Journal of Geophysical Research: Biogeosciences, 2013
ABSTRACT [1] Evidence suggests that respiration acclimation (RA) to temperature in plants can hav... more ABSTRACT [1] Evidence suggests that respiration acclimation (RA) to temperature in plants can have a substantial influence on ecosystem carbon balance. To assess the influence of RA on ecosystem response variables in the presence of global change drivers, we incorporated a temperature-sensitive Q10 of respiration and foliar basal RA into the ecosystem model PnET-CN. We examined the new algorithms' effects on modeled net primary production (NPP), total canopy foliage mass, foliar nitrogen concentration, net ecosystem exchange (NEE), and ecosystem respiration/gross primary production ratios. This latter ratio more closely matched eddy covariance long-term data when RA was incorporated in the model than when not. Averaged across four boreal ecotone sites and three forest types at year 2100, the enhancement of NPP in response to the combination of rising [CO2] and warming was 9% greater when RA algorithms were used, relative to responses using fixed respiration parameters. The enhancement of NPP response to global change was associated with concomitant changes in foliar nitrogen and foliage mass. In addition, impacts of RA algorithms on modeled responses of NEE closely paralleled impacts on NPP. These results underscore the importance of incorporating temperature-sensitive Q10 and basal RA algorithms into ecosystem models. Given the current evidence that atmospheric [CO2] and surface temperature will continue to rise, and that ecosystem responses to those changes appear to be modified by RA, which is a common phenotypic adjustment, the potential for misleading results increases if models fail to incorporate RA into their carbon balance calculations.
Leaf area index (LAI) is a key biophysical variable in most process-based forest-ecosystem models... more Leaf area index (LAI) is a key biophysical variable in most process-based forest-ecosystem models. However, most such models require LAI as an input, typically obtained from empirical observations. We tested whether scaling principles based on trade-offs between single leaf and canopy properties could be effectively used to model LAI, thereby obviating the need for empirical observations. To do so, we used the process-oriented model, PnET, configured to estimate LAI from these same scaling principles. We derived biologically based LAI predictions (LAI PnET ) for the Harvard Forest (Massachusetts, USA) eddy covariance tower site, a predominately mixed deciduous hardwood forest, using PnET, and compared these with a locally observed phenology record and with LAI estimates from both local (ground-based) photosynthetically active radiation transmittance (LAI TRANS ) and normalized difference vegetation index satellite data (LAI NDVI ). We generated the LAI PnET trajectory by running the PnET model with meteorological observations from the flux tower as model drivers. We derived LAI TRANS from measurements of above-and below-canopy photosynthetically active radiation at the flux tower, and LAI NDVI from observations from the Advanced Very High Resolution Radiometer (AVHRR) satellite-borne sensor of surface greenness for the 1 km 2 cell containing the flux tower. Over a 5-year period, LAI PnET and LAI TRANS values were comparable intra-and interannually, with maximum values differing by less than 0.1 to 0.2 LAI units (m 2 m -2 ). Values of LAI NDVI were similar to LAI PnET and LAI TRANS in midsummer, but higher LAI values were predicted in the early and late portions of the growing season.
Forest ecosystems across the Northwoods will face direct and indirect impacts from a changing cli... more Forest ecosystems across the Northwoods will face direct and indirect impacts from a changing climate over the 21st century. This assessment evaluates the vulnerability of forest ecosystems in the Laurentian Mixed Forest Province of northern Wisconsin and western Upper Michigan under a range of future climates. We synthesized and summarized information on the contemporary landscape, provided information on past climate trends, and described a range of projected future climates. This information was used to parameterize and run multiple vegetation impact models, which provided a range of potential vegetative responses to climate. Finally, we brought these results before a multidisciplinary panel of scientists and land managers familiar with the forests of this region to assess ecosystem vulnerability through a formal consensus-based expert elicitation process.
This poster presents a parallel-computing framework developed recently through the collaborative ... more This poster presents a parallel-computing framework developed recently through the collaborative efforts between the College of Food, Agriculture and Natural Resources Sciences, The Institute of the Environment, and the Supercomputing Institute at the University of Minnesota. The goal of this project is to address the challenges encountered in the modeling of biotic dynamics in a forest region across different time and spatial scales. By implementing the parallel computing framework on Itasca, we have been able to reduce the computing time from 46 days (if the simulation runs on a single desktop) to a half day by using 8 nodes for a study region of northern Minnesota, Wisconsin, and Michigan with 1 km grid resolution. This will allow us to attack computationally challenging problems, such as assessing the impact of critical events like the 1999 BWCA blowdown and finer resolution events such as controlled burns on future forest productivity and stability.
