Articles | Volume 10, issue 2
https://doi.org/10.5194/ascmo-10-69-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/ascmo-10-69-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
22 Jul 2024
| 22 Jul 2024
Spatiotemporal methods for estimating subsurface ocean thermal response to tropical cyclones
Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, PA, USA
Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA
Mikael Kuusela
Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, PA, USA
Ann B. Lee
Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, PA, USA
Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA
Donata Giglio
Department of Atmospheric and Oceanic Sciences, University of Colorado Boulder, Boulder, CO, USA
Kimberly M. Wood
Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, AZ, USA
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Viet Dung Nguyen, Sergiy Vorogushyn, Katrin Nissen, Lukas Brunner, and Bruno Merz
Adv. Stat. Clim. Meteorol. Oceanogr., 10, 195–216, https://doi.org/10.5194/ascmo-10-195-2024, https://doi.org/10.5194/ascmo-10-195-2024, 2024
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Juliette Mukangango, Amanda Muyskens, and Benjamin W. Priest
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Benjamin James Washington, Lynne Seymour, and Thomas L. Mote
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Katarina Lashgari, Gudrun Brattström, Anders Moberg, and Rolf Sundberg
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Katarina Lashgari, Anders Moberg, and Gudrun Brattström
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Qiuyi Wu, Julie Bessac, Whitney Huang, Jiali Wang, and Rao Kotamarthi
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 205–224, https://doi.org/10.5194/ascmo-8-205-2022, https://doi.org/10.5194/ascmo-8-205-2022, 2022
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Timothy DelSole and Michael K. Tippett
Adv. Stat. Clim. Meteorol. Oceanogr., 7, 73–85, https://doi.org/10.5194/ascmo-7-73-2021, https://doi.org/10.5194/ascmo-7-73-2021, 2021
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realisticdoes not mean that the detailed patterns on a particular day are correct, but rather that the statistics over many years are realistic. Past approaches to answering this question often neglect correlations in space and time. This paper proposes a method for answering this question that accounts for correlations in space and time.
Julie Bessac and Philippe Naveau
Adv. Stat. Clim. Meteorol. Oceanogr., 7, 53–71, https://doi.org/10.5194/ascmo-7-53-2021, https://doi.org/10.5194/ascmo-7-53-2021, 2021
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We propose a new forecast evaluation scheme in the context of models that incorporate errors of the verification data. We rely on existing scoring rules and incorporate uncertainty and error of the verification data through a hidden variable and the conditional expectation of scores. By considering scores to be random variables, one can access the entire range of their distribution and illustrate that the commonly used mean score can be a misleading representative of the distribution.
Eric Gilleland
Adv. Stat. Clim. Meteorol. Oceanogr., 7, 13–34, https://doi.org/10.5194/ascmo-7-13-2021, https://doi.org/10.5194/ascmo-7-13-2021, 2021
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Verifying high-resolution weather forecasts has become increasingly complicated,
and simple, easy-to-understand summary measures are a good alternative. Recent work has demonstrated some common pitfalls with many such summaries. Here, new summary measures are introduced that do not suffer from these drawbacks, while still providing meaningful information.
Thomas Patrick Leahy
Adv. Stat. Clim. Meteorol. Oceanogr., 7, 1–11, https://doi.org/10.5194/ascmo-7-1-2021, https://doi.org/10.5194/ascmo-7-1-2021, 2021
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This study looked at estimating damages caused by hurricanes in the United States. It assessed the relationship between the maximum wind speed at landfall and the resulting damage caused. The study found that the complex processes that determine the size of the damages inflicted could be estimated using this simple relationship. This work could be used to examine how often extreme damage events are likely to occur and the impact of stronger hurricane winds on the US Atlantic and Gulf coasts.
Yoann Robin and Aurélien Ribes
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 205–221, https://doi.org/10.5194/ascmo-6-205-2020, https://doi.org/10.5194/ascmo-6-205-2020, 2020
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We have developed a new statistical method to describe how a severe weather event, such as a heat wave, may have been influenced by climate change. Our method incorporates both observations and data from various climate models to reflect climate model uncertainty. Our results show that both the probability and the intensity of the French July 2019 heatwave have increased significantly in response to human influence. We find that this heat wave might not have been possible without climate change.
Timothy DelSole and Michael K. Tippett
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 159–175, https://doi.org/10.5194/ascmo-6-159-2020, https://doi.org/10.5194/ascmo-6-159-2020, 2020
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Scientists often are confronted with the question of whether two time series are statistically distinguishable. This paper proposes a test for answering this question. The basic idea is to fit each time series to a time series model and then test whether the parameters in that model are equal. If a difference is detected, then new ways of visualizing those differences are proposed, including a clustering technique and a method based on optimal initial conditions.
