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Gregory R. Foltz
,
Michael J. McPhaden
, and
Rick Lumpkin

Abstract

In the first half of 2009, anomalous cooling of sea surface temperatures (SSTs) in the equatorial North Atlantic (ENA; 2°–12°N) triggered a strong Atlantic meridional mode event. During its peak in April–May, SSTs in the ENA were 1°C colder than normal and SSTs in the equatorial South Atlantic (5°S–0°) were 0.5°C warmer than normal. Associated with the SST gradient were anomalous northerly winds, an anomalous southward shift of the intertropical convergence zone, and severe flooding in Northeast Brazil. This study uses in situ and satellite observations to examine the mechanisms responsible for the anomalous cooling in the ENA during boreal winter and spring of 2009. It is found that the cooling was initiated by stronger than normal trade winds during January and February 2009 associated with an anomalous strengthening of the subtropical North Atlantic high pressure system. Between 6° and 12°N, unusually strong trade winds cooled the ocean through wind-induced evaporation and deepened the mixed layer anomalously by 5–20 m. Closer to the equator, surface equatorial winds responded to the anomalous interhemispheric SST gradient, becoming northwesterly between the equator and 6°N. The anomalous winds drove upwelling of 0.5–1 m day−1 during March–April, a period when there is normally weak downwelling. The associated vertical turbulent heat flux at the base of the mixed layer led to unusually cool SSTs in the central basin, further strengthening the anomalous interhemispheric SST gradient. These results emphasize the importance of mixed layer dynamics in the evolution of the meridional mode event of 2009 and the potential for positive coupled feedbacks between wind-induced upwelling and SST in the ENA.

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Allyson Rugg
,
Gregory R. Foltz
, and
Renellys C. Perez

Abstract

This study examines the causes of observed sea surface temperature (SST) anomalies in the tropical North Atlantic between 1982 and 2015. The emphasis is on the boreal winter and spring seasons, when tropical Atlantic SSTs project strongly onto the Atlantic meridional mode (AMM). Results from a composite analysis of satellite and reanalysis data show important forcing of SST anomalies by wind-driven changes in mixed layer depth and shortwave radiation between 5° and 10°N, in addition to the well-known positive wind–evaporation–SST and shortwave radiation–SST feedbacks between 5° and 20°N. Anomalous surface winds also drive pronounced thermocline depth anomalies of opposite signs in the eastern equatorial Atlantic and intertropical convergence zone (ITCZ; 2°–8°N). A major new finding is that there is strong event-to-event variability in the impact of thermocline depth on SST in the ITCZ region, in contrast to the more consistent relationship in the eastern equatorial Atlantic. Much stronger anomalies of meridional wind stress, thermocline depth, and vertical turbulent cooling are found in the ITCZ region during a negative AMM event in 2009 compared to a negative event in 2015 and a positive event in 2010, despite SST anomalies of similar magnitude in the early stages of each event. The larger anomalies in 2009 led to a much stronger and longer-lived event. Possible causes of the inconsistent relationship between thermocline depth and SST in the ITCZ region are discussed, including the preconditioning role of the winter cross-equatorial SST gradient.

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Gregory R. Foltz
,
Jérôme Vialard
,
B. Praveen Kumar
, and
Michael J. McPhaden

