M. Altaf Arain

McMaster University, Hamilton, Ontario, Canada

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Publications (88)273.64 Total impact

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    ABSTRACT: Evapotranspiration (E) in the Amazon connects forest function and regional climate via its role in precipitation recycling. However, the mechanisms regulating water supply to vegetation and its demand for water remain poorly understood, especially during periods of seasonal water deficits In this study, we address two main questions: First, how do mechanisms of water supply (indicated by rooting depth and groundwater) and vegetation water demand (indicated by stomatal conductance and intrinsic water use efficiency) control evapotranspiration (E) along broad gradients of climate and vegetation from equatorial Amazonia to Cerrado, and second, how do these inferred mechanisms of supply and demand compare to those employed by a suite of ecosystem models? We used a network of eddy covariance towers in Brazil coupled with ancillary measurements to address these questions. With respect to the magnitude and seasonality of E, models have much improved in equatorial tropical forests by eliminating most dry season water limitation, diverge in performance in transitional forests where seasonal water deficits are greater, and mostly capture the observed seasonal depressions in E at Cerrado. However, many models depended universally on either deep roots or groundwater to mitigate dry season water deficits, the relative importance of which we found does not vary as a simple function of climate or vegetation. In addition, canopy stomatal conductance (gs) regulates dry season vegetation demand for water at all except the wettest sites even as the seasonal cycle of E follows that of net radiation. In contrast, some models simulated no seasonality in gs, even while matching the observed seasonal cycle of E. We suggest that canopy dynamics mediated by leaf phenology may play a significant role in such seasonality, a process poorly represented in models. Model bias in gs and E, in turn, was related to biases arising from the simulated light response (gross primary productivity, GPP) or the intrinsic water use efficiency of photosynthesis (iWUE). We identified deficiencies in models which would not otherwise be apparent based on a simple comparison of simulated and observed rates of E. While some deficiencies can be remedied by parameter tuning, in most models they highlight the need for continued process development of belowground hydrology and in particular, the biological processes of root dynamics and leaf phenology, which via their controls on E, mediate vegetation-climate feedbacks in the tropics.
    Agricultural and Forest Meteorology 06/2014; 191:33-50. · 3.42 Impact Factor
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    ABSTRACT: Biogeosciences Discussions This discussion paper is/has been under review for the journal Biogeosciences (BG). As a key component of the carbon cycle, soil respiration (SR) is being increasingly studied to improve our mechanistic understanding of this important carbon flux. Predicting ecosystem responses to climate change often depends on extrapolation of current relationships between ecosystem processes and their climatic drivers to conditions not yet experienced by the ecosystem. This raises the question to what extent these relationships remain unaltered beyond the current climatic window for which observations are available to constrain the relationships. Here, we evaluate whether current responses of SR to fluctuations in soil temperature and soil water content can be used to predict SR under altered rainfall patterns, which are projected for many regions around the world. Using data from 38 precipitation manipulation experiments conducted in various environments, we tested the hypothesis that a model parameterized with data from the control plots (using soil temperature and water content as predictor variables) could adequately predict SR measured in the manipulated treatment. Only for seven of these 38 experiments, this hypothesis was rejected, suggesting that there are no serious problems associated with extrapolating current moisture responses to future climate conditions. However, the experiments providing daily measurements of SR did reveal the limits to applying current soil moisture responses for predicting this flux under altered precipitation regimes. Regression tree analysis demonstrated that measurement frequency was crucial; our hypothesis could be rejected only for experiments with measurement intervals of less than 11 days, and was not rejected for any of the 24 experiments with larger measurement intervals. This highlights the importance of high-frequency measurements when studying effects of altered precipitation on SR, probably because infrequent measurement schemes have insufficient capacity to detect shifts in the climate-dependencies of SR. We strongly recommend that future experiments focus more strongly on establishing response functions across a broader range of precipitation regimes and soil moisture conditions. Such experiments should make accurate measurements of water availability, they require high-frequency SR measurements and they should consider both instantaneous responses and the potential legacy effects of climate extremes.
