Late-fall satellite-based soil moisture observations show clear connections to subsequent spring streamflow.

Because runoff production is more efficient over wetter soils, and because soil moisture has an intrinsic memory, soil moisture information can potentially contribute to the accuracy of streamflow predictions at seasonal leads. In this work, we use surface (0-5 cm) soil moisture retrievals obtained with the National Aeronautics and Space Administration's Soil Moisture Active Passive satellite instrument in conjunction with streamflow measurements taken within 236 intermediate-scale (2000-10,000 km) unregulated river basins in the conterminous United States to show that late-fall satellite-based surface soil moisture estimates are indeed strongly connected to subsequent springtime streamflow. We thus show that the satellite-based soil moisture retrievals, all by themselves, have the potential to produce skillful seasonal streamflow predictions several months in advance.

GEOS-S2S Version 2: The GMAO High Resolution Coupled Model and Assimilation System for Seasonal Prediction.

The Global Modeling and Assimilation Office (GMAO) has recently released a new version of the Goddard Earth Observing System (GEOS) Sub-seasonal to Seasonal prediction (S2S) system, GEOS-S2S-2, that represents a substantial improvement in performance and infrastructure over the previous system. The system is described here in detail, and results are presented from forecasts, climate equillibrium simulations and data assimilation experiments. The climate or equillibrium state of the atmosphere and ocean showed a substantial reduction in bias relative to GEOS-S2S-1.

Global relationships among traditional reflectance vegetation indices (NDVI and NDII), evapotranspiration (ET), and soil moisture variability on weekly timescales.

Monitoring the effects of water availability on vegetation globally using satellites is important for applications such as drought early warning, precision agriculture, and food security as well as for more broadly understanding relationships between water and carbon cycles. In this global study, we examine how quickly several satellite-based indicators, assumed to have relationships with water availability, respond, on timescales of days to weeks, in comparison with variations in root-zone soil moisture (RZM) that extends to about 1 m depth. The satellite indicators considered are the normalized difference vegetation and infrared indices (NDVI and NDII, respectively) derived from reflectances obtained with moderately wide (20-40 nm) spectral bands in the visible and near-infrared (NIR) and evapotranspiration (ET) estimated from thermal infrared observations and normalized by a reference ET.

Global Assessment of the SMAP Level-4 Surface and Root-Zone Soil Moisture Product Using Assimilation Diagnostics.

The Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides 3-hourly, 9-km resolution, global estimates of surface (0-5 cm) and root-zone (0-100 cm) soil moisture and related land surface variables from 31 March 2015 to present with ~2.5day latency. The ensemble-based L4_SM algorithm assimilates SMAP brightness temperature (Tb) observations into the Catchment land surface model.

Estimating Basin-Scale Water Budgets with SMAP Soil Moisture Data.

Soil Moisture Active Passive (SMAP) Level-2 soil moisture retrievals collected during 2015-2017 are used in isolation to estimate 10-day warm-season precipitation and streamflow totals within 145 medium-sized (2,000-10,000 km) unregulated watersheds in the conterminous United States. The precipitation estimation algorithm, derived from a well documented approach, includes a locally-calibrated loss function component that significantly improves its performance. For the basin-scale water budget analysis, the precipitation and streamflow algorithms are calibrated with two years of SMAP retrievals in conjunction with observed precipitation and streamflow data and are then applied to SMAP retrievals alone during a third year.

Improved Hydrological Simulation Using SMAP Data: Relative Impacts of Model Calibration and Data Assimilation.

The assimilation of remotely sensed soil moisture information into a land surface model has been shown in past studies to contribute accuracy to the simulated hydrological variables. Remotely sensed data, however, can also be used to improve the model itself through the calibration of the model's parameters, and this can also increase the accuracy of model products. Here, data provided by the Soil Moisture Active/Passive (SMAP) satellite mission are applied to the land surface component of the NASA GEOS Earth system model using both data assimilation and model calibration in order to quantify the relative degrees to which each strategy improves the estimation of near-surface soil moisture and streamflow.

Verification of land-atmosphere coupling in forecast models, reanalyses and land surface models using flux site observations.

We confront four model systems in three configurations (LSM, LSM+GCM, and reanalysis) with global flux tower observations to validate states, surface fluxes, and coupling indices between land and atmosphere. Models clearly under-represent the feedback of surface fluxes on boundary layer properties (the atmospheric leg of land-atmosphere coupling), and may over-represent the connection between soil moisture and surface fluxes (the terrestrial leg). Models generally under-represent spatial and temporal variability relative to observations, which is at least partially an artifact of the differences in spatial scale between model grid boxes and flux tower footprints.

Evaluation and enhancement of permafrost modeling with the NASA Catchment Land Surface Model.

