Retrieval And Application Of Arctic Sea Ice Thickness Based On Improved Snow Depth Model

Under the background of rapid global climate and environmental changes,changes in Arctic sea ice have a significant impact on the Arctic and global atmosphere,oceans,and ecosystems.Accurately obtain the information of the Arctic sea ice changes is the key to studying a range of major issues such as climate warming,extreme weather events,and environmental and ecological security in the polar regions.It is particularly urgent to strengthen the in-depth study of Arctic sea ice changes with the continuous and dramatic sea ice change.The thermal conductivity of snow covered on the surface of sea ice is less than an order of magnitude compared to sea ice,making it a significant insulation effect.Therefore,the thin snow cover on sea ice can effectively hinder the heat exchange between sea ice and the outside atmosphere,which has a great influence on the heat flux between the sea ice surface and the atmosphere,besides,the snow depth on sea ice is also a key parameter for retrieving sea ice thickness from satellite altimetry.The accuracy of snow depth estimation directly affects the estimation accuracy of sea ice thickness and volume.Meanwhile,highly accurate sea ice thickness from satellite altimetry will be beneficial to obtain the high-precision information of the change in sea ice thickness.Furthermore,it can provide basic information and reference for research in polar related fields,and provide important services and support for the development and utilization of Arctic waterway and the construction of“Silk Road on Ice”in China.At present,snow depth models on Arctic sea ice are based on early observation data.As the sea ice is rapidly shifting from multi-year ice to first-year ice rapidly,the error of the snow depth derived from the existing models is more and more prominent,Therefore,the model snow depths cannot reflect the accurate snow depth on Arctic sea ice.Meanwhile,the current model snow depths were used to calculate sea ice thickness from satellite altimeter?laser or radar?,thus,it has an important impact on the uncertainty of sea ice thickness estimation.In response to the above problems,this paper has carried out the following research:?1?Inter-calibration study of multi-generation passive microwave satellite data brightness temperaturesThe optimal calibration model of passive microwave remote sensing data F13-SSM/I and F17-SSMIS is discussed by comparing Meier and Cavalieri?MC?and Dai?DA?cross calibration methods.Analyzing the result anomaly of F17-SSMIS calibrated using F13 as the baseline and F13-SSM/I calibrated using F17 as the baseline,furthermore,a consistent long-time series brightness temperatures data set was construct.?2?Sea ice surface snow depth model reconstruction based on multi-source observation dataUsing A passive microwave brightness temperature data?TB?and airborne data?OIB?to construct the snow depth model?LPJF?on first-year?FY?sea ice.By comparing between LPJF derived snow depth?SD?and Markus model derived SD,furthermore,more accurate SD on FY sea ice will be obtained.Attempting to construct a multi-year?MY?sea ice surface SD model?LPJM?by using IMB SD observed by a material balance buoy.And compare LPJM SD and W99 SD,then,more accurate SD on MY sea ice will be obtained.?3?Comparison and evaluation of sea ice thickness from satellite altimeter based on different snow depth parametersThe paper set IMB sea ice thickness as verification data,and compare and evaluate the sea ice thickness?ICESat and CryoSat-2?based on LPJF+LPJM SD with sea ice thickness based on 0.5W99+W99 SD and Markus+W99 SD.Meanwhile,comparing and analyzing the sea ice thickness based on LPJF+LPJM SD with NASA-JPL sea ice thickness products and ESA-AWI sea ice thickness products,so as to obtain the high-precision sea ice thickness.?4?Arctic route planning study and system development considering sea ice thickness informationBased on the current Arctic shipping route which only considers the information of sea ice concentration,the research carried out the arctic shipping route planning considering multi-source ice condition parameters by integrating the information of sea ice thickness.By designing and developing a ship route planning system based on UWP platform,.NET framework and GIS development platform for realizing automatic routes planning and ship routes visualization.The main conclusions can be summarized as follows:?1?By comparing the Inter-calibration results of DA and MC methods,the thesis figures out that the accurate of the MC models are higher than DA models for the channel of 19H,19V,22V,37H and 37V.Besides,the results are better at F17-SSMIS calibrated using F13 as the baseline than using F17 as the baseline to calibrate F13-SSM/I.Choosing the F13-SSM/I as the baseline for constructing the consistent long-time series TB set is more suitable.?2?GR^H_ice?37H,19H?can be selected for use as the key parameter of LPJF model to calculate SD on FY sea ice.Meanwhile,the bias and RMSE of LPJM SD are smaller than that of W99 SD when compared to the IMB SD.It shows that LPJF model and LPJM model improve the estimation accuracy of the snow depth in the Arctic.?3?The comparison between the sea ice thickness?ICESat and CryoSat-2?based on LPJF+LPJM SD with sea ice thickness based on 0.5W99+W99 SD and Markus+W99 SD shows that the precision of sea ice thickness retrieved from LPJF+LPJM SD is highest and sea ice thickness retrieved from LPJF+LPJM SD is higher than the sea ice thickness products of NASA-JPL and ESA-AWI.?4?In the case of considering only the sea ice concentration single factor ice condition,the planned route passes through the sea ice area with low sea ice concentration value,as well as,ship routes based on sea ice concentration and sea ice thickness pass through sea ice areas with slightly higher sea ice concentration but thinner sea ice thickness,making the planned route shorter.Besides,the Arctic route planning system is developed with functions such as intelligent acquisition of sea ice information,dynamic route planning,and visualization of multi-dimensional sea ice information.