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Semelhante a Greening of the Arctic: Climate change and circumpolar Arctic vegetation
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Greening of the Arctic: Climate change and circumpolar Arctic vegetation
- 1. Greening of the Arctic: Climate change and circumpolar Arctic vegetation D.A. Walker, University of Alaska Fairbanks Fulbright Lecture Ceske Budejovice, 27 April 2011 Photo: P. Kuhry, http://www.ulapland.fi/home/arktinen/tundra/tu-taig.htm:
- 4. Albedo effects of ice Courtesy of NSIDC: http://nsidc.org/seaice/processes/albedo.html
- 5. Trend in surface temperatures Courtesy of NASA: http://data.giss.nasa.gov/cgi-bin/gistemp/ Land-surface temperatures of North America north of 60˚ N rose at rate of 0.84± 0.18 ˚C per decade between 1978 and 2006 (Comiso 2006). Dec-Feb Anomalies (2000-2010) Jun-Aug Anomalies (2000-2010)
- 6. Normalized Difference Vegetation Index (NDVI): An index of greenness CAVM Team. 2003 In general, land cover with high reflectance in the NIR and low reflectance in the visible portion of the spectrum has dense green vegetation. Reflectance spectra of common ground-cover types Normalized Difference Vegetation Index NDVI = (NIR - VIS) / (NIR + VIS) NIR = spectral reflectance in the near-infrared band (0.7 - 1.1µm), where light scattering from the cell-structure of the leaves dominates. VIS = reflectance in the visible, chlorophyll-absorbing portion of the spectrum (0.4 to 0.7µm). Circumpolar patterns of NDVI
- 16. Two transects through all 5 Arctic bioclimate subzones Bioclimate subzones Sub- Zone MJT Shrubs A 1-3 ˚C none B 3-5 ˚C prostrate dwarf-shrubs C 5-7 ˚C hemi-prostrate dwarf shrubs D 7-9 ˚C erect dwarf-shrubs E 9-12 ˚C low-shrubs CAVM Team 2003
- 19. Why focus on these dumb spots?
- 22. North American Arctic Transect Dalton Highway (7 locations) Arctic Bioclimate Subzones Canada Sub-zone MJT (˚C) SWI (˚C mo) A <3 <6 B 3-5 6-9 C 5-7 9-12 D 7-9 12-20 E 9-12 20-35 Forest >12 >35
- 23. Expeditions and Timeline: Dalton Highway: 2001-2002 Green Cabin: 2003 Mould Bay: 2004 Isachsen: 2005 Synthesis: 2006-2008 2001-02 2003 2004 2005
- 24. Field Camp at Green Cabin, Banks Island
- 25. Trend in patterned-ground along the Arctic bioclimate gradient Sub- zone: A B C D E Drawings modified from Chernov and Matveyeva 1997 Small non-sorted polygons Non-sorted circles Earth hummocks
- 26. Subzone A Isachsen, Ellef Ringnes Island, mean July temperature = 3 ˚C, SWI = 4 ˚C mo
- 27. Subzone C Howe Island, Ak and Green Cabin, Banks Island, MJT, 8 ˚C, SWI = 16 ˚C mo
- 28. Subzone E Tuktuyaktuk, NWT, Happy Valley, AK, MJT = 12 ˚C, SWI = 30 ˚C mo
- 31. Martha Raynolds Anja Kade Vegetation mapping and analysis of of active-layer/heave/vegetation relationships
- 35. Contrast in vegetation on and between frost features Kade et al 2005 Deadhorse Subzone C Braya purpurascens-Puccinellia angustata community Dryas integrifolia-Salix arctica community Nonsorted Circle Between Circles
- 39. The Eurasia Arctic Transect: Vegetation Analysis and Mapping Funded by NASA as part of the Land-cover Land-Use Change (LCLUC) Program Yamal Peninsula, Russia. Photo: D.A. Walker
- 48. The Nenets people and their reindeer Florian Stammler: Yamal LCLUC Workshop, Moscow, 28-30 Jan 2008; Central photo: D.A. Walker.
