satellite detection of increasing northern hemisphere non-frozen seasons from 1979 to 2008:...

Remote Sensing of Environment 121 (2012) 472–487

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Remote Sensing of Environment

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Satellite detection of increasing Northern Hemisphere non-frozen seasons from 1979to 2008: Implications for regional vegetation growth

Youngwook Kim a,b,⁎, J.S. Kimball a,b, K. Zhang c, K.C. McDonald d,e

a Flathead Lake Biological Station, The University of Montana, Polson, MT 59860, United Statesb Numerical Terradynamic Simulation Group, The University of Montana, Missoula, MT 59812, United Statesc Dept of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, United Statesd The City College of New York, New York, NY 10031, United Statese Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, United States

⁎ Corresponding author at: Flathead Lake Biological StatPolson, MT 59860, United States.

E-mail address: [email protected] (Y. K

0034-4257/$ – see front matter © 2012 Elsevier Inc. Alldoi:10.1016/j.rse.2012.02.014

a b s t r a c t
a r t i c l e i n f o
Article history:Received 19 May 2011Received in revised form 28 November 2011Accepted 12 February 2012Available online 22 March 2012

Keywords:Freeze thawSMMRSSM/IClimate changeGlobal warmingMODISNDVIVegetation growing seasonPhenologyESDRCDRNASA MEaSUREs

The landscape freeze–thaw (FT) signal from satellite microwave remote sensing is closely linked to vegetationphenology and land–atmosphere trace gas exchangewhere seasonal frozen temperatures are amajor constraintto plant growth. We applied a temporal change classification of 37 GHz brightness temperature (Tb) series fromthe Scanning Multichannel Microwave Radiometer (SMMR) and Special Sensor Microwave Imager (SSM/I) toclassify daily FT status over global land areaswhere seasonal frozen temperatures influence ecosystemprocesses.A temporally consistent, long-term (30 year) FT record was created, ensuring cross-sensor consistency throughpixel-wise adjustment of the SMMRTb record based on empirical analyses of overlapping SMMRand SSM/Imea-surements. The resulting FT record showedmean annual spatial classification accuracies of 91 (+/−8.6) and 84(+/−9.3) percent for PM and AM overpass retrievals relative to in situ air temperature measurements from theglobal weather station network. The FT results were compared against other measures of biosphere activity in-cluding CO2 eddy flux tower measurements and satellite (MODIS) vegetation greenness (NDVI). The FT definednon-frozen season largely bounds the period of active vegetation growth and net ecosystem CO2 uptake fortower sites representing major biomes. Earlier spring thawing and longer non-frozen seasons generally benefitvegetation growth inferred from NDVI spring and summer growth anomalies where the non-frozen season isless than approximately 6 months, with greater benefits at higher (>45 °N) latitudes. A strong (Pb0.001)increasing (0.189 days yr−1) trend in theNorthernHemispheremean annual non-frozen season is largely drivenby an earlier (−0.149 days yr−1) spring thaw trend and coincideswith a 0.033 °C yr−1 regional warming trend.The FT record also shows a positive (0.199 days yr−1) trend in the number of transitional (AM frozen and PMnon-frozen) frost days, which coincide with reduced vegetation productivity inferred from tower CO2 andMODISNDVImeasurements. The relative benefits of earlier and longer non-frozen seasons for vegetation growthunder global warming may be declining due to opposing increases in disturbance, drought and frost damagerelated impacts.

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1. Introduction

Approximately 66 million km2 (~52.5%) of the global land areaundergoes seasonal freezing that constrains ecosystem processes (Kimet al., 2011). At high northern latitudes, the landscape freeze–thaw(FT) status is closely linked to potential growing season length andvegetation productivity (Jarvis et al., 2000; Kimball et al., 2004; Tanjaet al., 2003), land-atmosphere trace gas exchange (Kurganova et al.,2007; Potter, 2004), the timing of seasonal snowmelt (Rawlins et al.,2005; Stone et al., 2002) and the release of nutrients in plant availableform (Grogan & Jonasson, 2003). The timing of seasonal thawing and

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snowmelt in spring also coincides with the onset of the growing seasonand influences boreal ecosystem sink activity for atmospheric CO2

(Ensminger et al., 2004; Goulden et al., 1998).Global surface air temperatures have experienced a general

warming trend since the 1980s (Alley et al., 2003; IPCC, 2007). Recentwarming has contributed to earlier onset and lengthening of the po-tential growing season (McDonald et al., 2004; Rosenzweig et al.,2008; Steltzer & Post, 2009), which is attributed to be a major driverof recent increases in vegetation greenness and productivity (Craineet al., 2003; Kimball et al., 2006; Lucht et al., 2002), and associatedshifts in atmospheric CO2 seasonal cycles (Randerson et al., 2009). Re-cent warming has also contributed to potentially adverse vegetationimpacts, including increasing water stress (Angert et al., 2005;Schindler & Donahue, 2006; Zhang et al., 2009), decreasing cold har-diness (Kreyling, 2010), higher tree mortality rates (Mantgem et al.,

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473Y. Kim et al. / Remote Sensing of Environment 121 (2012) 472–487

2009) and increasing fire frequency (Westerling et al., 2006). Whileearly spring thawing may promote increased vegetation productivityand net ecosystem CO2 uptake (Picard et al., 2005; Tanja et al., 2003),earlier bud-burst and canopy development may offset potentialcarbon gains by increasing vulnerability to spring frost damage(Gu et al., 2008; Inouye, 2008).

