Weather and climate extremes have been varying and changing on many different time scales. In recent decades, heat waves have generally become more frequent across the United States, while cold waves have been decreasing. While this is in keeping with expectations in a warming climate, it turns out that decadal variations in the number of U.S. heat and cold waves do not correlate well with the observed U.S. warming during the last century. Annual peak flow data reveal that river flooding trends on the century scale do not show uniform changes across the country. While flood magnitudes in the Southwest have been decreasing, flood magnitudes in the Northeast and north-central United States have been increasing. Confounding the analysis of trends in river flooding is multiyear and even multidecadal variability likely caused by both large-scale atmospheric circulation changes and basin-scale “memory” in the form of soil moisture. Droughts also have long-term trends as well as multiyear and decadal variability. Instrumental data indicate that the Dust Bowl of the 1930s and the drought in the 1950s were the most significant twentieth-century droughts in the United States, while tree ring data indicate that the megadroughts over the twelfth century exceeded anything in the twentieth century in both spatial extent and duration. The state of knowledge of the factors that cause heat waves, cold waves, floods, and drought to change is fairly good with heat waves being the best understood.
Data obtained from NOAA’s Geostationary Operational Environmental Satellite (GOES) and NASA’s Tropical Rainfall Measuring Mission (TRMM) satellites were used to investigate the relationships between topography, large-scale circulation, and the climatology of precipitation and cloudiness in the Andes—specifically over Peru and the Altiplano Plateau—at diurnal, seasonal, and interannual time scales. The spatial variability of cloudiness was assessed through empirical orthogonal function (EOF) analysis of GOES brightness temperatures. Results indicate that landform is the principal agent of the space–time variability of moist atmospheric processes in the Andes, with the first mode explaining up to 70% of all observed variability. These results substantiate the differences between “continental” (Andes and Himalayas) and “maritime” (Western Cordillera) orographic precipitation regimes, reflecting the degree to which upwind landmasses modulate moisture transport toward and across mountain barriers. GOES brightness temperatures show that afternoon convective activity during the rainy season is more intense on wet hydrometeorological years such as 2001, whereas the space–time structure of nighttime cloudiness at the foothills and outlets of deep interior valleys does not change during the monsoon and from one year to another independently of large-scale conditions. This suggests that daytime cloud formation and precipitation is strongly dependent on large-scale moisture transport. Interactions between mesoscale and ridge–valley circulations, which are locked to the topography, determine the space–time organization of clouds and precipitation at nighttime. This leads to strong clustering of precipitation features associated with enhanced convection at high elevations along the ridges and near the headwaters of the major river systems in the TRMM data.
Data from NASA's TRMM satellite and NOAA's GOES satellites were used to survey the orographic organization of cloud precipitation in central and southern Mexico during the monsoon with two main objectives: 1) to investigate large-scale forcing versus local landform controls, and 2) to compare the results with previous work in the Himalayas. At large scales, the modes of spatial variability of cloudiness were estimated using the empirical orthogonal function (EOF) analysis of GOES brightness temperatures. Terrain modulation of synoptic-scale high-frequency variability (3-5-and 6-9-day cycles normally associated with the propagation of easterly waves) was found to cause higher dispersion in the EOF spectrum, with the first mode explaining less than 30% of the spatial variability in central and southern Mexico as opposed to 50% and higher in the Himalayas. A detailed analysis of the first three EOFs for 1999, an average La Niña year with above average rainfall, and for 2001, a weak La Niña year with below average rainfall, shows that landform (mountain peaks and land-ocean contrast) and large-scale circulation (moisture convergence) alternate as the key controls of regional hydrometeorology in dry and wet years, or as active and break (midsummer drought) phases of the monsoon, respectively. The diurnal cycle is the dominant time scale of variability in 2001, as it is during the midsummer drought in all years. Strong variability at time scales beyond two weeks is only present during the active phases of the monsoon. At the river basin scale, the data show increased cloudiness over the mountain ranges during the afternoon, which moves over the low-lying regions at the foot of the major orographic barriers [the Sierra Madre Occidental (SMO)/Sierra Madre del Sur (SMS) and Trans-Mexican Volcanic Belt (TMVB)], specifically the Balsas and the Rio de Santiago basins at nighttime and in the early morning. At the ridge-valley scale (ϳ100-200 km), robust day-night (ridge-valley) asymmetries suggest strong local controls on cloud and precipitation, with convective activity along the coastal region of the SMO and topographically forced convection at the foothills of headwater ridges in the Altiplano and the SMS. These day-night spatial shifts in cloudiness and precipitation are similar to those found in the Himalayas at the same spatial scales.
in Wiley InterScience (www.interscience.wiley.com).Gas transfer experiments in bubbly flow are conducted inside a deep bubble column/ airlift reactor containing air and water with a maximum aerated water height of 23.4 m and diameter of 1.06 m. The effects of geometry and operating conditions on mixing and gas transfer are determined. Fluorescence measurements are used to estimate dispersion coefficients for several operating conditions, while bubble-water gas transfer measurements are made using dissolved oxygen (DO) probes. A two-phase convection-dispersion model is fit to the DO measurements using the liquid film coefficient (k L ) as a fitting parameter. Sparger differences had a substantial effect upon k L , and the gas transfer coefficient for the airlift reactor was four times that of the bubble column. Results are characterized using Sherwood, Reynolds, and Bond numbers. A low Reynolds number exponent was found, indicating that k L in a deep column tends toward a constant and is not highly dependent upon air discharge.
Flood susceptibility in the Lower Connecticut River Valley Region attributable to nonclimatic flood risk factors is mapped using a quantitative method using logistic regression. Flood risk factors considered include elevation, slope, curvature (concave, convex, or flat), distance to water, land cover, vegetative density, surficial materials, soil drainage, and impervious surface. Values of factors at point locations were correlated to whether a location was located within or outside of the U.S. Federal Emergency Management Agency 100‐year Special Flood Hazard Area (SFHA). The Lower Connecticut River Valley Region was divided into urban, rural, and coastal subregions to assess the differences in factor contributions to flood susceptibility between different region types; for each region flood risk factors were extracted from 4,000 points, of which an equal number were within or outside of the 100‐year SFHA. Logistic regression coefficients were obtained. It was found that elevation and distance to water have the greatest contribution to flood susceptibility in the urban and coastal subregions, whereas distance to water and surficial materials dominate in the rural subregion. The contribution of land use to flood susceptibility increased by over 200% between the rural and urban regions. Probabilities of flooding were computed using each regional logistic regression equation. Several areas classified as very high risk (80–100%) and high risk (60–80%) were located outside of the SFHA and included several types of infrastructure critical for human health, safety, and education. This study demonstrates the utility of logistic regression as an efficient methodology to map regional flood susceptibility.
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