G. James Collatz

Physiological and environmental regulation of stomatal conductance , photosynthesis and transpiration : a model that includes a laminar boundary layer

Abstract This paper presents a system of models for the simulation of gas and energy exchange of a leaf of a C3 plant in free air. The physiological processes are simulated by sub-models that: (a) give net photosynthesis (An) as a function of environmental and leaf parameters and stomatal conductance (gs); (b) give g, as a function of the concentration of CO2 and H2O in air at the leaf surface and the current rate of photosynthesis of the leaf. An energy balance and mass transport sub-model is used to couple the physiological processes through a variable boundary layer to the ambient environment. The models are based on theoretical and empirical analysis of gs, and An measured at the leaf level, and tests with intact attached leaves of soybeans show very good agreement between predicted and measured responses of gs and An over a wide range of leaf temperatures (20–35°C), CO2 concentrations (10–90 Pa), air to leaf water vapor deficits (0.5–3.7 kPa) and light intensities (100–2000 μmol m−2s−1). The combined models were used to simulate the responses of latent heat flux (λE) and gs for a soybean canopy for the course of an idealized summer day, using the ‘big-leaf’ approximation. Appropriate data are not yet available to provide a rigorous test of these simulations, but the response patterns are similar to field observations. These simulations show a pronounced midday depression of λE and gs at low or high values of boundary-layer conductance. Deterioration of plant water relations during midday has often been invoked to explain this common natural phenomenon, but the present models do not consider this possibility. Analysis of the model indicates that the simulated midday depression is, in part, the result of positive feedback mediated by the boundary layer. For example, a change in gs affects An and λE. As a consequence, the temperature, humidity and CO2 concentration of the air in the proximity of the stomata (e.g. the air at the leaf surface) change and these, in turn, affect gs. The simulations illustrate the possible significance of the boundary layer in mediating feedback loops which affect the regulation of stomatal conductance and λE. The simulations also examine the significance of changing the response properties of the photosynthetic component of the model by changing leaf protein content or the CO2 concentration of the atmosphere.

A Revised Land Surface Parameterization (SiB2) for Atmospheric GCMS. Part II: The Generation of Global Fields of Terrestrial Biophysical Parameters from Satellite Data

Abstract The global parameter fields used in the revised Simple Biosphere Model (SiB2) of Sellers et al. are reviewed. The most important innovation over the earlier SiB1 parameter set of Dorman and Sellers is the use of satellite data to specify the time-varying phonological properties of FPAR, leaf area index. and canopy greenness fraction. This was done by processing a monthly 1° by 1° normalized difference vegetation index (NDVI) dataset obtained farm Advanced Very High Resolution Radiometer red and near-infrared data. Corrections were applied to the source NDVI dataset to account for (i) obvious anomalies in the data time series, (ii) the effect of variations in solar zenith angle, (iii) data dropouts in cold regions where a temperature threshold procedure designed to screen for clouds also eliminated cold land surface points, and (iv) persistent cloud cover in the Tropics. An outline of the procedures for calculating the land surface parameters from the corrected NDVI dataset is given, and a brief d...

Effects of climate and atmospheric CO2 partial pressure on the global distribution of C4 grasses: present, past, and future

