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Keywords:

  • land surface modeling;
  • Simple Biosphere;
  • Biosphere 2;
  • tropical rain forest;
  • net ecosystem exchange;
  • temperature acclimation

Abstract

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Biosphere 2 Tropical Rain Forest Biome
  5. 3. Data
  6. 4. Version 3 of the Simple Biosphere Land Surface Model
  7. 5. Results
  8. 6. Discussion
  9. 7. Summary and Conclusions
  10. Appendix A:: Contributions to Reduction in Shortwave Radiation in B2-TRF
  11. Acknowledgments
  12. References
  13. Supporting Information

[1] Tropical rain forests contribute substantially to regional and global energy, water, and carbon exchanges between the land surface and the atmosphere, and better understanding of the mechanisms of vegetation response to different environmental stresses is needed. The Biosphere 2 facility provides an opportunity to link laboratory-scale and plot-scale studies in a controllable environment. We compiled a consistent quality-controlled time series of climate data from Biosphere 2 and used it to drive the Simple Biosphere model (SiB3) to test how well it represented the behavior of soils and vegetation inside the tropical rain forest biome of Biosphere 2 (B2-TRF). We found that soil respiration parameterization in SiB3 was not suitable for use in B2-TRF, so several alternative parameterizations were tested. None gave outstanding results, but a modified version of the parameterization originally proposed for SiB3 gave the best results. With this modification, SiB3 well simulated the observed net ecosystem exchange in B2-TRF but, significantly, only after additionally modifying parameters describing the thermal tolerance of plants so that photosynthetic capacity was reduced on average but maintained to higher temperatures. This implies either that tropical rain forest species can acclimate to higher temperatures than allowed for by vegetation models or that the plant community assembly in B2-TRF has shifted to allow continued functioning at higher temperatures, and plants in natural ecosystems could also. In either case, this suggests that the Amazon rain forest may be more resilient to climate change than hitherto thought.

1. Introduction

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Biosphere 2 Tropical Rain Forest Biome
  5. 3. Data
  6. 4. Version 3 of the Simple Biosphere Land Surface Model
  7. 5. Results
  8. 6. Discussion
  9. 7. Summary and Conclusions
  10. Appendix A:: Contributions to Reduction in Shortwave Radiation in B2-TRF
  11. Acknowledgments
  12. References
  13. Supporting Information

[2] Tropical forests account for about half the world's forests and hold about as much carbon as all other terrestrial areas combined [Houghton, 2005]. The Amazon rain forest, in particular, is known to play a major role in the annual variability of carbon uptake by the terrestrial biosphere. Such variability may be enhanced by increased atmospheric CO2 [Tian et al., 2000; Potter et al., 2001] or limited by water availability in the dry season and/or during El Niño events [Tian et al., 1998; Botta et al., 2002]. Despite improvements in ecosystem modeling over the last few decades, uncertainty remains large when estimating carbon fluxes in Amazonia [Miller et al., 2004; Saleska et al., 2003] because aspects of the soil-vegetation-atmosphere interaction (including soil water dynamics, CO2 uptake during the dry season, and their interrelationship) remain poorly defined locally [Bruno et al., 2006; Saleska et al., 2003] and regionally [Xiao et al., 2006; Saleska et al., 2007; Myneni et al., 2007, Samanta et al., 2010].

[3] Biosphere 2 is a large-scale Earth science facility near Tucson (Arizona, USA) which houses five natural biomes [Nelson et al., 1993], including a tropical rain forest mesocosm with an area of about 1900 m2 that contains plant species from different tropical regions of South and Central America [Leigh et al., 1999]. Engineering aspects of this closed facility have been extensively discussed in the past [e.g., Allen and Nelson, 1999; Dempster, 1999; Zabel et al., 1999], and this discussion is not reproduced here. Sealed off from the outside world [Osmond et al., 2004], Biosphere 2 provides a unique controlled laboratory for carrying out experiments to investigate rain forest biome behavior in response to imposed stresses at plot scale, thereby providing a link between the laboratory scale and the real world.

[4] Short-term drought and CO2 enrichment experiments have, for example, been successfully conducted on tropical forest vegetation inside the Biosphere 2 facility. These showed that net carbon assimilation is inhibited when drought conditions are sustained for 4–6 weeks but fully recovers after rewetting [Rascher et al., 2004], while CO2 enrichment enhances net carbon assimilation by an amount that depends on the radiation available and so changes between winter and summer at this midlatitude location [Lin et al., 1998, 1999]. Such studies arguably complement experiments in natural ecosystems such as throughfall exclusion experiments [Nepstad et al., 2002] and Free-Air CO2 Enrichment (FACE) experiments [Norby et al., 2002; Nowak et al., 2004] in which field implementation is complicated by the need to maintain a sample of vegetation that is open to the atmosphere [Clark, 2004].

[5] A few experiments have sought to assess the effects of warming on mature tropical forest trees [e.g., Doughty and Goulden, 2008], some by combining modeling with field data [e.g., Lloyd and Farquhar, 2008]. Such studies have suggested that tropical forests may be highly sensitive to temperature [Clark et al., 2003], and because such ecosystems already operate at relatively high temperatures, they may be among the first class of vegetation to show negative impacts in response to atmospheric warming [Clark, 2004]. However, acclimatization may modify the responses of the tropical rain forest ecosystem as temperature increases [Amthor and Baldocchi, 2001; Baldocchi and Amthor, 2001]. Clark [2004] concludes that available field data are inadequate to reach a firm conclusion on this topic, indicating that taking advantage of the unique characteristics of Biosphere 2 to reduce some of the uncertainties encountered in natural ecosystems is appropriate.

[6] Although environmental conditions in Biosphere 2 can be controlled to assess vegetation response to defined stress conditions [e.g., Adams et al., 2009] and to evaluate the interactions between different ecosystems [Huxman et al., 2009], lack of repetitions poses limitations on the analysis of experimental results (the facility contains only one mesocosm of each biome). Integration of experiments and modeling has been shown to advance scientific knowledge in the earth sciences [e.g., Sellers et al., 1997], and use of a well-established land surface parameterization scheme may mitigate the lack of repetitions in Biosphere 2 by providing a reliable assessment of a specific biome under a variety of conditions, including simulation of imposed perturbations.

[7] Land surface models seek to describe biosphere-atmosphere interactions by implementing parameterizations that simulate the different exchanges (momentum, energy, water, and biogeochemical gases) in the soil-plant-atmosphere continuum [Sellers et al., 1996a]; the form of these parameterizations is sometimes calibrated or validated against real-world field data. However, surface flux observations over natural ecosystems are prone to measurement errors. The eddy covariance measurement method is most accurate under steady atmospheric conditions over homogeneous vegetation on flat terrain [Baldocchi, 2003], conditions which are not always met, and nighttime fluxes are particularly prone to error. Any uncertainty in measured fluxes will be transferred into land surface models through calibration and validation. The tropical rain forest biome of Biosphere 2 (B2-TRF) has the positive aspects of a cuvette or chamber, including the ability to measure the diurnal variation in carbon flux and its response to imposed environmental conditions but applied at a much larger scale than conventional chambers, and in B2-TRF unmeasured advection is practically nonexistent. Arguably, the net carbon flux deduced using this large cuvette may be used to better test and validate models.

[8] In this study we take a land surface parameterization scheme which has been widely applied in natural ecosystems, namely version 3 of the Simple Biosphere model (SiB3) [Baker et al., 2003, 2008], and we challenge it to simulate the main aspects of the biosphere-atmosphere exchanges inside the Biosphere 2 tropical rain forest biome (B2-TRF). In order to run and analyze the model results, a comprehensive, high-quality set of meteorological forcing data were first produced for B2-TRF. Using this, model simulations were then made which include B2-TRF operating under normal operational conditions and also when subject to short-term experimental perturbations, such as imposed drought conditions and/or exposure to different atmospheric CO2 concentrations. The main objectives of this study are (1) to provide a high-quality database of the atmospheric forcing variables needed for land surface modeling inside B2-TRF, (2) to investigate whether SiB3 is capable of representing the vegetation's response in the B2-TRF controlled environment and, assuming it is capable of providing such representation, (3) to use SiB3 to assess the responses of the tropical rain forest vegetation inside B2-TRF to potential environmental stress.

2. Biosphere 2 Tropical Rain Forest Biome

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Biosphere 2 Tropical Rain Forest Biome
  5. 3. Data
  6. 4. Version 3 of the Simple Biosphere Land Surface Model
  7. 5. Results
  8. 6. Discussion
  9. 7. Summary and Conclusions
  10. Appendix A:: Contributions to Reduction in Shortwave Radiation in B2-TRF
  11. Acknowledgments
  12. References
  13. Supporting Information

[9] The tropical rain forest (B2-TRF) biome is one of the five original ecological ecosystems built in Biosphere 2 (B2) in the early 1990s. In the biome Amazonian plant species from Brazil and Venezuela dominate species found in other rain forests such as in Puerto Rico and Belize [Leigh et al., 1999]. The total area of the rain forest biome is ∼1936 m2, and at its highest point the roof is approximately 27 m above the lowest soil level in the biome [Arain et al., 2000]. The total volume of B2-TRF is 35,000 m3, which is ∼17% of the total volume of B2 as a whole. The climate (rainfall, temperature, and humidity) inside B2-TRF is controlled to be broadly comparable to conditions in natural rain forest.

[10] Inside B2-TRF the temperature is regulated by air handlers located in the basement of B2 which also create air movement. Tree growth is restricted to about 15 m by temperature stress due to the presence of a strong daytime inversion in temperature in the biome at this height which inhibits cooling at higher levels [Arain et al., 2000]. The structure is constructed of sealed glass and a supporting space frame, both of which reduce the amount of incoming solar radiation [Arain et al., 2000], as discussed in more detail later. The enclosed glass structure also prevents UV (UV-A and UV-B) radiation from reaching the inside of B2-TRF. For a detailed discussion about the lack of UV radiation in the Biosphere 2 facility, please refer to Cockell et al. [2000].

[11] Arain et al. [2000] compared the meteorological characteristics in B2-TRF with three Amazonian sites, i.e., near Manaus and in the Brazilian states of Rondônia and Pará. They found substantial differences in the seasonal cycle of solar radiation because B2-TRF is located at midlatitude rather than in the tropics. The air temperature in B2-TRF increases substantially during the Northern Hemisphere summer, and as a result, there is also significant seasonality in vapor pressure deficit (VPD). In addition, the glass sealed space frame greatly reduces incoming solar radiation so that only about half reaches inside B2-TRF. Because of this, monthly average solar radiation during the Northern Hemisphere summer is comparable (but still lower) to what is observed in Amazonia during the wet period when the latter is reduced by the greater cloud cover. Monthly average air temperature inside B2-TRF during the Northern Hemisphere winter is similar to that during the dry season in Amazonia, but it is much higher (5°C–7°C higher) in the Northern Hemisphere summer (the yearly cycle in Amazonian air temperature is small), and at this time of year VPD can be 3 times higher in B2-TRF (∼1.5 kPa compared to ∼0.5 kPa).

[12] Diurnal variations inside B2-TRF also differ greatly from those in the Amazon basin. According to Arain et al. [2000], during the Northern Hemisphere winter there is a much less pronounced diurnal variation in incoming solar radiation compared to during the equatorial solstice in the Amazon. However, in the Northern Hemisphere summer the reduction in radiation caused by the space frame compensates and the amount of radiation inside B2-TRF has a diurnal variation similar to that observed in the Amazon during the rainy season. However, the same is not true for air temperature and VPD. Although the diurnal variations in these variables during the Northern Hemisphere winter are comparable to that in the Amazon in the dry season, during the Northern Hemisphere summer the diurnal amplitude of air temperature and VPD is much higher relative to Amazonia. In the Amazon basin air temperature has a diurnal amplitude of about 5°C compared to ∼15°C in B2-TRF. While VPD in the Amazon ranges from near zero to about 1.5 kPa in the early afternoon, in B2-TRF this range is from ∼0.5 to ∼3.5 kPa.

[13] An interesting feature in B2-TRF is the occurrence of an inversion layer above the mean canopy level which decouples the air above from that below. Hence, there are two very distinct temperature environments during the day. The air is comparatively cool and reasonably well mixed below 10 m (with a temperature broadly similar to that in the Amazon rain forest), with hot, stable air above the canopy level. Arain et al. [2000] observed very little turbulence inside B2-TRF except near the base of the canopy, which suggests the transfer of energy and mass more likely occurs by mass flow rather than by turbulent mixing. The observed range of carbon dioxide concentration inside B2-TRF is also greater than for natural systems at both the daily and annual time scales because of the relatively small ratio of atmosphere to the vegetation biomass [Lin et al., 1998, 1999].

