An Unmanned Aerial Vehicle campaign to measure variability of Arctic albedo over glaciers, sea ice, and ice sheets. Measurements will be made 'operationally' on a weekly basis during the spring and early summer. Furthermore, during intensive pollutant transport episodes, flight frequency will be increased to assess whether there is a detectable change in the albedo.
||VAUUAV is a core project of CICCI
||Transport Forecasts are online
NORKLIMA III: Variability of Albedo Using an Unmanned Aerial Vehicle
John F Burkhart, The Norwegian Institute for Air Research, Kjeller, Norway
Johan Ström, The Norwegian Polar Institute, Tromsø, Norway
Rune Storvold, Norut IT, Tromsø, Norway
|VAUUAV 2009||VAUUAV 2010|
1 Project summary
A particularly important process in the Arctic surface energy budget is the snow/ice albedo feedback which contributes considerably to polaramplification of global warming (Køltzow et al., 2007). Several recent studies of pollutant transport, in particular black carbon, to the Arctic indicate that the deposition of black carbon on snow/ice surfaces may have a significant effect on the energy balance (Flanner et al., 2007; Law and Stohl, 2007; Quinn et al., 2007a/b). Currently, there are insufficient measurements to evaluate black carbon induced changes on albedo in a quantitative manner and satellite measurements lack the required precision to monitor this effect. This is a fundamental parameter for climate modelling and requires attention. Thus we propose an innovative solution relying upon state-of-the-art technology to acquire an improved assessment of Arctic albedo variability as well as pollutant induced changes of albedo.
Our measurement platform relies principally on the ‘operational’ deployment of a proven Unmanned Aerial Vehicle (UAV). Norway has a strong capacity in the operation of UAVs, yet, to date this expertise has not been fully utilized. The UAV platform will measure albedo over a wide range of Arctic terrain providing a valuable time series of baseline variability. Furthermore, utilizing forecast products from the FLEXPART Lagrangian transport model, we will initiate ‘Intensive Observing Periods’ (IOPs) during unique pollutant transport episodes in which it is suspected black carbon will be delivered efficiently to our sampling locations. The UAV will be flown ‘operationally’ during two campaigns, each covering approximately six months. The first campaign will take place in Svalbard, Norway with the objective of measuring snow albedo on polythermal glaciers and over sea ice. The second campaign will focus on the dry snow zone of the Greenland Ice Sheet operating out of the Summit Station observatory. The contrast between these two environments will not only provide an excellent opportunity for comparative measurements, but also enables the development of a data product for albedo with broad application to regional Arctic environments.
The primary goals of this project are to: a) establish highest quality measurements of Arctic albedo and background variability, b) quantify induced changes of albedo over snow/ice surfaces driven by transport/deposition of pollutants; c) contribute improved albedo measurements to climate modelers to evaluate feedback processes; d) further develop shared expertise between U.S. and Norwegian researchers working with climate data in Svalbard, Norway and throughout the Arctic.
A particularly important process in the Arctic surface energy budget is the snow/ice albedo feedback which contributes considerably to polaramplification of global warming (Køltzow et al., 2007; Flanner et al., 2007). This positive feedback process, whereby increased temperatures cause melting of highly reflective snow/ice covered areas, increases absorption of solar radiation due to a decreased regional albedo; ultimately increasing the temperature. Despite extensive studies demonstrating the sensitivity of the Arctic to global warming, there is a need to better understand surface albedo feedback process in the Arctic and the interaction with the global climate system (Solomon, 2007; Walsh, 1991). This process is largely related to increased temperatures driven by the accumulation of long-lived greenhouse gasses. However, as demonstrated by Law and Stohl (2007) and Hansen and Nazarenko (2004) the deposition of aerosols, particularly black carbon, also likely effect the energy budget through alteration of the surface albedo. Addressing this variable has particular importance in the immediate term for the Arctic. The recent work of Quinn et al. (2007b) outlines mitigation efforts to address Arctic climate change targeting short-lived pollutants, as even rapid decreases in CO2 will not likely delay the melting of the Arctic due to the long lifetime of the compound.
Curry et al (1995) outlined the surface albedo feedback mechanism and demonstrated a direct response to model perturbations. Recent investigations have shown that not only is the Arctic subject to change, but may also drive change at lower latitudes. Specifically, Dethloff et al. (2006) using an atmosphere-ocean general circulation model highlighted the process by which alterations to the polar energy sink in the Arctic exert strong influence on the North Atlantic Oscillation, ultimately influencing European climate. Subsequent studies have clearly shown albedo, particularly in the Arctic, to be a key parameter for climate modelling (Køltzow, 2007; Stamnes, 1999). Winton et al. (2006) also maintain amplified Arctic climate change as a function of albedo feedback is at least as important as grouped non-albedo feedback mechanisms. Following the work of Curry (2001), Køltzow et al. (2007) show the albedo feedback scheme employed in a climate model has dominant role in the representation of the model’s energy balance, particularly over sea ice. What is most pressing is the lack of a unified understanding of this process; no albedo feedback scheme employed to date properly captures the entire annual cycle, with the common deficiency of over-estimating summertime albedo and an underestimation by temperature dependent albedo feedback schemes during the spring to summer transition.
