Contact: NOVELTIS, Pascal Prunet, carbones@noveltis.fr LSCE, Philippe Peylin, peylin@lsce.ipsl.fr Dec. 2012

CARBONES Product Presentation

www.carbones.eu

The CARBONES project provides self-consistent 20 year reanalysis of carbon fluxes and stocks. These reanalysis are obtained with the help of the Carbon Cycle Data Assimilation System (CCDAS) that couples a prognostic model of the terrestrial carbon cycle (ORCHIDEE) with an optimization system in order to estimate identified process parameters of the terrestrial model. The optimization is done by assimilating various data sources (flux measurements, carbon inventory data, satellite products). The assimilation procedure consists of minimizing a misfit function that measures the mismatch between i) the model outputs, depending on the searched parameters, and the various observation streams, and ii) some a priori knowledge on these parameters and their optimized value. The assimilation framework requires that the model quantities (depending on the parameter to optimize) can be mapped to the various observation streams, and that the a priori error statistics (uncertainty) are known. Given the uncertainties on the prior values of the parameters and on each observation source, a CCDAS allows deriving the optimized model parameters as well as the corresponding uncertainties. The generated reanalysis of carbon fluxes and stocks and the associated uncertainties are based on the optimized parameters. Moreover, in CARBONES, as the terrestrial model used is fully prognostic, we can apply knowledge about the current terrestrial carbon cycle gained during the parameter optimization to predict its evolution into the future (with corresponding uncertainty on the predicted quantities).

As shown in the figure below, which depicts the graphical representation of the assimilation system, three different types of data streams are used in CARBONES: assimilation data (used for the optimization of ORCHIDEE parameters), evaluation data (used for validation of the CARBONES products) and forcing data (other data streams used in the CCDAS simulations).

Carbon data assimilation

system

Meteo. data Prior param. calibration

Optimized Carbone fluxes & pools

and model parameters

(values & uncertainties)

Satellite NDVI

Atmos. [CO2]

FluxNet data

Assimilation data Evaluation data

Ocean pCO2 data &

Physical fields

Forcing data

Ocean OCVR

model

Biomass bur fluxes

Fossil fuel fluxes

Forest C stock

Soil C stock

Independent land C fluxes

Independent

ocean C fluxes

Independent Atmos. [CO2]

Schematic representation of the CCDAS and different datastreams used

CARBONES DATA ASSIMILATION SYSTEM

General description of the

ORCHIDEE terrestrial model

ORCHIDEE is a global process-oriented Terrestrial Biosphere Model [Krinner et al., 2005]. It calculates carbon, water and energy fluxes between land surfaces and the atmosphere. The water and energy module computes major biophysical variables (albedo, roughness height, soil humidity) and solves the energy and hydrological balances at a half-hourly time step. The carbon module is called on a daily basis and calculates a prognostic LAI, allocates carbon towards leaves, roots, sapwood, reproductive structures and carbohydrate reserves. A turnover rate is applied to biomass pools and produces litterfall. Litter is decomposed and goes into three soil organic carbon pools with different residence times (active, slow and passive) following the CENTURY model [Parton et al., 1988]. The link between the water and carbon modules is done through photosynthesis, which is based on the work of Farquhar et al. [1980] for C3 plants and Collatz et al. [1992] for C4 plants. The stomatal conductance is based on Ball et al. [1987]. The carbon module first calculates LAI and the water and energy module then calculates GPP and stomatal conductance. The carbon module calculates growth and maintenance respirations [Ruimy et al., 1996] and Net Primary Production (NPP), heterotrophic respiration and Net Ecosystem Exchange (NEE). ORCHIDEE is the land surface component of the IPSL-CM5 Earth System Model (Dufresne et al., submitted), currently used for CMIP5 simulations.

Schematic representation of the ORCHIDEE model

Atmospheric transport model LMDz

The atmospheric transport model is an essential component of the CCDAS that provides the link between the fluxes and the atmospheric CO2 measurements which are one of the main assimilation datastreams.

