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Atmospheric Profiles, Clouds, and the Evolution of Sea Ice Cover in the Beaufort 

and Chukchi Seas: 

Atmospheric Observations and Modeling as Part of the Seasonal Ice Zone 

Reconnaissance Surveys 

Axel Schweiger 

Applied Physics Laboratory, University of Washington, 1013 NE 40 th St., Seattle, Wa. 98105 
phone: (206) 543-1312 fax: (206) 616-3142 email: axel@apl.washington.edu 
Ron Lindsay, Applied Physics Laboratory, University of Washington 
Jinlun Zhang, Applied Physics Laboratory, University of Washington 
Zheng Liu, Applied Physics Laboratory, University of Washington 
Dale A. Lawrence, Department of Aerospace Engineering, University of Colorado 
James Maslanik, Department of Aerospace Engineering, University of Colorado 
Award Number: N00014-12-1-0232 
http ://psc. apl.uw.edu 


LONG-TERM GOALS 

The goal of this project is to examine the role of sea-ice and atmospheric interactions in the retreat of 
the SIZ. As sea ice retreats further, changes in lower atmospheric temperature, humidity, winds, and 
clouds are likely to result from changed sea ice concentrations and ocean temperatures. These changes 
in turn will affect the evolution of the SIZ. An appropriate representation of this feedback loop in 
models is critical if we want to advance prediction skill in the SIZ. To do so, we will conduct a 
combination of targeted measurements and modeling experiments as part of the atmospheric 
component of the Seasonal Ice Zone Reconnaissance Survey project (SIZRS). Combined with 
oceanographic and sea ice components of the SIZRS project, this project provides a multi-year 
observational and modeling framework that will advance our understanding of the variability of the 
seasonal ice zone and which is needed to improve predictions from daily to climate time scales. 

OBJECTIVES 

• Assess the ability of global atmospheric reanalyses and forecast models to reflect the details of 
the seasonal evolution of atmosphere-ice-ocean interactions in the Beaufort Sea SIZ through 
the use coordinated multi-year atmospheric, ice, ocean measurements, 

• investigate how regional meso-scale models can improve the representation of atmosphere-ice 
interactions in the SIZ spring through fall, 

• determine how changes in sea ice and sea surface conditions in the SIZ affect changes in cloud 
properties and cover, 

• develop novel instrumentation including low cost, expendable, air-deployed micro-aircraft to 
obtain temperature and humidity profiles and cloud top and base heights 

• Integrate atmospheric, oceanographic, and sea ice measurements and models to advance our 
understanding of seasonal ice zone variability. 


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APPROACH 


To achieve these long-term objectives we are conducting observation and model experiments. The 
SIZRS project is an integrated observation and modeling program aimed at understanding the interplay 
of atmosphere, ice, and ocean in the SIZ of the Beaufort and Chukchi seas (BCSIZ). Seasonally 
changing surface conditions are expected to provide a present day analog for expected future ice 
retreat. SIZRS takes advantage of routine Coast Guard C-130 domain awareness missions that take 
place at bi-weekly intervals from May through November. As the atmospheric observation component 
of SIZRS, this project deploys dropsondes during SIZRS flights planned at least monthly from June 
through October to obtain atmospheric profiles of temperature, humidity, and winds from the time of 
ice edge retreat in spring to advance in fall. Transects following 150W and 140W from 72N to 77N 
are typically obtained. Cloud top heights will be retrieved using infrared imagers and a LIDAR 
provided by other SIZRS projects. In addition, we are contributing to technology development by 
adapting and deploying a new generation of truly expendable (<$700) micro-aerial vehicles 
(Glidersonde, SmartSonde) designed to obtain detailed high-vertical-resolution temperature, humidity 
and wind profiles and cloud layering information that cannot be obtained with traditional dropsondes.. 
In addition a dropsonde (IR dropsonde) capable of detecting cloud tops and bases is being developed. 
Satellite data from MODIS, CloudSat-Calipso as well as high resolution passive microwave and visible 
band optical images are utilized. Ship-based observations (Radiosonde and Cloud Ceilometer) are 
coordinated with SIZRS. Land based station data from the Department of Energy Atmospheric 
Radiation Program (ARM) are utilized to validate instrumentation. Sea surface temperatures, ice 
concentrations, and floe size distributions are measured by other components of the SIZRS project. 

