Submitted to: Journal of the American Water Resources Association
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/1/1999
Publication Date: N/A
Citation: Interpretive Summary: Scientific understanding and prediction of continental atmospheric weather pattern have greatly increased in the last decade. This has enabled NOAA to produce monthly and seasonal climate forecasts for broad regions called climate divisions. To fully take advantage of this climate forecast information for agricultural applications, the relationship between regional forecast for monthly precipitation and actual monthly precipitation at the farm scale must be quantified. Such a relationship was developed for the Central Climate Division of Oklahoma (CCDO). This study shows that the CCDO has an increasing precipitation gradient from west to east. The gradient is seasonal, with a low value in August and a high value in April and October during the two wet periods of the year. The CCDO has also a high random precipitation variability from one location to another. The size of this random variability between locations ranges between 27 and 48% of the inter-annual variability. This significant random variability leads to large localized departures from regional mean precipitation values used in the climate forecasts, and clearly demonstrates the critical role that the random variations plays in the utilization of regional climate forecasts for local agricultural applications. The results of this study also quantify the uncertainty range for local precipitation estimates that are derived from regional climate forecasts.
Technical Abstract: To fully take advantage of regional climate forecast information for agricultural applications, the relationship between divisional and station scale precipitation characteristics must be quantified. The spatial variability of monthly precipitation is assumed to consist of two components: a systematic and a random component. The systematic component is defined by differences in long-term mean precipitation between stations within a climate division, and the random component by differences in the standardized values between station and divisional values. For the Central Climate Division of Oklahoma, the systematic component has a positive precipitation gradient from west to east with a slope ranging between 3 to 16 mm of precipitation per 100 km depending on the month of the year. On the other hand, the random component ranges between 27 to 48% of the mean temporal variation of the monthly precipitation. This significant random spatial variability leads to large localized departures from divisional values, and clearly demonstrates the critical influence of the random component in the utilization of divisional climate forecasts for local agricultural applications. The results of this study also provide an uncertainty range for local monthly precipitation projections that are derived from divisional climate forecasts.