Nonlinear Variations In Ocean And Climate Change

The system of ocean and climate is nonlinear and non-stationary. It can’t reflectthe inherent nonlinear characteristic of the system very well by only considering thelinear and smooth variability. The empirical mode decomposition can decompose thenonlinear and non-stationary data into several mode functions with the inherentphysical processes, by which we can analysis the physical processes of every modefunctions. This makes us have the opportunity to know the information andmechanisms of the data representation, understand the nonlinear and non-stationary ofthe physical phenomena of the data and its physical mechanisms. In this paper, weused Hilbert-Huang Transfrom (HHT) to study the nonlinear and non-stationarycharacteristics of seasonal cycle of satellite remote sensing observations. Andunderstand its corresponding physical mechanisms.In the first part of this study, we used Empirical Mode Decomposition (EMD)method to study seasonal variability and nonlinear trend of AERONET AerosolOptical Depth and PM10mass concentrations in Hong Kong during2005-2011. AODis highly correlated with PM10in semi-annual and annual time scales. On thesemi-annual scale, both AOD and PM10can capture the two maxima in March andOctober, respectively, with much stronger amplitude in March probably due to thelong-range transport of dust storm. On the annual cycle, the AOD and PM10, whichare negatively correlated with the precipitation and solar radiation, vary coherentlywith the maxima in February. This annual peak occurs about one month earlier thanthe first peak of the semi-annual variability in March, but with only half amplitude.During the research period, both AOD and PM10exhibit the pronounced decreasingtrend. The regression analysis showed a nonlinear relationship between both. In the second part of this study, we focused on the chlorophyll a, SST and seasurface height (SSH) observed by ocean satellite remote sensing. The nonlinearcharacteristics of the seasonal changes in the physical quantities are clearly provedbased on the HHT method. The results showed that the nonlinear seasonal variationsof the three quantities are corresponding to different physical processes. To take theregion of northern pacific in mid-latitude as an example, chlorophyll a has thesignificant nonlinear semi-annual and annual, while SST and SSH only possess theannual variation. For chlorophyll a, the pronounced nonlinear bimodal occur in Mayand October, respectively, and the second peak amplitude is remarkable higher thanthat of in May. While there are many differences of annual variation in every year,with the mean amplitude occurred in July, and its mean value (0.012mg/m3) is lessthan the second peak value of semi-annual (0.08mg/m3). And for SST, there is astationary annual variation, as the high value in the autumn and low value in thewinter. And its peak value is in August and September. Although the annual cyclecharacteristic of SSH looks similar to SST, its peak value lags one month behind thatof SST. The instantaneous frequency of both annual component of SST and SSH isquite difference in the rates of change of the peak, mainly displayed as the SST wasslow cooling after quick heated to peak, while the SSH changes on the contrary. Itwas this differences that reflect the discrepancy nonlinear physical processdetermining the seasonal cycle between SST and SSH.In the third part of this study, we detected the nonlinear trend of globalchlorophyll a observed from MODIS based on EMD method. And we discussed itsresponse mechanisms of dominating factors. The results showed that chlorophyll adisplayed a significant nonlinear increase trend, with its rising rate being rise first andthen fall in western pacific warm pool. In the pacific equatorial divergence region, thechlorophyll a manifest a pronounced nonlinear decrease trend, with the decline ratebeing accelerated first and then decelerate. Both not present the reverse phaserelationship with SST. All the results indicate that the correlation between both trendcomponent of SST and chlorophyll a still need to be further confirmed. And the effect of global warming resulted in human activity on chlorophyll a also remains a furtherprobe.The EMD method is applied to analysis multiple type data of ocean and climatederived from satellite remote sensing in this article. The study results showed thenonlinear characteristics of seasonal cycle, long-term trend of these physicalquantities. It is noted that the nonlinear process revealed in some observed data cannotbe simulated in the available model. To some extent, it reflects the impacts of theproblems, which are ignored in the past data analysis, on the model development.Therefore, the results of this article can contribute to improve the simulated capabilityof ocean and climate model.