Parameterization, Sensitivity, and Uncertainty of 1-D Thermodynamic Thin-ice Thickness Retrieval

Tianyu Zhang Mohammed Shokr Zhida Zhang Fengming Hui Xiao Cheng Zhilun Zhang Jiechen Zhao Chunlei Mi

Tianyu Zhang, Mohammed Shokr, Zhida Zhang, Fengming Hui, Xiao Cheng, Zhilun Zhang, Jiechen Zhao, Chunlei Mi. Parameterization, Sensitivity, and Uncertainty of 1-D Thermodynamic Thin-ice Thickness Retrieval[J]. Acta Oceanologica Sinica. doi: 10.1007/s13131-023-2210-x
Citation: Tianyu Zhang, Mohammed Shokr, Zhida Zhang, Fengming Hui, Xiao Cheng, Zhilun Zhang, Jiechen Zhao, Chunlei Mi. Parameterization, Sensitivity, and Uncertainty of 1-D Thermodynamic Thin-ice Thickness Retrieval[J]. Acta Oceanologica Sinica. doi: 10.1007/s13131-023-2210-x

doi: 10.1007/s13131-023-2210-x

Parameterization, Sensitivity, and Uncertainty of 1-D Thermodynamic Thin-ice Thickness Retrieval

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  • 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.

    Table  1.   Summary of the previous uncertainty in TIR-TIT retrieval. The symbols are specified in the symbol list of Appendix 2. The data sources of AVHRR or MODIS are used for $ {T}_{s} $, and the National Centers for Environmental Prediction (NCEP), High Resolution Limited Area Model (HIRLAM), or ERA-Interim reanalysis data are the other meteorological input variables of the 1-D model. In the third column, the green color denotes the analysis scheme for the errors in the input variables, the values on the right side of the positive and negative signs ‘±’ are the analyzed variable’s errors, and the blue color is for comparing the sources of the different input variables.

    Reference Data source (data volume) Uncertainty analysis scheme Findings
    (Yu and Rothrock, 1996) AVHRR (13 images) $ {T}_{s} $ ± 1 K, $ {T}_{a} $ ± 1.6 K,
    $ u $ ± 2 m/s
    1. The uncertainty of the TIT caused by the error in the variables increases with the ice thickness.
    2. The uncertainty of the cumulative distribution is no more than 3% for ice thinner than 0.2 m.
    (Willmes et al., 2010) AVHRR + NCEP (1 image) $ {T}_{a} $ ± 5 K, $ u $ ± 3 m/s Results in max. TIT errors of ±20% (for TIT ≤ 0.5 m)
    (Wang et al., 2010) MODIS (case study) $ {T}_{s} $ ± 2 K, $ u $ ± 1 m/s For TIT < 0.3 m, TIT is −0.172 m or +0.179 m when $ {T}_{s} $+ 2 K or −2 K; TIT is +0.166 m or −0.133 m when $ u $+1 m/s or −1 m/s.
    (Mäkynen et al., 2013) MODIS + HIRLAM
    (199 images)
    The largest TIT uncertainty comes from air temperature.
    (Adams et al., 2013) MODIS + NCEP / COSMO (two-winter data) $ {T}_{s} $ ± 1.6 K, $ {T}_{a} $ ± 4.5 K;
    $ u $ ± 1.3 m/s, NCEP vs COSMO
    1. For all the variables’ uncertainties, the TIT varies by ±0.37 m for TIT of ≤ 0.5 m.
    2. The NCEP $ {T}_{a} $ leads to overestimated ice thicknesses, in comparison to COSMO $ {T}_{a} $.
    (Zeng et al., 2016) MODIS + ERA-Interim
    (four images)
    Altering the value of $ {T}_{a}-{T}_{s} $ 1. For $ {T}_{a}-{T}_{s} $ > +3 K, the $ {T}_{a}-{T}_{s} $ error of only +1 K causes the TIT to vary by +0.1 m.
    2. For $ {T}_{a}-{T}_{s} $ ≤ 0 K or > +2 K, the TIT decreases by 1 to 2 cm or 9 cm when U increases from 0 m/s to 12 m/s.
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    Table  2.   The specifications of the sonar measurements used in this study

