Missing data in long-term eddy covariance measurements of latent heat flux produce errors in the estimation of evapotranspiration and the water budget. Because no standard method of gap filling has been widely accepted, identification of optimal filling methods for gaps is crucial for determining total evapotranspiration. In this study we evaluate the application of a Kalman filter for filling gaps in latent heat flux data collected from an agricultural research station. The filtering approach was compared with several gap-filling methods including mean diurnal variation, multiple regressions, 2-week average Priestley-Taylor coefficient, and multiple imputation. The results demonstrated that a Kalman filtering approach developed using the relationship between latent heat flux, available energy, and vapour pressure deficit provides a closer approximation of the original data and introduces smaller errors than the other methods evaluated. Evaluation of the Kalman filter approach demonstrates the efficiency of this technique in replacing data in both small and large gaps of up to several days. #

11A new land-surface parameterization scheme, namely the Soil, Vegetation, and Snow 12 (SVS) scheme, has recently been developed at Environment and Climate Change Canada to 13 replace the operationally used ISBA (Interactions between Soil, Biosphere, and Atmosphere) 14 scheme. The new scheme is designed to address a number of weaknesses and limitations of 15 ISBA that have been identified over the last decade. Unlike ISBA, which calculates a single 16 energy budget for the different land-surface components, SVS introduces a new tiling approach 17 that includes separate energy budgets for bare ground, vegetation, and two different snow packs 18 (over bare ground and low vegetation, and under high vegetation). The inclusion of a 19 photosynthesis module as an option to determine the surface stomatal resistance is another 20 significant addition in SVS. The representation of vertical water transport through soil has also 21 been substantially improved in SVS with the introduction of multiple soil layers. Overall, offline 22 simulations conducted in the present study demonstrated clear improvements in warm season 23 Manuscript (non-LaTeX) Click here to download Manuscript (non-LaTeX) Husain_et_al_20160331-TEXT-and-FIGURES.docx 2 meteorological predictions with SVS compared to the ISBA scheme. The results also revealed 1 considerable reduction of standard error in the SVS-predicted L-band brightness temperature. 2This demonstrates the scheme's ability for better hydrological prediction and its potential for 3 providing more accurate soil moisture analysis. The impact of the photosynthesis module within 4 the current implementation of SVS is, however, found to be negligible on near-surface 5 meteorological prediction and slightly negative for brightness temperature. 6 7

A new land surface scheme has been developed at Environment and Climate Change Canada (ECCC) to provide surface fluxes of momentum, heat, and moisture for the Global Environmental Multiscale (GEM) atmospheric model. In this study, the performance of the Soil, Vegetation, and Snow (SVS) scheme in estimating the surface and root-zone soil moisture is evaluated against the Interactions between Soil, Biosphere, and Atmosphere (ISBA) scheme currently used operationally at ECCC within GEM for numerical weather prediction. In addition, the sensitivity of SVS soil moisture results to soil texture and vegetation data sources (type and fractional coverage) has been explored. The performance of SVS and ISBA was assessed against a large set of in situ observations as well as the brightness temperature data from the Soil Moisture Ocean Salinity (SMOS) satellite over North America. The results indicate that SVS estimates the time evolution of soil moisture more accurately, and compared to ISBA, results in higher correlations with observations and reduced errors. The sensitivity tests carried out during this study revealed that the SVS soil moisture results are not affected significantly by the soil texture data from different sources. The vegetation data source, however, has a major impact on the soil moisture results predicted by SVS, and accurate specification of vegetation characteristics is therefore crucial for accurate soil moisture prediction.

Accurate specification of the soil moisture in land-surface models has the potential to improve the evapotranspiration estimates from these models. However, soil moisture is highly variable in space and time due to variability in climatic, topographic, vegetative, and soil properties. It is anticipated that including information on soil moisture variability into a land-surface model will improve model estimates of evapotranspiration. In this experiment, the spatial variability of soil moisture was measured over an agricultural field in southern Ontario over a 70 m × 70 m area ten times during the growing season. These data were used to update the Canadian Land Surface Scheme (CLASS) using three assimilation techniques. The techniques evaluated included two versions of ensemble Kalman filter (EnKF), and direct insertion of soil moisture data into the model. The results showed that assimilating observed soil moisture variability into CLASS improves model latent heat flux estimates by up to 14%. The amount of improvement depends on the method and timing of assimilation. The effect was largest at the beginning of the growing season, while it was smallest at peak growth. Application of EnKF, considering both instrumental error and field variability, resulted in greater improvement in latent heat flux estimates compared to the other two methods. This study showed that assimilation of soil moisture variability into CLASS can result in greater improvement in modelled ET comparing to assimilating of the mean of the sampling area.

In the second phase of the Canadian Historical Forecasting Project (HFP2), four different Atmospheric General Circulation Models (AGCMs) were run to produce a series of four-month forecasts over a 33-year period. For the HFP2 project, the land surface wetness state was initialized from model climatology rather than estimates of the true initial soil moisture state. In this study, the impact of soil moisture initialization on the monthly forecasts of air temperature is evaluated using one of these four models, AGCM3 from the Canadian Centre for Climate modelling and Analysis (CCCma), which employs the Canadian Land Surface Scheme (CLASS) to represent land processes. A realistic global estimation of soil moisture was produced by running CLASS offline using a bias-corrected meteorological dataset over the global land surface. The relationship between errors in forecasts of near-surface air temperature over the month and the uncertainty in the surface soil moisture initialization is identified for five northern hemisphere warm-season months during the period 1979-2002. The results demonstrate that soil moisture initialization has a statistically significant impact on monthly air temperature forecasts over many regions. The greatest impact was found over the Sahel region in Africa, India and East Asia, regions in Brazil, and Central North America. The intensity and areal extent of this impact increases over extreme dry and wet soil moisture anomalies.RÉSUMÉ [Traduit par la rédaction] Dans la deuxième phase du Projet de prévisions historiques (PPH2) canadien, quatre modèles de circulation générale de l'atmosphère (MCGA) ont été exécuté pour produire une série de prévisions de quatre mois sur une période de 33 ans. Pour le projet PPH2, les conditions d'humidité de la surface du terrain étaient initialisées à partir de la climatologie du modèle plutôt que par estimations des conditions d'humidité initiales réelles de la surface du terrain. Dans cette étude,