Collaborative Research: Correcting sea surface temperature proxies for seasonal biases: combined data and model investigation

合作研究：修正海面温度代理的季节性偏差：结合数据和模型研究

• 批准号: 2202911
• 负责人: Samantha Bova
• 金额: $ 30.7万
• 依托单位: San Diego State University Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2202911&HistoricalAwards=false
• 关键词: Collaborative; Research; Correcting; sea; surface

Warmer than present periods in Earth history can provide insight into how the climate system will respond to rising global temperatures. Knowledge of ocean conditions during past warm periods comes, in large part, from geochemical records (proxies) of past sea surface temperature. These proxies are generally interpreted to reflect the average yearly temperature. Many SST proxies suggest that prior to the industrial revolution, global temperatures had been cooling for 6000 years. This result is at odds with climate model simulations which show warming over that period in response to rising atmospheric greenhouse gas levels. This mismatch has cast doubts on model predictions of future climate change and the warming impact of greenhouse gases. However, a recent study suggested the mismatch arises from the interpretation of the proxies, not from problems with the climate models. That study suggested that proxies often record temperature during a particular season, rather than the yearly average. When the seasonality of the proxy was considered the models and proxies were consistent. This study will test and refine approaches for extracting the seasonal and mean annual temperature from proxies of past sea surface temperatures. In addition, new geochemical records of past temperature will be produced across three recent warm periods in Earth history. These new data will aid in ground-truthing model predictions of future climate change. The project will also provide training opportunities for early career scientists, including students from underrepresented groups in the sciences. Results from the project will be communicated via public lectures and a K-12 science outreach program.Accounting for seasonal biases in sea surface temperature (SST) proxies is critical for understanding the drivers of SST in response to internal and external forcings. The proposed work aims to provide a way forward to identify seasonal biases and remove them from proxy SST records to improve paleoceanographic reconstructions. The study will include a series of test of the seasonal to annual mean transformation method in fully coupled and idealized climate models to explore the impact of ice sheet forcing, greenhouse gases, nonlinearities in the climate system, and sampling errors on the efficacy of the method in different regions of the global ocean and during multiple interglacial periods. The study will produce new paired foraminifer-magnesium/calcium and alkenone derived SST records from seven locations. At each of these sites, data from the Holocene, last interglacial, and Marine isotope stage 9 will be used to explore the stability of biases and the drivers of seasonal and mean annual climate change during multiple examples of a warm interglacial.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

比地球历史上目前的时期更温暖，可以让我们洞察气候系统将如何应对全球气温上升。对过去暖期海洋状况的了解，在很大程度上来自过去海洋表面温度的地球化学记录(替代物)。这些指标通常被解释为反映年平均气温。许多SST指标表明，在工业革命之前，全球气温已经冷却了6000年。这一结果与气候模型模拟结果不一致，气候模型模拟显示，在这段时间内，气候变暖是对大气温室气体水平上升的反应。这种不匹配让人们对未来气候变化和温室气体变暖影响的模型预测产生了怀疑。然而，最近的一项研究表明，这种不匹配是由于对代理的解释，而不是因为气候模型的问题。这项研究表明，气候指标通常记录的是特定季节的气温，而不是全年的平均气温。当考虑代理的季节性时，模型和代理是一致的。这项研究将测试和改进从过去海洋表面温度的替代物中提取季节性和平均年温度的方法。此外，还将在地球历史上最近的三个暖期内产生关于过去温度的新的地球化学记录。这些新数据将有助于对未来气候变化的地面真实模型预测。该项目还将为早期职业科学家提供培训机会，包括来自科学界代表性不足群体的学生。该项目的结果将通过公开讲座和K-12科学推广计划进行交流。考虑海洋表面温度(SST)替代指标的季节性偏差对于理解SST响应内部和外部强迫的驱动因素至关重要。这项拟议的工作旨在提供一种方法，以识别季节性偏差，并从代理SST记录中消除它们，以改进古海洋重建。这项研究将包括在完全耦合和理想化的气候模式中对季节到年度平均转换方法的一系列测试，以探索冰盖强迫、温室气体、气候系统中的非线性和采样误差对全球海洋不同区域和多个间冰期期间该方法有效性的影响。这项研究将从七个地点产生新的成对有孔虫-镁/钙和烯酮SST记录。在这些地点中的每一个，来自全新世、最后一次间冰期和海洋同位素阶段9的数据将被用来探索在多个温暖的间冰期的例子中偏差的稳定性以及季节性和平均年气候变化的驱动因素。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。