1. SeaWiFS Views Smoke from the California Fires Across Entire U.S. 970.2/Gene Feldman, NASA GSFC, Laboratory for Hydrospheric Processes, Office for Global Carbon Studies (gene.c.feldman@nasa.gov)gene.c.feldman@nasa.gov On Thursday, October 30, 2003 (left), a yellowish aerosol was visible in the SeaWiFS image over the Midwest region of the United States encompassing Kansas, Nebraska, Iowa, and Illinois. This aerosol emanated from the fires plaguing the southern part of California. The image on the right, taken on Friday, October 31, shows the eastward transport of this same yellowish aerosol over Cape Cod and the Bay of Fundy–Gulf of Maine area.

2. SeaWiFS Views Smoke from the California Fires Across Entire U.S. October 30, 2003 October 31, 2003

3. Seasonal to Interannual Soil Moisture and Climate Feedback over Eurasia Jiarui Dong 1,2, Wenge Ni-Meister 1,2, Paul R. Houser 1, and Randal D. Koster 1 1. Hydrological Sciences Branch, NASA Goddard Space Flight Center, Greenbelt, MD 20771 2. Goddard Earth Sciences and Technology Center, University of Maryland, Baltimore County, Baltimore, MD 20771 Soil has the ability to store the precipitated water from the periods of excess for later evaporation during the periods of shortage. Subsurface moisture stores exhibit persistence on seasonal to interannual time scales and this persistence of soil moisture anomalies MAY have tremendous impact on the seasonal cycle of the atmosphere. Understanding the control and the influence of soil moisture on regional climate has profound implications for improving seasonal to interannual climate predictions. The purpose of this study is to investigate the relations between soil moisture and near-surface climate at seasonal to interannual scales and emphasizes the influences of cold season processes and vegetation types on these relations. We used the following data sets for our analysis: (1) Observed plant available soil moisture data at the top 1-m layer from Global Soil Moisture Data Bank for the former Soviet Union (130 stations with temporal resolution at 10-11 days); (2) Monthly half-degree gridded climate data, air surface temperature and precipitation. (3) Remote sensing land cover map at a spatial resolution 1  1 km and with 14 land cover types from University of Maryland. Soil moisture measured at sites with forest as a dominant cover type, averaged over growing season and total eight years, is persistently larger than soil moisture measured at grassland dominant sites, because of the continuously large precipitation and relative low air temperature at the sites with forest as a dominant type (Figure 1). At an interannual scale, the strong positive/negative correlation between soil moisture and precipitation/temperature is found over grassland, but is weak over forest regions (Figure 2). Grassland will maintain the precipitation information due to the less evaporation from its weak root system, but the strong root system over forest regions tapping deeper soil water substantially reduces the near temporal link between soil moisture and precipitation. At seasonal scale, our analysis over the former Soviet Union data suggests that soil can remember spring soil moisture to enhance the subsequent precipitation in summer at the regions with forest as dominant land cover (Figure 3). Also, the cumulative snow in previous winter is used to build spring soil moisture bank (Figure 4). Forest dominant sites show strong abilities in memorizing spring soil moisture to enhance summer precipitation because the extensive root system can utilize the deep soil water, contrasting with the weak performance from grassland and cropland in tapping deep layer soil moisture. Snow accumulation in winter is an important reservoir to wet spring soil, and leaves soil water in deep layers undisturbed at the early stage of growing season. High temperature and the extensive forest root system will evaporate soil water back to the atmosphere in summer. This work may contribute to NASA Seasonal-to-Interannual Prediction Project (NSIPP) in providing the facts that can be used to validate model prediction on a relative longer time scale.

4. Interannual Figure 1. Comparison of soil moisture, precipitation and temperature averaged over sub-samples with the fraction of forest or grassland greater than a pre-defined threshold. Figure 2. Comparison of the interannual variability between soil moisture and climate among stations in the former Soviet Union averaged over growing season for forest and grassland dominant sites.

5. Snow Precipitation Seasonal Figure 3. a) Correlation between mean soil moisture in spring (March and April) and snowfall during previous winter with the former Soviet Union data, and b) The correlation coefficients between previous winter snowfall and soil moisture in the following months. 12 3 6 9 1 2 4 57 8 10 11 Figure 4. Correlation between soil moisture in early spring (March and April) and precipitation in summer (July & August) for the stations with forest fraction above 51% (left) and with grassland fraction above 51% (right). MONTHS Soil moisture

6. Comparison of Model with L-Band Radiometer Measurements The spectral window at L-band is important for measuring soil moisture and ocean salinity; At L-band radiation from celestial sources is strong and corrections are needed for the down-welling radiation reflected into the sensor; A model has been developed for this purpose modern using radio astronomy measurements (LeVine and Abraham, 2004); Comparison with remote sensing radiometer data is underway to validate the model; The accompanying slide shows the comparison with the PALS radiometer (Wilson et al, 2001). –Measured Vertical Polarized: Green –Measured Horizontal Polarized: Red –Model Prediction: Blue References : –Le Vine, D.M. and S. Abraham, “Galactic Noise and Passive Microwave Remote Sensing from Space at L-Band,” IEEE Trans. Geoscience and Remote Sensing, accepted for publication, 2004. –Wilson, W.J. et al, “Passive active L- and S-band (PALS) microwave sensor for ocean salinity and soil moisture measurements,” IEEE Trans. Geoscience and Remote Sensing, Vol: 39 # 5, pp 1039 -1048, May 2001 975/David Le Vine, NASA GSFC, Laboratory for Hydrospheric Processes, Microwave Sensors Branch (david.m.levine@nasa.gov)

7. Comparison of Model with L-Band Radiometer Measurements Measurements: –Vertical Pol: green –Horizontal Pol: red Model prediction: Blue Data are the average of measurements from Sept 21-23, 2001