GEOS-Chem model output for 2018-2019 CAMMPCAN Aurora Australis voyages

The dataset provides model output from the GEOS-Chem chemical transport model to support the CAMMPCAN and MARCUS 2018-2019 voyages.

The model version used was GEOS-Chem v12.8.1, with DOI: 10.5281/zenodo.3837666. The DOI should be cited when using this dataset. Modifications were made to the standard v12.8.1 to include abiotic ocean emissions of volatile organic compounds as implemented in Travis et al. (2020).

The model was running using MERRA-2 meteorology at 2°x2.5° (latitude x longitude) horizontal resolution with 47 vertical levels throughout the entire period of the voyages. These model runs were preceded by the model runs for the 2017-2018 voyages, in turn preceded a 6-month spin-up at 4°x5° beginning 1 May 2017.

The following output types are included (for variable names, see below):

1. Output along the shiptrack
Filenames: mrg20m_SS_ss_YYYYMMDD.txt
The model has been sampled to directly match the location of the ship at every minute during the voyages. The output is then averaged to ensure there is only one data point for each unique model gridbox-timestep combination. The resulting dataset is 1-dimensional, with values for latitude, longitude, and time along with the data variables. These are text files (with space as separator character).
2. Global monthly means
Filenames: GEOSChem.{DATA_TYPE}.{YYYYMM}01_monmean.nc4
{DATA_TYPE} can be any of SpeciesConc, StateMet, Aerosols, AerosolMass (see below).
{YYYYMM} is the year and month for the data included in the file.
The model output has been averaged on a monthly timescale and is provided for all model gridboxes globally. The resulting dataset is 3-dimensional (longitude, latitude, level). These are netcdf files.
3. Regional daily means
Filenames: GEOSChem.{DATA_TYPE}.{YYYYMM}01_regional.nc4
{DATA_TYPE} can be any of SpeciesConc, StateMet, Aerosols, AerosolMass (see below).
{YYYYMM} is the year and month for the data included in the file.
The model output has been averaged on a daily timescale and is provided for all gridboxes in the region bounded by 30-90°S (inclusive). The resulting dataset is 4-dimensional (longitude, latitude, level, time). These are netcdf files.
4. Emissions
Filenames: HEMCO_diagnostics.{YYYYMM}01.nc4
{YYYYMM} is the year and month for the data included in the file.
The model emissions (and related variables) have been averaged on a monthly timescale and are provided for all gridboxes globally. The resulting dataset includes both 2-dimensional (longitude, latitude) and 3-dimensional (longitude, latitude, level) variables. These are netcdf files.

Output data types (correspond to filenames given above):
1. SpeciesConc: Concentrations of advected model species.
2. StateMet: Meteorological fields and other derived quantities.
3. Aerosols: Diagnostics for aerosol optical depth and related quantities from full-chemistry simulations.
4. AerosolMass: Diagnostics for aerosol mass and particulate matter

Variable names for each output data type are provided in the file GEOSChem_Diagnostics.xlsx (one tab for each output data type). For species names (used in the SpeciesConc files and along-shiptrack files) and properties, see file GEOS-Chem_Species_Database.json. For emission diagnostic names (used in the HEMCO_diagnostics files) see file HEMCO_Diagn.rc.

References:
The International GEOS-Chem User Community. (2020, May 21). geoschem/geos-chem: GEOS-Chem 12.8.1 (Version 12.8.1). Zenodo. http://doi.org/10.5281/zenodo.3837666

Travis, K. R., Heald, C. L., Allen, H. M., Apel, E. C., Arnold, S. R., Blake, D. R., Brune, W. H., Chen, X., Commane, R., Crounse, J. D., Daube, B. C., Diskin, G. S., Elkins, J. W., Evans, M. J., Hall, S. R., Hintsa, E. J., Hornbrook, R. S., Kasibhatla, P. S., Kim, M. J., Luo, G., McKain, K., Millet, D. B., Moore, F. L., Peischl, J., Ryerson, T. B., Sherwen, T., Thames, A. B., Ullmann, K., Wang, X., Wennberg, P. O., Wolfe, G. M., and Yu, F.: Constraining remote oxidation capacity with ATom observations, Atmos. Chem. Phys., 20, 7753–7781, https://doi.org/10.5194/acp-20-7753-2020, 2020.

