Noah Land Surface Model Research Articles

Key parameters in urban surface radiation budget and energy balance modeling

Urban land surface models (ULSMs) are important tools for studying the climatic effects of human activity. Various kinds of ULSMs have been developed to simulate the land-atmosphere interactions in urban areas. Although the parameterization schemes of these models have become increasingly complicated, the influencing mechanisms and approaches of human-made land surface construction on urban land-atmosphere interactions still need further research. In this paper, some key parameters that are associated with urban land surface modeling were determined through a mechanistic study of the urban surface radiation budget and energy balance schemes in the integrated urban land model (IUM). These parameters are the surface albedo, three-dimensional fractional vegetation cover (FVC) and the correction factors for the net radiation absorption and surface roughness length. Then, these parameters were parameterized and optimized through a comparison with the observed flux data at the 325-m meteorology tower in downtown Beijing to improve the simulation ability of the IUM. The simulation results from the Noah land surface model coupled with the single-layer urban canopy model (Noah/SLUCM) were used and compared with the results from the IUM to demonstrate the better performance of the IUM model. The results indicate that after parameterizing and optimizing these key parameters, both the fluxes associated with the radiation budget and energy balance are substantially improved.

Evaluating the Impact of Planetary Boundary Layer, Land Surface Model, and Microphysics Parameterization Schemes on Cold Cloud Objects in Simulated GOES‐16 Brightness Temperatures

AbstractInfrared brightness temperatures (BTs) from the Geostationary Observing Environmental Satellite‐16 Advanced Baseline Imager are used to examine the ability of several microphysics and planetary boundary layer (PBL) schemes, as well as land surface models (LSM) and surface layers, to simulate upper‐level clouds. Six parameterization configurations were evaluated. Cloud objects are identified using the Method for Object‐Based Diagnostic Evaluation (MODE) and analyzed using the object‐based threat score, mean‐error distance, and pixel‐based metrics including the mean absolute error and mean bias error (MBE) for matched objects where the displacement between objects has been removed. Objects are identified using either a fixed BT threshold of 235 K or the 6.5th percentile of BTs for each model configuration. Analysis of the MODE‐identified cloud objects shows that, compared to a configuration with the Thompson microphysics scheme, Mellor‐Yamanda‐Nakanishi‐Niino (MYNN) PBL, Global Forecasting System (GFS) surface layer, and Noah LSM, the configuration employing the National Severe Storms Laboratory microphysics produced more cloud objects with higher BTs. Changing the PBL from MYNN to Shin‐Hong or Eddy‐Diffusivity Mass‐Flux also resulted in a slightly lower accuracy, though these changes result in configurations which more accurately reproduced the number of observation cloud objects and slightly reduced the high MBE. Changing the LSM from Noah to RUC reduces forecast accuracy by producing too many cloud objects with too low BTs. As the forecast hour increases, this accuracy reduction increases at a greater rate than occurred when changing the microphysics or PBL scheme and is further enhanced when using the MYNN surface layer rather than the GFS.

Quantitative assessment of the parameterization sensitivity of the Noah-MP land surface model with dynamic vegetation using ChinaFLUX data

