高分辨率海水表层二氧化碳分压重构以大西洋为例

海洋是自然界中重要的碳汇,海−气二氧化碳通量通常利用大气和海水表层的二氧化碳分压（pCO₂）差进行估算。受制于时空分布不均匀的观测样本和预测数据,目前已有海水表层二氧化碳分压的重构结果在空间分辨率上仍有较大可提升空间。为在高空间分辨率下更好地拟合时空变化,基于表层大洋二氧化碳地图（SOCAT）的海水表层二氧化碳逸度（f CO₂）数据集和遥感卫星等多源数据,利用XGBoost模型建立了海水表层二氧化碳分压值与海洋物理、生物、光学等要素的非线性关系,并根据样本时空频率构建权重模型,最终重构了2000−2018年大西洋0.041 7°×0.041 7°下月度海水表层二氧化碳分压分布。预测结果的相关系数为0.966,均方根误差为8.087 μatm,平均偏差为4.012 μatm,与同类重构结果相比,海水表层二氧化碳分压的时空变化趋势一致性强,且在空间分辨率上具有优势。     

北太平洋表层海水pH值的重建

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-a、pCO₂参数模拟最佳,BP神经网络模型基于SST、SSS、Chl-a、pCO₂参数模拟最佳。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。     

海水表层盐度数据的时-频联合分析初探

时－频联合分析法是近年来才兴起的一种信号分析方法，它是分析处理频率随时间而改变的非稳态信号的强有力工具。本文对这种方法作了简单介绍，并采用这种方法处理了嵊山海洋站海水表层盐度观测资料。所得结果表明，这种方法与传统方法相结合可以更好地分析海洋观测数据。     

基于神经网络的全球三维温盐场重构技术研究

文章利用果蝇优化广义回归神经网络算法FOAGRNN (fruit fly optimization algorithm, FOA; generalized regression neural network, GRNN)对SODA (simple ocean data assimilation)再分析数据进行训练, 构建海表温度、盐度、海面高度与次表层温盐场之间的投影关系模型, 并在全球范围使用SODA和卫星遥感数据评估了模型的应用性能。首先, 利用独立的2016年SODA海表数据作为模型输入进行理想重构试验, 结果显示全球重构温、盐平均均方根误差(MRMSE)分别为0.36℃和0.08‰, 与世界海洋图集WOA13资料相比减小约50%和60%。然后, 利用卫星观测的海表信息作为模型输入进行实际应用试验, 并与Argo观测剖面进行比较评估。试验结果表明, 重构模型能有效表征海水温、盐特征, 其中重构温、盐MRMSE分别为0.79℃和0.16‰, 相比WOA气候态减小27%和11%。误差的垂向分布显示, 重构温度RMSE从海表向下迅速增大, 至100m达到峰值1.35℃, 而后又迅速回落,至250m处为0.81℃, 跃层往下不断减小; 重构盐度RMSE基本随深度增大而减小, 误差峰值位于25m附近, 约为0.25‰。此外, Argo浮标跟踪分析和区域水团统计结果也表明模型能够较好地刻画海洋三维温盐场的内部结构特征。     

Short-term variability of fCO2 in seawater and air–sea CO2 fluxes in a coastal upwelling system (Ría de Vigo, NW Spain)

Coastal upwelling systems are regions with highly variable physical processes and very high rates of primary production and very little is known about the effect of these factors on the short-term variations of CO₂ fugacity in seawater (fCO₂^w). This paper presents the effect of short-term variability (<1 week) of upwelling–downwelling events on CO₂ fugacity in seawater (fCO₂^w), oxygen, temperature and salinity fields in the Ría de Vigo (a coastal upwelling ecosystem). The magnitude of fCO₂^w values is physically and biologically modulated and ranges from 285 μatm in July to 615 μatm in October. There is a sharp gradient in fCO₂^w between the inner and the outer zone of the Ría during almost all the sampling dates, with a landward increase in fCO₂^w.CO₂ fluxes calculated from local wind speed and air–sea fCO₂ differences indicate that the inner zone is a sink for atmospheric CO₂ in December only (−0.30 mmol m⁻² day⁻¹). The middle zone absorbs CO₂ in December and July (−0.05 and −0.27 mmol·m⁻² day⁻¹, respectively). The oceanic zone only emits CO₂ in October (0.36 mmol·m⁻² day⁻¹) and absorbs at the highest rate in December (−1.53 mmol·m⁻² day⁻¹).     

