Heat Exchange and Ice Production in the Arctic Ocean as Derived from ICESat

Nathan Kurtz, Thorsten Markus, Code 614.1, NASA GSFC

The rate at which heat is lost from the Arctic Ocean to the cold polar atmosphere depends strongly on the thickness of the interlaying sea ice cover. Thin ice areas allow much more heat to escape from the ocean and are also areas of greater ice production than thick ice areas.

Making use of the full spatial resolution of the laser altimeter aboard ICESat, we have developed a method to determine the sea ice thickness of the Arctic at a very small spatial scale. Knowledge of the ice thickness allows us to calculate the exchange of heat between the ocean and the atmosphere and the rate at which the sea ice grows.

This is the first time that high resolution satellite data have been used to find information on the ocean-atmosphere heat transfer and rate of sea ice growth across the Arctic basin. Figure 2: Sea ice growth rates during fall and

winter time periods for 2005 and 2006.

Hydrospheric and Biospheric Sciences Laboratory

Figure 1: This figure shows the amount of heat being transferred from the ocean to the atmosphere during fall and winter time periods for 2005 and 2006. The grey contour line is the boundary between seasonal and perennial ice areas.

Name: Nathan Kurtz, Thorsten Markus NASA/GSFC, Code 614.1

E-mail: nathan.t.kurtz@nasa.gov, thorsten.markus@nasa.gov

Phone: 301-614-5882

References:Kurtz, N.T., T. Markus, D.J. Cavalieri, L.C. Sparling, W.B. Krabill, A.J. Gasiewski, J.G. Sonntag, Estimation

of sea ice thickness distributions through the combination of snow depth and satellite laser altimetry data, J. Geophys. Res., (in press).

Data Sources: ICESat and Aqua-AMSR-E data were used to determine the sea ice thickness and estimate heat flux and ice production for the Arctic basin.

Technical Description of the Image:Figure 1: A method to combine snow depth and ICESat sea ice freeboard measurements to determine sea

ice thickness at the 70 m resolution of ICESat has been developed. These satellite measurements allow for the determination of the sea ice thickness distribution within 25 km x 25 km areas for the entire Arctic basin. This figure shows the ocean-atmosphere heat flux as calculated for typical Arctic winter conditions using the sea ice thickness distributions derived from ICESat data for the 2005 fall season and the 2006 winter season.

Figure 2: This figure shows the sea ice growth rate across the Arctic for the 2005 fall season and the 2006 winter season. The thicker perennial ice area has a much lower rate of growth than the thinner seasonal ice area.

Scientific Significance: Knowledge of sea ice thickness is critical for climate studies which seek to understand the interactions between the ocean, sea ice, and atmosphere. The spatial resolution with which the sea ice thickness is known is important as well. While knowledge of the large-scale average sea ice thickness is important for assessing the overall state of the sea ice cover, estimates of the sea ice thickness distribution are needed to better understand physical processes such as heat exchange and ice growth. This study shows that high resolution satellite laser altimetry data can be used to find the sea ice thickness at the spatial scale necessary to better understand physical processes which affect the climate.

Relevance to future science and relationship to Decadal Survey: This study provides a method to utilize the full spatial resolution of ICESat to determine the sea ice thickness distribution of a 25 km x 25 km area. The methods developed in the study will allow for a better understanding of sea ice growth and heat exchange between the Arctic Ocean and atmosphere which is particularly important given recently observed losses in the sea ice cover and warming temperatures in the Arctic.

Hydrospheric and Biospheric Sciences Laboratory

Impacts of Climate Variability on Primary Productivity and Impacts of Climate Variability on Primary Productivity and Carbon Distributions on the East Coast (CliVEC)Carbon Distributions on the East Coast (CliVEC)

Antonio Mannino, Code 614.2, NASA GSFCAntonio Mannino, Code 614.2, NASA GSFC

The CliVEC project goal is to examine how inter-annual and decadal-scale climate variability affects primary productivity and carbon distributions on the continental shelf and slope of the northeastern U.S. using observations from the MODIS and SeaWiFS time-series (1997-2012) and field measurements from the 7 extensive field campaigns. Validation of carbon and primary production ocean color products is a critical component of this project.