Our atmospheric observations are being examined in the context of varying surface and weather 
conditions (sea ice concentration, ice thickness, and SST, synoptic type) to increase our understanding 
of atmosphere-ice-ocean interactions and to initialize, validate, and improve our meso-scale 
atmospheric model. Forecast experiments to assess our current ability to forecast sea ice variability at 
different time scales are conducted. 

WORK COMPLETED 

Observations: 

• We adapted a commercial GPS-based radiosonde system to operate in a dropsonde mode that can 
be launched from aircraft. This provides an inexpensive alternative to a very limited choice in 
commercial suppliers of dropsonde systems. 

• We completed the Aircraft Configuration Control Board (ACCB) process including safety of flight 
test (SOFT) and obtained approval for deploying dropsondes during ADA flights. 

• Helped advance the dropsonde design with the commercial vendor. Initial deployments were made 
using a converted radiosonde which now has been transitioned into a dropsonde design that is 
suitable for tube launches. We helped develop a simple parachute system that achieves 5 m/sec 
descent rates and yield high resolution vertical profiles. 

• Conducted successful deployments of dropsondes during 22 flights fom June -2013 - Oct 2016, 
with a total of ca. 150 profiles collected. 

• We compared satellite retrievals of cloud fraction and cloud top height with observer estimates 
from C-130 cockpit. 

• We helped advance the dropsonde design with the commercial vendor. Initial deployments were 
made using a converted radiosonde which now has been transitioned into a dropsonde design that 


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is suitable for tube launches. We helped develop a simple parachute system that achieves 5 m/sec 
descent rates and yield high-resolution vertical profiles. 

• We utilized IR-camera images obtained through the C-130 ramp during launches to obtain cloud 
top temperatures. Use of CUPLIS-X system (PI, Mark Tschudi, University of Colorado) with IR 
and LIDAR instrumentation to provide cloud top information was recently approved for flight 
(First flight originally planned for Oct-6, 2015 was cancelled due to aircraft problems). 

• Worked with the vendor to expand the system for multi-channel operation allowing multiple 
sondes in the air. This will allow for denser sampling an resolution of smaller scale features such as 
low level jets. ‘Conducted the first flight with the CUPLIS-X system (PI, Mark Tschudi, University 
of Colorado) with IR and LIDAR instrumentation to provide cloud top information information. 
Analysis of data has begun 

• Completed successful flight in June 2016 and obtained 8 atmospheric profiles along the 150W line 
from 72.6N to 75.7N. In general, the lower atmosphere is cold and below freezing point at all 
levels. From 74N northward, there is a cold layer around 1.5 km and at the same level, there is a 
sharp decrease in relative humidity from near saturation, which suggests the presence of cloud top. 
This is confirmed by the cloud top height identified by IR-dropsonde at 75.7N, as is shown in Fig. 

1 by white stars. Two cloud tops are found at 75.7N: a higher cloud top at 1960 m and a lower top 
at 770 m 

• We conducted comparisons of dropsondes and radiosondes launched from ship by a separately 
funded ONR project (Overland). 



1250 1280 1310 1340 1370 1400 

10 mf& umL Cm) 


1250 1280 1310 1340 1370 1400 

10 m/s - Linit- tin} 


1250 1280 1310 1340 1370 1400 

10 m/s - (m) 


Fig. 1. Synoptic maps over the SIZRS domain for the June 19 (left), July 16 (middle) and August 16 
(right) fights. The nested domain boundaries are shown in thick black lines. The 850-1000 hPa thickness 
is shown in color shading, surface pressure contours in thin gray lines and the surface winds in green 
vectors. The 15% sea ice concentration contours are shown in thick blue lines. The locations of 
dropsondes for the June, July, and August SIZRS fights are shown as light green dots in left panel, middle 
panel, and right panel respectively. 


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150W: 20160615 



Fig 2. The temperature (top) and relative humidity (bottom) cross section along 150W observed 
during the SIZRS flight on June 15, 2016. The white stars mark the location of the cloud top 
height at 75.7N identified by the IR-dropsonde. 


Modeling: 

• A post-doctoral research associate, Zheng Liu, was hired to conduct WRF model experiments. 

• We conducted Weather Research and Forecast (WRF) model simulations for the summer of 2014 
and compared with the NCEP Global Forecast System (GFS) and ERA-Interim reanalysis data. 

The results are consistent with our previous study of the 2013 simulations (Liu et al., 2015). . 

• We constructed a /c-mean clustering synoptic classification algorithm using ERA-Interim reanalysis 
data to investigate the role of synoptic conditions on the vertical structure of atmosphere, cloud, 
and their interactions with sea ice. 