Contributing to the uncertainty of the feedback process is the high degree of uncertainty in the measurement of albedo itself (Key, 2001; Xiong, 2007, Stroeve, 2001, 2005). Albedo is the ratio of reflected to incident radiation. Broadband or spectral measurements can be made in terms of a bi-directional reflectance, or as hemispheric measurements. Direct measurements for the Arctic exist for many locations and times (Perovich 1994, 2002, 2004; Inoue, 2005; Grenfell, 1984; Gerland, 1999a, 1999b, 2000, 2004; Winther, 1999, 2002), but few consistent datasets exist that have both high spatial and temporal resolution. The surface reflectivity and albedo are complicated by numerous variables that affect them, including snow/ice grain shape and size, surface roughness, solar zenith angle, snow water and impurity content (Grenfell, 1994; Curry, 1996; Liu, 2007). Retrievals from satellites in the Arctic are complicated by a bright surface, low sun angles, and often cloudy conditions. For the Arctic use of both the advanced very high resolution radiometer (AVHRR) and MODIS has been employed to develop a product, but with an admittedly high degree of error particularly during the spring when atmospheric water vapors are high (Xiong, 2007). A significant challenge is to capture variability when overall seasonal changes are small (Stroeve, 2001). To date, many of the current modelling studies examining the albedo feedback mechanism rely on a singular set of measurements from 1998 developed during the SHEBA campaigns (Perovich, 2007).
Perhaps the most significant open question concerning the Arctic and albedo is the influence of black carbon and other short-lived pollutants on the regional climate system, and the quantitative role in the albedo feedback process for climate change (Flanner et al, 2007). As a result of its influence on both the absorption of radiation in the atmosphere, as well as having a direct impact on snow/ice albedo, black carbon has significant implications for radiative forcing of the Earth-atmosphere system (Hansen and Nazarenko, 2004). In the Arctic this has even greater significance due to the highly reflective ice and snow surfaces further enhancing the atmospheric absorption and their susceptibility to soot deposition.
Aerosol driven perturbations of the energy balance are complex with effects both at the surface and in the atmosphere. Aerosols which strongly scatter light will tend to cool the atmosphere through the reflection of incoming radiation, while strongly absorbing aerosols will have the opposite effect. However, even moderately absorbing aerosols may cause heating of the atmosphere over the high albedo surfaces found in the Arctic (Quinn et al., 2007b). Slight changes in albedo can shift the feedback between positive and negative modes as a result of interaction and energy balances between the ground, aerosols and clouds. Myhre et al. (submitted) examined aerosol optical depth (AOD) measurements acquired during a transport event in Svalbard, Norway as originally reported by Stohl et al. (2007). Using a multi-stream radiative transfer model with ground-based and satellite AOD measurements as inputs they calculated the direct radiative forcing (DRF) for the event and single scattering albedo (SSA) over Ny Ålesund. In their study, the results indicate a potential cooling as a result of the intensive aerosol loading. However the sign of the DRF is highly dependent on the ‘turnover value’ which is a function of surface reflectance (Haywood and Shine, 1997). In the Arctic SSA values as high as 0.98 can still yield a heating effect (Pueschel and Kinne, 1995) due to the high albedo of the ice/snow surfaces. Thus the current ambiguity in albedo products has direct consequence for our ability to understand and predict climate response resulting from the ice albedo feedback mechanisms.
Recent efforts to understand the sources of black carbon in the Arctic have shown that while significant contributions come from the southeast Asian continent (Koch and Hansen, 2005), the work of Stohl (2006) indicates the most significant source may be from biomass burning in the boreal region. Though seasonal, this source delivers black carbon to the Arctic during the austral summer when incoming radiation is greatest and when the albedo feedback process will have the greatest implications for the climate system. In fact, Stohl (2006) demonstrated boreal fires in western Canada delivered significant amounts of black carbon throughout the Arctic with a measurable impact on albedo at Summit, Greenland – over 4000 km. from the fires. In another study, the author of this proposal was witness to dramatic changes in albedo on Holtedahlfonna Isbree, Svalbard driven by extremely efficient transport of pollutants from agricultural burning in Eastern Europe (Stohl et al., 2007). Unfortunately, direct albedo measurements were not available at the time, but image processing techniques were employed on photographs to demonstrate a drop in reflectance of ~8% from the snow surface. These studies demonstrate the need for albedo measurements which can quantitatively resolve changes in albedo driven by black carbon deposition.
