The Greening Earth*department of geography, boston university, cybele.bu.eduRanga B. Myneni* & Compton Jim TuckerWith contributions from:Alexeyev, Anderson, Asrar, Bogaert, Bousquet, Buermann, Ceulemans, Cramer, Dickinson, Dong,Friedlingstein, Hashimoto, Hughes, Jolly, Kaufmann, Kauppi, Keeling, Knyazikhin, Lucht,Liski, Nemani, Piper, Potter, Prentice, Running,Shabanov, Sitch, Slayback, Song, Smith and ZhouThis research was funded by NASA-ESE.(1 of 37)Gustav Klimt (1862-1918): Der Park (1910)MOMA-NY

publications

Myneni, R. B., et al., 1997. Increased plant growth in the northern high latitudes from 1981-1991. Nature, 386:698-701.

Myneni, R. B., et al., 1998. Interannual variations in satellite-sensed vegetation index data from 1981 to 1991. J. Geophys. Res., 103 (D6): 6145-6160. Kaufmann et al., 2000. Effect of orbital drift and sensor changes on the time series of AVHRR vegetation index data. IEEE Trans. Geosci. Remote Sens., 38: 2584-2597. Zhou et al., 2001. Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999, J. Geophys. Res., 106 (D17): 20069-20083. Myneni and Dong et al., 2001. A large carbon sink in the woody biomass of northern forests. Proc. Natl. Acad. Sci. USA., 98(26): 14784-14789.Tucker et al., 2001. Higher northern latitude NDVI and growing season trends from 1982 to 1999. Int. J. Biometeorol., 45:184-190. Shabanov et al., 2002. Analysis of interannual changes in northern vegetation activity observed in AVHRR data during 1981 to 1994. IEEE Trans. Geosci. Remote Sens., 40:115-130. Lucht et al., 2002. Climatic control of the high-latitude vegetation greening trend and Pinatubo effect. Science, 296:1687-1689 (May-31-2002). Bogaert et al., 2002. Evidence for a persistent and extensive greening trend in Eurasia inferred from satellite vegetation index data. J. Geophys. Res., Vol. 107 (D11), 10.1029/2001JD001075. Kaufmann et al., 2002. Reply to Comment on "Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981- 1999" by J. R. Ahlbeck. J. Geophys. Res., Vol. 107(D11), 10.1029/2001JD001516. Dong et al., 2003. Remote sensing of boreal and temperate forest woody biomass: Carbon pools, Sources and Sinks, Remote Sens. Environ. 84: 393410.Buermann et al., 2002. Circulation anomalies explain interannual covariability in northern hemisphere temperatures and greenness. J. Geophys. Res. (accepted Dec 2002). Zhou et al., 2002. Relation between interannual variations in satellite measures of vegetation greenness and climate between 1982 and 1999. J. Geophys. Res. 108(D1), doi:10.1029/2002JD002510.Nemani et al., 2003. Climate driven increases in terrestrial net primary production from 1982 to 1999. Science (in review Apr 2003).(2 of 37)http://cybele.bu.edu/

outlineBackground on NDVI and NDVI data sets- the northern latitude greening trend during the 1980s and 1990s- persistence of greening in Eurasia vs North America- the temperature connection- the connection to circulation anomalies- northern latitude greening and the forest woody biomass carbon sinkThe greening earth and increasing terrestrial net primary productionGreening in the north(3 of 37)

reflectance spectrum of a green leafPigments in green leaves (notably chlorophyll) absorb strongly at red and blue wavelengths. Lack of such absorption at near-infrared wavelengths results in strong scatter from leaves.upper epidermispalisade layerspongy tissuelower epidermisimage credit: Govaerts et al.(4 of 37)

normalized difference vegetation index, ndviThe contrast between red and near-infrared reflectance of vegetation is captured by the greenness index, NDVI, as [(nir-red)/(nir+red)].image credit: Huete et al.(5 of 37)

avhrr ndvi data setsThe Advanced Very High Resolution Radiometers, AVHRR,have been flown on NOAA polar orbiting afternoon-viewing platforms

NOAA-07: jul 81 to jan 85 NOAA-09: feb 85 to oct 88 NOAA-11: nov 88 to sep 94 NOAA-14: jan 85 to oct 01.(6 of 37)GIMMS and PAL NDVI Data Sets HaveCalibrationPartial Atmospheric CorrectionCorrections for Stratospheric Aerosols10 or 15-day Maximum Value CompositesRun from July 1981 to about mid-2001GIMMS: GLOBAL INVENTORY MONITORING AND MODELING SYSTEMSPAL: PATHFINDER AVHRR LAND

outlineBackground on NDVI and NDVI data sets- the northern latitude greening trend during the 1980s and 1990s- persistence of greening in Eurasia vs North America- the temperature connection- the connection to circulation anomalies- northern latitude greening and the forest woody biomass carbon sinkThe greening earth and increasing terrestrial net primary productionGreening in the northUse with caution - download from http://cybele.bu.edu/(7 of 37)

greening trend in the northIncreasechanges in growing season durationchanges in greenness magnitudeIn the north, where vegetation growth is seasonal, the cumulative growing season greenness, which is the area under the NDVI curve, can change either due to a longer photosynthetically active growing season or due to increased greenness magnitude, or both.1) Define vegetated pixels in the study area using a land cover map2) Use NDVI values greater than zero only to avoid sparsely vegetated areas, pixels with snow and any corrupted data3) Assess changes in peak seasonal greenness from July and August average NDVI4) Use NDVI threshold to assess changes in dates of spring green-up and autumn green- down (assess sensitivity to threshold value)(8 of 37)

greening trend in the north (1980s & 90s)

NDVI averaged over boreal growing season months of May to September increased by about 10%,the timing of spring green-up advanced by about 6 days.

