¹ IIASA, Schlossplatz 1, A-2361 Laxenburg, Austria
² CGIAR Program on Climate Change, Agriculture and Food Security (CCAFS), ILRI, PO Box 30709, Nairobi 00100, Kenya
³ International Food Policy Research Institute, 2033 K Street, NW, Washington DC, 20006, USA
⁴ School of Nature Conservation, Beijing Forestry University, 35 Qinghua East Road, Beijing, China
⁵ Department of Geography, University of Maryland, 2181 LeFrak Hall, College Park, Maryland, 20742, USA
⁶ JRC, Via Fermi 2749, TP 266, Ispra, Italy
⁷ ILRI, PO Box 30709, Nairobi 00100, Kenya

Photo of cropland areas. Credit: dreamstime.com

This paper describes the start of a data sharing process to develop a consolidated community cropland map, which was initiated through a recent workshop on characterizing and validating global agricultural land cover. Participants from different organizations around the world were asked to contribute their various cropland maps prior to the workshop. Other data such as geo-tagged photos, in-situ data, classified satellite images and videos also were provided as part of this process. The data are now available online at agriculture.geo-wiki.org. This data sharing exercise, which has culminated in a new Sub-task on Agricultural Mapping as part of the GEO Agricultural Monitoring Task, will continue as an ongoing process and represents an effective model for how data sharing could be facilitated across the GEO community.

The Need for a Consolidated Community Cropland Product

Global land cover products provide important baseline information for resource assessments as well as inputs to a variety of land use models. Accurate estimates of cropland are crucial for determining land availability and for food security purposes, yet global land cover products do not provide consensus on the spatial distribution or total amount of cropland in production currently. For example, the global area under cropland is estimated to be between 1.22 to 1.71 billion hectares, at a 90 percent confidence level [1], which indicates a high uncertainty with a 40 percent difference between the upper and lower estimates. One source of information on croplands is the different medium- to coarse-resolution satellite-derived land cover datasets that are available, including the GLC-2000 [2], the MODIS v.5 land cover products [3] and GlobCover 2005/2009 [4] [5], which have classes for cultivated areas and mosaics of cropland and natural vegetation. Although, with the potential for being up-to-date, they were developed using different data and classification algorithms and do not have particularly high accuracy for estimation of cropland or crop types. Other global cropland products exist, some of which have been calibrated using cropland statistics, such as the M3-Cropland layer of agricultural lands for 2000 [1], the cropland probability layer from MODIS [6] and a global map of rain-fed cropland areas [7]. However, a product is needed with a minimum spatial resolution, and must be of sufficient quality to meet the needs of the food security and land use modeling communities, with an accuracy of at least 80 to 85 percent. It is thus a challenge for individuals and organizations working with these datasets to find a reliable picture of cultivation in one dataset. Africa often is the focus region for agricultural development due to consistent hunger and poverty issues, and weak local capacity results in huge data gaps in national statistics. A consistent and accurate measure of agricultural land derived from satellites could help fill these gaps partly, and provide valuable basic data for designing development programs as well as in monitoring and evaluating food security in the continent.

Home page for the Characterizing and Validating Global Land Cover Workshop with links to the presentations and final report

A Land Cover Workshop to Facilitate Data Sharing

To initiate this process of sharing data, with the aim of developing an enhanced cropland product, the Characterizing and Validating Global Agricultural Landcover workshop was held at the International Institute for Applied Systems Analysis (IIASA), from June 13-15. Data sharing was not limited to cropland maps but also included geo-tagged photos and other related in-situ data. The land cover workshop was hosted by IIASA and the CGIAR Consortium for Spatial Information, in close collaboration with the Group on Earth Observation (GEO), GOFC-GOLD and the Joint Research Centre of the European Commission (JRC). More than 70 international experts on remote sensing, land cover, land use, cropland and rangeland mapping, crop type mapping, area estimation and crowd-sourcing attended the workshop representing universities, national mapping agencies, research institutes and several international organizations including the Food and Agriculture Organization of the United Nations (FAO), the International Food Policy Research Institute (IFPRI), the International Livestock Research Institute (ILRI) and the International Crops Research Institute for the Semi-Arid-Tropics (ICRISAT). The emphasis was on improving African cropland maps, but the workshop discussions were broadened to a global scope and included participants from all major continents. Funding was provided by the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) and the JRC to allow participants from African countries and other developing nations to contribute their data and expertise during the workshop.

Screenshot of Home page for the Characterizing and Validating Global Land Cover Workshop with links to the presentations and final report

A number of key issues were raised during the two-day workshop from the plenary presentations and the breakout groups. Specific issues discussed include methods for cropland and crop-type mapping; crop area estimation; rangeland mapping, the value of crowd-sourcing for training, calibration and validation of land cover; integration of remote sensing and socio-economic data; and the availability of cropland data at the national and regional level. A key action from the workshop was to establish a Sub-Task on Agricultural Mapping under the GEO Agriculture Monitoring Task to be led by IIASA. A follow-up workshop on rangeland mapping and monitoring was also recommended in the framework of a new Sub-Task on Rangeland Productivity.

Group photo of participants who attended the Characterizing and Validating Global Land Cover Workshop

One of the first aims of the new Sub-Task on Agricultural Mapping will be to build a living, community-based consolidated cropland map. This initial product will provide the agricultural monitoring, food security and land use change communities with a better cropland product than currently exists, and will be freely available to researchers and the general public. All workshop participants agreed for their data contributions to be used in the development of an integrated cropland product. This new cropland extent map will integrate all the products contributed by workshop participants using a methodology similar to that reported in [10] at a 1-kilometer resolution. The map will also be calibrated with national and sub-national crop statistics as available. Validation will involve the wider community using crowd-sourced data and Google Earth. The first version of the map was published at the end of 2011 and is downloadable from the same site (agriculture.geo-wiki.org). Crop experts with knowledge of crop locations can use the new tools that have been developed to undertake qualitative validation with drawing tools and commenting facilities. These tools will be trialed as part of outreach activities in upcoming workshops.

The community-based consolidated cropland product will be updated when more crop information becomes available at the national and regional level. In this way, the product will become a living map, which will continue to improve with more contributions. The process of data sharing, which began with the workshop, should be seen as the start of an ongoing process that will continue through the new Sub-Task on Agricultural Mapping. The broader agricultural mapping community is encouraged to take part by providing more national and regional data on croplands, to help validate the product, and to improve our current knowledge of how much cropland there is, its location, and if data quality improves sufficiently or changes over time.

Hybrid cropland map of Africa produced by IIASA/IFPRI developed as part of (10)

The workshop was used as a vehicle to kick-start the data sharing process. A number of lessons have been learned that may be of value to those wanting to compile global datasets based on national and regional data products such as in socio-economic, geological, ecological and earth systems science in general:

• Target the right individuals: It is important to invite people who have data to contribute. Names were initially provided through the steering committee and through evolving contacts with potential participants but not all areas of Africa were covered. The benefits of sharing and contributing actively to the workshop and the community as a whole were used as arguments to persuade other organizations to contribute. This proved to be a very effective approach that gained momentum as the date of the workshop approached;

• Provide incentives: Two main types of incentives were provided to the participants. Payment or partial payment of travel expenses to attend the workshop from developing countries was offered on the conditions that the data were shared before the workshop. A second incentive was co-authorship on a scientific paper to all participants;

• Follow up after the workshop: Data contributions that were promised during the workshop were actively followed up by email along with new leads for sources of data.

