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Characterizing Multi-decadal temperature and precipitation variability in the Southeast U.S.

Characterizing Multi-decadal temperature and precipitation variability in the Southeast U.S. Marcus Williams August 4,2008. Outline. Objectives Background Temperature Trends Precipitation Trends Interannual Variability Observed Variability Scientific Questions Applications. Objectives.

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Characterizing Multi-decadal temperature and precipitation variability in the Southeast U.S.

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  1. Characterizing Multi-decadal temperature and precipitation variability in the Southeast U.S. Marcus Williams August 4,2008

  2. Outline • Objectives • Background • Temperature Trends • Precipitation Trends • Interannual Variability • Observed Variability • Scientific Questions • Applications

  3. Objectives • Characterize the nature of multi-decadal temp and precip variability in the Southeast U.S.(FL,GA,AL) • Seasonality • Spatial Coherence-Cluster analysis • Magnitude in comparison to other variability • Extremes • Correlation with known climate indices/teleconnect

  4. Temperature Trends • Easterling et al. 1997 • Analyzed monthly averaged min and max temperatures and DTR at 5400 observing stations around the world • Calculated anomalies from the mean of the base period of 1961 to 1985 for all station in a 5° x 5° lat-lon grid box

  5. Temperature Trends • Livezey and Smith 1998 • Used Rotated canonical correlation analysis between seasonal and longer mean global SST and U.S surface temps or 700 hPa heights in the Pacific North American region • Analysis found three distinct signals working a once • Interannual Variability(ENSO), Interdecadal variability(NAO), Global signal • Global signal was found to be greater than interannual variability. • Data used was gridded model data from 1950-1993 • Christy 2002 • Reconstructed the temperature data records for homogeneity purposes

  6. Temp Trends

  7. IPCC fourth assessment Global Historical Climatology Network: Peterson and Vose 1997 GHCN data interpolated to a 5° x 5° lat-lon grid box Precipitation has mostly increased over land in high northern latitudes, while decreases have dominated from 10°s to 30°n since 1970 Precipitation Trends

  8. D.B. Enfield et al. 2001 Examined the multidecadal and interannual behavior of precip over the U.S as they relate to the altering phase of oceanic AMO Data sets used were monthly reanalysis of global SST anomalies, and monthly rainfall over the U.S. summarized by climate divisions. P.O.R. was 1856-1999 Indexed the AMO with a ten-year running mean of Atlantic SSTA north of the Equator. Determined that there was a negative correlation between the AMO and Rainfall patterns over the U.S, with the exception of parts of the Northeast U.S. and Florida Precipitation Trends

  9. Interannual Variability • The El Nino/La Nina cycle is the predominant mode of year to year climate variability. Other modes include: • Pacific decadal oscillation • North Atlantic oscillation • Atlantic multidecadal oscillation • Ropewlewski and Halpert 1995 • Quantified the relasionship between precip patterns and ENSO • Found that median precipitation amounts shift on the order of 20 percentile points • Considerable spatial variations in the typical patterns of ENSO related precipitation percentiles in some regions • Sittel 1994 • Examined Max temp and total precip extemes of the ENSO cycle vs. Neutral across the U.S. during ten three month seasons during a year. • Bove 2000 • Examined the influence of the PDO on ENSO temp and precip anomalies for each ENSO/PDO subset • Each subset is then compared to it’s respective ENSO-only patterns • Concluded that positive PDO generally enhances ENSO anomaly patterns and negative PDO interferes with ENSO anomaly patterns.

  10. Data • DS 3200 and DS 3206 • DS 3200 is a daily summary of the day data set that consist of 8,000 active observing stations recording daily values of max temp, min temp and precip. • DS 3206 is the data rescue of data, converting paper records to digital copy’s • USHCN version 2 • The USHCN data set consist of approximately 1200 observing stations recording monthly values of max, min and mean temperature and precip. • Data is adjusted to remove biases due to station moves, instrument changes, and urbanization effects.

  11. Observed Variability

  12. Observed Variability

  13. Observed Variability

  14. Observed Variability

  15. Scientific Questions • Is this a physical cycle or low frequency noise • Can this variability be attributed to known climate oscillation • Any anthropogenic influences • Can knowledge of this variability lead to longer horizon predictability

  16. Applications • Agriculture • Extreme events • Length of growing season, chill accumulations • Water Resources • 5 -20 year planning window • Energy Planning

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