Arbuscular Mycorrhizae in Sand Dune Plants of the North Atlantic Coast of the U.S.: Field and Greenhouse Inoculation and Presence of Mycorrhizae in Planting Stock

1997 ◽  
Vol 50 (3) ◽  
pp. 251-264 ◽  
Author(s):  
J.N. Gemma ◽  
R.E. Koske
2014 ◽  
Vol 41 (12) ◽  
pp. 4300-4307 ◽  
Author(s):  
Paige E. Newby ◽  
Bryan N. Shuman ◽  
Jeffrey P. Donnelly ◽  
Kristopher B. Karnauskas ◽  
Jeremiah Marsicek

1911 ◽  
Vol 11 (1) ◽  
pp. 1 ◽  
Author(s):  
Edwin M. Borchardt

2021 ◽  
pp. 1-38
Author(s):  
Xi Guo ◽  
James P. Kossin ◽  
Zhe-Min Tan

AbstractTropical cyclone (TC) translation speed (TCTS) can affect the duration of TC-related disasters, which is critical to coastal and inland areas. The long-term variation of TCTS and their relationship to the variability of the mid-latitude jet stream and storm migration are discussed here for storms near the North Atlantic coast during 1948-2019. Our results reveal the prominent seasonality in the long-term variation of TCTS, which can be largely explained by the seasonality in the covariations of the mid-latitude jet stream and storm locations. Specifically, significant increases of TCTS occur in June and October during the past decades, which may result from the equatorward displacement of the jet stream and poleward migration of storm locations. Prominent slowdown of TCTS is found in August, which is related to the weakened jet strength and equatorward storm migration. In September, the effects of poleward displacement and weakening of the jet stream on TCTS are largely compensated by the poleward storm migration, therefore, no significant change in TCTS is observed. Meanwhile, the multidecadal variability of the Atlantic may contribute to the multidecadal variability of TCTS. Our findings emphasize the significance in taking a seasonality view in discussing the variability and trends of near-coast Atlantic TCTS under climate change.


Author(s):  
Tao Li

Sample day selection method plays an important role in managerial decisions which require analyses that are prohibitively expensive to apply to a large number of days. We develop a general sample day selection model that selects sample days based on the cumulative distributions of airspace conditions and characteristics (C&C) by considering factors such as sampling targets, degree of diversity and coverage of the selected days. We introduce indicators that capture the airspace C&C of the North Atlantic region (NAT) and apply the model to select sample days for the NAT. The results show that the model outperforms the methods used by the U.S. Federal Aviation Administration.


2018 ◽  
Vol 31 (13) ◽  
pp. 4981-4989 ◽  
Author(s):  
Jessica S. Kenigson ◽  
Weiqing Han ◽  
Balaji Rajagopalan ◽  
Yanto ◽  
Mike Jasinski

Recent studies have linked interannual sea level variability and extreme events along the U.S. northeast coast (NEC) to the North Atlantic Oscillation (NAO), a natural internal climate mode that prevails in the North Atlantic Ocean. The correlation between the NAO index and coastal sea level north of Cape Hatteras was weak from the 1960s to the mid-1980s, but it has markedly increased since around 1987. The causes for the decadal shift remain unknown. Yet understanding the abrupt change is vital for decadal sea level prediction and is essential for risk management. Here we use a robust method, the Bayesian dynamic linear model (DLM), to explore the nonstationary NAO impact on NEC sea level. The results show that a spatial pattern change of NAO-related winds near the NEC is a major cause of the NAO–sea level relationship shift. A new index using regional sea level pressure is developed that is a significantly better predictor of NEC sea level than is the NAO and is strongly linked to the intensity of westerly winds near the NEC. These results point to the vital importance of monitoring regional changes of wind and sea level pressure patterns, rather than the NAO index alone, to achieve more accurate predictions of sea level change along the NEC.


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