The physical structures of snow and sea ice in the Arctic section of 150°-180°W during the summer of 2010

The physical structures of snow and sea ice in the Arctic section of 150°-180°W were observed on the basis of snow-pit, ice-core, and drill-hole measurements from late July to late August 2010. Almost all the investigated floes were first-year ice, except for one located north of Alaska, which was probably multi-year ice transported from north of the Canadian Arctic Archipelago during early summer. The snow covers over all the investigated floes were in the melting phase, with temperatures approaching 0℃ and densities of 295-398 kg/m3 . The snow covers can be divided into two to five layers of different textures, with most cases having a top layer of fresh snow, a round-grain layer in the middle, and slush and/or thin icing layers at the bottom. The first-year sea ice contained about 7%-17% granular ice at the top. There was no granular ice in the lower layers. The interior melting and desalination of sea ice introduced strong stratifications of temperature, salinity, density, and gas and brine volume fractions. The sea ice temperature exhibited linear cooling with depth, while the salinity and the density increased linearly with normalized depth from 0.2 to 0.9 and from 0 to 0.65, respectively. The top layer, especially the freeboard layer, had the lowest salinity and density, and consequently the largest gas content and the smallest brine content. Both the salinity and density in the ice basal layer were highly scattered due to large differences in ice porosity among the samples. The bulk average sea ice temperature, salinity, density, and gas and brine volume fractions were-0.8℃, 1.8, 837 kg/m3 , 9.3% and 10.4%, respectively. The snow cover, sea ice bottom, and sea ice interior show evidences of melting during mid-August in the investigated floe located at about 87°N, 175°W.     

Phylogenetic diversity of planktonic bacteria in the Chukchi Borderland region in summer

Planktonic bacteria are abundant in the Chukchi Borderland region. However, little is known about their diversity and the roles of various bacteria in the ocean. Seawater samples were collected from two stations K2S and K4S where sea ice was melting obviously. The analysis of water samples with fluorescence in situ hybridization (FISH) showed that DMSP-degrading bacteria accounted for 13% of the total bacteria at the station K2S. No aerobic anoxygenic phototrophic (AAP) bacteria were detected in both samples. The bacterial communities were characterized by two 16S rRNA gene clone libraries. Sequences fell into four major lineages of the domain Bacteria, including Proteobacteria (Alpha, Beta and Gamma subclasses), Bacteroidetes, Actinobacteria and Firmicutes. No significant difference was found between the two clone libraries. SAR11 and Rhodobacteraceae clades of Alphaproteobacteria and Pseudoalteromonas of Gammapro-teobacteria constituted three dominant fractions in the clone libraries. A total of 191 heterotrophic bacterial strains were isolated and 76% showed extracellular proteolytic activity. Phylogenetic analysis reveals that the isolates fell into Gammaproteobacteria, Bacteroidetes, Actinobacteria and Firmicutes. The most common genus in both the bacterial isolates and protease-producing bacteria was Pseudoalteromonas. UniFrac data showed suggestive differences in bacterial communities between the Chukchi Borderland and the northern Bering Sea.     

二维EMD 方法在微尺度波斜率波数谱提取中的应用

进行海面微尺度波频率分析时,为消除重力波影响,引入二维 EMD(经验模态分解)方法滤除造成频率混叠的长重力波,然后进行傅立叶变换,得到海面微尺度波谱,进一步处理得到斜率波数谱。斜率波数谱提取结果表明：采用二维EMD方法可以有效滤除图像中混叠的长波信息,在此基础上进行微尺度斜率波谱计算的结果与国外已有结果基本一致。文中还将二维 EMD 方法与小波方法的处理结果进行了比对,结果表明在滤除长波的影响时,二维EMD方法具有其较明显的优势。     

