Large-ensemble simulations of the atmosphere-only time-slice experiments for the Polar Amplification Model Intercomparison Project (PAMIP) were carried out by the model group of the Chinese Academy of Sciences (CAS) Flexible Global Ocean-Atmosphere-Land System (FGOALS-f3-L). Eight groups of experiments forced by different combinations of the sea surface temperature (SST) and sea ice concentration (SIC) for pre-industrial, present-day, and future conditions were performed and published. The time-lag method was used to generate the 100 ensemble members, with each member integrating from 1 April 2000 to 30 June 2001 and the first two months as the spin-up period. All of these model datasets will contribute to PAMIP multi-model analysis and improve the understanding of polar amplification.
HE Bian
This data is the simulation of Antarctic sea ice density data from 2020 to 2100 under the medium emission scenario (ssp245) of the 6th International Coupled Model Comparison Program (CMIP6). The 25 mode data of CMIP6 were uniformly interpolated and then aggregated averaged. The size of sea ice density data is 0-1, the data time range is from January 2020 to December 2100, the time resolution is month, the spatial range is south of 45 ° S, and the spatial resolution is 1 ° × 1°。 This data provides the status and evolution of Antarctic sea ice under the medium emission scenario, and can provide reference for future changes in Antarctica.
LI Shuanglin, WANG Hui
(1) Data content: data set of Antarctic sea ice extent (Northernmost Latitude of Sea Ice Edge (NLSIE) [°N]) in the past 200 years; (2) Data source and processing method: the data is generated based on the statistical model using six annual resolution proxies (ice core MSA, accumulation rate, etc.); (3) Data quality description: annual resolution; Areas: Indian and western Pacific sector of the Southern Ocean (50 ° – 150 ° E, indwpac), Ross Sea (160 ° E – 140 ° W, RS), Amundsen Sea (90 ° – 140 ° W, as), Bellingshausen Sea (50 ° – 90 ° W, BS), Weddell Sea (50 ° W – 20 ° E, WS); (4) It can be used to study the interdecadal variability of Antarctic sea ice.
YANG Jiao
The data is the result of the prediction of Arctic sea ice density and sea ice coverage by the climate system model FGOALS independently developed by the project members. The correct selection of assimilation technology is an important factor for Arctic sea ice prediction. In the sea ice data assimilation technology, the singular value evolutionary interpolation Kalman filter (seik) is a relatively early but still commonly used filtering algorithm. However, due to the calculation of error covariance between all grid points, there is a false teleconnection error. Therefore, it is considered to develop a local filtering method to assimilate sea ice density and sea ice thickness. In the climate system model FGOALS, the project will initialize and process the sea ice thickness data retrieved by the European Space Agency (ESA) cryosat-2 and soil moisture and ocean salinity (SMOs) satellite remote sensing.
SONG Mirong
The data is the result of the prediction of Arctic sea ice density and sea ice coverage by the climate system model FGOALS independently developed by the project members. The correct selection of assimilation technology is an important factor for Arctic sea ice prediction. In the sea ice data assimilation technology, the singular value evolutionary interpolation Kalman filter (seik) is a relatively early but still commonly used filtering algorithm. However, due to the calculation of error covariance between all grid points, there is a false teleconnection error. Therefore, it is considered to develop a local filtering method to assimilate sea ice density and sea ice thickness. In the climate system model FGOALS, the project will initialize and process the sea ice thickness data retrieved by the European Space Agency (ESA) cryosat-2 and soil moisture and ocean salinity (SMOs) satellite remote sensing.