    Data source Data form Number of sites Data period Number of samples Nominal error (cm)
    NPEO 10-min ice draft data 2 2002, 2005 10 10
    BGEP Daily average draft statistics 34 2003–2021 737 5–10
    IOS-EBS 4-min ice draft data 1 2003 1724 5
    IOP 5-min ice draft data 5 2005–2008 3305 10
    Note: NPEO is the abbreviation for the North Pole Environmental Observatory (Morison, 2009). BGEP denotes the Beaufort Gyre Exploration Project. IOS and EBS are the short forms for the Institute of Ocean Sciences and the Eastern Beaufort Sea, respectively (Melling and Riedel, 2008). IOP denotes the Integrative Observational Platforms from the University of Washington.
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    Table  3.   The station ID and geographic locations of the wind speed observation stations

    Data sourceStation IDLongitude, latitudeTemporal resolution
    NWS99950−8.46, 71.01 hour
    9971019.00, 74.50
    9972025.01, 76.51
    9974028.89, 78.91
    9975216.54, 76.47
    9979013.63, 78.07
    9992716.24, 80.06
    9993525.00, 80.65
    9993831.46, 80.10
    NDBCULRA2−160.78, 63.876 min
    PRDA2−148.53, 70.40
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    Table  4.   The parameterized variable test schemes. All the symbols and equations in the third and fourth columns are introduced in Appendix 1.

    Test scheme no. Parameter Default scheme Parameterization scheme
    (equation name / equation number)
    T1 Atmospheric emissivity ($ {\varepsilon }_{a} $) $ {\varepsilon }_{a\_\mathrm{J}\mathrm{X}06} $ (+$ {\mathrm{E}}_{\mathrm{A}\mathrm{E}96}^{\mathrm{\text{'}}} $) $ {\varepsilon }_{a\_\mathrm{E}\mathrm{F}61}(+{\mathrm{E}}_{\mathrm{A}\mathrm{E}96}^{\mathrm{\text{'}}}) $ Eq. A1 (+ Eq. A6)
    T2 $ {\varepsilon }_{a\_\mathrm{K}\mathrm{L}94} $ Eq. A2
    T3 $ {\varepsilon }_{a\_\mathrm{J}\mathrm{X}06} $ (+$ {\mathrm{E}}_{\mathrm{M}78} $) (denoted as $ {\varepsilon }_{a\_J/M} $) Eq. A3 (+ Eq. A4)
    T4 $ {\varepsilon }_{a\_\mathrm{J}\mathrm{X}06} $ (+$ {\mathrm{E}}_{\mathrm{A}\mathrm{E}96} $) ($ {\varepsilon }_{a\_J/A} $) Eq. A3 (+ Eq. A5)
    T5 Air density ($ {\rho }_{a} $, $ \mathrm{k}\mathrm{g}/{\mathrm{m}}^{3} $) $ {\rho }_{a}=1.3 $ $ {\rho }_{a\_GL} $ Eq. A7
    T6 Specific heat of air ($ {c}_{p} $, $ \mathrm{J}/(\mathrm{k}\mathrm{g}\cdot\mathrm{K}) $) $ {c}_{p}= $1004 $ {\mathrm{c}}_{\mathrm{p}\mathrm{w}} $ Eq. A8
    T7 Latent heat of sublimation ($ {L}_{s} $,$ \mathrm{J}/\mathrm{k}\mathrm{g} $) $ {L}_{s}=2.5\times {10}^{6} $ $ {L}_{sw} $ Eq. A9
    T8 Ice conductivity ($ {k}_{i} $, $ \mathrm{W}/(\mathrm{m}\cdot\mathrm{K}) $) $ {k}_{i}=2.03 $ $ {k}_{i\_\mathrm{U}64} $ (+$ {k}_{0\_\mathrm{N}64}+{S}_{i\_\mathrm{J}94} $) ($ {k}_{i\_UNJ} $) Eq. A10 (+Eq. A11 + Eq. A15)
    T9 $ {k}_{i\_\mathrm{U}64} $ (+$ {k}_{0\_\mathrm{S}78}+{S}_{i\_\mathrm{J}94} $) ($ {k}_{i\_USJ} $) Eq. A10 (+Eq. A12 + Eq. A15)
    T10 $ {k}_{i\_\mathrm{U}64} $ (+$ {k}_{0\_\mathrm{C}10}+{S}_{i\_\mathrm{J}94} $) ($ {k}_{i\_UCJ} $) Eq. A10 (+Eq. A13 + Eq. A15)
    T11 $ {k}_{i\_\mathrm{U}64} $ (+$ {k}_{0\_\mathrm{C}10}+{S}_{i\_\mathrm{C}74} $) ($ {k}_{i\_UCC} $) Eq. A10 (+Eq. A13 + Eq. A14)
    T12 Snow depth ($ {h}_{s} $, m) $ {h}_{s}=0 $ $ {h}_{s\_\mathrm{D}71}(+{k}_{s1}) $ Eq. A16
    T13 $ {h}_{s\_\mathrm{M}19}(+{k}_{s1}) $ Eq. A17
    T14 Snow conductivity ($ {k}_{s} $, $ \mathrm{W}/(\mathrm{m}\cdot\mathrm{K}) $) $ {k}_{s1}=0.33 (+{h}_{s\_M19}) $ $ {k}_{s2}=0.21(+{h}_{s\_\mathrm{M}19}) $ (+ Eq. A17)
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    Table  5.   The mean deviation of TIT (in %) for the 14 parameterization schemes with respect to the default scheme. Calculations are for the three shown ice thickness bins as well as the average TIT over the entire thickness range of 0–0.5 m.

