{"title":"Deciphering Temperature Seasonality in Earth's Ancient Oceans","authors":"L. Ivany, E. Judd","doi":"10.1146/annurev-earth-032320-095156","DOIUrl":null,"url":null,"abstract":"Ongoing global warming due to anthropogenic climate change has long been recognized, yet uncertainties regarding how seasonal extremes will change in the future persist. Paleoseasonal proxy data from intervals when global climate differed from today can help constrain how and why the annual temperature cycle has varied through space and time. Records of past seasonal variation in marine temperatures are available in the oxygen isotope values of serially sampled accretionary organisms. The most useful data sets come from carefully designed and computationally robust studies that enable characterization of paleoseasonal parameters and seamless integration with mean annual temperature data sets and climate models. Seasonal data sharpen interpretations of—and quantify overlooked or unconstrained seasonal biases in—the more voluminous mean temperature data and aid in the evaluation of climate model performance. Methodologies to rigorously analyze seasonal data are now available, and the promise of paleoseasonal proxy data for the next generation of paleoclimate research is significant. Expected final online publication date for the Annual Review of Earth and Planetary Sciences, Volume 50 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.","PeriodicalId":8034,"journal":{"name":"Annual Review of Earth and Planetary Sciences","volume":"3367 1","pages":""},"PeriodicalIF":11.3000,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annual Review of Earth and Planetary Sciences","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1146/annurev-earth-032320-095156","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ASTRONOMY & ASTROPHYSICS","Score":null,"Total":0}
引用次数: 8
Abstract
Ongoing global warming due to anthropogenic climate change has long been recognized, yet uncertainties regarding how seasonal extremes will change in the future persist. Paleoseasonal proxy data from intervals when global climate differed from today can help constrain how and why the annual temperature cycle has varied through space and time. Records of past seasonal variation in marine temperatures are available in the oxygen isotope values of serially sampled accretionary organisms. The most useful data sets come from carefully designed and computationally robust studies that enable characterization of paleoseasonal parameters and seamless integration with mean annual temperature data sets and climate models. Seasonal data sharpen interpretations of—and quantify overlooked or unconstrained seasonal biases in—the more voluminous mean temperature data and aid in the evaluation of climate model performance. Methodologies to rigorously analyze seasonal data are now available, and the promise of paleoseasonal proxy data for the next generation of paleoclimate research is significant. Expected final online publication date for the Annual Review of Earth and Planetary Sciences, Volume 50 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
期刊介绍:
Since its establishment in 1973, the Annual Review of Earth and Planetary Sciences has been dedicated to providing comprehensive coverage of advancements in the field. This esteemed publication examines various aspects of earth and planetary sciences, encompassing climate, environment, geological hazards, planet formation, and the evolution of life. To ensure wider accessibility, the latest volume of the journal has transitioned from a gated model to open access through the Subscribe to Open program by Annual Reviews. Consequently, all articles published in this volume are now available under the Creative Commons Attribution (CC BY) license.