Inter-comparison and evaluation of Arctic sea ice type products
<p>Arctic sea ice type (SITY) variation is a sensitive indicator of climate change. However, systematic inter-comparison and analysis for SITY products are lacking. This study analysed eight daily SITY products from five retrieval approaches covering the winters of 1999–2019, including purely...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2023-01-01
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| Series: | The Cryosphere |
| Online Access: | https://tc.copernicus.org/articles/17/279/2023/tc-17-279-2023.pdf |
| Summary: | <p>Arctic sea ice type (SITY) variation is a sensitive
indicator of climate change. However, systematic inter-comparison and
analysis for SITY products are lacking. This study analysed eight daily SITY
products from five retrieval approaches covering the winters of 1999–2019,
including purely radiometer-based (C3S-SITY), scatterometer-based (KNMI-SITY
and IFREMER-SITY) and combined ones (OSISAF-SITY and Zhang-SITY). These SITY
products were inter-compared against a weekly sea ice age product (i.e. NSIDC-SIA – National Snow and Ice Data Center sea ice age) and evaluated with five synthetic aperture radar (SAR) images. The
average Arctic multiyear ice (MYI) extent difference between the SITY
products and NSIDC-SIA varies from <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M1" display="inline" overflow="scroll" dspmath="mathml"><mrow><mo>-</mo><mn mathvariant="normal">1.32</mn><mo>×</mo><msup><mn mathvariant="normal">10</mn><mn mathvariant="normal">6</mn></msup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="59pt" height="14pt" class="svg-formula" dspmath="mathimg" md5hash="2aa22a9ffdc850c2c372527cc31a192d"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="tc-17-279-2023-ie00001.svg" width="59pt" height="14pt" src="tc-17-279-2023-ie00001.png"/></svg:svg></span></span> to <span class="inline-formula">0.49×10<sup>6</sup></span> km<span class="inline-formula"><sup>2</sup></span>. Among them, KNMI-SITY and
Zhang-SITY in the QuikSCAT (QSCAT) period (2002–2009) agree best with NSIDC-SIA and
perform the best, with the smallest bias of <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M4" display="inline" overflow="scroll" dspmath="mathml"><mrow><mo>-</mo><mn mathvariant="normal">0.001</mn><mo>×</mo><msup><mn mathvariant="normal">10</mn><mn mathvariant="normal">6</mn></msup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="65pt" height="14pt" class="svg-formula" dspmath="mathimg" md5hash="afce16036921b8afb86c180e43b2000f"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="tc-17-279-2023-ie00002.svg" width="65pt" height="14pt" src="tc-17-279-2023-ie00002.png"/></svg:svg></span></span> km<span class="inline-formula"><sup>2</sup></span> in first-year ice (FYI) extent and
<span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M6" display="inline" overflow="scroll" dspmath="mathml"><mrow><mo>-</mo><mn mathvariant="normal">0.02</mn><mo>×</mo><msup><mn mathvariant="normal">10</mn><mn mathvariant="normal">6</mn></msup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="59pt" height="14pt" class="svg-formula" dspmath="mathimg" md5hash="bd104094256357e92162c3b880dbf7b4"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="tc-17-279-2023-ie00003.svg" width="59pt" height="14pt" src="tc-17-279-2023-ie00003.png"/></svg:svg></span></span> km<span class="inline-formula"><sup>2</sup></span> in MYI
extent. In the Advanced Scatterometer (ASCAT) period (2007–2019), KNMI-SITY tends to
overestimate MYI (especially in early winter), whereas Zhang-SITY and
IFREMER-SITY tend to underestimate MYI. C3S-SITY performs well in some early
winter cases but exhibits large temporal variabilities like OSISAF-SITY.
Factors that could impact performances of the SITY products are analysed and
summarized. (1) The Ku-band scatterometer generally performs better than the C-band
scatterometer for SITY discrimination, while the latter sometimes identifies FYI more accurately, especially when surface scattering
dominates the backscatter signature. (2) A simple combination of scatterometer
and radiometer data is not always beneficial without further rules of
priority. (3) The representativeness of training data and efficiency of
classification are crucial for SITY classification. Spatial and temporal
variation in characteristic training datasets should be well accounted for in the
SITY method. (4) Post-processing corrections play important roles and should
be considered with caution.</p> |
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| ISSN: | 1994-0416 1994-0424 |