ReferencesAustralian Government, Bureau of Meteorology web site, http://www.bom.gov.au/climate/. Accessed January 2007.Elliott, W. P. and J. K. Angell. 1988. Evidence for changes in Southern Oscillation relationships during the last 100 years. Journal of Climate 1:729-737.Hipel, K. W. and A. I. McLeod. 1994. Time Series Modelling of Water Resources and Environmental Systems. Elsevier, Amsterdam.National Center for Atmospheric Research, Climate & Global Dynamics Division, Climate Analysis Section web site: http://www.cgd.ucar.edu/cas/jhurrell/indices.html.

Accessed January 2007.NOAA, National Weather Service, National Centers for Environmental Prediction, Climate Prediction Center web site: http://www.cpc.noaa.gov/. Accessed January 2007.Trenberth, K. E. 1984. Signal versus noise in the Southern Oscillation. Monthly Weather Review 112:326-332.

Detecting climate signals in river discharge and precipitation data for the central Georgia coastJoan Sheldon* and Adrian Burd

Department of Marine Sciences, University of Georgia, Athens, GA, *email: jsheldon@uga.edu

AbstractIdentifying the effects of global change requires identifying global-scale signals in local data. We are seeking evidence of climate signals such as the Southern Oscillation Index (SOI) and the North Atlantic Oscillation Index (NAOI) in long-term data for the Georgia coast. The NAOI leads the SOI by 1 month and the two are very weakly correlated, so these two signals could explain different patterns in local data. Monthly standardized anomalies of Altamaha River discharge and Georgia coastal precipitation were constructed by normalizing, deseasonalizing, and detrending those data. Climate indices were also transformed if necessary, and all series were prewhitened prior to cross-correlation analyses to determine the most appropriate lags between series. Altamaha discharge and coastal precipitation are weakly negatively correlated, suggesting that coastal and inland precipitation patterns are different. However, climate signals explain very little of the variability in the local data (R2 < 0.02). This analysis will be extended to other datasets for coastal Georgia.

IntroductionLarge-scale patterns in atmospheric pressure, circulation and oceanic temperatures have important influences on weather at global and regional scales.

El Niño / Southern Oscillation (ENSO)The atmospheric component of this well-known pattern, the Southern Oscillation, is reflected in air pressure differences between the western and eastern tropical Pacific. The Southern Oscillation Index (SOI), one measure of this pattern, is usually calculated based on the anomaly (difference from normal) in air pressure between Tahiti and Darwin, Australia and corresponds well with changes in eastern tropical Pacific Ocean temperatures.

North Atlantic Oscillation (NAO)This prominent large-scale pattern describes fluctuations in air pressure between the higher and central latitudes of the North Atlantic Ocean, with one region over Greenland and the other spanning the central North Atlantic, eastern U.S. and western Europe between latitudes 35°N and 40°N. The NAO is associated with changes in the intensity and location of the North Atlantic jet stream, storm tracks, and patterns of temperature and precipitation from eastern North America to western and central Europe.

Map from NOAA, National Weather Service, National Centers for Environmental Prediction, Climate Prediction Center web site.

Georgia Coastal Ecosystems Long-Term Ecological Research(GCE-LTER) ProjectThe project study site, located on the Georgia coast in the vicinity of Sapelo Island, encompasses the lower Altamaha River estuary, Doboy Sound, and Sapelo Sound. The Altamaha River watershed is one of the largest watersheds on the east coast of the U.S. (36,718 km2) and delivers freshwater to the coast at an average rate of 400 m3 s-1, whereas Sapelo and Doboy Sounds receive little or no direct riverine inflow and are influenced more by local rainfall and runoff. The first six years of the project have focused on the differences and interactions among these three estuaries, especially with regard to the effects of these differences in freshwater inputs.

