Local Soil Moisture Research Articles

Detecting Ground Level Enhancements Using Soil Moisture Sensor Networks

AbstractGround level enhancements (GLEs) are space weather events that pose a potential hazard to the aviation environment through single event effects in avionics and increased dose to passengers and crew. The existing ground level neutron monitoring network provides continuous and well‐characterized measurements of the radiation environment. However, there are only a few dozen active stations worldwide, and there has not been a UK‐based station for several decades. Much smaller neutron detectors are increasingly deployed throughout the world with the purpose of using secondary neutrons from cosmic rays to monitor local soil moisture conditions (COSMOS). Space weather signals from GLEs and Forbush decreases have been identified in COSMOS data. Monte Carlo simulations of atmospheric radiation propagation show that a single COSMOS detector is sufficient to detect the signal of a medium‐strength (10%–100% increase above background) GLE at high statistical significance, including at fine temporal resolution. Use of fine temporal resolution would also provide a capability to detect Terrestrial Gamma Ray Flashes (via secondary neutrons) which are produced by certain lightning discharges and which can provide a hazard to aircraft, particularly in tropical regions. We also show how the COsmic‐ray Soil Moisture Observing System‐UK detector network could be used to provide warnings at the International Civil Aviation Organization “Moderate” and “Severe” dose rate thresholds at aviation altitudes, and how multiple‐detector hubs situated at strategic UK locations could detect a small GLE at high statistical significance and infer crucial information on the nature of the primary spectrum.

Response of deep soil moisture to different vegetation types in the Loess Plateau of northern Shannxi, China

Deep soil moisture is a highly important source of water for vegetation in the semiarid Loess Plateau of China, vegetation restoration reduced the deep soil moisture, but how to better quantify the impact of vegetation restoration on deep soil moisture is lack of certain understanding. To explore the impact exerted by different types of vegetation on deep layers of the soil moisture, the 0–10 m soil moisture content profile was measured before and after the rainy season in Armeniaca sibirica, Robinia pseudoacacia, Populus simonii, Pinus tabuliformis, Hippophae rhamnoides and in natural grassland in Wuqi County in Shannxi Province. These results showed that the highest soil moisture in the shallow layers (0–200 cm) was exhibited in the P. simonii forest, which was followed by that in the natural grassland. Both of these results were significantly higher than that those of the A. sibirica, P. tabuliformis, H. rhamnoides and R. pseudoacacia forests. The soil moisture in the deep layer (200–1000 cm) of the natural grassland was significantly higher than that of the other vegetation types. The annual precipitation that recharges the depth of soil moisture was the highest in natural grassland and the lowest in P. simonii. The inter-annual soil moisture replenishment is primarily affected by rainfall and vegetation types. Compared with the natural grassland, the CSWSD (the comparison of the soil moisture storage deficit) of different vegetation types varies. In the shallow soil layer, P. simonii is the lowest, and R. pseudoacacia is the highest. In the deep soil layer, R. pseudoacacia and P. simonii are the highest; H. rhamnoides is the second highest, and A. sibirica and P. tabuliformis are the lowest. These results indicate that vegetation restoration can significantly reduce the amount of water in the deep layers of the soil. In the future vegetation restoration, we suggest emphasizing natural development more strongly, since it can better maintain the local vegetation stability and soil moisture balance.

Computation Approach for Quantitative Dielectric Constant from Time Sequential Data Observed by CYGNSS Satellites

