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Michael Horn
,
Kevin Walsh
,
Ming Zhao
,
Suzana J. Camargo
,
Enrico Scoccimarro
,
Hiroyuki Murakami
,
Hui Wang
,
Andrew Ballinger
,
Arun Kumar
,
Daniel A. Shaevitz
,
Jeffrey A. Jonas
, and
Kazuyoshi Oouchi

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.

Full access
Hiroyuki Murakami
,
Gabriel A. Vecchi
,
Seth Underwood
,
Thomas L. Delworth
,
Andrew T. Wittenberg
,
Whit G. Anderson
,
Jan-Huey Chen
,
Richard G. Gudgel
,
Lucas M. Harris
,
Shian-Jiann Lin
, and
Fanrong Zeng

Abstract

A new high-resolution Geophysical Fluid Dynamics Laboratory (GFDL) coupled model [the High-Resolution Forecast-Oriented Low Ocean Resolution (FLOR) model (HiFLOR)] has been developed and used to investigate potential skill in simulation and prediction of tropical cyclone (TC) activity. HiFLOR comprises high-resolution (~25-km mesh) atmosphere and land components and a more moderate-resolution (~100-km mesh) sea ice and ocean component. HiFLOR was developed from FLOR by decreasing the horizontal grid spacing of the atmospheric component from 50 to 25 km, while leaving most of the subgrid-scale physical parameterizations unchanged. Compared with FLOR, HiFLOR yields a more realistic simulation of the structure, global distribution, and seasonal and interannual variations of TCs, as well as a comparable simulation of storm-induced cold wakes and TC-genesis modulation induced by the Madden–Julian oscillation (MJO). Moreover, HiFLOR is able to simulate and predict extremely intense TCs (Saffir–Simpson hurricane categories 4 and 5) and their interannual variations, which represents the first time a global coupled model has been able to simulate such extremely intense TCs in a multicentury simulation, sea surface temperature restoring simulations, and retrospective seasonal predictions.

Full access
Mitchell Bushuk
,
Yongfei Zhang
,
Michael Winton
,
Bill Hurlin
,
Thomas Delworth
,
Feiyu Lu
,
Liwei Jia
,
Liping Zhang
,
William Cooke
,
Matthew Harrison
,
Nathaniel C. Johnson
,
Sarah Kapnick
,
Colleen McHugh
,
Hiroyuki Murakami
,
Anthony Rosati
,
Kai-Chih Tseng
,
Andrew T. Wittenberg
,
Xiaosong Yang
, and
Fanrong Zeng

Abstract

Research over the past decade has demonstrated that dynamical forecast systems can skillfully predict pan-Arctic sea ice extent (SIE) on the seasonal time scale; however, there have been fewer assessments of prediction skill on user-relevant spatial scales. In this work, we evaluate regional Arctic SIE predictions made with the Forecast-Oriented Low Ocean Resolution (FLOR) and Seamless System for Prediction and Earth System Research (SPEAR_MED) dynamical seasonal forecast systems developed at the NOAA/Geophysical Fluid Dynamics Laboratory. Compared to FLOR, we find that the recently developed SPEAR_MED system displays improved skill in predicting regional detrended SIE anomalies, partially owing to improvements in sea ice concentration (SIC) and thickness (SIT) initial conditions. In both systems, winter SIE is skillfully predicted up to 11 months in advance, whereas summer minimum SIE predictions are limited by the Arctic spring predictability barrier, with typical skill horizons of roughly 4 months. We construct a parsimonious set of simple statistical prediction models to investigate the mechanisms of sea ice predictability in these systems. Three distinct predictability regimes are identified: a summer regime dominated by SIE and SIT anomaly persistence; a winter regime dominated by SIE and upper-ocean heat content (uOHC) anomaly persistence; and a combined regime in the Chukchi Sea, characterized by a trade-off between uOHC-based and SIT-based predictability that occurs as the sea ice edge position evolves seasonally. The combination of regional SIE, SIT, and uOHC predictors is able to reproduce the SIE skill of the dynamical models in nearly all regions, suggesting that these statistical predictors provide a stringent skill benchmark for assessing seasonal sea ice prediction systems.