The workshop was an intensive process that required more effort than a conventional workshop due to the exchange, reformatting, display and documentation of the datasets. However, the success of the workshop in terms of data sharing, networking and the initiation of an ongoing agricultural mapping process fully justifies the efforts.

If you have a cropland product you want to contribute to the consolidated community global cropland map, please contact us. The map will be registered in the GEOSS portal and become a citable product that will include your authorship details.

References

[1] N. Ramankutty, A. T. Evan, C. Monfreda, and J. A. Foley, “Farming the planet: 1. Geographic distribution of global agricultural lands in the year 2000”, Global Biogeochemical Cycles, vol. 22, GB1003, doi:10.1029/2007GB002952, 2008.

[2] E. Bartholomé and A. S. Belward, “GLC2000: A new approach to global land cover mapping from earth observation data”, International Journal of Remote Sensing, vol. 26(9), pp. 1959-1977, 2005.

[3] M. A. Friedl, D. Sulla-Menashe, B. Tan, A. Schneider, N. Ramankutty, A. Sibley and X. Huang, “MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets”, Remote Sensing of Environment, vol. 114(1), pp. 168-182, 2010.

[4] P. Bicheron, P. Defourny, C. Brockmann, L. Schouten, C. Vancutsem, M. Huc, S. Bontemps, M. Leroy, F. Achard, M. Herold, F. Ranera and O. Arino (2008). GlobCover: Products Description and Validation Report, 18, Toulouse, France. [Online]. Available: http://ionia1.esrin.esa.int/docs/GLOBCOVER_Products_Description_Validation_Report_I2.1.pdf

[5] S. Bontemps, P. Defourney, E. Van Bogaert, O. Arino, V. Kalogirou and J. R. Perez (2011) GLOBCOVER 2009: Products Description and Validation Report. [Online]. Available: http://ionia1.esrin.esa.int/docs/GLOBCOVER2009_Validation_Report_2.2.pdf.

[6] K. Pittman, M. C. Hansen, I. Becker-Reshef, P. V. Potapov, and C. O. Justice, “Estimating global cropland extent with multi-year MODIS data”, Remote Sensing, vol. 2, pp. 1844-1863, 2010.

[7] C.M. Biradar, P.S. Thenkabail, P. Noojipady, Y. Li, V. Dheeravath, H. Turral, M. Velpuri, M. K. Gumma, O. R. P. Gangalakunta, X. L. Cai, X. Xiao, M.A. Schull, R. D. Alankara, S. Gunasinghe and S. Mohideen, “A global map of rainfed cropland areas (GMRCA) at the end of the last millennium using remote sensing”, International Journal of Applied Earth Observation and Geoinformation, vol. 11, pp. 114-129, 2009.

[8] US Department of the Interior (2010). United States Launches New Global Initiative to Track Changes in Land Cover and Use. [Online]. Available: http://www.doi.gov/news/pressreleases/United-States-Launches-New-Global-Initiative-to-Track-Changes-in-Land-Cover-and-Use-Data-Sharing-Will-Assist-Land-Managers-Worldwide.cfm.

[9] European Space Agency (ESA) (2011). GMES Sentinels. [Online]. Available: http://www.esa.int/esaLP/SEM097EH1TF_LPgmes_0.html.

[10] S. Fritz, L. You, A. Bun, L. See, I. McCallum, C. Schill, C. Perger, J. Liu, M. Hansen and M. Obersteiner, “Cropland for sub-Saharan Africa: A synergistic approach using five land cover data sets”, Geophysical Research Letters, 38, L04404, doi:10.1029/2010GL046213, 2010.

[11] IIASA, “Characterizing and Validating Global Land Cover Workshop. IIASA 13-15 June 2011. Workshop Report”, http://www.iiasa.ac.at/Research/FOR/lc/IIASAWorkshopReportJun2011.pdf, 2011.

GIS in Education

For centuries, maps have stirred imaginations and inspired explorations of the unknown. Today, maps are used to help understand relationships across areas and regions. These spatial relationships are analyzed using digital maps within a Geographic Information Systems (GIS) environment. For the past 40 years, GIS has quietly transformed everyday decision making in academia, government, nonprofit, and in business through the manipulation of satellite imagery, maps, graphs, databases, and multimedia in a decision-making framework. Agriculture was one of the first fields to embrace GIS, applied to everything from precision agriculture to invasive weed eradication to sustainable practices.

In the classroom, GIS offers a powerful decision-making toolkit that helps students understand content in a variety of disciplines, such as geography, history, mathematics, language arts, environmental studies, chemistry, biology, and civics. GIS is used as an inquiry-driven, problem-solving, standards-based set of tasks that incorporates fieldwork and provides career pathways that are increasingly in demand. It helps students think critically, use real data, and connects them to their own community. It does so in informal, primary, secondary, and university settings and appeals to today’s visual learners. Geotechnologies, along with biotechnologies and nanotechnologies, are the three key skills and job markets identified by the US Department of Labor for the 21^st Century (Gewin 2004). The National Academy of Sciences (2005) identified GIS as being essential to K-12 learning because of its ability to foster spatial thinking (Gersmehl and Gersmehl 2006).

What is the relationship between birth rate and life expectancy? How does acid mine drainage in a mountain range affect water quality downstream? How will climate change affect global food production? With GIS, students explore the relationships between people, climate, land use, vegetation, river systems, aquifers, landforms, soils, natural hazards, and much more.

Using GIS provides a way of exploring not only a body of content knowledge, but provides a way of thinking about the world. When epidemiologists study the spread of diseases, scientists study climate change, or businesspersons determine where to locate a new retail establishment, they use spatial thinking and analysis. In each case, GIS provides critical tools for studying these issues and for solving very real problems on a daily basis.

GIS-based questions begin with the “whys of where” – why are cities, ecoregions, and earthquakes located where they are, and how are they affected by their proximity to nearby things and by invisible global interconnections and networks? After asking geographic questions, students acquire geographic resources and collect data online and from their own fieldwork. They analyze geographic data and discover relationships across time and space (Bednarz 2004). Geographic investigations are often value-laden and involve critical thinking skills. The following illustrates just one example of how GIS can be used in education.

Analyzing the Pattern of Four Crops in GIS

What did you have to eat today? Where was your food grown? Where was the cotton in your shirt cultivated? An increasing number of books and research initiatives are aimed at helping students to reconnect with the importance of agriculture. A new resource on the ArcLessons library (http://edcommunity.esri.com/arclessons/lesson.cfm?id=416) invites investigation of four different crops—soybeans (shown on the map below), wheat, corn (maize), and cotton – in a spatial context using Geographic Information Systems (GIS) technology.

Learners work through the following scenario: The US Department of Agriculture has heard about your extensive skills in GIS and spatial analysis, and has hired you to investigate the patterns of 4 crops as part of its National Crop Assessment Program (NCAP). They would like you to produce a report detailing the results of the following investigation: What are the cultural and physical geographic reasons for the spatial distribution, spatial patterns, and the amount of soybeans, cotton, wheat, and corn grown in the USA?

Learners conduct research on the origin of the four crops, examine the spatial distribution of those crops, and investigate the similarities and differences among them. They discover the most productive counties for each crop, and consider the proximity of major cities and the influence of climate on each. They determine which areas are planted with winter wheat versus spring wheat, based on the evidence. GIS skills developed include investigation of the “G” part of GIS (the maps) and the “I” part of GIS (the tables) through constructing queries, sorting, and creating summary statistics. They also select and identify data, create various thematic maps using different classification methods, including natural breaks, quantile, equal area, and standard deviation. Content emphases include national and global considerations of why different crops are grown, the influence on urban areas on crops, and the social and physical reasons for the spatial concentration or diffusion of the cultivation of those crops.

