MONARCH-A Toulouse Meeting

1. MONARCH-A Toulouse Meeting W.P 1.4: Land cover and Fire and their representation in models. Objective 1: To amalgamate high latitude land cover maps derived from remote sensing, assess their consistency and uncertainty, and transform them into forms suitable for carbon, water and climate models. Objective 2: To amalgamate the different sources of information about fire at high latitudes derived from remote sensing, assess their consistency and whether they reveal any trends, and define methods to interface these data with carbon and climate models. Map of the Arctic (1595) by Mercator

2. Land Cover Data Sets 1) MODIS Product MCD12C1 (MODIS LC) Source: MODIS sensor on-board the Terra and Aqua satellites. Classes:17 classes according to the International Geosphere Biosphere Programme (IGBP). Spatial Resolution: Global coverage at 0.05 degrees Temporal Resolution: Monthly, 2001-2007. Available Format: HDF-EOS. Obtained From: https://lpdaac.usgs.gov/ MONARCH Availability: Global coverage at 0.5 degrees for year 2001-2007 in NetCDF format. 2)Global Land Cover 2000 (GLC2000) Source: VEGETATION sensor on-board the SPOT-4 satellite. Classes: 23 classes according to the Land Cover Classification System (LCCS) by the Food and Agriculture Organization (FAO). Spatial Resolution: Global coverage at 0.009 degrees (~1 km at equator). Temporal Resolution: Annually, 2000. Available Format: Binary/TIFF/ESRI/IMG. Obtained From:http://bioval.jrc.ec.europa.eu/products/glc2000/glc2000.php MONARCH Availability: Global Coverage at 0.5 degrees for year 2000 in NetCDF format. 3) GlobCover Source: MERIS sensor on-board the ENVISAT satellite. Classes: 23 classes based on the Land Cover Classification System (LCCS) by the Food and Agriculture Organization (FAO). Spatial Resolution: Global coverage at 300 m. Temporal Resolution: Annually and bimonthly, 2005 & 2009. Available Format: GeoTIFF. Obtained From:http://ionia1.esrin.esa.int/ MONARCH Availability: Global coverage at 0.5 degrees for years 2005 and 2009 in Netcdf format. 4) MODIS Product MOD44B (MODIS VCF) Source: MODIS sensor on-board the Terra and Aqua satellites. Classes: 3 classes, tree, herbaceous vegetation and bare ground coverage. Spatial Resolution: Global coverage at ~0.0045 degrees Temporal Resolution: Annually, 2001 only. Available Format: TIFF. Transformed into NetCDF format for the MONARCH project. Obtained From:http://www.landcover.org/data/vcf/ MONARCH Availability: Global Coverage at 0.50 degrees for year 2001 in NetCDF format.

3. Comparing Land Covers Individual classes of MODIS,GLC2000 and GlobCover were grouped into 3 broad Classes: Tree, herbaceous and bare ground coverage. e.g. GlobCover: Sparse(<15%) Vegetation: 0% Tree, 15% Herbaceous and 85% Bare Ground. Fig 1: Area in N. America assigned to tree, herbaceous and bare ground coverage for the 4 land cover data sets and the LPJ model. • Methodology is to some extend subjective. • MODIS VCF showed significantly more herbaceous coverage in both regions. • Despite relatively straight-forward conversion even BareGround coverage demonstrated significant deviations, • especially for GlobCover. • LPJs naturally driven land cover is also shown. Fig 2: Area in Eurasia assigned to tree, herbaceous and bare ground coverage for the 4 land cover data sets and the LPJ model.

4. Fig 3: Maps of tree coverage in N.America for the 4 land cover data sets and LPJ.

5. Fig 4: Maps of herbaceous coverage in N.America for the 4 land cover data sets and LPJ.

6. Fig 5: Maps of bare ground coverage in N.America for the 4 land cover data sets and LPJ.

7. Fig 6: Maps of tree coverage in Eurasia for the 4 land cover data sets and LPJ.

8. Fig 7: Maps of herbaceous coverage in Eurasia for the 4 land cover data sets and LPJ.

9. Fig 8: Maps of bare ground coverage in Eurasia for the 4 land cover data sets and LPJ.

10. Driving Model with Land Covers SDGVM was driven using GLC2000,MODIS and MODIS VCF classifications with their classes converted into the relevant PFTs. Results are presented for the northern latitudes (lat ≥ 50) for the last ten years of CRU3 climatology: 1997-2006. Fig 9: SDGVM carbon fluxes for GLC2000, MODIS and MODIS VCF, (Gt y-1). • EVT was lower and subsequently Runoff increased for classifications with more herbaceous cover but not significantly. • Significant deviations in NBP were observed at high latitudes due to classifications differences in bare ground/herbaceous coverage. Fig 11: SDGVM Runoff and EVT for GLC2000, MODIS and MODIS VCF, (Gt y-1).

