Stomatal conductance in three conifer species at different elevations during summer in Wyoming

1988 ◽  
Vol 18 (2) ◽  
pp. 242-246 ◽  
Author(s):  
Gregory A. Carter ◽  
William K. Smith ◽  
Julian L. Hadley

Stomatal conductances to water vapor diffusion in Engelmann spruce (Piceaengelmannii Parry ex Engelm.), subalpine fir (Abieslasiocarpa (Hook.) Nutt.), and lodgepole pine (Pinuscontorta Engelm.) were compared to determine environmental influences on conductance at higher (3220 m) and lower (2860 m) elevations in the central Rocky Mountains. Measurements were taken on clear days, and soil water potentials remained at or greater than −0.1 MPa. Interspecific differences were small between spruce and fir at either site, but pine conductance was generally higher than spruce or fir at 2860 m. Daily maximum conductance in spruce and fir at 3220 m did not increase above 1.0 mm s−1 until daily minimum air temperature (early morning) increased to near 1 °C in early summer. Increases in maximum conductance above 2.0 mm s−1 occurred at both elevations when minimum air temperature rose above approximately 5 °C. At the lower elevation site, increases in maximum conductance during late July and mid-August appeared to depend strongly on soil temperature increasing above 7–8 °C. The persistence of cold soil temperatures in the highest elevations of the subalpine forest may serve to inhibit stomatal opening in spruce and fir in comparison to spruce, fir, and pine in lower elevation forests.

Author(s):  
Shengpan Lin ◽  
Nathan J. Moore ◽  
Joseph P. Messina ◽  
Mark H. DeVisser ◽  
Jiaping Wu

1988 ◽  
Vol 78 (2) ◽  
pp. 235-240 ◽  
Author(s):  
J. N. Matthiessen ◽  
M. J. Palmer

AbstractIn studies in Western Australia, temperatures in air and one- and two-litre pads of cattle dung set out weekly and ranging from one to 20 days old were measured hourly for 438 days over all seasons, producing 1437 day x dung-pad observations. Daily maximum temperatures (and hence thermal accumulation) in cattle dung pads could not be accurately predicted using meteorological data alone. An accurate predictor of daily maximum dung temperature, using multiple regression analysis, required measurement of the following factors: maximum air temperature, hours of sunshine, rainfall, a seasonal factor (the day number derived from a linear interpolation of day number from day 0 at the winter solstice to day 182 at the preceding and following summer solstices) and a dung-pad age-specific intercept term, giving an equation that explained a 91·4% of the variation in maximum dung temperature. Daily maximum temperature in two-litre dung pads was 0·6°C cooler than in one-litre pads. Daily minimum dung temperature equalled minimum air temperature, and daily minimum dung temperatures occurred at 05.00 h and maximum temperatures at 14.00 h for one-litre and 14.30 h for two-litre pads. Thus, thermal summation in a dung pad above any threshold temperature can be computed using a skewed sine curve fitted to daily minimum air temperature and the calculated maximum dung temperature.


2014 ◽  
Vol 53 (8) ◽  
pp. 1932-1942 ◽  
Author(s):  
Andrea J. Coop ◽  
Kenneth G. Hubbard ◽  
Martha D. Shulski ◽  
Jinsheng You ◽  
David B. Marx

AbstractClimate data are increasingly scrutinized for accuracy because of the need for reliable input for climate-related decision making and assessments of climate change. Over the last 30 years, vast improvements to U.S. instrumentation, data collection, and station siting have created more accurate data. This study explores the spatial accuracy of daily maximum and minimum air temperature data in Nebraska networks, including the U.S. Historical Climatology Network (HCN), the Automated Weather Data Network (AWDN), and the more recent U.S. Climate Reference Network (CRN). The spatial structure of temperature variations at the earth’s surface is compared for timeframes 2005–09 for CRN and AWDN and 1985–2005 for AWDN and HCN. Individual root-mean-square errors between candidate station and surrounding stations were calculated and used to determine the spatial accuracy of the networks. This study demonstrated that in the 5-yr analysis CRN and AWDN were of high spatial accuracy. For the 21-yr analysis the AWDN proved to have higher spatial accuracy (smaller errors) than the HCN for both maximum and minimum air temperature and for all months. In addition, accuracy was generally higher in summer months and the subhumid area had higher accuracy than did the semiarid area. The findings of this study can be used for Nebraska as an estimate of the uncertainty associated with using a weather station’s data at a decision point some distance from the station.