The resource includes not only the lesson, but the data needed to run the lesson. The data includes agricultural information at the county level from the US Census of Agriculture, climate data from the Natural Resources Conservation Service and the Spatial Climate Analysis Service at Oregon State University, and base layers (states, rivers, roads, lakes) from ESRI. The lesson contains 55 questions, but additional investigation can certainly be done, by students of secondary, university, and informal (such as 4-H) programs.

To find out more about the use of GIS in education, see http://edcommunity.esri.com, and the library of lessons on http://edcommunity.esri.com/arclessons.

References

Bednarz, Sarah W. 2004. Geographic information systems: A Tool to support geography and environmental education? GeoJournal 60: 191-199.

Gersmehl, Phil, & Gersmehl, Carol. 2006. Wanted: A concise list of neurologically defensible and assessable spatial thinking skills. Research in Geographic Education 8.

Gewin, Virginia. 2004. Mapping opportunities. Nature 427: 376-377.

National Academy of Sciences. 2006. Learning to Think Spatially—GIS as a Support System in the K-12 Curriculum. Washington DC: The National Academies Press, 313 p.

Benefits of GEOSS: A Panel Discussion

There are over six billion people on this planet, 193 countries and more than five thousand languages. No matter the nationality or language spoken or the location, everyone is inextricably linked and hence affected by global environmental change. To better understand our earth, thousands of artificial satellites have been launched, tens of thousands of surface stations have been deployed on the water and the land, and aircraft daily analyze the atmosphere and the land surface below them. This massive amount of data helps us in many ways, from weather prediction to disaster reduction. However, better usage of Earth environmental data across disciplinary, geographic and political boundaries is essential for addressing the environmental crises the world community faces. To coordinate the collection and analysis of Earth data, the intergovernmental Group on Earth Observations (GEO) is leading a worldwide effort to build a Global Earth Observation System of Systems (GEOSS) over a 10 year period. The following panel discussion by members who have been involved with the creation of GEOSS explains what GEOSS is and how it will affect us in our daily lives.

The panel members are:

Dr. Jose Achache, Director of the GEO Secretariat;
Dr. Jay Pearlman, IEEE representative for GEO and Chair of the IEEE Committee on Earth Observation
Helen Wood, Secretariat Director of the Ad Hoc GEO (predecessor to GEO)
Prof. Harold Annegarn University of Johannesburg, South Africa

Dr. Jose Achache, Director of the GEO Secretariat

Moderator: GEOSS is an effort to integrate the world’s Earth observing systems and make Earth information universally available for the benefit of society. GEOSS will impact our health, well being and our future. How can this very challenging goal be achieved? This is best done through cooperation on a global level, with scientists and engineers across all disciplines from industry, government and academia, working in partnership with leaders and decision makers.

Jose Achache: GEO is also an experience in creating a new kind of international intergovernmental organization. It’s not a UN organization. And the rules are very simple. In order to be a member, you have to accept the basic principles of GEO, which are the sharing of data and information and the sharing of observation systems. Once you are a member, the whole process of building GEOSS is a voluntary process.

Moderator: Using technology based on international standards, GEOSS builds upon existing national, regional, and international systems to provide comprehensive, coordinated Earth observations from thousands of instruments worldwide. The resulting system of cooperation provides an international mechanism to broaden the utility of these data into vital information for society.

Jay Pearlman: The challenge in creating GEOSS is the merging of many global systems that are built independently by governments and by organizations for their own purposes. The objective for GEOSS is the development of a unified framework so that diverse systems can work together and produce information that can be used for analysis of the earth as a whole, or for regions with issues such as tsunami response, precipitation and water, agriculture, as well as other areas directly impacting society.

Dr. Jay Pearlman, IEEE representative for GEO and Chair of the IEEE Committee on Earth Observation

Helen Wood: GEOSS is an interoperable framework to allow different systems to work together. This requires the creation of common standards and sharing agreements for both data and information. The systems themselves are built upon the technology that is typically addressed by standards – computing technology, communications technology, even at the level of sensor interface and communication formats.

Jose Achache: A big challenge to GEO and the success of GEOSS will be data sharing and data policies. Today, the basic principle is that members agree to share data openly and freely, but recognize national or commercial limitations.

Jay Pearlman: There are always issues when you have data of detailed Earth observations that people who want to use them for their own purposes may try to keep them to themselves. We have to overcome that. Two things have to happen. One is to build trust. So, if they release data, they understand the way it’s going to be handled. The second thing is understanding that the data are of benefit to everybody.

Harold Annegarn: I believe that information is one of the necessary key components to alleviating poverty. GEOSS, by providing information on the spatial distribution of resources, whether it is rainwater, warning of floods, ownership of land, urban infrastructure, or weather, provides information in a democratic way that all individuals can have access to and hence enables good decision-making. In the long run, the success of GEOSS depends on economic benefits.

Helen Wood: What are we trying to do? Are we trying to save lives? Are we trying to improve our agricultural output for a stronger economy? Are we trying to improve our water management to avoid or minimize the likelihood of drought and famine? It’s all of these that we’re trying to achieve.

Prof. Harold Annegarn University of Johannesburg, South Africa

Jay Pearlman: The vision of GEOSS is to provide a service on a global basis to all users. We need to reach out to the people who are making the decisions. These are people from Europe, and the United States, but also from Africa, South America, Asia, all parts of the world. These are people that don’t all have the same level of capability, the same education, or even the same tools.

Harold Annegarn: The importance of GEOSS in Africa is that it provides support for systems at a critical level that is currently lacking, namely we don’t have communities of practice in geospatial data and in remote sensing. We are looking to GEOSS as one international mechanism, as a system of cooperation in which this shortage could be addressed. Many developing countries lack infrastructure – transportation, electric power, and communications. Access to technology is a key issue.

Jay Pearlman: The speed of Internet access in the United States or Europe may not exist in all countries. And so when we reach out, we need to reach out in ways that people can understand from their own context.

Harold Annegarn: For instance, a farmer in Africa may need to know information on weather systems at some remote location. He doesn’t have a computer, he cannot afford a computer, but he is highly likely, nevertheless, to have a cellular telephone. So I believe that, at the lowest level of delivery, the cellular telephone networks in Africa will greatly facilitate and enhance the concept of GEOSS in being able to give even the common person, man or woman, access to advanced technology information.

Helen Wood, Secretariat Director of the Ad Hoc GEO (predecessor to GEO)

Moderator: The Global Earth Observation System of Systems may best be understood as a beginning. As it becomes operational, the next crucial steps involve making the system relevant to sustaining healthy ecosystems on Earth.

Helen Wood: It’s very clear that Earth observations alone are not going to solve our problems across societal benefit areas. If you haven’t planned within your community, whatever your community is, on how to interpret these data and what decisions need to be made as a result of these data, and then you’ve lost an enormously valuable opportunity.

Moderator: As GEOSS moves forward, the ultimate challenge will be making wise use of this data as decisions are made. By building trust and sharing information across international and disciplinary boundaries, the Global Earth Observation System of Systems can have a positive affect worldwide.

IEEE-TV GEOSS

Figure 1. Illustratration of airborne radio echo sounding showing the grayscale echogram and the a-scope plot of received signal power vs. depth.

