Atmospheric Circulation Response to Short-Term Arctic Warming in an Idealized Model

Recent Arctic sea ice loss in fall has been posited to drive midlatitude circulation changes into winter and even spring. Past work has shown that sea ice loss can indeed trigger a weakening of the stratospheric polar vortex, which can lead to delayed surface weather changes. But the mechanisms of such changes and their relevant time scales have remained unclear. This study uses large ensembles of idealized GCM simulations to identify how and over what time scales the atmospheric circulation responds to short-term surface heat flux changes in high latitudes. The ensemble-mean response of the atmospheric circulation is approximately linear in the amplitude of the surface forcing. It is also insensitive to whether the forcing is zonally asymmetric or symmetric, that is, whether stationary waves are generated or not. The circulation response can be decomposed into a rapid thermal response and a slower dynamic adjustment. The adjustment arises through weakening of vertical wave activity fluxes from the troposphere into the stratosphere in response to polar warming, a mechanism that differs from sudden stratospheric warmings yet still results in a weakened stratospheric circulation. The stratospheric response is delayed and persists for about 2 months because the thermal response of the stratosphere is slow compared with that of the troposphere. The delayed stratospheric response feeds back onto the troposphere, but the tropospheric effects are weak compared with natural variability. The general pathway for the delayed response appears to be relatively independent of the atmospheric background state at the time of the anomalous surface forcing

Atmospheric Circulation Response to Short-Term Arctic Warming in an Idealized Model

Recent Arctic sea ice loss in fall has been posited to drive midlatitude circulation changes into winter and even spring. Past work has shown that sea ice loss can indeed trigger a weakening of the stratospheric polar vortex, which can lead to delayed surface weather changes. But the mechanisms of such changes and their relevant time scales have remained unclear. This study uses large ensembles of idealized GCM simulations to identify how and over what time scales the atmospheric circulation responds to short-term surface heat flux changes in high latitudes. The ensemble-mean response of the atmospheric circulation is approximately linear in the amplitude of the surface forcing. It is also insensitive to whether the forcing is zonally asymmetric or symmetric, that is, whether stationary waves are generated or not. The circulation response can be decomposed into a rapid thermal response and a slower dynamic adjustment. The adjustment arises through weakening of vertical wave activity fluxes from the troposphere into the stratosphere in response to polar warming, a mechanism that differs from sudden stratospheric warmings yet still results in a weakened stratospheric circulation. The stratospheric response is delayed and persists for about 2 months because the thermal response of the stratosphere is slow compared with that of the troposphere. The delayed stratospheric response feeds back onto the troposphere, but the tropospheric effects are weak compared with natural variability. The general pathway for the delayed response appears to be relatively independent of the atmospheric background state at the time of the anomalous surface forcing

Current and emerging developments in subseasonal to decadal prediction

Weather and climate variations of subseasonal to decadal timescales can have enormous social, economic and environmental impacts, making skillful predictions on these timescales a valuable tool for decision makers. As such, there is a growing interest in the scientific, operational and applications communities in developing forecasts to improve our foreknowledge of extreme events. On subseasonal to seasonal (S2S) timescales, these include high-impact meteorological events such as tropical cyclones, extratropical storms, floods, droughts, and heat and cold waves. On seasonal to decadal (S2D) timescales, while the focus remains broadly similar (e.g., on precipitation, surface and upper ocean temperatures and their effects on the probabilities of high-impact meteorological events), understanding the roles of internal and externally-forced variability such as anthropogenic warming in forecasts also becomes important. The S2S and S2D communities share common scientific and technical challenges. These include forecast initialization and ensemble generation; initialization shock and drift; understanding the onset of model systematic errors; bias correct, calibration and forecast quality assessment; model resolution; atmosphere-ocean coupling; sources and expectations for predictability; and linking research, operational forecasting, and end user needs. In September 2018 a coordinated pair of international conferences, framed by the above challenges, was organized jointly by the World Climate Research Programme (WCRP) and the World Weather Research Prograame (WWRP). These conferences surveyed the state of S2S and S2D prediction, ongoing research, and future needs, providing an ideal basis for synthesizing current and emerging developments in these areas that promise to enhance future operational services. This article provides such a synthesis

Time-Varying Empirical Probability Densities of Southern Ocean Surface Winds: Linking the Leading Mode to SAM and Quantifying Wind Product Differences