This paper describes a computation method for obtaining dielectric constant using Global Navigation Satellite System reflectometry (GNSS-R) products. Dielectric constant is a crucial component in the soil moisture retrieval process using reflected GNSS signals. The reflectivity for circular polarized signals is combined with the dielectric constant equation that is used for radiometer observations. Data from the Cyclone Global Navigation Satellite System (CYGNSS) mission, an eight-nanosatellite constellation for GNSS-R, are used for computing dielectric constant. Data from the Soil Moisture Active Passive (SMAP) mission are used to measure the soil moisture through its radiometer, and they are considered as a reference to confirm the accuracy of the new dielectric constant calculation method. The analyzed locations have been chosen that correspond to sites used for the calibration and validation of the SMAP soil moisture product using in-situ measurement data. The retrieved results, especially in the case of a specular point around Yanco, Australia, show that the estimated results track closely to the soil moisture results, and the Root Mean Square Error (RMSE) in the estimated dielectric constant is approximately 5.73. Similar results can be obtained when the specular point is located near the Texas Soil Moisture Network (TxSON), USA. These results indicate that the analysis procedure is well-defined, and it lays the foundation for obtaining quantitative soil moisture content using the GNSS reflectometry results. Future work will include applying the computation product to determine the characteristics that will allow for the separation of coherent and incoherent signals in delay Doppler maps, as well as to develop local soil moisture models.

Asian meteorological droughts on three time scales and different roles of sea surface temperature and soil moisture

AbstractThe present study utilizes the standard precipitation index (SPI) to differentiate Asian summer droughts that are associated with variations of precipitation over periods of 3, 9 and 24 months (termed as short‐term, medium‐term and long‐term droughts, respectively). Then, the relationships between these droughts and atmospheric circulation, sea surface temperature (SST), and soil moisture are investigated. While the spatial and temporal characteristics of short‐term and medium‐term droughts are fairly similar, long‐term droughts display different spatial and temporal features. The leading spatial mode of short‐term and medium‐term droughts exhibits a dipole pattern with opposite loading north and south of approximately 30°N. The second spatial mode displays a tripole pattern with the same sign of loading over India and north‐northeast China–Mongolia and opposite loading in between. Temporal variations of short‐term and medium‐term droughts are dominated by interannual variabilities. For long‐term droughts, the first two spatial modes switch their order, and the long‐term trends are as important as the interannual and interdecadal variabilities. The droughts of the dipole mode are associated with SST anomalies featuring an El Niño‐like pattern in the tropical Indo‐Pacific Ocean that affect the drought variations through inducing large‐scale divergence and convergence. The interannual variability of the droughts with the dipole mode is also associated with the SST anomalies in the North Atlantic Ocean that affect the North Atlantic‐Eurasian atmospheric circulation. Local soil moisture conditions are more important for the formation of long‐term droughts than short‐term and medium‐term droughts.

Improving Australian Rainfall Prediction Using Sea Surface Salinity

AbstractThis study uses sea surface salinity (SSS) as an additional precursor for improving the prediction of summer [December–February (DJF)] rainfall over northeastern Australia. From a singular value decomposition between SSS of prior seasons and DJF rainfall, we note that SSS of the Indo-Pacific warm pool region [SSSP (150°E–165°W and 10°S–10°N) and SSSI (50°–95°E and 10°S–10°N)] covaries with Australian rainfall, particularly in the northeast region. Composite analysis that is based on high or low SSS events in the SSSP and SSSI regions is performed to understand the physical links between the SSS and the atmospheric moisture originating from the regions of anomalously high or low, respectively, SSS and precipitation over Australia. The composites show the signature of co-occurring La Niña and negative Indian Ocean dipole with anomalously wet conditions over Australia and conversely show the signature of co-occurring El Niño and positive Indian Ocean dipole with anomalously dry conditions there. During the high SSS events of the SSSP and SSSI regions, the convergence of incoming moisture flux results in anomalously wet conditions over Australia with a positive soil moisture anomaly. Conversely, during the low SSS events of the SSSP and SSSI regions, the divergence of incoming moisture flux results in anomalously dry conditions over Australia with a negative soil moisture anomaly. We show from the random-forest regression analysis that the local soil moisture, El Niño–Southern Oscillation (ENSO), and SSSP are the most important precursors for the northeast Australian rainfall whereas for the Brisbane region ENSO, SSSP, and the Indian Ocean dipole are the most important. The prediction of Australian rainfall using random-forest regression shows an improvement by including SSS from the prior season. This evidence suggests that sustained observations of SSS can improve the monitoring of the Australian regional hydrological cycle.

