Articles

Authors:	Jinsheng You, Kenneth G Hubbard, Martha Shulski
Volume:	Volume 2015, No. 1, 31 Jan 2015
DOI:	http://www.doi.org/10.46275/JoASC.2015.01.001
Abstract:	Serially complete climate datasets with no missing data are necessary for a diverse group of users working in many economic sectors. In this article we describe the procedures used to create a Serially Complete Data set (SCD) for the U.S. We include the selection criterion applied to potential SCD stations, the various procedural steps and the details applied to each step. A few observations that were not previously digitized were obtained from observers official paper reports. The methods used to estimate missing data are the Spatial Regression Test and the Inverse Distance Weighting technique. Using the criterion for selecting stations we were able to include 2144 stations for the SCD that had at least 1 element (maximum/minimum temperature and/or precipitation) for a continuous period of at least 40 years. In addition, the quality control procedure assigned confidence intervals to all observations and many of the estimates. We continue to explore the options for estimating any missing data that remain after our 3 step approach and we look forward to changing the base data set form TD 3200 to GHCN.
Link:	https://stateclimate.org/pdfs/journal-articles/2015_You_et_al_2015.pdf

Authors:	F. Adnan Akyüz, Joel K. Ransom
Volume:	Volume 2015, No. 2, 18 Dec 2015
DOI:	http://www.doi.org/10.46275/JoASC.2015.12.001
Abstract:	The use of environmental temperature and its effects on plant development have been useful in determining growth stages of plants. This paper compares two Corn Growing Degree Day (GDD) calculation methods that are widely used in the US and why one is more suitable in the northern edge of the US corn-belt areas than the other. The comparison between the two accumulated GDD calculations for corn during the last 67-year period from 1948 to 2014 growing seasons for Fargo, ND, indicates that one method systematically underestimates accumulated GDDs during the days when maximum temperatures are above and minimum temperatures are below the base temperature of 50°F. Furthermore, the ratio of the difference between the two seasonal accumulations to the required accumulated GDD necessary to mature the type of corn grown in this area becomes more significant than those grown in other parts of the US where corn requires higher seasonal GDD accumulations.
Link:	https://stateclimate.org/pdfs/journal-articles/2015_Adnan_et_al.pdf

Authors:	Aavudai Anandhi, Mary Knapp
Volume:	Volume 2016, No. 1, 23 May 2016
DOI:	http://www.doi.org/10.46275/JoASC.2016.05.001
Abstract:	Drought is a complex, least understood and one of the most expensive natural disaster. Drought impacts many sectors of environment and society. A regular question is how a current drought compares to previous droughts. Water managers, resource managers, news media and the general public want to place the event in context as they evaluate impacts, and as they attempt to plan for future events. There are many definitions of drought (meteorological, agricultural, hydrological and socioeconomic) resulting in a large number of drought metrics and indices in literature. In this study we have used Standardized Precipitation Index (SPI), a useful tool to answer these questions. SPI is a transformation of the probability of a given amount of precipitation in a set period of months. This allows for the comparison of wet/dry spells over extremely different climates and over various time scales from one month to two years (24 months).
Link:	https://stateclimate.org/pdfs/journal-articles/2016_Anandhi_Knapp.pdf

Authors:	Russell S. Vose, Mike Squires, Derek Arndt, Imke Durre, Chris Fenimore, Karin Gleason, Matthew J. Menne, James Partain, Claude N. Williams, Jr., Peter A. Bieniek
Volume:	Volume 2017, No. 1, 4 Oct 2017
DOI:	http://www.doi.org/10.46275/JoASC.2017.10.001
Abstract:	This paper describes the construction of temperature and precipitation time series for climate divisions in Alaska for 1925-2015. Designed for NOAA climate monitoring applications, these new series build upon the divisional data of Bieniek et al. (2014) through the inclusion of additional observing stations, temperature bias adjustments, supplemental temperature elements, and enhanced computational techniques (i.e., climatologically aided interpolation). The new NOAA series are in general agreement with Bieniek et al. (2014), differences being attributable to the underlying methods used to compute divisional averages in each dataset. Trends in minimum temperature are significant in most divisions whereas trends in maximum temperature are generally not significant in the eastern third of the state. Likewise, the statewide rate of warming in minimum temperature (0.158°C dec-1 ) is roughly 50% larger than that of maximum temperature (0.101 °C dec-1 ). Trends in precipitation are not significant for most divisions or for the state as a whole.
Link:	https://stateclimate.org/pdfs/journal-articles/2017-Ross-etal.pdf