Data Exchange Portal

The BGI department used to host a data exchange portal under www.bgc-jena.mpg.de/geodb. The data portal was decommisioned. Most of the datasets have been moved to a new place. Please look through the list below if you are looking for a certain dataset to find the link to the new dataset download location. If a link is missing, please contact the dataset owner or Fabian Gans (fgans@bgc-jena.mpg.de) about ways to access the data.

BGI

The public files of the Department for Biogechemical Integration of the Max-Planck Institute for Biogeochemistry are made available here for download.

▶ Global 1km forest age datasets

Description This dataset provides an ensemble of global estimation of 1km global forest age which is derived from forest inventories, biomass and climate data. Provided by: Max Planck Institute for Biogeochemistry, Hans Knöll Strasse 10, 07745 Jena, Germany Reference: Mapping global forest age from forest inventories, biomass and climate data, Simon Besnard, Sujan Koirala, Maurizio Santoro, Ulrich Weber, Jacob Nelson, Jonas Gütter, Bruno Herault, Justin Kassi, Anny NGuessan, Christopher Neigh, Benjamin Poulter, Tao Zhang, Nuno Carvalhais, Earth System Science Data (ESSD), to be submitted.
Owners Nuno Carvalhais (nuno.carvalhais@bgc-jena.mpg.de), Simon Besnard (sbesnard@bgc-jena.mpg.de)
Publication Date 2021-03-02
Version 1.0.0

▶ Gross Primary Production on Land

Description GPP derived by upscaling observations from the current global network of eddy-covariance towers (FLUXNET, Jung et al 2011 Journal of Geophysical Research, 116 G00J07). For the upscaling a model tree ensemble approach was used as described in (Jung, M., Reichstein, M., Bondeau, A. 2009 Biogeosciences, 6). For this dataset the flux partitioning was based on LASSLOP, G. et al. (2010), Global Change Biology, 16: 187â??208
Owners Martin Jung (mjung@bgc-jena.mpg.de)
Publication Date 2013-02-12
Version May12
DOI doi:10.17617/3.IJ65JT
Link https://doi.org/10.17617/3.IJ65JT

▶ Ecosystem turnover times database

Description This dataset provides an ensemble of global estimation of ecosystem turnover times which is derived from current state-of-the-art observation-based products of soil, vegetation and GPP. Provided by: Max Planck Institute for Biogeochemistry, Hans Knöll Strasse 10, 07745 Jena, Germany Reference: Apparent ecosystem carbon turnover time: uncertainties and robust features Naixin Fan, Sujan Koirala, Markus Reichstein, Martin Thurner, Valerio Avitabile, Maurizio Santoro, Bernhard Ahrens, Ulrich Weber, Nuno Carvalhais Earth System Science Data (ESSD), to be submitted
Owners Nuno Carvalhais (nuno.carvalhais@bgc-jena.mpg.de), Naixin Fan (nfan@bgc-jena.mpg.de)
Publication Date 2019-11-13
Version 1.0.0
DOI 10.17871/bgitau.201911

▶ Gross Primary Production on Land

Description GPP derived by upscaling observations from the current global network of eddy-covariance towers (FLUXNET, Jung et al 2011 Journal of Geophysical Research, 116 G00J07). For the upscaling a model tree ensemble approach was used as described in (Jung, M., Reichstein, M., Bondeau, A. 2009 Biogeosciences, 6). For this dataset the flux partitioning was based on Reichstein, M., et al. (2005), Global Change Biology, 11: 1424â??1439
Owners Martin Jung (mjung@bgc-jena.mpg.de)
Publication Date 2013-02-12
Version May12
DOI doi:10.17617/3.CQLBPE
Link https://doi.org/10.17617/3.CQLBPE

▶ Latent heat flux on land

Description Latent heat flux derived by upscaling observations from the current global network of eddy-covariance towers (FLUXNET, Jung et al 2011 Journal of Geophysical Research, 116 G00J07). For the upscaling a model tree ensemble approach was used as described in (Jung, M., Reichstein, M., Bondeau, A. 2009 Biogeosciences, 6).
Owners Martin Jung (mjung@bgc-jena.mpg.de)
Publication Date 2013-05-15
Version May12
DOI doi:10.17617/3.JEMIDA
Link https://doi.org/10.17617/3.JEMIDA

▶ Biomass dataset description file

Description Northern-hemispheric (~ 30 – 80°N) carbon density at 0.01° resolution. This dataset is based on Growing Stock Volume (GSV) derived from Envisat ASAR data applying the BIOMASAR algorithm (Santoro et al. 2011, 2013, in prep.). Carbon density was estimated from GSV using information on wood density, biomass allometric relationships and GLC2000 land cover information (Thurner et al. 2013). Additionally, an uncertainty estimate is given. Non-forest pixels have been masked out using GLC2000 (JRC 2003), land-cover classes 1-10 were considered to be forest. The user is referred to Thurner et al. (2013) and supporting material for further details. Please have a look at README_biomass.docx for a detailed description of the dataset and difference between versions before downloading!!! Dataset Reference: Thurner, M., Beer, C., Santoro, M., Carvalhais, N., Wutzler, T., Schepaschenko, D., Shvidenko, A., Kompter, E., Ahrens, B., Levick, S.R. & Schmullius, C. (2013) Carbon stock and density of northern boreal and temperate forests. Global Ecology and Biogeography, accepted.
Owners Christian Beer, Nuno Carvalhais (nuno.carvalhais@bgc-jena.mpg.de), Martin Thurner
Publication Date 2013-10-10
Version 1

▶ Gross Primary Production on Land

Description GPP derived by upscaling observations from the current global network of eddy-covariance towers (FLUXNET, Jung et al 2011 Journal of Geophysical Research, 116 G00J07). For the upscaling a model tree ensemble approach was used as described in (Jung, M., Reichstein, M., Bondeau, A. 2009 Biogeosciences, 6). For this dataset the flux partitioning was based on Reichstein, M., et al. (2005), Global Change Biology, 11: 1424–1439
Owners Martin Jung (mjung@bgc-jena.mpg.de)
Publication Date 2013-04-18
Version missing
DOI doi:10.17617/3.Z3GFRI
Link https://doi.org/10.17617/3.Z3GFRI

▶ Gross Primary Production on Land

Description GPP derived by upscaling observations from the current global network of eddy-covariance towers (FLUXNET, Jung et al 2011 Journal of Geophysical Research, 116 G00J07). For the upscaling a model tree ensemble approach was used as described in (Jung, M., Reichstein, M., Bondeau, A. 2009 Biogeosciences, 6). For this dataset the flux partitioning was based on LASSLOP, G. et al. (2010), Global Change Biology, 16: 187–208
Owners Martin Jung (mjung@bgc-jena.mpg.de)
Publication Date 2013-04-18
Version missing
DOI doi:10.17617/3.6QGYDS
Link https://doi.org/10.17617/3.6QGYDS

▶ Stem Biomass Version 3

Description Northern-hemispheric (~ 30 – 80°N) carbon density at 0.01° resolution. This dataset is based on Growing Stock Volume (GSV) derived from Envisat ASAR data applying the BIOMASAR algorithm (Santoro et al. 2011, 2013, in prep.). Carbon density was estimated from GSV using information on wood density, biomass allometric relationships and GLC2000 land cover information (Thurner et al. 2013). Additionally, an uncertainty estimate is given. Non-forest pixels have been masked out using GLC2000 (JRC 2003), land-cover classes 1-10 were considered to be forest. The user is referred to Thurner et al. (2013) and supporting material for further details. Please have a look at README_biomass.docx for a detailed description of the dataset and difference between versions before downloading!!! Dataset Reference: Thurner, M., Beer, C., Santoro, M., Carvalhais, N., Wutzler, T., Schepaschenko, D., Shvidenko, A., Kompter, E., Ahrens, B., Levick, S.R. & Schmullius, C. (2013) Carbon stock and density of northern boreal and temperate forests. Global Ecology and Biogeography, accepted.
Owners Christian Beer, Nuno Carvalhais (nuno.carvalhais@bgc-jena.mpg.de), Martin Thurner
Publication Date 2013-10-10
Version 1

▶ Total Biomass Version 3

Description Northern-hemispheric (~ 30 – 80°N) carbon density at 0.01° resolution. This dataset is based on Growing Stock Volume (GSV) derived from Envisat ASAR data applying the BIOMASAR algorithm (Santoro et al. 2011, 2013, in prep.). Carbon density was estimated from GSV using information on wood density, biomass allometric relationships and GLC2000 land cover information (Thurner et al. 2013). Additionally, an uncertainty estimate is given. Non-forest pixels have been masked out using GLC2000 (JRC 2003), land-cover classes 1-10 were considered to be forest. The user is referred to Thurner et al. (2013) and supporting material for further details. Please have a look at README_biomass.docx for a detailed description of the dataset and difference between versions before downloading!!! Dataset Reference: Thurner, M., Beer, C., Santoro, M., Carvalhais, N., Wutzler, T., Schepaschenko, D., Shvidenko, A., Kompter, E., Ahrens, B., Levick, S.R. & Schmullius, C. (2013) Carbon stock and density of northern boreal and temperate forests. Global Ecology and Biogeography, accepted.
Owners Christian Beer, Nuno Carvalhais (nuno.carvalhais@bgc-jena.mpg.de), Martin Thurner
Publication Date 2013-10-10
Version 1

▶ Sensible Heat Fluxes on Land

Description Sensible Heat Fluxes on Land
Owners Martin Jung (mjung@bgc-jena.mpg.de)
Publication Date 2013-06-03
Version May12
DOI doi:10.17617/3.6W15ZP
Link https://doi.org/10.17617/3.6W15ZP

▶ Aboveground Biomass Version 3

Description Northern-hemispheric (~ 30 – 80°N) carbon density at 0.01° resolution. This dataset is based on Growing Stock Volume (GSV) derived from Envisat ASAR data applying the BIOMASAR algorithm (Santoro et al. 2011, 2013, in prep.). Carbon density was estimated from GSV using information on wood density, biomass allometric relationships and GLC2000 land cover information (Thurner et al. 2013). Additionally, an uncertainty estimate is given. Non-forest pixels have been masked out using GLC2000 (JRC 2003), land-cover classes 1-10 were considered to be forest. The user is referred to Thurner et al. (2013) and supporting material for further details. Please have a look at README_biomass.docx for a detailed description of the dataset and difference between versions before downloading!!! Dataset Reference: Thurner, M., Beer, C., Santoro, M., Carvalhais, N., Wutzler, T., Schepaschenko, D., Shvidenko, A., Kompter, E., Ahrens, B., Levick, S.R. & Schmullius, C. (2013) Carbon stock and density of northern boreal and temperate forests. Global Ecology and Biogeography, accepted.
Owners Christian Beer, Nuno Carvalhais (nuno.carvalhais@bgc-jena.mpg.de), Martin Thurner
Publication Date 2013-10-10
Version 1

▶ Branches Biomass Version 3

Description Northern-hemispheric (~ 30 – 80°N) carbon density at 0.01° resolution. This dataset is based on Growing Stock Volume (GSV) derived from Envisat ASAR data applying the BIOMASAR algorithm (Santoro et al. 2011, 2013, in prep.). Carbon density was estimated from GSV using information on wood density, biomass allometric relationships and GLC2000 land cover information (Thurner et al. 2013). Additionally, an uncertainty estimate is given. Non-forest pixels have been masked out using GLC2000 (JRC 2003), land-cover classes 1-10 were considered to be forest. The user is referred to Thurner et al. (2013) and supporting material for further details. Please have a look at README_biomass.docx for a detailed description of the dataset and difference between versions before downloading!!! Dataset Reference: Thurner, M., Beer, C., Santoro, M., Carvalhais, N., Wutzler, T., Schepaschenko, D., Shvidenko, A., Kompter, E., Ahrens, B., Levick, S.R. & Schmullius, C. (2013) Carbon stock and density of northern boreal and temperate forests. Global Ecology and Biogeography, accepted.
Owners Christian Beer, Nuno Carvalhais (nuno.carvalhais@bgc-jena.mpg.de), Martin Thurner
Publication Date 2013-10-10
Version 1

