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Folders: ROOT > ScienceBase Catalog > National and Regional Climate Adaptation Science Centers > Southwest CASC > FY 2013 Projects > Understanding and Communicating the Role of Natural Climate Variability in a Changing World ( Show direct descendants )

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_ScienceBase Catalog
__National and Regional Climate Adaptation Science Centers
___Southwest CASC
____FY 2013 Projects
_____Understanding and Communicating the Role of Natural Climate Variability in a Changing World
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A new method for automatic detection of atmospheric rivers (ARs) is developed and applied to an atmospheric reanalysis, yielding an extensive catalog of ARs land-falling along the west coast of North America during 1948–2017. This catalog provides a large array of variables that can be used to examine AR cases and their climate-scale variability in exceptional detail. The new record of AR activity, as presented, validated and examined here, provides a perspective on the seasonal cycle and the interannual-interdecadal variability of AR activity affecting the hydroclimate of western North America. Importantly, AR intensity does not exactly follow the climatological pattern of AR frequency. Strong links to hydroclimate...
Abstract (from AGU 100): This study investigates snowmelt and streamflow responses to cloudiness variability across the mountainous parts of the western United States. Twenty years (1996–2015) of Geostationary Operational Environmental Satellite‐derived cloud cover indices (CC) with 4‐km spatial and daily temporal resolutions are used as a proxy for cloudiness. The primary driver of nonseasonal fluctuations in daily mean solar insolation is the fluctuating cloudiness. We find that CC fluctuations are related to snowmelt and snow‐fed streamflow fluctuations, to some extent (correlations of <0.5). Multivariate linear regression models of daily snowmelt (MELT) and streamflow (ΔQ) variations are constructed for each...
Natural climate variability can strongly temporarily enhance or obscure long-term trends in regional weather due to global climate change. We planned to explore (from our original proposal): (1) The influence of low frequency climate variability (interannual and decadal) on the seasonal probability distributions of daily weather (temperature and precipitation) within the Southwest, with a view on how natural variability modulates regional trends due to global warming. We explored natural climate variability and its impacts on extreme temperatures in Guirguis et al. (2015). We also explored natural climate variability and its impacts on precipitation extremes in Cavanaugh et al. (2015), Cavanugh and Gershunov (2015)...
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Abstract (from http://journals.ametsoc.org/doi/abs/10.1175/JHM-D-16-0194.1): This study investigates the spatial and temporal variability of cloudiness across mountain zones in the western United States. Daily average cloud albedo is derived from a 19-yr series (1996–2014) of half-hourly Geostationary Operational Environmental Satellite (GOES) images. During springtime when incident radiation is active in driving snowmelt–runoff processes, the magnitude of daily cloud variations can exceed 50% of long-term averages. Even when aggregated over 3-month periods, cloud albedo varies by ±10% of long-term averages in many locations. Rotated empirical orthogonal functions (REOFs) of daily cloud albedo anomalies over high-elevation...
Abstract (from Springer): Analyses of observed non-Gaussian daily minimum and maximum temperature probability distribution functions (PDFs) in the Southwest US highlight the importance of variance and warm tail length in determining future heat wave probability. Even if no PDF shape change occurs with climate change, locations with shorter warm tails and/or smaller variance will see a greater increase in heat wave probability, defined as exceedances above the historical 95th percentile threshold, than will long tailed/larger variance distributions. Projections from ten downscaled CMIP5 models show important geospatial differences in the amount of warming expected for a location. However, changes in heat wave probability...
Abstract (from http://link.springer.com/article/10.1007%2Fs00382-015-2517-1): Daily precipitation variability as observed from weather stations is heavy tailed at most locations around the world. It is thought that diversity in precipitation-causing weather events is fundamental in producing heavy-tailed distributions, and it arises from theory that at least one of the precipitation types contributing to a heavy-tailed climatological record must also be heavy-tailed. Precipitation is a multi-scale phenomenon with a rich spatial structure and short decorrelation length and timescales; the spatiotemporal scale at which precipitation is observed is thus an important factor when considering its statistics and extremes....
Abstract (from http://onlinelibrary.wiley.com/doi/10.1002/2015GL063238/pdf): The probability tail structure of over 22,000 weather stations globally is examined in order to identify the physically and mathematically consistent distribution type for modeling the probability of intense daily precipitation and extremes. Results indicate that when aggregating data annually, most locations are to be considered heavy tailed with statistical significance. When aggregating data by season, it becomes evident that the thickness of the probability tail is related to the variability in precipitation causing events and thus that the fundamental cause of precipitation volatility is weather diversity. These results have both theoretical...
Abstract (from http://iopscience.iop.org/article/10.1088/1748-9326/10/12/124023/meta): Temperature variability in the Southwest US is investigated using skew-normal probability distribution functions (SN PDFs) fitted to observed wintertime daily maximum temperature records. These PDFs vary significantly between years, with important geographical differences in the relationship between the central tendency and tails, revealing differing linkages between weather and climate. The warmest and coldest extremes do not necessarily follow the distribution center. In some regions one tail of the distribution shows more variability than does the other. For example, in California the cold tail is more variable while the warm...