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ISSN 1673-1719
CN 11-5368/P
Current Issue Archive Issue Online First
??30 July 2019, Volume 15 Issue 4 Previous Issue ??
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Changes in Climate System
Research progress for the bidirectional coupling of the Earth system model and integrated assessment model ? Collect
Shi-Li YANG,Wen-Jie DONG,Jie-Ming CHOU,Chang-Xin LIU
Climate Change Research. 2019, 15 (4): 335-342. ? DOI: 10.12006/j.issn.1673-1719.2018.166
Abstract ( 90 ) ? HTML ( 7 ) ? ? PDF (839KB) ( 43 ) ?

This paper summarizes the advantages and disadvantages of the Earth system model (ESM) and integrated assessment model (IAM) in investigating the human activities and climate change, and then clarifies the necessity of bidirectional coupling of these two kinds of models. The main difficulties in the bidirectional coupling process are objectively analyzed, and the methods and latest development in solving the coupling difficulties are also summarized at the international and domestic levels, and the uncertainty of bidirectional coupling and the method to solve the uncertainty are discussed in the last part. This paper provides a new guidance for the bidirectional coupling between the ESM and IAM in China.

Uncertainties in the simulation of 1.5℃ and 2℃warming threshold-crossing time arising from model internal variability based on CMIP5 models ? Collect
Di-Fei JI,Li LIU,Li-Juan LI,Chao SUN,Xin-Zhu YU,Rui-Zhe LI,Cheng ZHANG,Bin WANG
Climate Change Research. 2019, 15 (4): 343-351. ? DOI: 10.12006/j.issn.1673-1719.2018.157
Abstract ( 220 ) ? HTML ( 3 ) ? ? PDF (3063KB) ( 62 ) ?

Model internal variability has been recognized as an important source of uncertainties of climate simulation results. However, the impact of model internal variability on the uncertainties in the simulation of 1.5℃ and 2℃ warming threshold-crossing time has not been explored to date. In this paper, such impact and the corresponding sensitivity to different future emission scenarios are investigated based on the outputs of Coupled Model Intercomparison Project Phase5 (CMIP5) models. The results show that the effect of internal variability on uncertainties in the simulation of threshold-crossing time is equivalent to that of external forcing. The difference between the threshold-crossing time of model members reaching 1.5℃ or 2℃ global warming is 2-12 years. The influence of internal variability has a clear spatial distribution. Maximum uncertainties are observed at the ocean northern of Eurasia, the area around the Bering Strait, the northeastern North America and the ocean between it and Greenland, and the high latitudes in the Southern Hemisphere. Model internal variability causes greater uncertainties in the low emission scenario than the high emission scenario.

Climate classification over China based on K?ppen climate classification in the context of ENSO ? Collect
Yi-Man LI,Qian YE
Climate Change Research. 2019, 15 (4): 352-362. ? DOI: 10.12006/j.issn.1673-1719.2018.177
Abstract ( 115 ) ? HTML ( 2 ) ? ? PDF (9216KB) ( 58 ) ?

In this study, the traditional K?ppen climate classification was used to composite analyze the climate of 22 El Ni?o and La Ni?a years in China during the period 1966-2015, and the results were compared with those in the 50 years from 1966 to 2015. The results are as follows. During ENSO years, there are no significant changes on the distribution of the climate zones as a whole. China is mainly dominant by four climate zones. The climate types and subtypes, however, are changed significantly during El Ni?o and La Ni?a years and next years, especially in southern, northeastern and northwestern China. There are five sub-regions in China, namely southeastern Tibet, the central of Shaanxi province, the central of Sichuan province, Liaodong Peninsula and eastern Inner Mongolia, which are sensitive to both El Ni?o and La Ni?a years, and these sensitive regions tend to be drier in winter. There are still two sub-regions in China, namely the middle and lower reaches of the Yangtze River and South China, which are sensitive to La Ni?a years and also tend to be drier in winter. In addition, there are four sub-regions in China, namely the central of Shaanxi province, the central of Sichuan province, Liaodong Peninsula and eastern Inner Mongolia, which are sensitive to the next years of both El Ni?o and La Ni?a. There are also four sub-regions in China, namely the middle and lower reaches of the Yangtze River, South China, the east of Yunnan province and the central of Guangdong and Guangxi Hills, which are sensitive to the next years of El Ni?o.