[14] Overhead sprinklers mounted near the roof are the main mechanisms for providing artificial rainfall inside B2-TRF, usually applied at every 3–4 days. Other water reservoirs are the soil, water storage tanks, surface aquatic habitats, and plants and other biota [Leigh et al., 1999]. Major water flows include rainfall, subsoil drainage, reverse osmosis system flow, condensation mist, evapotranspiration, root uptake, and diffusion. B2-TRF receives an annual rainfall of approximately 1.3 m, 35% less than the average rainfall observed at some Amazonian sites such as near Manaus, Santarém, and Ji-Paraná [see Rosolem et al., 2008]. Given the observed differences in the meteorological characteristics of B2-TRF relative to the natural Amazonian rain forest, it is tempting to speculate whether B2-TRF can be considered an analog of the Amazon rain forest exposed to the future climate as predicted by climate models [e.g., Betts et al., 2004]. It is warmer (by about 2°C), drier (the rainfall rate is ∼3.5 mm d–1 compared to 5–5.5 mm d–1 at Amazonian sites), and is exposed to much higher CO2 concentrations (∼500 ppmv, with diurnal amplitudes ranging between 400 and 600 ppmv on average). However, it is important to recognize that B2-TRF does not have pronounced seasonality in annual rainfall and the radiation regime is quite different from that predicted by climate models for the Amazon basin.

[15] At installation the soils in B2-TRF were designed to be deep enough to allow expansion of the roots needed to stabilize above ground canopy expansion, and they were separately added as topsoil and subsoil during construction [Scott, 1999]. The subsoil was extracted from a local quarry and is very sandy loam of uniform composition with rocky and pebbly characteristics and with a depth that varies from 0 to 5 m. The topsoil, which is approximately 0.9 m deep, is a mixture of local desert grassland silt loam with organic and/or gravelly sand amendments. Leigh et al. [1999] describe the topsoil as having pockets of pure clay soil mixed with sandy or rocky pockets. The bulk density for the first 60 cm varies in the range 1.1–1.32 g cm−3 in the lowland habitat of the biome [Scott, 1999], which is the largest and tallest region inside B2-TRF, and which is considered typical of a wet equatorial forest. In this region, the topsoil is 50% loam, 25% gravelly sand, and 25% coarse organic material [Leigh et al., 1999].

3. Data

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Biosphere 2 Tropical Rain Forest Biome
  5. 3. Data
  6. 4. Version 3 of the Simple Biosphere Land Surface Model
  7. 5. Results
  8. 6. Discussion
  9. 7. Summary and Conclusions
  10. Appendix A:: Contributions to Reduction in Shortwave Radiation in B2-TRF
  11. Acknowledgments
  12. References
  13. Supporting Information

[16] Meteorological data relevant to the modeling studies described in this paper were intermittently available from both outside and inside the B2 tropical rain forest biome (B2-TRF). However, the physical environment of B2-TRF and the impact of this on the microclimate to which the vegetation is exposed mean that care is required to optimally define the values of the variables needed to force the SiB3 model. This section describes the data available and how these data were combined and/or adapted to give the consistent and physically meaningful time series of forcing variables required.

3.1. Available Data

[17] Some micrometeorological data, occasional surface flux data, and surrogate flux data (in the form of measured changes in concentrations inside B2-TRF) are available for the period November 1997 to October 1998 and for the years 2000, 2001, and 2002. However, these data are available with different sampling intervals (15 and 30 min, hourly, daily) and for different configurations of sensors, and they include some periods when data were missing. The 1997–1998 data set [Arain et al., 2000] was collected when B2-TRF was following its normal operating procedure designed to maintain the forest biome (i.e., without planned manipulation of the environment). The available data during this period were taken with two automatic weather stations, one outside and one inside B2-TRF, the latter with sensors mounted just above the forest canopy at 15 m. An eddy covariance system was also installed experimentally for about 3–4 weeks in 1998, but the lack of turbulence inside B2-TRF meant the results obtained were of little value other than to provide some hourly measurements of CO2 and horizontal wind speed. Daily data on B2-TRF “rainfall” (i.e., the water supplied to the overhead sprinklers in the biome) were also available for the period November 1997 to October 1998.

[18] Data for the period 2000–2002 were obtained from diverse sources over different periods, and they varied in nature. For additional information on the instrumentation deployed over this period, see Rosenthal et al. [1999] and Rascher et al. [2004]. Routine forest maintenance operating procedures were used in B2-TRF most of the time over this period, but in some periods, short-term drought experiments were performed [Rascher et al., 2004]. The data are available with 15 min sampling intervals and comprise standard micrometeorological variables, with CO2 concentration measured in four different locations inside B2-TRF. However, only the average data from the central and southeastern locations were used in this study in order to maintain consistency with the earlier 1997–1998 data which were acquired at a location near these two locations. For some periods data are also available from an automatic weather station located outside but near B2-TRF. Data measured outside B2-TRF during both periods for which data were available are hereafter referred to as outside weather station (OWS) data.

[19] The net ecosystem exchange of CO2 (NEE) is derived from the rate of change of CO2 inside B2-TRF, originally estimated in 15-min intervals [Lin et al., 1998]. The CO2 budget inside the mesocosm can be written as

  • equation image

where d[CO2]a/dt is the rate of change in CO2 concentration in the air, Ma is the number of moles of air in the mesocosm per unit ground area (in square meter), Fleak is the CO2 flux between the TRF mesocosm and neighboring mesocosm due to air leakage through the partition curtains, and Fconc is the rate of CO2 uptake by the concrete structure due to the carbonation reaction between CO2 and calcium oxide. The values of NEE had been previously derived from equation (1) prior to this study, but quality-control procedures were applied to identify outliers and any unrealistic hourly measurements were removed from the analysis.

3.2. Deriving Realistic and Consistent Forcing Data

[20] This section describes the methods used to obtain hourly sampled data for model forcing that were continuous, quality controlled, and representative of the meteorological conditions inside B2-TRF at a height of 15 m.

3.2.1. Temperature, Pressure, and Humidity

[21] Measurements inside B2-TRF include air temperature measured at 15 and 20 m (here referred to as Tair and T20, respectively), barometric surface pressure (Psurf), relative humidity (RH), and downward shortwave radiation (SWdown). Because these measurements were sometimes intermittent, interrelationships were developed to allow calculation of estimated values when missing data occurred. If Tair (an important driver in model studies) was missing (approximately 1.5% of the time), it was estimated from an empirical relationship with T20 that was derived when both were available, i.e.,

  • equation image

[22] The barometric surface pressure inside B2-TRF does not greatly differ from that outside, and when a measurement of Psurf was missing (approximately 19.8% of the time, mainly in 2000), its value was estimated from the outside pressure, Psurf-OWS, using an interrelationship derived when both were available, i.e.,

  • equation image

Specific humidity was calculated from air temperature, relative humidity, and surface pressure whenever available using standard meteorological formulae [Stull, 1988; Wallace and Hobbs, 1977].

3.2.2. Downward Shortwave Radiation

[23] The glass structure of B2 and metal supporting space frame greatly affect the downward shortwave radiation inside B2-TRF. A proportion g of both diffuse and direct components of solar radiation are absorbed by the (dirty) glass enclosing the biome and, on average, an additional fraction f of both diffuse and direct solar radiation is blocked out by the space frame. However, the blocking of diffuse and direct solar radiation by the space frame happens in different ways. The space frame blocks an additional fraction f of the distributed source of incoming diffuse solar radiation everywhere in the biome, but blocking of the incoming direct solar radiation occurs because at any point in time the space frame shades a fraction f of the biome from direct sunlight. As a result, there are intermittent rapid reductions in the measured value of shortwave radiation when a shortwave radiation sensor mounted in B2-TRF is shaded from the direct beam of solar radiation, with reductions occurring in a superficially haphazard way and with timing that changes with season. For modeling studies it is SWinbiome, the time series of the biome average (rather than the sensor location specific) value of shortwave radiation inside B2-TRF, that is required. For this reason, rather than using SWin, the measured point value inside B2-TRF, it is preferable to establish and use a time-average relationship between SWin and SWout, the value measured by the nearby external weather station outside B2, and to calculate SWinbiome from the relationship

  • equation image

where equation image and equation image are the time-average values of measured solar radiation inside and outside B2-TRF. On average, SWin is measured to be 50.15% of SWout; hence the solar radiation used as model forcing is given by

  • equation image

[24] This is consistent with the estimate made by Arain et al. [2000] using the 1997–1998 data. The reduction in shortwave radiation does not change the relative proportions of diffuse and direct radiation in the solar beam; see Appendix A.

3.2.3. Rainfall

[25] Estimates of rainfall in B2-TRF are based on daily records of water flow to the overhead sprinklers. Daily total volume of water is converted to millimeters depth of rain by normalizing to the area of the tropical rain forest biome (1936 m2), and estimates were made for any days with missing water flow data in the 2000–2002 period using equivalent days of the year in the 1997–1998 data. Because no information is available on the timing of the rainfall in B2-TRF, a visual inspection of the record of soil moisture measured at 30 cm depth was made (when available) to establish the approximate time of the day when rainfall occurred, and rainfall was assumed to have been applied at that time. When no soil moisture information was available on a particular day, rainfall was assumed to have occurred at the most frequent time suggested by soil moisture records on other days in each year. Typically this was between 1000 and 1200 local time, which is consistent with normal operation time of B2-TRF (J. van Haren, personal communication, 2009). Despite these efforts to define the timing of hourly rainfall, it remains uncertain in model simulations not only because detailed information on time of application is unavailable, but also because rainfall was applied via overhead sprinklers located in four quadrants of B2-TRF and may not have occurred simultaneously in all of these.

3.2.4. Carbon Dioxide Concentration as Forcing Data

[26] Because reliable observations of CO2 concentration are available inside the B2-TRF [Rosenthal et al., 1999], it is not necessary to assume a constant concentration in model simulations. Consequently, CO2 concentration (expressed in terms of partial pressure) is treated as forcing data in this study. On days when measurements of CO2 concentration were not available, the mean diurnal variation of B2-TRF was used.

3.2.5. Wind Speed

[27] Horizontal wind speed inside B2-TRF is very low, with observed values typically below 1 m s–1. Arain et al. [2000] recorded mean wind speed for a few weeks in 1998 using a centrally located sonic anemometer. The measurements showed an unusual diurnal pattern in which maximum wind speed occurred at night and wind speed was at near zero values during the day. This anomalous behavior likely indicates that during the day the sensor was above the strong inversion layer which was known to occur above the canopy each day because there were no open air vents in the roof of B2-TRF at that time. In addition, wind speed measurements were made for about 1 month using a standard cup anemometer in June 2009. Although the measured wind speed is low, the mean diurnal pattern of these latter data (Figure 1a) is more realistic, with greater wind speed during the day, little wind at night, and no evidence of the effect of a daytime inversion layer affecting the measurement. In the absence of any better alternative, this average diurnal cycle of wind speed was adopted and applied repeatedly each day during modeling. However, because wind speed inside B2-TRF is so low, it was necessary to relax the constraint on minimum horizontal wind speed imposed in the model from 1.0 to 0.1 m s−1. Fortunately, in practice, the numerical consequences of the low measured wind speeds and the implausibility of using a logarithmic extrapolation of wind speed into the canopy inside B2-TRF have less impact than may have been anticipated on modeled exchanges. This is because the measurement height is near the top of the canopy and in the model the calculated aerodynamic resistance between the measurement level and canopy air stream is much smaller than the calculated leaf boundary layer and stomatal resistances.

image

Figure 1. (a) Mean diurnal cycle of horizontal wind speed calculated for June 2009. The error bars indicate the standard errors. (b) Comparison of the downward longwave radiation estimated using equation (6) with observations. The 1:1 line is shown as a black line.

