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Renaud Barbero, John T. Abatzoglou, and Katherine C. Hegewisch


The skill of two statistical downscaled seasonal temperature and precipitation forecasts from the North American Multimodel Ensemble (NMME) was evaluated across the western United States at spatial scales relevant to local decision-making. Both statistical downscaling approaches, spatial disaggregation (SD) and bias correction spatial disaggregation (BCSD), exhibited similar correlative skill measures; however, the BCSD method showed superior tercile-based skill measures since it corrects for variance deflation in NMME ensemble averages. Geographic and seasonal variations in downscaled forecast skill revealed patterns across the complex topography of the western United States not evident using coarse-scale skill assessments, particularly in regions subject to inversions and variability in orographic precipitation ratios. Similarly, differences in the skill of cool-season temperature and precipitation forecasts issued when the fall El Niño–Southern Oscillation (ENSO) signal was strong versus ENSO-neutral years were evident across topographic gradients in the northwestern United States.

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David W. Pierce, Daniel R. Cayan, Edwin P. Maurer, John T. Abatzoglou, and Katherine C. Hegewisch


Global climate model (GCM) output typically needs to be bias corrected before it can be used for climate change impact studies. Three existing bias correction methods, and a new one developed here, are applied to daily maximum temperature and precipitation from 21 GCMs to investigate how different methods alter the climate change signal of the GCM. The quantile mapping (QM) and cumulative distribution function transform (CDF-t) bias correction methods can significantly alter the GCM’s mean climate change signal, with differences of up to 2°C and 30% points for monthly mean temperature and precipitation, respectively. Equidistant quantile matching (EDCDFm) bias correction preserves GCM changes in mean daily maximum temperature but not precipitation. An extension to EDCDFm termed PresRat is introduced, which generally preserves the GCM changes in mean precipitation. Another problem is that GCMs can have difficulty simulating variance as a function of frequency. To address this, a frequency-dependent bias correction method is introduced that is twice as effective as standard bias correction in reducing errors in the models’ simulation of variance as a function of frequency, and it does so without making any locations worse, unlike standard bias correction. Last, a preconditioning technique is introduced that improves the simulation of the annual cycle while still allowing the bias correction to take account of an entire season’s values at once.

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Justin L. Huntington, Katherine C. Hegewisch, Britta Daudert, Charles G. Morton, John T. Abatzoglou, Daniel J. McEvoy, and Tyler Erickson


The paucity of long-term observations, particularly in regions with heterogeneous climate and land cover, can hinder incorporating climate data at appropriate spatial scales for decision-making and scientific research. Numerous gridded climate, weather, and remote sensing products have been developed to address the needs of both land managers and scientists, in turn enhancing scientific knowledge and strengthening early-warning systems. However, these data remain largely inaccessible for a broader segment of users given the computational demands of big data. Climate Engine ( is a web-based application that overcomes many computational barriers that users face by employing Google’s parallel cloud-computing platform, Google Earth Engine, to process, visualize, download, and share climate and remote sensing datasets in real time. The software application development and design of Climate Engine is briefly outlined to illustrate the potential for high-performance processing of big data using cloud computing. Second, several examples are presented to highlight a range of climate research and applications related to drought, fire, ecology, and agriculture that can be rapidly generated using Climate Engine. The ability to access climate and remote sensing data archives with on-demand parallel cloud computing has created vast opportunities for advanced natural resource monitoring and process understanding.

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