Land Cover CCI
Land Cover CCI CCI Phase 1 results Climate research perspectives Multi-level validation strategy CCI-LC products 3 LC state products for the 2000, 2005 and 2010 epochs Fate of anthropogenic CO2 emissions (2003-2012 average) Dynamic global vegetation model Land component of Earth System Model Simulates the Energy, Water and Carbon balance LSCE – ORCHIDEE model • Surface description: a tile approach • A mosaïc of vegetation Land cover map • 10 to 15 different PFTs MOHC – Joint UK Land Environment Simulator (JULES) • JULES uses a tiled model of sub-grid heterogeneity • 9 Land surface types: BL tree, NL tree, C3 grass, C4 grass, Shrub, Urban, Water, Ice, Bare Soil MPI-M – JSBACH model • MPI-Earth Surface Model (ESM), with JSBACH as land surface component which can be run independently (offline simulations) • 13 different PFTs Earth system models comparison 1. Does the use of CCI-LC maps improve model-data comparisons? 2. Does the use of CCI-LC maps reduce model-model differences? 3. Does consistency in land cover improve model comparisons? Modeling Team Offline Original LC LC_CCI LC Online Dynamic vegeta9on Original LC LC_CCI LC LSCE-‐ ORCHIDEE V V V V V MOHC-‐JULES V V V V V MPI-‐JSBACH V V ✗ V V -‐ -‐ -‐ Same climate forcing for offline simulaAons (WATCH WFDEI) Same Epoch (2010) for LC_CCI inputs – but modeling groups define climate zones… Treatment of land use fluxes and disturbance up to the modeling teams Current land cover map • Outdated land cover inputs • IGBP land cover (Belward et al. 1999) and, • Olson vegetaAon map with 96 classes (1983) ESA LC_CCI PFT datasets Land Cover -‐> PFT Conversion Tool (BEAM) LC-CCI to improve initial model conditions Köppen-Geiger climate zones Land cover Phenology & Physiognomy Plant Functional Types PFT comparison - Bare fraction JSBACH JULES Possibly too high in high latitudes due to 85% allocation from sparse vegetation categories ORCHIDEE PFT comparison - C3 / crops fractions JSBACH (crops) JULES (All C3) Decrease in high latitudes due to replacement by bare soil/barren ORCHIDEE (natural C3 fraction) PFT comparison - BoBS fraction ORCHIDEE Notable increase in E Europe at expense of evergreens Fractional difference (-) By-model experiment Carbon, energy and water budget • LAI • Albedo • Gross Primary Production • Runoff (GPP) • Net Primary Production (NPP) • Total precipitation changes • Validation of Dynamic Global • Radiation & moisture fluxes Vegetation Model (in • Heat sensible flux comparison with IGBP map) • Latent sensible flux • Surface temperature Evapotranspiration • Heterotrophic respiration LAI comparison ORCHIDEE: LAI comparison between Olson and CCI-LC Mean difference in LAI (Olson – CCI_LC) Red: higher LAI with Olson-derived simulations Blue: higher LAI with CCI_LC-derived simulations LAI comparison ORCHIDEE: LAI comparison between Olson and CCI-LC • Comparison to GIMMS3gSurface parameters • AVHRR GIMMS3g LAI dataset (Zhu et al. 2013 Remote Sensing) • RMSE and bias between simulations of LAI and GIMMS3g data with Olson and CCI-LC derived PFT maps 2 0.8 0.6 1 Bias RMSE 1.5 0.5 0.4 0.2 0 0 Olson CCI Olson CCI Carbon budget - GPP JULES: Increase in global GPP in both online and offline simulations Offline Control LC_CCI 2010 GPP (Gt C yr-1) 152.5 166.7 Global Mean Annual GPP 125 100 Source Estimates of global annual GPP: • 123 ± 8 Gt C yr-1 (Beer et al. 2010) • 119.4 ± 5.9 Gt C yr-1 (Jung et al. 2011) • 120 Pg C yr−1 (Denman et al. 2007) • 109.29 Pg C yr−1 (Zhao et al. 2005) for 2001-2003 period • Discrepancy between offline and online GPP likely due to variations in JULES GtC yr−1 HadGEM2A: Control HadGEM2A: LC_CCI 2000 75 HadGEM2A: LC_CCI 2005 HadGEM2A: LC_CCI 2010 MODIS GPP MOD17A3 Jung et al 50 25 0 1980 1985 1990 1995 Year 2000 2005 Carbon budget - GPP