[1] Remotely sensed, multiannual data sets of shortwave radiative surface fluxes are now available for assimilation into land surface schemes (LSSs) of climate and/or numerical weather prediction models. The RAMI4PILPS suite of virtual experiments assesses the accuracy and consistency of the radiative transfer formulations that provide the magnitudes of absorbed, reflected, and transmitted shortwave radiative fluxes in LSSs. RAMI4PILPS evaluates models under perfectly controlled experimental conditions in order to eliminate uncertainties arising from an incomplete or erroneous knowledge of the structural, spectral and illumination related canopy characteristics typical for model comparison with in situ observations. More specifically, the shortwave radiation is separated into a visible and near‐infrared spectral region, and the quality of the simulated radiative fluxes is evaluated by direct comparison with a 3‐D Monte Carlo reference model identified during the third phase of the Radiation transfer Model Intercomparison (RAMI) exercise. The RAMI4PILPS setup thus allows to focus in particular on the numerical accuracy of shortwave radiative transfer formulations and to pinpoint to areas where future model improvements should concentrate. The impact of increasing degrees of structural and spectral subgrid variability on the simulated fluxes is documented and the relevance of any thus emerging biases with respect to gross primary production estimates and shortwave radiative forcings due to snow and fire events are investigated.

Gross canopy photosynthesis (P_g) can be simulated with canopy models or retrieved from turbulent carbon dioxide (CO₂) flux measurements above the forest canopy. We compare the two estimates and illustrate our findings with two case studies. We used the three-dimensional canopy model MAESTRA to simulate P_g of two spruce forests differing in age and structure. Model parameter acquisition and model sensitivity to selected model parameters are described, and modeled results are compared with independent flux estimates. Despite higher photon fluxes at the site, an older German Norway spruce (Picea abies L. (Karst.)) canopy took up 25% less CO₂ from the atmosphere than a young Scottish Sitka spruce (Picea sitchensis (Bong.) Carr.) plantation. The average magnitudes of P_g and the differences between the two canopies were satisfactorily represented by the model. The main reasons for the different uptake rates were a slightly smaller quantum yield and lower absorptance of the Norway spruce stand because of a more clumped canopy structure. The model did not represent the scatter in the turbulent CO₂ flux densities, which was of the same order of magnitude as the non-photosynthetically-active-radiation-induced biophysical variability in the simulated P_g. Analysis of residuals identified only small systematic differences between the modeled flux estimates and turbulent flux measurements at high vapor pressure saturation deficits. The merits and limitations of comparative analysis for quality evaluation of both methods are discussed. From this analysis, we recommend use of both parameter sets and model structure as a basis for future applications and model development.

Photosynthetically active radiation (Q)-use efficiency (ɛ) is an important parameter for deriving carbon fluxes between forest canopies and the atmosphere from meteorological ground and remote sensing data. A common approach is to assume gross primary production (P_g) and net primary production (P_n) are proportional to Q absorbed by vegetation (Q_abs) by defining the proportionality constants ɛ_Pg and ɛ_Pn (for P_g and P_n, respectively). Although remote sensing and climate monitoring provide Q_abs and other meteorological data at the global scale, information on ɛ is particularly scarce in remote tropical areas. We used a 16-month continuous CO₂ flux and meteorological dataset from a mountainous tropical rain forest in central Sulawesi, Indonesia to derive values of ɛ_Pg and to investigate the relationship between P_g and Q_abs. Absorption was estimated with a 1D SVAT model from measured canopy structure and short wave radiation. The half-hourly P_g data showed a saturation response to Q_abs. The amount of Q_abs required to saturate P_g was reduced when water vapor saturation deficit (D) was high. Light saturation of P_g was still evident when shifting from half-hourly to daily and monthly time scales. Thus, for a majority of observations, P_g was insensitive to changes in Q_abs. A large proportion of the observed seasonal variability in P_g could not be attributed to changes in Q_abs or D. Values of ɛ_Pg varied little around the long-term mean of 0.0179 mol CO₂ (mol photon)⁻¹ or 0.99 g C MJ⁻¹ (the standard deviations were ± 0.006 and ± 0.0018 mol CO₂ (mol photon)⁻¹ for daily and monthly means, respectively). In both cases, ɛ_Pg values were more sensitive to Q_abs than to daytime D. These findings show that the current ɛ-approaches fail to predict P_g at our tropical rain forest site for two reasons: (1) they neglect saturation of P_g when Q_abs is high; and (2) they do not include factors, other than Q_abs and D, that determine seasonality and annual sums of P_g.