scholarly journals Regional Moisture Budget and Land‐Atmosphere Coupling Over the U.S. Southern Great Plains Inferred From the ARM Long‐Term Observations

2019 ◽  
Vol 124 (17-18) ◽  
pp. 10091-10108 ◽  
Author(s):  
Cheng Tao ◽  
Yunyan Zhang ◽  
Shuaiqi Tang ◽  
Qi Tang ◽  
Hsi‐Yen Ma ◽  
...  
2011 ◽  
Vol 24 (18) ◽  
pp. 4831-4843 ◽  
Author(s):  
P. Jonathan Gero ◽  
David D. Turner

Abstract A trend analysis was applied to a 14-yr time series of downwelling spectral infrared radiance observations from the Atmospheric Emitted Radiance Interferometer (AERI) located at the Atmospheric Radiation Measurement Program (ARM) site in the U.S. Southern Great Plains. The highly accurate calibration of the AERI instrument, performed every 10 min, ensures that any statistically significant trend in the observed data over this time can be attributed to changes in the atmospheric properties and composition, and not to changes in the sensitivity or responsivity of the instrument. The measured infrared spectra, numbering more than 800 000, were classified as clear-sky, thin cloud, and thick cloud scenes using a neural network method. The AERI data record demonstrates that the downwelling infrared radiance is decreasing over this 14-yr period in the winter, summer, and autumn seasons but it is increasing in the spring; these trends are statistically significant and are primarily due to long-term change in the cloudiness above the site. The AERI data also show many statistically significant trends on annual, seasonal, and diurnal time scales, with different trend signatures identified in the separate scene classifications. Given the decadal time span of the dataset, effects from natural variability should be considered in drawing broader conclusions. Nevertheless, this dataset has high value owing to the ability to infer possible mechanisms for any trends from the observations themselves and to test the performance of climate models.


Tellus B ◽  
2011 ◽  
Vol 63 (2) ◽  
Author(s):  
Margaret S. Torn ◽  
Sebastien C. Biraud ◽  
Christopher J. Still ◽  
William J. Riley ◽  
Joe A. Berry

2015 ◽  
Vol 213 ◽  
pp. 209-218 ◽  
Author(s):  
Naama Raz-Yaseef ◽  
Dave P. Billesbach ◽  
Marc L. Fischer ◽  
Sebastien C. Biraud ◽  
Stacey A. Gunter ◽  
...  

2021 ◽  
Vol 310 ◽  
pp. 108631
Author(s):  
Pradeep Wagle ◽  
Prasanna H. Gowda ◽  
Brian K. Northup ◽  
James P.S. Neel ◽  
Patrick J. Starks ◽  
...  

2015 ◽  
Vol 28 (14) ◽  
pp. 5813-5829 ◽  
Author(s):  
Joseph A. Santanello ◽  
Joshua Roundy ◽  
Paul A. Dirmeyer

Abstract The coupling of the land with the planetary boundary layer (PBL) on diurnal time scales is critical to regulating the strength of the connection between soil moisture and precipitation. To improve understanding of land–atmosphere (L–A) interactions, recent studies have focused on the development of diagnostics to quantify the strength and accuracy of the land–PBL coupling at the process level. In this paper, the authors apply a suite of local land–atmosphere coupling (LoCo) metrics to modern reanalysis (RA) products and observations during a 17-yr period over the U.S. southern Great Plains. Specifically, a range of diagnostics exploring the links between soil moisture, evaporation, PBL height, temperature, humidity, and precipitation is applied to the summertime monthly mean diurnal cycles of the North American Regional Reanalysis (NARR), Modern-Era Retrospective Analysis for Research and Applications (MERRA), and Climate Forecast System Reanalysis (CFSR). Results show that CFSR is the driest and MERRA the wettest of the three RAs in terms of overall surface–PBL coupling. When compared against observations, CFSR has a significant dry bias that impacts all components of the land–PBL system. CFSR and NARR are more similar in terms of PBL dynamics and response to dry and wet extremes, while MERRA is more constrained in terms of evaporation and PBL variability. Each RA has a unique land–PBL coupling that has implications for downstream impacts on the diurnal cycle of PBL evolution, clouds, convection, and precipitation as well as representation of extremes and drought. As a result, caution should be used when treating RAs as truth in terms of their water and energy cycle processes.


