美国西部牧场管理应用的优化 NMME 气候预测

IF 2.4 3区 环境科学与生态学 Q2 ECOLOGY Rangeland Ecology & Management Pub Date : 2024-04-17 DOI:10.1016/j.rama.2024.03.008
Merilynn C. Schantz , Stuart P. Hardegree , Roger L. Sheley , John T. Abatzoglou , Katherine C. Hegewisch , Emile E. Elias , Jeremy J. James , Corey A. Moffet
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引用次数: 0

摘要

降水和温度输入是影响牧场生态区净初级植物产量的主要因素。娴熟的季节性气候预测有可能直接帮助为美国西部多种农业和自然资源应用的管理提供信息。鉴于牧场生态区降水和温度的时空异质性很高,使用这些预报的一个主要限制因素是无法获得跨牧场生态区的高分辨率气候预报。本研究考察了气候预测的技能或气候预测与实际数据之间的相关性。根据参与北美多模式集合(NMME)的模式,对最多 7 个月提前期的季节性气候预测进行了统计降尺度。季节性气候预报的技能通常在区域范围内进行评估,这可能会影响预报在具体地点管理应用中的效用。技能评估通常也只在单个模式或所有模式的集合平均值上进行,这可能会忽略子采样模式的增值技能。在此,我们对美国西部多个牧场生态区的七个 NMME 模型得出的月降水量和温度预报的统计降尺度技能进行了评估。我们发现,与单个模式预报和集合平均值相比,多模式集合预报通常具有协同技能。我们还发现,预报技能取决于地点、预报月份和预报长度。我们建议,针对具体地点和时间优化多模式集合技能,将提高这些预报的潜在效用,为牧场管理决策提供信息。
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Optimized NMME Climate Forecasts for Rangeland Management Applications in the Western United States

Precipitation and temperature inputs are the primary factors affecting net primary plant production across rangeland ecoregions. Skillful seasonal climate forecasts have the potential to directly aid in informing management for multiple agricultural and natural resource applications across the western United States. A key limitation in using these forecasts has been the availability of high-resolution climate forecasts across rangeland ecoregions given the high spatiotemporal heterogeneity in precipitation and temperature across these regions. This study examines the skill, or the correlation between of a climate forecast to actual data. Seasonal climate forecasts at lead times of up to 7 months lead times are statistically downscaled from models participating in the North American Multi-Model Ensemble (NMME). The skill of seasonal climate forecasts has typically been assessed at regional scales which may obscure forecast utility for site-specific management applications. Skill assessments are also typically only conducted on individual models, or the ensemble mean of all models which may overlook value-added skill by subsampling models. Here, we evaluated the skill of statistically downscaled monthly precipitation and temperature forecasts derived from seven NMME models for multiple rangeland ecoregions across the western United States. We found that multimodel aggregate forecasts often had synergistic skill when compared to both individual model forecasts as well as the ensemble mean. We also found that forecast skill was dependent on location, the month in which the forecast was made, and the forecast length. We suggest that a site and time-specific optimization of multimodel ensemble skill would increase the potential utility of these forecasts to inform rangeland management decision-making.

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来源期刊
Rangeland Ecology & Management
Rangeland Ecology & Management 农林科学-环境科学
CiteScore
4.60
自引率
13.00%
发文量
87
审稿时长
12-24 weeks
期刊介绍: Rangeland Ecology & Management publishes all topics-including ecology, management, socioeconomic and policy-pertaining to global rangelands. The journal''s mission is to inform academics, ecosystem managers and policy makers of science-based information to promote sound rangeland stewardship. Author submissions are published in five manuscript categories: original research papers, high-profile forum topics, concept syntheses, as well as research and technical notes. Rangelands represent approximately 50% of the Earth''s land area and provision multiple ecosystem services for large human populations. This expansive and diverse land area functions as coupled human-ecological systems. Knowledge of both social and biophysical system components and their interactions represent the foundation for informed rangeland stewardship. Rangeland Ecology & Management uniquely integrates information from multiple system components to address current and pending challenges confronting global rangelands.
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