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北太平洋表层海水pH值的重建
作者:王洁1 2  毛景景1  吕阳阳1  王杰1  栾奎峰1 2 
单位:1. 上海海洋大学 海洋科学学院, 上海 201306;
2. 上海河口海洋测绘工程技术研究中心, 上海 201306
关键词:线性回归 BP神经网络 表层海水pH值 模型 重建 
分类号:P734.2+5
出版年·卷·期(页码):2023·40·第一期(46-56)
摘要:
以 1993-2018年北太平洋海表面温度(SST)、海表面盐度(SSS)、叶绿素a浓度(Chl-a)、二氧化碳分压(pCO2)等数据为基础,利用传统线性回归分析和 BP 神经网络算法,建立表层海水pH值的预测模型。结果表明:两种方法对于重建北太平洋表层海水pH值都能达到较高的精度,其中线性回归模型基于SSS、Chl-apCO2参数模拟最佳,BP神经网络模型基于SST、SSS、Chl-apCO2参数模拟最佳。对比两种最佳模型的均方根误差和拟合系数发现,BP神经网络模型优于线性回归模型。除此之外,最佳BP神经网络模型在4个季节的拟合效果均很好,不同季节的适用性远高于最佳线性回归模型。表层海水 pH 值受到多种因素的综合影响,与 pCO2、SST 呈负相关关系,与SSS、Chl-a呈正相关关系。应用最佳 BP神经网络模型重建北太平洋表层海水 pH 值发现,本研究模型的预测结果与已有研究、哥白尼欧洲地球观测计划数据、站点实测数据都存在很好的一致性,表层海水pH值冬季高于夏季,整体呈现西北高东南低的趋势。
Based on the data of sea surface temperature (SST), sea surface salinity (SSS), chlorophyll-a (Chl-a) concentration and carbon dioxide partial pressure (pCO2) in the North Pacific from 1993 to 2018, a prediction model for the pH value of surface seawater in the North Pacific is established using the traditional linear regression and the BP neural network algorithm. The results show that the two methods have good consistency for the reconstruction of the pH value of the surface seawater in the North Pacific. The linear regression model is of the best performance based on the parameters of SSS, Chl-a, pCO2, and the BP neural network model is of the best performance based on the parameters of SST, SSS, Chl-a and pCO2. Comparing the root mean square error and fitting coefficient of the two best models, it is found that the BP neural network model is better than the linear regression model. In addition, the applicability of the best BP neural network model in spring, summer, autumn and winter is much higher than that of the best linear regression model. The pH value of surface seawater is affected by many factors, which shows a negative correlation with pCO2 and SST and a positive correlation with SSS and Chl-a. Using the best BP neural network model to reconstruct the surface seawater pH value in the North Pacific, it is found that the prediction results of the model are in good agreement with the existing research, Copernicus Marine Environment Monitoring Service data and the measured site data. The pH value of the surface seawater in winter is higher than that in summer with the overall trend being higher in the northwest and lower in the southeast.
参考文献:
[1] 陈清华, 彭海君. 海洋酸化的生态危害研究进展[J]. 科技导报, 2009, 27(19):108-111. CHEN Q H, PENG H J. Ecological impact of ocean acidification[J]. Science & Technology Review, 2009, 27(19):108-111.
[2] 郭景腾. 15万年来热带西太平洋表层pH和pCO2演化及其影响因素[D]. 青岛:中国科学院大学(海洋研究所), 2015. GUO J T. The variations and controls of sea surface pH and pCO2 in the tropical western Pacific during the last 150 Ka[D]. Qingdao:University of Chinese Academy of Sciences (Institute of Oceanography), 2015.
[3] 陈烈庭, 吴仁广. 太平洋各区海温异常对中国东部夏季雨带类型的共同影响[J]. 大气科学, 1998, 22(5):718-726. CHEN L T, WU R G. The joint effects of SST anomalies over different pacific regions on summer rainbelt patterns in eastern China[J]. Scientia Atmospherica Sinica, 1998, 22(5):718-726.
[4] HUANG H C, FENG R D, ZHU J, et al. Prediction of pH value by multi-classification in the Weizhou island area[J]. Sensors, 2019, 19(18):3875.
[5] GONSKI S F, HORWITH M J, ALBERTSON S, et al. Monitoring ocean acidification within state borders:lessons from Washington State (USA)[J]. Coastal Management, 2021, 49(5):487-509.
[6] 於维樱, 张灿影, 冯志纲, 等. 全球海洋酸化研究态势与最新进展分析[J]. 海洋科学集刊, 2016:296-307. YU W Y, ZHANG C Y, FENG Z G, et al. Bibliometric analysis on research trends and latest developments of global ocean acidification[J]. Studia Marina Sinica, 2016:296-307.
