Data Model Intercomparison of Ecosystem Carbon & Water Surface Fluxes Across Amazonia
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1 Data Model Intercomparison of Ecosystem Carbon & Water Surface Fluxes Across Amazonia Bradley Christoffersen University of Arizona Organizers: Scott R. Saleska, Inez Fung, Luis Gustavo G. de Gonçalves, Humberto da Rocha Flux Tower PIs: Alessandro Araújo, Bart Kruijt, Antonio Manzi, Celso von Randow, Humberto da Rocha, Scott R. Saleska Modelers & Analysis Team: Michel N. Muza, Ian Baker, Marcos Costa, Hewlley Imbuziero, Ben Poulter, Koichi Sakaguchi, Natalia Restrepo Coupe, Xubin Zeng
2 Tapajos km67 Moist Forest Site Photo: B. Christoffersen
3 Tapajos km67 Moist Forest Site Photo: B. Christoffersen Tapajos km77 Agriculture Site Photo: B. Christoffersen
4 Motivation 25 5% of precipitation in Amazon originates from transpired water through leaf stomata. Amazon is a locus for potential positive feedback to climate change. (Eltahir & Bras 1994; Betts et al. 24)
5 Motivation 25 5% of precipitation in Amazon originates from transpired water through leaf stomata. Amazon is a locus for potential positive feedback to climate change. (Eltahir & Bras 1994; Betts et al. 24) Drought frequency Stomatal closure
6 Motivation 25 5% of precipitation in Amazon originates from transpired water through leaf stomata. Amazon is a locus for potential positive feedback to climate change. (Eltahir & Bras 1994; Betts et al. 24) Drought frequency Stomatal closure
7 Motivation 25 5% of precipitation in Amazon originates from transpired water through leaf stomata. Amazon is a locus for potential positive feedback to climate change. (Eltahir & Bras 1994; Betts et al. 24) Drought frequency Stomatal closure Forest dieback
8 Motivation 25 5% of precipitation in Amazon originates from transpired water through leaf stomata. Amazon is a locus for potential positive feedback to climate change. (Eltahir & Bras 1994; Betts et al. 24) Drought frequency Rising atmospheric CO 2 Stomatal closure Forest dieback
9 Motivation 25 5% of precipitation in Amazon originates from transpired water through leaf stomata. Amazon is a locus for potential positive feedback to climate change. (Eltahir & Bras 1994; Betts et al. 24) Land surface models have typically misrepresented dry season ecosystem metabolism across the seasonally dry Amazon (Shuttleworth 1991, Bonan et al. 1998, Dickinson et al. 26) Drought frequency Rising atmospheric CO 2 Stomatal closure Forest dieback
10 Misrepresentation of dry season ecosystem metabolism Evapotranspiration (ET) 1 site Model Model Data Shuttleworth (1991)
11 Misrepresentation of dry season ecosystem metabolism Evapotranspiration 1 site Modeled Evapotranspiration basinwide dry season W m -2 Dickinson et al. (26) Shuttleworth (1991)
12 Motivation 25 5% of precipitation in Amazon originates from transpired water through leaf stomata. Amazon is a locus for potential positive feedback to climate change. (Eltahir & Bras 1994; Betts et al. 24) Land surface models have typically predicted water limited control on ET across the seasonally dry Amazon (Shuttleworth 1991, Bonan et al. 1998, Dickinson et al. 26) Recent eddy tower syntheses show little evidence for water limited evapotranspiration (ET) in the Amazon (Hasler and Avissar 27, Juarez et al. 27, Fisher et al. 29) Drought frequency Rising atmospheric CO 2 Stomatal closure Forest dieback
13 Motivation 25 5% of precipitation in Amazon originates from transpired water through leaf stomata. Amazon is a locus for potential positive feedback to climate change. (Eltahir & Bras 1994; Betts et al. 24) Land surface models have typically predicted water limited control on ET across the seasonally dry Amazon (Shuttleworth 1991, Bonan et al. 1998, Dickinson et al. 26) Recent eddy tower syntheses show little evidence for water limited evapotranspiration (ET) in the Amazon (Hasler and Avissar 27, Juarez et al. 27, Fisher et al. 29) Drought frequency Rising atmospheric CO 2 Stomatal closure Question: How realistic are the simulated water & carbon fluxes across the Amazon basin by land surface models of varying complexity? Forest dieback