The urgency to understand these processeses cannot be understated. As shown by Stroeve et al. (2007), even the ‘business-as-usual’ International Panel on Climate Change Fourth Assessment Report (IPCC AR4) sea ice models are too conservative and it is likely the Arctic will move to an ice free state well within this century. Addressing the CO2 burden is fundamental, but it is likely the greatest efforts to mitigate these changes will result from understanding and addressing changes driven by Arctic air pollution (Quinn et al., 2007a). The proposal set forth here has the specific objective of developing a comprehensive record of albedo measurements over a variety of Arctic terrain and assessing pollutant driven changes on the surface albedo feedback process.
3 Proposed work
This proposal employs an innovative solution relying upon state-of-the-art technologies to investigate earth system processes at the regional scale. Measurement and modelling approaches will be used in parallel to optimize sampling and interpretation of collected data. The goals and objectives of program elements are broken down into 5 individual work packages (WP): Coordination (WP1); Unmanned Aerial Vehicle Instrumentation (WP2); Measurements (WP3); Intensive Observing Periods (WP4); and Synthesis and Dissemination (WP5).
3.1 Work Package 1: Coordination
Leader: J.F. Burkhart (NILU)
The success of this project relies upon the expertise of numerous investigators. Each component of the research has specific logistical, technical, and scientific requirements. Coordinating these activities will require strong communication between the groups. The lead scientist of this proposal is familiar with coordinating intensive sampling programs and has experience co-leading the secretariat for the IPY POLARCAT program as well as the Summit, Greenland Science Coordination Office (SCO). Specific goals and objectives for this work package include:
Maintain robust communication amongst all project participants: In order to facilitate communication between investigators across Norway, a centralized web-portal will be established. Through this portal investigators will have access to shared workspace for data analysis and manuscript preparation. As required, organizational meetings will be hosted at the Tromsø offices of NILU.
Coordinate data collection and archiving for efficient delivery to national data centres: An online database of the measurements will be maintained and available at all times to all project participants. The database will be developed following the guidelines of ISO 19115 metadata management. Following this structure allows for greater interoperability between data archive centres creating data elements that allow users to identify, evaluate, select, obtain, and use the datasets. Furthermore, geographic metadata elements allow the user to understand without ambiguity spatial extents of the data set.
Engage the public in Arctic research activities: The innovative nature of this project will certainly draw public attention. We intend to capitalize on the interest to broadly educate viewers about issues regarding the Arctic and atmospheric transport. We will provide near real-time (possibly real time) updates of the flight tracks, and images from each flight. Basic flight data will also be posted on the website. Several educational modules will be created providing fundamental information about albedo in the Arctic and the technical specifications of the UAV.
3.2 Work Package 2: Unmanned Aerial Vehicle Instrumentation
Leader: R. Storvold (Norut); co-Leaders: J.F.Burkhart (NILU), S. Gerland; C.A. Pedersen; T. Svenøe (NPI)
The two principal goals of this project are to assess natural variability of albedo in a variety of Arctic environments, and to address induced changes in albedo from the deposition of pollutants to snow/ice surfaces. To address these goals we propose two operational ‘modes’ of the CryoWing Unmanned Aerial Vehicle (UAV); 1) a ‘baseline mode’ will allow for the measurement and determination of background or natural variability in albedo; 2) An intensive observing period mode (‘IOP mode’ – see Section 3.3) will provide a unique opportunity to capture changes induced specifically by pollutant transport events.
3.2.1 Payload components in the UAV
The UAV will carry: 2 radiometers with RS232 interface, 1, digital camera, 1 GPS sensor, 1 Gyro, 1 Tiltmeter, 1 air temperature sensor, 1 wind speed sensor, and 1 small personal computer. Scientifically, the radiometers and the digital camera are most central, therefore in the following they are more commented in detail.
The radiometers cover a wavelength range from 320 to 950 nm (CCD sensor), and are produced by TriOS (type Ramses). Contrary to the standard radiomter version usually sold by TriOS, this version will be modified to meet low-weight demands for the UAV. One sensor will be oriented upwards, the other downwards, in order to log incoming and reflected solar radiation. The sensor logging the incoming radiation will have a cosine receptor foreoptics, the one that measured the reflected radiation will have a limited view (less than 20 degree). The digital camera will take images downwards simultaneously to radiometer measurements. Standards will be adjusted according to flight elevation and speed (e.g. 2 sec. interval), in order to obtain continuous (overlapping) data/images. Data and images will be recorded along with the current position and time on the UAV PC.
