Protocol

2019 Global Carbon Budget: Land modelling protocol (Trendy-v8)

Contact: Stephen Sitch () and Pierre Friedlingstein ()

  1. Deadline for submission of simulations:

GCB 2019 simulations: S0, S1, S2, S3 simulations latest: August 16th 2019

Additional Trendy simulations: S4, S5, S6, S7: October 16th 2019

Goal:  To provide the land components of the Global Carbon Project 2019 Budget, and an ensemble of land carbon cycle simulations to be used by the scientific community. The Trendy-v8 data will be made available to GCP’s REgional Carbon Cycle Assessment Project phase 2 (RECCAP2), and represent Tier 1 products (regional C balance & component fluxes based on global products). Usual Trendy data-use policy will apply for RECCAP2.

Also the FIREmip group of fire enabled DGVMs will utilise this protocol as a basis for their new historical simulations.

2. Model simulations

Models can have static or dynamic natural vegetation but all will use prescribed cropland and grazing (=pasture+rangeland) distribution. The models will be forced over the 1700-2018 period with changing CO2, climate and land use according to the following simulations.

2.1. GCB 2019 simulations (see more detailed protocol below)

S0: Control. No forcing change (time-invariant “pre-industrial” CO2, climate and land use mask). S0 is needed to diagnose any “cold start” issues or model drift

S1: CO2 only (time-invariant “pre-industrial” climate and land use mask)

S2: CO2 and climate only (time-invariant “pre-industrial” land use mask)

S3: CO2, climate and land use (all forcing time-varying)

Models with N cycle should have time-varying N inputs for S1, S2 and S3 (see Annex 3).

2.2.  Additional Trendy simulations (not for GCB 2019 ESSD paper).

a) Land Use analysis (lead: Julia Pongratz)

S4: Land use only – PI (time-invariant “pre-industrial” climate and CO2, annual time-varying land use mask 1700-2018)

S5: Land use only – PD (time-invariant present-day climate and CO2, annual time-varying land use mask 1700-2018)

S6: Control to S5 (time-invariant present-day climate and CO2, time-invariant “pre-industrial” land use mask)

Note: both S4 and S5 require a control (S0 and S6, respectively) because those experiments in TRENDYv2 showed that NBP=0 does not hold even when averaged over a climate cycle in most models.

b) Nitrogen fertilisation (lead: Sönke Zaehle). For N-enabled models only

S7: No Ndeposition (time-invariant “pre-industrial” Nitrogen deposition)

c) There are further requests from the wider community which the trendy modelling teams will be invited to join in due course. These include:

  • The effects of diffuse radiation on the land carbion cycle and the BIM (budget imbalance), lead Mike O’Sullivan (Uni Exeter) – will require new simulation(s) (protocol to be devised).
  •  14C emulator to constrain turnover times, lead Ashley Ballantyne (Uni Montana/LSCE) – will require more detailed information on fluxes and C pools at monthly timestep.
  • Traceability framework analysis, lead (Yiqi Luo, Northern Arizona Uni), will require models to include their own matrix versions, which can possibly participate in the matrix-based traceability analysis

3. Criteria for budget inclusion

As in the past, we will apply three criteria for minimum model realism by including only those models with :

(1) steady state after spin-up. Diagnosed from S0 run. Steady-State defined as an offset < 0.10 GtC/yr, drift < 0.05 GtC/yr per century (i.e. first is the average over 1700-2018, second is the slope x 100).

(2) net annual land flux (Sland-ELUC) is a carbon sink over the 1990s and/or 2000s as constrained by global atmospheric and oceanic observations (Keeling and Manning 2014). Diagnosed from S3 run.

(3) global net annual land use flux (ELUC) is a carbon source over the 1990s.  Diagnosed from S3-S2 runs.

Note– as last year, DGVM results will be evaluated in the iLamb benchmarking system and summary statistics will be given for each model (in summary table/figures) and included in the supplementary material of the ESSD paper. This will enable us to document model improvement each year, and to identify possible issues / model deficiencies to aid model development. We do not envisage using the benchmarking results as criteria for budget inclusion at the moment, but potentially in future years after further consultation among participating groups.

  1. Dataset provided and data access

4.1 CRU Climate forcing:

0.5 degree CRU monthly historical forcing over 1901-2018.