• We applied the synoptic classification algorithm to determine the synoptic conditions of the SIZRS 
flights and studied the relationship between synoptic conditions and the observed atmospheric 
profiles 

• We conducted forecast experiments with the Marginal Ice Zone Modeling and Assimilation System 
(MIZMAS) and assessed the quality of sea ice drift forecasts from 6 hours to 9 days. We examined 
the role of wind forcing and ice edge position errors. 


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Advanced Observation Platforms (IR Dropsonde, GliderSonde, SmartSonde): 

• Work on a SmartSonde development to obtain detailed atmospheric parameters and cloud top and 
base and can be launched from C-130 is progressing. In order to accelerate approval we modified 
the initial design from a motorized SmartSonde to a GliderSonde concept. We conducted SOFT 
tests for “GliderSonde” systems. ACCB approval is reportedly imminent. A more detailed report is 
submitted separately by Co-Investigators Lawrence and Maslanik. 

• In collaboration with the vendor (MeteoModem) we modified the standard dropsonde platform to 
host an additional set of IR radiometers (IR dropsonde). These sensors are designed to provide 
cloud top and base heights. We validated the methodology using a balloon launched sensor 
package in Colorado and have conducted several field deployments with overflights of ice-breaker 
based and land-based ceilometers. Initial results indicate a good agreement of cloud top and base 
heights visually retrieved from air craft (See also separate project report by Co-Investigators 
Lawrence and Maslanik) 


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RESULTS 


To better understand the atmospheric profiles observed from the SIZRS flights and their relationship to 
the synoptic weather conditions and the interaction with sea ice in the BCSIZ domain, we constructed 
four distinct synoptic mean states from the ERA-Interim reanalysis using the k- mean clustering 
algorithm. 



Fig. 3. Synoptic maps of the 700 hPa temperature (color shading, in °C), geopotential height (in solid 
gray lines, darker for higher values), and horizontal winds of the four synoptic regimes mean state of 
June, July, August, and September. The mean states are generated using the ERA-Interim 6-hourly data 
between 2007 and 2011. The dashed red lines in upper left panel outline the boundaries of the BCSIZ 
domain used for the synoptic classification. 


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The map of temperature, winds, and geopotential height of the mean states of the four regimes at 700 
hPa level are shown in Fig. 3 as an example. Both the BCSIZ domain (outlined by the dashed red lines 
in the upper left panel) and the larger domain are presented to more clearly demonstrate the ambient 
synoptic conditions of the four mean states. The first state (SOI) is associated with a high pressure over 
the western part of the BCSIZ domain and northerly winds to the east, where there is strong 
baroclinicity and cold advection from the central Arctic. The baroclinicity is shown in Fig. 2 by the 
gradient of geopotential height and temperature, and the crossing angle between the geopotential 
height contours and temperature contours. The second state (S02) is associated with low pressure over 
the northeastern part of the BCSIZ domain and is dominated by easterly and northeasterly winds. 
Compared to SOI, S02 has much weaker baroclinicity and weaker cold advection from the Siberian 
side of the Arctic Ocean. The third state (S03) is associated with a high pressure system to the 
southeast, far into the North America continent and the low pressure system is also far to the northeast 
over the Siberian side of the Arctic Ocean. The fourth state (S04) is characterized by a high pressure 
centered over the Beaufort Sea, which results in very strong warm advection in the western half of the 
BCSIZ domain and cold advection to the east, close to the eastern edge of the BCSIZ domain. Similar 
to SOI, S03 is also a state with strong baroclinicity. The conditions of S03 are more equivalent 
barotropic (“barotropic” hereafter for simplicity) over Alaska and more baroclinic over the BCSIZ 
domain, although not as strong as in SOI and S04. In addition to the weaker baroclinicity compared 
S04, the warmest air mass in S03 resides deep inland, while in S04 the warmest air mass is over 
Alaska and very close to the strongest southerly winds. The combined effect is a much stronger warm 
advection and warmer atmosphere in S04 than in S03. 





Fig. 3. The vertical profiles of the ERA-Interim mean temperature, water vapor mixing ratio, relative 
humidity, and horizontal wind speed during JJAS in the BCSIZ domain. 