AbstractSouthern Ocean (SO) surface winds are essential for ventilating the upper ocean by bringing heat and CO2 to the ocean interior. The relationships between mixed-layer ventilation, the Southern Annular Mode (SAM), and the storm tracks remain unclear because processes can be governed by short-term wind events as well as long-term means.In this study, observed time-varying 5-day probability density functions (PDFs) of ERA5 surface winds and stresses over the SO are used in a singular value decomposition to derive a linearly independent set of empirical basis functions. The first modes of wind (72% of the total wind variance) and stress (74% of the total stress variance) are highly correlated with a standard SAM index (r = 0.82) and reflect SAM’s role in driving cyclone intensity and, in turn, extreme westerly winds. This Joint PDFs of zonal and meridional wind show that southerly and less westerly winds associated with strong mixed-layer ventilation are more frequent during short and distinct negative SAM phases. The probability of these short-term events might be related to mid-latitude atmospheric circulation. The second mode describes seasonal changes in the wind variance (16% of the total variance) that are uncorrelated with the first mode.The analysis produces similar results when repeated using 5-day PDFs from a suite of scatterometer products. Differences between wind product PDFs resemble the first mode of the PDFs. Together, these results show a strong correlation between surface stress PDFs and the leading modes of atmospheric variability, suggesting that empirical modes can serve as a novel pathway for understanding differences and variability of surface stress PDFs

Identifying Ocean Swell Generation Events from Ross Ice Shelf Seismic Data

AbstractStrong surface winds under extratropical cyclones exert intense surface stresses on the ocean that lead to upper-ocean mixing, intensified heat fluxes, and the generation of waves, that, over time, lead to swell waves (longer than 10-s period) that travel long distances. Because low-frequency swell propagates faster than high-frequency swell, the frequency dependence of swell arrival times at a measurement site can be used to infer the distance and time that the wave has traveled from its generation site. This study presents a methodology that employs spectrograms of ocean swell from point observations on the Ross Ice Shelf (RIS) to verify the position of high wind speed areas over the Southern Ocean, and therefore of extratropical cyclones. The focus here is on the implementation and robustness of the methodology in order to lay the groundwork for future broad application to verify Southern Ocean storm positions from atmospheric reanalysis data. The method developed here combines linear swell dispersion with a parametric wave model to construct a time- and frequency-dependent model of the dispersed swell arrivals in spectrograms of seismic observations on the RIS. A two-step optimization procedure (deep learning) of gradient descent and Monte Carlo sampling allows detailed estimates of the parameter distributions, with robust estimates of swell origins. Median uncertainties of swell source locations are 110 km in radial distance and 2 h in time. The uncertainties are derived from RIS observations and the model, rather than an assumed distribution. This method is an example of supervised machine learning informed by physical first principles in order to facilitate parameter interpretation in the physical domain

Identifying Ocean Swell Generation Events from Ross Ice Shelf Seismic Data

AbstractStrong surface winds under extratropical cyclones exert intense surface stresses on the ocean that lead to upper-ocean mixing, intensified heat fluxes, and the generation of waves, that, over time, lead to swell waves (longer than 10-s period) that travel long distances. Because low-frequency swell propagates faster than high-frequency swell, the frequency dependence of swell arrival times at a measurement site can be used to infer the distance and time that the wave has traveled from its generation site. This study presents a methodology that employs spectrograms of ocean swell from point observations on the Ross Ice Shelf (RIS) to verify the position of high wind speed areas over the Southern Ocean, and therefore of extratropical cyclones. The focus here is on the implementation and robustness of the methodology in order to lay the groundwork for future broad application to verify Southern Ocean storm positions from atmospheric reanalysis data. The method developed here combines linear swell dispersion with a parametric wave model to construct a time- and frequency-dependent model of the dispersed swell arrivals in spectrograms of seismic observations on the RIS. A two-step optimization procedure (deep learning) of gradient descent and Monte Carlo sampling allows detailed estimates of the parameter distributions, with robust estimates of swell origins. Median uncertainties of swell source locations are 110 km in radial distance and 2 h in time. The uncertainties are derived from RIS observations and the model, rather than an assumed distribution. This method is an example of supervised machine learning informed by physical first principles in order to facilitate parameter interpretation in the physical domain