本数据集提供GEOS-Chem化学传输模型的模拟输出，用于支撑CAMMPCAN与MARCUS 2018-2019年科考航次。

所用模型版本为GEOS-Chem v12.8.1，其数字对象唯一标识符（DOI, Digital Object Identifier）为10.5281/zenodo.3837666，使用该数据集时需引用此DOI。针对标准v12.8.1版本进行了修改，纳入了Travis等人（2020）所实现的非生物海洋挥发性有机化合物排放。

本模型采用MERRA-2气象场驱动，水平分辨率为2°×2.5°（纬度×经度），在整个航次周期内共设置47个垂直层数。本次模拟的前置流程为2017-2018年航次的模型运行，而该航次模拟的前置步骤为2017年5月1日启动、分辨率为4°×5°的6个月自旋启动阶段。

本数据集包含以下四类输出产物（变量名详见下文）：

1. 沿航迹输出
文件名格式：mrg20m_SS_ss_YYYYMMDD.txt
模型采样点直接匹配航次期间每分钟的船舶实时位置，随后对数据进行平均处理，确保每个唯一的模式网格单元-时间步长组合仅对应一个数据点。最终生成的数据集为一维结构，包含纬度、经度、时间以及各类数据变量。该类数据为以空格作为分隔符的文本文件。

2. 全球逐月平均数据
文件名格式：GEOSChem.{DATA_TYPE}.{YYYYMM}01_monmean.nc4
其中{DATA_TYPE}可选值为SpeciesConc、StateMet、Aerosols、AerosolMass（详见下文）；{YYYYMM}为文件包含数据的年份与月份。
本模型输出已按月尺度进行平均，覆盖全球所有模式网格单元。最终生成的数据集为三维结构（经度、纬度、垂直层数），采用NetCDF格式存储。

3. 区域逐日平均数据
文件名格式：GEOSChem.{DATA_TYPE}.{YYYYMM}01_regional.nc4
其中{DATA_TYPE}可选值为SpeciesConc、StateMet、Aerosols、AerosolMass（详见下文）；{YYYYMM}为文件包含数据的年份与月份。
本模型输出已按日尺度进行平均，覆盖南纬30°至90°（含边界）范围内的所有网格单元。最终生成的数据集为四维结构（经度、纬度、垂直层数、时间），采用NetCDF格式存储。

4. 排放数据
文件名格式：HEMCO_diagnostics.{YYYYMM}01.nc4
其中{YYYYMM}为文件包含数据的年份与月份。
本模型排放（及相关变量）已按月尺度进行平均，覆盖全球所有网格单元。最终生成的数据集同时包含二维（经度、纬度）与三维（经度、纬度、垂直层数）变量，采用NetCDF格式存储。

### 输出数据类型（与上述文件名对应）
1. SpeciesConc：平流输送的模式物种的浓度数据。
2. StateMet：气象场及其他衍生物理量。
3. Aerosols：全化学模拟中气溶胶光学厚度及相关参量的诊断结果。
4. AerosolMass：气溶胶质量与颗粒物相关的诊断结果。

各类输出数据类型对应的变量名详见文件GEOSChem_Diagnostics.xlsx（每个输出类型对应一个工作表）。关于SpeciesConc文件及沿航迹输出文件中使用的物种名称与属性，请参阅文件GEOS-Chem_Species_Database.json。关于HEMCO_diagnostics文件中使用的排放诊断名称，请参阅文件HEMCO_Diagn.rc。

### 参考文献
1. 国际GEOS-Chem用户社区. (2020, 5月21日). geoschem/geos-chem: GEOS-Chem 12.8.1（版本12.8.1）[软件]. Zenodo. https://doi.org/10.5281/zenodo.3837666
2. Travis, K. R., Heald, C. L., Allen, H. M., Apel, E. C., Arnold, S. R., Blake, D. R., Brune, W. H., Chen, X., Commane, R., Crounse, J. D., Daube, B. C., Diskin, G. S., Elkins, J. W., Evans, M. J., Hall, S. R., Hintsa, E. J., Hornbrook, R. S., Kasibhatla, P. S., Kim, M. J., Luo, G., McKain, K., Millet, D. B., Moore, F. L., Peischl, J., Ryerson, T. B., Sherwen, T., Thames, A. B., Ullmann, K., Wang, X., Wennberg, P. O., Wolfe, G. M., & Yu, F. (2020). Constraining remote oxidation capacity with ATom observations. *Atmos. Chem. Phys.*, 20, 7753–7781. https://doi.org/10.5194/acp-20-7753-2020