Matching Items (3)
Filtering by
- All Subjects: Climate Change
Description
Ten regional climate models (RCMs) and atmosphere-ocean generalized model parings from the North America Regional Climate Change Assessment Program were used to estimate the shift of extreme precipitation due to climate change using present-day and future-day climate scenarios. RCMs emulate winter storms and one-day duration events at the sub-regional level. Annual maximum series were derived for each model pairing, each modeling period; and for annual and winter seasons. The reliability ensemble average (REA) method was used to qualify each RCM annual maximum series to reproduce historical records and approximate average predictions, because there are no future records. These series determined (a) shifts in extreme precipitation frequencies and magnitudes, and (b) shifts in parameters during modeling periods. The REA method demonstrated that the winter season had lower REA factors than the annual season. For the winter season the RCM pairing of the Hadley regional Model 3 and the Geophysical Fluid-Dynamics Laboratory atmospheric-land generalized model had the lowest REA factors. However, in replicating present-day climate, the pairing of the Abdus Salam International Center for Theoretical Physics' Regional Climate Model Version 3 with the Geophysical Fluid-Dynamics Laboratory atmospheric-land generalized model was superior. Shifts of extreme precipitation in the 24-hour event were measured using precipitation magnitude for each frequency in the annual maximum series, and the difference frequency curve in the generalized extreme-value-function parameters. The average trend of all RCM pairings implied no significant shift in the winter annual maximum series, however the REA-selected models showed an increase in annual-season precipitation extremes: 0.37 inches for the 100-year return period and for the winter season suggested approximately 0.57 inches for the same return period. Shifts of extreme precipitation were estimated using predictions 70 years into the future based on RCMs. Although these models do not provide climate information for the intervening 70 year period, the models provide an assertion on the behavior of future climate. The shift in extreme precipitation may be significant in the frequency distribution function, and will vary depending on each model-pairing condition. The proposed methodology addresses the many uncertainties associated with the current methodologies dealing with extreme precipitation.
ContributorsRiaño, Alejandro (Author) / Mays, Larry W. (Thesis advisor) / Vivoni, Enrique (Committee member) / Huang, Huei-Ping (Committee member) / Arizona State University (Publisher)
Created2013
Description
This study performs numerical modeling for the climate of semi-arid regions by running a high-resolution atmospheric model constrained by large-scale climatic boundary conditions, a practice commonly called climate downscaling. These investigations focus especially on precipitation and temperature, quantities that are critical to life in semi-arid regions. Using the Weather Research and Forecast (WRF) model, a non-hydrostatic geophysical fluid dynamical model with a full suite of physical parameterization, a series of numerical sensitivity experiments are conducted to test how the intensity and spatial/temporal distribution of precipitation change with grid resolution, time step size, the resolution of lower boundary topography and surface characteristics. Two regions, Arizona in U.S. and Aral Sea region in Central Asia, are chosen as the test-beds for the numerical experiments: The former for its complex terrain and the latter for the dramatic man-made changes in its lower boundary conditions (the shrinkage of Aral Sea). Sensitivity tests show that the parameterization schemes for rainfall are not resolution-independent, thus a refinement of resolution is no guarantee of a better result. But, simulations (at all resolutions) do capture the inter-annual variability of rainfall over Arizona. Nevertheless, temperature is simulated more accurately with refinement in resolution. Results show that both seasonal mean rainfall and frequency of extreme rainfall events increase with resolution. For Aral Sea, sensitivity tests indicate that while the shrinkage of Aral Sea has a dramatic impact on the precipitation over the confine of (former) Aral Sea itself, its effect on the precipitation over greater Central Asia is not necessarily greater than the inter-annual variability induced by the lateral boundary conditions in the model and large scale warming in the region. The numerical simulations in the study are cross validated with observations to address the realism of the regional climate model. The findings of this sensitivity study are useful for water resource management in semi-arid regions. Such high spatio-temporal resolution gridded-data can be used as an input for hydrological models for regions such as Arizona with complex terrain and sparse observations. Results from simulations of Aral Sea region are expected to contribute to ecosystems management for Central Asia.
ContributorsSharma, Ashish (Author) / Huang, Huei-Ping (Thesis advisor) / Adrian, Ronald (Committee member) / Herrmann, Marcus (Committee member) / Phelan, Patrick E. (Committee member) / Vivoni, Enrique (Committee member) / Arizona State University (Publisher)
Created2012
Description
Flowering phenology offers a sensitive and reliable biological indicator of climate change because plants use climatic and other environmental cues to initiate flower production. Drylands are the largest terrestrial biome, but with unpredictable precipitation patterns and infertile soils, they are particularly vulnerable to climate change. There is a need to increase our comprehension of how dryland plants might respond and adapt to environmental changes. I conducted a meta-analysis on the flowering phenology of dryland plants and showed that some species responded to climate change through accelerated flowering, while others delayed their flowering dates. Dryland plants advanced their mean flowering dates by 2.12 days decade-1, 2.83 days °C-1 and 2.91 days mm-1, respectively, responding to time series, temperature, and precipitation. Flowering phenology responses varied across taxonomic and functional groups, with the grass family Poaceae (-3.91 days decade1) and bulb forming Amaryllidaceae (-0.82 days decade1) showing the highest and lowest time series responses respectively, while Brassicaceae was not responsive. Analysis from herbarium specimens collected across Namibian drylands, spanning 26 species and six families, revealed that plants in hyper-arid to arid regions have lower phenological sensitivity to temperature (-9 days °C-1) and greater phenological responsiveness to precipitation (-0.56 days mm-1) than those in arid to semi-arid regions (-17 days °C-1, -0.35 days mm-1). The flowering phenology of serotinous plants showed greater sensitivity to both temperature and precipitation than that of non-serotinous plants. I used rainout shelters to reduce rainfall in a field experiment and showed that drought treatment advanced the vegetative and reproductive phenology of Cleome gynandra, a highly nutritional and medicinal semi-wild vegetable species. The peak leaf length date, peak number of leaves date, and peak flowering date of Cleome gynandra advanced by six, 10 and seven days, respectively. Lastly, I simulated drought and flood in a greenhouse experiment and found that flooding conditions resulted in higher germination percentage of C. gynandra than drought. My study found that the vegetative, and flowering phenology of dryland plants is responsive to climate change, with differential responses across taxonomic and functional groups, and aridity zones, which could alter the structure and function of these systems.
ContributorsKangombe, Fransiska Ndiiteela (Author) / Throop, Heather (Thesis advisor) / Sala, Osvaldo (Committee member) / Vivoni, Enrique (Committee member) / Pigg, Kathleen (Committee member) / Hultine, Kevin (Committee member) / Kwembeya, Ezekeil (Committee member) / Arizona State University (Publisher)
Created2023