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| DOI | 10.1155/2021/8278579 |
| A GA-Based BP Artificial Neural Network for Estimating Monthly Surface Air Temperature of the Antarctic during 1960-2019 | |
| Fang, Miao | |
| 通讯作者 | Fang, M (通讯作者)，Chinese Acad Sci, Northwest Inst Ecoenvironm & Resource, Lanzhou 730000, Peoples R China. |
| 发表日期 | 2021 |
| ISSN | 1687-9309 |
| EISSN | 1687-9317 |
| 卷号 | 2021 |
| 英文摘要 | The spatial sparsity and temporal discontinuity of station-based SAT data do not allow to fully understand Antarctic surface air temperature (SAT) variations over the last decades. Generating spatiotemporally continuous SAT fields using spatial interpolation represents an approach to address this problem. This study proposed a backpropagation artificial neural network (BPANN) optimized by a genetic algorithm (GA) to estimate the monthly SAT fields of the Antarctic continent for the period 1960-2019. Cross-validations demonstrate that the interpolation accuracy of GA-BPANN is higher than that of two benchmark methods, i.e., BPANN and multiple linear regression (MLR). The errors of the three interpolation methods feature month-dependent variations and tend to be lower (larger) in warm (cold) months. Moreover, the annual SAT had a significant cooling trend during 1960-1989 (trend = -0.07 degrees C/year; p=0.04) and a significant warming trend during 1990-2019 (trend = 0.06 degrees C/year; p=0.05). The monthly SAT did not show consistent cooling or warming trends in all months, e.g., SAT did not show a significant cooling trend in January and December during 1960-1989 and a significant warming trend in January, June, July, and December during 1990-2019. Furthermore, the Antarctic SAT decreases with latitude and the distance away from the coastline, but the eastern Antarctic is overall colder than the western Antarctic. Spatiotemporal inconsistencies on SAT trends are apparent over the Antarctic continent, e.g., most of the Antarctic continent showed a cooling trend during 1960-1989 (trend = -0.20 similar to 0 degrees C/year; p=0.01 similar to 0.27) with a peak over the central part of the eastern Antarctic continent, while the entire Antarctic continent showed a warming trend during 1990-2019 (trend = 0 similar to 0.10 degrees C/year; p=0.04 similar to 0.42) with a peak over the higher latitudes. |
| 关键词 | SPATIAL INTERPOLATIONMASS-BALANCEICE SHELVESCLIMATEPRECIPITATION21ST-CENTURYELEVATION |
| 语种 | 英语 |
| WOS研究方向 | Meteorology & Atmospheric Sciences |
| WOS类目 | Meteorology & Atmospheric Sciences |
| WOS记录号 | WOS:000667065400001 |
| 来源期刊 | ADVANCES IN METEOROLOGY
 |
| 来源机构 | 中国科学院西北生态环境资源研究院 |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/253949 |
| 作者单位 | [Fang, Miao] Chinese Acad Sci, Northwest Inst Ecoenvironm & Resource, Lanzhou 730000, Peoples R China |
| 推荐引用方式 GB/T 7714 | Fang, Miao. A GA-Based BP Artificial Neural Network for Estimating Monthly Surface Air Temperature of the Antarctic during 1960-2019[J]. 中国科学院西北生态环境资源研究院,2021,2021. |
| APA | Fang, Miao.(2021).A GA-Based BP Artificial Neural Network for Estimating Monthly Surface Air Temperature of the Antarctic during 1960-2019.ADVANCES IN METEOROLOGY,2021. |
| MLA | Fang, Miao."A GA-Based BP Artificial Neural Network for Estimating Monthly Surface Air Temperature of the Antarctic during 1960-2019".ADVANCES IN METEOROLOGY 2021(2021). |
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