3.2.2 Operational UAV Flights
Year 1 of the project will be focused on integration, development, and testing of the UAV instruement packages. The integration of these systems requires some small modifications to the airframe and data collection system. During this first year, flights will be conducted out of Tromsø and possibly Svalbard to test payload performance under the same environmental conditions as is expected during the Svalbard and Greenland campaigns. This will also provide test datasets which can be used in preparation for the deployments. During Years 2 and 3 of the project, operational deployment will take place from March through June on Svalbard and March through August in Greenland. In the ‘baseline mode’ the CryoWing will be flown once a week to establish a baseline record of albedo variability over varying terrain. Repeat overflights will be conuducted throughout the deployments. Concurrent with each flight, snow samples (see Section 3.2.4) will be collected from nearby the Zeppelin Mtn. observatory (in Year 2) and from the undisturbed snow sector at the Summit Station Observatory in Greenland (in Year 3). Both sites provide clean undisturbed snow free of local impacts. During the ‘IOP mode’ flights and snow sampling will be increased as appropriate.
Critical to the success of these objectives is the effective and operational deployment of the CryoWing. Norut has developed, tested, and deployed the CryoWing prototype UAV successfully on several shorter campaigns in the Arctic (Figure 1). Based on this experience, a new platform has been aquired with extended range and payload capabilities. During the IPY Norwegian – US Antarctic traverse in the 2007/2008 and 2008/2009 seasons this system will be deployed with a geophysical payload over the Antarctic Platau. The
platform consists of an airframe, autopilot with satellite link, PC for payload control and data logging, and a ground control station. The latest version of the CryoWing (first airplanes received in May 2007) has a range of 1500-2000 km, depending on payload.
The UAV operation will be performed similarly to that on the Antarctic Traverse. Norut will have a Pilot/payload specialist deployed throughout the campaigns to work with the local station technicians (This person will be swapped every 6-8 weeks). This individual will work with two technicians who will be trained on-site to support during launch and recovery. In addition Norut engineers and scientists will provide remote support from Tromsø as the UAV system is complex and consistes of many subsystems which requires different fields of expertice.
The deployment in Year 2 will start in March 2009 in Ny-Ålesund when the sun returns and continue through June 2009. In Ny Ålesund, Trond Svenøe will work with Norut at the Sverdrup station to organize flight operations. The Year 3 deployment on Greenland will similarly be conducted from March through August 2010. The shorter period on Svalbard versus Greenland is chosen because of unfavourable snow conditions/lack of snow and sea ice, and frequent occurency of fog/poor visibility in summer on Svalbard.
The first days of the campaigns will be designated to system setup and testing after transport and training of local support personnel. Thereafter we will perform a local area pattern shakedown flight before regular flights start which will be approximately once a week depending on weather. Additional flights in connection with transport events identified by FLEXPART modelling (see Section 3.3) will be conducted as needed. Along with regular deployments, flights of opportunity will be coordinated, in time and area of interest, with Aqua and Terra MODIS overflights when feasible weather conditions (cloud free) exist so that direct comparison between satellite products and aerial measurements may be performed. Due to the changing weather condtions we realize that there could be periods where it would not be possible to fly due to precipitation, fog and supercooled low clouds. The airplane is equipped with an icing sensor that will warn the operator of icing and allow us to abort/change the mission to avoid loss of equipment.
3.2.3 Target terrain
A primary goal of ths project is to develop a baseline record of albedo variability in the Arctic. As discussed in Curry et al. (2001), despite the importance of the sea-ice albedo feedback process, due to a lack of larger, consistent observational data sets, most models use overly simplistic represenations of the parameter. A robust set of data collected during the SHEBA campaigns provided a new set of measurements enabling modelers to objectively tune the parameterization (Liu et al., 2006; Perovich et al., 2007). However, this dataset is also limited to a singular year and is applicable to sea ice only. We have chosen our two locations based on the access to terrain that will enable acquisition of baseline measurements over a variety of key Arctic snow/ice surfaces.
The chosen locations for the campaign deployments represent two diverse locations, each with uniquely valuable characteristics. The Svalbard flights will cover sea ice and polythermal glaciers North and East of Ny-Ålesund, Spitsbergen, respectively. (Figure 2) We expect the highest degree of variability to occur over the glaciers where temperature changes will alter the physical structure of the snowpack significantly. Fortunately, a recently installed transect of weather stations along the Kongsvegen glacier will provide valuable information to control for this process (pers. comm J.Kohler). For flights over the fast ice in Kongsfjorden, information from the longterm Kongsfjorden fast ice monitoring project of the NPI (Gerland and Hall, 2006; Gerland and Renner, in press) will be used along with optical measurements done on reference spots during the campaigns. The Greenland Flights on the other hand will enable measurements over a relatively homogeneous surface with a much lower degree of temperature driven variability. These transect will go to the East and West of the ice sheet (Figure 3) with emphasis placed on the dry snow zone (regions >2000 m.a.s.l) where variability will be most constrained. For both campaigns a detailed route will be loaded into the autopilot (up to 1000 waypoints) which will be repeated for each flight. The autopilot will keep the airplane within an approximately 30 m wide corridor of the scheduled track independent of winds, allowing routine repeat measurements.
