多情景下的全球海平面指纹演化研究

刘宇欣; 邓珊珊; 张雯茜; 胡安戈

doi:10.12284/hyxb2024093

多情景下的全球海平面指纹演化研究

doi: 10.12284/hyxb2024093

刘宇欣^{1, 2, 3,},
邓珊珊^{1, 2, 3, ,},
张雯茜^{1, 2, 3},
胡安戈^{1, 2, 3}

1.
广西南海珊瑚礁研究重点实验室，广西南宁 530004
2.
广西大学珊瑚礁研究中心，广西南宁 530004
3.
广西大学海洋学院，广西南宁 530004

基金项目: 国家自然科学基金（42201024）；广西自然科学基金(2024GXNSFBA010158)；广西科技基地和人才专项基金（桂科AD23026069）。

详细信息

作者简介:
刘宇欣（1999—），女，湖南省邵阳市人，主要从事陆海水储量再分布重构研究。E-mail：liuyuxin_1205@163.com

通讯作者:
邓珊珊，助理教授，主要从事气候变化背景下的全球水循环研究。E-mail：dengss@gxu.edu.cn

中图分类号: P941.8
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出版历程
- 收稿日期: 2024-01-30
- 修回日期: 2024-07-30
- 网络出版日期: 2024-08-16
- 刊出日期: 2024-10-10

The evolution of global sea level fingerprints under multiplescenarios

Liu Yuxin^{1, 2, 3
,},
Deng Shanshan^{1, 2, 3
, ,},
Zhang Wenxi^{1, 2, 3},
Hu Ange^{1, 2, 3}

1.
Guangxi Laboratory on the Study of Coral Reefs in the South China Sea, Nanning 530004, China
2.
Coral Reef Research Center of China, Guangxi University, Nanning 530004, China
3.
School of Marine Sciences, Guangxi University, Nanning 530004, China

摘要

摘要: 气候变化背景下，区域质量海平面变化速率不一。其中，陆地向海水输送的淡水在负荷自吸效应与极移反馈共同作用下形成质量海平面的时空异质性变化，即海平面指纹，是质量海平面中的重要组成部分。本文利用三套陆地水储量异常数据，采用考虑负荷自吸效应与极移反馈的海平面方程，模拟3种情景下的海平面指纹，分别为：（1）吻合实际冰川物质平衡的情景；（2）吻合近期气候变化速率的情景；（3）仅考虑气候自然变率的情景。基于模拟结果，分析多情景下海平面指纹的演化特征，并评估其对观测质量海平面异常的贡献。研究表明：格陵兰岛、阿拉斯加、高加索及中东地区、南安第斯山脉和南极洲等地区的冰川消融主导海平面指纹的趋势项。吻合实际冰川物质平衡的情景能更好地再现观测质量海平面异常趋势项的全球分布格局，表现为与GRACE/GRACE-FO结果的空间相似系数为0.31、与测高卫星结果的空间相似系数为0.71。非冰川区域陆地水储量异常则更好地再现观测质量海平面异常的振幅项，表现为仅考虑气候自然变率情景下的海平面指纹与GRACE/GRACE-FO结果的空间相似系数为0.67、与测高卫星结果的空间相似系数为0.84。低纬海域质量海平面异常主要贡献源是海平面指纹。
- 海平面指纹 /
- 海平面方程 /
- 多情景
Abstract: Under the backdrop of climate change, mass sea level change rates are varied across regions. Therein, under the combined effect of the self-attraction and loading effect and polar motion feedback, freshwater transported from land to sea resulted in the spatiotemporal heterogeneous change of mass sea level, termed sea level fingerprints. The sea level fingerprints are important components of mass sea level. This study utilized three terrestrial water storage anomalies datasets to simulate sea level fingerprints under three scenarios, following a sea level equation that incorporated the self-attraction and loading effect along with polar motion feedback. The simulated scenarios were: (1) aligning with the actual glacial mass balance; (2) consistent with the recent climate change rates; (3) considering climate natural variability alone. Based on simulation results, this study analyzed the evolution ofsea level fingerprints under multiple scenarios and assessed their contribution to observed mass sea level anomalies. The study revealed that glacier melting in regions such as Greenland, Alaska, the Caucasus and Middle East, the Southern Andes, and Antarctica dominated the trend term of sea level fingerprints. The sea level fingerprints, which align with the actual glacier mass balance, better replicated the global distribution pattern of the observed mass sea level anomalies trend term, as shown by the spatial similarity coefficients of 0.31 with the GRACE/GRACE-FO results and 0.71 with the altimetry satellite results. Non-glacial regional terrestrial water storage anomalies better captured the amplitude term of the observed mass sea level anomalies, as shown by sea level fingerprints, which consider climate natural variability alone, having spatial similarity coefficients of 0.67 with the GRACE/GRACE-FO results and 0.84 with the altimetry satellite results. The sea level fingerprints were the primary contributing source to mass sea level anomalies in low-latitude regions.
- sea level fingerprints /
- sea level equation /
- multiple scenarios