The application of radio-echo sounding (RES) to thickness measurements of glacial and sheet ice has been demonstrated since the early 1960s. The concept for this approach can be traced to 1933 at Admiral Byrd’s base, Little America, Antarctica where the first indication that snow and ice are transparent to high frequency radio signal was observed. An investigation by U.S. Army researchers, prompted by pilot reports of the “uselessness” of radar altimeters over ice suggesting the transparency of polar ice and snow in the VHF and UHF bands, led Amory Waite and others to demonstrate that a radar altimeter (the SCR 718 operating at 440 MHz) could measure the thickness and other features of polar glaciers in 1957 (Waite and Schmidt, 1961; Robin, 1972 ). This observation resulted in one of the most important technical advances in glaciology, namely the development and wide application of radio-echo sounding systems.

Following Waite’s demonstration, Stan Evans at Cambridge University’s Scott Polar Research Institute (SPRI) developed the first of several VHF systems specifically for radio echo sounding in 1963. Within the next few years several other research groups began developing and using RES systems including the United States Army Electronics Laboratory (USAEL), the British Antarctic Survey, the Arctic and Antarctic Scientific Research Institute in Leningrad, the Geophysical and Polar Research Center (GPRC) at the University of Wisconsin, the U.S. Army Cold Regions Research Laboratory (CRREL), the Canadian Department of Energy, the Technical University of Denmark (TUD), Stanford Research Institute,and the Institut Geografii of the U.S.S.R. Akademiya Nauk (Evans, 1967; Gudmandsen, 1969; Evans and Smith, 1969; Weber and Andreiux, 1970; Robin, 1975a; Macheret and Zhuravlev, 1982). These systems, all short pulse-type radar systems with operating frequencies ranging from 30 MHz to 600 MHz, successfully sounded ice sheets, ice caps and glaciers (both temperate and polar) in Greenland and Antarctica. Both surface-based and airborne measurements were conducted. Thickness estimates from RES systems agree with those from seismic and gravity based estimates (Drewry, 1975). Reflections from internal layers have also been observed since the first RES observations and a variety of sources for these internal reflections have been identified including layers of liquid water (Bamber, 1987; Davis, Dean, and Xin, 1990), layering involving small permittivity changes due to changes in acidity (from large volcanic events) (Millar, 1981), changes in the size or shape of air bubbles within the ice (Ackley and Keliher, 1979), and variations in ice crystal orientation and density (Harrison, 1973). Layers produced by the small permittivity changes represent isocrones and are useful in the interpretation of climate information (Gudmandsen, 1975; Jacobel and Hodge, 1995). In addition to measuring ice thickness and layers, other features and characteristics have been observed such as regions of bottom melting and freezing (Neal, 1979), ice bottom sliding velocity (Doake 1975), glacier velocity (Doake, Gorman and Paterson, 1976), sub-ice lakes formed by pressure melting (Owsald, 1975) and bottom crevasses (Jezek, Bentley and Clough, 1979). Information on other ice parameters including signal absorption (Neal, 1976), signal fading patterns (Harrison, 1971; Berry, 1975), propagation velocity (Robin, 1975b), and birefringence (Bentley, 1975; Hargreaves, 1977; Woodruff and Doake, 1979) have been obtained from RES data.

In the next generation of RES systems we see more specialization. For example, to reduce the reflections from nearby walls in sounding valley glaciers, the system frequency is increased to improve system directivity. A 620-MHz system succeeded in sounding the Rusty Glacier in the Yukon Territory where a 35-MHz system and conventional seismic systems had previously been unsuccessful due to echo obscuration by the transmitted radio and seismic pulses and due to the proximity of the valley walls (Clarke and Goodman, 1975). Also, several low frequency RES systems were developed specifically for sounding temperate glaciers where absorption losses are significant due to higher ice temperatures and the presence of liquid water. Systems with frequencies ranging from 1 to 32 MHz have successfully sounded temperate glaciers by numerous researchers (Strangway et al., 1974; Watts and England, 1976; Bjornsson et al., 1977; Sverrisson, Johannesson and Bjornsson, 1980; Watts and Wright, 1981).

Techniques for determining additional information on both the geography and roughness of subglacial terrain have also developed. Significant changes in the polarization of the returned echoes from land ice and an ice shelf indicate that tidal strain on crystal orientation at the hinge zone results in a large change in birefringence of the ice, indicating that polarization could be used to distinguish floating from grounded ice (Woodruff and Doake, 1979). A method for continuously monitoring the returned echo power from a sub-glacial ice/rock or ice/water interface was also developed for detecting changes in the reflection coefficient, the absorption properties of the ice, as well as identifying the changing nature of the basal reflection properties along a flow line (Neal, 1976). As part of a physics experiment investigating a theoretical fifth force affecting Newton’s inverse square law for gravitation, an extensive series of RES measurements were made around the DYE-3 complex in southern Greenland. Sounding data were collected along 124 radial lines, each about 5 km in length producing a map of bedrock topography with an uncertainty of less than 5 m over most of the survey area (Fisher et al., 1989). A precise grid pattern was flown over the summit region of Greenland with the TUD RES system to obtain both ice surface and ice bottom topography to accuracies of ±6 m and ±50 to 125 m, depending on the bottom roughness (Hodge et al., 1990). Synthetic-aperture radar (SAR) techniques were applied to RES data by researchers from the British Antarctic Survey to produce two dimensional maps of echo strength showing the grounding line of a glacier in the Antarctic Peninsula (Musil and Doake, 1987).

Figure 2. (top) Ground-based wideband SAR/depth-sounder system deployed July 2005 at Summit, Greenland. Leading sled has the transmit antennas while the trailing sled has the receive antennas. (Vehicle-mounted antennas are not for the SAR.) (bottom) SAR mosaic of the ice-bed surface images produced from data collected along eight east-west traverses. The origin is at Summit Camp, Greenland (72.5783º N and 38.4596º W).

As enabling technologies emerged, RES systems became more capable. Digital data acquisition, signal processing and recording have significantly improved system capabilities not the least of which is dynamic range (Goodman, 1975; Sivaprasad, 1978; Wright, Bradley, and Hodge, 1989). Walford and others developed a coherent RES system permitting the measurement of both the amplitude and phase of the received (Walford, Holdorf and Oakberg, 1977; Walford and Harper, 1981). The Coherent Antarctic Radar depth sounder (CARDS), the first RES system designed completely with solid-state, computerized components, which is coherent and employs pulse compression to reduce peak transmit power requirements, was field tested in Antarctica by researchers at the University of Kansas (Raju, Xin and Moore, 1990). Subsequent upgrades to this system incorporate microwave monolithic integrated circuits (MMICs) (Gogineni, Legarsky, and Thomas, 1998), alternating transmit waveforms, and multiple independent receive channels (Lohoefener, 2006). Figure 1 illustrates the concept of airborne radio echo sounding and sample data products. The advent of systems with multiple receive antennas (each with a dedicated receive channel and digitizer) enable digital beam steering, null steering (for clutter suppression), and interferometric processing. Another such system is the ground-based, 8-channel SAR for bed imaging first applied near Summit, Greenland to produce maps of basal backscatter over an area extending 6 km x 28 km with VV polarization and spatial resolution of 30 m with 15 looks to reduce speckle (Paden et al., 2004; Allen et al., 2008), as shown in Figure 2. Interferometric processing of data from this 8-channel system also permitted discrimination of off-nadir scattering sources providing further insight regarding basal topography.

