Abstract. Parameterizations of aerodynamic resistance to heat and water transfer have a significant impact on the accuracy of models of land -atmosphere interactions and of estimated surface fluxes using spectro-radiometric data collected from aircrafts and satellites. We have used measurements from an eddy correlation system to derive the aerodynamic resistance to heat transfer over a bare soil surface as well as over a maize canopy. Diurnal variations of aerodynamic resistance have been analyzed. The results showed that the diurnal variation of aerodynamic resistance during daytime (07:00 h-18:00 h) was significant for both the bare soil surface and the maize canopy although the range of variation was limited. Based on the measurements made by the eddy correlation system, a comprehensive evaluation of eight popularly used parameterization schemes of aerodynamic resistance was carried out. The roughness length for heat transfer is a crucial parameter in the estimation of aerodynamic resistance to heat transfer and can neither be taken as a constant nor be neglected. Comparing with the measurements, the parameterizations by Choudhury et al. (1986), Viney (1991), Yang et al. (2001 and the modified forms of Verma et al. (1976) and Mahrt and Ek (1984) by inclusion of roughness length for heat transfer gave good agreements with the measurements, while the parameterizations by Hatfield et al. (1983) and Xie (1988) showed larger errors even though the roughness length for heat transfer has been taken into account.

[1] Predicting the global carbon and water cycle requires a realistic representation of vegetation phenology in climate models. However most prognostic phenology models are not yet suited for global applications, and diagnostic satellite data can be uncertain and lack predictive power. We present a framework for data assimilation of Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) and Leaf Area Index (LAI) from the MODerate Resolution Imaging Spectroradiometer (MODIS) to constrain empirical temperature, light, moisture and structural vegetation parameters of a prognostic phenology model. We find that data assimilation better constrains structural vegetation parameters than climate control parameters. Improvements are largest for drought-deciduous ecosystems where correlation of predicted versus satellite-observed FPAR and LAI increases from negative to 0.7-0.8. Data assimilation effectively overcomes the cloud-and aerosol-related deficiencies of satellite data sets in tropical areas. Validation with a 49-year-long phenology data set reveals that the temperature-driven start of season (SOS) is light limited in warm years. The model has substantial skill (R = 0.73) to reproduce SOS inter-annual and decadal variability. Predicted SOS shows a higher inter-annual variability with a negative bias of 5-20 days compared to species-level SOS. It is however accurate to within 1-2 days compared to SOS derived from net ecosystem exchange (NEE) measurements at a FLUXNET tower. The model only has weak skill to predict end of season (EOS). Use of remote sensing data assimilation for phenology model development is encouraged but validation should be extended with phenology data sets covering mediterranean, tropical and arctic ecosystems.

[1] Synoptic events may play an important role in determining the CO 2 spatial distribution and temporal variations on a regional scale. In this study, we chose a front that passed the WLEF tower site on 16 August 2001, which had the most significant CO 2 concentration variation in our case pool. The CO 2 concentration, or [CO 2 ], at the WLEF site had a strong dip and an increasing trend before the front arrived and a decreasing trend afterward. The concentration at 396 m above the ground varied by more than 40 ppm within 36 hours. We investigated the CO 2 variations associated with this frontal case using a fully coupled model of land surface physics and carbon exchange (SiB 2.5) and the atmosphere (RAMS 5.04), in which CO 2 was treated as a free variable and used to determine photosynthesis rate. under overcast sky condition were also partially responsible for the quick CO 2 accumulation at the lower levels at the WLEF site before the front's arrival. This case study confirmed the existence of mixing signals from at least two different scales: large-scale horizontal advection and local ecosystem response to the changing weather. SiB-RAMS showed its strength in simulating the coherent anomalies in biospheric CO 2 flux and in the regional weather pattern. Further refinement of the model is needed to better capture the timing and location of synoptic events and CO 2 signals that travel across North America. Exploitation of continuous tower data in data assimilation and inverse modeling to determine regional sources and sinks will require careful error attribution to either transport or surface flux estimates.

Proper simulation of soil temperature and permafrost at high latitudes in land surface models requires proper simulation of the processes that control snowpack development. The Simple Biosphere/Carnegie‐Ames‐Stanford Approach (SiBCASA) did not account for depth hoar development and wind compaction, which dominate snow processes at high latitudes. Consequently, SiBCASA had difficulty properly simulating seasonal soil freeze/thaw and permafrost. We improved simulated soil temperatures at high latitudes by (1) incorporating a snow classification scheme that includes depth hoar development and wind compaction, (2) including the effects of organic matter on soil physical properties, and (3) increasing the soil column depth. We ran test simulations at eddy covariance flux tower sites using the North American Regional Reanalysis (NARR) as input meteorology. The NARR captured the observed variability in air temperature, but tended to overestimate precipitation. These changes produced modest improvements in simulated soil temperature at the midlatitude sites because the original snow model already included the weight compaction, thermal aging, and melting processes that dominate snowpack evolution at these locations. We saw significant improvement in simulated soil temperatures and active layer depth at the high‐latitude tundra and boreal forest sites. Adding snow classifications had the biggest effect on simulated soil temperatures at the tundra site while the organic soil properties had the biggest effect at the boreal forest site. Implementing snow classes, a deeper soil column, or organic soil properties separately was not sufficient to produce realistic soil temperatures and freeze/thaw processes at high latitudes. Only the combined effects of simultaneously implementing all three changes significantly improved the simulated soil temperatures and active layer depth at the tundra and boreal sites.