Background/Question/Methods While large changes in atmospheric CO2, temperature and precipitation... more Background/Question/Methods While large changes in atmospheric CO2, temperature and precipitation are predicted by 2100, the long-term consequences for carbon cycling in forests are poorly understood. We used the PnET-CN ecosystem model to evaluate the effects of changing climate and atmospheric CO2 on productivity in forests of the North American Great Lakes region at 1 km spatial resolution. We examined two statistically downscaled and contrasting climate projections (PCM B1 and GFDL A1FI, with the latter predicting more warming and reduced precipitation) to represent a range in potential future climate. Each climate projection was simulated under two CO2 scenarios (constant and increasing atmospheric CO2 concentration) to separate the effects of rising CO2 from warming and precipitation changes. Results/Conclusions Increased productivity under future climate projections and higher CO2 was largely driven by CO2 fertilization effects on reduced stomatal conductance and water stress...
The response of respiration to temperature in plants can be considered at both short-and long-ter... more The response of respiration to temperature in plants can be considered at both short-and long-term temporal scales. Short-term temperature responses are not well described by a constant Q 10 of respiration, and longer-term responses often include acclimation. Despite this, many carbon balance models use a static Q 10 of respiration to describe the short-term temperature response and ignore temperature acclimation.
Climate, tree species traits, and soil fertility are key controls on forest productivity. However... more Climate, tree species traits, and soil fertility are key controls on forest productivity. However, in most forest ecosystems, natural and human disturbances, such as wind throw, fire, and harvest, can also exert important and lasting direct and indirect influence over productivity. We used an ecosystem model, PnET-CN, to examine how disturbance type, intensity, and frequency influence net primary production (NPP) across a range of forest types from Minnesota and Wisconsin, USA. We assessed the importance of past disturbances on NPP, net N mineralization, foliar N, and leaf area index at 107 forest stands of differing types (aspen, jack pine, northern hardwood, black spruce) and disturbance history (fire, harvest) by comparing model simulations with observations. The model reasonably predicted differences among forest types in productivity, foliar N, leaf area index, and net N mineralization. Model simulations that included past disturbances minimally improved predictions compared to simulations without disturbance, suggesting the legacy of past disturbances played a minor role in influencing current forest productivity rates. Modeled NPP was more sensitive to the intensity of soil removal during a disturbance than the fraction of stand mortality or wood removal. Increasing crown fire frequency resulted in lower NPP, particularly for conifer forest types with longer leaf life spans and longer recovery times. These findings suggest that, over long time periods, moderate frequency disturbances are a relatively less important control on productivity than climate, soil, and species traits.
Large changes in atmospheric CO 2 , temperature, and precipitation are predicted by 2100, yet the... more Large changes in atmospheric CO 2 , temperature, and precipitation are predicted by 2100, yet the long-term consequences for carbon (C), water, and nitrogen (N) cycling in forests are poorly understood. We applied the PnET-CN ecosystem model to compare the long-term effects of changing climate and atmospheric CO 2 on productivity, evapotranspiration, runoff, and net nitrogen mineralization in current Great Lakes forest types. We used two statistically downscaled climate projections, PCM B1 (warmer and wetter) and GFDL A1FI (hotter and drier), to represent two potential future climate and atmospheric CO 2 scenarios. To separate the effects of climate and CO 2 , we ran PnET-CN including and excluding the CO 2 routine. Our results suggest that, with rising CO 2 and without changes in forest type, average regional productivity could increase from 67% to 142%, changes in evapotranspiration could range from -3% to +6%, runoff could increase from 2% to 22%, and net N mineralization could increase 10% to 12%. Ecosystem responses varied geographically and by forest type. Increased productivity was almost entirely driven by CO 2 fertilization effects, rather than by temperature or precipitation (model runs holding CO 2 constant showed stable or declining productivity). The relative importance of edaphic and climatic spatial drivers of productivity varied over time, suggesting that productivity in Great Lakes forests may switch from being temperature-to water-limited by the end of the century.