    Test scheme0.0–0.1 m0.2–0.3 m0.4–0.5 m0–0.5 m
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    Table  6.   The sensitivity of the model’s output to increments in TIT of 0.1 m for the 14 parameterization equation schemes

    Test schemeSensitivity (%)Test schemeSensitivity (%)
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    Table  7.   The deviation (in percentage) of the model TIT with respect to the ULS TIT based on the different parameterization schemes. The deviation was calculated according to Eq. (7) in Section 3.

    SchemeIce bin (cm)
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    Table  8.   Statistics of the difference between the model and ULS TIT based on the different parameterization schemes.

    Scheme Bias (m) RMSE (m) MAE (m) $ {\mathrm{R}}^{2} $ $ \rho $
    Def. 0.027 0.103 0.088 0.741 0.861
    T1 0.031 0.101 0.086 0.766 0.875
    T2 0.032 0.106 0.089 0.693 0.832
    T5 0.010 0.100 0.087 0.766 0.875
    T8 0.028 0.101 0.088 0.755 0.869
    T9 0.018 0.106 0.091 0.663 0.814
    T12 0.004 0.120 0.102 0.367 0.606
    T13 −0.004 0.120 0.102 0.407 0.638
    T14 −0.018 0.118 0.102 0.511 0.715
    *footnote: RMSE = root-mean-square error, MAE = mean absolute error, $ {R}^{2} $ is the coefficient of determination, $ \rho $ is the Pearson’s linear correlation coefficient. The p-value of rho for each scheme is no more than 0.05.
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    Table  9.   Bias of $ {T}_{a} $ (ERA5 minus IABP measurements) and $ {T}_{s} $ (MODIS minus IABP) for the different ice bins and temperature bins. Correlation coefficients $ \rho $ of the data from the two sources are also shown.

    Ice bin (m)Bias for different temperature bins (K)Bias (K)$ \rho $
    $ {T}_{a} $0.0–0.1-−2.08−1.17−1.96−2.84−3.59−2.240.86
    $ {T}_{s} $0.2–0.35.713.94−0.50−3.69--−0.570.47
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    Table  10.   Statistics of the difference between ERA5 $ u $ and the observed $ u $ for different ice bins and wind speed bins

    Ice bin (m)Bias for different wind speed bins (m/s)Bias (m/s)$ \rho $
    $ u $0.0–0.10.93−1.1−3.61−5.12−1.170.65
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    Table  11.   The uncertainty (bias, m) of TIT due to the uncertainty of the input variables of $ {T}_{a} $, $ {T}_{s} $, and $ u $. ​Columns 2–5 correspond to the uncertainty of the input variables in Fig.s 6b, 6d, 6f, and 7b.

    Ice bin (m)$ {T}_{a} $$ {T}_{s}(\mathrm{v}\mathrm{s}.\mathrm{ }\mathrm{I}\mathrm{A}\mathrm{B}\mathrm{P}) $$ {T}_{s}(\mathrm{v}\mathrm{s}.\mathrm{ }\mathrm{E}\mathrm{R}\mathrm{A}5) $$ u $
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  • 收稿日期:  2023-02-22
  • 录用日期:  2023-05-04
  • 网络出版日期:  2024-01-03