arXiv subjects

Edwin P. Gerber

Publications and source records attributed to Edwin P. Gerber.

8 recordsLinked to original sources

arXiv2026

Interpretable Neural Networks to Predict Momentum Fluxes of Orographic Gravity Waves

State-of-the-art Earth system models (ESMs) cannot explicitly resolve many small-scale atmospheric processes such as atmospheric gravity waves, and thus must represent, or parameterise, their effects on the resolved state. Machine learning (ML) has the potential to improve these parameterisations. In our study, we train neural networks (NNs) on ERA5 reanalysis data to predict momentum fluxes of orographic gravity waves as a function of the state variables at the resolution of a coarse ESM. Employing a full year of data, we extract inertia-gravity waves using the software MODES, which applies linear theory for wave filtering, and train ML models on data coarse-grained to the ESM's target resolution. We consider four different cases: the full spectrum of inertia-gravity waves resolved in ERA5, or just the part of the spectrum that is subgrid-scale in the target ESM, both over all land or just over mountainous terrain. Our NNs successfully predict momentum fluxes, with a global coefficient of determination ($R^2$) ranging from 0.72 to 0.56, depending on the case, when evaluated offline with data from another year. An analysis of our models using SHAP values, an explainable AI technique, suggests that the networks learned physically meaningful relationships. In addition, we give a comparison with the physics-based parameterisation scheme by Lott and Miller. This work forms the basis for the development of operational ML-based parameterisations to improve the representation of gravity waves and their effects in climate models.

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arXiv2025

Earth's Infrared Background: A Physics-Based Null Hypothesis for the Global-Scale Subannual Variability of Outgoing Longwave Radiation

Much of the Outgoing Longwave Radiation (OLR) emitted to space can be described as a noisy "background" of random variability. A rigorous characterization of this background provides an objective null spectrum and enables the isolation of atmospheric phenomena within OLR observations, such as waves and storms. Previously, the background has only been considered in the Tropics. Here we study the background on global, subannual scales and focus on its physical origins. We define the background as isotropic fluctuations implied by the fluctuation-dissipation theorem in response to internal atmospheric variability on small spatiotemporal scales. We use a stochastically forced energy balance climate model that generates a red spectrum in space and time, consistent with observations. By fitting the model to OLR measurements from satellites, we find that the background fluctuations have an upper bound of about 400~km and 2.5~days on their spatiotemporal (de)correlations, between meso-scale and synoptic-scale weather.

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arXiv2024

The QBO, the annual cycle, and their interactions: Isolating periodic modes with Koopman analysis

The Quasi-Biennial Oscillation (QBO) is the dominant mode of variability in the equatorial stratosphere. It is characterized by alternating descending easterly and westerly jets over a period of approximately 28 months. It has long been known that the QBO interactions with the annual cycle, e.g., through variation in tropical upwelling, leading to variations in the descent rate of the jets and, resultingly, the QBO period. Understanding these interactions, however, has been hindered by the fact that conventional measures of the QBO convolve these interactions. Koopman formalism, derived from dynamical systems, allows one to decompose spatio-temporal datasets (or nonlinear systems) into spatial modes that evolve coherently with distinct frequencies. We use a data-driven approximation of the Koopman operator on zonal-mean zonal-wind to find modes that correspond to the annual cycle, the QBO, and the nonlinear interactions between the two. From these modes, we establish a data-driven index for a "pure" QBO that is independent of the annual cycle and investigate how the annual cycle modulates the QBO. We begin with what is already known, quantifying the Holton-Tan effect, a nonlinear interaction between the QBO and the annual cycle of the polar stratospheric vortex. We then use the pure QBO to do something new, quantifying how the annual cycle changes the descent rate of the QBO, revealing annual variations with amplitudes comparable to the $30 \, \mathrm{m} \, \mathrm{day}^{-1}$ mean descent rate. We compare these results to the annual variation in tropical upwelling and interpret then with a simple model.

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arXiv2024

Using Explainable AI and Transfer Learning to understand and predict the maintenance of Atlantic blocking with limited observational data

Blocking events are an important cause of extreme weather, especially long-lasting blocking events that trap weather systems in place. The duration of blocking events is, however, underestimated in climate models. Explainable Artificial Intelligence are a class of data analysis methods that can help identify physical causes of prolonged blocking events and diagnose model deficiencies. We demonstrate this approach on an idealized quasigeostrophic model developed by Marshall and Molteni (1993). We train a convolutional neural network (CNN), and subsequently, build a sparse predictive model for the persistence of Atlantic blocking, conditioned on an initial high-pressure anomaly. Shapley Additive ExPlanation (SHAP) analysis reveals that high-pressure anomalies in the American Southeast and North Atlantic, separated by a trough over Atlantic Canada, contribute significantly to prediction of sustained blocking events in the Atlantic region. This agrees with previous work that identified precursors in the same regions via wave train analysis. When we apply the same CNN to blockings in the ERA5 atmospheric reanalysis, there is insufficient data to accurately predict persistent blocks. We partially overcome this limitation by pre-training the CNN on the plentiful data of the Marshall-Molteni model, and then using Transfer Learning to achieve better predictions than direct training. SHAP analysis before and after transfer learning allows a comparison between the predictive features in the reanalysis and the quasigeostrophic model, quantifying dynamical biases in the idealized model. This work demonstrates the potential for machine learning methods to extract meaningful precursors of extreme weather events and achieve better prediction using limited observational data.

