[1] Continental to global-scale modeling of the carbon cycle using process-based models is subject to large uncertainties. These uncertainties originate from the model structure and uncertainty in model forcing fields; however, little is known about their relative importance. A thorough understanding and quantification of uncertainties is necessary to correctly interpret carbon cycle simulations and guide further model developments. This study elucidates the effects of different state-of-the-art land cover and meteorological data set options and biosphere models on simulations of gross primary productivity (GPP) over Europe. The analysis is based on (1) three different process-oriented terrestrial biosphere models (Biome-BGC, LPJ, and Orchidee) driven with the same input data and one model (Biome-BGC) driven with (2) two different meteorological data sets (ECMWF and REMO), (3) three different land cover data sets (GLC2000, MODIS, and SYNMAP), and (4) three different spatial resolutions of the land cover (0.25°fractional, 0.25°d ominant, and 0.5°dominant). We systematically investigate effects on the magnitude, spatial pattern, and interannual variation of GPP. While changing the land cover map or the spatial resolution has only little effect on the model outcomes, changing the meteorological drivers and especially the model results in substantial differences. Uncertainties of the meteorological forcings affect particularly strongly interannual variations of simulated GPP. By decomposing modeled GPP into their biophysical and ecophysiological components (absorbed photosynthetic active radiation (APAR) and radiation use efficiency (RUE), respectively) we show that differences of interannual GPP variations among models result primarily from differences of simulating RUE. Major discrepancies appear to be related to the feedback through the carbon-nitrogen interactions in one model (Biome-BGC) and water stress effects, besides the modeling of croplands. We suggest clarifying the role of nitrogen dynamics in future studies and revisiting currently applied concepts of carbon-water cycle interactions regarding the representation of canopy conductance and soil processes. , et al. (2007), Uncertainties of modeling gross primary productivity over Europe: A systematic study on the effects of using different drivers and terrestrial biosphere models, Global Biogeochem. Cycles, 21, GB4021,
Abstract. Globally, the year 2003 is associated with one of the largest atmospheric CO 2 rises on record. In the same year, Europe experienced an anomalously strong flux of CO 2 from the land to the atmosphere associated with an exceptionally dry and hot summer in Western and Central Europe. In this study we analyze the magnitude of this carbon flux anomaly and key driving ecosystem processes using simulations of seven terrestrial ecosystem models of different complexity and types (process-oriented and diagnostic). We address the following questions: (1) how large were deviations in the net European carbon flux in 2003 relative to a shortterm baseline (1998)(1999)(2000)(2001)(2002) C for Western Europe and between 24 and -129 Tg C for Central Europe depending on the model used. All models responded to a dipole pattern of the climate anomaly in 2003. In Western and Central Europe NEP was reduced due to heat and drought. In contrast, lower than normal temperatures and higher air humidity decreased NEP over Northeastern Europe. While models agree on the sign of changes in simulated NEP and gross primary productivity in 2003 over Western and Central Europe, models diverge in the estimates of anomalies in ecosystem respiration. Except for two process models which simulate respiration increase, most models simulated a decrease in ecosystem respiration in 2003. The diagnostic models showed a weaker decrease in ecosystem respiration than the process-oriented models.Based on the multi-model simulations we estimated the total carbon flux anomaly over the 2003 growing season in Europe to range between -0.02 and -0.27 Pg C relative to the net carbon flux in 1998-2002.
The objective of this study is to investigate the effects of urban land on the climate in Europe on local and regional scales. Effects of urban land cover on the climate are isolated using the fifth-generation Pennsylvania State University-National Center for Atmospheric Research (PSU-NCAR) Mesoscale Model (MM5) with a modified land surface scheme based on the Town Energy Budget model. Two model scenarios represent responses of climate to different states of urbanization in Europe: 1) no urban areas and 2) urban land in the actual state in the beginning of the twenty-first century. By comparing the simulations of these contrasting scenarios, spatial differences in near-surface temperature and precipitation are quantified. Simulated near-surface temperatures and an urban heat island for January and July over a period of 6 yr (2000-05) agree well with corresponding measurements at selected urban areas. The conversion of rural to urban land results in statistically significant changes to precipitation and near-surface temperature over areas of the land cover perturbations. The diurnal temperature range in urbanized regions was reduced on average by 1.26°Ϯ 0.71°C in summer and by 0.73°Ϯ 00.54°C in winter. Inclusion of urban areas results in an increase of urban precipitation in winter (0.09 Ϯ 00.16 mm day Ϫ1) and a precipitation reduction in summer (Ϫ0.05 Ϯ 0.22 mm day Ϫ1).
Abstract.Mixing ratio measurements of atmospheric tracers like CO 2 can be used to estimate regional surface-air tracer fluxes using inverse methods, involving a numerical transport model. Currently available transport models are either global but rather coarse, or more accurate but only over a limited spatial and temporal domain. To obtain higher-resolution flux estimates within a region of interest, existing studies use zoomed or coupled models. The two-step scheme developed here uses global and regional models sequentially in separate inversion steps, coupled only via the data vector. This provides a nested atmospheric inversion scheme without the necessity of a direct coupled model implementation. For example, the scheme allows an easy nesting of Lagrangian models with their potential of very high resolution into global inversions based on Eulerian models.
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