Downscaling humidity with Localized Constructed Analogs (LOCA) over the conterminous United States
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Humidity is important to climate impacts in hydrology, agriculture, ecology, energy demand, and human health and comfort. Nonetheless humidity is not available in some widely-used archives of statistically downscaled climate projections for the western U.S. In this work the Localized Constructed Analogs (LOCA) statistical downscaling method is used to downscale specific humidity to a 1°/16° grid over the conterminous U.S. and the results compared to observations. LOCA reproduces observed monthly climatological values with a mean error of ~0.5 % and RMS error of ~2 %. Extreme (1-day in 1- and 20-years) maximum values (relevant to human health and energy demand) are within ~5 % of observed, while extreme minimum values (relevant to agriculture and wildfire) are within ~15 %. The asymmetry between extreme maximum and minimum errors is largely due to residual errors in the bias correction of extreme minimum values. The temporal standard deviations of downscaled daily specific humidity values have a mean error of ~1 % and RMS error of ~3 %. LOCA increases spatial coherence in the final downscaled field by ~13 %, but the downscaled coherence depends on the spatial coherence in the data being downscaled, which is not addressed by bias correction. Temporal correlations between daily, monthly, and annual time series of the original and downscaled data typically yield values >0.98. LOCA captures the observed correlations between temperature and specific humidity even when the two are downscaled independently.
KeywordsStatistical downscaling Climate modeling Hydrology
We would like to thank a reviewer who made valuable suggestions that improved this work. This work was made possible by support from California Energy Commission, agreement #500-10-041, which is gratefully acknowledged. Additional support was provided by the NOAA California Nevada Applications Program (CNAP) RISA award NOAA NA11OAR4310150, and the Department of Interior’s (U.S. Geological Survey) Southwest Climate Science Center, grant USGS G12AC20518. Computational resources in partial support of this work were provided by the NASA Earth Exchange (NEX) collaborative through the NASA Advanced Supercomputing (NAS) Division at Ames Research Center.
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