3.2.4 Work Package 3: Measurements
Three basic datasets will be collected during the flights: 1) spectral albedo measurements, 2) black carbon in snow, and 3) basic meteorologic information. This data will be collected during each flight and submitted to the online database for access by project participants. The leader of this work package has extensive experience in both the collection of spectral albedo measurements and black carbon measurements. Knowledge gained during the RCN Projects: Spectral Reflectance of Snow and Sea Ice (1997-2000), Atmosphere-Ice-Ocean Interaction Studies in Svalbard Fjords (2002-2006), and Climate Effects of Reducing Black Carbon Emissions (2005-2008) will be employed to optimize sampling during this project. Goals of this component include: a) collect high-accuracy spectral albedo measurements from the UAV; and b) collect a concurrent set of black carbon concentrations in snow.
3.2.5 Spectral albedo from air
Using the instrumentation as detailed in detail in WP2, continuous spectral albedo transects will be gainedfrom UAV transects. In principle, there are planned weekly flights out from Ny-Ålesund (first phase) and out of Summit, Greenland (second phase).
3.2.6 Spectral albedo from ground
A mobile, autonomous albedo monitoring station with identical instruments as used in the UAV will be installed at a reference spot near Ny-Ålesund (2009) and at summit on Greenland (2010). The station will consist of two TriOS Ramses VIS radiometers (320-950 nm). A similar setup is already used by the NPI for certain periods as a part of the EU research project DAMOCLES in Storfjorden, at the drifting station “Tara” and in the Fram Strait (IPY project iAOOS Norway, NFR). Data will be recorded on a datalogg each 30 min., and the measurement frequency will be increased to 1 min. during flights. This setup will, along with weather station data, give us a good picture on the seasonal surface albedo changes related to snow metamorphosis and snow thickness. The data will also have the function for calibration and validation of the more complex UAV optical data.
3.2.7 Other optics from ground and snow properties
During shorter field campaigns, additional optical data and physical properties of snow will be obtained. Albedo data with very high wavelength resolution and a broad range using a more advanced spectroradiometer (ASD Fieldspec, range 350-2500 nm) will be recorded on selected spots and occasions. In snowpits, standard physical snow properties including snow temperature, density, moisture, grain size, snow classification is determined along vertical profiles (see e.g. Gerland et al. 1999b), with highest attention to the surface layers, being most important for the albedo.
3.2.8 Black Carbon concentration in snow
A key issue of this project is the investigation of the effect of black carbon (BC, soot) on the surface albedo on a larger, regional scale than it is done in current ongoing activities. Most of the project team members are involved in current BC projects (IPY Polarcat, NFR-BC (Programme Norklima), NFR Norwegian-US BC (ProgrammePolar Research)), and they are familiar with the issue/field.
Here, funding is asked for to measure a total of 100 snow samples for BC content. The samples will be collected on spots that are below flight lines of regulary (weekly flights), and at the position where the albedo logging station is installed. The upper centimetres of the snow will be sampled (sample size about 2 litres). Snow is melted, filtred and the filters then analysed at the University of Stockholm, Sweden (J. Ström), for BC content. In conjunction with our own measurements, we have formed an agreement with Stephen Warren of the University of Washington in the United States. Under an existing program they are analyzing snow samples for BC throughout the Arctic and will provide access to that dataset as a part of this collaboration.
3.3 Work Package 4: Intensive Observing Periods
Leader: J.F. Burkhart (NILU)
To address the open research question regarding changes in snow/ice albedo resulting from the deposition of black carbon we propose to establish a series of ‘on-demand’ intensive observing periods (IOPs). Using the Lagrangian transport model, FLEXPART, we will set up an automated forecast system (Figure 4) to produce alerts for periods of potentially unique synoptic scale transport episodes. The FLEXPART model in this same ‘forecast‘ configuration has been used in prior investigations including the recent ASTAR, INTEX-B, and MILAGRO atmospheric chemistry campaigns (e.g. see: http://data.eol.ucar.edu/codiac/dss/id=93.027). The alert system will be based on regular output of forecast products. Certain grid cells will be monitored for activity, if concentrations of the carbon monoxide (CO) tracer exceed a predefined value for a period of time, an alert will be generated. CO is the chosen tracer as a result of its 20 day lifetime and its representation of both anthropogenic and biomass burning emissions. Once alerted to the episode, we will track the progress and initiate increased UAV flights and corresponding snow sampling if appropriate.