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3.2.6. Downward Longwave Radiation

[28] Downward longwave radiation, LWdown, is required in the model simulations but measurements of LWdown are not available for the period over which simulations were made. Consequently, the value had to be estimated using an empirical relationship calibrated during the limited period when measurements were available in June 2009. The approach used was based on that proposed by Idso [1981], but the specific empirical relationship Idso proposed relates to vertically integrated contributions to longwave radiation of an overlying atmosphere and is therefore inappropriate inside B2-TRF. Consequently, an expression with the same functional form was adopted, but with new empirical values assigned to the coefficients by optimization using the June 2009 data. The resulting equation is

  • equation image

where σ is the Stefan-Boltzmann constant (σ = 5.67 × 10−8 W m−2 K−4), Tair is the air temperature at 15 m in kelvin, and e is the measured vapor pressure deficit in hPa. A comparison between this calibrated version of the Idso formula and observations is shown in Figure 1b.

[29] Although many of the gaps in the available data were removed by using the various interrelationships described above, some remained, and to produce a continuous set of values the following data gap filling rules were applied:

[30] 1. If the gap was less than 3 h, it was filled by linear interpolation.

[31] 2. If the gap was greater than 3 h, the missing hours were replaced by average values for the same hours averaged over the previous and subsequent 15 days.

[32] 3. If any additional gap filling was needed, the missing data were replaced by the average value for the specific hour on the specific day of the year calculated from the entire multiyear data set.

4. Version 3 of the Simple Biosphere Land Surface Model

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Biosphere 2 Tropical Rain Forest Biome
  5. 3. Data
  6. 4. Version 3 of the Simple Biosphere Land Surface Model
  7. 5. Results
  8. 6. Discussion
  9. 7. Summary and Conclusions
  10. Appendix A:: Contributions to Reduction in Shortwave Radiation in B2-TRF
  11. Acknowledgments
  12. References
  13. Supporting Information

[33] The Simple Biosphere model (SiB) [Sellers et al., 1986] was originally developed to represent biophysical aspects of the land surface in general circulation models (GCMs) [Sato et al., 1989]. The modeled physiology responds to atmospheric forcing with some features of the vegetation cover, including vegetation type and greenness, prescribed as a function of location and season. The second generation of the SiB model (SiB2) [Sellers et al., 1996a] was substantially modified to incorporate linkage between the energy/water and carbon exchanges of vegetation surfaces by including a more realistic photosynthesis-conductance submodel [Sellers et al., 1992]. In this second version some important vegetation parameters were defined from remotely sensed data [Sellers et al., 1996b] rather than from the literature. The SiB2 model was recently modified (SiB2.5) to allow for simulations of mixed C3/C4 plants as discussed by Colello et al. [1998] and to incorporate prognostic modeling of canopy air space variables (temperature, vapor pressure, and CO2 partial pressure) in order to allow for in-canopy storage of heat, water, and carbon [Vidale and Stöckli, 2005]. Version 3 of the model (SiB3) used in this study, which is described by Baker et al. [2003], also includes a user-determined number of soil layers, the soil water stress parameterization described by Baker et al. [2008], and a soil representation based on the Community Land Model [Dai et al., 2003].

[34] Observations in the top meter of soil in B2-TRF in 2009 (K. Dontsova, personal communication, 2009) report the soil porosity and saturated hydraulic conductivity as 0.444 and 14.73 cm d−1, respectively, while the percentage of sand and clay are 45% and 20%, respectively. Other soil property parameters (e.g., the exponent b defined by Clapp and Hornberger [1978] and the soil tension at saturation) were prescribed in SiB3 following the work of Cosby et al. [1984] using empirical relationships with sand and clay fractions. The leaf area index (LAI) was set to 5 m2 m−2 [Rascher et al., 2004], the vegetation cover fraction was set to 85% [Lin et al., 1998, 1999], and the canopy top height (ztop) was set to 14 m [Arain et al., 2000], with surface roughness length (z0) and zero plane displacement height (zpdisp) then calculated from ztop according to Stensrud [2007], i.e., z0 = ztop/8 = 1.75 m and zpdisp = 0.75ztop = 10.50 m. All remaining parameter values were taken from the original SiB2 look-up tables for evergreen broadleaf forest [Sellers et al., 1996a, 1996b] unless otherwise specified below.

[35] In this study it was necessary to include some modifications to SiB3 to develop a more accurate conceptual model of the B2-TRF biome. Specification of these modifications represents the results of this study, and they are therefore described in section 5.

5. Results

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Biosphere 2 Tropical Rain Forest Biome
  5. 3. Data
  6. 4. Version 3 of the Simple Biosphere Land Surface Model
  7. 5. Results
  8. 6. Discussion
  9. 7. Summary and Conclusions
  10. Appendix A:: Contributions to Reduction in Shortwave Radiation in B2-TRF
  11. Acknowledgments
  12. References
  13. Supporting Information

5.1. Modifications to SiB3 Associated With Structural Characteristics of B2-TRF

5.1.1. Radiation Partitioning and Estimated Cloud Fraction

[36] The SiB3 model requires specification of the relative proportions of diffuse and direct radiation and visible to near-infrared radiation in the shortwave solar beam; these two ratios are calculated within the model using formulae that are based solely on the extent of cloud cover. As discussed in greater detail in section 3.2.2 and in Appendix A, the whole biome average value of the ratio of the diffuse to direct radiation inside B2-TRF is not altered by absorption of shortwave radiation by the glass that encloses the biome nor (as an area average and time average) by the shading that results from the space frame which supports the glass. Similarly, the relative proportion of visible to near-infrared radiation is also the same inside and outside B2-TRF. Consequently, the values of these two ratios for the shortwave radiation to which the vegetation inside B2-TRF is exposed can be calculated if the true extent of cloud cover can be calculated from the value of shortwave radiation measured outside B2-TRF.

[37] The original SiB3 model included a simple and (it is assumed) empirical formula to calculate the effective cloud cover from the measured solar radiation, the origin of which was not defined. In practice using this formula gave poor simulations in this study because it systematically overestimated the cloud cover, which then affected radiation partitioning and the calculated net photosynthesis and NEE. Consequently, a more realistic physically based estimate of the cloud fraction, C, was implemented in SiB3 based on the methodology proposed by Deardorff [1978] and discussed by Crawford and Duchon [1999]. Using this approach,

  • equation image

where s is the ratio of the measured solar irradiance (SWout) to the clear-sky irradiance (fmSc cos θ), i.e., the total amount of solar radiation incident at the top of the atmosphere (TOA) reduced by a fraction fm. The clear-sky irradiance is calculated from Sc, the solar constant (assumed to be 1367 W m–2), θ is the solar zenith angle calculated for the latitude and longitude of B2-TRF (32°34′N, 110°51′W), and fm is the fraction of the solar radiation at the TOA which reaches the ground in clear-sky conditions.

[38] The value of fm was estimated from the measured solar radiation during days when the daily pattern of solar radiation was consistent with clear-sky conditions (totally clear skies are not uncommon in Arizona, regardless of time of year), i.e., applying

  • equation image

in clear-sky conditions. On average fm is about 0.72 with some seasonal variations, from 0.70–0.75 throughout most of the year reducing to 0.60–0.65 during the monsoon months (July and August), with the additional absorption arguably being related to the moister atmosphere in these months. To allow for the observed seasonality, monthly average values of fm were calculated and used in equation (7) to calculate cloud cover during model simulations. When calculated using this method, the cloud fraction is not overestimated and the resulting improvement in the calculated relative proportions of diffuse and direct radiation and visible to near-infrared radiation in the shortwave solar beam reduced the root-mean-square error (RMSE) between modeled and measured NEE of by approximately 10% (see later).

5.1.2. Subsoil Drainage

[39] Unlike natural tropical forest ecosystems where soils can be tens of meters deep and where trees can sometimes access the water table, the soils in B2 are of limited depth, on average between 3 and 5 m (J. van Haren, personal communication, 2010), and they are free draining. Tubiello et al. [1999] reported that subsoil drainage in B2-TRF can be as high as 3000 L d−1, and to accommodate this in the model it is necessary to prescribe an artificially lower saturated hydraulic conductivity to the lowest soil layer in SiB3 (the total soil column prescribed in SiB3 is ∼3.4 m). The value 0.158 cm d−1 for the saturated hydraulic conductivity of the lowest modeled soil layer was found by trial and error to give the best representation of the daily total subsoil drainage from the whole of B2-TRF, and this value was therefore selected. Although using this value also influences the amount of soil water available in the deeper layers in the model, this does not have a major impact on the simulated energy, water, and carbon fluxes in simulations (not shown). Likely this is because few plants have roots at these depths (see below).

5.1.3. Rooting Profile

[40] The default depths of the individual soil simulated layers in SiB3 are defined to increase exponentially rather than linearly with depth, and a root fraction distribution is assigned to each soil layer following the method of Jackson et al. [1996]. However, in B2-TRF the observed root distribution in the soil is limited to approximately 60 cm and the default formulation in SiB3 is inappropriate. Scott [1999] made measurements of root density as a function of depth in B2-TRF, and from this information, root density values were defined for each level in SiB3 constrained by the conditions that there were no roots at the surface or below 60 cm in the soil. This soil root density profile was then converted to the root fraction profile required by SiB3 by normalizing by the soil density of the entire soil column. This approach produces root distribution which is arguably more realistic for B2-TRF (not shown) with, for example, 60% of the roots above 10 cm depth rather than 45% as in the default distribution, and no roots below 60 cm depth rather than 45% as in the default distribution.

5.2. Modifications to SiB3 When Modeling Vegetation Responses of B2-TRF

5.2.1. Parameterization of Soil Respiration

[41] The original formulation of soil respiration in SiB3 [Baker et al., 2003] was a slightly modified version of the submodel described by Denning et al. [1996] which was based on the approach of Raich et al. [1991] used in the Terrestrial Ecosystems Model (TEM). In this formulation the relative intensity of soil respiration (R*) is calculated from the soil moisture fraction and soil temperature (in kelvin) for each soil layer at each time step, as follows:

  • equation image

where

  • equation image

with

  • equation image

In equations (9)(11) the influence of soil moisture on decomposition is defined by f(M), which has a minimum value of 0.2. The percentage of pore space occupied by water in each soil layer is w, and zm defines the skewness of the curve relating relative respiration to soil moisture [see Raich et al., 1991, Figure A5]. The parameter wsat determines the value of f(M) when the soil pore space is saturated with water. Soil respiration is greatest when the soil moisture is wopt but less in drier or moister conditions.

[42] The original version of SiB3 assumes that carbon storage of terrestrial ecosystems is in a steady state on an annual basis, i.e., that the annual sum of respiration loss is equal to the annual sum of canopy net carbon assimilation, and the net annual flux of CO2 is zero. This assumption is implemented by calculating individual monthly scaling factors of relative soil respiration (i.e., the ratio of monthly R* values to annual sum of R*) which are then applied to the canopy net assimilation. The approach has been successfully used in a variety of ecosystems [Baker et al., 2003, 2008; Hanan et al., 2005; Stöckli and Vidale, 2005] and even in a global assessment of CO2 exchange between land surfaces and atmosphere [Randall et al., 1996]. However, Denning et al. [1996] pointed out that the constraint of imposing an annual balance in carbon fluxes results in a loss of generality and is unsuitable to the assessment of sources and sinks of CO2 for periods longer than 1 year.

[43] In the controlled environment B2-TRF imposing an annual carbon balance is unrealistic, not least because perturbation experiments can be performed for periods longer than 1 year. Consequently, the original soil respiration formulation used in SiB3 is arguably not appropriate. A modified approach was therefore developed in which the total soil respiration Rs is calculated as the sum of layer-by-layer contributions R*(i) defined by applying equation (9) to each layer, weighted by the root fraction root f(i) in the i layers, and then normalized to the observed long-term carbon balance of B2-TRF using a reference soil respiration rate (〈R0〉), i.e.,

  • equation image

This approach is similar to the original formulation of soil respiration proposed by Raich et al. [1991] which also contained a scaling factor to be determined by model calibration on a vegetation-specific basis. However, the net flux is now envisioned as being a multilayer weighted sum assuming a link between decomposition rates and the presence of roots in the soil, with a common scaling factor then applied for all layers. In TEM [Raich et al., 1991], monthly average rates were calculated, but in SiB3 they are calculated at each time step (i.e., hourly in this study.)

