{"title":"Should we think of observationally constrained multidecade climate projections as predictions?","authors":"Tong Li, Francis W. Zwiers, Xuebin Zhang","doi":"10.1126/sciadv.adt6485","DOIUrl":null,"url":null,"abstract":"<div >Empirical evidence indicates that the range of model-projected future warming can be successfully narrowed by conditioning the projected warming on past observed warming. We demonstrate that warming projections conditioned on the entire instrumental annual surface temperature record are of sufficiently high quality and should be considered as long-term predictions rather than merely as projections. We support this view by considering the skill of predicted 20- and 50-year lead temperature changes under the Shared Economic Pathway (SSP)1-2.6 and SSP5-8.5 emission scenarios in climates of different sensitivities. Using climate model simulations, we show that adjusting raw multimodel projections of future warming with the Kriging for Climate Change (KCC) method eliminates most biases and reduces the uncertainty of warming projections irrespective of the sensitivity of the climate being considered. Simpler methods, or using only the more recent part of the temperature record, provide less effective constraints. The high-skill future warming predictions obtained via KCC have a serious place in informing global climate policies.</div>","PeriodicalId":21609,"journal":{"name":"Science Advances","volume":"11 20","pages":""},"PeriodicalIF":11.7000,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.science.org/doi/reader/10.1126/sciadv.adt6485","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Science Advances","FirstCategoryId":"103","ListUrlMain":"https://www.science.org/doi/10.1126/sciadv.adt6485","RegionNum":1,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Empirical evidence indicates that the range of model-projected future warming can be successfully narrowed by conditioning the projected warming on past observed warming. We demonstrate that warming projections conditioned on the entire instrumental annual surface temperature record are of sufficiently high quality and should be considered as long-term predictions rather than merely as projections. We support this view by considering the skill of predicted 20- and 50-year lead temperature changes under the Shared Economic Pathway (SSP)1-2.6 and SSP5-8.5 emission scenarios in climates of different sensitivities. Using climate model simulations, we show that adjusting raw multimodel projections of future warming with the Kriging for Climate Change (KCC) method eliminates most biases and reduces the uncertainty of warming projections irrespective of the sensitivity of the climate being considered. Simpler methods, or using only the more recent part of the temperature record, provide less effective constraints. The high-skill future warming predictions obtained via KCC have a serious place in informing global climate policies.
经验证据表明,模式预估未来变暖的范围可以通过将预估变暖调节到过去观测到的变暖来成功地缩小。我们证明,以整个仪器年表面温度记录为条件的变暖预估具有足够高的质量,应被视为长期预测,而不仅仅是预估。我们通过考虑在不同敏感性气候的共享经济路径(SSP)1-2.6和SSP5-8.5排放情景下预测的20年和50年铅温度变化的技能来支持这一观点。利用气候模式模拟,我们发现用Kriging for climate Change (KCC)方法调整原始的多模式未来变暖预估消除了大多数偏差,并降低了与所考虑的气候敏感性无关的变暖预估的不确定性。更简单的方法,或者只使用最近的温度记录部分,提供的约束效果较差。通过KCC获得的高技能未来变暖预测在为全球气候政策提供信息方面具有重要作用。
期刊介绍:
Science Advances, an open-access journal by AAAS, publishes impactful research in diverse scientific areas. It aims for fair, fast, and expert peer review, providing freely accessible research to readers. Led by distinguished scientists, the journal supports AAAS's mission by extending Science magazine's capacity to identify and promote significant advances. Evolving digital publishing technologies play a crucial role in advancing AAAS's global mission for science communication and benefitting humankind.