- 50. 1. Extensive nutrient-poor surface sands with lichens that are easily overgrazed by reindeer. 2. Underlain by permafrost with massive pure ice. 3. Extensive landslides are rapidly eroding the landscape. 4. This exposes salt-rich and nutrient-rich clays. 5. Complex vegetation succession process that results in willow-shrub tundra and much greener vegetation in the eroded valleys. High-ice Permafrost Landscapes Photos: D.A. Walker and M. Liebman (upper right)
- 51. Extensive azonal willow shrublands due to landslide disturbances Greening associated with eroded sandy uplands
- 53. Erosional patterns are clearly discernable on new GeoEye scene. Vaskiny Dachi 0.41 m resolution should permit analysis of rate of erosion and related greening.
- 54. Nenets camp on Yamal in Salix low shrub tundra Reindeer grazing Salix thickets in Nenets Okrug. If they grow over ≈ 2 m high, herders can lose sight of animals. The changes in willow growth are affecting reindeer management. Forbes et al. 2009 PNAS , ENSINOR project Photos courtesy of Bruce Forbes.
- 58. Zonal vegetation along both transects Eurasia Transect A - Hayes Island B - Ostrov Belyy C – Kharasavey D - Vaskiny Dachi E - Laborovaya North America transect A - Isachsen B- Mould Bay C - Green Cabin D - Sagwon MNT E - Happy Valley
- 59. Summer Warmth Index Vegetation (CAVM Team 2003) Variation in summer land temperature and vegetation along the transects
- 66. Use of ground data to calibrate regional AVHRR-NDVI /biomass data
- 67. Circumpolar aboveground biomass derived from NDVI Raynolds et al. 2011 submitted, Geophysical Research Letters
- 69. Changes in summer land-surface temperature (1982-2010)
- 70. Temporal patterns of NDVI in relationship to changes in area of summer open water Walker et al. 2011. Bulletin American Meteorological Society. State of the Climate . New analysis based on new GIMMS 3g AVHRR NDVI data by Pinzon et al. 2010 (in progress) and sea ice data by Comiso et al. 2010. Pct. Change MaxNDVI (1982-2010) 26% increase +4% increase Beaufort Kara Beaufort Kara Oceans: % change of May-Aug open water (1982-2010) Tundra land areas: % change in TI-NDVI (1982-2010) From Bhatt et al. 2010, AGU Fall Meeting
Notas do Editor
- If the melting of the sea ice continues, we may see an ice free Arctic Ocean in late summer within our life times as portrayed on this vegetation map of the Arctic Tundra. What is striking in this polar view of the Arctic Tundra Biome is the close proximity of all the biome to the Arctic Ocean or seasonally frozen sea water. It really is a maritime biome. This map gives a good impression of just how closely tied the tundra biome is to the ocean. 61% of the tundra is within 50 km of sea ice (blue buffer), 80% is within 100 km (magenta and blue buffers), and 100% is within 350 km. Upland areas above 333 m (gray areas) were excluded from the analysis because these area experience sufficient adiabatic cooling to shift to colder elevation belts.
- These two diagrams show the state of the multi-year (i.e. thick) sea ice in 1980 and 2009. These are two ends of an animation that Ignatius Rigor made that show the rapid decline in multi-year sea ice during the period of satellite observations. The red dots are ocean and ice buoys that monitor ice thickness and position. The three shades of blue show
- (Left) The best tools we have for looking at Arctic wide vegetation change are the multi-channel sensors aboard earth orbiting satellites that sense in the visible and near infrared portions of the electromagnetic spectrum. This graph shows the reflectance of several common ground cover types. Those types with a lot of chlorophyll have spectra similar to those shown by the red and green lines (alfalfa and maple trees). Green vegetation in general tends to reflect strongly in the near infrared portion of the spectrum apparently due to light scattering at the cell level within the leaves, and the amount of reflectance in the NIR varies considerably between species and can be used to help identify species. Chlorophyll also strongly absorbs light in the red portion of the spectrum. And the difference in the reflectance in the Infrared and Visible is an index of the amount of green vegetation in a given pixel. Normalizing by the sum of the NIR and VIS channel helps to account for the affects of shadows and the affects of different lighting on slopes. (Right) This circumpolar view of NDVI focuses on the tundra region. It was derived from images taken during two relatively warm years in the Arctic, and shows the Arctic a maximum greenness. Most other views have shown the tundra only as part of the global patterns, so variations within the tundra are minimized. This view shows the strong differences across the Arctic tundra zone. The blue colors a sparse vegetation generally found in the High Arctic, the greens and yellows indicate more peaty tundra types of the low arctic. The oranges and reds are the shrub tundras, many of which occur in permafrost free areas of the Arctic. As you can see there is a lot spatial variability in the NDVI. Some of these differences are caused by climate and some is caused by many other factors suc as areas of recent glaciation, mountain ranges, wetlands, and the influence of ocean and atmospheric circulation patterns. The greening of the Arctic project is focusing on the spatial variation of NDVI and “how has the patterned changed in recent years?”