NDVI averaged over boreal growing season months of May to September increased by about 10%,the timing of spring green-up advanced by about 6 days.8.4%/18 yrs (p

the northern latitudes (>40N) have greened since the early 1980soutlineBackground on NDVI and NDVI data sets- the northern latitude greening trend during the 1980s and 1990s- persistence of greening in Eurasia vs North America- the temperature connection- the connection to circulation anomalies- northern latitude greening and the forest woody biomass carbon sinkThe greening earth and increasing terrestrial net primary productionGreening in the northUse with caution download from http://cybele.bu.edu(10 of 37)

spatial pattern of greeningFrom Zhou et al., (JGR, 106(D17):20069-20083, 2001)Analyses of pixel-based persistence indices from GIMMS (v1) NDVI data for the period 1981 to 1999 indicate that:About 61% of the total vegetated area between 40N-70N in Eurasia shows a persistent increase in growing season NDVI over a broad contiguous swath of land from Central Europe through Siberia to the Aldan plateau, where almost 58% (7.3 million km2) is forests and woodlands.North America, in comparison, shows a fragmented pattern of change, notable only in the forests of the southeast and grasslands of the upper Midwest.These results are further substantiated from a study that evaluated patch characteristics using landscape ecology metrics (Bogaert et al., JGR, 107(D11):10.1029/2001jd001075, 2002).(11 of 37)

the greening in Eurasia is more persistent than in North Americaoutline the northern latitudes (>40N) have greened since the early 1980sBackground on NDVI and NDVI data sets- the northern latitude greening trend during the 1980s and 1990s- persistence of greening in Eurasia vs North America- the temperature connection- the connection to circulation anomalies- northern latitude greening and the forest woody biomass carbon sinkThe greening earth and increasing terrestrial net primary productionGreening in the northUse with caution download from http://cybele.bu.edu(12 of 37)

greenness and surface temperature T GISS temperature; EA Eurasia; NA - North AmericaFrom Zhou et al., (JGR, 106(D17):20069-20083, 2001)The temporal changes and continental differences in NDVI are consistent with ground based measurements of temperature, an important determinant of biological activity in the north(13 of 37)

Chart7

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820.35359-0.484030.354060.29397820.3806070.014383-1.87839810890.2145490.527825-1.32350494960.3733750.016061-1.20260257770.4251480.395952-0.3312977331

830.358450.096010.352630.35301830.3806070.014383-1.54049920040.2145490.527825-0.22458011650.3733750.016061-1.29163812960.4251480.395952-0.1821887502

840.36494-0.017850.34457-0.00421840.3806070.014383-1.08927205730.2145490.527825-0.44029555250.3733750.016061-1.7934748770.4251480.395952-1.0843688124

850.3716-0.355870.359820.09673850.3806070.014383-0.6262254050.2145490.527825-1.08069720080.3733750.016061-0.84396986490.4251480.395952-0.8294389219

860.38668-0.030170.362320.18676860.3806070.0143830.42223458250.2145490.527825-0.4636366220.3733750.016061-0.68831330550.4251480.395952-0.6020628763

870.391050.799180.35941-0.30561870.3806070.0143830.7260654940.2145490.5278251.10762279160.3733750.016061-0.86949754060.4251480.395952-1.8455721906

880.377660.719560.374610.70381880.3806070.014383-0.20489466730.2145490.5278250.9567773410.3733750.0160610.07689434030.4251480.3959520.7037772255

890.384210.534310.372940.53216890.3806070.0143830.25050406730.2145490.5278250.60580874340.3733750.016061-0.02708424130.4251480.3959520.2702650826

900.385610.478710.389770.76608900.3806070.0143830.34784120140.2145490.5278250.50047080.3733750.0160611.02079571630.4251480.3959520.8610437629

910.396810.64620.384220.86674910.3806070.0143831.12653827440.2145490.5278250.81779188180.3733750.0160610.67523815450.4251480.3959521.1152664969

920.36456-0.655590.36578-0.00698920.3806070.014383-1.11569213660.2145490.527825-1.64853692040.3733750.016061-0.47288462740.4251480.395952-1.0913646099

930.37359-0.08530.36538-0.01574930.3806070.014383-0.48786762150.2145490.527825-0.56808411880.3733750.016061-0.49778967690.4251480.395952-1.1134885037

940.382710.687030.384020.84224940.3806070.0143830.14621428070.2145490.5278250.89514706580.3733750.0160610.66278562980.4251480.3959521.0533903099

950.38670.21530.391280.91953950.3806070.0143830.4236251130.2145490.5278250.00142282010.3733750.0160611.11481227820.4251480.3959521.2485907383

960.37537-0.343720.380110.06683960.3806070.014383-0.36411040810.2145490.527825-1.05767820770.3733750.0160610.41933877090.4251480.395952-0.9049531256

970.39192-0.043550.398290.88085970.3806070.0143830.78655357020.2145490.527825-0.48898593280.3733750.0160611.55127327070.4251480.3959521.1509021296

980.40671.389990.386530.6203980.3806070.0143831.81415560040.2145490.5278252.22695211480.3733750.0160610.81906481540.4251480.3959520.4928678224

990.398770.311680.395010.85619990.3806070.0143831.26281026210.2145490.5278250.18402121920.3733750.0160611.34705186480.4251480.3959521.0886218532

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Chart6

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820.35359-0.484030.354060.29397820.3806070.014383-1.87839810890.2145490.527825-1.32350494960.3733750.016061-1.20260257770.4251480.395952-0.3312977331

830.358450.096010.352630.35301830.3806070.014383-1.54049920040.2145490.527825-0.22458011650.3733750.016061-1.29163812960.4251480.395952-0.1821887502

840.36494-0.017850.34457-0.00421840.3806070.014383-1.08927205730.2145490.527825-0.44029555250.3733750.016061-1.7934748770.4251480.395952-1.0843688124

850.3716-0.355870.359820.09673850.3806070.014383-0.6262254050.2145490.527825-1.08069720080.3733750.016061-0.84396986490.4251480.395952-0.8294389219

