Scott Capps

Estimated global ocean wind power potential from QuikSCAT observations, accounting for turbine characteristics and siting

For the first time, global ocean usable wind power is evaluated for modern offshore turbine characteristics including hub height, usable portion of the wind speed distribution, and siting depth. Mean wind power increases by 30%, 69%, and 73% within the tropics and Northern and Southern Hemisphere extratropics, respectively, between hub heights of 10 m and 100 m. A turbine with a cut-out speed of 25 m s−1 (30 m s−1) within the Northern Hemisphere storm track harvests between 55% (82%) and 85% (>98%) of available power. Within this region, a 2–3 m s−1 change in cut-out speed can result in a 5–7% change in usable power. Eighty meter wind power accumulates at a rate of 20–45 MW km2 m−2 per meter depth increase from the shore to the shelf break. Beyond the shelf break, wind power accumulates at a slower rate (<12 MW km2 m−2 m−1). The combined impact of all three characteristics on available wind power is assessed for three technology tiers: existing, planned, and future innovations. Usable percent of 80 m available global ocean wind power ranges from 0.40% for existing to 2.73% for future envisioned turbine specifications. Offshore wind power production is estimated using three offshore wind turbine power curves, three ocean depth limits and two siting densities. Global offshore wind power is as much as 39 TW (54% of onshore) and is maximized for the smallest and least powerful of the three turbine specifications evaluated.

Contrasting controls on wildland fires in Southern California during periods with and without Santa Ana winds

Wildland fires in Southern California can be divided into two categories: fall fires, which are typically driven by strong offshore Santa Ana winds, and summer fires, which occur with comparatively weak onshore winds and hot and dry weather. Both types of fire contribute significantly to annual burned area and economic loss. An improved understanding of the relationship between Southern Californias meteorology and fire is needed to improve predictions of how fire will change in the future and to anticipate management needs. We used output from a regional climate model constrained by reanalysis observations to identify Santa Ana events and partition fires into those occurring during periods with and without Santa Ana conditions during 1959–2009. We then developed separate empirical regression models for Santa Ana and non-Santa Ana fires to quantify the effects of meteorology on fire number and size. These models explained approximately 58% of the seasonal and interannual variation in the number of Santa Ana fires and 36% of the variation in non-Santa Ana fires. The number of Santa Ana fires increased during years when relative humidity during Santa Ana events and fall precipitation were below average, indicating that fuel moisture is a key controller of ignition. Relative humidity strongly affected Santa Ana fire size. Cumulative precipitation during the previous three winters was significantly correlated with the number of non-Santa Ana fires, presumably through increased fine fuel density and connectivity between infrastructure and nearby vegetation. Both relative humidity and the preceding wet season precipitation influenced non-Santa Ana fire size. Regression models driven by meteorology explained 57% of the temporal variation in Santa Ana burned area and 22% of the variation in non-Santa Ana burned area. The area burned by non-Santa Ana fires has increased steadily by 1.7% year−1 since 1959 (p < 0.006); the occurrence of extremely large Santa Ana fires has increased abruptly since 2003. Our results underscore the need to separately consider the fuel and meteorological controls on Santa Ana and non-Santa Ana fires when projecting climate change impacts on regional fire.

Observed and CAM3 GCM Sea Surface Wind Speed Distributions: Characterization, Comparison, and Bias Reduction

Abstract Climatological surface wind speed probability density functions (PDFs) estimated from observations are characterized and used to evaluate, for the first time, contemporaneous wind PDFs predicted by a GCM. The observations include NASA’s global Quick Scatterometer (QuikSCAT) dataset, the NCEP/Department of Energy Global Reanalysis 2 (NCEP-2) 6-hourly reanalysis, and the Tropical Atmosphere Ocean (TAO)/Triangle Trans-Ocean Buoy Network (TRITON) moored buoy data, all from 2000 to 2005. Wind speed mean, 90th percentile, standard deviation, and Weibull shape parameter climatologies are constructed from these data. New features that emerge from the analysis include the identification of a stationary pattern to the wind speed variance in the equatorial Pacific. Interestingly, a distinct wind speed shape anomaly migrates with the ITCZ across this stationary background. The GCM despite its coarser spatial and temporal resolution predicts wind speed PDFs in general agreement with observations. Relative to ...

A Hybrid Dynamical–Statistical Downscaling Technique. Part I: Development and Validation of the Technique

AbstractIn this study (Part I), the mid-twenty-first-century surface air temperature increase in the entire CMIP5 ensemble is downscaled to very high resolution (2 km) over the Los Angeles region, using a new hybrid dynamical–statistical technique. This technique combines the ability of dynamical downscaling to capture finescale dynamics with the computational savings of a statistical model to downscale multiple GCMs. First, dynamical downscaling is applied to five GCMs. Guided by an understanding of the underlying local dynamics, a simple statistical model is built relating the GCM input and the dynamically downscaled output. This statistical model is used to approximate the warming patterns of the remaining GCMs, as if they had been dynamically downscaled. The full 32-member ensemble allows for robust estimates of the most likely warming and uncertainty resulting from intermodel differences. The warming averaged over the region has an ensemble mean of 2.3°C, with a 95% confidence interval ranging from 1...

Global ocean wind power sensitivity to surface layer stability

Global ocean wind power has recently been assessed (W. T. Liu et al., 2008) using scatterometry-based 10 m winds. We characterize, for the first time, wind power at 80 m (typical wind turbine hub height) above the global ocean surface, and account for the effects of surface layer stability. Accounting for realistic turbine height and atmospheric stability increases mean global ocean wind power by +58% and −4%, respectively. Our best estimate of mean global ocean wind power is 731 W m−2, about 50% greater than the 487 W m−2 based on previous methods. 80 m wind power is 1.2–1.5 times 10 m power equatorward of 30° latitude, between 1.4 and 1.7 times 10 m power in wintertime storm track regions and >6 times 10 m power in stable regimes east of continents. These results are relatively insensitive to methodology as wind power calculated using a fitted Weibull probability density function is within 10% of power calculated from discrete wind speed measurements over most of the global oceans.

Twenty-First-Century Precipitation Changes over the Los Angeles Region*

AbstractA new hybrid statistical–dynamical downscaling technique is described to project mid- and end-of-twenty-first-century local precipitation changes associated with 36 global climate models (GCMs) in phase 5 of the Coupled Model Intercomparison Project archive over the greater Los Angeles region. Land-averaged precipitation changes, ensemble-mean changes, and the spread of those changes for both time slices are presented. It is demonstrated that the results are similar to what would be produced if expensive dynamical downscaling techniques were instead applied to all GCMs. Changes in land-averaged ensemble-mean precipitation are near zero for both time slices, reflecting the region’s typical position in the models at the node of oppositely signed large-scale precipitation changes. For both time slices, the intermodel spread of changes is only about 0.2–0.4 times as large as natural interannual variability in the baseline period. A caveat to these conclusions is that interannual variability in the tro...