Open access
Liwei Jia
,
Thomas L. Delworth
,
Sarah Kapnick
,
Xiaosong Yang
,
Nathaniel C. Johnson
,
William Cooke
,
Feiyu Lu
,
Matthew Harrison
,
Anthony Rosati
,
Fanrong Zeng
,
Colleen McHugh
,
Andrew T. Wittenberg
,
Liping Zhang
,
Hiroyuki Murakami
, and
Kai-Chih Tseng

Abstract

This study shows that the frequency of North American summertime (June–August) heat extremes is skillfully predicted several months in advance in the newly developed Geophysical Fluid Dynamics Laboratory (GFDL) Seamless System for Prediction and Earth System Research (SPEAR) seasonal forecast system. Using a statistical optimization method, the average predictability time, we identify three large-scale components of the frequency of North American summer heat extremes that are predictable with significant correlation skill. One component, which is related to a secular warming trend, shows a continent-wide increase in the frequency of summer heat extremes and is highly predictable at least 9 months in advance. This trend component is likely a response to external radiative forcing. The second component is largely driven by the sea surface temperatures in the North Pacific and North Atlantic and is significantly correlated with the central U.S. soil moisture. The second component shows largest loadings over the central United States and is significantly predictable 9 months in advance. The third component, which is related to the central Pacific El Niño, displays a dipole structure over North America and is predictable up to 4 months in advance. Potential implications for advancing seasonal predictions of North American summertime heat extremes are discussed.

Full access
Liping Zhang
,
Thomas L. Delworth
,
Sarah Kapnick
,
Jie He
,
William Cooke
,
Andrew T. Wittenberg
,
Nathaniel C. Johnson
,
Anthony Rosati
,
Xiaosong Yang
,
Feiyu Lu
,
Mitchell Bushuk
,
Colleen McHugh
,
Hiroyuki Murakami
,
Fanrong Zeng
,
Liwei Jia
,
Kai-Chih Tseng
, and
Yushi Morioka

Abstract

One of the most puzzling observed features of recent climate has been a multidecadal surface cooling trend over the subpolar Southern Ocean (SO). In this study we use large ensembles of simulations with multiple climate models to study the role of the SO meridional overturning circulation (MOC) in these sea surface temperature (SST) trends. We find that multiple competing processes play prominent roles, consistent with multiple mechanisms proposed in the literature for the observed cooling. Early in the simulations (twentieth century and early twenty-first century) internal variability of the MOC can have a large impact, in part due to substantial simulated multidecadal variability of the MOC. Ensemble members with initially strong convection (and related surface warming due to convective mixing of subsurface warmth to the surface) tend to subsequently cool at the surface as convection associated with internal variability weakens. A second process occurs in the late-twentieth and twenty-first centuries, as weakening of oceanic convection associated with global warming and high-latitude freshening can contribute to the surface cooling trend by suppressing convection and associated vertical mixing of subsurface heat. As the simulations progress, the multidecadal SO variability is suppressed due to forced changes in the mean state and increased oceanic stratification. As a third process, the shallower mixed layers can then rapidly warm due to increasing forcing from greenhouse gas warming. Also, during this period the ensemble spread of SO SST trend partly arises from the spread of the wind-driven Deacon cell strength. Thus, different processes could conceivably have led to the observed cooling trend, consistent with the range of possibilities presented in the literature. To better understand the causes of the observed trend, it is important to better understand the characteristics of internal low-frequency variability in the SO and the response of that variability to global warming.

Full access
Hsi-Yen Ma
,
A. Cheska Siongco
,
Stephen A. Klein
,
Shaocheng Xie
,
Alicia R. Karspeck
,
Kevin Raeder
,
Jeffrey L. Anderson
,
Jiwoo Lee
,
Ben P. Kirtman
,
William J. Merryfield
,
Hiroyuki Murakami
, and
Joseph J. Tribbia

