Atmospheric processes

Abstract:

Euro‐Atlantic regimes are typically identified using either the latitude of the North Atlantic jet or clustering algorithms in the phase space of 500‐hPa geopotential (Z500). However, while robust trimodality is visibly apparent in jet latitude indices, Z500 clusters require highly sensitive significance tests to distinguish them from autocorrelated noise. This leads to considerable decadal variability in regime patterns, confounding many potential applications. A clear‐cut choice of the optimal number of regimes is also hard to justify. We argue that the jet speed, a near‐Gaussian distribution projecting strongly onto the Z500 field, is the source of these difficulties. Once its influence is removed, the phase space becomes visibly non‐Gaussian, and clustering algorithms easily recover three regimes, closely corresponding to the jet latitude modes. Further analysis supports the existence of two additional blocking regimes, corresponding to a tilted and split jet. All five regimes are approximately stationary across the twentieth century.

Abstract:

Information in decadal climate prediction arises from a well initialised ocean state and from the predicted response to an external forcing. The length of time over which the initial conditions benefit the decadal forecast depends on the start date of the forecast. We characterise this state-dependent predictability for decadal forecasts of upper ocean heat content in the Community Earth System Model. We find regionally dependent initial condition predictability, with extended predictability generally observed in the extra-tropics. We also detect state-dependent predictability, with the year of loss of information from the initialisation varying between start dates. The decadal forecasts in the North Atlantic show substantial information from the initial conditions beyond the ten-year forecast window, and a high degree of state-dependent predictability. We find some evidence for state dependent predictability in the ensemble spread in this region, similar to that seen in weather and subseasonal-to-seasonal forecasts. For some start dates, an increase of information with lead time is observed, for which the initialised forecasts predict a growing phase of the Atlantic Multidecadal Oscillation. Finally we consider the information in the forecast from the initial conditions relative to the forced response, and quantify the crossover timescale after which the forcing provides more information. We demonstrate that the climate change signal projects onto different patterns than the signal from the initial conditions. This means that even after the crossover timescale has been reached in a basin-averaged sense, the benefits of initialisation can be felt locally on longer timescales.

Abstract:

Euro-Atlantic regimes are typically identified using either the latitude of the eddy-driven jet, or clustering algorithms in the phase space of 500hPa geopotential height (Z500). However, while robust trimodality is visibly apparent in jet latitude indices, Z500 clusters require highly sensitive significance tests to distinguish them from autocorrelated noise. As a result, even small shifts in the time-period considered can notably alter the diagnosed regimes. Fixing the optimal regime number is also hard to justify. We argue that the jet speed, a near-Gaussian distribution projecting strongly onto the Z500 field, is the source of this lack of robustness. Once its influence is removed, the Z500 phase space becomes visibly non-Gaussian, and clustering algorithms easily recover three extremely stable regimes, corresponding to the jet latitude regimes. Further analysis supports the existence of two additional regimes, corresponding to a tilted and split jet. This framework therefore naturally unifies the two regime perspectives.

Abstract:

Stochastic parameterizations account for uncertainty in the representation of unresolved subgrid processes by sampling from the distribution of possible subgrid forcings. Some existing stochastic parameterizations utilize data‐driven approaches to characterize uncertainty, but these approaches require significant structural assumptions that can limit their scalability. Machine learning models, including neural networks, are able to represent a wide range of distributions and build optimized mappings between a large number of inputs and subgrid forcings. Recent research on machine learning parameterizations has focused only on deterministic parameterizations. In this study, we develop a stochastic parameterization using the generative adversarial network (GAN) machine learning framework. The GAN stochastic parameterization is trained and evaluated on output from the Lorenz '96 model, which is a common baseline model for evaluating both parameterization and data assimilation techniques. We evaluate different ways of characterizing the input noise for the model and perform model runs with the GAN parameterization at weather and climate time scales. Some of the GAN configurations perform better than a baseline bespoke parameterization at both time scales, and the networks closely reproduce the spatiotemporal correlations and regimes of the Lorenz '96 system. We also find that, in general, those models which produce skillful forecasts are also associated with the best climate simulations.