Besides soil hydrology and snow processes, the NASA Catchment Land Surface Model (CLSM) simulates soil temperature in six layers from the surface down to 13m depth. In this study, to examine CLSM's treatment of subsurface thermodynamics, a baseline simulation produced subsurface temperatures for 1980-2014 across Alaska at 9-km resolution. The results were evaluated using in situ observations from permafrost sites across Alaska.

The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2).

The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) is the latest atmospheric reanalysis of the modern satellite era produced by NASA's Global Modeling and Assimilation Office (GMAO). MERRA-2 assimilates observation types not available to its predecessor, MERRA, and includes updates to the Goddard Earth Observing System (GEOS) model and analysis scheme so as to provide a viable ongoing climate analysis beyond MERRA's terminus. While addressing known limitations of MERRA, MERRA-2 is also intended to be a development milestone for a future integrated Earth system analysis (IESA) currently under development at GMAO.

A Data-Driven Approach for Daily Real-Time Estimates and Forecasts of Near-Surface Soil Moisture.

NASA's Soil Moisture Active Passive (SMAP) mission provides global surface soil moisture retrievals with a revisit time of 2-3 days and a latency of 24 hours. Here, to enhance the utility of the SMAP data, we present an approach for improving real-time soil moisture estimates ("nowcasts") and for forecasting soil moisture several days into the future. The approach, which involves using an estimate of loss processes (evaporation and drainage) and precipitation to evolve the most recent SMAP retrieval forward in time, is evaluated against subsequent SMAP retrievals themselves.

Hydroclimatic Variability and Predictability: A Survey of Recent Research.

Recent research in large-scale hydroclimatic variability is surveyed, focusing on five topics: (i) variability in general, (ii) droughts, (iii) floods, (iv) land-atmosphere coupling, and (v) hydroclimatic prediction. Each surveyed topic is supplemented by illustrative examples of recent research, as presented at a 2016 symposium honoring the career of Professor Eric Wood. Taken together, the recent literature and the illustrative examples clearly show that current research into hydroclimatic variability is strong, vibrant, and multifaceted.

Large Scale Influences on Summertime Extreme Precipitation in the Northeastern United States.

Observations indicate that over the last few decades there has been a statistically significant increase in precipitation in the Northeastern United States and that this can be attributed to an increase in precipitation associated with extreme precipitation events. Here we use a state-of-the-art atmospheric reanalysis to examine such events in detail. Daily extreme precipitation events defined at the 75 and 95 percentile from gridded gauge observations are identified for a selected region within the Northeast.

A Modeling Study of the Causes and Predictability of the Spring 2011 Extreme US Weather Activity.

This study examines the causes and predictability of the spring 2011 U.S. extreme weather using the Modern-Era Retrospective analysis for Research and Applications (MERRA) reanalyses and Goddard Earth Observing System, version 5 (GEOS-5) Atmospheric General Circulation Model simulations.

Precipitation Estimation Using L-Band and C-Band Soil Moisture Retrievals.

An established methodology for estimating precipitation amounts from satellite-based soil moisture retrievals is applied to L-band products from the Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) satellite missions and to a C-band product from the Advanced Scatterometer (ASCAT) mission. The precipitation estimates so obtained are evaluated against (gauge-based) precipitation observations from across the globe. The precipitation estimation skill achieved using the L-band SMAP and SMOS datasets is higher than that obtained with the C-band product, as might be expected given that L-band is sensitive to a thicker layer of soil and thereby provides more information on the response of soil moisture to precipitation.

Confronting weather and climate models with observational data from soil moisture networks over the United States.

Four land surface models in uncoupled and coupled configurations are compared to observations of daily soil moisture from 19 networks in the conterminous United States to determine the viability of such comparisons and explore the characteristics of model and observational data. First, observations are analyzed for error characteristics and representation of spatial and temporal variability. Some networks have multiple stations within an area comparable to model grid boxes; for those we find that aggregation of stations before calculation of statistics has little effect on estimates of variance, but soil moisture memory is sensitive to aggregation.

Regions of strong coupling between soil moisture and precipitation.

Previous estimates of land-atmosphere interaction (the impact of soil moisture on precipitation) have been limited by a lack of observational data and by the model dependence of computational estimates. To counter the second limitation, a dozen climate-modeling groups have recently performed the same highly controlled numerical experiment as part of a coordinated comparison project. This allows a multimodel estimation of the regions on Earth where precipitation is affected by soil moisture anomalies during Northern Hemisphere summer.

On the cause of the 1930s Dust Bowl.

During the 1930s, the United States experienced one of the most devastating droughts of the past century. The drought affected almost two-thirds of the country and parts of Mexico and Canada and was infamous for the numerous dust storms that occurred in the southern Great Plains. In this study, we present model results that indicate that the drought was caused by anomalous tropical sea surface temperatures during that decade and that interactions between the atmosphere and the land surface increased its severity.