LMDz belongs to the class of models called the General Circulation Models (GCM). It has been widely used to model climate (IPCC, 2001, 2007) as well as transport and chemistry of gas and particles. The dynamical part of the LMDz is based on a finite-difference formulation of the primitive equations solved on a 3D Eulerian grid with a horizontal resolution of 3.75° (longitudes) x 2.5° (latitudes) and 19 sigma-pressure layers up to 3hPa. This discretization corresponds to a vertical resolution of about 300-500 m in the planetary boundary layer (first level at 70 m height) and about 2 km at the tropopause (with 7-9 levels located in the stratosphere). The calculated winds (u and v) are relaxed to ECMWF analyzed meteorology with a relaxation time of 2.5h (nudging) in order to realistically account for large scale advection (Hourdin et al., 2000). The advection of tracers is calculated based on the finite-volume, second-order scheme as described by Hourdin and Armengaud (1999). Deep convection is parameterized according to the scheme of Tiedtke (1989) and the turbulent mixing in the planetary boundary layer is based on a local second-order closure formalism.

Representation of vertical and horizontal grids of LMDZ, Colors represent simulated surface and air temperatures, arrows

represent wind’s direction. © L. Fairhead (IPSL/LMD)

OCVR : Ocean Carbon Variational

Re-analyzer

The ocean plays a crucial role as it contributes to an uptake of about a quarter to a third of the

anthropogenic emissions with significant year-to-year variations.

The air-sea CO2 flux is typically represented with the formula : F =Kex . ∆pCO2 where Kex is the

exchange coefficient (dependent on parameters such as wind speed, salinity and temperature) and

∆pCO2 represents the difference between the pCO2 in surface seawater and in the overlying air. ∆pCO2

represents the thermodynamic driving potential for the exchange flux at the sea-air interface. The

uncertainties in the estimation of the CO2 flux are mainly driven by the variability in the sea surface

pCO2. Indeed the seasonal and geographical variations of surface water pCO2 are much greater (from

150 to 750 microatm) than the corresponding atmospheric variations.

In the CARBONES project, these fluxes are estimated with the help of an innovative model (OCVR)

that calculates CO2 partial pressure in surface water. OCVR is a neural network framework

developed by CLIMMOD within the CARBONES project. The model takes as input observations

from satellites for Surface Chlorophyll and Sea Surface Temperature as well as in-situ

observations and model estimations of temperature, salinity, and Mixed Layer Depth. A

variational data assimilation scheme efficiently incorporates new sets of pCO2 observations (trend and

seasonal adjustments), and takes into account extreme events like El Nino. The system then uses

supplied atmospheric CO2 concentration to calculate air-sea flux according to a selectable exchange

parameterization (e.g. Wanninkhof 1992, Nightingale 2000, Takahashi 2009). The results obtained

with the OCVR-system are illustrated as global maps below.

Ocean surface pCO2 calculated by OCVR for January and for 1991, 1999 and 2009.

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Jan-1991

Jan-1999

Jan-2009

Assimilation Data

MODIS NDVI data MODIS collection 5 surface reflectance data (from 2000-2010) in the red (R) and near-infrared (NIR) bands at 5km resolution are used to optimise the phenology-related parameters of ORCHIDEE. The data have been processed by LSCE at global scale to correct for directional effects, following Vermote et al. (2009). From this the Normalised Difference Vegetation Index (NDVI) is calculated using the following equation:

The NDVI is used as a measure of the vegetation “greenness”, and thus can be used a proxy for the phenological cycle of the vegetation (Tucker et al., 1979).

Atmospheric CO2 observations We are using a set of atmospheric CO2 concentration data from selected stations around the world. This data stream integrates the fluxes over a large area and provides the basis to optimize the large global patterns of carbon fluxes correctly. The figure below displays the location of the stations. These data come from three large databases: The NOAA Earth System Laboratory (ESRL) archive, the CarboEurope IP project, and the World Data Centre for Greenhouse Gases (WDCGG) of the World Meteorological Organization (WMO) Global Atmospheric Watch Programme. The three databases include both in situ measurements made by automated quasi-continuous analyzers and air samples collected in flasks and later analysed at central facilities.