860.38668-0.030170.362320.18676860.3806070.0143830.42223458250.2145490.527825-0.4636366220.3733750.016061-0.68831330550.4251480.395952-0.6020628763

870.391050.799180.35941-0.30561870.3806070.0143830.7260654940.2145490.5278251.10762279160.3733750.016061-0.86949754060.4251480.395952-1.8455721906

880.377660.719560.374610.70381880.3806070.014383-0.20489466730.2145490.5278250.9567773410.3733750.0160610.07689434030.4251480.3959520.7037772255

890.384210.534310.372940.53216890.3806070.0143830.25050406730.2145490.5278250.60580874340.3733750.016061-0.02708424130.4251480.3959520.2702650826

900.385610.478710.389770.76608900.3806070.0143830.34784120140.2145490.5278250.50047080.3733750.0160611.02079571630.4251480.3959520.8610437629

910.396810.64620.384220.86674910.3806070.0143831.12653827440.2145490.5278250.81779188180.3733750.0160610.67523815450.4251480.3959521.1152664969

920.36456-0.655590.36578-0.00698920.3806070.014383-1.11569213660.2145490.527825-1.64853692040.3733750.016061-0.47288462740.4251480.395952-1.0913646099

930.37359-0.08530.36538-0.01574930.3806070.014383-0.48786762150.2145490.527825-0.56808411880.3733750.016061-0.49778967690.4251480.395952-1.1134885037

940.382710.687030.384020.84224940.3806070.0143830.14621428070.2145490.5278250.89514706580.3733750.0160610.66278562980.4251480.3959521.0533903099

950.38670.21530.391280.91953950.3806070.0143830.4236251130.2145490.5278250.00142282010.3733750.0160611.11481227820.4251480.3959521.2485907383

960.37537-0.343720.380110.06683960.3806070.014383-0.36411040810.2145490.527825-1.05767820770.3733750.0160610.41933877090.4251480.395952-0.9049531256

970.39192-0.043550.398290.88085970.3806070.0143830.78655357020.2145490.527825-0.48898593280.3733750.0160611.55127327070.4251480.3959521.1509021296

980.40671.389990.386530.6203980.3806070.0143831.81415560040.2145490.5278252.22695211480.3733750.0160610.81906481540.4251480.3959520.4928678224

990.398770.311680.395010.85619990.3806070.0143831.26281026210.2145490.5278250.18402121920.3733750.0160611.34705186480.4251480.3959521.0886218532

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modeling the temperature connectionFrom Lucht et al., (Science, 296:1687-1689, 2002)A biogeochemical model of vegetation using observed climate data predicted the high northern latitude greening trend over the past two decades observed by satellites and a marked setback in this trend after the Mount Pinatubo volcano eruption in 1991.The observed trend toward earlier spring budburst and increased maximum leaf area is produced by the model as a consequence of biogeochemical vegetation responses mainly to changes in temperature.(14 of 37)

it is temperature, not co2, that is the related to greeningoutline the greening in Eurasia is more persistent than in North America the northern latitudes (>40N) have greened since the early 1980sBackground on NDVI and NDVI data sets- the northern latitude greening trend during the 1980s and 1990s- persistence of greening in Eurasia vs North America- the temperature connection- the connection to circulation anomalies- northern latitude greening and the forest woody biomass carbon sinkThe greening earth and increasing terrestrial net primary productionGreening in the northUse with caution download from http://cybele.bu.edu(15 of 37)

cca analysisare these relations valid at finer (pixel) scales?what is causing these correlations?NDVI data: FASIR GIMMS v1 (1982-98) (Courtesy of Los et al.)Surface temperature from GISS (Hansen et al., 1999)NINO3 index (Reynolds and Smith, 1994)AO index (first EOF of NH SLP 20N-90N; Thompson and Wallace, 1998)DataFrom Buermann and Anderson et al. (JGR, in press)Zhou et al., (2001)Canonical Correlation Analysis (CCA) to isolate coupled spatial patterns between temperature and NDVI and assess their possible relationship to large-scale circulation anomaliesMethod(16 of 37)

the enso modeFirst canonical factor: the ENSO signalTemperature (r=0.78)NDVI (r=0.76)NINO3 SON (81-97)Principal ComponentSpring (MAM) Temp and Spring NDVI, 10N-90NGeographic Plot of the Grid-point Correlations for the 1st CFTEMPERATURENDVI These figures indicate that the first factor captures the NH spring ENSO tele-connection signal in the surface temperature and NDVI fields.During warm ENSO events, warmer and greener conditions prevail in spring over North America, far east Asia and to some extent over Europe. The ENSO related patterns explain 10.8% (13.5%) of the total spring surface temperature (NDVI) variability.(17 of 37)

the ao modeTemperature (r=0.85)NDVI (r=0.83)AO (DJB), 82-98NAO (DJF), 82-98Principal ComponentSpring (MAM) Temp and Spring NDVI, 10N-90NSecond canonical factor: the AO signalGeographic Plot of the Grid-point Correlations for the 2nd CFTEMPERATURENDVIThe fact that there is quantitative agreement between the temporal and spatial features isolated via the CCA algorithm and those associated with ENSO and AO indices suggests that surface temperature signatures associated with these two predominant modes of global climate variability are also important drivers for variability in northern hemisphere greenness.(18 of 37)These figures indicate that the first factor captures the NH spring AO teleconnection signal in the surface temperature and NDVI fields.

