Reconstruction of Zonal Precipitation From Sparse Historical Observations Using Climate Model Information and Statistical Learning
OPEN ACCESS
Author / Producer
Date
2022-12-16
Publication Type
Journal Article
ETH Bibliography
yes
Citations
Altmetric
OPEN ACCESS
Data
Rights / License
Abstract
Future projected changes in precipitation substantially impact societies worldwide. However, large uncertainties remain due to sparse historical observational coverage, large internal climate variability, and climate model disagreement. Here, we present a novel reconstruction of seasonally averaged zonal precipitation metrics from sparse rain-gauge data using regularized regression techniques that are trained across climate model simulations. Subsequently, we test the reconstruction on independent satellite data and reanalyzed precipitation, and find a large fraction of historical zonal mean precipitation (ZMP) variability is recovered, in particular over the Northern hemisphere and in parts of the tropics. Finally, we demonstrate that the reconstructed ZMP trends are outside the variability of pre-industrial control simulations, and are largely consistent with the range of historical simulations driven by external forcing. Overall, we illustrate a novel way of estimating seasonally averaged zonal precipitation from gauge data, and trends therein that show a signal very likely caused by human influence.
Permanent link
Publication status
published
External links
Editor
Book title
Journal / series
Volume
49 (23)
Pages / Article No.
Publisher
American Geophysical Union
Event
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
precipitation; reconstruction; statistical learning; infilling; large ensemble; hydrological cycle
Organisational unit
03777 - Knutti, Reto / Knutti, Reto
Notes
Funding
167215 - Combining theory with Big Data? The case of uncertainty in prediction of trends in extreme weather and impacts (SNF)
101003469 - Extreme Events: Artificial Intelligence for Detection and Attribution (EC)
101003469 - Extreme Events: Artificial Intelligence for Detection and Attribution (EC)