Atmospheric Carbon Dioxide Concentrations Simulated With A Terrestrial Ecosystem Model (BEPS) And An Atmospheric Transport Model (TM5)

Transcription

1 Atmospheric Carbon Dioxide Concentrations Simulated With A Terrestrial Ecosystem Model (BEPS) And An Atmospheric Transport Model (TM5) Feng Deng 1, Jing M. Chen 1, Gang Mo 1, Wouter Peters 2, Maarten Krol 3 1 Department of Geography, University of Toronto, 100 St. George Street, Toronto, Ontario, Canada M5S 3G3 2 NOAA Earth System Research Lab & Wageningen Research University 3 Wageningen Research University TransCom Meeting Utrecht, The Netherlands June 2-5, 2008

2 Outline Introduction Terrestrial Ecosystem Model Atmospheric Transport Model Key Datasets Primary Results and Analysis Conclusion Future work

3 Introduction Process-based terrestrial ecosystem modeling is one approach to estimate fluxes over large areas. Only limited tower flux data available for validating the modeling results. Coupling of an ecosystem model with an atmospheric transport model would allow us to translate the surface carbon fluxes into atmospheric CO 2 concentration. Comparing with CO 2 measurements over the globe could validate the global scale modeling results, and find potential problems of an ecosystem model. It is also an important step in global inverse modeling of the surface carbon flux.

4 Introduction Predicted Atmospheric CO2 concentration Measured Atmospheric CO2 concentration Atmospheric Transport Model TM5 Fire emission Fossil Fuel emission Ocean surface exchange Land surface exchange Meteorology Land Cover Remote sensed vegetation properties Terrestrial Ecosystem Model BEPS

5 The Boreal Ecosystem Productivity Simulator (BEPS) A module for photosynthesis calculation (Chen et al., 1999); Canopy is separated into sunlit and shaded components in calculating photosynthesis. A soil biogeochemical module for soil C, N and heterotrophic respiration simulation following CENTURY (Parton et al., 1993); A land surface scheme driven by remotely sensed surface parameters. Liu et al., 1997, 1999, 2002; Ju et al., 2004; Sun et al., 2004, Wang et al., 2003; Matsushita et al.,

6 The Boreal Ecosystem Productivity Simulator (BEPS) Hourly global Net Ecosystem Exchange (NEE) with resolution 1 x1 was produced for a fiveyear period ( ). Examples: 3-hour average of hourly land surface NEE results produced from BEPS A short movie shows the diurnal NEE variation of 3 days

7 The Boreal Ecosystem Productivity Simulator (BEPS)

8 TM5 Offline atmospheric transport model Meteorology from ECMWF Global simulation 6 x 4 Zooming to 1 x 1 (Target region) 25 vertical layers Krol et al. (2005) Peters et al. (2004)

9 Key Datasets: Global LAI Dynamics Data sources: SPOT VEGETATION 10-day composites, 1 km resolution, 2003 Algorithm: BRDF-SR+RSR, U of T Atmospheric Correction: SMAC, VITO Seasonal Trajectory: LACC0.5, U of T Deng et al. (2006, TGARS) Chen et al. (2006,

10 Key Datasets - Fossil Fuels -Fires Fossil Fuels The Global, regional and national fossil-fuel CO 2 emission from 1871 to 2004 (CIDAC) (Marland, Boden, and Andres, 2007); The EDGAR 3.2 database provides global annual CO 2 emission on a 1x1 degree grid for 1990 and 1995 ( edgar/model/) Fires the Global Emissions Fire Database version 2 (GFEDv2). (van der Werf et al., 2003; Giglio et al., 2006).

11 Key Datasets - Ocean Results of CO 2 fluxes using the OPA-PISCES-T model Forced by daily wind stress and heat and water fluxes from the NCEP reanalyzed data for 1948 to 2004 Buitenhuis et al. (2006) Data provided by Dr. Corinne Le Quéré

12 Key Datasets - CO2 Observations GLOBALVIEW-CO2: Cooperative Atmospheric Data Integration Project - Carbon Dioxide. NOAA ESRL, Boulder, Colorado

13 Temperature and heterotrophic respiration Heterotrophic respiration Rh is a temperature dependent function. The temperature response function is often written as where T s is soil temperature, Q 10 is a quotient of change in respiration caused by a change in temperature by 10.

14 Temperature and heterotrophic respiration In BEPS, we used the equation of Lloyld and Taylor (1994) or allow Q 10 to linearly decrease with T:

15 Primary results Measured vs. simulated CO2 concentration at Mauna Loa, Hawaii, United States (19.54 N, W) from 2002 to LT Q 10 (T) Red - weekly average of measured concentration Blue simulated hourly concentration

16 Primary results Measured vs. simulated CO2 concentration at Park Falls, Wisconsin, United States (45.93 N, W) from 2002 to LT Q 10 (T) Red - weekly average of measured concentration Blue simulated hourly concentration

17 Primary results Measured vs. simulated CO 2 concentration at Schauinsland, Germany (48.00 N, 8.00 E) from 2002 to LT Q 10 (T) Red - weekly average of measured concentration Blue simulated hourly concentration

18 Primary results Measured vs. simulated CO 2 concentration at Fraserdale, Canada (49.88 N, W) from 2002 to LT Q 10 (T) Red - weekly average of measured concentration Blue simulated hourly concentration

19 Primary results Modeling carbon exchange components at the local grid of Fraserdale, Canada for 2003 Measured vs. simulated CO2 concentration at Fraserdale, Canada (49.88 N, W) from 2002 to 2004.

20 Conclusion The combination of BEPS and TM5 was used to simulate the global atmospheric CO 2 distribution with reasonable success. The simulation appears to be very sensitive to the respiration scheme. A temperature dependent Q 10 function (linear) seems to perform the best.

21 Future work Predicted Atmospheric CO2 concentration Measured Atmospheric CO2 concentration Atmospheric Transport Model TM5 Fire emission Fossil Fuel emission Ocean surface exchange InTEC Land surface exchange Meteorology Land Cover Remote sensed vegetation properties Terrestrial Ecosystem Model BEPS

22 The End