The Climate Data Toolbox for MATLAB

Greene, Chad A.; Thirumalai, Kaustubh; Kearney, Kelly; Delgado, José Miguel; Schwanghart, Wolfgang; Wolfenbarger, Natalie S.; Thyng, Kristen M.; Gwyther, David; Gardner, Alex; Blankenship, D. D.

doi:10.1029/2019gc008392

Cited by 173 publications

(112 citation statements)

References 32 publications

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“…The TC track data are obtained from the International Best Track Archive for Climate Stewardship (IBTrACS), version 4 44 , which contains interpolated~3hourly data for storm positions. Data are overlaid using the Climate Data Toolbox for MATLAB 45 .…”

Section: Methodsmentioning

confidence: 99%

Effects of climate change on the movement of future landfalling Texas tropical cyclones

et al. 2020

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The movement of tropical cyclones (TCs), particularly around the time of landfall, can substantially affect the resulting damage. Recently, trends in TC translation speed and the likelihood of stalled TCs such as Harvey have received significant attention, but findings have remained inconclusive. Here, we examine how the June-September steering wind and translation speed of landfalling Texas TCs change in the future under anthropogenic climate change. Using several large-ensemble/multi-model datasets, we find pronounced regional variations in the meridional steering wind response over North America, but-consistently across models-stronger June-September-averaged northward steering winds over Texas. A cluster analysis of daily wind patterns shows more frequent circulation regimes that steer landfalling TCs northward in the future. Downscaling experiments show a 10-percentagepoint shift from the slow-moving to the fast-moving end of the translation-speed distribution in the future. Together, these analyses indicate increases in the likelihood of faster-moving landfalling Texas TCs in the late 21 st century.

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Section: Methodsmentioning

confidence: 99%

Effects of climate change on the movement of future landfalling Texas tropical cyclones

et al. 2020

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“…The MATLABⓇ codes for the coral PSM algorithms that contributed to the analysis and results in this study are publicly available on the GitHub repository for the lead author: https://github.com/lawmana/coralPSM. We also acknowledge the use the Climate Data Toolbox (CDT) for MATLAB® [Greene et al, 2019]. The CDT is publicly available on GitHub: https://github.com/chadagreene/CDT.…”

Section: Code Availabilitymentioning

confidence: 99%

Developing a Coral Proxy System Model to Compare Coral and Climate Model Estimates of Changes in Paleo‐ENSO Variability

Lawman

Partin

Dee

et al. 2020

Paleoceanog and Paleoclimatol

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“…The statistics of the wind speed and direction were performed by converting wind speed and direction into an east and north vector; all the east vectors were averaged, and all the north vectors were averaged, and then the resulting two vectors were combined to obtain a mean wind vector. The wind velocity data were then converted to wind stress using the wind stress function found in the Climate Data Toolbox for MATLAB (Greene et al, 2019).…”

Section: Windsmentioning

confidence: 99%

Annual and Seasonal Surface Circulation over the Mid Atlantic Bight Continental Shelf Derived from a Decade of High Frequency Radar Observations

Roarty

Glenn

Brodie

et al. 2020

Preprint

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A decade (2007-2016) of hourly 6-km-resolution maps of the surface currents across the Mid-Atlantic Bight (MAB) generated by a regional-scale High Frequency Radar network are used to reveal new insights into the spatial patterns of the annual and seasonal mean surface flows. Across the 10-year time series, temporal means and interannual and intra-annual variability are used to quantify the variability of spatial surface current patterns. The 10-year annual mean surface flows are weaker and mostly cross-shelf near the coast, increasing in speed and rotating to more alongshore directions near the shelfbreak, and increasing in speed and rotating to flow off-shelf in the southern MAB. The annual mean surface current pattern is relatively stable year to year compared to the hourly variations within a year. The 10-year seasonal means exhibit similar current patterns, with winter and summer more cross-shore while spring and fall transitions are more alongshore. Fall and winter mean speeds are larger and correspond to when mean winds are stronger and cross-shore. Summer mean currents are weakest and correspond to a time when the mean wind opposes the alongshore flow. Again, intra-annual variability is much greater than interannual, with the fall season exhibiting the most interseasonal variability in the surface current patterns. The extreme fall seasons of 2009 and 2011 are related to extremes in the wind and river discharge events caused by different persistent synoptic meteorological conditions, resulting in more or less rapid fall transitions from stratified summer to well-mixed winter conditions. Plain Language Summary A coordinated High Frequency Radar network operated between Cape Cod, MA, and Cape Hatteras, NC, generates hourly maps of ocean surface currents. A decade-long study revealed the detailed structure of the surface flows. These flows were compared to wind and river flow data to explain the patterns observed in the flow. Near the coast, the average currents flow offshore. Away from the coast, the average currents flow along the coast toward the south. Fall is the season with the most variability from year to year. Its higher variability can be traced to different regional weather patterns that change the wind fields and the amount of freshwater delivered by the rivers to the coastal ocean. This is the first study to use a decade of observed surface current maps that uniquely and simultaneously observe the changing patterns of the average flow structure along a segment of eastern United States. The improved understanding of the coastal circulation over a wide area, and what drives its variability, has implications for pollutant transport, plankton transport at the base of the food chain, fish and shellfish reproduction, and multiple ocean-based human activities including fishing, marine transportation, and offshore wind energy development.

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The Climate Data Toolbox for MATLAB

Cited by 173 publications

References 32 publications

Effects of climate change on the movement of future landfalling Texas tropical cyclones

Effects of climate change on the movement of future landfalling Texas tropical cyclones

Developing a Coral Proxy System Model to Compare Coral and Climate Model Estimates of Changes in Paleo‐ENSO Variability

Annual and Seasonal Surface Circulation over the Mid Atlantic Bight Continental Shelf Derived from a Decade of High Frequency Radar Observations

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