Arctic-ICE (Arctic – Ice-Covered Ecosystem in a Rapidly Changing Environment)
2011 Field Report
A sea ice-based study in Allen Bay, NU, Canada
Compiled and Edited by: Karley Campbell Virginie Galindo Jack Landy
Table of Contents
1.0 Introductory Summary ....................................................................................................... 1 2.0 Physical Oceanography ...................................................................................................... 7 3.0 Snow on Sea Ice ..................................................................................................................... 15 4.0 Sea Ice Surface Morphology ............................................................................................. 23 5.0 Air-Ice-Ocean Gas Exchanges and Microclimate ...................................................... 36 6.0 Sea Ice Geochemistry .......................................................................................................... 45 7.0 Sea Ice Physics and Nutrients .......................................................................................... 57 8.0 Sea Ice and Water Column Climate Relevant Compounds ................................... 68 9.0 Sea Ice, Water Column and Melt Pond Algae ............................................................. 87 10.0 Ice Algae and Bio-Optics .................................................................................................. 99 11.0 Literature Cited................................................................................................................... 113 12.0 Appendix ............................................................................................................................... 122
[Type a quote from 1the document or the summary
1.0
Introductory Summary
C.J. Mundy Centre for Earth Observation Science (CEOS) Department of Environment and Geography CHR Faculty of Environment, Earth, and Resources University of Manitoba Winnipeg, Manitoba Canada, R3T 2N2 The Arctic ice-covered environment is rapidly changing with recent accelerated declines in sea ice cover (Comiso et al., 2008), consistent disappearance of old (thick) multiyear ice (Maslanik et al., 2007; Barber et al., 2009) and changes to water mass characteristics and distribution (McLaughlin et al., 2002; Polykov et al., 2005). The Canadian Arctic Archipelago (CAA) has not been exempt of these changes with the recent opening of the Northwest Passage via M’Clure Strait in fall 2007 renewing national and international interest in this northern shipping route. The northern-most area of the CAA still remains covered by multiyear ice, but this is expected to change given current trends (Maslanik et al., 2007). The connection of the ice-covered marine system to these changes is readily apparent, but the extent of these changes and future changes on the ecosystem is unknown. Points to consider include: (1) changes to bottom-ice transmitted irradiance associated with changes to snow depth, ice thickness and timing of melt onset; (2) changes in nutrient supply associated with changes to water mass characteristics and distribution; (3) relative contribution of ice algae and under-ice phytoplankton to total primary production; (4) timing of ice algae release into the water column; (5) role of microbial processes; and (6) change between a pelagic- versus benthic-based 1
ecosystem. It has been hypothesized that the direction of carbon flow among the lower trophic levels of the food web and the extent of sympagic-pelagic-benthic coupling in the ice-covered ecosystem will provide sensitive indicators of directional change for the system as a whole. Therefore, the underlying objective of Arctic-ICE is to determine the various pathways of carbon flow within the lower trophic levels of the ice-covered ecosystem using observational process studies in support of one- and three-dimensional ecosystem models. The Arctic continental shelves cover 53% of the total surface area of the high Arctic Ocean, yet account for approximately 85% of its primary production (Sakshaug, 2004). The CAA encompasses more than 50% of Canada’s Arctic shelf waters, but overall primary production and its biogeochemical role in the marine ecosystem are still poorly understood (Michel et al., 2006). Barrow Strait/Resolute Passage, in the center of the CAA, represents a model study area for a number of reasons. (1) It is the site of previous multidisciplinary studies on the ice algal ecosystem and carbon flow (e.g., Welch et al., 1992; Fukuchi et al., 1997; Vézina et al., 1997; Conover et al., 1999; Michel et al., 2006), providing a baseline with which to compare new observations and models, although key knowledge gaps still exist (e.g., Welch et al., 1992; Michel et al., 2006). (2) The region is consistently covered by landfast ice from December to July, facilitating the study of key bio-physical processes in the system through time-series data collection. (3) Barrow Strait has an enhanced surface nutrient supply relative to adjacent regions (McLaughlin et al., 2006), which results in high sea ice and pelagic primary production, nourishing higher trophic levels and therefore supporting high local concentrations of marine 2
mammals and birds (Welch et al., 1992). (4) Marine wildlife in the vicinity of Barrow Strait provides sustenance and is a source of commerce to the hamlet of Resolute Bay, Nunavut. (5) A declining ice cover, expectation of increased shipping traffic, and political pressure for an increased understanding of our North highlight the need to better document and model the current ice-associated ecosystem to predict its future structure and function. From 16 April and 9 July 2011, we accomplished the second field study under the umbrella of the Arctic-ICE (Arctic – Ice-Covered Ecosystem in a Rapidly Changing Environment) program, located in Allen Bay and based out of the Polar Continental Shelf Program (PCSP) base, located in Resolute Bay, Nunavut, Canada (Fig. 1.1). Also highlighted is the location of the field site relative to the ice edge in Barrow Strait, demonstrating the relative consistency of the ice edge throughout the study and the transition from snow-covered to melt pond-covered sea ice surface (Fig. 1.1c and d, respectively). Our purpose was to investigate physical and biological aspects of the atmosphere, sea ice and ocean in support of the main objective of Arctic-ICE stated above. Table 1.1 lists the Arctic-ICE 2011 field participants and their associated institutions, field activities that they were responsible for, and their principle investigator(s). This field report is split into sections based on different types of measurements and analyses carried out in the field and on individual student projects. The sections include: Section 1.0 Introductory Summary; Section 2.0 Physical Oceanography; Section 3.0 Snow Characteristics and Distribution; Section 4.0 Sea Ice Surface Morphology; Section 5.0 Air-Ice-Ocean gas exchanges and microclimate; Section 6.0 3
Sea Ice Geochemistry; Section 7.0 Sea Ice Physics and Nutrients; Section 8.0 Sea ice and water column climate relevant compounds; Section 9.0 Sea Ice, Water Column and Melt Ponds Algae; Section 10.0 Algae and Bio-Optics; Section 11 Literature cited; Section 12 Appendix. a.
c.
b. )
d.