Beginning in the decade of the 1990s, a new generation of specialized impulsive RES systems emerged to address specific glaciological questions. A miniature impulse RES system capable of operating from 1 to 200 MHz was developed and field tested by Canadian researchers for sounding glaciers and ice caps (Narod and Clarke, 1994). Similarly the British Antarctic Survey (King, Woodward, and Smith, 2007) has used the Deep Look Radio Echo Sounder (DELORES) system (1 to 20 MHz tunable) for sounding the 3-km thick Rutford Ice Stream in West Antarctica. Researchers from the University of Alaska at Fairbanks have used a 1.7 MHz impulse-type RES system to measure a cross-section of Taku Glacier, Alaska and estimate the mass-balance flux (Nolan et al., 1995). University of Washington researchers probed the bed reflectivity in an active ice stream in West Antarctica using a 2-MHz impulsive system (Raymond et al., 2006). A 3-MHz impulsive radar was used to collect 1850 km of RES data in 2001 along the U.S. leg of the International Trans-Antarctic Scientific Expedition traverse (US-ITASE) (Welch and Jacobel, 2003). Backpack-portable impulsive radar operating with a 5-MHz center frequency was used to map 300-m deep topography in temperate valley glaciers (Matsuoka, Saito, and Naruse, 2004). Researchers from the University of Munster developed and fielded two ground-based 35 MHz RES systems, the first a single pulse system intended to measure the reflections from internal layering with high resolution while the second system uses a burst transmitter designed to penetrate the sheet ice and observe the underlying bedrock (Hempel and Thyssen, 1992).

Commercial ground penetrating radars (GPRs) have also been used for ice probing. Units from Geophysical Survey Systems, Inc. (GSSI) have been used by the U.S. Army Cold Regions Research and Engineering Laboratory (CRREL) to study the firn in Alaska at 135 MHz (Arcone, 2002), in Antarctica at 400 MHz (Arcone, Spikes, and Hamilton, 2005) and at 80 MHz (Clarke et al. 2000), and in Svalbard at 900 MHz (Wadham et al., 2006). A GSSI system was also used to map ground ice in Canada’s Northwest Territory at 200 and 400 MHz (De Pascale, Pollard, and Williams, 2008). Products from Sensors and Software Inc. have been operated at 100 MHz to sound the internal and basal characteristics of Svalbard glaciers (Murray et al., 2000) and at 100 and 200 MHz to study crevasse formation in Antarctica (Nath and Vaughan, 2003). Equipment from Mala Geoscience was used in Svalbard at 50 MHz to map snow accumulation variability on the Nordenskjöldbreen glacier (Palli et al., 2002) and at 50 and 200 MHz to probe the firn-ice transition-zone (Palli, Moore, and Rolstad, 2003), and at the Amundsenisen plateau in Antarctica at 200 and 250 MHz to map variations in the ice sheet’s top 100 m (Rotschky et al. 2004).

Network analyzers have been configured to operate as continuous-wave, step-frequency radars by various researchers to study ice sheets and glaciers. Norwegian researchers have applied synthetic-aperture radar techniques to image the internal structure of the subpolar glacier Slakbreen in Spitsbergen, Svalbard operating from 5 to 20 MHz and from 320 to 370 MHz (Hamran and Aarholt, 1993) and to characterize the Riiser–Larsenisen ice shelf in Antarctica from 10 to 30 MHz, 155 to 170 MHz, and 330 to 360 MHz (Hamran et al., 1998). British researchers used a vector network analyzer configured as a polarimetric radar was used to study the birefringence of Antarctic ice shelves from 200 to 400 MHz (Doake et al. 2003) and to study the basal reflection at the grounding line of the Rutford Ice Stream, Antarctica from 270 to 321 MHz (Jenkins et al., 2006). Researchers from Sweden (Pettersson, Jansson, and Holmlund, 2003) studied the cold temperate transition on Storglaciären in Sweden from 320 to 360 MHz and 700 to 900 MHz. A bistatic basal sounder was fashioned around a network analyzer to characterize the basal scattering and ice attenuation parameters over the 110 to 500 MHz frequency range (Paden et al., 2005).

FM-CW (frequency modulated, continuous wave) radars have been designed specifically for cryospheric research. CRREL researchers used ground-based C-band (3.95 to 5.89 GHz) and X-band (8.2 to 12.4 GHz) FM-CW radars to profile frozen lakes in Alaska (Arcone, Yankielun, and Chacho, 1997) and an airborne L-band (1.12 to 1.76 GHz) FM-CW radar to sound the temperate Black Rapids Glacier in Alaska from a helicopter (Arcone and Yankielun, 2000). Using airborne UHF (600 to 900 MHz) FM-CW radar researchers at Kansas University have mapped the accumulation rate variability from a NASA P-3 aircraft over the Greenland ice sheet (Kanagaratnam et al., 2004).

Antenna technologies used for RES have changed as well over the years. For ground-based applications, resistively-loaded, half-wave antennas have been used (Watts and Wright, 1981). The stepped FM system (Strangway et al., 1974) that operates at discrete frequencies between 1 and 32 MHz uses two orthogonal, horizontal electric dipoles for transmit and a set of three orthogonal receiving coils on a vehicle. For airborne applications, two resistively loaded wire antennas have been towed from the aircraft wing tips, one for transmit, the other for receive (Watts and Wright, 1981).

A variety of antennas have been used with the 35-MHz SPRI system. For ground-based applications, a simple trailing wire (end-fed) has been towed behind the vehicle has been used resulting in a bandwidth of 6 MHz, a rigid, two-conductor half-wave dipole arrangement has been used yielding a bandwidth of 10 MHz, and a loaded dipole with a tank circuit balun has been used (bandwidth not reported), primarily as it preserves the pulse shape (Robin, Evans, and Bailey, 1969). The 35-MHz TUD system has used two folded dipoles about 4-m long suspended about 1 m beneath each wing, one for transmit and the other for receive (Gudmandsen, 1969).

Table 1 – Parameters of time-domain radio depth sounder systems reported in the literature (excludes GPR, impulse, and FM-CW systems)

At 60 MHz, Danish researchers at TUD use linear array of four dipoles suspended a quarter wavelength beneath the wing resulting in a narrow 22º beamwidth in the cross-track plane and 110º in the along-track plane (Gudmandsen, 1976; Drewry and Meldrum, 1978). An antenna system composed of two half-wave dipoles mounted beneath opposite wings (one for transmit, the other for receive) has also been used at 60 MHz. By using the wings as reflectors, a gain of about 8 dB is obtained (Gorman and Cooper, 1987).

Finally, the value of radio-echo sounding data is reduced without accurate knowledge of where the data were collected. Therefore, navigation and position measurement techniques are of great significance to RES systems. When RES systems were first field tested, navigation relied on observation by system operators, which is significantly hampered in regions without discernible landmarks. In 1967 navigation records including a galvanometer showing aircraft heading, air temperature, static pressure and airspeed were recorded along with airborne radio echo sounding data to obtain position information. By 1971 an inertial navigation system (INS), the Litton 51C, was used to annotate the RES data with position information (Evans, Drewry and Robin, 1972; Robin, 1975a). Position measurement systems employing microwave signals and transponders at known geographic points have been used to obtain position data within limited areas with an accuracy of ±10 m in ground based RES experiments (Goodman, 1975). By the late 1970s, LORAN and satellite navigation were available providing less accurate yet affordable position information on a global basis suitable for RES applications (Sverrisson, Johannesson and Bjornsson, 1980). A Doppler navigator linked through a Tactical Air Navigation System (TANS) navigation computer was used in 1983 to provide a continuous read-out of latitude and longitude with sub-kilometer accuracy (Gorman and Cooper, 1987; Drewry and Liestol, 1985). Currently differential GPS data is routinely collected with RES measurements reducing positional uncertainties to ±2 to 5 m (Nolan et al., 1995) and with post-processing to within ±10 cm (Krabill et al., 1995).

