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Dynamical Prediction of the Terrestrial Ecosystem and the Global Carbon Cycle: a 25-year Hindcast Experiment Jin-Ho Yoon Dept. Atmospheric and Oceanic.

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Presentation on theme: "Dynamical Prediction of the Terrestrial Ecosystem and the Global Carbon Cycle: a 25-year Hindcast Experiment Jin-Ho Yoon Dept. Atmospheric and Oceanic."— Presentation transcript:

1 Dynamical Prediction of the Terrestrial Ecosystem and the Global Carbon Cycle: a 25-year Hindcast Experiment Jin-Ho Yoon Dept. Atmospheric and Oceanic Science University of Maryland Co-Authors: Ning Zeng, Augustin Vintzileos, G. James Collatz, Eugenia Kalnay, Annarita Mariotti, Arun Kumar, Antonio Busalacchi, Stephen Lord Collaborators: C. Roedenbeck, P Wetzel, E. Maier-Reimer, Brian Cook, H. Qian, R. Iacono, E. Munoz March 5, 2008 CPASW

2 Breathing of the biosphere: CO2 as a major indicator of climateBreathing of the biosphere: CO2 as a major indicator of climate Terrestrial carbon model forced by observed climate variability ENSO, Pinatubo, drought and other signals Modeled land-atmo flux vs. MLO CO2 growth rate

3 Seasonal-interannual Prediction of Ecosystem and Carbon Cycle Two strands of recent research made this a real possibility Significantly improved skill in atmosphere-ocean prediction system, such as CFS at NCEP Development of dynamic ecosystem and carbon cycle models that are capable of capturing major interannual variabilities, when forced by realistic climate anomalies A pilot study at U Maryland: Feasibility study using a prototype eco-carbon prediction system dynamical vs. statistical

4 Targets Predict spatial patterns and temporal variability of carbon fluxes and pool size Example: Reduced biosphere productivity and enhanced fire and CO2 from Amazon. Predict atmospheric CO2 concentration and growth rate. Atmospheric CO2 can be a climate index indicating anomalies in the global ecosystem

5 The NCEP Climate Forecast System (CFS, Saha et al. 2006) CFS captures major ENSO and other seasonal-interannual variability

6 Photosynthesis Autotrophic respiration Carbon allocation Turnover Heterotrophic respiration 4 Plant Functional Types: Broadleaf tree Needleleaf tree C3 Grass (cold) C4 Grass (warm) 3 Vegetation carbon pools: Leaf Root Wood 3 Soil carbon pools: Fast Intermediate Slow Atmospheric CO2 The VEgetation-Global Atmosphere-Soil Model (VEGAS) NPP=60 Pg/y Rh=60Pg/y NEE = Rh – NPP = + 3 (Interannual)

7 Forecasting Procedure I CFS (9mon, 15 members) VEGAS Output 9mon, 15 members Month 2 CFS (9mon, 15 members) VEGAS Output 9mon, 15 members Month 1 1 mo forecast ensemble mean I Initialization Climate Predition Ecosystem+ Carbon Model Predicted Eco-carbon Spinup Precip Temp Precip Temp

8 Forecasting procedure II L=1 L=0 L=3 L=2 tt-1 t+1 Ensemble mean

9 NEE(validation') and MLO CO2 NEE (land-atmo C flux): VEGAS forced by observed climate (Precip, T) This will be called validation as there is no true observation available Ocean contribution smaller, so NEE can be compared with atmo CO2 Using regression of inversion/OCMIP with Nino3.4/MEI?

10 NEE(validation') and Inversion (from MPI)

11 First look: Productivity (NPP)

12 'Observed' Predicted global cabon flux (Fta) Lead time from 0 to 8 months 1. CFS/VEGAS captures most of the interannual variability, but 2. Amplitude is underestimated

13 Anomaly Correlation Fta High skills in South America Indonesia southern Africa eastern Australia western US central Asia

14 Summary of skill for anomaly correlation

15 Correlation Regression Correlation.vs. Regression (Amplitude)

16 Benchmark Forecast: Do we need dynamical forecast? Relaxation or Damping of climate forcing Anomaly at L=0 will persist or damped to zero with decorrelation time scale. Persistence Damping L=1L=0 L=2

17 Benchmark Forecast Do we need dynamic forecast system?

18 Benchmark Forecast

19 Beyond ENSO: Drought during

20 Beyond ENSO: Fire in the US Natural and anthropogenic factors Observation Model

21 Conclusions: prediction Encouraging results (better than expected) Seasonal prediction of Prec/Tas(CFS) + Dynamic ecosystem model (VEGAS) Parallel project on Fire danger (potential) prediction. Working on operational mode.

22 Issues Overestimation at midlatitude Implications of prediction Applications to ecosystem and carbon cycle A new framework for study eco-carbon response and feedback to climate Identifying ways of incorporating eco-carbon dynamics in the next generation of climate prediction models Conclusions: prediction

23 Thank you! Questions?

24 The NCEP Climate Forecast System (CFS, Saha et al. 2006)

25 Summary of skill for anomaly correlation


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