Sabbatical report by wuyunqing


									                                                    Table of Contents

1. Executive Summary ..................................................................................................... 1

2. Research on influences on the variability of Kansas climate ................................... 1
   2.1 Background and objective ..................................................................................... 1
      2.1.1 The importance of climate in Kansas ............................................................ 1
      2.1.2 Influences on the Kansas climate, real and potential .................................. 2
      2.1.3 Objective .......................................................................................................... 3
   2.2 Methodology ........................................................................................................... 3
      2.2.1 General approach............................................................................................. 3
      2.2.2 Data and processing ......................................................................................... 3
      2.2.3 Analysis ............................................................................................................. 5
   2.3 Results ..................................................................................................................... 6
      2.3.1 The El Niño Southern Oscillation (ENSO) ................................................... 6
      2.3.2 The Pacific-North American (PNA) pattern ................................................ 7
      2.3.3 The Pacific Decadal Oscillation (PDO) ......................................................... 8
      2.3.4 The North Atlantic Oscillation (NAO) .......................................................... 9
      2.3.5 The Quasi-Biennial Oscillation (QBO) ....................................................... 10
      2.3.6 Summary of relationships to teleconnections ............................................. 11
      2.3.7 Conclusions .................................................................................................... 13
      2.3.8 References ...................................................................................................... 14

3. Research on the remote sensing of sea ice ............................................................... 16
   3.1        Research conducted as part of the AMSRIce03 sea ice validation project 16
   3.2        Collaborative sea ice research at the Jet Propulsion Laboratory .............. 16

4. Plans for disseminating the sabbatical results ........................................................ 17

                                              Supplementary materials

Heinrichs, J., J. Maslanik, M. Sturm, D. Perovich, J. Stroeve, J. Richter-Menge, D. Cavalieri, T.
        Markus, J. Holmgren, K. Tape, and A. Gasiewski, The AMSRIce03 validation project:
        activities and results. Proc. SPIE Int. Soc. Opt. Eng., 5977-06, Bruges, Belgium,
        September 19-22, 2005, 9 pp., 2005.

Heinrichs, J., D. Cavalieri, and T. Markus, Assessment of the AMSR-E sea ice concentration
        product at the ice edge using RADARSAT imagery and operational ice charts, IEEE
        Trans. Geosci. Rem. Sens., submitted November 2005.

Heinrichs, J., The Morphology of the Sea Ice Edge at Multiple Scales From Remote Sensing
        Data, poster presented at the American Geophysical Union annual meeting, San
        Francisco, CA, December 5-9, 2006.
                                 Sabbatical report
                                      Fall 2005
                                Dr. John F. Heinrichs
                Fort Hays State University Department of Geosciences

1. Executive Summary

During the Fall Semester of 2005, I took a sabbatical leave from Fort Hays State
University. The objectives of the research to be conducted during the sabbatical were (1)
to definitively characterize the climate of Kansas and its variability and (2) to isolate and
determine the relative importance of the factors that control the climate of Kansas. As
can be seen in Section 2 below, this research produced a number of valuable results
which should be useful to a variety of stakeholders including agricultural producers and
planners preparing to mitigate natural hazards. In addition to the climate research
originally proposed for my sabbatical, important opportunities were provided to me under
the auspices of the National Aeronautics and Space Administration (NASA) to conduct
collaborative research on the use of satellite remote sensing imagery for measuring
characteristics of sea ice in the Arctic. Section 3 of this report outlines those
opportunities and the results of that research. Included with this report are copies of
papers and a poster that further detail the sea ice research. Section 4 describes the ways
the results of the sabbatical will be disseminated to the scientific community, the FHSU
community, and the citizens of Kansas.

I am very grateful to Fort Hays State University for providing me with the opportunity to
pursue these two major research initiatives through my sabbatical leave. In both cases, I
feel that I was able to generate results of significance. Furthermore, the collaborations
developed at NASA have already proved, and will continue to be, of great value for the
reputation of FHSU on a national and international level as well as enhancing
opportunities for research grants and engaging our students in research that augments
their classroom learning.