Joshua North, Zofia Stanley, William Kleiber, Wiebke Deierling, Eric Gilleland, and Matthias Steiner
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 79–90, https://doi.org/10.5194/ascmo-6-79-2020, https://doi.org/10.5194/ascmo-6-79-2020, 2020
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Very short-term forecasting, called nowcasting, is used to monitor storms that pose a significant threat to people and infrastructure. These threats could include lightning strikes, hail, heavy precipitation, strong winds, and possible tornados. This paper proposes a fast approach to nowcasting lightning threats using simple statistical methods. The proposed model results in fast nowcasts that are more accurate than a competitive, computationally expensive, approach.
Ola Haug, Thordis L. Thorarinsdottir, Sigrunn H. Sørbye, and Christian L. E. Franzke
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 1–12, https://doi.org/10.5194/ascmo-6-1-2020, https://doi.org/10.5194/ascmo-6-1-2020, 2020
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Trends in gridded temperature data are commonly assessed independently for each grid cell, ignoring spatial coherencies. This may severely affect the interpretation of the results. This article proposes a space–time model for temperatures that allows for joint assessments of the trend across locations. In a case study of summer season trends in Europe, it is found that the region with a significant trend under spatial coherency is vastly different from that under independent assessments.
Alexis Hannart
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 161–171, https://doi.org/10.5194/ascmo-5-161-2019, https://doi.org/10.5194/ascmo-5-161-2019, 2019
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In climate change attribution studies, one often seeks to maximize a signal-to-noise ratio, where the
signalis the anthropogenic response and the
noiseis climate variability. A solution commonly used in D&A studies thus far consists of projecting the signal on the subspace spanned by the leading eigenvectors of climate variability. Here I show that this approach is vastly suboptimal – in fact, it leads instead to maximizing the noise-to-signal ratio. I then describe an improved solution.
Moritz N. Lang, Georg J. Mayr, Reto Stauffer, and Achim Zeileis
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 115–132, https://doi.org/10.5194/ascmo-5-115-2019, https://doi.org/10.5194/ascmo-5-115-2019, 2019
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Accurate wind forecasts are of great importance for decision-making processes in today's society. This work presents a novel probabilistic post-processing method for wind vector forecasts employing a bivariate Gaussian response distribution. To capture a possible mismatch between the predicted and observed wind direction caused by location-specific properties, the approach incorporates a smooth rotation of the wind direction conditional on the season and the forecasted ensemble wind direction.
X. Joey Wang, John R. J. Thompson, W. John Braun, and Douglas G. Woolford
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 57–66, https://doi.org/10.5194/ascmo-5-57-2019, https://doi.org/10.5194/ascmo-5-57-2019, 2019
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This paper presents the analysis of data from small-scale laboratory experimental smouldering fires that were digitally video-recorded. The video images of these fires bear a resemblance to remotely sensed images of wildfires and provide an opportunity to fit and assess a spatial model for fire spread that attempts to account for uncertainty in fire growth. We found that the fitting method is feasible, and the spatial model provides a suitable mathematical for the fire spread process.
Robin Tokmakian and Peter Challenor
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 17–35, https://doi.org/10.5194/ascmo-5-17-2019, https://doi.org/10.5194/ascmo-5-17-2019, 2019
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As an example of how to robustly determine climate model uncertainty, the paper describes an experiment that perturbs the initial conditions for the ocean's temperature of a climate model. A total of 30 perturbed simulations are used (via an emulator) to estimate spatial uncertainties for temperature and precipitation fields. We also examined (using maximum covariance analysis) how ocean temperatures affect air temperatures and precipitation over land and the importance of feedback processes.
Thorsten Simon, Georg J. Mayr, Nikolaus Umlauf, and Achim Zeileis
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 1–16, https://doi.org/10.5194/ascmo-5-1-2019, https://doi.org/10.5194/ascmo-5-1-2019, 2019
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Lightning in Alpine regions is associated with events such as thunderstorms,
extreme precipitation, high wind gusts, flash floods, and debris flows.
We present a statistical approach to predict lightning counts based on
numerical weather predictions. Lightning counts are considered on a grid
with 18 km mesh size. Skilful prediction is obtained for a forecast horizon
of 5 days over complex terrain.
Tony E. Wong
Adv. Stat. Clim. Meteorol. Oceanogr., 4, 53–63, https://doi.org/10.5194/ascmo-4-53-2018, https://doi.org/10.5194/ascmo-4-53-2018, 2018
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Millions of people worldwide are at a risk of coastal flooding, and this number will increase as the climate continues to change. This study analyzes how climate change affects future flood hazards. A new model that uses multiple climate variables for flood hazard is developed. For the case study of Norfolk, Virginia, the model predicts 23 cm higher flood levels relative to previous work. This work shows the importance of accounting for climate change in effectively managing coastal risks.
