# Presentation: 793914

## High Spatial and Temporal Resolution Ecosystem CO2, Latent Heat, and Sensible Heat Fluxes in the SGP: Tools for Cloud Models, CLASIC, and NACP

**High Spatial and Temporal Resolution Ecosystem CO****2****, Latent Heat, and Sensible Heat Fluxes in the SGP: Tools for Cloud Models, CLASIC, and NACP**
**William J. Riley****1 ****(wjriley@lbl.gov****),Sébastien C. Biraud****1****, Marc L. Fischer****1****, Margaret S. Torn****1****, Joseph A. Berry****2**
**1: Earth Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA**
**2: Dept. Global Ecology,**** ****Carnegie Institution of Washington, Stanford, CA**

**INTRODUCTION**

**RESULTS**

**CONCLUSIONS**

**REFERENCES**

- 	Estimating spatially distributed and temporally resolved ecosystem fluxes is important for ARM cloud modeling, the 2007 CLASIC experiment, and for the North American Carbon Program.  Because the land surface is very spatially heterogeneous in the ACRF (LAI and vegetation cover change on scales of about 200 m), characterizing vegetation type and characteristics is critical to accurately estimating CO2 and energy surface exchanges with the atmosphere. We describe here a methodology to estimate surface energy fluxes and net ecosystem CO2 Exchange (NEE) continuously over the Southern Great Plains, using (1) meteorological forcing data from Mesonet facilities; (2) the USGS soil database; (3) NEXRAD 4 km precipitation estimates; (4) eddy covariance data from several sites over several years in the ACRF; and (6) a thoroughly tested land-surface model (ISOLSM). This approach allows us to estimate fluxes in periods and areas where meteorological forcing data are unavailable.
- 	In this poster we present our method, compare site level model estimates of CO2 and energy exchanges, compare regional model predictions to measurements made with eddy correlation flux systems, and discuss estimates of regional means and variability.

**ACKNOWLEDGEMENTS**

- Aranibar, J.N., J.A. Berry, W.J. Riley, D.R. Bowling, J.R. Ehleringer, D.E. Pataki, and B.E. Law (2005) Modeling environmental controls of carbon isotope discrimination, carbon, and energy fluxes at the canopy scale in a semi-arid pine forest, 12, 710–730, doi: 10.1111/j.1365-2486.2006.01121.x, *Global Change Biology.*
- Billesbach, D. P., M. L. Fischer, M. S. Torn and J. A. Berry. A Portable Eddy Covariance System for the Measurement of Ecosystem-Atmosphere Exchange of CO2, Water Vapor, and Energy. Journal of Atmospheric and Oceanic Technology 21(4): 639-650, 2004.** **
- Bonan, G. B., A land surface model (LSM version 1.0) for ecological, hydrological, and atmospheric studies: Technical description and user's guide, pp. 150, NCAR, Boulder, CO, 1996.
- Bonan, G. B., K. J. Davis, D. Baldocchi, D. Fitzgerald, and H. Neumann, Comparison of the NCAR LSM I land surface model with BOREAS aspen and jack pine tower fluxes, Journal of Geophysical Research, 102 (C12), 29,065-29,076, 1997.
- Christensen, L., W.J. Riley, and I. Ortiz-Monasterio (2006) Nitrogen cycling in an irrigated wheat system in Sonora, Mexico: Measurements and modeling, DOI 10.1007/s10705-006-9025-y, *Nutrient Cycling in Agroecosystems*.
- Cooley, H.S., W.J. Riley, M.S. Torn, and Y. He, (2005) Effect of harvest on regional climate and soil moisture and temperature, **110**, D03113, doi:10.1029/2004JD005160, *JGR – Atmospheres.*
- Doran, J. C., J. M. Hubbe, J. C. Liljegren, W. J. Shaw, G. J. Collatz, D. R. Cook, R. L. Hart. A technique for determining the spatial and temporal distributions of surface fluxes of heat and moisture over the Southern Great Plains Cloud and Radiation Testbed. Journal of Geophysical Research-Atmospheres 103(D6): 6109-6121, 1998.
- Henderson-Sellers, A., M. Fischer, K. McGuffie, W.J. Riley, G. Schmidt, K. Sturm, K. Yoshimura (2006) Stable Water Isotope Simulation by Current Land-surface Schemes: Results of iPILPS Phase 1, 51, 34-58, doi:10.1016/j.gloplacha.2006.01.003, *Global and Planetary Change*.
- Lai, C., W.J. Riley, C. Owensby, J. Ham, A. Schauer, and J. R. Ehleringer (2006) Seasonal and interannual variations of carbon and oxygen isotopes of respired CO2 in a tallgrass prairie: Measurements and modeling results from 3 years with contrasting water availability, 111, D08S06, doi:10.1029/2005JD006436, *J. Geophys. Res.*
- Riley, W.J., C.J. Still, M.S. Torn, and J.A. Berry (2002) A mechanistic model of H218O and C18OO fluxes between ecosystems and the atmosphere:  Model description and sensitivity analyses, *Global* *Biogeochemical Cycles, 16, 1095-1109.*
- Riley, W.J., C.J. Still, B.R. Helliker, M. Ribas-Carbo, and J.A. Berry (2003) Measured and modeled 18O in CO2 and H2O above a tallgrass prairie, **9**, 1567-1581, *Global Change Biology*
- Riley, W.J., J.T. Randerson, P.N. Foster, and T.J. Lueker (2005) The influence of terrestrial ecosystems and topography on coastal CO2 measurements: A case study at Trinidad Head, California, *JGR-Biogeosciences*, No. G1, G01005, 10.1029/2004JG000007
- Riley, W.J. (2005) A Modeling Study of the Impact of the δ18O Value of Near-Surface Soil Water on the δ18O Value of the Soil-Surface CO2 Flux, **69**(8), 1939–1946, *Geochimica et Cosmochimica Acta.*
- Still, C.J., W.J. Riley, B.A. Helliker, and J.A. Berry (2005) Simulation of ecosystem oxygen‐18 CO2 isotope fluxes in a tallgrass prairie: Biological and physical controls, In Stable Isotopes and Biosphere-Atmosphere Interactions (Eds. Flanagan, L.B., Ehleringer, J.R. & D.E. Pataki). Elsevier-Academic Press, Physiological Ecology Series.

