青海湖湖温预估研究

doi:10.11755/j.issn.1006-7639(2024)-01-0064

干旱气象 ›› 2024, Vol. 42 ›› Issue (1): 64-74.DOI: 10.11755/j.issn.1006-7639(2024)-01-0064

青海湖湖温预估研究

董靖玮¹^,²(), 文莉娟²(), 于涛¹, 杨永顺³, 罗颖⁴, 王梦晓², 牛瑞佳²

1.兰州理工大学石油化工学院，甘肃兰州 730050
2.中国科学院西北生态环境资源研究院，青海湖综合观测研究站，甘肃兰州 730000
3.青海省海西州生态环境局，青海德令哈 817099
4.青海湖裸鲤救护中心，青海西宁 810016

收稿日期:2023-01-22 修回日期:2023-10-24 出版日期:2024-02-29 发布日期:2024-03-06
通讯作者: 文莉娟（1978—），女，山西人，研究员，主要从事陆面过程与区域气候变化研究。E-mail: wlj@lzb.ac.cn。
作者简介:董靖玮（1998—），女，辽宁朝阳人，硕士研究生，主要从事气候变化与水环境研究。E-mail: 214623225@qq.com。
基金资助:
甘肃省自然科学基金(22JR5RA073);国家重点研发计划-政府间国际科技创新合作重点专项(2019YFE0197600);国家自然科学基金项目(42275044);中国科学院“西部之光”项目(E129030101);中国科学院“西部之光”项目(Y929641001)

Prediction of the future temperature in Qinghai Lake

DONG Jingwei¹^,²(), WEN Lijuan²(), YU Tao¹, YANG Yongshun³, LUO Ying⁴, WANG Mengxiao², NIU Ruijia²

1. College of Petrochemical Engineering，Lanzhou University of Technology，Lanzhou 730050，China
2. Northwest Institute of Eco-Environment and Resources Chinese Academy of Sciences，Qinghai Lake Comprehensive Observation and Research Station，Lanzhou 730000，China
3. Haixi State Ecology and Environment Bureau of Qinghai Province，Delingha 817099，Qinghai，China
4. Qinghai Naked Carp Rescue Center，Xining 810016，China

Received:2023-01-22 Revised:2023-10-24 Online:2024-02-29 Published:2024-03-06

摘要/Abstract

摘要：

青海湖对青藏高原的生态安全有着重要作用，深入理解青海湖未来湖表温度变化特征至关重要。本文通过站点观测数据和再分析数据评估第六次国际耦合模式比较计划（Coupled Model Intercomparison Project 6，CMIP6）中3个全球气候模式的常规气象数据，并利用观测数据和MODIS地表温度数据评估一维湖泊模式（Freshwater Lake Model，FLake）在青海湖的适用性，预测4种不同排放情景（SSP126、SSP245、SSP370、SSP585）青海湖未来湖温的演变趋势，阐明湖温变化的驱动机制及其对青海湖裸鲤生存环境的潜在影响。结果表明：（1）再分析数据和CMIP6多模式集合的历史气象数据优于单个模式。2015—2100年，青海湖年均湖表温度持续升高，但由于社会共享经济路径（Shared Socioeconomic Pathway，SSP）和辐射强迫的差异，CMIP6 4种不同排放情景Flake模拟的湖表温度升温速率表现出明显差异。SSP126情景2050年后的升温速率低于历史水平，而在SSP245、SSP370和SSP585情景，2050年前后的升温速率均高于历史水平，气温是湖温上升的主要驱动因素。（2）未来不同时期的月、日最高湖表温度均出现在8月，近期（2021—2040年）各情景之间湖表温度差异较小，日均湖表温度基本在20.00 ℃以下；中期（2041—2080年）各情景间差异逐渐增大；远期（2081—2100年）8月日均湖表温度将超过20.00 ℃，多年日均湖表温度最高可达23.31 ℃，可对青海湖裸鲤的生长造成严重威胁，使其处于较高风险。

关键词: 青海湖, 湖表温度, CMIP6预估, 多模式集合, 青海湖裸鲤

Abstract:

Qinghai Lake plays an important role in the ecological security of the Qinghai-Tibet Plateau, and it is very important to deeply understand the characteristics of future lake surface temperature changes of Qinghai Lake. We evaluated the conventional meteorological data of three global climate models in the Coupled Model Intercomparison Project 6 (CMIP6) through site observations and reanalysis data. Freshwater Lake Model (FLake) was evaluated by using the observed data and MODIS (Moderate-resolution Imaging Spectroradiometer) surface temperature data to predict the evolution trend of lake temperature under four different discharge scenarios (SSP126、SSP245、SSP370、SSP585)in the future, and to elucidate the driving mechanism of lake surface temperature change and its potential impact on the living environment of Gymnocyprisprzewalskii. The results are as follows:(1) The historical meteorological data of the reanalysis data and the CMIP6 multi-model collection are superior to the single model. From 2015 to 2100, the average annual lake surface temperature of Qinghai Lake continued to rise under four scenarios. Under SSP126 scenarios, the warming rate after 2050 is lower than that in the historical period, while under SSP245, SSP370 and SSP585 scenarios, the warming rate before and after 2050 is higher than that in the historical period, in which air temperature is the main driving factor of lake surface temperature. (2)In the future different periods, the monthly and daily maximum lake surface temperature in different stages will appear in August, and in the recent period (2020-2041), there is little difference among different scenarios. In the middle period (2041-2080), the differences between the scenarios gradually increased. In the long run (2081-2100), the average daily lake surface temperature in August basically exceeded 20.00 ℃, and the maximum daily temperature even reached 23.31 ℃, Gymnocyprisprzewalskii would be at a high risk.