The vertical profiles of domain mean temperature, moisture, and winds of the mean states are shown in 
Fig. 4. The two states associated with cold advection (SOI and S02) are colder and hold less water 
vapor than the other two state associated with warm advection (S03 and S04). The state S04 is not only 
the warmest state but also strongly stratified in the lower levels, with a 4 K mean inversion around 750 
m. Although the water vapor mixing ratio is highest in S04, its relative humidity is lowest. This is 
likely due to the continental origin of the air mass in the BCSIZ domain and the downslope warming 
and drying over the Brooks Range. Both SOI and S04 have much stronger mean winds in the lower 
500 m and more evident low-level jets (LLJ) than the other two states, which is likely due to their 
stronger baroclinicity. The other two states (S02 and S03) have weaker near surface wind than higher 
up at 2.5 km. 


7 



















wspd [m/s] 


Fig. 4. The vertical profiles of the mean temperature, water vapor mixing ratio, relative humidity, and 
horizontal wind speed of the SIZRS observations under different synoptic conditions. 


Using the constructed synoptic mean states, the synoptic conditions for the SIZRS flights in 2013 and 
2014 are classified. Over half of the observed profiles are obtained under condition S02, which is 
associated with cold and wet atmosphere, as is shown in Fig. 4. The lower stratification and wind 
speed of S02 are consistent with the ERA-Interim 5-year averages. The other three states are warmer 
and more strongly stratified, especially the two states associated with warm advection (S03 and S04). 
In general the observed atmospheric profiles under different synoptic conditions agree qualitatively 
with ERA-Interim reanalysis multi-year averages. The most obvious differences from the ERA-Interim 
mean vertical profiles shown in Fig. 3 are that S03 and S04. The synoptic condition with the strongest 
mean wind and lowest relative humidity below 1 km is S04 in ERA-Interim reanalysis and S03 in the 
SIZRS observations. For states SOI, S03, and S04, each consists of observations from two SIZRS 
flights and less than 10 profiles. So mean profiles for these three states are much noisier. With only 
two SIZRS flights in S03 and S04, the mean profiles can be biased by extreme cases. With the addition 
of the 2015 and future SIZRS dropsonde data, we expect the comparison with ERA-Interim reanalysis 
will be improved. In addition our visual inspection of the synoptic conditions for the corresponding 
SIZRS flights found that these synoptic conditions are in between the mean states of the two warm 
advection conditions, S03 and S04. Our current synoptic classification uses only 5 years of reanalysis 
data. Using a longer time record will more distinctively define the mean states of the synoptic 
conditions and reduce the ambiguity during classification. Future work will include comparisons with 
other models such as the Navy NAVGEM. 


IR Dropsonde 

During a September 29 2014 SIZRS mission we had the opportunity to overfly the Canadian Coast 
Guard Ice Breaker Luis St. Laurent. The ship was equipped with a laser ceilometer and radiosonde 
launch facility. We coordinated an overflight with simultaneous dropsonde and radiosonde launches to 
allow for independent validation of the dropsonde data. The overflight of a cloud ceilometer allowed a 
first test of the IR-dropsonde. A comparison of ship-based ceilometer data and IR dropsonde data is 
shown in Figure 5. The IR dropsonde clearly identifies the two cloud layers apparent in the ceilometer 
data. 


8 





























Figure 5 Time (bottom x-axis) height (y-axis) section oflidar backscatter from the ship-based 
ceilometer. Red to blue show backscatter values with increasing magnitude. The 1R sonde profile is 
shown in white relative to the top x axis (IR units) with a smoothed version overplotted in red, The 
ceilometer backscatter data shows two distinct cloud layers which are clearly identified by the IR 
dropsonde 