[44] The plausibility of this modified version of the SiB3 submodel soil respiration after calibration against B2-TRF data was investigated relative to alternative calibrated submodels of soil respiration. These alternatives included assuming a fixed respiration rate and submodels that had been applied in earlier versions of SiB (Norman et al. [1992] described by Colello et al. [1998]) or developed for tropical rain forest ecosystems [Sotta et al., 2004; Chambers et al., 2004; da Rocha et al., 1996; Malhi et al., 1998] (see Table 1). In each case submodel calibration was made against observed nighttime average values of NEE when photosynthesis does not occur, and assuming soil respiration to be the dominant component to CO2 flux as supported by Chambers et al. [2004], Malhi et al. [1999], and Meir et al. [1996]. Other studies in the Amazon, however, report soil respiration to be only about 30%–40% of the total ecosystem respiration [Hutyra et al., 2008; Saleska et al., 2003], and that may introduce some uncertainty in our calibration approach. Each of the eight different soil respiration parameterizations contains parameters that were calibrated against nighttime NEE by generating 1000 parameter sets using the Latin Hypercube Random Sampling method and then selecting the best set of parameters from among these using a multiobjective approach [Gupta et al., 1998]. Selection was made to minimize two objective functions simultaneously, i.e., the mean absolute error (MAE) and (1 – ρ), where ρ is the Pearson correlation coefficient. The first objective function is a measure of closeness to the observed nighttime NEE, while the second is a measure of the timing of changes in soil respiration rates within the time series. The parameter set selected is here referred to as the compromise solution, this being the solution that tries to the minimize MAE and (1 – ρ) as much as possible by minimizing the normalized (i.e., ranging from 0 to 1, where 0 is the minimum observed objective value and 1 is the maximum) average of the two objectives. Typically, “mean-square” quantities (e.g., root-mean-square error, RMSE) are used when calibrating energy, water, and carbon fluxes with these models [Gupta et al., 1999; Liu et al., 2004, 2005; Rosolem et al., 2005]. MAE and RMSE are measures of dispersion of the model residual around zero and cannot therefore reasonably be considered as unrelated. The choice of MAE was made based on how we would like to weight small and large errors (i.e., deviations from measurements) in our simulation. We have decided to use MAE because we assume NEE values are reasonably good regardless of whether it is daytime or nighttime (an assumption not often met in natural ecosystems). Nighttime differences tend to be smaller (flux is more steady at night), but we would like to weight these differences equally relative to daytime errors (when NEE follows closely the diurnal cycle; see section 5.2.2).

Table 1. Soil Respiration Submodels Tested in This Study Including the Original Formulation Used in SiB3 Based on Denning et al. [1996], a Constant Soil Respiration Rate, and the Alternative of the Original Approach Introduced in This Paper (See Equation (12))a
Soil Respiration ModelEquationCalibrated Parameters
  • a

    The β coefficients are empirical values appropriate for each submodel and do not necessarily represent the same quantity. LAI is the leaf area index (m2 m−2), θ is the volumetric soil moisture (m3 m−3), and Ts,z is soil temperature at depth z. To maintain consistency with SiB3 soil layers, soil moisture and/or soil temperature specified to be at 1, 5, and 10 cm depth in some models are assumed to be those modeled at 0.7, 6, and 12 cm.

Denning et al. [1996] (SiB3 original form)Relative soil respiration (R*) calculated to achieve zero net annual flux of CO2; see equations (9)(11)wopt = 74.1; wsat = 0.75; zm = 0.60
ConstantRs = 〈R0R0〉 = 4.9 μmol m−2 s−1
Norman et al. [1992]Rs = imageβ1 = 0.092; β2 = 0.023; β3 = 0.011; β4 = 23.2°C
Sotta et al. [2004]Rs = imageR0〉 = 9.9 μmol m−2 s−1; k = −0.0289
Chambers et al. [2004]Rs = imageβ1 = 0.830; β2 = −0.012; β3 = 0.905; β4 = −0.519
Rocha et al. [1996]Rs = imageβ1 = 0.062; β2 = 0.72
Malhi et al. [1998]Rs = 〈R0〉eequation imageimage β)R0〉 = 5.4 μmol m−2 s−1; Q10 = 1.001; β = 25.6°C
This studyequation (12)wopt = 71.3; wsat = 0.75; zm = −0.87; 〈R0〉 = 5.0 μmol m−2 s−1

[45] Figure 2 shows that the majority of soil respiration submodels tested represent the observed B2-TRF nighttime soil respiration rates very poorly even after they had been calibrated. Note that after calibration the Malhi et al. [1998] and Sotta et al. [2004] submodels give a near-constant soil respiration rate, which also translates into a near-constant ecosystem respiration rate (i.e., soil plus canopy respirations) in Figure 2. Although we adopted a multiobjective approach when selecting SiB3 soil respiration submodels, ultimately our selection is subjective and based on results shown in Figure 2. For example, although the correlation coefficient (ρ) measures the strength of a linear relationship between simulated and observed values, this does not necessarily reflect a 1:1 relationship in Figure 2. Thus, had our selection been solely based on the two objective functions with no subjective analysis, we would have selected the model of Chambers et al. [2004], but this does not represent the observed variation in nighttime NEE successfully (i.e., being as close as possible to the 1:1 line). On the basis of Figure 2, only three models gave acceptable agreement with measurements: the SiB3 original formulation (Figure 2a), the model of Norman et al. [1992] (Figure 2c), and the revised SiB3 formulation given as equation (12) (Figure 2g). Although the performance from none of these three was outstanding, these three submodels were selected for further consideration in the study of the thermal tolerance of B2-TRF plant species described next.

image

Figure 2. Comparison between nighttime averages of simulated and observed NEE inside B2-TRF (μmol m−2 s−1) for each soil respiration submodel tested in SiB3. The mean absolute error (MAE) and correlation coefficient (ρ) are also shown. The 1:1 line is shown as a black line.

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5.2.2. Thermal Tolerance

[46] In SiB3, Sellers et al. [1992, 1996a] calculated photosynthetic rates based on the model of Farquhar et al. [1980] following the approach proposed by Collatz et al. [1991] scaled to canopy levels. Photosynthesis is calculated as the minimum of three potential limiting factors, namely the efficiency of the photosynthetic enzyme system (ribulose 1,5-bisphosphate carboxylase/oxygenase, rubisco), the amount of photosynthetically active radiation captured by the leaf chlorophyll, and the capacity of the leaf to export or utilize the products of photosynthesis. Dark respiration rates are also scaled to the leaf carboxylase content (rubisco). The effect of temperature is included in each limiting factor, e.g., the Michaelis-Menten constants for CO2 and O2 in the case of the catalytic capacity of rubisco. For a detailed description of the temperature stress functions used in SiB, see the work by Sellers et al. [1996a, Appendix C].

[47] A previous study [Rosolem et al., 2005] using observed surface flux data taken in the Amazon basin showed that simulated surface fluxes were not sensitive to the parameter values in temperature stress functions used in the SiB model and that the default values given by Sellers et al. [1996b] for evergreen broadleaf forests were therefore acceptable. More recently, Baker et al. [2008] showed good agreement between simulated and observed NEE values at a Tapajós National Forest site (KM83) using a slightly modified version of SiB3. However, none of their modifications relate to the parameterization of temperature stress, suggesting that the parameterization currently used in SiB3 is appropriate for Amazon rain forest sites. Further analyses of SiB3 parameters at Amazon sites are being made under the Large-Scale Biosphere-Atmosphere Experiment in Amazonia Data-Model Intercomparison Project (LBA-DMIP) (R. Rosolem et al., manuscript in preparation, 2010). As previously discussed, temperatures inside B2-TRF differ substantially from those in the Amazon. The annual mean temperature inside B2-TRF is on average about 1.9°C warmer than those observed above the forest at the Manaus, Tapajós, and Reserva Jarú LBA eddy covariance tower forest sites in the Amazon and, on average, also has a larger diurnal amplitude (12.2°C) in air temperature than those observed above the canopy at these Amazon forest sites (4.6°C) [de Gonçalves et al., 2010]. When compared to climatological data from weather stations near these Amazon sites [Rosolem et al., 2008], the mean annual temperature inside B2-TRF is less warm, i.e., about +0.7°C.

[48] The lack of sensitivity of SiB3 to temperature stress parameters in simulating forests in the Amazon, as reported by Rosolem et al. [2005], presumably means that current conditions in natural tropical forest do not activate an internal mode in SiB3 which allows these parameters to play a major role. However, it seems that the large diurnal amplitude and higher air temperatures inside B2-TRF trigger sensitivity to these parameters, and it is therefore of interest to investigate the model's temperature stress sensitivity under these conditions. The parameters directly related to temperature stress functions in SiB3 were therefore calibrated against data from B2-TRF using a calibration procedure similar to that used to evaluate soil respiration parameterizations (see above), but with MAE and (1 − ρ) now calculated for hourly measurements of NEE measured during daylight hours when the photosynthesis is the dominant component of CO2 exchange and likely to be most influenced by temperature stress.

[49] In fact, when the three submodels of soil respiration retained after the analysis described in section 5.2.1 were used to calculate hourly daytime NEE, there were few discernible differences in their performances relative to hourly daytime observations; see Figures 3a–3c. However, the cumulative NEE for hours when observations are available is much better simulated when the revised SiB3 formulation given as equation (12) is used to describe soil respiration rather than either the original SiB3 formulation or the submodel of Norman et al. [1992] (see Figure 3d). This is also reflected in the calculated mean bias for each submodel. The original SiB3 formulation and the submodel of Norman et al. [1992] have mean biases of −0.35 and 0.48 μmol m−2 s−1, respectively, while the revised SiB3 formulation has a mean bias of just −0.07 μmol m−2 s−1. The revised SiB3 submodel is therefore preferred and at this stage was adopted and used during the calibration of temperature stress related parameters in SiB3 although, in practice, this submodel selection had little influence on the calibrated values of temperature stress parameters since these are mainly determined by the ability of SiB3 to simulate daytime photosynthesis.

image

Figure 3. Comparison between simulated and observed hourly NEE (μmol m−2 s−1) for (a) the original soil respiration model in SiB3, (b) the soil respiration model of Norman et al. [1992], and (c) and the alternative soil respiration model in SiB3 introduced in this paper (see equation (12)). In each case the mean absolute error (MAE) and correlation coefficient (ρ) are also shown. Daytime data are shown in green; nighttime data are shown in red. (d) Cumulative NEE (g m−2) calculated by summing available observations, or summing only those simulated values that correspond to available observations. The blue line is for the original soil respiration model in SiB3, and the red line is for the model of Norman et al. [1992]. Simulation results with the alternative of the original approach introduced in this paper are shown as a green line and the observed NEE is shown as a black line. All the results shown are for each model after calibration of soil respiration submodel parameters using observed nighttime NEE and after calibration of temperature stress parameters using hourly observed daytime NEE. Note that cumulative NEE shown in Figure 3d does not correspond to seasonal or interannual variations in NEE because the results shown are constrained to periods when observational data were available. The 1:1 line is shown as a black line in Figures 3a–3c.

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[50] In SiB3 the maximum catalytic capacity of rubisco (Vm) is calculated by

  • equation image

where Vmax is defined by

  • equation image

and Vmax0 is defined as the Vmax for top of canopy leaves; Qt is calculated as

  • equation image

where Topt is a prescribed coefficient in the model (see Table 2). In SiB3 the temperature stress function, ft(Tcanopy), is calculated as follows:

  • equation image

and the leaf respiration rate (Rd) is calculated as a fraction (fd) of Vm:

  • equation image
Table 2. Description of the Parameters Associated With Temperature Stress Functions in SiB3 and Their Default and Calibrated Values
ParameterDescriptionUnitsDefault Value [Sellers et al., 1996a, 1996b]Calibrated Value [This Study]
Vmax0Maximum rubisco capacity at canopy topμmol m−2 s−110061
fdRespiration fraction of Vm0.0150.017
s1Temperature inhibition parameter for C3 VmK−10.31.7
s2Half-inhibition temperature for C3 VmK313319
s5Temperature inhibition parameter for C3 RdK−11.30.3
s6Half-inhibition temperature for C3 RdK328339
ToptTemperature coefficient used to calculate Q10K298296

[51] The soil water stress function (fW) is calculated on the basis of the formulation proposed by Baker et al. [2008]. All temperature variables are calculated in kelvin.

