J. S. Famiglietti

Realistic Initialization of Land Surface States: Impacts on Subseasonal Forecast Skill

Forcing a land surface model (LSM) offline with realistic global fields of precipitation, radiation, and nearsurface meteorology produces realistic fields (within the context of the LSM) of soil moisture, temperature, and other land surface states. These fields can be used as initial conditions for precipitation and temperature forecasts with an atmospheric general circulation model (AGCM). Their usefulness is tested in this regard by performing retrospective 1-month forecasts (for May through September, 1979‐93) with the NASA Global Modeling and Assimilation Office (GMAO) seasonal prediction system. The 75 separate forecasts provide an adequate statistical basis for quantifying improvements in forecast skill associated with land initialization. Evaluation of skill is focused on the Great Plains of North America, a region with both a reliable land initialization and an ability of soil moisture conditions to overwhelm atmospheric chaos in the evolution of the meteorological fields. The land initialization does cause a small but statistically significant improvement in precipitation and air temperature forecasts in this region. For precipitation, the increases in forecast skill appear strongest in May through July, whereas for air temperature, they are largest in August and September. The joint initialization of land and atmospheric variables is considered in a supplemental series of ensemble monthly forecasts. Potential predictability from atmospheric initialization dominates over that from land initialization during the first 2 weeks of the forecast, whereas during the final 2 weeks, the relative contributions from the two sources are of the same order. Both land and atmospheric initialization contribute independently to the actual skill of the monthly temperature forecast, with the greatest skill derived from the initialization of both. Land initialization appears to contribute the most to monthly precipitation forecast skill.

Terrestrial water mass load changes from Gravity Recovery and Climate Experiment (GRACE)

Recent studies show that data from the Gravity Recovery and Climate Experiment (GRACE) is promising for basin- to global-scale water cycle research. This study provides varied assessments of errors associated with GRACE water storage estimates. Thirteen monthly GRACE gravity solutions from August 2002 to December 2004 are examined, along with synthesized GRACE gravity fields for the same period that incorporate simulated errors. The synthetic GRACE fields are calculated using numerical climate models and GRACE internal error estimates. We consider the influence of measurement noise, spatial leakage error, and atmospheric and ocean dealiasing (AOD) model error as the major contributors to the error budget. Leakage error arises from the limited range of GRACE spherical harmonics not corrupted by noise. AOD model error is due to imperfect correction for atmosphere and ocean mass redistribution applied during GRACE processing. Four methods of forming water storage estimates from GRACE spherical harmonics (four different basin filters) are applied to both GRACE and synthetic data. Two basin filters use Gaussian smoothing, and the other two are dynamic basin filters which use knowledge of geographical locations where water storage variations are expected. Global maps of measurement noise, leakage error, and AOD model errors are estimated for each basin filter. Dynamic basin filters yield the smallest errors and highest signal-to-noise ratio. Within 12 selected basins, GRACE and synthetic data show similar amplitudes of water storage change. Using 53 river basins, covering most of Earths land surface excluding Antarctica and Greenland, we document how error changes with basin size, latitude, and shape. Leakage error is most affected by basin size and latitude, and AOD model error is most dependent on basin latitude.

Evaluation of 10 Methods for Initializing a Land Surface Model

Abstract Improper initialization of numerical models can cause spurious trends in the output, inviting erroneous interpretations of the earth system processes that one wishes to study. In particular, soil moisture memory is considerable, so that accurate initialization of this variable in land surface models (LSMs) is critical. The most commonly employed method for initializing an LSM is to spin up by looping through a single year repeatedly until a predefined equilibrium is achieved. The downside to this technique, when applied to continental- to global-scale simulations, is that regional annual anomalies in the meteorological forcing accumulate as artificial anomalies in the land surface states, including soil moisture. Nine alternative approaches were tested and compared using the Mosaic LSM and 15 yr of global meteorological forcing. Results indicate that the most efficient way to initialize an LSM, if possible and given that multiple years of preceding forcing are not available, is to use climatologi...

Global terrestrial water storage capacity and flood potential using GRACE

Terrestrial water storage anomaly from the Gravity Recovery and Climate Experiment (GRACE) and precipitation observations from the Global Precipitation Climatology Project (GPCP) are applied at the regional scale to show the usefulness of a remotely sensed, storage-based flood potential method. Over the GRACE record length, instances of repeated maxima in water storage anomaly that fall short of variable maxima in cumulative precipitation suggest an effective storage capacity for a given region, beyond which additional precipitation must be met by marked increases in runoff or evaporation. These saturation periods indicate the possible transition to a flood-prone situation. To investigate spatially and temporally variable storage overflow, a monthly storage deficit variable is created and a global map of effective storage capacity is presented for possible use in land surface models. To highlight a flood-potential application, we design a monthly global flood index and compare with Dartmouth Flood Observatory flood maps.

Monitoring groundwater storage changes in complex basement aquifers: An evaluation of the GRACE satellites over East Africa

Although the use of the Gravity Recovery and Climate Experiment (GRACE) satellites to monitor groundwater storage changes has become commonplace, our evaluation suggests that careful processing of the GRACE data is necessary to extract a representative signal especially in regions with significant surface water storage (i.e. lakes/reservoirs). In our study, we use cautiously processed datasets, including GRACE, lake altimetry and model soil moisture, to reduce scaling factor bias and compare GRACE-derived groundwater storage changes to in-situ groundwater observations over parts of East Africa. Over the period 2007-2010, a strong correlation between in-situ groundwater storage change and GRACE-groundwater estimates (Spearmans ρ = 0.6) is found. Piecewise trend analyses for the GRACE-groundwater estimates reveal significant negative storage changes that are attributed to groundwater use and climate variability. Further analysis comparing groundwater and satellite precipitation datasets permits identification of regional groundwater characterization. For example, our results identify potentially permeable and/or shallow groundwater systems underlying Tanzania and deep and/or less permeable groundwater systems underlying the Upper-Nile basin. Regional groundwater behaviors in the semi-arid regions of Northern Kenya are attributed to hydraulic connections to recharge zones outside the sub-basin boundary. Our results prove the utility of applying GRACE in monitoring groundwater resources in hydrologically complex regions that are under-sampled and where policies limit data accessibility. This article is protected by copyright. All rights reserved.

Terrestrial Water Storage

Special supplement to the Bulletin of the American Meteorological Society vol.94, No. 8, August 2013

Filters to estimate water storage variations from GRACE

We examine the problem of obtaining average surface mass load changes in a local area (for example, water content in a river basin) from time-variable Stokes coefficients available from the GRACE mission. A basin function is unity inside a defined geographical region, and zero outside, and can be represented exactly only if Stokes Coefficients of all degrees and orders are available. GRACE Stokes coefficients will contain errors that generally increase with degree, and will be of limited degree range, perhaps to 100 or so. Load variations within a basin should be estimated by minimizing some quantity that accounts for both GRACE measurement error, and leakage error, associated with a finite degree range. To understand this problem, we use Fourier series, the 1-D equivalent to spherical harmonics. The solution of this problem is not unique, and we examine several different approaches. We use Monte Carlo experiments to test the performance of various methods derived. Time series of gridded soil moisture, snow, ocean bottom pressure, atmospheric surface pressure and Gaussian random noise are utilized in an experiment to recover load variations within the Nile basin.