Figure 1.1. Map of the Arctic-ICE 2011 field study location (red dot; (74° 43 N; 95° 09 W).) relative to (a) the Arctica (b) the Canadian Arctic Archipelago, (c) the ice edge on 25 June and (d) Allen Bay, the hamlet of Resolute bay and the PCSP baseice edge on 25 June. Image (c) was obtained from the MODIS-Terra satellite real time website, http://rapidfire.sci.gsfc.nasa.gov/realtime/.
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Table 1.1. List of Arctic-ICE 2011 field participants and their associated field responsibilities, dates in the field and principal investigators (PI). Field Personnel
Status
C.J. Mundy (ISMER, UQAR; now at CEOS, UM)
Postdoc; Assistant Professor
Michael Fischer (AWI) Karley Campbell (CEOS, UM) Bruce Johnson (CEOS, UM)
PhD Stu.
Kristy Hugill (CEOS, UM) Virginie Galindo (ULaval)
BSc Stu.
Robin Bénard (ULaval)
BSc Stu.
MSc Stu. Technician
PhD Stu.
John Iacozza (CEOS, UM) Yi-Ching Chung (CEOS, UM) Jack Landy (CEOS, UM) Andrew Osipa (CEOS, UM) Marty Davelaar (IOS,DFO)
Instructor
Michel Gosselin (ISMER UQAR)
Professor
Postdoc PhD Stu. BSc Stu. Technician
Field Responsibilities Camp Manager Sea ice-water column nutrients, algae and biooptics Ocean-sea iceatmosphere gas fluxes Sea ice radiative transfer and bio-optics Meteorology and atmosphere-sea ice gas fluxes Sea ice radiative transfer and bio-optics Sea ice-water column climate relevant compounds Sea ice-water column climate relevant compounds Snow on sea ice Snow on sea ice Sea ice surface morphology Sea ice surface morphology Sea ice-water column inorganic carbon chemistry Sea ice-water column nutrients, algae and biooptics
Dates in Resolute 16 April to 7 May; 28 May to 9 July 16 April to 9 July 16 April to 9 July 16 April to 7 May 7 May to 9 July 7 May to 9 July
PI Michel Gosselin (ISMER, UQAR) C.J. Mundy (CEOS, UM) Gerhard Dieckmann (AWI) Dieter Wolf-Gladrow (AWI) David Barber (CEOS, UM) Tim Papakyriakou (CEOS, UM) David Barber (CEOS, UM) Maurice Levasseur (ULaval) Michael Scarratt (MLI, DFO)
7 May to 9 July
Maurice Levasseur (ULaval)
7 May to 28 May 7 May to 28 May 28 May to 9 July 28 May to 9 July 28 May to 18 June
David Barber (CEOS, UM)
28 May to 18 June
Michel Gosselin (ISMER, UQAR)
David Barber (CEOS, UM) David Barber (CEOS, UM) David Barber (CEOS, UM) Lisa Miller (IOS, DFO)
The Arctic-ICE 2011 program data use policy maintains rights to all data collected during the field season. For purpose of publications, field members and their respective supervisors have first rights to the data, which will hold until publication or completion of their respective degree documents. In closing, we would like to note that this type of field project could not be undertaken without the help of others behind the scenes. Therefore, we would like 5
to express our sincere gratitude to all of those organizations and individuals who helped to make the Arctic-ICE 2011 field program a success. In particular, we would like to thank the Polar Continental Shelf Program (PCSP) and their Resolute Base field personnel for their logistical support, without which, there would be no ArcticICE 2011 field program. We would like to thank the hamlet of Resolute Bay, NU, and the various research licensing organizations (Government of Nunavut, Nunavut Research Institute, Nunavut Impact Review Board, Fisheries and Oceans Canada, etc.) who supported the undertaking of our research. We would also like to thank the Canadian Coast Guard and ArcticNet Inc. who helped transport equipment between Resolute Bay and Quebec. Direct funds in support of this project came through individual NSERC grants to participating University-based PIs and the Northern Scientific Training Program grant to Karley Campbell. We would also like to thank a multitude of research associates, technicians and contractors who helped assist with preparing for field work and providing analyses for samples brought back. These people included: Joannie Ferland, Martine Lizotte, Emmelia Stainton, Marjolaine Blais, Melanie Simard, and Sylvie Lessard. For more information on the Arctic-ICE 2011 program, please contact the lead principal investigator: Dr. C.J. Mundy Centre for Earth Observation Science (CEOS) CHR Faculty of Environment, Earth, and Resources University of Manitoba Winnipeg, Manitoba Canada, R3T 2N2 Tel: 1-204-272-1571 Fax: 1-204-474-8129 E-mail:
[email protected] 6
2.0 Physical Oceanography
Karley Campbell M.Sc. Student Supervisor – David G. Barber (CEOS, UM) Centre for Earth Observation Science (CEOS) Faculty of Environment, Earth and Resources University of Manitoba Winnipeg, Manitoba and Virginie Galindo Ph.D. Student Supervisors – Maurice Levasseur (ULaval), Michael Scarratt (IML-DFO) Québec-Océan & Département de biologie Université Laval Québec, Québec
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2.1 Introduction During the Arctic-ICE 2011 program, an ice camp was established in the center of Allen Bay, northwest of Resolute Bay, NU (74° 43 N; 95° 09 W; Figure 1.1). For the duration of the camp physical oceanographic variables were recorded as part of daily water column measurements as well as from ice tethers logging data. In the following section the collection methodology and datasets obtained are described.
2.2 Data collection 2.2.1. Conductivity, Temperature and Depth (CTD) casts CTD casts were made daily in the laboratory tent around 8 am as well as at approximately 8 pm time permitting. All casts were collected between April 23 (Cast 28) and June 25 (Cast 105) 2011, and are listed in the Table 2.1.
Table 2.1. List of CTD casts collected during the Arctic-ICE 2011 field program.
