Abstract: Unresolved sub-grid processes, those which are too small or dissipate too quickly to be captured within a model's spatial resolution, are not adequately parameterized by conventional numerical climate models. Sub-grid heterogeneity is lost in parameterizations that quantify only the `bulk effect' of sub-grid dynamics on the resolved scales. A unique solution, one unreliant on increased grid resolution, is the employment of stochastic parameterization of the sub-grid to reintroduce variability. We administer this approach in a coupled land-atmosphere model, one that combines the single-column Community Atmosphere Model and the single-point Community Land Model, by incorporating a stochastic representation of sub-grid latent heat flux to force the distribution of precipitation. Sub-grid differences in surface latent heat flux arise from the mosaic of Plant Functional Types (PFT's) that describe terrestrial land cover. With the introduction of a stochastic parameterization framework to affect the distribution of sub-grid PFT's, we alter the distribution of convective precipitation over regions with high PFT variability. The stochastically forced precipitation probability density functions show lengthened tails demonstrating the retrieval of rare events. Through model data analysis we show that the stochastic model increases both the frequency and intensity of rare events in comparison to conventional deterministic parameterization.

Abstract: Climate modelling as it is generally practised is the act of generating large volumes of simu- lated weather through integration of primitive-equation/general circulation model-based Earth system models (ESMs) and subsequent statistical analysis of these large volumes of model-generated history files. This ap- proach, though highly successful, entails explosively growing data volumes, and may not be practicable on exascale computers. This situation begs the question: Can we model climate’s governing dynamics directly? If we pursue this tactic, there are two clear avenues to pursue: i) analysis of the combined primitive equations and subgridscale parameterisations to formulate an “envelope theory” applicable to the system’s larger spa- tiotemporal scales; and ii) a search for governing dynamics through analysis of the existing corpus of climate observation assimilated and simulated data. Our work focuses on strategy ii). Climate data analysis concentrates primarily on statistical moments, quantiles, and extremes, but rarely on the most complete statistical descriptor—the probability density function (PDF). Long-term climate variabil- ity motivates a moving-window-sampled PDF, which we call a time-dependent PDF (TDPDF). The TDPDF resides within a PDF/information-theoretic framework that provides answers to several key questions of cli- mate variability, stability, and change, including: How does the climate evolve in time? How representative is any given sampling interval of the whole record? How rapidly is the climate changing? In this study, we pursue probability density estimation globally sampled climate data using two techniques that are readily applicable to spatially weighted data and yield closed-form PDFs: the Edgworth expansion and kernel smoothing. We explore our concerns regarding serial correlation in the data and effective sample size due to spatiotemporal correlations. We introduce these concepts for a simple dataset: the Central England Temperature Record. We then apply these techniques to larger, spatially-weghted climate data sets, including the USA National Center for Environmental Predictions NCEP-1 Reanalysis, the Australian Water Availability Project (AWAP) dataset, and the Australian Water and Carbon Observatory dataset.

Abstract: In this paper, an implementation study was undertaken to employ Artificial Neural Networks (ANN) in third-generation ocean wave models for direct mapping of wind-wave spectra into exact nonlinear interactions. While the investigation expands on previously reported feasibility studies of Neural Network Interaction Approximations (NNIA), it focuses on a new robust neural network that is implemented in Wavewatch III (WW3) model. Several idealistic and real test scenarios were carried out. The obtained results confirm the feasibility of NNIA in terms of speeding-up model calculations and is fully capable of providing operationally acceptable model integrations. The ANN is able to emulate the exact nonlinear interaction for single- and multi-modal wave spectra with a much higher accuracy then Discrete Interaction Approximation (DIA). NNIA performs at least twice as fast as DIA and at least two hundred times faster than exact method (Web-Resio-Tracy, WRT) for a well trained dataset. The accuracy of NNIA is network configuration dependent. For most optimal network configurations, the NNIA results and scatter statistics show good agreement with exact results by means of growth curves and integral parameters. Practical possibilities for further improvements in achieving fast and highly accurate emulations using ANN for emulating time consuming exact nonlinear interactions are also suggested and discussed.