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This study estimated the trends in the number of days that fall within the near-zero (0?°C) range of the temperature continuum. This narrow range has importance for potential transportation hazards and freeze-thaw cycles. While the tails of the air-temperature distribution and their trends often are closely examined under the climate change context, the frequency and trend of other portions of the air-temperature distribution can be equally important, as many societal impacts are caused by events in the non-tail region, such as near-zero °C temperatures (NZT). Examining the trend of the number of NZT days over the conterminous USA for the period of 1948–1949 through 2010–2011, we found three distinct spatial clusters. The most distinctive spatial clusters are found along the West Coast (positive temperature trends leading to a decrease in NZT days), the High Plains and Northern Rockies (positive temperature trends leading to an increase in NZT days), and the southeastern USA (negative temperature trends leading to an increase in NZT days). While trends in the number of NZT days are linked to changes in mean minimum air temperature, increasing minimum temperature leads to a positive trend at NZT days only at some locations. 相似文献
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Sungho Lee Soojong Pak Christopher J. Davis Robeson M. Herrnstein T. R. Geballe Paul T. P. Ho J. Craig Wheeler 《Monthly notices of the Royal Astronomical Society》2003,341(2):509-516
We have detected the v = 1 → 0 S(1) (λ= 2.1218 μm) and v = 2 → 1 S(1) (λ= 2.2477 μm) lines of H2 in the Galactic Centre, in a 90 × 27 arcsec2 region between the north-eastern boundary of the non-thermal source Sgr A East, and the giant molecular cloud (GMC) M−0.02 − 0.07 . The detected H2 v = 1 → 0 S(1) emission has an intensity of 1.6–21 × 10−18 W m−2 arcsec−2 and is present over most of the region. Along with the high intensity, the large linewidths (FWHM = 40–70 km s−1 ) and the H2 v = 2 → 1 S(1) to v = 1 → 0 S(1) line ratios (0.3–0.5) can be best explained by a combination of C-type shocks and fluorescence. The detection of shocked H2 is clear evidence that Sgr A East is driving material into the surrounding adjacent cool molecular gas. The H2 emission lines have two velocity components at ∼+50 and ∼0 km s−1 , which are also present in the NH3 (3, 3) emission mapped by McGary, Coil & Ho. This two-velocity structure can be explained if Sgr A East is driving C-type shocks into both the GMC M−0.02 − 0.07 and the northern ridge of McGary et al. 相似文献
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Scott M. Robeson 《Climatic change》2002,52(1-2):219-238
Using daily minimum air-temperature (Tmin) data from the state of Illinois, the dates of spring and fall freezes – and the resulting growing-season length – are examined for trends during theperiod 1906–1997. Of the stations in the Daily Historical Climate Network, mostshow trends toward earlier spring freezes; however, trends in fall freezes are not consistent over the station network. Although the time series are highly variable (noisy), results suggest that the growing-season length in Illinois became roughly one week longer during the 20thcentury. To examine how changing freeze-date statistics relate to changing air-temperature probability distributions, percentiles of Tmin formoving 10-year periods were analyzed for trends during the typical times for spring and fall freezes in Illinois (i.e., the months of April and October). The lower portion of the April probability distribution shows substantially larger warming (0.5–0.7 ° C/100 yrs) than the upper portion of the distribution (0.2–0.3 ° C/100 yrs), suggesting that although cold events are warming during April, warm events are not warming as fast. Conversely, the lower portion of the October probability distribution shows modest cooling in Tmin (–0.2 ° C/100yrs for the 10th percentile), while middle and upper portions of the distribution show very large rates of cooling (up to –1.5 ° C/100 yrs for the 40th–70th percentiles). Analysis ofthe entire probability distribution provides a more-comprehensive perspective on climatic change than does the traditional focus on central tendency. 相似文献
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Uneven and changing spatial distributions of air temperature stations can produce unrepresentative samples of the space-time variability of near-surface air temperature. Over the last century, station networks have varied from less than 300 stations that are largely limited to the Northern Hemisphere to nearly 1700 stations that are fairly well distributed over the terrestrial surface. As a result, estimates of air temperature change derived from historical observation networks often contain network-induced variability.Spatial resampling methods are used to estimate network-induced variability in terrestrially averaged air temperature anomalies and trends. Random resampling from the station networks allows pseudo confidence intervals and other statistics of variability to be estimated. Network-induced variability appears to be substantial during the late 1800s, especially in the Southern Hemisphere. Most networks from the 1900s and the Northern Hemisphere, however, appear to produce reliable estimates of spatially averaged air temperature anomalies and trends.A non-random, but historically appropriate, resampling method - sampling a given year's air temperature anomaly field using another year's station distribution - also is used. Using 1987 - one of the warmest years on record - as an example, station distributions from 1881-1988 are used to sample the 1987 air temperature anomaly field. This sampling procedure produces spatially averaged air temperature anomalies for 1987 that vary by over 0.3°C. A combinatorial process of resampling a given year's anomaly field is repeated for every year and every station network to produce terrestrial average air temperature anomaly estimates that vary by more than 0.3°C solely due to network changes. 相似文献