Monthly CRU data are provided by Ian Harris at UEA 1901-2018 and available from the following website:

https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.03/

4.2 CRU-JRA climate forcing

0.5 degree CRU-JRA55 6-hourly historical forcing over 1901- 2018

6 hourly CRU-JRA55 climatology provided by Ian Harris at UEA 1901-2018 and available from from Exeter ftp site (see instructions below) in directory:

./input/CRUJRA2019

Groups needing daily forcing can start from either monthly CRU or 6-hourly CRU-JRA55. In any case they have to make sure that monthly totals are conserved. Groups aggregating from 6-hourly to daily might find the following repository of bash+cdo+nco scripts useful: https://github.com/MagicForrest/CRUJRA_4_GUESS. See annex 1 for description of JRA-55 and differences with NCEP previously used up to TRENDYv6).

4.3 Global atmospheric CO2 

1700-2018 annual time-series, derived from from ice core CO2 data merged with NOAA annual resolution from 1958 onwards. Prepared by C Le Quéré for the Global Carbon Project. Most small differences with the 2017 data are from the revisions of the trend between MLO and SPO which is used to fill missing SPO data. This dataset is intended to be used as atmospheric forcing for modelling the evolution of carbon sinks. Data from March 1958 are monthly average from MLO and SPO provided by NOAA’s Earth System Research Laboratory http://www.esrl.noaa.gov/gmd/ccgg/trends/. When no SPO data are available (including prior to 1975), SPO is constructed from the 1976-2017 average MLO-SPO trend and average monthly departure. Data for 2016-2018 are preliminary values. The data from 1980 through 2006 were reprocessed in 2011 to bring them into the WMO X2007 scale. Data prior to March 1958 are estimated with a cubic spline fit to ice core data from Joos and Spahni 2008 Rates of change in natural and anthropogenic radiative forcing over the past 20,000 years PNAS.

Annual mean fields are generated from these monthly data. DGVMs may also wish to run directly with monthly CO2 fields.

CO2 data are available from Exeter ftp site (see instructions below) in directory:

./input/CO2field/global_co2_ann_1700_2018.txt

4.4 Land use change:

Land-use Harmonization (LUH) data for GCB 2019 is provided in 3 separate files, which can be downloaded from the following links (for the states, transitions, and management data layers respectively):

http://gsweb1vh2.umd.edu/LUH2/LUH2_GCB_2019/states.nc

http://gsweb1vh2.umd.edu/LUH2/LUH2_GCB_2019/transitions.nc

http://gsweb1vh2.umd.edu/LUH2/LUH2_GCB_2019/management.nc

These files are based on updated data from HYDE for the years 1960-2019 (which is based on updated data from FAO), as well as the latest FAO wood harvest data. A summary of the methods we used are described in annex two.

The data files are for the years 850-2019, although most TRENDY modelling teams will only be using the years from 1950 onwards in those files. We thought it would be helpful to keep the file format consistent with the LUH2 data produced for CMIP6 though, hence the start year of 850. The land-use states for the years 850-1950 will be identical to the LUH2 v2h dataset, and the states for the years 1951-2019 will be different from the LUH2 v2h dataset, primarily in Brazil (the transitions for years 1950-2018 are new for GCB 2019).

4.5 Misc. Datasets

Each group will use its own data source for soil properties etc.

  1. Experiment protocol
  • Model spin up:
    • 1700 CO2 concentration (276.59ppm), 2018 CO2 (S5, S6)
    • recycling climate mean and variability from the early decades of the 20th century (i.e. 1901-1920).
    • constant 1700 LUC (crops and pasture distribution).
    • For S5 and S6 – recycling climate mean and variability from recent decades of the 21st century (i.e. 1999-2018)
  • 1701-1900 transient simulation:
    • varying CO2 (S1, S2, S3). 1700 CO2 (S0, S4), 2018 CO2 (S5, S6)
    • continue recycling spin up climate (all simulations; for S5 & S6 start 1999-2018 cycle with 2001 climate in model year 1701 such that 1999 – 2018 model years will have 1999-2018 climate)
    • varying LUC  (S3, S4, S5, S7). 1700 LUC, as in spin-up (S0, S1, S2, S6)
  • 1901-2018 transient simulation:
    • varying CO2 (S1, S2, S3, S7). 1700 CO2 (S0, S4). 2018 CO2 (S5, S6).
    • varying climate (S2, S3, S7). Continue recycling spin up climate (1901-1920: S0, S1, S4; 1999-2018: S5, S6)
    • varying LUC  (S3, S4, S5, S7). 1700 LUC, as in spin-up (S0, S1, S2, S6)

