Collaborative Research: Characterizing Land Surface Memory to Advance Climate Prediction

合作研究：表征陆地表面记忆以推进气候预测

• 批准号: 0232616
• 负责人: C Schlosser
• 金额: $ 9.89万
• 依托单位: University of Maryland Baltimore County
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-07-01 至 2004-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0232616&HistoricalAwards=false
• 关键词: Collaborative; Research; Characterizing; Land; Surface

0232616SchlosserWe hypothesize that interactions between the land and atmosphere may be a considerable source of land surface memory, and that this memory poses an element of coupled land-atmosphere predictability in the climate system that can advance water-cycle prediction. However, accuracy of simulated land memory must be assessed, strength of land-atmosphere water recycling must be identified, and systematic errors in global prediction systems need to be corrected in order for climate models to faithfully harvest any degree of land-atmosphere predictability and advance global water-cycle prediction. An integrated and collaborative effort is proposed.Investigations into land surface memory include analysis of land-model simulations driven by observed meteorology (in conjunction with the Global Soil Wetness Project Phase 2) and available in situ observations to produce a global picture of soil moisture persistence; forward and backward water vapor trajectory analysis to quantify the extent of land-atmosphere water recycling across the globe; and contemporaneous and lagged correlations between the two analyses above to determine if indeed strong recycling is the cause of persistent soil moisture anomalies. Climate model sensitivity studies will verify the role of high-memory regions in the global water cycle suggested by the above analyses.Recent research indicates that removal of systematic errors in climate models improves their sensitivity to land surface anomalies, and improves predictive skill. We will also pursue development of an empirical correction of land and atmospheric errors in a global prediction system to improve the numerical simulation and seasonal-to-interannual prediction of the land-atmosphere branch of the global water cycle, with the ultimate aim of application of empirical correction to further improve climate prediction capability.Adjustments to work plan:At the request of NSF, the original budget has been revised to reflect a 30% reduction. This had been achieved by roughly equal percentage cuts among the three collaborating institutions. Details of the changes that affect the original work plan are listed below.* The post-doc level of effort has been reduced to 6 months during year one, anticipating a later start date for the post-doc.* Levels of effort for the PIs have been reduced, meaning a slower rate of progress than originally proposed.* The work plan as been adjusted (below), shifting more work to later in the project and moving the final elements of the originally proposed work to a follow-on for which we will seek support from a future program solicitation. This shift will primarily delay the final synthesis between characterization of land memory and predictability, identification regions of strong land-atmosphere water recycling, and advancement of water-cycle prediction through these identifications of land memory and empirical correction of systematic errors in climate models.Characterizing Land Surface Memory to Advance Climate PredictionDirmeyer & DelSole (COLA), Brubaker (UMCP), and Schlosser (UMBC/GEST).

[00:23 . 26] schlosser我们假设陆地和大气之间的相互作用可能是陆地表面记忆的一个重要来源，而这种记忆在气候系统中构成了陆地-大气耦合可预测性的一个要素，可以促进水循环的预测。然而，必须评估模拟陆地记忆的准确性，必须确定陆地-大气水循环的强度，并且需要纠正全球预测系统中的系统误差，以便气候模式能够忠实地收集任何程度的陆地-大气可预测性并推进全球水循环预测。提出了一种综合和协作的努力。对陆地表面记忆的调查包括分析由观测到的气象学驱动的陆地模式模拟（与全球土壤湿度项目第2阶段一起）和现有的现场观测，以产生全球土壤湿度持久性的图像；正反向水汽轨迹分析量化全球陆地-大气水循环程度以及上述两种分析之间的同期和滞后相关性，以确定强烈的再循环是否确实是持续土壤湿度异常的原因。气候模式敏感性研究将验证上述分析提出的高记忆区在全球水循环中的作用。最近的研究表明，消除气候模式中的系统误差可以提高其对地表异常的敏感性，并提高预测技能。发展陆地和大气误差全球预报系统的经验校正，完善全球水循环陆地-大气分支的数值模拟和季节-年际预测，最终实现利用经验校正进一步提高气候预报能力。工作计划调整：应国家科学基金会的要求，对原预算进行了修订，以反映30%的削减。这是通过三个合作机构之间大致相同的百分比削减来实现的。下面列出了影响原始工作计划的变更细节。*第一年的博士后工作时间减少到6个月，预计博士后开始时间会推迟。* pi的努力程度已经降低，这意味着进展速度比最初提议的要慢。*工作计划进行了调整（见下文），将更多工作转移到项目的后期，并将最初建议工作的最后部分转移到后续工作中，我们将从未来的项目征集中寻求支持。这一转变将主要延迟陆地记忆和可预测性特征的最终综合，陆地-大气强水循环区域的识别，以及通过这些陆地记忆识别和气候模式系统误差的经验校正来推进水循环预测。表征陆地表面记忆以推进气候预测[j] . dimeyer & DelSole (COLA), Brubaker (UMCP), Schlosser （UMBC/GEST）。