A multi-sourced assessment of the spatiotemporal dynamics of soil moisture in the MARINE flash flood model

Abstract. The MARINE (Model of Anticipation of Runoff and INundations for Extreme events) hydrological model is a distributed model dedicated to flash flood simulation. Recent developments of the MARINE model are explored in this work. On one hand, transfers of water through the subsurface, formerly relying on water height, now take place in a homogeneous soil column based on the soil saturation degree (SSF model). On the other hand, the soil column is divided into two layers, which represent, respectively, the upper soil layer and the deep weathered rocks (SSF–DWF model). The aim of the present work is to assess the accuracy of these new representations for the simulation of soil moisture during flash flood events. An exploration of the various products available in the literature for soil moisture estimation is performed. The efficiency of the models for soil saturation degree simulation is estimated with respect to several products either at the local scale or spatially distributed: (i) the gridded soil moisture product provided by the operational modeling chain SAFRAN-ISBA-MODCOU; (ii) the gridded soil moisture product provided by the LDAS-Monde assimilation chain, which is based on the ISBA-A-gs land surface model and assimilating satellite derived data; (iii) the upper soil water content hourly measurements taken from the SMOSMANIA observation network; and (iv) the Soil Water Index provided by the Copernicus Global Land Service (CGLS), which is derived from Sentinel-1 C-SAR and ASCAT satellite data. The case study is performed over two French Mediterranean catchments impacted by flash flood events over the 2017–2019 period. The local comparison of the MARINE outputs with the SMOSMANIA measurements, as well as the comparison at the basin scale of the MARINE outputs with the gridded LDAS-Monde and CGLS data, lead to the following conclusion: both the dynamics and the amplitudes of the soil saturation degree simulated with the SSF and SSF–DWF models are better correlated with both the SMOSMANIA measurements and the LDAS-Monde data than the outputs of the base model. Finally, the soil saturation degree simulated by the two-layers model for the deep layer is compared to the soil saturation degree provided by the LDAS-Monde product at corresponding depths. In conclusion, the developments presented for the representation of subsurface flow in the MARINE model enhance the soil saturation degree simulation during flash floods with respect to both gridded data and local soil moisture measurements.

Soil Water Characteristics of Gleysols in the Bamenda (Cameroon) Wetlands and Implications for Agricultural Management Strategies

Water budgeting in agriculture requires local soil moisture information as crops depend mainly on moisture available at root level. The present paper aims to evaluate the soil moisture characteristics of Gleysols in the Bamenda (Cameroon) wetlands and to evaluate the link between soil moisture content and selected soil characteristics affecting crop production. The work was conducted in the field and laboratory, and data were analyzed by simple descriptive statistics. The main results showed that the soils had a silty clayey to clayey texture, high bulk density, high soil organic carbon content, and high soil organic carbon stocks. The big difference between moisture contents at wilting point and at field capacity testified to very high plant-available water content. Also, the soils displayed very high contents of readily available water and water storage contents. The soil moisture characteristics give sigmoid curves and enabled noting that the Gleysols attain their full water saturation at a range of 57.68 to 91.70% of dry soil. Clay and SOC contents show a significant positive correlation with most of the soil moisture characteristics, indicating that these soil properties are important for soil water retention. Particle density, coarse fragments, and sand contents correlated negatively with the soil moisture characteristics, suggesting that they decrease soil water-holding capacity. The principal component analysis (PCA) enabled reducing 17 variables described to only three principal components (PCs) explaining 73.73% of the total variance; the first PC alone expressed 45.12% of the total variance, associating clay, SOC, and six soil moisture characteristics, thus portraying a deep correlation between these eight variables. Construction of contoured ditches, deep tillage, and raised ridges management techniques during the rainy season while channeling water from nearby water bodies into the farmland, opportunity cropping, and usage of water cans and other irrigation strategies are used during the dry season to combat water constraints.