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图 1 海平面方程解算流程

Fig. 1 Sea level equation solution flowchart

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图 2 海平面指纹全球平均距平值时间序列及各组分变化特征图

a. SLF_D，b. SLF _L，c. SLF _H全球平均距平值

Fig. 2 Characteristic plots of the SLF global mean anomalies time series and its components.

a. SLF_D, b. SLF _L, c. SLF _H global mean anomalies

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图 3 海平面指纹季节振幅全球分布特征

a.横坐标为经度，纵坐标为季节振幅经向余弦加权平均值；e.横坐标为季节振幅纬向算数平均值，纵坐标为纬度；b .SLF_D、c. SLF_L、d. SLF_H全球各格网季节振幅

Fig. 3 Characteristis of the global distributions of SLF seasonal amplitudes

a. Horizontal coordinate is longitude, vertical coordinate is longitudinal cosine-weighted mean of seasonal amplitudes; e. horizontal coordinate is latitudinal arithmetic mean of seasonal amplitudes, vertical coordinate is latitude; b. SLF_D, c. SLF_L, d. SLF_H global seasonal amplitudes for each grid

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图 4 20°S至50°N陆地水储量异常驱动的海平面指纹季节振幅与全球陆地水储量异常驱动的海平面指纹季节振幅的比值全球分布特征

a. SLF_D、b. SLF_L、c. SLF_H全球各格网季节振幅占比

Fig. 4 Characteristics of the global distributions of the ratio of SLF seasonal amplitudes driven by TWS anomalies from 20°S to 50°N to that driven by the global TWS anomalies

a. SLF_D, b. SLF_L, c. SLF_Hglobal ratio of seasonal amplitudes for each grid

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图 5 海平面指纹变化趋势全球分布特征

a.横坐标为经度，纵坐标为变化趋势经向余弦加权平均值；e.横坐标为变化趋势纬向算数平均值，纵坐标为纬度；b. SLF_D、c. SLF_L、d. SLF_H全球各格网变化趋势

Fig. 5 Characteristics of the global distributions of SLF trends

a. Horizontal coordinate is longitude, vertical coordinate is longitudinal cosine-weighted mean of trends; e. horizontal coordinate is latitudinal arithmetic mean of trends, vertical coordinate is latitude; b. SLF_D, c. SLF_L, d. SLF_H global trends for each grid

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图 6 海平面指纹年际变异波动全球分布特征

a.横坐标为经度，纵坐标为年际变异波动经向余弦加权平均值；e.横坐标为年际变异波动纬向算数平均值，纵坐标为纬度；b. SLF_D、c. SLF_L、d. SLF_H全球各格网年际变异波动

Fig. 6 Characteristis of the global distributions of SLFinterannual variation fluctuations

a. Horizontal coordinate is longitude, vertical coordinate is longitudinal cosine-weighted mean of interannual variation fluctuations; e. horizontal coordinate is latitudinal arithmetic mean of interannual variation fluctuations, vertical coordinate is latitude; b. SLF_D, c. SLF_L, d. SLF_H global interannual variation fluctuations for each grid