Accuracy Assessment of the TOPEX/Poseidon Ionosphere Measurements

We conducted an assessment of the TOPEX dual-frequency nadir ionosphere observations in the TOPEX/Poseidon (T/P) GDR by comparing TOPEX with the Center for Orbit Determination in Europe (CODE) Global Ionosphere Map (GIM), the climatological model IRI2001, and the DORIS (onboard T/P) relative ionosphere delays. We investigated the TOPEX (TOPEX Side A and TOPEX Side B altimeters, TSA and TSB, respectively) ionosphere observations for the time period 1995–2001, covering periods of low, intermediate, and high solar activity. Here, we use absolute path delays (at Ku-band frequency of the TOPEX altimeter and with positive signs) rather than Total Electron Content (TEC). We found significant biases between GIM and TOPEX (GIM–TOPEX) nadir ionosphere path delays: ?8.1 ± 0.4 {mm} formal uncertainties and equivalent to 3.7 TECu) and ?9.0 ± 0.7 {mm} (4.1 TECu) for TSA and TSB, respectively, indicating that the TOPEX path delay is longer (or with higher TECu) than GIM. The estimated relative biases vary with latitude and with daytime or nighttime passes. The estimated biases in the path delays (DORIS–TOPEX) are: ?10.9 ± 0.4 {mm} (5.0 TECu) and ?14.8 ± 0.6 {mm} (6.7 TECu), for TSA and TSB, respectively. There is a distinct jump of the DORIS path delays (?3.9 ± 0.7 {mm}, TSA delays longer than TSB delays) at the TSB altimeter switch in February 1999, presumably due to inconsistent DORIS processing. The origin of the bias between GIM (GPS, L-band) and TOPEX (radar altimeter, Ku-band) is currently unknown and warrants further investigation. Finally, the estimated drift rates between GIM and TSA, DORIS and TSA ionosphere path delays for the 6-year study span are ?0.4 mm/yr and ?0.8 mm/yr, respectively, providing a possible error bound for the TOPEX/Poseidon sea level observations during periods of low and intermediate solar activity.     

Altimeter Signal-to-Noise for Deep Ocean Processes in Operational Systems

The ocean signal for this study is the sea surface height due to the slowly varying (greater than 5-day) ocean processes, which are predominantly the deep ocean mesoscale. These processes are the focus of present assimilation systems for monitoring and predicting ocean circulation due to ocean fronts and eddies and the associated environmental changes that impact real time activities in areas with depths greater than about 200 m. By this definition, signal-to-noise may be estimated directly from altimeter data sets through a crossover point analysis. The RMS variability in crossover differences is due to instrument noise, errors in environmental corrections to the satellite observation, and short time period oceanic variations. The signal-to-noise ratio indicates that shallow areas are typically not well observed due to the high frequency fluctuations. Many deep ocean areas also contain significant high frequency variability such as the subpolar latitudes, which have large atmospheric pressure systems moving through, and these in turn generate large errors in the inverse barometer correction. Understanding the spatial variations of signal to noise is a necessary prerequisite for correct assimilation of the data into operational systems.     

Retracking of Jason-1 Data

We present the results of retracking 18 cycles (15 from the Jason-TOPEX collinear period) of Jason-1 data. We used the retracking method of Rodriguez which simultaneously solves for all relevant waveform parameters using a 26 Gaussian model of the altimeter point target response. We find significant differences from the Jason-1 Project retracking in the key parameters of range and significant wave height (SWH) in the second version of the Project SGDRs. The differences from the Jason-1 data have a strong dependence on off-nadir angle and some dependence on SWH. The dependence of range on SWH is what is called sea state bias. The retracking technique also estimates surface skewness. For Jason-1 with its very clean waveforms we make the first direct estimates of the skewness effect on altimeter data. We believe that the differences found here and thus in overall sea surface height are the result of the standard project processing using a single Gaussian approximation to the Point Target Response (PTR) and not solving simultaneously for off nadir angle. We believe that the relatively large sea state bias errors estimated empirically for Jason-1 during the cal/val phase result from sensitivity of quantities, particularly SWH, in project GDRs to off nadir angle. The TOPEX-Jason-1 bias can be determined only when a full retracking of Jason-1 is done for the collinear period.     