▶ Foliage Biomass Version 3

Description Northern-hemispheric (~ 30 – 80°N) carbon density at 0.01° resolution. This dataset is based on Growing Stock Volume (GSV) derived from Envisat ASAR data applying the BIOMASAR algorithm (Santoro et al. 2011, 2013, in prep.). Carbon density was estimated from GSV using information on wood density, biomass allometric relationships and GLC2000 land cover information (Thurner et al. 2013). Additionally, an uncertainty estimate is given. Non-forest pixels have been masked out using GLC2000 (JRC 2003), land-cover classes 1-10 were considered to be forest. The user is referred to Thurner et al. (2013) and supporting material for further details. Please have a look at README_biomass.docx for a detailed description of the dataset and difference between versions before downloading!!! Dataset Reference: Thurner, M., Beer, C., Santoro, M., Carvalhais, N., Wutzler, T., Schepaschenko, D., Shvidenko, A., Kompter, E., Ahrens, B., Levick, S.R. & Schmullius, C. (2013) Carbon stock and density of northern boreal and temperate forests. Global Ecology and Biogeography, accepted.
Owners Christian Beer, Nuno Carvalhais (nuno.carvalhais@bgc-jena.mpg.de), Martin Thurner
Publication Date 2013-10-10
Version 1

▶ Root Biomass (Belowground Biomass) Version 3

Description Northern-hemispheric (~ 30 – 80°N) carbon density at 0.01° resolution. This dataset is based on Growing Stock Volume (GSV) derived from Envisat ASAR data applying the BIOMASAR algorithm (Santoro et al. 2011, 2013, in prep.). Carbon density was estimated from GSV using information on wood density, biomass allometric relationships and GLC2000 land cover information (Thurner et al. 2013). Additionally, an uncertainty estimate is given. Non-forest pixels have been masked out using GLC2000 (JRC 2003), land-cover classes 1-10 were considered to be forest. The user is referred to Thurner et al. (2013) and supporting material for further details. Please have a look at README_biomass.docx for a detailed description of the dataset and difference between versions before downloading!!! Dataset Reference: Thurner, M., Beer, C., Santoro, M., Carvalhais, N., Wutzler, T., Schepaschenko, D., Shvidenko, A., Kompter, E., Ahrens, B., Levick, S.R. & Schmullius, C. (2013) Carbon stock and density of northern boreal and temperate forests. Global Ecology and Biogeography, accepted.
Owners Christian Beer, Nuno Carvalhais (nuno.carvalhais@bgc-jena.mpg.de), Martin Thurner
Publication Date 2013-10-10
Version 1

▶ Subset of data for turnover time of land carbon (tau) for model evaluations using ESMValTool

Description

This archive consists of the data needed for evaluation of turnover time of ecosystem carbon as calculated in the ESMValTool (see Eyring et al., 2020). The implementation in ESMValTool is based on the study of Carvalhais et al., 2014.

Note that the full dataset of turnover time from Carvalhais et al., 2014 is available at http://www.bgc-jena.mpg.de/geodb/BGI/tau.php.

Overview of resolutions

The dataset includes data at 11 different resolutions to match the selected models in Carvalhais et al., 2014. A summary of the reference models and corresponding resolutions is presented in the following table.

Reference	target_grid	grid_label*
Observation	0.5x0.5	gn
NorESM1-M	2.5x1.875	gr
bcc-csm1-1	2.812x2.813	gr1
CCSM4	1.25x0.937	gr2
CanESM2	2.812x2.813	gr3
GFDL-ESM2G	2.5x2.0	gr4
HadGEM2-ES	1.875x1.241	gr5
inmcm4	2.0x1.5	gr6
IPSL-CM5A-MR	2.5x1.259	gr7
MIROC-ESM	2.812x2.813	gr8
MPI-ESM-LR	1.875x1.875	gr9

*The grid_label is suffixed with z for data in zonal/latitude coordinates: the zonal turnover and zonal correlation.

Data files and conventions

At each spatial resolution, four data files are provided:

• tau_ctotal_fx_Carvalhais2014_BE_gn.nc - global data of tau_ctotal
• tau_ctotal_fx_Carvalhais2014_BE_gnz.nc - zonal data of tau_ctotal
• r_tau_ctotal_tas_fx_Carvalhais2014_BE_gnz.nc - zonal correlation of tau_ctotal and tas, controlled for pr
• r_tau_ctotal_pr_fx_Carvalhais2014_BE_gnz.nc - zonal correlation of tau_ctotal and pr, controlled for tas.

The data is produced in obs4MIPs standards, and provided in netCDF4 format. The filenames use the convention:

{variable}_{frequency}_{source_label}_{variant_label}_{grid_label}.nc

• {variable}: variable name
• {frequency}: temporal frequency of data
• {source_label}: observational source
• {variant_label}: observation variant
• {grid_label}: a standard label of spatial grids

Refer to Obs4MIPs Data Specifications (https://esgf-node.llnl.gov/site_media/projects/obs4mips/ODSv2p1.pdf) for details of the definitions above. All data variables have additional variables ({variable}_5 and {variable}_95) in the same file.

How to cite

This dataset should be referred to with the following citations:

• Carvalhais, N., Forkel, M., Khomik, M., Bellarby, J., Jung, M., Migliavacca, M., ... & Ahrens, B. (2014). Global covariation of carbon turnover times with climate in terrestrial ecosystems. Nature, 514(7521), 213-217.
• Eyring, V., Bock, L., Lauer, A., Righi, M., Schlund, M., Andela, B., ... & Carvalhais, N. (2020). ESMValTool v2.0–extended set of large-scale diagnostics for quasi-operational and comprehensive evaluation of Earth system models in CMIP. Geoscientific Model Development.

Usage notes

The usage of this data should follow the guidelines in the documentation of the ESMValTool recipe at https://esmvaltool.readthedocs.io/en/latest/recipes/recipe_carvalhais2014nat.html.

This dataset is intended for research uses through ESMValTool only. Any questions related to the data, as well as its use for other research purpose should be corresponded to Nuno Carvalhais (ncarval@bgc-jena.mpg.de) or Sujan Koirala (skoirala@bgc-jena.mpg.de) at the Max Planck Institute for Biogeochemistry.

Owners Nuno Carvalhais (nuno.carvalhais@bgc-jena.mpg.de), Sujan Koirala (skoirala@bgc-jena.mpg.de)
Publication Date 2020-04-08
Version 1.0
DOI doi:10.17617/3.L6A1WU
Link https://doi.org/10.17617/3.L6A1WU

▶ FPAR monthly time-series for the period 1982-2011

Description A harmonised long-term global fAPAR record from 1982-2011 by merging the NDVI product of the Global Inventory Modeling and Mapping Studies (GIMMS) based on the Advanced Very High Resolution Radiometer(AVHRR) [Tucker, et al., 2005] with the fAPAR product using Sea-viewing Wide Field-of-view Sensor (SeaWiFS) [Gobron, et al., 2006], and the MEdium Resolution Imaging Spectrometer (MERIS) [Gobron, et al., 2008] at 0.5o x 0.5O spatial resolution. The harmonization procedure of different remote sensing products, covering different periods, adjusts the mean seasonal cycle for each pixel based on the overlapping period (see Jung, et al., 2010 for details). The final long-term harmonized fAPAR product is based on adjusted GIMMS from 1982-1997, SeaWiFS from 1998-2005, and adjusted MERIS from 2006-2011
Owners Martin Jung (mjung@bgc-jena.mpg.de)
Publication Date 2013-03-06
Version 1

▶ FPAR 16-day 0.25° dataset tiled by vegetation type (preliminary)

Description The global 16day, 0.01° FAPAR product from the two-stream inversion package (TIP, Pinty et al. 2011a, Pinty et al 2011b) in conjunction with a global high resolution land use map from MODIS (Friedl et al 2010) was used to generate global 16 day and 0.25° resolution FAPAR datasets for each vegetation type. TIP assimilates albedos from MODIS in the visible and near infrared domain into a radiative transfer model. The TIP product was screened for good quality data and aggregated to 0.25° for each vegetation type and time step. The following IGBP vegetation types are considered: evergreen boradleaved forest, evergreen needle-leaved forest, deciduous broadleaved forest, deciduous needle-leaved forest, mixed forest, woody savannas, savannas, closed shrubland, open shrubland, grassland, cropland, cropland natural vegetation mosaic. Grasslands and croplands were further separeted into C3 and C4 photosynthetic pathway using ancillary datasets (Winslow et al 2003, Monfreda et al 2008). Long systematic gaps (e.g. due to polar night or periodic snow cover in northern high latitudes in winter) were filled with random data from the lower 0.2 quantile of the time series. Remaining gaps are filled based on an iterative SSA (Singular Spectrum Analysis) scheme (Kondrashov and Ghil, 2006), which detects periodic components in the signal to interpolate gap values
Owners Martin Jung (mjung@bgc-jena.mpg.de)
Publication Date 2013-03-08
Version 0.9 (preli
DOI doi:10.17617/3.AFGA00
Link https://doi.org/10.17617/3.AFGA00

▶ Harmonized ERA-Interim WATCH dataset

Description This climate dataset covers the period 1901-2010 and is planned to be gradually extended to 2013 as the original data will become available. It's the result of the harmonization of WATCH and ERA-Interim. We applied a correction of the seasonal cycle using the WATCH database as reference. The year to year variability was made comparable for the period 1901-2001 (original WATCH) and the period 2001-2010 (harmonized ERA-Interim). Since precipitation is a not continuous field, a specific correction was applied in a way to account just for the rainy events. The original spatial resolution was 0.5 degrees. The harmonized dataset was spatially downscaled to 0.25 degrees using CRU2.0. The spatial anomalies on montlhy basis were extracted from the CRU grids and overlapped to the harmonized dataset.
Owners Enrico Tomerelli
Publication Date 2013-04-18
Version missing

▶ Water availability index

Description
Owners Martin Jung (mjung@bgc-jena.mpg.de), Ulrich Weber (uweber@bgc-jena.mpg.de)
Publication Date 2013-04-18
Version

▶ Terrestrial Ecosystem respiration

Description missing
Owners Enrico Tomerelli
Publication Date 2013-04-18
Version missing

▶ Gross Primary Production on Land

Description missing
Owners Enrico Tomerelli
Publication Date 2013-04-18
Version missing

▶ Yield table data collection

Description MS-Access Database of european yield tables: tables in which one can find forest stand characteristics at the hectare scale at varying ages. In addition: Excel-addin to interpolate between yield table data.
Owners Thomas Wutzler (twutz@bgc-jena.mpg.de)
Publication Date 2013-05-03
Version 1304
DOI doi:10.17617/3.MC00CF
Link https://doi.org/10.17617/3.MC00CF

▶ Water fraction derived from SYNMAP at half degree, global. static

Description fraction of water per half degree gridcell
Owners Martin Jung (mjung@bgc-jena.mpg.de)
Publication Date 2013-06-03
Version v1
DOI doi:10.17617/3.CQ5KGI
Link https://doi.org/10.17617/3.CQ5KGI

▶ Aboveground Biomass Version 2

Description Northern-hemispheric (~ 30 – 80°N) carbon density at 0.01° resolution. This dataset is based on Growing Stock Volume (GSV) derived from Envisat ASAR data applying the BIOMASAR algorithm (Santoro et al. 2011, 2013, in prep.). Carbon density was estimated from GSV using information on wood density, biomass allometric relationships and GLC2000 land cover information (Thurner et al. 2013). Additionally, an uncertainty estimate is given. Non-forest pixels have been masked out using GLC2000 (JRC 2003), land-cover classes 1-10 were considered to be forest. The user is referred to Thurner et al. (2013) and supporting material for further details. Please have a look at README_biomass.docx for a detailed description of the dataset and difference between versions before downloading!!! Dataset Reference: Thurner, M., Beer, C., Santoro, M., Carvalhais, N., Wutzler, T., Schepaschenko, D., Shvidenko, A., Kompter, E., Ahrens, B., Levick, S.R. & Schmullius, C. (2013) Carbon stock and density of northern boreal and temperate forests. Global Ecology and Biogeography, accepted.
Owners Christian Beer, Nuno Carvalhais (nuno.carvalhais@bgc-jena.mpg.de), Martin Thurner
Publication Date 2013-10-09
Version 1

▶ Branches Biomass Version 2

Description Northern-hemispheric (~ 30 – 80°N) carbon density at 0.01° resolution. This dataset is based on Growing Stock Volume (GSV) derived from Envisat ASAR data applying the BIOMASAR algorithm (Santoro et al. 2011, 2013, in prep.). Carbon density was estimated from GSV using information on wood density, biomass allometric relationships and GLC2000 land cover information (Thurner et al. 2013). Additionally, an uncertainty estimate is given. Non-forest pixels have been masked out using GLC2000 (JRC 2003), land-cover classes 1-10 were considered to be forest. The user is referred to Thurner et al. (2013) and supporting material for further details. Please have a look at README_biomass.docx for a detailed description of the dataset and difference between versions before downloading!!! Dataset Reference: Thurner, M., Beer, C., Santoro, M., Carvalhais, N., Wutzler, T., Schepaschenko, D., Shvidenko, A., Kompter, E., Ahrens, B., Levick, S.R. & Schmullius, C. (2013) Carbon stock and density of northern boreal and temperate forests. Global Ecology and Biogeography, accepted.
Owners Christian Beer, Nuno Carvalhais (nuno.carvalhais@bgc-jena.mpg.de), Martin Thurner
Publication Date 2013-10-10
Version 1