ENSO and AO are partly responsible for the correlation between temperature and NDVIoutline it is temperature, not co2, that is the related to greening the greening in Eurasia is more persistent than in North America the northern latitudes (>40N) have greened since the early 1980sBackground on NDVI and NDVI data sets- the northern latitude greening trend during the 1980s and 1990s- persistence of greening in Eurasia vs North America- the temperature connection- the connection to circulation anomalies- northern latitude greening and the forest woody biomass carbon sinkThe greening earth and increasing terrestrial net primary productionGreening in the northUse with caution download from http://cybele.bu.edu(19 of 37)

biomass carbon stocks, sources and sinksFrom Myneni and Dong et al. (PNAS, 98(26):14784-14789, 2001)About 1 to 2 giga (10^9) tons of carbon (Gt C) a year are suggested to be sequestered in pools on northern land.This study is limited to analysis of the carbon pool in the woody biomass of northern temperate and boreal forests, which cover an area of about 1.4 to 1.5 billion hectares.We define forests as the following remote sensing land covers: broad leaf forests, needle leaf forests, mixed forests and woody savannas.Motivation(20 of 37)Debate is currently underway regarding which of the forest biomass sinks can be used by the industrialized nations to meet their commitments under the Kyoto protocol.

inventory data and greennessYear-to-year changes in biomass are quite small, about two orders of magnitude smaller than the biomass pool. At decadal and longer time scales, the biomass changes can be considerable due to accrual of the differences between gains and losses.

Potentially, these can be observed as low frequency variations in decadal scale greenness, in much the same way as century scale greenness changes are suggestive of successional changes.We use 5-yr averages of growing season NDVI total (GIMMS v1), the area under seasonal NDVI curve and above a threshold, which captures both the average seasonal level of greenness and growing season duration, and therefore is an ideal measure of seasonal greenness.Forest inventory data from 171 provinces in six countries that represent a wide variety of inventory practices, provincial forest area, ecosystem types, age structures and time periods.(21 of 37)

uncertainty analysisThe relation between woody biomass and seasonal greenness is estimated as, 1/biomass = a + [(1/ndvi)/latitude^2] + g latitudewhere, biomass: inventory estimate (tons/ha), ndvi: cumulative growing season ndvi averaged over five years of the inventory, latitude: average of latitudes over forest pixels in each province and, , and : regression coefficientsThe relative difference between remote sensing and inventory estimates is 27% for above-stump biomass (10.4 tons C/ha), 33% for total biomass (16.1 tons C/ha), 50% for changes in pool size (0.33 tons C/ha/yr) (22 of 37)

spatial picture of the biomass sinkPool changes were then evaluated as the difference between the late 1990s and early 1980s pool estimates, pixel-by-pixel, and quoted on a per year basis.The carbon pool in the woody biomass of northern forests (1.5 billion ha) is estimated to be 61 20 Gt C during the late 1990s.

Our sink estimate for the woody biomass during the 1980s and 1990s is 0.680.34 Gt C/yr.(23 of 37)

country analysisThe estimates of the three large countries, Canada, Russia and the USA, are crucial because they account for 78% of the pool, 73% of the sink and 77% of the forest area.(24 of 37)For Canada, we estimate a sink of about 73 Mt C/yr which is comparable to an inventory estimate by the Canadian forest service about 85 Mt C/yr.

country analysis(25 of 37)Our pool, sink and forest area estimates for the USA are are comparable to TBFRA-2000 estimates. Our sink estimate for the USA (142 Mt C/yr) is comparable to most estimates for the 1980s (110 to 150 Mt C/yr).

country analysis(26 of 37)Estimates for Russia differ principally because of differences in the definition of forest area. When expressed on a per unit forest area basis, the various estimates are comparable.

reasonsThe spatial patterns, however, offer some clues:a) longer growing seasons from warming in the northern latitudes possibly explain some of the changes, andb) increased incidences of fires and infestations in Canadac) fire suppression and forest re-growth in the USAd) declining harvests in Russiae) improved silviculture in Nordic and European countriesf) forest expansion and re-growth in China (27 of 37)

biomass carbon sinks represent 10% of the annual fossil fuel emissions ENSO and AO are partly responsible for the correlation between temperature and NDVI it is temperature, not co2, that is the related to greening the greening in Eurasia is more persistent than in North America the northern latitudes (>40N) have greened since the early 1980sBackground on NDVI and NDVI data sets- the northern latitude greening trend during the 1980s and 1990s- persistence of greening in Eurasia vs North America- the temperature connection- the connection to circulation anomalies- northern latitude greening and the forest woody biomass carbon sinkThe greening earth and increasing terrestrial net primary productionGreening in the northUse with caution download from http://cybele.bu.eduoutline(28 of 37)

motivationGlobal environmental changes between 1980 and 2000 have been significant:- Two of the warmest decades in the instrumental record- Three intense El Nino events (1982-83; 1987-88; 1997-98)- Changes in tropical cloudiness and monsoon dynamics- A 9.3% increase in atmospheric co2 concentration- A 36% increase in global population (4.45 billion in 1980 to 6.08 billion in 2000)A substantial incentive to understand trends and variability in terrestrial Net Primary Production because NPP: - is the foundation of food, fiber and fuel for human consumption- determines seasonal and interannual variations in atmospheric co2- integrates climatic, ecological, geochemical and human influences on the biosphereHow have global environmental changes affected (eased or strengthened) climatic constraints to plant growth and NPP? Image credit: IPCCImage credit: FAO(29 of 37)From Nemani et al., (Science; in review, 2003)

step 1: limiting factorsPlant growth is assumed to be principally limited by sub-optimal climatic conditions such as low temperatures, inadequate rainfall and cloudiness (Churkina and Running, 1998). We used 1960-1990 average climate data (Leemans and Cramer, 1991) to develop scaling factors between 0 and 1 that indicate the reduction in growth potential.Dominant Controlswater availability 40%temperature 33%solar radiation 27%Total vegetated area: 117 M km2(30 of 37)

step 2: trends in climate dataInterannual trends in daily average temperature 1982-99Interannual trend in vapor pressure deficit 1982-99Interannual trend in solar radiation 1982-99Data: Reanalysis data (6-hourly 2 m height temperatures, 2-m height specific humidity, and incident solar radiation) from NCEP to represent climate variability from 1982 to 1999.Potential Climate Limits for Plant GrowthThe observed climatic changes have been mostly in the direction of reducing climatic constraints to plant growth. Therefore, it seems likely that vegetation responded to such changes positively.(31 of 37)

step 3: npp evaluationStep 1convert absorbed radiation to optimal gross productionStep 2downgrade by climate limiting factors to obtain gppStep 3subtract respiration to obtain nppAverage of interannual trends (1982-99) in growing season NPP estimated with GIMMS and PAL (v3) FPAR