Observed Soil Moisture Impact on Strong Convection over Mountainous Tibetan Plateau

AbstractConvection over the Tibetan Plateau (TP) has been linked to heavy rain and flooding in downstream parts of China. Understanding processes which influence the development of convection on the TP could contribute to better forecasting of these extreme events. TP scale (~1000 km) soil moisture gradients have been shown to influence formation of convective systems over the eastern TP. The importance of smaller-scale (~10 km) variability has been identified in other regions (including the Sahel and Mongolia) but has yet to be investigated for the TP. In addition, compared to studies over flat terrain, much less is known about soil moisture–convection feedbacks above complex topography. In this study we use satellite observations of cold cloud, land surface temperature, and soil moisture to analyze the effect of mesoscale soil moisture heterogeneity on the initiation of strong convection in the complex TP environment. We find that strong convection is favored over negative (positive) land surface temperature (soil moisture) gradients. The signal is strongest for less vegetation and low topographic complexity, though still significant up to a local standard deviation of 300 m in elevation, accounting for 65% of cases. In addition, the signal is dependent on background wind. Strong convective initiation is only sensitive to local (tens of kilometers) soil moisture heterogeneity for light wind speeds, though large-scale (hundreds of kilometers) gradients may still be important for strong wind speeds. Our results demonstrate that, even in the presence of complex topography, local soil moisture variability plays an important role in storm development.

Outcrop distribution and formation of carbonate rhizoliths in Badain Jaran Desert, NW China

Carbonate rhizoliths and related features are environmental and ecological indicators of not only palaeosols, but also modern soils. They designate subaerial vadose and pedogenic diagenesis environments. Rhizoliths are commonly used as a tool for paleoenvironmental reconstructions, but uncertainty remains regarding their formation mechanisms. Field characteristics of the dune rhizoliths in the Badain Jaran Desert, NW China were analyzed to investigate formation mechanisms and interpret paleoenvironmental significance. Systematic field sampling and intensive examination showed that the tube-like rhizoliths without morphological structures of plant roots have been placed at the dune soil surface following erosion and weathering but the underground rhizoliths are still connected to the dead Artemisia roots. Rhizoliths occurred only along the windward, long gentle slopes of mega-dunes. Their distribution patterns of both types in connection with plant distribution and landscapes have been studied. Their petrological and mineralogical characters were performed in detail using microscopic and spectroscopic techniques (cathodoluminescence, scanning electronic microscopy and energy dispersive X-ray spectroscopy) in laboratory. Altogether, field and laboratory analyses showed that rhizolith cement is calcium carbonate and despite the high porosity of soil and high aridness of climate, soil moisture plays an important role in sustaining Artemisia growth and rhizolith formation. Desert rhizoliths can therefore be used as a proxy to infer local soil moisture and plant communities. Moreover, as soil moisture content is related to climate and landscape position, rhizoliths could indirectly be indicators of aboveground environmental conditions, e.g. annual precipitation or humidity.

Monitoring Soil Moisture Dynamics Using Electrical Resistivity Tomography under Homogeneous Field Conditions.

There is a gap between lab experiments where resistivity–soil moisture relations are generally very good and field studies in complex environmental settings where relations are always less good and complicated by many factors. An experiment was designed where environmental settings are more controlled, the best outside laboratory, to assess the transferability from lab to outdoor. A field experiment was carried out to evaluate the use of electric resistivity tomography (ERT) for monitoring soil moisture dynamics over a period of 67 days. A homogeneous site in the central part of The Netherlands was selected consisting of grass pasture on an aeolian sand soil profile. ERT values were correlated to gravimetric soil moisture samples for five depths at three different dates. Correlations ranged from 0.43 to 0.73 and were best for a soil depth of 90 cm. Resistivity patterns over time (time-lapse ERT) were analyzed and related to rainfall events where rainfall infiltration patterns could be identified. Duplicate ERT measurements showed that the noise level of the instrument and measurements is low and generally below 3% for the soil profile below the mixed layer but above the groundwater. Although the majority of the measured resistivity patterns could be well explained, some artefacts and dynamics were more difficult to clarify, even so in this homogeneous field situation. The presence of an oak tree with its root structure and a ditch with surface water with higher conductivity may have an impact on the resistivity pattern in the soil profile and over time. We conclude that ERT allows for detailed spatial measurement of local soil moisture dynamics resulting from precipitation although field experiments do not yield accuracies similar to laboratory experiments. ERT approaches are suitable for detailed spatial analyses where probe or sample-based methods are limited in reach or repeatability.

