新疆温度和降水变化的CMIP6模式预估

张晓璐; 王晓欣; 华丽娟; 姜大膀

doi:10.3878/j.issn.1006-9895.2201.21182

新疆温度和降水变化的CMIP6模式预估

doi: 10.3878/j.issn.1006-9895.2201.21182

张晓璐^1,,
王晓欣²,
华丽娟¹,
姜大膀^{2, 1, ,}

1.
中国科学院大学地球与行星科学学院, 北京100049
2.
中国科学院大气物理研究所, 北京100029

基金项目: 国家自然科学基金项目41991284

详细信息

作者简介:
张晓璐，女，1995年出生，硕士研究生，主要从事气候变化研究。E-mail: zhangxiaolu191@mails.ucas.ac.cn

通讯作者:
姜大膀，E-mail: jiangdb@mail.iap.ac.cn

中图分类号: P467
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出版历程
- 收稿日期: 2021-09-20
- 录用日期: 2022-01-18
- 网络出版日期: 2022-01-21
- 刊出日期: 2023-03-15

Projections of Temperature and Precipitation over Xinjiang Based on CMIP6 Models

1.
College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049
2.
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029

Funds: National Natural Science Foundation of China (NSFC) (Grant 41991284)

摘要

摘要: 本文使用国际耦合模式比较计划第六阶段（CMIP6）中对新疆当代温度和降水模拟能力较好的20个模式的试验数据，在三种共享社会经济路径（SSPs）情景下，预估了新疆21世纪温度和降水的年和季节变化。根据多模式中位数，相对于1995～2014年，新疆21世纪不断升温，盆地增幅大于山区。在SSP1-2.6、SSP2-4.5和SSP5-8.5情景下，2015～2099年年平均增温趋势分别为0.1°C (10 a)⁻¹、0.3°C (10 a)⁻¹和0.7°C (10 a)⁻¹；2080～2099年区域平均分别升温1.3°C、2.6°C和5.3°C，其中夏季增幅最大。各模式预估的年和季节温度变化符号的区域平均一致性大于90%，模式结果间不确定性范围随时间增加，SSP5-8.5情景下的不确定性较SS1-2.6和SSP2-4.5的更大；除春季外，模式对其它季节温度预估的不确定性高于年平均。新疆21世纪降水不断增加，降水百分比变化的大值区位于塔里木盆地中部，末期SSP5-8.5情景下增幅超过50%。在SSP1-2.6、SSP2-4.5和SSP5-8.5情景下，2015～2099年年降水增幅分别是0.2% (10 a)⁻¹、2% (10 a)⁻¹和4% (10 a)⁻¹；2080～2099年区域平均降水分别增加5%、13%和25%，其中冬季降水增幅更大。各模式预估的新疆降水变化符号的一致性较好，且随时间有所提高，但仍较温度的小；对新疆降水百分比变化预估的不确定性范围随时间增加，其中在SSP5-8.5情景下的最大；各季节降水预估的不确定性较年平均偏大。
- CMIP6 /
- 温度 /
- 降水 /
- 预估 /
- 新疆
Abstract: In this study, we project the changes in temperature and precipitation over Xinjiang during the period 2015–2099 relative to the historical period 1995–2014 using 20 global climate models, which have exhibited good performance in simulating climatological temperature and precipitation over this region, from the Coupled Model Intercomparison Project Phase 6 (CMIP6) under the three shared socioeconomic pathways (SSPs). Multimodel median results indicate that annual and seasonal temperatures will increase during the 21st century, with larger values in the basins than those in the mountains. The trends of annual temperature changes under SSP1-2.6, SSP2-4.5, and SSP5-8.5 are expected to be 0.1°C (10 a)⁻¹, 0.3°C (10 a)⁻¹, and 0.7°C (10 a)⁻¹, respectively. The regionally averaged temperature will increase by 1.3°C, 2.6°C, and 5.3°C, respectively, during the period 2080–2099, with the strongest warming occurring in the summertime. The regionally averaged consistency in the sign of the projected annual and seasonal temperature changes is greater than 90%, and the inter-model uncertainty will increase with time, with larger values occurring under SSP5-8.5 than those occurring under SSP1-2.6 and SSP2-4.5. Except for springtime, larger uncertainties occur in the projection of seasonal temperatures than that in the annual case. Precipitation is expected to increase over Xinjiang during the 21st century. The projected maximum increase of more than 50% will be located in the central Tarim Basin under the SSP5-8.5 scenario during 2080–2099. Under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, the trends of changes in annual precipitation from 2015 to 2099 are 0.2% (10 a)⁻¹, 2% (10 a)⁻¹, and 4% (10 a)⁻¹, respectively, and annual precipitation increases by a regional average of 5%, 13%, and 25% during 2080–2099. The largest increase in precipitation will occur in winter. The inter-model consistency in the sign of projected annual and seasonal precipitation changes increases with time but is weaker than its temperature counterpart. The inter-model uncertainty for precipitation projections is expected to increase with time, with the largest magnitude occurring under SSP5-8.5. The inter-model uncertainty of seasonal precipitation projections is larger than that of annual projections.
- CMIP6 /
- Temperature /
- Precipitation /
- Projection /
- Xinjiang