11. Driving Model with Land Covers • The land covers are significantly • Different but no clear reason to prefer • a specific one. • The different approach of • MODIS-VCF means that it can be • used to modify other land use products. Fig.11: SDGVM Evapotranspiration difference: MODIS VCF - GLC2000, (mm). Deviations are mainly located in agricultural regions were the model was driven with different crop fractional cover. Fig. 12: SDGVM NBP difference: MODIS VCF – GLC2000, (g m-2 y-1). Differences are located in latitudes>70°

12. Fire Data Sets 1) MODIS Product MCD45A1 Source: MODIS sensor on-board the Terra and Aqua satellites. Type: Active Fire product Spatial Resolution: Global Coverage at 500 m Temporal Resolution: Monthly, 2000-2010 Available Format: GeoTIFF Obtained From:http://modis-fire.umd.edu/Burned_Area_Products.html 5) Global Fire Emissions Data (GFED) Burned Area Product Source: Derived mainly from MODIS images. Type: Burned area Spatial Resolution: Global at 0.5 degrees. Temporal Resolution: Monthly, 1996-2009 Available Format: txt Obtained From: http://www.globalfiredata.org/ 2) MODIS Product MOD14CMH & MYD14CMH Source: MODIS sensor on-board the Terra and Aqua satellites. Type: Fire Radiative Power Spatial Resolution: Global Coverage at 0.5 degrees. Temporal Resolution: Monthly, 2002-2010 Available Format: HDF Obtained From: http://modis-fire.umd.edu/Active_Fire_Products.html 3) Global Fire Emissions Data (GFED) Emissions Product Source: Derived mainly from MODIS images, land cover data sets and biochemical models. Type: Carbon Emissions Spatial Resolution: Global at 0.5 degrees. Temporal Resolution: Monthly, 1997-2009 Available Format: txt Obtained From: http://www.falw.vu/~gwerf/GFED/ 4) Along Track Scanning Radiometer, World Fire Atlas (ATSR WFA) Source: ATSR/ATSR-2/AATSR instrument on-board the ERS-1/ERS-2/ENVISAT satellite. Type: Active Fires Product (night only) Spatial Resolution: Global at 1km. Temporal Resolution:Monthly,1996-2011 Available Format:txt Obtained From: http://wfaa-dat.esrin.esa.int/

13. Overview of Fire Data Sets Fig 14: Comparison of burned area for N America between MODIS and GFED. Carbon emissions are shown on the secondary y-axis. Differences between the Burned Area products are attributed to the different methodologies used. Fig.13: Burned Area and Carbon Emissions according to GFED. Note that even though more carbon emissions originate from Eurasia, emissions per area burned is higher for N. America.

14. Overview of Fire Data Sets Fig.15: Fraction of area burned for fire and non-fire years in N. America and Eurasia • Significant temporal and spatial variations of fire activity. • Fire Return Interval in these latitudes is in the region of hundreds of years, • an order of magnitude greater than the temporal range of the data sets.

15. Fire Disturbance in Models • Fraction of area burned in models is strictly based on climatology (precipitation, fuel humidity). • Due to previous, models and especially CLM4.0 show significantly smaller variance compared to the data sets. • Models show good agreement of their long term mean of area burned compared to the data sets. • LPJ outputs considerable more carbon emissions, probably due to higher carbon content of burned vegetation. Fig. 16: Burned Area and Carbon Emissions for different data sets and models

16. Fire Disturbance in Models • Models cannot capture the stochastic nature of fires. • All 3 of our models burn the majority of the grid cells by a very small fraction, usually less than 1%. • Cumulatively they are in agreement with the data sets where few grid cells are burned by a considerably larger fraction. Fig. 17: Fraction (%) of Area Burned for 3 models

17. Adding Fire Variation to Models • Fire data sets record is not long enough to drive the fire disturbances • in models. • Instead we kept the fire disturbance algorithm of the SDGVM model • but added the variance of the GFED Burned Area data set (SDGVM*). Fig. 18: Burned Area (in mHc) for GFED, SDGVM and SDGVM* with the added variance. Fig. 19: Carbon Emissions (Mt C) for SDGVM and SDGVM* • The added variance of the burned area was subsequently transferred to the carbon emissions. • NBP didn’t exhibit any significant differences probably because even though variance increased, the % of area burned for each grid cell remained at levels lower than 3%. Fig. 20: NBP (Mt C) for SDGVM and SDGVM*

18. Adding Fire Variation to Models • Biomass is reduced with the adjusted higher variance fire regime. • An entire new approach is required to • model the fire regime, describing its • stochastic nature. • At this point models simply try to • capture the spatial average carbon • emissions. • Describe the behaviour of the system • for higher fractions of area burned • per grid cell. • GFED seems the most complete • product since it offers both burned • area and carbon emissions. Fig. 21: Biomass (kg m-2) for SDGVM and SDGVM* Fig 22: BIOMASS Average 1997-2006: (Adjusted – Control) /Control x 100.