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Daniel T. Myers Darren L. Ficklin Scott M. Robeson Ram P. Neupane Alejandra Botero-Acosta Pedro M. Avellaneda 《水文研究》2021,35(2):e14045
The selection of calibration and validation time periods in hydrologic modelling is often done arbitrarily. Nonstationarity can lead to an optimal parameter set for one period which may not accurately simulate another. However, there is still much to be learned about the responses of hydrologic models to nonstationary conditions. We investigated how the selection of calibration and validation periods can influence water balance simulations. We calibrated Soil and Water Assessment Tool hydrologic models with observed streamflow for three United States watersheds (St. Joseph River of Indiana/Michigan, Escambia River of Florida/Alabama, and Cottonwood Creek of California), using time period splits for calibration/validation. We found that the choice of calibration period (with different patterns of observed streamflow, precipitation, and air temperature) influenced the parameter sets, leading to dissimilar simulations of water balance components. In the Cottonwood Creek watershed, simulations of 50-year mean January streamflow varied by 32%, because of lower winter precipitation and air temperature in earlier calibration periods on calibrated parameters, which impaired the ability for models calibrated to earlier periods to simulate later periods. Peaks of actual evapotranspiration for this watershed also shifted from April to May due to different parameter values depending on the calibration period's winter air temperatures. In the St. Joseph and Escambia River watersheds, adjustments of the runoff curve number parameter could vary by 10.7% and 20.8%, respectively, while 50-year mean monthly surface runoff simulations could vary by 23%–37% and 169%–209%, depending on the observed streamflow and precipitation of the chosen calibration period. It is imperative that calibration and validation time periods are chosen selectively instead of arbitrarily, for instance using change point detection methods, and that the calibration periods are appropriate for the goals of the study, considering possible broad effects of nonstationary time series on water balance simulations. It is also crucial that the hydrologic modelling community improves existing calibration and validation practices to better include nonstationary processes. 相似文献
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Chunfeng?HuangEmail author Haimeng?Zhang Scott?M.?Robeson 《Mathematical Geosciences》2011,43(6):721-733
Covariance and variogram functions have been extensively studied in Euclidean space. In this article, we investigate the validity
of commonly used covariance and variogram functions on the sphere. In particular, we show that the spherical and exponential
models, as well as power variograms with 0<α≤1, are valid on the sphere. However, two Radon transforms of the exponential model, Cauchy model, the hole-effect model and
power variograms with 1<α≤2 are not valid on the sphere. A table that summarizes the validity of commonly used covariance and variogram functions on
the sphere is provided. 相似文献
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Hourly wind data from a network of climate stations in the north-central United States (drawn from the states of Illinois, lowa, Kansas, Minnesota, Missouri, Nebraska, North Dakota, South Dakota, and Wisconsin) are analyzed to evaluate the efficacy of spatial analyses of near-surface wind speed and power. Spatial autocorrelation functions (acfs) were calculated at a number of timescales: annual, monthly, daily, and hourly. Annual wind speeds have virtually no coherent distance-decay relationship; monthly data produce a more consistent relationship, but still exhibit a large amount of scatter. Both daily and hourly data have classical decay with increasing distance between stations and there appears to be an optimal level of temporal aggregation, near the daily timescale, for spatial analysis of wind. In general, however, spatial acfs overestimate the spatial coherence of both wind speed and power. Temporal nonstationarities in wind data (i.e., diurnal and annual cycles) bias spatial autocorrelation functions and need to be removed before using spatial acfs to estimate characteristics of wind fields. Because mean absolute differences (MAD) of interstation wind speed and power are less affected by temporal nonstationarities, they produce more-robust representations of the spatial variability of wind speed and power. As a result, spatial MADs are recommended over spatial acfs for analyzing spatial coherence and decay of any spatial variable that contains nonstationarities. Methods for improving the spatial analysis of wind are discussed. [Key words: wind energy, spatial autocorrelation, spatial analysis, nonstationarity, north-central United States.] 相似文献
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