Soil moisture is an important variable in the climate system. Understanding and predicting variations of surface temperature, drought, and flood depend critically on knowledge of soil moisture variations, as do impacts of climate change and weather forecasting. An observational dataset of actual in situ measurements is crucial for climatological analysis, for model development and evaluation, and as ground truth for remote sensing. To that end, the Global Soil Moisture Data Bank, a Web site (http://climate.envsci.rutgers.edu/soil_moisture) dedicated to collection, dissemination, and analysis of soil moisture data from around the globe, is described. The data bank currently has soil moisture observations for over 600 stations from a large variety of global climates, including the former Soviet Union, China, Mongolia, India, and the United States. Most of the data are in situ gravimetric observations of soil moisture; all extend for at least 6 years and most for more than 15 years. Most of the stations have grass vegetation, and some are agricultural. The observations have been used to examine the temporal and spatial scales of soil moisture variations, to evaluate Atmospheric Model Intercomparison Project, Project for Intercomparison of Land-Surface Parameterization Schemes, and Global Soil Wetness Project simulations of soil moisture, for remote sensing of soil moisture, for designing new soil moisture observational networks, and to examine soil moisture trends. For the top 1-m soil layers, the temporal scale of soil moisture variation at all midlatitude sites is 1.5 to 2 months and the spatial scale is about 500 km. Land surface models, in general, do not capture the observed soil moisture variations when forced with either model-generated or observed meteorology. In contrast to predictions of summer desiccation with increasing temperatures, for the stations with the longest records summer soil moisture in the top 1 m has increased while temperatures have risen. The increasing trend in precipitation more than compensated for the enhanced evaporation.

Abstract. Scales of soil moisture variations are important for understanding patterns of climate change, for developing and evaluating land surface models, for designing surface soil moisture observation networks, and for determining the appropriate resolution for satellitebased remote sensing instruments for soil moisture. Here we take advantage of a new archive of in situ soil moisture observations from Illinois and Iowa in the United States, and from Russia, Mongolia, and China, to evaluate the observed temporal and spatial scales of soil moisture variations. We separate the variance into two components, the very small scale, determined by soils, topography, vegetation, and root structure, and the large scale forced by the atmosphere. This larger scale, determined by precipitation and evaporation patterns, is of interest for global climate modeling. We characterize the small scale as white noise for our analysis, keeping in mind that it is an important component of soil moisture variations for other problems. We find that the atmospheric spatial scale for all regions is about 500 km. The atmospheric temporal scale is about 2 months for the top 1-m soil layer. The temporal scale for the top 10-cm layer is slightly less than 2 months. The white noise component of the variance for temporal variations ranges from 50% for the top 10 cm to 20-40% for the top 1 m. For spatial variations the white noise component is the same for all depths but varies with region from 30% for Illinois to around 70% for Mongolia. Nevertheless, the red noise (atmospheric component) can be seen in all regions. These results are for Northern Hemisphere midlatitudes and would not necessarily apply to other latitudes. The results are based on observations taken from grassland or agricultural areas, and may not be similar to those of areas with other vegetation types. In China, a region with substantial latitudinal variation, the temporal scale for the top 1 rn varies from 1 month in the south to 2.5 months in the north, demonstrating the control of potential evaporation on the temporal scales. Seasonal analysis of the scales of soil moisture for Illinois shows that during the winter the temporal scales are long, though the spatial scales are short. We suggest that these variations are both attributable to the seasonal cycle of potential evaporation.

Summary More than half of human cancers have aberrantly upregulated phosphoinositide signals; yet how phospholipid signals are controlled during tumorigenesis is not fully understood. We report here that TIPE3 (TNFAIP8L3) is the transfer protein of phosphoinositide second messengers that promote cancer. High-resolution crystal structure of TIPE3 shows a large hydrophobic cavity that is occupied by a phospholipid-like molecule. TIPE3 preferentially captures and shuttles two lipid second messengers, i.e., phosphatidylinositol 4,5-bisphosphate and phosphatidylinositol 3,4,5-trisphosphate, and increases their levels in the plasma membrane. Importantly, human cancers have markedly upregulated TIPE3 expression. Knocking out TIPE3 diminishes tumorigenesis whereas enforced TIPE3 expression enhances it in vivo. Thus, the function and metabolism of phosphoinositide second messengers are controlled by a specific transfer protein during tumorigenesis.

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Using 19 yr of Chinese soil moisture data from 1981 to 1999, the authors evaluate soil moisture in three reanalysis outputs: the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) ReAnalysis (ERA-40); the National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) reanalysis 1 (R-1); and the NCEP-Department of Energy (DOE) reanalysis 2 (R-2) over China. R-2 shows improved interannual variability and better seasonal patterns of soil moisture than R-1 as the result of the incorporation of observed precipitation, but not for all stations. ERA-40 produces a better mean value of soil moisture for most Chinese stations and good interannual variability. Limited observations in the spring indicate a spring soil moisture peak for most of the stations. ERA-40 generally reproduced this event, while R-1 or R-2 generally did not capture this feature, either because the soil was already saturated or the deep soil layer was too thick and damped such a response. ERA-40 and R-1 have a temporal time scale comparable to observations, but R-2 has a memory of nearly 5 months for the growing season, about twice the temporal scale of the observations. The cold season tends to prolong soil moisture memory by about 3 months for R-2 and 1 month for ERA-40. The unrealistic long temporal scale of R-2 can be attributed to the deep layer of the land surface model, which is too thick and dominates the soil moisture variability. R-1 has the same land surface scheme as R-2, but shows a temporal scale close to observations, which is actually because of soil moisture nudging to a fixed climatology. This new long time series of observed soil moisture will prove valuable for other studies of climate change, remote sensing, and model evaluation.