Air–Sea Gas Exchange Due To Rain and Wind Larry Bliven/Instrumentation Sciences Branch, Hydrospheric & Biospheric Sciences Laboratory Introduction: Air–sea CO 2 is an important process that contributes to the global carbon cycle. CO 2 exchange is thought to be controlled predominantly by subsurface turbulence, which is in turn driven by environmental processes such as wind and rain. History: The effect of wind on gas exchange has historically received a great deal of attention, and recently, the relationship between rain and gas exchange has been examined. Yet the combined effect of rain and wind on air–sea gas exchange has not been studied. Research and Results (see next slide for graphical results): A series of experiments were conducted at the University of Delaware's Air–Sea Interaction Laboratory to examine the combined effects of rain and wind on air–sea gas exchange. For the experimental conditions, rain and wind combine linearly to influence air–water gas exchange. Raindrop size measurements were necessary for this investigation and the NASA\GSFC Rain Imaging System (RIS) provided the unique capability to obtain the data. Impact: Rain significantly enhances air–sea CO 2, even during windy conditions. The exchange rates from the combined wind and rain cases are modeled by simple addition of exchange rates from independent wind and rain algorithms. These results are important for modeling and assessing field air-sea CO 2 fluxes.

First Rain and Wind Gas Flux Experiment Figure 3: Comparison between modeled and measured gas flux rates. The model assumes that the effects of rain and wind are additive. The model and the data are highly correlated. Figures 1 and 2: Schematic diagrams of the laboratory setup at the University of Delaware.

The impact of irrigation on land surface water and energy balance Mutlu Ozdogan, Matthew Rodell and Hiroko Kato, Hydrological Sciences Branch Problem Statement: Irrigated croplands have been shown to influence local and regional climates and hydrology by modifying the partitioning of water and energy between the land surface and the atmosphere. Historical Significance: The climate/hydrological modeling community has been reluctant to adopt irrigation schemes within numerical land surface models (LSMs). While it has been consistently shown that accurate and realistic LSM-based initialization of land surface moisture and energy states in numerical weather prediction models is critical for short- and medium-term meteorological and hydrological predictions, significant modification of these states by human activities such as irrigation and the impact of these modifications on prediction accuracies have so far been overlooked. One reason for this lack of attention is that reliable data on croplands, and in particular, irrigated croplands, over large areas have not been available. Moreover, LSMs have traditionally relied on generalized, and often static, land cover types that do not incorporate information on management (i.e. irrigation and crop type). Objective and Approach: The research objective was to better understand the impact of irrigated croplands on land surface water and energy balance, and by extension, on the skill and accurate initialization of LSMs. To achieve this goal we first developed an up-to-date, remote sensing based database on irrigated croplands (Figure 1a). This dataset, along with a dataset on major crop types over the Continental US, were then integrated into the newly developed irrigation module within an LSM. The irrigation module was forced by contemporary atmospheric fields and computed daily irrigation water requirements (Figure 1b). A year-long simulation of land surface water and energy states was run within the Land Information System (LIS) framework with (irrigated) and without (control) additional moisture from the irrigation module. Results from these two experiments were compared to each other and to field observations to assess the role of irrigation on modifying land surface water and energy balance components. Results and Discussion (please see graphical results on next 2 slides): Simulation results show that irrigation has a major impact on evapotranspiration from the land surface (Figure 1c). Up to 2 mm/day additional (on top of precipitation) evapotranspiration losses are possible over irrigated sites which could significantly influence the water balance as well as boundary layer processes. Impact: Irrigation increases evaporative cooling, which reduces daytime surface temperatures. The numerical model simulation successfully captured this effect after satellite derived irrigation data were incorporated

Figure 1. A) Irrigated area in 2003; B) Warm season irrigation amount; C) Contribution of irrigation to evapotranspiration in the warm season; D) Contribution of irrigation to base flow in the warm season (mm/d). Note that irrigation significantly influences evapotranspiration. AB CD

Figure 2. Impact of irrigation on surface energy balance components. Irrigation’s influence on latent heat flux (A), onsensible heat flux (B), and ground heat flux (C). Inclusion of irrigation also improves the accuracy of model-predicted max. and min. surface temperatures (dark solid line in D is closer to observed values [dotted line] then the gray-colored solid line). A D C B

All-Weather Land Surface Temperature Retrieval Manfred Owe, Hydrological Sciences Branch, Hydrospheric & Biospheric Sciences Laboratory Research Statement: 37 GHz vertical polarization microwave brightness temperatures have a strong physical relationship with land surface temperature which is essential for soil moisture retrieval by microwave sensors. Data Comparisons: This technology is less affected by atmospheric contaminants than thermal infrared (example of thermal sensor susceptibility is shown in Figure 1-next slide), and provides near all-weather capability. It also:  Compares well with ECMWF assimilation LST products.  Compares well with LST modeled from OK Mesonet 5 cm soil temperatures (Figure 2-next slide). Acronyms: ECMWF-European Center for Medium range Weather Forecasting LST-Land Surface Temperature MODIS-Moderate Resolution Imaging Spectroradiometer SSM/I-Special Sensor Microwave Imager AMSR-E-Advanced Microwave Scanning Radiometer - EOS Impact: Microwave retrieval techniques are less susceptible to cloud contamination and have a near-all weather capability, thus improving both the spatial and temporal coverage of global land surface temperature data products.

SSM/I 18 Sept LocalMODIS Night LST Bondville 18 Sept 2001 * white areas indicate no data Figure 1: Comparison of MODIS and SSM/I Land Surface Temperature over Illinois for 18 September Although observation times for the two images are not the same, the susceptibility of thermal sensors to adverse atmospheric conditions, such as clouds is clearly illustrated. (MODIS data courtesy of Mike Bosilovich, NASA-GSFC-GMAO) AMSR-E Ts (K) OK Mesonet Ts (K) Figure 2: Scatter plot of AMSR-E 37 GHz LST and Mesonet surface temperature for a 0.5 degree test site in East Central Oklahoma for 2003

Coastal Research Along the Delmarva Peninsula and Southern Virginia Antonio Mannino (614.2), Mary Russ (UMBC-GEST), and Stan Hooker (614.2) Program: Researchers from the Ocean Sciences Branch (614.2) have conducted a series of oceanographic expeditions in along the Delmarva peninsula and the southern Virginia coast as well as day cruises within outflow waters of the Chesapeake Bay. Purpose: The research cruises represent a continuing collaboration between GSFC scientists and Old Dominion University to study coastal ocean carbon and ecosystems by linking in situ observations with MODIS and SeaWiFS observations. Overarching Objective: To expand the product suite derived from ocean color satellite data to improve our understanding of the coastal carbon cycle and expand our capabilities to study potential impacts of climate change on coastal ecosystems. Research and Results (please see graphical results on next 2 slides): Our current work is focusing on validating these algorithms and evaluating the seasonal variability and interannual consistency of the DOC to a CDOM relationship. CDOM is colored dissolved organic matter. Future Direction and Scalability: With the capability to retrieve a CDOM and DOC from ocean color sensors, we will be able to monitor the seasonal and interannual variability of a CDOM, DOC and the DOC reservoir for this coastal region. Impact: Dissolved organic carbon-DOC is significantly correlated to the absorption coefficient of CDOM (a CDOM ). This relationship varies seasonally but thus far is consistent interannually for the winter-spring period.