Abstract C4 photosynthetic physiologies exhibit fundamentally different responses to temperature and atmospheric CO2 partial pressures (pCO2) compared to the evolutionarily more primitive C3 type. All else being equal, C4 plants tend to be favored over C3 plants in warm humid climates and, conversely, C3 plants tend to be favored over C4 plants in cool climates. Empirical observations supported by a photosynthesis model predict the existence of a climatological crossover temperature above which C4 species have a carbon gain advantage and below which C3 species are favored. Model calculations and analysis of current plant distribution suggest that this pCO2-dependent crossover temperature is approximated by a mean temperature of 22°C for the warmest month at the current pCO2 (35 Pa). In addition to favorable temperatures, C4 plants require sufficient precipitation during the warm growing season. C4 plants which are predominantly graminoids of short stature can be competitively excluded by trees (nearly all C3 plants) – regardless of the photosynthetic superiority of the C4 pathway – in regions otherwise favorable for C4. To construct global maps of the distribution of C4 grasses for current, past and future climate scenarios, we make use of climatological data sets which provide estimates of the mean monthly temperature to classify the globe into areas which should favor C4 photosynthesis during at least 1 month of the year. This area is further screened by excluding areas where precipitation is <25 mm per month during the warm season and by selecting areas classified as grasslands (i.e., excluding areas dominated by woody vegetation) according to a global vegetation map. Using this approach, grasslands of the world are designated as C3, C4, and mixed under current climate and pCO2. Published floristic studies were used to test the accuracy of these predictions in many regions of the world, and agreement with observations was generally good. We then make use of this protocol to examine changes in the global abundance of C4 grasses in the past and the future using plausible estimates for the climates and pCO2. When pCO2 is lowered to pre-industrial levels, C4 grasses expanded their range into large areas now classified as C3 grasslands, especially in North America and Eurasia. During the last glacial maximum (∼18 ka BP) when the climate was cooler and pCO2 was about 20 Pa, our analysis predicts substantial expansion of C4 vegetation – particularly in Asia, despite cooler temperatures. Continued use of fossil fuels is expected to result in double the current pCO2 by sometime in the next century, with some associated climate warming. Our analysis predicts a substantial reduction in the area of C4 grasses under these conditions. These reductions from the past and into the future are based on greater stimulation of C3 photosynthetic efficiency by higher pCO2 than inhibition by higher temperatures. The predictions are testable through large-scale controlled growth studies and analysis of stable isotopes and other data from regions where large changes are predicted to have occurred.

Carbon 13 exchanges between the atmosphere and biosphere

We present a detailed investigation of the gross 12C and 13C exchanges between the atmosphere and biosphere and their influence on the δ13C variations in the atmosphere. The photosynthetic discrimination Δ against 13C is derived from a biophysical model coupled to a general circulation model [Sellers et al., 1996a], where stomatal conductance and carbon assimilation are determined simultaneously with the ambient climate. The δ13C of the respired carbon is calculated by a biogeochemical model [Potter et al., 1993; Randerson et al., 1996] as the sum of the contributions from compartments with varying ages. The global flux-weighted mean photosynthetic discrimination is 12–16‰, which is lower than previous estimates. Factors that lower the discrimination are reduced stomatal conductance and C4 photosynthesis. The decreasing atmospheric δ13C causes an isotopic disequilibrium between the outgoing and incoming fluxes; the disequilibrium is ∼0.33‰ for 1988. The disequilibrium is higher than previous estimates because it accounts for the lifetime of trees and for the ages rather than turnover times of the biospheric pools. The atmospheric δ13C signature resulting from the biospheric fluxes is investigated using a three-dimensional atmospheric tracer model. The isotopic disequilibrium alone produces a hemispheric difference of ∼0.02‰ in atmospheric δ13C, comparable to the signal from a hypothetical carbon sink of 0.5 Gt C yr−1 into the midlatitude northern hemisphere biosphere. However, the rectifier effect, due to the seasonal covariation of CO2 fluxes and height of the atmospheric boundary layer, yields a background δ13C gradient of the opposite sign. These effects nearly cancel thus favoring a stronger net biospheric uptake than without the background CO2 gradient. Our analysis of the globally averaged carbon budget for the decade of the 1980s indicates that the biospheric uptake of fossil fuel CO2 is likely to be greater than the oceanic uptake; the relative proportions of the sinks cannot be uniquely determined using 12C and 13C alone. The land-ocean sink partitioning requires, in addition, information about the land use source, isotopic disequilibrium associated with gross oceanic exchanges, as well as the fractions of C3 and C4 vegetation involved in the biospheric uptake.