基于ST-ConvLSTM的南海海表面CO2分压的空间和时间序列预测

海表面二氧化碳分压(pCO₂)的未来变化趋势,对统计评估全球碳收支以及理解全球气候变化背景下的海洋酸化现象至关重要。目前传统的海面pCO₂预测方法大部分基于有限的实测数据,然而实测数据存在着时间和地理方面的制约,且计算成本较高。近年来,随着时空观测数据的爆炸性增长,基于深度学习的数据驱动模型在海表面pCO₂预测方面中表现出良好的潜力。然而,由于多种环境因素与海表面pCO₂之间的关系错综复杂,到目前为止尚无十分简单有效的相关模型来对海表面pCO₂进行预测。为应对这一挑战,利用时空卷积长短时记忆神经网络(ST-ConvLSTM)模型,通过海面温度(sea surface temperature, SST)、海面盐度(sea surface salinity, SSS)、叶绿素a浓度(chl a)和海面pCO₂数据,预测南海的海面pCO₂,并将2019年1～12月的数据作为测试集对模型的表现进行了验证。结果显示, ST-ConvLSTM模型...     

Uptake of atmospheric carbon dioxide in Arctic shelf seas: evaluation of the relative importance of processes that influence pCO2 in water transported over the Bering–Chukchi Sea shelf

The uptake of atmospheric carbon dioxide in the water transported over the Bering–Chukchi shelves has been assessed from the change in carbon-related chemical constituents. The calculated uptake of atmospheric CO₂ from the time that the water enters the Bering Sea shelf until it reaches the northern Chukchi Sea shelf slope (1 year) was estimated to be 86±22 g C m⁻² in the upper 100 m. Combining the average uptake per m³ with a volume flow of 0.83×10⁶ m³ s⁻¹ through the Bering Strait yields a flux of 22×10¹² g C year⁻¹. We have also estimated the relative contribution from cooling, biology, freshening, CaCO₃ dissolution, and denitrification for the modification of the seawater pCO₂ over the shelf. The latter three had negligible impact on pCO₂ compared to biology and cooling. Biology was found to be almost twice as important as cooling for lowering the pCO₂ in the water on the Bering–Chukchi shelves. Those results were compared with earlier surveys made in the Barents Sea, where the uptake of atmospheric CO₂ was about half that estimated in the Bering–Chukchi Seas. Cooling and biology were of nearly equal significance in the Barents Sea in driving the flux of CO₂ into the ocean. The differences between the two regions are discussed. The loss of inorganic carbon due to primary production was estimated from the change in phosphate concentration in the water column. A larger loss of nitrate relative to phosphate compared to the classical ΔN/ΔP ratio of 16 was found. This excess loss was about 30% of the initial nitrate concentration and could possibly be explained by denitrification in the sediment of the Bering and Chukchi Seas.     

fCO2 variability at the CARIACO tropical coastal upwelling time series station

Monthly seawater pH and alkalinity measurements were collected between January 1996 and December 2000 at 10°30′N, 64°40′W as part of the CARIACO (CArbon Retention In A Colored Ocean) oceanographic time series. One key objective of CARIACO is to study temporal variability in Total CO₂ (TCO₂) concentrations and CO₂ fugacity (fCO₂) at this tropical coastal wind-driven upwelling site. Between 1996 and 2000, the difference between atmospheric and surface ocean CO₂ concentrations ranged from about − 64.3 to + 62.3 μatm. Physical and biochemical factors, specifically upwelling, temperature, primary production, and TCO₂ concentrations interacted to control temporal variations in fCO₂. Air–sea CO₂ fluxes were typically depressed (0 to + 10 mmol C m^{− 2} day^{− 1}) in the first few months of the year during upwelling. Fluxes were higher during June–November (+ 10 to 20 mmol C m^{− 2} day^{− 1}). Fluxes were generally independent of the slight changes in salinity normally seen at the station, but low positive flux values were seen in the second half of 1999 during a period of anomalously heavy rains and land-derived runoff. During the 5 years of monthly data examined, only two episodes of negative air–sea CO₂ flux were observed. These occurred during short but intense upwelling events in March 1997 (−10 mmol C m^{− 2} day^{− 1}) and March 1998 (− 50 mmol C m^{− 2} day^{− 1}). Therefore, the Cariaco Basin generally acted as a source of CO₂ to the atmosphere in spite of primary productivity in excess of between 300 and 600 g C m^{− 2} year^{− 1}.     