CAST
DATE
28
23-april
29
24-april
30
TIME(CT D)
FILENAME
OPERATORS
14:10
AI_2011_04_24_001
CJ/Karley
18:40
AI_2011_04_25_002
CJ/Karley
25-april
08:00
AI_2011_04_26_003
CJ/Karley
31
26-april
08:30
AI_2011_04_27_004
CJ/Karley
32
27-april
08:30
AI_2011_04_28_005
CJ/Karley
33
28-april
08:30
AI_2011_04_29_006
CJ/Karley
34
29-april
12:30
AI_2011_04_30_007
CJ/Karley
35
30-april
08:00
AI_2011_05_01_008
CJ/Karley
36
01-may
08:00
AI_2011_05_02_009
CJ/Karley
37
02-may
08:00
AI_2011_05_03_010
CJ/Karley
38
03-may
08:00
AI_2011_05_04_011
CJ/Karley
8
COMMENTS
CTD frozen overnight
39
04-may
08:00
40
05-may
08:00
AI_2011_05_05_012 AI_2011_05_06_013
CJ/Karley CJ/Karley
06-may
No ctd - leg exchange day
41
07-may
08:00
AI_2011_05_08_014
CJ/Karley
42
07-may
19:30
AI_2011_05_08_015
Karley/Virginie
43
08-may
44
08-may
08:00
AI_2011_05_08_016
K/V/Kristy
45
08-may
20:00
AI_2011_05_09_017
K/V/Robin
46
09-may
08:00
AI_2011_05_10_018
Virginie/Robin
47
09-may
20:00
AI_2011_05_10_019
K/V/Robin
48
10-may
08:00
AI_2011_05_11_020
Virginie/Robin
49
10-may
22:30
AI_2011_05_11_021
K/V/Robin
50
11-may
51
11-may
08:10
AI_2011_05_12_022
Virginie/Robin
52
11-may
19:30
AI_2011_05_12_023
K/V/Robin
53
12-may
08:20
AI_2011_05_13_024
Virginie/Robin
54
12-may
20:20
AI_2011_05_13_025
K/V/Robin
55
13-may
08:15
AI_2011_05_14_026
Virginie/Robin
56
14-may
08:15
AI_2011_05_15_027
Virginie/Robin
57
15-may
08:30
AI_2011_05_16_028
Virginie/Kristy
58
16-may
08:15
AI_2011_05_17_029
Virginie/Kristy
59
16-may
21:00
AI_2011_05_17_030
Virginie/Robin
60
17-may
08:45
AI_2011_05_18_031
Virginie/Robin
61
17-may
20:30
AI_2011_05_18_032
Virginie/Robin
62
18-may
08:30
AI_2011_05_19_033
Virginie/Robin
63
19-may
08:30
AI_2011_05_20_034
K/V/R
64
20-may
08:00
AI_2011_05_21_035
Virginie/Robin
65
20-may
20:30
AI_2011_05_21_036
Virginie/Robin
66
21-may
08:30
AI_2011_05_22_037
Virginie/Robin
67
22-may
08:15
AI_2011_05_23_038
Virginie/Robin
68
23-may
08:00
AI_2011_05_24_039
Virginie/Robin
69
24-may
08:30
AI_2011_05_25_040
Virginie/Robin
70
25-may
08:30
AI_2011_05_26_041
Virginie/Robin
71
25-may
20:30
AI_2011_05_26_042
Virginie/Robin
72
26-may
08:30
AI_2011_05_27_043
Virginie/Robin
73
27-may
08:00
AI_2011_05_28_044
Virginie/Robin
74
28-may
08:00
AI_2011_05_29_045
Virginie/Robin
75
28-may
76
28-may
21:05
AI_2011_05_29_046
CJ/Virginie
77
29-may
08:00
AI_2011_05_30_047
CJ/Virginie
78
29-may
20:30
AI_2011_05_30_048
CJ/Virginie
79
30-may
08:30
AI_2011_05_31_049
CJ/Virginie
K/V/Kristy
No good : tube ON
No good : tube ON
Generator trouble during upwards cast Strong SW current Strong SW current
Change batteries - no cast
9
80
31-may
08:30
AI_2011_06_01_050
CJ/Virginie
81
01-june
07:17
AI_2011_06_02_051
CJ/Virginie
82
02-june
10:40
AI_2011_06_03_052
CJ
83
03-june
08:30
AI_2011_06_04_053
CJ
84
04-june
07:30
AI_2011_06_05_054
CJ/Robin
85
05-june
08:05
AI_2011_06_06_055
CJ/Virginie
86
06-june
08:30
AI_2011_06_07_056
CJ
87
07-june
08:20
AI_2011_06_08_057
CJ
88
08-june
08:10
AI_2011_06_09_058
CJ/Robin
89
09-june
07:40
AI_2011_06_10_059
CJ/Virginie
90
10-june
09:40
AI_2011_06_11_060
CJ
91
11-june
08:50
AI_2011_06_12_061
CJ
92
12-june
08:10
AI_2011_06_13_062
CJ/Robin
93
13-june
08:30
AI_2011_06_14_063
CJ/Virginie
94
14-june
09:00
AI_2011_06_15_064
CJ
95
15-june
08:50
AI_2011_06_16_065
CJ
96
16-june
07:50
AI_2011_06_17_066
CJ/Robin
97
17-june
07:00
AI_2011_06_18_067
CJ/Virginie
98
18-june
09:40
AI_2011_06_19_068
CJ
99
19-june
09:00
AI_2011_06_20_069
CJ
100
20-june
08:00
AI_2011_06_21_070
CJ/Robin
101
21-june
07:00
AI_2011_06_22_071
CJ/Virginie
102
22-june
08:30
AI_2011_06_23_072
CJ
103
23-june
09:00
AI_2011_06_24_073
CJ
104
24-june
08:50
AI_2011_06_25_074
CJ/Robin
105
25-june
09:10
AI_2011_06_25_075
CJ/Virginie
The casts were done through the main water sampling hole of the propane heated laboratory tent (ca. 1 by 1 m square). Prior to casts the hole was cleaned to remove sea ice and frost which formed during the cold period of fieldwork (April – May) and ice algae aggregates that occurred during melt (June). Sometimes, other visitors were seen as well! (Figure 2.2).
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a)
b)
Figure 2.2. Examples of organisms observed in the main sampling hole during Arctic-ICE 2011. (a) Algae aggregates and (b) Jellyfish.