Abstract

Sea surface temperature (SST) in the southwestern tropical Indian Ocean exerts a significant influence on global climate through its influence on the Indian summer monsoon and Northern Hemisphere atmospheric circulation. In this study, measurements from a long-term moored buoy are used in conjunction with satellite, in situ, and atmospheric reanalysis datasets to analyze the seasonal mixed layer heat balance in the thermocline ridge region of the southwestern tropical Indian Ocean. This region is characterized by a shallow mean thermocline (90 m, as measured by the 20°C isotherm) and pronounced seasonal cycles of Ekman pumping and SST (seasonal ranges of −0.1 to 0.6 m day−1 and 26°–29.5°C, respectively). It is found that surface heat fluxes and horizontal heat advection contribute significantly to the seasonal cycle of mixed layer heat storage. The net surface heat flux tends to warm the mixed layer throughout the year and is strongest during boreal fall and winter, when surface shortwave radiation is highest and latent heat loss is weakest. Horizontal heat advection provides warming during boreal summer and fall, when southwestward surface currents and horizontal SST gradients are strongest, and is close to zero during the remainder of the year. Vertical turbulent mixing, estimated as a residual in the heat balance, also undergoes a significant seasonal cycle. Cooling from this term is strongest in boreal summer, when surface wind and buoyancy forcing are strongest, the thermocline ridge is shallow (<90 m), and the mixed layer is deepening. These empirical results provide a framework for addressing intraseasonal and interannual climate variations, which are dynamically linked to the seasonal cycle, in the southwestern tropical Indian Ocean. They also provide a quantitative basis for assessing the accuracy of numerical ocean model simulations in the region.

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Karthik Balaguru
,
Gregory R. Foltz
,
L. Ruby Leung
,
Samson M. Hagos
, and
David R. Judi

Abstract

Sea surface temperature (SST) and tropical cyclone heat potential (TCHP) are metrics used to incorporate the ocean’s influence on hurricane intensification into the National Hurricane Center’s Statistical Hurricane Intensity Prediction Scheme (SHIPS). While both SST and TCHP serve as useful measures of the upper-ocean heat content, they do not accurately represent ocean stratification effects. Here, it is shown that replacing SST within the SHIPS framework with a dynamic temperature T dy, which accounts for the oceanic negative feedback to the hurricane’s intensity arising from storm-induced vertical mixing and sea surface cooling, improves the model performance. While the model with SST and TCHP explains about 41% of the variance in 36-h intensity changes, replacing SST with T dy increases the variance explained to nearly 44%. These results suggest that representation of the oceanic feedback, even through relatively simple formulations such as T dy, may improve the performance of statistical hurricane intensity prediction models such as SHIPS.

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Chunzai Wang
,
Shenfu Dong
,
Amato T. Evan
,
Gregory R. Foltz
, and
Sang-Ki Lee

Abstract

Most studies of African dust and North Atlantic climate have been limited to the short time period since the satellite era (1980 onward), precluding the examination of their relationship on longer time scales. Here a new dust dataset with the record extending back to the 1950s is used to show a multidecadal covariability of North Atlantic SST and aerosol, Sahel rainfall, and Atlantic hurricanes. When the North Atlantic Ocean was cold from the late 1960s to the early 1990s, the Sahel received less rainfall and the tropical North Atlantic experienced a high concentration of dust. The opposite was true when the North Atlantic Ocean was warm before the late 1960s and after the early 1990s. This suggests a novel mechanism for North Atlantic SST variability—a positive feedback between North Atlantic SST, African dust, and Sahel rainfall on multidecadal time scales. That is, a warm (cold) North Atlantic Ocean produces a wet (dry) condition in the Sahel and thus leads to low (high) concentration of dust in the tropical North Atlantic, which in turn warms (cools) the North Atlantic Ocean. An implication of this study is that coupled climate models need to be able to simulate this aerosol-related feedback in order to correctly simulate climate variability in the North Atlantic. Additionally, it is found that dust in the tropical North Atlantic varies inversely with the number of Atlantic hurricanes on multidecadal time scales because of the multidecadal variability of both direct and indirect influences of dust on vertical wind shear in the hurricane main development region.