    Biogeosciences 03/2014; 11(11):853-899. · 3.75 Impact Factor
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    ABSTRACT: A fundamental question connecting terrestrial ecology and global climate change is the sensitivity of key terrestrial biomes to climatic variability and change. The Amazon region is such a key biome: it contains unparalleled biological diversity, a globally significant store of organic carbon, and it is a potent engine driving global cycles of water and energy. The importance of understanding how land surface dynamics of the Amazon region respond to climatic variability and change is widely appreciated, but despite significant recent advances, large gaps in our understanding remain. Understanding of energy and carbon exchange between terrestrial ecosystems and the atmosphere can be improved through direct observations and experiments, as well as through modeling activities. Land surface/ecosystem models have become important tools for extrapolating local observations and understanding to much larger terrestrial regions. They are also valuable tools to test hypothesis on ecosystem functioning. Funded by NASA under the auspices of the LBA (the Large-Scale Biosphere–Atmosphere Experiment in Amazonia), the LBA Data Model Intercomparison Project (LBA-DMIP) uses a comprehensive data set from an observational network of flux towers across the Amazon, and an ecosystem modeling community engaged in ongoing studies using a suite of different land surface and terrestrial ecosystem models to understand Amazon forest function. Here an overview of this project is presented accompanied by a description of the measurement sites, data, models and protocol.
    Agricultural and Forest Meteorology 12/2013; 182-183(SI):111-127. · 3.42 Impact Factor
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    ABSTRACT: Even though dissolved organic carbon (DOC) is the most active carbon (C) cycling that takes place in soil organic carbon (SOC) pools, it is missing from the global C budget. Fluxes in DOC are critical to aquatic ecosystem inputs and contribute to C balances of terrestrial ecosystems. Only a few ecosystem models have attempted to integrate DOC dynamics into terrestrial C cycling. This study introduces a new process-based model, TRIPLEX-DOC that is capable of estimating DOC dynamics in forest soils by incorporating both ecological drivers and biogeochemical processes. TRIPLEX-DOC was developed from Forest-DNDC, a biogeochemical model simulating C and nitrogen (N) dynamics, coupled with a new DOC process module that predicts metabolic transformations, sorption/desorption, and DOC leaching in forest soils. The model was validated against field observations of DOC concentrations and fluxes at white pine forest stands located in southern Ontario, Canada. The model was able to simulate seasonal dynamics of DOC concentrations and the magnitudes observed within different soil layers, as well as DOC leaching in the age-sequence of these forests. Additionally, TRIPLEX-DOC estimated the effect of forest harvesting on DOC leaching, with a significant increase following harvesting, illustrating that change in land use is of critical importance in regulating DOC leaching in temperate forests as an important source of C input to aquatic ecosystems.
    Geoscientific Model Development Discussions 06/2013; 6(2):3473-3508.
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    ABSTRACT: Phenology, by controlling the seasonal activity of vegetation on the land surface, plays a fundamental role in regulating photosynthesis and other ecosystem processes, as well as competitive interactions and feedbacks to the climate system. We conducted an analysis to evaluate the representation of phenology, and the associated seasonality of ecosystem-scale CO2 exchange, in 14 models participating in the North American Carbon Program Site Synthesis. Model predictions were evaluated using long-term measurements (emphasizing the period 20002006) from 10 forested sites within the AmeriFlux and Fluxnet-Canada networks. In deciduous forests, almost all models consistently predicted that the growing season started earlier, and ended later, than was actually observed; biases of 2 weeks or more were typical. For these sites, most models were also unable to explain more than a small fraction of the observed interannual variability in phenological transition dates. Finally, for deciduous forests, misrepresentation of the seasonal cycle resulted in over-prediction of gross ecosystem photosynthesis by +160 145 g C m-2 yr-1 during the spring transition period and +75 +/- 130 g C m-2 yr-1 during the autumn transition period (13% and 8% annual productivity, respectively) compensating for the tendency of most models to under-predict the magnitude of peak summertime photosynthetic rates. Models did a better job of predicting the seasonality of CO2 exchange for evergreen forests. These results highlight the need for improved understanding of the environmental controls on vegetation phenology and incorporation of this knowledge into better phenological models. Existing models are unlikely to predict future responses of phenology to climate change accurately and therefore will misrepresent the seasonality and interannual variability of key biosphereatmosphere feedbacks and interactions in coupled global climate models.