[52] The parameter nomenclature used by Sellers et al. [1996a, 1996b] was adopted in this study and is used in Table 2 to summarize the calibration results for the seven parameters that determine temperature inhibition functions in SiB3. The calibrated value of the maximum rubisco capacity at canopy top (Vmax0) is 61 μmol m−2 s−1 in B2-TRF, much lower than the default value. For comparison, da Rocha et al. [1996] calibrated SiB2 using Amazon data and found Vmax0 = 81 μmol m−2 s−1 while Rosolem et al. [2005] report values in the range 83–101 μmol m−2 s−1 in a similar calibration using data from the LBA Tapajós KM83 site in eastern Amazonia. Upper leaf values of Vmax reported by Meir et al. [2002] for the Manaus site in the Amazon were in the range 35–50 μmol m−2 s−1, and using slightly different temperature sensitivity functions to describe the Vmax of an Amazonian rain forest site, Lloyd et al. [1995] estimated a leaf level (as opposed to canopy top) value of 68 μmol m–2 s–1 at 25°C. In fact, Lin et al. [1998] successfully used this last value when analyzing plant response to CO2 enrichment inside B2-TRF and, in unpublished data, also report similar estimates of Vmax from gas exchange measurements on upper leaves of two canopy trees in B2-TRF, which suggests that Vmax0 may have a similar value.

[53] The low value of Vmax0 found by calibration in this study may be associated with nitrogen available in leaves: the leaf nitrogen profile observed in B2-TRF [Lin et al., 1998] is similar to the profile reported by Lloyd et al. [1995]. However, the reduced radiation levels that result from artificial space frame shading inside B2-TRF may also be significant. According to Bonan [2002], plant species growing in a shaded environment achieve no photosynthetic gain by investing in energetically expensive rubisco and so have low values of Vmax, while low leaf nitrogen content is directly related to low photosynthetic capacity in low radiation environments [Meir et al., 2002; Carswell et al., 2000].

[54] Curvature of the exponential temperature stress functions are described by the several s terms in equation (16), and these are presented in Table 2; s1 and s2 specify the temperature stress function that affects Vm for the photosynthetic process in C3 plants (hereinafter referred to as C3 Vm), whereas s5 and s6 specify the function associated with Vm when calculating dark respiration (Rd) (hereinafter referred to as Rd Vm). Small (large) values of s1 and s5 correspond to a more gradual (abrupt) change in the curvature of these functions, while s2 and s6 define the half-inhibition points associated with high temperature. The temperature stress functions for C3 Vm calculated using parameters before and after calibration are shown in Figure 4a. There is a marked difference in the change from default to calibrated values for parameters associated with function curvature (s1 and s5), but the half-inhibition temperatures for both the C3 Vm and Rd Vm temperature functions (s2 and s6) are higher, by 6 and 11 K, respectively, suggesting that plants inside B2-TRF have a higher thermal tolerance than that given with default parameter values. The fraction of Vm that characterizes canopy respiration, fd, and the temperature parameter (Topt) associated with Qt in the model are little changed by calibration, as are canopy respiration rates, which are around ∼1 μmol m−2 s−1 at 25°C and in excellent agreement with estimated rates from Lloyd et al. [1995]. The range of air temperature typical of both B2-TRF and the Amazon tropical rain forest are also shown in Figure 4a. These ranges are the observed minimum and maximum temperatures recorded in B2-TRF (Tmin = 20.4°C and Tmax = 46.9°C) and in the Amazon (i.e., Tmin = 17.2 ± 3.7°C and Tmax = 33.6 ± 1.4°C), respectively, with the values for the Amazon being the mean values of minimum and maximum temperatures for the Manaus K34, Tapajós K67 and K83, and Reserva Jarú LBA rain forest sites [de Gonçalves et al., 2010].

image

Figure 4. (a) Relationships of Vm temperature stress factor (applied to photosynthesis in C3 plants), (b) maximum catalytic capacity of rubisco, (c) net assimilation, and (d) soil respiration modeled by SiB3 during daytime hours versus modeled canopy temperature using default parameters (black circles) and calibrated parameters (red crosses). (e) Difference between the modeled NEE given by SiB3 and the observed value during daytime hours as a function of modeled canopy temperature before (black circles) and after (red crosses) calibration, respectively. The ranges of air temperature (ΔTair, in °C) typical of B2-TRF (solid red line) and natural Amazon rain forest (solid black line) are shown in Figure 4a. Units are in μmol m−2 s−1, except for fT(Tcanopy) in Figure 4a, which is unitless.

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[55] The effect of canopy temperature on the model parameter Vm before and after calibration is shown in Figure 4b. There is a marked difference between the two curves. Vm is systematically lower after calibration compared to the default value for the range where air temperatures commonly observed both in the Amazon and inside B2-TRF coincide with each other (i.e., 22°C ≤ ΔTair ≤ 34°C). Over this range, the mean value of Vm calculated prior to calibration is 136 ± 19 μmol m−2 s−1 but it is 104 ± 20 μmol m−2 s−1 after parameters are calibrated. With the default parameter values, Vm reaches a maximum value at about 36°C and then declines, a result that is consistent with the idea that tropical rain forest plant species are near a high-temperature threshold. However, after calibration, Vm increases substantially up to around 43°C and then sharply declines to near zero before 50°C, which suggests that this high-temperature threshold appears to occur at a much higher temperature not yet observed in natural tropical rain forests. Within the range of air temperatures observed inside B2-TRF but not in the Amazon (i.e., 34°C ≤ ΔTair ≤ 47°C), the mean value of Vm calculated after calibration is 174 ± 38 μmol m−2 s−1 compared with the mean value calculated with the default parameter of 156 ± 26 μmol m−2 s−1. The mean Vm calculated over the entire range of air temperature observed inside B2-TRF (i.e., 22°C ≤ ΔTair ≤ 47°C) is 125 ± 41 μmol m−2 s−1 after calibration, representing the higher end of the Vm range reported by Wullschleger [1993] for tropical forest species.

[56] Net assimilation calculated as photosynthesis minus canopy respiration is shown in Figure 4c. The overall effect of using calibrated (as opposed to default) parameters is to reduce the modeled net assimilation rate but then to maintain this rate to higher temperatures. However, note that SiB3 simulates leaf but not stem respiration, and stems will experience a different temperature regime from soil, and net assimilation and stomatal conductance are strongly related [Collatz et al., 1991; Wong et al., 1985a, 1985b, 1985c]. Soil respiration, which is about an order of magnitude less than net assimilation, is little altered by the calibration of temperature stress parameters (Figure 4d). Figure 4e shows a comparison between observed and modeled NEE for default and calibrated temperature stress parameters. There is substantial variability in Figure 4e associated with the effect of other controls on NEE, but the generally improved description given with the calibration of temperature stress parameters is still apparent. NEE measurements were available for approximately 63% of the time, but unfortunately no measurements were available in the summers of 2000 and 2002 (the two hottest years compared to 2001); hence the range of canopy temperatures presented in Figure 4e is slightly smaller relative to those shown in Figures 4a–4d.

[57] As discussed by Arain et al. [2000], radiation has a strong seasonal signal inside B2 because of its midlatitude location, and Figure 5 shows the average observed and model-calculated diurnal cycle of NEE (prior to and after calibration of temperature stress parameters) in the winter (Figure 5, left) and summer (Figure 5, right), with the winter months being centered on December and the summer months centered on June. Overall, the simulated results after calibration (red lines) show remarkable agreement with observations (black lines), especially when the percentage of observations available in a month is high (percentage availability is given in Figure 5). In winter (see Figure 5, left) SiB3 simulations prior to calibration calculate unrealistically higher assimilation (more negative NEE) during daylight hours compared with both observations and simulated values after calibration. Nighttime fluxes confirm the improvement obtained when the new soil respiration parameterization is used and also exhibit good agreement relative to observations. We suspect the quality of the observations made in December 2001 when the nighttime and to a lesser extent daytime fluxes agree less well, but given a lack of evidence for discarding these data, we retained them in the calibration and comparison.

image

Figure 5. Average diurnal variation of NEE (μmol m−2 s−1) inside B2-TRF simulated prior to (blue lines) and after calibration (red lines) of the temperature stress parameters in SiB3 compared to observations (black circles), calculated for (left) winter (i.e., November–December–January) and (right) summer (May–June–July). Error bars and shading correspond to one standard deviation calculated for observations and simulations, respectively.

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[58] In the morning during summer (see Figure 5, right) the results are similar to those found in winter, but there is observed and correctly simulated reduction in assimilation in the afternoon (i.e., NEE is less negative). This is consistent with a limitation on rubisco activity starting around midday, as reported by Lloyd et al. [1995]. There is also an abrupt fall in simulated assimilation, that is, an abrupt increase in NEE during morning to afternoon transition in the summer months caused by the substantial increase in temperatures, and this is more pronounced in the two hottest summers (2000 and 2002). Unfortunately, summertime observations were only available for 2001 to confirm this modeled behavior, but the agreement in the summer of 2001 suggests that the simulated fluxes are arguably realistic in this respect. Again, nighttime NEE fluxes are simulated well by SiB3 during the summer, confirming the improvement obtained with the revised parameterization soil respiration.

[59] The time variation of the soil water stress factor in SiB3 indicated that the vegetation inside B2-TRF was never under significant water stress during the period of this simulation (not shown) and B2-TRF released approximately 4850 kg of C ha−1 during this 3 year period (2000–2002), which on a per unit area basis is comparable to values reported for sites in the eastern Amazon [Saleska et al., 2003].

6. Discussion

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Biosphere 2 Tropical Rain Forest Biome
  5. 3. Data
  6. 4. Version 3 of the Simple Biosphere Land Surface Model
  7. 5. Results
  8. 6. Discussion
  9. 7. Summary and Conclusions
  10. Appendix A:: Contributions to Reduction in Shortwave Radiation in B2-TRF
  11. Acknowledgments
  12. References
  13. Supporting Information

[60] In order to compile a set of forcing data adequate for land surface modeling, assumptions and simplifications were needed which are described in detail in section 3. Measurements made in 1997–1998 and 2000–2002 in central and southeastern portions of B2-TRF were selected as being of sufficient consistency, but observations with dubious quality made elsewhere in the biome in 2000–2002 were discarded. In order to produce a continuous input data, some gap filling was also needed, most notably in the case of wind speed when a mean diurnal cycle based on measurements taken for only a short period of time was used. However, using a daily average variation in wind speed (rather than a fixed value) did improve the quality of the simulations relative to observations (not shown), as did using measured (rather than an assumed fixed) CO2 concentration (also not shown). Arguably the biggest uncertainty in the model input data is in rainfall which was derived from records of daily water flow to overhead sprinklers, with visual inspection of 30 cm depth soil moisture used to estimate the approximate time of the day when watering occurred. Fortunately, a variety of micrometeorological and soil sensors are now being installed in B2-TRF biome to give forcing data with high temporal and spatial resolution and this should facilitate future modeling of the biome.

[61] One methodological aspect of this study of general relevance is that when using observations to optimize parameters in models of CO2 exchange, it is helpful to use nighttime and daytime NEE values separately to provide distinctive information on contributing processes that dominate at these different times of day. In this study we were able to make largely independent calibrations of model parameters that influence soil respiration rates using nighttime data and of model parameters that influence photosynthetic uptake using daytime data. However, the separation of daytime and nighttime NEE measurements which is feasible in an enclosed environment such as B2-TRF may not be feasible in natural ecosystems.

[62] In general, SiB3 successfully simulated the behavior of B2-TRF, after modification of model formulation and parameter values. The modifications required to simulate the observed NEE reveal important aspects of ecosystem function in B2-TRF, especially in three areas: CO2 concentration, soil respiration, and photosynthesis in this high-temperature environment.

[63] First, with regard to CO2 concentrations, we found that a fixed value of 375 ppmv for CO2 concentration, conventionally assumed in SiB3, gave poor results because the concentration and diurnal variation of CO2 inside B2-TRF routinely differ from this value. Discrepancies in model simulations relative to observations were reduced when CO2 was allowed to follow the actual measured concentration (not shown).

[64] Second, we evaluated seven different submodels of soil respiration in addition to the original SiB3 formulation. These included empirical parameterizations, assuming a constant rate, and a submodel which is a revision of the original formulation but with normalization to observed nighttime values of NEE rather than forcing an annual equilibrium in carbon exchange. All but three of these submodels provided very poor simulations of observed variations in nighttime-average NEE. Most of the unsuccessful submodels relate soil respiration to soil temperature in a single layer and were developed for the Amazon rain forest, where variations in soil temperature are not large. Both the original SiB3 submodel and the differently normalized form of this submodel gave better results, perhaps because soil respiration is calculated for each soil layer in these two models. The third reasonably successful model was originally developed by Norman et al. [1992] for grassland where variations of soil temperature may be larger than in the tropics.