Trends in NPP are positive over 55% of the global vegetated area and are statistically more significant than the declining trends observed over 19% of the vegetated area.The NPP Algorithm(32 of 37)

climate, npp and atmospheric co2 growth rate Analyses of variation in the plant photosynthesis-respiration balance, expressed as NPP/GPP ratio (right panel), showed observed declines in NPP during El Nio years to be dominated by increases in respiration due to warmer temperatures.Interannual variations in global NPP are correlated withglobal atmospheric CO2 growth rates (r = 0.70, p

npp trends by latitudeA strong decline in NPP following the Mt. Pinatubo eruption (1991) was evident only at the high latitudes of the Northern Hemisphere. Cooler temperatures resulting from the eruption decreased the growing season length at high latitude.Ecosystems in all tropical regions and those in the high latitudes of the Northern Hemisphere accounted for 80% of the increase in global NPP between 1982 and 1999. El Nio impacts are strong at low latitudes when compared to mid- and high latitudes. The same cooling may have promoted plant growth in low latitude ecosystems by reducing the evaporative demand and respiration losses.(34 of 37)

npp trends in the tropicsWe suggest increases in solar radiation, as a result of declining cloud cover, in these predominantly radiation-limited forests as a plausible explanation for the increased NPP.The evergreen forests of the Amazon region showed NPP increases, on average, of >1.0% / yr, contributing to over 40% of the global NPP increases between 1982 and 1999.An increase in NPP of 1%/yr as in the case of Amazonia require a fertilization effect greater than 0.5% per ppm of CO2 increase, which appears to be much greater than those reported by field experiments.(35 of 37)

Tropical and northern ecosystems drive increases in terrestrial npp as a result of easing of climatic limits to plant growthoutline biomass carbon sinks represent 10% of the annual fossil fuel emissions ENSO and AO are partly responsible for the correlation between temperature and NDVI it is temperature, not co2, that is the related to greening the greening in Eurasia is more persistent than in North America the northern latitudes (>40N) have greened since the early 1980sBackground on NDVI and NDVI data sets- the northern latitude greening trend during the 1980s and 1990s- persistence of greening in Eurasia vs North America- the temperature connection- the connection to circulation anomalies- northern latitude greening and the forest woody biomass carbon sinkThe greening earth and increasing terrestrial net primary productionGreening in the northUse with caution download from http://cybele.bu.edu(36 of 37)

bottomline half the vegetated lands greened by about 11% 15% of the vegetated lands browned by about 3% 1/3rd of the vegetated lands showed no changes.Since the early 1980s about,The End(37 of 37)These changes are due to easing of climatic constraints to plant growth.

---Ladies and gentlemen,Thank you for the attendance.I have 37 visuals and my talk may last about 45 minutes, during which I shall present evidence for a greener earth based on our work with satellite data of the past 20 years.------The presentation today is a summary of about 15 articles published since 1997.Obviously we do not have time discuss all the details. So, I suggest to those further interested on this theme to obtain the articles from my web site.------I will begin with a brief background on vegetation remote sensing and satellite data sets of greenness.Then, I will present evidence for greening in the northern temperate and boreal latitudes, contrast the spatial pattern of greening in Eurasia and North America, show the correlation between greening and temperature, and the dynamical basis for this correlation, and close this section by assessing the implications for the carbon cycle.In the final section, I will demonstrate the greening, now at the global scale, to be consistent with the notion of easing of climatic constraints to plant growth since the early 1980s.------Some introductory comments and background information.Pigments in green leaves absorb strongly at red and blue wavelengths.The reflectance and transmittance spectra of green leaves therefore show local minima at these wavelengths.There is no such absorption at near-infrared wavelengths, as such the spectra show a maximum at this broad wavelength band.This contrast between red and near-infrared wavelengths is truly unique to green leaves and canopies of leaves and is the basis for optical remote sensing of vegetation.------The contrast between red and near-infrared reflectance of vegetation is the basis for the greenness index, normalized difference vegetation index, or NDVI.NDVI is negative for water bodies and snow, is close to zero for barren areas and gradually increases with leaf area, but saturates in the case of dense vegetation canopies.There is ample theoretical and experimental basis for the the claim that NDVI is suggestive of canopy green leaf area, but the relation between the two is not easy to formulate.---

---Data from the Advanced Very High Resolution Radiometers, AVHRRs, have been processed by various groups and individuals to produce NDVI data sets.The two notable data sets currently in use, are the Global Inventory Monitoring and Modeling Studies (GIMMS) and the Pathfinder AVHRR Land (PAL) data sets.The processing of raw data to NDVI includes calibration and inter-sensor alignment, some form of atmospheric correction, corrections for anomalous events, such as the eruption of volcanoes, and compositing.We have recently developed cleaner versions of the two data sets, by spatially and temporally aggregating the data (the so-called version 3 data sets).--- ---So, that is a brief background on NDVI and NDVI data sets. If you wish to obtain the version 3 NDVI or LAI data sets, please download these from my web site. And, use with caution.Let us now discuss the greening trend in the temperate and boreal zones of the northern hemisphere.---

---The cumulative growing season greenness is the area under the seasonal NDVI curve. This can change either due to a change in the magnitude of greenness or due to a change in the growing season, or both.We assess changes in greenness magnitude from NDVI data of July and August.To assess changes in growing season duration, we chose a NDVI threshold, and count the number of days with NDVI values above this threshold, and how this number changes with time.Such analyses are performed for vegetated pixels only and using NDVI values greater than zero, all of which minimizes artifacts related to data corruption due to snow or barren lands.---

