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Multi-model ensemble predictions of precipitation and temperature using machine learning algorithms 期刊论文
ATMOSPHERIC RESEARCH, 2020, 236
作者:  Ahmed, Kamal;  Sachindra, D. A.;  Shahid, Shamsuddin;  Iqbal, Zafar;  Nawaz, Nadeem;  Khan, Najeebullah
收藏  |  浏览/下载:29/0  |  提交时间:2020/07/02
General circulation models  Multi-model ensemble  Taylor skill score  Machine learning algorithms  Temperature and precipitation  Pakistan  
The projected timing of abrupt ecological disruption from climate change 期刊论文
NATURE, 2020, 580 (7804) : 496-+
作者:  Gorgulla, Christoph;  Boeszoermenyi, Andras;  Wang, Zi-Fu;  Fischer, Patrick D.;  Coote, Paul W.;  Padmanabha Das, Krishna M.;  Malets, Yehor S.;  Radchenko, Dmytro S.;  Moroz, Yurii S.;  Scott, David A.;  Fackeldey, Konstantin;  Hoffmann, Moritz;  Iavniuk, Iryna;  Wagner, Gerhard;  Arthanari, Haribabu
收藏  |  浏览/下载:80/0  |  提交时间:2020/05/13

As anthropogenic climate change continues the risks to biodiversity will increase over time, with future projections indicating that a potentially catastrophic loss of global biodiversity is on the horizon(1-3). However, our understanding of when and how abruptly this climate-driven disruption of biodiversity will occur is limited because biodiversity forecasts typically focus on individual snapshots of the future. Here we use annual projections (from 1850 to 2100) of temperature and precipitation across the ranges of more than 30,000 marine and terrestrial species to estimate the timing of their exposure to potentially dangerous climate conditions. We project that future disruption of ecological assemblages as a result of climate change will be abrupt, because within any given ecological assemblage the exposure of most species to climate conditions beyond their realized niche limits occurs almost simultaneously. Under a high-emissions scenario (representative concentration pathway (RCP) 8.5), such abrupt exposure events begin before 2030 in tropical oceans and spread to tropical forests and higher latitudes by 2050. If global warming is kept below 2 degrees C, less than 2% of assemblages globally are projected to undergo abrupt exposure events of more than 20% of their constituent species  however, the risk accelerates with the magnitude of warming, threatening 15% of assemblages at 4 degrees C, with similar levels of risk in protected and unprotected areas. These results highlight the impending risk of sudden and severe biodiversity losses from climate change and provide a framework for predicting both when and where these events may occur.


Using annual projections of temperature and precipitation to estimate when species will be exposed to potentially harmful climate conditions reveals that disruption of ecological assemblages as a result of climate change will be abrupt and could start as early as the current decade.


  
Relating anomalous large-scale atmospheric circulation patterns to temperature and precipitation anomalies in the East Asian monsoon region 期刊论文
ATMOSPHERIC RESEARCH, 2020, 232
作者:  Yang, Ye;  Gao, Meng;  Xie, Naru;  Gao, Zhiqiang
收藏  |  浏览/下载:24/0  |  提交时间:2020/07/02
Atmospheric circulation patterns  Self-organizing map  Temperature and precipitation anomalies  East Asian Monsoon  
Projected spatial patterns in precipitation and air temperature for China's northwest region derived from high-resolution regional climate models 期刊论文
INTERNATIONAL JOURNAL OF CLIMATOLOGY, 2019
作者:  Yin, Zhenliang;  Feng, Qi;  Yang, Linshan;  Deo, Ravinesh C.;  Adamowski, Jan F.;  Wen, Xiaohu;  Jia, Bing;  Si, Jianhua
收藏  |  浏览/下载:26/0  |  提交时间:2020/02/17
climate change  CORDEX-EA  northwestern China  precipitation and temperature projection  regional climate models  
Stand carbon density drivers and changes under future climate scenarios across global forests 期刊论文
FOREST ECOLOGY AND MANAGEMENT, 2019, 449
作者:  Guo, Yanrong;  Peng, Changhui;  Trancoso, Ralph;  Zhu, Qiuan;  Zhou, Xiaolu
收藏  |  浏览/下载:20/0  |  提交时间:2019/11/27
Aboveground and belowground carbon densities  Stand age  Mean annual precipitation  Mean annual temperature  Dryness index  Clay content  
Changes in Extreme Precipitation Over Dry and Wet Regions of China During 1961-2014 期刊论文
JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 2019, 124 (11) : 5847-5859
作者:  Han, Jingya;  Du, Haibo;  Wu, Zhengfang;  He, Hong S.
收藏  |  浏览/下载:18/0  |  提交时间:2019/11/26
China  dry and wet regions  extreme precipitation  air temperature  
High-resolution mapping of daily climate variables by aggregating multiple spatial data sets with the random forest algorithm over the conterminous United States 期刊论文
INTERNATIONAL JOURNAL OF CLIMATOLOGY, 2019, 39 (6) : 2964-2983
作者:  Hashimoto, Hirofumi;  Wang, Weile;  Melton, Forrest S.;  Moreno, Adam L.;  Ganguly, Sangram;  Michaelis, Andrew R.;  Nemani, Ramakrishna R.
收藏  |  浏览/下载:22/0  |  提交时间:2019/11/26
daily surface climate  machine learning  NEX-GDM  precipitation  random forest  solar radiation and wind speed  temperature  
Impacts of global warming on the surface water balance components in Iran as simulated by RegCM4 期刊论文
INTERNATIONAL JOURNAL OF CLIMATOLOGY, 2019, 39 (5) : 2646-2658
作者:  Moslemzadeh, Elham;  Irannejad, Parviz;  Alizadeh-Choobari, Omid
收藏  |  浏览/下载:12/0  |  提交时间:2019/11/26
global warming  precipitation  RCP4  5 and RCP8  5 scenarios  RegCM4  surface temperature  
Temperature and precipitation variability in regional climate models and driving global climate models: Total variance and its temporal-scale components 期刊论文
INTERNATIONAL JOURNAL OF CLIMATOLOGY, 2019, 39 (3) : 1276-1286
作者:  Crhova, Lenka;  Holtanova, Eva
收藏  |  浏览/下载:14/0  |  提交时间:2019/04/09
CMIP5  EURO-CORDEX  fast Fourier transformation  global climate model  Kolmogorov-Zurbenko filter  long-term variability  regional climate model  seasonal-scale variability  short-term variability  temperature and precipitation variability  
Spatiotemporal trends of dryness/wetness duration and severity: The respective contribution of precipitation and temperature 期刊论文
ATMOSPHERIC RESEARCH, 2019, 216: 176-185
作者:  Wu, Jiefeng;  Chen, Xiaohong
收藏  |  浏览/下载:12/0  |  提交时间:2019/04/09
Dryness/wetness  Duration and severity  Precipitation  Temperature  Contribution  Pearl River basin (PRB)