2. Research on influences on the variability of Kansas climate

2.1 Background and objective

2.1.1 The importance of climate in Kansas

Kansas is a major agricultural state, producing more wheat than any other state in the US
(USDA, 2004). Most of the grain production in Kansas is unirrigated, so production
depends heavily upon soil moisture, which is in turn dependent on both temperature and
precipitation. Kansas suffers from frequent severe weather, resulting in many deaths and
injuries as well as substantial property damage. The occurrence of severe weather in
Kansas exhibits substantial variation at all time scales, from annual to multidecadal.
Kansas is also subject to droughts, which can last several years, as well as major floods.
One of the most devastating floods in recent years occurred in 1993, in which the

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northern and eastern portions of the state suffered hundreds of millions of dollars in
damage (Josephson, 1994). Understanding the factors that influence climate in Kansas
can enable agricultural producers to take advantage of beneficial conditions as well as
apply appropriate mitigation and adaptation strategies to address climate-related
adversity. Furthermore, additional understanding of these climate factors could allow
disaster responders to make appropriate plans.

2.1.2 Influences on the Kansas climate, real and potential

From a climatological point of view, Kansas is a transitional state. It is located in a zone
in which major air masses mix. The positions of jet streams, particularly the polar jet,
play a large role in influencing which air masses move over the state and mix. The jet
streams, in turn, are known to be influenced by teleconnections. Teleconnections are
recurrent, seasonally varying patterns of atmospheric circulation anomalies (AMS, 2003).
The best known example, as well as the one that has the most dramatic impacts, is the El
Niño Southern Oscillation (ENSO). Preliminary research (Heinrichs, unpublished)
indicated Kansas climate is predominantly driven by factors outside the state. Previous
research on teleconnection impacts in the Great Plains has generally focused on larger
spatial scales (climate division and up)

The El Niño Southern Oscillation (ENSO): The ENSO is the most significant mode of
variability in the Earth’s climate at time scales of a few years. ENSO is characterized by
an east-west shift of warm water in the equatorial Pacific Ocean (Diaz and Markgraf
(2000). The El Niño phase of ENSO involves the eastwards movement of the warm pool
to the central Pacific from its normal position in the vicinity of Indonesia. The cold phase
of ENSO , called “La Niña”, involves the movement of the warm pool to its furthest
westward position and the presence of particularly cool water in the eastern Pacific. The
shift between the El Niño and La Niña phases of ago has a period of 2-7 years. As the
warm pool shifts back and forth, the convection associated with it moves as well, altering
the global zonal circulation pattern around the Equator. The influence of ENSO extends
significantly north and south of the equatorial regions, mainly by changing the upper air
flow and particularly the subtropical jet stream. The ENSO cycle produces major changes
in the polar and subtropical jet streams over the US. During El Niño, the southern tier of
states tends to be warmer, while the northern tier is cooler (Diaz and Markgraf, 2000).
The southwestern US is known to receive more precipitation during El Niño.

The Pacific-North American (PNA) pattern: The PNA pattern is a prominent mode of
low-frequency variability in the Northern Hemisphere (Wallace and Gutzler, 1981), and
is characterized by atmospheric flow in which the west coast of North America is out of
phase with the Eastern Pacific and Southeast United States. During the PNA positive
phase, wave-like flow exists over the North American continent, the Northwest
experiences increased temperatures and decreased storminess, and cold temperatures
predominate in the Southeast. The positive phase of the PNA is often associated with
weak El Niño episodes. During the negative phase of the PNA, airflow over North
America has a stronger zonal (east-west) orientation. The Northwest experiences
decreased temperatures and increased precipitation, and the Southeast is warmer.