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Gregory R. Foltz
,
Amato T. Evan
,
H. Paul Freitag
,
Sonya Brown
, and
Michael J. McPhaden

Abstract

Long-term and direct measurements of surface shortwave radiation (SWR) have been recorded by the Prediction and Research Moored Array in the Tropical Atlantic (PIRATA) since 1997. Previous studies have shown that African dust, transported westward from the Sahara and Sahel regions, can accumulate on mooring SWR sensors in the high-dust region of the North Atlantic (8°–25°N, 20°–50°W), potentially leading to significant negative SWR biases. Here dust-accumulation biases are quantified for each PIRATA mooring using direct measurements from the moorings, combined with satellite and reanalysis datasets and statistical models. The SWR records from five locations in the high-dust region (8°, 12°, and 15°N along 38°W; 12° and 21°N along 23°W) are found to contain monthly-mean accumulation biases as large as −200 W m−2 and record-length mean biases on the order of −10 W m−2. The other 12 moorings, located mainly between 10°S and 4°N, are in regions of lower atmospheric dust concentration and do not show statistically significant biases. Seasonal-to-interannual variability of the accumulation bias is found at all locations in the high-dust region. The moorings along 38°W also show decreasing trends in the bias magnitude since 1998 that are possibly related to a corresponding negative trend in atmospheric dust concentration. The dust-accumulation biases described here will be useful for interpreting SWR data from PIRATA moorings in the high-dust region. The biases are also potentially useful for quantifying dust deposition rates in the tropical North Atlantic, which at present are poorly constrained by satellite data and numerical models.

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Karthik Balaguru
,
Gregory R. Foltz
,
L. Ruby Leung
,
John Kaplan
,
Wenwei Xu
,
Nicolas Reul
, and
Bertrand Chapron

Abstract

Tropical cyclone (TC) rapid intensification (RI) is difficult to predict and poses a formidable threat to coastal populations. A warm upper ocean is well known to favor RI, but the role of ocean salinity is less clear. This study shows a strong inverse relationship between salinity and TC RI in the eastern Caribbean and western tropical Atlantic due to near-surface freshening from the Amazon–Orinoco River system. In this region, rapidly intensifying TCs induce a much stronger surface enthalpy flux compared to more weakly intensifying storms, in part due to a reduction in SST cooling caused by salinity stratification. This reduction has a noticeable positive impact on TCs undergoing RI, but the impact of salinity on more weakly intensifying storms is insignificant. These statistical results are confirmed through experiments with an ocean mixed layer model, which show that the salinity-induced reduction in SST cold wakes increases significantly as the storm’s intensification rate increases. Currently, operational statistical–dynamical RI models do not use salinity as a predictor. Through experiments with a statistical RI prediction scheme, it is found that the inclusion of surface salinity significantly improves the RI detection skill, offering promise for improved operational RI prediction. Satellite surface salinity may be valuable for this purpose, given its global coverage and availability in near–real time.

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Karthik Balaguru
,
Gregory R. Foltz
,
L. Ruby Leung
,
John Kaplan
,
Wenwei Xu
,
Nicolas Reul
, and
Bertrand Chapron
Full access
Janet Sprintall
,
Victoria J. Coles
,
Kevin A. Reed
,
Amy H. Butler
,
Gregory R. Foltz
,
Stephen G. Penny
, and
Hyodae Seo
Full access
Janet Sprintall
,
Victoria J. Coles
,
Kevin A. Reed
,
Amy H. Butler
,
Gregory R. Foltz
,
Stephen G. Penny
, and
Hyodae Seo

Abstract

Process studies are designed to improve our understanding of poorly described physical processes that are central to the behavior of the climate system. They typically include coordinated efforts of intensive field campaigns in the atmosphere and/or ocean to collect a carefully planned set of in situ observations. Ideally the observational portion of a process study is paired with numerical modeling efforts that lead to better representation of a poorly simulated or previously neglected physical process in operational and research models. This article provides a framework of best practices to help guide scientists in carrying out more productive, collaborative, and successful process studies. Topics include the planning and implementation of a process study and the associated web of logistical challenges; the development of focused science goals and testable hypotheses; and the importance of assembling an integrated and compatible team with a diversity of social identity, gender, career stage, and scientific background. Guidelines are also provided for scientific data management, dissemination, and stewardship. Above all, developing trust and continual communication within the science team during the field campaign and analysis phase are key for process studies. We consider a successful process study as one that ultimately will improve our quantitative understanding of the mechanisms responsible for climate variability and enhance our ability to represent them in climate models.

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