- This study by Jiong Jia prompted this interest in NDVI. The top graph traces the history of Maximum (Peak) NDVI values in the three major bioclimate subzones and the average for the entire Arctic Slope of Alaska. The bottom line in the top graph represents is the 20-yr record of MaxNDVI derived from AVHRR 8-km data in the coldest subzone near the coast and the top line is the warmest subzone in the foothills. The top blue line is the average Peak NDVI for the whole of the Arctic Slope. There is an average 17 ± 6% increase in NDVI across all of the Arctic Slope. Based on regressions of biomass vs. NDVI from the same study, this corresponds to about an average 170 g m-2 increase in biomass. The bottom graph shows the long-term temperature data from three stations in each of the subzones, Barrow in subzone C, Prudhoe Bay in subzone D, and Umiat in Subzone E. The shaded area corresponds to the same time interval shown in the top graph. And the red and blue arrows show positive and negative fluctuations in the yearly temperature which correspond to responses of the NDVI. These are the best data currently available that demonstrate a regional change in the vegetation over the past few decades. However, they are from only a small part of the Arctic, all of which is in the Low Arctic.
- This study by Jiong Jia prompted this interest in NDVI. The top graph traces the history of Maximum (Peak) NDVI values in the three major bioclimate subzones and the average for the entire Arctic Slope of Alaska. The bottom line in the top graph represents is the 20-yr record of MaxNDVI derived from AVHRR 8-km data in the coldest subzone near the coast and the top line is the warmest subzone in the foothills. The top blue line is the average Peak NDVI for the whole of the Arctic Slope. There is an average 17 ± 6% increase in NDVI across all of the Arctic Slope. Based on regressions of biomass vs. NDVI from the same study, this corresponds to about an average 170 g m-2 increase in biomass. The bottom graph shows the long-term temperature data from three stations in each of the subzones, Barrow in subzone C, Prudhoe Bay in subzone D, and Umiat in Subzone E. The shaded area corresponds to the same time interval shown in the top graph. And the red and blue arrows show positive and negative fluctuations in the yearly temperature which correspond to responses of the NDVI. These are the best data currently available that demonstrate a regional change in the vegetation over the past few decades. However, they are from only a small part of the Arctic, all of which is in the Low Arctic.
- The focus of this talk is the ground measurements of biomass, NDVI, and leaf area index and their correlations with observations from space mainly with the Advanced Very High Resolution Radiometer measurements.
- Ground observations were conducted along two transects through all 5 Arctic bioclimate subzones as defined by the Circumpolar Arctic Vegetation Map (Walker et al. 2005). The subzones are defined by difference in the dominant plant growth forms on zonal sites and by the mean July temperatures.
- The map in the lower left shows the location of the North America and Eurasia Arctic transects. North America Arctic Transect: Completed in 2006 as part of the Biocomplexity of Arctic Patterned Ground Ecosystems Project (NSF). Eurasian Arctic Transect: Field seasons in 2007-2010 (NASA).
- We had these two primary questions: How do biological and physical processes interact to form the patterned ground ecosystems? How do these systems change across the Arctic climate gradient?
- Why focus on small patterned-ground features? The processes involved in the formation of patterned-ground landscapes are not well understood. The importance of patterned ground with respect to biogeochemical cycling, carbon sequestration and other ecosystem processes is poorly known. They are an ideal natural system to to help predict the consequences of climate change of disturbed and undisturbed tundra.