[65] It is reassuring that the calculated percentage contribution of soil respiration to total ecosystem respiration given by the modified form of the SiB3 submodel ultimately adopted as the preferred formulation during this study is comparable with that observed in the Amazon (60%–84%) as reported by Chambers et al. [2004], Malhi et al. [1999], and Meir et al. [1996]. A reduction in nighttime NEE rates was sometimes observed during the summer, which may be associated with drier soils, suggesting that it is a combination of soil temperature and soil moisture which determines variations in soil respiration inside B2-TRF. However, our results suggest soil respiration rate is not sensitive to changes in CO2 concentration, a result consistent with observations made during CO2 enrichment experiments in B2-TRF [Lin et al., 1998, 1999].

[66] Third, we found that for SiB3 to accurately simulate the observed behavior of plant photosynthetic assimilation in the high-temperature environment of B2-TRF, the parameters describing temperature stress in SiB3 had to be adjusted from the default values that had previously been found adequate when modeling the behavior of Amazonian forest [Rosolem et al., 2005; Baker et al., 2008]. The required half-inhibition point of the temperature stress curve for photosynthetic capacity (i.e., C3 Vm) in B2-TRF (in Table 2) is ∼6 K higher than originally proposed for tropical forests in SiB3 [Sellers et al., 1996a, 1996b]. This modification represents vegetation whose photosynthetic capacity has greater tolerance for high-temperature environments.

[67] At the same time, we found that the overall photosynthetic capacity (Vmax0) for plants in B2-TRF had to be adjusted downward. This may be associated with a reduction in leaf nitrogen concentration [Bonan, 2002] in this space frame shaded, lower radiation environment, or this may represent an intrinsic trade-off for the simultaneously increased thermal tolerance.

[68] The photosynthesis modifications, required to accurately represent the B2 tropical forest (but not to represent natural Amazon tropical forests), are perhaps the most intriguing result of this study. They suggest (1) that tropical rain forest vegetation can acclimate to higher temperatures than previously thought, and this is revealed when they are grown in the warmer environment of B2-TRF, (2) that the species composition of plants in B2-TRF has shifted to disproportionately represent those species able to function at higher temperatures, or (3) some combination of (1) and (2). Over half of the plant species originally in the B2-TRF in the early 1990s had died off by the time of the observations used in this study (T. Taylor, personal communication, 2010), representing a significant opportunity for selection to shift species composition toward a community with higher aggregate thermal tolerance. Shifts in community assembly or relative abundance can control ecosystem response to environmental change in some systems [Shaver et al., 2001; Saleska et al., 2002; Bradley and Pregitzer, 2007], but this mechanism has so far not been seen in large-stature long-lived tropical forests. Whether they can explain the aggregate thermal tolerance of the B2-TRF remains to be tested by further work in Biosphere 2.

[69] The thermal tolerance of tropical rain forest communities remains a subject of debate. Some field experiments suggest that Amazon rain forests are near their maximum temperature threshold [Doughty and Goulden, 2008], a suggestion that is consistent with a SiB3 simulation using the default parameters (compare the default temperature stress function and the typical range for Amazon rain forests in Figure 4a), while other studies that combine observations and modeling suggest they have significant capacity to acclimate to higher temperatures [Lloyd and Farquhar, 2008]. However, the two studies just cited and the present study all suggest reductions in carbon assimilation for leaf temperatures greater than ∼35°C, which might be explained by stomatal closure in response to increased evaporative demand.

[70] Evidence for ability of species in B2-TRF to acclimate was presented by Adams and Berry [1999], who used chlorophyll fluorescence measurements to determine critical temperatures for lower-canopy, midcanopy, and upper-canopy leaves inside B2-TRF during winter and summer. They found that upper-canopy species increased their critical temperature during the summer, but neither the midcanopy nor lower-canopy species did so because seasonal temperature differences are less pronounced at these two levels. The observed range of critical temperature for upper-canopy species (Ceiba pentandra and Hura crepitans) was from 47.2°C to 50.7°C, consistent with the temperature range we obtained in this modeling study (see Figures 4a–4c).

[71] Photosynthetic temperature response has generally been found to be very plastic and to vary seasonally and among ecotypes within a species [Baldocchi and Amthor, 2001], implying that acclimation to increased temperature should be expected. However, it has also been suggested that tropical plant species may be less capable of acclimating to a changing climate because they have evolved in tropical climates with lower seasonal variability than have temperate forests [Janzen, 1967; Hogan et al., 1991]. However, our modeling results suggests that the tropical plant species inside B2-TRF, which are exposed to a much greater diurnal fluctuations in climatic variables, may well be capable of adjusting to higher temperatures than those currently observed in natural tropical rain forests such as in the Amazon basin.

7. Summary and Conclusions

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Biosphere 2 Tropical Rain Forest Biome
  5. 3. Data
  6. 4. Version 3 of the Simple Biosphere Land Surface Model
  7. 5. Results
  8. 6. Discussion
  9. 7. Summary and Conclusions
  10. Appendix A:: Contributions to Reduction in Shortwave Radiation in B2-TRF
  11. Acknowledgments
  12. References
  13. Supporting Information

[72] This study implemented and challenged a land surface parameterization scheme widely used in natural ecosystems to simulate the behavior of vegetation inside the closed tropical rain forest biome in Biosphere 2 (B2-TRF). In order to accomplish this, data from different sources with differing availability and temporal resolution were pooled to create a time series of quality-controlled forcing data appropriate for land surface modeling application inside B2-TRF. In particular, the methods used to make an allowance for the unique radiation and wind speed environment of B2-TRF were major challenges. These methods represent an important enabling component of this study that is potentially likely to be of value in future modeling studies in B2-TRF.

[73] SiB3 proved capable of simulating the observed behavior of NEE for plants in B2-TRF, but some modifications to model formulation and parameter values were required that are important. Most significant were two modifications: a revised parameterization of soil respiration and the modification of the parameters describing thermal tolerance of vegetation. The preferred representation of soil respiration was a modified version of the formulation originally used in SiB3 in which soil respiration is calculated as the sum of layer-by-layer contributions weighted by root fraction in each layer, all normalized to match the observed long-term carbon balance of B2-TRF rather than being forced to assume a zero carbon balance as in the original form.

[74] To adequately simulate NEE in B2-TRF, the model parameters describing thermal stress in SiB3 had to be modified by calibration in such a way as to reduce the modeled net assimilation rate but then to maintain this rate to higher temperatures. This result suggests that tropical rain forest species can acclimate to higher temperatures than previously thought or that the plants in B2-TRF have shifted their composition to allow the aggregate community to function at higher temperatures, and plants in natural ecosystems could also. Given that environmental conditions in B2-TRF (warmer, drier, with higher CO2 concentration) are broadly comparable to those forecast by climate models, it is tempting to speculate that the Amazon rain forest may be more resilient to climate change than hitherto thought.

[75] The motivation for the present investigation was the belief that the Biosphere 2 tropical rain forest biome can potentially provide a currently missing bridge between laboratory-based studies and regional/large-scale experiments in natural tropical ecosystems. This study shows that land surface modeling in combination with available in situ observations can enhance understanding of physical and physiological aspects of an enclosed tropical rain forest ecosystem maintained in controlled meteorological conditions. Because the land surface model used has been widely applied in natural ecosystems around the globe, the study also establishes a direct link between controlled and natural ecosystems. Thus, B2-TRF may serve not only to provide a consistency test of models derived from real-world field experiments, but also as a basis for advancing modeling strategies and new parameterizations in an environment with well-defined boundary conditions. We invite the scientific community to challenge other ecosystem models inside Biosphere 2 to test their universality.

Appendix A:: Contributions to Reduction in Shortwave Radiation in B2-TRF

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Biosphere 2 Tropical Rain Forest Biome
  5. 3. Data
  6. 4. Version 3 of the Simple Biosphere Land Surface Model
  7. 5. Results
  8. 6. Discussion
  9. 7. Summary and Conclusions
  10. Appendix A:: Contributions to Reduction in Shortwave Radiation in B2-TRF
  11. Acknowledgments
  12. References
  13. Supporting Information

[76] Figure A1 is a schematic diagram of the time series of downward solar radiation measured at a single point in B2-TRF on a cloud-free day when the space frame shaded the sensor once. For most of the day (for example, for the time period A) the sensor is not shaded from the direct beam and the measured solar radiation during these periods is given by

  • equation image

where SWoutdiff and SWoutdir are the diffuse and direct components, respectively, of the solar beam outside B2. However, for a fraction f of the time, i.e., for the period B, the sensor is shaded and the measured total solar radiation is therefore only the diffuse component and given by

  • equation image
image

Figure A1. Schematic diagram of the diurnal cycle of downward solar radiation measured at a single point on a cloud-free day when the space frame shaded the radiation sensor just once.

Download figure to PowerPoint

[77] Because the sun is not a point source, the transition between these two values is not instantaneous; consequently the period B is in fact the “effective” period of total shading which comprises a period with total shading and two periods with partial shading. The biome average value of solar radiation inside B2 is the time-average sum of these nonshaded and shaded single point measured values, i.e., the weighted average:

  • equation image

[78] Substituting equations (A1) and (A2) into equation (A3), it can be shown that

  • equation image

thus confirming that the ratio of diffuse to direct shortwave radiation is the same inside B2-TRF as it is outside and not altered by the glass and space frame shading because, when averaged across the whole biome, both components are reduced by the common factor (1 − g)(1 − f).

[79] Figure A2 shows the cumulative downward solar radiation measured inside and outside B2-TRF on days that were selected to have clear sky together with the cumulative value for these same days of the downward shortwave radiation that would have been measured inside B2-TRF had there been no shading by the space frame. The latter is estimated from the measured value of solar radiation outside B2 reduced by a factor which is calculated as the time-average ratio of the measured components inside and outside during periods when the sensor was not shaded on these days (e.g., the ratio during the period A in Figure A1). On these selected clear-sky days, Figure A2 shows that the time-average fractional reduction in measured shortwave radiation inside B2-TRF relative to that outside is by a factor 0.485 (i.e., SWin/SWout), whereas the time-average fractional reduction in measured shortwave radiation inside B2-TRF relative to that outside would be by a factor 0.515 had there been no shading by the space frame (i.e., SWin*/SWout). From equation (A4) it therefore follows that

  • equation image

and from equation (A1) that

  • equation image

Substituting equation (A5) into (A6) and rearranging gives

  • equation image
image

Figure A2. Cumulative downward solar radiation measured inside (SWin) and outside (SWout) B2-TRF on days that were selected to have clear sky, together with the cumulative value for these same days of the downward shortwave radiation that would have been measured inside B2-TRF had there been no shading by the space frame (SWin*).

Download figure to PowerPoint

[80] On cloud-free days the average ratio of diffuse to total solar radiation (i.e., SWoutdiff/SWout) and direct to total solar radiation (i.e., SWoutdir/SWout) outside B2 are, respectively, 0.19 and 0.81; hence, from equation (A6) it follows that g = 0.48, and substituting this value of g into equation (A5) gives f = 0.07. In summary, on average the (dirty) glass therefore reduces solar radiation by 48% and the space frame reduces solar radiation by a further 7%, but neither reduction alters the ratio of diffuse to direct sunlight.

Acknowledgments

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Biosphere 2 Tropical Rain Forest Biome
  5. 3. Data
  6. 4. Version 3 of the Simple Biosphere Land Surface Model
  7. 5. Results
  8. 6. Discussion
  9. 7. Summary and Conclusions
  10. Appendix A:: Contributions to Reduction in Shortwave Radiation in B2-TRF
  11. Acknowledgments
  12. References
  13. Supporting Information

[81] This study was supported by B2-Earthscience, NSF Amazon-PIRE under grant 0730305, by NASA Earth and Science Fellowship under grant NNX09AO33H, and by the NSF Center for Sustainability of semi-Arid Hydrology and Riparian Areas (SAHRA) under the STC Program of the National Science Foundation, Agreement EAR-9876800 and NSF award DEB-0415977. The authors would like to acknowledge Ian T. Baker and A. Scott Denning for providing the original version of the SiB3 model; Hoshin Gupta for advice on parameter optimization; Muhammad Altaf Arain, Blake Farnsworth, and Uwe Rascher for providing the raw data from Biosphere 2; Joost van Haren, Katerina Dontsova, J. C. Villegas, Greg Barron-Gafford, Javier Espeleta, and John Adams for providing additional information on the biome and its soil properties; and James Broermann, Nate Bryant, and Michael Leuthold for technical support on model simulations. The authors would also like to thank Dennis Baldocchi along with the Associate Editor and two anonymous reviewers for valuable comments that substantially improved the manuscript.