[7] 贺仕昌, 张远辉, 陈立奇, 等. 海洋酸化研究进展[J]. 海洋科学, 2014, 38(6):85-93. HE S C, ZHANG Y H, CHEN L Q, et al. Advances in the studies of ocean acidification[J]. Marine Science, 2014, 38(6):85-93.
[8] 唐启升, 陈镇东, 余克服, 等. 海洋酸化及其与海洋生物及生态系统的关系[J]. 科学通报, 2013, 58(14):1307-1314. TANG Q S, CHEN Z D, YU K F, et al. The effects of ocean acidification on marine organisms and ecosystem[J]. Chinese Science Bulletin, 2013, 58(14):1307-1314.
[9] 李福荣. 1985年8月黄河口邻近海区海水pH的分布特征及影响因素[J]. 海洋湖沼通报, 1988(4):33-38. LI F R. The variation and distribution of pH and its effect factors at Yellow River estuary in August 1985[J]. Transactions of Oceanology and Limnology, 1988(4):33-38.
[10] 石强, 杨鹏金, 霍素霞, 等. 近36年来渤海海水酸化进程[C]//2013中国环境科学学会学术年会浦华环保优秀论文集. 昆明:中国环境科学学会, 2013:114-121. SHI Q, YANG P J, HUO S X, et al. Process of seawater acidification in Bohai Sea in recent 36 years[C]//2013, Academic Annual Meeting of the Chinese Society of Environmental Sciences. Kunming:China Society of Environmental Sciences, 2013:114-121.
[11] 杨顶田, 单秀娟, 刘素敏, 等. 三亚湾近10年pH的时空变化特征及对珊瑚礁石影响分析[J]. 南方水产科学, 2013, 9(1):1-7. YANG D T, SHAN X J, LIU S M, et al. Spatial and temporal distribution of pH in Sanya Bay in recent 10 years and its effects on coral reef[J]. South China Fisheries Science, 2013, 9(1):1-7.
[12] NAKANO Y, WATANABE Y W. Reconstruction of pH in the surface seawater over the North Pacific basin for all seasons using temperature and chlorophyll-a[J]. Journal of Oceanography, 2005, 61(4):673-680.
[13] ALIN S R, FEELY R A, DICKSON A G, et al. Robust empirical relationships for estimating the carbonate system in the southern California Current System and application to CalCOFI hydrographic cruise data (2005-2011)[J]. Journal of Geophysical Research:Oceans, 2012, 117(C5):C05033.
[14] LI B F, WATANABE Y W, YAMAGUCHI A. Spatiotemporal distribution of seawater pH in the North Pacific subpolar region by using the parameterization technique[J]. Journal of Geophysical Research:Oceans, 2016, 121(5):3435-3449.
[15] SRIDEVI B, SARMA V V S S. Role of river discharge and warming on ocean acidification and pCO2 levels in the Bay of Bengal[J]. Tellus B:Chemical and Physical Meteorology, 2021, 73(1):1971924.
[16] WOOTTON J T, PFISTER C A, FORESTER J D. Dynamic patterns and ecological impacts of declining ocean pH in a highresolution multi-year dataset[J]. Proceedings of the National Academy of Sciences of the United States of America, 2008, 105(48):18848-18853.
[17] GREGOR L, GRUBER N. OceanSODA-ETHZ:a global gridded data set of the surface ocean carbonate system for seasonal to decadal studies of ocean acidification[J]. Earth System Science Data, 2021, 13(2):777-808.
[18] 郭燕娟,杨修群. 全球海气系统年际和年代际变化的时空特征分析[J].气象科学, 2002, 22(2):127-138. GUO Y J, YANG X Q. Temporal and spatial characteristics of interannual and interdecadal variations in the global oceanatmosphere system[J]. Scientia Meteorologica Sinica, 2002, 22(2):127-138.
[19] FRIEDRICH T, OSCHLIES A. Neural network-based estimates of North Atlantic surface pCO2 from satellite data:A methodological study[J]. Journal of Geophysical Research:Oceans, 2009, 114(C3):C03020.
[20] LARUELLE G G, LANDSCHÜTZER P, GRUBER N, et al. Global high-resolution monthly pCO2 climatology for the coastal ocean derived from neural network interpolation[J]. Biogeosciences, 2017, 14(19):4545-4561.
[21] BOSTOCK H C, FLETALHER S E M, WILLIAMS M J M. Estimating carbonate parameters from hydrographic data for the intermediate and deep waters of the Southern Hemisphere oceans[J]. Biogeosciences, 2013, 10(10):6199-6213.
[22] SASSE T P, MCNEIL B I, ABRAMOWITZ G. A novel method for diagnosing seasonal to inter-annual surface ocean carbon dynamics from bottle data using neural networks[J]. Biogeosciences, 2013, 10(6):4319-4340.
[23] VELO A, PÉREZ F F, TANHUA T, et al. Total alkalinity estimation using MLR and neural network techniques[J]. Journal of Marine Systems, 2013, 111-112:11-18.