HTML全文

图 1 新疆地理位置（红色矩形框内：34°N～50°N，71°E～99°E）及其地形分布（填色，单位：m）

Figure 1. Geographical location (curve in the red rectangle: 34°N–50°N, 71°E–99°E) and topography (shading, units: m) of Xinjiang

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图 2 相对于1995～2014年，在SSP1-2.6（蓝色）、SSP2-4.5（橙色）和SSP5-8.5（红色）情景下，20个CMIP6模式模拟的2015～2099年新疆区域平均年温度变化时间序列（左图，单位：°C）和2080~2099年的年温度变化（右图，单位：°C）。实线表示多模式中位数，阴影区为多模式模拟结果的5%～95%范围

Figure 2. Time series of regionally averaged annual temperature changes over Xinjiang from 2015 to 2099 (left-hand side panel) and annual temperature changes during 2080–2099 (right panel) relative to 1995–2014 (units: °C), as derived from 20 CMIP6 models under SSP1-2.6 (blue), SSP2-4.5 (orange), and SSP5-8.5 (red), respectively. The solid line and the shading represent the multimodel median and the range of 5%–95% of individual models, respectively

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图 3 相对于1995～2014年，在SSP1-2.6、SSP2-4.5和SSP5-8.5情景下，基于20个CMIP6模式的中位数预估，21世纪近期（左列）、中期（中间列）和末期（右列）新疆年温度变化（填色，单位：°C）及多模式预估结果的不确定性（阴影，单位：°C）。黑色实心点表示大于80%的模式通过95%的显著性检验；各图左上角数字代表区域平均的年温度变化（不确定性）

Figure 3. Changes in annual temperature (color, units: °C) in the near- (left column), the middle- (middle column), and the long- (right column) terms during the 21st century relative to 1995–2014 over Xinjiang as derived from the median of 20 CMIP6 models under SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively, and inter-model uncertainty (shading, units: °C) of temperature projections from individual models. The black solid dots indicate that more than 80% of individual models are statistically significant at the 95% confidence level. The regionally averaged annual temperature change (inter-model uncertainty) is provided in the top-left corner of each panel

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图 4 同图3，但为SSP5-8.5情景下的春季（MAM）、夏季（JJA）、秋季（SON）和冬季（DJF）温度变化（单位：°C）。空心点表示50%~80%的模式通过95%的显著性检验

Figure 4. Same as Fig. 3, but for the temperature changes in spring (MAM), summer (JJA), autumn (SON), and winter (DJF) under SSP5-8.5 (units: °C). The hollow dots indicate that 50%–80% of individual models are statistically significant at the 95% confidence level

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图 5 同图2，但为年降水变化

Figure 5. Same as Fig. 2, but for the changes in annual precipitation

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图 6 同图3，但为年降水变化（填色）及多模式预估结果的不确定性（阴影）

Figure 6. Same as Fig. 3, but for the changes in annual precipitation (color) and associated inter-model uncertainty (shading)

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图 7 同图6，但为SSP5-8.5情景下季节降水变化（填色）

Figure 7. Same as Fig. 6, but for the changes of seasonal precipitation under SSP5-8.5 (color)

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表 1 本文使用的20个CMIP6全球气候模式的基本信息

Table 1. Basic information of the 20 CMIP6 (Coupled Model Intercomparison Project Phase 6) global climate models used in this study