Co2 record at Mauno Loa station and locations of CO2 measurements stations used in the assimilation

Eddy-covariance flux measurements Eddy covariance flux measurements for Net Carbon Exchange and for Latent Heat flux from the global network of observation sites are used to constrain ecosystem physiology and fast processes from the synoptic variations to the seasonality of fluxes in ORCHIDEE.

Harmonized quality checked and gap filled data (LEVEL4) from a new global synthesis called the LaThuile dataset are used. This dataset forms a unique collection of 600 site-years of online hourly measurements of CO2, water vapour, and heat fluxes. In order to avoid dealing with the large error correlations both in the model and the measurements, daily mean values of Net Ecosystem Exchange and Latent Heat flux are used in the CCDAS.

Localization of the different FLUXNET sites that were used in the CCDAS (www.fluxdata.org); View of a typical Flux

Torwer measurement; Exemple of weekly mean NEE variations over one year for a deciduous forest

Partial pressure of CO2 at the ocean surface Observations of CO2 partial pressure at the surface of the ocean (pCO2) are used to train the OCVR statistical ocean model that calculates the air-sea carbon fluxes.

Two types of pCO2 data streams are used:

• pCO2 climatology built by compiling of over 3 million records from the past 30 years re- projected and interpolated to construct a climatological reference for the year 2000 (Takahashi et al. 2009).

• Raw pCO2 data including measurements from El Niño years that are not present in the climatology. They are used to account for specific air-sea fluxes during El Niño condition within the OCVR system.

As soon as they are available, the Takahashi pCO2 dataset shall be replaced by pCO2 data from SOCAT (http://www.SOCAT.info). These will include data from more than 1250 cruises from 1968 to 2007 with approximately 4.5 million measurements of various carbon parameters.

Spatial coverage of the pCO2 data used

Forcing Data

Biomass burning emissions Biomass burning emissions data from the Global Fire Data (GFED version 3; http://www.globalfiredata.org/Data/index.html) are used. Monthly CO2 emissions fields at 0.5°x0.5° degree resolution and over 1997-2010 period are considered. Figure below illustrates the mean annual fire emission over the globe from this GFED release.

Annual carbon emissions (as g C m-2 year-1), averaged over 1997-2010 from GFED database

The GFED data are broken down into 6 sectors: deforestation, peat fires, savanna fires, agriculture, forest fires, and woodland. With these data, we generated fluxes of CO2 emissions relevant for typical “burning – regrowth” processes as detailed hereafter. These fluxes are then prescribed to the CCDAS over the period of 1989-2009. Before 1997, we use for each year, the mean annual field derived from

the fluxes over the 1997-2010 period. In order to account for fundamental differences between the six initial categories, we grouped the GFED emissions in 3 types: Emissions from deforestation and peat fires, Emissions from agriculture and savanna fires , Emissions from woodland and burnt forests. A specific treatment is applied for each of these types. The overall biomass burning flux considered in the CCDAS for the optimization process is the sum of the three fluxes as described above.

Meteorological forcing data: IERA reanalysis The IERA Meteorological data are used as boundary conditions to the ORCHIDEE land surface model and to guide the atmospheric transport model LMDz towards the actual weather. The variables have been extracted at a 3-hourly temporal and 0.7°x0.7° spatial resolutions from the archive of the ECMWF interim reanalysis (ERA-I, Berisford et al. 2009) over the period 1989-2009. We have chosen this dataset for its consistency over the 20-yr target period. The meteorological fields are used to force two models:

• Land surface model ORCHIDEE which uses: 2D Surface temperature and dew point

temperature fields, 2D 10-meter wind fields, 2D surface precipitation fields (rain and snow),

2D surface pressure fields, 2D surface specific humidity fields, and 2D downward surface

radiation fields (solar and thermal)

• Atmospheric transport model LMDz which uses 3D wind fields

Fossil fuel emissions

Specific fossil fuel emissions were constructed in the framework of CARBONES by USTUTT/IER who combined the 0.1° x 0.1° resolution EDGAR 4.2 CO2 fossil fuel emission dataset with:

• Monthly profiles (derived for different emission sectors, countries, and years)

• Daily profiles (derived for different sectors and countries)

• Hourly profiles (derived for different countries, days of the week and time zones).

