In order to study the temporal variations of correlations between two time series,a running correlation coefficient(RCC)could be used.An RCC is calculated for a given time window,and the window is then moved sequentially through time.The current calculation method for RCCs is based on the general definition of the Pearson product-moment correlation coefficient,calculated with the data within the time window,which we call the local running correlation coefficient(LRCC).The LRCC is calculated via the two anomalies corresponding to the two local means,meanwhile,the local means also vary.It is cleared up that the LRCC reflects only the correlation between the two anomalies within the time window but fails to exhibit the contributions of the two varying means.To address this problem,two unchanged means obtained from all available data are adopted to calculate an RCC,which is called the synthetic running correlation coefficient(SRCC).When the anomaly variations are dominant,the two RCCs are similar.However,when the variations of the means are dominant,the difference between the two RCCs becomes obvious.The SRCC reflects the correlations of both the anomaly variations and the variations of the means.Therefore,the SRCCs from different time points are intercomparable.A criterion for the superiority of the RCC algorithm is that the average value of the RCC should be close to the global correlation coefficient calculated using all data.The SRCC always meets this criterion,while the LRCC sometimes fails.Therefore,the SRCC is better than the LRCC for running correlations.We suggest using the SRCC to calculate the RCCs.
The Arctic Ocean and Arctic sea ice have undergone a series of rapid changes. Oceanographic surveying has become one of the key missions of the Chinese National Arctic Research Expeditions since 1999. Using the data obtained in these surveys and from other sources, Chinese researchers have carried out a series of studies in the field of Arctic physical oceanography. The Near Sea-surface Temperature Maximum, freshwater content and heat flux in different regions of the Arctic have drawn wide attention from Chinese researchers. Arctic circulation is changing with the decline of sea ice, which is also influencing the structure and distribution of water masses. Studies have also focused on these issues. In this paper, the main results of research on water masses, currents, the structure of the upper ocean and other major hydrological phenomena over the past two decades are summarized.
In this study, we measured the droplet size distribution(DSD) and visibility of sea fog using a fog droplet spectrometer and visibility meter, respectively, during the July 23-August 3 and August 22-September 13 periods of the 2016 Chinese National Arctic Research Expedition. We calculated the visibility using the Mie theory and the DSD data and then compared the calculated with the observed visibility. The comparison shows that the deviations in the calculated visibility caused by DSD data sampling errors cannot be ignored. During navigation, wind and ship speeds tended to push or pull the sampled air and cause turbulence pulsation, which influenced the sampling of the fog droplet spectrometer. This influence is weak when the liquid water content(LWC) is high but becomes stronger as the LWC decreases. Taking the sailing speed and heading into consideration, the wind speed component parallel and perpendicular to the air inlet of the fog droplet spectrometer exhibit different laws in the deviation. By performing a fitting analysis of the calculated and observed visibilities under different wind speeds and wind directions, here, we present two sets of correction coefficients for the two wind-speed components and a method for correcting the calculated visibility. This correction method shows excellent results.
Oceanic heat flux(Fw) is the vertical heat flux that is transmitted to the base of sea ice. It is the main source of sea ice bottom melting. The residual method was adopted to study oceanic heat flux under sea ice. The data acquired by 28 ice mass balance buoys(IMBs) deployed over the period of 2004 to 2013 in the Arctic Ocean were used. Fw values presented striking seasonal and spatial variations. The average summer Fw values for the Canada Basin, Transpolar Drift, and Multiyear Ice area were 16.8, 7.7, and 5.9 W m^-2, respectively. The mean summer F-w for the whole Arctic was 10.1 W m^-2, which was equivalent to a bottom melt of 0.4 m. Fw showed an autumn peak in November in the presence of the near-surface temperature maximum(NSTM). The average Fw for October to December was 3.7 W m^-2. And the average Fw for January to March was 1.0 W m^-2, which was approximately one third of the average Fw in the presence of NSTM. The summer Fw was almost wholly attributed to the incident solar radiation that enters the upper ocean through leads and the open water. Fw calculated through the residual method using IMB data was compared with that calculated through the parameterization method using Autonomous Ocean Flux Buoy data. The results revealed that the Fw provided by the two methods were consistent when the sea ice concentration exceeded 70% and mixing layer temperature departure from freezing point was less than 0.15℃. Otherwise, the Fw yielded by the residual method was approximately one third smaller than that provided by the parameterization method.
This study used the synthetic running correlation coefficient calculation method to calculate the running correlation coefficients between the daily sea ice concentration(SIC) and sea surface air temperature(SSAT) in the Beaufort-Chukchi-East Siberian-Laptev Sea(BCEL Sea), Kara Sea and southern Chukchi Sea, with an aim to understand and measure the seasonally occurring changes in the Arctic climate system. The similarities and differences among these three regions were also discussed. There are periods in spring and autumn when the changes in SIC and SSAT are not synchronized, which is a result of the seasonally occurring variation in the climate system. These periods are referred to as transition periods. Spring transition periods can be found in all three regions, and the start and end dates of these periods have advancing trends. The multiyear average duration of the spring transition periods in the BCEL Sea, Kara Sea and southern Chukchi Sea is 74 days, 57 days and 34 days, respectively. In autumn, transition periods exist in only the southern Chukchi Sea, with a multiyear average duration of only 16 days. Moreover, in the Kara Sea, positive correlation events can be found in some years, which are caused by weather time scale processes.
