State-of-the-Art of the Simulation of Net Primary Production of Tropical Forest Ecosystems Marcos Heil Costa, Edson Luis Nunes, Monica C. A. Senna, Hewlley M. A. Imbuzeiro Universidade Federal de Viçosa Manaus, November 18 th 2008

1. Introduction Atmospheric CO 2 is increasing at faster rates – : 1.3 ppm.yr -1 – : 1.6 ppm.yr -1 – : 1.5 ppm.yr -1 – : 1.9 ppm.yr -1 Of the total CO 2 emissions –24% is absorbed by oceans –30% is absorbed by the atmosphere –46% remains in the atmosphere (Data for Canadell et al. PNAS 2007) Changes in the terrestrial carbon sink may cause deep modifications in the global carbon cycle

atmospheric CO 2 ocean land fossil fuel emissions deforestation CO 2 flux (Pg C y -1 ) Sink Source Why does the land carbon sink varies so much from year to year? Why there isn’t a trend in the land carbon sink. like in the other two pools? How long will the land carbon pool hold? Where is this carbon sink happening?

Nemani et al.. Science 2003 Carbon sink by the Amazon rainforest increased by 1.4 Pg-C in the period or 77 Tg-C/yr (these numbers may be underestimated). Trends in carbon sequestration by vegetation, period

Carbon sink by undisturbed terrestrial ecosystems: NEE = NPP– R H –NEE: net ecosystem exchange –NPP: net primary production –R H : heterotrophic respiration –NEE > 0  carbon sink –NPP increases/decreases  carbon sink increases/decreases –NPP may change due to changes in climate (incoming PAR. available soil moisture) or to increasing CO 2 concentration (physiological effect of CO 2 ) Monitoring NPP is necessary to understand the present terrestrial carbon sink dynamics Predictive capability is also desirable

2. Objective Our goal here is to evaluate the state-of-the-art of the simulation of net primary production (NPP) by tropical rainforest ecosystems –two diagnostic configurations simulation at micrometeorological sites simulation forced by climate datasets and remote sensing products –one prognostic configuration simulation by a coupled climate-biosphere model

3. Models tested SITE 0-D. simple terrestrial ecosystem dynamics model. forced by hourly meteorological data; diagnostic capability. IBIS 0-D 2-D. terrestrial ecosystem dynamics model. forced by hourly meteorological data. dynamically simulates leaves phenology; diagnostic capability. RATE 2-D. terrestrial ecosystem dynamics model. forced by gridded datasets (reanalysis or other). assimilates remote sensing information (MODIS LAI and FAPAR); diagnostic capability. CCM3-IBIS coupled climate-biosphere model. Generates its own climate and the associated vegetation dynamics; prognostic capability.

4. Data Site Nearest City CoordinatesPeriodVegetation Available data Source Flona Tapajós K67Belterra – PA55.04º W; 2.86º S2004 Amazon tropical rainforest MS; NPPTurner et al. (2006) BA712 Teixeira de Freitas – BA 39.67º W; 17.29º S2006 Atlantic rainforest MS; NPPNunes (2008) Flona Tapajós K67Belterra – PA55.02º W; 2.86º S2001 Amazon tropical rainforest NPPVieira et al. (2004) UFAC Rio Branco – AC 68.03º W; 10.12º S Amazon tropical rainforest NPPVieira et al. (2004) ZF-2Manaus – AM60.18º W; 2.97º S Amazon tropical rainforest NPPVieira et al. (2004) CaxiuanãMelgaço – PA Several parcels between 1°41’S and 1°46’S and between 51°30’W and 51°23’W Long-term Amazon tropical rainforest NPPMalhi et al. (2008) Flona Tapajós K67Belterra – PA Several parcels between 2°50’S and 3°18’S; and between 55°05’W and 54°55’W Long-term Amazon tropical rainforest NPPMalhi et al. (2008) ManausManaus – AM Several parcels between 2°20’S and 2°38’S; and between 60°15’W and 59°58’W Long-term Amazon tropical rainforest NPPMalhi et al. (2008)

5. Results SiteYearModel NPP (kg-C m -2 yr -1 ) Observed Value Simulated Value Relative Error Flona Tapajós K672004SITE-MS % Flona Tapajós K672004IBIS-MS % BA SITE-MS % BA IBIS-MS % Average relative error– % Average |relative error|–––6.6% Models forced by Meteorological Station (MS) Data

SiteYearModel NPP (kg-C m -2 yr -1 ) Observed Value Simulated Value Relative Error Flona Tapajós K672004SITE-MS % Flona Tapajós K672004IBIS-MS % BA SITE-MS % BA IBIS-MS % Average relative error– % Average |relative error|–––12.2% Models forced by Reanalysis (RA) Data

SiteYearModel NPP (kg-C m -2 yr -1 ) Observed Value Simulated Value Relative Error Flona Tapajós K672001RATE % Flona Tapajós K672004RATE % ZF-22001RATE % ZF-22002RATE % UFAC2001RATE % UFAC2002RATE % BA RATE % Average relative error– % Average |relative error|–––8.9% Model forced by Reanalysis and Remote Sensing Data

SiteModel NPP (kg-C m -2 yr -1 ) Observed Value Simulated Value Relative Error Manaus (long-term)CCM3-IBIS % Flona Tapajós (long-term)CCM3-IBIS % Caxiuanã (longo-term)CCM3-IBIS % Average relative error % Average |relative error|––16.7% Coupled climate-biosphere model for present climate

6. Summary and Conclusions Group of models |Average relative error| (%) Average |relative error| (%) Maximum |relative error| (%) Models forced by meteorological station data Models forced by reanalysis and remote sensing data Coupled biosphere- atmosphere model Expected error for regional estimates Expected error for site estimates Maximum expected error for site estimates

1.|Average relative error| is very similar in all cases, varying between 3.3% and 4.7% This is probably closer to the minimum limit of this error, defined by the uncertainties in the meteorological and NPP data. This means that state-of-the-art NPP models are able to simulate the average NPP of a group of points, no matter whether they run in diagnostic or prognostic mode. This translates in a strong confidence in these models for simulation of average regional values.

2.Average |relative error| increases as model becomes less dependent on local observations. When models are forced by meteorological station data, average error is 6.6%, increasing to 8.9% when model is forced by reanalysis and remote sensing data, reaching 16.7% in the coupled climate-biosphere model. These values indicate that state-of-the-art models have na expected error at each site smaller than 10% in the diagnostic mode, and smaller than 20% in the prognostic mode.

3.Maximum |relative error| behaves similar to average |relative error|, growing as model becomes less dependent on local observations. When models are forced by meteorological station data, average error is 11.1%, increasing to 19.0% when model is forced by reanalysis and remote sensing data, reaching 21.8% in the coupled climate-biosphere model.

Overall conclusion State-of-the-art NPP models are capable of doing unbiased estimates of average regional values, both in diagnostic and prognostic mode, with errors smaller than 5%, while the expected error at each site is smaller than 10% in diagnostic mode and smaller than 20% in prognostic mode.