ORCHIDEE Development Section

Project CAMS41
Optimization of the relationship between CHTESSEL GPP and SIF data [per REGION and per SEASON]

All input data (SIF, GPP Jung and GPP CTESSEL) are smoothed with the 3-month running averaging window

PFT1 (Crops, mixed farming)
PFT2 (Short grass)
PFT3 (Evergreen needleleaf)
PFT4 (Deciduous needleleaf)
PFT5 (Deciduous broadleaf)
PFT6 (Evergreen broadleaf)
PFT7 (Tall grass)
PFT9 (Tundra)
PFT10 (Irrigated crops)
PFT11 (Semidesert)
PFT13 (Bogs and marshes)
PFT16 (Evergreen shrubs)
PFT17 (Deciduous shrubs)
PFT18 (Mixed forest - Wood)
PFT19 (Interrupted forest)

  1. Optimize \( (a,b) \) per CTESSEL PFT from the relationship
    \( SIF_\mathsf{SAT} = a \cdot GPP_\mathsf{Jung} + b \)
  2. Calculate inverse coefficients \( (\alpha,\beta) \)
    \( \alpha = \frac{1}{a} \)
    \( \beta = -\frac{b}{a} \)
  3. Compute CTESSEL SIF
    \( SIF_\mathsf{CTESSEL} = a \cdot GPP_\mathsf{CTESSEL} + b\)
  1. Compare SIF for CTESSEL vs SAT
    \( \Delta SIF = SIF_\mathsf{SAT} - SIF_\mathsf{CTESSEL} \)
  2. Convert difference from SIF to GPP
    \( \Delta GPP_\mathsf{SAT} = \alpha \cdot \Delta SIF \)
  3. Compare with GPP difference
    \( \Delta GPP = GPP_\mathsf{Jung} - GPP_\mathsf{CTESSEL} \)

PFT maps
SIF data
Posterior data for GOSAT
a_post
REGIONYEARWINTERSPRINGSUMMERAUTUMN
010.1140.1320.1160.1070.127
01_N0.1080.1580.1080.1060.132
01_T0.1080.1120.110.1180.114
01_S0.1220.1330.1350.140.116
b_post
REGIONYEARWINTERSPRINGSUMMERAUTUMN
010.03770.03080.0290.03920.0457
01_N0.03260.02180.02110.03210.0415
01_T0.06980.08310.05480.04130.0712
01_S0.04830.008590.05730.03750.058
n_points
REGIONYEARWINTERSPRINGSUMMERAUTUMN
01363228750923295528788
01_N250825936629367706083
01_T63771657168914921539
01_S48631157125012901166
RMSD (GOSAT vs Jung)
REGIONMODEYEARWINTERSPRINGSUMMERAUTUMN
01all data0.1620.1710.160.1610.156
seasonal0.1620.1710.160.1610.155
01_Nall data0.1550.1680.1540.1510.144
seasonal0.1540.1680.1540.1510.143
01_Tall data0.1820.1710.1730.2080.178
seasonal0.1820.170.1720.2080.177
01_Sall data0.1670.1810.1650.1430.181
seasonal0.1670.1790.1630.1430.181

Posterior data for GOME2-Joiner
a_post
REGIONYEARWINTERSPRINGSUMMERAUTUMN
010.1560.1490.1530.1470.154
01_N0.1850.2470.1880.1590.205
01_T0.1150.09470.1030.1250.121
01_S0.1460.150.1640.1490.131
b_post
REGIONYEARWINTERSPRINGSUMMERAUTUMN
010.070.05290.05860.1020.0833
01_N0.05260.02920.03570.1020.0606
01_T0.1090.1190.10.09490.129
01_S0.0840.06430.05990.08310.124
n_points
REGIONYEARWINTERSPRINGSUMMERAUTUMN
0125761556294670026716367156
01_N16158132294429904315243145
01_T5408313512135241352413523
01_S4195110488104881048710488
RMSD (GOME2-Joiner vs Jung)
REGIONMODEYEARWINTERSPRINGSUMMERAUTUMN
01all data0.1020.1160.09430.1040.0941
seasonal0.10.1140.09330.1010.0933
01_Nall data0.09280.1140.08610.0950.078
seasonal0.09080.1120.08480.09210.076
01_Tall data0.1090.1080.1030.1160.111
seasonal0.1070.1060.09950.1160.107
01_Sall data0.1040.1090.09170.0920.12
seasonal0.1020.1080.09040.0920.117

GPP
SIF (all data optimization)
SIF (seasonal optimization)
Delta GPP