- Why focus on small patterned-ground features? The processes involved in the formation of patterned-ground landscapes are not well understood. The importance of patterned ground with respect to biogeochemical cycling, carbon sequestration and other ecosystem processes is poorly known. They are an ideal natural system to to help predict the consequences of climate change of disturbed and undisturbed tundra.
- The patterned ground forms change along the transect in a predictable way. This diagram of the changes on zonal surfaces along the climate gradient is adapted from Nadya Matveyeva’s work that described the transitions on the Taimyr Peninsula in Russia. Her conceptual model also fits the situation in North America quite well. Generally, at the northern end of the transect we see a dominance of small non-sorted polygons formed by cracking processes. Toward the middle of the transect, we see larger features that are formed by differential frost heaving with a strong increase in the amount of vegetation particularly around the margins of the heave features. And in the southern part of the gradient, vegetation overtops the features.
- Plant cover: Insulates the surface decreasing the heat flux and summer soil temperatures. stabilizes cryoturbation and limits needle-ice formation. Promotes nitrogen and carbon inputs to the soil. The effect of vegetation on patterned ground morphology increases toward the south.
- These are maps of three of the grids that show the vegetation, active layer and snow depths There are two posters that will be presented tomorrow that show the trends in the vegetation and morphology of patterned ground along the transect. Corinne Munger’s poster presents the maps and analysis. Martha Raynolds poster presents many photographs of the patterned ground forms along the temperature and moisture gradients.
- Biomass affects NDVI along the transect. The NDVI is a measure of vegetation greenness that is derived from the difference in the reflectance of plants in the red and near-infrared portions of the electromagnetic spectrum. More dense vegetation generally has higher NDVI values. These graphs show some of the work of Howie Epstein and Alexia Kelley: T he upper graph shows that there is about a 2-fold increase in the average NDVI of vegetation on zonal surfaces along the transect, with quite a bit of scatter. Much of the scatter is due to small-scale variations caused by patterned ground. The lower graph shows the NDVI on and between patterned ground features. The contrast in NDVI on patterned ground features (gray bars) vs. the tundra between features (green bars) decreases toward the south.
- Especially our Russian colleagues who have made the Eurasia transect possible. In August 2010 we completed the second of the transects with an expedition to Franz Josef Land in northern Russia. This gave us observations from Bioclimate subzone A in Eurasia to compare with results from the same subzone in Canada. Other members of the team also drilled the northernmost permafrost borehole at 80˚ 37’ N.
- In the midst of of the The Nenets people and their reindeer themselves have a significant impact. The Nenets people have privitized their herding activities, and both their own population and their herds have increased markedly over the past 80 years. The nomadic portion of the people population has recently stabililzed at about 5000. But the private reindeer herds have increased 3x since 1981 to about 156,000 animals.
- There are numerous effects of all these animals, including Overgrazing and Trampling of the vegetation Transformation of previous sedge and shrub-dominated tundra vegetation into grasslands Wind erosion of the some of he trampled areas. Some of these effects are be quantified using remote sensing tools by Timo Kumpula
- Extraordinarily Sensitive Permafrost Landscapes: Extensive nutrient-poor surface sands with lichens that are easily overgrazed by reindeer. Underlain by permafrost with massive pure ice. Extensive landslides are rapidly eroding the landscape. This exposes salt-rich and nutrient-rich clays. Complex vegetation succession process that results in willow-shrub tundra in the interior parts of the peninsula.
- Willow communities on old landslides at Vaskiny Dachi Low-willow shrublands develop on landslides during 200-yr succession, greatly changing biomass and NDVI.
- The changes in willow growth are affecting reindeer management. If they grow over ≈ 2 m high, herders can lose sight of animals. Extensive willow shrublands are avoided during migration..
- A large portion of the summer pastureland land of both of these brigades is no longer available to them. In general the Nenets see the changes going on due to both resource extraction and climate change. They recognize the threat of resource extraction to be a much greater threat than that of climate change. But there are also positive aspects of the resource development. In general the herders view the gas field activity positively because the influx of gas field workers provide a market for their reindeer that did not previously exist.