Table 1 is an updated summary of the characteristics of the various time-domain radar sets first tabulated by Goodman (Goodman, 1975) and later augmented (Gogineni et al., 1998).

This work was supported by the National Science Foundation (ANT-0424589 & OPP0122520), NASA (NAG5-12659), the Kansas Technology Enterprise Corporation (KTEC), and the University of Kansas.

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Introduction

Figure 1. The coupled Sun-Earth system – a schematic. Primary regions of the Sun-Earth system include the Sun, which is the system’s source of energy (shown in yellow), the heliosphere and space environment near Earth (shown in green) and the Earth’s atmosphere and surface (shown in blue). Arrows indicate the flow of energy among regions. Galactic cosmic rays originate beyond the Sun-Earth system and are modulated by it.

Our planet inhabits the neighborhood of the Sun, a somewhat capricious, middle-aged star. The Sun’s output – in the form of energetic particles (mainly electrons and protons) and magnetic fields – is sufficiently stable to maintain a terrestrial environment that supports human life and enables space-based technologies. But the energy output is not constant. A dynamo located beneath the Sun’s visible surface causes solar activity to wax and wane with a cycle of roughly 11 years. Solar activity – a measure of the amount of magnetic flux that erupts onto the Sun’s surface and into its atmosphere, driven by the dynamo below – changes the Sun’s energy outputs. Even the total electromagnetic output, called the total solar irradiance (TSI, in units of Watt per meter² at a distance of one Astronomical Unit), is higher during peaks of the solar activity cycle, warming Earth’s surface slightly. In atmospheric layers at increasingly higher altitudes above the Earth’s surface, the terrestrial impact of the solar cycle grows dramatically, dominating the variations in temperature and density beyond 100 km. In addition, erratic solar eruptions expel particles and magnetic fields into the solar wind, further roiling the Earth’s outer atmosphere and the space environment near earth.

With the advent of the space era – which commenced barely fifty years ago – fledgling, intermittent observations of the Sun and Earth have evolved to databases of multiple solar and terrestrial parameters with unprecedented spatial and temporal coverage. These databases provide a phenomenal opportunity to characterize the changing Sun and the changing Earth, and to explore, understand and specify the complex coupled system (Figure 1) in which we live.

Faces of the Sun

Figure 2. Solar phenomena and time scales of solar variability.Images of the Sun made in different wavelengths of radiation reveal myriad phenomena including bright, coronal loops (upper left), flares and active regions (upper middle, movie), coronal mass ejections (lower left) and dark sunspots (lower middle, movie). Flares and coronal mass ejections produce energy output variations on time scales of minutes to hours. Rotation of sunspots, faculae and plage across the Sun’s face generate energy output variations on times scales of days and weeks. A dynamo inside the Sun (upper right), located at the bottom of the convention zone, generates the 11-year activity cycle, shown in sunspot numbers (right) and in coronal evolution seen in soft X-ray images made from the Yohkoh spacecraft (lower right). Images and Movies: NASA SOHO & Trace, ISAS Yohkoh

Were the Sun the pristine, blemish-free orb that Galileo‘s seventeenth century Catholic Church decreed, solar energy output would vary little on time scales of days to decades. However, the Sun’s face exhibits myriad features (Figure 2) that are distinguished from the background “quiet” Sun by their altered levels of magnetic field strength. Magnetic features and associated phenomena emerge, evolve and decay with increased occurrence and size when the Sun is active. Observations at different wavelengths reveal aspects of the different features. For example, compact black spots, sunspots, are apparent in visible light on the nominal “surface” of the Sun (defined as the gaseous shell of the Sun’s atmosphere which emits visible photons). Large sunspots, many times the size of the Earth, may cover a few percent of the Sun’s disk. Multiple clumps of bright regions, called faculae (Latin for “torch”), often surround sunspots. When viewed in ultraviolet light that is emitted from higher in the Sun’s atmosphere, above the visible surface, larger and brighter plage are seen to overlie the faculae. In the outer layers of the Sun’s atmosphere – the corona – larger still, and even brighter, regions of strong magnetic flux sometimes cover a significant fraction of the Sun’s face. Also populating the corona are extended dark patches, called coronal holes, where mass from the solar atmosphere flows into the surrounding heliosphere (the sphere of “helio”, Greek for “Sun”), producing the solar wind that envelops the Earth and planets.

Sunspots have been recorded since their telescopic discovery in the early 1600s, and variations in sunspot numbers (Figure 2) characterize the archetypal solar activity cycle. Sunspots, faculae, plages and coronal holes are all generated by protrusions of magnetic field into the Sun’s atmosphere, driven by the subsurface dynamo. Because electromagnetic radiation from these magnetic features is altered relative to the quiet Sun, their occurrence modulates, in distinct ways, the Sun’s net outward flowing energy. Thus, superimposed on the dominant 11-year activity cycle (Figure 2) are semi-regular fluctuations on time scales of days to weeks, produced by the Sun’s rotation on its axis and the evolution of magnetic regions. On shorter time scales still, active region magnetic fields can reconnect, producing large fluxes of electromagnetic radiation at very high energies, called flares. The rapid and unpredictable eruptions that produce flares also spew massive amounts of matter into the solar wind, called coronal mass ejections, which accelerate particles and tangle fields throughout the heliospshere.

Figure 3. Variations in total solar irradiance.Variations in the total solar irradiance (upper left) are currently made by the Total Irradiance Monitor (TIM) on the Solar Radiation and Climate (SORCE) spacecraft (lower right), to be followed by the Glory mission in solar cycle 24. The primary sources of the observed total solar irradiance variations are bright faculae and dark sunspots whose contributions are shown over the solar cycle (lower left) and during a short-term period when large sunspots on the disk produced a significant irradiance reduction (upper right). Images: NASA SOHO, BBSO, LASP

Since the space era coincides approximately with the Modern Maximum of high solar activity (characterized by high sunspot numbers after ∼1950 in Figure 2), most of our observational knowledge of the Sun and its variations pertains to overall high activity levels. The dearth of sunspots during the Maunder Minimum (from 1645 to 1715 in Figure 2) indicates an absence of magnetic fields presumably accompanied by reduced energy outputs.

Bathed in Light

Photons carry the bulk of the Sun’s energy directly to Earth, depositing most of it – at near-UV, visible and IR wavelengths – near the surface. Although historically referred to as the solar “constant”, the total solar irradiance varies because dark sunspots and bright faculae on the Sun’s disk reduce and enhance, respectively, the net photon emission (Figure 3). There is an overall increase in total solar irradiance during the solar cycle because enhanced emission in bright faculae more than offsets (by a factor of ∼2) the decreased emission in sunspots. However, when solar rotation carries large sunspots onto the face of the Sun visible at the Earth, short-term sunspot dimming can exceed facular brightening by as much as a factor of 5 (Figure 3). This produces significant depletions in radiant energy that are seen in Figure 3 superimposed on the 11-year solar irradiance cycle.

Solar photons at wavelengths less than 300 nm deposit their energies at increasingly higher altitudes in the Earth’s atmosphere (Figure 4); UV radiation is absorbed in the stratosphere (~25 to 50 km above the surface) and the extreme UV (EUV) radiation in the thermosphere and ionosphere (100 to 500 km above the surface). Electromagnetic radiation at the shorter (EUV) wavelengths of the solar spectrum are formed higher in the solar atmosphere, absorbed higher in the Earth’s atmosphere, and vary much more than does radiation in the visible spectrum. For example, solar cycle changes in EUV radiation exceed 100%, compared with the 0.1% cycle of the visible spectrum. This is because plage emission at EUV wavelengths is an order of magnitude (or more) brighter than the background Sun, whereas the contrast of the underlying visible light faculae that alter the total irradiance is only a few percent.