---Zhou et al. in a paper published in JGR 2001 reported analysis of GIMMS NDVI data for the period 1981 to 1999.The two left panels depict trends in greenness magnitude separately for North America and Eurasia.Likewise, the two right panels depict trends in growing season duration. Zhou et al. conclude - a larger increase in growing season NDVI magnitude (12 vs 8%) and a longer active growing season (18 vs 12 days) brought about by an early spring and delayed autumn are observed in Eurasia relative to North America,suggesting that vegetation in Eurasia was photosynthetically more active than vegetation in North America.---

---So, we wrap this subsection by concluding that the northern latitudes (>40N) have greened since the early 1980s.This is generally consistent with various other reports of changes in the north.Now, let us look at the spatial pattern of greening.------Zhou et al characterized the persistence in the greening trend by examining the tendency for sustaining the greening with increase in record length, pixel-by-pixel.The spatial pattern of such persistence in the greening trend is shown here. - About 61% of the total vegetated area between 40N-70N in Eurasia shows a persistent increase in growing season NDVI over a broad contiguous swath of land from Central Europe through Siberia to the Aldan plateau, where almost 58% (7.3 million km2) is forests and woodlands- North America, in comparison, shows a fragmented pattern of change, notable only in the forests of the southeast and grasslands of the upper Midwest.Bogaert et al. in a 2002 JGR article used landscape metrics to quantify the spatial patterns of persistence and concluded that- regions of high NDVI persistence values in Eurasia exhibit higher connectivity with large dominant patches, lower patch density, higher patch coherence, more pixel clustering, more contiguous pixels, more aggregation, and a higher probability of finding orthogonal neighbors.- in North America, the spatial pattern of long-term NDVI increases is fragmented with a higher patch density, smaller patches, a few large connected regions, less coherence, and higher values of the fragmentation index.---

---So we wrap up this subsection by concluding that the greening in Eurasia is more temporally persistent and spatially coherent than in North America.Next, let us look at the temperature connection.---

---The two panels to the left depict growing season, that is, May to September, average greenness for all vegetated areas between 40 and 70N and the surface temperature observations for the same period and regions, separately for North America and Eurasia. Our statistical analyses indicate that North American surface temperature and greenness are correlated and likewise Eurasian surface temperature and greenness are correlated.However, North American temperature and Eurasian greenness are NOT correlated. Similarly, North American greenness and Eurasian surface temperature are NOT correlated.Thus, we conclude that the temporal changes and continental differences in NDVI are consistent with ground based measurements of temperature, an important determinant of biological activity in the north.------Lucht et al. forced a dynamic vegetation model, the LPJ model, with observed climate data, and were able to reproduce the northern latitude greening trend recorded in the satellite NDVI data.The figure shows changes in peak seasonal LAI, onset of spring green-up and autumn die-down as simulated by the model and inferred from satellite data.In particular, they were able to reproduce accurately the interannual variations in greenness, especially the downturn during the cooler period, July 1991 to December 1992 as a result of Mount Pinatubo eruption.They report that the observed trend toward earlier spring budburst and increased maximum leaf area is produced by the model as a consequence of biogeochemical vegetation responses mainly to changes in temperature.---

---From these two studies, we conclude that the unprecedented warming of the northern lands during the past 2 decades is the primary driver of the greening trend.Questions have been raised as to whether this trend could be due to CO2 fertilization. Kaufmann et al. article in JGR 2002 dispels this notion convincingly. The details not presented here, but I would be happy to discuss this offline.Now, let us explore this temperature connection further.------The correlation between surface temperature and satellite greenness estimates reported by Zhou et al., and shown here, raises at least two questions:1) Is this correlation valid at all spatial and temporal scales?2) What mechanisms are responsible for this correlation?In an attempt to answer these questions, we performed canonical correlation analysis, CCA, in which the predictor fields are NH (10N-90N) surface temperature anomalies and the predictand fields are greenness anomalies.We focused on the spring time period because of the sensitivity of plant growth to temperature.

The CCA is designed to select those temporal features in the greenness fields that are best correlated with temporal features in surface temperature fields.In particular, the CCA attempts to minimize the variance between a subset of the NDVI and temperature EOF time series, to produce a set of canonical factor time series that isolate modes of greatest correlation within the two data sets (as opposed to isolating modes of greatest variance explained as in the case of standard EOF analysis).The subsets of EOFs used for CCA in this study was limited to the first six. Together they explain 76% and 64% of NH spring temperature and NDVI variability, respectively.------The left panel shows the 1st canonical factor time series of spring (MAM) surface temperature and NDVI and the preceding autumn (SON) NINO3 time series, appropriately normalized.The correlation between the NINO3 and the temperature time series is 0.78; likewise, the correlation between the NINO3 and NDVI time series is 0.76.The color coded maps to the right show the spatial pattern of correlations between the first canonical factor and the original grid-point anomaly data, temperature at the top, NDVI at the bottom.These patterns are quite similar to correlation maps between temperature and NINO3, and NDVI and NINO3 (not shown).Together, these results indicate that the first canonical factor captures the NH spring ENSO teleconnection signal in the surface temperature and NDVI fields.During warm ENSO events, warmer and greener conditions prevail in spring over North America, far east Asia and to some extent over Europe.The ENSO related patterns explain 10.8% and 13.5% of the total spring surface temperature and NDVI variability, respectively.It is important to note that the CCA is designed to isolate the best-correlated modes of temperature/NDVI co-variability and not necessarily the modes that explain the largest amount of variance (or covariance). As such, it is possible for the CCA algorithm to isolate patterns within the temperature/NDVI fields that may have well correlated features, but that do not explain much variance in the overall system.---

