School of Geospatial Engineering and Science, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China
2.
Key Laboratory of Comprehensive Observation of Polar Environment (Sun Yat-sen University), Ministry of Education, Zhuhai 519082, China
3.
State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China
4.
Science and Technology Branch, Environment and Climate Change Canada, Toronto, ON M3H5T4, Canada
5.
Qingdao Innovation and Development Base (Centre) of Harbin Engineering University, Qingdao 266500, China
6.
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
7.
University of Chinese Academy of Sciences, Beijing 100049, China
Retrieval of thin-ice thickness (TIT) using thermodynamic modeling is sensitive to the parameterization of the independent variables (coded in the model) and the uncertainty of the measured input variables. This article examines the deviation of the classical model’s TIT output when using different parameterization schemes and the sensitivity of the output to the ice thickness. Moreover, it estimates the uncertainty of the output in response to the uncertainties of the input variables. The parameterized independent variables include atmospheric longwave emissivity, air density, specific heat of air, latent heat of ice, conductivity of ice, snow depth, and snow conductivity. Measured input parameters include air temperature, ice surface temperature, and wind speed. Among the independent variables, the results show that the highest deviation is caused by adjusting the parameterization of snow conductivity and depth, followed ice conductivity. The sensitivity of the output TIT to ice thickness is highest when using parameterization of ice conductivity, atmospheric emissivity, and snow conductivity and depth. The retrieved TIT obtained using each parameterization scheme is validated using in situ measurements and satellite-retrieved data. From in situ measurements, the uncertainties of the measured air temperature and surface temperature are found to be high. The resulting uncertainties of TIT are evaluated using perturbations of the input data selected based on the probability distribution of the measurement error. The results show that the overall uncertainty of TIT to air temperature, surface temperature, and wind speed uncertainty is around 0.09 m, 0.049 m, and −0.005 m, respectively.
Figure 1. The geographic locations of (a) the sonar sites, (b) the IceBridge footprints, (c) the IABP buoys, and (d) the wind speed stations used in this study.
Figure 2. Flowchart of the study scheme.
Figure 3. The relative deviation of the model output TIT using each test scheme (T1, T2, … T14) from the TIT obtained using the default scheme. The solid points are mean values of the deviation, and the vertical lines represent one standard deviation. The colors denote the upper limit of the different TIT bins, from 0.1 m to 0.5 m.
Figure 4. TIT from the 1-D model versus the ULS data, obtained for different TIT intervals. The solid dots represent the mean value from the model, and the error bars represent 0.5 standard deviation.
Figure 5. Comparison between the model-retrieved TIT using the combination of schemes T5 and T9 and the TIT from the ULS, IceBridge (IB), and SMOS/SMAP products, respectively. The Cmb. denotes the combination scheme. The interval between the data on the horizontal scale is 0.05 m. The error bars represent 0.5 standard deviation.
Figure 6. Comparisons of (a) ERA5 $ {T}_{a} $ versus IABP $ {T}_{a} $, (c) MODIS $ {T}_{s} $ versus IABP $ {T}_{s} $, and (e) ERA5 $ u $ versus NWS-observed $ u $, and their corresponding probability distributions of the differences in (b), (d) and (f), respectively, with fitted curves (black lines). The error bars represent one standard deviation. The numbers between brackets in (a) and (e) denote the data counts. Fig.s (a) and (c) share the same data counts. The numbers in (b), (d), and (f) at the peak of each curve are the mean value of the differences.
Figure 7. (a) Plot between $ {T}_{s} $ from MODIS and ERA5. (b) Histogram of the difference between the measurements. The shadow colors in (a) show the counts of data. The error bars represent one standard deviation. The black lines in (b) are the fitted Gaussian curves, and the number at the peak is the mean difference.
Figure 8. Plots showing the retrieved TIT obtained using the original $ {T}_{s} $, $ {T}_{a} $, and $ u $ as inputs (values in the horizontal axis) and the retrieved TIT with perturbations added representing the error in measurements from (a)$ {T}_{a} $ (with respect to IABP $ {T}_{a} $ ), (b) $ {T}_{s} $ (with respect to IABP $ {T}_{s} $) , (c) $ {T}_{s} $ (with respect to ERA5 skin temperature), and (d) $ u $ (with respect to the NWS/NDBC observations). The error bars represent one standard deviation. The shadow colors show the counts of the data points.