Land-atmosphere Interface Research Articles

Towards an improved prediction of soil-freezing characteristic curve based on extreme gradient boosting model

As an essential property of frozen soils, change of unfrozen water content (UWC) with temperature, namely soil-freezing characteristic curve (SFCC), plays significant roles in numerous physical, hydraulic and mechanical processes in cold regions, including the heat and water transfer within soils and at the land–atmosphere interface, frost heave and thaw settlement, as well as the simulation of coupled thermo-hydro-mechanical interactions. Although various models have been proposed to estimate SFCC, their applicability remains limited due to their derivation from specific soil types, soil treatments, and test devices. Accordingly, this study proposes a novel data-driven model to predict the SFCC using an extreme Gradient Boosting (XGBoost) model. A systematic database for SFCC of frozen soils compiled from extensive experimental investigations via various testing methods was utilized to train the XGBoost model. The predicted soil freezing characteristic curves (SFCC, UWC as a function of temperature) from the well-trained XGBoost model were compared with original experimental data and three conventional models. The results demonstrate the superior performance of the proposed XGBoost model over the traditional models in predicting SFCC. This study provides valuable insights for future investigations regarding the SFCC of frozen soils.

Position-specific isomers of monohydroxy fatty acids in the land-atmosphere interface: identification and quantification

Due to a wide variety and similar physicochemical properties of monohydroxy saturated fatty acids (OH-FAs) isomers as biomarkers, previously reported OH-FAs in environmental samples were mainly restricted to the α-, β-, (ω-1)- and ω-OH-FA isomers. Here, N,N-dimethylethylenediamine (DMED) labeling coupled with ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS) analysis with multiple reaction monitoring mode (MRM) was developed to screen, identify and quantify position-specific isomers of OH-FAs (C8-C18). An identification strategy of positional isomers of OH-FAs, including α-, β-, 4 to (ω-2)-, (ω-1)- and ω-OH-FAs, was established by integrating the characteristics of peak intensity ratios of product ions based on the library of OH-FAs. Meanwhile, d4-DMED-labeled OH-FA standards as internal standards were adopted for the relative quantification of positional isomers. The extraction processes were optimized for different interface-environmental samples. Our method offers a promising tool to investigate position-specific isomers of OH-FAs in the land-atmosphere interface.

Improving monthly mean land surface temperature estimation by merging four products using the generalized three-cornered hat method and maximum likelihood estimation

Land surface temperature (LST) is a critical parameter associated land surface energy and water balance at the land–atmosphere interface. Monthly mean LST (MMLST) is used not only as an input parameter for land surface models, but also as an indicator for general climate trend studies. Four MMLST products (i.e., NESDC, TPDC, ZENODO, and ERA5) were generated by different research teams and institutes using different algorithms. In this study, a new method was presented to improve MMLST estimation by merging these four products using the generalized three-cornered hat (TCH) method and maximum likelihood estimation (MLE). The generalized TCH method was used to simultaneously calculate the relative uncertainties of these four MMLST products during the period 2003–2019. In general, the smallest uncertainties are obtained for the TPDC MMLST product in most areas of the globe, whereas the largest uncertainties are achieved for the ERA5 MMLST product, especially in high-latitude regions. The MLE method was used to merge these four MMLST products and to generate a new MMLST product in consideration of the uncertainty of each individual MMLST product. In situ measurements at 25 stations from the AmeriFlux and SURFRAD networks during the period 2003–2019 were used to evaluate the accuracies of the individual and merged MMLST products. The largest root mean squared error (RMSE) of approximately 2.4 K is obtained for the TPDC MMLST product, whereas the smallest RMSE of around 1.6 K is reached for the merged MMLST product. The NESDC, ZENODO, and ERA5 MMLST products have similar RMSE of approximately 2 K. The results indicate that the merged MMLST product outperforms each individual MMLST product. The merged MMLST product shows promising prospects for climate- and global-scale studies and applications.

