Embedding machine-learnt sub-grid variability improves climate model biases
The under-representation of cloud formation is a long-standing bias associated with climate simulations. Parameterisation schemes are required to capture cloud processes within current climate models but have known biases. We overcome these biases by embedding a Multi-Output Gaussian Process (MOGP) trained on high resolution Unified Model simulations to represent the variability of temperature and specific humidity within a climate model. A trained MOGP model is coupled in-situ with a simplified Atmospheric General Circulation Model named SPEEDY. The temperature and specific humidity profiles of SPEEDY are perturbed at fixed intervals according to the variability predicted from the MOGP. Ten-year predictions are generated for both control and ML-hybrid models. The hybrid model reduces the global precipitation bias by 18\% and over the tropics by 22\%. To further understand the drivers of these improvements, physical quantities of interest are explored, such as the distribution of lifted index values and the alteration of the Hadley cell. The control and hybrid set-ups are also run in a plus 4K sea-surface temperature experiment to explore the effects of the approach on patterns relating to cloud cover and precipitation in a warmed climate setting.
Code (0)
등록된 구현이 없습니다.
Tasks
4kMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Time-Aware Word Embeddings for Three Lebanese News Archives
Word embeddings have proven to be an effective method for capturing semantic relations among distinct terms within a large corpus. In this paper, we present a set of word embeddings learnt from three large Lebanese news …
Optical Character Recognition (OCR)Word EmbeddingsExploiting Oxide Based Resistive RAM Variability for Bayesian Neural Network Hardware Design
Uncertainty plays a key role in real-time machine learning. As a significant shift from standard deep networks, which does not consider any uncertainty formulation during its training or inference, Bayesian deep networks…
EmbedTalk: Triplane-Free Talking Head Synthesis using Embedding-Driven Gaussian Deformation
Real-time talking head synthesis increasingly relies on deformable 3D Gaussian Splatting (3DGS) due to its low latency. Tri-planes are the standard choice for encoding Gaussians prior to deformation, since they provide a…
SafePowerGraph-LLM: Novel Power Grid Graph Embedding and Optimization with Large Language Models
Efficiently solving Optimal Power Flow (OPF) problems in power systems is crucial for operational planning and grid management. There is a growing need for scalable algorithms capable of handling the increasing variabili…
Graph EmbeddingIn-Context LearningManagementUniversal Differential Equations for Scientific Machine Learning of Node-Wise Battery Dynamics in Smart Grids
Universal Differential Equations (UDEs), which blend neural networks with physical differential equations, have emerged as a powerful framework for scientific machine learning (SciML), enabling data-efficient, interpreta…