- This work was supported by the ARM Program, U.S. Department of Energy Office of Science, Biological and Environmental Research Program, under Agreement DE-FC03-90ER61010.

**ISOLSM Land Surface Model**:
- 	ISOLSM (developed from LSM1.0, Bonan, 1996, 1997) is a “big-leaf” land surface model that simulates CO2, H2O, energy and isotope (e.g., C18OO, HDO, H218O) fluxes between ecosystems and the atmosphere. ISOLSM simulates aboveground fluxes of radiation, momentum, sensible heat, and latent heat; energy and water fluxes below ground; and coupled CO2, H2O, and isotope exchange between soil, plants, and atmosphere. Soil hydraulic characteristics are determined from sand, silt, and clay content. We modified the surface types in ISOLSM to correspond to the dominant land cover in the ARM-SGP domain, and calibrated the model to these land covers using our eddy covariance measurements over several years. ISOLSM has been applied in a number of studies in the ACRF (Riley et al., (2002, 2003); Cooley et al. (2005); Riley (2005); Lai et al. (2006); Still et al. (2005, 2007)) and in other sites and regions (Henderson-Sellers et al. (2006); Aranibar et al. (2006), Riley et al. (2005)).
- 	We used 250 m MODIS NDVI data to estimate LAI using the approach of Sellers et al. (1996). Vegetation cover type was estimated using archetypal LAI profiles for each vegetation type and the MODIS data. Average percent vegetation cover for four quadrants (NW, NE, SW, and SE) in the ACRF determined from the MODIS NDVI compared well with the 2002 Census data (Figure 3), and captured the large spatial variability in C3 winter crop versus pasture prevalence. To ensure that the simulations are tractable computationally, we sub-sample ten of each vegetation type cells per user-defined “macrocell” (here 10 km).

- 	We incorporate meteorological data from distributed Oklahoma and Kansas Mesonet sites (Doran et al., 1998), satellite derived vegetation cover and characteristics, and NEXRAD precipitation estimates into a distributed Land Surface Model (ISOLSM, Riley et al. (2002, 2003)) of fluxes between ecosystems and the atmosphere. The meteorological datasets are compiled by ARM and the MODIS satellite data are available from NASA.
**Interpolation of Climate Measurements:**
**	**We apply the approach of Doran et al. (1998) to generate the model climate forcing (wind speed, downward solar radiation, and air temperature, pressure, and humidity) from the Mesonet data. The interpolation procedure provides the climate forcing for ISOLSM at a user-specified resolution. Given the spacing of the Mesonet stations, the minimum justifiable resolution is about 10 km, which we use in the results shown here. For example, Figure 1 shows the result of the interpolation for air temperature. Note that the density of stations in KS is much lower than in the OK portion of the ACRF. We calculated precipitation inputs from the 4 km NEXRAD production (Figure 2).