▶ Foliage biomass Version 2

Description Northern-hemispheric (~ 30 – 80°N) carbon density at 0.01° resolution. This dataset is based on Growing Stock Volume (GSV) derived from Envisat ASAR data applying the BIOMASAR algorithm (Santoro et al. 2011, 2013, in prep.). Carbon density was estimated from GSV using information on wood density, biomass allometric relationships and GLC2000 land cover information (Thurner et al. 2013). Additionally, an uncertainty estimate is given. Non-forest pixels have been masked out using GLC2000 (JRC 2003), land-cover classes 1-10 were considered to be forest. The user is referred to Thurner et al. (2013) and supporting material for further details. Please have a look at README_biomass.docx for a detailed description of the dataset and difference between versions before downloading!!! Dataset Reference: Thurner, M., Beer, C., Santoro, M., Carvalhais, N., Wutzler, T., Schepaschenko, D., Shvidenko, A., Kompter, E., Ahrens, B., Levick, S.R. & Schmullius, C. (2013) Carbon stock and density of northern boreal and temperate forests. Global Ecology and Biogeography, accepted.
Owners Christian Beer, Nuno Carvalhais (nuno.carvalhais@bgc-jena.mpg.de), Martin Thurner
Publication Date 2013-10-10
Version 1

▶ Root Biomass (Belowground Biomass) Version 2

Description Northern-hemispheric (~ 30 – 80°N) carbon density at 0.01° resolution. This dataset is based on Growing Stock Volume (GSV) derived from Envisat ASAR data applying the BIOMASAR algorithm (Santoro et al. 2011, 2013, in prep.). Carbon density was estimated from GSV using information on wood density, biomass allometric relationships and GLC2000 land cover information (Thurner et al. 2013). Additionally, an uncertainty estimate is given. Non-forest pixels have been masked out using GLC2000 (JRC 2003), land-cover classes 1-10 were considered to be forest. The user is referred to Thurner et al. (2013) and supporting material for further details. Please have a look at README_biomass.docx for a detailed description of the dataset and difference between versions before downloading!!! Dataset Reference: Thurner, M., Beer, C., Santoro, M., Carvalhais, N., Wutzler, T., Schepaschenko, D., Shvidenko, A., Kompter, E., Ahrens, B., Levick, S.R. & Schmullius, C. (2013) Carbon stock and density of northern boreal and temperate forests. Global Ecology and Biogeography, accepted.
Owners Christian Beer, Nuno Carvalhais (nuno.carvalhais@bgc-jena.mpg.de), Martin Thurner
Publication Date 2013-10-10
Version 1

▶ Stem Biomass Version 2

Description Northern-hemispheric (~ 30 – 80°N) carbon density at 0.01° resolution. This dataset is based on Growing Stock Volume (GSV) derived from Envisat ASAR data applying the BIOMASAR algorithm (Santoro et al. 2011, 2013, in prep.). Carbon density was estimated from GSV using information on wood density, biomass allometric relationships and GLC2000 land cover information (Thurner et al. 2013). Additionally, an uncertainty estimate is given. Non-forest pixels have been masked out using GLC2000 (JRC 2003), land-cover classes 1-10 were considered to be forest. The user is referred to Thurner et al. (2013) and supporting material for further details. Please have a look at README_biomass.docx for a detailed description of the dataset and difference between versions before downloading!!! Dataset Reference: Thurner, M., Beer, C., Santoro, M., Carvalhais, N., Wutzler, T., Schepaschenko, D., Shvidenko, A., Kompter, E., Ahrens, B., Levick, S.R. & Schmullius, C. (2013) Carbon stock and density of northern boreal and temperate forests. Global Ecology and Biogeography, accepted.
Owners Christian Beer, Nuno Carvalhais (nuno.carvalhais@bgc-jena.mpg.de), Martin Thurner
Publication Date 2013-10-10
Version 1

▶ Total Biomass Version 2

Description Northern-hemispheric (~ 30 – 80°N) carbon density at 0.01° resolution. This dataset is based on Growing Stock Volume (GSV) derived from Envisat ASAR data applying the BIOMASAR algorithm (Santoro et al. 2011, 2013, in prep.). Carbon density was estimated from GSV using information on wood density, biomass allometric relationships and GLC2000 land cover information (Thurner et al. 2013). Additionally, an uncertainty estimate is given. Non-forest pixels have been masked out using GLC2000 (JRC 2003), land-cover classes 1-10 were considered to be forest. The user is referred to Thurner et al. (2013) and supporting material for further details. Please have a look at README_biomass.docx for a detailed description of the dataset and difference between versions before downloading!!! Dataset Reference: Thurner, M., Beer, C., Santoro, M., Carvalhais, N., Wutzler, T., Schepaschenko, D., Shvidenko, A., Kompter, E., Ahrens, B., Levick, S.R. & Schmullius, C. (2013) Carbon stock and density of northern boreal and temperate forests. Global Ecology and Biogeography, accepted.
Owners Christian Beer, Nuno Carvalhais (nuno.carvalhais@bgc-jena.mpg.de), Martin Thurner
Publication Date 2013-10-10
Version 1

▶ Characterising the response of vegetation cover to water limitation in Africa using geostationary satellites

Description Ecohydrological metrics of vegetation over Africa at 0.0417° resolution. The data are derived from the Fraction of Vegetation Cover (FVC) product estimated by Trigo et al., 2011 using the retrievals of the geostationary satellite Meteosat Second Generation. The data consist 3 files as: (i) asymptotic minimum and maximum FVC, (ii) start day, duration, and FVC integral of the dry season, and (iii) the decay rate of FVC during dry-down. Files (ii) and (iii) has variation as a quality diagnostic. In addition, (iii) has number of convergences as another quality diagnostic. Dataset Reference: Küçük Ç., Koirala S., Carvalhais N., Miralles D.G., Reichstein M., and Jung M. (2020) Characterising the response of vegetation cover to water limitation in Africa using geostationary satellites.
Owners Martin Jung (mjung@bgc-jena.mpg.de), Caglar Kucuk (ckucuk@bgc-jena.mpg.de)
Publication Date 2020-07-15
Version
DOI 10.17871/bgi_ehydro_afr_2020

▶ NABP - Global forest carbon changes for the period 1993-2018

Description Yearly dataset at 0.25 deg resolution describing the annual variability in above ground biomass (AGB), termed as net above ground productivity (NABP) as defined in Besnard et al., 2021.
Owners Nuno Carvalhais (nuno.carvalhais@bgc-jena.mpg.de), Simon Besnard (sbesnard@bgc-jena.mpg.de)
Publication Date 2021-11-20
Version 1.0
DOI doi:10.17617/3.ALEVKU
Link https://doi.org/10.17617/3.ALEVKU

Fluxcom

An initiative to upscale biosphere-atmosphere fluxes from FLUXNET sites to continental and global scales

▶ FLUXCOM Global Land Carbon Fluxes

FLUXCOM used machine learning to merge carbon flux measurements from FLUXNET eddy covariance towers with remote sensing and meteorological data to estimate gross primary productivity (GPP), terrestrial ecosystem respiration (TER) and net ecosystem exchange (NEE) and their uncertainties. The resulting database comprises more than 100 global gridded products in two setups: (1) using exclusively MODIS remote sensing data (RS) on a 0.0833° grid, and (2) using remote sensing and meteorological data (RS+METEO) at 0.5° resolution. Within each setup we use a full factorial design across multiple machine learning methods, climate forcing datasets (for RS-METEO) and two flux partitioning approaches. Here we provide these data on a monthly resolution and provide also FLUXCOM ensemble products. Please see Jung et al. 2020, BG and Tramontana et al. 2016, BG before using the data, and cite both papers in publications. These data are freely available on a CC4.0 BY license.
Publication Date 2020-01-28
Version v1
DOI 10.17871/FLUXCOM_CarbonFluxes_v1
The dataset is available through the MPI BGC FTP server. Please note that the server is not browsable. Please-copy-paste the exact dataset path in your ftp client.
FTP Server ftp.bgc-jena.mpg.de
FTP Path /outgoing/FluxCom/CarbonFluxes

▶ FLUXCOM (RS+METEO) Global Land Carbon Fluxes using CRUNCEP climate data

Please cite both, Jung et al., 2016 and Tramontana et al., 2016, when using any of the data for publications. This data set provides 1) subfolder "raw": global land carbon fluxes (GPP, TER) on daily to annual resolution from 1980-2013 generated by 3 machine learning methods (RF, ANN, MARS) which were forced with CRUNCEPv6 meteorological data and mean seasonal cycles of several MODIS based variables (RS+METEO setup, see Tramontana et al., 2016 for details); 2) subfolder "AnomaliesClim": detrended carbon flux anomalies driven by air temperature (Tair), water availability (WAI2), shortwave radiation (Rg) as described in Jung et al., 2016. Two variants of GPP and TER refer to 2 flux partitioning methods, where _HB refers to Lasslop et al., 2010, and no specifier refers to Reichstein et al. 2005. Usage notes: Long-term trends and interannual variations in this data set originate exclusively from direct effects of changing climate; i.e. the climate forcing data set CRUNCEPv6 used here. Results from using other climate forcing data will also be available. Potential effects due to e.g. CO2 fertilization or vegetation greening are not accounted for. Magnitudes of interannual variations appear to be too small in the datasets, and a normalization of the anomalies is recommended when analysing interannual varibility. Analysing long-term mean TER is not recommended due to a likely bias of mean annual TER; the same hold for mean annual NEE computed from TER-GPP. The choice of flux partitioning variant is usually not critical; we would give a slight preference to Reichstein et al., 2005 variants.
Publication Date 2020-01-20
Version v1
DOI 10.17871/FLUXCOM_RS_METEO_CRUNCEPv6_1980_2013_v1
The dataset is available through the MPI BGC FTP server. Please note that the server is not browsable. Please-copy-paste the exact dataset path in your ftp client.
FTP Server ftp.bgc-jena.mpg.de
FTP Path /outgoing/FluxCom/CarbonFluxes_v1_2017/RS+METEO/CRUNCEPv6

▶ FLUXCOM Global Land Energy Fluxes

FLUXCOM used machine learning to merge energy flux measurements from FLUXNET eddy covariance towers with remote sensing and meteorological data to estimate net radiation (Rn), latent (LE) and sensible heat (H) and their uncertainties. The resulting database comprises 147 global gridded products in two setups: (1) using exclusively MODIS remote sensing data (RS) on a 0.0833° grid, and (2) using remote sensing and meteorological data (RS+METEO) at 0.5° resolution. Within each setup we use a full factorial design across multiple machine learning methods, climate forcing datasets (for RS-METEO) and corrections approaches for biases of FLUXNET data. Here we provide these data on a monthly resolution and provide also FLUXCOM ensemble products. Please see Jung et al. 2019, and Tramontana et al. 2016 before using the data, and cite both papers in publications. A related manuscript was submitted to Scientific Data. A preprint of the submitted version is available at arXiv (http://arxiv.org/abs/1812.04951). These data are freely available on a CC4.0 BY license.
Publication Date 2018-11-26
Version v1
DOI 10.17871/FLUXCOM_EnergyFluxes_v1
The dataset is available through the MPI BGC FTP server. Please note that the server is not browsable. Please-copy-paste the exact dataset path in your ftp client.
FTP Server ftp.bgc-jena.mpg.de
FTP Path /outgoing/FluxCom/EnergyFluxes/

SoMo.ml

Global soil moisture generated from in-situ measurements using machine learning. Data files are freely available under a Creative Commons Attribution 4.0 International

▶ SoMo.ml - Global soil moisture generated from in situ measurements using machine learning. Layer 1

Description SoMo.ml provides global soil moisture at three different layers: 0-10 cm, 10-30 cm, and 30-50 cm, corresponding to Layer 1, Layer 2, and Layer 3, respectively. The data has a spatiotemporal resolution of 0.25° and daily, covering the period of 2000 to 2019. Lincense (for files): Creative Commons Attribution 4.0 International
Owners Rene Orth (rorth@bgc-jena.mpg.de), Sungmin O (sungmin.o@bgc-jena.mpg.de)
Publication Date 2020-08-12
Version v1.0
DOI 10.17871/bgi_somo.ml_v1_2020

▶ SoMo.ml - Global soil moisture generated from in situ measurements using machine learning. Layer 3

Description SoMo.ml provides global soil moisture at three different layers: 0-10 cm, 10-30 cm, and 30-50 cm, corresponding to Layer 1, Layer 2, and Layer 3, respectively. The data has a spatiotemporal resolution of 0.25° and daily, covering the period of 2000 to 2019. Lincense (for files): Creative Commons Attribution 4.0 International
Owners Rene Orth (rorth@bgc-jena.mpg.de), Sungmin O (sungmin.o@bgc-jena.mpg.de)
Publication Date 2020-08-12
Version v1.0
DOI 10.17871/bgi_somo.ml_v1_2020

▶ SoMo.ml - Global soil moisture generated from in situ measurements using machine learning. Layer 2

Description SoMo.ml provides global soil moisture at three different layers: 0-10 cm, 10-30 cm, and 30-50 cm, corresponding to Layer 1, Layer 2, and Layer 3, respectively. The data has a spatiotemporal resolution of 0.25° and daily, covering the period of 2000 to 2019. Lincense (for files): Creative Commons Attribution 4.0 International
Owners Rene Orth (rorth@bgc-jena.mpg.de), Sungmin O (sungmin.o@bgc-jena.mpg.de)
Publication Date 2020-08-12
Version v1.0
DOI 10.17871/bgi_somo.ml_v1_2020

RECCAP-2

REgional Carbon Cycle Assessment and Processes’, Phase 2 (RECCAP-2) is coordinated by the Global Carbon Project, and collects and synthesises regional data for 14 large regions of the globe with a requirement of harmonisation sufficient to be able to scale these budgets to the globe and to compare different regions. Key policy-relevant challenges for the scientific community and objectives of the RECCAP-2 project are to: Improve quantification of anthropogenic greenhouse gas emissions and their sources; Develop robust observation-based estimates of changes in carbon storage and greenhouse gas emissions and sinks by the oceans and terrestrial ecosystems, distinguishing whenever possible anthropogenic vs. natural fluxes and their driving processes; Gain science-based evidence of the response of marine and terrestrial regional GHG budgets to climate change and direct anthropogenic drivers.