JULES: Spatial distribution of differences Gross Primary Production ( gC m−2 yr−1 ) LC_CCI 2010 − Control 50°N 0° 50°S 0° 100°W −2000 −1500 −1000 −500 0 100°E 500 1000 1500 2000 Carbon budget - GPP Gross Primary Production ( gC m−2 yr−1 ) JULES: Comparison to estimates from Jung et al. (2011) Model − Observations Control 50°N • LC_CCI 2010 shows reduction in biases in: • West Africa • Southern Africa • Amazon • India • South East Asia 0° 50°S LC_CC:2010 0° 100°W −3000 −2000 −1000 0 100°E 1000 2000 3000 Carbon budget - NPP ORCHIDEE: NPP comparison (CCI-LC – Olson) • Large increase in NPP over croplands • Decrease in high latitudes due to decrease in grass cover KgC/m2/yr Carbon budget - Evaluation using atm. CO2 concentration ORCHIDEE LMDz transport: comparison to GlobalView data ! improvement of the CO2 seasonal cycle with reduced amplitude Radiation & moisture fluxes Tropical Bare C3 C4 GPP 0.20 0.15 0.10 0.05 0.00 cVeg 0 −1 −2 −3 JULES: West Africa Tropical Increase in C4 Grass, replacing BL tree or C3 grass in old maps C3 value value -0.40 J F M A M J J A S O N D J F M A M J J A Bare -0.10 0.00 -0.04 -0.08 0.00 0.03 0.00 0.00 -0.12 0.00 J F M A M J J A S O N D J F M A M Ice J J Month A S O N D J F M A M J J A S O N D JJ FF M M AA M M JJ JJ AA SS O O NN DD J C4, 66% (10) C4, 71% (11) F M A M J J Month A S O N D J F M A M J J r20mm Water r20mm Urban 0 S O N −2D 4 2 0 −2 −4 1.0 0.5 0.0 −0.5 −1.0 C4, 45% (14) Pr90pctl Month Shrub 2 Pr90pctl −2 4 2 0 −2 −4 1.0 0.5 0.0 −0.5 −1.0 4 C4, 39% (8) Tmax90pct 0.00 Leads to: Increase in GPP Reduction in veg. C Increase in extreme max temp in Sept (10) • Extreme precipitation earlier in season (11) C4, 32% (21) LH -0.21 • • • • SH 11 0.66 0.71 0 −25 −50 −75 Bare, 50% (9) Alb -0.17 SM 0.00 ET -0.28 cVeg 10 40 20 0 −20 r20mm Tmax90pct C4 C4, 66% (10) C3, 63% (13) Pr90pctl LH C3 0.00 Tmax90pct SH NL GPP 0.08 Alb BL C4, 45% (14) C4, 71% (11) PFT, %increase (point ID) LH ID 00 −25−2 −50−4 −751.0 40.5 0.0 2 −0.5 −1.0 0 C4, 32% (21) SH SM 0−2 4 −20 2 C3, 63% (13) C4, 39% (8) 0.04 40 2 20 0 PFT, %increase (point ID) Bare, 50% (9) 100 75 50 25 0 10 5 0 −5 −10 C4 grass C4 10 ET C3 grass 0 −1 −2 −3 Alb BL Tree Tropical C3 0.20 0.15 0.10 0.05 0.00 SM cVeg 0 1040 520 00 −5 −20 −10 0 −25 0.08 −50 0.04 −75 0.00 4 11 Bare C4 value Bare ET GPP 100 75 0.2050 0.1525 0.10 0 0.05 0.0010 5 00 −1−5 −10 −2 −3 0.08 100 75 0.04 50 0.00 25 A S O N D Radiation & moisture fluxes JULES Maximum temperature Energy budget Visible albedo Change in albedo (-) ORCHIDEE (CCI-LC – Olson) • Increase in high lat. albedo with increased bare soil • Decrease in W Canada with increasing tree cover • Decrease in some cropland areas By catchment experiment Surface albedo JSBACH • Over high- and mid-latitude catchments: CCI-LC albedo higher than reference • Over arid and semi-arid regions: improvement with CCI-LC albedo By catchment experiment WFDEI 2m temperature JSBACH • Effect of albedo difference visible in estimated 2m t° (↑ albedo " ↓t°) • General improvement over most catchments By catchment experiment Uncertainty due to epoch JSBACH • Notable difference in CCI-LC maps only in Amazon and Congo (large-scale deforestation " increased albedo) ! need for simulation with LC change • For other regions, using the 2010 epoch is a reasonable assumption Models inter-comparison Albedo Surface Albedo 0.4 MODIS 0.35 GlobAlbedo 0.3 WFDEI-‐REF 0.25 WFDEI-‐CCI 0.2 ECHAM6-‐REF 0.15 ECHAM6-‐CCI Over Giorgi regions AMZ = Amazonia WAF = West Africa SAF = South Africa NAS = North Asia SAS = South Asia NAU = North Australia JULES-‐REF 0.1 JULES-‐CCI 0.05 HadGEM2A-‐REF 0 • HadGEM2A-‐CCI AMZ WAF SAF NAS SAS NAU Surface Albedo Stronger sensitivity of JSBACH than JULES to CCILC maps • Overestimation over the Northern Asia region (w.r.t. GlobaAlbedo) • Over