In terms of the characteristic topology parameters of climate complex networks,the spatial connection structural complexity of the circulation system and the influence of four teleconnection patterns are quantitatively described.Results of node degrees for the Northern Hemisphere (NH) mid-high latitude (30°N-90°N) circulation system (NHS) networks with and without the Arctic Oscillations (AO),the North Atlantic Oscillations (NAO) and the Pacific-North American pattern (PNA) demonstrate that the teleconnections greatly shorten the mean shortest path length of the networks,thus being advantageous to the rapid transfer of local fluctuation information over the network and to the stability of the NHS.The impact of the AO on the NHS connection structure is most important and the impact of the NAO is the next important.The PNA is a relatively independent teleconnection,and its role in the NHS is mainly manifested in the connection between the NHS and the tropical circulation system (TRS).As to the Southern Hemisphere mid-high latitude (30°S-90°S) circulation system (SHS),the impact of the Antarctic Arctic Oscillations (AAO) on the structural stability of the system is most important.In addition,there might be a stable correlation dipole (AACD) in the SHS,which also has important influence on the structure of the SHS networks.
In this paper we try to extract stable components in the extended-range forecast for the coming 10–30 days by using empirical orthogonal function(EOF) analysis, similarity coefficient, and some other methods based on the National Center for Environmental Prediction(NCEP)/National Center for Atmospheric Research(NCAR) reanalysis daily data. The comparisons of the coefficient of variance of climatological background field and truth data in winter between 2010 and 2011 are made. The method of extracting stable components and climatological background field can be helpful to increase forecasting skill. The forecasting skill improvement of air temperature is better than geopotential height at 500 hPa. Moreover, this method improves the predictability better in the Pacific Ocean. In China, the forecast in winter in Northeast China is more uncertain than in the other parts.
A climate network of six climate indices of the North Pacific air-sea system is constructed during the period of 1948-2009.In order to find out the inherent relationship between the intrinsic mechanism of climate index network and the important climate shift,the synchronization behaviour and the coupling behaviour of these indices are investigated.Results indicate that climate network synchronization happened around the beginning of the 1960s,in the middle of the 1970s and at the beginnings of the 1990s and the 2000s separately.These synchronization states were always followed by the decrease of the coupling coefficient.Each synchronization of the network was well associated with the abrupt phase or trend changes of annually accumulated abnormal values of North Pacific sea-surface temperature and 500-hPa height,among which the one that happened in the middle of the 1970s is the most noticeable climate shift.We can also obtain this mysterious shift from the first mode of the empirical orthogonal function of six indices.That is to say,abrupt climate shift in North Pacific air-sea system is not only shown by the phase or trend changes of climate indices,but also might be indicated by the synchronizing and the coupling of climate indices.Furthermore,at the turning point of 1975,there are also abrupt correlation changes in the yearly mode of spatial degree distribution of the sea surface temperature and 500-hPa height in the region of the North Pacific,which further proves the probability of climate index synchronization and coupling shift in air-sea systems.
With the influence of global warming,the global climate has undergone significant inter-decadal variation since the late 1970s.Although El Nio-Southern Oscillation(ENSO)has been the strongest signal for predicting global climate inter-annual variability,its relation with the summer rainfall in China has significantly changed,and its indicative function on the summer rainfall in China has weakened.This has led to a significant decrease in the accuracy rate of early conceptual prediction models for the Three Rainfall Patterns in the summer of eastern China.On the basis of the difference analysis of atmospheric circulation system configuration in summer,as well as the interaction of ocean and atmospheric in previous winter between two phases,i.e.before and after the significant global warming(1951 to 1978 and 1979 to 2012,respectively),we concluded that(1)Under different inter-decadal backgrounds,the atmospheric circulations that impacted the Three Rainfall Patterns in the summer of eastern China showed consistency,but in the latter phase of the global warming,the Western Pacific Subtropical High(WPSH)was on the strong side,the position of which was in the south,and the blocking high in the Eurasia mid-high latitudes was active,while the polar vortex extended to the south,and meridional circulation intensified.This circulation background may have been conducive to the increase of the circulation frequency of Patterns II and III,and the decrease of the circulation frequency of Pattern I,thus leading to more Patterns II and III and fewer Pattern I in the summer rainfall of eastern China.(2)In the former phase,the corresponding previous winter SST fields of different rainfall patterns showed visible differences.The impact of ENSO on North Pacific Oscillation(NPO)was great,and the identification ability of which on Patterns I and II of summer rainfall was effective.In the latter phase,this identification ability decreased,while the impact of ENSO on the Pacific/North American(PNA)teleconnection pattern increased,and
By establishing the Markov model for a long-range correlated time series (LRCS) and analysing its evolutionary characteristics,this paper defines a physical effective correlation length (ECL) τ,which reflects the predictability of the LRCS.It also finds that the ECL has a better power law relation with the long-range correlated exponent γ of the LRCS:τ=K exp(γ/0.3) + Y,(0 < γ < 1)-the predictability of the LRCS decays exponentially with the increase of γ.It is then applied to a daily maximum temperature series (DMTS) recorded at 740 stations in China between the years 1960-2005 and calculates the ECL of the DMTS.The results show the remarkable regional distributive feature that the ECL is about 10-14 days in west,northwest and northern China,and about 5-10 days in east,southeast and southern China.Namely,the predictability of the DMTS is higher in central-west China than in east and southeast China.In addition,the ECL is reduced by 1-8 days in most areas of China after subtracting the seasonal oscillation signal of the DMTS from its original DMTS;however,it is only slightly altered when the decadal linear trend is removed from the original DMTS.Therefore,it is shown that seasonal oscillation is a significant component of daily maximum temperature evolution and may provide a basis for predicting daily maximum temperatures.Seasonal oscillation is also significant for guiding general weather predictions,as well as seasonal weather predictions.
This paper refers to the CNOP-related algorithms and formulates the practical method and forecast techniques of extracting predictable components in a numerical model for predictable components on extended-range scales.Model variables are divided into predictable components and unpredictable chaotic components from the angle of model prediction error growth.The predictable components are defined as those with a slow error growth at a given range.A targeted numerical model for predictable components is established based on the operational dynamical extended-range forecast(DERF)model of the National Climate Center.At the same time,useful information in historical data are combined to find the fields for predictable components in the numerical model that are similar to those for the predictable components in historical data,reducing the variable dimensions in a similar judgment process and further correcting prediction errors of predictable components.Historical data is used to obtain the expected value and variance of the chaotic components through the ensemble forecast method.The numerical experiment results show that this method can effectively improve the forecast skill of the atmospheric circulation field in the 10–30 days extended-range numerical model and has good prospects for operational applications.