[24] DESPORTES C, GARRIC G, RÉGNIER C, et al. Quality information document for global ocean reanalysis multi-model ensemble products GREP[EB/OL]. (2019-01). https://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-GLO-QUID-001-031.pdf.
[25] LAMOUROUX J, PERRUCHE C, MIGNOT A, et al. Quality information document for global biogeochemical analysis and forecast product[EB/OL]. (2019-04-19). https://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-GLO-QUID-001-028.pdf.
[26] LO MONACO C, METZL N, FIN J, et al. Distribution and longterm change of the sea surface carbonate system in the Mozambique Channel (1963-2019)[J]. Deep Sea Research Part II:Topical Studies in Oceanography, 2021, 186-188:104936.
[27] 张景阳, 潘光友. 多元线性回归与BP神经网络预测模型对比与运用研究[J]. 昆明理工大学学报(自然科学版), 2013, 38(6):61-67. ZHANG J Y, PAN G Y. Comparison and application of multiple regression and BP neural network prediction model[J]. Journal of Kunming University of Science and Technology (Natural Science Edition), 2013, 38(6):61-67.
[28] 张玉超, 钱新, 钱瑜, 等. 基于机器学习方法的太湖叶绿素a定量遥感研究[J]. 环境科学, 2009, 30(5):1321-1328. ZHANG Y C, QIAN X, QIAN Y, et al. Quantitative retrieval of chlorophyll a concentration in Taihu Lake using machine learning methods[J. Environmental Science, 2009, 30(5):1321-1328.
[29] DUARTE C M, HENDRIKS I E, MOORE T S, et al. Is ocean acidification an open-ocean syndrome? Understanding anthropogenic impacts on seawater pH[J]. Estuaries and Coasts, 2013, 36(2):221-236.
[30] MIDORIKAWA T, ISHII M, SAITO S, et al. Decreasing pH trend estimated from 25-yr time series of carbonate parameters in the western North Pacific[J]. Tellus B:Chemical and Physical Meteorology, 2010, 62(5):649-659.
[31] BATES N R, BEST M H P, NEELY K, et al. Detecting anthropogenic carbon dioxide uptake and ocean acidification in the North Atlantic Ocean[J]. Biogeosciences, 2012, 9(7):2509-2522.
[32] RAVEN J, CALDEIRA K, ELDERFIELD H, et al. Ocean acidification due to increasing atmospheric carbon dioxide[M]. London:The Royal Society, 2005:5-13.
[33] ZEEBE R E. History of seawater carbonate chemistry, atmospheric CO2, and ocean acidification[J]. Annual Review of Earth and Planetary Sciences, 2012, 40:141-165.
[34] 肖钲霖. 楚科奇海与北欧海海洋酸化研究[D]. 厦门:国家海洋局第三海洋研究所, 2015. XIAO Z L. Study on ocean acidification at the Chukchi sea and the Nordic Sea[D]. Xiamen:Third Institute of Oceanography, MNR, 2015.
[35] DENG X W, CHEN J, LARS-ANDERS H, et al. Eco-chemical mechanisms govern phytoplankton emissions of dimethylsulfide in global surface waters[J]. National Science Review, 2021, 8(2):38-45.
[36] 季轩梁. 西北太平洋海洋生态系统碳循环数值模拟研究[D]. 北京:国家海洋环境预报中心, 2013. JI X L. Numerical study of marine ecosystem and carbon numerical study of marine ecosystem and carbon[D]. Beijing:National Marine Environmental Forecasting Center, 2013.
[37] JIANG L Q, CARTER B R, FEELY R A, et al. Surface ocean pH and buffer capacity:past, present and future[J]. Scientific Reports, 2019, 9(1):18624.
[38] TAKAHASHI T, SUTHERLAND S C, CHIPMAN D W, et al. Climatological distributions of pH, pCO2, total CO2, alkalinity, and CaCO 3 saturation in the global surface ocean, and temporal changes at selected locations[J]. Marine Chemistry, 2014, 164:95-125.
[39] DUARTE C M, HENDRIKS I E, MOORE T S, et al. Is ocean acidification an open-ocean syndrome? Understanding anthropogenic impacts on seawater pH[J]. Estuaries and Coasts, 2013, 36(2):221-236.
[40] 曲宝晓, 宋金明, 李学刚. 海洋酸化之时间序列研究进展[J]. 海洋通报, 2020, 39(3):281-290. QU B X, SONG J M, LI X G. Advances in ocean acidification timeseries studies[J]. Marine Science Bulletin, 2020, 39(3):281-290.
[41] 陈雪霏, 韦刚健, 邓文峰, 等. 珊瑚礁海水pH变化及其对海洋酸化的意义[J]. 热带地理, 2016, 36(1):41-47. CHEN X F, WEI G J, DENG W F, et al. Reef water pH variation and its implications for ocean acidification[J]. Tropical Geography, 2016, 36(1):41-47.
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