The Impact of Melting Ice on the Ecosystems in Greenland by Bo Qu

By Bo Qu

Arctic marine ecosystems are mostly impacted by way of adjustments linked to international warming. the ocean ice in Greenland Sea performs an incredible function in neighborhood and worldwide weather procedure. The publication examine the relationships among phytoplankton biomass, measured utilizing remotely sensed chlorophyll-a (CHL), aerosol optical intensity (AOD) and sea-ice disguise (ICE) within the Greenland Sea (20°W-10°E, 65-85°N) over the interval 2003-2012. First hand satellite tv for pc info was once used to do correlation research. more desirable records tools, resembling lag regression approach and cointegration research process are used for correlation and regression research among 2 variables (up to three variables). ARMA version was once used to prediction time sequence sooner or later three years. The ebook not just provides define of atmosphere in Greenland Sea, how the ice influence to the neighborhood ecosystems, but additionally presents worthy statistical equipment on research correlations and predicting the longer term ecosystems.

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Additional resources for The Impact of Melting Ice on the Ecosystems in Greenland Sea: Correlations on Ice Cover, Phytoplankton Biomass, AOD and PAR

Example text

We use EViews set up a distributed lag model. 0003) for CHL regression coefficient test. That means when ICE lagged 3 months behind, ICE had the most significant influence on CHL. This is consistent with the previews results. 9. 5922, respectively. 5844) is greater than the critical values; hence, we cannot refuse original hypothesis. This shows CHL had unit root which was non-stationary sequence. We then do unit root-test for the firstorder differential sequence. 8268 is less than all critical values; hence, it can refuse original hypothesis.

It is noticed that NAO was 3 months ahead of MI. This result is confirmed by Eviews. 21 is the weekly two time series for year 2010 in the same region. 21a). 21b). 57. It means with the increase of NAO, MI would decrease. On the other hand, with decrease of NAO, MI would increase. 21 Weekly time series of MI and NAO in 75°N–80°N in year 2010 If considering the positive correlations for monthly data, the time lag is much larger. For years 2011 and 2012, the time lag between MI and NAO is 3 months.

We do the regression analysis for the two variables: CHL and ICE, then check the smoothness of the regression residuals. 2406, less than the correspondent critical value. It shows the residuals sequence does not have unit root, it is stationary sequence. That is, CHL and ICE had co-integration relationship, that means CHL and ICE had long-term equilibrium relationship. 18. NAO had inter-annual variations. Apart from year 2010 where NAO was negative throughout the year, NAO in other years had more fluctuations through one year.

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