序号	模式名称	所属国家或地区	所属机构简称	水平分辨率（纬向×经向）	积分时段
01	AWI-CM-1-1-MR	德国	AWI	0.9375°×～0.9°	2015～2100
02	BCC-CSM2-MR	中国	BCC	1.125°×～1.1°	2015～2100
03	CAMS-CSM-1-0	中国	CAMS	1.125°×～1.1°	2015～2099
04	CESM2-WACCM	美国	NCAR	1.25°×～0.9°	2015～2100
05	CanESM5	加拿大	CCCma	2.8125°×～2.8°	2015～2100
06	EC-Earth3-Veg	欧洲十国	EC-Earth-Consortium	0.703125°×～0.7°	2015～2100
07	EC-Earth3	欧洲十国	EC-Earth-Consortium	0.703125°×～0.7°	2015～2100
08	FGOALS-f3-L	中国	CAS	1.25°×1.0°	2015～2100
09	FGOALS-g3	中国	CAS	2.0°×～2.0°	2015～2100
10	GFDL-ESM4	美国	NOAA-GFDL	1.25°×1.0°	2015～2100
11	INM-CM4-8	俄罗斯	INM	2.0°×1.5°	2015～2100
12	INM-CM5-0	俄罗斯	INM	2.0°×1.5°	2015～2100
13	IPSL-CM6A-LR	法国	IPSL	2.5°×～1.3°	2015～2100
14	KACE1.0-G	韩国	NIMS-KMA	1.875°×1.25°	2015～2100
15	MIROC6	日本	MIROC	1.40625°×～1.4°	2015～2100
16	MPI-ESM1-2-HR	德国	MPI-M	0.9375°×～0.9°	2015～2100
17	MPI-ESM1-2-LR	德国	MPI-M	1.875°×～1.9°	2015～2100
18	NESM3	中国	NUIST	1.875°×～1.9°	2015～2100
19	NorESM2-LM	挪威	NCC	2.5°×～1.9°	2015～2100
20	NorESM2-MM	挪威	NCC	1.25°×～0.9°	2015～2100
注：第5列“～”表示“约”

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表 2 根据20个CMIP6模式中位数，相对于1995～2014年，在SSP1-2.6、SSP2-4.5和SSP5-8.5情景下，21世纪近期（2021～2040年）、中期（2041～2060年）和末期（2080～2099年），新疆的年和季节温度、降水变化的区域平均值及模式间预估结果的不确定性

Table 2. Regionally averaged annual and seasonal temperature and precipitation changes over Xinjiang projected by the median of 20 CMIP6 models under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios in the near (2021–2040)-, middle (2041–2060)-, and long (2080–2099)-terms of the 21st century relative to 1995–2014. The regionally averaged inter-model uncertainty of the projected temperature and precipitation changes among the models is listed in the brackets

		温度变化/°C（不确定性/°C）			降水变化（不确定性）
		前期	中期	末期	前期	中期	末期
SSP1-2.6	年平均	0.9（1.1）	1.3（1.1）	1.3（1.1）	4％（10％）	7％（11％）	5％（12％）
	春季	0.8（0.8）	1.2（0.9）	1.3（0.9）	8％（18％）	8％（18％）	6％（17％）
	夏季	1.1（1.4）	1.5（1.4）	1.6（1.4）	−1％（22％）	−1％（22％）	1％（25％）
	秋季	1.0（1.3）	1.4（1.4）	1.3（1.4）	1％（19％）	6％（21％）	2％（20％）
	冬季	0.9（1.3）	1.3（1.2）	1.3（1.2）	12％（21％）	12％（21％）	11％（20％）
SSP2-4.5	年平均	1.0（1.1）	1.8（1.1）	2.6（1.2）	4％（10％）	9％（14％）	13％（18％）
	春季	0.8（0.9）	1.4（1.0）	2.2（1.0）	4％（16％）	10％（18％）	17％（24％）
	夏季	1.1（1.3）	2.0（1.5）	3.0（1.5）	2％（30％）	2％（30％）	1％（29％）
	秋季	0.9（1.4）	1.8（1.4）	2.6（1.5）	4％（18％）	7％（21％）	12％（27％）
	冬季	0.9（1.3）	1.7（1.2）	2.6（1.4）	18％（23％）	18％（23％）	29％（29％）
SSP5-8.5	年平均	1.1（1.1）	2.3（1.3）	5.3（1.8）	4％（11％）	9％（15％）	25％（27％）
	春季	0.9（1.0）	2.0（1.1）	4.8（1.7）	5％（16％）	11％（20％）	28％（34％）
	夏季	1.4（1.4）	2.6（1.6）	5.8（2.2）	1％（24％）	−1％（26％）	2％（38％）
	秋季	1.1（1.4）	2.3（1.6）	5.3（2.2）	5％（21％）	9％（25％）	22％（38％）
	冬季	1.0（1.2）	2.1（1.3）	5.1（1.9）	10％（20％）	21％（26％）	59％（54％）

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