---The left panel shows the 2nd canonical factor time series of spring (MAM) surface temperature and NDVI and the winter Arctic Oscillation (AO) and North Atlantic Oscillation (NAO) time series, appropriately normalized.The correlation between the AO and the temperature time series is 0.85; likewise, the correlation between the NINO3 and NDVI time series is 0.83.The temporal and spatial patterns of the second canonical factor suggest that this factor represents a NH spring AO teleconnection signal in surface temperature and NDVI fields.These AO related patterns explain 18.4% (9.9%) of the total spring surface temperature (NDVI) variability.The main spatial features associated with the positive phase of the AO during NH spring appear to be enhanced warm and green conditions over large regions in Europe and Asian Russia with the opposite conditions in the eastern half of North America.The fact that there is quantitative agreement between the temporal and spatial features isolated via the CCA algorithm and those associated with ENSO and AO indices suggests that surface temperature signatures associated with these two predominant modes of global climate variability are also important drivers for variability in northern hemisphere greenness.---

---So, this previous discussion was motivated by the questions,What mechanisms are responsible for the observed correlation between surface temperature and greennessIf this correlation is valid at finer spatial scales.The answer is, ENSO and AO are partly responsible for the correlation between surface temperature and NDVI, and it appears that the correlations may be valid at finer scales in view of our analysis with grid point dataand the spatial coherence in derived spatial patterns.Now, let us discuss the consequences of this greening trend for the carbon cycle.------The motivation for assessing the carbon content of vegetation is two-fold: - about 1 to 2 billion tons of carbon a year are suggested to be sequestered in pools on northern lands - there is a possibility of using some of the forest biomass carbon sinks by nations to meet their emissions reductions commitments under the Kyoto Protocol.Carbon on land is contained in various active pools such as vegetation, detritus, soil, black carbon residue from fires, harvested products, etc.This study is limited to the biomass carbon pool of the northern temperate and boreal forests, shown here in this color-coded image, which cover an area of about 1.4 to 1.5 billion hectares.We define forests as the following remote sensing land covers: broad leaf forests, needle leaf forests, mixed forests and woody Savannas. This definition is different than the one used by the FAO based on land-use to define FOWL. Woody biomass consists of wood, twigs, bark, stumps, branches and roots of live trees, shrubs and bushes.The vegetation pool gains carbon from photosynthetic investment in these organs and loses carbon due to aging, mortality, disease, harvest, insect attacks, fire, windthrow, etc.---

---Altimeters or lidars in space are required to remotely estimate forest biomass, and we do not have lidars in space (ICESAT?).It is important to note that year-to-year changes in biomass are about 2 orders of magnitude smaller than the biomass pool. Therefore, biomass changes difficult to measure accurately.However, at decadal and longer time scales, biomass changes can be considerable, and hopefully these can be measured as low frequency variations in decadal scale greenness, in much the same way as century scale greenness changes are suggestive of successional changes.The figure here shows woody biomass in tons/ha estimated from 1980s and 90s forest inventory data from 171 provinces in six countries covering over 1 billion ha of temperate and boreal forests, and which represent a wide variety of inventory practices, provincial forest area, ecosystem types, age structures and time periods. These are plotted against 5-year averages of growing season cumulative NDVI, which is an ideal measure of greenness, for it captures both the average seasonal level of greenness and growing season duration.The outliers represent high biomass, old growth forests of the Pacific northwestern statesBritish Columbia in Canada; Washington, Oregon and (northern) California in the United Statessituations where the satellite NDVI data saturate.------The biomass and NDVI data shown previously were transformed and used to estimate a statistically significant relation between biomass and seasonal greenness totals. The specification used is shown in this slide.The results indicate that biomass increases with NDVI and varies with latitude, with the largest values in temperate latitudes. The ability of this equation to represent the relation between biomass and NDVI across spatial, temporal, and ecological scales was evaluated by testing the null hypothesis that the regression coefficients do not vary among nations, time periods, NDVI, or latitude. The results indicate that the coefficient associated with NDVI is stable across a large portion of the observed range for NDVI, latitude, and among nations.Thus, the regression model obtained from pooled data were used to generate all biomass estimates discussed here.Uncertainties in our estimates of biomass pool (left panel) and changes (right panel) were evaluated by comparing these to national, provincial and state estimates. This is an out-of-sample comparison.The average absolute difference between remote sensing and these inventory estimates is 10.4 tons C/ha for above-stump biomass, 16.1 tons C/ha for total biomass, and 0.33 tons C/ha per year for changes in pool size, or 27%, 33%, and 50% of the mean inventory estimates, respectively. There is no bias in the estimation of biomass pools and changes to the pools. The national inventory sink estimates, shown in the right panel here, were derived from wood volume increment and loss data (natural and fellings), unlike remote sensing estimates which are biomass differences betweentwo time periods. The comparability of the two estimates is thus noteworthy.---

---Biomass pools were estimated for two periods, the early 1980s (1982-86) and the late 1990s (1995-99). Pool changes were then evaluated as the difference between these two pool estimates, pixel-by-pixel, and quoted on a per year basis. The color-coded image depicts the estimated pool changes.the details.Carbon gains, in excess of 0.3 tons of C/ha per year, in Eurasian boreal and North American temperate forests, and carbon losses, greater than 0.1 tons C/ha per year, in some Canadian boreal forests. The gains are observed in Eurasia over a large, broad, nearly contiguous swath of land, from Sweden through Finland, European Russia, central Siberia to trans-Baikalia. In North America, similarly large gains are seen in the eastern temperate forests of the United States and in southern Ontario and Quebec below the 50th parallel. Carbon losses are seen in Canadas boreal forests, from Newfoundland to the Northwest territories, except in smallerfragments in northern Saskatchewan and Alberta, where gains are observed.The carbon pool in the woody biomass of northern forests (1.5 billion ha) is estimated to be 61 20 Gt C during the late 1990s.This is comparable to the TBFRA-2000 reports (80 Gt C), but on 2.5 billion ha of forests and other wooded land.Our sink estimate for the woody biomass during the 1980s and 1990s is 0.680.34 Gt C/yr.This is in the mid-range of estimates by Sedjo for mid-1980s (0.36 Gt C/yr) and TBFRA-2000 for early and mid-1990s (0.81 Gt C/yr).---