The derivation of the time profiles is done on the basis of statistical data sets as well as correlations (e.g. the consumption of fossil fuel for residential heating as a function of air temperature in winter). Examples of statistical data sets used are e.g. Eurostat, ENTSO-E, UN monthly bulletin, etc. Currently, the temporal profile for the globe are derived from data sets over Europe extrapolated with information on climate zone, average monthly temperature for the seasonal cycles and similarity in socio-economic parameters like population and GDP.

The derived fossil fuel fluxes are directly used in the CCDAS as imposed surface carbon fluxes.

In addition, the fossil emissions are split into three categories based on their emission heights:

• Above 781 m: cruise, climb and descent emissions from aircraft, parts from combustion processes from energy and manufacturing industry above

• between 92m and 781m: main part of emissions caused by combustions from energy and manufacturing industries as well as households

• below 92m: surface emissions like transport, residential sectors and industrial processes

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Spatial distribution of the hourly fossil fuel emission considering three different emissions height classes for the January

16, 2008 at 00 UTC.

Validation Data

Forest biomass data

ALTERRA has compiled a database on forest area, growing stock, increment, harvest levels (scattered info only) and age classes (Europe only), based on historical and recent international assessments and national forest inventory statistics. Data of different assessments are being harmonised to reflect both forest area and other wooded land as required by the project and to be comparable between countries and assessments. These data are converted to estimates of carbon stock in biomass and NBP using default biomass expansion factors from the IPPC Good Practice Guidance.

An algorithm has been developed at EFI to provide time series of global maps for these variables (1950-2010). Variable values for years without existing inventory information are linearly interpolated at the country level. If necessary, country-level data can be downscaled to finer resolution based on land use maps and biogeographical information. Uncertainty estimates resulting from data assessment, harmonisation, interpolation and downscaling are provided for the results, mostly based on expert estimates.

Global Maps of growing stock in 2005, 1x1 degree resolution.

The maps below demonstrate results for growing stock and forest area by grid cell (1 x 1 degree) in selected European countries for the years 1950 and 1980.

Maps of forest area and growing stock in 1950 and 1980, 1x1 degree resolution.

Soil Organic Carbon dataset

Several soil organic data sets have been used and combined to evaluate the soil carbon estimated with ORCHIDEE in the CCDAS.

The databases contain information on soil parameters to a depth of 1 metre (organic carbon, pH, water storage capacity, soil depth, cation exchange capacity of the soil and the clay fraction, total exchangeable nutrients, lime and gypsum contents, sodium exchange percentage, salinity, textural class and granulometry). In each soil unit up to 9 different soils are represented and their respective proportion is given.

WISE5min Version 1.0 (Batjes, 2006)

The spatial resolution of this database is 5 arc minutes. The profile is provided in 20 cm intervals to a depth of 1 m. Standard deviations are also given for this dataset.

The database draws information from WISE version 3.0, which holds 10,100 globally distributed soil profiles.

Soil Organic Carbon content: WISE5min version 1.0

HWSD Version 1.1 (Nachtergaele et al., 2009)

The spatial resolution of the database is 1 km (30 arc seconds by 30 arc seconds). The profile is divided in topsoil (0 – 30 cm) and subsoil (30 – 100 cm).

The database draws information from:

• Soil map of the world

• SOTER regional studies

• European soil databases

• The soil map of China

• WISE version 2.0

Soil Organic Carbon content : HWSD version 1.1. Extended inland white areas are greater than 80 kg m-2.

Independent satellite and atmospheric CO2 data Independent satellite measurements as well as atmospheric CO2 data not used in the assimilation process are used to evaluate the CCDAS output.

