Search NASA⌕ Search

DOE OSTI · 1923920

Decadal climate variability in the tropical Pacific: Characteristics, causes, predictability, and prospects

Abstract

Decadal climate variability and change affects nearly every aspect of our world, including weather, agriculture, ecosystems, and the economy. Predicting its expression is thus of critical importance on multiple fronts. Power et al. review what is known about tropical Pacific decadal climate variability and change, the degree to which it can be simulated and predicted, and how we might improve our understanding of it. Here, more accurate projections will require longer and more detailed instrumental and paleoclimate records, improved climate models, and better data assimilation methods. —HJS.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Power, Scott, Lengaigne, Matthieu, Capotondi, Antonietta, Khodri, Myriam, Vialard, Jérôme, Jebri, Beyrem, Guilyardi, Eric, McGregor, Shayne, Kug, Jong-Seong, Newman, Matthew, McPhaden, Michael J., Meehl, Gerald, Smith, Doug, Cole, Julia, Emile-Geay, Julien, Vimont, Daniel, Wittenberg, Andrew T., Collins, Mat, Kim, Geon-Il, Cai, Wenju, Okumura, Yuko, Chung, Christine, Cobb, Kim M., Delage, François, Planton, Yann Y., Levine, Aaron, Zhu, Feng, Sprintall, Janet, Di Lorenzo, Emanuele, Zhang, Xuebin, Luo, Jing-Jia, Lin, Xiaopei, Balmaseda, Magdalena, Wang, Guojian, Henley, Benjamin J.. 2021-10-01. Decadal climate variability in the tropical Pacific: Characteristics, causes, predictability, and prospects. https://doi.org/10.1126/science.aay9165

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

Water4Energy Step-1 Band-M Ready-to-Train Samples for TVA Weeks-to-Years Prediction, Version 0

AI-ready Band-M (monthly) labelled training pack for the Water4Energy Genesis Task-1 project on weeks-to-years prediction of Tennessee Valley temperature and precipitation. The deposit includes leakage-aware issue-time samples (samples_M_v0.nc; N=486), train-only scalers, issue-time split table, supporting monthly panels, and Python generation scripts to recreate the pack from the companion Tier-1 raw observation collection (https://doi.org/10.13139/ORNLNCCS/3398576). Each sample pairs a 12-month lookback of teleconnection indices and SST box anomalies with TVA-mean ERA5 anomaly targets (t2m, tp, msl) at leads 1–3 months.

54 ENVIRONMENTAL SCIENCES↗

Multi-Angle Snowflake Camera, particle analysis

The c1 level data product for the Mutli-Angle Snowflake Camera contains snowflake fall speeds and particle size, among other analysis for images associated with each hydrometeor.

54 ENVIRONMENTAL SCIENCES↗

Multi-Angle Snowflake Camera, time bins

The c1 level data product for the Mutli-Angle Snowflake Camera contains snowflake fall speeds and particle size, among other analysis for images associated with each hydrometeor.

54 ENVIRONMENTAL SCIENCES↗