Amy Braverman, Snigdhansu Chatterjee, Megan Heyman, and Noel Cressie
Adv. Stat. Clim. Meteorol. Oceanogr., 3, 93–105, https://doi.org/10.5194/ascmo-3-93-2017, https://doi.org/10.5194/ascmo-3-93-2017, 2017
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In this paper, we introduce a method for expressing the agreement between climate model output time series and time series of observational data as a probability value. Our metric is an estimate of the probability that one would obtain two time series as similar as the ones under consideration, if the climate model and the observed series actually shared the same underlying climate signal.
Joshua P. French, Seth McGinnis, and Armin Schwartzman
Adv. Stat. Clim. Meteorol. Oceanogr., 3, 67–92, https://doi.org/10.5194/ascmo-3-67-2017, https://doi.org/10.5194/ascmo-3-67-2017, 2017
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We assess the mean temperature effect of global and regional climate model combinations for the North American Regional Climate Change Assessment Program using varying classes of linear regression models, including possible interaction effects. We use both pointwise and simultaneous inference procedures to identify regions where global and regional climate model effects differ. We conclusively show that accounting for multiple comparisons is important for making proper inference.
László Varga and András Zempléni
Adv. Stat. Clim. Meteorol. Oceanogr., 3, 55–66, https://doi.org/10.5194/ascmo-3-55-2017, https://doi.org/10.5194/ascmo-3-55-2017, 2017
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This paper proposes a new generalisation of the block bootstrap methodology, which allows for any positive real number as expected block size. We use this bootstrap for determining the p values of a homogeneity test for copulas. The methods are applied to a temperature data set - we have found some significant changes in the dependence structure between the standardised temperature values of pairs of observation points within the Carpathian Basin.
Andrew Poppick, Elisabeth J. Moyer, and Michael L. Stein
Adv. Stat. Clim. Meteorol. Oceanogr., 3, 33–53, https://doi.org/10.5194/ascmo-3-33-2017, https://doi.org/10.5194/ascmo-3-33-2017, 2017
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We show that ostensibly empirical methods of analyzing trends in the global mean temperature record, which appear to de-emphasize assumptions, can nevertheless produce misleading inferences about trends and associated uncertainty. We illustrate how a simple but physically motivated trend model can provide better-fitting and more broadly applicable results, and show the importance of adequately characterizing internal variability for estimating trend uncertainty.
John Tipton, Mevin Hooten, and Simon Goring
Adv. Stat. Clim. Meteorol. Oceanogr., 3, 1–16, https://doi.org/10.5194/ascmo-3-1-2017, https://doi.org/10.5194/ascmo-3-1-2017, 2017
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We present a statistical framework for the reconstruction of historic temperature patterns from sparse, irregular data collected from observer stations. A common statistical technique for climate reconstruction uses modern era data as a set of temperature patterns that can be used to estimate the spatial temperature patterns. We present a framework for exploration of different assumptions about the sets of patterns used in the reconstruction while providing statistically rigorous estimates.
Georgina Davies and Noel Cressie
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 155–169, https://doi.org/10.5194/ascmo-2-155-2016, https://doi.org/10.5194/ascmo-2-155-2016, 2016
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Sea surface temperature (SST) is a key component of global climate models, particularly in the tropical Pacific Ocean where El Niño and La Nina events have worldwide implications. In our paper, we analyse monthly SSTs in the Niño 3.4 region and find a transformation that removes a spatial mean-variance dependence for each month. For 10 out of 12 months in the year, the transformed monthly time series gave more accurate or as accurate forecasts than those from the untransformed time series.
Eric Gilleland, Melissa Bukovsky, Christopher L. Williams, Seth McGinnis, Caspar M. Ammann, Barbara G. Brown, and Linda O. Mearns
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 137–153, https://doi.org/10.5194/ascmo-2-137-2016, https://doi.org/10.5194/ascmo-2-137-2016, 2016
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Several climate models are evaluated under current climate conditions to determine how well they are able to capture frequencies of severe-storm environments (conditions conducive for the formation of hail storms, tornadoes, etc.). They are found to underpredict the spatial extent of high-frequency areas (such as tornado alley), as well as underpredict the frequencies in the areas.