The CTD was supported by a thin rope connected to a custom-built winch that was operated using a Dewalt 9 volt drill. Different colors of tape were placed on the cord to differentiate sampling depths as well as the maximum depth of CTD possible without reaching the bottom of the water column and touching sediments. The depth of the water column ranged from approximately 45 to 60m potentially due to the presence of an ocean floor ridge. As a result of this uncertainty casts were only done to a maximum depth of 40 meters. The CTD was a Sea-Bird SBE 19plusV2.1 series 6280 and was composed a number of sensors: chlorophyll a fluorometer (Wet Labs ECO-FL(RT)) attached to the external voltage 0, scalar photosynthetically active radiation (PAR) sensor (Biospherical QSP-2300L) attached to external voltage 1, and nitrate sensor (Satlantic ISUS V3) attached to external voltage 2. The operational procedure for CTD casts was order specific (Figure 2.3).
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a)
b)
Figure 2.3. The two first different stages of a CTD cast (a) Put instrument gently into the water, and (b) Leave at the surface for 5 minutes. After installation of the nitrate plug a waiting period of approximately 15 minutes was required to warm up the sensor. During this time other sensor caps (fluorometer, PAR, CTD) were removed and the CTD was positioned close to the hole for deployment. The CTD and associated sensors was submerged in the water immediately after turning it on and was maintained at the surface during a minimum of 90 seconds to permit the acclimation of all the sensors. Lastly, the CTD was lowered at a speed of approximately 0.9 m.s-1 until 40 meters depth, held at 40 meters for about 30 seconds, and raised back to the surface at the same speed. The speed of CTD descent and ascent was depending on the drill operator and was therefore not constant. It would be helpful to control the speed for future CTD sampling. After each cast, the nitrate sensor was removed and replaced by a dummy plug. The fluorometer and PAR sensors were cleaned with fresh water, and dried with a Kim wipes. Fresh water was injected 3 times with a 60 mL syringe in the conductivity cells to remove any salt residue and the CT sensor was cleaned with ethanol after which caps and tubing were replaced to protect the sensors. Between measurements the CTD was stored in the warmest camp tent (kitchen tent) to avoid 12
freezing of the conductivity cell. CTD data was downloaded weekly followed by conversion of the *.hex files using of the SBEDataProcessing-win32 software.
2.2.2. CT tethers Two ice tethers were constructed by attachment of ALEC Compact CT and ALEC MKV-L PAR sensors to a rope 40 meters in length at depths 2, 5, 7.5, 10, 20, 30, and 40 meters from the air-ice surface. At 40 meters depth an RBR DR-1050 pressure sensor was also secured in addition to a weight of approximately 8lbs to reduce tether movement from ocean currents. One of the tethers also sported ALEC Infinity-EM current sensors at intervals of 2.5, 12.5, and 25 meters (Figure 2.4).
Figure 2.4. Diagram of CT tether structure and positioning in the sea ice, taken from ArcticICE 2010 data report.
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The tethers were deployed through 8-inch auger holes in the sea ice and were anchored to an aluminum pole resting across the bore hole. The sites located at 74o43.103N, 95o10.031W and 74o43.108N, 95o09.990W were selected based on low (18 cm) snow depth categories during the April installation. Sensors recorded values through the entire camp duration and the snow depth surrounding the tethers was measured daily to account for changes in PAR.
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3.0. Snow on Sea Ice
Yi-Ching Chung Research Associate Centre for Earth Observation Science (CEOS) Faculty of Environment, Earth and Resources University of Manitoba Winnipeg, Manitoba
15
3.1 Introduction Snow cover plays a primary role in determining the thermodynamic state of sea ice by controlling both radiative and mass transfers across the sea ice-atmosphere interface due to its low thermal conductivity and high albedo (Maykut and Untersteiner, 1971; Flato and Brown, 1996; Eicken, 2003; Yackel and Barber, 2007; Langlois and Barber, 2007). The timing and magnitude of snow precipitation and blowing snow also control the thickness of sea ice and ice accretion and ablation rates (Maykut, 1978; Brown and Cote, 1992; Ledley, 1993; Flato and Brown, 1996; Iacozza and Barber, 2001; Barber and Hanesiak, 2004; Langlois and Barber, 2007). Early snow melt events on snow-covered first year sea ice (FYI) may explain variability in the timing of melt onset to pond coverage (Yackel et al. 2007). A thin snow cover can decrease snow insulation and force ice growth, leading to a thicker ice pack whereas a thick snow cover can insulate the colder sea ice from the warming atmosphere (e.g., Huwald et al., 2005; Chung et al., 2010). Chung et al. (2011) found that blowing snow may lead to earlier ice melt onset by approximately 6 days, with a slight increase of 4 cm on average for ice thickness, and a decrease of 0.4 K for the temperature at the snow-ice interface, by exploring the effect of blowing snow model on a snow-ice physical model system. Mundy et al. (2007a) also suggested that snow cover might play a role in brine channel density and size in the snow-sea ice system. A brine-wetted, saline basal snow layer associated with the temperature at the snow-ice interface will prohibit heat flows in snow and sea ice (Fukusako, 1990). Biologically, snow on sea ice affects the timing 16
and magnitude of sub-ice primary production (e.g., ice algae and pelagic algae) because the transmission of photosynthetically active radiation (PAR) is highly dependent on the snow cover thickness (Welch and Bergmann, 1989; Carmack et al., 2006; Mundy et al., 2007a; Barber et al., 2008). Changes in snowfall over sea ice have particular implications for the habitat of ice-dwelling species (e.g. ringed seals and polar bears) (Barber and Iacozza, 2004). Despite these physical and biological implications, the spatial and temporal evolution of snow in the Arctic remains poorly studied (Barber et al. 1995a; Markus et al., 2006; Langlois and Barber, 2007).