▶ RECCAP-1 synthesis

Description Supplementary data for the RECCAP-1 final global synthesis paper: Philippe Ciais et al. Empirical estimates of regional carbon budgets imply reduced global soil heterotrophic respiration, National Science Review, nwaa145, https://doi.org/10.1093/nsr/nwaa145
Owners Ana Bastos
Publication Date 2020-11-20
Version v2

▶ MIROC4-ACTM inversion fluxes for carbon dioxide (CO2)

Description
Owners Prabir Patra
Publication Date 2022-04-01
Version

▶ CO2 evasion from lakes and reservoirs, Surface areas of lakes and reservoirs

Description
Owners Ronny Lauwerwall
Publication Date 2014-11-14
Version

▶ RECCAP Future CMIP6 data

Description updated data from CMIP6 projections.
Owners Chris Jones
Publication Date 2022-03-03
Version 2

▶ CO2 evasion from rivers, updated version with surface area corrections for big rivers based on data set by Lehner and Döll, 2004

Description Results from 50 runs of a Monte-Carlos simulation based on the statistical model and standard errors of predictors
Owners Ronny Lauwerwall
Publication Date 2015-02-04
Version 2

▶ GCP wetland CH4 estimates for 2000-2017

Description The files include summaries of model estimates for monthly wetland CH4 (Unit: Tg CH4 mon-1) using two different approaches for inundation dynamics, a diagnostic approach that uses a satellite-based product Development of a global dataset of Wetland Area and Dynamics for Methane Modeling (WAD2M), and a prognostic approach that uses a hydrologic model within the corresponding model if exist. Ref for WAD2M: Zhang, Z., Fluet-Chouinard, E., Jensen, K., McDonald, K., Hugelius, G., Gumbricht, T., Carroll, M., Prigent, C., Bartsch, A., and Poulter, B.: Development of a global dataset of Wetland Area and Dynamics for Methane Modeling (WAD2M), Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2020-262, in review, 2020.
Owners Ben Poulter
Publication Date 2021-05-04
Version

▶ FLUXCOM v1 Carbon Fluxes (GPP, NEE, TER) from RS ensemble and RS+METEO ERA-5 ensemble.

Description The RECAP-2 extracts contain estimates of Carbon Fluxes from FLUXCOM version 1. Two ensemble estimates are provided 1) RS - Remote Sensing only, which is produced at 8km - 8daily intervals, and has been first spatially aggregated to half degree and linearly regressed to daily temporal resolution before monthly aggregates have been calculated. 2) RS+Meteo, which combines RS information with Meteo forcings based on daily resolution and 0.5 degree, base to the monthly aggregates. Jung et al. (2020). doi.org/10.5194/bg-17-1343-2020, https://www.biogeosciences.net/17/1343/2020/
Owners Ulrich Weber (uweber@bgc-jena.mpg.de)
Publication Date 2021-05-31
Version FLUXCOM_v1

▶ Gridded annual soil N2O emissions during 1861-2016

Description It is the ensemble mean of six models participated in NMIP: DLEM, LPX-Bern, OCN, ORCHIDEE, ORCHIDEE-CNP and VISIT
Owners Hanqin Tian, Naiqing Pan
Publication Date 2021-09-14
Version

▶ Synthesis of inland water GHG emission estimates for RECCAP2

Description We give a sysnthesis of published estimates on inland water GHG emissions which we rescaled to consistent estimates of inland water surface area, and corrected for effects of seasonal ice cover and ice melt.
Owners Ronny Lauerwald
Publication Date 2022-03-25
Version Corrected

▶ MIROC4-ACTM inversion fluxes for methane (CH4)

Description
Owners Prabir Patra
Publication Date 2022-04-01
Version

▶ MIROC4-ACTM inversion for CO2

Description Annual and monthly mean emissions for the 10 land regions and 5 ocean regions of RECCAP2 are provided as ensemble means of 16 inversions
Owners Prabir Patra
Publication Date 2022-04-19
Version 1

▶ Fluvial exports of C

Description
Owners Ronny Lauwerwall
Publication Date 2014-11-14
Version

▶ CAMS inversion surface fluxes based on OCO-2 and surface observations.

Description Contains two .tar files: CAMSFT20r2_OCO2.tar Global surface fluxes inferred by the Copernicus Atmosphere Monitoring Service atmospheric inversion. Assimilation of XCO2 retrievals from OCO2. The .tar includes monthly netCDF files covering the period from 2014-09 to 2020-08. References: Chevallier et al. (JGR, 2005, JGR, 2010, ACP, 2019) CAMSv20r1_SURF.tar Global surface fluxes inferred by the Copernicus Atmosphere Monitoring Service atmospheric inversion. Assimilation of surface air-sample measurements. The .tar includes monthly netCDF files covering the period from 1979-01 to 2020-05. References: Chevallier et al. (JGR, 2005, JGR, 2010); Chevallier (GMD, 2013) Original files at: https://atmosphere.copernicus.eu/
Owners Ana Bastos, Frédéric Chevallier
Publication Date 2021-01-21
Version 2020

▶ ELUC from bookkeeping models from GCB2020

Description Julia Pongratz, 2021/03 Description of file Country_ELUC_090222021_upload.xlsx: Contains the GCB2020 net land-use change flux on country, RECCAP region, IPCC region levels for the two bookkeeping models BLUE (Hansis et al) and Houghton&Nassikas 2017 * H&N_2017_countries: HN2017 original values (HN countries), but with extrapolation beyond 2015 as done for GCB2020; error of global sum <1%. * BLUE_GCB2020_HN_countries: Hansis et al BLUE model as used in GCB2020, but with the 0.25 degree spatially explicit data aggregated to HN countries (i.e., the list of countries used by HN); error of global sum <1%. * BLUE_GCB2020_IPCC_countries: Hansis et al BLUE model as used in GCB2020, but with the 0.25 degree spatially explicit data aggregated to IPCC (as used in AR6, WG3) countries; error of global sum <1%. * BLUE_GCB2020_IPCC_regions: Hansis et al BLUE model as used in GCB2020, but with the 0.25 degree spatially explicit data aggregated to IPCC (AR6, WG3) 10 regions; error of global sum <1%. * BLUE_GCB2020_RECCAP_regions_x: Hansis et al BLUE model as used in GCB2020, but with the 0.25 degree spatially explicit data aggregated to RECCAP 18 regions. Note: this data is preliminary and puts coastal regions into “other"; please contact Julia Pongratz for news. * H&N_2017_IPCC_regions: HN2017 original values, but with extrapolation beyond 2015 as done for GCB2020 and country data aggregated to IPCC (AR6, WG3) 10 regions; error of global sum <1%. * H&N_2017_RECCAP_regions: HN2017 original values, but with extrapolation beyond 2015 as done for GCB2020 and country data aggregated to RECCAP 18 regions; error of global sum <1%. Units are TgC/year. All data has peat emissions (burning, drainage) added; see Friedlingstein et al., 2020 for details of peat datasources (which differ for BLUE and HN). Contact Julia Pongratz (julia.pongratz@lmu.de), if you use this data, for updates and report any conspicuous features. Description of file Gross_Country_ELUC_090222021_upload.xlsx: Contains the GCB2020 gross land-use change sources and sinks on country, RECCAP region, IPCC region levels for the two bookkeeping models BLUE (Hansis et al) and Houghton&Nassikas 2017 * Gross sources and gross sinks for the same models and regional splits as above, though errors are partly larger. The 2019 correction for BLUE was not applied for gross fluxes (thus no 2019 values are provided) and HN was not extrapolated for gross fluxes (thus HN timeseries stops in 2015). Can be delivered upon request: OSCAR regional data.
Owners Ana Bastos, Julia Pongratz
Publication Date 2021-03-14
Version v202103

▶ RECCAP2 Tier1 Regional Land Carbon Fluxes TRENDYv9

Description
Owners Mike O Sullivan
Publication Date 2021-03-15
Version

▶ Soil N2O emissions estimated by NMIP models

Description
Owners Ana Bastos, Hanqin Tian
Publication Date 2021-03-17
Version

▶ N2O emissions estimated by atmospheric inversion models

Description
Owners Ana Bastos, Hanqin Tian
Publication Date 2021-03-17
Version

▶ Top-down CH4 fluxes from the Global Methane Budget (Saunois et al., 2020)

Description Regional emissions estimated by top-down inverse systems. Unit is Tg of CH4/year. The categories are these from the Global Methane Budget (Saunois et al., 2020). The regional estimates have been extracted based on the RECCAP2-mask - allowing some extention to the ocean to avoid misapportionment to land surface due to the rather coarse resolution of the models (see Suppl of Saunois et al, 2020).
Owners Marielle Saunois
Publication Date 2021-03-24
Version 24.03.2021

▶ ELUC, LASC and land sink from OSCAR v3.1.1

Description Same as Gasser et al. (2020), but with finest possible regional aggregation. Best guess estimates only. Ends in 2018. Not all countries available separately. Any missing country is aggregated in one of the Rest of regions.
Owners Thomas Gasser
Publication Date 2021-05-11
Version v1

▶ CMIP6 data for Future-RECCAP2 analysis

Description single tar file containint folders by region with CMIP6 .csv files for historical, ssp126 and ssp370 scenarios for basic variables
Owners Chris Jones
Publication Date 2021-06-03
Version 1

▶ TOMCAT-based posterior gridded global N2O fluxes for 1995 – 2018

Description N2O fluxes on the native inversion 5.6 degree grid and regridded onto a 1x1 degree grid. A landmask for the 5.6 degree grid is also included.
Owners Rona Thompson
Publication Date 2021-06-04
Version

▶ Annual top down land CO2 fluxes by RECCAP2 region from GCP2021 inversions for 1990-2020

Description Annual land fluxes by RECCAP2 region, derived from the 6 GCP2021 CO2 inversions (see co-owners). These fluxes have been regridded and aggregated by region, and adjusted to facilitate the comparison to bottom up estimates. This adjustment includes 3 aspects: - Fossil fuel adjustment to account for minor remaining differences to GridFED v2021_2 (this includes emissions from cement production). - Cement carbonation sink adjustment which was not included in GridFED, but is in the E_FOS term in GCB. - Lateral river flux adjustment as provided by Ronny Lauerwald (The file is based on GlobalNEWS2 for organic C and the weathering CO2 sink after Harmann et al. 2009 as used in Zscheischler et al 2017. But in this version, the organic C loads after GlobalNEWS are twice rescaled: 1) to the latitudinal pattern from Resplandy et al. (2018 NatGeo) and 2) to a synthesis of global estimates of organic C exports of about 500 Tg C/yr (for this you could for the time being cite Regnier et al. 2013, Nat Geo).)
Owners Ingrid Luijkx
Publication Date 2021-11-24
Version 1.1

▶ Annual top down land CO2 fluxes by RECCAP2 region from GCP2021 inversions for 1990-2020