Characteristics of extreme temperature and precipitation in China in 2017 based on ETCCDI indices ? Collect
Hong YIN,Ying SUN
Climate Change Research. 2019, 15 (4): 363-373. ? DOI: 10.12006/j.issn.1673-1719.2018.164
Abstract ( 179 ) ? HTML ( 2 ) ? ? PDF (3802KB) ( 94 ) ?

Based on the homogenized daily data in 2419 stations in China from 1961 to 2017, we calculated 26 extreme temperature and precipitation indices as defined by the Expert Team on Climate Change Detection and Indices (ETCCDI), and analyzed the characteristics of extreme temperature and precipitation in China in 2017. For China average, all the high temperature indices in 2017 were above the 30-year average of 1961—1990 and the extreme low temperature indices were lower than their corresponding 1961—1990 average. The annual minima of daily maximum temperature (TXn) and daily minimum temperature (TNn) reached the highest recorded value, while the number of cold nights (TN10p), cold days (TX10p), and cold spell duration index (CSDI) reached the lowest recorded value. Some indices were ranked at the second or third place since 1961, including annual maxima of daily maximum temperature (TXx) and daily minimum temperature (TNx), warm nights (TN90p), frost days (FD), icing days (ID), tropical nights (TR), and growing season length (GSL). Other extreme temperature indices were ranked in the top 10 since 1961. Meanwhile, 7 out of 10 extreme precipitation indices averaged over China in 2017 were within the range of one standard deviation of precipitation indices during 1961-2017, indicating a normal situation for extreme precipitation in 2017.

The spatiotemporal characteristics and long-term trends of surface-air temperatures difference in China ? Collect
Yao-Ming LIAO,Deliang CHEN,Qiu-Feng LIU
Climate Change Research. 2019, 15 (4): 374-384. ? DOI: 10.12006/j.issn.1673-1719.2018.199
Abstract ( 196 ) ? HTML ( 3 ) ? ? PDF (4164KB) ( 89 ) ?

In this study, we analyzed the temporal and spatial distributions and long-term linear trend of surface-air temperatures difference (ΔT) in China. The values of ΔT were calculated based on the daily surface and air temperature measured at 825 weather stations from 1961 to 2016. The results showed that the annual ΔT was above 2.5℃ in most of western China and parts of South China, while below 2.5℃ in most of central and eastern China. In spring and summer, the ΔT was positive and generally distributed meridionally, high in western China and low in eastern China. In autumn and winter, the ΔT was generally distributed zonally, high in southern China and low in northern China. Especially in some areas of northern China, ΔT was negative in winter. The average monthly ΔT in China was positive, but it was relatively low in winter (January and December) and high in summer. The intra-annual distribution characteristics of ΔT varied across regions. The annual ΔT in Tibet was the highest in China with the maximum in May prior the rainy season. The regional average ΔT in the Northeast China, North China, Huanghuai region, Northwest China and Inner Mongolia reached the peak in June before the arrival of the rainy season. The maximum ΔT appeared in July or August after the rainy season in Yangtze-Huaihe region, Jianghan, south of the Yangtze River and South China. The monthly ΔT in Southwest China had smaller change and had two peaks respectively in May before the rainy season and August after the rainy season. From 1961 to 2016, the countrywide mean ΔT displayed an upward linear trend in April and from April to October, while there was no significant linear trend in July and October. The ΔT had an increasing trend in Northeast China, Northwest China, Inner Mongolia, and western Tibet, while a decreasing trend was identified in central and eastern China.

Reversal trends of atmospheric temperature in spring over the Tibetan Plateau after 2008 and possible links with total ozone trends ? Collect
Qing WANG,Fu-Xiang HUANG,Xue-Qi XIA
Climate Change Research. 2019, 15 (4): 385-394. ? DOI: 10.12006/j.issn.1673-1719.2019.061
Abstract ( 92 ) ? HTML ( 3 ) ? ? PDF (3127KB) ( 38 ) ?