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图 7 海平面指纹与GRACE/GRACE-FO观测的质量海平面异常的评估结果空间分布

b、f、j和 c、g、k分别为SLF_D、SLF_L、SLF_H与GRACE/GRACE-FO观测的质量海平面异常间的CorrCoef和EV；a、e、i和 d、h、l分别为SLF_D、SLF_L、SLF_H的CorrCoef和EV纬向算术平均值。结果均仅显示观测范围内显著相关区域（p < 0.05）

Fig. 7 Spatial distribution of correlation between SLF and GRACE/GRACE-FO observed mass sea level anomalies

b, f, j, and c, g, k. The CorrCoef and EV between SLF_D, SLF_L, and SLF_H and the GRACE/GRACE-FO observed mass sea level anomalies, respectively; a, e, i, and d, h, l. the SLF_D, SLF_L, and SLF_H CorrCoef and EV latitudinal arithmetic means, respectively. The results all show only regions of significant correlation (p < 0.05) within the observed range

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图 8 海平面指纹与测高质量海平面异常的评估结果空间分布

b、f、j和c、g、k分别为SLF_D、SLF_L、SLF_H与GRACE/GRACE-FO观测的质量海平面异常间的CorrCoef和EV；a、e、i和d、h、l分别为SLF_D、SLF_L、SLF_H的CorrCoef和EV纬向算术平均值。结果均仅显示观测范围内显著相关区域（p < 0.05）

Fig. 8 Spatial distribution of correlation between SLF and altimetry observed mass sea level anomalies

b, f, j, and c, g, k. The CorrCoef and EV between SLF_D, SLF_L, and SLF_H and the altimetry observed mass sea level anomalies, respectively; and, a, e, i, and d, h, l. the SLF_D, SLF_L, and SLF_H CorrCoef and EV latitudinal arithmetic means, respectively. The results all show only regions of significant correlation (p < 0.05) within the observed range

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表 1 参数常数

Tab. 1 Parameters and constants

符号	参数/常数	数值	单位
$ g $	重力加速度	9.81	m·s⁻²
$ r $	地球平均半径	6.3710×10⁶	m
$ {\rho }_{w} $	水密度	1000	kg·m⁻³
$ {\rho }_{e} $	地球平均密度	5512	kg·m⁻³
$ {k}_{2} $	2阶潮汐勒夫数（表面势）	0.3055	−
$ {h}_{2} $	2阶潮汐勒夫数（位移）	0.6149	−
$ {\varOmega} $	地球平均旋转速度	7.2921×10⁻⁵	s⁻¹
$ {A} $	赤道平均转动惯量	8.0077×10³⁷	kg·m²
$ {C} $	极转动惯量	8.0345×10³⁷	kg·m²
$ \varrho $	钱德勒摆动频率	1.6490×10⁻⁷	s⁻¹

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表 2 TWS异常重构数据集

Tab. 2 TWS anomalies reconstruction dataset

数据集	TWS_D	TWS_L	TWS_H
时间跨度	1981年1月至2020年6月	1979年7月至2020年6月	1901年1月至2019年7月
空间分辨率	0.5°×0.5°	1°×1°	1°×1°
强迫数据	土壤湿度、雪深、降水、地表温度、冰川质量变化	降水、地表温度、海表温度、蒸发、径流、土壤湿度以及其他17种气候系统指数	地表温度、降水
GRACE/GRACE-FO产品	JPL MasconRL06_v02	CSR Mascon RL06	JPL Mascon RL06、GSFC Mascon v2.4
引用	文献[42−43]	文献[36−37]	文献[35]

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表 3 海平面指纹与观测质量海平面异常的比较表

Tab. 3 Comparisons between SLF and satellite-observed mass sea level anomalies

		重力卫星时期			测高卫星时期
		SLF_D	SLF_L	SLF_H	SLF_D	SLF_L	SLF_H
SimCoef	长期趋势	0.31	0.30	0.14	0.71	0.71	0.60
SimCoef	振幅	0.65	0.65	0.67	0.83	0.83	0.84
MAD ± STD/mm		13.89 ± 22.79	13.95 ± 22.79	15.92 ± 22.50	22.97 ± 16.74	22.99 ± 16.73	25.60 ± 16.35
注：重力卫星时期和测高卫星时期的时空范围不同。

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