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The interactions between crops and the atmosphere significantly impact surface energy and hydrology budgets, climate, crop yield, and agricultural management. In this study, a multipass land data assimilation scheme (MLDAS) is proposed based on the Noah‐MP‐Crop model. The ensemble Kalman filter (EnKF) method is used to jointly assimilate the leaf area index (LAI), soil moisture (SM), and solar‐induced chlorophyll fluorescence (SIF) observations to predict sensible (H) and latent (LE) heat fluxes, gross primary productivity (GPP), etc. Such joint assimilation is demonstrated to be effective in constraining the model state variables (i.e., leaf biomass and SM) and optimizing key crop‐model parameters (i.e., specific leaf area [SLA], and maximum rate of carboxylation, Vcmax). The performance of the MLDAS is evaluated against observations at two AmeriFlux cropland sites, revealing good an agreement with the observed H, LE, and GPP. When using optimized model parameters (SLA and Vcmax) and jointly assimilating LAI, SM, and SIF observations, the MLDAS produces 34.28%, 26.90%, and 51.82% lower root mean square deviations for daily H, LE, and GPP estimates compared with the Noah‐MP‐Crop open loop simulation. Our findings also indicate that the H and LE predictions are more sensitive to SM measurements, while the GPP simulations are more affected by LAI and SIF observations. The results indicate that performances of physical models can be greatly improved by assimilating multi‐source observations within MLDAS. Plain Language Summary: Accurate estimations of water and carbon fluxes in croplands are required for monitoring crop yield, hydrology, and irrigation scheduling. Thus, many crop models are aimed at modeling crop dynamics to establish more accurate water, carbon, and energy processes. The data assimilation (DA) method can combine land surface and vegetation phenological information with a process‐based model to reduce the uncertainty in model variables and optimize model parameters. In this study, multi‐source observations of the land surface and vegetation phenology are assimilated into a land‐surface‐crop model to improve the water, carbon, and energy fluxes over croplands. This research involved the construction of a DA framework with multipass ensemble Kalman filter method. The results show that the assimilation of soil moisture observations significantly improves the surface heat fluxes estimates, while the assimilation of vegetation phenological observations significantly improves the vegetation dynamics and crop yield estimates. Key Points: The Multipass Land Data Assimilation Scheme (MLDAS) is proposed based on the Noah‐MP‐Crop modelLeaf area index (LAI), soil moisture (SM), and solar‐induced chlorophyll fluorescence (SIF) measurements are assimilated into the MLDAS to predict sensible heat flux (H), latent heat flux (LE), and gross primary productivity (GPP)H and LE estimates are more sensitive to SM measurements, while the GPP retrievals are more affected by LAI and SIF observations [ABSTRACT FROM AUTHOR]

Uncertainties in representing land–atmosphere interactions can substantially influence regional climate simulations. Among these uncertainties, the surface exchange coefficient Ch is a critical parameter, controlling the total energy transported from the land surface to the atmosphere. Although it directly impacts the coupling strength between the surface and atmosphere, it has not been properly evaluated for regional climate models. This study assesses the representation of surface coupling strength in a stand-alone Noah-MP land surface model and in coupled 4-km Weather Research and Forecasting (WRF) model simulations. The data collected at eight FLUXNET sites of the Canadian Carbon Program and seven AMRIFLUX sites are used to evaluate the offline Noah-MP simulations. Nine of these FLUXNET sites are used for the evaluation of the coupled WRF simulations. These sites are categorized into three land use types: grassland, cropland, and forest. The surface exchange coefficients derived using three formulations in Noah-MP simulations are compared to those calculated from observations. Then, the default C zil = 0 and new canopy-height dependent C zil are used in coupled WRF simulations over the spring and summer in 2006 to compare their effects on surface heat flux, temperature, and precipitation. When the new canopy-height dependent C zil scheme is used, the simulated Ch exchange coefficient agrees better with observation and improves the daily maximum air temperature and heat flux simulation over grassland and cropland in the US Great Plains. Over grassland, the modeled Ch shows a different diurnal cycle than that for observed Ch, which makes WRF lag behind the observed diurnal cycle of sensible heat flux and temperature. The difference in precipitation between the two schemes is not as clear as the temperature difference because the impact of changing Ch is not local. [ABSTRACT FROM AUTHOR]

The fluxes of water and energy between the land surface and atmosphere involve many complex non-linear processes. In this study, the Noah and Noah-MP land surface models with multiple groundwater sub-models are used to assess how the treatment of canopy processes and interactions with deep groundwater affect 6 month regional climate simulations in two contrasting years, 2002 and 2010. Unlike the free drainage models, the models with groundwater capability have upward flux from the aquifer at different periods in the simulation. The inter-model Noah-MP soil moisture and latent heat flux results are consistent with recharge differences: the stronger upward flux capability with interactive groundwater results in the highest soil moisture and latent heat flux of the Noah-MP models. The increased latent heat effect on increased precipitation is small, which may result from negligible differences in convective precipitation. The Noah-MP model, independent of groundwater option, improves upon a cold and dry bias in the spring Noah simulations both during the day and night. The results for summer are region dependent and also differ between year and time of day. For a majority of the simulation period, there is little groundwater effect on the Noah-MP near-surface diagnostic fields. However, when the Noah-MP model produces large warm/dry biases in the 2010 summer, the aquifer interactions in Noah-MP improve the air temperature bias by 1-2 °C and dew point temperature bias by 1 °C. [ABSTRACT FROM AUTHOR]