Whitney K. Huang, Michael L. Stein, David J. McInerney, Shanshan Sun, and Elisabeth J. Moyer
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 79–103, https://doi.org/10.5194/ascmo-2-79-2016, https://doi.org/10.5194/ascmo-2-79-2016, 2016
Sergei N. Rodionov
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 63–78, https://doi.org/10.5194/ascmo-2-63-2016, https://doi.org/10.5194/ascmo-2-63-2016, 2016
David Bolin, Arnoldo Frigessi, Peter Guttorp, Ola Haug, Elisabeth Orskaug, Ida Scheel, and Jonas Wallin
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 39–47, https://doi.org/10.5194/ascmo-2-39-2016, https://doi.org/10.5194/ascmo-2-39-2016, 2016
Julie Bessac, Pierre Ailliot, Julien Cattiaux, and Valerie Monbet
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 1–16, https://doi.org/10.5194/ascmo-2-1-2016, https://doi.org/10.5194/ascmo-2-1-2016, 2016
Short summary
Short summary
Several multi-site stochastic generators of zonal and meridional components of wind are proposed in this paper. Various questions are explored, such as the modeling of the regime in a multi-site context, the extraction of relevant clusterings from extra variables or from the local wind data, and the link between weather types extracted from wind data and large-scale weather regimes. We also discuss the relative advantages of hidden and observed regime-switching models.
E. M. Schliep, A. E. Gelfand, and D. M. Holland
Adv. Stat. Clim. Meteorol. Oceanogr., 1, 59–74, https://doi.org/10.5194/ascmo-1-59-2015, https://doi.org/10.5194/ascmo-1-59-2015, 2015
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There is considerable demand for accurate air quality information in human health analyses. The sparsity of ground monitoring stations across the US motivates the need for advanced statistical models to predict air quality metrics. We propose a statistical model that jointly models ground-monitoring station data and satellite-obtained data allowing for temporal and spatial misalignment, missingness, and spatially and temporally varying correlation to enhance prediction of particulate matter.
R. Philbin and M. Jun
Adv. Stat. Clim. Meteorol. Oceanogr., 1, 29–44, https://doi.org/10.5194/ascmo-1-29-2015, https://doi.org/10.5194/ascmo-1-29-2015, 2015
T. K. Doan, J. Haslett, and A. C. Parnell
Adv. Stat. Clim. Meteorol. Oceanogr., 1, 15–27, https://doi.org/10.5194/ascmo-1-15-2015, https://doi.org/10.5194/ascmo-1-15-2015, 2015
W. B. Leeds, E. J. Moyer, and M. L. Stein
Adv. Stat. Clim. Meteorol. Oceanogr., 1, 1–14, https://doi.org/10.5194/ascmo-1-1-2015, https://doi.org/10.5194/ascmo-1-1-2015, 2015
Cited articles
Argo: Argo float data and metadata from Global Data Assembly Centre (Argo GDAC), SEANOE [data set], https://doi.org/10.17882/42182, 2000. a
Argo Program: Implementation status, https://argo.ucsd.edu/about/status/ (last access: 10 December 2020), 2020. a
Balaguru, K., Chang, P., Saravanan, R., Leung, L. R., Xu, Z., Li, M., and Hsieh, J.-S.: Ocean barrier layers' effect on tropical cyclone intensification, P. Natl. Acad. Sci. USA, 109, 14343–14347, https://doi.org/10.1073/pnas.1201364109, 2012. a
Balaguru, K., Foltz, G. R., Leung, L. R., Asaro, E. D., Emanuel, K. A., Liu, H., and Zedler, S. E.: Dynamic Potential Intensity: An improved representation of the ocean's impact on tropical cyclones, Geophys. Res. Lett., 42, 6739–6746, https://doi.org/10.1002/2015GL064822, 2015. a
Bender, M. A. and Ginis, I.: Real-Case Simulations of Hurricane–Ocean Interaction Using A High-Resolution Coupled Model: Effects on Hurricane Intensity, Mon. Weather Rev., 128, 917–946, https://doi.org/10.1175/1520-0493(2000)128<0917:RCSOHO>2.0.CO;2, 2000. a, b, c
