Cornell University
Library
Cornell UniversityLibrary

eCommons

Help
Log In(current)
  1. Home
  2. College of Engineering
  3. Earth and Atmospheric Sciences
  4. Climate Sciences
  5. Dataset for Intrinsic Century-Scale Variability in Tropical Pacific Sea Surface Temperatures and their Influence on Western US Hydroclimate

Dataset for Intrinsic Century-Scale Variability in Tropical Pacific Sea Surface Temperatures and their Influence on Western US Hydroclimate

File(s)
EvansEtAl_GRL2022_LIM-CORRReadme.rtf (50.24 KB)
EvansEtAl_GRL2022_LIM-CORRData.zip (608.39 MB)
Permanent Link(s)
https://doi.org/10.7298/8d7j-wt40
https://hdl.handle.net/1813/112210
Collections
Climate Sciences
Author
Evans, Colin P.
Coats, Sloan
Carrillo, Carlos M.
Li, Xiaolu
Alessi, Marc J.
Herrera, Dimitris A.
Benton, Brandon N.
Ault, Toby R.
Abstract

These files contain the data supporting the results of the Evans, et al, 2022 paper, “Intrinsic Century Scale Variability in Tropical Pacific Sea Surface Temperatures and their Influence on Southwestern US Hydroclimate”. In Evans et al 2022, we found: Hydroclimate variability of the southwest United States (SWUS) is influenced by the tropical Pacific Ocean, particularly through the teleconnection to El Niño/Southern Oscillation (ENSO), which is expected to be altered by climate change. Natural variability in this teleconnection has not been robustly quantified, complicating the detection of anthropogenic climate change. Here, we use a linear inverse model (LIM) to quantify natural variability in the ENSO-SWUS teleconnection. The LIM yields realistic teleconnection patterns with century-scale variability comparable to simulations from the Last Millennium Ensemble project and the Climate Model Intercomparison Project Phases 5 and 6. The variability quantified by the LIM illuminates two aspects of our understanding of ENSO and its impacts: the inherent statistics of the observable system can produce century-long periods with a wide range of correlations to SWUS hydroclimate, including nonsignificant correlations, and thus that detecting changes in ENSO-related hydroclimate variability is challenging in a changing climate.

Sponsorship
This work was made possible through funding from the National Science Foundation awards AGS1751535 and AGS1602564.
Date Issued
2022-11-22
Keywords
statistical modeling
•
hydroclimate teleconnections
•
water resources
Rights
CC0 1.0 Universal
Rights URI
http://creativecommons.org/publicdomain/zero/1.0/
Type
dataset

Site Statistics | Help

About eCommons | Policies | Terms of use | Contact Us

copyright © 2002-2026 Cornell University Library | Privacy | Web Accessibility Assistance