    Global Change Biology 02/2013; 18:566-584. · 6.91 Impact Factor
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    ABSTRACT: There is a continued need for models to improve consistency and agreement with observations [Friedlingstein et al., 2006], both overall and under more frequent extreme climatic events related to global environmental change such as drought [Trenberth et al., 2007]. Past validation studies of terrestrial biosphere models have focused only on few models and sites, typically in close proximity and primarily in forested biomes [e.g., Amthor et al., 2001; Delpierre et al., 2009; Grant et al., 2005; Hanson et al., 2004; Granier et al., 2007; Ichii et al., 2009; Ito, 2008; Siqueira et al., 2006; Zhou et al., 2008]. Furthermore, assessing model‐data agreement relative to drought requires, in addition to high‐quality observedCO2 exchange data, a reliable drought metric as well as a natural experiment across sites and drought conditions.
    Journal of Geophysical Research 02/2013; 115. · 3.17 Impact Factor
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    ABSTRACT: Export Date: 25 July 2013, Source: Scopus, Article in Press
    Agricultural and Forest Meteorology; 01/2013
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    ABSTRACT: Weather effects on forest productivity are not normally represented in inventory-based models for carbon accounting. To represent these effects, a meta-analysis was conducted on modeling results of five process models (ecosys, CN-CLASS, Can-IBIS, InTEC and TRIPLEX) as applied to a 6275 ha boreal forest landscape in Eastern Canada. Process model results showed that higher air temperature (Ta) caused gains in CO2 uptake in spring, but losses in summer, both of which were corroborated by CO2 fluxes measured by eddy covariance (EC). Seasonal changes in simulated CO2 fluxes and resulting inter-annual variability in NEP corresponded to those derived from EC measurements. Simulated long-term changes in above-ground carbon (AGC) resulting from modeled NEP and disturbance responses were close to those estimated from inventory data. A meta-analysis of model results indicates a robust positive correlation between simulated annual NPP and mean maximum daily air temperature (Tamax) during May–June in four of the process models. We therefore, derived a function to impart climate sensitivity to inventory-based models of NPP: NPP′i = NPPi + 9.5 (Tamax −16.5) where NPPi and NPP′i; are the current and temperature-adjusted NPP, 16.5 is the long-term mean Tamax during May–June, and Tamax is that for the current year. The sensitivity of net CO2 exchange to Ta is nonlinear. Although, caution should be exercised while extrapolating this algorithm to regions beyond the conditions studied in this landscape, results of our study are scalable to other regions with a humid continental boreal climate dominated by black spruce. Collectively, such regions comprise one of the largest climatic zones in the 450 Mha North American boreal forest ecosystems.
    Ecological Modelling 01/2013; 260:25-35. · 2.07 Impact Factor
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    ABSTRACT: The eddy-covariance method often underestimates fluxes under stable, low-wind conditions at night when turbulence is not well developed. The most common approach to resolve the problem of nighttime flux underestimation is to identify and remove the deficit periods using friction-velocity (u*) threshold filters (u*(Th)). This study modifies an accepted method for u*(Th) evaluation by incorporating change-point-detection techniques. The original and modified methods are evaluated at 38 sites as part of the North American Carbon Program (NACP) site-level synthesis. At most sites, the modified method produced u*(Th) estimates that were higher and less variable than the original method. It also provided an objective method to identify sites that lacked a u*(Th) response. The modified u*(Th) estimates were robust and comparable among years. Inter-annual u*(Th) differences were small, so that a single u*(Th) value was warranted at most sites. No variation in the u*(Th) was observed by time of day (dusk versus mid or late night), however, a few sites showed significant u*(Th) variation with time of year. Among-site variation in the u*(Th) was strongly related to canopy height and the mean annual nighttime u*. The modified u*(Th) estimates excluded a high fraction of nighttime data - 61% on average. However, the negative impact of the high exclusion rate on annual net ecosystem production (NEP) was small compared to the larger impact of underestimating the u*(Th). Compared to the original method, the higher u*(Th) estimates from the modified method caused a mean 8% reduction in annual NEP across all site-years, and a mean 7% increase in total ecosystem respiration (R-e). The modified method also reduced the u*(Th)-related uncertainties in annual NEP and R-e by more than 50%. These results support the use of u*(Th) filters as a pragmatic solution to a complex problem. (C) 2012 A.G. Barr. Published by Elsevier B.V. All rights reserved.
