量化经验降尺度气候数据中空间明确的不确定性

IF 3.5 3区 地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES
Nicole C. Inglis, Taylor R. Brown, Ashley B. Cale, Theodore Hartsook, Adriano Matos, Johanson Onyegbula, Jonathan A. Greenberg
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引用次数: 0

摘要

包括森林和植被生长模型在内的生态模拟需要与模拟过程的分辨率和范围相匹配的气候输入。气候输入的分辨率通常比许多生态系统过程的尺度更粗。机器学习模型经过训练后,可利用地形变量(如海拔、地势和其他特定地点因素)将气候数据的空间分辨率缩减到精细(30 米)。统计降尺度气候模型在空间上会有不同的不确定性,这些不确定性通常没有纳入降尺度技术,以便将误差传播到以后的模型中,而且通常应用于较小的区域,对于许多建模技术来说分辨率不够精细,或者并不总是可以扩展到大的空间范围。目前仍有机会利用机器学习的进步,将气候降尺度到非常精细(30 米)的分辨率,并结合相关的空间不确定性,以在生态模型中表示微气候变化。在这项研究中,我们使用量化机器学习技术生成了加利福尼亚州 30 米降尺度气温和降水数据以及相关模型预测的不确定性。气温模型能准确地将 4 公里的气候数据降尺度到 30 米,在高坡、低坡和高海拔地区,尤其是气象观测较少的地区,表现优于 4 公里的数据。降水模型的预测结果与 4 千米尺度相比并没有全面改善,但在高海拔地区、太阳辐射较强的斜坡和山谷地区更为准确。对于所有气候变量,通过 90% 预测区间增加的空间明确不确定性细节,对根据经验缩小气候尺度的实用性提供了重要的启示。由此产生的 30 米空间连续输出结果可用作生态模型输入,并进行不确定性传播,以阐明作为细尺度空间因素函数的气候随时间变化的趋势,并突出空间显式不确定性区域。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Quantifying spatially explicit uncertainty in empirically downscaled climate data

Ecological simulations including forest and vegetation growth models require climate inputs that match the resolution and extent of the process being modelled. Climate inputs are often derived at resolutions coarser than the scale of many ecosystem processes. Machine learning models can be trained to spatially downscale climate data to fine (30 m) resolution using topographic variables such as elevation, aspect and other site-specific factors. Statistically downscaled climate models will have spatially varying uncertainty that is not usually incorporated into downscaling techniques for error propagation into later models, are often applied on smaller areas, are not fine enough resolutions for many modelling techniques, or are not always scalable to large spatial extents. There remains opportunity to leverage machine learning advancements to downscale climate to very fine (30 m) resolutions with associated spatially explicit uncertainty to represent microclimatic variation in ecological models. In this study, we used quantile machine learning to produce 30 m downscaled temperature and precipitation data and associated model prediction uncertainty for the state of California. Temperature models were accurate at downscaling 4 km climate data to 30 m, performing better than the 4 km data at high and low slope positions and at high elevations, especially where there were fewer weather observations. Precipitation model predictions did not show global improvement over the 4 km scale, but were more accurate at high elevations, slopes with higher solar radiation and in valleys. For all climate variables, the added detail of spatial explicit uncertainty via 90% prediction intervals provides critical insight into the utility of empirically downscaled climate. The resulting 30 m spatially contiguous outputs can be used as ecological model inputs with uncertainty propagation, to illuminate climate trends over time as a function of fine-scale spatial factors, and to highlight areas of spatially explicit uncertainty.

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来源期刊
International Journal of Climatology
International Journal of Climatology 地学-气象与大气科学
CiteScore
7.50
自引率
7.70%
发文量
417
审稿时长
4 months
期刊介绍: The International Journal of Climatology aims to span the well established but rapidly growing field of climatology, through the publication of research papers, short communications, major reviews of progress and reviews of new books and reports in the area of climate science. The Journal’s main role is to stimulate and report research in climatology, from the expansive fields of the atmospheric, biophysical, engineering and social sciences. Coverage includes: Climate system science; Local to global scale climate observations and modelling; Seasonal to interannual climate prediction; Climatic variability and climate change; Synoptic, dynamic and urban climatology, hydroclimatology, human bioclimatology, ecoclimatology, dendroclimatology, palaeoclimatology, marine climatology and atmosphere-ocean interactions; Application of climatological knowledge to environmental assessment and management and economic production; Climate and society interactions
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