First mapping of polarization-dependent vegetation optical depth and soil moisture from SMAP L-band radiometry

Soil moisture (SM) and vegetation optical depth (VOD) estimates using passive microwave remote sensing at L-band (1.4 GHz) are essential for attaining a better understanding of water exchanges at the land-atmosphere interface. However, current retrieval algorithms often ignore the polarization dependence of vegetation effects. This study proposed a parameter self-calibrating framework for the multi-channel collaborative algorithm (MCCA) and presented a new SM and the first polarization-dependent VOD product based on the dual-polarized L-band observations at a fixed angle (40°) from the NASA Soil Moisture Active Passive (SMAP) mission. The parameter self-calibrating framework utilizes an information theory-based approach to obtain surface roughness and effective scattering albedo globally. Furthermore, the MCCA does not require auxiliary data for vegetation or soil moisture to constrain the retrieval process. Comparison with other SM and VOD products, such as MT-DCA version 5, DCA, SCA-H, SCA-V from SMAP Level-3 products version 8, and SMAP-IB, demonstrate analogous spatial patterns. The MCCA-derived SM exhibits the lowest unbiased root mean square deviation (ubRMSD, about 0.055 m3/m3), followed by SMAP-IB and DCA (0.061 m3/m3), with an overall Pearson's correlation coefficient of 0.744 (SMAP-IB performed best with R = 0.764) when evaluated against in-situ observations from 18 dense soil moisture networks. The MCCA generates VOD values for both vertical and horizontal polarization, demonstrating a slight polarization difference of vegetation effect at the satellite scale. Both VODs exhibit a strong linear relationship with above-ground biomass and canopy height. The polarization difference of VODs is primarily observed in densely vegetated and arid areas.

Use of a new Tibetan Plateau network for permafrost to characterize satellite-based products errors: An application to soil moisture and freeze/thaw

Two basic properties of soil moisture, namely water content and its phase status (freeze/thaw, referred to as F/T), strongly influence the pattern and efficiency of water and heat exchanges at the land-atmosphere interface. Therefore, accurate soil moisture and F/T retrievals are crucial to explore the impact of soil moisture and temperature on multi-sphere interactions, especially in the permafrost region of the third pole (Tibetan Plateau, TP), which is highly sensitive to climate changes. However, given the difficulty of installing and maintaining ground stations in harsh environments, dense ground networks are scarce over the TP with an average altitude of 4000 m. In this study, we reported a new soil moisture and temperature wireless sensor network consisting of 10 stations, which were established over the permafrost in the WuDaoLiang region (referred to as SMN-WDL) of the TP in 2019. In situ measurements from the SMN-WDL were used to simultaneously assess satellite-based surface soil moisture (SSM) and F/T products. The reliability of categorical triple collocation (CTC) method was investigated by comparing CTC-derived and ground-based relative performance rankings. The results reveal that the low-frequency (L-band) passive microwave SSM and F/T products outperformed both high-frequency (C/X/Ka-bands) passive and active microwave products, especially SMAP (Soil Moisture Active Passive) SSM and F/T products displayed the best statistical metrics to SMN-WDL ground-based measurements. The existing satellite F/T products had varying degrees of earlier freezing in autumn and later thawing in spring (mainly in ASCAT F/T) within SMN-WDL, which resulted in unsatisfactory thaw accuracy (< 85% in most cases) and overall accuracy (33.16% to 94.92%). In SMN-WDL, using existing F/T products as a masking criterion to filter out frozen soil for SSM validation studies would incorrectly mask out too many data samples and, lead to a decrease of the performance of SSM products. The overestimation of F/T thresholds may be one of the main reasons for the above misclassification of satellite F/T products. Compared to existing F/T products, the overall accuracy of threshold-optimal F/T retrievals can be increased by >10% during nighttime after optimizing thresholds based on ground-based measurements. CTC can provide consistent performance ranking with ground-based results in most cases, but the risk of CTC performing incorrect rankings was higher within the triplet where two products strongly covariate or one of the products has a significant error. This study is the first to use dense ground-based measurements in the permafrost region (SMN-WDL) to characterize the errors of four satellite SSM and F/T products. These results are expected to provide a reference with smaller representativeness errors for the further refinement of satellite retrievals in the permafrost region of the third pole.