Chu, J.-H., Sampson, C. R., Levine, A. S., and Fukada, E.: The Joint Typhoon Warning Center Tropical Cyclone Best-Tracks, 1945–2000, Tech. Rep. NRL/MR/7540-02-16, Joint Typhoon Warning Center, https://www.metoc.navy.mil/jtwc/products/best-tracks/tc-bt-report.html (last access: 10 December 2020), 2002. a
Cione, J. J. and Uhlhorn, E. W.: Sea Surface Temperature Variability in Hurricanes: Implications with Respect to Intensity Change, Mon. Weather Rev., 131, 1783–1796, https://doi.org/10.1175//2562.1, 2003. a
Cressie, N. A. C.: Statistics for Spatial Data, Wiley Series in Probability and Statistics, John Wiley & Sons, Inc., ISBN 9781119115182, 1993. a
Dare, R. A. and McBride, J. L.: Sea Surface Temperature Response to Tropical Cyclones, Mon. Weather Rev., 139, 3798–3808, https://doi.org/10.1175/MWR-D-10-05019.1, 2011. a
D'Asaro, E. A., Sanford, T. B., Niiler, P. P., and Terrill, E. J.: Cold wake of Hurricane Frances, Geophys. Res. Lett., 34, L15609, https://doi.org/10.1029/2007GL030160, 2007. a, b, c
Daubechies, I., Guskov, I., Schröder, P., and Sweldens, W.: Wavelets on irregular point sets, Philos. T. Roy. Soc. Lond. Ser.-A, 357, 2397–2413, https://doi.org/10.1098/rsta.1999.0439, 1999. a
DelSole, T. and Yang, X.: Field Significance of Regression Patterns, J. Climate, 24, 5094–5107, https://doi.org/10.1175/2011JCLI4105.1, 2011. a
Draper, N. R. and Smith, H.: Applied regression analysis, Vol. 326, John Wiley & Sons, ISBN 9780471170822, https://doi.org/10.1002/9781118625590, 1998. a, b, c, d
Duchon, J.: Splines minimizing rotation-invariant semi-norms in Sobolev spaces, in: Constructive theory of functions of several variables 85–100, Springer, ISBN 978-3-540-08069-5, https://doi.org/10.1007/BFb0086566, 1977. a, b, c
Elsberry, R. L., Fraim, T. S., and Trapnell, R. N.: A mixed layer model of the oceanic thermal response to hurricanes, J. Geophys. Res., 81, 1153–1162, https://doi.org/10.1029/JC081i006p01153, 1976. a, b
Emanuel, K.: Contribution of tropical cyclones to meridional heat transport by the oceans, J. Geophys. Res.-Atmos., 106, 14771–14781, https://doi.org/10.1029/2000JD900641, 2001. a, b, c
Emanuel, K.: 100 Years of Progress in Tropical Cyclone Research, Meteorol. Monogr., 59, 15.1–15.68, https://doi.org/10.1175/AMSMONOGRAPHS-D-18-0016.1, 2018. a
Emanuel, K. A.: An Air-Sea Interaction Theory for Tropical Cyclones. Part I: Steady-State Maintenance, J. Atmos. Sci., 43, 585–605, https://doi.org/10.1175/1520-0469(1986)043<0585:AASITF>2.0.CO;2, 1986. a, b, c
Emanuel, K. A.: Thermodynamic control of hurricane intensity, Nature, 401, 665–669, https://doi.org/10.1038/44326, 1999. a, b, c
Fisher, E. L.: The Exchange of Energy Between the Sea and the Atmosphere in Relation to Hurricane Behavior, J. Meteorol., 15, 164–171, https://doi.org/10.1175/1520-0469(1958)015<0164:TEOEBT>2.0.CO;2, 1957. a
Fritsch, F. N. and Carlson, R. E.: Monotone Piecewise Cubic Interpolation, SIAM J. Numer. Anal., 17, 238–246, https://doi.org/10.1137/0717021, 1980. a
Gentemann, C. L., Donlon, C. J., Stuart-Menteth, A., and Wentz, F. J.: Diurnal signals in satellite sea surface temperature measurements, Geophys. Res. Lett., 30, 1140, https://doi.org/10.1029/2002GL016291, 2003. a
Green, P. J. and Silverman, B. W.: Nonparametric regression and generalized linear models: a roughness penalty approach, no. 58 in: Monographs on statistics and applied probability, Chapman & Hall, London; New York, 1st Edn., ISBN 9780429161056, https://doi.org/10.1201/b15710, 1994. a, b, c, d, e, f
Haakman, K., Sayol, J.-M., van der Boog, C. G., and Katsman, C. A.: Statistical Characterization of the Observed Cold Wake Induced by North Atlantic Hurricanes, Remote Sens., 11, 2368, https://doi.org/10.3390/rs11202368, 2019. a, b
Haas, T. C.: Lognormal and Moving Window Methods of Estimating Acid Deposition, J. Am. Stat. Assoc., 85, 950–963, https://doi.org/10.1080/01621459.1990.10474966, 1990. a
