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Task Forces The Arctic Council currently has two active Task Forces working on different issues, such as the Arctic marine environment and telecommunications The active layer, the ground overlying permafrost that thaws in the summer, tends to be thinner in areas of continuous permafrost. Though the active layer can support vegetation, roots typically spread laterally and permafrost is a barrier to rooting depth.
Obtaining quantitative information about permafrost using remotely-sensed data is a very difficult endeavor. For one thing, the active layer obscures the frozen ground below. Also, it is difficult to distinguish from space between seasonally frozen ground and permafrost, though surface clues can help. The remote sensing of permafrost is in its infancy, however, much important research has been accomplished.
In this section, we highlight some work to provide the reader with information about the state of the art of the remote sensing of permafrost for climate-change trend studies. As discussed earlier, many studies have shown convincing observational and modeling evidence for a sustained warming of the Arctic.
Surface temperature is the primary climatic factor that governs the existence, spatial distribution, and thermal regime of permafrost. In fact, a general increase in permafrost temperatures is observed during the last several decades not only in Alaska but also in northwest Canada and Siberia 96 — Figure 6. When the permafrost temperature increases, this results in a thickening of the active layer, and development of thermokarst which is an irregular land surface of marshy hollows and small hummocks following the thawing of the permafrost.
The data are presented as annual percentage deviations from the mean value for the period of observations indicated in each graph. Solid red lines show mean values. Dashed gray lines represent maximum and minimum values. Thaw depth observations from the end of the thawing season were used.
The number of CALM sites within each region varies and is indicated in each graph. Copyright John Wiley and Sons; Ref Copyright The Northern Publisher Salckhard.laxgertnandbald.tk
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In Figure 6 , changes in the thickness of the active layer, or active-layer thickness ALT are shown for nine different areas in the NH. Permafrost thawing and thermokarst development are associated with improved drainage, i. Peatlands that are underlain by permafrost emit CH 4 methane to the atmosphere, thus permafrost thawing contributes to the release of a significant amount of greenhouse gas emission, primarily CH 4 and carbon dioxide CO 2 stored in organic frozen soils.
And CH 4 release has been documented from various locations in the Arctic and sub-Arctic. Over , seeps of CH 4 gas have been mapped in the pan-Arctic through permafrost, resulting from the shrinkage of glaciers, often through active faults, and the Greenland ice sheet.
Surface albedo is an important component of the climate system because of its role in controlling the amount of heat from the sun that is absorbed by the surface. The albedo of ice and snow is among the highest on the surface of the Earth but the value varies depending on surface type and can cover a large range. For snow-covered surfaces, albedo is affected by many factors including the grain size, age, thickness, and the liquid content of the snow.
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The surface albedo in the Arctic thus changes during the onset of melt when liquid water covers individual snow grains and when diurnal melt and refreeze occurs, causing the snow grain size to increase. The mean albedo of the Arctic region has declined considerably because of the aforementioned shrinkage in the sea ice and snow cover. Such decline has been exacerbated by increases in the amount of soot particles that are transported to the polar regions. However, intercalibration can be improved through the use of reference targets, like Greenland or Antarctica, at certain times of the year when they have predictable albedo values.
To illustrate how albedo has been changing during the satellite era, color-coded averages of narrow-band albedo for the years — and from to are presented in Figure 7 a and b , respectively. The values are actually normalized radiances at 0. Averages are very similar for the two periods with the exception of the sea ice cover where large changes from the first to second period have been observed. For comparison, Figure 7 d is the result of trend analysis for each pixel and it is apparent that the trends for the entire period are very similar to the difference of mean albedo for the first and second period.
Multiyear averages of advanced very high resolution radiometer AVHRR narrow-band albedo normalized to range from 0 to 1 from a to and b to ; c difference of a and b. Average monthly albedo over land and sea ice from to for e April and f May. To assess how the Arctic albedo has been changing during spring, the average monthly albedo for April and May in the NH are presented in Figures 7 e and f , respectively.
The negative trends are consistent with observed changes in surface characteristics and the negative trends in the spring snow-cover and sea ice extent. The errors indicated are again the statistical errors of the trends which are relatively large mainly on account of large interannual variability.
There are also large systematic errors associated with imperfect cloud masking and intercalibration of the different sensors that are difficult to quantify but the year to year variations of such errors are not considered significant and likely would not affect the trend. The important role of clouds on the Arctic climate, especially in association with feedback effects, has been reported in many studies. Clouds can cause a cooling or a warming of the Earth's surface depending on cloud type.