Description Annual land fluxes by RECCAP2 region, derived from the 6 GCP2021 CO2 inversions (see co-owners). These fluxes have been regridded and aggregated by region, and adjusted to facilitate the comparison to bottom up estimates. This adjustment includes 3 aspects: - Fossil fuel adjustment to account for minor remaining differences to GridFED v2021_2 (this includes emissions from cement production). - Cement carbonation sink adjustment which was not included in GridFED, but is in the E_FOS term in GCB. - Lateral river flux adjustment as provided by Ronny Lauerwald (The file is based on GlobalNEWS2 for organic C and the weathering CO2 sink after Harmann et al. 2009 as used in Zscheischler et al 2017. But in this version, the organic C loads after GlobalNEWS are twice rescaled: 1) to the latitudinal pattern from Resplandy et al. (2018 NatGeo) and 2) to a synthesis of global estimates of organic C exports of about 500 Tg C/yr (for this you could for the time being cite Regnier et al. 2013, Nat Geo).)
Owners Ingrid Luijkx
Publication Date 2021-11-24
Version 1.1

▶ MIROC4-ACTM inversion fluxes for nitrous oxide (N2O)

Description
Owners Prabir Patra
Publication Date 2022-04-01
Version

▶ RECCAP2-LOAC-group II “Estuaries and coastal ‘blue carbon’ ecosystems”

Description Excel summary table, method description and overview figures. Please README. Excel table that summarises the methods and results and includes overview figures for: Estuary (tidal systems and deltas, lagoons and fjords) CO2, CH4 and N2O emissions, coastal wetland (mangrove, salt marshes, seagrasses) NEE, and CH4 and N2O emissions, estuary TN and OC burial, marginal C input, estuary and coastal wetlands surface areas. The methods, figures and results summarized in the excel table are unpublished, subject to change and accessible only to registered RECCAP2 members (password protected). The data shared in this context is to be used for RECCAP2 only purposes. Further use of data for unrelated activities needs to be allowed by the contributors, with acknowledgement of RECCAP2.
Owners Judith Rosentreter
Publication Date 2022-04-04
Version III

▶ Gridded top down CO2 fluxes from GCP2021 inversions for 1970-2020

Description In this file, we include the data from the GCP2021 inversions regridded to 1x1 degrees. The data has been regridded and adjusted to facilitate comparison with bottom-up estimates for RECCAP2 regions. This adjustment includes 3 aspects: - Fossil fuel adjustment to account for minor remaining differences to GridFED v2021_2 (this includes emissions from cement production). - Cement carbonation sink adjustment which was not included in GridFED, but is in the E_FOS term in GCB. - Lateral river flux adjustment as provided by Ronny Lauerwald (The file is based on GlobalNEWS2 for organic C and the weathering CO2 sink after Harmann et al. 2009 as used in Zscheischler et al 2017. But in this version, the organic C loads after GlobalNEWS are twice rescaled: 1) to the latitudinal pattern from Resplandy et al. (2018 NatGeo) and 2) to a synthesis of global estimates of organic C exports of about 500 Tg C/yr (for this you could for the time being cite Regnier et al. 2013, Nat Geo).)
Owners Ingrid Luijkx
Publication Date 2022-04-06
Version 1.2

▶ RECCAP2 Tier1 Regional Land Carbon Fluxes TRENDYv9 version 2

Description
Owners Mike O Sullivan
Publication Date 2022-04-09
Version

▶ MIROC4-ACTM inversion for CH4

Description Annual and monthly mean emissions for the 10 land regions of RECCAP2 are provided for 2 inversions
Owners Prabir Patra
Publication Date 2022-04-19
Version 1

▶ MIROC4-ACTM inversion for N2O

Description Annual and monthly mean emissions for the 10 land regions and 5 ocean regions of RECCAP2 are provided as ensemble means of 16 inversions
Owners Prabir Patra
Publication Date 2022-04-19
Version 1

▶ Gridded top down CO2 fluxes from GCP2021 inversions for 1970-2020 with adjustment fluxes

Description Gridded top down CO2 fluxes from GCP2021 inversions for 1970-2020 but now with each flux used for adjustments provided separately
Owners Ingrid Luijkx
Publication Date 2022-06-28
Version 2.2

▶ Monthly wetland methane emissions for RECCAP2 regions by model (diagnostic and prognostic), 2000-2017

Description These are the monthly prognostic and diagnostic wetland methane emissions used in Saunois et al., 2017. The summaries are provided by RECCAP2 region and by model. The data are to be used for the Global Carbon Project RECCAP2 activity.
Owners Benjamin Poulter
Publication Date 2022-11-29
Version 1

BACI

Our overarching objective is to tap into the unrealized potential of existing and scheduled space-borne Earth observation data streams to detect changes in ecosystem functioning and services that have repercussions for essential biodiversity variables, land use potentials, and land-atmosphere interactions.

▶ Upscaled diurnal cycles of carbon and energy fluxes

Description
Owners Martin Jung (mjung@bgc-jena.mpg.de), Paul Bodesheim (pbodes@bgc-jena.mpg.de)
Publication Date 2017-02-22
Version v1
DOI https://doi.org/10.17871/BACI.224
The dataset is available through the MPI BGC FTP server. Please note that the server is not browsable. Please-copy-paste the exact dataset path in your ftp client.
FTP Server ftp.bgc-jena.mpg.de
FTP Path /outgoing/BACI_upscaling/DiurnalCycles/Bgwqcl2cCN2hWfu1t2Dl4PVIc5QozAha/

▶ Global and European tree-ring data

Description These datasets contain: 1) A global network of annually resolved tree-ring width chronologies (~4000 sites) 2) A European network of annually resolved tree-ring width and above-ground biomass increment data (48 sites) 3) A European network of tree-ring stable isotope (13C and 18O) chronologies (43 sites)
Owners David Frank, Flurin Babst
Publication Date 2016-09-26
Version 1

▶ Upscaled tree ring increments for Europe

Description This data product contains upscaled annual tree ring increments across Europe for the six most dominant genera: Abies, Fagus, Larix, Picea, Pinus, and Quercus. Estimated tree ring growth rates are provided at two different spatial resolutions: half-degree (0d50) and 1 km^2 (0d0083). Machine learning methods have been used to create this dataset and predictions of two regression approaches are incorporated: random decision forests (RDF) and Gaussian processes (GP).
Owners Miguel Mahecha (mmahecha@bgc-jena.mpg.de), Paul Bodesheim (pbodes@bgc-jena.mpg.de)
Publication Date 2021-03-05
Version v1
DOI https://www.doi.org/10.17871/BACI.248
The dataset is available through the MPI BGC FTP server. Please note that the server is not browsable. Please-copy-paste the exact dataset path in your ftp client.
FTP Server ftp.bgc-jena.mpg.de
FTP Path /outgoing/BACI_upscaling/TreeRingIncrements/DTDcPyJSzvHBJ7tIRrcfuSQK2GPfQi3a/

▶ WP3_MS10_itask3.5_fieldGhana_WH

Description WP3- MS10 - First version of the ground data examples available for the other WPs Field data for Ghana
Owners Gaia Vaglio Laurin
Publication Date 2016-09-26
Version

▶ WP3_MS10_task3.5_fieldSierra_TJ

Description WP3-MS10 First version of the ground data examples available for the other WPs field data for Sierra Leone
Owners Gaia Vaglio Laurin
Publication Date 2016-09-26
Version

▶ WP3_MS10_task3.5_harwood_UK

Description WP3-ML10 - First version of the ground data examples available for the other WPs MSC thesis documenting analysis and data for Harwood UK site. The full data set is published at http://www2.geog.ucl.ac.uk/~mdisney/fieldwork/Harwood/2003/ARSF_DATA
Owners Gaia Vaglio Laurin
Publication Date 2016-09-26
Version

▶ WP3_MS10_task3.5_Roda_lidarDSM

Description WP3 - MS10 -First version of the ground data examples available for the other WPs Lidar-derived DSM for Roda, Germany
Owners Gaia Vaglio Laurin
Publication Date 2016-09-26
Version

▶ FLux data example

Description The file is one example of the FLUXNET data that will be produced for WP3. The final version is expected to be slightly different only due to additional variables provided and not changes expected what already included now.
Owners Dario Papale
Publication Date 2016-09-28
Version 0

▶ WP3_MS10_task3.1_plantsbirds_data

Description WP3 MS10 Synthesis of plants and birds diversity data
Owners Gaia Vaglio Laurin
Publication Date 2016-09-29
Version

▶ Plant Trait Synthesis Dataset

Description We here provide a synthesis dataset of plant traits, which is a data product based on original trait observations from more than 150 original datasets (see original data references) compiled in the TRY database version 3.0 (Kattge et al. 2011a,b; www.try-db.org). The synthesis dataset comprises four individual datasets 1) Gap-filled trait predictions 2) Species mean traits 3) Categorical traits look-up table 4) Environmental site information for geo-referenced trait observations
Owners Jens Kattge (jkattge@bgc-jena.mpg.de)
Publication Date 2016-09-29
Version 1

▶ CABLAB cube BACI_all

Description Cutout from CABLAB datat cube for the whole BACI region
Owners Miguel Mahecha (mmahecha@bgc-jena.mpg.de), Fabian Gans (fgans@bgc-jena.mpg.de)
Publication Date 2017-03-15
Version

▶ CABLAB data cube EastEurope

Description
Owners Miguel Mahecha (mmahecha@bgc-jena.mpg.de), Fabian Gans (fgans@bgc-jena.mpg.de)
Publication Date 2017-03-15
Version

▶ CABLAB cube NorthEurope

Description CutOut from the CABLAB data cube for NorthEurope
Owners Miguel Mahecha (mmahecha@bgc-jena.mpg.de), Fabian Gans (fgans@bgc-jena.mpg.de)
Publication Date 2017-03-15
Version

▶ CABLAB cutout SouthAfrica

Description Cutout from CABLAB data cube for SouthAfrica
Owners Miguel Mahecha (mmahecha@bgc-jena.mpg.de), Fabian Gans (fgans@bgc-jena.mpg.de)
Publication Date 2017-03-15
Version

▶ CABLAB cube EastAfrica

Description Cutout from CABLAB data cube for East Africa
Owners Miguel Mahecha (mmahecha@bgc-jena.mpg.de), Fabian Gans (fgans@bgc-jena.mpg.de)
Publication Date 2017-03-15
Version

▶ CABLAB data cube for WestAfrica

Description
Owners Miguel Mahecha (mmahecha@bgc-jena.mpg.de), Fabian Gans (fgans@bgc-jena.mpg.de)
Publication Date 2017-03-15
Version

▶ CABLAB data cube for SouthEurope

Description
Owners Miguel Mahecha (mmahecha@bgc-jena.mpg.de), Fabian Gans (fgans@bgc-jena.mpg.de)
Publication Date 2017-03-15
Version

▶ Global annual Light Use Efficiency

Description
Owners Dario Papale
Publication Date 2017-04-04
Version

▶ Global annual Water Use efficiency

Description
Owners Paul Bodesheim (pbodes@bgc-jena.mpg.de)
Publication Date 2017-04-04
Version

▶ Global annual Precipitation Use efficiency

Description
Owners Paul Bodesheim (pbodes@bgc-jena.mpg.de)
Publication Date 2017-04-04
Version

▶ Global annual Bowen Ratio

Description
Owners Paul Bodesheim (pbodes@bgc-jena.mpg.de)
Publication Date 2017-04-04
Version

▶ GPPmax

Description GPPmax estimated as the 90th percentile of daily GPP over the year. Daily GPP data were derived as the daily integral of half hourly GPP.
Owners Paul Bodesheim (pbodes@bgc-jena.mpg.de)
Publication Date 2017-04-04
Version 1

▶ Percentiles of Kernel Density Anomaly Scores of selected variables describing the Atmosphere: Temperature, Soil Moisture, Radiation, Precipitation, relative humidity

Description KDE estimates of BIO (gross primary productivity, latent evapotranspiration, heat flux, fraction of absorbed photosynthetic radiation) or ATMOS (temperature, relative humidity, soil moisture, global radiation, precipiation) variables
Owners Milan Flach (mflach@bgc-jena.mpg.de)
Publication Date 2017-06-16
Version 1

▶ Percentiles of Kernel Density Anomaly Scores of selected variables describing the Biosphere: Gross Primary Productivity, Latent Heat Flux, Sensible Heat Flux, FAPAR

Description KDE estimates of BIO (gross primary productivity, latent evapotranspiration, heat flux, fraction of absorbed photosynthetic radiation) or ATMOS (temperature, relative humidity, soil moisture, global radiation, precipiation) variables
Owners Milan Flach (mflach@bgc-jena.mpg.de)
Publication Date 2017-06-16
Version 1

▶ BACI Index

Description
Owners Joachim Denzler, Yanira Guanche Garcia
Publication Date 2017-09-19
Version

▶ Synthesis of relevant ecosystem scale functional parameters (EFPs, cf. definitions) derived by Fluxnet sites records and ancillary information, characterized by uncertainty estimation.

