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Shu Q.,State Oceanic Administration | Shu Q.,Key Laboratory of Marine Science and Numerical Modeling | Song Z.,State Oceanic Administration | Song Z.,Key Laboratory of Marine Science and Numerical Modeling | And 2 more authors.
Cryosphere | Year: 2015

The historical simulations of sea ice during 1979 to 2005 by the Coupled Model Intercomparison Project Phase 5 (CMIP5) are compared with satellite observations, Global Ice-Ocean Modeling and Assimilation System (GIOMAS) output data and Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS) output data in this study. Forty-nine models, almost all of the CMIP5 climate models and earth system models with historical simulation, are used. For the Antarctic, multi-model ensemble mean (MME) results can give good climatology of sea ice extent (SIE), but the linear trend is incorrect. The linear trend of satellite-observed Antarctic SIE is 1.29 (±0.57) × 105 km2 decade-1; only about 1/7 CMIP5 models show increasing trends, and the linear trend of CMIP5 MME is negative with the value of g-3.36 (±0.15) × 105 km2 decade-1. For the Arctic, both climatology and linear trend are better reproduced. Sea ice volume (SIV) is also evaluated in this study, and this is a first attempt to evaluate the SIV in all CMIP5 models. Compared with the GIOMAS and PIOMAS data, the SIV values in both the Antarctic and the Arctic are too small, especially for the Antarctic in spring and winter. The GIOMAS Antarctic SIV in September is 19.1 × 103 km3, while the corresponding Antarctic SIV of CMIP5 MME is 13.0 × 103 km3 (almost 32% less). The Arctic SIV of CMIP5 in April is 27.1 × 103 km3, which is also less than that from PIOMAS SIV (29.5 × 103 km3). This means that the sea ice thickness simulated in CMIP5 is too thin, although the SIE is fairly well simulated. © 2015 Author(s).

Shu Q.,State Oceanic Administration | Shu Q.,Key Laboratory of Marine Science and Numerical Modeling | Shu Q.,Ocean University of China | Ma H.,State Oceanic Administration | And 4 more authors.
Ocean Dynamics | Year: 2012

The drift trajectory of a floe near the North Pole (87° N, 175° W) was observed during 8-19 August, 2010 based on the fourth Chinese National Arctic Research Expedition. The trajectory of the floe showed circular motions superimposed on straight drift. Each cycle had a period of about 12 h. The circular motion is inertial oscillation. The largest amplitude of inertial oscillation speed can reach 20 cm/ s. After removing the inertial oscillation, the floe drift direction is about 40° on average to the right of the observed 10-m wind which is much larger than previous reports on the angle between sea-ice velocity and the geostrophic wind, and floe drift moves with a speed of about 1.4 % of the observed 10-m wind speed throughout the whole observation period. A simple dynamic sea ice-ocean coupled model and a threedimensional sea ice-ocean coupled model are employed to simulate the floe drift. Both numerical models are with the widely used quadratic water-drag formulation, i.e., the stress is proportional to the square of the ice velocity relative to the ocean surface current. The inertial oscillation of the floe is successfully simulated by the simple passive drag model, while the floe drift amplitudes simulated from the threedimensional model are relatively small. © Springer-Verlag 2012.

Ren G.,Ocean University of China | Ren G.,First Institute of Oceanography | Zhang J.,First Institute of Oceanography | Zhang J.,Key Laboratory of Marine Science and Numerical Modeling | And 3 more authors.
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2010

Because of the environment conditions of the ground targets have been changing when the remote sensing images are acquired, so it is known that labeled samples from one remote sensing image is almost imposable to be used in another image classification, because the spectral signatures are various. But once it can be done successfully it will lead to great resource preserving and high work efficiency. This article proposes a classification samples spatial-time domain expanding method to address this issue. In this method, we choose training samples from a reference image, then classify the images in space and time neighborhood of the reference image by the classifier which is trained with these labeled samples. Before classifying, the relative radio-correcting (or to say radiometric normalization) of the images to be classified need be done, and it is the key step. Three classification experiments, which were the reference image and the image need be classified have only different acquisition time, only different cover region, and both the different acquisition time and different cover region, are successfully carried out. The results prove that our method has done well in classification samples expanding application in time domain, in space domain and both in the two domains. © 2010 Copyright SPIE - The International Society for Optical Engineering.

Jiang L.,First Institute of Oceanography | Zhang J.,First Institute of Oceanography | Zhang J.,Key Laboratory of Marine Science and Numerical Modeling | Ma Y.,First Institute of Oceanography | Ma Y.,Key Laboratory of Marine Science and Numerical Modeling
European Space Agency, (Special Publication) ESA SP | Year: 2010

Texture is an inherent property of virtually all surfaces. It contains important information about the structural arrangement of surfaces and the relationships with the surrounding environment. Image texture is a kind of important feature for object recognition. The paper has taken the ENVISAT ASAR image in the Yellow River Delta as an example, and analyzed four typical wetland types (aquaculture, salt field, tidal flat, and reservoir) and two other land-use categories(farmland and sea) based on gray level co-occurrence matrix(GLCM). We ascertained the factors of GLCM on SAR wetland image such as orientation, displacement, quantization, and window size. The optimum orientation was average of the four orientations of 0°, 45°, 90° and 135°. The optimum quantization in these texture parameters was 64-level. The optimum window size was 13×13. With regard of the displacement, the optimum was 6. The textural analysis results indicated that reservoirs had lower gray value, marked boundary and the difference of texture curve between it and other wetland types; salt field and aquaculture had similarity on spatial structure and gray value, so their texture curve were close. Because the image had not been filtered before texture analysis, there were noise, the texture of sea was close to that of tidal flat. Farmland has a feature of higher gray value, complex spatial structure and confused orientation, so it was easy to distinguish it from others, although its texture feature was close to others.

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