SONG Mirong
Snow over sea ice controls the energy budgets, affects the sea ice growth/melting, and thus has essential climatic effects. Snow depth, one of the fundamental properties of snow cover, is essential for understanding of the rapid change in Antarctic climate and for sea ice thickness estimation. Passive microwave radiometer can be used for basin-scale snow depth estimation in daily scale, however, previous published methods applied for Antarctic snow depth shows clear underestimation, which limits their further application. Here, we construct a new and robust linear regression equation for snow depth retrieval using microwave radiometers by including lower frequencies, and we produce the snow depth product over Antarctic sea ice from 2002 to 2020 from AMSR-E, AMSR-2, SSMIS based on this method. A regression analysis using 7 years of Operation IceBridge (OIB) airborne snow depth measurements shows that the gradient ratio (GR) calculated using brightness temperatures in vertical polarized 37 and 19 GHz, i.e., GR(37/7), is the optimal one for deriving Antarctic snow depth with an root mean square deviation (RMSD) of 8.92 cm and a correlation coefficient of -0.64, the related equation coefficients are then derived. GR(37/19) is used to retrieve snow depth from SSMIS data to fill the observation gaps between AMSR-E and AMSR-2, and the estimated snow depth is corrected for the consistence with these from AMSR-E/2. An averaged uncertainty of 3.81 cm is found based on a Gaussian error propagation, which accounts for 12% of the estimated mean snow depth. The evaluation of proposed method with in-situ measurements from Australian Antarctic Data Centre shows that the proposed method outperforms the previous available method, with a mean difference of 5.64 cm and an RMSD of 13.79 cm, comparing to -14.47 cm and 19.49 cm. Comparison to shipborne observations from Antarctic Sea Ice Processes and Climate indicates that the proposed method shows slight better performance than previous method (RMSDs of 16.85 cm and 17.61 cm, respectively); and comparable performances in growth and melting seasons suggests that the proposed method can still be used in the melting season. We generate a complete snow depth product over Antarctic sea ice from 2002 to 2020 in daily scale, and negative trends can be found in all sea sectors and seasons. This dataset can be further used in the reanalysis data evaluation, sea ice thickness estimation, climate model and other aspects.
SHEN Xiaoyi, KE Changqing
The original data of the Arctic and Antarctic sea ice data set is generated by the National Snow and Ice Data Center (NSIDC) through remote sensing data. The data format is GeoTIFF format and image format. The spatial resolution of the data is 25km and the time resolution is day. The data content is the sea ice range and sea ice density of the north and south poles. In this study, NetCDF format products are generated by post-processing the extent and density of sea ice in the north and south poles. The product data includes the sea ice range and sea ice density data of the north and south poles from 1979 to 2019. The time resolution is day by day, the coverage range is the South Pole and the north pole, and the horizontal spatial resolution is 12.5km. The data value of 1 in the sea ice range matrix indicates that the grid is sea ice, and the sea ice density is expressed by 0-1000. The grid value divided by 10 is the sea ice density value of the grid.
YE Aizhong
Sea ice is the ice formed by the freezing of sea water on the sea surface, and the re freezing of precipitation on the sea ice surface also becomes a part of sea ice. Sea ice changes not only affect the stratification, stability and convection of the ocean, but also affect the large-scale temperature and salt environment. In addition, due to the high albedo and insulation of sea ice, it can change the radiation state of the polar surface and affect the energy and material exchange between air and sea. The change of sea ice not only affects the local marine ecological environment and the local atmospheric environment, but also affects the weather and climate of other regions in the way of remote correlation through complex feedback process. Through the evaluation, this data set presents four parameters related to polar sea ice: sea ice density, range, thickness and albedo. To provide a basis for the study of polar and global climate change.
QIU Yubao
Because of its unique natural conditions and geographical location, the Arctic region plays a very important role in global change. Polar sea ice, as an important influencing factor of climate change, is a sensitive instrument of global climate change. The Yellow River Station, one of China's research stations in the Arctic, focuses on supporting the three scientific fields of global change and its regional response, the polar space environment and space climate, and the life characteristics and processes in the polar environment, providing an important platform for China's in-depth scientific research activities in the Arctic. Therefore, the product data set of data validation for key areas of Arctic sea ice in recent years is constructed to monitor the key areas of Arctic sea ice.