Amazon and West African, JSBACH closer to GlobAlbedo but farther away from MODIS 0.3 0.25 MODIS 0.2 GlobAlbedo ORCHIDEE-‐Olson 0.15 ORCHIDEE-‐CCI ORCHIDEEonl-‐Olson 0.1 ORCHIDEEonl-‐CCI 0.05 ORCHIDEE-‐DGVM 0 AMZ WAF SAF NAS SAS NAU • Significant improvement of ORCHIDEE with CCI-LC maps Models inter-comparison Evapotranspiration Deviation from LandFlux Diag ET mean 60.0% LF Min LF Max WFDEI-‐REF WFDEI-‐CCI ECHAM6-‐REF ECHAM6-‐CCI JULES-‐REF JULES-‐CCI HadGEM2A-‐REF HadGEM2A-‐CCI 40.0% 20.0% 0.0% -‐20.0% -‐40.0% Over Giorgi regions AMZ = Amazonia WAF = West Africa SAF = South Africa NAS = North Asia SAS = South Asia NAU = North Australia • -‐60.0% AMZ WAF SAF NAS SAS NAU Direction of change when using CCI-LC data: • Deviation from LandFlux Diag ET mean 60.0% • LF Min 40.0% LF Max 20.0% ORCHIDEE-‐Olson 0.0% ORCHIDEE-‐CCI ORCHIDEEonl-‐Olson -‐20.0% ORCHIDEEonl-‐CCI -‐40.0% ORCHIDEEoff-‐DGVM -‐60.0% -‐80.0% AMZ WAF SAF NAS SAS NAU • Is consistent between offline & online simulation for JULES and JSBACH Opposite direction for ORCHIDEE over AMZ, WAF and SAS Improvement over high latitude for 3 models Summary • Significant differences are observed in the land surface description when using CCI-LC maps w.r.t. older reference maps • • • Boreal region with the continuum bare – sparse vegetation – grassland – JULES & ORCHIDEE: vegetation to bare soil è increased albedo associated with decrease in extreme temperatures – JSBACH: more forest & less bare soil è increases of LAI, biomass and surface albedo è decrease of surface temperature and sensible heat flux JSBAC model: less forest and more bare soil in West Africa, Sahel, Congo – LAI and biomass decrease, albedo increase – reduced NPP, GPP and evapotranspiration – soil moisture and runoff increases. Globally, large reduction in C3 grass cover and increase in C4 grass cover ! generally increases GPP, but decreases cVeg, associated with improvements in max temp bias Summary • All models show high sensitivity to land cover as an initial condition for both stocks and fluxes • Mainly changes in high latitudes and tropics • Improvement in high latitude NPP, in GPP online • Improvement in terrestrial carbon sink estimate and seasonality • For Amazon and Congo, albedo is increasing from the earliest 2000 Epoch to the latest 2010 Epoch ! indication of large-scale deforestation of tropical rainforest • Mixed response of biomass, depending on region BUT global reduction in positive bias • Improvement to atmospheric data at high latitudes • Improved representation of 2m air temperature and surface albedo in some regions • CCI-LC constrains the problem of where to improve existing model processes and parameters • Implementation of fully consistent changes of land cover (not only input data but all associated parameters – e.g. PFTs distributions) would lead to a more larger impact of CCI data 2 papers to come Paper 1 - “Land cover uncertainty in land carbon cycle processes” … Making sense of multiple benchmarks … Paper 2 - “A plant functional type classification for Earth System Models from the European Space Agency Essential Climate Variables Program” … Toward regular PFT classification from ESA-CCI … For the future • Further discussion/development of cross-walking procedure (LCCS class into PFTs) • In Phase 1: assess the impact of LC uncertainty with all models with their own reference map Next step: Same experiment with all models with the same reference map • Model optimization for new CCI-LC maps • More links between C and water/energy expertise in modelling groups • Create a LC change scenario (yearly) and assess its impact on the C, water and energy fluxes (especially on on-going deforestation and associated fluxes) • Use of WB product and/or LC condition products in models
© Copyright 2024