                                        Page 2
The Pacific Decadal Oscillation (PDO): The PDO refers to a long-term change in ocean
temperature patterns in the Pacific (Francis and Hare, 1994). The temperature patterns
persist for 20-30 years and have a spatial configuration similar to the ENSO cycle.
During the warm phase of the PDO, ocean surface temperatures in the northern Pacific
are cooler while ocean surface temperatures in the eastern Pacific are warmer. During
the cool phase, this pattern is reversed. The PDO has been noted to modulate the ENSO
cycle and produces long-term impacts on US west coastal climate (Gershunov and
Barrett, 1998; Hare and Francis, 1995)

The North Atlantic Oscillation (NAO): Unlike the three Pacific Ocean patterns described
above, the NAO is a north-south pressure pattern focused in the North Atlantic Ocean
(Hurrell, 1995). During the NAO’s positive phase, the Icelandic low is deeper and high
pressure systems in the Atlantic mid-latitudes are stronger. These often act as blocking
highs, slowing east-west circulation over the US. The NAO is associated with wetter
conditions in the southeastern US. During the negative phase of the NAO, both lows and
highs over the Atlantic are weaker, and the southeastern US is drier. The NAO is known
to be connected to what is called the Arctic Oscillation, in which the anticyclone over the
Arctic Ocean becomes alternately stronger and weaker.

The Quasi-Biennial Oscillation (QBO) : All of the teleconnections discussed so far occur
in the troposphere, the portion of the atmosphere closest to the Earth’s surface. The
QBO, on the other hand, is characterized by a periodic reversal of winds in the
stratosphere (at pressure levels of 30 to 50 mb). The dominant period of the QBO is 28
months. The QBO is driven by vertically propagating gravity waves (Baldwin et al.,
2001) and is known to have an effect on convection in the troposphere through a coupling
with the polar vortex (Thompson et al., 2002).

2.1.3 Objective

The specific objective of this research was to examine the spatial relationship of the five
major teleconnections described above to the interannual variability of temperature and
precipitation in Kansas at high spatial resolution.

2.2 Methodology

2.2.1 General approach

The general approach to the study was straightforward, involving a regression analysis of
Kansas temperature and precipitation data from stations against data characterizing the
five major teleconnections described above.

2.2.2 Data and processing

Kansas temperature and precipitation data: Temperature and precipitation data for 31
Kansas stations (Figure 1) were obtained from the Historical Climatology Network

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dataset (Vose et al., 1992) obtained from the NOAA National Climatic Data Center. A
study period of 1953-2000 was selected in order to maximize the number of stations as
well as to maintain consistency between the teleconnection indices (described below).
The annual averages were calculated from the monthly temperature and precipitation

        Figure 1. Map of 31 meteorological stations in Kansas used for the study

Teleconnection data: There are a number of ways to characterize the state of the ENSO ,
but the most commonly used is the Southern Oscillation Index (SOI), which is the
normalized difference in sea level pressure (SLP) between Darwin, Australia and Tahiti
in the central Pacific (Ropelewski and Jones, 1987). Because the low pressure moves
with the warm pool , during an El Niño episode, the SLP over Tahiti will be lower than
that over Darwin, making the SOI negative. During the cold (La Niña) phase, the
situation is reversed and the SOI becomes positive. Monthly and annual SOI data were
obtained from the Joint Institute for Study of the Atmosphere and Ocean (JISAO) at the
University of Washington. Average values for the SOI for the December-January-
February period were calculated.

To characterize the PNA, an index developed by Wallace and Gutzler (1981) was used.
This index is defined as

PNA = 0.25 * [Z(20° N,160° W) - Z(45° N,165° W) + Z(55° N,115° W) - Z(30° N,85°

where Z are standardized 500 hPa geopotential height values at the locations given. PNA
index data for 1948-2000 were obtained from JISAO, and the DJF averages of the index
were calculated.

