Monthly averages of ED2 model simulations initialised with airborne lidar structure, Jan 1981-Dec 2018, Brazilian Amazon

Summary

This dataset provides output results from three ED2 model simulations that used a combination of forest structure derived from a regional airborne lidar survey across the Brazilian Amazon carried out in 2016, which was led by the Brazilian National Institute for Space Research (INPE), and two forest structure change scenarios. These results are presented in the following manuscript:

Longo, M., M. Keller, L. M. Kueppers, K. Bowman, O. Csillik, A. Ferraz, P. R. Moorcroft, J. P. Ometto, B. S. Soares-Filho, X. Xu, M. L. F. de Assis, E. B. Görgens, E. J. L. Larson, J. F. Needham, E. M. Ordway, F. R. S. Pereira, E. Rangel Pinagé, L. Sato, L. Xu and S. Saatchi. 2025. Degradation and deforestation increase the sensitivity of the Amazon Forest to climate extremes. In review.

For all simulations, we used bias-corrected hourly reanalyses (WFDE5) for most meteorological drivers, except for precipitation, which was obtained from CHIRPS. The meteorological drivers used in the study span 38 years (Jan 1981–Dec 2018). The output results correspond to the last 38 years of simulation (one full cycle of meteorological drivers), in which ED2 simulations used static stand structure (i.e., the forest structure was held constant). The following files are provided:

ED2_emean_Global_R004_BrAmaz_s1c0t0l0f0.nc. This corresponds to the Control simulation. The forest structure was obtained from the airborne lidar.

ED2_emean_Global_R005_BrAmaz_s1c0t1l1f0.nc. This corresponds to the Degraded simulation. The forest structure was obtained from a spin-up simulation initialized with airborne lidar and a scenario that expanded deforestation and selective logging across the Amazon.

ED2_emean_Global_R006_BrAmaz_s1c0t1l0f0.nc. This corresponds to the Recovery simulation. The forest structure was obtained from a spin-up simulation initialized with airborne lidar and a scenario that completely halted deforestation and degradation, allowing degraded forests to recover for 38 years.

ED2_zones_R004_BrAmaz_s1c0t0l0f0.nc. This file classifies each grid cell into zones used in the reference manuscript: 1: Southeast. 2: South. 3: West. 4: Central. 5: Northeast. 6: North. 7: Northwest". Index 0 corresponds to grid cells excluded from sub-region analyses because they were dominated by flooded forests, deforestation, and naturally non-forest vegetation.

The variables included in the NetCDF files contain metadata describing the quantity and the units. Important: For biomass-related variables, units shown in kg actually correspond to kg C (~50% of oven-dry biomass).

Data characteristics

Spatial coverage: Brazilian Amazon Biome (74°W–45°W; 14°S—5°N)

Spatial resolution: 1×1°, with sub-grid information available for several variables. Sub-grid information include data aggregated by plant functional type, by plant size, by disturbance history, and by edaphic characteristics (soil texture or soil depth).

Temporal coverage: Jan 1981–Dec 2018 (based on meteorological drivers)

Temporal resolution: Monthly

Methods

Step 1. To carry out the ED2 simulations, we used ED2 initialization files generated following the algorithm described in Longo et al. (2020) and available on Zenodo.Step 2. We carried out ED2 simulations using the version tag v.2.2.1-BrAmazALS2, which is available both on GitHub and on a permanent archive. Using the boundary conditions archived on Zenodo, we carried out 5 sets of simulations using the configuration settings (archived here), and used the initial post-processing R scripts available on the same archive.Step 3. The consolidated R objects were converted to NetCDF files using the R Markdown notebooks available on Zenodo, and defining the output for NetCDF files to span from 1981 to 2018.

Version history

v1.0.1. This fixes a previous upload that only had part of the monthly averages in the NetCDF files. For this version, we deleted the multiple domain and zone files, because they did not provide any unique information.

v1.0.0. First submission.