- The areas that we looked at on the ground were mainly zonal sites, those where the soils and vegetation correspond to the long-term climatic climax. These photos show representative sites along the Eurasia and North America transects in each bioclimate subzone.
- Our goal was to correlate the NDVI patterns with other remote sensing data such as the summer warmth index derived from the AVHRR thermal bands and a variety of the other information such as that contained in the Circumpolar Arctic Vegetation Map GIS data base.
- The biomass data from the transects were examined in a couple of ways. The first is a plot-level determination, which is the of the clip harvest data from the zonal plots at each sample location. Both transects show the general expected pattern of increased biomass along the bioclimate gradient, but there are also major differences related to differences in substrate, precipitation, and relative position of the locations within each subzone.
- Some of the most important differences appear to be related to differences in disturbance regimes. For example, along the NAAT there is much more evergreen-shrub and lichen biomass. This could be the result of the much more intensive grazing pressure by reindeer along the the EAT. This phenomenon occurs across most of the Russian Arctic causing major differences in the structure of the vegetation.
- Hand-held measurements of NDVI also had different values for equivalent levels of biomass and LAI along both transects, probably reflecting the the different structure of the vegetation.
- These differences in structure are also evident in the LAI-Biomass relationship along both transects. An equivalent amount of biomass has consistently much higher LAI values along the NAAT than along the EAT and the difference increases at higher biomass values
- These differences are reflected in graphs of the AVHRR-NDVI and biomass vs. summer warmth. The biomass shown here is a slightly different number which is a landscape-level of biomass that is derived from the biomass data in combination with detailed vegetation maps. In general the EAT is greener than the NAAT in equivalent climates.
- In spite of the structural and composition differences between the transects, there is overall a very strong relationship between 1-km AVHRR NDVI values and biomass along both transects and in the combined data set.
- This gives us quite a bit of confidence that we can extrapolate the the zonal landscape-level biomass values from the transect to the broader Arctic using the AVHRR data.
- We have also examined the temporal relationships of the AVHRR NDVI compared to the coastal sea ice trends and summer land surface temperature trends. Uma Bhatt published a key paper this year that showed the strong year-to-year circumpolar correlations between coastal sea-ice, land temperatures, and NDVI values. This has now been updated with a new NDVI data set provided by Jorge Pinzon and Jim Tucker at NASA-Goddard. The two graphs in the lower right show the 1982-2010 trends in NDVI along the two transects. Several things are worth noting: The trend in tundra NDVI peaked in 1989 along the Yamal transect and 2004 along the North America Transect. Since 2004 it has been declining along both transects. A particular strong dip occurred in 2009. This occurred in every area of the Arctic. In western Eurasia, a further dip occurred in 2010. The trend along the Kara has been almost flat especially since 1990, when it reached its highest value, whereas the trend in North America has been a remarkable 24%.
- Much of the change in the Alaska is thought to be due to changes in shrub cover. Ken Tape, Matthew Sturm and Chuck Racine have shown Over 30% increase in shrub cover on stable valley slopes dramatic increase in shrub cover on river terraces, colonization by new alders. less cover of sandbars, more vegetation in river floodplains
- Broad similarities in biomass between North America and Eurasia along Arctic temperature gradient, but also major differences likely related to different disturbance regimes, geology, and precipitation patterns. Very good correlation between AVHRR NDVI and zonal landscape-level biomass. Analysis of Landsat-derived NDVI trends for a similar period did not provide corroboration for magnitude of 1982-2010 NDVI change indicated by GIMMS 3g. AVHRR NDVI is still one of the best tools we presently have for looking at long-term terrestrial response to climate change. It is highly correlated with a wide variety of the biophysical properties, but we need better calibration of remote sensing data sets and ground measurements to give us confidence in the indicated temporal trends.
- This project is a joint collaboration by three groups of institutions in the U.S., Russia and Finland. Five funded projects were collaborating in the project including. The primary logistic funding came from NASA and NSF in the U.S. and the Russian Academy of Science in Russia.