The Noah land surface model with multiparameterization options (Noah-MP) depicts the water, heat, and carbon exchange processes occurring between the land and atmosphere. Including dynamic vegetation in the model gives tens of thousands of possible model parameterization combinations, making it difficult to select the optimal scheme for practical applications. Quantitative assessment of parameterization sensitivity can mitigate this difficulty. In this study, we combined summertime observation data from eight sites in the ChinaFLUX network with the multiple parameterization schemes of four physical subprocesses to design 72 simulation tests for each site to assess the model. Subsequently, Sobol sensitivity analysis was performed to quantitatively evaluate the sensitivity of the four physical subprocesses in simulating sensible heat flux (SHF), latent heat flux (LHF), and net ecosystem exchange (NEE). These subprocesses include the soil moisture factor which controls stomatal resistance (BTR), surface turbulence exchange coefficient (SFC), runoff and groundwater (RUN), and radiation transfer (RAD). Finally, the role of the sensitive subprocess parameterization schemes on the simulation results was explored. The results showed that a combination of parameterization schemes reasonably simulated diurnal variations in SHF and LHF, although a significant difference was shown in simulated NEE diurnal variation. The SFC and RAD subprocess parameterizations were sensitive to simulated SHF at most sites, as were the BTR and SFC subprocesses to NEE. With respect to LHF, the sensitive processes were not consistent at each site. Comparison of the parameterization schemes of the sensitive processes showed that RAD and SFC process parameterization affected heat, water, and carbon flux mainly via the canopy and ground temperature, BTR by transpiration and carbon assimilation, and RUN by soil moisture.

Quantifying the observational requirements of a space-borne LiDAR snow mission

This study quantifies the level of observational accuracy required from a spaceborne light detection and ranging (LiDAR) snow depth retrieval mission for enabling beneficial impacts for snow estimation. The study is conducted over a region in Western Colorado using a suite of observing system simulation experiments (OSSEs). The Joint UK Land Environment Simulator, version 5.0 (JULES v5.0) is employed to simulate a suite of idealized LiDAR observations, considering a range of LiDAR snow depth retrieval errors, different hypothetical sensor swath widths, and the impact of cloud cover on observability. These simulated observations are then assimilated into the Noah land surface model with multi-parameterization options, version 3.6 (Noah-MP v3.6) model. This data assimilation setup is used to systematically evaluate the potential utility of LiDAR observations for improving modeled snow water equivalent (SWE) estimates and water budget variables such as runoff. Results from the OSSE runs show that, in general, assimilation of synthetic LiDAR observations provide beneficial impacts when the LiDAR snow depth retrieval error standard deviation (σerror) is below 60 cm. Based on comparisons between the realistic (i.e., swath-limited and cloud-attenuated) case and the idealized (i.e., infinite swath width in the absence of cloud cover) case, this study concludes that observations with a conservative error standard deviation threshold of 40 cm (i.e., upper limit of the snow depth retrieval error that adds value to the SWE estimates via assimilation) are needed for improving modeled snow estimates. More than a 33% reduction in SWE root mean square errors and more than a 15% increase in correlation coefficients are achieved when σerror ≤ 40 cm using a 170-km sensor swath width in the presence of cloud attenuation effects. Further, the integrated hydrologic response, as represented by total (surface and subsurface) runoff estimates during the snow ablation season, are also enhanced when assimilating synthetic LiDAR snow depth retrievals with errors below this level.

Satellite soil moisture data assimilation impacts on modeling weather variables and ozone in the southeastern US – Part 1: An overview