---The estimates of the three large countries (Canada, Russia, and the United States) are crucial to overall accuracy because they account for 78% of the pool, 73% of the sink, and 77% of the forest area. Our pool, sink, and forest area estimates for Canada and the United States are comparable to TBFRA-2000. The losses observed in some Canadian boreal forests are consistent with reports of disturbances from fires and insects during the 1980s and 1990. For the entire country, however, we estimate a sink of about 0.073 Gt C/year, which is comparable to an inventory estimate by the Canadian Forest Service (0.091 Gt C/year) for 19821991.------Our sink estimate for the United States (0.142 Gt C/year) is comparable to most other estimates for the 1980s (0.020.15 Gt C/year). ------Estimates for Russia are especially crucial and they tend to differ. The remote sensing estimate of forest area, 642 Mha, is about 130180 Mha lower, possibly because of the resolution of satellite data, which may be too coarse for detecting tree stands in the forest-tundra of Russia, where small lots of sparse stands with extremely low growing stock are distributed among the vast peatlands. But, when expressed on per-ha forest area basis, the various pool estimates are comparable (3843 tons C/ha). The difference in sink estimates between remote sensing and TBFRA-2000 is smaller (0.44 vs. 0.53; in tons C/ha per year). Nilsson et al.s sink estimate, 0.058 Gt C/year, is significantly lower than our (0.292 Gt C/year) and TBFRA-2000 estimates (0.423 Gt Cyyear).------The reasons for the observed changes in the forest woody biomass pool are not known, which implies uncertainty regarding the future of biomass sinks and therefore the need for monitoring.The spatial patterns, however, offer some clues: - longer growing seasons from warming in the northern latitudes possibly explain some of the changes, and - increased incidence of fires and infestations in Canada - fire suppression and forest re-growth in the USA - declining harvests in Russia - improved silviculture in the Nordic and European countries - forest expansion and regrowth in Chinapossibly explain some of the changes.---

---So, we wrap up our study on northern forest woody biomass sinks by noting that such sinks represent 10% of annual fossil fuel emissions.The final topic of my presentation is based on some recent work done with the latest versions of GIMMS and PAL NDVI data sets that were produced at Boston University. These data sets are of a quality where we feel that we can now conduct global studies. Based on our work with the MODIS program, the lessons learned there, and the field data collected during the past 6 years, we were able to generate LAI and FPAR data sets from the new NDVI data sets. This allowed us to use Net Primary Production as the study variable, rather than NDVI, as was the case up until now.---

---Global environmental changes between 1980 and 2000 are significant and include a global warming trend resulting in two of the warmest decades in the instrumental record (1980s and 1990s), three intense and persistent El Nio events (1982-83, 1987-88 and 1997-98), changes in tropical cloudiness and monsoon dynamics, a 9.3% increase in atmospheric CO2, and a 36% increase in global population (4.45 billion in 1980 to 6.08 billion in 2000). Because net primary production integrates climatic, ecological, geo-chemical, and human influences on the biosphere, there is a substantial incentive to understand the trends and variability of NPP both for its role in determining seasonal and inter-annual variations in atmospheric CO2 and as the foundation of food, fiber and fuel for human consumption. Therefore, the question is, how have global environmental changes affected (by which I mean,eased or strengthened) climatic constraints to plant growth and NPP?------In order to evaluate the world-wide significance of climatic changes in the context of limiting factors to plant growth, we derived a global map, shown here, of the relative influence of climate factors that regulate plant growth (temperature, water and solar radiation) using long-term (1960-90) 0.5 x 0.5 grided monthly climate data from Leemans and Cramer . We found that water availability acts as a dominant control over 40% of the Earths vegetated area of 117 M km2, followed by temperature (33%) and radiation (27%). Often, more than one climatic factor regulates plant growth during the growing season. Plant growth is limited by - temperature and radiation (cold winters and cloudy summers) over Eurasia, shown here in cyan,- temperature and water (cold winters and dry summers) over western North America, shown here in magenta,- and radiation and water (wet-cloudy and dry-hot periods induced by rainfall seasonality) in the tropics, shown here in yellow.These limits vary by season; for example, high latitude regions are limited by temperature in the winter and by either water or radiation in the summer.------To determine the magnitude and spatial distribution of climate changes from 1982 to 1999, we estimated the trends in growth limiting climate factors from the National Center for Environmental Prediction (NCEP) daily reanalysis data. Spring air temperatures that regulate the initiation of the growing season have increased over temperature-limited regions of North America and Northwest Europe promoting earlier plant growth. Wetter rainfall regimes and the associated reduction in vapor pressure deficits in the 1990s are important for the water-limited ecosystems of Australia, Africa and the Indian sub-continent. Significant increases in incident solar radiation are evident over radiation-limited regions of Eurasia and the equatorial tropics. While the increased solar radiation over Eurasia may be related to changes in the North Atlantic Oscillation, a decline in cloud cover along with increases in outgoing long-wave radiation, as a consequence of changes in tropical circulation patterns, has recently been reported over tropical regions. Interestingly, the observed climatic changes have been mostly in the direction of reducing climatic constraints to plant growth. Therefore, it seems likely that vegetation responded to such changes positively.---

---To assess quantitatively the effect of observed changes in climate on net primary production, we used a production efficiency model that combines satellite-derived monthly estimates of FPAR and LAI with daily NCEP climatic data to estimate monthly and annual gross primary production, plant respiration and net primary production at 0.5 o x 0.5 resolution. A schematic of the algorithm for NPP is shown here.In water and radiation limited regions NPP showed the highest increase (6.5%) followed by those in temperature and radiation (5.7%), and temperature and water (5.4%) limited regions. Globally all biomes, except open-shrubs, showed an increasing NPP trend from 1982 to 1999 with the largest increase in evergreen broadleaf forests. Trends in NPP are positive over 55% of the global vegetated area and are statistically more significant than the declining trends observed over 19% of the vegetated area.---

---A moderately increasing trend (6% or 3.42 PgC/18yr, p