Models having a nitrogen cycle should use time varying Nitrogen inputs (see annex 3)

  1. Required outputs
  • trendy_listofvariables_GCP2019v6
  • For all simulations (S0 to S7): Ascii file with five columns: year, annual global NBP, annual northern extra tropics NBP, annual tropical NBP, annual southern extra-tropics NBP (see excel file for definition and sign convention); one row per year, 1700-2018. Name convention: Model_zonalNBP.txt, e.g. JULES_zonalNBP.txt. Units are PgCyr-1. One dataset per simulation S0-S7, seven in total. Global; North = north of 30oN; Tropics = 30oN to 30oS; South = south of 30oS.
  • List of gridded output variables: See companion Excel file.
    • Level 1 variables: essential
    • Level 2 variables: desirable for additional analysis/studies
    • Additional N-cycle variables where applicable (see end of excel file)
  • Time period: 1700-2018
  • Time resolution: as specified in the file
  •  Spatial resolution: 0.5×0.5 (or at a coarser resolution if necessary; ideally at 0.5 or 1 degree)
  • Format netcdf (see Excel file). ***Important*** See annex 5 for netcdf formats developed with input from iLamb team.
  • Please define PFTs in the header of Vegtype level netcdf files , e.g. PFT 1 = broadleaf tree, PFT2 = … Please supply Fractional Land Cover [0-1] of PFT for each simulation as requested (1=total land). If Dynamic Vegetation is not enabled in your DGVM (i.e. changing natural PFT fraction in response to climate) please indicate (e.g. include information in an associated README file). Note the ocean fraction of any given gridcell may not be zero (e.g. at coastal gridcells). Please provide your results in units /m2 land fraction. Please upload the land-sea mask that you are applying. This is particularly relevant for the regional analysis as part of RECCAP2.
  • Note– in previous years we have received identical outputs for different experiments (e.g., same S1 and S2 outputs), different units for different experiments – please double check before submission.
  1. Output file name convention

One file per variable, entire time-series

Model_Simulation_variable.nc (e.g. JULES_S1_mrso.nc)

Please see Annex 5 for an example netcdf header for variable nomenclature

  1. ftp Instructions for Output

please contact, Stephen Sitch, s.a.sitch@exeter.ac.uk

  1. PLEASE update model description tables and nominate author

Please report any new info (for new models) or changes from Tables 4 and A1 in the ESSD paper, and identify who the ESSD nominated author should be for each model, and to identify specific sources of funding if relevant (Table A5).

GCP2019_Table4_Model_specific_changes

https://drive.google.com/file/d/1VQoFq4VyaHdakhQEpkLJ71_kWnWLFl-1/view?usp=sharing

GCP2019_TableA1_DGVM_processes_setup

https://drive.google.com/file/d/1nIq5_bUrVHYabcU-C1_K4jrNc82UsLTs/view?usp=sharing

GCP2019_TableA5_Funders

https://drive.google.com/file/d/1Mbp0-bbMybioibaK_TK2BmYjSinkC2FD/view?usp=sharing

Annex 1  Description of CRU-JRA55 and differences from CRU-NCEP

New: Ian Harris (UEA), in collaboration with Nicolas Viovy, has kindly agreed to merge the “new generation” reanalysis from JRA-55 (Japanese 55-year Reanalysis) with the CRU TS dataset.

  1. All JRA-55 data are regridded to the CRU 0.5° grid using appropriate NCL routines based on the Spherepack package, and masked to give a land-only (excluding Antarctica) dataset.
  2. For the four variables tmp, dswrf, shum and pre, JRA-55 is aligned to CRU TS (v4.03) tmp, cld, vap and pre (also wet) respectively over land, using the same transformations as previously. The other four variables (pres, ugrd, vgrd, dlwrf) pass through without further modification.
  3. For years between 1958 and 2018, JRA-55 is used. Alignment to CRU TS occurs where appropriate.
  4. For years between 1901 and 1957, random (but fixed) years from JRA-55 for 1958-1967 are used to fill. Alignment to CRU TS applies separately to each instance, as appropriate (ie, using the appropriate CRU TS year). It means that we use the same method as with CRUNCEP except for version V8 where the NCEP 1901-2014 reanalysis was used to generate years before 1948.

