Climate Intervention Analysis using AI Model Guided by Statistical Physics Principles
Authors: 
Sookyung Kim, Kalai Ramea, Salva Rühling Cachay, Haruki Hirasawa, Subhashis Hazarika, Dipti Hingmire, Peetak Mitra, Philip J. Rasch, Hansi SinghAuthors Info & Claims
Sookyung Kim, Kalai Ramea, Salva Rühling Cachay, Haruki Hirasawa, Subhashis Hazarika, Dipti Hingmire, Peetak Mitra, Philip J. Rasch, Hansi SinghAuthors Info & ClaimsPages 4653 - 4659
Abstract
In this study, we propose a solution to estimating system responses to external forcings or perturbations. We utilize the Fluctuation-Dissipation Theorem (FDT) from statistical physics to extract knowledge using an AI model that can rapidly produce scenarios for different external forcings by leveraging FDT and analyzing a large dataset from Earth System Models. Our model, AiBEDO, accurately captures the complex effects of radiation perturbations on global and regional surface climate, enabling faster exploration of the impacts of spatially-heterogenous climate forcings. We demonstrate its effectiveness by applying AiBEDO to Marine Cloud Brightening, a climate intervention technique, aiming to optimize cloud brightening patterns for regional climate targets and prevent climate tipping points. Our approach has broader applicability to other scientific disciplines with computationally demanding simulation models. Source code of AiBEDO framework is made available at https://github.com/kramea/cikm_aibedo. A sample dataset is made available at https://doi.org/10.5281/zenodo.7597027. Additional data available upon request.
References
[1]
Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton. 2016. Layer normalization. arXiv preprint arXiv:1607.06450 (2016).
[2]
Matthew Chantry, Hannah Christensen, Peter Dueben, and Tim Palmer. 2021. Opportunities and challenges for machine learning in weather and climate modelling: hard, medium and soft AI. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 379, 2194 (April 2021), 20200083. https://doi.org/10.1098/rsta.2020.0083
[3]
I. Cionni, G. Visconti, and F. Sassi. 2004. Fluctuation dissipation theorem in a general circulation model: FDT IN A GENERAL CIRCULATION MODEL. Geophysical Research Letters 31, 9 (May 2004), n/a--n/a. https://doi.org/10.1029/2004GL019739
[4]
Michaël Defferrard, Martino Milani, Frédérick Gusset, and Nathanaël Perraudin. 2020. DeepSphere: a graph-based spherical CNN. arXiv preprint arXiv:2012.15000 (2020).
[5]
Peter D. Dueben and Peter Bauer. 2018. Challenges and design choices for global weather and climate models based on machine learning. Geoscientific Model Development 11, 10 (Oct. 2018), 3999--4009. https://doi.org/10.5194/gmd-11- 3999--2018
[6]
Alistair S Dunham, Pedro Beltrao, and Mohammed AlQuraishi. 2022. Highthroughput deep learning variant effect prediction with Sequence UNET. bioRxiv (2022).
[7]
Gregory M Flato. 2011. Earth system models: an overview. Wiley Interdisciplinary Reviews: Climate Change 2, 6 (2011), 783--800.
[8]
Shaojia Ge, Hong Gu,Weimin Su, Jaan Praks, and Oleg Antropov. 2022. Improved semisupervised unet deep learning model for forest height mapping with satellite sar and optical data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 15 (2022), 5776--5787.
[9]
Filippo Giorgi and Xue-Jie GAO. 2018. Regional earth system modeling: review and future directions. Atmospheric and Oceanic Science Letters 11, 2 (2018), 189-- 197.
[10]
AS Gritsoun, G Branstator, and VP Dymnikov. 2002. Construction of the linear response operator of an atmospheric general circulation model to small external forcing. Russian Journal of Numerical Analysis and Mathematical Modelling 17, 5 (2002), 399--416.
[11]
Andrey Gritsun and Grant Branstator. 2007. Climate response using a threedimensional operator based on the fluctuation--dissipation theorem. Journal of the atmospheric sciences 64, 7 (2007), 2558--2575.
[12]
John Guibas, Morteza Mardani, Zongyi Li, Andrew Tao, Anima Anandkumar, and Bryan Catanzaro. 2021. Efficient Token Mixing for Transformers via Adaptive Fourier Neural Operators. In International Conference on Learning Representations.
[13]
Dan Hendrycks and Kevin Gimpel. 2016. Gaussian error linear units (gelus). arXiv preprint arXiv:1606.08415 (2016).