It is still a daunting challenge for land surface models (LSMs) to correctly represent surface heat exchange for water-limited desert steppe ecosystems. This study aims to improve the ability of the Noah LSM to simulate surface heat fluxes through addressing uncertainties in precipitation forcing conditions, rapidly evolving vegetation properties, soil hydraulic properties (SHPs), and key parameterization schemes. Three years (2008-10) of observed surface heat fluxes and soil temperature over a desert steppe site in Inner Mongolia, China, are used to verify model simulations. The proper seasonal distribution of precipitation, along with more realistic vegetation parameters, can improve the simulation of sensible heat flux (SH) and the seasonal variability of latent heat flux. Correctly representing the low-surface exchange coefficient is crucial for improving SH for short vegetation like this desert steppe site. Relating Czil, the coefficient in the Noah surface exchange coefficient calculation, with canopy height h improves the simulated SH and the diurnal range of soil temperature over the simulation compared with using the default constant Czil. The exponential water stress formulation proposed here for the Jarvis scheme improves the partitioning between soil evaporation and transpiration. It is found that the surface energy fluxes are very sensitive to SHPs. This study highlights the important role of the proper parameter values and appropriate parameterizations for the surface exchange coefficient and water stress function in canopy resistance in capturing the observed surface energy fluxes and soil temperature variations for this desert steppe site. [ABSTRACT FROM AUTHOR]

Utilizing the data of the intensive observation period (May-June 2000) of the Dunhuang land-surface process field experiment supported by the 'Atmosphere-land Interactive Field Experiment over Arid Regions of Northwest China(NWC-ALIEX)', the bulk momentum transfer coefficient ( C) and the bulk sensible heat transfer coefficient ( C) between the surface and the atmosphere over the arid Gobi Desert region are determined using three different methods. The results indicate that these coefficients, especially the means, are the same order of magnitude. The influence of the building near the observational station on the results is significant. When the building effect exists, the diurnal variation of the atmospheric bulk transfer coefficients and the bulk Richardson number are irregular. After the building effect is eliminated through analysing the wind direction, the bulk Richardson number and the range of the typical values of the bulk transfer coefficients over the Gobi are obtained. The diurnal variations of the bulk transfer coefficients are smoother than that without the building affect. The bulk transfer coefficients are larger in the daytime than in the nighttime. It is also worth noting that the variations of the bulk transfer coefficients and the bulk Richardson number are just opposite in phase, and that the bulk transfer coefficient for sensible heat flux is more related to the bulk Richardson number than that for momentum flux. The results are more reasonable than that after removing the building effect. The relation between the bulk transfer coefficients is also discussed. Copyright © 2010 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

Observations of the surface heat fluxes can be used to evaluate and improve land-surface models (LSMs). There are significant uncertainties, however, in measured surface-energy budgets, especially for the heterogeneous Tibetan Plateau (TP) region where the observation conditions are harsh. In this study, summer (July-October 2014) surface flux data were obtained using the eddy covariance method from ten sites over the TP during the Third Tibetan Plateau Atmospheric Scientific Experiments. Data analysis was performed to assess the surface-energy balance ratio (SEBR= H + LE/(Rn-G)) and associated uncertainties across various land-cover types and elevation heights. Measured latent heat fluxes were positive during nighttime and exhibit substantially greater uncertainty than the sensible heat fluxes. The ten-site averaged SEBR was 74.2 ± 5.4%, largely on par with reported SEBR for other regions. SEBR values were similar among homogeneous sites, and the averaged SEBR (93.4%) for those sites was better than that (67.3%) for the heterogeneous sites. The soil heat storage term represents the most significant source of uncertainty (8.2%) than the canopy storage term (0.22%) to closing the surface energy budget. The SEBR showed a strong diurnal cycle and the midday (10:00∼15:00 local time) values were higher than those nearest sunrise and sunset times. The late-night SEBR (00:00∼6:00 local time) at sites located at higher elevations were more reliable than those at lower elevation sites, because of the frequent occurrence of neutral conditions (instead of stable or very stable conditions) at high terrains. The relationships between SEBR and surface-layer turbulent parameters (ξ, u*,θ*) and wind direction were investigated. An uncertainty range for measured surface heat fluxes was derived to provide a meaningful guidance for applying these observations in evaluating LSMs. [ABSTRACT FROM AUTHOR]