Greening of Arctic: Knowledge and Uncertainties

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1 Greening of Arctic: Knowledge and Uncertainties Jiong Jia, Hesong Wang Chinese Academy of Science Howie Epstein Skip Walker Moscow, January 28, 2008

2 Global Warming and Its Impact IMPACTS FOR HIGH WARMING SCENARIO OBSERVED Some increase in extreme climate events Small positive or negative net monetary impacts (most people adversely affected) Net negative for some regions Risks to some systems Risks of large scale discontinuities Net negative monetary impacts Net negative for many regions Risks to many systems Extreme and irreversible effects Aggregate impacts Distribution of impacts Unique and threatened systems Temperature anomaly ( o C) Global mean annual temperature change relative to preindustrial IPCC High IPCC Low OBSERVED Some increase in extreme climate events Small positive or negative net monetary impacts (most people adversely affected) Net negative for some regions Risks to some systems IMPACTS FOR LOW WARMING SCENARIO Extreme and irreversible effects Aggregate impacts Distribution of impacts Unique and threatened systems

3 Available Data NASA GIMMS bi-monthly 8km Recent update for (September 2007) NASA PAL monthly 4km (Piao( Piao,, Zhou, et al.) Long-term data record (LTDR( LTDR), daily ~2000 (Missing data over 70 degree north) MODIS , collection 5, 4 (ORNL subsets) Landsat MSS, TM, ETM+ over many locations since early 1970s with 5-20 yr interval (Bruce, Jiong) Near future: NPOESS-VIIRS

4 Knowledge Base What are the Consequences of Land Cover & Land Use Change for the Sustainability of Ecosystems & Economic Productivity? NASA Joint Unfunded Landsats-4,5 &7 Landsat-7 Landsat-7/ASTER IKONOS Data buy Aqua/MODIS Terra/MODIS, MISR & ASTER NOAA AVHRR Aqua/MODIS AVHRR global 1 km land cover; Local to regional scale inventories of land cover & land use change using Landsat 5 data. Terra/ MODIS Spatially explicit socio-economic & ecological models of land cover/ land use change working synergistically NPP-VIIRS Landsat Data Continuity Mission NPOESS- VIIRS Land Use & Integrated Assessment Models Ecological, biogeochemical, and land use model simulation results incorporating actual land cover observations Quantitative inventory of global forest cover Understanding the physical and socio-economic impacts of land use changes Global and regional land cover change products detailing loss of biodiversity, habitat degradation and fragmentation, changes in water resources and changes in the forestry and agriculture sectors; changes in urban areas, wetlands and coastal zones Global estimation of above-ground biomass carbon storage and sources Land cover and land use change effects on atmospheric, surface radiation and hydrological processes Environmental & Climate Data Records at moderate spatial resolution Continuous high-resolution global seasonally-refreshed land cover and land use data Continued improvement in algorithms for land cover classification, direct parameterizations and change detection Evaluate consequences of observed and predicted changes and further understanding of their impact on environmental goods and services, the carbon and water cycles and the management of natural resources Forecast the extent & impacts of land use change at local, national, and regional scales with models integrating imagery across scales (e.g., MODIS to IKONOS) with socioeconomic & climate data

5 AVHRR Data Corrections Compared to newer sensors (e.g. MODIS, MISR), AVHRR lacks of onboard calibration; Low SNR due to cloud contamination and water vapor (Mostly corrected with MVC); Geometric registration errors (Mostly corrected); Volcanic aerosol effects for and (partly corrected); Residual sensor degradation and viewing angle effects due to satellite drift (partly corrected, problem over recent years)

6 Use of EMD technique and the removal of SZAcorrelated trends Top: Before draft correction, Middle: trend removed Bottom: resulting series.

7 What we have done Unmix the signal of vegetation and select areas with relatively homogeneous vegetation with MODIS and Landsat ETM+ for each subzone and boreal forest; Mask pixels of water and ice/snow in analysis; Temporal-spatial analysis of peak and time-integrated NDVI by continents and subzones Analysis of vegetation phenology Examining the uncertainties in interpreting trends of greenness

8 Observations Remotely sensed In situ measurements Meta analysis Scaling up

9 Modeled Landscape

10 Circumpolar Greenness

11 Vegetation in Yamal

12 Vegetation shown on photos and Landsat ETM+ images Subzone A Subzone B Subzone C

13 Vegetation shown on photos and Landsat ETM+ images Subzone D Subzone E

14 Circumpolar Peak Vegetation Greenness

15 Semi-decadal comparison of peak NDVI over circumpolar region

16 Annual peak NDVI Yamal Arctic tundra +0.44%/yr, R2=0.61, p< Years ! 0.75 Yamal Arctic south of 70 degree north +0.34%/yr, r2=0.36, p<0.01 Changes of annual peak vegetation greenness (NDVI) over tundra biome from (top) and below 70 degree north from (bottom) in Yamal Arctic, Russia as detected by NOAA AVHRR time series data. Annual peak NDVI Years