Description Light Use Efficiency, site level, daily time step.
Owners Dario Papale, Gianluca Tramontana
Publication Date 2017-10-12
Version 1

▶ Synthesis of relevant ecosystem scale functional parameters (EFPs, cf. definitions) derived by Fluxnet sites records and ancillary information, characterized by uncertainty estimation.

Description Water Use efficiency derived by FLUXNET data: site level, daily time step.
Owners Dario Papale, Gianluca Tramontana
Publication Date 2017-10-12
Version 1

▶ Synthesis of relevant ecosystem scale functional parameters (EFPs, cf. definitions) derived by Fluxnet sites records and ancillary information, characterized by uncertainty estimation.

Description Inherent Water Use Efficiency derived by FLUXNET data. Site level, daily time resolution.
Owners Dario Papale, Gianluca Tramontana
Publication Date 2017-10-12
Version 1

▶ Synthesis of relevant ecosystem scale functional parameters (EFPs, cf. definitions) derived by Fluxnet sites records and ancillary information, characterized by uncertainty estimation.

Description Bowen ratio estimated by FLUXNET data. Site level, daily time resolution.
Owners Dario Papale, Gianluca Tramontana
Publication Date 2017-10-12
Version 1

▶ Synthesis data product of ecosystem parameters derived at Fluxnet sites, characterized by uncertainty estimation

Description
Owners Dario Papale
Publication Date 2017-10-12
Version 1

▶ Daily 90th quantile GPP halfhourly and daily integral GPP half hourly (plus bootstrapping)

Description there are 6 variables in the files; # daily estimates are based on a 5 day moving window. GPP90: daily 90th quantile of half hourly GPP GPPcum: daily integral of half hourly GPP # for every 5 day moving window a bootstrapping of 500 repetition using a sample size of 50 with replacement is also used. # then the average of 500 GPP90 or GPPcum estimates is assign to the mid day of the moving window. GPP90.b.avg: averaged bootstrapped daily 90th quantile of half hourly GPP GPPcum.b.avg: averaged bootstrapped daily integral of half hourly GPP # for every 5 day moving window a bootstrapping of 500 repetition using a sample size of 50 with replacement is used. # then the standard deviation of the 500 GPP90 or GPPcum estimates is assign to the mid day of the moving window. GPP90.b.sd: averaged bootstrapped daily 90th quantile of half hourly GPP GPPcum.b.sd: averaged bootstrapped daily integral of half hourly GPP
Owners Miguel Mahecha (mmahecha@bgc-jena.mpg.de), Paul Bodesheim (pbodes@bgc-jena.mpg.de), Talie Musavi (tmusavi@bgc-jena.mpg.de)
Publication Date 2017-12-27
Version v1

▶ Second Version of BACIndex

Description
Owners Joachim Denzler, Yanira Guanche Garcia
Publication Date 2018-11-19
Version

GEOCARBON

GEOCARBON is a European FP7 project with a global perspective, with the ultimate aim to lay the foundations for an operational Global Carbon Observing and Analysis System in support to both science and policy.
All files relating to this project can

▶ Forest Aboveground Biomass map

Description Global map at 0.01 degree of AGB in Forest areas
Owners Valerio Avitabile
Publication Date 2015-09-10
Version v3

▶ Collection of all fluxes

Description This file contains all fluxes that were used for the final synthesis study by Zscheischler et al.
Owners Miguel Mahecha (mmahecha@bgc-jena.mpg.de), Fabian Gans (fgans@bgc-jena.mpg.de), Jakob Zscheischler
Publication Date 2017-07-13
Version v1
DOI doi:10.17617/3.7PJQM1
Link https://doi.org/10.17617/3.7PJQM1

▶ Collection of all fluxes and synthesis

Description All fluxes and integrated net C exchange that were used for the final GeoCarbon synthesis study by
Jakob Zscheischler, Miguel D. Mahecha, Valerio Avitabile, Leonardo Calle, Nuno Carvalhais, Philippe Ciais, Fabian Gans, Nicolas Gruber, Jens Hartmann, Martin Herold, Kazuhito Ichii, Martin Jung, Peter Landschützer, Goulven G. Laruelle, Ronny Lauerwald, Dario Papale, Philippe Peylin, Benjamin Poulter, Deepak Ray, Pierre Regnier, Christian Rödenbeck, Rosa M. Roman-Cuesta, Christopher Schwalm, Gianluca Tramontana, Alexandra Tyukavina, Riccardo Valentini, Guido van der Werf, Tristram O. West, Julie E. Wolf, and Markus Reichstein (2017) An empirical spatiotemporal description of the global surface–atmosphere carbon fluxes: opportunities and data limitations. Biogeosciences.
Owners Miguel Mahecha (mmahecha@bgc-jena.mpg.de), Fabian Gans (fgans@bgc-jena.mpg.de), Jakob Zscheischler
Publication Date 2017-08-01
Version v2
DOI doi:10.17617/3.XCOEYU
Link https://doi.org/10.17617/3.XCOEYU

▶ GEOCARBON synthesis dataset

Description Synthesis dataset of Carbon fluxes ensembles for different sources. All units are in PgC/month.
Owners Miguel Mahecha (mmahecha@bgc-jena.mpg.de), Fabian Gans (fgans@bgc-jena.mpg.de), Jakob Zscheischler
Publication Date 2017-07-13
Version v2
DOI doi:10.17617/3.P0SW9I
Link https://doi.org/10.17617/3.P0SW9I

▶ GLODAP Observational Data

Description Global Ocean Data Analysis Project (GLODAP) is a synthesis database covering the global oceans making carbon, nutrient and tracer samples from research vessels available in a uniform format with performed secondary quality controlled. GLODAP contains the following parameters: total carbon, alkalinity, pH, nutrients, oxygen, temperature, salinity and tracers (SF6, CFC). Due to primary and secondary quality control are the error estimates for both products below the accuracy of the measurement devices and quality flags indicate the certainty of measurements. For more information please visit http://cdiac.ornl.gov/oceans/glodap/\n
Owners Benjamin Pfeil
Publication Date 2013-02-12
Version 1.1

▶ Wood Harvest

Description
Owners Benjamin Poulter
Publication Date 2013-09-25
Version 2

▶ Best available Global Land Cover map

Description These global land cover maps are provided by Climate Change Initiative: Land Cover project by the ESA. More information about the project can be at http://www.esa-landcover-cci.org/. This project provides 3-epoch series of global land cover maps at 300m spatial resolution, where each epoch covers a 5-year period (2008-2012, 2003-2007, 1998-2002. Each pixel value corresponds to the label of a land cover class defined using UN-LCCS classifiers. In addition, 4 quality flag maps are also provided for each epoch, and it can be accessed at http://maps.elie.ucl.ac.be/CCI/viewer/download.php Only major land cover changes were detected at 1km spatial resolution and limited to a certain number of classes (see CRDP user guide). Dedicated user tool is available for sub-setting, re-projecting and re-sampling the CCI-LC maps and converting the LCCS legend to user-specific PFTs.
Owners Nandinerdene Tsendbazar
Publication Date 2014-11-01
Version 1.3 and 1.

▶ SOCAT Observational data SOCAT Version 1.5

Description Surface Ocean CO2 Atlas (SOCAT) provides quality controlled underway CO2 and physical oceanographic (eg sea surface temperature and salinity) data for the time frame 1968-2007. All data is in the same format. The following parameters relevant for GEOCARBON are available within SOCAT: fugacity of carbon dioxide, temperature, salinity, and atmospheric pressure. The error estimates for fCO2 within SOCAT is approx. 2 uatm. For more information please visit the SOCAT website (www.socat.info). Please cite the SOCAT V1.5: Pfeil, B et al. (2012) A uniform, quality controlled Surface Ocean CO2 Atlas (SOCAT), Earth Syst. Sci. Data Discuss., 5, 735-780, doi:10.5194/essdd-5-735-2012, 2012
Owners Benjamin Pfeil
Publication Date 2013-02-12
Version 1.5

▶ CARINA

Description Carbon Dioxide in the Atlantic (CARINA) is a synthesis database covering the Atlantic Ocean and Southern Ocean making carbon, nutrient and tracer samples from research vessels available in a uniform format with performed secondary quality controlled. CARINA contains the following parameters: total carbon, alkalinity, pH, nutrients, oxygen, temperature, salinity and tracers (SF6, CFC). Due to primary and secondary quality control are the error estimates for both products below the accuracy of the measurement devices and quality flags indicate the certainty of measurements. The main coverage is data obtained in the Atlantic Ocean and the Southern Ocean. For more information please visit http://cdiac.ornl.gov/oceans/CARINA/about_carina.html Please cite CARINA as Tanhua, T. et al (2008) CARINA Data Synthesis Project. ORNL/CDIAC-157, NDP-091. Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy, Oak Ridge, Tennessee. doi:10.3334/CDIAC/otg.ndp091
Owners Benjamin Pfeil
Publication Date 2013-02-12
Version 1.2

▶ GLODAP Gridded Data

Description This is the gridded version of Global Ocean Data Analysis Project (GLODAP) covering the global oceans making carbon, nutrient and tracer samples from research vessels available in a uniform format with performed secondary quality controlled. GLODAP contains the following parameters: total carbon, alkalinity, pH, nutrients, oxygen, temperature, salinity and tracers (SF6, CFC). For more information please visit http://cdiac.ornl.gov/oceans/glodap/ \n
Owners Benjamin Pfeil
Publication Date 2013-02-12
Version 1.1

▶ GEOCARBON_CropHarvest_LCSE_v1

Description Global agricultural crop harvest data
Owners Philippe Peylin, Daniel McInerney, Benjamin Poulter
Publication Date 2013-02-18
Version 1

▶ Annual updates of SOCAT

Description This compilation contains public subset of data from SOCATV2 (data with no intellectual property rights issues) The entire SOCATV2 collection will be published in June 2013 at the ICDC in Beijing. The data does not contain all the SOCAT features - for detailed information please contact Benjamin Pfeil (benjamin.pfeil@gfi.uib.no)
Owners Benjamin Pfeil
Publication Date 2013-03-08
Version SOCAT publ

▶ Neural Network based spatiotemporal global air-see CO2 flux estimate

Description Neural Network based spatiotemporal global air-see CO2 flux estimate
Owners Ute Schuster, Nicolas Gruber, Peter Landschützer
Publication Date 2013-09-26
Version

▶ Continental Driver Data

Description Bias-corrected climate data, based on WATCH-ERA-Interim.
Owners Ulrich Weber (uweber@bgc-jena.mpg.de), Christian Beer
Publication Date 2013-09-27
Version 1.0

▶ Continental Driverdata projections based on KNMI

Description
Owners Ulrich Weber (uweber@bgc-jena.mpg.de), Christian Beer
Publication Date 2013-09-27
Version 1.0

▶ GLOBAL WOOD HARVEST DATASET

Description Global Forest Harvest Statistics derived from National Forest Inventories and the UN-FAO Forest Resources Assesssment programme
Owners Daniel McInerney, Benjamin Poulter
Publication Date 2013-10-16
Version 2

▶ Global forest age distributions (fractions of grid cell)

Description This dataset provides at 0.5 degree resolution the age distribution of forests at the plant functional type level. Age classes are in 10-year intervals, from 0 to 140 years, and then mixed age/unmanaged/old growth classified to > 140 years. The data are derived from forest inventory for temperate and boreal regions and from biomass-age curves for tropical regions.
Owners Benjamin Poulter
Publication Date 2013-11-13
Version 0

▶ Global sea surface pCO2 and air-sea flux from a 2-step neural network technique using SOCAT v2

Description Global sea surface pCO2 and air-sea flux from a 2-step neural network technique using SOCAT v2
Owners Nicolas Gruber, Peter Landschützer
Publication Date 2014-05-07
Version TR01

▶ Best available Global Land Cover map

Description These global land cover maps are provided by Climate Change Initiative: Land Cover project by the ESA. More information about the project can be at http://www.esa-landcover-cci.org/. This project provides 3-epoch series of global land cover maps at 300m spatial resolution, where each epoch covers a 5-year period (2008-2012, 2003-2007, 1998-2002. Each pixel value corresponds to the label of a land cover class defined using UN-LCCS classifiers. In addition, 4 quality flag maps are also provided for each epoch, and it can be accessed at http://maps.elie.ucl.ac.be/CCI/viewer/download.php Only major land cover changes were detected at 1km spatial resolution and limited to a certain number of classes (see CRDP user guide). Dedicated user tool is available for sub-setting, re-projecting and re-sampling the CCI-LC maps and converting the LCCS legend to user-specific PFTs.
Owners Nandinerdene Tsendbazar
Publication Date 2014-11-01
Version 1.3 and 1.

