
Know How ◽  
Developed Economies ◽  
River Deltas ◽  
Global Population

Abstract Climate change is intensifying tropical cyclones, accelerating sea-level rise, and increasing coastal flooding. River deltas are especially vulnerable to flooding because of their low elevations and densely populated cities. Yet, we do not know how many people live on deltas and their exposure to flooding. Using a new global dataset, we show that 339 million people lived on river deltas in 2017 and 89% of those people live in the same latitudinal zone as most tropical cyclone activity. We calculate that 41% (31 million) of the global population exposed to tropical cyclone flooding live on deltas, with 92% (28 million) in developing or least developed economies. Furthermore, 80% (25 million) live on sediment-starved deltas, which cannot naturally mitigate flooding through sediment deposition. Given that coastal flooding will only worsen, we must reframe this problem as one that will disproportionately impact people on river deltas, particularly in developing and least-developed economies.

scholarly journals Conversion of the Knutson et al. (2020) Tropical Cyclone Climate Change Projections to Risk Model Baselines

2021 ◽  
Author(s):  
Stephen Jewson
Keyword(s):  
Climate Change ◽  
Tropical Cyclone ◽  
Surface Temperature ◽  
Tropical Cyclones ◽  
Risk Model ◽  
Computer Code ◽  
Risk Modeling ◽  
Risk Models ◽  
Global Mean Surface Temperature ◽  
Global Mean

AbstractKnutson et al. (2020) recently published a meta-study that gives multi-model projections for changes in various properties of tropical cyclones under climate change. They considered frequency of tropical cyclones, frequency of very intense tropical cyclones, intensity of tropical cyclones, and total rainfall rate of tropical cyclones. For each of these properties, they reported changes globally and by basin for the six major tropical cylone basins. The changes were presented as the change that would occur with 2 °C warming of global mean surface temperature. These projections are potentially of great use to the tropical cyclone risk modeling community. However, most risk models use temporal baselines, such as the period from 1950 to 2019, and the Knutson et al. results can only be applied to risk models after some steps of adjustment involving past and future global mean surface temperature values. We derive the necessary adjustments and present and discuss some of the resulting projections, for different properties, basins, RCPs and baselines. We find that the results are sensitive to the baseline being used, which implies that users of tropical cyclone risk models need to make sure they clearly understand what baseline their model represents before they adjust the model for climate change. One part of our analysis derives estimates of the implied impact of climate change so far on TC properties, relative to a representative baseline. The computer code we use to calculate the adjustments is available online.

scholarly journals Forecasted attribution of the human influence on Hurricane Florence

2020 ◽  
Vol 6 (1) ◽  
pp. eaaw9253 ◽  
Author(s):  
K. A. Reed ◽  
A. M. Stansfield ◽  
M. F. Wehner ◽  
C. M. Zarzycki
Keyword(s):  
Climate Change ◽  
Numerical Model ◽  
Tropical Cyclone ◽  
Tropical Cyclones ◽  
Local Maximum ◽  
Extreme Weather ◽  
Anthropogenic Climate Change ◽  
Human Influence ◽  
Impact Of Climate Change ◽  
The Impact

Changes in extreme weather, such as tropical cyclones, are one of the most serious ways society experiences the impact of climate change. Advance forecasted conditional attribution statements, using a numerical model, were made about the anthropogenic climate change influence on an individual tropical cyclone, Hurricane Florence. Mean total overland rainfall amounts associated with the forecasted storm’s core were increased by 4.9 ± 4.6% with local maximum amounts experiencing increases of 3.8 ± 5.7% due to climate change. A slight increase in the forecasted storm size of 1 to 2% was also attributed. This work reviews our forecasted attribution statement with the benefit of hindsight, demonstrating credibility of advance attribution statements for tropical cyclones.

scholarly journals Pacific subsurface ocean temperature as a long-range predictor of South China tropical cyclone landfall

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Nathan Sparks ◽  
Ralf Toumi
Keyword(s):  
Tropical Cyclone ◽  
Tropical Cyclones ◽  
South China ◽  
Southern Oscillation ◽  
Warm Pool ◽  
Ocean Temperature ◽  
Seasonal Forecasts ◽  
Subsurface Ocean ◽  
Landfalling Tropical Cyclones ◽  
Tropical Cyclone Landfall

Abstract Seasonal forecasts of the tropical cyclones which frequently make landfall along the densely populated South China coast are highly desirable. Here, we analyse observations of landfalling tropical cyclones in South China and of subsurface ocean temperatures in the Pacific warm pool region, and identify the possibility of forecasts of South China tropical cyclone landfall a year ahead. Specifically, we define a subsurface temperature index, subNiño4, and build a predictive model based on subNiño4 anomalies with a robust double cross-validated forecast skill against climatology of 23%, similar in skill to existing forecasts issued much later in the spring. We suggest that subNiño4 ocean temperatures precede the surface El Niño/Southern Oscillation state by about 12 months, and that the zonal shifts in atmospheric heating then change mid-level winds to steer tropical cyclones towards landfall in South China. We note that regional subsurface ocean temperature anomalies may permit atmospheric predictions in other locations at a longer range than is currently thought possible.

scholarly journals The use of the Conway-Maxwell-Poisson in the seasonal forecasting of tropical cyclones

2021 ◽  
Author(s):  
Timothy D. Mitchell ◽  
Joanne Camp
Keyword(s):  
Tropical Cyclone ◽  
Poisson Process ◽  
Tropical Cyclones ◽  
Dispersion Characteristic ◽  
Seasonal Forecast ◽  
Seasonal Forecasting ◽  
Mean And Variance ◽  
The Mean ◽  
Over Dispersion ◽  
Tropical Cyclone Landfall

AbstractThe Conway-Maxwell-Poisson distribution improves the precision with which seasonal counts of tropical cyclones may be modelled. Conventionally the Poisson is used, which assumes that the formation and transit of tropical cyclones is the result of a Poisson process, such that their frequency distribution has equal mean and variance (‘equi-dispersion’). However, earlier studies of observed records have sometimes found over-dispersion, where the variance exceeds the mean, indicating that tropical cyclones are clustered in particular years. The evidence presented here demonstrates that at least some of this over-dispersion arises from observational inhomogeneities. Once this is removed, and particularly near the coasts, there is evidence for equi-dispersion or under-dispersion. In order to more accurately model numbers of tropical cyclones, we investigate the use of the Conway-Maxwell-Poisson as an alternative to the Poisson that represents any dispersion characteristic. An example is given for east China where using it improves the skill of a prototype seasonal forecast of tropical cyclone landfall.

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