Satellite remote sensing records have been effectively used to char-acterize recent climate driven changes to global vegetation (Goetz et al.,2005; Nemani et al., 2003; Zhao & Running, 2010). The normalizeddifference vegetation index (NDVI) derived from optical–infraredremote sensing is closely related to the amount, structure and conditionof vegetation photosynthetic biomass (Tucker et al., 1985). These NDVIrecords have been widely used to document spatial patterns, annualvariability and trends in vegetation activity in response to recent globalwarming (Myneni et al., 1997; Slayback et al., 2003). However, relative-ly coarse (8–16 day) temporal compositing of these data needed toreduce cloud and atmospheric aerosol contamination, and snow andlow solar illumination effects degrades optical-IR sensor capabilities toresolve vegetation seasonal cycles over many areas of the globe,especially at higher latitudes (Alcaraz-Segura et al., 2010; Cihlar et al.,1998; Nagol et al., 2009).

Satellitemicrowave remote sensing is sensitive to temporal changesin the predominant frozen or thawed state of water in the landscape,including surface snow, soil and vegetation canopy elements, and isrelatively insensitive to atmosphere and solar illumination effects atlower microwave frequencies. Satellite microwave radiometers suchas the Special Sensor Microwave Imager (SSM/I) have relatively longrecords and have been used for global daily mapping of terrestrial FTdynamics at moderate (~25-km) spatial scales (Kim et al., 2011). Inareas that undergo seasonal freezing, the FT parameter from satellitemicrowave remote sensing has been shown to be sensitive to a numberof biophysical parameters influencing terrestrial energy, water andcarbon cycles, including the timing and duration of seasonal snowcover and frozen soils (Smith et al., 2004), and onset and duration ofpotential growing seasons (Kimball et al., 2004; McDonald et al.,2004). The FT parameter is also compatible with other vegetation andland surface ‘skin’ temperature measurements available from satelliteoptical-infrared remote sensing, but with relative insensitivity to solarillumination and atmosphere contamination effects (Jones et al., 2010;Kimball et al., 2006).

Previous studies have documented temporal anomalies and regionaltrends in FT cycles over northern (>50 °N) land areas from the SSM/Irecord (McDonald et al., 2004; Smith et al., 2004); these studies showa general advance in the timing of spring thaw and a lengthening ofthe non-frozen season coincident with global warming, with generallypositive impacts to vegetation productivity (Kimball et al., 2006). How-ever, continued warming and widespread drought and disturbance inrecent years have increased vegetation stress and mortality (Allenet al., 2010; Mantgem et al., 2009), and decreased annual vegetationproductivity for many areas (Beck et al., 2011; Goetz et al., 2005;Zhang et al., 2008). In addition, potential reductions in cold hardinessfrom continued warming and increasing winter thaw events mayincrease frost damage risk in boreal and sub-Arctic vegetation(Bokhorst et al., 2009; Bourque et al., 2005; Strimbeck et al., 1995).The negative impacts of recent warmingmay be offsetting the potentialbenefits of earlier and longer growing seasons for vegetation growth.

Previously, a satellite based global FT record was developed for eco-system studies using a daily temporal change classification of SSM/I37 GHz brightness temperatures and extending over a 20-year periodfrom 1988 to 2007 (Kim et al., 2011). The FT classification schemedistinguishes four discrete categories on a daily basis, including non-frozen, frozen, transitional (AM frozen and PMnon-frozen), and inversetransitional (AM non-frozen and PM frozen) conditions. A global FTclassification domain was developed that encompasses all vegetatedland areas where seasonal cold temperatures are a major constraint toannual vegetation productivity, excluding permanent snow and ice,

urban and barren land areas, and grid cells with greater than 20% frac-tional openwater cover. The cold temperature constraintswere definedusing a simple growing season index (GSI) andminimum daily air tem-perature threshold driven by global reanalysis daily surface meteorolo-gy (Jolly et al., 2005; Nemani et al., 2003). A global 1-km resolution, 16class land cover map (MOD12Q1; Friedl et al., 2002) was used to definesub-grid scale land cover and fractional open water cover within each25-km resolution grid cell.

The objectives of this investigation are to quantify regional patterns,variability and recent trends in the Northern Hemisphere (NH)non-frozen season, and verify the ecological significance of the satellitemicrowave FT signal for regional vegetation growth under a warmingclimate. For the current investigation, we construct a spatially andtemporally consistent and continuous global FT record spanning a lon-ger 30-year period (1979–2008) by combining similar Tb measure-ments from the SMMR (1979–1987) and subsequent SSM/I records,and using a similar FT classification scheme and domain developedfrom a previous study (Kim et al., 2011).

