CCI Phase 1 results Climate research perspectives

Land Cover CCI

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)

Land component of Earth System Model Simulates the Energy, Water and Carbon balance

Dynamic global vegetation model

Land cover map

LSCE – ORCHIDEE model

•  Surface description: a tile approach •  A mosaïc of vegetation

•  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

Modeling  Team  

Offline   Online  

Original  LC   LC_CCI  LC   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  

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?

LC-CCI to improve initial model conditions

Current  land  cover  map  • Outdated  land  cover  inputs  

•  IGBP  land  cover  (Belward  et  al.  1999)  and,  

•  Olson  vegetaAon  map  with  96  classes  (1983)  

 Land cover Köppen-Geiger

climate zones

Phenology & Physiognomy

Plant Functional Types Land

 Cover  -­‐>

 PFT  

Conversion

 Too

l  (BEA

M)  

ESA  LC_CCI  PFT  datasets  

PFT comparison - Bare fraction

Possibly too high in high latitudes due to 85% allocation from sparse vegetation categories

ORCHIDEE

JULES JSBACH

PFT comparison - C3 / crops fractions

Decrease in high latitudes due to replacement by bare soil/barren

ORCHIDEE (natural C3 fraction)

JULES (All C3)

JSBACH (crops)

PFT comparison - BoBS fraction

Fractional difference (-)

ORCHIDEE Notable increase in E Europe at expense of evergreens

By-model experiment Carbon, energy and water budget

•  LAI

•  Gross Primary Production

(GPP)

•  Net Primary Production (NPP)

•  Radiation & moisture fluxes

•  Heat sensible flux

•  Latent sensible flux

•  Surface temperature

Evapotranspiration

•  Heterotrophic respiration

•  Albedo

•  Runoff

•  Total precipitation changes

•  Validation of Dynamic Global

Vegetation Model (in

comparison with IGBP map)

LAI comparison

Red: higher LAI with Olson-derived simulations

Blue: higher LAI with CCI_LC-derived simulations

ORCHIDEE: LAI comparison between Olson and CCI-LC Mean difference in LAI (Olson – 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

LAI comparison

0

0.5

1

1.5

2

Olson CCI

RM

SE

Bia

s

0

0.2

0.4

0.6

0.8

Olson CCI

ORCHIDEE: LAI comparison between Olson and CCI-LC

Carbon budget - GPP

0

25

50

75

100

125

1980 1985 1990 1995 2000 2005Year

GtC

yr−1

SourceHadGEM2A: ControlHadGEM2A: LC_CCI 2000HadGEM2A: LC_CCI 2005HadGEM2A: LC_CCI 2010MODIS GPP MOD17A3Jung et al

Global Mean Annual GPP

Offline Control LC_CCI 2010

GPP (Gt C yr-1) 152.5 166.7

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

JULES: Increase in global GPP in both online and offline simulations

Carbon budget - GPP

Gross Primary Production ( gC m−2 yr−1 )LC_CCI 2010 − Control

50°S

0°

50°N

100°W 0° 100°E

−2000 −1500 −1000 −500 0 500 1000 1500 2000

JULES: Spatial distribution of differences

Carbon budget - GPP Gross Primary Production ( gC m−2 yr−1 )

Model − Observations

50°S

0°

50°N

Control

100°W 0° 100°E

LC_CC:2010

−3000 −2000 −1000 0 1000 2000 3000

•  LC_CCI 2010 shows reduction in biases in:

•  West Africa •  Southern Africa •  Amazon •  India •  South East Asia

JULES: Comparison to estimates from Jung et al. (2011)

KgC/m2/yr

Carbon budget - NPP

•  Large increase in NPP over croplands •  Decrease in high latitudes due to decrease in grass cover

ORCHIDEE: NPP comparison (CCI-LC – Olson)

LMDz transport: comparison to GlobalView data ! improvement of the CO2 seasonal cycle with

reduced amplitude

Carbon budget - Evaluation using atm. CO2 concentration

ORCHIDEE

Bare C3 C4

0.000.050.100.150.20

−3−2−1

0

0255075

100

−10−5

05

10

0.000.040.08

−200

2040

−75−50−25

0

−2024

−4−2

024

−1.0−0.5

0.00.51.0

GPP

cVegET

SMAlb

SHLH

Tmax90pct

Pr90pctlr20m

m

J F M A M J J A S O N D J F M A M J J A S O N D J F M A M J J A S O N DMonth

valu

e

PFT, %increase (point ID)Bare, 50% (9)C3, 63% (13)C4, 32% (21)C4, 39% (8)C4, 45% (14)C4, 66% (10)C4, 71% (11)