Haas, T. C.: Local Prediction of a Spatio-Temporal Process with an Application to Wet Sulfate Deposition, J. Am. Stat. Assoc., 90, 1189–1199, https://doi.org/10.1080/01621459.1995.10476625, 1995. a
Haney, S., Bachman, S., Cooper, B., Kupper, S., McCaffrey, K., Van Roekel, L., Stevenson, S., Fox-Kemper, B., and Ferrari, R.: Hurricane wake restratification rates of one-, two- and three-dimensional processes, J. Mar. Res., 70, 824–850, https://doi.org/10.1357/002224012806770937, 2012. a, b, c, d
Hu, A. J.: huisaddison/tc-ocean-methods: v0.0.1 (v0.0.1), Zenodo [code], https://doi.org/10.5281/zenodo.11882987, 2024. a
Jansen, M. and Ferrari, R.: Impact of the latitudinal distribution of tropical cyclones on ocean heat transport, Geophys. Res. Lett., 36, L06604, https://doi.org/10.1029/2008GL036796, 2009. a, b
Jansen, M. F., Ferrari, R., and Mooring, T. A.: Seasonal versus permanent thermocline warming by tropical cyclones: thermacline warming by tropical cyclones, Geophys. Res. Lett., 37, L03602, https://doi.org/10.1029/2009GL041808, 2010. a, b
Joint Typhoon Warning Center: Frequently Asked Questions – Naval Oceanography Portal, https://www.usno.navy.mil/JTWC/frequently-asked-questions-1/frequently-asked-questions (last access: 30 December 2022), 2015. a
Korty, R. L., Emanuel, K. A., and Scott, J. R.: Tropical Cyclone–Induced Upper-Ocean Mixing and Climate: Application to Equable Climates, J. Climate, 21, 638–654, https://doi.org/10.1175/2007JCLI1659.1, 2008. a, b
Landsea, C. W. and Franklin, J. L.: Atlantic Hurricane Database Uncertainty and Presentation of a New Database Format, Mon. Weather Rev., 141, 3576–3592, https://doi.org/10.1175/MWR-D-12-00254.1, 2013. a
Leipper, D. F.: Observed Ocean Conditions and Hurricane Hilda, 1964, J. Atmos. Sci., 24, 182–186, https://doi.org/10.1175/1520-0469(1967)024<0182:OOCAHH>2.0.CO;2, 1966. a
Lewiner, T., Lopes, H., Vieira, A. W., and Tavares, G.: Efficient Implementation of Marching Cubes' Cases with Topological Guarantees, J. Graphics Tools, 8, 1–15, https://doi.org/10.1080/10867651.2003.10487582, 2012. a
Lin, I.-I., Wu, C.-C., Emanuel, K. A., Lee, I.-H., Wu, C.-R., and Pun, I.-F.: The Interaction of Supertyphoon Maemi (2003) with a Warm Ocean Eddy, Mon. Weather Rev., 133, 2635–2649, https://doi.org/10.1175/MWR3005.1, 2005. a
Lin, S., Zhang, W.-Z., Shang, S.-P., and Hong, H.-S.: Ocean response to typhoons in the western North Pacific: Composite results from Argo data, Deep-Sea Res. Pt. I, 123, 62–74, https://doi.org/10.1016/j.dsr.2017.03.007, 2017. a, b
Liu, Z., Xu, J., Zhu, B., Sun, C., and Zhang, L.: The upper ocean response to tropical cyclones in the northwestern Pacific analyzed with Argo data, Chin. J. Oceanol. Limn. 25, 123–131, https://doi.org/10.1007/s00343-007-0123-8, 2007. a
Livezey, R. E. and Chen, W. Y.: Statistical Field Significance and its Determination by Monte Carlo Techniques, Mon, Weather Rev., 111, 46–59, https://doi.org/10.1175/1520-0493(1983)111<0046:SFSAID>2.0.CO;2 1982. a
Lloyd, I. D. and Vecchi, G. A.: Observational Evidence for Oceanic Controls on Hurricane Intensity, J. Climate, 24, 1138–1153, https://doi.org/10.1175/2010JCLI3763.1, 2011. a
Lorensen, W. E. and Cline, H. E.: Marching cubes: A high resolution 3D surface construction algorithm, ACM SIGGRAPH Computer Graphics, 21, 163–169, https://doi.org/10.1145/37402.37422, 1987. a
Luo, Z., Wahba, G., and Johnson, D. R.: Spatial–temporal analysis of temperature using smoothing spline ANOVA, J. Climate, 11, 18–28, 1998. a
Mainelli, M., DeMaria, M., Shay, L. K., and Goni, G.: Application of Oceanic Heat Content Estimation to Operational Forecasting of Recent Atlantic Category 5 Hurricanes, Weather Forecast., 23, 3–16, https://doi.org/10.1175/2007WAF2006111.1, 2008. a, b
