Search Results (8)

Surface soil moisture (SSM) and root-zone soil moisture (RZSM) are key hydrological variables for the agricultural water cycle and vegetation growth. Accurate SSM and RZSM forecasting at sub-seasonal scales would be valuable for agricultural water management and preparations. Currently, weather model-based soil moisture predictions are subject to large uncertainties due to inaccurate initial conditions and empirical parameterization schemes, while the data-driven machine learning methods have limitations in modeling long-term temporal dependences of SSM and RZSM because of the lack of considerations in the soil water process. Thus, here, we innovatively integrate the model-based soil moisture predictions from a sub-seasonal-to-seasonal (S2S) model into a data-driven stacked deep learning model to construct a hybrid SSM and RZSM forecasting framework. The hybrid forecasting model is evaluated over the Yangtze River Basin and parts of Europe from 1- to 46-day lead times and is compared with four baseline methods, including the support vector regression (SVR), random forest (RF), convolutional long short-term memory (ConvLSTM) and the S2S model. The results indicate substantial skill improvements in the hybrid model relative to baseline models over the two study areas spatiotemporally, in terms of the correlation coefficient, unbiased root mean square error (ubRMSE) and RMSE. The hybrid forecasting model benefits from the long-lead predictive skill from S2S and retains the advantages of data-driven soil moisture memory modeling at short-lead scales, which account for the superiority of hybrid forecasting. Overall, the developed hybrid model is promising for improved sub-seasonal SSM and RZSM forecasting over global and local areas. Full article

Exploring multi-timescale tidal variability is fundamental and necessary for numerous practical purposes, such as flood protection, marine cultivation, and ocean transport. It is well known that tides show significant seasonal, inter-annual, and 18.61-year nodal variability. Less known and less discussed is the subseasonal tidal variability (i.e., ter-annual, quarter-annual, and penta-annual cycles) in the coastal ocean. In this study, we explore subseasonal tidal modulations in the Gulf of Tonkin via the combination of four tide gauges and 27-year multi-satellite altimeter observations. Both tide gauges and satellite altimeters indicate that tidal subseasonality is significant in the Gulf of Tokin, although the amplitudes of subseasonal variations are much smaller than those of seasonal variations. Compared to spatially limited tide gauges, satellite altimeters successfully derive the basin-scale tidal subseasonality in the Gulf of Tonkin. The largest amplitude of subseasonal tidal constituents originated from the subseasonality of main tidal constituents, and can reach as high as 31.8 mm. It is suggested that subseasonal variations in ocean environments (e.g., sea levels and ocean stratification) induce tidal subseasonality through changing tidal propagation and dissipation. Although powerful, satellite altimeters also have some defects. Due to tidal aliasing related to long-period sampling intervals, some subseasonal tidal constituents are indistinguishable in satellite altimeter records. Full article

The precise tracking of distance variations between two satellites in low Earth orbit can provide key data for the understanding of the Earth’s system, specifically on seasonal and sub-seasonal water cycles and their impact on water levels. Measured distance variations, caused by local variations in gravitational field, serve as inputs to complex gravity models with which the movement of water on the globe can be identified. Satellite missions GOCE (2009–2013) and GRACE (2002–2017) delivered a significant improvement to our understanding of spatial and temporal gravity variations. Since 2018, GRACE Follow-On has been providing data continuity and features for the first time through the use of a laser interferometer as the technology demonstrator, in addition to a microwave ranging system as the main instrument. The laser interferometer provides an orders-of-magnitude lower measurement noise, and thereby could enable a significant improvement in the measurement of geoids if combined with suitable improvements in auxiliary instrumentation and Earth system modelling. In order to exploit the improved ranging performance, the ESA is investigating the design of a ‘Next Generation Gravity Mission’, consisting of two pairs of satellites with laser interferometers, improved accelerometers and improved platform performance. In this paper, we present the current design of the laser interferometer developed by us, the development status of the individual instrument units and the options available. Full article

In this study, the Chinese Academy of Sciences’ Earth System Model Version 2 (CAS-ESM2) and its atmospheric component were evaluated for the ability to simulate the East Asian summer monsoon (EASM), in terms of climatology and composites in El Niño decaying years (EN) and La Niña years (LN). The results show that the model can realistically simulate the El Niño Southern Oscillation (ENSO) annual cycle, the interannual variation, the evolution process, and the prerequisites of ENSO, but the trend of developing and decaying is faster than that of the observations. With regard to the climatological mean state in the EASM, the coupled model run can largely improve the precipitation and 850 hPa wind simulated in the atmospheric model. Moreover, the coupled run can also reduce the mid-latitude bias in the atmospheric model simulation. Composite methods were then adopted to examine performance in different phases of the ENSO, from a mature winter to a decaying summer. The atmospheric model can well reproduce the Western North Pacific Anomalous Anticyclone (WNPAC)/Western North Pacific Anomalous Cyclone (WNPC) during EN/LN well, but the westerly/easterly anomalies and the associated precipitation anomalies over the equatorial Central Eastern Pacific are somewhat overestimated. Compared with the atmospheric model, these anomalies are all underestimated in the coupled model, which may be related to the ENSO-related SST bias appearing in the Eastern Indian Ocean. Due to the ENSO and ITCZ bias in the historical simulations, the simulated ENSO-related SST and the precipitation anomaly are too equator-trapped in comparison with the observations, and the cold tongue overly extends westward. This limits the ability of the model to simulate ENSO-related EASM variability. For the subseasonal simulations, though atmospheric model simulations can reproduce the westward extension of the Western Pacific subtropic high (WPSH) in EN decaying summers, the eastward retreat of the WPSH in LN is weak. The historical simulations show limited improvement, indicating that the subseasonal variation in the EASM is still a considerable challenge for current generation models. Full article

