Revealing short-term dynamics of tropical cyclone wind speeds from satellite synthetic aperture radar.

Both unresolved physics in numerical models and limited theoretical understanding of the small-scale diffusion processes occurring near the ocean surface hamper predictability of tropical cyclone (TC) wind changes. An analytical model is here developed to diagnose the short-term evolution of the TC wind profile. An effective frictional parameter is introduced to control the unknown diffusion effects. When this frictional parameter is adjusted to match the TC intensity change, solutions are consistent with observed high-resolution ocean surface wind speeds from satellite synthetic aperture radar (SAR). The initial high-resolution estimate of the near-core wind structure is then found to strongly modulate the wind profile evolution. The frictional parameter can, unfortunately, not efficiently be calibrated using outer-core wind speed changes. Low-resolution observations or standard numerical weather predictions may thus not be directly used to reinterpret and anticipate short-term TC wind changes. The expected accumulation of orbiting SAR sensors as well as improved measurements of the ocean-atmosphere boundary layer characteristics shall then become essential to more precisely monitor TC dynamics.

Underestimation of extremes in sea level surge reconstruction.

Statistical models are an alternative to numerical models for reconstructing storm surges at a low computational cost. These models directly link surges to metocean variables, i.e., predictors such as atmospheric pressure, wind and waves. Such reconstructions usually underestimate extreme surges. Here, we explore how to reduce biases on extremes using two methods-multiple linear regressions and neural networks-for surge reconstructions. Models with different configurations are tested at 14 long-term tide gauges in the North-East Atlantic. We found that (1) using the wind stress rather than the wind speed as predictor reduces the bias on extremes. (2) Adding the significant wave height as a predictor can reduce biases on extremes at a few locations tested. (3) Building on these statistical models, we show that atmospheric reanalyses likely underestimate extremes over the 19th century. Finally, it is demonstrated that neural networks can effectively predict extreme surges without wind information, but considering the atmospheric pressure input extracted over a sufficiently large area around a given station. This last point may offer new insights into air-sea interaction studies and wind stress parametrization.

Shift of the storm surge season in Europe due to climate variability.

Along the European coasts, changes in the timing of the storm surge season are analyzed. Using 10 long-term tide gauges located in western Europe, a consistent spatio-temporal shift emerged in the storm surge season between 1950 and 2000. Temporal shifts are positive (later events) in the North, negative (earlier events) in the South. Extreme surge events occurred about 4 days/decade later in northern Europe, and 5 days/decade earlier in southern Europe. Such a tendency is similar to the one already reported for European river floods between 1960 and 2010. In northern Europe, extreme surges are known to occur during the positive North Atlantic Oscillation phase (NAO+). Identified spatio-temporal shifts likely trace that NAO+ storms tend to occur later between 1950 and 2000. A new index measuring the timing of the NAO+ and NAO- persistent situations is shown to help capture this spatial distribution in the timing of the storm surge seasons.

Eigenvalues of autocovariance matrix: A practical method to identify the Koopman eigenfrequencies.

To infer eigenvalues of the infinite-dimensional Koopman operator, we study the leading eigenvalues of the autocovariance matrix associated with a given observable of a dynamical system. For any observable f for which all the time-delayed autocovariance exist, we construct a Hilbert space H_{f} and a Koopman-like operator K that acts on H_{f}. We prove that the leading eigenvalues of the autocovariance matrix has one-to-one correspondence with the energy of f that is represented by the eigenvectors of K. The proof is associated to several representation theorems of isometric operators on a Hilbert space, and the weak-mixing property of the observables represented by the continuous spectrum. We also provide an alternative proof of the weakly mixing property. When f is an observable of an ergodic dynamical system which has a finite invariant measure µ, H_{f} coincides with closure in L^{2}(X,dµ) of Krylov subspace generated by f, and K coincides with the classical Koopman operator. The main theorem sheds light to the theoretical foundation of several semi-empirical methods, including singular spectrum analysis (SSA), data-adaptive harmonic analysis (DAHD), Hankel DMD, and Hankel alternative view of Koopman analysis (HAVOK). It shows that, when the system is ergodic and has finite invariant measure, the leading temporal empirical orthogonal functions indeed correspond to the Koopman eigenfrequencies. A theorem-based practical methodology is then proposed to identify the eigenfrequencies of K from a given time series. It builds on the fact that the convergence of the renormalized eigenvalues of the Gram matrix is a necessary and sufficient condition for the existence of K-eigenfrequencies. Numerical illustrating results on simple low dimensional systems and real interpolated ocean sea-surface height data are presented and discussed.

Winter weather controls net influx of atmospheric CO2 on the north-west European shelf.

Shelf seas play an important role in the global carbon cycle, absorbing atmospheric carbon dioxide (CO2) and exporting carbon (C) to the open ocean and sediments. The magnitude of these processes is poorly constrained, because observations are typically interpolated over multiple years. Here, we used 298500 observations of CO2 fugacity (fCO2) from a single year (2015), to estimate the net influx of atmospheric CO2 as 26.2 ± 4.7 Tg C yr-1 over the open NW European shelf. CO2 influx from the atmosphere was dominated by influx during winter as a consequence of high winds, despite a smaller, thermally-driven, air-sea fCO2 gradient compared to the larger, biologically-driven summer gradient. In order to understand this climate regulation service, we constructed a carbon-budget supplemented by data from the literature, where the NW European shelf is treated as a box with carbon entering and leaving the box. This budget showed that net C-burial was a small sink of 1.3 ± 3.1 Tg C yr-1, while CO2 efflux from estuaries to the atmosphere, removed the majority of river C-inputs. In contrast, the input from the Baltic Sea likely contributes to net export via the continental shelf pump and advection (34.4 ± 6.0 Tg C yr-1).