Causal interaction in high frequency turbulence at the biosphere–atmosphere interface: Structural behavior

High-frequency (e.g., 10 Hz) eddy covariance measurements are typically used to estimate fluxes at the land–atmosphere interface at timescales of 15–60 min. These multivariate data contain information about the interdependency at high frequency between the interacting variables such as wind, humidity, temperature, and CO2. We use data at 10 Hz from an eddy covariance instrument located at 25 m above agricultural land in the Midwestern US, which offers an opportunity to move beyond the traditional spectral analyses to explore causal dependency among variables. In this study, we quantify the structure of inter-dependencies of interacting variables at high frequency represented by a directed acyclic graph (DAG). We compare DAGs to investigate changes in structural differences in causal interactions. We then apply a distance-based classification and k-means clustering approach to identify the evolution of the causal structure represented by a DAG. Our method selects an unbiased number of clusters of similar structures and characterizes the similarities and differences between them. We explore a range of dynamic behavior using data from a clear sky day and during a solar eclipse in 2017. Our results show well-defined clusters of similar causal dependencies as the system evolves. Our approach provides a methodological framework to understand how causal dependence in turbulence manifests in high-frequency data when represented through a DAG.

Evaluation of Remote Sensing and Reanalysis Products for Global Soil Moisture Characteristics

Soil moisture (SM) exists at the land-atmosphere interface and serves as a key driving variable that affects global water balance and vegetation growth. Its importance in climate and earth system studies necessitates a comprehensive evaluation and comparison of mainstream global remote sensing/reanalysis SM products. In this study, we conducted a thorough verification of ten global remote sensing/reanalysis SM products: SMAP DCA, SMAP SCA-H, SMAP SCA-V, SMAP-IB, SMOS IC, SMOS L3, LPRM_C1, LPRM_C2, LPRM_X, and ERA5-Land. The verification was based on ground observation data from the International SM Network (ISMN), considering both static factors (such as climate zone, land cover type, and soil type) and dynamic factors (including SM, leaf area index, and land surface temperature). Our goal was to assess the accuracy and applicability of these products. We analyzed the spatial and temporal distribution characteristics of global SM and discussed the vegetation effect on SM products. Additionally, we examined the global high-frequency fluctuations in the SMAP L-VOD product, along with their correlation with the normalized difference vegetation index, leaf area index, and vegetation water content. Our findings revealed that product quality was higher in regions located in tropical and arid zones, closed shrubs, loose rocky soil, and gray soil with low soil moisture, low leaf area index, and high average land surface temperature. Among the evaluated products, SMAP-IB, SMAP DCA, SMAP SCA-H, SMAP SCA-V, and ERA5-Land consistently performed better, demonstrating a good ability to capture the spatial and temporal variations in SM and showing a correlation of approximately 0.60 with ISMN. SMOS IC and SMOS L3 followed in performance, while LPRM_C1, LPRM_C2, and LPRM_X exhibited relatively poor results in SM inversion. These findings serve as a valuable reference for improving satellite/reanalysis SM products and conducting global-scale SM studies.

Dynamic evolution of recent droughts in Central Asia based on microwave remote sensing satellite products

Under warm climates, the more complex and malleable relationship between water and heat and the changes in the water components at the land–atmosphere interface pose greater challenges to understanding hydro-meteorological processes. However, the emergence of the advanced microwave scanning radiometer (AMSR) has facilitated the precise monitoring of these changes. Therefore, this study investigated and analyzed the spatiotemporal transformation characteristics of drought–wetness and its driving mechanisms in Central Asia from July 2002 to December 2018 based on the AMSR surface wetness index (ASWI) coupled with multiple water components. Three major conclusions were drawn from this investigation. First, the spatiotemporal differences in multiple water components in Central Asia caused substantial spatiotemporal differences in the extent of changes in drought–wetness. For example, arid areas were mainly concentrated in the lower reaches of the Amu Darya and Syr Darya river basins, where the dominant drought periods were 2007 and 2018. Second, the centroids of the largest drought patches were mainly located in the northeast, northwest, and southwest regions of Central Asia. Central Asia experienced a continuous drought from 2002 to 2005, and then a gradual wetting from 2008 to 2017. Third, the drought–wetness variation can be attributed to the variation of water vapor flux in the typical barometric layer as well as the variation of precipitation and temperature. The ASWI may play an essential role in drought monitoring and tracking prediction, particularly for drought risk mitigation and aversion.