本数据集提供了三项ED2模型（ED2 model）模拟的输出结果，这些模拟结合了2016年由巴西国家空间研究院（INPE）主导的巴西亚马逊区域机载激光雷达（airborne lidar）调查获取的森林结构数据，以及两种森林结构变化情景。相关研究成果已提交至以下期刊论文：
Longo, M., M. Keller, L. M. Kueppers, K. Bowman, O. Csillik, A. Ferraz, P. R. Moorcroft, J. P. Ometto, B. S. Soares-Filho, X. Xu, M. L. F. de Assis, E. B. Görgens, E. J. L. Larson, J. F. Needham, E. M. Ordway, F. R. S. Pereira, E. Rangel Pinagé, L. Sato, L. Xu 及 S. Saatchi. 2025. 《退化与森林砍伐提升亚马逊森林对极端气候的敏感性》，目前处于审稿阶段。

所有模拟中，我们采用经过偏差校正的逐小时再分析数据（WFDE5）作为多数气象驱动因子，降水数据则来自CHIRPS。本研究使用的气象驱动因子时间跨度为38年（1981年1月—2018年12月）。模拟输出结果对应模拟的最后38年（完整覆盖一轮气象驱动因子周期），此时ED2模拟采用静态林分结构（即森林结构保持恒定）。

本次提供的文件如下：
1. `ED2_emean_Global_R004_BrAmaz_s1c0t0l0f0.nc`：对应对照模拟，其森林结构来自机载激光雷达数据。
2. `ED2_emean_Global_R005_BrAmaz_s1c0t1l1f0.nc`：对应退化模拟，其森林结构来自以机载激光雷达数据为初始条件的自旋模拟，同时叠加了亚马逊区域森林砍伐与选择性伐木扩张的情景。
3. `ED2_emean_Global_R006_BrAmaz_s1c0t0l0f0.nc`：对应恢复模拟，其森林结构来自以机载激光雷达数据为初始条件的自旋模拟，情景设定为完全停止森林砍伐与退化过程，允许退化森林恢复38年。
4. `ED2_zones_R004_BrAmaz_s1c0t0l0f0.nc`：该文件将每个网格单元归类为参考论文中定义的区域：1为东南部，2为南部，3为西部，4为中部，5为东北部，6为北部，7为西北部。索引0对应因被水淹森林、森林砍伐以及自然非森林植被主导而被排除在子区域分析之外的网格单元。

NetCDF文件中包含的变量均带有描述其物理量与单位的元数据。重要提示：与生物量相关的变量，标注单位为kg时，实际对应的是kg C（约占烘干生物量的50%）。

数据特征
空间覆盖范围：巴西亚马逊生物群区（74°W–45°W；14°S—5°N）
空间分辨率：1×1°，部分变量附带亚网格信息。亚网格信息包括按植物功能型、植物尺寸、扰动历史以及土壤特性（土壤质地或土壤深度）聚合的数据。
时间覆盖范围：1981年1月—2018年12月（基于气象驱动因子的时间跨度）
时间分辨率：月度

研究方法
步骤1：开展ED2模拟时，我们采用了遵循Longo等人（2020）所述算法生成的ED2初始化文件，该文件可在Zenodo平台获取。
步骤2：我们使用版本标记为v.2.2.1-BrAmazALS2的ED2模型开展模拟，该版本可在GitHub及永久存档平台获取。依托Zenodo平台存档的边界条件，我们依据存档的配置设置开展了5组模拟，并使用同一存档中提供的初始后处理R脚本进行处理。
步骤3：整合后的R对象通过Zenodo平台上的R Markdown笔记本转换为NetCDF文件，并将NetCDF文件的输出时间范围设定为1981年至2018年。

版本历史
v1.0.1：修复了此前上传版本中NetCDF文件仅包含部分月度平均值的问题。本版本删除了多个冗余的区域与分区文件，因其未提供独有信息。
v1.0.0：首次提交版本。