Wintertime Rossby Wave Breaking Persistence in Extended-range Seasonal Forecasts of Atlantic Tropical Cyclone Activity

In this study, we examine the wintertime environmental precursors of summer anticyclonic wave breaking (AWB) over the North Atlantic region and assess the applicability of these precursors in predicting AWB impacts on seasonal tropical cyclone (TC) activity. We show that predictors representing the environmental impacts of subtropical AWB on seasonal TC activity improve the skill of extended-range seasonal forecasts of TC activity. There is a significant correlation between boreal winter and boreal summer AWB activity via AWB-forced phases of the quasi-stationary North Atlantic Oscillation (NAO). Years with above-normal boreal summer AWB activity over the North Atlantic region also show above-normal AWB activity in the preceding boreal winter that tends to force a positive phase of the NAO that persists through the spring. These conditions are sustained by continued AWB throughout the year, particularly when El Niño-Southern Oscillation plays less of a role at forcing the large-scale circulation. While individual AWB events are synoptic and nonlinear with little predictability beyond 8-10 days, the strong dynamical connection between winter and summer wave breaking lends enough persistence to AWB activity to enable predictability of its potential impacts on TC activity. We find that the winter-summer relationship improves the skill of extended-range seasonal forecasts from as early as an April lead time, particularly for years when wave breaking has played a crucial role in suppressing TC development.

Testing Vertical Wind Shear and Nonlinear MJO/ENSO Interactions as Predictors for Subseasonal Atlantic Tropical Cyclone Forecasts

Hansen et al. (2020) found patterns of vertical wind shear, relative humidity (RH) and non-linear interactions between the Madden-Julian Oscillation and El Niño-Southern Oscillation that impact subseasonal Atlantic TC activity. We test whether these patterns can be used to improve subseasonal predictions. To do this we build a statistical-dynamical hybrid model using Navy-ESPC reforecasts as a part of the SUBX project. By adding and removing Navy-ESPC reforecasted values of predictors from a logistic regression model, we assess the contribution of skill from each predictor. We find that Atlantic SSTs and the MJO are the most important factors governing subseasonal Atlantic TC activity. RH contributes little to subseasonal TC predictions, however, shear predictors improve forecast skill at 5-10 day lead times, before forecast shear errors become too large. Non-linear MJO/ENSO interactions did not improve skill compared to separate linear considerations of these factors but did improve the reliability of predictions for high-probability active TC periods. Both non-linear MJO/ENSO interactions and the subseasonal shear signal appear linked to PV streamer activity. This study suggests that correcting model shear biases and improving representation of Rossby wave-breaking is the most efficient way to improve subseasonal Atlantic TC forecasts.

The unusually long cold spell and the snowstorm Filomena in Spain in January 2021

In early January 2021, Spain was affected by two extreme events – an unusually long cold spell and a heavy snowfall event associated with extratropical cyclone Filomena. For example, up to 50 cm of snow fell in Madrid and the surrounding areas in 4 days. Already during 9 days prior to the snowfall event, anomalously cold temperatures at 850 hPa and night frosts prevailed over large parts of Spain. During this period, anomalously cold and dry air was transported towards Spain from central Europe and even from the Barents Sea. The storm Filomena, which was responsible for major parts of the snowfall event, developed from a precursor low-pressure system over the central North Atlantic. Filomena intensified due to interaction with an upper-level potential vorticity (PV) trough, which was the result of anticyclonic wave breaking over Europe. In turn, this wave breaking was related to an intense surface anticyclone and upper-level ridge, whose formation was strongly influenced by a warm conveyor belt outflow of a cyclone off the coast of Newfoundland. The most intense snowfall occurred on 09 January and was associated with a sharp air mass boundary with an equivalent potential temperature difference at 850 hPa across Spain exceeding 20 K. Overall, the combination of pre-existing cold surface temperatures, the optimal position of the air mass boundary, and the dynamical forcing for ascent induced by Filomena and its associated upper-level trough were all essential – and in parts physically independent – ingredients for this extreme snowfall event to occur.

