Statistical Analysis in Climate Research

Storch, Hans von; Zwiers, Francis W.

doi:10.1017/cbo9780511612336

Cited by 2,433 publications

(2,246 citation statements)

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“…The main patterns of covariability between the ocean and the atmosphere are investigated with a lagged maximum covariance analysis (MCA) (von Storch and Zwiers 1999). The MCA isolates pairs of spatial patterns and their associated time series by performing a singular value decomposition of the covariance matrix between two fields.…”

Section: Methodsmentioning

confidence: 99%

The Influence of the AMOC Variability on the Atmosphere in CCSM3

2013

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The influence of the Atlantic meridional overturning circulation (AMOC) variability on the atmospheric circulation is investigated in a control simulation of the NCAR Community Climate System Model, version 3 (CCSM3), where the AMOC evolves from an oscillatory regime into a red noise regime. In the latter, an AMOC intensification is followed during winter by a positive North Atlantic Oscillation (NAO). The atmospheric response is robust and controlled by AMOC-driven SST anomalies, which shift the heat release to the atmosphere northward near the Gulf Stream/North Atlantic Current. This alters the low-level atmospheric baroclinicity and shifts the maximum eddy growth northward, affecting the storm track and favoring a positive NAO. The AMOC influence is detected in the relation between seasonal upper-ocean heat content or SST anomalies and winter sea level pressure. In the oscillatory regime, no direct AMOC influence is detected in winter. However, an upper-ocean heat content anomaly resembling the AMOC footprint precedes a negative NAO. This opposite NAO polarity seems due to the southward shift of the Gulf Stream during AMOC intensification, displacing the maximum baroclinicity southward near the jet exit. As the mode has somewhat different patterns when using SST, the wintertime impact of the AMOC lacks robustness in this regime. However, none of the signals compares well with the observed influence of North Atlantic SST anomalies on the NAO because SST is dominated in CCSM3 by the meridional shifts of the Gulf Stream/ North Atlantic Current that covary with the AMOC. Hence, although there is some potential climate predictability in CCSM3, it is not realistic.

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Section: Methodsmentioning

confidence: 99%

The Influence of the AMOC Variability on the Atmosphere in CCSM3

2013

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“…5, we perform a Monte Carlo simulation to test following null-Hypothesis: the cross-correlation is zero for large positive lags. To perform this test, we generate a random m data set using a moving average model (see von Storch and Zwiers 1999). The generated data set has the same auto-correlation as the real m up to lag 6 and then is exactly zero afterwards (this is the same point where the real m auto-correlation appears to be negligible).…”

Section: Appendix B Statistical Significance Of Cross-correlationmentioning

confidence: 99%

Eddy–Zonal Flow Feedback in the Southern Hemisphere

2001

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The variability of the zonal-mean zonal wind in the Southern Hemisphere is studied using EOF analysis and momentum budget diagnostics of NCEP Reanalysis data (1978)(1979)(1980)(1981)(1982)(1983)(1984)(1985)(1986)(1987)(1988)(1989)(1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997). The leading EOF of the zonal-mean zonal wind is well separated from the remaining EOF's and represents the north/south movement of the mid-latitude jet. Analysis of the momentum budget shows that a positive feedback between the zonal-mean wind anomalies and the eddy momentum fluxes accounts for the unusual persistence of EOF1 and plays an important role in the selection of the leading EOF of mid-latitude variability. Further analysis also shows a propagating feedback, common to both EOF1 and EOF2, which is responsible for the poleward drift of wind anomalies with time. The observations support the following feedback mechanism: anomalous baroclinic wave activity is generated at the latitude of anomalous temperature gradient which, by thermal wind, coincides with the latitude of the anomalous zonal jet. The net propagation of baroclinic wave activity away from the jet gives momentum fluxes into the jet. This positive feedback is partially offset by low-frequency, equivalent barotropic eddies which propagate into the jet and remove momentum from it. The bias toward equatorward wave propagation on a sphere contributes to the poleward drift of the wind anomalies.1

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“…The daily mean parameter in (1) is equivalent to the seasonal mean. However, the variance of the seasonal mean temperature depends not only on the daily variance, but also on the first-order autocorrelation coefficient (e.g., chapter 17 in von Storch and Zwiers, 1999). The shift in the mean of daily minimum and maximum temperature, but not necessarily in the variance or autocorrelation, with the BHI (Tables VII and VIII) is consistent with the corresponding result that only the median (or mean) of seasonal mean temperatures is apparently related to the BHI (Figures 2-5).…”

Section: Aggregative Properties Of Stochastic Weather Modelsmentioning

confidence: 99%

Stochastic Modeling of the Effects of Large-Scale Circulation on Daily Weather in the Southeastern U.S.

Katz

Parlange

Tebaldi

2003

Issues in the Impacts of Climate Variability and Change on Agriculture

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Abstract. Statistical methodology is devised to model time series of daily weather at individual locations in the southeastern U.S. conditional on patterns in large-scale atmosphere-ocean circulation. In this way, weather information on an appropriate temporal and spatial scale for input to crop-climate models can be generated, consistent with the relationship between circulation and temporally and/or spatially aggregated climate data (an exercise sometimes termed 'downscaling'). The Bermuda High, a subtropical Atlantic circulation feature, is found to have the strongest contemporaneous correlation with seasonal mean temperature and total precipitation in the Southeast (in particular, stronger than for the El Niño-Southern Oscillation phenomenon). Stochastic models for time series of daily minimum and maximum temperature and precipitation amount are fitted conditional on an index indicating the average position of the Bermuda High. For precipitation, a multi-site approach involving a statistical technique known as 'borrowing strength' is applied, constraining the relationship between daily precipitation and the Bermuda High index to be spatially the same. In winter (the time of greatest correlation), higher daily maximum and minimum temperature means and higher daily probability of occurrence of precipitation are found when there is an easterly shift in the average position of the Bermuda High. Methods for determining aggregative properties of these stochastic models for daily weather (e.g., variance and spatial correlation of seasonal total precipitation) are also described, so that their performance in representing low frequency variations can be readily evaluated.

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Statistical Analysis in Climate Research

Cited by 2,433 publications

References 1 publication

The Influence of the AMOC Variability on the Atmosphere in CCSM3

The Influence of the AMOC Variability on the Atmosphere in CCSM3

Eddy–Zonal Flow Feedback in the Southern Hemisphere

Stochastic Modeling of the Effects of Large-Scale Circulation on Daily Weather in the Southeastern U.S.

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