This work investigates the utility of the National Oceanic and Atmospheric Administration’s National Water Model (NWM) for water management operations by assessing the total inflow into a select number of reservoirs across the Central and Western U.S. Total inflow is generally an unmeasured quantity, though critically important for anticipating both floods and shortages in supply over a short-term (hourly) to sub-seasonal (monthly) time horizon. The NWM offers such information at over 5000 reservoirs across the U.S., however, its skill at representing inflow processes is largely unknown. The goal of this work is to understand the drivers for both well performing and poor performing NWM inflows such that managers can get a sense of the capability of NWM to capture natural hydrologic processes and in some cases, the effects of upstream management. We analyzed the inflows for a subset of Bureau of Reclamation (BoR) reservoirs within the NWM over the long-term simulations (retrospectively, seven years) and for short, medium and long-range operational forecast cycles over a one-year period. We utilize ancillary reservoir characteristics (e.g., physical and operational) to explain variation in inflow performance across the selected reservoirs. In general, we find that NWM inflows in snow-driven basins outperform those in rain-driven, and that assimilated basin area, upstream management, and calibrated basin area all influence the NWM’s ability to reproduce daily reservoir inflows. The final outcome of this work proposes a framework for how the NWM reservoir inflows can be useful for reservoir management, linking reservoir purposes with the forecast cycles and retrospective simulations. Full article

Impact on the hydrology cycle is projected to be one of the most noticeable consequences of climate change. An increase in regional dry and wet extremes has already been observed, resulting in large socioeconomic losses. The 2014 wet conditions in Bulgaria present a valuable case study for analyzing the interaction between multiple drivers that are essential for early forecasting and warning of flood events. In this paper, time series analysis of temperature, precipitation and Terrestrial Water Storage Anomaly (TWSA) is performed and cross-correlations between observations and climate variability indices are computed for a 12-year period. In Bulgaria, a positive linear temperature trend was found with precipitation and TWSA exhibiting negative trends for the period 2003–2014. The year 2014 started with a drier and warmer than usual winter followed by five consecutive wet months from March to July. We found the following long-term variations: (1) temperature showing a local minimum in November 2014, (2) precipitation peaks in July 2014 and (3) a local TWSA maximum in December 2014. Over a 12-year period, weak to moderate negative correlations were observed between the long-term components of temperature, precipitation and TWSA. Moderate positive correlations with a 3 to 6-month lag were obtained between precipitation and TWSA long-term components. The long-term trends of temperature and precipitation from surface observations and atmospheric reanalysis showed very good alignment. Very large subseasonal precipitation residuals from observations and atmospheric reanalysis were obtained for April and September 2014. Two oscillation indices showed: (1) weak correlations with precipitation and (2) weak to moderate correlations with TWSA. Full article

The impacts of the Madden–Julian Oscillation (MJO), Kelvin waves, and Equatorial Rossby (ER) waves on the diurnal cycle of rainfall and types of deep convection over the Maritime Continent are investigated using rainfall from the Tropical Rainfall Measurement Mission Multisatellite Precipitation Analysis and Infrared Weather States (IR–WS) data from the International Satellite Cloud Climatology Project. In an absolute sense, the MJO produced its strongest modulations of rainfall and organized deep convection over the islands, when and where convection is already strongest. The MJO actually has a greater percentage modulation over the coasts and seas, but it does not affect weaker diurnal cycle there. Isolated deep convection was also more prevalent over land during the suppressed phase, while organized deep convection dominated the enhanced phase, consistent with past work. This study uniquely examined the effects of Kelvin and ER waves on rainfall, convection, and their diurnal cycles over the Maritime Continent. The modulation of convection by Kelvin waves closely mirrored that by the MJO, although the Kelvin wave convection continued farther into the decreasing phase. The signals for ER waves were also similar but less distinct. An improved understanding of how these waves interact with convection could lead to improved subseasonal forecast skill. Full article

Soil moisture observations are expected to play an important role in monitoring global climate trends. However, measuring soil moisture is challenging because of its high spatial and temporal variability. Point-scale in-situ measurements are scarce and, excluding model-based estimates, remote sensing remains the only practical way to observe soil moisture at a global scale. The ESA-led Soil Moisture and Ocean Salinity (SMOS) mission, launched in 2009, measures the Earth’s surface natural emissivity at L-band and provides highly accurate soil moisture information with a 3-day revisiting time. Using the first six full annual cycles of SMOS measurements (June 2010–June 2016), this study investigates the temporal variability of global surface soil moisture. The soil moisture time series are decomposed into a linear trend, interannual, seasonal, and high-frequency residual (i.e., subseasonal) components. The relative distribution of soil moisture variance among its temporal components is first illustrated at selected target sites representative of terrestrial biomes with distinct vegetation type and seasonality. A comparison with GLDAS-Noah and ERA5 modeled soil moisture at these sites shows general agreement in terms of temporal phase except in areas with limited temporal coverage in winter season due to snow. A comparison with ground-based estimates at one of the sites shows good agreement of both temporal phase and absolute magnitude. A global assessment of the dominant features and spatial distribution of soil moisture variability is then provided. Results show that, despite still being a relatively short data set, SMOS data provides coherent and reliable variability patterns at both seasonal and interannual scales. Subseasonal components are characterized as white noise. The observed linear trends, based upon one strong El Niño event in 2016, are consistent with the known El Niño Southern Oscillation (ENSO) teleconnections. This work provides new insight into recent changes in surface soil moisture and can help further our understanding of the terrestrial branch of the water cycle and of global patterns of climate anomalies. Also, it is an important support to multi-decadal soil moisture observational data records, hydrological studies and land data assimilation projects using remotely sensed observations. Full article