TropicalBare C3 C4

0.000.050.100.150.20

−3−2−1

0

0255075

100

−10−5

05

10

0.000.040.08

−200

2040

−75−50−25

0

−2024

−4−2

024

−1.0−0.5

0.00.51.0

GPP

cVegET

SMAlb

SHLH

Tmax90pct

Pr90pctlr20m

m

J F M A M J J A S O N D J F M A M J J A S O N D J F M A M J J A S O N DMonth

valu

e

PFT, %increase (point ID)Bare, 50% (9)C3, 63% (13)C4, 32% (21)C4, 39% (8)C4, 45% (14)C4, 66% (10)C4, 71% (11)

Tropical11

10

Shrub Urban Water Bare Ice

-0.10 0.00 -0.04 -0.08 0.00

0.03 0.00 0.00 -0.12 0.00

ID BL NL C3 C4

10 -0.28 0.00 -0.17 0.66 11 -0.21 0.00 -0.40 0.71

C4 grass BL Tree C3 grass •  Leads to: •  Increase in GPP •  Reduction in veg. C •  Increase in extreme

max temp in Sept (10)

•  Extreme precipitation earlier in season (11)

Bare C3 C4

0.000.050.100.150.20

−3−2−1

0

0255075

100

−10−5

05

10

0.000.040.08

−200

2040

−75−50−25

0

−2024

−4−2

024

−1.0−0.5

0.00.51.0

GPP

cVegET

SMAlb

SHLH

Tmax90pct

Pr90pctlr20m

m

J F M A M J J A S O N D J F M A M J J A S O N D J F M A M J J A S O N DMonth

valu

e

PFT, %increase (point ID)Bare, 50% (9)C3, 63% (13)C4, 32% (21)C4, 39% (8)C4, 45% (14)C4, 66% (10)C4, 71% (11)

TropicalRadiation & moisture fluxes

JULES: West Africa Increase in C4 Grass, replacing BL tree or C3 grass in old maps

Radiation & moisture fluxes

Maximum temperature JULES

Energy budget - Visible albedo

Cha

nge

in a

lbed

o (-

)

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

•  Over high- and mid-latitude catchments: CCI-LC albedo higher than reference •  Over arid and semi-arid regions: improvement with CCI-LC albedo

JSBACH

By catchment experiment WFDEI 2m temperature

•  Effect of albedo difference visible in estimated 2m t° (↑ albedo " ↓t°)

•  General improvement over most catchments

JSBACH

By catchment experiment Uncertainty due to epoch

•  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

JSBACH

Models inter-comparison Albedo

•  Stronger sensitivity of JSBACH than JULES to CCI-LC 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

•  Significant improvement of ORCHIDEE with CCI-LC maps

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

AMZ WAF SAF NAS SAS NAU

Surface  Albedo

MODIS

GlobAlbedo

WFDEI-­‐REF

WFDEI-­‐CCI

ECHAM6-­‐REF

ECHAM6-­‐CCI

JULES-­‐REF

JULES-­‐CCI

HadGEM2A-­‐REF

HadGEM2A-­‐CCI

0

0.05

0.1

0.15

0.2

0.25

0.3

AMZ WAF SAF NAS SAS NAU

Surface  Albedo

MODIS

GlobAlbedo

ORCHIDEE-­‐Olson

ORCHIDEE-­‐CCI

ORCHIDEEonl-­‐Olson

ORCHIDEEonl-­‐CCI

ORCHIDEE-­‐DGVM

Over Giorgi regions AMZ = Amazonia WAF = West Africa SAF = South Africa NAS = North Asia SAS = South Asia NAU = North Australia

Models inter-comparison Evapotranspiration

•  Direction of change when using CCI-LC data:

•  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

Over Giorgi regions AMZ = Amazonia WAF = West Africa SAF = South Africa NAS = North Asia SAS = South Asia NAU = North Australia

-­‐60.0%

-­‐40.0%

-­‐20.0%

0.0%

20.0%

40.0%

60.0%

AMZ WAF SAF NAS SAS NAU

Deviation  from  LandFlux  Diag  ET  mean

LF  MinLF  MaxWFDEI-­‐REFWFDEI-­‐CCIECHAM6-­‐REFECHAM6-­‐CCIJULES-­‐REFJULES-­‐CCIHadGEM2A-­‐REFHadGEM2A-­‐CCI

-­‐80.0%

-­‐60.0%

-­‐40.0%

-­‐20.0%

0.0%

20.0%

40.0%

60.0%

AMZ WAF SAF NAS SAS NAU

Deviation  from  LandFlux  Diag  ET  mean

LF  Min

LF  Max

ORCHIDEE-­‐Olson

ORCHIDEE-­‐CCI

ORCHIDEEonl-­‐Olson

ORCHIDEEonl-­‐CCI

ORCHIDEEoff-­‐DGVM

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