[14]
Haruki Hirasawa, Dipti Hingmire, Hansi Singh, Philip J Rasch, Sookyung Kim, Subhashis Hazarika, Peetak Mitra, and Kalai Ramea. 2022. Impact of Regional Marine Cloud Brightening Interventions on Climate Tipping Points. In Preparation (2022).
[15]
Mohammad Enamul Hoque and Mohd Azlan Shah Zaidi. 2020. Global and country-specific geopolitical risk uncertainty and stock return of fragile emerging economies. Borsa Istanbul Review 20, 3 (2020), 197--213.
[16]
R Kubo. 1966. The fluctuation-dissipation theorem. Reports on Progress in Physics 29, 1 (jan 1966), 255. https://doi.org/10.1088/0034--4885/29/1/306
[17]
Thorsten Kurth, Shashank Subramanian, Peter Harrington, Jaideep Pathak, Morteza Mardani, David Hall, Andrea Miele, Karthik Kashinath, and Animashree Anandkumar. 2022. Fourcastnet: Accelerating global high-resolution weather forecasting using adaptive fourier neural operators. arXiv preprint arXiv:2208.05419 (2022).
[18]
Remi Lam, Alvaro Sanchez-Gonzalez, Matthew Willson, Peter Wirnsberger, Meire Fortunato, Alexander Pritzel, Suman Ravuri, Timo Ewalds, Ferran Alet, Zach Eaton-Rosen, et al. 2022. GraphCast: Learning skillful medium-range global weather forecasting. arXiv preprint arXiv:2212.12794 (2022).
[19]
C.H. Leith. 1975. Climate Response and Fluctuation Dissipation. Journal of the Atmospheric Sciences 32 (1975), 2022--2026.
[20]
Zongyi Li, Nikola Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, AndrewStuart, and Anima Anandkumar. 2020. Fourier neural operator for parametric partial differential equations. arXiv preprint arXiv:2010.08895 (2020).
[21]
Fukai Liu, Jian Lu, Oluwayemi Garuba, L. Ruby Leung, Yiyong Luo, and Xiuquan Wan. 2018. Sensitivity of Surface Temperature to Oceanic Forcing via q-Flux Green's Function Experiments. Part I: Linear Response Function. Journal of Climate 31, 9 (May 2018), 3625--3641. https://doi.org/10.1175/JCLI-D-17-0462.1
[22]
Ilya Loshchilov and Frank Hutter. 2017. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017).
[23]
Musawenkoi LH Mabaso, Penelope Vounatsou, Stanely Midzi, Joaquim Da Silva, and Thomas Smith. 2006. Spatio-temporal analysis of the role of climate in inter-annual variation of malaria incidence in Zimbabwe. International Journal of Health Geographics 5, 1 (2006), 1--9.
[24]
D. G. MacMartin, D. Visioni, B. Kravitz, J.H. Richter, T. Felgenhauer, W. R. Lee, D. R. Morrow, E. A. Parson, and M. Sugiyama. 2022. Scenarios for modeling solar radiation modification. Proceedings of the National Academy of Sciences 119, 33 (Aug. 2022), e2202230119. https://doi.org/10.1073/pnas.2202230119
[25]
Andrew J. Majda, Rafail Abramov, and Boris Gershgorin. 2010. High skill in low-frequency climate response through fluctuation dissipation theorems despite structural instability. Proceedings of the National Academy of Sciences 107, 2 (Jan. 2010), 581--586. https://doi.org/10.1073/pnas.0912997107
[26]
Masaru Matsumoto, Tohru Saitoh, Masahiko Kitazawa, Hiromichi Shirato, and Takayuki Nishizaki. 1995. Response characteristics of rain-wind induced vibration of stay-cables of cable-stayed bridges. Journal of Wind Engineering and Industrial Aerodynamics 57, 2--3 (1995), 323--333.
[27]
Joe McGlinchy, Brian Johnson, Brian Muller, Maxwell Joseph, and Jeremy Diaz. 2019. Application of UNet Fully Convolutional Neural Network to Impervious Surface Segmentation in Urban Environment from High Resolution Satellite Imagery. In IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. 3915--3918. https://doi.org/10.1109/IGARSS.2019.8900453
[28]
Ji Hwan Park, Shinjae Yoo, and Balu Nadiga. 2019. Machine learning climate variability. In Proceedings of the 33rd Conference on Neural Information Processing Systems (NeurIPS), Vancouver, BC, Canada. 8--14.