A three-dimensional synthesis study of δ18O in atmospheric CO2: 1. Surface fluxes

The isotope O-18 in CO2 is of particular interest in studying the global carbon cycle because it is sensitive to the processes by which the global land biosphere absorbs and respires CO2. Carbon dioxide and water exchange isotopically both in leaves and in soils, and the O-18 character of atmospheric CO2 is strongly influenced by the land biota, which should constrain the gross primary productivity and total respiration of land ecosystems, In this study we calculate the global surface fluxes of O-18 for vegetation and soils using the SiB2 biosphere model coupled with the Colorado State University general circulation model. This approach makes it possible to use physiological variables that are consistently weighted by the carbon assimilation rate and integrated through the vegetation canopy, We also calculate the air-sea exchange of O-18 and the isotopic character of fossil emissions and biomass burning. Global mean values of the isotopic exchange with each reservoir are used to close the global budget of O-18 in CO2 results confirm the fact that the land biota exert a dominant control on the delta(18)O of the atmospheric reservoir, At the global scale, exchange with the canopy produces an isotopic enrichment of CO2, whereas exchange with soils has the opposite effect.

Global Interannual Variations in Sea Surface Temperature and Land Surface Vegetation, Air Temperature, and Precipitation

Abstract Anomalies in global vegetation greenness, SST, land surface air temperature, and precipitation exhibit linked, low-frequency interannual variations. These interannual variations were detected and analyzed for 1982–90 with a multivariate spectral method. The two most dominant signals for 1982–90 had periods of about 2.6 and 3.4 yr. Signals centered at 2.6 years per cycle corresponded to variations in the El Nino–Southern Oscillation index and explained about 28% of the variance in anomalies of SST, land surface air temperature, precipitation, and vegetation; these signals were most pronounced in 1) SST anomalies in the eastern equatorial Pacific Ocean, 2) land surface vegetation and precipitation anomalies in tropical and subtropical regions, and 3) land surface vegetation, precipitation, and temperature anomalies in North America. Signals at 3.4 years per cycle corresponded to variations in the North Atlantic oscillation index and explained 8.6% of the variance in the combined datasets; their occ...

Forecasting Fire Season Severity in South America Using Sea Surface Temperature Anomalies

Sea surface temperature anomalies can predict annual fire season severity in South America up to 3 to 5 months in advance. Fires in South America cause forest degradation and contribute to carbon emissions associated with land use change. We investigated the relationship between year-to-year changes in fire activity in South America and sea surface temperatures. We found that the Oceanic Niño Index was correlated with interannual fire activity in the eastern Amazon, whereas the Atlantic Multidecadal Oscillation index was more closely linked with fires in the southern and southwestern Amazon. Combining these two climate indices, we developed an empirical model to forecast regional fire season severity with lead times of 3 to 5 months. Our approach may contribute to the development of an early warning system for anticipating the vulnerability of Amazon forests to fires, thus enabling more effective management with benefits for climate and air quality.

Forest Disturbance and North American Carbon Flux

North Americas forests are thought to be a significant sink for atmospheric carbon. Currently, the rate of sequestration by forests on the continent has been estimated at 0.23 petagrams of carbon per year, though the uncertainty about this estimate is nearly 50%. This offsets about 13% of the fossil fuel emissions from the continent [Pacala et al., 2007]. However, the high level of uncertainty in this estimate and the scientific communitys limited ability to predict the future direction of the forest carbon flux reflect a lack of detailed knowledge about the effects of forest disturbance and recovery across the continent. The North American Carbon Program (NACP), an interagency initiative to better understand the distribution, origin, and fate of North American sources and sinks of carbon, has highlighted forest disturbance as a critical factor constraining carbon dynamics [Wofsy and Harris, 2002]. National forest inventory programs in Canada, the United States, and Mexico provide important information, but they lack the needed spatial and temporal detail to support annual estimation of carbon fluxes across the continent. To help with this, the NACP recommends that scientists use detailed remote sensing of the land surface to characterize disturbance.