17 0.60 Annual peak NDVI North American Arctic 0.64%/yr, r2=0.71, p< Years 0.8 Changes of annual peak vegetation greenness (NDVI) over tundra biome from (top) and below 70 degree north from (bottom) in North American Arctic (Alaska and Canada) as detected by NOAA AVHRR time series data. Annual peak NDVI North American Arctic south of 70 degree 0.58%/yr, r2=0.70, p< Years

18 Canadian High Arctic Peak NDVI Subzone A: 0.492%/yr Subzone B: 0.55%/yr Subzone C: 0.806%/yr Year

19 Canadian Low Arctic Peak NDVI Subzone D: 0.672%/yr Subzone E: 0.354%/yr Taiga: %/yr Year

20 Russia High Arctic Peak NDVI Subzone A: 0.556%/yr Subzone B: 0.537%/yr Subzone C: 0.407%/yr Year

21 Russia Low Arctic and Taiga Peak NDVI Subzone D: 0.371%/yr Subzone E: 0.283%/yr Taiga: %/yr Year

22 Seasonal Pattern of Vegetation Greenness

23 NDVI Subzone A Subzone A A Arctic Subzone A a 5b 6a 6b 7a 7b 8a 8b 9a 9b Subzone B Subzone B B 0.18 NDVI 0.16 Arctic Subzone B a 5b 6a 6b 7a 7b 8a 8b 9a 9b

24 Subzone C Subzone C C NDVI Arctic Subzone C a 5b 6a 6b 7a 7b 8a 8b 9a 9b Subzone D Subzone D D Arctic Subzone D NDVI a 5b 6a 6b 7a 7b 8a 8b 9a 9b

25 Subzone E Subzone E E NDVI Arctic Subzone E a 5b 6a 6b 7a 7b 8a 8b 9a 9b Bimonthly periods

26 What s s Behind the Greening General trend of global and regional warming Stronger warming signals Get closer to Canadian Arctic (1km ~2005) Revisit Arctic Alaska (1km, weekly, ~2006) Spatial patterns of NDVI changes Impact of surface temperature and sea-ice on vegetation greenness

27 MODIS + AVHRR over Siberia (Graversen et al, 2008, Nature)

28 Temperature anomalies (Graversen et al, 2008, Nature)

29 Fire and NPP anomalies (Bond-Lamberty, 2007, Nature)

30 Accumulative effect on biomass and NPP Standing biomass Cumulative NPP Time Time Exclosure Low/Moderate intensity grazing High intensity grazing

31 Uncertainties Major uncertainties came from data calibrations; Draft of AVHRR sensors and differences in spectra and algorisms between AVHRR and MODIS; Confused from pixels contain mixture of land cover types respond to warming in different ways; Time periods considered in analysis; Geographic extents in analysis; Reluctance in facing the inconvenient truth

32 MODIS vs. AVHRR NDVI

33 AVHRR over Siberia

34 MODIS + AVHRR over Siberia

35 0.5 Peak NDVI Year 0.7 Peak NDVI Year How trend of greening is affected by the length and period of time series

36 3.0! TI-NDVI TI-NDVI Year! Year In case of time-integrated NDVI

37 Goetz et al. (2005)

38 :966 8: %,%-./.012%3% %,%6/6780 GAC Peak NDVI NA boreal GAC summer NDVI NA boreal *%+%,-.,/01%2%3434/ 5 / %+%-.6,63 6,0-- 6, /--- /--0 /-6-

39 7<000 7/000 7;000 7: &.&/012345&6&70/ &.&0173:7 GAC Peak NDVI NA Subzone D ; ; ; :: 37::: 322:: GAC Peak NDVI NA Subzone E 32::: 3<2:: 3<::: -(.(/012345(6(24/ (.(:1304; 3/2:: 34;: 34;2 344: ::: 9::2 9:3:

40 Plans for 2008 Revise JGR paper; Submit Alaska revisit paper; Prepare and submit global trends and uncertainty paper; Resubmit Arctic vegetation RS proposal to NSFC, with Howie and Skip int l l collaborators; Continue support a student on GOA work

41 Thanks! Questions?

42 Bunn et al, 2007, EOS