Assessment of Land Surface Schemes from the WRF-Chem for Atmospheric Modeling in the Andean Region of Ecuador

Surface interactions occur near the land–atmosphere interface, thus affecting the temperature, convection, boundary layer, and stability of the atmosphere. A proper representation of surface interactions is a crucial component for numerical atmospheric and air quality modeling. We assessed four land surface schemes—1. 5–layer thermal diffusion scheme (1 5-Layer); 2. unified Noah land surface model (2 Noah); 3. rapid update cycle (3 RUC) land surface model; and 4. Pleim–Xiu land surface model (4 Pleim–Xiu)—from the Weather Research and Forecasting with Chemistry (WRF-Chem V3.2) model for the purposes of atmospheric modeling in Cuenca, which is a region with a complex topography and land use configuration and which is located in the Southern Andean region, in Ecuador. For this purpose, we modeled the meteorological and air quality variables during September 2014. It was found that the meteorological and short-term air quality variables were better modeled through the 2 Noah scheme. Long-term (mean monthly) air quality variables were better modeled by the 1 5–Layer and 3 RUC options. On average, the 2 Noah scheme was better at modeling meteorology and air quality. In addition, we assessed the 2 Noah scheme combined with the urban canopy model (UCM) (5 Noah UCM), which was developed as an option to represent the urban effects at a subgrid-scale. Results indicated that the performance of the 5 Noah UCM scheme was not better at modeling than the 2 Noah scheme alone. Moreover, the 5 Noah UCM scheme notably decreased the modeling performance for carbon monoxide and fine particulate matter. These results complement previous assessments of other schemes, allowing us to recommend a basic configuration of parameters for atmospheric modeling in the Andean region of Ecuador.

Isoprene and monoterpene simulations using the chemistry–climate model EMAC (v2.55) with interactive vegetation from LPJ-GUESS (v4.0)

Abstract. Earth system models (ESMs) integrate previously separate models of the ocean, atmosphere and vegetation into one comprehensive modelling system enabling the investigation of interactions between different components of the Earth system. Global isoprene and monoterpene emissions from terrestrial vegetation, which represent the most important source of volatile organic compounds (VOCs) in the Earth system, need to be included in global and regional chemical transport models given their major chemical impacts on the atmosphere. Due to the feedback of vegetation activity involving interactions with weather and climate, a coupled modelling system between vegetation and atmospheric chemistry is recommended to address the fate of biogenic volatile organic compounds (BVOCs). In this work, further development in linking LPJ-GUESS, a global dynamic vegetation model, to the atmospheric-chemistry-enabled atmosphere–ocean general circulation model EMAC is presented. New parameterisations are included to calculate the foliar density and leaf area density (LAD) distribution from LPJ-GUESS information. The new vegetation parameters are combined with existing LPJ-GUESS output (i.e. leaf area index and cover fractions) and used in empirically based BVOC modules in EMAC. Estimates of terrestrial BVOC emissions from EMAC's submodels ONEMIS and MEGAN are evaluated using (1) prescribed climatological vegetation boundary conditions at the land–atmosphere interface and (2) dynamic vegetation states calculated in LPJ-GUESS (replacing the “offline” vegetation inputs). LPJ-GUESS-driven global emission estimates for isoprene and monoterpenes from the submodel ONEMIS were 546 and 102 Tg yr−1, respectively. MEGAN determines 657 and 55 Tg of isoprene and monoterpene emissions annually. The new vegetation-sensitive BVOC fluxes in EMAC are in good agreement with emissions from the semi-process-based module in LPJ-GUESS. The new coupled system is used to evaluate the temperature and vegetation sensitivity of BVOC fluxes in doubling CO2 scenarios. This work provides evidence that the new coupled model yields suitable estimates for global BVOC emissions that are responsive to vegetation dynamics. It is concluded that the proposed model set-up is useful for studying land–biosphere–atmosphere interactions in the Earth system.

Land–Atmosphere Feedbacks Weaken the Cooling Effect of Soil Organic Matter Property toward Deep Soil on the Eastern Tibetan Plateau