3.2 Methods 3.2.1 Study site In this study an intense observation period (IOP) from 7 to 28 May, 2011 was completed as part of the Arctic-ICE program. Snow pits were dug between one and six times a day in areas surrounding the Allen Bay ice camp (Figure 3.1). Figure 4.1 shows that the sampling site were plotted using the ArcGIS software. Table 3.1 shows the location of the 61 snow pits completed (accuracy ±3 meters). Snow samples were collected from various points, with snowmobiles needed to access remote study sites (maximum distance of 1.42 km between points), which were generally located between the ice camp (74°43.165’ N, 95°10.099’ W) and meteorological tower (74°42.850’ N, 95°11.98’ W). This was done to limit spatial heterogeneity and disturbance within the snowpack. Snow pits were also selected in conjunction with ice core collection, snow clear, and transect sampling sites (Section 17
10). The snow pits near ice core sampling locations were dug according to the following snow depth characteristics: low (< 10 cm), medium (10~18 cm), and high (> 18cm). A total of two snow pits were excavated on the snow clear sites prior to snow removal and snow pits were excavated at the East and West ends of transects constructed on 13, 17, 21 and 25 May, as well as at some points between. Table 3.1 describes the latitude and longitude positions of the snow pits relative to the location of ice camp base, meteorological tower, and radiation tower. The coordinates of some points along the transect lines were linearly interpolated.
Figure 3.1. The sampling pits near the ice camp and atmospheric tower. The grids are plotted by taking into account the curvature of the earth.
3.2.2 Data Collection In order to reduce sampling error, data were acquired in the following order after a snow pit was excavated: (a) Snow thermography; (b) Depth; (c) Temperature; (d) Dielectric constant/moisture; (e) Grain size; (f) Density; and (g) Salinity. (a): Once the snow pit was dug, the thermography of the snow wall was 18
immediately taken by a FLIR, which was started 10 to 15 minutes before use to maintain accuracy under very cold temperatures. The distance between FLIR and the object was less than 1 meter to minimize the effect of intervening water vapour in the air. The thermography and traditional pictures were taken at the same time by FLIR as shown in Figure 3.2. (b): Snow depth was measured using a folding ruler. (c): Snow temperature profiles were recorded at a 2 cm vertical resolution by inserting a temperature probe (Traceable Digital Thermometer, Model 4000; accuracy ± 0.1 °C) into the snow wall. For (d): The hydra probe was also inserted to the snow wall at a 3 cm vertical resolution to measure the complex dielectric constant (ε) at a frequency of 50 MHz under mild wind conditions. The sensor was calibrated for ε´ using 99% isopropyl alcohol (accuracy ± 0.6%), and for ε″ using a saline solution of known conductivity (accuracy ± 0.7%) in order to retrieve snow moisture content (Geldsetzer et al., 2009). (e): A 66.36 cm3 density cutter was inserted into the snow wall immediately beside the point where temperature and dielectric constant were measured (Drobot and Barber, 1998). Density samples were removed at 2 cm vertical intervals and placed into sealed plastic bags (Nasco Whirl-Pak®). Sampling was done from the snow surface towards the snow–ice interface.
19
Figure 3.2. Non-calibrated examples of (a) traditional micro-photographs, and (b) noncalibrated InfraRed (IR) thermography.
(e): Snow samples were immediately brought back to the working tent to take pictures of snow grains on the snow plate card (i.e., grain size grid plate) using the Nikon Coolpix P6000 Digital Camera (13.5 megapixels). Variability in photograph acquisition angle and distance may have resulted in resolution error during analyses (Mundy et al., 2007b). The example picture and post image processing to binarize grains are shown in Figure 3.3. (f): Following image capture, samples were weighed on a digital scale and converted to density in kilograms per cubic meter (accuracy ± 30 kg m−3) using the gravimetric technique (e.g., Garrity and Burns, 1988; Yackel and Barber, 2007). (g): Finally, the samples were melted at room temperature to measure salinity using a handheld salinometer (Hoskin Scientific Cond 330i; accuracy ±0.5% of reading). All the sites included thousands of snow measurements during IOP, for example, 2340 measurements were obtained for snow salinity.
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Figure 3.3. (a) Traditional picture taken at the snow layer located 4 cm from ground at local time 1530h on May 10, 2011; (b) contour map to create the image of binarized grains.
Table 3.1. List of snow pits sampled in May during the Arctic-ICE 2011 field program.
ID
Date
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
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Time (local) 11:00 10:30 13:30 8:30 15:30 22:00 8:30 14:00 22:00 9:30 18:30 8:00 16:30 15:30 17:30 10:30 16:00 21:30 8:30 18:30
Coordinates 74°43.140'N, 95°11.117'W 74°42.980'N, 95°11.218'W 74°43.186'N, 95°11.980'W 74°42.974'N, 95°11.304'W 74°43.166'N, 95°10.110'W 74°43.167'N, 95°10.111'W 74°42.974'N, 95°11.304'W 74°42.974'N, 95°11.304'W 74°43.175'N, 95°10.156'W 74°43.175'N, 95°10.156'W 74°42.980'N, 95°11.218'W 74°43.175'N, 95°10.156'W 74°43.069'N, 95°10.036'W 74°43.069'N, 95°10.097'W 74°43.068'N, 95°10.157'W 74°43.175'N, 95°10.156'W 74°43.175'N, 95°10.156'W 74°43.175'N, 95°10.156'W 74°43.177'N, 95°10.152’W 74°43.179'N, 95°10.145'W 21
Facility or project nearby Camp Tower Variogram Tower Camp Camp Tower Tower Camp Camp Tower Camp Transect Transect Transect Camp Camp Camp Camp Camp