Estimation of Wave-Breaking Index by Learning Nonlinear Relation Using Multilayer Neural Network

Estimating wave-breaking indexes such as wave height and water depth is essential to understanding the location and scale of the breaking wave. Therefore, numerous wave-flume laboratory experiments have been conducted to develop empirical wave-breaking formulas. However, the nonlinearity between the parameters has not been fully incorporated into the empirical equations. Thus, this study proposes a multilayer neural network utilizing the nonlinear activation function and backpropagation to extract nonlinear relationships. Existing laboratory experiment data for the monochromatic regular wave are used to train the proposed network. Specifically, the bottom slope, deep-water wave height and wave period are plugged in as the input values that simultaneously estimate the breaking-wave height and wave-breaking location. Typical empirical equations employ deep-water wave height and length as input variables to predict the breaking-wave height and water depth. A newly proposed model directly utilizes breaking-wave height and water depth without nondimensionalization. Thus, the applicability can be significantly improved. The estimated wave-breaking index is statistically verified using the bias, root-mean-square errors, and Pearson correlation coefficient. The performance of the proposed model is better than existing breaking-wave-index formulas as well as having robust applicability to laboratory experiment conditions, such as wave condition, bottom slope, and experimental scale.

Well-posedness, wave breaking, Holder continuity and periodic peakons for a nonlocal sine-µ-Camassa-Holm equation

In this paper, we investigate the initial value problem of a nonlocal sine-type µ-Camassa-Holm (µCH) equation, which is the µ-version of the sine-type CH equation. We first discuss its local well-posedness in the framework of Besov spaces. Then a sufficient condition on the initial data is provided to ensure the occurance of the wave-breaking phenomenon. We finally prove the H¨older continuity of the data-to-solution map, and find the explicit formula of the global weak periodic peakon solution.

Development of a Fully Convolutional Neural Network to Derive Surf-Zone Bathymetry from Close-Range Imagery of Waves in Duck, NC

Timely observations of nearshore water depths are important for a variety of coastal research and management topics, yet this information is expensive to collect using in situ survey methods. Remote methods to estimate bathymetry from imagery include using either ratios of multi-spectral reflectance bands or inversions from wave processes. Multi-spectral methods work best in waters with low turbidity, and wave-speed-based methods work best when wave breaking is minimal. In this work, we build on the wave-based inversion approaches, by exploring the use of a fully convolutional neural network (FCNN) to infer nearshore bathymetry from imagery of the sea surface and local wave statistics. We apply transfer learning to adapt a CNN originally trained on synthetic imagery generated from a Boussinesq numerical wave model to utilize tower-based imagery collected in Duck, North Carolina, at the U.S. Army Engineer Research and Development Center’s Field Research Facility. We train the model on sea-surface imagery, wave conditions, and associated surveyed bathymetry using three years of observations, including times with significant wave breaking in the surf zone. This is the first time, to the authors’ knowledge, an FCNN has been successfully applied to infer bathymetry from surf-zone sea-surface imagery. Model results from a separate one-year test period generally show good agreement with survey-derived bathymetry (0.37 m root-mean-squared error, with a max depth of 6.7 m) under diverse wave conditions with wave heights up to 3.5 m. Bathymetry results quantify nearshore bathymetric evolution including bar migration and transitions between single- and double-barred morphologies. We observe that bathymetry estimates are most accurate when time-averaged input images feature visible wave breaking and/or individual images display wave crests. An investigation of activation maps, which show neuron activity on a layer-by-layer basis, suggests that the model is responsive to visible coherent wave structures in the input images.

Dynamical drivers of Greenland blocking in climate models

Blocking over Greenland is known to lead to strong surface impacts, such as ice sheet melting, and a change in its future frequency can have important consequences. However, as previous studies demonstrated, climate models underestimate the blocking frequency for the historical period. Even though some improvements have recently been made, the reasons for the model biases are still unclear. This study investigates whether models with realistic Greenland blocking frequency in winter have a correct representation of its dynamical drivers, most importantly, cyclonic wave breaking (CWB). Because blocking is a rare event and its representation is model-dependent, we use a multi-model large ensemble. We focus on two models that show typical Greenland blocking features, namely a ridge over Greenland and an equatorward-shifted jet over the North Atlantic. ECHAM6.3-LR has the best representation of CWB of the models investigated but only the second best representation of Greenland blocking frequency, which is underestimated by a factor of 2. While MIROC5 has the most realistic Greenland blocking frequency, it also has the largest (negative) CWB frequency bias, suggesting that another mechanism leads to blocking in this model. Composites over Greenland blocking days show that the present and future experiments of each model are very similar to each other in both amplitude and pattern and that there is no significant change in Greenland blocking frequency in the future. However, these projected changes in blocking frequency are highly uncertain as long as the mechanisms leading to blocking formation and maintenance in models remain poorly understood.