Long-term Atlantic Ocean and Gulf Stream (GS) variability were linked in past studies to coastal sea level (CSL) change along the U.S. East Coast – a weakening GS can lead to rise in CSL and increased coastal flooding. However, high frequency variability (HFV) in CSL is, in most cases, attributed to atmospheric weather events. This study is focused on HFV (intraseasonal variations with periods between ~ 1 week and ~ 2 months) in the GS and in CSL. First, wavelet and spectral analysis of observations of the Florida Current transport and CSL characterized the HFV in the data, and then idealized numerical simulations were conducted to study the response of CSL to imposed GS variations with known frequencies. Three experiments were conducted: a control run with constant surface and boundary forcing, and two experiments with imposed oscillations in the Florida Current transport into the model domain- a “high-frequency experiment” (HFE) and a “low-frequency experiment” (LFE), where the period of the GS oscillations were ~ 1–2 weeks and ~ 1–2 months, respectively. The observations and the model show statistically significant anticorrelation between the GS flow and the CSL, but the LFE resulted in higher GS-CSL correlations and was more like the observations than the HFE was. The results also show large spatial differences in the CSL response to GS variations - the South-Atlantic Bight (SAB) responded more strongly to the LFE while the Mid-Atlantic Bight (MAB) responded more strongly to the HFE. Power spectra of the model simulations show that even small, imposed GS oscillations at high frequency, can interact with natural variability to excite unpredictable CSL variabilities over a wide range of frequencies, including oscillations at much longer time scales than the forcing. The variability in the Florida Current flow generated a northward propagating signal along the GS path and a southward propagating sea level signal along the coast. However, the topography near Cape Hatteras seems to partly block communication between the MAB and the SAB. The study demonstrates the important contribution of high frequency GS variability to CSL variability - better understanding of the role of remote forcing on coastal sea level can help to improve prediction of coastal sea level variations and associated flooding.

Abstract We analyse the spreading distribution of directional wave spectra across different sea states using a 3D fully nonlinear model and measurements from a wave basin. Using the spreading coefficient, we propose a transfer function for adaptive methods such as the Maximum Likelihood Method (MLM), Maximum Entropy Method (MEM), and Wavelet Directional Method (WDM), with the estimated spectra from 2FFT as a baseline. Our numerical analysis reveals that nonlinear effects broaden the directional distribution at peak frequency, particularly in narrow spectra. This broadening becomes more pronounced as wave steepness increases. With the proposed transfer function, we found that MLM tends to overestimate spreading, resulting in spectra that are 14% broader. In contrast, MEM and WDM produced narrower spectra, by 6% and 3% respectively. When analysing the spreading coefficient across relative frequencies, WDM performs best in the range of 0.8 to 1.15 $$f/f_p$$ , while MEM yields superior results at higher relative frequencies, specifically above 1.3 $$f/f_p$$ . We validate the transfer function using laboratory measurements, which effectively capture the behaviour of the adaptive methods within the error limits. Additionally, due to the plane correction transformation, our laboratory analysis using stereo-imaging measurements showed that adaptive methods generally produced a slightly broader spectrum than those based on probe measurements. These findings highlight the importance of carefully selecting methods depending on the desired balance between accuracy and stability in wave spectral analysis.

Abstract We conducted 6-month deterministic and ensemble forecasts initialized from the ensemble mean analyses and 128-member ensemble analyses, respectively, on the first day of each month from 2016 to 2018 (i.e., 36 cases in total) to compare the forecast accuracies of the Kuroshio south of Japan. The results showed that the ensemble mean forecasts captured the Kuroshio paths better in general compared with the deterministic forecasts, especially during the Kuroshio large meander period after the summer of 2017. For 2016–2018, the predictability limit for the ensemble mean forecast is estimated to be 108 days on average, demonstrating for the first time that the ensemble mean forecasts achieve a significantly longer predictability limit than the 74-day limit of the deterministic forecasts for the Kuroshio. We also investigated the sensitivity of the forecast accuracy to the ensemble size using a resampling method. The results indicated that for the Kuroshio prediction, the ensemble mean forecasts are more accurate than the deterministic forecasts when the ensemble size exceeds 32 members. When the ensemble size is limited to 32 members or less, combining the deterministic and ensemble forecasts could improve the short-term Kuroshio forecasting. This study also identified substantial northward shift biases in the Kuroshio forecasts, suggesting that further system development is necessary to achieve more significant improvements in the forecast accuracy.