21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61
15 21:30 16 9:30 16 11:30 17 9:30 17 14:30 17 15:30 17 16:00 17 16:30 17 17:30 18 13:00 19 9:30 19 17:30 20 8:30 20 15:00 21 9:00 21 18:00 21 18:15 21 18:30 21 19:00 21 20:30 22 8:30 22 22:00 23 8:15 23 11:30 23 12:00 23 12:20 23 18:00 24 9:00 24 15:30 25 8:30 25 12:30 25 18:30 25 19:00 25 19:15 26 11:00 26 14:15 27 9:30 27 13:00 27 13:15 27 13:30 27 20:30 Reference Reference Reference
74°43.337'N, 95°09.621'W 74°43.180'N, 95°10.150'W 74°43.174'N, 95°10.154'W 74°43.169'N, 95°10.166'W 74°43.050'N, 95°10.020'W 74°43.050'N, 95°10.035'W 74°43.050'N, 95°10.050'W 74°43.050'N, 95°10.060'W 74°43.050'N, 95°10.080'W 74°42.980'N, 95°11.218'W 74°43.177'N, 95°10.131'W 74°43.137'N, 95°09.769'W 74°43.176'N, 95°10.178'W 74°42.977'N, 95°11.297'W 74°42.182'N, 95°10.123'W 74°43.038'N, 95°09.892'W 74°42.037'N, 95°09.912'W 74°42.035'N, 95°09.933'W 74°42.036'N, 95°09.951'W 74°43.120'N, 95°09.884'W 74°43.183'N, 95°10.148'W 74°43.005'N, 95°11.105'W 74°43.183'N, 95°10.125'W 74°43.144'N, 95°09.699'W 74°43.150'N, 95°09.659'W 74°43.144'N, 95°09.675'W 74°43.005'N, 95°11.105'W 74°43.183'N, 95°10.125'W 74°42.974'N, 95°11.299'W 74°43.189'N, 95°10.131'W 74°43.179'N, 95°10.109'W 74°43.003'N, 95°09.983'W 74°43.001'N, 95°10.068'W 74°43.004'N, 95°09.963'W 74°43.181'N, 95°10.104'W 74°42.977'N, 95°11.294'W 74°43.190'N, 95°10.140'W 74°43.138'N, 95°09.655'W 74°43.136'N, 95°09.631'W 74°43.132'N, 95°09.609'W 74°43.127'N, 95°09.942'W 74°43.165'N, 95°10.099'W 74°42.850'N, 95°11.980'W 74°42.851'N, 95°12.640'W
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Lidar Camp Camp Tower Transect Transect Transect Transect Transect Tower Camp Camp Camp Tower Camp Transect Transect Transect Transect Snow clear sites Camp Tower Camp Core Core Core Tower Camp Tower Camp Camp Transect Transect Transect Camp Tower Camp Core Core Core Snow clear site Camp base Tower Radiation tower
4.0 Sea Ice Surface Morphology
Jack Landy Ph.D. Student Supervisor – David G. Barber (UM) Centre for Earth Observation Science (CEOS) Faculty of Environment, Earth and Resources University of Manitoba Winnipeg, Manitoba
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4.1 Introduction It is apparent that climate change will have, and may already be having (Grebmeier et al., 2006), a measurable effect on the Arctic marine ecosystem (Li et al., 2009). Not only is the geographic distribution of sea ice decreasing, evidence also suggests the ice is becoming thinner (Tschudi et al., 2007). Greater temperatures cause a decrease in sea ice extent of approximately 100,000 km2 per year with some models predicting an entirely seasonal Arctic ice cover within the next century (Serreze et al., 2007). With the reduction in extent and thickness of Arctic sea ice there is an associated drop in surface albedo as greater areas of water become exposed (Perovich et al., 2008). Solar radiation is absorbed more easily by water than the reflective surfaces of sea ice and snow, which in turn further accelerates the rate of ice melt. The positive feedback mechanism described is referred to as the icealbedo feedback (Tschudi et al, 2007). Melt ponds form as melt water from snow and ice pools in surface depressions of first-year and multi-year ice during the late spring and early summer. The formation of sea ice surface melt ponds is one of the primary drivers in the decrease of marine Arctic albedo and therefore plays a significant role in the ice-albedo feedback, but there is still great uncertainty concerning their overall contribution to changes in Arctic climate (Derksen et al, 1997). To gain a better understanding of the transition from snow covered to melt pond covered sea ice, it is essential to study physical characteristics of snow, ice and melt ponds.
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Gathering data about the transition from snow to pond covered ice has predominately been accomplished using satellite and aerial remote sensing techniques, with direct in situ physical data collected as either a compliment to, or a validation tool for, remotely acquired data. Light Detection and Ranging (LiDAR) offers a method for acquiring physical data over snow, ice and melt pond surfaces at unprecedented spatial resolution. In this study, data were acquired remotely with a LiDAR system from a terrestrial vantage point close to the sea ice surface. Although the range of the terrestrial system is typically several orders of magnitude lower than an aerial or satellite system, the spatial and temporal resolution, and accuracy of the data collected in this study were several orders of magnitude higher. Given that melt pond dimensions are characteristically lower than the resolution of satellite sensors, LiDAR offers a more effective technique for studying the transition of snow to melt pond covered sea ice.
4.2 Methods 4.2.1 Terrestrial Laser Scanning A basic LiDAR system (laser scanner) measures the time-of-flight for a discrete laser pulse to travel to and from an object. The Leica ScanStation C10 Scanner used in this study was operated at a wavelength of 532 nm (visible: green), which is reflected strongly by snow (up to 90%) and ice (ca. 50%) and is absorbed completely by water. The contrast between snow, ice, and water aids in the definition of meltpond edges. The ScanStation C10 was operated with a pulse rate of up to 50000 25
points per second, so a ‘point cloud’ (three-dimensional co-ordinate array of laser returns) was acquired over the sea ice surface at a sampling site with an average spatial resolution of less than 3 cm in a couple of hours.