Abstract

The correspondence between mean sea surface temperature (SST) biases in retrospective seasonal forecasts (hindcasts) and long-term climate simulations from five global climate models is examined to diagnose the degree to which systematic SST biases develop on seasonal time scales. The hindcasts are from the North American Multimodel Ensemble, and the climate simulations are from the Coupled Model Intercomparison Project. The analysis suggests that most robust climatological SST biases begin to form within 6 months of a realistically initialized integration, although the growth rate varies with location, time, and model. In regions with large biases, interannual variability and ensemble spread is much smaller than the climatological bias. Additional ensemble hindcasts of the Community Earth System Model with a different initialization method suggest that initial conditions do matter for the initial bias growth, but the overall global bias patterns are similar after 6 months. A hindcast approach is more suitable to study biases over the tropics and subtropics than over the extratropics because of smaller initial biases and faster bias growth. The rapid emergence of SST biases makes it likely that fast processes with time scales shorter than the seasonal time scales in the atmosphere and upper ocean are responsible for a substantial part of the climatological SST biases. Studying the growth of biases may provide important clues to the causes and ultimately the amelioration of these biases. Further, initialized seasonal hindcasts can profitably be used in the development of high-resolution coupled ocean–atmosphere models.

Full access
Suzana J. Camargo
,
Claudia F. Giulivi
,
Adam H. Sobel
,
Allison A. Wing
,
Daehyun Kim
,
Yumin Moon
,
Jeffrey D. O. Strong
,
Anthony D. Del Genio
,
Maxwell Kelley
,
Hiroyuki Murakami
,
Kevin A. Reed
,
Enrico Scoccimarro
,
Gabriel A. Vecchi
,
Michael F. Wehner
,
Colin Zarzycki
, and
Ming Zhao

Abstract

Here we explore the relationship between the global climatological characteristics of tropical cyclones (TCs) in climate models and the modeled large-scale environment across a large number of models. We consider the climatology of TCs in 30 climate models with a wide range of horizontal resolutions. We examine if there is a systematic relationship between the climatological diagnostics for the TC activity [number of tropical cyclones (NTC) and accumulated cyclone energy (ACE)] by hemisphere in the models and the environmental fields usually associated with TC activity, when examined across a large number of models. For low-resolution models, there is no association between a conducive environment and TC activity, when integrated over space (tropical hemisphere) and time (all years of the simulation). As the model resolution increases, for a couple of variables, in particular vertical wind shear, there is a statistically significant relationship in between the models’ TC characteristics and the environmental characteristics, but in most cases the relationship is either nonexistent or the opposite of what is expected based on observations. It is important to stress that these results do not imply that there is no relationship between individual models’ environmental fields and their TC activity by basin with respect to intraseasonal or interannual variability or due to climate change. However, it is clear that when examined across many models, the models’ mean state does not have a consistent relationship with the models’ mean TC activity. Therefore, other processes associated with the model physics, dynamical core, and resolution determine the climatological TC activity in climate models.

Free access
Mitchell Bushuk
,
Michael Winton
,
F. Alexander Haumann
,
Thomas Delworth
,
Feiyu Lu
,
Yongfei Zhang
,
Liwei Jia
,
Liping Zhang
,
William Cooke
,
Matthew Harrison
,
Bill Hurlin
,
Nathaniel C. Johnson
,
Sarah B. Kapnick
,
Colleen McHugh
,
Hiroyuki Murakami
,
Anthony Rosati
,
Kai-Chih Tseng
,
Andrew T. Wittenberg
,
Xiaosong Yang
, and
Fanrong Zeng

Abstract

Compared to the Arctic, seasonal predictions of Antarctic sea ice have received relatively little attention. In this work, we utilize three coupled dynamical prediction systems developed at the Geophysical Fluid Dynamics Laboratory to assess the seasonal prediction skill and predictability of Antarctic sea ice. These systems, based on the FLOR, SPEAR_LO, and SPEAR_MED dynamical models, differ in their coupled model components, initialization techniques, atmospheric resolution, and model biases. Using suites of retrospective initialized seasonal predictions spanning 1992–2018, we investigate the role of these factors in determining Antarctic sea ice prediction skill and examine the mechanisms of regional sea ice predictability. We find that each system is capable of skillfully predicting regional Antarctic sea ice extent (SIE) with skill that exceeds a persistence forecast. Winter SIE is skillfully predicted 11 months in advance in the Weddell, Amundsen/Bellingshausen, Indian, and west Pacific sectors, whereas winter skill is notably lower in the Ross sector. Zonally advected upper-ocean heat content anomalies are found to provide the crucial source of prediction skill for the winter sea ice edge position. The recently developed SPEAR systems are more skillful than FLOR for summer sea ice predictions, owing to improvements in sea ice concentration and sea ice thickness initialization. Summer Weddell SIE is skillfully predicted up to 9 months in advance in SPEAR_MED, due to the persistence and drift of initialized sea ice thickness anomalies from the previous winter. Overall, these results suggest a promising potential for providing operational Antarctic sea ice predictions on seasonal time scales.