14 The LBA Data Model Intercomparison Project Identify model mechanisms leading to deficiencies Identify mechanisms necessary for good performance Discriminate among realistic / unrealistic mechanisms (get the right answer for the right reason) > 1 participating models across > 1 research groups Ecuador Mean Annual Precipitation Peru Colombia Venezuela Kilometers $ K34 K83 CAX $ $ $$ $ RJA FNS Bolivia Guyana Surinam K67 K77 French Guyana $ JAV PDG $ Brasil Map design: N. Restrepo-Coupe
15 Methodology Flux tower meteorology checked for outliers and gap filled, 1 hour timestep SW/LW radiation, precip, temp, relative humidity, wind speed, pressure ~3 years of data per site Site characteristics provided Soil texture classes, dominant vegetation functional type, seasonal leaf area indices (LAI) Specifications for model spinup Until inter annual deviation in mean monthly soil moisture < 1% Equilibrium soil carbon NCAR Community Land Model (CLM): Focal model for improvements to representation of roots
16 Fazenda Nossa Senhora (FNS) Pasture Rebio Jaru (RJA) Semideciduous Pe de Gigante (PDG) Savanna Rio Javaes (BAN) Seasonally Flooded Forest Credit: R.G. Aguiar Credit: H.R. da Rocha Credit: Credit: A. Steele Colombia Venezuela Guyana Surinam French Guyana Ecuador K34 K83 CAX $ $ $$ $ K67 K77 Brasil $ RJA FNS $ JAV Peru Kilometers Bolivia Manaus (K34) Wet Forest PDG $ Tapajos (K67) Moist Forest Tapajos (K77) Agriculture Tapajos (K83) Logged Forest Credit: Credit: B. Christoffersen Credit: B. Christoffersen Credit: M. Litvak
17 Southern Amazon (Semi-Deciduous) Jaru Equatorial Amazon (Evergreen) Tapajos Outside Amazon (Deciduous Savanna) Pe-de Gigante Colombia Venezuela Guyana Surinam French Guyana Ecuador K34 K83 CAX $ $ $$ $ K67 K77 Brasil $ RJA FNS $ JAV Peru Kilometers Bolivia PDG $ Map design: N. Restrepo-Coupe
18 Specific Questions Ecosystem water use: What are the seasonal drivers of evapotranspiration (ET)? Belowground mechanisms: How does soil moisture storage capacity and representation of roots influence model performance? Ecosystem level metabolism (gross productivity; GPP) How well do models capture observed magnitude of GPP? How well do models capture seasonality of light use efficiency (LUE)? How is leaf phenology related to seasonality of carbon exchange?
19 Ecosystem water use: K67 mm/month depth (m) (seasonal moist tropical forest) Observations IBIS CLM3. CLM3.5 Noah LE W/m Net radiation (w /m2) Net Radiation (W/m2)
20 Ecosystem water use: K67 mm/month depth (m) (seasonal moist tropical forest) Observations IBIS CLM3. CLM3.5 Noah Precipitation (mm/month) Net radiation (mm/month water equivalent) ET (mm/month) Soil moisture (normalized to maximum) LE W/m Net radiation (w /m2) Red: Dry season ET Blue: Wet season ET Net Radiation (W/m2)
21 Ecosystem water use: K67 mm/month depth (m) (seasonal moist tropical forest) Observations IBIS CLM3. CLM3.5 Noah ET peaks in the dry season Deep drying & root water uptake LE W/m Net radiation (w /m2) Available energy explains 86% of variation in ET Net Radiation (W/m2)
22 Ecosystem water use: K67 mm/month depth (m) (seasonal moist tropical forest) Observations IBIS CLM3. CLM3.5 Noah LE W/m Net Radiation (W/m2)
23 Ecosystem water use: K67 mm/month depth (m) (seasonal moist tropical forest) Observations IBIS 8m deep CLM3. Deficient CLM3.5 Improved Noah roots hydrology hydrology Encouraging: Some models capture ET seasonal pattern Release of moisture stress; available energy controls ET LE W/m Net Radiation (W/m2)