                                      Page 4
The NAO is typically characterized by the north-south pressure difference in the Atlantic
Ocean. For this study, an NAO index developed by Hurrell (1995) was employed. This
NAO index is defined as the normalized Lisbon, Portugal minus Stykkisholmur, Iceland
average sea level pressure anomaly. Monthly and annual data for 1864-2001 were
obtained from JISAO, and as for the other indices above, DJF averages were calculated.

The Pacific Decadal Oscillation (PDO) Index is defined as the leading principal
component of North Pacific monthly sea surface temperature variability poleward of 20°
N (Mantua et al., 1997). PDO index data for 1900-2004 were obtained from JISAO and
the DJF averages were calculated.

The QBO zonal wind index is the concatenation of 30 hPa wind speed values at Canton
Island (3° S,172° W) for January 1953 through August 1967; Gan/Maldives (1° S, 73° E)
for September 1967 through December 1975; and Singapore (1° N,104° E) for January
1976 through 2001. Negative values of the QBO index indicate stratospheric easterlies
(from the east) and positive values are westerlies. QBO index data for 1953-2001 were
acquired from JISAO and the DJF averages were calculated.

2.2.3 Analysis

A regression analysis was conducted between the annual temperature and precipitation
data for each station and the teleconnection indices, as well as a multiple linear regression
between the station data and all five teleconnection indices together. Outputs from the
regression analysis included correlation coefficients, the coefficient of determination
(which indicates the percentage of a priori variance explained by the independent
variable) and the significance level of the linear relationship. All of the calculations were
performed using the Interactive Data Language (IDL) produced by Research Systems
Incorporated (RSI). Cartographic products illustrating the statistical results were
prepared using the ArcView 3.3 Geographic Information System (GIS) produced by the
Environmental Systems Research Institute (ESRI). The ArcView Spatial Analyst was
used to interpolate the statistical results between the station locations.

                                         Page 5
2.3 Results

2.3.1 The El Niño Southern Oscillation (ENSO)

Both temperature and precipitation show spatially coherent relationships to the SOI
(Figure 2). Temperature in western Kansas has a direct relationship to the SOI (i.e.,
cooler after El Niño events or warmer after La Niña), and eastern Kansas has a noticeably
weaker inverse relationship (i.e., warmer after El Niño or cooler after La Niña). The
entire state has an inverse relationship to SOI (i.e., wetter after El Niño or drier after La
Niña), and this relationship is strongest towards the northern and northeastern portions of
the state, with correlations in the 0.4 range and significance greater than 90% for several
of the stations.

   Figure 2. Spatial relationship between the December-January-February average of the Southern
   Oscillation Index and Kansas temperature (top) and precipitation (bottom).

                                            Page 6
2.3.2 The Pacific-North American (PNA) pattern

As Figure 3 shows, the temperature over almost the entire state has a weak to moderate
inverse relationship to the PNA index, so that Kansas is in general cooler during the PNA
positive phase. The relationship of the PNA to precipitation in Kansas, however, is both
weaker and less spatially homogeneous. A weak inverse relationship of precipitation to
the PNA (i.e., less precipitation during the positive PNA phase or more precipitation
during the negative PNA phase) exists in the central and northeastern portions of the
state, while a weak positive relationship (i.e., more precipitation during the positive PNA
phase or drier conditions during the negative phase) exists in the southern and
northwestern portions.

   Figure 3. Spatial relationship between the December-January-February average of the Pacific-
   North American pattern index and Kansas temperature (top) and precipitation (bottom).

                                            Page 7
2.3.3 The Pacific Decadal Oscillation (PDO)

As seen in Figure 4, most of Kansas exhibits a weak to moderate inverse relationship
between temperature and the PDO index. This relationship is stronger in the western
portion of the state, but has some spatial incoherence. Kansas precipitation has a
uniformly direct relationship to PDO index, which is particularly strong in the eastern
part of the state where many stations exhibit a significance above the 0.95 level. It has
been long noted that floods in Kansas occur with a repeat period of about 20-22 years,
and the relationship between precipitation and the PDO may be a factor in the observed

   Figure 4. Spatial relationship between the December-January-February average of the Pacific
   Decadal Oscillation index and Kansas temperature (top) and precipitation (bottom).