    Agricultural and Forest Meteorology 01/2013; 171:31-45. · 3.42 Impact Factor
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    Biogeosciences 01/2013; 10(11):6893-6909. · 3.75 Impact Factor
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    ABSTRACT: The energy balance at most surface-atmosphere flux research sites remains unclosed. The mechanisms underlying the discrepancy between measured energy inputs and outputs across the global FLUXNET tower network are still under debate. Recent reviews have identified exchange processes and turbulent motions at large spatial and temporal scales in heterogeneous landscapes as the primary cause of the lack of energy balance closure at some intensively-researched sites, while unmeasured storage terms cannot be ruled out as a dominant contributor to lack of energy balance closure at many other sites. We analyzed energy balance closure across 173 ecosystems in the FLUXNET database and explored the relationship between energy balance closure and landscape heterogeneity using MODIS products and GLOBEstat elevation data. Energy balance closure per research site ( C EB,s ) averaged 0.84 ± 0.20, with best average closures in evergreen broadleaf forests and savannas (0.91-0.94) and worst average closures in crops, deciduous broadleaf forests, mixed forests and wetlands (0.70-0.78). Half-hourly or hourly energy balance closure on a percent basis increased with friction velocity ( u * ) and was highest on average under near-neutral atmospheric conditions. C EB,s was significantly related to mean precipitation, gross primary productivity and landscape-level enhanced vegetation index (EVI) from MODIS, and the variability in elevation, MODIS plant functional type, and MODIS EVI. A linear model including landscape-level variability in both EVI and elevation, mean precipitation, and an interaction term between EVI variability and precipitation had the lowest Akaike's information criterion value. C EB,s in landscapes with uniform plant functional type approached 0.9 and C EB,s in landscapes with uniform EVI approached 1. These results suggest that landscape-level heterogeneity in vegetation and topography cannot be ignored as a contributor to incomplete energy balance closure at the flux network level, although net radiation measurements, biological energy assimilation, unmeasured storage terms, and the importance of good practice including site selection when making flux measurements should not be discounted. Our results suggest that future research should focus on the quantitative mechanistic relationships between energy balance closure and landscape-scale heterogeneity, and the consequences of mesoscale circulations for surface-atmosphere exchange measurements.
    Agricultural and Forest Meteorology 01/2013; 171-172:137-152. · 3.42 Impact Factor
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    ABSTRACT: Phenology, by controlling the seasonal activity of vegetation on the land surface, plays a fundamental role in regulating photosynthesis and other ecosystem processes, as well as competitive interactions and feedbacks to the climate system. We conducted an analysis to evaluate the representation of phenology, and the associated seasonality of ecosystem-scale CO2 exchange, in 14 models participating in the North American Carbon Program Site Synthesis. Model predictions were evaluated using long-term measurements (emphasizing the period 20002006) from 10 forested sites within the AmeriFlux and Fluxnet-Canada networks. In deciduous forests, almost all models consistently predicted that the growing season started earlier, and ended later, than was actually observed; biases of 2 weeks or more were typical. For these sites, most models were also unable to explain more than a small fraction of the observed interannual variability in phenological transition dates. Finally, for deciduous forests, misrepresentation of the seasonal cycle resulted in over-prediction of gross ecosystem photosynthesis by +160 145 g C m-2 yr-1 during the spring transition period and +75 +/- 130 g C m-2 yr-1 during the autumn transition period (13% and 8% annual productivity, respectively) compensating for the tendency of most models to under-predict the magnitude of peak summertime photosynthetic rates. Models did a better job of predicting the seasonality of CO2 exchange for evergreen forests. These results highlight the need for improved understanding of the environmental controls on vegetation phenology and incorporation of this knowledge into better phenological models. Existing models are unlikely to predict future responses of phenology to climate change accurately and therefore will misrepresent the seasonality and interannual variability of key biosphereatmosphere feedbacks and interactions in coupled global climate models.