The relative accuracy of the FT classifications is assessed using in situdaily air temperature measurements from the global weather stationnetwork. The daily FT classifications for each grid cell are then pro-cessed to determine annual anomalies and trends in seasonal onset, off-set and duration of the non-frozen season for NH portion of the globalFT domain. The ecological significance of the FT seasonalmetrics is eval-uated against other surrogatemeasures of vegetation activity, includingin situ CO2 eddy flux tower measurement basedmetrics from represen-tative ecoregions and satellite (MODIS) remote sensing based NDVIspring and summer growth anomalies. An empirical analysis of theserelationships is used to quantify regional patterns and variability inthe timing and length of the annual non-frozen period, and the impactsof these changes on regional vegetation growth. A regional trend anal-ysis is also conducted to document long-term (30-year) changes in FTseasonal cycles under a warming climate.

2. Data and methods

2.1. FT record construction and validation

Satellitemicrowave remote sensing provides an effectivemeasure ofcharacteristically large landscape dielectric changes that occur as waterin vegetation, surface snow and soil element transitions between pre-dominantly frozen and thawed conditions (McDonald & Kimball,2005). For this investigation, we define landscape FT state as the pre-dominant (frozen or thawed) condition of water within the satellitesensor field-of-view (FOV). The FOV is frequency dependent and pro-vides an approximate 25-km spatial resolution for SMMR and SSM/I37 GHz brightness temperature (Tb) retrievals. Sensitivity of the satel-lite FT signal to individual landscape elements is also frequency depen-dent and largely reflects conditions in the upper vegetation canopylayer and surface soil and snow layers at the 37 GHz frequency (Ulabyet al., 1982). The SMMR and SSM/I sensors have compatible 37 GHzfrequency channels and sensor characteristics (e.g. altitude, incidenceangle and footprint size). The combined SMMR and SSM/I series ofsatellites provide overlapping Tb records that enable the production ofa continuous global daily FT record extending over more than threedecades, and providing one of the longest satellite-based land parame-ter records available for global change studies.

The SMMR and SSM/I sensor characteristics are summarized inTable 1. The Nimbus-7 SMMR Pathfinder dataset contains Tb retrievalsfrom October 25, 1978 to August 20, 1987. The sun-synchronous,polar orbiting SMMR sensor obtained global Tb retrievals at fivefrequencies (6.6, 10.7, 18, 21 and 37 GHz) in both horizontal (H) andvertical (V) polarizations, at a constant 50.31° incidence angle andlocal noon (ascending orbit) andmidnight (descending orbit) equatori-al crossings (i.e. overpass times). SMMR acquired data every other dayto conserve power, resulting in nearly continuous global mapping

Table 1Comparative operating characteristics of the SMMR and SSM/I sensors.

Parameter SMMR (Nimbus-7) SSM/I (DMSP)

Frequencies (GHz) 6.6, 10.7, 18, 21, 37 19.3, 22.3, 37, 85.5Altitude (km) 955 860Antenna size (m) 0.79 0.60Incidence angle (deg) 50.3 53.1Footprint size at 37 GHz (km) 27 35Swath width (km) 780 1400Periods of record 1978–1987 1987–present

474 Y. Kim et al. / Remote Sensing of Environment 121 (2012) 472–487

twice every 6 days. SMMRwas a predecessor to the SSM/I sensor series,but the mission effectively ended August 20, 1987 due to sensor failure.The SMMR Pathfinder dataset provides an extensive reprocessing of theoriginal sensor Tb record to mitigate known calibration anomalies andremap the data to a consistent geographic grid (Njoku et al., 1995,1996).

The SSM/I is part of the Defense Meteorological Satellite Program(DMSP) platform series and provides continuous global, daily AM andPM Tb measurements from July 9, 1987 to present. The SSM/I measuresV and H polarized Tb at 19, 37 and 85 GHz frequencies, and V polarizedTb at 22 GHz frequency, with a constant 53.1° incidence angle and6 AM/PM equatorial crossings (Armstrong et al., 1994). The SSM/I(Version 6) Tb product was used to obtain improved Tb calibration,cross-platform stability and more precise geolocation resampling overearlier product versions (Brodzik & Armstrong, 2008). The SMMR andSSM/I data for this study were acquired as daily global cylindricalEqual-Area Scalable Earth grid (EASE-Grid) Tb series with 25 km gridcell resolution for the 1979–2008 period (Brodzik & Armstrong, 2008;Brodzik & Knowles, 2002).

The overlap between SMMR and SSM/I operations encompassed a6 week period between Jul 9 and Aug 20, 1987. However, less than20 days of overlapping Tb retrievals are available between the twosensors because of the coarser SMMR two-day revisit time and largecoverage gaps at lower latitudes for both sensors.

Linear regression analysis was used to fill missing 1987 daily Tb valuesfrom the SMMR and SSM/I records using empirical relationships devel-oped between sensor Tb records and coincident model reanalysis (NNR,see below) surface air temperatures on a grid cell-wise basis. The reanaly-sis daily minimum andmaximum air temperature (Tmn and Tmx) recordswere used to define empirical relationships for respective satellite AMand PM overpasses; this was done to fill large gaps in the sensor Tb re-cords in January and December of 1987 for SMMR (DOY=4, 6, 8, 10,12, 14) and SSM/I (DOY 337 to 365). All other missing Tb values foreach grid cell were gap filled using temporal linear interpolation of adja-cent Tb values following previously developedmethods (Kim et al., 2011).