                                            Page 8
2.3.4 The North Atlantic Oscillation (NAO)

Figure 5 shows that the NAO has a moderate to strong inverse relationship to temperature
(i.e., cooler during the NAO positive phase or warmer during the negative phase) in
western Kansas. The relationship is direct in northeastern Kansas. A number of the
stations have significance above the 0.95 level, meaning that the observed spatial
incoherence is a real phenomenon that requires additional explanation. There is a
widespread direct relationship of precipitation to the NAO index (wetter during the NAO
positive phase or drier during the NAO negative phase) across the state, which is
strongest in southeastern Kansas where the correlation coefficients approach 0.4.

   Figure 5. Spatial relationship between the December-January-February average of the North
   Atlantic Oscillation index and Kansas temperature (top) and precipitation (bottom).

                                            Page 9
2.3.5 The Quasi-Biennial Oscillation (QBO)

The relationship of the QBO to temperature (Figure 6) is generally weak, inverse, and
with substantial spatial inhomogeneity. The same is true of the relationship of the QBO
to precipitation in Kansas. There are two “hot spots” of moderate correlation neat the
center of the state.

   Figure 6. Spatial relationship between the December-January-February average of the Quasi-
   Biennial Oscillation index and Kansas temperature (top) and precipitation (bottom).

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2.3.6 Summary of relationships to teleconnections

Figure 7 summarizes the strength of the relationship between all of the teleconnections
taken together and temperature and precipitation. The coefficient of determination,
which is calculated as the square of the correlation coefficient, gives the fraction of the a
priori variance of the dependent variable (i.e., temperature or precipitation) explained by
the independent variables in a multiple linear regression. It can be seen that almost half
of the interannual variance of temperature at some stations is explained by the
teleconnections, while other stations have only a slight influence. The pattern is not
spatially coherent, suggesting that local effects may be important. For precipitation, on
the other hand, the influence of the teleconnections is clearly greatest in eastern Kansas,
with 30-40% of the interannual variability in the southeastern area explained by the five

   Figure 7. Spatial relationship between all teleconnections indices taken together and Kansas
   temperature (top) and precipitation (bottom).

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Figure 8 shows the relative degree to which temperature at each of the stations has a
relationship to each of the teleconnections. The pie graphs for each station have
slices sized in proportion to the amount of variance each teleconnection explains.
The blue slices represent the Pacific teleconnections. The spatial pattern of the
relationship to temperature is not spatially coherent. The Pacific teleconnections have
their strongest influences in the southeastern (the PDO is noteworthy in this area),
south-central, and northwestern regions, while the NAO is significant in the northeast.
The QBO has some influence in the northeastern part of the state. In contrast, the
spatial pattern of the precipitation/teleconnection relationship (Figure 9) has much
more spatial coherence. The Pacific teleconnections dominate in the northeast,
southeast and northwest, where the NAO has its greatest influence in southern and
central Kansas. The QBO is a minor factor, except in the very center of the state.

   Figure 8. The relative influence of each of the five teleconnections on Kansas temperature.

  Figure 9. The relative influence of each of the five teleconnections on Kansas precipitation.

                                         Page 12
2.3.7 Conclusions

The research described above has revealed interesting patterns between major
teleconnections and temperature and precipitation in Kansas at a finer spatial scale than
previously examined. Perhaps the most important conclusion of the research is the
quantification of the degree to which the Kansas climate is influenced by teleconnections.
Another key observation, and a somewhat surprising one, is the magnitude of the
influence on Kansas climate of events occurring thousands of kilometers away over the
Pacific Ocean. The spatial coherence of the pattern of influences on precipitation, in
particular, suggests that the impact of the Pacific teleconnections is not just an artifact,
but a real physical phenomenon. The spatial inhomogeneity of several of the patterns,
and in general for temperature, is yet another observation of interest.