Climate ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 158
Author(s):  
Carla Mateus ◽  
Aaron Potito

Accurate long-term daily maximum and minimum air temperature series are needed to assess the frequency, intensity, distribution, and duration of extreme climatic events. However, quality control and homogenisation procedures are required to minimise errors and inhomogeneities in climate series before the commencement of climate data analysis. A semi-automatic quality control procedure consisting of climate consistency, internal consistency, day-to-day step-change, and persistency tests was applied for 12 long-term series registered in Ireland from 1831–1968, Armagh Observatory (Northern Ireland) from 1844–2018, and for 21 short-term series dating to the mid-19th century. There were 976,786 observations quality-controlled, and 27,854 (2.9%) values flagged. Of the flagged records, 98.5% (n = 27,446) were validated, 1.4% (n = 380) corrected and 0.1% (n = 28) deleted. The historical long-term quality-controlled series were merged with the modern series quality-controlled by Met Éireann and homogenised using the software MASHv3.03 in combination with station metadata for 1885–2018. The series presented better homogenisation outcomes when homogenised as part of smaller regional networks rather than as a national network. The homogenisation of daily, monthly, seasonal, and annual series improved for all stations, and the homogenised records showed stronger correlations with the Central England long-term temperature series.


2015 ◽  
Vol 30 (4) ◽  
pp. 359-370 ◽  
Author(s):  
Carlos Antonio Costa dos Santos ◽  
Tantravahi Venkata Ramana Rao ◽  
Ricardo Alves de Olinda

ABSTRACT This study attempts to provide new information on seasonal and annual trends, on a regional scale, using records of daily air temperature over Idaho, USA, through the analysis of the Growing Season Length (GSL), and maximum and minimum air temperature data from multiple stations in the region, as well as, to obtain the temporal correlation between the daily air temperature and Sea Surface Temperature (SST) indices. The analyses were conducted using long-term and high quality data sets for 35 meteorological stations for the period between 1970 and 2006. The results suggest that both daily maximum and minimum temperatures had increasing trends, but the minimum air temperature is increasing faster than the maximum air temperature. On average, the GSL has increased by 7.5 days/decade during the period 1970-2006, associated with increasing temperatures. Trends in regional air temperature and their indication of climate change are of interest to Idaho and the rest of the world. The trends obtained herein corroborate with the general idea that during the last century the globe has warmed.


Author(s):  
Stephen D. Sebestyen ◽  
Elon S. Verry ◽  
Arthur E. Elling ◽  
Richard L. Kyllander ◽  
Daniel T. Roman ◽  
...  

2007 ◽  
Vol 24 (2) ◽  
pp. 206-213 ◽  
Author(s):  
Kenneth G. Hubbard ◽  
Nathaniel B. Guttman ◽  
Jinsheng You ◽  
Zhirong Chen

Abstract TempVal is a spatial component of data quality assurance algorithms applied by the National Climatic Data Center (NCDC), and it has been used operationally for about 4 yr. A spatial regression test (SRT) approach was developed at the regional climate centers for climate data quality assurance and was found to be superior to currently used quality control (QC) procedures for the daily maximum and minimum air temperature. The performance of the spatial quality assessment procedures has been evaluated by assessing the rate with which seeded errors are identified. A complete dataset with seeded errors for the year 2003 for the contiguous United States was examined for both the maximum and minimum air temperature. The spatial regression quality assessment component (SRT), originating in the Automated Climate Information System (ACIS), and TempVal, originating in the NCDC database, were applied separately and evaluated through the ratio of identified seeded errors to the total number of seeds. The spatial regression test applied in the ACIS system was found to perform better in identifying the seeded errors. For all months, the relative frequency of correct identification of wrong data is 0.72 and 0.83 for TempVal and SRT, respectively. The goal of the comparison was to evaluate quality assurance techniques that could improve data quality assessment at the NCDC, and the results of the comparison led to the recommendation that the SRT be included in the NCDC quality assessment methodology.


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