# THOUGHTS ON ASSESSING DECADAL PRECIPITATION VARIATIONS AS SURROGATE FORECASTS Jeanne M. Schneider USDA Agricultural Research Service Grazinglands Research.

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THOUGHTS ON ASSESSING DECADAL PRECIPITATION VARIATIONS AS SURROGATE FORECASTS Jeanne M. Schneider USDA Agricultural Research Service Grazinglands Research Laboratory El Reno, OK Jeanne M. Schneider USDA Agricultural Research Service Grazinglands Research Laboratory El Reno, OK

But first, a quick review/tutorial on probability of exceedance functions…. which illustrates why someone might want to use them.

Probability of Exceedance = 1 - (cumulative probability density function) "a posteriori"

Precipitation (inches) Probability of Exceedance (%) 67% 3.2 Two in three chance for more than 3.2 inches.

Precipitation (inches) Probability of Exceedance (%) 67% 3.24.2 50% Two in three chance for more than 3.2 inches. Fifty-fifty chance for more than 4.2 inches.

Precipitation (inches) Probability of Exceedance (%) 67% 33% 3.26.14.2 50% Two in three chance for more than 3.2 inches. Fifty-fifty chance for more than 4.2 inches. One in three chance for more than 6.1 inches.

Precipitation (inches) Probability of Exceedance (%) 67% 33% 3.26.14.2 50% Two in three chance for more than 3.2 inches. Fifty-fifty chance for more than 4.2 inches. One in three chance for more than 6.1 inches. If you can associate a potential financial loss with each of these outcomes, then you have a definition of risk.

So, what do we plan to do when the NOAA/CPC forecasts offer nothing beyond the 30-year climatology ("EC")? Or when the forecast skill is so low as to preclude practical use? Do we have any other options for climate- conditioned decision support for agriculture in areas where ENSO impacts are marginal? So, what do we plan to do when the NOAA/CPC forecasts offer nothing beyond the 30-year climatology ("EC")? Or when the forecast skill is so low as to preclude practical use? Do we have any other options for climate- conditioned decision support for agriculture in areas where ENSO impacts are marginal? Continuing where we left off last year….

0 0.2 0.4 0.6 0.8 1 01234567891011 Dry periods 1900-1904 1909-1917 1936-1939 1952-1956 1963-1972 1976-1980 Wet periods 1905-1908 1941-1945 1957-1961 1982-2000 Probability of Exceedance of September Precipitation Climate Division 3405; Central Oklahoma; 1895-2005 Prob. of Exceedance Precipitation [in/mo] Dry Periods Mean = 3.2 [in] 38 Years Wet Periods Mean = 4.6 [in] 33 Years 1895-2005 Mean = 3.7 [in] 111 Years Preliminary data, subject to revision USDA-ARS-GRL

Assume that the earth/ocean/atmosphere system behaves as a chaotic system with a very large number of degrees of freedom, but that the net expression of that chaos on the precipitation processes at any location can be usefully described as a small collection of states (dry, wet, perhaps transition), where each state persists for several years. A faint flavor of chaotic dynamics:

Climate changes: To what degree does the past predict the future? Data is incomplete and imperfect: if a predictive signal exists in the phenomena, can we discern it in the record? Where, when, and how do we search for the signal? And on and on, ad nauseum…. Climate changes: To what degree does the past predict the future? Data is incomplete and imperfect: if a predictive signal exists in the phenomena, can we discern it in the record? Where, when, and how do we search for the signal? And on and on, ad nauseum…. But there are problems with statistical approaches to climate forecasts:

However, as has been noted by several presenters already: "….decisions have to be made".

We need monthly, location specific guidance relative to precipitation for producers, agricultural extension agents, and others involved with small to medium scale agriculture. To be useful in the near term, that guidance needs to be built using existing data and tools. We need monthly, location specific guidance relative to precipitation for producers, agricultural extension agents, and others involved with small to medium scale agriculture. To be useful in the near term, that guidance needs to be built using existing data and tools. The good news is that we have a couple of simplifying constraints:

Can we, or can we not, produce probabilistic monthly precipitation guidance for specific locations that is more reliable than the standard 30-year climatology, given data and tools currently in hand? Can we, or can we not, produce probabilistic monthly precipitation guidance for specific locations that is more reliable than the standard 30-year climatology, given data and tools currently in hand? The Acid Test:

Klaus Wolter's Experimental Climate Divisions http://www.cdc.noaa.gov/people/klaus.wolter/ClimateDivisions/

Klaus Wolter's Experimental Climate Divisions http://www.cdc.noaa.gov/people/klaus.wolter/ClimateDivisions/ Because these are based on precipitation variability, I will use these experimental climate divisions to define "location specific".

Century-scale monthly data from PRISM http://www.prism.oregonstate.edu/ Select a grid point near the center of each experimental forecast division, and use the 103-year long PRISM time series data to define the decadal variations in precipitation. I will test different lengths of base record, de-trending options, and definitions of state and persistence.

Use individual station data to test the reliability of both the decadal and 30-year climatology as probabilistic forecasts over the last decade. Given the short period, use as many stations as possible within each experimental forecast division for the assessment. Use individual station data to test the reliability of both the decadal and 30-year climatology as probabilistic forecasts over the last decade. Given the short period, use as many stations as possible within each experimental forecast division for the assessment. NOAA/NCDC Coop Station Data

Conceptually, this is so simple it can be done graphically for each month and experimental forecast division. But realistically, we will build assessment PoEs from the station data, and adapt or develop measures of relative reliability. More next year….

Jeanne Schneider jeanneschneider@mac.com Jeanne.Schneider@ars.usda.gov 405-884-2656

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