A general regression neural network approach to reconstruct global 1°×1° resolution sea surface pCO2
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摘要: 表层海水二氧化碳分压是评估海洋碳源汇强度的关键参数，但其实测数据较少、时空分布极不均匀，导致二氧化碳交换通量的估算有很大的不确定性，海洋源汇特征就不能确切获取。为了解决这个难题，在收集的表层大洋二氧化碳地图(Surface Ocean CO2 Atlas，SOCAT)实测数据集基础上，运用广义回归神经网络建立二氧化碳分压与经纬度、时间、温度、盐度和叶绿素浓度间的非线性关系，构建了1998−2018年间全球1°×1°经纬度的表层海水二氧化碳分压格点数据，其标准误差为16.93 μatm，平均相对误差为2.97%，优于现有研究中的前反馈神经网络、自组织映射神经网络和机器学习算法等方法。根据构建的数据所绘制的全球表层海水二氧化碳分压的分布与现有研究有较好的一致性。
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关键词:
- 广义回归神经网络 /
- 表层海水二氧化碳分压 /
- 全球大洋格点数据
Abstract: Sea surface partial pressure of carbon dioxide (pCO2) is a crucial parameter for estimating ocean carbon source and sink term, but its sparse and uneven in situ measurements in space and time lead to large uncertainty in the estimate of sea-air CO2 flux and characteristics of ocean carbon source and sink. To eliminate this uncertainty, a general regression neural network approach using the Surface Ocean CO2 Atlas (SOCAT) dataset, based on the non-liner regression of pCO2 and longitude, latitude, time, temperature, salinity and concentration of chlorophyll, was successfully used in the reconstruction of global 1°×1° resolution monthly sea surface pCO2 from 1998 to 2018, with a root mean square error (RMSE) of 16.93 μatm and a mean relative error (MRE) of 2.97%, lower than existing feed-forward neural network (FFNN), self-organizing neural network (SOM) and machine learning approaches. The global distribution of pCO2 obtained by this approach agrees well with existing researches. -
图 1 1998–2018年间SOCAT二氧化碳分压数据分布情况
Fig. 1 Spatial distribution of pCO2 observations in SOCAT dataset from 1998 to 2018
图 2 SOCAT pCO2实测数据时间分布
Fig. 2 Temporal distribution of pCO2 observations in SOCAT dataset
图 6 GRNN与实测值及其他神经网络方法同时间点pCO2数据结果对比
Fig. 6 Comparation of pCO2 distribution between in situ measurements, GRNN and other approaches
表 1 数据来源
Tab. 1 Data source
参数 时间范围 分辨率 数据来源 pCO2 1957−2018年 − Surface Ocean CO2 Atlas (https://www.socat.info/) 盐度 1940−2018年 1°×1° IAP, Global Ocean Heat Content Change (http://159.226.119.60/cheng/) 叶绿素 1997−2019年 1°×1° European Service for Ocean Colour, Globcolour Project (http://www.globcolour.info/) 温度 1981−2019年 0.25°×0.25° NOAA OI SST V2 High Resolution Dataset (https://www.esrl.noaa.gov/psd/data/gridded/data.noaa.oisst.v2.highres.html) 
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表 2 GRNN与其他方法误差对比
Tab. 2 Comparation of errors between GRNN and other approaches
区域 标准误差/μatm FFNN[13] 机器学习算法[17] SOM[11, 12, 18-19] SOM-FFNN[20-21] GRNN 全球大洋 17.97 9.10(墨西哥湾) 17.60～20.20 20.00～22.80 16.93(2.97%)b 全球大洋及近岸a − − − − 21.60(3.52%)b 北极 22.05 − − − 23.92(6.00%)b 近极地大西洋 22.99 − − − 21.39(3.49%)b 近极地太平洋 34.77 − − − 24.57(4.59%)b 亚热带大西洋 17.28 − − − 13.87(2.66%)b 亚热带太平洋 15.86 − − − 11.50(2.15%)b 赤道大西洋 17.27 − − − 14.26(2.52%)b 赤道太平洋 15.73 − − − 10.45(1.93%)b 南大西洋 17.81 − − − 15.37(2.60%)b 南太平洋 13.52 − − − 9.97(2.04%)b 印度洋 17.25 − − − 11.64(2.25%)b 南大洋 17.40 − − − 24.59(4.87%)b 近岸区域a − − − 42.40～48.00 46.87(8.83%)b 注：a表示此处近岸区域指水深小于200 m的海域；b表示括号内为本文方法的平均相对误差；−表示无数据；FFNN：前反馈神经网络；SOM：自组织映射神经网络；SOM-FFNN：自组织映射神经网络与前反馈神经联用法；GRNN：本文所使用的广义回归神经网络。
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