The SMMR 37 V GHz Tb values were adjusted to the correspondingSSM/I Tb record using empirical models derived from linear regressionrelationships between high quality (QC), overlapping SMMR and SSM/I Tb values from the 1987 DOY 192 to 232 mission overlap period. Thehigh quality Tb values were obtained from cells having 0% open waterfraction and homogeneous land cover (i.e. >95% of the grid cell havinga single land cover type) defined from the underlying 1-km resolutionglobal land cover classification, and homogeneous terrain conditions(i.e. elevation standard deviationb10.0 m within the grid cell) definedfrom a similar fine scale digital elevation map (DEM; Hasting et al.,1999). This approach assumes minimal seasonal variation in therelationship between SMMR and SSM/I records, which have the samefrequency and similar altitude and incidence angle. Previous studiesusing merged SSM/I and SMMR data records have also employedsimilar assumptions and approaches with favorable results (Comiso &Nishio, 2008; Royer & Poirier, 2010).

A seasonal threshold-based temporal change classification (STA)approach was used to classify daily (AM and PM) FT status using a Tbbased FT threshold defined annually on a grid cell-wise basis in relationto surface air temperatures from global model reanalysis (Kim et al.,

2011). A base surface air temperature of 0 °C was selected as the FTthreshold following Kim et al. (2011). In this investigation we apply asimilar STA classification using SMMR 37 V GHz Tb series from 1979 to1987. The resulting SMMRAMand PM overpass based FT classificationsare combined for each EASE-Grid cell to define a single composite dailyFT state value (CO; hereafter composite FT classification). The SSM/I37 V derived landscape CO FT daily record used for this study extendsfrom 1988 to 2008 andwas obtained fromdata holdings of the NationalSnow and Ice Data Center (NSIDC; Kim et al., 2010).

Daily surface air temperature records from the National Centers forEnvironmental Prediction and National Center for AtmosphericResearch (NCEP-NCAR) reanalysis (NNR; Kalnay et al., 1996; Kistleret al., 2001)were used for FT algorithm calibration. The NNRwas select-ed because it is the only reanalysis product that covers the entire com-bined SMMR and SMM/I record, is updated on a regular basis and waspreviously used for developing the SSM/I based global FT record (Kimet al., 2011). The NNR provides surface meteorological data four timesper day in a global Gaussian grid with approximately 1.9° by 1.875°spatial resolution. The NNR derived daily precipitation (Prcp) and sur-face Tmn and Tmx (~2 m height) records were used to identify SMMRand SSM/I Tb values under NNR defined frozen (≤0 °C) and non-frozen reference conditions; these Tb thresholds were derived annuallyon a grid cell-wise basis and used for FT algorithm calibration.

We used independent in situ air temperature measurements fromthe globalWMOweather station network (NWS, 1988) to assess the ac-curacy of SMMR and SSM/I derived FT classification results followingpreviously developedmethods (Kim et al., 2011). The availableweatherstations were first screened to be regionally representative of environ-mental conditions within overlying EASE-Grid cells on the basis ofselecting the closest station to the center of each EASE-Grid cell andbeing located within cells with regionally homogeneous land coverand terrain conditions. Approximately 2774±159 [temporal-SD]weather stations were selected for the 1979–1987 period. The numberof validation stations varied annually because of variability in the num-ber of reporting stations and observations. The in situ daily Tmn and Tmx

observations at each station location were used to define local FT(frozen: T≤0 °C; non-frozen: T>0 °C) conditions and compared withcorresponding satellite-based FT classification results of the overlyingEASE-Grid cells and respective AMand PMoverpass periods. Thismeth-od assumes that landscape diurnal air temperature variability generallyfollows a sinusoid curve and that the local timing of daily Tmx and Tmn

occurs near the SMMR and SSM/I equatorial crossing times. The EASE-Grid cells containing co-located satellite and station based FT classifica-tion results were counted and expressed as a proportion (%) of the totalWMO station cells represented within the global FT classification do-main to determine overall spatial classification accuracy on a dailybasis. Errors of omission and commission and the kappa coefficientwere also determined and evaluated to assess FT classification accuracy(Congalton & Mead, 1983).

Additional quality control and quality assessment (QC/QA) informa-tion on the resulting FT classification record was determined, includingmetadata on global daily classification accuracy from the in situ stationobservations and data quality maps identifying spatial and temporalgaps in the satellite Tb record and complex terrain and land coverareas. Details of these data are described elsewhere (Kim et al., 2011).