▶ Best available Global Land Cover map

Description These global land cover maps are provided by Climate Change Initiative: Land Cover project by the ESA. More information about the project can be at http://www.esa-landcover-cci.org/. This project provides 3-epoch series of global land cover maps at 300m spatial resolution, where each epoch covers a 5-year period (2008-2012, 2003-2007, 1998-2002. Each pixel value corresponds to the label of a land cover class defined using UN-LCCS classifiers. In addition, 4 quality flag maps are also provided for each epoch, and it can be accessed at http://maps.elie.ucl.ac.be/CCI/viewer/download.php Only major land cover changes were detected at 1km spatial resolution and limited to a certain number of classes (see CRDP user guide). Dedicated user tool is available for sub-setting, re-projecting and re-sampling the CCI-LC maps and converting the LCCS legend to user-specific PFTs.
Owners Nandinerdene Tsendbazar
Publication Date 2014-11-01
Version 1.3 and 1.

▶ Maps of monthly CO2 flux anomalies

Description Using a novel 2-step neural network technique and the available sea surface pCO2 observations from the Surface Ocean Carbon Atlas (SOCAT) version 2 (Bakker et al, 2014) we were able to reconstruct the sea surface pCO2 and the resulting air-sea CO2 flux on a monthly temporal and a global spatial scale, using a fine 1x1 degree grid, as described in Landschützer et al (2013) and Landschützer et al (2014). Compared to previous work we were now able to extend our analysis 30 years period (1982-2011) backwards in time, providing a total of 360 monthly pCO2 maps, allowing us to investigate inter-annual as well as inter-decadal CO2 flux variabilities solely based on shipboard observations.
Owners Nicolas Gruber, Peter Landschützer
Publication Date 2014-11-11
Version netETH30yr

▶ CO2 evasion from rivers

Description 50 runs of riverine FCO2
Owners Ronny Lauwerwall
Publication Date 2014-11-14
Version

▶ Estuarine FCO2 (0.5 degree)

Description 0.5 degree aggregation of estuarine CO2 outgasing / These values were calculated on larger regions (COSCAT/MARCATS) and these files were created for visualisation purposes and integration with other global datasets for GEOCARBON
Owners Goulven Laurelle
Publication Date 2014-11-14
Version

▶ Estuarine FCO2 (1 degree)

Description 1 degree aggregation of estuarine CO2 outgasing / These values were calculated on larger regions (COSCAT/MARCATS) and these files were created for visualisation purposes and integration with other global datasets for GEOCARBON
Owners Goulven Laurelle
Publication Date 2014-11-14
Version

▶ SOCAT Update

Description SOCAT Version 2 plus updates from public cruises
Owners Benjamin Pfeil
Publication Date 2014-11-17
Version v2

▶ Update of SOCAT

Description
Owners Benjamin Pfeil
Publication Date 2014-11-17
Version v2

▶ Air-sea CO2 flux - 10 realizations

Description Air-sea CO2 flux - 10 realizations for uncertainty calculation. Each run has been created by withholding a random 10% fraction of surface ocean pCO2 data
Owners Nicolas Gruber, Peter Landschützer
Publication Date 2014-12-05
Version ETH30yr01

▶ Global wood consumption, following transport from harvested sites using GEOCARBON dataset GEOCARBON_WOODHARVEST_LSCE_V2.nc

Description A simple consumption-based model, to laterally transport wood harvest to sites of consumption, was also developed. We based the model roughly on the definitions of Peters et al. (2012) accounting for fluxes of ‘apparent consumption’ and ignoring processing related losses. The redistribution of wood harvest was based on first estimating country-level wood use from import and export statistics from FAOSTAT (http://faostat3.fao.org) and then using the Gridded Population of the World version 4 dataset (GPWv4) to calculate per capita wood consumption. Country-level wood use was determined as, consumption = production + imports – exports, and then the per-capita consumption estimated as the summed country level wood consumption divided by total country-level population (using national boundaries from the Global Administrative Dataset version 2.8). These per-capita consumption rates were then multiplied by the 1-degree GPWv4 dataset to calculate gridded total wood consumption, which (reaggregated to the country level) was equivalent to the net production, import and export statistics from FAOSTAT. Lastly, we rescaled the FAOSTAT per-pixel wood consumption product to match the global sum of our wood harvest product by first calculating the fractional consumption of global wood harvest for each pixel (i.e., per-pixel wood harvest divided by the global wood harvest from the FAOSTAT approach), and then multiplying the per-pixel fractional consumption grid by the new global wood harvest product. The final redistribution of wood harvest was thus consistent with the original total global wood harvest estimate of 0.89 PgC, and took into account per-capita consumption rates and population density.
Owners Benjamin Poulter
Publication Date 2016-10-06
Version V1

DEHESyRE

Integration of multitemporal/multiscale optical and thermal data to interpret and monitor ecosystem-scale water, carbon and nutrient fluxes in Mediterranean areas with complex vegetation structure

▶ DEHESyRE

Description DEHESHyrE 2015. APRIL AHS + CASI1500i SURVEY FLIGHT CAMPAIGN PROGRAM
Owners Mirco Migliavacca (mmiglia@bgc-jena.mpg.de)
Publication Date 2015-05-18
Version 1

▶ DEHESyRE

Description DEHESHyrE 2015. MAY AHS + CASI1500i SURVEY FLIGHT CAMPAIGN PROGRAM
Owners Mirco Migliavacca (mmiglia@bgc-jena.mpg.de)
Publication Date 2015-05-18
Version 1

▶ DEHESyRE

Description DEHESHyrE 2015. APRIL. IMAGE OF TEMPERATURE AHS
Owners Mirco Migliavacca (mmiglia@bgc-jena.mpg.de)
Publication Date 2015-06-18
Version 1

▶ DEHESyRE

Description DEHESHyrE 2015. APRIL. HEADER FILE'S OF TEMPERATURE IMAGE AHS
Owners Mirco Migliavacca (mmiglia@bgc-jena.mpg.de)
Publication Date 2015-06-18
Version 1

▶ DEHESyRE

Description DEHESHyrE 2015. APRIL. IMAGE OF TEMPERATURE AHS
Owners Mirco Migliavacca (mmiglia@bgc-jena.mpg.de)
Publication Date 2015-06-18
Version 1

▶ DEHESyRE

Description DEHESHyrE 2015. APRIL. HEADER FILE'S OF TEMPERATURE IMAGE AHS
Owners Mirco Migliavacca (mmiglia@bgc-jena.mpg.de)
Publication Date 2015-06-18
Version 1

▶ DEHESyRE

Description DEHESHyrE 2015. APRIL. IMAGE OF TEMPERATURE AHS
Owners Mirco Migliavacca (mmiglia@bgc-jena.mpg.de)
Publication Date 2015-06-18
Version 1

▶ DEHESyRE

Description DEHESHyrE 2015. APRIL. HEADER FILE'S OF TEMPERATURE IMAGE AHS
Owners Mirco Migliavacca (mmiglia@bgc-jena.mpg.de)
Publication Date 2015-06-18
Version 1

▶ DEHESyRE

Description DEHESHyrE 2015. APRIL. IMAGE OF TEMPERATURE AHS
Owners Mirco Migliavacca (mmiglia@bgc-jena.mpg.de)
Publication Date 2015-06-18
Version 1

▶ DEHESyRE

Description DEHESHyrE 2015. APRIL. HEADER FILE'S OF TEMPERATURE IMAGE AHS
Owners Mirco Migliavacca (mmiglia@bgc-jena.mpg.de)
Publication Date 2015-06-18
Version 1

▶ DEHESyRE

Description DEHESHyrE 2015. APRIL. IMAGE OF TEMPERATURE AHS
Owners Mirco Migliavacca (mmiglia@bgc-jena.mpg.de)
Publication Date 2015-06-18
Version 1

▶ DEHESyRE

Description DEHESHyrE 2015. APRIL. HEADER FILE'S OF TEMPERATURE IMAGE AHS
Owners Mirco Migliavacca (mmiglia@bgc-jena.mpg.de)
Publication Date 2015-06-18
Version 1

▶ DEHESyRE

Description DEHESHyrE 2015. APRIL. IMAGE OF TEMPERATURE AHS
Owners Mirco Migliavacca (mmiglia@bgc-jena.mpg.de)
Publication Date 2015-06-18
Version 1

▶ DEHESyRE

Description DEHESHyrE 2015. APRIL. HEADER FILE'S OF TEMPERATURE IMAGE AHS
Owners Mirco Migliavacca (mmiglia@bgc-jena.mpg.de)
Publication Date 2015-06-18
Version 1

▶ DEHESyRE

Description DEHESHyrE 2015. APRIL. IMAGE OF TEMPERATURE AHS
Owners Mirco Migliavacca (mmiglia@bgc-jena.mpg.de)
Publication Date 2015-06-18
Version 1

▶ DEHESyRE

Description DEHESHyrE 2015. APRIL. HEADER FILE'S OF TEMPERATURE IMAGE AHS
Owners Mirco Migliavacca (mmiglia@bgc-jena.mpg.de)
Publication Date 2015-06-18
Version 1

▶ DEHESyRE

Description DEHESHyrE 2015. APRIL. IMAGE OF TEMPERATURE AHS
Owners Mirco Migliavacca (mmiglia@bgc-jena.mpg.de)
Publication Date 2015-06-18
Version 1

▶ DEHESyRE

Description DEHESHyrE 2015. APRIL. HEADER FILE'S OF TEMPERATURE IMAGE AHS
Owners Mirco Migliavacca (mmiglia@bgc-jena.mpg.de)
Publication Date 2015-06-18
Version 1

▶ DEHESyRE

Description DEHESHyrE 2015. APRIL. IMAGE OF TEMPERATURE AHS
Owners Mirco Migliavacca (mmiglia@bgc-jena.mpg.de)
Publication Date 2015-06-18
Version 1

▶ DEHESyRE

Description DEHESHyrE 2015. APRIL. HEADER FILE'S OF TEMPERATURE IMAGE AHS
Owners Mirco Migliavacca (mmiglia@bgc-jena.mpg.de)
Publication Date 2015-06-18
Version 1

▶ DEHESyRE

Description DEHESHyrE 2015. APRIL. IMAGE OF REFLECTANCE AHS
Owners Mirco Migliavacca (mmiglia@bgc-jena.mpg.de)
Publication Date 2015-06-19
Version 1

▶ DEHESyRE

Description DEHESHyrE 2015. APRIL. HEADER FILE'S OF REFLECTANCE IMAGE AHS
Owners Mirco Migliavacca (mmiglia@bgc-jena.mpg.de)
Publication Date 2015-06-19
Version 1

▶ DEHESyRE

Description DEHESHyrE 2015. APRIL. IMAGE OF REFLECTANCE AHS
Owners Mirco Migliavacca (mmiglia@bgc-jena.mpg.de)
Publication Date 2015-06-19
Version 1

▶ DEHESyRE

Description DEHESHyrE 2015. APRIL. HEADER FILE'S OF REFLECTANCE IMAGE AHS
Owners Mirco Migliavacca (mmiglia@bgc-jena.mpg.de)
Publication Date 2015-06-19
Version 1

▶ DEHESyRE

Description DEHESHyrE 2015. APRIL. IMAGE OF REFLECTANCE CASI
Owners Mirco Migliavacca (mmiglia@bgc-jena.mpg.de)
Publication Date 2015-06-19
Version 1

▶ DEHESyRE

Description DEHESHyrE 2015. APRIL. HEADER FILE'S OF REFLECTANCE IMAGE CASI
Owners Mirco Migliavacca (mmiglia@bgc-jena.mpg.de)
Publication Date 2015-06-19
Version 1

▶ Majadas flux and meteo data

Description Meteo and flux data from Majadas including a gap filled best overall meteo file for incoming variables. Each tower has one csv file with the data.
Owners Tarek El-Madany (telmad@bgc-jena.mpg.de)
Publication Date 2018-02-07
Version v1

CARBO-EXTREME

CARBO-Extreme aims to

• improve our understanding of the terrestrial carbon cycle in response to climate variability and extreme events
• represent and apply this knowledge over Europe with predictive terrestrial carbon cycle modelling
  
  ▶ Ancillary data for model data fusion at long-term eddy flux sites

  Description missing
  Owners Miguel Mahecha (mmahecha@bgc-jena.mpg.de)
  Publication Date 2013-04-18
  Version missing
  