Key words: Qinghai Lake, lake surface temperature, CMIP6 estimation, multimode set, Gymnocyprisprzewalskii

中图分类号:

P461+.5

董靖玮, 文莉娟, 于涛, 杨永顺, 罗颖, 王梦晓, 牛瑞佳. 青海湖湖温预估研究[J]. 干旱气象, 2024, 42(1): 64-74.

DONG Jingwei, WEN Lijuan, YU Tao, YANG Yongshun, LUO Ying, WANG Mengxiao, NIU Ruijia. Prediction of the future temperature in Qinghai Lake[J]. Journal of Arid Meteorology, 2024, 42(1): 64-74.

图/表 9

图1 研究区和观测站点

Fig.1 Location of study area and observation station

表1 第六次国际耦合模式比较计划（CMIP6）3个模式的基本信息

Tab.1 Basic information of three models in the Coupled Model Intercomparison Project Phase 6（CMIP6）

序号	模式名称	所属国家/地区	研发机构简称	分辨率（纬度×经度）
1	CMCC-CM2-SR5	意大利	CMCC	0.94°×1.25°
2	MIROC6	日本	MIROC	1.4°×1.4°
3	MPI-ESM1-2-LR	德国	MPI-M	1.875°×1.875°

表2 1979—2014年青海湖观测数据与再分析数据和CMIP6模式日数据的相关性

Tab.2 Correlations of observation data of Qinghai Lake with reanalysis data and CMIP6 model daily data from 1979 to 2014

图2 1979—2014年青海湖气温（a）、风速（b）和比湿（c）的年际变化

Fig.2 Inter-annual variation of air temperature (a), wind speed (b) and specific humidity (c) in Qinghai Lake from 1979 to 2014

图3 青海湖2021年模拟温度与0.5 m观测湖水温度（a）、2013—2014年模拟与观测湖表温度（b）及2000—2014年模拟与MODIS地表温度（c）

Fig.3 The simulated temperature and the observed lake water temperature at 0.5 m in 2021 (a), simulated and observed lake surface temperature from 2013 to 2014 (b) and simulated temperature and MODIS land surface temperature from 2000 to 2014 (c) in Qinghai Lake

表3 CMIP6各模式及多模式集合模拟的统计结果

Tab.3 Statistics results of CMIP6 simulation for each models and multi-model assemble

模式	rr		bias/℃		RMSE/℃
模式	白天	夜晚	白天	夜晚	白天	夜晚
CMCC-CM2-SR5	0.96	0.92	0.36	3.16	3.30	4.65
MIROC6	0.95	0.91	0.79	3.62	3.11	4.47
MPI-ESM1-2-LR	0.96	0.91	0.77	3.60	3.02	4.65
CMIP6模式集合	0.96	0.92	0.63	3.46	2.88	4.64

图4 2015—2100年CMIP6模式集合不同情景青海湖湖表温度（a）、气温（b）、向下长波辐射（c）、比湿（d）、风速（e）及向下短波辐射（f）的预估结果（a为青海湖湖表温度模拟结果；rr为湖表温度与不同因素的相关系数，阴影区表示模拟结果正负一个标准差，*表示通过了99%置信水平的显著性检验）

Fig.4 The prediction result of lake surface temperature (a), air temperature (b), downward longwave radiation (c), specific humidity (d), wind speed (e), downward shortwave radiation (f) of Qinghai Lake in different scenarios based on CMIP6 model from 2015 to 2100 （a is the prediction result of lake surface temperature, rr is correlation coefficient between lake surface temperature and different factors, shadow represents plus/minus one standard deviation among the models from their ensemble mean, the star indicates passed the 99% significance test）

图5 青海湖历史时期（2000—2014年）（a）及不同排放情景近期（2021—2040年）（b）、中期（2041—2080年）（c）和远期（2081—2100年）（d）逐月湖表温度

Fig.5 Monthly lake surface temperature of historical period (2000-2014) (a) and different scenarios such as near-term (2020-2040) (b), mid-term (2041-2080) (c) and long-term (2081-2100) (d) in Qinghai Lake

图6 2050—2100年Flake模式不同情景青海湖湖表日均最高温度超过20.00 ℃的天数

Fig.6 The numbers of days when the daily average highest temperature of the lake surface exceeds 20 °C from 2050 to 2100 simulated by FLake model in different scenarios

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青海湖湖温预估研究

Prediction of the future temperature in Qinghai Lake

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