Abstract Soil organic matter (SOM) is enriched on the eastern Tibetan Plateau, but its effects on the hydrothermal state of the coupled land–atmosphere system remain unclear. This study comprehensively investigates these effects during summer from multiple perspectives based on regional climate modeling, land surface modeling, and observations. Using a regional climate model, we show that accounting for SOM effects lowers cold and wet biases in simulations of this region. SOM increases 2-m air temperature, decreases 2-m specific/relative humidity, and reduces precipitation in coupled simulations. Inclusion of SOM also warms the shallow soil while cooling the deep soil, which may help to preserve frozen soil in this region. This cooling effect is captured by both observations and offline land surface simulations, but it is overestimated in the offline simulations due to no feedback from the atmosphere compared to the coupled ones. Including SOM in coupled climate models could therefore not only imrove their representations of atmospheric energy and water cycles, but also help to simulate the past, present, and future evolution of frozen soil with increased confidence and reliability. Note that these findings are from one regional climate model and do not apply to wetlands. Significance Statement The eastern Tibetan Plateau is rich in soil organic matter (SOM), which increases the amount of water the soil can hold while decreasing the rate at which heat moves through it. Although SOM is expected to preserve frozen soil by insulating it from atmospheric warming, researchers have not yet tested the effects of coupled land–atmosphere interactions on this relationship. Using a regional climate model, we show that SOM typically warms and dries the near-surface air, warms the shallow soil, and cools the deep soil by modifying both soil properties and energy exchanges at the land–atmosphere interface. The results suggest that the cooling effect of SOM on deep soil is overestimated when atmospheric feedbacks are excluded.

Systematic Evaluation of a High-Resolution CLM5 Simulation over Continental China for 1979–2018

Abstract The land surface model is extensively used to simulate turbulence fluxes and hydrological and momentum variables at the land–atmosphere interface. In this study, the Community Land Model, version 5 (CLM5), driven by the 0.1° × 0.1° Chinese Meteorological Forcing Dataset (CMFD) and the field-surveyed soil parameters, is used to simulate land surface processes during 1979–2018. Various high-quality land surface datasets are adopted to assess the model simulations. In general, the CLM5 well captures the monthly variations of 0–10-cm soil moisture in subregions, particularly in the Tibetan Plateau, with an anomaly correlation coefficient between 0.56 and 0.88. However, the simulated soil moisture shows overall wet biases in the whole country, resulting from several reasons. The model simulation is skillful in replicating both the magnitude and spatial pattern when they are compared with the MODIS snow cover dataset. Compared with in situ measured soil temperature in multiple soil layers within 320-cm soil depth from 1980 to 2018, the simulations accurately capture spatial patterns, vertical profiles, and long-term warming trends. For land surface energy components, the simulations have a highly temporal correlation with the observation of Chinese Flux Observation and Research Network (ChinaFLUX) cropland and grassland sites, except for four forest sites, where biases exist in both atmospheric forcing variables and surface vegetation phenology in the model default input dataset. In summary, this study reveals the overall capability of CLM5 in reproducing land surface energy fluxes and hydrological variables over conterminous China, and the validation results may also provide some references for future model improvement and application. Significance Statement The offline Community Land Model, version 5 (CLM5), driven by a 0.1° × 0.1° (∼10 km) horizontal resolution atmospheric forcing dataset and a set of field-surveyed soil parameters, are used to simulate the land surface hydrological and heat fluxes in continental China for 1980–2018. The simulated hydrological variables and energy fluxes are validated with various sources of high-quality observation-based datasets. From our systematic evaluations, the current CLM5 high–resolution simulation accurately captures the spatial patterns and temporal variations in most of the water and energy balance components, although biases exist in some simulated variables. Overall, this study reveals the capability of the offline CLM5 simulation in conterminous China and provides the reference for future model improvement and application.

Evolution of turbulent flow characteristics in a hedgerow vineyard during the growing season

The characteristics of turbulent flow at the land-atmosphere interface and within plant canopies are strongly modified by the interactions with vegetation elements. However, only few experimental studies were conducted so far on the effect of changing leaf area on within-canopy turbulence statistics. This aspect is important for deciduous forests and perennial woody crops (orchards and vineyards), where the variation of leaf area is recurrent and substantial during the growing season. Increasing the understanding on canopy turbulence is fundamental to improve the parameterization of multi-layer models to predict vegetation-atmosphere exchanges. In this context, we conducted an experimental campaign in a hedgerow vineyard in North-East Italy carrying out measurements with a vertical array of 3D sonic anemometers, together with canopy structure characterization, in order to analyze the evolution of turbulent flow characteristics from a leafless canopy to full development. Additionally, the effects of wind direction with respect to rows and of atmospheric stability were analyzed. We found that the aerodynamic properties of the vineyard were not only influenced by canopy height and total leaf area, but also by the vertical distribution of leaf density, with the thickness of the mid-upper layer playing a major role in determining the canopy roughness and the mean level of momentum absorption. The characteristics of within-canopy turbulent flow became more similar to a well-defined mixing-layer type flow as foliage developed, but only for diagonal and across-row wind. In contrast, with wind parallel to rows the effect of increasing leaf density was lower and the vineyard was more similar to an open canopy. The influence of atmospheric stability was less important compared to wind direction or leaf density, except for the free convection class. Nevertheless, a variation of canopy aerodynamic parameters with stability was observed and this should be taken into account in atmospheric models.