Open access
Kevin J. E. Walsh
,
Suzana J. Camargo
,
Gabriel A. Vecchi
,
Anne Sophie Daloz
,
James Elsner
,
Kerry Emanuel
,
Michael Horn
,
Young-Kwon Lim
,
Malcolm Roberts
,
Christina Patricola
,
Enrico Scoccimarro
,
Adam H. Sobel
,
Sarah Strazzo
,
Gabriele Villarini
,
Michael Wehner
,
Ming Zhao
,
James P. Kossin
,
Tim LaRow
,
Kazuyoshi Oouchi
,
Siegfried Schubert
,
Hui Wang
,
Julio Bacmeister
,
Ping Chang
,
Fabrice Chauvin
,
Christiane Jablonowski
,
Arun Kumar
,
Hiroyuki Murakami
,
Tomoaki Ose
,
Kevin A. Reed
,
Ramalingam Saravanan
,
Yohei Yamada
,
Colin M. Zarzycki
,
Pier Luigi Vidale
,
Jeffrey A. Jonas
, and
Naomi Henderson

Abstract

While a quantitative climate theory of tropical cyclone formation remains elusive, considerable progress has been made recently in our ability to simulate tropical cyclone climatologies and to understand the relationship between climate and tropical cyclone formation. Climate models are now able to simulate a realistic rate of global tropical cyclone formation, although simulation of the Atlantic tropical cyclone climatology remains challenging unless horizontal resolutions finer than 50 km are employed. This article summarizes published research from the idealized experiments of the Hurricane Working Group of U.S. Climate and Ocean: Variability, Predictability and Change (CLIVAR). This work, combined with results from other model simulations, has strengthened relationships between tropical cyclone formation rates and climate variables such as midtropospheric vertical velocity, with decreased climatological vertical velocities leading to decreased tropical cyclone formation. Systematic differences are shown between experiments in which only sea surface temperature is increased compared with experiments where only atmospheric carbon dioxide is increased. Experiments where only carbon dioxide is increased are more likely to demonstrate a decrease in tropical cyclone numbers, similar to the decreases simulated by many climate models for a future, warmer climate. Experiments where the two effects are combined also show decreases in numbers, but these tend to be less for models that demonstrate a strong tropical cyclone response to increased sea surface temperatures. Further experiments are proposed that may improve our understanding of the relationship between climate and tropical cyclone formation, including experiments with two-way interaction between the ocean and the atmosphere and variations in atmospheric aerosols.

Full access
Kevin J. E. Walsh
,
Suzana J. Camargo
,
Gabriel A. Vecchi
,
Anne Sophie Daloz
,
James Elsner
,
Kerry Emanuel
,
Michael Horn
,
Young-Kwon Lim
,
Malcolm Roberts
,
Christina Patricola
,
Enrico Scoccimarro
,
Adam H. Sobel
,
Sarah Strazzo
,
Gabriele Villarini
,
Michael Wehner
,
Ming Zhao
,
James P. Kossin
,
Tim LaRow
,
Kazuyoshi Oouchi
,
Siegfried Schubert
,
Hui Wang
,
Julio Bacmeister
,
Ping Chang
,
Fabrice Chauvin
,
Christiane Jablonowski
,
Arun Kumar
,
Hiroyuki Murakami
,
Tomoaki Ose
,
Kevin A. Reed
,
Ramalingam Saravanan
,
Yohei Yamada
,
Colin M. Zarzycki
,
Pier Luigi Vidale
,
Jeffrey A. Jonas
, and
Naomi Henderson
Full access