CliVEC focuses on the impacts of variable river discharge, freshening of shelf-slope water along the U.S. East Coast from the melting of Greenland’s ice sheet, sea-surface temperatures, and wind stress as well as regional and global indices of climate variability on primary production, new N inputs from N2 fixation, community structure, and organic carbon distributions.

Figure 1: Study area sampling stations and chlorophyll a for August 2009 field campaign, which occurred in the midst of hurricane Bill & tropical storm Danny.

Figure 3: Primary productivity model to be refined with CliVEC field observations and validated satellite data products

Hydrospheric and Biospheric Sciences Laboratory

Figure 2: New northeastern U.S. coastal chlorophyll a satellite algorithm.

Name: Antonio Mannino, NASA/GSFC E-mail: Antonio.Mannino@nasa.govPhone: 301-286-0182

References:Pan, X., A. Mannino, M.E. Russ, S.B. Hooker, L.W. Harding, Jr., 2009. Remote Sensing of Phytoplankton Pigment Distribution in the United States Northeast Coast, Submitted Sept. 2009, Remote Sensing of Environment.

Data Sources: The CliVEC project is a joint effort between NASA-GSFC (project lead, carbon field measurements, validation of ocean color satellite algorithms, and climate variability analysis), Old Dominion University (field sampling including primary productivity and N2 fixation measurements), and NOAA (research vessel and sampling logistics, primary productivity model development, and satellite time-series data processing).

Surface ocean chlorophyll a observations (Figure 1) derived from merged NASA SeaWiFS and MODIS-Aqua images provided by the NOAA team [K. Hyde, NOAA NEMFS] during the field campaign. Surface ocean chlorophyll distributions (Figure 2) [Pan et al. 2009]. Northeastern U.S. coastal ocean primary productivity (PP) for April 2002 from the NOAA team (Figure 3) [J. O’Reilly and K. Hyde, NOAA NEMFS].

Technical Description of Image:Figure 1: Sampling stations of the first CliVEC oceanographic cruise (August 17-30, 2009) overlain on top of surface ocean chlorophyll a derived from merged NASA SeaWiFS and MODIS-Aqua images on August 7, 2009. The satellite imagery was provided by the NOAA NEMFS team during the field campaign to helpFigure 2: New chlorophyll a satellite algorithm (8-day mean from August 1-8, 2006) for the northeastern U.S. developed from previous field campaigns that improves upon the operational product distributed by the Ocean Biology Processing Group at GSFC. [Pan et al. submitted]Figure 3: Satellite-based primary productivity for the northeastern U.S. coastal ocean for April 2002 computed using the Ocean Productivity from Absorption of Light (OPAL) model (Marra et al. 2007). OPAL utilizes satellite-derived chlorophyll a, phytoplankton and CDOM absorption and sea-surface temperatures to derive photic-zone integrated PP. The field data collected on the CliVEC field campaigns and satellite algorithms developed will be applied to significantly improve the OPAL model.

Scientific significance: The U.S. Middle Atlantic Bight, George’s Bank and Gulf of Maine stand at the crossroads between major ocean circulation features – the Gulf Stream and Labrador slope-sea and shelf currents – and are influenced by highly variable river discharge, summer upwelling, warm core rings, and intense seasonal stratification. Satellite products and field observations generated from this work will be crucial for developing and evaluating biogeochemical models while also providing some context to evaluate climate change scenarios for coastal carbon cycling and ecosystem variability. Estimates of daily primary productivity (PP) will be computed using the Ocean Productivity from Absorption of Light (OPAL) model. OPAL will be validated with new field measurements of PP. Field measurements of particulate (POC) and dissolved organic carbon (DOC), chlorophyll a, and the absorption coefficients of phytoplankton (aph) and colored dissolved organic matter (aCDOM) will allow us to extend the validation range (temporally and spatially) for our coastal algorithms (Mannino et al. 2008; Pan et al. 2008, 2009) and reduce the uncertainties in satellite-derived estimates of OPAL PP, POC, DOC, chlorophyll a, aph and aCDOM. We will rigorously validate and compare multiple band-ratio and multivariate neural network algorithms.

Relevance for future science and relationship to Decadal Survey: Coastal marine ecosystems play an important role in the balance of air-sea CO2 exchange and are vulnerable to climate variability and anthropogenic activities. Ocean color satellite algorithm refinement and validation of organic carbon products and primary productivity models in coastal ocean regions are relevant for future ocean color missions such as the Tier 2 ACE and GEO-CAPE missions. The techniques, models and algorithms developed in this work will provide the necessary tools and data products to apply future ocean color satellite observations to the coastal ocean.