Abstract. This study evaluates the impact of satellite soil moisture (SM) data assimilation (DA) on regional weather and ozone (O3) modeling over the southeastern US during the summer. Satellite SM data are assimilated into the Noah land surface model using an ensemble Kalman filter approach within National Aeronautics and Space Administration's Land Information System framework, which is semicoupled with the Weather Research and Forecasting model with online Chemistry (WRF-Chem; standard version 3.9.1.1). The DA impacts on the model performance of SM, weather states, and energy fluxes show strong spatiotemporal variability. Dense vegetation and water use from human activities unaccounted for in the modeling system are among the factors impacting the effectiveness of the DA. The daytime surface O3 responses to the DA can largely be explained by the temperature-driven changes in biogenic emissions of volatile organic compounds and soil nitric oxide, chemical reaction rates, and dry deposition velocities. On a near-biweekly timescale, the DA modified the mean daytime and daily maximum 8 h average surface O3 by up to 2–3 ppbv, with the maximum impacts occurring in areas where daytime surface air temperature most strongly (i.e., by ∼2 K) responded to the DA. The DA impacted WRF-Chem upper tropospheric O3 (e.g., for its daytime-mean, by up to 1–1.5 ppbv) partially via altering the transport of O3 and its precursors from other places as well as in situ chemical production of O3 from lightning and other emissions. Case studies during airborne field campaigns suggest that the DA improved the model treatment of convective transport and/or lightning production. In the cases that the DA improved the modeled SM, weather fields, and some O3-related processes, its influences on the model's O3 performance at various altitudes are not always as desirable. This is in part due to the uncertainty in the model's key chemical inputs, such as anthropogenic emissions, and the model representation of stratosphere–troposphere exchanges. This can also be attributable to shortcomings in model parameterizations (e.g., chemical mechanism, natural emission, photolysis and deposition schemes), including those related to representing water availability impacts. This study also shows that the WRF-Chem upper tropospheric O3 response to the DA has comparable magnitudes with its response to the estimated US anthropogenic emission changes within 2 years. As reductions in anthropogenic emissions in North America would benefit the mitigation of O3 pollution in its downwind regions, this analysis highlights the important role of SM in quantifying air pollutants' source–receptor relationships between the US and its downwind areas. It also emphasizes that using up-to-date anthropogenic emissions is necessary for accurately assessing the DA impacts on the model performance of O3 and other pollutants over a broad region. This work will be followed by a Noah-Multiparameterization (with dynamic vegetation)-based study over the southeastern US, in which selected processes including photosynthesis and O3 dry deposition will be the foci.

Performance evaluation of reanalysis precipitation products in Egypt using fuzzy entropy time series similarity analysis

AbstractSelection of suitable gridded precipitation data is deemed for hydroclimatic assessment and climate change impact analysis, especially in regions where long‐term reliable precipitation data is unavailable. A novel approach based on fuzzy entropy similarity analysis (FESA) is proposed to evaluate the performance of four reanalysis gridded precipitation datasets (GPDs) for Egypt, namely European Reanalysis v.5. (ERA5), TerraClimate, Global Land Data Assimilation System (GLDAS)—Noah Land Surface Model L4 v.2 and Climatologies at high resolution for the Earth's land surface areas (CHELSA), against gauge records. The proposed method was verified using conventional statistics. Besides, the relative performance of different GPDs was verified according to their response to the influence of North Atlantic Oscillation (NAO) and Mediterranean Oscillation (MO) on winter precipitation. The performance of the best reanalysis GPD was also compared with the gauge‐based global precipitation climatology centre (GPCC) dataset to show its reliability. The FESA revealed CHELSA as the best reanalysis GPD for Egypt. The performance assessment of GDPs based on conventional statistical metrics and visual presentation confirms the results obtained using FESA. CHELSA showed significant correlations with NAO (r = 0.3627) and MO (r = 0.624) like that obtained for gauge records. CHELSA also showed a better representation of precipitation in Egypt than GPCC at nearly half of the gauge locations. As CHELSA has a much higher spatial resolution than GPCC, it can be recommended as the proxy of gauge records in Egypt. The FESA can be used for performance analysis of gridded climate data by avoiding the complexities of using multiple statistical metrics.

Ensemble Skill Gains Obtained From the Multi‐Physics Versus Multi‐Model Approaches for Continental‐Scale Hydrological Simulations

AbstractMulti‐physics ensembles have emerged as a promising approach to hydrological simulations. As multi‐physics ensembles are constructed by perturbing the model physics, the ensemble members share a substantial portion of the same physics and hence are not independent of each other. It is unknown whether and to what extent this nonindependence affects the skill gain of the ensemble method, especially compared with the multi‐model ensemble approach. This study compares a multi‐physics ensemble configured from the Noah land surface model with multi‐parameterization options (Noah‐MP) with the North American Land Data Assimilation System (NLDAS) multi‐model ensemble. The two ensembles are evaluated in terms of the annual cycle and interannual anomaly at 12 River Forecast Centers over the conterminous United States. The ensemble skill gain is measured by the difference between the performance of the ensemble mean and the average of the ensemble members' performance, and the inter‐member independence is measured by error correlations. Results show that, due to the improved model physics, the Noah‐MP configurations outperform, on average, the NLDAS models, especially in the snow‐dominated areas. The Noah‐MP ensemble almost always obtains an outstanding member that performs the best among the two ensembles, reflecting its dense sampling of the feasible model physics space. However, these two performance superiorities do not lead to a superiority of the ensemble mean. The Noah‐MP ensemble has a lower ensemble skill gain, which corresponds to the lower inter‐member independence. These results highlight the importance of inter‐member independence, particularly when most hydrological ensemble methods have overlooked it.