Figure 4. Deposition of solar radiation in the Earth’s atmosphere.Shown on the left, by the red curve, is the approximate altitude at which solar electromagnetic radiation at different wavelengths is absorbed in the Earth’s atmosphere. Primary atmospheric “spheres” – the troposphere, stratosphere and thermosphere – are identified. On the right are the average temperature (T) and density (ρ) profiles of the atmosphere, with the arrows indicating approximately where the energy in specific bands is deposited. Radiation at wavelengths longer than ∼300 nm reaches the surface and troposphere. Solar energy heating warms the atmosphere, reversing the cooling trend with altitude in the troposphere and producing the hot “thermo” sphere. Images: NRL, NASA, Google Image

There is much debate about how the Sun’s changing energy outputs influence Earth’s climate, including speculation that solar variations, rather than anthropogenic gases, have caused significant recent global warming. The Sun’s activity cycle does influence the Earth’s surface temperature, but the changes are modest and difficult to detect. The solar signal must be isolated from other climatic influences that operate simultaneously. The El Niño Southern Oscillation (ENSO, an atmospheric-ocean coupling in the tropical Pacific), volcanic aerosols, increasing concentrations of greenhouse gases (GHGs) and industrial aerosols, and land use changes all affect the surface and troposphere, in different ways, in different amounts, in different geographical regions. When these changes are accounted for in the surface temperature record, a solar-driven global temperature cycle of about 0.1 K (from activity min to max) is identified (Figure 5). For comparison, the globe warmed 0.2 K during the 1997 “super” ENSO event, cooled 0.3 K as a result of the Pinatubo volcano, and has warmed 0.4 K since 1980 in response to changes in anthropogenic gas concentrations. Recent global warming is therefore primarily anthropogenic, rather than solar, in origin.

Temperatures in the Earth’s atmosphere respond to the Sun’s changing energy outputs with increasing amplitude at increasing height above the surface (Figure 5). Anthropogenic influences significantly exceed the solar cycle signal near the surface, but they are comparable in the stratosphere. In the thermosphere-ionosphere, for example near 450 km, the solar cycle global temperature increase of 400 K overwhelms the 3 K long-term anthropogenic cooling in recent decades. Here, the deposition of solar EUV radiation (Figure 4) causes the atmosphere to be much hotter (∼1,000 K) than at the surface (288 K), and solar energy output changes are the undisputed orchestrator of temperature and density variations. For example, solar activity is the dominant cause of total electron content (TEC) variability, which alters the ionosphere transmission and refraction of radio waves, affecting systems such as the Global Positioning System (GPS) which utilize these frequencies for communication and navigation around the globe.

Figure 5. Global temperature responses to solar, anthropogenic, volcanic and ENSO influences. Compared are the natural and anthropogenic sources of global temperature change at the Earth’s surface (left)) and in the lower stratosphere (right), obtained by multiple regression. The observed global surface and atmosphere temperature changes since 1980 (shown as symbols in the two bottom panels) are modeled by linear combinations of four different signals; solar irradiance and anthropogenic gases (greenhouse gases, GHG, and chlorofluorocarbons, CFC, upper panels) the El Niño Southern Oscillation (ENSO) and volcanic aerosols (middle panels). Images: NASA, Google Image.

The magnitude and terrestrial consequences of long-term solar activity – especially on Earth’s climate – is uncertain because of the lack of comprehensive observations prior to the space era. Four of the largest amplitude sunspot cycles have occurred in the space era but during the Maunder Minimum sunspots were absent from the Sun’s disk for extended periods, the cycle perhaps interrupted by a lethargic dynamo. Presumably solar energy outputs were reduced, but the actual levels of brightness and solar wind speed is speculative. One approach for estimating past changes in the Sun’s energy output is to model the dynamo-driven transport of surface magnetic flux (by diffusion, meridional flow and differential rotation as the Sun rotates on its axis) using sunspots as indicators of emerging magnetic fields. Long-term evolution of the total magnetic flux is then assumed to produce irradiance changes (Figure 6). When simulated in this way, solar irradiance variations account for less than 10% of the total global warming in the past century.

Buffeted by Wind

A steady stream of mass – mainly protons – flows from coronal holes, where magnetic fields in the Sun’s outer atmosphere extend into the heliosphere, rather than being anchored, loop-like, at the Sun’s surface. With a speed of about 450 km per sec, this steady solar wind transports particles and magnetic fields to Earth (150 million km downstream) in a few days. At Earth, semi-regular, recurring wind streams occur as dark coronal holes rotate across the Sun’s face.

Impinging upon the Earth, the flowing solar wind contorts and reshapes the geomagnetic field into an elongated teardrop, with an upwind bow shock at about 10 R_E and a much longer, elongated tail that extends some 100 R_E downwind. The magnetosphere envelops the plasmasphere (a region of low density ions where magnetic field lines are anchored at Earth), which in turn surrounds – and protects – the Earth’s outer atmosphere and ionosphere.

Figure 6. Estimating long-term changes in solar activity and irradiance. A sub-surface dynamo (upper image, far left) causes magnetic fields to penetrate the solar surface. The pattern of magnetic flux (upper image, second left) evolves continually as surface magnetic flux is transported by differential rotation, meridional flow and diffusion (three images, upper right). These transport processes alter the amount of closed and open flux, which in turn alter solar irradiance and the solar wind, respectively. Flux transport model calculations simulate long-term solar energy output changes, and suggest that solar irradiance has increased 0.05% since the seventeenth century Maunder Minimum (lower graph). Images: NASA, Y.-M. Wang.

Magnetic fields on the Sun can reconnect abruptly, propelling explosions of photons and mass towards the Earth (Figure 7). Flare photons arrive almost immediately (in 8 minutes), followed by highly energetic solar particles (within hours) then, over a few days, by the ejected coronal mass. As coronal mass ejections propagate through the heliosphere, solar wind speeds can increase to over 700 km per sec.

The bow shock is first to feel the brunt of the agitated solar wind impact as heliospheric and terrestrial fields intermix and reconnect, producing at times dramatic reverberations that reconfigure the entire magnetosphere (Figure 8, upper left image). Particles that were trapped in belts of magnetic field are funneled into the Earth’s polar regions where the fields converge, depositing their energy by collisions with oxygen and nitrogen gases in the Earth’s lower thermosphere (near 100 km altitude). Auroras are visible evidence of the resultant resistive heating. The heated gases expand upward and outward, fountain-like, transporting energy and mass over the poles to the night side of the Earth, and eventually to mid latitudes around the globe, further carried by the Earth’s rotation (Figure 8, middle). Dramatic temperature and composition changes ensue, including rapid alterations of ionospheric electron densities that impact communication and navigation in sudden (and unpredictable) ways, and neutral densities (Figure 8, lower left) that impose severe drag (acceleration) on spacecraft in low-earth orbit.

Geomagnetic storm impacts are muted in the stratosphere and at lower altitudes because the increasingly dense atmosphere (at decreasing altitudes, Figure 4, right panel) strongly attenuates all but the most energetic particles. Nevertheless, surges of solar energetic particles – high energy protons – can penetrate deep into the Earth’s atmosphere and alter ozone chemistry. Galactic cosmic rays also penetrate to the lower atmosphere; their flux attenuated by the intervening heliosphere such that high solar activity diminishes the flux and reduces the content of ¹⁴C in tree rings and ¹⁰Be in ice cores. Thus these cosmogenic isotope archives preserve in terrestrial format a history of solar activity over past millennia, specifically the changes in the open magnetic flux that modulate the helisopshere.