The Record-Breaking High Temperature over Europe in June of 2019

Observational and reanalysis data show that the surface air temperature (SAT) over most parts of Europe in June of 2019 broke the highest temperature on record. In this study, we investigate the factors for the formation of this record-breaking high temperature over Europe, focusing on the role of atmospheric circulation anomalies. A strong anomalous anticyclone appeared over Europe, with a quasi-barotropic vertical structure. On one hand, the downward motion anomalies associated with this anomalous anticyclone led to less cloud cover and an increase in downward shortwave radiation, which contributed to the SAT warming over Europe. On the other hand, southerly wind anomalies to the west side of the anomalous anticyclone also resulted in SAT warming via carrying warmer and wetter air northward from lower latitudes. The formation of the anticyclonic anomaly over Europe in June of 2019 was closely related to an atmospheric wave train propagating eastward from the mid-high latitudes of the North Atlantic to Eurasia. The atmospheric wave train over the North Atlantic–Eurasia region is suggested to be mainly related to the Atlantic–Eurasia teleconnection pattern. Further analysis indicates that a decrease in the local soil moisture over Europe may also have escalated the surface temperature warming through a positive land–atmosphere feedback.

Where Resource‐Acquisitive Species Are Located: The Role of Habitat Heterogeneity

AbstractRising temperatures with increased drought pose two challenges for management of future biodiversity. First, are the most vulnerable species concentrated in specific regions and habitats? Second, where can landscape heterogeneity potentially mitigate impacts? We conducted a comprehensive trait analysis of forest plots spanning the eastern United States to quantify how resource‐acquisitive species respond to moisture‐soil‐climate interactions. We found that resource‐acquisitive species, including nutrient‐acquisitive and moisture‐acquisitive species, respond disproportionately to environmental gradients, and their response is largely explained by soil variation. We showed that the strong boundary of resource‐acquisitive species occurs near the last glacial limit, highlighting one of the clearest indicators of soil controls. Although local soil moisture may reduce drought‐induced stress for moisture‐acquisitive species, nutrient‐acquisitive species remain vulnerable on wet soils in dry climates. The results suggest that theories explaining species distributions should devote close attention to the combination of local drainage and soil type.

The Long-Term Change of Latent Heat Flux over the Western Tibetan Plateau

The Tibetan Plateau (TP) has been experiencing warming and wetting since the 1980s. Under such circumstances, we estimated the summer latent heat flux (LE) using the maximum entropy production model driven by the net radiation, surface temperature, and soil moisture of three reanalysis datasets (ERA5, JRA-55, and MERRA-2) at the Ali site over the western TP during 1980–2018. Compared with the observed LE of the Third Tibetan Plateau Atmospheric Scientific Experiment, the coefficient of determination, root-mean-square error, and mean bias error of the estimated summer LE are 0.57, 9.3 W m−2, and −2.25 W m−2 during 2014–2016, respectively, which are better than those of LE of the reanalysis datasets. The estimated long-term summer LE presents a decreasing (an increasing) trend of −7.4 (1.8) W m−2 decade−1 during 1980–1991 (1992–2018). The LE variation is closely associated with the local soil moisture influenced by precipitation, glacier, and near-surface water conditions at the Ali site. The summer soil moisture also presents a decreasing (an increasing) trend of −0.082 (0.022) decade−1 during 1980–1991 (1992–2018). The normalized difference vegetation index generally shows the consistent trend with LE at the Ali site.