Development of a Flash Drought Intensity Index

Flash droughts are characterized by a period of rapid intensification over sub-seasonal time scales that culminates in the rapid emergence of new or worsening drought impacts. This study presents a new flash drought intensity index (FDII) that accounts for both the unusually rapid rate of drought intensification and its resultant severity. The FDII framework advances our ability to characterize flash drought because it provides a more complete measure of flash drought intensity than existing classification methods that only consider the rate of intensification. The FDII is computed using two terms measuring the maximum rate of intensification (FD_INT) and average drought severity (DRO_SEV). A climatological analysis using soil moisture data from the Noah land surface model from 1979–2017 revealed large regional and interannual variability in the spatial extent and intensity of soil moisture flash drought across the US. Overall, DRO_SEV is slightly larger over the western and central US where droughts tend to last longer and FD_INT is ~75% larger across the eastern US where soil moisture variability is greater. Comparison of the FD_INT and DRO_SEV terms showed that they are strongly correlated (r = 0.82 to 0.90) at regional scales, which indicates that the subsequent drought severity is closely related to the magnitude of the rapid intensification preceding it. Analysis of the 2012 US flash drought showed that the FDII depiction of severe drought conditions aligned more closely with regions containing poor crop conditions and large yield losses than that captured by the intensification rate component (FD_INT) alone.

Interannual Variability of Atmospheric CH4 and Its Driver Over South Korea Captured by Integrated Data in 2019

Understanding the temporal variability of atmospheric methane (CH4) and its potential drivers can advance the progress toward mitigating changes to the climate. To comprehend interannual variability and spatial characteristics of anomalous CH4 mole fractions and its drivers, we used integrated data from different platforms such as in situ measurements and satellites (TROPOspheric Monitoring Instrument (TROPOMI) and Greenhouse Gases Observing SATellite (GOSAT)) retrievals. A pronounced change of annual growth rate was detected at Anmyeondo (AMY), Republic of Korea, ranging from −16.8 to 31.3 ppb yr−1 as captured in situ through 2015–2020 and 3.9 to 16.4 ppb yr−1 detected by GOSAT through 2014–2019, respectively. High growth rates were discerned in 2016 (31.3 ppb yr−1 and 13.4 ppb yr−1 from in situ and GOSAT, respectively) and 2019 (27.4 ppb yr−1 and 16.4 ppb yr−1 from in situ and GOSAT, respectively). The high growth in 2016 was essentially explained by the strong El Niño event in 2015–2016, whereas the large growth rate in 2019 was not related to ENSO. We suggest that the growth rate that appeared in 2019 was related to soil temperature according to the Noah Land Surface Model. The stable isotopic composition of 13C/12C in CH4 (δ13-CH4) collected by flask-air sampling at AMY during 2014–2019 supported the soil methane hypothesis. The intercept of the Keeling plot for summer and autumn were found to be −53.3‰ and −52.9‰, respectively, which suggested isotopic signature of biogenic emissions. The isotopic values in 2019 exhibited the strongest depletion compared to other periods, which suggests even a stronger biogenic signal. Such changes in the biogenic signal were affected by the variations of soil temperature and soil moisture. We looked more closely at the variability of XCH4 and the relationship with soil properties. The result indicated a spatial distribution of interannual variability, as well as the captured elevated anomaly over the southwest of the domain in autumn 2019, up to 70 ppb, which was largely explained by the combined effect of soil temperature and soil moisture changes, indicating a pixel-wise correlation of XCH4 anomaly with those parameters in the range of 0.5–0.8 with a statistical significance (p < 0.05). This implies that the soil-associated drivers are able to exert a large-scale influence on the regional distribution of CH4 in Korea.