[29]
Jaideep Pathak, Shashank Subramanian, Peter Harrington, Sanjeev Raja, Ashesh Chattopadhyay, Morteza Mardani, Thorsten Kurth, David Hall, Zongyi Li, Kamyar Azizzadenesheli, et al. 2022. Fourcastnet: A global data-driven highresolution weather model using adaptive fourier neural operators. arXiv preprint arXiv:2202.11214 (2022).
[30]
G Reyne, J Sabonnadiere, J Coulomb, and P Brissonneau. 1987. A survey of the main aspects of magnetic forces and mechanical behaviour of ferromagnetic materials under magnetisation. IEEE Transactions on magnetics 23, 5 (1987), 3765--3767.
[31]
Keith B. Rodgers, Sun-Seon Lee, Nan Rosenbloom, Axel Timmermann, Gokhan Danabasoglu, Clara Deser, Jim Edwards, Ji-Eun Kim, Isla R. Simpson, Karl Stein, Malte F. Stuecker, Ryohei Yamaguchi, Tamás Bódai, Eui-Seok Chung, Lei Huang, Who M. Kim, Jean-François Lamarque, Danica L. Lombardozzi, William R. Wieder, and Stephen G. Yeager. 2021. Ubiquity of human-induced changes in climate variability. Earth System Dynamics 12, 4 (Dec. 2021), 1393--1411. https://doi.org/10.5194/esd-12--1393--2021
[32]
Dionysius M Siringoringo and Yozo Fujino. 2012. Observed along-wind vibration of a suspension bridge tower. Journal of wind engineering and industrial aerodynamics 103 (2012), 107--121.
[33]
Kazi Sohag, Rogneda Vasilyeva, Alina Urazbaeva, and Valentin Voytenkov. 2022. Stock market synchronization: the role of geopolitical risk. Journal of Risk and Financial Management 15, 5 (2022), 204.
[34]
Kevin Trebing, Tomasz Stanczyk, and Siamak Mehrkanoon. 2021. SmaAt-UNet: Precipitation nowcasting using a small attention-UNet architecture. Pattern Recognition Letters 145 (2021), 178--186.
[35]
Jujie Wang, Wenyu Zhang, Jianzhou Wang, Tingting Han, and Lingbin Kong. 2014. A novel hybrid approach for wind speed prediction. Information Sciences 273 (2014), 304--318.
[36]
Fu Y Lu R Zhang Y Yin XX, Sun L. 2022. U-Net-Based Medical Image Segmentation. Journal of healthcare engineering (2022). https://doi.org/10.1155/2022/4189781
[37]
CF Zukoski. 1993. Material properties and the electrorheological response. Annual Review of Materials Science 23, 1 (1993), 45--78.
Index Terms
- Climate Intervention Analysis using AI Model Guided by Statistical Physics Principles
Recommendations
Climate Informatics: Accelerating Discovering in Climate Science with Machine Learning
Given the impact of climate change, understanding the climate system is an international priority. The goal of climate informatics is to inspire collaboration between climate scientists and data scientists, in order to develop tools to analyze complex ...
Is the climate getting WARMer? A framework and tool for climate data comparison
AbstractClimate informatics connects climate science and data analytics to further the knowledge and understanding of climate data and its applications. The utilization of climate models has been at the core of climate informatics research. However, the ...
Highlights- Suggested analytics framework comparing predicted and observed climate parameters.
- Visualization tool Weather Analysis Regional Model (WARM) implements the framework.
- Case studies demonstrate the framework workflow as well as ...
Downscaling statistical model techniques for climate change analysis applied to the Amazon region
The Amazon is an area covered predominantly by dense tropical rainforest with relatively small inclusions of several other types of vegetation. In the last decades, scientific research has suggested a strong link between the health of the Amazon and the ...
Comments
Information & Contributors
Information
Published In
October 2023
5508 pages
ISBN:9798400701245
DOI:10.1145/3583780
- General Chairs:
- Ingo Frommholz,
- Frank Hopfgartner,
- Mark Lee,
- Michael Oakes,
- Program Chairs:
- Mounia Lalmas,
- Min Zhang,
- Rodrygo Santos
Copyright © 2023 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 21 October 2023
Check for updates
Author Tags
Qualifiers
- Research-article
Funding Sources
- DARPA
Conference
CIKM '23: The 32nd ACM International Conference on Information and Knowledge Management
October 21 - 25, 2023
Birmingham, United Kingdom
Acceptance Rates
Overall Acceptance Rate 1,861 of 8,427 submissions, 22%
Upcoming Conference
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 118Total Downloads
- Downloads (Last 12 months)118
- Downloads (Last 6 weeks)5
Reflects downloads up to 30 Aug 2024
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in