4.2.2 Sampling Site A square 100 x 100 m site over landfast first-year sea ice located approximately 300 m west of the ice camp was selected for collecting LiDAR data. The site was first surveyed with leveling apparatus and then left as a non-invasive area. Following this the site was only accessed from the corners and side mid-points via a set walking paths located approximately 20 m outside the site to minimize further disturbance(Figure 4.1). Travel by skidoo near the study area was only permitted outside the walking paths. On the northwest side of the sample area, along the physical data sampling transect (Figure 4.1), travel by skidoo was forbidden and travel by foot was restricted to five passes per day to limit the chances of artificial pond initiation from human interference. The size of the observation site was chosen to be 4 to 5 times the characteristic length of snow drifts and melt ponds over low topography, first-year sea ice to ensure that a representative sample of surface features was collected. LiDAR data were acquired over the non-invasive sampling site almost daily over a three week period from 08 to 26 June. One day’s point cloud comprised four separate LiDAR scans taken from scan positions at each of the site vertices (Figure 4.1). Scans were acquired from these different angles to limit ‘shadowing’ of areas inside the observation area where snow drifts could have obstructed the laser path. 26
The LiDAR system was mounted approximately 2.5 m above the ice surface on a tripod, which was fixed to a wooden platform frozen into the ice before sampling began (Figure 4.2 a). Each scan covered a field-of-view (FOV) of 90° horizontal and 45° vertical. Data were acquired at 5 cm resolution at 100 m range (i.e. most of the scan would be better than 5 cm resolution and part would be less than 5 cm) which corresponded to approximately 15 minutes per scan. The four scans constituting one day’s LiDAR data were combined by registering individual point clouds to a rigid target reflector array. Leica High-Definition Surveying (HDS) reflectors were used as targets and were frozen into the ice prior to sampling to act both as a rigid, arbitrary 100 x 100 m co-ordinate system for scan registration.as well as a means of measuring rates of ablation across the site because of their constant height above sea level which was independent of snow and ice melt. Ice freeboard (to the laser height) was also measured daily at the Eastern platform, so the transition from freshwater to saline ponds following vertical brine movement through the ice could be identified. The total time required to take a LiDAR scan from one vertex equaled approximately 45 minutes and comprised: tripod and scanner setup, scanner heat-up (see below), data acquisition, target acquisition and travel to next vertex; meaning that one day’s LiDAR data took approximately 3 hours to obtain.
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Figure 4.1. 100 x 100 m site map.
a)
b)
Figure 4.2. (a) Scanner platform frozen into the ice prior to sampling, and (b) acquiring data with the terrestrial LiDAR. 28
Physical, in situ data were collected along a transect on the northwest edge of the site to compliment and validate the LiDAR data (Figure 4.1). Complimentary data included snow and pond depth measurements recorded every meter (where relevant) along the transect. Validation data included ice, snow and pond surface measurements recorded every meter with leveling equipment, photographs of surface characteristics recorded every 5 meters and measurements of ice ablation recorded every 20 meters along the transect using PVC ablation stakes frozen into the ice prior to sampling.
4.2.3 Target Testing Spherical targets, painted orange and frozen into the ice, were initially used as they can remain in position throughout the sampling period without needing to be turned towards the scanner each time data is acquired from a different vertex, unlike the HDS reflectors. By using static targets, error introduced into the data when registering scans was minimized. The shape of a spherical target, rather than the intensity of reflected laser energy, was modeled by the scanner during target acquisition and the center was located. However, the spherical targets were often not located by the scanner in the correct position during target acquisition, so a test was carried out in an area away from the non-invasive sampling site to attempt to understand and possibly address this issue.
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Spherical targets were tested by placing them next to HDS targets at known distances and angles from the laser scanner (Figure 4.3). Both types of targets were repeatedly scanned using a range of values for spherical target acquisition parameters (modeling resolution and target size). This was done so that patterns in spherical target mis-locations could be traced for example, to a certain distance or modeling parameter. No such pattern could be identified but it was clear that the issue was more associated with varying target
Figure 4.3. Testing spherical targets next to pairs of HDS targets.
modeling parameters than scanning distances or angles. A comprehensive evaluation of spherical target modeling parameters would have to be undertaken before these targets could be utilized over sea ice at long distances.
4.3 Initial Results
LiDAR data from the same viewing perspective at four regular intervals over the sampling period are shown in Figure 4.4. Melt ponds initially formed on the surface on 9 June and at a peak on 14 and15 June covered over 70% of the surface. Melt water then began to drain from the ice surface through seal holes and brine drainage channels. This period was accompanied by a transition between isolated ponds on 14 and15 June to an increasingly channelized hydrological regime by 26 June. Pond
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levels appeared to be lowest at the end of the sampling period on 26 June, just a few days before the ice broke up. The accuracy of LiDAR data was dependent on a number of mitigating factors. Sources of error include positional error (poor quality registration between scans), error relating to optical transfer (attenuation and scattering of the laser beam off dust particles or precipitation) and error relating to the detection of returning laser energy (differences between echoes from the same emitted pulse that are smaller than the sensor range resolution). Air temperature, barometric pressure and relative humidity all affect the speed of light, but to such a small degree that related errors in point locations are negligible. Given that LiDAR data were not acquired in poor weather conditions and the range resolution of the Scanstation C10 was not a limiting factor for acquiring data over sea ice, the accuracy of the collected data was primarily dependent on registration error. Seventeen sets of LiDAR data were acquired over the site during the three week sampling period and average absolute registration error for these data sets was 2.9 cm. Minimum error was 1.6 cm on 16 and 21 June and maximum error was 7 cm on 25 June.
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8 June
14 June
20 June
Figure 4.4. LiDAR data viewed from the same perspective at four intervals over the sampling period. Orange-coloured LiDAR points can be seen over snow and ice, and dark areas where the laser was absorbed can be seen over water (melt ponds).
