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Carbon soil moisture and fAPAR assimilation

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Carbon soil moisture and fAPAR assimilation Powered By Docstoc
					      Carbon, soil moisture and
         fAPAR assimilation
                     Wolfgang Knorr
          Max-Planck Institute of Biogeochemistry
                     Jena, Germany 1

Acknowledgments: Nadine Gobron 2, Marko Scholze
3, Peter Rayner 4, Thomas Kaminski 5,

Ralf Giering 5, Heinrich Widmann1
2              3                  4             5 FastOpt
    IES/JRC        QUEST /            LSCE
               Overview

• CO2 – climate linkages
• Satellite fAPAR as soil moisture indicator
• Assimilation of fAPAR
         Atmospheric CO2
          Measurements
                                 CCDAS inverse modelling period



... and more stations in CCDAS
                   Carbon Cycle Data Assimilation
                         System (CCDAS)
                                                               Prescribed                                           Assimilated
             Assimilated
        satellite fAPAR +                                       Phenology                                            CO2
              Uncert.                                           Hydrology                                          + Uncert.

                                                                              CCDAS Step 2
          CCDAS Step 1                                                        BETHY+TM2
           full BETHY                                                      only Photosynthesis,
                                                                         Energy&Carbon Balance

                 Background                                 Optimized Params                                      Diagnostics
                 CO2 fluxes*                                    + Uncert.                                          + Uncert.

    *
* ocean: Takahashi et al. (1999), LeQuere et al. (2000); emissions: Marland et al. (2001), Andres et al. (1996); land use: Houghton et al. (1990)
Terr. biosphere–atmosphere
         CO2 fluxes    ENSO




       … preliminary results from extended CCDAS run
ENSO and global climate
 normalized anomalies




                 ENSO
–precipitation            temperature

                 … global drying and warming trend
for more information see:


    http://www.CCDAS.org
               Overview

• CO2 – climate linkages
• Satellite fAPAR as soil moisture indicator
• Assimilation of fAPAR
         
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1 developed by Nadine Gobron, Bernard Pinty,
  Frédéric Melin, IES/JRC, Ispra
1 3-channel algorithm taylored to SeaWiFS ocean color
  instrument (blue, red, near-infrared)
1 cloud screening algorithm
1 requires no atmospheric correction
1 starts 10/1997, continuing...
1 being extended by same product for MERIS

2
55357

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55357 89
9

   precipitation   gridded station data



  soil moisture    BETHY simulations



 leaf area index



     fAPAR         satellite observations
                   BETHY simulations
     precipitation vs.
       fAPAR from
    SeaWiFS satellite
           obs.
                                                     1-month lag
   percent area with 99%
   significant correlation
         r>0
         r<0




                                                     4-month lag

0.5°x0.5°, 123456789
5

51245
87568


   precipitation vs.
       fAPAR:
      satellite and
         model
                                                    1-month lag
                          SeaWiFS fAPAR
percent area with 99%
significant correlation
      r>0
      r<0




                            BETHY simulated fAPAR
   precipitation vs.
       fAPAR:
      satellite and
         model
                                                    4-month lag
                          SeaWiFS fAPAR
percent area with 99%
significant correlation
      r>0
      r<0




                            BETHY simulated fAPAR
  precipitation vs.
  satellite fAPAR
 and simulated soil
     moisture
                                                            1-month lag
                          SeaWiFS fAPAR
percent area with 99%
significant correlation
      r>0
      r<0




                            BETHY simulated soil moisture
  precipitation vs.
  satellite fAPAR
 and simulated soil
     moisture

                                                            4-month lag
                          SeaWiFS fAPAR
percent area with 99%
significant correlation
      r>0
      r<0




                            BETHY simulated soil moisture
17
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9
  327
235
                  3-month lag
               Overview

• CO2 – climate linkages
• Satellite fAPAR as soil moisture indicator
• Assimilation of fAPAR
               89
9795535


               PFT distribution*   ecosystem model
Prescribed
                                     parameters
 climate &                                        optimization
soils data**

                             BETHY
                                                    mismatch



                carbon and water    model-derived    satellite
                     fluxes           fAPAR          fAPAR
                      2776
5

Measure of the mismatch (cost function):


