The Influence Of The ENSO And COWL On Global-mean Surface Temperatures

The earth is undergoing a continuous rising trend for nearly a century,. Somescientists think that the next50to100years the temperature rising trend will continue,this trend will impacts the ecology and economy of the world greatly, and it willthreats our survival and development. The cause of global warming is very complex,most of scientists think there are three main kinds of factors at work: the first iscaused by greenhouse gas produced by human activity; the second is the naturalfactors, including volcanic aerosols and the solar radiation; third is the influence ofthe time rate of change of internal climate system, such as ENSO, COWL. Analysis ofeach factor effects will help us to analysis and predict the global temperature changetrend.Based on the Optimum partition method and the Multichannel Singular SpectrumAnalysis, study is made in detail of the diagnosis analysis on phased global climatechange and its relation to the ENSO signals in terms of the relationship betweenglobal temperature and sea surface temperature(SST) in Nino areas. Results show that(1) there exist several distinct phases in the global temperature in the past100years;and the climatic variability differs evidently between adjacent phases;(2) the phasesof the global temperature have a comparatively obvious influence on ESNOinterannual and interdecadal quasi-periodical oscillations;(3) Markedquasi-four-and-two-year oscillations are revealed between the interannual variabilitiesof the global temperature and Nino area SST.The signal of temperature advection over the high latitudes of the NorthernHemisphere is manifested in the so-called ‘cold oceans–warm land’(COWL) pattern.Duringmonths with abnormally strong westerly winds at the surface, there isenhanced advection of relatively warm marine air masses over the colder continentsand cold continental air masses over the warmer oceans. The continents have a lower heat capacity than the oceans, hence the warming of the land exceeds the cooling ofthe ocean, and the global-mean temperature for that month is anomalously high.Months with abnormally weak surface westerlies are marked by global-meantemperature anomalies in the opposite sense. The COWL index time series accountsfor a substantial amount of the month-to-month weather-related ‘noise’ in the timeseries of global-mean surface temperatures, but also has weak secular variability duein part to trends in the atmospheric circulation.Finally, the influences of ENSO and the COWL pattern on surface temperatureswere removed by subtracting the linearly fitted ENSO and COWL index time seriesfrom the time series of global-mean surface temperatures using low-pass-filteredmethod. Filtering out ENSO and the COWL pattern reduces substantially the amountof interannual and month-to-month variance in the time series of global-mean surfacetemperatures without reducing its temporal resolution. Consequently, the resultingresidual global-mean temperature time series provides a cleaner rendition of theinterdecadal variability in the time series of twentieth-century global-meantemperatures while retaining and increasing the prominence of numerous discretedrops in it.After analysis and research, we draw the following conclusion: the influence ofENSO on the global temperature is mainly associated with interannual and theinfluence of COWL on the global temperature is mainly associated with month.bothof ENSO and COWL have little influence on the long-term trend of globaltemperature variability, while the influence of the greenhouse effect to larger timescales on the long-term trend of global temperature variability is very important.