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26 June
4.4 Method Evaluation Following critical evaluation of the method, a number of comments are raised regarding difficulties, initial solutions and future recommendations for time-series based LiDAR data acquisition over sea ice: 1. Targets – Spherical target locations were often translated in space to a point in the background of the target acquisition window, generating up to > 25 m registration error. In order to benefit from the advantages spherical targets offer over, for example, HDS reflectors (e.g. zero human interference over sampling period); comprehensive tests must be performed to calibrate spherical targets to software modeling routines. It is recommended that these tests focus on the influences of target distance, target colour and the method and resolution of modeling routines on error in acquired target position. 2. Leica Scanstation C10 system – The scanner failed to operate at temperatures below approximately 5 degrees Celsius, especially during windy conditions. The combination of space and battery heaters under and around the scanner with a tarpaulin cover failed to heat the scanner sufficiently, even after the space heater was raised to a position directly below the scanner. The use of heat packs to pre-heat the equipment before it was brought out of its transport case and the direct heating of the scanner through its ventilation intake on start-up with a 1500 W hairdryer succeeded, but was temperamental and required an additional 15 minutes or so preparation time before each scan. Several ideas had been mentioned for a long-term solution to this problem, but the best seems to be a heated case capable of bringing 33
the equipment to a temperature above what is required for scanning so that it can even partially cool down during start-up and still work. 3. Field of view – It would be useful to have a compass mounted on the bracket below the scanner so that a reasonable estimate of the required field of view could be made prior to scanning, without the need for the laptop. Data could then be acquired more easily if the laptop or connection to the scanner stopped functioning properly. 4. Multi-path error in LiDAR data – A strong returning laser signal was observed in one small area over a number of scans, causing artificial LiDAR points to be recorded at a distance much greater than the laser range. It is suggested that this is a result of the sun reflecting off the surface of a melt pond near to the scanner, leading to saturation of the scanner’s sensor. This may be unavoidable in future, but these artificial points are quite easily removed during post-processing and if needed a function could be added to the laser sampling procedure to ignore returns above a certain intensity (i.e. with very high energy). 5. Acquisition of physical data – When using the leveling apparatus to measure surface elevation, minor movement of the tripod caused significant error in measured height. To keep the tripod stable, it is recommended that a platform be frozen into the ice prior to sampling in addition to the four (or more) platforms installed around the site for acquiring LiDAR data. This would also facilitate calculations of ablation between transects of surface elevation recorded on subsequent days. 34
Table 4.1. Data Sampling Log Date (2011) 27 May 28 May 29 May
LiDAR Data Acquisition
Physical Data Acquisition
Log Travel Travel, platform building Platform building, time-series site relocation Time-series site relocation, platform peg modification Platform installation, fixed spherical target installation
30 May 31May 1 June 2 June 3 June 4 June 5 June 6 June 7 June 8 June 9 June 10 June 11 June 12 June 13 June 14 June 15 June 16 June 17 June 18 June 19 June 20 June
21 June 22 June 23 June 24 June 25 June 26 June 27 June 28 June 29 June 30 June 1 July 2 July
Fail with blanket Fail with heater Fail with heater Fail with heat packs Success with hairdryer Fail to register
Full-site scan Full-site scan Fail weather Full-site scan Full-site scan Full-site scan Full-site scan Full-site scan Full-site scan Full-site scan 24-hour sampling – 3 scans Full-site scan Full-site scan Fail weather Full-site scan Full-site scan
Heater platform and cover building Heater platform modification Transect marker placement
Up to 25 m spherical target error Target testing – spherical vs. HDS Target testing and error evaluation Target testing – range of acquisition parameters Fixed HDS target installation Full transect Full transect Full transect Full transect Full transect Full transect Full transect Full transect
Full transect Full transect Full transect Full transect Ice break-up Ice break-up Ice break-up Nothing Nothing Nothing
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5.0 Air-Ice-Ocean gas exchanges and Microclimate
Michael Fischer Ph.D. Student Supervisors – Dieter Wolf-Gladrow (AWI), Gerhard Dieckmann (AWI), Sabine Kasten (AWI), Gernot Nehrke (AWI) Alfred Wegener Institute for Polar and Marine Research Bremerhaven, Germany and Tim N. Papakyriakou Associate Professor Centre for Earth Observation Science (CEOS) Faculty of Environment, Earth and Resources University of Manitoba Winnipeg, Manitoba and Bruce Johnson Centre for Earth Observation Science (CEOS) Faculty of Environment, Earth and Resources University of Manitoba Winnipeg, Manitoba
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5.1 Introduction The Air-Ice-Ocean gas exchanges and microclimate program was designed to record basic meteorological conditions, snow and sea ice temperature structure, basic exchanges of heat, radiation, momentum and mass (CO2 and H2O) over seasonal sea ice and in situ pCO2 in the water column, sea ice, melt ponds and boundary layer. The main purpose of the monitoring program was to acquire a data set that will (1) allow multiple measures of the CO2 flux for cross comparison, (2) provide a gradient of pCO2 from water through the ice to the atmosphere and (3), support an investigation into the drivers of the surface CO2 flux. Additional information on the surface microclimate generally supports other studies conducted at the site.
5.2 Micrometeorology and Surface Radiation A micrometeorological tower 4.5 m tall and a radiation tower (Figure 5.1 and 5.2) were deployed between April 26 and June 27 2011.The micro-meteorological tower operated on the land fast sea ice in Allen Bay (74° 42.850’ N; 95° 11.980’ W), approximately 1 km south of the main base camp. The radiation tower was deployed at 74° 42.851’ N, 95° 12.640’ W, 35 m west of the main flux tower. Ice thickness was 1.58 m at the time of the tower installation. Snow thickness ranged between 4 and 15 cm in the vicinity of the towers over the sampling period. Equipment was powered by a battery bank, which was continually charged by a diesel generator located on a pallet and protected by a wooden box at 74° 42.876’ N, 95° 11.936’ W, 50 m in a N/NE direction from the main flux tower. The batteries and 37
charger were located at an equidistant position between the generator and tower. Instruments deployed on or near the tower are described in Table 5.1.
Figure 5.1. Flux tower. The eddy covariance system was at the top of the tower as well as the sampling inlet for the closed path analyzer.
Figure 5.2. Radiation tower.
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Table 5.1. Description of instruments. Sensor
Variables
Units
Ht from surface (m)
Scan (s) /Ave (min)
Wind monitor (RMYoung 05103)
wind direction
M s-1; º
4.65
2 sec/ 1 min
ºC; %
4.20
1 sec/ 1 min
µmol m-2 sec-1
4.50
Temperature/relative humidity probe (Vasailla T and RH HMP45C212) Photsynthetically active radiation (Kipp & Zonen PARLite)
PAR
3D wind velocity (Campbell Scientific CSAT3) ic anemometer)
u,v,w, speed of sound (SOS)
m s-1
LI7500 open path gas analyzer
CO2/H2O
µmol m-1 4.0 mmol/ m
LI7000 closed path gas analyzer
CO2/H2O
µmol m-1 inlet at m mmol/ 3.75
Net radiation: (Kipp&Zonen CNR1)
SWin, SWout LWin, LWout
UVA,B (Kipp&Zonen Pressure transducer (RM Young, 61205V) Ice and snow temperature structure
1 sec/ 1 min
Wm
±0.6 m/s ±3º deg Humidity ±2% @ 20ºC ±3% @ 20ºC 0.05% RH/ºC ± 0.1 ºC < ± 0.1 % / °C quantum response matching error < 10%
20 Hz
RMS noise