Ground temperature and snow depth variability within a subarctic peat plateau landscape

AbstractSubarctic permafrost peatlands cover extensive areas and store large amounts of soil organic carbon that can be remobilized as active layer deepening and thermokarst formation increase in a future warmer climate. Better knowledge of ground thermal variability within these ecosystems is important for understanding future landscape development and permafrost carbon feedbacks. In a peat plateau complex in Tavvavuoma, northern Sweden, ground temperatures and snow depth have been monitored in six different landscape units: on a peat plateau, in a depression within a peat plateau, along a peat plateau edge (close to a thermokarst lake), at a thermokarst lake shoreline, in a thermokarst lake and in a fen. Permafrost is present in all three peat plateau landscape units, and mean annual ground temperature (MAGT) in the central parts of the peat plateau is −0.3°C at 2 m depth. In the three low‐lying wetter or saturated landscape units (along the thermokarst lake shoreline, in the lake and the fen) taliks are present and MAGT at 1 m depth is 1.0–2.7°C. Topographical differences between the elevated and low‐lying units affect both local snow depth and soil moisture, and are important for ground thermal patterns in this landscape. Permafrost exists in landscape units with a shallow mean December–April snow depth (<20 cm) whereas snow depths >40 cm mostly result in absence of permafrost.

Sensitivity of the 2018 UK summer heatwave to local sea temperatures and soil moisture

AbstractThe impact of local climate conditions on air temperatures during a hot summer period over the United Kingdom in 2018 is studied using simple sensitivity experiments with a state‐of‐the‐art regional numerical weather prediction system. The experiments are designed to investigate the influence of sea surface temperature (SST) and soil moisture on the air temperatures over land. They involved applying changes to the analysed SST and soil moisture patterns with magnitudes consistent with the differences between forecast analysis and climatology. The results from daily 5‐day forecasts over an 11‐day period show that a 3°C reduction in SSTs reduces the simulated air temperatures averaged over all land points by just over 1°C. Moistening the soil while using the control SSTs reduces the temperatures by a little less than 1°C on average although this can be larger than 3°C locally. While the reduction in SST impacts the daily maximum and minimum temperatures to a similar extent, the increase in soil moisture had little impact on the daily minimum temperatures.

Soil Moisture Monitoring of the Plant Root Zone by Using Phenology as Context in Remote Sensing

In this study, the phenological behavior and energy balance of plants are used as a sensory mechanism for root-zone soil moisture monitoring using both in-situ and satellite remote sensing data. The commonly used in-situ measurements are not feasible for mapping soil moisture at large-scale agricultural areas. Local direct root-zone soil moisture measurements cannot be reliably interpolated owing to the high spatial variability of soil structure and the vegetative content. Remote sensing methods are negatively affected by vegetation coverage and density regarding penetration and backscattering characteristics. In order to overcome these limitations, we propose a root-zone soil moisture estimation method utilizing a context-aware data clustering process, which can be applied prior to any statistical analysis, for empirical evaluation of data. In this aspect, the crops’ phenological stages and soil–air temperature differences are defined as the two contexts for data clustering. Parameters such as canopy–air temperature difference, land surface temperature, and solar radiation with respect to plant energy and water processes are used for the analysis. The proposed model is utilized using piecewise linear regression of data obtained from 16 rainfed wheat parcels distributed across Turkey, under different climatic and topographic conditions. It is shown that the proposed context-aware data clustering process enables the nonlinear plant behavior to be analyzed linearly. The correlation value of the whole season increased from 21% to a range between 78% and 95% for different clusters. The outliers became relevant and the parameters became significant after the proposed context-aware data clustering.

LiDAR derived terrain wetness indices to infer soil moisture above underground pipelines

Abstract Corrosion of external cast-iron pipe surfaces, a major contributor to pipe failure, has been attributed to the free water in the soil surrounding the pipe. Because observation at pipe depth is difficult, a potential proxy is the soil surface moisture. Herein highly accurate elevation data derived from airborne light detection and ranging is used to model the distribution of soil water in urban catchments containing pipe infrastructure. The results are compared with local soil moisture Theta Probe measurements along the pipe. The results show potential to identify wetter spots above underground infrastructure, which may inform its corrosion potential, without digging up the asset.

