The Correlation Between Qingdao Temperature Anomalies And Global Sea Surface Temperature Anomalies

With the aggravating of global warming, the land temperature variation is always ahot topic in the field of atmospheric research. There are different reasons according totemperature variation in different regions because they are complex and various. So, it isalways a important and difficult point in this field to find out the correlation between thetemperature variation predictability and the change features of the sensitive factors whichinfluence the regional temperature.The influencing factors of Qingdao city temperature are very various. Until now thereare no explicit results about the correlation between the global SST (sea surfacetemperature) and the land temperature which can support the forecast of Qingdao citytemperature. The impact of sea-air interaction to the Qingdao city temperature is not onlylimited to the El Nino region based on previous researches. The relationship between airtemperature anomalies and SST anomalies is not also limited to the Pacific or other thespecific sea areas. Therefore, it is necessary to make further research about influencingfactors of Qingdao city temperature from the worldwide SST anomalies.In this dissertation, historical data of Qingdao city temperature from160nationalstations and ERSST data is used to analyze the trend and cyclical changes of Qingdao citytemperature anomalies and global SST anomalies. The methods of SSA (singularspectrum analysis) and AR model are used to predict the Qingdao city temperature.Research found that the Qingdao city average temperature is significantly enhanced innearly30years, the series of temperature anomaly are reconstituted with the method of theSSA principal component analysis that the coincidence rate has reached0.7497, andreceive the main cycles of Qingdao city temperature anomaly are6.5years,2years,1yearand3or4months. The time series of three different lengths is used to forecast theQingdao city temperature anomaly with the method of AR model that the maximumcorrelation coefficient between live series and reconstitute series has reached0.9453which received a good prediction effect, and forecast the Qingdao city temperature anomaly of next three years with this method. Meanwhile, Research found that theaverage global SST appears rising trend in nearly61years through the research of theglobal SST data, the series of global SST anomaly are reconstituted with the method of theSSA principal component analysis that the coincidence rate has reached0.9897, andreceive the cycles of global SST anomaly are4.5years,2.5years,1.5years,1year and9months; The time series of three different lengths is used to forecast the global SSTanomaly with the method of AR model that the maximum correlation coefficient betweenlive series and reconstitute series has reached0.9306.Meanwhile, linear correlation and sliding correlation are used to find out thecorrelation of the Qingdao temperature anomalies and global SST anomaly according tothree kinds of time scales (61Years,30Years,10Years) and two kinds of time series(natural time series and monthly series). Research found that both of the linear correlationcoefficient and sliding correlation coefficient are not enough0.7according to the data ofnature time series (61Y,30Y,10Y) which is not enough to be a reference factor toforecast the Qingdao city temperature anomaly. The correlation coefficients according tothe monthly time series (61Y,30Y,10Y) have improved significantly, and the results of30years is better than the others, the maximum correlation coefficient has reached0.7880which prove there are a big correlation between them, the point in8N,49W and thesliding region is17, meanwhile, other region also have the correlation which above0.7.In this dissertation, two kinds of methods of SSA and AR model is used to forecastthe Qingdao city temperature anomaly which received a good prediction effect, andresearch the correlation of Qingdao temperature anomaly and global SST anomaly withthree kinds of time scales (61Y,30Y,10Y) and two kinds of time series (natural timeseries and monthly series), the results can be used as the reference factors to forecast theQingdao temperature anomaly, and provide more accurate reference for the Qingdaotemperature forecasting. The Concrete analysis about the influence on Qingdao citytemperature will be researched in the future study with the Conclusions from thisdissertation.