Journal of Geophysical Research: Biogeosciences, 2013
ABSTRACT [1] Evidence suggests that respiration acclimation (RA) to temperature in plants can hav... more ABSTRACT [1] Evidence suggests that respiration acclimation (RA) to temperature in plants can have a substantial influence on ecosystem carbon balance. To assess the influence of RA on ecosystem response variables in the presence of global change drivers, we incorporated a temperature-sensitive Q10 of respiration and foliar basal RA into the ecosystem model PnET-CN. We examined the new algorithms' effects on modeled net primary production (NPP), total canopy foliage mass, foliar nitrogen concentration, net ecosystem exchange (NEE), and ecosystem respiration/gross primary production ratios. This latter ratio more closely matched eddy covariance long-term data when RA was incorporated in the model than when not. Averaged across four boreal ecotone sites and three forest types at year 2100, the enhancement of NPP in response to the combination of rising [CO2] and warming was 9% greater when RA algorithms were used, relative to responses using fixed respiration parameters. The enhancement of NPP response to global change was associated with concomitant changes in foliar nitrogen and foliage mass. In addition, impacts of RA algorithms on modeled responses of NEE closely paralleled impacts on NPP. These results underscore the importance of incorporating temperature-sensitive Q10 and basal RA algorithms into ecosystem models. Given the current evidence that atmospheric [CO2] and surface temperature will continue to rise, and that ecosystem responses to those changes appear to be modified by RA, which is a common phenotypic adjustment, the potential for misleading results increases if models fail to incorporate RA into their carbon balance calculations.
Leaf area index (LAI) is a key biophysical variable in most process-based forest-ecosystem models... more Leaf area index (LAI) is a key biophysical variable in most process-based forest-ecosystem models. However, most such models require LAI as an input, typically obtained from empirical observations. We tested whether scaling principles based on trade-offs between single leaf and canopy properties could be effectively used to model LAI, thereby obviating the need for empirical observations. To do so, we used the process-oriented model, PnET, configured to estimate LAI from these same scaling principles. We derived biologically based LAI predictions (LAI PnET ) for the Harvard Forest (Massachusetts, USA) eddy covariance tower site, a predominately mixed deciduous hardwood forest, using PnET, and compared these with a locally observed phenology record and with LAI estimates from both local (ground-based) photosynthetically active radiation transmittance (LAI TRANS ) and normalized difference vegetation index satellite data (LAI NDVI ). We generated the LAI PnET trajectory by running the PnET model with meteorological observations from the flux tower as model drivers. We derived LAI TRANS from measurements of above-and below-canopy photosynthetically active radiation at the flux tower, and LAI NDVI from observations from the Advanced Very High Resolution Radiometer (AVHRR) satellite-borne sensor of surface greenness for the 1 km 2 cell containing the flux tower. Over a 5-year period, LAI PnET and LAI TRANS values were comparable intra-and interannually, with maximum values differing by less than 0.1 to 0.2 LAI units (m 2 m -2 ). Values of LAI NDVI were similar to LAI PnET and LAI TRANS in midsummer, but higher LAI values were predicted in the early and late portions of the growing season.
Forest ecosystems across the Northwoods will face direct and indirect impacts from a changing cli... more Forest ecosystems across the Northwoods will face direct and indirect impacts from a changing climate over the 21st century. This assessment evaluates the vulnerability of forest ecosystems in the Laurentian Mixed Forest Province of northern Wisconsin and western Upper Michigan under a range of future climates. We synthesized and summarized information on the contemporary landscape, provided information on past climate trends, and described a range of projected future climates. This information was used to parameterize and run multiple vegetation impact models, which provided a range of potential vegetative responses to climate. Finally, we brought these results before a multidisciplinary panel of scientists and land managers familiar with the forests of this region to assess ecosystem vulnerability through a formal consensus-based expert elicitation process.
This poster presents a parallel-computing framework developed recently through the collaborative ... more This poster presents a parallel-computing framework developed recently through the collaborative efforts between the College of Food, Agriculture and Natural Resources Sciences, The Institute of the Environment, and the Supercomputing Institute at the University of Minnesota. The goal of this project is to address the challenges encountered in the modeling of biotic dynamics in a forest region across different time and spatial scales. By implementing the parallel computing framework on Itasca, we have been able to reduce the computing time from 46 days (if the simulation runs on a single desktop) to a half day by using 8 nodes for a study region of northern Minnesota, Wisconsin, and Michigan with 1 km grid resolution. This will allow us to attack computationally challenging problems, such as assessing the impact of critical events like the 1999 BWCA blowdown and finer resolution events such as controlled burns on future forest productivity and stability.
