Biases in Model-Simulated Surface Energy Fluxes During the Indian Monsoon Onset Period

Abstract

2019. Biases in modelsimulated surface energy fluxes during the Indian monsoon onset period. 170 (2).Abstract We use eddy covariance measurements over a semi-natural grassland in the cen-8 tral Indo-Gangetic Basin to investigate biases in the energy fluxes simulated by the Noah 9 land-surface model (LSM) for two monsoon onset periods: one with rain (2016) and one 10 completely dry (2017). In the preliminary run with default parameters, the offline Noah 11 LSM overestimates the midday (1000 to 1400 local time) sensible heat flux (H) by 279% 12 (in 2016) and 108% (in 2017) and underestimates the midday latent heat flux (LE) by 56% 13 (in 2016) and 67% (in 2017). These discrepancies in simulated energy fluxes propagate to 14 and are amplified in coupled Weather Research and Forecasting (WRF) model simulations, 15 as seen from the High Asia Reanalysis (HAR) dataset. One-dimensional Noah simulations 16 with modified site-specific vegetation parameters not only improve the partitioning of the 17 energy fluxes (Bowen ratio of 0.90 in modified run versus 3.09 in the default run), but also 18 2 Tirthankar Chakraborty et al.reduce the overestimation of the model-simulated soil and skin temperature. Thus, use of 19 ambient site parameters in future studies is warranted to reduce uncertainties in short-term 20 and long-term simulations over this region. Finally, we examine how biases in the model 21 simulations can be attributed to lack of closure in the measured surface energy budget. The 22 bias is smallest when the sensible heat flux post-closure method is used (5.2 W m -2 for H 23 and 16 W m -2 for LE in 2016; 0.17 W m -2 for H and 2.8 W m -2 for LE in 2017). The 24 study shows the importance of taking into account the surface energy imbalance at eddy 25 covariance sites when evaluating LSMs. 26 Keywords Eddy covariance · Energy balance closure · Land-surface model · Model 27 evaluation · Surface energy balance 28 1 Introduction 29The Earth is a complex system and its principal components, the atmosphere, the ocean, and 30 the land, interact with each other on a wide range of spatial and temporal scales (Suni et al. 31 2015). The impact of land-atmosphere interactions on climatic variabilities has received 32 much attention in recent years (Seneviratne and Stöckli 2008). The land surface represents 33 the lower boundary for the atmosphere and interacts with it through the exchange of energy, 34 water, and a variety of chemical species (Entekhabi et al. 1999). Solar radiation warms the 35 Earth's surface, and the total available energy is primarily partitioned into sensible heat flux 36 (henceforth, H), latent heat flux (henceforth, LE), and ground heat flux (henceforth, G s ), 37 collectively representing the surface energy balance (Trenberth et al. 2009). Studies have 38 shown that the heterogeneity of the Earth's land surface makes the feedbacks between land 39 use and the energy fluxes dynamic in space and time (Giorgi and Avissar 1997; Pielke 2001; 40 Suni et al. 2015). Thus, forecasting both climate and weather ...

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