Collaborative Research: Characterizing Land Surface Memory to Advance Climate Prediction

0232616 Schlosser我们假设，陆地和大气之间的相互作用可能是陆面记忆的一个重要来源，这种记忆构成了气候系统中耦合陆-气可预测性的一个要素，可以推进水循环预测。 然而，必须评估模拟的土地记忆的准确性，必须确定陆-气水循环的强度，并需要纠正全球预测系统中的系统性误差，以使气候模式忠实地收获任何程度的陆-气可预测性和推进全球水循环预测。陆面记忆的研究包括由观测气象驱动的陆面模式模拟分析（与全球土壤湿度项目第二阶段结合）和现有的实地观测，以绘制全球土壤湿度持续性的图像;前后水蒸气轨迹分析，以量化整个地球仪的陆地-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，以确定强循环是否确实是持续土壤水分异常的原因。 气候模式敏感性研究将验证上述分析所提出的高记忆区域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陆面异常的敏感性，并提高预测能力。 此外，为了提高全球水循环陆-气分支的数值模拟和季节-年际预测能力，我们还将致力于开发全球预测系统中陆-气误差的经验修正，最终目的是应用经验修正，进一步提高气候预测能力。工作计划的调整：应NSF的要求，对原预算进行了修改，减少了30%。 这是通过三个合作机构之间大致相等的削减百分比实现的。 影响原工作计划的变动详情如下。在第一年，博士后的工作量已减少到6个月，预计博士后的开始日期将推迟。方案执行机构的工作量有所减少，这意味着进展速度比最初提议的要慢。工作计划已经调整（见下文），将更多的工作转移到项目后期，并将最初提出的工作的最后部分转移到后续工作中，我们将寻求未来计划招标的支持。 这种转变将主要延迟土地记忆和可预测性的表征，确定强陆-气水循环的区域，并通过这些识别土地记忆和气候模型中的系统误差的经验校正来推进水循环预测之间的最终合成。表征陆面记忆，以推进气候预测Dirmeyer DelSole（COLA），Brubaker（UMCP）和Schlosser（UMBC/GEST）。

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).