lafferty-sriver_2023_npjCliAtm

Downscaling and bias-correction contribute considerable uncertainty to local climate projections in CMIP6

David Lafferty^1*, Ryan Sriver¹

¹ Department of Atmospheric Sciences, University of Illinois Urbana-Champaign

* corresponding author: [email protected]

Abstract

Efforts to diagnose the risks of a changing climate often rely on downscaled and bias-corrected climate information, making it important to understand the uncertainties and potential biases of this approach. Here, we perform a variance decomposition to partition uncertainty in global climate projections and quantify the relative importance of downscaling and bias-correction. We analyze simple climate metrics such as annual temperature and precipitation averages, as well as several indices of climate extremes. We find that downscaling and bias-correction often contribute substantial uncertainty to local decision-relevant climate outcomes, though our results are strongly heterogeneous across space, time, and climate metrics. Our results can provide guidance to impact modelers and decision-makers regarding the uncertainties associated with downscaling and bias-correction when performing local-scale analyses, as neglecting to account for these uncertainties may risk overconfidence relative to the full range of possible climate futures.

Journal reference

Lafferty, D.C. & Sriver, R.L. Downscaling and bias-correction contribute considerable uncertainty to local climate projections in CMIP6. npj Clim Atmos Sci 6, 158 (2023). https://doi.org/10.1038/s41612-023-00486-0

How to reproduce the experiment

Preliminaries

1. The CIL-GDPCIR and carbonplan ensembles are initially analyzed on Microsoft Planetary Computer (MPC) so you will need to create an account there. Additionally, intermediate results from MPC are transferred to Azure Blob Storage where they can be accessed publicly via azcopy. To install azcopy download the relevant executable file from this link and add it to your path.
2. The NEX-GDDP and ISIMIP ensembles were downloaded locally (to a high-performance computing cluster). Details on how to download each ensemble can be found on their respective websites.

Instructions

To reproduce the entire anaylsis, sequentially execute all notebooks/scripts in the code directory. The table below provides some additional information.

Script Number	Purpose	Run on MPC?
01a	Calculates historical temperature and precipitation quantiles from ERA5 dataset	x
01b	Calculates historical temperature and precipitation quantiles from GMFD dataset
01c	Transfers ERA5 historical quantiles local storage, transfers GMFD historical quantiles to Azure
01d	Transfers GMFD historical quantiles to MPC	x
02a	Calculates suite of impact metrics for CIL-GDPCIR ensemble	x
02b	Calculates suite of impact metrics for NEX-GDDP ensemble
02c	Calculates suite of impact metrics for ISIMIP ensemble
02d	Calculates suite of impact metrics for carbonplan ensemble	x
03	Transfers output data from 02a and 02d to local storage
04	Conservatively regrids all output data from 02 to a common 0.25 degree grid
05	Performs the uncertainty analysis
06	Produces all main text figures
07a	Runs ANOVA analysis for select cities to infer interaction effects
07b	Produces all supplementary figures

Dependencies

• Python: environment.yml
• R: Rsession.log