References

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Biosphere 2 Tropical Rain Forest Biome
  5. 3. Data
  6. 4. Version 3 of the Simple Biosphere Land Surface Model
  7. 5. Results
  8. 6. Discussion
  9. 7. Summary and Conclusions
  10. Appendix A:: Contributions to Reduction in Shortwave Radiation in B2-TRF
  11. Acknowledgments
  12. References
  13. Supporting Information
  • Adams, H. D., M. Guardiola-Claramonte, G. A. Barron-Gafford, J. C. Villegas, D. D. Breshears, C. B. Zou, P. A. Troch, and T. E. Huxman (2009), Temperature sensitivity of drought-induced tree mortality portends increased regional die-off under global-change-type drought, Proc. Natl. Acad. Sci. U. S. A., 106(17), 70637066.
  • Adams, J., and J. Berry (1999), Thermal tolerance of rainforest species, paper presented at Plant Biology Annual Meeting, Am. Soc. of Plant Physiol., Baltimore, Md.,
  • Allen, J., and M. Nelson (1999), Biospherics and Biosphere 2, mission one (1991–1993): Overview and design, Ecol. Eng., 13(1–4), 1529.
  • Amthor, J. S., and D. D. Baldocchi (2001), Terrestrial higher plant respiration and net primary production, in Terrestrial Global Productivity, edited by J. Roy, B. Saugier, and H. A. Mooney, pp. 3359, Academic, San Diego, Calif.,
  • Arain, M. A., W. J. Shuttleworth, B. Farnsworth, J. Adams, and O. L. Sen (2000), Comparing micrometeorology of rain forests in Biosphere-2 and Amazon basin, Agric. For. Meteorol., 100(4), 273289.
  • Baker, I., A. S. Denning, N. Hanan, L. Prihodko, M. Uliasz, P. L. Vidale, K. Davis, and P. Bakwin (2003), Simulated and observed fluxes of sensible and latent heat and CO2 at the WLEF-TV tower using SiB2.5, Global Change Biol., 9(9), 12621277.
  • Baker, I. T., L. Prihodko, A. S. Denning, M. Goulden, S. Miller, and H. R. da Rocha (2008), Seasonal drought stress in the Amazon: Reconciling models and observations, J. Geophys. Res., 113, G00B01, doi:10.1029/2007JG000644.
  • Baldocchi, D. D. (2003), Assessing the eddy covariance technique for evaluating carbon dioxide exchange rates of ecosystems: Past, present, and future, Global Change Biol., 9, 479492.
  • Baldocchi, D. D., and J. S. Amthor (2001), Canopy photosynthesis: History, measurements, and models, in Terrestrial Global Productivity, edited by J. Roy, B. Saugier, and H. A. Mooney, pp. 931, Academic, San Diego, Calif.,
  • Betts, R. A., P. M. Cox, M. Collins, P. P. Harris, C. Huntingford, and C. D. Jones (2004), The role of ecosystem-atmosphere interactions in simulated Amazonian precipitation decrease and forest dieback under global climate warming, Theor. Appl. Climatol., 78, 157175, doi: 10.1007/s00704-004-0050-y.
  • Bonan, G. (2002), Ecological climatology: Concepts and Applications, 1st ed., 678 pp., Cambridge Univ. Press, New York.
  • Botta, A., N. Ramankutty, and J. A. Foley (2002), Long-term variations of climate and carbon fluxes over the Amazon basin, Geophys. Res. Lett., 29(9), 1319, doi:10.1029/2001GL013607.
  • Bradley, K. L., and K. S. Pregitzer (2007), Ecosystem assembly and terrestrial carbon balance under elevated CO2, Trends Ecol. Evol., 22(10), 538547.
  • Bruno, R. D., H. R. da Rocha, H. C. de Freitas, M. L. Goulden, and S. D. Miller (2006), Soil moisture dynamics in an eastern Amazonian tropical forest, Hydrol. Processes, 20(12), 24772489.
  • Carswell, F. E., P. Meir, E. V. Wandelli, L. C. M. Bonates, B. Kruijt, E. M. Barbosa, A. D. Nobre, J. Grace, and P. G. Jarvis (2000), Photosynthetic capacity in a central Amazonian rain forest, Tree Physiol., 20(3), 179186.
  • Chambers, J. Q., E. S. Tribuzy, L. C. Toledo, B. F. Crispim, N. Higuchi, J. dos Santos, A. C. Araujo, B. Kruijt, A. D. Nobre, and S. E. Trumbore (2004), Respiration from a tropical forest ecosystem: Partitioning of sources and low carbon use efficiency, Ecol. Appl., 14(4), S72S88.
  • Clapp, R. B., and G. M. Hornberger (1978), Empirical equations for some soil hydraulic properties, Water Resour. Res., 14(4), 601604.
  • Clark, D. A. (2004), Sources or sinks? The responses of tropical forests to current and future climate and atmospheric composition, Philos. Trans. R. Soc. London, Ser. B, 359, 477491, doi:10.1098/rstb.2003.1426.
  • Clark, D. A., S. C. Piper, C. D. Keeling, and D. B. Clark (2003), Tropical rain forest tree growth and atmospheric carbon dynamics linked to interannual temperature variation during 1984–2000, Proc. Natl. Acad. Sci. U. S. A., 100(10), 58525857.
  • Cockell, C. S., A. Southern, and A. Herrera (2000), Lack of UV radiation in Biosphere 2: Practical and theoretical effects on plants, Ecol. Eng., 16(2), 293299.
  • Colello, G. D., C. Grivet, P. J. Sellers, and J. A. Berry (1998), Modeling of energy, water, and CO2 flux in a temperate grassland ecosystem with SiB2: May–October 1987, J. Atmos. Sci., 55(7), 11411169.
  • Collatz, G. J., J. T. Ball, C. Grivet, and J. A. Berry (1991), Physiological and environmental regulation of stomatal conductance, photosynthesis and transpiration: A model that includes a laminar boundary-layer, Agric. For. Meterol., 54(2–4), 107136.
  • Cosby, B. J., G. M. Hornberger, R. B. Clapp, and T. R. Ginn (1984), A statistical exploration of the relationships of soil moisture characteristics to the physical properties of soils, Water Resour. Res., 20(6), 682690.
  • Crawford, T. M., and C. E. Duchon (1999), An improved parameterization for estimating effective atmospheric emissivity for use in calculating daytime downwelling longwave radiation, J. Appl. Meterol., 38(4), 474480.
  • Dai, Y. J., et al. (2003), The Common Land Model, Bull. Am. Meteorol. Soc., 84(8), 1013.
  • da Rocha, H. R., P. J. Sellers, G. J. Collatz, I. R. Wright, and J. Grace (1996), Calibration and use of the SiB2 model to estimate water vapour and carbon exchange at the ABRAC0S forest sites, in Amazonian Deforestation and Climate, edited by J. H. C. Gash, et al., pp. 459451, John Wiley, Chichester, U. K.,
  • Deardorff, J. W. (1978), Efficient prediction of ground surface temperature and moisture with inclusion of a layer of vegetation, J. Geophy. Res., 83, 18891903.
  • de Gonçalves, L. G. G., I. Baker, B. Christoffersen, M. Costa, N. Restrepo-Coupe, H. da Rocha, S. Saleska, and M. N. Muza (2010) The Large Scale Biosphere-Atmosphere Experiment in Amazônia Model-Data Intercomparison Project (LBA-DMIP) protocol, 24 pp., available at http://www.climatemodeling.org/lba-mip/(accessed 30 August 2010).
  • Dempster, W. F. (1999), Biosphere 2 engineering design, Ecol. Eng., 13(1–4), 3142.
  • Denning, A. S., G. J. Collatz, C. G. Zhang, D. A. Randall, J. A. Berry, P. J. Sellers, G. D. Colello, and D. A. Dazlich (1996), Simulations of terrestrial carbon metabolism and atmospheric CO2 in a general circulation model. 1. Surface carbon fluxes, Tellus, Ser. B, 48(4), 521542.
  • Doughty, C. E., and M. L. Goulden (2008), Are tropical forests near a high temperature threshold? J. Geophys. Res., 113, G00B07, doi:10.1029/2007JG000632.
  • Farquhar, G. D., S. V. Caemmerer, and J. A. Berry (1980), A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species, Planta, 149(1), 7890.
  • Gupta, H. V., S. Sorooshian, and P. O. Yapo (1998), Toward improved calibration of hydrologic models: Multiple and noncommensurable measures of information, Water Resour. Res., 34(4), 751763.
  • Gupta, H. V., L. A. Bastidas, S. Sorooshian, W. J. Shuttleworth, and Z. L. Yang (1999), Parameter estimation of a land surface scheme using multicriteria methods, J. Geophys. Res., 104(D16), 19,49119,503.
  • Hanan, N. P., J. A. Berry, S. B. Verma, E. A. Walter-Shea, A. E. Suyker, G. G. Burba, and A. S. Denning (2005), Testing a model of CO2, water and energy exchange in Great Plains tallgrass prairie and wheat ecosystems, Agric. For. Meterol., 131(3–4), 162179.
  • Hogan, K. P., A. P. Smith, and L. H. Ziska (1991), Potential effects of elevated CO2 and changes in temperature on tropical plants. Plant Cell Environ., 14, 763778.
  • Houghton, R. A. (2005), Aboveground forest biomass and the global carbon balance, Global Change Biol., 11(6), 945958.
  • Hutyra, L. R., J. W. Munger, E. Hammond-Pyle, S. R. Saleska, N. Restrepo-Coupe, B. C. Daube, P. B. de Camargo, and S. C. Wofsy (2008), Resolving systematic errors in estimates of net ecosystem exchange of CO2 and ecosystem respiration in a tropical forest biome, Agric. For. Meterol., 148(8–9), 12661279.
  • Huxman, T., P. Troch, J. Chorover, D. D. Breshears, S. Saleska, J. Pelletier, and X. Z. J. Espeleta (2009), The hills are alive: Earth science in a controlled environment, Eos Trans. AGU, 90(14), 120, doi:10.1029/2009EO140003.
  • Idso, S. B. (1981), A set of equations for full spectrum and 8 μm to 14 μm and 10.5 μm to 12.5 μm thermal radiation from cloudless skies, Water Resour. Res., 17(2), 295304.
  • Jackson, R. B., J. Canadell, J. R. Ehleringer, H. A. Mooney, O. E. Sala, and E. D. Schulze (1996), A global analysis of root distributions for terrestrial biomes, Oecologia, 108(3), 389411.
  • Janzen, D. H. (1967), Why mountain passes are higher in the tropics, Am. Nat., 101(919), 233249, doi:10.1086/282487.
  • Leigh, L. S., T. Burgess, B. D. V. Marino, and Y. D. Wei (1999), Tropical rainforest biome of Biosphere 2: Structure, composition and results of the first 2 years of operation, Ecol. Eng., 13(1–4), 6593.
  • Lin, G. H., B. D. V. Marino, Y. D. Wei, J. Adams, F. Tubiello, and J. A. Berry (1998), An experimental and modeling study of responses in ecosystems carbon exchanges to increasing CO2 concentrations using a tropical rainforest mesocosm, Aust. J. Plant Physiol., 25(5), 547556.
  • Lin, G. H., J. Adams, B. Farnsworth, Y. D. Wei, B. D. V. Marino, and J. A. Berry (1999), Ecosystem carbon exchange in two terrestrial ecosystem mesocosms under changing atmospheric CO2 concentrations, Oecologia, 119(1), 97108.
  • Liu, Y., H. V. Gupta, S. Sorooshian, L. A. Bastidas, and W. J. Shuttleworth (2004), Exploring parameter sensitivities of the land surface using a locally coupled land-atmosphere model, J. Geophys. Res., 109, D21101, doi:10.1029/2004JD004730.
  • Liu, Y., H. V. Gupta, S. Sorooshian, L. A. Bastidas, and W. J. Shuttleworth (2005), Constraining land surface and atmospheric parameters of a locally coupled model using observational data, J. Hydrometerol., 6(2), 156172.
  • Lloyd, J., and G. D. Farquhar (2008), Effects of rising temperatures and CO2 on the physiology of tropical forest trees, Philos. Trans. R. Soc. B, 363(1498), 18111817.
  • Lloyd, J., J. Grace, A. C. Miranda, P. Meir, S. C. Wong, B. S. Miranda, I. R. Wright, J. H. C. Gash, and J. McIntyre (1995), A simple calibrated model of Amazon rainforest productivity based on leaf biochemical properties, Plant Cell Environ., 18(10), 11291145.