Hydrospheric and Biospheric Sciences Laboratory

NASA Provides a Global Soil Moisture Product for the USDACrop Forecasting System

John D. Bolten, Code 614.3, NASA GSFC

The integration of Aqua AMSR-E soil moisture estimates into the USDA Foreign Agricultural Service (FAS) crop forecasting system provides better characterization of surface wetness conditions which enables more accurate crop monitoring in key agricultural areas.

Figure 1. NASA/USDA blended soil moisture product

Figure 2: Soil moisture error reduction (red) or increase (blue) over the continental U.S.

Figure 3. Soil moisture error reduction (red) or increase (blue) over West Africa

Hydrospheric and Biospheric Sciences Laboratory

Name: John D. Bolten, NASA/GSFC E-mail: John.Bolten@nasa.govPhone: 301-614-6529

References:

J.D. Bolten, W. Crow, X. Zhan, C. Reynolds, T. Jackson, Evaluating the Utility of Remotely-Sensed Soil Moisture Retrievals for Operational Agricultural Drought Monitoring, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, In Press.

J.D. Bolten, W. Crow, X. Zhan, C. Reynolds, T. Jackson, Evaluation of a soil moisture data assimilation system over West Africa, In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2009.

J.D. Bolten, W. Crow, X. Zhan, C. Reynolds, T. Jackson, Assimilation of a satellite-based soil moisture product in a two-layer water balance model for a global crop production decision support system, in Data Assimilation for Atmospheric, Oceanic, and Hydrologic Applications, (Ed. Springer-Verlag), 2009.

Data Sources: This is a collaborative effort involving NASA GSFC, USDA FAS, USDA ARS, and NOAA NESDIS. The remotely sensed soil moisture data are from the EOS Advanced Microwave Scanning Radiometer (AMSR-E). Global precipitation and temperature data are provided by the Air Force Weather Agency (AFWA) and World Meteorological Organization (WMO).

Technical Description of Image:Figure 1: Global root-zone soil moisture product operationally delivered to the USDA-FAS as displayed in Google Map. This global product is delivered to the USDA FAS every 3-5 days, as well as a surface soil moisture product (not shown) and soil moisture anomaly product (not shown).

Figure 2: For this analysis, a data denial experimental approach is utilized to isolate the added utility of integrating remotely-sensed soil moisture by comparing Ensemble Kalman Filter (EnKF) soil moisture results obtained using (relatively) low-quality precipitation products obtained from real-time satellite imagery integrated with AMSR-E observations to baseline USDA FAS model runs forced with higher quality rainfall. This figure shows root- zone delta RMSE soil moisture results over North America for the 5 year data denial experiment. Negative (positive) values shaded in red (blue) indicate areas of improvement (degradation) relative open loop upon application of the EnKF. [Bolten et al., 2009a]

Figure 3: Following the approach described above, this figure presents root- zone delta r soil moisture anomaly results over West Africa for the 5-year data denial experiment. Positive (negative) values shaded in blue (red) indicate areas of improvement (degradation) relative open loop upon application of the EnKF. [Bolten et al., 2009b] The larger areas of increased soil moisture error (blue) are partly due to the benchmark precipitation product being poorly gauge-corrected over this region, thus the improvements from integrating AMSR-E data into the real-time precipitation product are less pronounced.

Scientific significance: This analysis suggests that the assimilation of AMSR-E surface soil moisture retrievals can add significant value to USDA root-zone predictions derived from real-time satellite precipitation products. This is the first time remotely sensed soil moisture observations have been operationally applied by the USDA crop forecasting system.

Relevance for future science and relationship to Decadal Survey: This work holds promise for applying remotely-sensed soil moisture observations for more accurate characterization of root-zone conditions at the regional scale, with possible application in crop yield forecasting and the monitoring of anomalous agro-meteorological events. Studies demonstrating the added benefit of using remotely sensed soil moisture observations as shown here are essential given the expected launch of several soil moisture-focused missions in the near future. For example the NASA Soil Moisture Active/Passive mission scheduled for launch before 2014 will both provide improved global soil moisture observations that can be used to further enhance the global characterization of agricultural drought conditions.

Hydrospheric and Biospheric Sciences Laboratory