Trends of atmospheric temperature during 1980-2007 over the Tibetan Plateau were evaluated using the ERA-Interim and MERRA-2 reanalysis data sets. Analyses of different time scales from annual to seasonal and monthly show that remarkable reversal trends of atmospheric temperature after 2008: temperatures at the lower stratosphere (150-50 hPa) are increasing with rates of 1.0-2.7℃/10a, while in the upper troposphere (300-175 hPa), temperatures are decreasing with rates of -3.1- -1.0℃/10a. Trends of atmospheric temperature in spring after 2008 over the Tibetan Plateau are reversal with those of 1980-2007. Analysis of total ozone trends over the Tibetan Plateau during 1980-2017 were carried out and results show significant increasing trends of monthly total ozone from end of winter to spring, especially in May with the highest increasing rate of 13.7 DU/10a. Analysis results show that variations of atmospheric temperature are closely linked with variation of total ozone, and the reversal trends of atmospheric temperature in spring maybe the results of total ozone recovery after 2008 over the Tibetan Plateau.

Impacts of Climate Change
Interpretation of IPCC SR1.5 on cryosphere change and its impacts ? Collect
Bo SU,Xue-Jie GAO,Cun-De XIAO
Climate Change Research. 2019, 15 (4): 395-404. ? DOI: 10.12006/j.issn.1673-1719.2019.139
Abstract ( 214 ) ? HTML ( 4 ) ? ? PDF (2241KB) ( 126 ) ?

The cryosphere is highly sensitive to climate change among the five major spheres of climate system. Past decades, with the anthropogenic climate warming, has seen an accelerated retreat of the global cryosphere (including mountain glacier, frozen soil, snow cover and sea ice, etc.), which has also seriously affected global climate system and regional water resources, eco-environment, socio-economic development and human well-being. The IPCC Special Report on Global Warming of 1.5℃ (SR1.5) was issued in October 2018, it systematically presented basic scientific understanding of 1.5℃ global warming above pre-industrial levels and related global greenhouse gas emission pathways in the context of sustainable development and poverty eradication. In the cryosphere and related aspects, SR1.5 mainly projected some cryospheric changes (mainly sea ice, permafrost) and their impacts on the atmosphere, hydrosphere, biosphere, lithosphere and anthroposphere at a global average warming of 1.5℃ and higher levels of warming. It also focused on many climate change hotspots and tipping points under different global temperature goals, most of which are related to the cryosphere. As the temperature continues to rise, the risks to the cryosphere and its associated hotspots and tipping points will continue to increase. Limiting global warming to 1.5℃ compared to 2℃ or higher level is projected to lower the risks. However, frankly, SR1.5 has not given deep attention to the change and its impacts. In the future, it is necessary to deepen the research on the changes of the cryosphere and its related impacts and adaptation under different climatic scenarios, especially with the global 1.5℃ and 2℃ temperature goals and tipping points, thereby to explore a more sustainable and resilient pathway in the cryosphere affected regions.

Climate change impacts on runoff in the upper Yangtze River basin ? Collect
Peng-Cheng QIN,Min LIU,Liang-Min DU,Hong-Mei XU,Lyu-Liu LIU,Chan XIAO
Climate Change Research. 2019, 15 (4): 405-415. ? DOI: 10.12006/j.issn.1673-1719.2018.168
Abstract ( 220 ) ? HTML ( 4 ) ? ? PDF (7182KB) ( 88 ) ?

To assess the impacts of climate change on river runoff in the upper Yangtze River basin, the climate change and its impacts on spatial-temporal trend of annual, seasonality and extremes of runoff were examined using Soil and Water Assessment Tool (SWAT) model, based on a subset of five global circulation models under three Representative Concentration Pathways (RCP2.6, RCP4.5, RCP8.5). The projected average annual temperature presents a significant upward trend, with an increase of 1.5-5.5℃ by the end of the 21st century relative to the reference period 1986-2005, and the overall precipitation is projected to increase after 2030s, with an increase of 5%-15% by the end of the 21st century. There is considerable spatial variation in the projected changes in annual temperature and precipitation across the upper Yangtze River basin, with the upper sub-basins (Jinsha River and Mintuo River) having a generally warmer and wetter conditions comparing with the whole study area. Changes of climate will result in an increase in the simulated mean annual runoff in the upper Yangtze River basin after 2030s, with an increase of 4%-8% in middle of the 21st century, and 10%-15% increase by the end of the 21st century. Additionally, the intra-annual distribution of monthly runoff is simulated generally more uniform. However, the inter-annual variation of runoff is simulated to increase, which indicates more frequent and severe extreme flood and drought events. With respect to spatial differences in simulated runoff, the sub-basins of Jinsha River and Mintuo River show a relative small change in the annual water availability as well as the inter-annual and intra-annual variability, whereas the sub-basins of Jialing, Wujiang, and mainstream of the upper Yangtze River show a larger increase in water availability and hydrological extremes.

