HECT: High-Dimensional Ensemble Consistency Testing for Climate Models

• URL: http://arxiv.org/abs/2010.04051v2
• Date: Mon, 30 Nov 2020 22:48:17 GMT
• Title: HECT: High-Dimensional Ensemble Consistency Testing for Climate Models
• Authors: Niccol\`o Dalmasso, Galen Vincent, Dorit Hammerling, Ann B. Lee
• Abstract summary: Climate models play a crucial role in understanding the effect of environmental changes on climate to help mitigate climate risks and inform decisions. Large global climate models such as the Community Earth System Model (CESM), are very complex with millions of lines of code describing interactions of the atmosphere, land, oceans, and ice. Our work uses probabilistics like tree-based algorithms and deep neural networks to perform a statistically rigorous goodness-of-fit test of high-dimensional and man-made data.
• Score: 1.7587442088965226
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Climate models play a crucial role in understanding the effect of environmental and man-made changes on climate to help mitigate climate risks and inform governmental decisions. Large global climate models such as the Community Earth System Model (CESM), developed by the National Center for Atmospheric Research, are very complex with millions of lines of code describing interactions of the atmosphere, land, oceans, and ice, among other components. As development of the CESM is constantly ongoing, simulation outputs need to be continuously controlled for quality. To be able to distinguish a "climate-changing" modification of the code base from a true climate-changing physical process or intervention, there needs to be a principled way of assessing statistical reproducibility that can handle both spatial and temporal high-dimensional simulation outputs. Our proposed work uses probabilistic classifiers like tree-based algorithms and deep neural networks to perform a statistically rigorous goodness-of-fit test of high-dimensional spatio-temporal data.

Related papers

• Efficient Localized Adaptation of Neural Weather Forecasting: A Case Study in the MENA Region [62.09891513612252]
  We focus on limited-area modeling and train our model specifically for localized region-level downstream tasks. We consider the MENA region due to its unique climatic challenges, where accurate localized weather forecasting is crucial for managing water resources, agriculture and mitigating the impacts of extreme weather events. Our study aims to validate the effectiveness of integrating parameter-efficient fine-tuning (PEFT) methodologies, specifically Low-Rank Adaptation (LoRA) and its variants, to enhance forecast accuracy, as well as training speed, computational resource utilization, and memory efficiency in weather and climate modeling for specific regions.
  arXiv  Detail & Related papers  (2024-09-11T19:31:56Z)
• Probabilistic Emulation of a Global Climate Model with Spherical DYffusion [15.460280166612119]
  We present the first conditional generative model able to produce global climate ensemble simulations that are accurate and physically consistent. Our approach beats relevant baselines and nearly reaches a gold standard for successful climate model emulation.
  arXiv  Detail & Related papers  (2024-06-21T00:16:55Z)
• Towards Causal Representations of Climate Model Data [18.82507552857727]
  This work delves into the potential of causal representation learning, specifically the emphCausal Discovery with Single-parent Decoding (CDSD) method. Our findings shed light on the challenges, limitations, and promise of using CDSD as a stepping stone towards more interpretable and robust climate model emulation.
  arXiv  Detail & Related papers  (2023-12-05T16:13:34Z)
• Climate Intervention Analysis using AI Model Guided by Statistical Physics Principles [6.824166358727082]
  We propose a novel solution by utilizing a principle from statistical physics known as the Fluctuation-Dissipation Theorem (FDT) By leveraging, we are able to extract information encoded in a large dataset produced by Earth System Models. Our model, AiBEDO, is capable of capturing the complex, multi-timescale effects of radiation perturbations on global and regional surface climate.
  arXiv  Detail & Related papers  (2023-02-07T05:09:10Z)
• ClimaX: A foundation model for weather and climate [51.208269971019504]
  ClimaX is a deep learning model for weather and climate science. It can be pre-trained with a self-supervised learning objective on climate datasets. It can be fine-tuned to address a breadth of climate and weather tasks.
  arXiv  Detail & Related papers  (2023-01-24T23:19:01Z)
• Multi-scale Digital Twin: Developing a fast and physics-informed surrogate model for groundwater contamination with uncertain climate models [53.44486283038738]
  Climate change exacerbates the long-term soil management problem of groundwater contamination. We develop a physics-informed machine learning surrogate model using U-Net enhanced Fourier Neural Contaminated (PDENO) In parallel, we develop a convolutional autoencoder combined with climate data to reduce the dimensionality of climatic region similarities across the United States.
  arXiv  Detail & Related papers  (2022-11-20T06:46:35Z)
• Deep Learning Based Cloud Cover Parameterization for ICON [55.49957005291674]
  We train NN based cloud cover parameterizations with coarse-grained data based on realistic regional and global ICON simulations. Globally trained NNs can reproduce sub-grid scale cloud cover of the regional simulation. We identify an overemphasis on specific humidity and cloud ice as the reason why our column-based NN cannot perfectly generalize from the global to the regional coarse-grained data.
  arXiv  Detail & Related papers  (2021-12-21T16:10:45Z)
• Climate-Invariant Machine Learning [0.8831201550856289]
  Current climate models require representations of processes that occur at scales smaller than model grid size. Recent machine learning (ML) algorithms hold promise to improve such process representations, but tend to extrapolate poorly to climate regimes they were not trained on. We propose a new framework - termed "climate-invariant" ML - incorporating knowledge of climate processes into ML algorithms.
  arXiv  Detail & Related papers  (2021-12-14T07:02:57Z)
• DeepClimGAN: A High-Resolution Climate Data Generator [60.59639064716545]
  Earth system models (ESMs) are often used to generate future projections of climate change scenarios. As a compromise, emulators are substantially less expensive but may not have all of the complexity of an ESM. Here we demonstrate the use of a conditional generative adversarial network (GAN) to act as an ESM emulator.
  arXiv  Detail & Related papers  (2020-11-23T20:13:37Z)
• Dynamical Landscape and Multistability of a Climate Model [64.467612647225]
  We find a third intermediate stable state in one of the two climate models we consider. The combination of our approaches allows to identify how the negative feedback of ocean heat transport and entropy production drastically change the topography of Earth's climate.
  arXiv  Detail & Related papers  (2020-10-20T15:31:38Z)
• Augmented Convolutional LSTMs for Generation of High-Resolution Climate Change Projections [1.7503398807380832]
  We present auxiliary informed-temporal neural architecture for statistical downscaling. Current study performs daily downscaling of precipitation variable from an ESM output at 1.15 degrees (115 km) to 0.25 degrees (25 km) over the world's most climatically diversified country, India.
  arXiv  Detail & Related papers  (2020-09-23T17:52:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.