Capture unique transport episodes: By developing an algorithm which automatically ingests FLEXPART forecast output we will be able to continuously monitor our sampling locations for potential unique transport episodes. A series of gridcells of interest for each sampling location will be defined. FLEXPART forecast output at those gridcells will be monitored. As concentrations of the chosen tracer exceed a certain limit for a specified period of time, an automated alert system will be invoked sending email messages to appropriate project investigators. At that point, closer investigation of the episode will be initiated.
Initiate Intensive Observing Periods (IOPs) during transport events: If the transport episode initiating the alert system warrants increased sampling frequency, station personnel will be notified as well as the appropriate investigators. NorutIT personnel and on-site technicians will be able to increase the flight frequency as well as the snow sampling frequency. Through this approach we will be able to efficiently deploy the resources to capture the events, while maintaining baseline measurements to gain an understanding of the overall variability in the process.
Figure 4. FLEXPART Forecast System schematic and example products (Panels a-d)
The principal goal of this work package is to develop a quantitative measure of changes in albedo driven by deposition of atmospheric pollutants. We will address the spring Arctic Haze period, but focus on summer episodes. Traditional studies of Arctic air pollution have focused on Arctic Haze, a phenomenom occurring during the late winter or early spring in which the slow decay of pollutants during the dark winter months causes the highest aerosol levels of the year (Figure 4a). However, there are several factors causing summertime aerosol loads to have a potential for greater impact on the climate system. First, in the summer, deposition of aerosols is more efficient as a result slower transport and greater water vapor content. The more efficient deposition of aerosols can clearly be seen in the history of size distribution collected at the Zeppelin Mtn. observatory (Figure 4b). There is a distinct shift in the size distribution from at the end of winter with smaller aerosols dominating during the summer. Another important factor regarding the summertime period versus the winter is the availability of sunlight. In the summer, there is plenty of incoming solar radiation thus the effect of reduced albedo will have much greater significance to the radiative feedback and energy balance than during winter or late spring.
A newly recognized component to the summer season is the corresponding forest fire season in the boreal region of the sub-Arctic. In a climatological investigation of Arctic transport pathways, Stohl (2006) showed that black carbon from boreal forest fires in Siberia and North American continent can have a significantly higher potential for transport than anthropogenic pollution. As there is increasing evidence that climate change is increasing the duration of the forest fire season as well as the size of the fires (Westerling, 2006) it is critical that we understand the impact these events will have on the Arctic. In a recent investigation following the 2004 forest fire season, the efficient transport of forest fire plumes throughout the Arctic was shown to have measurable and significant effects on AOD at four different observatories including Zeppelin as far as 7000 kilometers from the source of the fires (Stohl et al., 2007). Furthermore, that same study demonstrated a measurable effect on albedo at Summit Station, Greenland – though a lack of sufficient data restricted the analysis to being qualitative. This study will address the existing data gap and enable quantitative investigations into the effect of soot deposition on albedo during such episodes.
As a result of several components of this project including: autonomous flight technology, field deployed UAVs operated by local technicians, and the use of accurate and proven forecast capabilities of FLEXPART, we will have fine-grained control over our sampling program. The application of this systems approach to our sampling method allows great efficiency and benefit to the data collection. Yet, by having an ongoing ‘baseline’ measurement program we will be able to conduct sound statistical analysis of the the variability induced by black carbon deposition.
4 Work Package 5: Synthesis & Dissemination
Leader: J.F. Burkhart (NILU); co-Leaders: S. Gerland (NP); J.O. Hagen (UiO)
Due to the interdisciplinary nature of the problem, data processing, analysis, and interpretation will necessarily be a staged process. Initial priority will be placed on the development of sound albedo measurements and integration of our measurement system into the UAV. While in theory, albedo is simple measurement, it is complicated by numerous factors including environmental conditions (e.g. cloud cover, rhime), snow metamorphism, and slope – as examples. For proper interpretation modelling will be required to evaluate the effect of local variables on the measurement. Furthermore, the changes we expect to see will be slight, resulting in the need for highly accurate data.
Radiative transfer calculations will be an important component of our data analysis and integration. Our goal is to provide our dataset to the modelling community for incorpration into climate models to improve assessment of the albedo feedback process in the Arctic with sound measurements. A Ph.D. student will be hired to work closely on this aspect of the research. Publication of the results will be produced in leading peer reviewed journal publications
Curry, J. A. (1995), Interactions among aerosols, clouds, and climate of the Arctic-Ocean. Science of the Total Environment, 161, 777-791.