Greenhouse Gas Emissions
Estimation of embodied energy and carbon emissions in Sino-U.S. trade based on Multi-Region Input-Output Model ? Collect
Zhong HAN,Gang WANG
Climate Change Research. 2019, 15 (4): 416-426. ? DOI: 10.12006/j.issn.1673-1719.2018.154
Abstract ( 122 ) ? HTML ( 2 ) ? ? PDF (1275KB) ( 46 ) ?

Based on the Multi-Regional Input-Output Model (MRIO), using the World Input-Output Tables (WIOT) and environmental account data developed by the EU, the scale and net value of the value-added trade between China and the U.S. from 1995 to 2009 were measured, and the environmental accounts were used. Energy consumption and carbon emissions data measure the overall level of Sino-U.S. foreign trade embodied energy and embodied carbon emissions and its industry structure. Results showed that China’s value-added exports to the U.S. continued to grow in 1995-2009, especially after China’s accession to the World Trade Organization (WTO), but then decreased due to the global economic crisis in 2008. In comparison with the U.S., China’s value-added energy consumption and carbon emission levels are relatively high, resulting in larger-scale embodied energy and embodied carbon exports, long-term net exporting country status of embodied energy and embodied carbon, and net output. The scale shows an upward trend. From the perspective of industry structure, the energy industry such as manufacturing and electricity, gas and water supply is the main source of China’s foreign trade embodied energy consumption and embodied carbon emissions.

Carbon market, sector competitiveness and carbon leakage: steel sector case ? Collect
Wen-Bin LIN,A-Lun GU,Bin LIU,Zhao-Xin WANG,Ling-Ling ZHOU
Climate Change Research. 2019, 15 (4): 427-435. ? DOI: 10.12006/j.issn.1673-1719.2018.190
Abstract ( 76 ) ? HTML ( 2 ) ? ? PDF (2080KB) ( 22 ) ?

In December 2017, China officially announced the launch of the national carbon market, which will provide institutional guarantees for China’s carbon emissions peaking as early as possible and coordinating environmental issues. The industries covered by the national carbon market include energy intensive sectors such as electricity, steel, cement, chemicals, paper, and non-ferrous metals. The carbon market policy is bound to have certain impacts on their competitiveness. By constructing a partial equilibrium model, this paper quantitatively analyzes the impact of carbon market on the competitiveness of China’s steel industry from the aspects of price, output, trade and carbon leakage, and makes sensitivity analysis on key parameters affecting the model results, including emission reduction cost curve, quota allocation method and trade elasticity. The research results show that the carbon market has not much impact on the competitiveness of the steel industry, but more attention should be paid to the carbon leakage issue.

Forum
Main focus of adaptation under the implementation rules of the Paris Agreement and China’s future measures ? Collect
Shuo LIU,Yu-E LI,Xiao-Bo QIN,Qing-Zhu GAO,Yun-Fan WAN
Climate Change Research. 2019, 15 (4): 436-444. ? DOI: 10.12006/j.issn.1673-1719.2019.020
Abstract ( 131 ) ? HTML ( 3 ) ? ? PDF (792KB) ( 50 ) ?

Adaptation is an important part of negotiations under the United Nations Framework Convention on Climate Change and its Paris Agreement. The 24th Conference of the Parties (COP24), held in December 2018, reached consensus on the follow-up implementation of adaptation issues, bringing new opportunities and challenges to global climate governance. In the future global climate governance, how to use the new achievements to promote the steady development of domestic adaptation and actively play China’s role is an important issue that needs to be considered urgently under the new situation. Based on this, this paper combed the focus of COP24 adaptation issues, the standpoints of groups and Parties, looked forward to the main arrangements of adaptation issues in the period of 2019-2025, and put forward some suggestions for future adaptation work in China, including: (1) analyzing the linkages between international information reporting system and domestic information, and sorting out and highlighting domestic information, in order to construct high-quality reports; (2) building a cross-sectoral and cross-regional collaboration mechanism, strengthening information collection and improvement, effectively improving the functions of data and information statistics; (3) strengthening the role of scientific research on climate change adaptation technologies, norms and standards, so as to provide incorporate relevant technical requirements and improve policy provisions in formulating policies and regulations as a kind of operational services.

