Abstract
Predicting Gross Primary Productivity in Terrestrial Ecosystems
Highlights
Our goal was to constructa simple, highlyaggregatedmodel, drivenby available data sets, thataccuratelypredictedterrestriagl ross primaryproductivity (GPP; carboxylationplus oxygenation)in diverseenvironmentasnd ecosystems.Our starting pointwas a fine-scale,multilayermodel of half-hourlycanopyprocesses thathas been parametrizedforHarvard Forest,Massachusetts.Over varied growingseason conditions, this fine-scalemodel predictedhourlycarbon and latentenergyfluxesthatwere in good agreementwithdata fromeddy covariance studies.Using an heuristicprocess,we derived a simpleaggregatedset of equationsoperatingon cumulativeor averagevalues ofthemost sensitivedrivingvariables.We calibratedthe aggregatedmodel to provide estimatesof GPP similar to those of the fine-scalemodel across a wide range of these drivingvariables
Budgeting the ofresults.Recently,Curtis(1996) has shownthatmetaglobal C cycle reveals a missingsinkof 1.4 Tg C/yr analysiscan overcometheselimitationsto examinethe (Schimel 1995), and inversecalculations suggestthis relativeimportance sink may be located in the terrestriabl iosphere(Tans of various factorsaffectingnet CO2 assimilationand et al 1990, Entingand Mansbridge 1991, Denning et photosyntheticacclimationunderincreased CO2 conal. 1995)
Soil-Plant-Atmosphere),a fine-scalemodel of canopy processes developed forHarvardForest,Massachusetts (Williams et al 1996), operatesat a 30-mintimestep, Thefine-scale model and incorporates10 canopy layers. This spatial and The fine-scale,soil-plant-atmospherecanopymodel temporal detail allows the model to correctlyscale (MBL/SPA; see Williamset al. 1996 fora fulldescripmanyof thenonlinearprocesses, such as diurnalvari- tion)is a multilayersimulationofC3-canopyprocesses ationin lightattenuationthroughthecanopy,and parametrizedfor a temperatedeciduous forest.The resolvetheinteractionbetweenmicroclimateandphys- model employssome well-testedtheoreticalrepreseniology
Summary
Our goal was to constructa simple, highlyaggregatedmodel, drivenby available data sets, thataccuratelypredictedterrestriagl ross primaryproductivity (GPP; carboxylationplus oxygenation)in diverseenvironmentasnd ecosystems.Our starting pointwas a fine-scale,multilayermodel of half-hourlycanopyprocesses thathas been parametrizedforHarvard Forest,Massachusetts.Over varied growingseason conditions, this fine-scalemodel predictedhourlycarbon and latentenergyfluxesthatwere in good agreementwithdata fromeddy covariance studies.Using an heuristicprocess,we derived a simpleaggregatedset of equationsoperatingon cumulativeor averagevalues ofthemost sensitivedrivingvariables (leaf area index, mean foliarN concentrationc, anopy height, average daily temperatureand temperaturreange,atmospherictransmittancel,atitude,day of year,atmosphericCO2 concentrationa,nd an index of soil moisture).We calibratedthe aggregatedmodel to provide estimatesof GPP similar to those of the fine-scalemodel across a wide range of these drivingvariables. Soil-Plant-Atmosphere),a fine-scalemodel of canopy processes developed forHarvardForest,Massachusetts (Williams et al 1996), operatesat a 30-mintimestep, Thefine-scale model and incorporates10 canopy layers. This spatial and The fine-scale,soil-plant-atmospherecanopymodel temporal detail allows the model to correctlyscale
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