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arXiv2024

Overcoming set imbalance in data driven parameterization: A case study of gravity wave momentum transport

Machine learning for the parameterization of subgrid-scale processes in climate models has been widely researched and adopted in a few models. A key challenge in developing data-driven parameterization schemes is how to properly represent rare, but important events that occur in geoscience datasets. We investigate and develop strategies to reduce errors caused by insufficient sampling in the rare data regime, under constraints of no new data and no further expansion of model complexity. Resampling and importance weighting strategies are constructed with user defined parameters that systematically vary the sampling/weighting rates in a linear fashion and curb too much oversampling. Applying this new method to a case study of gravity wave momentum transport reveals that the resampling strategy can successfully improve errors in the rare regime at little to no loss in accuracy overall in the dataset. The success of the strategy, however, depends on the complexity of the model. More complex models can overfit the tails of the distribution when using non-optimal parameters of the resampling strategy.

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arXiv2023

Revealing the statistics of extreme events hidden in short weather forecast data

Extreme weather events have significant consequences, dominating the impact of climate on society. While high-resolution weather models can forecast many types of extreme events on synoptic timescales, long-term climatological risk assessment is an altogether different problem. A once-in-a-century event takes, on average, 100 years of simulation time to appear just once, far beyond the typical integration length of a weather forecast model. Therefore, this task is left to cheaper, but less accurate, low-resolution or statistical models. But there is untapped potential in weather model output: despite being short in duration, weather forecast ensembles are produced multiple times a week. Integrations are launched with independent perturbations, causing them to spread apart over time and broadly sample phase space. Collectively, these integrations add up to thousands of years of data. We establish methods to extract climatological information from these short weather simulations. Using ensemble hindcasts by the European Center for Medium-range Weather Forecasting (ECMWF) archived in the subseasonal-to-seasonal (S2S) database, we characterize sudden stratospheric warming (SSW) events with multi-centennial return times. Consistent results are found between alternative methods, including basic counting strategies and Markov state modeling. By carefully combining trajectories together, we obtain estimates of SSW frequencies and their seasonal distributions that are consistent with reanalysis-derived estimates for moderately rare events, but with much tighter uncertainty bounds, and which can be extended to events of unprecedented severity that have not yet been observed historically. These methods hold potential for assessing extreme events throughout the climate system, beyond this example of stratospheric extremes.

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arXiv2022

Data-driven transition path analysis yields a statistical understanding of sudden stratospheric warming events in an idealized model

Atmospheric regime transitions are highly impactful as drivers of extreme weather events, but pose two formidable modeling challenges: predicting the next event (weather forecasting), and characterizing the statistics of events of a given severity (the risk climatology). Each event has a different duration and spatial structure, making it hard to define an objective "average event." We argue here that transition path theory (TPT), a stochastic process framework, is an appropriate tool for the task. We demonstrate TPT's capacities on a wave-mean flow model of sudden stratospheric warmings (SSWs) developed by Holton and Mass (1976), which is idealized enough for transparent TPT analysis but complex enough to demonstrate computational scalability. Whereas a recent article (Finkel et al. 2021) studied near-term SSW predictability, the present article uses TPT to link predictability to long-term SSW frequency. This requires not only forecasting forward in time from an initial condition, but also \emph{backward in time} to assess the probability of the initial conditions themselves. TPT enables one to condition the dynamics on the regime transition occurring, and thus visualize its physical drivers with a vector field called the \emph{reactive current}. The reactive current shows that before an SSW, dissipation and stochastic forcing drive a slow decay of vortex strength at lower altitudes. The response of upper-level winds is late and sudden, occurring only after the transition is almost complete from a probabilistic point of view. This case study demonstrates that TPT quantities, visualized in a space of physically meaningful variables, can help one understand the dynamics of regime transitions.

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arXiv2021

Learning forecasts of rare stratospheric transitions from short simulations

Rare events arising in nonlinear atmospheric dynamics remain hard to predict and attribute. We address the problem of forecasting rare events in a prototypical example, Sudden Stratospheric Warmings (SSWs). Approximately once every other winter, the boreal stratospheric polar vortex rapidly breaks down, shifting midlatitude surface weather patterns for months. We focus on two key quantities of interest: the probability of an SSW occurring, and the expected lead time if it does occur, as functions of initial condition. These \emph{optimal forecasts} concretely measure the event's progress. Direct numerical simulation can estimate them in principle, but is prohibitively expensive in practice: each rare event requires a long integration to observe, and the cost of each integration grows with model complexity. We describe an alternative approach using integrations that are \emph{short} compared to the timescale of the warming event. We compute the probability and lead time efficiently by solving equations involving the transition operator, which encodes all information about the dynamics. We relate these optimal forecasts to a small number of interpretable physical variables, suggesting optimal measurements for forecasting. We illustrate the methodology on a prototype SSW model developed by Holton and Mass (1976) and modified by stochastic forcing. While highly idealized, this model captures the essential nonlinear dynamics of SSWs and exhibits the key forecasting challenge: the dramatic separation in timescales between a single event and the return time between successive events. Our methodology is designed to fully exploit high-dimensional data from models and observations, and has the potential to identify detailed predictors of many complex rare events in meteorology.

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