In terms of format, CRUJRA is very similar to CRUNCEP. One exception is that latitude values now run from south to north.

In addition to the fact that JRA-55 will differ from NCEP in term of meteorological model, an important difference is the resolution of JRA is 0.5 degree instead of 2.5° for NCEP. This means that now resolution of reanalysis is compatible with resolution of the CRU dataset. This will not change the monthly fields that are still aligned to CRU TS but obviously it will change the spatial and high frequency temporal variability of the fields which, in general, is expected to be higher (and more realistic) than in CRUNCEP. So it also means that model results will probably differ from previous years which require models to redo the simulation over all the full period 1901-2018 (and then not just extend with 2018).

Annex 2 LULCC forcing

Land-use states for the years 850-1950, and land-use transitions for the years 850-1949, are the same as LUH2 v2h (released for CMIP6).

Land-use states for the years 1951-2019, and land-use transitions for the years 1950-2018, are new, based on new inputs from HYDE, and new FAO data for the national wood harvest demands, as described below. The majority of differences between the LUH2-GCB2019 data and the LUH2 v2h data will be seen in the country of Brazil, where a correction based on new FAO data was made.

HYDE inputs: The version of HYDE used for LUH2 v2h was based on a previous FAO release that included data up to and including the year 2012 – those years have very small changes for all countries except Brazil in this new GCB dataset (and changes to countries other than Brazil should not impact country-level statistics in any meaningful way). The data for Brazil in this version of HYDE is different from that used for LUH2 for the years 1951-2012 in order to correct an error found for that country in the previous version. The new data from HYDE, prepared for GCB 2019, is based on the most recent FAO release, which includes data up to and including the year 2015 (HYDE applied annual changes in FAO data to the year 2012 data from the previous release to get the new 2013-2015 data used for GCB 2019). After the year 2015 HYDE extrapolates the cropland, pasture, and urban data, based on a moving window of the previous 5 years, to generate data until the year 2019.

Wood harvest inputs: The version of wood harvest data used for LUH2 v2h was based on a previous FAO release that included data up to and including the year 2014 – those inputs remained the same in this new GCB dataset. The new wood harvest data, prepared for GCB 2019, is based on the most recent FAO release, which includes data up to and including the year 2017 (we applied annual changes in FAO WH data to the year 2014 data from the previous release to get the new 2015-2017 data used for GCB 2018). After the year 2017 we extrapolated the wood harvest data until the year 2019.

Conversion to pasture/rangeland

The LUH2 methodology uses the cropland, managed pasture, and rangeland layers from HYDE. DGVM groups in the past have requested more information on whether natural vegetation is lost in conversion to pasture and rangeland.

Following LUH2 simple guidelines (on their website): “all natural vegetation should be cleared for managed pasture, and only cleared for rangeland if it is forested”.

Using this rule/guideline gives maps of forest area, carbon density, and carbon emissions that are consistent with other published maps.

The “staticData_quarterdeg.nc” file on the LUH website contains a layer named fstnf which is 1 when the potential vegetation is forested, and 0 when it is not. This layer can be used to designate whether any rangeland increases should imply clearing of natural vegetation (yes, if fstnf is 1 and no if fstnf is 0).

Users can download this file from here:

http://gsweb1vh2.umd.edu/LUH2/LUH2_v2h/staticData_quarterdeg.nc

Annex 3 Nitrogen cycle

Models having a nitrogen cycle should use time varying Nitrogen inputs as follows:

S0 none (PI CO2, PI climate, PI LUC, PI Ndep, PI Nfert)

S1 CO2 + Ndep (PI Nfert)

S2 CO2 + climate  + Ndep (PI Nfert)

S3 CO2 + climate + LUC + Nfert + Ndep

S4 LUC + Nfert (PI Ndep, PI CO2, PI Climate)

S5 LUC + Nfert (PD Ndep, PD CO2, PD Climate)

S6 none (PI LUC, PI Nfert, PD Ndep, PD CO2, PD Climate)

S7 CO2 + climate + LUC + Nfert (PI Ndep)

Note: PI = 1700 for LUC, PI = 1850 for Nfert, PI= 1860 Ndep, PD = average 1999-2018 for Ndep, PD = recycle individual years, 1999-2018 for Climate, PD = 2018 for CO2.