Weather Reasearch and Forecasting Model Simulation of a Snowfall Event in Southern Brazil

This study evaluates the ability of the Weather Research and Forecasting (WRF) Model to simulate a snowfall event in the southern Brazil. The event in August 2013 was considered one of the most intense in recent years in the region with the highest topographic elevations between the states of Rio Grande do Sul (RS) and Santa Catarina (SC). The Snowfall in the mountain region of RS and SC was associated with the configuration involving a polar anticyclone and the intensification of an extratropical cyclone over the Atlantic Ocean. The WRF simulation results demonstrated the model's viability to predict the event, but without the magnitude representation of the phenomenon. The simulations represented few snow accumulations, with a maximum value of 1.2 mm, and occurred in only two meteorological monitoring stations among the seven evaluated, differently from what was reported in the media. These results may be linked to the simulation of the warmer air temperature near to the surface in relation to the observations, and consequently to the increase in the simulation of precipitation in liquid form. These results were attributed to the choice of WRF Single–moment 6–class (WSM6) microphysics and in the Noah Land Surface Model scheme. Despite these limitations, WRF has proved to be an important tool for predicting the spatial and temporal distribution of snowfall and precipitation in the higher regions of southern Brazil.

Assessing the simulated soil hydrothermal regime of the active layer from the Noah-MP land surface model (v1.1) in the permafrost regions of the Qinghai–Tibet Plateau

Abstract. Extensive and rigorous model intercomparison is of great importance before model application due to the uncertainties in current land surface models (LSMs). Without considering the uncertainties in forcing data and model parameters, this study designed an ensemble of 55 296 experiments to evaluate the Noah LSM with multi-parameterization (Noah-MP) for snow cover events (SCEs), soil temperature (ST) and soil liquid water (SLW) simulation, and investigated the sensitivity of parameterization schemes at a typical permafrost site on the Qinghai–Tibet Plateau (QTP). The results showed that Noah-MP systematically overestimates snow cover, which could be greatly resolved when adopting the sublimation from wind and a semi-implicit snow/soil temperature time scheme. As a result of the overestimated snow, Noah-MP generally underestimates ST, which is mostly influenced by the snow process. A systematic cold bias and large uncertainties in soil temperature remain after eliminating the effects of snow, particularly in the deep layers and during the cold season. The combination of roughness length for heat and under-canopy (below-canopy) aerodynamic resistance contributes to resolving the cold bias in soil temperature. In addition, Noah-MP generally underestimates top SLW. The runoff and groundwater (RUN) process dominates the SLW simulation in comparison to the very limited impacts of all other physical processes. The analysis of the model structural uncertainties and characteristics of each scheme would be constructive to a better understanding of the land surface processes in the permafrost regions of the QTP as well as to further model improvements towards soil hydrothermal regime modeling using LSMs.

Impact of Land Surface Processes on a Record‐Breaking Rainfall Event on May 06–07, 2017, in Guangzhou, China