Profiles of photosynthetically active radiation, nitrogen and photosynthetic capacity in the boreal forest: Implications for scaling from leaf to canopy

Profiles of photosynthetically active radiation (PAR), leaf nitrogen per unit leaf area (Narea), and photosynthetic capacity (Amax) were measured in an aspen, two jack pine, and two black spruce stands in the BOREAS northern study area. Narea decreased with decreasing %PAR in each stand, in all conifer stands combined (r=0.52) and in all stands combined (r=0.46). Understory alder had higher Narea for similar %PAR than did aspen early in the growing season. Amax decreased with decreasing Narea, except for the negative correlation between Narea and Amax during shoot flush for jack pine. For the middle and late growing season data, Narea and Amax had r values of 0.51 for all stands combined and 0.60 for all conifer stands combined. For similar Narea the aspen stand had higher Amax than did the conifer stands. Photosynthetic capacity expressed as a percentage of Amax at the top of the canopy (%Amax0) decreased with %PAR similarly in all stands, but %Amax0 decreased at a much slower rate than did %PAR. To demonstrate the implications of the vertical distribution of Amax, three different assumptions were used to scale leaf Amax to the canopy (Acan-max): (1) constant Amax with canopy depth, (2)Amax scaled proportionally to %PAR, and (3) a linear relationship between Amax and cumulative leaf area index derived from our data. The first and third methods resulted in similar Acan-max; the second was much lower. All methods resulted in linear correlations between normalized difference vegetation indices measured from a helicopter and Acan-max (r=0.97, 0.93, and 0.97, respectively), but the slope was strongly influenced by the scaling method.

Implications of the carbon cycle steady state assumption for biogeochemical modeling performance and inverse parameter retrieval

We analyze the impacts of the steady state assumption on inverse model parameter retrieval from biogeochemical models. An inverse model parameterization study using eddy covariance CO 2 flux data was performed with the Carnegie Ames Stanford Approach (CASA) model under conditions of strict and relaxed carbon cycle steady state assumption (CCSSA) in order to evaluate both the robustness of the models structure for the simulation of net ecosystem carbon fluxes and the assessment of the CCSSA effects on simulations and parameter estimation. Net ecosystem production (NEP) measurements from several eddy covariance sites were compared with NEP estimates from the CASA model driven by local weather station climate inputs as well as by remotely sensed fraction of photosynthetically active radiation absorbed by vegetation and leaf area index. The parameters considered for optimization are directly related to aboveground and belowground modeled responses to temperature and water availability, as well as a parameter (η) that relaxed the CCSSA in the model, allowing for site level simulations to be initialized either as net sinks or sources. A robust relationship was observed between NEP observations and predictions for most of the sites through the range of temporal scales considered (daily, weekly, biweekly, and monthly), supporting the conclusion that the model structure is able to capture the main processes explaining NEP variability. Overall, relaxing CCSSA increased model efficiency (21%) and decreased normalized average error (-92%). Intersite variability was a major source of variance in model performance differences between fixed (CCSSA f ) and relaxed (CCSSA r ) CCSSA conditions. These differences were correlated with mean annual NEP observations, where an average increase in modeling efficiency of 0.06 per 100 g Cm -2 a -1 (where a is years) of NEP is observed (α < 0.003). The parameter η was found to be a key parameter in the optimization exercise, generating significant model efficiency losses when removed from the initial parameter set and parameter uncertainties were significantly lower under CCSSA r . Moreover, modeled soil carbon stocks were generally closer to observations once the steady state assumption was relaxed. Finally, we also show that estimates of individual parameters are affected by the steady state assumption. For example, estimates of radiation-use efficiency were strongly affected by the CCSSA f indicating compensation effects for the inadequate steady state assumption, leading to effective and thus biased parameters. Overall, the importance of model structural evaluation in data assimilation approaches is thus emphasized.