Code supporting "A multi-method framework for global real-time climate attribution" by Gilford et al. (2022, ASCMO)
This repository is the Python code base for "A multi-method framework for global real-time climate attribution" by Gilford et al. (2022), current in press at Advances in Statistical Climatology, Meteorology and Oceanography.
Project abstract:
Human-driven climate change has caused a wide range of extreme weather events to become more frequent in recent decades. Although increased and intense periods of extreme weather are expected consequences of anthropogenic climate warming, it remains challenging to rapidly and continuously assess the degree to which human activity alters the probability of specific events. This study introduces a new framework to enable the production and communication of global real-time estimates of how human-driven climate change has changed the likelihood of daily weather events. The framework's multi-method approach implements one model-based and two observation-based methods to provide ensemble attribution estimates with accompanying confidence levels. The framework is designed to be computationally lightweight to allow attributable probability changes to be rapidly calculated using forecasts or the latest observations. The framework is particularly suited for highlighting ordinary weather events that have been altered by human-caused climate change. An example application using daily maximum temperature in Phoenix, AZ, USA highlights the framework's effectiveness in estimating the attributable human influence on observed daily temperatures (and deriving associated confidence levels). Global analyses show that the framework is capable of producing worldwide complementary observational- and model-based assessments of how human-caused climate change changes the likelihoods of daily maximum temperatures. For instance, over 56% of the Earth's total land area all three framework methods agree that maximum temperatures greater than the pre-industrial 99th percentile have become at least twice as likely in today's human-influenced climate. Additionally over 52% of land in the tropics, human-caused climate change is responsible for at least five-fold increases in the likelihood of pre-industrial 99th percentile maximum temperatures. By systematically applying this framework to near-term forecasts or daily observations, local attribution analyses can be provided in real time, worldwide. These new analyses create opportunities to enhance communication and provide input and/or context for policy, adaptation, human health and other ecosystem/human-system impact studies.
If you have any questions, comments, or feedback on this work or code, please contact Daniel or open an Issue in the repository.
If you use any part of this work, please cite this repository, Gilford et al. (2022), and include a link. The citation for Gilford et al. (2022) will be supplied upon full public release.
The Gridded Berkeley Earth Surface Maximum Temperature Anomaly Field was retrieved in March 2019 via the WMO Climate Explorer tool. The Met Office Hadley Centre/Climatic Research Unit global surface temperature data set, HadCRUT5 version 5.0.1.0, was retrieved on 17 August 2021 from the Met Office Hadley Centre. Coupled Model Intercomparison Project phase 5 records were retrieved in November 2018 from the Centre for Environmental Analysis.
Analysis output data files that are small enough to be included here are found in the data directory; the remaining output analyses are available from the author upon reasonable request.
Additional packages for visualization and validation include matplotlib.pyplot, cartopy, statsmodels, and xhistogram.
- descriptions forthcoming
- analysisfx - Set of utility functions used in the codebase
- utilities - Set of analysis functions used in the codebase
- Figure 1 - Flowchart of study's attribution analysis, including observation- and model-based methods
- Figure 2 - forthcoming
- Figure 3 - forthcoming
- Figure 4 - forthcoming
- Figure S1 - Timeseries of observed and modeled global mean surface temperature anomalies, 1880-present
- Figure S2 - Year after which Berkeley Daily Maximum Temperature data is maintained (through present)
- Figure S3 - Maps of monthly observation-based median scale factors
- Figure S4 - Maps of monthly/quantile observation-based quantile scale factors
- Figure S5 - Maps of monthly median-scaled probability ratios at the 96.7th percentile
- Figure S6 - Maps of monthly quantile-scaled probability ratios at the 96.7th percentile
- Figure S7 - forthcoming
- Figure S8 - forthcoming
- Figure S9 - forthcoming
- Figure S10 - forthcoming
- Figure S11a - Empirical CDFs of maximum temperatures before/after biasadjustment
- Figure S11b - Scatter plot of example climate model bias adjustments between forced and natural periods
- Figure S11c - Seasonal cycles of observed and modeled maximum temperature before/after bias
Supporting Figures S11d, S12, and S13 can be constructed through modification of BiasAdjust_comparison.ipynb
- Daniel M. Gilford, PhD - GitHub
- Andrew Pershing
- Kasten Haustein
Daniel M. Gilford: Methodology, Software, Validation, Formal analysis, Investigation, Writing – Original Draft and Review & Editing, and Visualization. Andrew Pershing: Conceptualization, Methodology, Writing – Review & Editing, Visualization, and Supervision. Benjamin H. Strauss: Conceptualization, Methodology, Writing – Review & Editing, and Project administration. Karsten Haustein: Software, Validation, Formal analysis, Investigation, Data Curation, and Writing – Review & Editing. Friederike E. L. Otto: Conceptualization, Methodology, and Writing – Review & Editing.
This project is licensed under the MIT License - see the LICENSE file for details.
Portions of this code, specifically the cmip5_bias_adjustment.py module and the BiasAdjust_model_run.ipynb notebook are licensed directly under the GNU Affero General Public License v3.0, which is compatible with the MIT License. Source code on which these software products are based is provided in /isimip3-source/, and is forked from the isimip3 repository as published on 7 March 2019.
The authors thank Claudia Tebaldi for helpful comments which improved this work. We also thank Lukasz Tracewski and Dan Dodson for maintaining the computing resources on which the climate attribution framework was developed and tested. For the research conducted in this study, Climate Central received funding, in part, from The Schmidt Family Foundation/The Eric and Wendy Schmidt Fund for Strategic Innovation.