AbstractA record‐breaking extreme rainfall event with a maximum rainfall amount over 24 h of 524.1 mm occurred in Guangzhou, China, on May 06–07, 2017. To study the impact of land surface processes on this extreme rainfall, two 21‐member convective‐permitting ensemble forecasts over South China were performed based on two land surface models (LSMs), Noah and Community Land Model (CLM), and 21 forecasting members of the Global Ensemble Forecast System (GEFS). The results showed that, in general, members using the Noah LSM could better simulate urban heat islands (UHIs) and urban convection than members using the CLM LSM in this case. By investigating the ensemble member that most resembled the observations, it was found that the high temperature center in the urban area caused a thermal low in the early stage. As the southerly winds strengthened, the low‐level convergence line continued moving northward and eventually triggered convection in the mountainous region. A sensitivity experiment showed that the impact of land surface heterogeneity on precipitation could be reflected on a finer scale, and heavy rainfall was very sensitive to the changes in small‐scale land surface forces, including terrain and land use. Slight variations in small‐scale land surface conditions caused great responses in the total precipitation, indicating that for the occurrence of such quasi‐stationary extreme rainfall, a subtle balance between different atmospheric and land surface factors may be required.

Evaluation of the Effect of Stability Schemes on the Simulation of Land Surface Processes at a Western Tibetan Site

The surface fluxes calculated in land surface models (LSMs) are sensitive to the determination of the stability parameter. Further, calculation of the surface fluxes over the Tibetan Plateau (TP) is crucial in the simulation of regional and global weather and climate. In this study, we use 2-year micrometeorological data measured from Shiquanhe, located in the western TP, to evaluate the performance of the widely used Noah LSM with five stability parameterization schemes. Results show that all five stability parameterization schemes can generally reproduce the observations, but the scheme proposed by Li has the smallest bias. The reason is that Li’s scheme is more accurate under the unstable condition, and the surface layer at Shiquanhe is mostly unstable. Further, the four non-iterative schemes show an advantage in terms of their computational efficiency compared to the iterative scheme adopted by the Noah LSM.

Evaluation of the temporal variations of groundwater storage and its interactions with climatic variables using GRACE data and hydrological models: A study from Turkey

AbstractMonitoring of the fluctuations of groundwater storage is particularly important in arid and semi‐arid regions where water scarcity brings about various challenges. Remote sensing data and techniques play a preponderant role in developing solutions to environmental problems. The launch of Gravity Recovery and Climate Experiment (GRACE) satellites has eased the remote monitoring and evaluation of groundwater resources with an unprecedented precision over large scales. Within the scope of the current study, the latest release (RL06) of GRACE mass concentrations (Mascons) from Jet Propulsion Laboratory (JPL) dataset as well as Global Land Data Assimilation System (GLDAS) models of Noah and Catchment Land Surface Model (CLSM) were used to provide Groundwater Storage Anomalies (GWSA) over Turkey. The temporal interactions of the estimated GWSA with the climatic variables of precipitation and temperature (derived from the reanalysis datasets of CHELSA [Climatologies at High resolution for the Earth's Land Surface Areas] and FLDAS [the Famine Early Warning Systems Network Land Data Assimilation System], respectively) were investigated statistically. The results suggest that there is a descending trend (from 2003 to 2016) for Terrestrial Water Storage Anomalies (TWSA) and GWSA over Turkey with a total loss of 11 and 6 cm of water, respectively. The statistical analysis results also indicate that the monthly variations of GWSA over Turkey are highly correlated with precipitation and temperature at 2‐month lag. The analysis of the climatology (long‐term) values of monthly GWSA, precipitation and temperature also revealed high agreement between the variables.

A Soil Moisture‐Dependent Model to Simulate Water Table Depth and Proportions of Surface and Subsurface Runoff and Its Validation at the Basin Scale