2019
Vol.15
No.3?
2019-05-30
pp.217-334
No.2
2019-03-30
pp.107-216
No.1
2019-01-30
pp.1-106
2018
Vol.14
No.6?
2018-11-30
pp.547-648
No.5
2018-09-30
pp.437-546
No.4
2018-07-30
pp.331-436
No.3
2018-05-31
pp.221-330
No.2
2018-03-30
pp.111-220
No.1
2018-01-31
pp.1-110
2017
Vol.13
No.6?
2017-11-30
pp.517-630
No.5
2017-09-30
pp.407-516
No.4
2017-07-30
pp.0-0
No.3
2017-05-30
pp.0-0
No.2
2017-03-30
pp.0-0
No.1
2017-01-30
pp.1-94
2016
Vol.12
No.6?
2016-11-30
pp.467-574
No.5
2016-09-30
pp.355-466
No.4
2016-07-30
pp.261-354
No.3
2016-05-31
pp.0-0
No.2
2016-03-30
pp.0-0
No.1
2016-01-30
pp.0-0
2015
Vol.11
No.6?
2015-11-30
pp.379-446
No.5
2015-09-30
pp.301-378
No.4
2015-07-31
pp.0-0
No.3
2015-05-31
pp.157-230
No.2
2015-03-30
pp.79-156
No.1
2015-01-30
pp.1-78
2014
Vol.10
No.6?
2014-11-30
pp.391-470
No.5
2014-09-30
pp.313-390
No.4
2014-07-30
pp.235-312
No.3
2014-05-30
pp.0-0
No.2
2014-03-30
pp.79-156
No.1
2014-01-31
pp.1-78
2013
Vol.9
No.6?
2013-11-30
pp.391-452
No.5
2013-09-30
pp.313-390
No.4
2013-07-30
pp.235-312
No.3
2013-05-30
pp.157-234
No.2
2013-03-30
pp.79-156
No.1
2013-01-31
pp.1-78
2012
Vol.8
No.6?
2012-11-30
pp.391-476
No.5
2012-09-30
pp.313-390
No.4
2012-07-30
pp.235-312
No.3
2012-05-30
pp.157-234
No.2
2012-03-30
pp.79-156
No.1
2012-01-30
pp.1-78
2011
Vol.7
No.6?
2011-11-30
pp.385-460
No.5
2011-09-30
pp.307-384
No.4
2011-07-30
pp.235-306
No.3
2011-05-30
pp.0-0
No.2
2011-03-30
pp.79-156
No.1
2011-01-30
pp.1-78


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For Selected: View Abstracts Toggle Thumbnails
A brief introduction and comment on ScenarioMIP endorsed by CMIP6
ZHANG Li-Xia, CHEN Xiao-Long, XIN Xiao-Ge
Climate Change Research ??
Introduction of NUIST-ESM model and its CMIP6 activities
CAO Jian, MA Li-Bin, LI Juan, WANG Bin, WANG Bo
Climate Change Research ??
Introduction of BCC models and its participation in CMIP6
XIN Xiao-Ge, WU Tong-Wen, ZHANG Jie, ZHANG Fang, LI Wei-Ping, ZHANG Yan-Wu, LU Yi-Xiong, FANG Yong-Jie, JIE Wei-Hua, ZHANG Li, DONG Min, SHI Xue-Li, LI Jiang-Long, CHU Min, LIU Qian-Xia, YAN Jing-Hui
Climate Change Research ??
Introduction of FIO-ESM v2.0 and its participation plan in CMIP6 Experiments
SONG Zhen-Ya, BAO Ying, QIAO Fang-Li
Climate Change Research ??
CMIP6 Carbon Dioxide Removal Model Intercomparison Project (CDRMIP)
JI Duo-Ying, ZHANG Qian, LUO Zhi-Cheng, CHEN Yang-Xin
Climate Change Research ??
Overview of the High Resolution Model Intercomparison Project (HighResMIP)
WANG Lei, BAO Qing, HE Bian
Climate Change Research ??
Long-term runoff change trend of Yalong River basin under future climate change scenarios
DONG Li-Jun, DONG Xiao-Hua, ZENG Qiang, WEI Chong, YU Dan, BO Hui-Juan, GUO Jing
Climate Change Research ??

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