Since wintertime teleconnection indices were used for the study, it should be possible for
Kansas agricultural producers and particularly those starting crops in the spring, to take
advantage of the wide availability of the indices to make appropriate crop management
decisions and thereby maximize their productivity and profit.

The study does have some important limitations. First, the accuracy of the temperature
and precipitation data from the Historical Climatology Network is between 6 and 10%
(Vose et al., 1992), and the accuracy of the teleconnection indices is about the same
magnitude. These errors are not large enough to cast doubt on the major influence
patterns observed, but may render some of the weaker patterns questionable. Another
concern is the relatively short period (47 years) when data is available for all 31 stations
and all 5 teleconnections. There may be longer-term modes of variability that are not
captured in the analysis, but which are very important. For one example, there is a long-
term cycle in temperature over the Great Plains of which the cold period in the early 20th
century and the Dust Bowl in the 1930s are a manifestation. Yet another limitation is the
exclusion from the analysis of long-term trends such as anthropogenic warming. Finally,
the nature of linear regression analysis means that the phase of an observed relationship is
not isolated. For example, if an inverse relationship is observed between a teleconnection
index and temperature, whether temperatures are cooler during the positive phase or
warmer during the negative phase is not distinguished.

There are many ways in which the work described here could be extended. The analysis
could be expanded to examine the seasonal relationships of temperature and precipitation
to the teleconnections. A seasonal focus should help resolve the ambiguity between the
influences of the phases of any given teleconnections. The examples of spatial coherence
could be investigated further to determine whether they are due to local effects (such as
topography, land cover, or human activity) or simply to noise in the data or climate
system. The study did not investigate the causal mechanisms by which the
teleconnections influences are coupled to Kansas climate, and an analysis of the synoptic
mesoscale weather patterns over the Great Plains would be valuable for establishing
specific coupling mechanisms.

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2.3.8 References

•   American Meteorological Society, 2003, Statement on Meteorological Drought
    (Adopted by AMS Council on 23 December 2003), Bull. Amer. Met. Soc., 85.
•   Baldwin, M. P., L. J. Gray, T. J. Dunkerton, K. Hamilton, P. H. Haynes, W. J.
    Randel, J. R. Holton, M. J. Alexander, I. Hirota, T. Horinouchi, D. B. A. Jones, J. S.
    Kinnersley, C. Marquardt, K. Sato, and M. Takahashi, 2001, The Quasi-Biennial
    Oscillation, Reviews of Geophys., 39, 179-229, 2001.
•   Diaz H .F., and V. Markgraf (eds.), 2000: El Niño and the Southern Oscillation:
    Multiscale Variability and Global and Regional Impacts, Cambridge University
    Press, 496 pp.
•   Francis, R. C. and S. R. Hare, 1994, Decadal-scale regime shifts in the large marine
    ecosystems of the Northeast Pacific: a case for historical science, Fish. Oceanogr, 3,
•   Gershunov, A. and T. P. Barnett. 1998, Interdecadal modulation of ENSO
    teleconnections, Bull. Amer. Meteor. Soc. 79, 2715-2725.
•   Hare, S. R. and R. C. Francis, 1995, Climate Change and Salmon Production in the
    Northeast Pacific Ocean, In R.J. Beamish [ed.] Ocean climate and northern fish
    populations. Can. spec. Pub. Fish. Aquat. Sci. 121, 357-372.
•   Hurrell, J. W., 1995, Decadal trends in the North Atlantic Oscillation: regional
    temperatures and precipitation. Science, 269, 676-679.
•   Josephson, D. H., 1994, The Great Midwest Flood of 1993, Natural Disaster Survey
    Report, Department of Commerce, NOAA, National Weather Service, Silver Spring,
•   Kiladis, G. N., and H. F. Diaz, 1989, Global climatic anomalies associated with
    extremes in the Southern Oscillation, J. Climate, 2, 1069-1090.
•   Mantua, N. J., The Pacific Decadal Oscillation and Climate Forecasting for North
    America, Climate Risk Solutions, retrieved from, March
•   Mantua, N. J., S. R. Hare, Y. Zhang, J. M. Wallace, and R. C. Francis, 1997, A
    Pacific interdecadal climate oscillation with impacts on salmon production, Bulletin
    of the American Meteorological Society, 78, 1069-1079.
•   Marshall, J. Y. Kushnir, D. Battisti, Ping Chang, A. Czaja, R.R. Diskson, J. Hurrell,
    M. McCartney, R. Saravanan and M. Visbeck, 2001, North Atlantic climate
    variability: phenomena, impacts and mechanisms, International J. Clim., 21, 1863-
•   Ropelewski, C.F. and P. D. Jones, 1987, An extension of the Tahiti-Darwin Southern
    Oscillation Index, Monthly Weather Review, 115, 2161-2165.
•   Thompson, David W., Mark P. Baldwin, and John M. Wallace, 2002, Stratospheric
    Connection to Northern Hemisphere Wintertime Weather: Implications for
    Prediction, J. Climate., 15, 12, 1421-1428.
•   U.S. Department of Agriculture, 2004, Kansas Wheat History, National Agricultural
    Statistics Service, Kansas Field Office, November 2004.