Evidence That Sea State Bias is Different for Ascending and Descending Tracks

Jason-1 and TOPEX/Poseidon (T/P) measured sea-surface heights (SSHs) are compared for five regions during the verification tandem phase. The five regions are of similar latitude and spatial extent and include the Gulf of Mexico, Arabian Sea, Bay of Bengal, and locations in the Pacific and Atlantic Oceans away from land. In all five regions, a bias, defined as Jason SSH—TOPEX-B SSH, exists that is different for ascending and descending tracks. For example, in the Gulf of Mexico the bias for ascending tracks was ?0.13 cm and the bias for descending tracks was 2.19 cm. In the Arabian Sea the bias for ascending tracks was ?2.45 cm and the bias for descending tracks was ?1.31 cm. The bias was found to depend on track orientation and significant wave height (SWH), indicating an error in the sea state bias (SSB) model for one or both altimeters. The bias in all five regions can be significantly reduced by calculating separate corrections for ascending and descending tracks in each region as a function of SWH. The correction is calculated by fitting a second-order polynomial to the bias as a function of SWH separately for ascending and descending tracks. An additional constraint is required to properly apply the correction, and we chose to minimize the sum of the TOPEX-B and Jason-1 root-mean-square (rms) crossover differences to be consistent with present SSB models. Application of this constraint shows that the correction, though consistent within each region, is different for each region and that each satellite contributes to the bias. One potential source that may account for a portion of the difference in bias is the leakage in the wave forms in TOPEX-B due to differing altitude rates for ascending and descending tracks. Global SSB models could be improved by separating the tracks into ascenders and descenders and calculating a separate SSB model for each track.     

The state‐of‐the‐art in marine navigation

Tide gauges distributed all over the world provide valuable information for monitoring mean sea level changes. The statistical models used in estimating sea level change from the tide gauge data assume implicitly that the random model components are stationary in variance. We show that for a large number of global tide gauge data this is not the case for the seasonal part using a variate-differencing algorithm. This finding is important for assessing the reliability of the present estimates of mean sea level changes because nonstationarity of the data may have marked impact on the sea level rate estimates, especially, for the data from short records.     

Traveling wavefront ray tracing and its applications

The current discussion of whether marine geodesy is something entirely new or part of oceanography or geodesy can be clarified by re‐examining our notions about the basic business of geodesy. This paper contends that the same basic geodetic services, which were needed for millenia to chart and control new territory, are being adapted now to the marine environment to suit modern accuracy standards. In a brief historical review it is shown that conceptually, the oceanic regions were always an integral part of geodetic concern. The oceans were certainly part of the world for the ancient map makers and for Pythagoras’ spherical earth model. Notions connected with the marine geoid were implied in ancient speculations. Distances and directions at sea, even depths, were determined to meet the requirements of the times. With the modern sophistication in geodetic theory and measuring techniques, these ideas became more refined and demanding in turn, but they were there all the time. For obvious reasons, land geodesy developed faster; marine geodesy will catch up now, because only now there is the technical capability and also an urgent economic motivation.     

Interannual Sea Level Variations in the South China Sea Over 1950–2009

Spatial patterns of interannual sea level variations in the South China Sea (SCS) are investigated by analyzing an EOF-based 2-dimensional past sea level reconstruction from 1950 to 2009 and satellite altimetry data from 1993 to 2009. Long-term tide gauge records from 14 selected stations in this region are also used to assess the quality of reconstructed sea levels and determine the rate of sea level along the coastal area. We found that the rising rate of sea levels derived from merged satellite altimetry data during 1993–2009 and past sea level reconstruction over 1950–2009 is about 3.9 ± 0.6 mm/yr and 1.7 ± 0.1 mm/yr, respectively. For the longer period, this rate is not significantly different from the global mean rate (of 1.8 ± 0.3 mm/yr). The interannual mean sea level of the SCS region appears highly correlated with Niño 4 indices (a proxy of El Niño-Southern Oscillation/ENSO), suggesting that the interannual sea level variations over the SCS region is driven by ENSO events. Interpolation of the reconstructed sea level data for 1950–2009 at sites where tide gauge records are of poor quality (either short or gapped) show that sea level along the Chinese coastal area is rising faster than the global mean rate of 1.8 mm/yr. At some sites, the rate is up to 2.5 mm/yr.