2.2. Deriving FT seasonal metrics

The merged SMMR and SSM/I daily CO FT record was used to assessNH regional variability and temporal trends in timing and duration ofthe annual non-frozen season over the 30-year (1979–2008) studyperiod. Seven FT metrics were derived from the daily CO FT classificationseries for each calendar year, including annual and seasonal non-frozenand transitional periods (days), and the timing (day of year, DOY) ofprimary seasonal thawing and freezing events. The non-frozen and tran-sitional periodswere derived from the FT record by summing the number


of classified non-frozen and transitional days, respectively, for each EASE-Grid cell and year within the domain. The CO FT parameters were alsosummed and analyzed for spring (MAM) and fall (SON) periods, whichlargely bound NH growing season (McDonald et al., 2004; Smith et al.,2004). The total number of classified non-frozen and transitional days inwinter (DJF)was also determined on a grid cell-wise basis and used to in-vestigate associated patterns and trends in winter processes. The day ofprimary seasonal thaw was determined as the first day (DOY) when 12out of 15 consecutive days (i.e. 80% rule) from January through Junewere classified as non-frozen, while the primary day of seasonal freezingwas selected as the first day when 12 out of 15 consecutive days fromSeptember through Decemberwere classified as frozen. The 15-daymov-ing window size and 80% rule was determined to provide consistentdetection of primary seasonal thaw and freeze events across the study re-gion relative to in situ station observations (Zhang et al., 2011).

2.3. Evaluation of FT metrics against site and regional indicators ofvegetation activity

We compared the seasonal pattern of daily FT status derived fromthe combined SMMR and SSM/I records against in situmeasures of veg-etation activity derived from CO2 eddy flux tower measurement basedestimates of daily gross primary production (GPP) and net ecosystemCO2 exchange (NEE). Four tower sites were selected for these compari-sons representingmajor ecoregionswithinNHdomain for 2005, includ-ing tundra, boreal evergreen needleleaf coniferous forest (ENF),grassland and temperate ENF types. The tower data were obtainedfrom the global FLUXNET database (Baldocchi, 2008; Turner et al.,2006) and included Abisko, Sweden (SE_Abi; 68.4 °N, 18.8 °E; tundra),Quebec, Canada (CA_Qfo; 49.7 °N, 74.3 °W; boreal ENF), Lethbridge,Canada (CA_Let; 49.7 °N, 112.9 °W; Grassland) and Tharandt, Germany(DE_Tha, 60.0 °N, 13.6 °E; Temperate ENF) sites. The tower sites wereselected on the basis of having relatively homogeneous land cover andterrain conditions within a 25-km×25-km window surrounding eachtower location. Daily Tmx and Tmn measurements at each tower sitewere averaged to estimate the mean daily air temperature (Tav),which was used with the tower carbon fluxes to evaluate coincidentsatellite based FT series of the overlying grid cell at each tower location.

The tower based carbon fluxes were used with coincident satelliteNDVI 16-day composite time series from the MODIS (MOD13C1; Hueteet al., 1999) record as indicators of vegetation activity to evaluate the eco-logical significance of the satellite FT signal. The NDVI data were obtainedat 0.05° (~5.6 km) spatial resolution and were resampled to the 25 kmglobal EASE grid format by spatially averaging sub-grid scale NDVI pixelswithin each 25 km resolution grid cell. The satellite FT signal derived fromhigher frequency (37 GHz) microwave remote sensing provides ameasure of surface canopy conditions and associated frozen temperatureconstraints to photosynthesis andGPP, but has generally lower sensitivityto underlying soil processes. The FT signal from the SMMR and SSM/I Tbretrievals is therefore expected to have lower correspondence with NEE,which represents the residual difference between GPP and ecosystemrespiration (Reco), where Reco is influenced by additional environmentalfactors including soil temperature and moisture status, and nutrient sup-ply (Dunn et al., 2007; Rocha et al., 2006). Therefore, the tower measure-ment based GPP estimates were selected as primary site level indicatorsof growing season onset and offset using a similar approach relative tothe method used to define onset and offset metrics from the FT record;the tower growing season onset was determined from the daily GPPseries as the first day (DOY) when 12 out of 15 consecutive days fromJanuary through Juneweremore than 25% of the seasonal GPPmaximum,while the growing season offset was selected as the first daywhen 12 outof 15 consecutive days from September through December were below25% of the seasonal GPP maximum.

The NDVI from satellite optical-infrared remote sensing is sensitiveto regional and global variations in vegetation greenness (Fensholtet al., 2009; Goetz et al., 2005) and was used to evaluate the influence

of the FT seasonalmetrics on vegetation canopy seasonal growth anom-alies. Relatively long-term global NDVI records have been producedfrom the NOAA Advanced Very High Resolution Radiometer (AVHRR),including the Global Inventory Modeling and Mapping Studies(GIMMS; Tucker et al., 2004) and Land Long Term Data Records(LTDR; Pedelty et al., 2007) products. For this study we used the globalNDVI record from the NASA Moderate Resolution Imaging Spectrome-ter (MODIS) to evaluate spatial and temporal correspondence betweenthe FT parameters and associated NDVI seasonal anomalies over the NHdomain. The MODIS record extends from 2000 and was designed withadvanced radiometric precision, sensor calibration and atmosphericscreening capabilities suitable for climate change studies (Beck et al.,2006; Fensholt & Sandholt, 2005; Fensholt et al., 2009; Huete et al.,1999; Vermote et al., 2002). The NDVI is used here as a surrogate mea-sure of regional vegetation growth changes similar to previous studies(Myneni et al., 1997; Nemani et al., 2003), though theNDVI is also influ-enced by other factors independent of vegetation growth changes,including fire disturbance (Alcaraz-Segura et al., 2010; Verbyla, 2008),tree mortality (Mantgem et al., 2009), solar angle and illuminationeffects, atmosphere cloud–aerosol and snow cover contamination(Beck et al., 2006; Cihlar et al., 2004). While MODIS has relativelyadvanced snow and cloud–aerosol screening, these adverse conditionsare particularly problematic at higher latitudes, resulting in reducedspatial and temporal fidelity of all satellite NDVI retrievals.

