Temporal complexity of daily precipitation records from different atmospheric environments: Chaotic and Lévy stable parameters
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This study investigates the difference of the predictability ofseasonal mean precipitation with Japan Meteorological Agency (JMA) Atmospheric General Circulation Model (AGCM), using two types of prescribed sea surface temperature (SST). One set of seasonal prediction simulations is called “SMIP” (which stands for “Seasonal prediction Model Intercomparison Project”). In the SMIP, observed SSTs are prescribed. The other set is called “HINDCAST”. In the HINDCAST, SST anomalies at initial time are assumed to persist during the forecast period.The December-January-February (DJF) averaged precipitation predictability over the tropical Pacific in SMIP is higher than that in HINDCAST, as was expected. However, it was found that the June-July-August (JJA) averaged precipitation predictability over the western tropical Pacific in SMIP was lower than that in HINDCAST.In the western tropical Pacific, there is a negative correlation between the observed precipitation anomalies and SST anomalies in JJA. The observed precipitation anomalies in early summer (May-June-July) are well correlated with the SST anomalies in spring (March-April-May). The simulated precipitation anomalies are strongly influenced by local SST anomalies in the same period. Because of this observed lag-correlation between precipitations and SSTs, and the property ofsimulated precipitation by the model, JJA averaged precipitation predictability over the western tropical Pacific in HINDCAST is higher than that in SMIP.
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Abstract The connection between the local SST and precipitation (SST–P) correlation and the prediction skill of precipitation on a seasonal time scale is investigated based on seasonal hindcasts from the National Centers for Environmental Prediction (NCEP) Climate Forecast System version 2 (CFSv2). The results demonstrate that there is good correspondence between the two: precipitation skill is generally high only over the regions where SST–P correlation is positive and is low where SST–P correlation is small or weakly negative. This result has fundamental implications for understanding the limits of precipitation predictability on seasonal time scale and helps explain spatial variations in the skill of seasonal mean precipitation. Over the regions where atmospheric variability drives the ocean variability (and consequently the local SST–P correlation is weakly negative), the inherently unpredictable nature of atmospheric variability leads to low predictability for seasonal precipitation. On the other hand, over the regions where slow time scale ocean variability drives the atmosphere (and the local SST–P correlation is large positive), the predictability of seasonal mean precipitation is also high.
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The predictability of a chaotic series is limited to a few future time steps due to its sensitivity to initial conditions and the exponential divergence of the trajectories. Over the years, streamflow has been considered as a stochastic system in many approaches. In this study, the chaotic nature of daily streamflow is investigated using autocorrelation function, Fourier spectrum, correlation dimension method (Grassberger-Procaccia algorithm) and false nearest neighbor method. Embedding dimensions of 6-7 obtained indicates the possible presence of low-dimensional chaotic behavior. The predictability of the system is estimated by calculating the system's Lyapunov exponent. A positive maximum Lyapunov exponent of 0.167 indicates that the system is chaotic and unstable with a maximum predictability of only 6 days. These results give a positive indication towards considering streamflow as a low dimensional chaotic system than as a stochastic system.
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Divergence (linguistics)
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