Satellite:

Several other vegetation indices are consider to i) evaluate the optimized ORCHIDEE model output (namely the normalized temporal variations of fAPAR) and ii) be assimilated in place of the MODIS NDVI. The data streams that are considered are:

• AVHRR: the LTDR v3.0 dataset (http://ltdr.nascom.nasa.gov/cgi- bin/ltdr/ltdrPage.cgi). The reflectances are already directionally corrected.

o Spatial resolution: 5km(CMG)

o Period:1981-2000

o Error evaluation: based on the Quality field

• MODIS Enhanced Vegetation Index (EVI), calculated from the same MODIS reflectance data. EVI has shown to be less prone to saturation at high LAI (e.g. Xiao et al., 2006), which may be useful when studying the phenology of Tropical Raingreen trees (PFT 3).

• Joint Research Center (JRC) fAPAR products (Gobron et al., 2002; Gobron et al., 2006).

• Biogeophysical Parameters (BioPar) Products from the European Commission 7th Framework Programme GEOLAND2 project (Lacaze et al., 2011).

Atmospheric CO2 data

It is planned to use the vertical CO2 profiles measured during specific campaigns, or measurements from the CONTRAIL database.

Independent air-sea flux estimates

The air-sea fluxes from CARBONES are evaluated against independent ocean fluxes, estimated for instance from ocean interior inversion.

Independent land flux products

CARBONES gross carbon land fluxes will be also compared against independent estimates. The data oriented approach described in Jung et al. 2011 and based on global spatial-temporal patterns of carbon and energy fluxes that were created using a machine-learning algorithm that upscales site-level eddy covariance measurements to the globe based on satellite and meteorological observations will be used.

Maps of upscaled gross primary productivity for January and July 1982 (Jung et al. 2011)

The Carbon Data Assimilation System delivers fluxes and stocks of carbon, as well as latent heat and sensible fluxes over land. These products were generated for a 20 year period (1990-2009) at a resolution of 1° x 1° degrees. The calculations for the current version were done at a coarser resolution of 2.5° x 3.75° and were then interpolated on the 1°x1° degree grid. Estimates of the uncertainty (Bayesian uncertainty calculated by the CCDAS) are provided based on the provided uncertainty of data streams and a priori information. The CCDAS products available with the current version are listed in below.

Type Sub-type units

Carbon Fluxes

ecosystem fluxes Net ecosystem flux kgC / m2 / hour

Photosynthetic flux

Respiration flux

Forest Net ecosystem

Grass Net ecosystem

Crop Net ecosystem

Total photosynthesis and respiration

Total land fluxes

Other natural fluxes Ocean Flux

Biomass burning

Total Natural fluxes

Anthropogenic fluxes Fossil Fuel Emissions

Total C fluxes Total Carbon flux

Other Fluxes Latent heat - W / m2

Sensible heat -

Carbon

Stocks

Biomass Total above ground kgC / m2

Total below ground

Total (above and below ground)

Soil Organic Matter --

Leaf Area Index - No unit

CARBONES Products

EXAMPLES OF PRELIMNARY

CARBONES PRODUCTS

Net Natural land and ocean net fluxes

20 year (mean: 1990-2010) Net Natural land and ocean net fluxes

Time series of yearly means of Net Ecosystem Flux

Ocean Fluxes

CARBONES OCVR ocean CO2 fluxes: mean for 1990-2004 (upper left), mean 1998 (upper right, El Nino event)

*

Comparison of the mean annual ocean fluxes for: OCVR (CARBONES) – red curve, Picse biogechemical ocean model –

green curve, Takahaski climatology – blue curve for three ocean regions.

Biomass burnign flux

20 year (mean: 1990-2010) Biomass Burning Flux

Fossil Fuel flux

Example of hourly CO2 emissions from fossil fuel combustion (for 00:00Z, 16-01-2009)

Fossil Fuel flux (cont)

20 year mean (1990-2010) Fossil Fuel Burning Flux

Time series of monthly fossil fuel emissions for : Global Land and Europe

Biomass Stocks

Aboveground forest biomass from CARBONES

FAO validation data with Above Ground Biomass