DICTUM

Lawns make up the largest irrigated crop by surface area in North America and carry with it a demand for over 7B gallons of freshwater each day. Despite recent developments in irrigation control and sprinkler technology, state-of-the-art irrigation systems do nothing to compensate for areas of turf with heterogeneous water needs. In this work, we overcome the physical limitations of the traditional irrigation system with the development of a sprinkler node that can sense the local soil moisture, communicate wirelessly, and actuate its own sprinkler based on a centrally computed schedule. A model is then developed to compute moisture movement from runoff, absorption, and diffusion. Integrated with an optimization framework, optimal valve scheduling can be found for each sprinkler node in the space. In a turf area covering over 10,000ft 2 , two separate deployments with four weeks of fine-grained data collection show that DICTUM can reduce water consumption by 23.4% over traditional campus scheduling, and by 12.3% over state-of-the-art evapotranspiration systems while substantially improving conditions for plant health. In addition to environmental, social, and health benefits, DICTUM is shown to return its investment in 16 to 18 months based on water consumption alone.

Ocean Salinity as a Precursor of Summer Rainfall over the East Asian Monsoon Region

AbstractThe sea surface salinity (SSS) varies largely as a result of the evaporation–precipitation difference, indicating the source or sink of regional/global water vapor. This study identifies a relationship between the spring SSS in the tropical northwest Pacific (TNWP) and the summer rainfall of the East Asian monsoon region (EAMR) during 1980–2017. Analysis suggests that the SSS–rainfall link involves the coupled ocean–atmosphere–land processes with a multifacet evolution. In spring, evaporation and water vapor flux divergence were enhanced in some years over the TNWP where an anomalous atmospheric anticyclone was established and a high SSS was well observed. As a result, the convergence of water vapor flux and soil moisture over the EAMR was strengthened. This ocean-to-land water vapor transport pattern was sustained from spring to summer and played a leading role in the EAMR rainfall. Moreover, the change in local spring soil moisture helped to amplify the summer rainfall by modifying surface thermal conditions and precipitation systems over the EAMR. As the multifacet evolution is closely related to the large-scale ocean-to-land water vapor transport, it can be well represented by the spring SSS in the TNWP. A random forest regression algorithm was used to further evaluate the relative importance of spring SSS in predicting summer rainfall compared to other climate indices. As the SSS is now monitored routinely by satellite and the global Argo float array, it can serve as a good metric for measuring the water cycle and as a precursor for predicting the EAMR rainfall.

United States Heat Wave Frequency and Arctic Ocean Marginal Sea Ice Variability

AbstractRecent studies point to a significant rise in the number of summer extreme weather events that correspond with the presence of amplified, quasistationary midtropospheric planetary waves, weakened atmospheric circulation in the Northern Hemisphere, and coincide with reduced summer Arctic sea ice cover. This study explores potential connections between 1979 and 2016 summer heat wave frequency across the USA and regional Arctic sea ice extent (SIE) in various Arctic basins. Most notable SIE interannual relationships exist across the southern Plains and southeastern US during low Hudson Bay summer SIE. Locally increased frequencies of summer heat waves coincide with unseasonably warm conditions developed and sustained by the presence of an omega blocking pattern situated over the southern US throughout summer. The block appears following anomalous atmospheric warming and reduced mean zonal winds observed throughout spring (March–May) over northeastern Canada, the northwestern Atlantic basin, and Greenland. Spring preconditioning of summer ice melt is favored by the presence of strong negative phase of the North Atlantic Oscillation and positive Greenland Blocking Index. Summer synoptic flow related to Hudson Bay ice melt over North America appears to be influenced by the background state of atmospheric variability, namely, the positive phase of the Atlantic Multidecadal Oscillation. Antecedent local humidity, soil moisture, and precipitation conditions are shown to influence the “flavor” of the heat waves, which are more likely to be oppressive in the southeastern US and extreme across the southern Plains during summers experiencing low Hudson SIE.