Sensitivity of Land Surface Processes and Its Variation during Contrasting Seasons over India

The study investigates the influence of near-surface atmospheric parameters on land surface processes at the land–atmosphere interface through the offline simulation of the 2D Noah Land Surface Model-based High-Resolution Land Data Assimilation System (HRLDAS). The HRLDAS is used to conduct sensitive experiments by introducing perturbation in the atmospheric parameters, and the experiments were conducted for the period 2011–2013 in India. In each sensitive experiment, a single parameter is perturbed at a time, keeping the rest of the forcing parameters unchanged, and the procedure is followed for all the forcing parameters. The results revealed that the downward longwave radiation and T2 are highly sensitive to land surface processes, while wind speed is the least sensitive. The land surface process sensitivity varies with soil moisture content. The annual mean soil moisture at the surface layer is increased (decreased) by 8% when long wave radiation is decreased (increased) by 20%. Similarly, the annual mean soil temperature increased (decreased) by 2.2 °C when T2 increased (decreased) by 1%. The latent heat flux is highly sensitive to longwave radiation over the wetter soil, while its sensitivity to rainfall is higher over the drier soil. This is attributed to evapotranspiration’s sensitivity to the preferred soil moisture state. Further, the land surface sensitivity varies with contrasting seasons. The sensitivity of soil moisture and latent heat flux is high in OND and JJA seasons, respectively, and are least sensitive in the MAM season. In contrast, the sensible heat flux is highly sensitive to solar radiation in the MAM season and comparatively less sensitive in the JJA season. The study suggests that the antecedent soil moisture state plays a critical role in modulating land surface process sensitivity, and, therefore, a realistic soil moisture state is important for land surface feedback processes.

Comparison of Methods for Reconstructing MODIS Land Surface Temperature under Cloudy Conditions

Land surface temperature (LST) is a vital parameter associated with the land–atmosphere interface. The Moderate Resolution Imaging Spectroradiometer (MODIS) LST product can provide precise LST with high time resolution, and is widely applied in various remote sensing temperature research. However, due to its inability to penetrate the cloud and fog, its quality is not able to meet the requirements of actual research. Hence, obtaining continuous and cloudless MODIS LST datasets remains challenging for researchers. The critical point is to reconstruct missing pixels. To compare the performance of different methods, first, three kinds of methods were used to reconstruct the missing pixels, namely, temporal, spatial, and spatiotemporal methods. The predicted values using these methods were validated by the automatic weather system data (AWS) in the Heihe river basin of China. The results demonstrated that, compared with other methods, linear temporal interpolation using Aqua data had the best performance in MODIS LST reconstruction in the Heihe river basin, with an RMSE of 7.13 K and an R2 of 0.82, and the NSE and PBias were 0.78 and −0.76%, respectively. Furthermore, the interpolation method was improved using adaptive windows and robust regression. First, the international Geosphere–Biosphere Program (IGBP) classification was employed to distinguish the different land surface types. Then, the invalid LST values were reconstructed using adjacent days’ effective LST values combined with a robust regression. Finally, a mean filter was applied to eliminate outliers. The overall results combined with ERA5 data were validated by AWS, with an RMSE of 6.96 K and an R2 of 0.79 and the NSE and PBias were 0.77 and −0.20%, respectively. The validation demonstrated that the scheme proposed in this paper is able to accurately reconstruct the missing values and improve the accuracy of the interpolation method to a certain extent when reconstructing MODIS LST.

