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德国观察组织发布《2024年气候变化绩效指数》报告 快报文章
气候变化快报,2024年第1期
作者:  廖琴
Microsoft Word(414Kb)  |  收藏  |  浏览/下载:461/0  |  提交时间:2024/01/06
Climate Change  Performance Index  
德国观察组织发布《2023年气候变化绩效指数》报告 快报文章
气候变化快报,2022年第23期
作者:  廖琴
Microsoft Word(182Kb)  |  收藏  |  浏览/下载:661/0  |  提交时间:2022/12/05
Climate Change Performance Index  
悉尼科技大学为全球各行业脱碳提出碳预算 快报文章
气候变化快报,2022年第10期
作者:  董利苹
Microsoft Word(16Kb)  |  收藏  |  浏览/下载:745/0  |  提交时间:2022/05/19
Global Warming  1.5 ˚C  Sectoral Pathways  Key Performance Indicators  
Germanwatch发布2022年气候变化绩效指数报告 快报文章
气候变化快报,2021年第23期
作者:  廖琴
Microsoft Word(726Kb)  |  收藏  |  浏览/下载:849/0  |  提交时间:2021/12/07
Climate Change Performance Index  
Germanwatch发布2021年气候变化绩效指数报告 快报文章
气候变化快报,2021年第1期
作者:  刘燕飞
Microsoft Word(20Kb)  |  收藏  |  浏览/下载:482/0  |  提交时间:2021/01/04
Climate Change Performance Index  GHG Emissions  Renewable Energy  Energy Use  Climate Policy  
ECMWF与Atos成立天气和气候模拟卓越中心 快报文章
气候变化快报,2020年第20期
作者:  刘燕飞
Microsoft Word(15Kb)  |  收藏  |  浏览/下载:378/0  |  提交时间:2020/10/20
Weather & Climate Modelling  High-performance Computing  ECMWF  Artificial Intelligence  
Impact of Higher Spatial Atmospheric Resolution on Precipitation Extremes Over Land in Global Climate Models 期刊论文
JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 2020, 125 (13)
作者:  Bador, Margot;  Boe, Julien;  Terray, Laurent;  Alexander, Lisa, V;  Baker, Alexander;  Bellucci, Alessio;  Haarsma, Rein;  Koenigk, Torben;  Moine, Marie-Pierre;  Lohmann, Katja;  Putrasahan, Dian A.;  Roberts, Chris;  Roberts, Malcolm;  Scoccimarro, Enrico;  Schiemann, Reinhard;  Seddon, Jon;  Senan, Retish;  Valcke, Sophie;  Vanniere, Benoit
收藏  |  浏览/下载:12/0  |  提交时间:2020/08/18
precipitation extremes  multimodel and multiproduct of observations framework  performance of the models  global climate models for CMIP6 and HighResMIP  sensitivity to atmospheric spatial resolution  
Characterization of Aerosol Type Over East Asia by 4.4 km MISR Product: First Insight and General Performance 期刊论文
JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 2020, 125 (13)
作者:  Tao, Minghui;  Wang, Jun;  Li, Rong;  Chen, Liangfu;  Xu, Xiaoguang;  Wang, Lunche;  Tao, Jinhua;  Wang, Zifeng;  Xiang, Juan
收藏  |  浏览/下载:10/0  |  提交时间:2020/08/18
aerosol type  MISR  East Asia  characterization  performance  
Potential for large-scale CO2 removal via enhanced rock weathering with croplands 期刊论文
NATURE, 2020, 583 (7815) : 242-+
作者:  David J. Beerling;  Euripides P. Kantzas;  Mark R. Lomas;  Peter Wade;  Rafael M. Eufrasio;  Phil Renforth;  Binoy Sarkar;  M. Grace Andrews;  Rachael H. James;  Christopher R. Pearce;  Jean-Francois Mercure;  Hector Pollitt;  Philip B. Holden;  Neil R. Edwards;  Madhu Khanna;  Lenny Koh;  Shaun Quegan;  Nick F. Pidgeon;  Ivan A. Janssens;  James Hansen;  Steven A. Banwart
收藏  |  浏览/下载:18/0  |  提交时间:2020/07/14

Enhanced silicate rock weathering (ERW), deployable with croplands, has potential use for atmospheric carbon dioxide (CO2) removal (CDR), which is now necessary to mitigate anthropogenic climate change(1). ERW also has possible co-benefits for improved food and soil security, and reduced ocean acidification(2-4). Here we use an integrated performance modelling approach to make an initial techno-economic assessment for 2050, quantifying how CDR potential and costs vary among nations in relation to business-as-usual energy policies and policies consistent with limiting future warming to 2 degrees Celsius(5). China, India, the USA and Brazil have great potential to help achieve average global CDR goals of 0.5 to 2gigatonnes of carbon dioxide (CO2) per year with extraction costs of approximately US$80-180 per tonne of CO2. These goals and costs are robust, regardless of future energy policies. Deployment within existing croplands offers opportunities to align agriculture and climate policy. However, success will depend upon overcoming political and social inertia to develop regulatory and incentive frameworks. We discuss the challenges and opportunities of ERW deployment, including the potential for excess industrial silicate materials (basalt mine overburden, concrete, and iron and steel slag) to obviate the need for new mining, as well as uncertainties in soil weathering rates and land-ocean transfer of weathered products.


  
International evaluation of an AI system for breast cancer screening 期刊论文
NATURE, 2020, 577 (7788) : 89-+
作者:  McKinney, Scott Mayer;  Sieniek, Marcin;  Godbole, Varun;  Godwin, Jonathan;  Antropova, Natasha;  Ashrafian, Hutan;  Back, Trevor;  Chesus, Mary;  Corrado, Greg C.;  Darzi, Ara;  Etemadi, Mozziyar;  Garcia-Vicente, Florencia;  Gilbert, Fiona J.;  Halling-Brown, Mark;  Hassabis, Demis;  Jansen, Sunny;  Karthikesalingam, Alan;  Kelly, Christopher J.;  King, Dominic;  Ledsam, Joseph R.;  Melnick, David;  Mostofi, Hormuz;  Peng, Lily;  Reicher, Joshua Jay;  Romera-Paredes, Bernardino;  Sidebottom, Richard;  Suleyman, Mustafa;  Tse, Daniel;  Young, Kenneth C.;  De Fauw, Jeffrey;  Shetty, Shravya
收藏  |  浏览/下载:15/0  |  提交时间:2020/07/03

Screening mammography aims to identify breast cancer at earlier stages of the disease, when treatment can be more successful(1). Despite the existence of screening programmes worldwide, the interpretation of mammograms is affected by high rates of false positives and false negatives(2). Here we present an artificial intelligence (AI) system that is capable of surpassing human experts in breast cancer prediction. To assess its performance in the clinical setting, we curated a large representative dataset from the UK and a large enriched dataset from the USA. We show an absolute reduction of 5.7% and 1.2% (USA and UK) in false positives and 9.4% and 2.7% in false negatives. We provide evidence of the ability of the system to generalize from the UK to the USA. In an independent study of six radiologists, the AI system outperformed all of the human readers: the area under the receiver operating characteristic curve (AUC-ROC) for the AI system was greater than the AUC-ROC for the average radiologist by an absolute margin of 11.5%. We ran a simulation in which the AI system participated in the double-reading process that is used in the UK, and found that the AI system maintained non-inferior performance and reduced the workload of the second reader by 88%. This robust assessment of the AI system paves the way for clinical trials to improve the accuracy and efficiency of breast cancer screening.