  • Malhi, Y., A. D. Nobre, J. Grace, B. Kruijt, M. G. P. Pereira, A. Culf, and S. Scott (1998), Carbon dioxide transfer over a Central Amazonian rain forest, J. Geophys. Res., 103(D24), 31,59331,612.
  • Malhi, Y., D. D. Baldocchi, and P. G. Jarvis (1999), The carbon balance of tropical, temperate and boreal forests, Plant Cell Environ., 22(6), 715740.
  • Meir, P., J. Grace, A. Miranda, and J. Lloyd (1996), Soil respiration in a rainforest in Amazonia and in cerrado in central Brazil, in Amazonian Deforestation and Climate, edited by J. H. C. Gash, et al., pp. 319329, John Wiley, Chichester, U. K.,
  • Meir, P., B. Kruijt, M. Broadmeadow, E. Barbosa, O. Kull, F. Carswell, A. Nobre, and P. G. Jarvis (2002), Acclimation of photosynthetic capacity to irradiance in tree canopies in relation to leaf nitrogen concentration and leaf mass per unit area, Plant Cell Environ., 25, 343357.
  • Miller, S. D., M. L. Goulden, M. C. Menton, H. R. da Rocha, H. C. de Freitas, A. Figueira, and C. A. D. de Sousa (2004), Biometric and micrometeorological measurements of tropical forest carbon balance, Ecol. Appl., 14(4), S114S126.
  • Myneni, R. B., et al. (2007), Large seasonal swings in leaf area of Amazon rainforests, Proc. Natl. Acad. Sci. U. S. A., 104(12), 48204823.
  • Nelson, M., T. Burgess, A. Alling, N. Alvarez-Romo, W. Dempster, R. Walford, and J. Allen (1993), Using a closed ecological system to study Earth's biosphere: Initial results from Biosphere 2, BioScience, 43(4), 225236.
  • Nepstad, D. C., et al. (2002), The effects of partial throughfall exclusion on canopy processes, aboveground production, and biogeochemistry of an Amazon forest, J. Geophys. Res., 107(D20), 8085, doi:10.1029/2001JD000360.
  • Norby, R. J., et al. (2002), Net primary productivity of a CO2-enriched deciduous forest and the implications for carbon storage, Ecol. Appl., 12(5), 12611266.
  • Norman, J. M., R. Garcia, and S. B. Verma (1992), Soil surface CO2 fluxes and the carbon budget of a grassland, J. Geophys. Res., 97(D17), 18,84518,853.
  • Nowak, R. S., D. S. Ellsworth, and S. D. Smith (2004), Functional responses of plants to elevated atmospheric CO2: Do photosynthetic and productivity data from FACE experiments support early predictions? New Phytol., 162(2), 253280.
  • Osmond, B., et al. (2004), Changing the way we think about global change research: Scaling up in experimental ecosystem science, Global Change Biol., 10(4), 393407.
  • Potter, C., S. Klooster, C. R. de Carvalho, V. B. Genovese, A. Torregrosa, J. Dungan, M. Bobo, and J. Coughlan (2001), Modeling seasonal and interannual variability in ecosystem carbon cycling for the Brazilian Amazon region, J. Geophys. Res., 106(D10), 10,42310,446.
  • Raich, J. W., E. B. Rastetter, J. M. Melillo, D. W. Kicklighter, P. A. Steudler, B. J. Peterson, A. L. Grace, B. Moore, and C. J. Vorosmarty (1991), Potential net primary productivity in South America: Application of a global model, Ecol. Appl., 1(4), 399429.
  • Randall, D. A., D. A. Dazlich, C. Zhang, A. S. Denning, P. J. Sellers, C. J. Tucker, L. Bounoua, S. O. Los, C. O. Justice, and I. Fung (1996), A revised land surface parameterization (SiB2) for GCMs. 3. The greening of the Colorado State University general circulation model, J. Clim., 9(4), 738763.
  • Rascher, U., et al. (2004), Functional diversity of photosynthesis during drought in a model tropical rainforest: The contributions of leaf area, photosynthetic electron transport and stomatal conductance to reduction in net ecosystem carbon exchange, Plant Cell Environ., 27(10), 12391256.
  • Rosenthal, Y., B. Farnsworth, F. V. R. Romo, G. H. Lin, and B. D. V. Marino (1999), High quality, continuous measurements of CO2 in Biosphere 2 to assess whole mesocosm carbon cycling, Ecol. Eng., 13(1–4), 249262.
  • Rosolem, R., L. A. Bastidas, W. J. Shuttleworth, L. G. G. de Goncalves, E. J. Burke, H. R. da Rocha, S. D. Miller, and M. L. Goulden (2005), Evaluation of effects of selective logging on energy-water and carbon exchange processes, in Regional Hydrological Impacts of Climatic Change: Hydroclimatic Variability, edited by S. Franks, et al., IAHS Publ., 296, 118125.
  • Rosolem, R., W. J. Shuttleworth, and L. G. G. de Goncalves (2008), Is the data collection period of the Large-Scale Biosphere-Atmosphere Experiment in Amazonia representative of long-term climatology? J. Geophys. Res., 113, G00B09, doi:10.1029/2007JG000628.
  • Saleska, S. R., M. R. Shaw, M. Fischer, J. Dunne, C. J. Still, M. Holman, and J. Harte (2002), Plant community composition mediates both large transient decline and predicted long-term recovery of soil carbon under climate warming, Global Biogeochem. Cycles, 16(4), 1055, doi:10.1029/2001GB001573.
  • Saleska, S. R., et al. (2003), Carbon in Amazon forests: Unexpected seasonal fluxes and disturbance-induced losses, Science, 302(5650), 15541557.
  • Saleska, S. R., K. Didan, A. R. Huete, and H. R. da Rocha (2007), Amazon forests green-up during 2005 drought, Science, 318(5850), 612.
  • Samanta, A., S. Ganguly, H. Hashimoto, S. Devadiga, E. Vermote, Y. Knyazikhin, R. R. Nemani, and R. B. Myneni (2010), Amazon forests did not green-up during the 2005 drought, Geophys. Res. Lett., 37, L05401, doi:10.1029/2009GL042154.
  • Sato, N., P. J. Sellers, D. A. Randall, E. K. Schneider, J. Shukla, J. L. K. III, Y.-T. Hou, and E. Albertazzi (1989), Implementing the Simple Biosphere model (SiB) in a general circulation model: Methodologies and results, NASA Conf. Rep., CR 185509.
  • Scott, H. J. (1999), Characteristics of soils in the tropical rainforest biome of Biosphere 2 after 3 years, Ecol. Eng., 13(1–4), 95106.
  • Sellers, P. J., Y. Mintz, Y. C. Sud, and A. Dalcher (1986), A Simple Biosphere model (SiB) for use within general circulation models, J. Atmos. Sci., 43(6), 505531.
  • Sellers, P. J., J. A. Berry, G. J. Collatz, C. B. Field, and F. G. Hall (1992), Canopy reflectance, photosynthesis, and transpiration. 3. A reanalysis using improved leaf models and a new canopy integration scheme, Remote Sens. Environ., 42(3), 187216.
  • Sellers, P. J., D. A. Randall, G. J. Collatz, J. A. Berry, C. B. Field, D. A. Dazlich, C. Zhang, G. D. Collelo, and L. Bounoua (1996a), A revised land surface parameterization (SiB2) for atmospheric GCMs. 1. Model formulation, J. Clim., 9(4), 676705.
  • Sellers, P. J., S. O. Los, C. J. Tucker, C. O. Justice, D. A. Dazlich, G. J. Collatz, and D. A. Randall (1996b), A revised land surface parameterization (SiB2) for atmospheric GCMs. 2. The generation of global fields of terrestrial biophysical parameters from satellite data, J. Clim., 9(4), 706737.
  • Sellers, P. J., et al. (1997), Modeling the exchanges of energy, water, and carbon between continents and the atmosphere, Science, 275(5299), 502509.
  • Shaver, G. R., S. M. Bret-Harte, M. H. Jones, J. Johnstone, L. Gough, J. Laundre, and F. S. Chapin (2001), Species composition interacts with fertilizer to control long-term change in tundra productivity, Ecology, 82(11), 31633181.
  • Sotta, E. D., P. Meir, Y. Malhi, A. D. Nobre, M. Hodnett, and J. Grace (2004), Soil CO2 efflux in a tropical forest in the central Amazon, Global Change Biol., 10(5), 601617.
  • Stensrud, D. J. (2007), Parameterization schemes: keys to understanding numerical weather prediction models, 1st ed., 459 pp., Cambridge Univ. Press, New York.
  • Stöckli, R., and P. L. Vidale (2005), Modeling diurnal to seasonal water and heat exchanges at European Fluxnet sites, Theor. Appl. Climatol., 80(2–4), 229243.
  • Stull, R. B. (1988), An Introduction to Boundary Layer Meteorology, 1st ed., 666 pp., Springer, New York.
  • Tian, H., J. M. Melillo, D. W. Kicklighter, A. D. McGuire, J. Helfrich, B. Moore, and C. J. Vorosmarty (2000), Climatic and biotic controls on annual carbon storage in Amazonian ecosystems, Global Ecol. Biogeogr., 9(4), 315335.
  • Tian, H. Q., J. M. Melillo, D. W. Kicklighter, A. D. McGuire, J. V. K. Helfrich, B. Moore, and C. J. Vorosmarty (1998), Effect of interannual climate variability on carbon storage in Amazonian ecosystems, Nature, 396(6712), 664667.
  • Tubiello, F. N., G. Lin, J. W. Druitt, and B. D. V. Marino (1999), Ecosystem-level evapotranspiration and water-use efficiency in the desert biome of Biosphere 2, Ecol. Eng., 13(1–4), 263271.
  • Vidale, P. L., and R. Stöckli (2005), Prognostic canopy air space solutions for land surface exchanges, Theor. Appl. Climatol., 80(2–4), 245257.
  • Wallace, J. M., and P. V. Hobbs (1977), Atmospheric Science: An Introductory Survey, 1st ed., 467 pp., Academic, New York.
  • Wong, S. C., I. R. Cowan, and G. D. Farquhar (1985a), Leaf conductance in relation to rate of CO2 assimilation. 1. Influence of nitrogen nutrition, phosphorus nutrition, photon flux density, and ambient partial pressure of CO2 during ontogeny, Plant Physiol., 78(4), 821825.
  • Wong, S. C., I. R. Cowan, and G. D. Farquhar (1985b), Leaf conductance in relation to rate of CO2 assimilation. 2. Effects of short-term exposures to different photon flux densities, Plant Physiol., 78(4), 826829.
  • Wong, S. C., I. R. Cowan, and G. D. Farquhar (1985c), Leaf conductance in relation to rate of CO2 assimilation. 3. Influences of water-stress and photoinhibition, Plant Physiol., 78(4), 830834.
  • Wullschleger, S. D. (1993), Biochemical limitation to carbon assimilation in C3 plants. A retrospective analysis of the A/Ci curves from 109 species, J. Exp. Bot., 44(262), 907920.
  • Xiao, X. M., S. Hagen, Q. Y. Zhang, M. Keller, and B. Moore (2006), Detecting leaf phenology of seasonally moist tropical forests in South America with multi-temporal MODIS images, Remote Sens. Environ., 103(4), 465473.
  • Zabel, B., P. Hawes, H. Stuart, and B. D. V. Marino (1999), Construction and engineering of a created environment: Overview of the Biosphere 2 closed system, Ecol. Eng., 13(1–4), 4363.

Supporting Information

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Biosphere 2 Tropical Rain Forest Biome
  5. 3. Data
  6. 4. Version 3 of the Simple Biosphere Land Surface Model
  7. 5. Results
  8. 6. Discussion
  9. 7. Summary and Conclusions
  10. Appendix A:: Contributions to Reduction in Shortwave Radiation in B2-TRF
  11. Acknowledgments
  12. References
  13. Supporting Information
FilenameFormatSizeDescription
jgrg724-sup-0001-t01.txtplain text document3KTab-delimited Table 1.
jgrg724-sup-0002-t02.txtplain text document1KTab-delimited Table 2.

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