McTaggart-Cowan, R., Davies, E. L., Fairman, J. G., Galarneau, T. J., and Schultz, D. M.: Revisiting the 26.5°C Sea Surface Temperature Threshold for Tropical Cyclone Development, B. Am. Meteorol. Soc., 96, 1929–1943, https://doi.org/10.1175/BAMS-D-13-00254.1, 2015. a, b
Mei, W. and Pasquero, C.: Restratification of the Upper Ocean after the Passage of a Tropical Cyclone: A Numerical Study, J. Phys. Oceanogr., 42, 1377–1401, https://doi.org/10.1175/JPO-D-11-0209.1, 2012. a
Mei, W. and Pasquero, C.: Spatial and Temporal Characterization of Sea Surface Temperature Response to Tropical Cyclones, J. Climate, 26, 3745–3765, https://doi.org/10.1175/JCLI-D-12-00125.1, 2013. a, b
Mei, W., Primeau, F., McWilliams, J. C., and Pasquero, C.: Sea surface height evidence for long-term warming effects of tropical cyclones on the ocean, P. Natl. Acad. Sci. USA, 110, 15207–15210, https://doi.org/10.1073/pnas.1306753110, 2013. a, b, c
National Hurricane Center: HURDAT2 best-track data, https://www.nhc.noaa.gov/data/#hurdat, of last access: 30 December 2022. a
Nychka, D. W.: Spatial-process estimates as smoothers, in: Smoothing and regression: approaches, computation, and application, edited by: Schimek, M. G., Wiley, vol. 329, p. 393, ISBN 9781118150658, https://doi.org/10.1002/9781118150658, 2000. a, b, c, d
Paciorek, C. J. and Schervish, M. J.: Spatial modelling using a new class of nonstationary covariance functions, Environmetrics, 17, 483–506, 2006. a
Park, J. J., Kwon, Y.-O., and Price, J. F.: Argo array observation of ocean heat content changes induced by tropical cyclones in the north Pacific, J. Geophys. Res.-Oceans, 116, C12025, https://doi.org/10.1029/2011JC007165, 2011. a
Pasquero, C. and Emanuel, K.: Tropical Cyclones and Transient Upper-Ocean Warming, J. Climate, 21, 149–162, https://doi.org/10.1175/2007JCLI1550.1, 2008. a
Potter, H., DiMarco, S. F., and Knap, A. H.: Tropical Cyclone Heat Potential and the Rapid Intensification of Hurricane Harvey in the Texas Bight, J. Geophys. Res.-Oceans, 124, 2440–2451, https://doi.org/10.1029/2018JC014776, 2019. a
Price, J. F.: Upper Ocean Response to a Hurricane, J. Phys. Oceanogr., 11, 153–175, https://doi.org/10.1175/1520-0485(1981)011<0153:UORTAH>2.0.CO;2, 1980. a, b
Qu, T., Song, Y. T., and Maes, C.: Sea surface salinity and barrier layer variability in the equatorial Pacific as seen from Aquarius and Argo, J. Geophys. Res.-Oceans, 119, 15–29, https://doi.org/10.1002/2013JC009375, 2014. a
Rao, C. R.: Linear statistical inference and its applications, vol. 2 of Wiley Series in Probability and Statistics, John Wiley & Sons, Inc., ISBN 9780470316436, https://doi.org/10.1002/9780470316436, 1973. a, b, c, d
Ridgway, K. R., Dunn, J. R., and Wilkin, J. L.: Ocean Interpolation by Four-Dimensional Weighted Least Squares–Application to the Waters around Australasia, J. Atmos. Ocean. Tech., 19, 1357–1375, https://doi.org/10.1175/1520-0426(2002)019<1357:OIBFDW>2.0.CO;2, 2002. a, b, c
Riser, S. C., Freeland, H. J., Roemmich, D., Wijffels, S., Troisi, A., Belbéoch, M., Gilbert, D., Xu, J., Pouliquen, S., Thresher, A., Le Traon, P.-Y., Maze, G., Klein, B., Ravichandran, M., Grant, F., Poulain, P.-M., Suga, T., Lim, B., Sterl, A., Sutton, P., Mork, K.-A., Vélez-Belchí, P. J., Ansorge, I., King, B., Turton, J., Baringer, M., and Jayne, S. R.: Fifteen years of ocean observations with the global Argo array, Nat. Clim. Change, 6, 145–153, https://doi.org/10.1038/nclimate2872, 2016. a
Ruppert, D. and Carroll, R. J.: Theory & Methods: Spatially-adaptive Penalties for Spline Fitting, Aust. NZ J. Stat., 42, 205–223, 2000. a
Russell, B. T., Risser, M. D., Smith, R. L., and Kunkel, K. E.: Investigating the association between late spring Gulf of Mexico sea surface temperatures and U.S. Gulf Coast precipitation extremes with focus on Hurricane Harvey, Environmetrics, 31, https://doi.org/10.1002/env.2595, 2020. a