AbstractIn land surface models, the ability to simulate runoff is a good diagnostic for the hydrological cycle and other surface parameterizations; however, the partitioning of total runoff into surface and subsurface runoff shows considerable uncertainties. The interaction between soil moisture and groundwater plays an important role in simulating the surface and subsurface runoff. Specific yield (Sy) is widely used to calculate water table depths, in which the error comes mainly from uncertainty in determining the Sy value. This study developed a new soil moisture‐dependent model (SMD‐model) to improve the simulations of water table depths and partitioning total runoff into subsurface and surface components. The validations against observations and the Community Noah Land Surface Model with MultiParameterization Options (Noah‐MP) in three basins indicate that the SMD‐model can simulate a realistic and stable water table series, which shows the detailed variations of observed water tables, avoiding the uncertainties in determining Sy; and it can generate accurate proportions of surface and subsurface runoff. However, when Sy is used to calculate the water table depth, keeping other processes the same as in the SMD‐model, it produces a series of smoother and deeper water tables, and underestimates the proportion of subsurface runoff to total runoff by 24%–28%. Noah‐MP captures the overall variation of observed water tables and overestimates the proportion of subsurface runoff by 20%–33%. This study can provide relatively accurate estimations of water tables and surface and subsurface runoff, and benefit the study of land‐atmosphere interactions.

Modeling Snow Ablation over the Mountains of the Western United States: Patterns and Controlling Factors

AbstractWhen compared with differences in snow accumulation predicted by widely used hydrological models, there is a much greater divergence among otherwise “good” models in their simulation of the snow ablation process. Here, we explore differences in the performance of the Variable Infiltration Capacity model (VIC), Noah land surface model with multiparameterization options (Noah-MP), the Catchment model, and the third-generation Simplified Simple Biosphere model (SiB3) in their ability to reproduce observed snow water equivalent (SWE) during the ablation season at 10 Snowpack Telemetry (SNOTEL) stations over 1992–2012. During the ablation period, net radiation generally has stronger correlations with observed melt rates than does air temperature. Average ablation rates tend to be higher (in both model predictions and observations) at stations with a large accumulation of SWE. The differences in the dates of last snow between models and observations range from several days to approximately a month (on average 5.1 days earlier than in observations). If the surface cover in the models is changed from observed vegetation to bare soil in all of the models, only the melt rate of the VIC model increases. The differences in responses of models to canopy removal are directly related to snowpack energy inputs, which are further affected by different algorithms for surface albedo and energy allocation across the models. We also find that the melt rates become higher in VIC and lower in Noah-MP if the shrub/grass present at the observation sites is switched to trees.

Assessment of surface exchange coefficients in the Noah‐MP land surface model for different land‐cover types in China

AbstractThe parameterization of surface exchange coefficients (Ch) representing land–atmosphere coupling strength plays a key role in land surface modelling. Previous studies have found that land–atmosphere coupling in land surface models (LSMs) is largely overestimated, which affects the predictability of weather and climate evolution. To improve the representation of land–atmosphere interactions in LSMs, this study investigated the impact of dynamic canopy‐height‐dependent coupling strength in an offline Noah LSM with multi‐parameterization options (Noah‐MP) when applied to China. Comparison with the default Noah‐MP LSM showed that the dynamic scheme significantly improved the Ch calculations and realistically reduced the biases of simulated surface energy and water components against observations. It is noteworthy that the improvements brought about by the dynamic scheme differed across land‐cover types. The scheme was superior in reproducing the observed Ch as well as surface energy and water variables for short vegetation (grass, crops, and shrubs), whereas the improvement for tall canopy vegetation (forest) was not significant although the estimations were reasonable. The improved version benefits from the treatment of the roughness length for heat. Further, the scheme can better reproduce the observed surface thermal components, while the improvement in hydraulic variables (such as soil moisture) remains limited. Overall, the dynamic coupling scheme markedly affects the simulation of land–atmosphere interactions, and altering the dynamics of surface coupling has the potential to improve the representation of land–atmosphere interactions and, thus, the further development of LSM.