24 Ecosystem water use: RJA mm/month Observations IBIS CLM3. CLM3.5 Noah depth (m) (seasonal transitional tropical forest) ET constant throughout dry season Contemporary soil moisture obs N/A LE W/m Net radiation (w /m2) Available energy explains 8% of variation in ET Net Radiation (W/m2)
25 mm/month Ecosystem water use: RJA depth (m) (seasonal transitional tropical forest) Observations IBIS 8m deep CLM3. Deficient CLM3.5 Improved Noah roots hydrology hydrology Release of moisture stress may overfix ET (overestimate) LE W/m Net Radiation (W/m2)
26 mm/month Ecosystem water use: PDG depth (m) (cerrado / savanna) Observations IBIS CLM3. CLM3.5 Noah ET trough in the dry season Soil moisture observations N/A LE W/m Net radiation (w /m2) Available energy explains only 34% of variation in ET Net Radiation (W/m2)
27 mm/month Ecosystem water use: PDG depth (m) (cerrado / savanna) Observations IBIS 4m deep CLM3. Deficient CLM3.5 Improved Noah roots hydrology hydrology ET seasonal pattern well captured by most models Some models still overestimate (soil too wet?) LE W/m Net Radiation (W/m2)
28 Seasonal Amplitude in Soil Moisture (mm) Soil moisture storage capacity and net radiation control of ET R2 of ET Net radiation relationship Obs Obs
29 Ecosystem metabolism (GPP) Observations IBIS CLM3. CLM3.5 Noah GPP (umol C m 2 s 1) GPP (umol C m 2 s 1) GPP (umol C m 2 s 1) K RJA PDG
30 Ecosystem metabolism (GPP) Observations IBIS CLM3. CLM3.5 Noah GPP (umol C m 2 s 1) GPP (umol C m 2 s 1) GPP (umol C m 2 s 1) K RJA PDG
31 Ecosystem metabolism (GPP) Observations IBIS CLM3. CLM3.5 Noah GPP (umol C m 2 s 1) GPP (umol C m 2 s 1) GPP (umol C m 2 s 1) K RJA PDG
32 Ecosystem metabolism (GPP) Observations IBIS CLM3. CLM3.5 Noah GPP (umol C m 2 s 1) GPP (umol C m 2 s 1) GPP (umol C m 2 s 1) K RJA PDG Reduced water stress in some models highlights deficiencies in representation of photosynthesis (large overestimation!)
33 month Seasonality of carbon exchange: Moist forest site (km67) Light use efficiency (LUE) 25 2 LUE
34 month Seasonality of carbon exchange: Moist forest site (km67) Light use efficiency (LUE) 25 2 LUE Coincident with leaf flush SPA captures this pattern well Prognostic leaf phenology still under development in most models
35 Summary & Conclusions Here, I explored: mechanisms leading to deficiencies, mechanisms necessary for good performance Ecosystem water use Deep roots: provides a fix in the right direction Seasonal soil moisture dynamics: capturing observed seasonal amplitude most important Ecosystem metabolism Release of water stress in some models highlights deficient leaf to ecosystem scaling of carbon flux Leaf phenology: essential for capturing seasonality of carbon flux
36 Ongoing & Future Work mechanisms leading to deficiencies, mechanisms necessary for good performance Modeling study of deep soil, deep roots, and root hydraulic redistribution in CLM4. (improved hydrology) Integrated assessment of leaf to canopy scaling schemes (across models) on both water & carbon exchange Assessment of model performance under simulated drought Discriminate among realistic / unrealistic mechanisms (get the right answer for the right reason) Belowground datasets needed to discriminate among model mechanisms: Vertical distributions of root water uptake (water balance & stable isotopes) Fine root phenology (minirhizotrons)
37 Thanks! DOE GCEP: Funding, research & travel support NSF Amazon PIRE at UofA: research support Faculty advisor: Scott Saleska DOE Mentor: Inez Fung Koichi Sakaguchi (CLM runs) LBA DMIP team (model runs, assistance in collating model runs) Natalia Restrepo Coupe (BrasilFlux database)
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