Nitrogen fertiliser input datasets are available via the NMIP project

(https://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-17-0212.1)

Note, N fertiliser data is available until 2014. As NMIP assume these N input data remain unchanged in years 2015, 2016, 2017 and 2018. N fertiliser is available only from 1860, please assume N Fertiliser at the 1860 value for years 1700-1860.

Manure is an organic fertiliser (animal waste put on fields). It’s fairly important from the N cycle perspective, because it’s one of the important pre-artificial fertiliser sources. However, as it’s based on organic N, it causes a problem with the model mass balance (you need to take the C and N from land, respire some of the C, and then add the remaining C:N onto the cropland. Doing this wrongly will have an effect on the C cycle simulation. For TRENDY, we recommend to not include it (however if you use it, you must tell us where you take the C and N from…). Note: If models choose to include manure, against our recommendation, then we need a manure application rate for S0-S4 Nfert.

In terms of artificial fertiliser, it’s fairly safe to assume that the per area rates haven’t changed much between 1700 and 1850. For manure, this would not be so easy.

N deposition (search for “N deposition” from): 

https://esgf-node.llnl.gov/search/input4mips/

Please use the historical N-deposition database (1850-2014) then transition onto the Future RCP8.5 N-deposition databases (2015-2100) for years 2015, 2016, 2017 and 2018.

N deposition is available only from 1850, please assume N deposition at the 1850 value for years 1700-1850.

NOTE: Peter Anthoni has kindly downloaded and regridded these N deposition files and uploaded them onto the Exeter server:

./input/ndep

Annex 4. Lightning ignition and population density

A gridded lightning climatology (for years 1700-2009) and time variant fields for 2010-2018 have been produced by Jed Kaplan (). This dataset can be considered as the number of ‘cloud to ground lightning strikes’ per month. A global scaling factor (1/0.164), can be applied which is based on a regional evaluation of the dataset for the Alaska region.

Given uncertainty around datasets, scaling factors, and potential need for model recalibration, and the fact in TRENDY we want models to supply their best C-cycle representation, groups are free to choose the lightning dataset they use (and whether or not they apply the global scaling factor to the most recent forcing dataset).

The new WWLLN Global gridded Lightning Climatology (WGLC) dataset here:

https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.pangaea.de%2F10.1594%2FPANGAEA.904253&amp;data=02%7C01%7CS.A.Sitch%40exeter.ac.uk%7C746bb93ce22640b8b0d908d71050bf52%7C912a5d77fb984eeeaf321334d8f04a53%7C0%7C1%7C636995811310644516&amp;sdata=EW8LeZjhaGahFzkkdyrR6xIPJZgz1XNPq3tcM2%2FOD7o%3D&amp;reserved=0

A complete data description paper is in preparation and will be submitted this summer.

Gridded population density based on HYDE3.2 is available on the TRENDYv8 server:

./input/pop_dens/

There is also included the total land fraction per gridcell (from HYDE), as this might be important for some models.

Annex 5 Output netcdf formats

Formatting Requests to aid analysis

  1. Please follow the protocol (or explicitly state why not).
  2. Report LAI per PFT Eg. Half the models incorrectly output LAI per grid.
  3. Using cf-complient units. Remove “C” for carbon and “N” for nitrogen from the units and don’t measure time in years or months, e.g. All CO2 stocks and fluxes were previously requested in units kgC m-2, and kgC m-2 s-1, respectively, please remove the letter C to be cf-complient in the netcdf files.
  1. Stress the importance of land fraction data (see earlier comment).
  2. All models should use a consistent file naming (e.g. JULES_S1_mrso.nc ). Eg. do not include annual/monthly/perpft tag.
  3. Following this, PFT labels are different among DGVMs (pft, PFT, vegtype…). Please all use nomenclature, PFT.
  4. Consistent latitude/longitude use (e.g. do not use lat/lon)
  5. Consistent fill value of -99999 to be used (e.g. not -9999)
  6. All data from -180-> 180 and -90->90.
  7. All data over the full latitude/longitude space. Eg, some models currently remove lower latitudes.
  8. All models output over the same time period, 1700-2018, e.g. until now some supply from 1700, others 1840, 1850, 1900, 1901.
  9. Consistent order of dimensions would be consistent eg. [longitude latitude PFT time]

To ensure accessibility by broad users, avoid to format netcdf files with netcdf library 4.4.0 or earlier, combined with libhdf5 1.10.0 or greater. There is a known issue with netcdf formatted by this set of libraries.