For this study we used the MODIS MOD13C1 16-day compositeNDVI series as a surrogate measure of vegetation seasonal growthchanges over the NH domain. The MOD13C1 data from Terra wereobtained from 2000 to 2008 and were resampled to the 25 km resolu-tion EASE-Grid of the SMMR and SSM/I based FT record using drop-in-the-bucket averaging (Jones et al., 2011). We selected only the highestquality (QC=0) pixels for spatial aggregation and analysis to removegap filled data, and to minimize the influence of lower quality andsnow/ice effects on the NDVI signal (Jones et al., 2011). Mean NDVIvalues were computed for summer (NDVIJJA) and spring (NDVIMAM)periods on a grid cell-wise basis and used to investigate the influenceof FT parameter changes on spring and summer vegetation growthanomalies. The aggregated MODIS NDVI series were also extractedfrom the nearest 25-km EASE-Grid cell overlying each tower locationand used with the coincident satellite FT records for the site level com-parisons against CO2 eddy flux tower measurements.

We evaluated grid cell-wise temporal correspondence betweenspring thaw timing and non-frozen period anomalies from the satelliteFT record, and seasonal vegetation growth changes defined from theMODIS NDVI record (2000–2008) over the NH domain. The annualnon-frozen period from January to August was defined from the FTrecord and compared against NDVIJJA, while the primary spring thawdatemetric was evaluated against bothNDVIJJA andNDVIMAM. The Pear-son's correlation coefficient (r) and least-squares linear regression slope(s) metrics were used to assess the sign, strength and sensitivity of theFT (independent variable) and NDVI (dependent variable) relation-ships, while significance was assessed at the 90% level. A global terres-trial ecoregion map (Olson et al., 2001) was used to distinguishtundra, boreal forest, grassland, and temperate forest areas within thedomain, and the FT and NDVI relationships were summarized forthese individual ecoregions. Temporal anomalies of the FT and NDVIparameter series were first computed as differences from averageconditions defined from the period of record; where a significanttrend was determined (p-valueb0.1), the temporal anomalies werederived as differences from the long-term detrended mean.

2.4. Determining and evaluating regional FT trends

The temporal trends in the FT seasonal metrics were defined on a gridcell-wise basis and summarized for individual ecoregions, and over thelarger NH domain. A Shapiro–Wilk test and histogram analysis of the FTdata series were employed to test for data normality prior to evaluating

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Fig. 1. Pixel-wise correlations (r) between overlapping SMMR and SSM/I 37 V GHz, PM overpass Tb series from Jul 9 to Aug 20, 1987 with the associated histogram of significant(pb0.1) correlations (in black on adjacent inset map) (a); the record lengths (days) used to derive the correlations are also presented (b). Land areas in white are outside ofthe NH FT domain and were masked from the analysis. (a) Pixel-wise correlation (r) between SMMR and SSM/I 37 V GHz, PM overpass Tb series for 1987. (b) Number of sampledays used to determine correlations between SMMR and SSM/I 37 V GHz, PM overpass Tb series for 1987.


the regional FT trends and their significance. An autocorrelation function(ACF) was used to identify temporal autocorrelation within a 95% confi-dence level. Because the FT time-series do not meet the parametricassumptions for data normality (e.g., Shapiro–Wilk test, pb0.1, rejectnormality hypothesis), the FT temporal trends were analyzed usingKendall's tau, which provides a robust, non-parametric measure of corre-lation and significance that is insensitive to impacts from seasonality,non-normality, heteroscedasticity, missing values and temporal autocor-relation (Hirsch & Slack, 1984). The secular FT trends were evaluatedusing a Sen's non-parametric estimator of slope approach, which takesthe median of slopes determined by all pairs of sample points and isinsensitive to outliers (Hirsch et al., 1982; Sen, 1968); this approach canalso be more accurate than simple linear regression for skewed and het-eroscedastic data and shows similar performance against the simpleleast squares method even for normally distributed data (Akritas et al.,1995; Sen, 1968). The regional FT trends and their significance werederived from the ZYP package (ver 0.9-1) in R statistics, and consideringpre-whitening to remove lag-1 correlations (Yue et al., 2002). When theFT trends were analyzed, outliers as a non-systematic variation wereidentified on a grid cell-wise basis as values exceeding±2 times the stan-dard deviation of the 30-yearmean (Moore, 2006) and screened from theanalysis, including anomalous FT metrics resulting from large gaps in the1987 sensor Tb records. All trends were evaluated using a 90% (p≤0.1)minimum significance threshold.