    Global Change Biology 11/2012; 18:566-584. · 6.91 Impact Factor
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    ABSTRACT: We describe a pragmatic approach for evaluating the spatial representativeness of flux tower measurements based on footprint climatology modeling analyses of land cover and remotely sensed vegetation indices. The approach was applied to the twelve flux sites of the Canadian Carbon Program (CCP) that include grassland, wetland, and temperate and boreal forests across an east–west continental gradient. The spatial variation within the footprint area was evaluated by examining the spatial structure of Normalized Difference Vegetation Index (NDVI) and land cover using geostatistical analyses of frequency distribution, variogram and window size. The results show that at most sites (i) the percentages of the target vegetation functional type (dominant land cover) observed by the CCP towers were higher than 60%; (ii) to some extent, most of the CCP sites presented anisotropically distributed patterns of NDVI in the 90% annual footprint climatology area; and (iii) the land surface heterogeneity within the flux footprint area differed among sites. Overall, the forest sites had larger fine-scale spatial variation than the grassland and wetland sites. The coniferous boreal forest sites had greater spatial variability than the two wetland sites and a coniferous temperate forest site. We conclude that the combination of footprint modeling, semivariogram and window size techniques, together with moderate spatial resolution remotely-sensed image data, is a pragmatic approach for assessing the spatial representativeness of flux tower measurements.
    Remote Sensing of Environment 09/2012; 124:742-755. · 5.10 Impact Factor
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    ABSTRACT: Increasing evidence links air pollution to the risk of cardiovascular disease. This study investigated the association between ischemic heart disease (IHD) prevalence and exposure to traffic-related air pollution (nitrogen dioxide [NO₂], fine particulate matter [PM₂.₅], and ozone [O₃]) in a population of susceptible subjects in Toronto. Local (NO₂) exposures were modeled using land use regression based on extensive field monitoring. Regional exposures (PM₂.₅, O₃) were modeled as confounders using inverse distance weighted interpolation based on government monitoring data. The study sample consisted of 2360 patients referred during 1992 to 1999 to a pulmonary clinic at the Toronto Western Hospital in Toronto, Ontario, Canada, to diagnose or manage a respiratory complaint. IHD status was determined by clinical database linkages (ICD-9-CM 412-414). The association between IHD and air pollutants was assessed with a modified Poisson regression resulting in relative risk estimates. Confounding was controlled with individual and neighborhood-level covariates. After adjusting for multiple covariates, NO₂ was significantly associated with increased IHD risk, relative risk (RR) = 1.33 (95% confidence interval [CI]: 1.2, 1.47). Subjects living near major roads and highways had a trend toward an elevated risk of IHD, RR = 1.08 (95% CI: 0.99, 1.18). Regional PM₂.₅ and O₃ were not associated with risk of IHD.