The ENSO and NAO may be expected to affect the GCE study site by different pathways. Pacific phenomena (ENSO) would propagate primarily via weather fronts from the west, reaching the Altamaha River watershed before reaching the GCE site itself. We might expect the SOI to be better correlated with Altamaha River discharge (as an integrator of watershed effects) than with coastal precipitation. The NAO indices (NAOI), based on Atlantic phenomena from the east, might be better correlated with coastal precipitation. However, the GCE study site is not in the higher correlation regions for either index. The purpose of this study is to determine if these two large-scale climate signals (SOI, NAOI) can be detected in observational data from the GCE site and surrounding area.

MethodsClimate signal indices are often calculated in slightly different ways depending on the sources and intended use of the data, but comparisons show that competing indices are generally well correlated (Elliott and Angell 1988). We used the SOI from the Australian Bureau of Meteorology (SOIaust) rather than one from NOAA because of its longer time series. We used two NAO indices: the shorter series from the NOAA National Centers for Environmental Prediction (NAOIncep) uses a more robust algorithm involving Rotated Principal Component Analysis (RPCA) of data from several stations, whereas the longer series from the National Center for Atmospheric Research (NAOIncar) uses only two stations in a simple difference of station pressure anomalies. We used data from the NWS station at Brunswick, GA, (BrunsPrecip) rather than the one on Sapelo Island for coastal precipitation because of its longer time series. Altamaha River discharge data (DoctDisch) was from the most downstream USGS station at Doctortown, GA.

The time series were processed for analysis of causality according to Hipel and McLeod (1994). Causality in this case is defined as: X causes Y if the present Y can be better predicted by using past values of X than by not doing so. River discharge and precipitation data were transformed to approximate normality, then each data value was deseasonalized by differencing from its respective monthly mean and dividing by its monthly std. deviation. These anomalies had standard normal distributions. The climate indices were already normalized anomalies, but with different variances due to different methods of calculation. These were transformed to standard normal as necessary. Series were then detrended, and finally autocorrelations were removed (“prewhitening”) to obtain residuals or “innovations” in the series. Cross-correlations between pairs of series were evaluated both before and after removing autocorrelations. Apparent cross-correlations of non-whitened series could be spurious due to autocorrelations within the individual time series (Elliott and Angell 1988; Hipel and McLeod 1994), but similar autocorrelations in two series could also reflect a common source of variability. Assuming that large-scale climate patterns would affect local observations within one year, we evaluated autocorrelations out to lags of 24 months, and cross-correlations out to 12 months. The SOI was more highly autocorrelated at short time scales than the NAOI, with a memory of 5 months vs. 1 month. River discharge was autocorrelated over 3 months, but coastal precipitation was not autocorrelated at the monthly scale.

Results and ConclusionsMany cross-correlations among the data have very low correlation coefficients (R) but are statistically significant only because of the large sample sizes. These cases are described as “weak” correlations with low explanations of variability (low R2 but p<0.05) as opposed to nonexistent (p>0.05). Only a few cross-correlations are both statistically significant and possibly substantial in effect. R and R2 values are reported for the transformed variables.

AcknowledgmentsWe thank Kristin Meehan and Sylvia Schaefer for providing maps of the GCE study area and the Altamaha River watershed, and Merryl Alber and Mark Ohman for discussions related to these analyses. This work was funded by the National Science Foundation through the Georgia Coastal Ecosystems LTER project (NSF Award OCE 99-82133).

Negative SOI, El Niño episode Positive SOI, La Niña episode

air pressure at Tahiti below normal above normal

air pressure at Darwin above normal below normal

eastern tropical Pacific Ocean abnormally warm abnormally cold

effect on southeastern U.S.

winter wet and cool dry and warm

summer relatively unaffected relatively unaffected

Positive NAO Negative NAO

air pressure at high latitudes below normal above normal

air pressure at central latitudes above normal below normal

effect on eastern U.S.

temperature above normal below normal

precipitation below normal above normal

Maps from NOAA, National Weather Service, National Centers for Environmental Prediction, Climate Prediction Center web site.