Impacts of Vegetation Changes on Land Evapotranspiration in China During 1982–2015

Evapotranspiration (ET) bridges the hydrological and energy cycle through vegetation transpiration (T), soil evaporation (ES), and canopy interception evaporation (EI). Transpiration to evapotranspiration ratio (T/ET) quantifies the water use efficiency of terrestrial ecosystems explaining the mechanism of vegetation water transport and water–carbon interactions. This study employed GIMMS LAI3g data to improve the CLM4.5 land surface scheme of RegCM4.6. We designed two simulation experiments, each with control (CTL) and sensitivity (SEN), simulating the interannual variability of vegetation on ET and T/ET in China from 1982 to 2015. Studies show China has experienced a greening trend, especially in mid-south China and South China. Leaf area index (LAI) increased significantly (0.002 m2m−2yr−1). ∆LAI (SEN input LAI data minus CTL input LAI data) and ∆T/ET (T/ET data output by SEN minus T/ET data output by CTL) have shown significant positive correlations. The impacts of LAI on T/ET are more prominent during spring and winter than in autumn and summer. Compared with T/ET and LAI (R = 0.70), the correlation between ET and LAI is moderate (R &amp;lt; 0.5), indicating that vegetation has a higher impact on T/ET than ET. The impact of vegetation anomalies (positive and negative LAI anomalies) on T/ET and ET is spatially different, mainly due to dominant factors affecting ET and T/ET changes. In spring, summer, and autumn, &amp;amp;T (transpiration changes corresponding to vegetation anomalies) is the leading factor affecting both ET and T/ET regionally, and &amp;amp;T has a stronger influence on T/ET than ET, especially in summer. Vegetation anomalies have a stronger influence on T/ET than ET; and the influence of positive vegetation anomalies on ET and T/ET is greater than that of negative vegetation anomalies, especially in spring and autumn. This study reveals the mechanisms behind vegetation processes and their influences on the water and heat fluxes at the land–atmosphere interface and provides a strong scientific basis for studying the water cycle under climate warming.

Occurrence of drought events at the land–atmosphere interface in Central Asia assessed via advanced microwave scanning radiometer data

AbstractThe timely and effective early warning of sudden drought events is of great importance for reducing disaster losses and drought risk. Satellite microwave remote sensing with the advanced microwave scanning radiometer (AMSR) has enabled the global monitoring of multiple components of water, such as soil moisture (SM), vapour pressure deficit (VPD), and open water fraction (OWF), on a daily scale since 2002. In this study, the applicability of the AMSR surface wetness index (ASWI) and drought features in Central Asia were studied from 2002 to 2017 based on AMSR data. This study concluded that (1) the spatial and temporal differences in the various water components and coefficient of variation of the Central Asia land–atmosphere interface were large, which led to large spatial and temporal differences under both dry and wet conditions in Central Asia; (2) although the ASWI was not significantly correlated with other drought indices (i.e., gravity recovery and climate experiment (GRACE) combined climatological deviation index (CCDI) and GRACE drought severity index (DSI)) over Central Asia, the maximum Pearson correlation coefficient (CC) between the ASWI and Palmer drought severity index (PDSI) in the Irtysh River Basin was 0.824 (p &lt; 0.05), corresponding to a moving average window of 11 months. The CCs of ASWI and Standardized Precipitation Evapotranspiration Index on a 6‐month scale (SPEI‐6) in Central Asia were generally more than 0.8 (p &lt; 0.05), indicating that the ASWI was effective for retrieving drought conditions in Central Asia. In addition, these four drought indices (ASWI, PDSI, GRACE‐DSI, and GRACE‐CCDI) revealed that drought occurred continuously after 2003 in Central Asia; (3) the spatial and temporal distribution of dry and wet conditions in Central Asia is closely related to the teleconnection indices (i.e., MEI, SOI, and PDO). Thus, AMSR data are critical for understanding drought events and their evolution in regions influenced by climate change.

