Identifying Climate Patterns Using Clustering Autoencoder Techniques
- a Department of Computer Science, University of Chicago, Chicago, Illinois
- b School of Freshwater Sciences, University of Wisconsin–Milwaukee, Milwaukee, Wisconsin
- c Earth and Environmental Sciences Area, Lawrence Berkeley National Laboratory, Berkeley, California
- d Department of Computer Science, Florida International University, Miami, Florida
- e Lawrence Berkeley National Laboratory, Berkeley, California
- f NASA Ames Research Center, Mountain View, California
- g Pasteur Labs and ISI, Brooklyn, New York
- h Department of Nuclear Science and Engineering, Department of Civil and Environmental Engineering, Cambridge, Massachusetts
Abstract The complexity of growing spatiotemporal resolution of climate simulations produces a variety of climate patterns under different projection scenarios. This paper proposes a new data-driven climate classification workflow via an unsupervised deep learning technique that can dimensionally reduce the vast volume of spatiotemporal numerical climate projection data into a compact representation. We aim to identify distinct zones that capture multiple climate variables as well as their future changes under different climate change scenarios. Our approach leverages convolutional autoencoders combined with k -means clustering (standard autoencoder) and online clustering based on the Sinkhorn–Knopp algorithm (clustering autoencoder) across the conterminous United States (CONUS) to capture unique climate patterns in a data-driven fashion from the Geophysical Fluid Dynamics Laboratory Earth System Model with GOLD component (GFDL-ESM2G). The developed approach compresses 70 years of GFDL-ESM2G simulation at 0.125° spatial resolution across the CONUS under multiple warming scenarios to a lower-dimensional space by a factor of 660 000 and then tested on 150 years of GFDL-ESM2G simulation data. The results show that five climate clusters capture physically reasonable and spatially stable climatological patterns matched to known climate classes defined by human experts. Results also show that using a clustering autoencoder can reduce the computational time for clustering by up to 9.2 times when compared to using a standard autoencoder. Our five unique climate patterns resulting from the deep learning–based clustering of the lower-dimensional space thereby enable us to provide insights on hydrometeorology and its spatial heterogeneity across the conterminous United States immediately without downloading large climate datasets. Significance Statement This paper presents a data-driven climate classification approach using unsupervised deep learning to dimensionally reduce climate model outputs and to identify distinct climate regions for their future changes. Our approach compresses climate information for 70 years of Geophysical Fluid Dynamics Laboratory Earth System Model data across the conterminous United States (CONUS) at 0.125° spatial resolution. The results reveal that five climate clusters capture reasonable and stable climatological patterns matched to known climate patterns. The embedded clustering process in deep learning provides ×9.2 times faster execution than the k -means clustering technique. These results give us insight about climate spatial patterns and heterogeneity of hydrological patterns across the conterminous United States without downloading large climate datasets.
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA); USDOE Office of Environmental Management (EM)
- Grant/Contract Number:
- NONE; AC02-05CH11231; AI0000001
- OSTI ID:
- 2439316
- Journal Information:
- Artificial Intelligence for the Earth Systems, Journal Name: Artificial Intelligence for the Earth Systems Journal Issue: 3 Vol. 3; ISSN 2769-7525
- Publisher:
- American Meteorological SocietyCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Similar Records
A unified ensemble soil moisture dataset across the continental United States
Deep structural clustering for single-cell RNA-seq data jointly through autoencoder and graph neural network