    Journal of Toxicology and Environmental Health Part A 04/2012; 75(7):402-11. · 1.73 Impact Factor
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    ABSTRACT: North American forests play an important role in the global carbon cycle because they offset a large portion of global fossil fuel carbon dioxide (CO2) emissions. A substantial fraction of these carbon sequestering forests are located in the north-eastern temperate climate zones, where spring through early summer is the most productive period of the growing season. Therefore, variations in carbon sequestration rates due to environmental constraints during this period may have a profound impact on seasonal and annual net ecosystem productivity (NEP) of forest ecosystems in the region. Recent studies suggest that, in the future, summer warming and drought events may be shifted to both spring and autumn shoulder seasons and concurrent heat and drought events may exacerbate the negative effects of drought on carbon cycling in these forests. In this study the impact of seasonal and annual climate variability as well as extreme climatic events on gross primary productivity (GEP), ecosystem respiration (RE), net ecosystem productivity (NEP) and evapotranspiration (Ec) in an age-sequence (72, 37 and 9 years old) of planted temperate pine (white pine, Pinus strobus L.) forests, north of Lake Erie in southern Ontario, Canada will be examined using eight years (2003-2010) of eddy covariance flux and meteorological data. These sites are known as the Turkey Point Flux Station and had been part of the Canadian Carbon Program (CCP) or the Fluxnet-Canada Research Network (FCRN. The response of canopy transpiration (Et) and forest growth rates to experimentally reduced precipitation during the early growing season will also be evaluated in the 72-year old forest. A 20 m x 20 m throughfall exclusion setup was established and throughfall was excluded from April 1 to July 3, 2009. During this period 270 mm precipitation (27% of annual total) fell, of which 90% was excluded excluded. Sapflow measurements suggested that Et was 14% less in the drought plot compared to the reference plot when evaluated at the end of growing season in November. Tree growth estimates at the end of growing season indicated 17% decrease in growth in the drought plot. Climate predictions foresee changes in precipitation patterns, more than total precipitation amounts. The short-term deficit in water supply during peak growing season may have important implications for forest ecosystems. The findings of this throughfall manipulation will help to quantify the impacts of spring and early summer water deficit on forest ecosystems and evaluate their potential responses to future climate regimes. Our study also suggests that the simultaneous occurrence of early growing season drought and extreme summer heat events may play a large role in reducing the annual net carbon uptake in mature and young forests in eastern North America.
    04/2012;
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    ABSTRACT: • It is well established that individual organisms can acclimate and adapt to temperature to optimize their functioning. However, thermal optimization of ecosystems, as an assemblage of organisms, has not been examined at broad spatial and temporal scales. • Here, we compiled data from 169 globally distributed sites of eddy covariance and quantified the temperature response functions of net ecosystem exchange (NEE), an ecosystem-level property, to determine whether NEE shows thermal optimality and to explore the underlying mechanisms. • We found that the temperature response of NEE followed a peak curve, with the optimum temperature (corresponding to the maximum magnitude of NEE) being positively correlated with annual mean temperature over years and across sites. Shifts of the optimum temperature of NEE were mostly a result of temperature acclimation of gross primary productivity (upward shift of optimum temperature) rather than changes in the temperature sensitivity of ecosystem respiration. • Ecosystem-level thermal optimality is a newly revealed ecosystem property, presumably reflecting associated evolutionary adaptation of organisms within ecosystems, and has the potential to significantly regulate ecosystem-climate change feedbacks. The thermal optimality of NEE has implications for understanding fundamental properties of ecosystems in changing environments and benchmarking global models.
    New Phytologist 03/2012; 194(3):775-83. · 6.74 Impact Factor
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    ABSTRACT: To guide the future development of CO2-atmospheric inversion modeling systems, we analyzed the errors arising from prior information about terrestrial ecosystem fluxes. We compared the surface fluxes calculated by a process-based terrestrial ecosystem model with daily averages of CO2flux measurements at 156 sites across the world in the FLUXNET network. At the daily scale, the standard deviation of the model-data fit was 2.5 gC·m-2·d-1; temporal autocorrelations were significant at the weekly scale (>0.3 for lags less than four weeks), while spatial correlations were confined to within the first few hundred kilometers (<0.2 after 200 km). Separating out the plant functional types did not increase the spatial correlations, except for the deciduous broad-leaved forests. Using the statistics of the flux measurements as a proxy for the statistics of the prior flux errors was shown not to be a viable approach. A statistical model allowed us to upscale the site-level flux error statistics to the coarser spatial and temporal resolutions used in regional or global models. This approach allowed us to quantify how aggregation reduces error variances, while increasing correlations. As an example, for a typical inversion of grid point (300 km × 300 km) monthly fluxes, we found that the prior flux error follows an approximate e-folding correlation length of 500 km only, with correlations from one month to the next as large as 0.6.