Impacts of land cover heterogeneity and land surface parameterizations on turbulent characteristics and mesoscale simulations

The impacts of surface heterogeneity and land surface parameterization on the mesoscale processes were studied. Experiments were conducted using the Weather Research and Forecasting (WRF) model coupled with a simple (slab) land surface model (LSM), a relatively complex Noah LSM, and a land data assimilation system (LDAS) with detailed surface fields. Three heterogeneity length scales: 1, 3, and 9 km, were employed to alter land cover and land use. A series of simulations were performed over the U.S. Southern Great Plains during the summer when the soil moisture was abundant. Results indicate that both the land surface parameterizations and fine-scale surface heterogeneity affect the model simulations; and the impact of land surface parameterization is found to be more important, particularly for low frequency ( $$f<{10}^{-4} \mathrm{hz}$$ ) eddies and mesoscale circulations. Substantial variations in turbulent spectra were also noted, and the energy spectra respond nonlinearly to the changes in the heterogeneous length scales. Three important conclusions emerge: (i) more detailed land surface representation reduces uncertainty in simulations of surface fluxes via improved turbulence characterization over a heterogeneous land surface, which helps improve simulations of land–atmosphere feedbacks; (ii) the impact of land surface heterogeneity on atmospheric feedbacks can be detected in the mesoscale circulations that are roughly four times of the spatial heterogeneity scale; and (iii) the land surface heterogeneity that can influence mesoscale circulations would be a function of grid spacing in the model.

Identification of Dominant Warm-Season Latent Heat Flux Patterns in the Lower Mississippi River Alluvial Valley

Warm-season precipitation in the Lower Mississippi River Alluvial Valley (LMRAV) is heavily dominated by the rates of evapotranspiration and surface heat fluxes and is a primary water resource for agriculture. However, the stochastic nature of LMRAV warm-season thunderstorms makes precipitation forecasts challenging. The Weather Research and Forecasting Hydrologic (WRF-Hydro) model, coupled with the multi-parameter Noah land surface (Noah-MP) model, has improved estimates of important warm-season precipitation process. Given the widespread agriculture and dominance of crop and forested landscapes over the region, proper assessment of land use / land cover (LULC) is critical in predicting warm-season precipitation patterns. The objective of this study is to quantify simulated latent heat flux sensitivity (important for warm-season precipitation) to temporally updated LULC datasets. Both the model default and annually updated LULC conditions were used to initialize a 16-year WRF-Hydro simulation from which warm-season latent heat flux estimates were obtained. Annual root mean square difference was computed at each gridpoint. Cluster analysis preprocessed with kernel principal component analysis was used to identify spatial RMSD structures that quantified sensitivity to updated LULC conditions. Results showed the largest impacts occurred directly in the LMRAV and for points slightly east and revealed a meteorological link between these regions.

Improving ASCAT Soil Moisture Retrievals With an Enhanced Spatially Variable Vegetation Parameterization

This study investigates the performance of the TU Wien soil moisture retrieval (TUW-SMR) algorithm by adapting the strength of the vegetation correction. The semiempirical change detection method TUW-SMR exploits the multiangle backscatter observations from spaceborne fan-beam scatterometer systems in order to derive surface soil moisture information expressed in the degree of saturation. The vegetation parameterization of TUW-SMR is controlled by the dry and wet crossover angles that are used to determine the dry and wet backscatter reference. Backscatter observations from the Advanced Scatterometer (ASCAT) are used to produce four soil moisture data sets based on different dry and wet crossover angles describing: 1) a static, respectively, no vegetation correction; 2) the currently used seasonal vegetation correction; 3) a stronger seasonal vegetation correction; and 4) a spatially variable seasonal vegetation correction with the stronger vegetation correction over vegetated areas and no vegetation correction over bare land. All four ASCAT soil moisture data sets are evaluated against soil moisture estimates from GLDAS-2.1 Noah land surface model and the European Space Agency (ESA) climate change initiative (CCI) Passive v04.5 soil moisture product using the triple collocation method and traditional correlation analysis. The results show that the spatially variable vegetation correction overall improves soil moisture estimates in both more densely vegetated areas, e.g., in large parts of North America and Europe, and more sparsely vegetated, e.g., Western Africa. Nonetheless, the experiment also provides insight into challenging retrieval conditions where the TUW-SMR fails to take all relevant backscatter processes into account, e.g., wetlands and bare soils with subsurface scattering.