We used a land surface model to quantify the causes and extents of biases in terrestrial gross primary production (GPP) due to the use of meteorological reanalysis datasets. We first calibrated the model using meteorology and eddy covariance data from 25 flux tower sites ranging from the tropics to the northern high latitudes and subsequently repeated the site simulations using two reanalysis datasets: NCEP/NCAR and CRUNCEP. The results show that at most sites, the reanalysis-driven GPP bias was significantly positive with respect to the observed meteorology-driven simulations. Notably, the absolute GPP bias was highest at the tropical evergreen tree sites, averaging up to ca. 0.45 kg C m(-2)  yr(-1) across sites (ca. 15% of site level GPP). At the northern mid-/high-latitude broadleaf deciduous and the needleleaf evergreen tree sites, the corresponding annual GPP biases were up to 20%. For the nontree sites, average annual biases of up to ca. 20-30% were simulated within savanna, grassland, and shrubland vegetation types. At the tree sites, the biases in short-wave radiation and humidity strongly influenced the GPP biases, while the nontree sites were more affected by biases in factors controlling water stress (precipitation, humidity, and air temperature). In this study, we also discuss the influence of seasonal patterns of meteorological biases on GPP. Finally, using model simulations for the global land surface, we discuss the potential impacts of site-level reanalysis-driven biases on the global estimates of GPP. In a broader context, our results can have important consequences on other terrestrial ecosystem fluxes (e.g., net primary production, net ecosystem production, energy/water fluxes) and reservoirs (e.g., soil carbon stocks). In a complementary study (Barman et al., ), we extend the present analysis for latent and sensible heat fluxes, thus consistently integrating the analysis of climate-driven uncertainties in carbon, energy, and water fluxes using a single modeling framework.

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We used a land surface model constrained using data from flux tower sites, to analyze the biases in ecosystem energy and water fluxes arising due to the use of meteorological reanalysis datasets. Following site-level model calibration encompassing major vegetation types from the tropics to the northern high-latitudes, we repeated the site and global simulations using two reanalysis datasets: the NCEP/NCAR and the CRUNCEP. In comparison with the model simulations using observed meteorology from sites, the reanalysis-driven simulations produced several systematic biases in net radiation (Rn ), latent heat (LE), and sensible heat (H) fluxes. These include: (i) persistently positive tropical/subtropical biases in Rn using the NCEP/NCAR, and gradually transitioning to negative Rn biases in the higher latitudes; (ii) large positive H biases in the tropics/subtropics using the NCEP/NCAR; (iii) negative LE biases using the NCEP/NCAR above 40°N; (iv) high tropical LE using the CRUNCEP in comparison with observationally derived global estimates; and (v) flux-partitioning biases from canopy and ground components. Across vegetation types, we investigated the role of the meteorological drivers (shortwave and longwave radiation, atmospheric humidity, temperature, precipitation) and their seasonal biases in controlling these reanalysis-driven uncertainties. At the global scale, our site-level analysis explains several model-data differences in the LE and H fluxes when compared with observationally derived global estimates of these fluxes. Using our results, we discuss the implications of site-level model calibration on subsequent regional/global applications to study energy and hydrological processes. The flux-partitioning biases presented in this study have potential implications on the couplings among terrestrial carbon, energy, and water fluxes, and for the calibration of land-atmosphere parameterizations that are dependent on LE/H partitioning.

[1] The present work reports the development of a nonlinear technique based on artificial neural network (ANN) for prediction of tsunami travel time in the Indian Ocean. The expected times of arrival (ETA) computation involved 250 representative coastal stations encompassing 35 countries. A travel time model is developed using ANN approach. The ANN model uses non-linear regression where a Multi-layer Perceptron (MLP) is used to tackle the non-linearity in the computed ETA. The back-propagation feed forward type network is used for training the system using the resilient back-propagation algorithm. The model demonstrates a high degree of correlation, proving its robustness in development of a real-time tsunami warning system for Indian Ocean.

We used a land surface model to (1) evaluate the influence of recent improvements in modeling cold-region soil/snow physics on near-surface permafrost physical characteristics (within 0-3 m soil column) in the northern high latitudes (NHL) and (2) compare them with uncertainties from climate and landcover data sets. Specifically, four soil/snow processes are investigated: deep soil energetics, soil organic carbon (SOC) effects on soil properties, wind compaction of snow, and depth hoar formation. In the model, together they increased the contemporary NHL permafrost area by 9.2 3 10 6 km 2 (from 2.9 to 12.3-without and with these processes, respectively) and reduced historical degradation rates. In comparison, permafrost area using different climate data sets (with annual air temperature difference of 0.58C) differed by up to 2.3 3 10 6 km 2 , with minimal contribution of up to 0.7 3 10 6 km 2 from substantial land-cover differences.Individually, the strongest role in permafrost increase was from deep soil energetics, followed by contributions from SOC and wind compaction, while depth hoar decreased permafrost. The respective contribution on 0-3 m permafrost stability also followed a similar pattern. However, soil temperature and moisture within vegetation root zone (0-1 m), which strongly influence soil biogeochemistry, were only affected by the latter three processes. The ecosystem energy and water fluxes were impacted the least due to these soil/snow processes. While it is evident that simulated permafrost physical characteristics benefit from detailed treatment of cold-region biogeophysical processes, we argue that these should also lead to integrated improvements in modeling of biogeochemistry.