The SMMR and SSM/I defined NH FT trends were evaluated againstregional air temperature trends determined from an ensemble meanof global reanalysis surface meteorology data. The National Centers forEnvironmental Prediction and the Department of Energy (NCEP–DOE)Atmospheric Model Intercomparison Project (AMIP-II) Reanalysis(hereafter NCEP2) was used to construct mean annual surface airtemperature series for the 30-year period; NCEP2 has the same spatialresolution and temporal fidelity as the NNR and fixes knownprocessingerrors in the NNR (Kanamitsu et al., 2002). The NCEP2 dailysurface (2 m height) Tav parameter was used to compute mean annualTav anomalies and trends over the NH domain. A similar 30-year Tavproduct generated by the MERRA GEOS-5 system (Rienecker et al.,2008) was also used to determinemean annual surface air temperaturetrends and anomalies. MERRA has finer (1/2°×1/3°) spatial resolutionthan NNR and NCEP2, and has relatively high accuracy, especially athigher latitudes (Yi et al., 2011). A composite Tav trend was computedfor the NH domain as the ensemble mean of the NNR, NCEP2 andMERRA based temperature series, while uncertainty in the Tav trendwas defined on an annual basis as the difference between maximumand minimum values of the regional mean annual Tav value from thethree temperature sets.

The Multivariate El Niño/Southern Oscillation (ENSO) Index (MEI)was used to examine relationships between ENSO-driven climate cyclesandmean non-frozen period variability defined from the FT record. The

Fig. 2.Meanannual FT spatial classification accuracies (left) over the global FT domain as derived from themerged SMMR and SSM/I record. The FT classification accuracywas determinedon a daily basis for individual satellite AM and PM orbital overpass retrievals in relation to grid cell-to-point comparisons with respective minimum andmaximum daily air temperaturemeasurements. The classification accuracy for 2006 PM orbital overpass represents the proportion (%) of station locations correctly identified as frozen or non-frozen on a daily basis inrelation to the in situ air temperature measurements (upper right). Approximately 3207±427 [SD]WMOweather stations were used to compute the classification accuracy, where thenumber of available validation stations varied on an annual basis (lower right). Themean annual FT classification results represent annual (Jan 1–Dec 31) averages of daily FT classificationaccuracy results. Grey shading in the left figures denotes the spatial variability [SD] of the mean annual FT classification accuracy.


MEI is derived from a principal component analysis of sea levelpressure, zonal and meridional winds, sea surface and air temperature,and total cloudiness over the tropical Pacific, and is computed on amonthly basis (Wolter & Timlin, 1993). The MEI and reanalysis basedTav results were then compared against the FT classification based NHnon-frozen period anomalies for the 30-year satellite record to evaluaterelative consistency between the FT trends and regional climate oscilla-tions, and longer term temperature trends.

3. Results

3.1. Merging the SMMR and SSM/I records

The overlapping SMMR and SMM/I daily 37 V GHz Tb records showconsiderable pixel-wise correspondence within the global domain(e.g., Fig. 1a). The number of cells used in the Tb correlation analysis in-creases at higher latitudes due to convergence of orbital swaths(Fig. 1b). Areas with low or negative Tb correspondence result fromone or more factors including a smaller available sample size used tocompute Tb correlations at lower latitudes, low Tb temporal variabilityfor some areas, and dynamic temporal changes in surface emissivityand temperature between SMMR and SSM/I local overpass times.

The SSM/I 37 V GHz Tb values have amean bias of approximately 1 Kand 4.6 K for respective AM and PM overpass retrievals relative tocorresponding 37 V GHz Tb retrievals from SMMR for the mission over-lap period (Derksen & Walker, 2003). The sensor Tb differences aremainly attributed to different acquisition times and variable calibrationof the respective retrievals (Royer & Poirier, 2010). The resulting equa-tions were used to calibrate the SMMR Tb record to the SSM/I Tb recordon a grid cell-wise basis over the global domain as:

SSM=I Tb ¼ 1:1503⁎SMMR Tb–35:79 AM overpass; r2 ¼ 0:991; pb0:001� �

ð1Þ

SSM=I Tb ¼ 1:1125⁎SMMR Tb–27:18 PM overpass; r2 ¼ 0:989; pb0:001� �

ð2Þ

The RMSE difference between SSM/I and SMMR derived Tb valuesfrom Eq. (1) and Eq. (2) was 3.41 K for AM overpass (52,037 cells)and 4.26 K for PM overpass (52,540 cells) retrievals, whereas

respective mean Tb differences (SMMR–SSM/I) between overlappingrecords was −2.1 K and −1.8 K. The respective degrees of freedom(DF) for the regression statistics were 52,539 and 52,036 for AM andPM overpasses. Themean annual non-frozen period detrended anoma-lies derived from SMMR (1979–1987) and SSM/I (1987–2008) portionsof the resulting FT record were near zero, while the residual trends ofboth periods were also close to zero. These results indicate that therewas no resulting FT discontinuity between the SMMR and SSM/Iportions of record.