Deconstructing the soil moisture-latent heat flux relationship: the range of coupling regimes experienced and the presence of nonlinearity within the sensitive regime

Abstract The control of latent heat flux (LE) by soil moisture (SM) is a key process affecting the moisture and energy budgets at the land-atmosphere interface. SM:LE coupling relationships are conventionally examined using metrics involving temporal correlation. However, such a traditional linear approach, which fits a straight line across the full SM:LE space to evaluate the dependency, leaves out certain critical information: nonlinear SM:LE relationships and the long-recognized thresholds that lead to dramatically different behavior in different ranges of soil moisture, delineating a dry regime, a transitional regime of high sensitivity, and a wet (energy-limited) regime. Using data from climate models, reanalyses, and observationally-constrained datasets, global patterns of SM:LE regimes are determined by segmented regression. Mutual information analysis is applied only for days when SM is in the transitional regime between critical points defining high sensitivity of LE to SM variations. Sensitivity is further decomposed into linear and nonlinear components. Results show discrepancies in the global patterns of existing SM regimes, but general consistencies among the linear and nonlinear components of SM:LE coupling. This implies that although models simulate differing surface hydroclimates, once SM is in the transitional regime, the locations where LE closely interacts with SM are well-captured and resemble the conventional distribution of “hot spots” of land-atmosphere interactions. This indicates that only the transitional SM regime determines the strength of coupling, and attention should focused on when this regime occurs. This framework can also be applied to investigate extremes and the shifting surface hydroclimatology in a warming climate.

Towards Consistent Soil Moisture Records from China’s FengYun-3 Microwave Observations

Soil moisture plays an essential role in the land-atmosphere interface. It has become necessary to develop quality large-scale soil moisture data from satellite observations for relevant applications in climate, hydrology, agriculture, etc. Specifically, microwave-based observations provide more consistent land surface records because they are unhindered by cloud conditions. The recent microwave radiometers onboard FY-3B, FY-3C and FY-3D satellites launched by China’s Meteorological Administration (CMA) extend the number of available microwave observations, covering late 2011 up until the present. These microwave observations have the potential to provide consistent global soil moisture records to date, filling the data gaps where soil moisture estimates are missing in the existing records. Along these lines, we studied the FY-3C to understand its added value due to its unique time of observation in a day (ascending: 22:15, descending: 10:15) absent from the existing satellite soil moisture records. Here, we used the triple collocation technique to optimize a benchmark retrieval model of land surface temperature (LST) tailored to the observation time of FY3C, by evaluating various soil moisture scenarios obtained with different bias-imposed LSTs from 2014 to 2016. The globally optimized LST was used as an input for the land parameter retrieval model (LPRM) algorithm to obtain optimized global soil moisture estimates. The obtained FY-3C soil moisture observations were evaluated with global in situ and reanalysis datasets relative to FY3B soil moisture products to understand their differences and consistencies. We found that the RMSEs of their anomalies were mostly concentrated between 0.05 and 0.15 m3 m−3, and correlation coefficients were between 0.4 and 0.7. The results showed that the FY-3C ascending data could better capture soil moisture dynamics than the FY-3B estimates. Both products were found to consistently complement the skill of each other over space and time globally. Finally, a linear combination approach that maximizes temporal correlations merged the ascending and descending soil moisture observations separately. The results indicated that superior soil moisture estimates are obtained from the combined product, which provides more reliable global soil moisture records both day and night. Therefore, this study aims to show that there is merit to the combined usage of the two FY-3 products, which will be extended to the FY-3D, to fill the gap in existing long-term global satellite soil moisture records.

Improvement in Modeling Soil Dielectric Properties During Freeze-Thaw Transitions

Soil freeze-thaw cycles have a profound impact on heat and water fluxes at the land-atmosphere interface and transport in soils. Microwave remote sensing is a widely used technique to detect near-surface soil freeze/thaw states due to significant changes in dielectric properties associated with water phase transitions in soils, where uncertainty remains. This letter proposes a new parameterization scheme for the estimation of unfrozen water content to improve the modeling of soil dielectric properties during freeze-thaw transitions. Predictions from the new model referred to as Zhang-Zhao&#x2019;s model were compared with dielectric measurements during thawing processes of soil samples collected from Baoding (silty clay soil), Zhangjiakou (loamy sandy soil), and Zhengzhou (clay loam soil) in China. The mean biases of the predictions were 3.25 (4.44 and 2.07 for the thawed value and frozen value, respectively) and 1.54 (2.22 and 0.88 for the thawed value and frozen value, respectively) for the real part and imaginary part, respectively. The model-predicted soil complex relative permittivity (CRP) was highly correlated with measurements, with correlation coefficients ranging from 0.7944 to 0.9865. The normalized root mean square errors of the predictions were 13.72&#x0025; (real part) and 25.41&#x0025; (imaginary part).