Background/Question/Methods While large changes in atmospheric CO2, temperature and precipitation... more Background/Question/Methods While large changes in atmospheric CO2, temperature and precipitation are predicted by 2100, the long-term consequences for carbon cycling in forests are poorly understood. We used the PnET-CN ecosystem model to evaluate the effects of changing climate and atmospheric CO2 on productivity in forests of the North American Great Lakes region at 1 km spatial resolution. We examined two statistically downscaled and contrasting climate projections (PCM B1 and GFDL A1FI, with the latter predicting more warming and reduced precipitation) to represent a range in potential future climate. Each climate projection was simulated under two CO2 scenarios (constant and increasing atmospheric CO2 concentration) to separate the effects of rising CO2 from warming and precipitation changes. Results/Conclusions Increased productivity under future climate projections and higher CO2 was largely driven by CO2 fertilization effects on reduced stomatal conductance and water stress...
The response of respiration to temperature in plants can be considered at both short-and long-ter... more The response of respiration to temperature in plants can be considered at both short-and long-term temporal scales. Short-term temperature responses are not well described by a constant Q 10 of respiration, and longer-term responses often include acclimation. Despite this, many carbon balance models use a static Q 10 of respiration to describe the short-term temperature response and ignore temperature acclimation.
Climate, tree species traits, and soil fertility are key controls on forest productivity. However... more Climate, tree species traits, and soil fertility are key controls on forest productivity. However, in most forest ecosystems, natural and human disturbances, such as wind throw, fire, and harvest, can also exert important and lasting direct and indirect influence over productivity. We used an ecosystem model, PnET-CN, to examine how disturbance type, intensity, and frequency influence net primary production (NPP) across a range of forest types from Minnesota and Wisconsin, USA. We assessed the importance of past disturbances on NPP, net N mineralization, foliar N, and leaf area index at 107 forest stands of differing types (aspen, jack pine, northern hardwood, black spruce) and disturbance history (fire, harvest) by comparing model simulations with observations. The model reasonably predicted differences among forest types in productivity, foliar N, leaf area index, and net N mineralization. Model simulations that included past disturbances minimally improved predictions compared to simulations without disturbance, suggesting the legacy of past disturbances played a minor role in influencing current forest productivity rates. Modeled NPP was more sensitive to the intensity of soil removal during a disturbance than the fraction of stand mortality or wood removal. Increasing crown fire frequency resulted in lower NPP, particularly for conifer forest types with longer leaf life spans and longer recovery times. These findings suggest that, over long time periods, moderate frequency disturbances are a relatively less important control on productivity than climate, soil, and species traits.
Large changes in atmospheric CO 2 , temperature, and precipitation are predicted by 2100, yet the... more Large changes in atmospheric CO 2 , temperature, and precipitation are predicted by 2100, yet the long-term consequences for carbon (C), water, and nitrogen (N) cycling in forests are poorly understood. We applied the PnET-CN ecosystem model to compare the long-term effects of changing climate and atmospheric CO 2 on productivity, evapotranspiration, runoff, and net nitrogen mineralization in current Great Lakes forest types. We used two statistically downscaled climate projections, PCM B1 (warmer and wetter) and GFDL A1FI (hotter and drier), to represent two potential future climate and atmospheric CO 2 scenarios. To separate the effects of climate and CO 2 , we ran PnET-CN including and excluding the CO 2 routine. Our results suggest that, with rising CO 2 and without changes in forest type, average regional productivity could increase from 67% to 142%, changes in evapotranspiration could range from -3% to +6%, runoff could increase from 2% to 22%, and net N mineralization could increase 10% to 12%. Ecosystem responses varied geographically and by forest type. Increased productivity was almost entirely driven by CO 2 fertilization effects, rather than by temperature or precipitation (model runs holding CO 2 constant showed stable or declining productivity). The relative importance of edaphic and climatic spatial drivers of productivity varied over time, suggesting that productivity in Great Lakes forests may switch from being temperature-to water-limited by the end of the century.
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