Shay, L. K.: Air-Sea Interactions in Tropical Cyclones, in: World Scientific Series on Asia-Pacific Weather and Climate, World Scientific, vol. 4, 93–131, https://doi.org/10.1142/9789814293488_0003, 2010. a, b
Shay, L. K. and Goni, G. J.: Effects of a Warm Oceanic Feature on Hurricane Opal, Mon. Weather Rev., 128, 1366–1383, https://doi.org/10.1175/1520-0493(2000)128<1366:EOAWOF>2.0.CO;2, 2000. a
Sriver, R. L., Huber, M., and Nusbaumer, J.: Investigating tropical cyclone-climate feedbacks using the TRMM Microwave Imager and the Quick Scatterometer, Geochem. Geophy. Geosy., 9, Q09V11, https://doi.org/10.1029/2007GC001842, 2008. a
Steffen, J. and Bourassa, M.: Barrier Layer Development Local to Tropical Cyclones based on Argo Float Observations, J. Phys. Oceanogr., 48, 1951–1968, https://doi.org/10.1175/JPO-D-17-0262.1, 2018. a
Sun, L., Yang, Y.-J., Xian, T., Wang, Y., and Fu, Y.-F.: Ocean Responses to Typhoon Namtheun Explored with Argo Floats and Multiplatform Satellites, Atmos.-Ocean, 50, 15–26, https://doi.org/10.1080/07055900.2012.742420, 2012. a
Trenberth, K. E., Cheng, L., Jacobs, P., Zhang, Y., and Fasullo, J.: Hurricane Harvey Links to Ocean Heat Content and Climate Change Adaptation, Earth's Future, 6, 730–744, https://doi.org/10.1029/2018EF000825, 2018. a, b
Trepanier, J. C.: North Atlantic Hurricane Winds in Warmer than Normal Seas, Atmosphere, 11, 293, https://doi.org/10.3390/atmos11030293, 2020. a
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S. J., Brett, M., Wilson, J., Jarrod Millman, K., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E., Carey, C., Polat, İ., Feng, Y., Moore, E. W., Vand erPlas, J., Laxalde, D., Perktold, J., Cimrman, R., Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M., Ribeiro, A. H., Pedregosa, F., van Mulbregt, P., and SciPy 1.0 Contributors: SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python, Nat. Methods, 17, 261–272, https://doi.org/10.1038/s41592-019-0686-2, 2020. a, b
Wahba, G.: Spline models for observational data, CBMS-NSF regional conference series in applied mathematics, SIAM, ISBN 978-0-89871-244-5, https://doi.org/10.1137/1.9781611970128, 1990. a, b, c, d
Wentz, F., Gentemann, C., and Hilburn, K.: Three years of ocean products from AMSR-E: evaluation and applications, in: Proceedings, 2005 IEEE International Geoscience and Remote Sensing Symposium, IGARSS'05, IEEE, Seoul, Korea, vol. 7, 4929–4932, https://doi.org/10.1109/IGARSS.2005.1526780, 2005. a
Wilks, D. S.: On “Field Significance” and the False Discovery Rate, J. Appl. Meteorol. Clim., 45, 1181–1189, https://doi.org/10.1175/JAM2404.1, 2006. a
Wood, S. N.: Thin plate regression splines, J. Roy. Stat. Soc. Ser. B, 65, 95–114, https://doi.org/10.1111/1467-9868.00374, 2003. a
Wunsch, C.: The past and future ocean circulation from a contemporary perspective, Geophys. Monogr., 173, https://doi.org/10.1029/173GM06, 2007. a
Xu, Z., Sun, Y., Li, T., Zhong, Z., Liu, J., and Ma, C.: Tropical Cyclone Size Change under Ocean Warming and Associated Responses of Tropical Cyclone Destructiveness: Idealized Experiments, J. Meteorol. Res., 34, 163–175, https://doi.org/10.1007/s13351-020-8164-4, 2020. a
Short summary
We introduce a new statistical framework to estimate the change in subsurface ocean temperature following the passage of a tropical cyclone (TC). Our approach combines tools handling seasonal variations and spatial dependence in the data, culminating in a three-dimensional characterization of the interaction between TCs and the ocean. Our work allows us to obtain new scientific insights, and we expect it to be generally applicable to studying the impact of TCs on other ocean phenomena.
We introduce a new statistical framework to estimate the change in subsurface ocean temperature...