_***Figure 1***_*: Interpolated air temperature field over the ARM-SGP domain on April 3, 2003 at 9:00 UT. Open circles show the location of Mesonet platforms and filled square**** ****shows the**** ****ARM Central Facility.*

**MATERIALS & METHODS**

_***Figure 5:***_* Time series of measured and simulated NEE of wheat (April 2003), pasture, and sorghum (June 2003). Red lines and blue dots correspond to simulated and measured CO**2** fluxes, respectively.*

_***Figure 4***_*: Comparison of interpolated (red line) and measured (blue dots): (a) net radiation; (b) sensible heat; and (c) latent heat.*

**Comparing climate interpolation with ECOR measurements:**
- 	Figure 3 shows interpolated (from the Mesonet data) and measured relative humidity, air temperature and wind speed at one wheat field in April 2003. The interpolated values are in good agreement with independent measurements (i.e. from locations not used in the interpolation). Similarly good agreement was obtained for our other eddy flux sites.

- 	Figure 5 shows a comparison between simulated and measured net ecosystem CO2 flux for 3 crop types (wheat, pasture, and sorghum). We calibrated the model for each vegetation type using a 2 approach with the maximum carboxylation rate (Vmax) and soil organic matter content.
- 	Midday NEE in April wheat was underestimated for the first two weeks. Estimates during the remainder of the month were good. NEE predictions in June for the pasture were accurate; peak uptake was approximately uniform across the month. Estimates in June for the sorghum field underestimated NEE for the first two weeks, and then accurately captured the decrease in NEE as the plants senesced. Overall, NEE during the nighttime was more accurately predicted than during the daytime. This result occurs because of the relative simplicity of mechanisms operating at night versus day.

**Regional Mean Ecosystem Exchanges**
**	** Because we sub-sample the 250 m pixels across the ACRF, regional estimates are made by combining (as mean and SD) estimates for each vegetation type, and then scaling to the 10 km grid using the relative prevalence of each vegetation type. Figure 5 shows mean and SD NEE predictions for a three-week period in early summer.

- The surface energy balance (latent heat, sensible heat, long-wave radiation) varied greatly over the domain, depending on the type of vegetation and its phenology.
- Characterizing vegetation type using archetypal LAI profiles and MODIS data resulted in good predictions as compared to census county level data.
-  Comparisons were good between interpolated and independently measured climate forcing
-  Comparisons were good between predicted and measured CO2, latent heat, and sensible heat fluxes at our portable eddy covariance sites.
- The forcing fields for the modeling work will be made available via the ARM website.

**Eddy flux measurements**:
- 	We compared simulated and ongoing measurements of land surface-atmosphere exchanges of CO2 and energy as well as ancillary data from pasture (C3/C4 grass mix), wheat (winter C3 crop), and sorghum (C4 crop) fields, which are the dominant crop types in the ARM-SGP domain (Fischer et al., 2007). These measurements are made with portable systems comprised of a sonic anemometer, an open-path infrared gas analyzer (IRGA), and a set of meteorological instruments that monitor net and photo-synthetically active radiation, air temperature, relative humidity, precipitation, soil heat flux, and soil moisture and temperature.  The measurement height is 4 m above the ground, allowing a minimum of ~3 m between the top of the canopy and the IRGA and sonic anemometer for crops included in this study.
- (see *http://www.arm.gov/instruments/carbon.stm* for measurement description).

_***Figure 6:***_* Regional mean (blue) NEE across the ARM-SGP domain  in June 2003. Red line corresponds to plus or minus one standard deviation around mean NEE.*

- 	For June, 2003, regional mean NEE varied between -15 and +5   mol m-2 s-1** **diurnally (Figure 6). Variations in midday NEE result from differences in meteorological forcing, consequent soil moisture, and vegetation characteristics. Spatial heterogeneity in NEE was largest midday (up to 20 mol m-2 s-1** **) and smaller at night (~2 mol m-2 s-1** **). In June, a majority of the cumulative carbon uptake was by C4 crops and pasture. The other vegetation types did not contribute substantially  to cumulative uptake. During April, the majority of the uptake over the region was by winter C3 crops (i.e., winter wheat).
- 	Comparable large variation across the ACRF were also seen in latent and sensible heat fluxes, implying that estimating regional exchanges requires accurate characterization of spatial heterogeneity in vegetation type, vegetation characteristics, and meteorological forcing.

**Simulated and measured NEE:**

_***Figure 2***_*: *Precipitation varies strongly from South to North, with about double the precipitation falling in the Southeast as compared to the Northwest.

_**Figure 3.**_** ***Predicted and USDA Census data for vegetation cover for the four quadrants surrounding the CF** *