                                       Page 14
•   Visbeck M. H., J. W. Hurrell, L. Polvani, and H. M. Cullen, 2001, The North Atlantic
    Oscillation: past, present, and future, Proc Natl Acad Sci., 98(23),12876-7.
•   Vose, R., R. Heim, R. Schmoyer, T. Karl, P. Steurer, J. Eischeid, and T. Peterson,
    1992, The Global Climatology Network: Long Term Monthly Temperature,
    Precipitation, Sea Level Pressure, and Station Pressure Data, Oak Ridge National
    Laboratory, Environmental Sciences Division, Publication No. 3912.
•   Wallace, J. M , and D. S. Gutzler, 1981, Teleconnections in the geopotential height
    field during the Northern Hemisphere winter, Mon. Wea. Rev., 109, 784-812.

                                      Page 15
3. Research on the remote sensing of sea ice

3.1 Research conducted as part of the AMSRIce03 sea ice validation project

From 2002 through 2005, I was involved in a NASA-funded project called AMSRIce03
to collect and analyze data about sea ice in the Beaufort, Chukchi, and Bering Seas. My
efforts on this project were focused primarily on integrating field, aircraft, and satellite
data sets using GIS, but also included evaluating the performance of sea ice geophysical
parameters from a passive microwave sensor called the Advanced Microwave Scanning
Radiometer (AMSR) using Synthetic Aperture Radar (SAR) data from a Canadian
satellite called RADARSAT. In September, I was asked to attend a NASA AMSR
validation workshop in Hawaii to present the latest results of this work. Because of my
unique position as the integrator of data from many sources, I had become familiar with
all of the aspects of the project, and also in September I gave an overview of the
AMSRIce03 project at an SPIE conference in Bruges, Belgium. Another outcome of this
conference was a proceedings paper (included as part of this report) about the entire
project on which I was the lead author with 8 colleagues as co-authors.