Curry, J. A., et al. (1996), Overview of Arctic cloud and radiation characteristics, Journal of Climate, 9(8), 1731-1764.
Curry, J. A., et al. (2001), Applications of SHEBA/FIRE data to evaluation of snow/ice albedo parameterizations, Journal of Geophysical Research-Atmospheres, 106(D14), 15345-15355.
Dethloff, K., et al. (2006), A dynamical link between the Arctic and the global climate system, Geophysical Research Letters, 33(3).
Flanner M, et al. (2007) Present day climate forcing and response from black carbon in snow. Journal of Geophysical Research, VOL. IN PRESS, doi:10.1029/,
Gerland, S., & Hall, R. (2006): Variability of fast ice thickness in Spitsbergen fjords. Annals of Glaciology, Vol. 44, pp. 231-239.
Gerland, S., & Renner, A.H.H. (in press): Sea ice mass balance in an Arctic fjord. Annals of Glaciology, Vol. 46.
Gerland, S., C. Haas, M. Nicolaus, and J.-G. Winther 2004. Seasonal development of structure and optical properties of fast ice in Kongsfjorden, Svalbard. In: Wiencke, C. (Ed.): The Coastal Ecosystem of Kongsfjorden, Svalbard. Synopsis of Biological Research Performed at the Koldewey Station in the Years 1991-2003. Reports on Polar and Marine Research, Alfred Wegener Institute, Bremerhaven, Germany. (492), 26-34.
Gerland, S., G. E. Liston, J.-G. Winther, J. B. Ørbæk, and B. Ivanov 2000. Attenuation of solar radiation in Arctic snow: field observations and modelling. Annals of Glaciology 31, 364-368.
Gerland, S., J.-G. Winther, J. B. Ørbæk, and B. V. Ivanov 1999a. Physical properties, spectral reflectance and thickness development of first year fast ice in Kongsfjorden, Svalbard. Polar Research 18 (2), 275-282.
Gerland, S., J.-G. Winther, J. B. Ørbæk, G. E. Liston, N. A. Øritsland, A. Blanco, and B. Ivanov 1999b. Physical and optical properties of snow covering Arctic tundra on Svalbard. Hydrological Processes 13 (14/15), 2331-2343.
Grenfell, T. C., and D. K. Perovich 1984. Spectral albedos of sea ice and incident solar irradiance in the southern Beaufort Sea. Journal of Geophysical Research 89 (C3), 3573-3580.
Grenfell, T. C., et al. (1994), Reflection of solar-radiation by the Antarctic snow surface at ultraviolet, visible, and near-infrared wavelengths. Journal of Geophysical Research-Atmospheres, 99(D9), 18669-18684.
Hansen, J., and L. Nazarenko (2004), Soot climate forcing via snow and ice albedos, Proceedings of the National Academy of Sciences of the United States of America, 101(2), 423-428.
Haywood, J. M., and K. P. Shine (1997), Multi-spectral calculations of the direct radiative forcing of tropospheric sulphate and soot aerosols using a column model, Quarterly Journal of the Royal Meteorological Society, 123(543), 1907-1930.
Inoue, J., et al. (2005), A drop in mid-summer shortwave radiation induced by changes in the ice-surface condition in the central Arctic, Geophysical Research Letters, 32(13).
Key, J. R., et al. (2001), Estimating the cloudy-sky albedo of sea ice and snow from space, Journal of Geophysical Research-Atmospheres, 106(D12), 12489-12497.
Koch, D., and J. Hansen (2005), Distant origins of Arctic black carbon: A Goddard Institute for Space Studies ModelE experiment, Journal of Geophysical Research-Atmospheres, 110(D4).
Koltzøw M. (2007), The effect of a new snow and sea ice albedo scheme on regional climate model simulations, Journal of Geophysical Research-Atmospheres, 112(D7).
Law, K. S., and A. Stohl (2007), Arctic air pollution: Origins and impacts, Science, 315(5818), 1537-1540.
Liu, J. P., et al. (2007), Evaluation of snow/ice albedo parameterizations and their impacts on sea ice simulations, International Journal of Climatology, 27(1), 81-91.
Liu, L., et al. (2006), Assessing Goddard Institute for Space Studies ModelE aerosol climatology using satellite and ground-based measurements: A comparison study, Journal of Geophysical Research-Atmospheres, 111(D20).
Mryhe C., et al. (submitted) Regional aerosol optical properties and radiative impact of the extreme smoke event in the European Arctic in spring 2006. Atmospheric Chemistry and Physics-Discussions.