Integrating the Sun-Earth System

Figure 7. Anatomy of the erupting Sun-Earth system. During the “Halloween” storm on 28 Oct 2003, a large active region, observed as a sunspot in a white light image made at the Big Bear Solar Observatory (upper left image) and plage brightening in EUV emission by the Extreme ultraviolet Imaging Telescope(EIT) on the Solar and Heliospheric Observatory (SOHO) (upper right image), erupted when it was near the center of the solar disk. Detectors on the Geostationary Operational Environmental Satellites (GOES) recorded an X-class flare (upper right plot). The Large Angle and Spectrometric Coronagraph Experiment (LASCO) on SOHO observed an associated coronal mass ejection (lower left image). Many hours later, the energetic particles associated with the eruption reached the LASCO detector at L1, upstream from Earth, and saturated its detectors (lower right image). GOES detectors recorded the proton fluxes (lower right plot), which were reported by the National Weather Service Space Weather Prediction Center. Images: BBSO, NASA/SOHO, NOA/SWPC.

The Sun-Earth system is huge, vastly unobserved, and not yet modeled as a fully-integrated regime. Of the entire volume from the Earth’s surface to 20,000 km (where GPS spacecraft orbit) more than 99% resides above 50 km. Solar-driven impacts are qualitatively recognized throughout the domain but quantitative representation is poor, in part because of the lack of regular, global observations in the upper atmosphere and space environment, and the daunting challenge of observing with adequate spatial and temporal coverage.

Physical models are therefore crucial to capture the entire system. Emerging is a next generation of terrestrial system models that encapsulates new understanding and assimilates observations. General circulation climate models are extending upwards from the surface. Rather than using a few token “buffer” layers in the stratosphere to represent the upper boundary of the climate system, the models are reaching to 100 km, with many stratosphere and mesospheric layers, interactive ozone chemistry and coupled chemical, dynamical and radiative schemes. For the space environment, geospace system models are being constructed by coupling models of individual regimes, integrating the thermosphere and ionosphere electrodynamically with the magnetosphere and heliosphere (Figure 9) and ingesting state-of-the-art solar drivers, including irradiance at all wavelengths, solar wind speed, density (and temperature) and magnetic field. Above about 50 km, however, current knowledge of dynamical motions, vertical interactions and system coupling mechanisms is fledgling compared with knowledge of the atmosphere and surface below. Whereas general circulation climate models have been under development for almost 50 years (since the 1960s) the first coupled thermosphere-ionosphere–magnetosphere model was attempted only in 2001.

Figure 8. Terrestrial impact of a solar-geomagnetic storm. The upper left image depicts a coronal mass ejection from the sun impinging on the magnetosphere, allowing trapped particles to spill into the thermosphere and ionosphere, producing a geomagnetic storm and a bright aurora. The image in the middle illustrates the consequences: Heating by precipitating particles at high latitudes produces upward mass flow over polar regions, and downwelling equatorial winds, which the Earth’s rotation transports to the day (sunlit) side, producing large scale changes in temperature and composition. Thermopsheric densities can change dramatically in response to such solar-terrestrial eruptions. In the bottom panel, the solar-terrestrial changes during the Halloween geomagnetic storm are seen to be superimposed on the more regular changes associated with the EUV photon variations during solar rotation. Image: R. Meier, NASA

Ultimately, assimilation of comprehensive observations will advance models throughout the entire Sun-Earth system, not just in the troposphere – as is done today. A future challenge is to secure the needed observations to constrain the models by developing the capability to image the geospace environment on large spatial scales, not just in selected domains, with adequate cadence to properly capture the space-time variations so as to extract geophysical parameters relevant to the models. Seamless specification and forecasting of the entire, expanded terrestrial system and its solar-driven fluctuations will then ensue.

Judith Lean
Space Science Division http://spacescience.nrl.navy.mil/
Naval Research Laboratory www.nrl.navy.mil
Washington DC 20375

Acknowledgements: Funded by NASA and ONR. Much appreciated and valued collaborations with David Rind, Mike Picone, Yi-Ming Wang, Joe Huba, Robert Meier and others at NRL, LASP and elsewhere.

Figure 9. Components of an Integrated Sun-Earth System Model.Components of the Sun-Earth system are integrated using fully-coupled physical models, enabling simulations of environmental change from the Sun to the Earth. The models are available at Community Coordinated Modeling Center (CCMC).

IEEE President and CEO, Lewis Terman

One of my major areas of focus this year is the application of engineering, science, and technology to societal problems. This is something the IEEE and the broader technical and engineering community must be concerned about, and it’s part of the recently adopted IEEE Envisioned Future strategy platform, which recognizes that by addressing societal issues, the IEEE can affect global prosperity and the quality of life. It is an area of great opportunity.

Historically, the IEEE and the technical community have focused primarily on advancing technology and applying it to the development of products. The focus of Envisioned Future is not on technology but on the critical needs of society. Any list of major problems during the next 50 years would include generating and conserving energy, eliminating pollution, ensuring safe drinking water and a safe global food supply, protecting the environment, improving education, eliminating poverty and disease, and addressing climate change. Solving these problems will require multiple technologies and cross-disciplinary approaches with which the IEEE is very familiar.

Given the IEEE’s technical scope and global presence, our 375,000 members have a tremendous opportunity to contribute. In fact, our members are already involved in a number of such projects. For example, the IEEE Committee on Earth Observation has since 2005 been involved in the International Group on Earth Observations and its effort to create a Global Earth Observation System of Systems. Through GEOSS, data obtained from all sources will be used to help create accurate models of Earth’s environments. Understanding what is happening will enable informed decisions that should reduce the impact of natural disasters, promote better health, improve weather forecasting, and protect natural resources for the sustainability of society.

Another effort is the IEEE partnership with the United Nations Foundation, through which members will help solve problems in such areas as health care and disaster response, mitigation, and recovery. Of course, other organizations have been applying technology to address such issues. Two examples of nonprofit organizations with which I am familiar are at MIT and the University of California at Berkeley. At MIT, the D-Lab and Edgerton Center are leaders in creating elegantly simple technical solutions for developing countries to such problems as food refrigeration, water testing and quality, and avoiding deforestation by turning waste materials into cooking fuel. Students from the university are accomplishing much by working at the local level in areas including Africa, Central America, and India. UC Berkeley’s “Center for Information Technology Research in the Interest of Science” has been using information technology to solve problems in health care, energy, the environment, and transportation. The effort involves hundreds of faculty and thousands of students, many of whom are IEEE members.

Individual IEEE members around the world are also involved in solving societal issues, often at the
local level. The projects involve mentoring teachers, search-and-rescue robots, a fiber-optic network, and low-cost medical technology.

These are just a few examples of what members can do, and as President of the IEEE, I am encouraging members and their colleagues everywhere to identify opportunities where they can become involved and make a difference. For the IEEE to help solve these problems requires sharing best practices and carefully coordinating what we do so that our financial and human resources are used effectively. This is a bold new direction for the IEEE and the technical community at-large and one of the most important challenges for our global community in the 21st century.

I welcome your comments at terman.column@ieee.org

Lewis Terman
IEEE President and CEO

Adapted from an Article in “The Institute”, published in March 2008 by the IEEE.