  
  ▶ Normalized Difference Vegetation index (AVHRR)

  Description We use the Land Long Term Data Record LTDR version 3, which include BRDF effects correction, cloud mask improvement and a better orbital drift correction, for the 1981-2000 period. The reprocessing of this version is not entirely finished (year 1984 is still missing), but around 90% of the data are ready so we decided to deliver a first version. We provide the Quality Assessment field delivered in the LTDR files (see details in http://ltdr.nascom.nasa.gov/ltdr/docs/LTDRDataFormatDescriptions_03_2010_30.pdf). NDVI data have been filtered against: \n QA bit number 1: Pixel is cloudy \nA bit number 2: Pixel contains cloud shadow \n QA bit number 3: Pixel is over water \n QA bit number 6: Pixel is at night (high solar zenith angle)
  Owners Fabienne Maignan
  Publication Date 2013-04-18
  Version missing
  
  
  ▶ Normalized Difference Vegetation index (MODIS)

  Description We provide directionally corrected NDVI MODIS/Terra daily data, at the Climate Modeling Grid resolution (CMG, 5 km, 0.05°), for the 2000-2008 period. The correction follows the algorithm described in Vermote et al. (2009). Over the European PFTs the noise is estimated to be less than 0.02.
  Owners Fabienne Maignan
  Publication Date 2013-04-18
  Version missing
  
  
  ▶ Continental model run BASFOR ()

  Description missing
  Owners Marcel van Ojen
  Publication Date 2013-04-18
  Version missing
  
  
  ▶ - Old - Continental model run EPIC (IIASA)

  Description
  Owners Juraj Balkovic, Marijn van der Velde
  Publication Date 2013-04-18
  Version
  
  
  ▶ Spatial simulations of crop lands in Europe

  Description Simulation results of DailyDayCent for control climate, unchanged variability, increasing atmospheric CO2 concentration. The simulations considered crop rotations that are different in each grid cell. The basis of the crop rotation and agricultural management information is the NitroEurope data set.
  Owners Matthias Kuhnert
  Publication Date 2013-05-15
  Version
  
  
  ▶ LPJmL Simulation with landuse and fire for control climate variability and control CO2

  Description LPJmL Simulation with landuse and fire for control climate variability and control CO2
  Owners Ariane Walz, Kirsten Thonicke, Anja Rammig, Susanne Rolinski
  Publication Date 2013-05-22
  Version
  
  
  ▶ LPJmL Simulation with landuse and fire for control climate variability and constant CO2

  Description LPJmL Simulation with landuse and fire for control climate variability and constant CO2
  Owners Ariane Walz, Kirsten Thonicke, Anja Rammig, Susanne Rolinski
  Publication Date 2013-05-22
  Version
  
  
  ▶ LPJmL Simulation with landuse and fire for control climate variability, but constant climate, and control CO2

  Description LPJmL Simulation with landuse and fire for control climate variability, but constant climate, and control CO2
  Owners Ariane Walz, Kirsten Thonicke, Anja Rammig, Susanne Rolinski
  Publication Date 2013-05-22
  Version
  
  
  ▶ LPJmL Simulation with landuse and fire for control climate variability, but constant climate and constant CO2

  Description LPJmL Simulation with landuse and fire for control climate variability, but constant climate and constant CO2
  Owners Ariane Walz, Kirsten Thonicke, Anja Rammig, Susanne Rolinski
  Publication Date 2013-05-22
  Version
  
  
  ▶ LPJmL Simulation with landuse and fire for reduced climate variability and control CO2

  Description LPJmL Simulation with landuse and fire for reduced climate variability and control CO2
  Owners Ariane Walz, Kirsten Thonicke, Anja Rammig, Susanne Rolinski
  Publication Date 2013-05-22
  Version
  
  
  ▶ LPJmL Simulation with landuse and fire for reduced climate variability and constant CO2

  Description LPJmL Simulation with landuse and fire for reduced climate variability and constant CO2
  Owners Ariane Walz, Kirsten Thonicke, Anja Rammig, Susanne Rolinski
  Publication Date 2013-05-22
  Version
  
  
  ▶ Maps of vulnerability and risk

  Description Full set of available scenario runs are analysed by the direct and indirect PRA
  Owners Susanne Rolinski
  Publication Date 2013-06-04
  Version
  
  
  ▶ INRA continental modelruns

  Description INRA continental modelruns
  Owners Christian Beer
  Publication Date 2013-06-13
  Version
  
  
  ▶ LSCE continental modelruns

  Description
  Owners Christian Beer
  Publication Date 2013-06-13
  Version
  
  
  ▶ Simulation results of the carbon cycle of cropland in Europe

  Description These data are simulation results of the model DAILYDAYCENT. Crop rotation and management are considered in the simulations and vary in space and time. Because of imitated data availability the study area is restricted to EU-27. The used climate data and atmospheric CO2 concentration is described in the data set.
  Owners Matthias Kuhnert
  Publication Date 2013-07-10
  Version version2
  
  
  ▶ Continental model run EPIC (IIASA)

  Description
  Owners Juraj Balkovic, Marijn van der Velde
  Publication Date 2013-07-11
  Version
  
  
  ▶ Continental Dirverdata projection

  Description Model prpjection based on REMO.
  Owners Ulrich Weber (uweber@bgc-jena.mpg.de), Christian Beer
  Publication Date 2013-09-27
  Version 1.0
  
  
  ▶ Simulation results of the carbon cycle of cropland in Europe

  Description These data are simulation results of the model DAILYDAYCENT. Crop rotation and management are considered in the simulations and vary in space and time. Because of imitated data availability the study area is restricted to EU-27. The used climate data and atmospheric CO2 concentration is described in the data set.
  Owners Matthias Kuhnert
  Publication Date 2013-11-14
  Version version 3
  
  
  ▶ Simulation results of the carbon cycle of cropland in Europe with reduced variable climate

  Description These data are simulation results of the model DAILYDAYCENT. Crop rotation and management are considered in the simulations and vary in space and time. Because of imitated data availability the study area is restricted to EU-27. The used climate data and atmospheric CO2 concentration is described in the data set.
  Owners Matthias Kuhnert
  Publication Date 2013-11-14
  Version version 3
  
  
  ▶ MPIBGC continental model runs

  Description
  Owners Christian Beer
  Publication Date 2014-01-31
  Version v2
  
  
  ▶ MPIBGC continental model runs

  Description
  Owners Christian Beer
  Publication Date 2014-01-31
  Version v2
  
  
  ▶ MPIBGC continental model runs

  Description
  Owners Christian Beer
  Publication Date 2014-01-31
  Version v2
  
  
  ▶ MPIBGC continental model runs

  Description
  Owners Christian Beer
  Publication Date 2014-01-31
  Version v2
  
  
  ▶ MPIBGC model runs CNTLVAR CNTLCLIM CNTLCO2

  Description
  Owners Christian Beer
  Publication Date 2014-01-31
  Version v2
  
  
  ▶ MPIBGC continental model runs

  Description
  Owners Christian Beer
  Publication Date 2014-01-31
  Version v2
  
  
  ▶ MPIBGC modelruns REDVAR CNTLCLIM CNTLCO2

  Description
  Owners Christian Beer
  Publication Date 2014-01-31
  Version
  
  
  ▶ MPIBGC model runs REDVAR CNTLCLIM CONSTCO2

  Description
  Owners Christian Beer
  Publication Date 2014-01-31
  Version v2
  
  
  ▶ Bias-corrected daily climate data at 0.5 degree during 1979-2010

  Description This dataset contains harmonized time series of daily climate variables at a global grid of 0.5 degree spatial resolution without projection. Each annual file contains daily data of minimum and maximum surface air temperature (deg C), precipitation (kg/m2/s), shortwave and longwave downward radiation fluxes (W/m2), wind speed (m/s), specific humidity (kg/kg), and surface pressure (Pa). The period 1901-1978 is covered by WATCH forcing data. The period 1979-2010 is covered by bias-corrected ECMWF ERA-Interim reanalysis results. The period 2011-2100 is covered by bias-corrected medium resolution results from CMIP5 experiments using the MPI-ESM. For this latter period three RCP results are used: RCP2.6, RCP4.5, and RCP8.5. Full description of methods is available in Beer et al., Journal of Climate, 2014. This article should be cited when using the data. The only difference is using global data instead of regional.
  Owners Ulrich Weber (uweber@bgc-jena.mpg.de), Christian Beer
  Publication Date 2014-12-09
  Version 1
  
  
  ▶ Bias-corrected daily climate data at 0.5 degree during 2011-2100 for RCP2.6

  Description This dataset contains harmonized time series of daily climate variables at a global grid of 0.5 degree spatial resolution without projection. Each annual file contains daily data of minimum and maximum surface air temperature (deg C), precipitation (kg/m2/s), shortwave and longwave downward radiation fluxes (W/m2), wind speed (m/s), specific humidity (kg/kg), and surface pressure (Pa). The period 1901-1978 is covered by WATCH forcing data. The period 1979-2010 is covered by bias-corrected ECMWF ERA-Interim reanalysis results. The period 2011-2100 is covered by bias-corrected medium resolution results from CMIP5 experiments using the MPI-ESM. For this latter period three RCP results are used: RCP2.6, RCP4.5, and RCP8.5. Full description of methods is available in Beer et al., Journal of Climate, 2014. This article should be cited when using the data. The only difference is using global data instead of regional.
  Owners Ulrich Weber (uweber@bgc-jena.mpg.de), Christian Beer
  Publication Date 2014-12-09
  Version
  
  
  ▶ Bias-corrected daily climate data at 0.5 degree during 2011-2100 for RCP4.5

  Description This dataset contains harmonized time series of daily climate variables at a global grid of 0.5 degree spatial resolution without projection. Each annual file contains daily data of minimum and maximum surface air temperature (deg C), precipitation (kg/m2/s), shortwave and longwave downward radiation fluxes (W/m2), wind speed (m/s), specific humidity (kg/kg), and surface pressure (Pa). The period 1901-1978 is covered by WATCH forcing data. The period 1979-2010 is covered by bias-corrected ECMWF ERA-Interim reanalysis results. The period 2011-2100 is covered by bias-corrected medium resolution results from CMIP5 experiments using the MPI-ESM. For this latter period three RCP results are used: RCP2.6, RCP4.5, and RCP8.5. Full description of methods is available in Beer et al., Journal of Climate, 2014. This article should be cited when using the data. The only difference is using global data instead of regional.
  Owners Ulrich Weber (uweber@bgc-jena.mpg.de), Christian Beer
  Publication Date 2014-12-09
  Version
  
  
  ▶ Bias-corrected daily climate data at 0.5 degree during 1901-1978

  Description This dataset contains harmonized time series of daily climate variables at a global grid of 0.5 degree spatial resolution without projection. Each annual file contains daily data of minimum and maximum surface air temperature (deg C), precipitation (kg/m2/s), shortwave and longwave downward radiation fluxes (W/m2), wind speed (m/s), specific humidity (kg/kg), and surface pressure (Pa). The period 1901-1978 is covered by WATCH forcing data. The period 1979-2010 is covered by bias-corrected ECMWF ERA-Interim reanalysis results. The period 2011-2100 is covered by bias-corrected medium resolution results from CMIP5 experiments using the MPI-ESM. For this latter period three RCP results are used: RCP2.6, RCP4.5, and RCP8.5. Full description of methods is available in Beer et al., Journal of Climate, 2014. This article should be cited when using the data. The only difference is using global data instead of regional.
  Owners Ulrich Weber (uweber@bgc-jena.mpg.de), Christian Beer
  Publication Date 2014-12-09
  Version
  
  
  ▶ Bias-corrected daily climate data at 0.5 degree during 2011-2100 for RCP8.5

  Description This dataset contains harmonized time series of daily climate variables at a global grid of 0.5 degree spatial resolution without projection. Each annual file contains daily data of minimum and maximum surface air temperature (deg C), precipitation (kg/m2/s), shortwave and longwave downward radiation fluxes (W/m2), wind speed (m/s), specific humidity (kg/kg), and surface pressure (Pa). The period 1901-1978 is covered by WATCH forcing data. The period 1979-2010 is covered by bias-corrected ECMWF ERA-Interim reanalysis results. The period 2011-2100 is covered by bias-corrected medium resolution results from CMIP5 experiments using the MPI-ESM. For this latter period three RCP results are used: RCP2.6, RCP4.5, and RCP8.5. Full description of methods is available in Beer et al., Journal of Climate, 2014. This article should be cited when using the data. The only difference is using global data instead of regional.
  Owners Ulrich Weber (uweber@bgc-jena.mpg.de), Christian Beer
  Publication Date 2014-12-15
  Version