as supplied transforms

frequency years std. anomaly? frequency normality std. normal remove trend remove autocorr

at lags

SOIaust monthly 1876-2006 10 * std. anomaly

of pressure diffs

not necessary not necessary divide by 10 detrend linear 1-5 mo

NAOIncep monthly 1950-2006 from RPCA of

std. pressure anomalies

not necessary not necessary not necessary detrend linear 1

NAOIncar monthly 1865-2003 diff of std. pressure

anomalies

not necessary not necessary monthwise std. anomaly

of diffs

not necessary 1

DoctDisch daily mean rate 1931-2006 no monthly mean rate ln monthwise std. anomaly detrend linear 1-3

BrunsPrecip daily total 1931-2005 no monthly mean daily total inverse square monthwise std. anomaly not necessary not necessary

Lag (mo) / R SOIaust NAOIncep NAOIncar DoctDisch

SOIaust ----------

NAOIncep -1 / -0.085 ----------

NAOIncar none 0 / 0.702 ----------

DoctDisch 2 / -0.156 7 / 0.079 none ----------

BrunsPrecip 1 / 0.144 -8 / 0.083 -8 / 0.103 -1 / -0.411

Cross-correlations prior to whitening series

Lag (mo) / R SOIaust NAOIncep NAOIncar DoctDisch

SOIaust ----------

NAOIncep -1 / -0.127 ----------

NAOIncar -1 / -0.057 0 / 0.702 ----------

DoctDisch 2 / -0.085 none -4 / 0.077 ----------

BrunsPrecip 8 / 0.108 -8 / 0.083 -8 / 0.100 (-1-)0 / -0.312

Cross-correlations after whitening series

Altamaha discharge and Brunswick precipitation are only weakly explained by the SOI (R2=0.02, lags 2 and 1 month, respectively), using the cross-correlation prior to whitening the series. Using the whitened series, these relationships become even weaker (R2=0.01). NAO indices explain even less of the variability in discharge and precipitation (R2<= 0.01).

As expected, the two NAO indices are positively correlated, with no lag, when they overlap (R=0.7). This is not as high as might be expected but is probably related to the fact that the NCAR index uses two stations whereas the NCEP index uses a RPCA of many stations. The SOI is not highly correlated with either NAO index (0>R>-0.1). Slight but significant negative correlations indicate that the NAO indices may lead the SOI by 1 month. The lack of substantial correlation between the SOI and the NAOI means that these two indicators could potentially explain different patterns of variability in local data.

Altamaha discharge and Brunswick precipitation are weakly negatively correlated, with coastal precipitation leading the river discharge series by up to 1 month (R=(-0.3)-(-0.4)). Coastal precipitation has an opposite pattern from freshwater delivery to the coast via the river (these data and D. Di Iorio, pers. comm.). This suggests that coastal and inland precipitation patterns are different and that both these weather patterns may influence freshwater delivery to the GCE site.

Future workLooking at monthly data and short-term lags are a starting point but they may not be the best frequency and time scale for detecting patterns in these data, if they exist. For example, similarly high correlations at multiple adjacent lags between some variables (even using the prewhitened data) suggest that it may be valuable to examine the data at a different frequency such as quarterly, in order to combine optimum lags spread over several months and to reduce the noise in the data (Trenberth 1984). Such broad correlation peaks were evident in several comparisons involving the Altamaha discharge data. Furthermore, some connections may be strong only in certain seasons; for example, the winter NAOI is often examined rather than the whole year. It may be beneficial to examine the data with regard to seasonal subsets of the indices.

In a few cases, prewhitening the data clarified a correlation that was apparent between the non-whitened series, but in other cases broad correlations at multiple adjacent lags in the non-whitened data disappeared in the analysis of residuals. This suggests that some apparent correlations may have been due to the interaction of two different autocorrelations. However, even if the cross-correlations of non-whitened series are real, the amount of variability in the local variables (discharge and precipitation) that is explained by climate indices is very low (2% by the SOI, less by NAOI). This supports the general observation that the GCE study site is not in the zones of greatest effects for either the ENSO or the NAO.

Doctortown

Brunswick

GCE-LTER

Altamaha River Sapelo

Island

Sapelo Snd.

Doboy Snd.

Time series processing