High clouds tend to cause a cooling effect since they shield the Earth's surface from solar radiation by reflecting the radiation back to outer space. Low and thick clouds usually have a warming effect since they absorb and reemit long wave radiation back to the surface.
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On the other hand, cloud-free conditions make the ice-albedo feedback phenomenon operate more effectively in light of the rapidly retreating sea ice cover in the summer. Interannual changes and trends in the Arctic cloud cover are largely unknown because of the lack of reliable long-term cloud data. To gain insight into how Arctic clouds have been changing, we present in Figure 8 plots of time series of monthly anomaly cloud fraction data as derived from AVHRR for the period from August to November Interannual changes are more apparent in the monthly anomaly plots and are shown separately for the ice covered and ice free regions of the Arctic in Figures 8 a and b , respectively.
The trends are consistently negative with the most negative over Greenland. Yearly averages of August of one year to July the following year are also provided showing similar distributions as the month running averages in red of the monthly anomalies and actual cloud fractions. The errors in the trends, as indicated, are statistical errors and are shown to be significantly lower than the trends. Trend analysis was also done using yearly averages and the results are consistent with those derived from the monthly anomalies but with larger errors.
The set of near-simultaneous data provides a very promising capability to perform cloud cover studies but more work needs to be done to obtain consistency in the estimates from among these three sensors MODIS provides more accurate cloud data than AVHRR but the current time series is too short for meaningful trend analysis. Its direct impacts on the sea ice cover and wind circulation patterns have been evaluated using AO indices as presented for the entire year on a monthly basis in Figure 9 a and for the winter period in Figure 9 b.
The plots show that the indices for both monthly and for the winter season are mainly positive since although there are years e.
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It has been previously reported that negative AO indices are associated with extensive ice cover while positive indices would correspond to a reduced sea ice cover. Recent analysis using data from to yielded very poor correlation of AO indices in winter with the sea ice extent and area for both perennial and multiyear ice see Figure 3 f. The influence of AO on the recent changes in the Arctic is currently not well understood and some studies even suggest that there has been a radical shift of atmospheric circulation that is leading to a rapid change in the Arctic climate system.
There is thus a need for a more in-depth understanding of the basic mechanisms associated with feedbacks and the interaction of observed changes in the Arctic with the Arctic climate system. With the Arctic changing so rapidly, it is important to gain insights into the key factors that may be contributing to the changes. The observational needs are many and include detailed and accurate measurements of ocean, atmospheric and ice parameters. The observed changes are the result of a complex interaction of these various parameters.
For example, to gain an improved understanding about the rapidly changing perennial and multiyear sea-ice cover, we need to know not just how the extent is changing but also how the thickness of the ice and its snow cover are changing. We also need measurements of ocean circulation, temperature and salinity to assess how the ice is affected by the underlying water and of atmospheric wind and temperature to quantify the influence of the atmosphere on the ice.
The most dependable measurements on thickness of sea ice have been the very sparse historical data provided by submarine upward-looking sonars. With the advent of satellite lidar and Doppler radar altimetry, we can have sea ice thickness at a good spatial and temporal resolution. The technique measures the thickness of the freeboard layer the layer above the water line and assumes knowledge of the density of the ice. Some radar systems flown on board aircraft have shown good potential but space-based systems also need to be developed. In this regard, ability to quantify the amount and rate of solid precipitation would also be desirable.
Such data would be useful not only for sea ice studies but also for studies of other cryospheric phenomena such as accumulation rates over the ice sheets. Passive microwave data have been very useful in this regard but the resolution of current systems is relatively coarse and for many applications high resolution data are needed.
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Such data would also be useful for studies of mountain glaciers and permafrost during the winter and during darkness. Quantitative observations of the amount of methane that is released from permafrost and other regions would also be desirable. As the Arctic continues to warm, the amount of methane released will increase and the impact of methane on global warming is likely to become a serious problem.
An ability to monitor atmospheric methane in permafrost regions using space-based sensors could provide much of the required information. The future state of the Arctic climate has been of intense interest because of the observation of large changes in the region during the last few decades. To improve our understanding of the significance of the observed changes, there is a need for a numerical model that incorporates the physics of the system and is able to simulate the observed variability and trends of the various parameters in the system.
Such a model could then be used to do sensitivity studies to test the influence of various parameters, including greenhouse gases, on the climate system.