Weather and Climate Systems

Long-term Analysis of the Asynchronicity between Temperature and Precipitation Maxima in the Great Plains

Paul Flanagan

School of Meteorology

06 April 2016, 3:00 PM

National Weather Center, Room 5600
120 David L. Boren Blvd.
University of Oklahoma
Norman, OK

The agricultural industry is of particular importance to the economy of the Great Plains (GP) region. Owing to this, knowledge of meteorological conditions during the growing season, and associated variability on differing time scales is critical to the region’s economy. During the warm season in the GP, an asynchronicity between precipitation and temperature maxima exists. Climatologically precipitation peaks in the spring (~April-June) with temperature peaking in the summer (~July-August). Thus, the early growing season is less dependent on outside moisture sources (irrigation, etc), while in the summer, crops are more dependent on these outside sources of water to continue maturing until harvest later in the year. Even with the importance of this feature of the GP climate, there exists a lack of literature investigating the impact of climate change on this asynchronicity between precipitation and temperature maxima. This study aims to begin filling this gap, by investigating a long-term observational dataset to provide an analysis of this asynchronicity and answer the question of how global climate change is impacting this feature of the GP climate.

With the requirement of a long-term dataset of observed temperature and precipitation amounts the Global Historical Climate Network Daily (GHCN-Daily) data was utilized for this study. This dataset has been in operation since the late 1800’s and includes numerous stations (~80,000) worldwide with approximately 1200 stations in the U.S. The nClimDiv dataset was also used for spatial analysis from monthly averaged data. Comprised of GHCN-Daily data, this dataset has nearly the same period of record as the GHCN-Daily data, however it is area averaged into climate divisions over the contiguous U.S and Alaska. 352 GHCN-Daily stations were identified based on specific criteria and the dates of the precipitation and temperature maxima for each year were identified at daily, weekly and monthly intervals. An Asynchronous Difference Index (ADI) was computed by taking the difference between these two dates, and then the ADI was averaged over each decade. Analysis of the time series of these means show a statistically significant decreasing decadal ADI trend in Kansas and an increasing trend in North Dakota with results from other states being less conclusive. Analysis on the standard deviation of the decadal ADI shows that several states are incurring more ADI variability in the last few decades then in the early 20th century. Finally, histograms of the ADI dataset revealed two different regimes of ADI (positive and negative), however more rigorous analysis is required in order to explain the cause of this feature in the ADI.