Sea Ice Forecast Experiments 

We tested our ability to forecast sea ice motion. Sea ice motion is strongly influenced by the surface 
wind and our ability to forecast winds accurately will likely be the key towards improvement in 
predicting the movement of sea ice. This in turn is tied to the ability to provide the sea ice model with 
accurate atmospheric forcings. So far, relatively little is published on how well we can forecast sea ice 
motion with the current generation of atmospheric forecast and sea ice models. To fill this gap we 
conducted a forecast experiment in which the Marginal Ice Zone Modeling and Assimilation System 
(MIZMAS), currently being developed under separate ONR funding, was forced with atmospheric 
forcings from the NOAA Climate Forecast System (CFS). Forecast ice motion for 1-9 days was 
compared with observed ice motion from buoys and other platforms (e.g. assets deployed during the 
ONR MIZ experiment). Forecast errors for speed and position are compared with reference forecasts 
generated using an ice velocity climatology driven by multi-year integrations of the same model. The 
results are examined in the context of a practical application: The scheduling of the acquisition of high- 
resolution images that need to follow buoys or scientific research platforms. RMS errors for ice speed 
are on the order of 5 km/day for 24 hr to 48 hr since forecast using the sea ice model compared with 9 
km/day using climatology. Predicted buoy position RMS errors are 6.3 km for 24 hr and 14 km for 72 
hr since forecast. Model biases in ice speed and direction can be reduced by adjusting the air drag 
coefficient and water turning angle, but the adjustments do not affect verification statistics. This 
suggests that improved atmospheric forecast forcing may further reduce the forecast errors. The model 
remains skillful for 8 days. Using the forecast model increases the probability of tracking a target 
drifting in sea ice with a 10x10 km image from 60% to 95% for a 24-hr forecast and from 27% to 73% 
for a 48-hr forecast (Figure 6). An initial assessment of drift error sources suggests that improvements 
in wind forecasts will likely yield the most immediate improvement in sea ice motion forecasts. Figure 


9 







7 shows the decorrelation of wind velocity between forecast (CFS) and analysis (NCEP) which shows 
a sharp drop over the initial 48 hours. 




a) b) 

Figure 6 Histogram of position errors for the forecast model and for positions predicted using 
climatology for a) 24 hr since forecast and b) 48 hr since forecast. Solid curves indicate cumulative 
probabilities. Vertical lines show median errors for forecast and climatology, respectively 


Wind Speed Furecast Quality: Analysis vs. Forecast 

1.0 


0-5 


3 day Forecast 


0.0 

R 




7 dgy Forecast 


May- Sept 2014, Correlation between Analysis and Forecast 


Figure 7 Correlation coefficient between wind velocity between CFS-V2 forecast and NCEP/NCAR 
reanalysis at 3 days and 7 days. 


10 





























































IMPACT/APPLICATIONS 


The vertical structure of the atmospheric profiles and cloud are regulated by the synoptic conditions. 
Using our k-mean classification algorithm, we are able to investigate the interactions between 
atmosphere, cloud, and the underlying sea ice under similar synoptic conditions. This approach allows 
us to focus on the different physical processes involved in these interactions. For example, under 
synoptic conditions S02, the weak stratification and wet conditions favor the cloud formation and 
maintenance. The cloud and radiative processes might be more important for the underlying sea ice. 
Under synoptic condition S04, the warm, dry, and strongly stratified atmosphere suppress the cloud 
formation. The associated stronger low level winds might be more important forcing for sea ice. In 
addition, evaluation of Polar-WRF simulations under different synoptic conditions will help to more 
clearly identify the deficiencies in the representation of these processes and identify the pathway to 
improve the weather and sea ice forecast in the BCSIZ region. New technology developments such as 
the IR dropsonde and the GliderSonder will provide opportunities to inexpensively obtain data that is 
otherwise not available (i.e. cloud base) and allow more detailed data collection across the ice-edge. 
Our evaluation of sea ice drift forecasts skills provides a baseline from which improvements in the 
future can be measured. As a multi-year integrated observation and modeling study, SIZRS is well 
positioned to advance our predictive capabilities in the BCSIZ. 

RELATED PROJECTS 

Zhang (PI) MIZMAS: Modeling the Evolution of Ice Thickness and Floe Size Distributions in the 
Marginal Ice Zone of the Chukchi and Beaufort Sea (ONR, MIZ DRI) 

Morison (PI) Ocean Profile Measurements During the SIZRS (ONR Core) 

Steele (PI). Uptempo buoys for understanding and prediction (ONR-Core) 

Lindsay (PI). Visible and Thermal Images of Sea Ice and Open Water from the Coast Guard Arctic 
Domain Awareness Flights (ONR-Core) 

Rigor (PI). International Arctic Buoy Program (ONR-Core) 

Morison (PI). SIZRS Coordination (ONR-Core) 

Tschudi (PI). CUPLIS-X (ONR-Core) 

Overland (PI). (ONR-Core) 

PUBLICATIONS 

Stem, H.L, A.J. Schweiger, M. Stark, J. Zhang, M. Steele, B. Hwang. The seasonal evolution of the 
sea ice floe size distribution in the Beaufort and Chukchi Seas, Elementa, [in review, refereed] 

Stern, H.L, A.J. Schweiger, M. Stark, J.Zhang, and M. Steele. Is it Possible to Renconcile Disparte 
Studies of the Sea-Ice Floe Size Distribution? Elementa, [accepted with revisions, refereed] 

