SeaWiFS Highlights April 2002 SeaWiFS Views Bright Water in the Rio de la Plata of South America Gene Feldman, NASA GSFC, Laboratory for Hydrospheric Processes, SeaWiFS SIMBIOS Project Office, On April 28, 2002 SeaWiFS captured this image of very bright, sediment-laden water in the Rio de la Plata of South America. Along the coast to the northwest, however, very little light was reflected back to the orbiting sensor. Perhaps a phytoplankton bloom fed by high-nutrient runoff was responsible for absorbing much of the solar radiation that was not absorbed by the water itself. The metropolitan area of Buenos Aires is readily visible as a large gray splotch just below the western end of the Rio de la Plata estuary. Other smaller splotches mark cities such as La Plata (just southeast of Buenos Aires) and Montevideo (on the northern side of the estuary).

SeaWiFS Views Bright Water in the Rio de la Plata of South America Sydney April 28, 2002

Initializing Soil Moisture in a Seasonal Forecast System 1 Randal Koster and 2 Max Suarez NASA GSFC, 1 Laboratory for Hydrospheric Processes, Hydrological Sciences Branch, 2 Laboratory for Atmospheres, Climate and Radiation Branch Science Problem: Climate modelers have long speculated that knowledge of soil moisture can contribute to precipitation predictability on seasonal timescales. The idea is that a soil moisture anomaly at the beginning of a forecast period can induce evaporation anomalies for weeks to months into the period, which in turn can affect precipitation in predictable ways. To what extent, though, is this speculation correct? Can we quantify the impact of soil moisture initialization on precipitation and temperature forecasts? Objectives: The goal of this study was to quantify, for the NASA Seasonal-to-Interannual Prediction Project (NSIPP) forecast system, the impact of soil moisture initialization on seasonal forecasts. In particular, we aimed to establish precisely where initialization has a significant impact on forecasts and where the impact is quickly drowned out by chaotic atmospheric processes. Methodology: For each boreal summer during , we generated two 16-member ensembles of 3-month simulations with the land and atmosphere components of the NSIPP seasonal forecast system. In one of the ensembles, soil moisture variables were realistically initialized on June 1, and in the other, no specific initialization was imposed – the initial conditions varied between the component simulations. The two ensembles were otherwise identical. A comparison of the two thus helps us isolate the impact of soil moisture initialization on forecasted variables. Highlights: The figure shows, for June, July, and August of 1999, differences in the mean fields of soil moisture, precipitation, and temperature generated by the two ensem-bles. Differences are plotted only where they are statistically significant at the 95% level – only where we can be reasonably sure that initialization had some impact on the fields. In this particular year (1999), land initialization produced a wet soil moisture anomaly in the central United States that persisted through June, July, and August (left column). For soil moisture in much of the eastern and western United States, the distinction between the two ensembles was rendered insignificant by random weather. The impact of the land initialization on precipitation and temperature is shown in the middle and right columns, respectively. The areas of impact lie within those identified for soil moisture. A comparison of the two ensembles on a global scale shows that soil moisture initialization has a statistically significant impact on summertime precipitation over only a handful of continental regions -- the impact of initialization on precipitation prediction is spatially limited. The impact on temperature prediction is spatially more extensive. The degree to which the initialization improves forecasts relative to observations is still being examined. This analysis, however, is limited by data availability. Preliminary evaluations with the limited datasets available are mixed, reflecting a critical need for the continued development of model parameterizations and data analysis strategies. ESE focus: The research relates directly to ESE objectives in seasonal-to-interannual climate variability and prediction.