During October I worked on a related project to determine how well the AMSR data
could be used to measure the position of the ice edge in the Bering Sea, again by
comparison with RADARSAT imagery. RADARSAT images have a spatial resolution
of 100 m, ideal for comparison with the AMSR data which has a resolution of 12.5 km.
This work was stimulated by discussions at the Hawaii workshop, and I was fortunate to
be able to work closely with two scientists at the NASA Goddard Space Flight Center,
Dr. Thorsten Markus and Dr. Don Cavalieri, who are very well known in the sea ice
remote sensing community and have studied the sea ice edge previously. This project
involved using a GIS to digitize an ice edge from the RADARSAT imagery and then
compare that edge with that obtained from the AMSR data using a 15% sea ice
concentration contour. The technique of comparison was a Fréchet distance calculation
of the distance between two complex plane curves. The results showed that the AMSR
ice edge was on average within one 12.5 km pixel of the RADARSAT edge, a result that
suggests that agencies producing operational ice charts for routing shipping could use the
AMSR data effectively. This is very desirable, since the AMSR covers the entire Arctic
basin daily while SAR sensors such as RADARSAT have a three-day repeat cycle. A
journal article on this work (included with this report) was written and submitted to a
special AMSR validation issue of Transactions on Geoscience and Remote Sensing in
November. This paper is now in peer review and should appear in print towards the end
of 2006.

3.2 Collaborative sea ice research at the Jet Propulsion Laboratory

In April, I was invited by Dr. Ben Holt of the NASA Jet Propulsion laboratory (JPL) in
Pasadena to come to JPL as a visiting scientist (unpaid). Arrangements were made for a
visit in November, and JPL provided me with an office and computer for a three-week
period. During my visit to JPL, I had the opportunity to discuss research projects in a

                                        Page 16
number of areas with JPL scientists, including Dr. Holt, Dr. Ron Kwok, who is an expert
on measuring sea ice characteristics with radar instruments, Dr. Eric Fielding, who is an
expert on measuring land subsidence with radar interferometry, Dr. Dimitris Menemenlis,
who is developing a sophisticated ocean model for use in the Arctic, and Dr. Michael
Hecht, who is the leading scientist studying the evolution of the polar caps on Mars. The
majority of my time at JPL was directed towards extending my sea ice edge work in
several interesting directions, primarily finding ways to measure the shape and tortuosity
(“curviness”) of the ice edge from satellite imagery and then examining the degree to
which the ice edge shape is independent of the resolution of the sensor used. This work
involved finding and then calculating a number of mathematical shape parameters using
sea ice edge curves derived from the AMSR, RADARSAT, and a visible/infrared sensor
called MODIS (Moderate Resolution Imaging Spectroradiometer). I also worked
extensively with Dr. Alexandra Piryatinska of JPL, an expert on geostatistical analysis,
on a technique called spectral coherence which allows the comparison of complex curves
on the basis of their spectral properties. The results of this work suggest that the shape of
the sea ice edge is dependent on the sensor used to observe the ice edge and also that the
spectral coherence method has great potential for application to sea ice edge
determination. At the end of my time at JPL, I presented a seminar to the members of the
Physical Oceanography group, which was very well received and stimulated much further
discussion. In addition, the following week I presented a poster (included with this
report) at the American Geophysical Union annual meeting in San Francisco describing
this work and its results.

4. Plans for disseminating the sabbatical results

Results of the Kansas climate research will be disseminated to the scientific community
via a journal article currently in preparation for submission to the Journal of the Kansas
Academy of Sciences. This article will be based largely on the material contained in
Section 2 of this report. Because of the relevance of this research to the citizens of
Kansas and public officials, additional means will be employed to distribute information
about the research conclusions. On February 3, 2006 I appeared on KAYS radio in Hays
as a guest on Mr. Michael Cooper’s “Friday Focus” program and described my climate
research. I also plan to contact news media and supply newspapers and other outlets with
a press release describing the research and its results.

The results of the sea ice research have already been disseminated to the scientific
community through two papers and a poster presentation. On January 18, 2006, I gave a
seminar entitled "The front line of climate change: the edge of the sea ice pack" to the
Sternberg Geosciences Club. Finally, I plan to give two public sabbatical presentations,
one on the Kansas climate variability research and the other on the sea ice remote sensing
research, during the FHSU Research and Creativity Week in April 2006.

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