2015 ◽  
Vol 106 ◽  
pp. 43-55 ◽  
Author(s):  
Caroline Parworth ◽  
Jerome Fast ◽  
Fan Mei ◽  
Tim Shippert ◽  
Chitra Sivaraman ◽  
...  

2017 ◽  
Vol 60 (4) ◽  
pp. 1189-1208 ◽  
Author(s):  
Meetpal S. Kukal ◽  
Suat Irmak

Abstract. Sustainable agricultural utilization of the limited water resources demands improvements in understanding the changes in crop water productivity (CWP) in space and time, which is often presented as a potential solution to relieve the growing pressure on fresh water resources. In addition, crop yield needs to be studied in relation to precipitation received annually and during the growing season for its contribution to reduce irrigation water requirements, which is quantified through precipitation use efficiency (PUE). Hence, systematic quantifications, mapping, and analyses of large-scale CWP and PUE levels are needed. This study aims to quantify long-term (1982-2013) information on grain yield, PUE, and CWP for maize and soybean in the U.S. Great Plains counties and to map and analyze them. Multiple public data sources were used, including weather, satellite, and yield datasets for the 834 counties over a 32-year period. Long-term average maize grain yield ranged from 1.56 to 12.81 t ha-1 with a regional average of 6.66 t ha-1. Long-term average soybean grain yield ranged from 0.47 to 3.46 t ha-1 with an average of 2.17 t ha-1. About 87% and 89% of the counties in the region showed increasing trends in grain yield for maize and soybean, respectively, with regional average increasing trends for maize and soybean yield of 0.1014 and 0.0328 t ha-1 year-1, respectively. The regional annual PUE (ANNPUE) and growing season PUE (GRSPUE) were 1.09 and 1.90 kg m-3, respectively, for maize and 0.32 and 0.55 kg m-3, respectively, for soybean. In addition, the regional average increasing trends in maize ANNPUE (exhibited by 88% of counties) and GRSPUE (exhibited by 85% of counties) were 0.0174 and 0.0316 kg m-3 year-1. For soybean, regional average increasing trends in ANNPUE (exhibited by 91% of counties) and GRSPUE (exhibited by 87% of counties) were 0.0048 and 0.0081 kg m-3 year-1. The magnitude of maize CWP varied from 0.30 to 2.97 kg m-3 with a regional average of 1.08 kg m-3, and soybean CWP varied from 0.15 to 0.67 kg m-3 with a regional average of 0.40 kg m-3. It was found that 79% and 86% of the counties showed positive trends in maize and soybean CWP, respectively, and the increasing trend magnitudes were 0.0144 and 0.0047 kg m-3 year-1. Pooled data from all counties and growing seasons were used to develop frequency distribution histograms to quantify the inter-annual variation and distribution characteristics. The level of CWP variability represented via maps revealed regions where opportunity exists for improvements in production system efficiency. A comprehensive understanding of the spatial and temporal patterns in these efficiency indices will provide a basis for decision-making in resource assessments, planning, evaluation, and investment by state and federal agencies and stakeholders. Keywords: Agriculture, Climate, Evapotranspiration, Great Plains, Water productivity.


2017 ◽  
Vol 122 (21) ◽  
pp. 11,524-11,548 ◽  
Author(s):  
Thomas J. Phillips ◽  
Stephen A. Klein ◽  
Hsi-Yen Ma ◽  
Qi Tang ◽  
Shaocheng Xie ◽  
...  

2006 ◽  
Vol 33 (18) ◽  
pp. n/a-n/a ◽  
Author(s):  
Stephen A. Klein ◽  
Xianan Jiang ◽  
Jim Boyle ◽  
Sergey Malyshev ◽  
Shaocheng Xie

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