Liu, Zheng, A. Schweiger (2017). Synoptic conditions, clouds, and sea ice melt-onset in the Beaufort 
and Chukchi Seasonal Ice Zone, Journal of Climate, [accepted with revisions, refereed] 

Zhang, J. L., A. Schweiger, M. Steele, and H. Stern (2015), Sea ice floe size distribution in the 
marginal ice zone: Theory and numerical experiments, J.Geophys.Res., 120(5), 3484-3498. [published 
refereed] 


11 



Schweiger, A. J., and J. Zhang (2015), Accuracy of short-term sea ice drift forecasts using a coupled 
ice-ocean model, Journal, 120, doi: 10.1002/2015jc011273. [published, referreed] 

Lindsay, R., and A. Schweiger (2015), Arctic sea ice thickness loss determined using subsurface, 
aircraft, and satellite observations. The Cryosphere, 9(1), 269-283, doi: 10.5194/tc-9-269-2015. 
[published, refereed] 

Liu, Z., A. Schweiger, and R. Lindsay (2014), Observations and Modeling of Atmospheric Profiles in 
the Arctic Seasonal Ice Zone, Journal, 143, doi: 10.1175/mwr-d-14-00118.1. [published, refereed] 

Zhang, J., R. Lindsay, A. Schweiger, and M. Steele (2013), The impact of an intense summer cyclone 
on 2012 Arctic sea ice retreat, Geophys. Res. Lett, n/a-n/a, doi: 10.1002/grl.50190 [published, 
refereed]. 

Zhang, J. L., R. Lindsay, A. Schweiger, and I. Rigor (2012), Recent changes in the dynamic properties 
of declining Arctic sea ice: A model study, Geophys. Res. Lett, 39 [published, refereed]. 

Lindsay, R., M. Wensnahan, A. Schweiger, and J. Zhang, Evaluation of seven different atmospheric 
reanalysis products in the Arctic, J. Climate, 27, 2588-2606, doi: http://dx.doi.org/10.1175/JCLI-D-13- 
00014.sl , 2014 [published, refereed], 

Zhang, J., A. Schweiger, M. Steele, and H. Stern, Sea ice floe size distribution in the marginal ice 
zone: Theory and numerical experiments, J. Geophys. Res., 120, doi:10.1002/2015JC010770, 2015 
[published, refereed]. 


12 




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3. DATES COVERED (From - To) 

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4. TITLE AND SUBTITLE 

Atmospheric Profiles, Clouds and the Evolution of Sea Ice 

5a. CONTRACT NUMBER 

5b. GRANT NUMBER 

N00014-12-1-0232 

5c. PROGRAM ELEMENT NUMBER 

6. AUTHOR(S) 

Axel Schweiger 

5d. PROJECT NUMBER 

5e. TASK NUMBER 

5f. WORK UNIT NUMBER 

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University of Washington - Applied Physics Laboratory 

4333 Brooklyn Avenue NE 

Seattle, WA 98105-6613 

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12. DISTRIBUTION / AVAILABILITY STATEMENT 


Distribution Statement A: Approved for public release; distribution is unlimited. 


13. SUPPLEMENTARY NOTES 


14. ABSTRACT 

The goal of this project was to examine the role of sea-ice and atmospheric interactions in 
the retreat of the SIZ. As sea ice retreats further, changes in lower atmospheric 
temperature, humidity, winds, and clouds are likely to result from changed sea ice 
concentrations and ocean temperatures. We conducted a combination of targeted measurements 
and modeling experiments as part of the atmospheric component of the Seasonal Ice Zone 
Reconnaissance Survey project (SIZRS). Combined with oceanographic and sea ice components of 
the SIZRS project. The projects identified biases in standard forecasting and reanalysis 
products relative to aircraft observed vertical atmospheric profiles. The biases, 
particularly a misrepresentation of the lower level atmospheric were found to be related to 
excessive vertical mixing in global models. We also examined the variability of clouds over 
the Beaufort/Chukchi sea area. We found a strong connection with synoptic variability and a 
strong connection of melt-onset with clear, warm advection events. 

15. SUBJECT TERMS 

Sea ice, atmosphere, sea ice retreat. Seasonal Ice Zone Reconnaissance Survey, SIZRS, model 


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19a. NAME OF RESPONSIBLE PERSON 

Axel Schweiger 

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