past GCMs Sup material PLOS ONE

These are text files with the information about the climatic conditions predicted by 9 General Circulation Models for the Last Glacial Maximum. see more at http://ecoclimate.org/ Data availability: The dataset includes simulations for modern (simulations for 1950-1999), historical (1900-1949), pre-industrial (~1760), Mid-Holocene (6ka), Last Glacial Maximum (21ka), and future conditions (mean of simulations for 2080-2100), for nine coupled atmosphere-ocean global climate models (AOGCMs). Future simulations include four representative concentration pathways (RCPs): RCP2.6 (low emissions scenarios), RCP4.5 and RCP6.0 (intermediate emissions scenarios), and RCP 8.5 (high emissions scenario) (see details in Taylor et al. 2009, 2012).<br>Data downscaling and interpolation: Monthly simulations of precipitation and mean, maximum and minimum temperature for all time periods and AOGCMs were downloaded in NetCDF format from CMIP5 and PMIP3, with spatial resolution originally ranging between 0.9o (e.g., CCSM4) to 2.8o (e.g., MIROC-ESM). All data were downscaled to 0.5o x 0.5o resolution, according to the standard change-factor approach (Wilby et al. 2004), namely: i) firstly we computed the change-factor (also called climate change trends or anomalies) between past/future and baseline climate for each raw variable at model-specific native spatial resolution, (ii) secondarily we downscaled the change-factor (instead of past/future climate values) and its respective baseline climate from each AOGCM to the standard 0.5o resolution, and (iii) thirdly applied the downscaled change-factor to the downscaled baseline climate to reconstitute values and obtain the downscaled layers for past and future climates. From downscaled data, we generated the 19 bioclimatic variables described in WorldClim. This procedure was done using a script developed by Matheus Lima-Ribeiro in https://github.com/macroecology/LGM_GCMs.<br>References: TAYLOR, KE; STOUFFER, RJ and MEEHL, GA (2012) An overview of CMIP5 and the Experiment Design. American Meteorological Society. 93: 485–498.<br>TAYLOR, KE; STOUFFER, RJ and MEEHL, GA (2009) A summary of the CMIP5 Experiment Design. Available in CMIP5. WILBY, RL; CHARLES, SP: ZORITA, E: TIMBAL, B, WHETTON, P, MEARNS ,LO (2004) Guidelines for use of climate scenarios developed from statistical downscaling methods. IPCC Task Group on Data and Scenario Support for Impact and Climate Analysis. http://www.ipcc data.org/guidelines/dgm_no2_v1_09_2004.pd People: Matheus Lima-Ribeiro Professor<br>Laboratory of Macroecology<br>Universidade Federal de Goiás<br>Regional Jataí, Brazil Levi Carina Terribile Professor<br>Laboratory of Macroecology<br>Universidade Federal de Goiás<br>Regional Jataí, Brazil Sara Varela Postdoctoral researcher<br>Department of Ecology<br>Charles University<br>Prague, Czech Republic Javier González-Hernández Software engineer<br>Berlin, Germany Guilherme de Oliveira Professor<br>Laboratory of Conservation Biogeography<br>Universidade Federal do Recôncavo da Bahia<br>Bahia, Brazil José Alexandre Felizola Diniz-Filho Professor<br>Department of Ecology<br>Universidade Federal de Goiás<br>Goiás, Brazil Acknowledgements Financial support for data processing and downscaling was provided by the Brazilian National Council for Scientific and Technological Development (CNPq) and Brazilian Federal Agency for the Support and Evaluation of Graduate Education (CAPES), through the Research Network GENPAC (Geographical Genetics and Regional Planning for Natural Resources in Brazilian Cerrado, project no 563727/2010-1). We thank the World Climate Research Programme (WCRP) and Working Group on Coupled Modelling (WGCM) by the CMIP5 and the PMIP3, from which climatic simulations were derived. We also thank Thiago Fernando Rangel (UFG) for help and suggestions. We dedicate the EcoClimate to Mariana Rocha (in memorian), who was enthusiastically interested in this project when integrating the early EcoClimate team.