                                                       model diagnostics   measurements


      21 1 21 21 -1 21 21 T 1 21 21 21 -1 21 21 21 T
    J(m ) = [m − m 0 ]C m 0 [m − m 0 ] + [ y (m ) − y0 ]C y [ y (m ) − y 0 ]
           2                            2
      assumed                            a priori error covariance                error covariance matrix
   model parameters                        matrix of parameters                      of measurements
                          a priori
                      parameter values




      aim: minimize J(m)
      [for each grid point separately]
                  27
3322

                                                               vector of prior
parameter vector m={m1,m2,m3}:             represents:         parameter
                                                               values m0:
 m1      ∆Tφ       shift of leaf           temperature
                   onset/shedding          limitation               ∆Tφ,0=0
                   temperature

 m2      wmax      maximum soil water      water limitation         wmax,0
                   holding capacity                                 (derived from
                                                                    FAO soil map)

 m3      fc        fraction of grid cell   residual,                fc,0
                   covered with            unmodelled               (function of
                   vegetation              limitations (nitrogen,   P/PET and
                                           land use)                Temp. of
                                                                    warmest
                                                                    month)
                 
57
33227

prior values:

Tφ=5°C
Tφ=12°C for crops
^
Tφ=15°C




   note: each 0.5°x0.5° has
   mixture of up to 6 PFTs


                         map reflects presence of crops; red: unvegetated
                        
57
33227!




bucket model:
                runoff
precipitation
                =overflow
=input
                      full
                      bucket:
                      wmax

                     current
                     bucket:
                     w

                                       wmax,0 [mm]
    
57
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                                         ^
fc,0=Pannual/PETannual∗ΛW(Twarmest month)/Λ
               
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error covariance matrix of parameters Cm0:



                  1K   2
                                   0                0    
                                                        
     C   m 0   =  0        (2 w   max,   0 )2      0    
                                                       2
                  0               0             0 . 25 


⇒ off-diagonal elements assumed 0 here
= no prior correlation between errors of different parameters
              27955327#33

model diagnostics vector y={y1,y2,...,y12}:

 yi          modelled fAPAR of month i


satellite-derived diagnostics vector y0={y0,1,y0,2,...,y0,12}:

 y0,i        SeaWiFS derived fAPAR of month i
   
57787$2322

error covariance matrix of measurements Cy:



                  0 .05 2   if valid measurement
  C y i, j   =
             i=j     ∞              if data gap
             =σ    2
                   y,i




⇒ off-diagonal elements again 0
= no prior correlation between errors of different months
         
33227!7%253&
57'32(
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5)




                       5   55*2




                                   local site
                  Local Simulations
   precipitation [mm/month]             fAPAR
                                                             Paragominas
                                                             Paragominas
                                                               3°S 48°W 63 m
                                                                3°S 48°W 63 m




                                remote sensing data

         1992
                                                          no remote sens. data
                                                          fAPAR prescribed
evapotranspiration [mm/month]   NPP [gC/(m2 month)]       fAPAR assimilated


                                                      -
                                                      -
       Measured Soil Moisture
precipitation [mm/month]           fAPAR
                                                      Paragominas
                                                      Paragominas
                                                        3°S 48°W 63 m
                                                         3°S 48°W 63 m




                           remote sensing data       no remote sens. data
                                                     fAPAR prescribed
     1992
                                                     fAPAR assimilated
      0...2m depth                 0...8m depth




                                                 -
                                                 -




     1992                           1992
233535 %253&


          5          55*2




        mm/year           mm/year
+)757527%253,7
         )723&


        5            55*2




                               Parag.




       mm                 mm
                   Conclusions

• The carbon cycle is highly sensitive to climate fluctuations
• Vegetation can be quantified reliably from space
• fAPAR lags precipitation by ~1–4(?) months
• seems to behave similar to soil moisture
• assimilation of fAPAR can deliver valuable information on
  soil moisture status
                 Conclusions

• Need to improve phenology model
• Implement sequential 2-D var assimilation scheme
• Assimilate fAPAR into coupled ECHAM5-BETHY model


 (hope not too distant) goal: make fAPAR what SST
 is for ocean-atmosphere interactions... and
 improve seasonal forecasts
Thank You For Your Attention!

				
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posted:7/27/2011
language:English
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