亚热带珊瑚礁海域枯水季pCO2的分布特征及CO2汇源转换机制-以深圳杨梅坑海域为例

由于存在极高的初级生产和高效的碳代谢速率, 珊瑚礁海域二氧化碳(CO₂)的汇/源属性仍存有争议。为明晰中国典型珊瑚礁海域CO₂的汇源属性及驱动因素, 作者基于2022年11月(秋季)和2023年2月(冬季)在深圳杨梅坑海域的调查结果并结合室内培养实验所获得的数据, 探究了枯水季节典型亚热带珊瑚礁海水二氧化碳分压(pCO₂)的分布特征及主要控制机制。结果表明, 调查期间pCO₂的变化较大, 其范围为233.3~465.3 μatm。秋季表现为大气CO₂的汇, CO₂吸收通量为1.66±0.41 mmol C/(m²/d);冬季表现为大气CO₂的弱源, 其释放通量为0.36±0.17 mmol C/(m²/d)。调查期间(枯水季)杨梅坑海域受淡水输入的影响较小, 季节性温度影响下的生物过程是驱动pCO₂变化的关键因素, 其贡献pCO₂总变化量的73.6%(表层)和66.5%(底层)。其中, 浮游植物光合作用的季节差异是导致海水CO₂汇源转变的主要成因, 而微生物呼吸作用的影响甚微。相比较, 物理过程(CO₂海-气交换、温度和盐度变化)对pCO₂的影响相对较小, 其作用结果远低于生物过程。此外, 珊瑚的代谢活动对杨梅坑局部海域pCO₂分布产生一定影响, 造成礁区pCO₂值高于非礁区。因此, 海气CO₂通量估算中不能忽视局部海域珊瑚代谢作用的影响。     

CO_2加富对盐生杜氏藻(Dunaliella salina)叶绿素荧光参数的影响

大气中CO_2浓度不断升高导致的海水酸化,已经引起了广泛的环境、生态和气候问题。本实验采用实验生态学的方法,以盐生杜氏藻(Dunaliella salina)为研究对象,分析其在CO_2加富的条件下叶绿素荧光参数的变化。研究表明,CO_2加富对盐生杜氏藻光系统Ⅱ最大光化学量子产额(Fv/Fm)和最大相对电子传递速率(rETRmax)无显著影响(P0.05),显著促进了光系统Ⅱ实际光合效率(P0.05)和光能利用效率(α)(P0.05),并且降低了饱和光强(Ek)(P0.05)。然而,CO_2升高增加了盐生杜氏藻的光抑制参数(β)(P0.05)和非光化学淬灭(NPQ)(P0.05),这说明在光照充足的情况下,CO_2加富会对盐生杜氏藻产生负面效应,使其更容易受到光抑制。     

南海东北部春季海表pCO_2分布及海-气CO_2通量

2013年南海东北部春季共享航次采用走航观测方式,现场测定了表层海水和大气的二氧化碳分压(pCO2)及相应参数。结合水文、化学等同步观测要素资料,对该海域pCO2的分布变化进行了探讨。结果表明,陆架区受珠江冲淡水、沿岸上升流及生物活动的影响,呈现CO2的强汇特征;吕宋海峡附近及吕宋岛西北附近海域受海表高温、黑潮分支"西伸"、吕宋岛西北海域上升流等因素影响,呈现强源特征。根据Wanninkhof的通量模式,春季整个南海东北部海域共向大气释放约4.25×104 t碳。     

考洲洋牡蛎养殖海域海-气界面CO2交换通量的时空变化

根据2018年7月、11月和2019年1月、4月对广东考洲洋牡蛎养殖海域进行4个季节调查获得的p H、溶解无机碳(DIC)、水温、盐度、溶解氧(DO)及叶绿素a(Chla)等数据,估算该区域表层海水溶解无机碳体系各分量的浓度、初级生产力(PP)、表层海水CO₂分压[p(CO₂)]和海-气界面CO₂交换通量(FCO₂),分析牡蛎养殖活动对养殖区碳循环的影响。结果表明:牡蛎养殖区表层海水中Chl a、DIC、HCO₃^–和PP显著低于非养殖区;养殖淡季表层海水中pH、DO、DIC、HCO₃^–、和CO₃^2–显著大于养殖旺季,养殖旺季的p(CO₂)和FCO₂显著大于养殖淡季。牡蛎养殖区表层海水夏季、秋季、冬季和春季的海-气界面CO₂交换通量FCO₂平均值分别是(42.04±9.56)、(276...     

富油微藻富碳培养下油脂积累及固碳研究

微藻固碳是一种新型节能减排技术,具有长期可持续发展的潜力。本文对两株富油微藻（球等鞭金藻和微拟球藻）进行了富碳培养下生长特性及中性脂积累特性的研究。两株富油微藻的最佳培养条件为10%CO₂浓度和f培养基。本研究对两株富油微藻的最大生物量产率、总脂含量、最大油脂产率、微藻的C含量和CO₂固定率进行了测定。球等鞭金藻的各参数指标分别为：142.42±4.58g/（m²·d）,39.95%±0.77%,84.47±1.56g/（m²·d）,45.98%±1.75%和33.74±1.65g/（m²·d）。微拟球藻的各参数指标分别为：149.92±1.80g/（m²·d）,37.91%±0.58%,89.90±1.98g/（m²·d）,46.88%±2.01%和34.08±1.32g/（m²·d）。实验结果显示,两株海洋微藻均属于高固碳优良藻株,适合应用于微藻烟气减排技术开发,具备用于海洋生物质能耦合CO₂减排开发的潜力。