本数据集包含9个通用循环模式（General Circulation Models）针对末次冰盛期（Last Glacial Maximum）的气候模拟结果文本文件。更多信息详见 http://ecoclimate.org/

数据可用性：本数据集涵盖9个耦合大气-海洋全球气候模型（Atmosphere-Ocean Global Climate Models, AOGCMs）的多时段气候模拟结果，包括现代时段（1950-1999年模拟结果）、历史时期（1900-1949年）、前工业时代（约1760年）、全新世中期（Mid-Holocene，6ka）、末次冰盛期（21ka），以及未来气候情景（2080-2100年模拟结果的平均值）。其中未来情景包含4种典型浓度路径（Representative Concentration Pathways, RCPs）：RCP2.6（低排放情景）、RCP4.5与RCP6.0（中等排放情景），以及RCP8.5（高排放情景），详细信息参见Taylor等人2009、2012年的研究。

数据降尺度与插值：本研究从CMIP5（耦合模式比较计划第五阶段）与PMIP3（古气候模式比较计划第三阶段）数据库中，下载了所有时段、所有AOGCM的降水、平均气温、最高气温与最低气温的月尺度模拟数据，数据格式为NetCDF，原始空间分辨率介于0.9°（如CCSM4模型）至2.8°（如MIROC-ESM模型）之间。所有数据均按照标准变化因子法（Wilby等人2004年提出）统一降尺度至0.5°×0.5°的分辨率，具体步骤如下：① 首先在各模型的原始空间分辨率下，计算各原始变量在过去/未来气候与基准气候之间的变化因子（亦称气候变化趋势或气候异常值）；② 其次将各AOGCM的变化因子（而非直接的过去/未来气候数值）及其对应的基准气候数据降尺度至标准0.5°分辨率；③ 最后将降尺度后的变化因子应用于降尺度后的基准气候数据，重构得到过去与未来气候的降尺度栅格图层。基于降尺度后的数据，我们生成了WorldClim标准定义的19个生物气候变量。本处理流程使用Matheus Lima-Ribeiro开发的脚本完成，代码仓库地址为https://github.com/macroecology/LGM_GCMs。

参考文献：
1. TAYLOR, KE; STOUFFER, RJ and MEEHL, GA (2012) 《耦合模式比较计划第五阶段（CMIP5）概况与试验设计概述》，美国气象学会会刊，93: 485–498。
2. TAYLOR, KE; STOUFFER, RJ and MEEHL, GA (2009) 《耦合模式比较计划第五阶段试验设计总结》，载于CMIP5官方资料。
3. WILBY, RL; CHARLES, SP; ZORITA, E; TIMBAL, B; WHETTON, P; MEARNS, LO (2004) 《统计降尺度法气候情景应用指南》，政府间气候变化专门委员会（IPCC）影响与气候分析数据与情景支持工作组，http://www.ipcc data.org/guidelines/dgm_no2_v1_09_2004.pd

参与人员：
- Matheus Lima-Ribeiro 教授 | 宏观生态学实验室 | 戈亚斯联邦大学雅塔伊校区 | 巴西
- Levi Carina Terribile 教授 | 宏观生态学实验室 | 戈亚斯联邦大学雅塔伊校区 | 巴西
- Sara Varela 博士后研究员 | 生态系 | 查理大学 | 布拉格，捷克共和国
- Javier González-Hernández 软件工程师 | 柏林，德国
- Guilherme de Oliveira 教授 | 保护生物地理学实验室 | 巴伊亚联邦大学雷孔卡沃分校 | 巴伊亚州，巴西
- José Alexandre Felizola Diniz-Filho 教授 | 生态系 | 戈亚斯联邦大学 | 戈亚斯州，巴西

致谢：本数据集的数据处理与降尺度工作获得了巴西国家科学技术发展委员会（Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq）与巴西高等教育支持与评估基金会（Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, CAPES）通过GENPAC研究网络（巴西塞拉多自然资源地理遗传学与区域规划，项目编号563727/2010-1）提供的经费支持。我们感谢世界气候研究计划（World Climate Research Programme, WCRP）与耦合模式工作组（Working Group on Coupled Modelling, WGCM）提供CMIP5与PMIP3的气候模拟数据。同时感谢Thiago Fernando Rangel（戈亚斯联邦大学）为本研究提供的帮助与建议。谨将本EcoClimate数据集献给Mariana Rocha（已故），她在加入早期EcoClimate团队时便对本项目抱有极高的热忱。