Perovich, D. K., et al. (2007), Seasonal evolution and interannual variability of the local solar energy absorbed by the Arctic sea ice-ocean system, Journal of Geophysical Research-Oceans, 112(C3), 13.
Perovich, D., and T. Grenfell 2004. Sunlight and sea-ice: the ice-albedo feedback and a warming Arctic. In: Gerland, S & Njåstad, B.: Arctic Climate Feedback Mechanisms. Proceedings of a Workshop at the Norwegian Polar Institute, Tromsø, November 2003. NPI Report Series (124), 41-42.
Perovich, D., T. Grenfell, B. Light, and P. Hobbs 2002. Seasonal evolution of the albedo of multiyear Arctic sea ice. Journal of Geophysical Research 107 (C10), 8044, doi: 10.1029/2000JC000438.
Perovich, D. K. 1994. Light reflection from sea ice during the onset of melt. Journal of Geophysical Research 99 (C2), 3351-3359.
Pueschel, R. F., and S. A. Kinne (1995), Physical and radiative properties of Arctic atmospheric aerosols. Science of the Total Environment, 161, 811-824.
Quinn, P. K., G. Shaw, E. Andrews, E. G. Dutton, T. Ruoho-Airola, S. L. Gong (2007a): Arctic haze: current trends and knowledge gaps. Tellus B 59, 99-114.
Quinn, P. K., T. S. Bates, E. Baum, N. Doubleday, A. Fiore, M. Flanner, A. Fridlind, T. Garrett, D. Koch, S. Menon, D. Shindell, A. Stohl, and S. G. Warren (2007b): Short-lived pollutants in the Arctic: Their climate impact and possible mitigation strategies. Submitted to EOS.
Solomon, S., D. Qin, M. Manning, R.B. Alley, T. Berntsen, N.L. Bindoff, Z. Chen, A. Chidthaisong, J.M. Gregory, G.C. Hegerl, M. Heimann, B. Hewitson, B.J. Hoskins, F. Joos, J. Jouzel, V. Kattsov, U. Lohmann, T. Matsuno, M. Molina, N. Nicholls, J. Overpeck, G. Raga, V. Ramaswamy, J. Ren, M. Rusticucci, R. Somerville, T.F. Stocker, P. Whetton, R.A. Wood and D. Wratt, 2007: Technical Summary. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.
Stamnes, K., et al. (1999), Review of science issues, deployment strategy, and status for the ARM North Slope of Alaska-Adjacent Arctic Ocean climate research site, Journal of Climate, 12(1), 46-63.
Stohl, A., et al. (2007), Arctic smoke - record high air pollution levels in the European Arctic due to agricultural fires in Eastern Europe in spring 2006, Atmospheric Chemistry and Physics, 7, 511-534.
Stohl, A. (2006): Characteristics of atmospheric transport into the Arctic troposphere. J. Geophys. Res. 111, D11306, doi:10.1029/2005JD006888.
Stroeve, J., et al. (2007), Arctic sea ice decline: Faster than forecast. Geophysical Research Letters. VOL. 34, L09501, doi: 10.1029 /2007GL029703, 2007
Stroeve, J., et al. (2005), Accuracy assessment of the MODIS 16-day albedo product for snow: comparisons with Greenland in situ measurements, Remote Sensing of Environment, 94(1), 46-60.
Stroeve, J. C., et al. (2001), Intercomparison between in situ and AVHRR polar pathfinder-derived surface Albedo over Greenland, Remote Sensing of Environment, 75(3), 360-374.
Walsh, J. E. (1991), Climate change – the Arctic as a bellwether. Nature, 352(6330), 19-20.
Warren, S. G., and W. J. Wiscombe 1980. A model for the spectral albedo of snow. II: Snow containing atmospheric aerosols. Journal of the Atmospheric Sciences 37, 2734-2745.
Westerling, A. L., et al. (2006), Warming and earlier spring increase western US forest wildfire activity, Science, 313(5789), 940-943.
Winther, J.-G., F. Godtliebsen, S. Gerland, and P. E. Isachsen 2002. Surface albedo in Ny-Ålesund, Svalbard: variability and trends during 1981-97. Global and Planetary Change 32, 127-139
Winther, J.-G., S. Gerland, J. B. Ørbæk, B. Ivanov, A. Blanco, and J. Boike 1999. Spectral reflectance of melting snow in a high Arctic watershed on Svalbard: some implications for optical satellite remote sensing studies. Hydrological Processes 13 (12/13), 2033-2049.
Winton, M. (2006), Amplified Arctic climate change: What does surface albedo feedback have to do with it?, Geophysical Research Letters, 33(3), 4.
Xiong X., et al. (2002) Surface albedo over the Arctic ocean derived from AVHRR and its validation with SHEBA data. Journal or Applied Meteorology. V(41). 413.