Chen Fu, QIU Yubao
1) This data is the reconstructed autumn sea ice from 1289 to 1993 in Barents Kara Sea, Arctic ; 2) Based on multiple statistical methods modeling, this sea ice time series is reconstructed by the ice core and tree ring proxy record; 3) This long term sea ice series is annual resolution and have a high reliability; 4) This data can help us know the historical changes of Arctic sea ice and its response and impact on climate change. The Barents Sea Kara Sea area is the key sea area where the extreme cold air flows southward in winter and spring in China. However, the lack of observation data limits our understanding of its mechanism. It is very important to reconstruct the characteristics of long-term Arctic sea ice change to study the Arctic sea ice change in the global context and its impact on China's historical climate.
XIAO Cunde
Under the summer sunlight, the snow covering the ice melts, forming different shapes and sizes of ice pools on the ice. The melting pool caused by the melting of the sea ice surface will reduce the sea ice albedo, which will have a significant impact on the energy balance in the polar region, increasing absorption and thus accelerating the sea ice melting process. Among the factors that affect the sea ice albedo, melting pool is one of the most important and most violent factors. With climate change, the rate of ice melting in summer is also getting faster and faster. The energy balance on the Earth's surface has a significant impact, and the acceleration of ice melting speed may also make the melting pool, an important natural phenomenon, one of the most significant ice surface features during the Arctic sea ice melting season. The albedo of melting pool is between sea water and sea ice. The study of melting pool on ice is also an important part of the study of the rapid change mechanism of Arctic sea ice. Due to the similar microwave signal characteristics between sea ice melting pools and the sea surface, and the significant uncertainty of using microwave data to map melting pool coverage due to factors such as wind speed and sea ice melting, the most reliable remote sensing method for melting pool coverage is to use medium resolution optical remote sensing data (such as MODIS) to map sub pixel melting pool coverage. This dataset includes the use of MODIS data for sub pixel decomposition inversion of Arctic sea ice melting pool coverage and sea ice concentration based on dynamic end element reflectance.
Xiong Chuan, REN Yan, QIU Yubao
The data sets include four sets of data obtained from the Scanning Multi-channel Microwave Radiometer (SMMR), Special Sensor Microwave Imager (SSM/I) and the Special Sensor Microwave Imager Sounder (SSMIS) sensors using passive microwave remote sensing inversion. SMMR was aboard the Nimbus-7 satellite, and its working period was from October 26, 1978 to July 8, 1987. Since July 1987, the data provided by the SSM/I and the SSMIS aboard the US Defense Meteorological Satellite Program (DMSP) satellite group have been used. The first three data sets contain sea ice concentration data, covering the Antarctic region with a spatial resolution of 25 km: (1) The data were obtained from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Version 1 by applying the NASA Team algorithm inversion. The temporal coverage is from November 1978 to February 2017, with a temporal resolution of one month. A bin file is stored every month. (2) The data source is the same as the first set. The temporal coverage is from 1978-10-26 to 2017-2-28. The temporal resolution is two days, and the spatial resolution is 25 km. A folder was stored every year, and a bin file was stored every other day. (3) The data were obtained from near-real-time DMSP SSMIS by applying the NASA Team algorithm inversion. The temporal coverage is from 2015-1-1 to 2018-2-3, and the temporal resolution is one day. A bin file is stored every day. Each file consists of a 300-byte file title (data time information, projection pattern, file name) and a 316*332 matrix. The fourth set of data is the sea ice coverage and sea ice area time series. The temporal coverage is from November 1978 to December 2017. This data set is a time series sequence of sea ice coverage and sea ice area in the Antarctic. The temporal resolution is one month, and an ASCII file is stored every month. Each file consists of a file title (time, data type), a 39*1 sea ice cover matrix and a 39*1 sea ice area matrix. For further details on the data, please visit the US Ice and Snow Data Center NSIDC website - Data Description http://nsidc.org/data/NSIDC-0051; http://nsidc.org/data/NSIDC-0081; http://nsidc.org/data/G02135
LI Shuanglin, LIU Na
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