    Global Biogeochemical Cycles 03/2012; 26. · 4.68 Impact Factor
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    ABSTRACT: Phenology, by controlling the seasonal activity of vegetation on the land surface, plays a fundamental role in regulating photosynthesis and other ecosystem processes, as well as competitive interactions and feedbacks to the climate system. We conducted an analysis to evaluate the representation of phenology, and the associated seasonality of ecosystem-scale CO2 exchange, in 14 models participating in the North American Carbon Program Site Synthesis. Model predictions were evaluated using long-term measurements (emphasizing the period 20002006) from 10 forested sites within the AmeriFlux and Fluxnet-Canada networks. In deciduous forests, almost all models consistently predicted that the growing season started earlier, and ended later, than was actually observed; biases of 2 weeks or more were typical. For these sites, most models were also unable to explain more than a small fraction of the observed interannual variability in phenological transition dates. Finally, for deciduous forests, misrepresentation of the seasonal cycle resulted in over-prediction of gross ecosystem photosynthesis by +160 145 g C m-2 yr-1 during the spring transition period and +75 +/- 130 g C m-2 yr-1 during the autumn transition period (13% and 8% annual productivity, respectively) compensating for the tendency of most models to under-predict the magnitude of peak summertime photosynthetic rates. Models did a better job of predicting the seasonality of CO2 exchange for evergreen forests. These results highlight the need for improved understanding of the environmental controls on vegetation phenology and incorporation of this knowledge into better phenological models. Existing models are unlikely to predict future responses of phenology to climate change accurately and therefore will misrepresent the seasonality and interannual variability of key biosphereatmosphere feedbacks and interactions in coupled global climate models.
    Global Change Biology 02/2012; 18(2):566-584. · 6.91 Impact Factor
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    [show abstract] [hide abstract]
    ABSTRACT: Phenology, by controlling the seasonal activity of vegetation on the land surface, plays a fundamental role in regulating photosynthesis and other ecosystem processes, as well as competitive interactions and feedbacks to the climate system. We conducted an analysis to evaluate the representation of phenology, and the associated seasonality of ecosystem-scale CO2 exchange, in 14 models participating in the North American Carbon Program Site Synthesis. Model predictions were evaluated using long-term measurements (emphasizing the period 2000–2006) from 10 forested sites within the AmeriFlux and Fluxnet-Canada networks. In deciduous forests, almost all models consistently predicted that the growing season started earlier, and ended later, than was actually observed; biases of 2 weeks or more were typical. For these sites, most models were also unable to explain more than a small fraction of the observed interannual variability in phenological transition dates. Finally, for deciduous forests, misrepresentation of the seasonal cycle resulted in over-prediction of gross ecosystem photosynthesis by +160 ± 145 g C m−2 yr−1 during the spring transition period and +75 ± 130 g C m−2 yr−1 during the autumn transition period (13% and 8% annual productivity, respectively) compensating for the tendency of most models to under-predict the magnitude of peak summertime photosynthetic rates. Models did a better job of predicting the seasonality of CO2 exchange for evergreen forests. These results highlight the need for improved understanding of the environmental controls on vegetation phenology and incorporation of this knowledge into better phenological models. Existing models are unlikely to predict future responses of phenology to climate change accurately and therefore will misrepresent the seasonality and interannual variability of key biosphere–atmosphere feedbacks and interactions in coupled global climate models.
    Global Change Biology 01/2012; 18(2):566 - 584. · 6.91 Impact Factor

Publication Stats

988 Citations
273.64 Total Impact Points

Institutions

  • 2003–2013
    • McMaster University
      • Department of Geography and Earth Sciences
      Hamilton, Ontario, Canada
  • 2012
    • University of Oklahoma
      Norman, Oklahoma, United States
  • 2000
    • University of British Columbia - Vancouver
      Vancouver, British Columbia, Canada