3.2. FT classification accuracy

Themean annual FT spatial classification accuracy over the global FTdomain and 30-year combined satellite record was 91.4±1.05 [inter-annual SD] and 84.2±0.92 [inter-annual SD] percent for respectivePM and AM orbital nodes (Fig. 2); the spatial variability in mean annualclassification accuracy was approximately ±8.6 and ±9.3 [SD] percentfor the PM and AM results. The classification accuracy was determinedfrom daily comparisons with in situ surface air temperature measure-ments from 3207±427 [SD] weather stations. The STA derived FT spa-tial classification accuracy of the PM orbital retrievals for the 1987SMMR and SSM/I overlap year was 89.2% and considerably lower thanthe other years of record, while the AM orbital results were similar(84.3%) to the 30-year average. The spatial variability in mean annualclassification accuracy was also larger (±12.9 and ±10.9 percent [SD]for respective PM and AM orbital nodes) for 1987 relative to otheryears of record; this was attributed to significant gaps in the 1987 sen-sor Tb records and greater associated uncertainty in the FT classifica-tions. The resulting FT classification errors of omission were 4.2±0.8%[inter-annual SD] and 14.1±1.9% [inter-annual SD] for respective PMand AM orbital nodes. The associated errors of commission were6.1±1.0% [inter-annual SD] and 9.8±1.1% [inter-annual SD] for PMand AM orbital nodes. Kappa-coefficients for the PM and AM FT classifi-cation results were between 0.985 and 0.988. These results indicatefavorable satellite FT classification accuracy results relative to surrogatemeasures of these processes available from the global in situ air temper-ature measurement network.

The mean annual FT classification accuracy results from thesatellite record show a positive trend toward greater mean accuracyof 0.095%yr−1 (r=0.798; pb0.001) and 0.080%yr−1 (r=0.765;pb0.001) for respective PM and AMorbital nodes (DF=29). Significant

(a)

(b)

Fig. 3. (a) Mean annual non-frozen period (days) and (b) non-frozen period annual variability (SD, days yr−1) derived from the merged SMMR and SSM/I 30-year (1979–2008)record.


relationships also exist between the ensemblemean annual air temper-atures for the domain and mean annual FT classification accuracies de-rived from PM (r=0.635, pb0.001) and AM (r=0.564, pb0.01) orbitalnode results (DF=29). The positive FT accuracy trend is an artifact ofgenerally increasing occurrence of non-frozen conditions under recentglobal warming, because classification accuracies tend to be lowerunder frozen conditions and heterogeneous seasonal FT transitionperiods (Kim et al., 2011). The residual differences in mean annual FTclassification accuracies from these secular trends show no significantdifferences in classification accuracies between the SMMR and SSM/Iportions of the 30-year FT record (p>0.1, F-test).

3.3. Northern Hemisphere FT patterns

The NH FT domain represents approximately 42.2% (54.4million km2 based on individual cell area of 628.38 km2) of the globalland area and encompasses a latitudinal range from 0–80 °N. The FTresults for the domain show a mean annual non-frozen period of209.3±3.35 [inter-annual SD] days yr−1. The timing of respective pri-mary seasonal thawand freeze dates in spring and fall are approximate-ly DOY 99.6±2.52 [inter-annual SD] and 312.7±3.00 [inter-annual SD]days yr−1. The average number of transitional (AM frozen and PMthawed) frost days for the domain is 59.0±2.54 [inter-annual SD]days yr−1, and ranges from 20.7±1.11 [inter-annual SD] days in spring(MAM) to 18.0±1.79 [inter-annual SD] days in fall (SON). The averagenumber of non-frozen and FT transitional days in winter (DJF) is32.7±2.12 [inter-annual SD] days for the 30-year record and the NHdomain.

The FT record shows large regional variability of approximately ±79.0 days [spatial SD] around the regional means, with generally earlierspring thawing, longer non-frozen period and more transitional days atlower latitudes, and along coastal margins, relative to higher latitudesand inland areas. The FT results show a strong N–S latitudinal increasein the average non-frozen period (Fig. 3a), with generally longer dura-tion along coastal margins relative to adjacent inland areas. The FT re-sults also show areas with relatively large annual variability in thenon-frozen period, including central North America and Europe(Fig. 3b), with large characteristic seasonal variability influenced bythe movement of regional air masses (e.g. maritime vs. continental).

Other areas of relatively extreme (>25 days yr−1) non-frozen periodtemporal variability occur over mountainous regions, including theTibetan plateau and Mexico.

3.4. Verification of satellite FT metrics at representative ecoregion tower sites

The satellite based daily FT record was compared against coincidentMODIS 16-day compositedNDVI time series, towermeasurement baseddaily GPP and NEE, and in situ air temperature measurements for 2005from 4 sites within the domain representing tundra, boreal evergreenneedleleaf coniferous forest (ENF), grassland and temperate ENF types(Fig. 4). At the tundra site (Fig. 4a), the satellite FT record shows severaltransitional (AM frozen, PM thawed) frost events in late-April, followedby the primary spring thaw event in late-May (DOY 154). These eventscoincidewith generally sustained Tav thawing, seasonal GPP onset (DOY161) and a subsequent NEE seasonal shift from terrestrial carbon (CO2)source activity during the frozen season to predominant carbon sink ac-tivity during the growing season. The MODIS data for this site show aninitial NDVI increase coincident with the onset of Tav thaw and the FTtransitional period. A relatively abrupt decrease in NDVI, GPP and NEEsink activity occurs after the primary spring thaw event and coincidentwith several FT transitional frost events; these events are followed bygeneral recovery of NDVI and GPP, and NEE sink activity. The satelliteFT record also shows a series of transitional frost events in early fall(~DOY 250) that coincide with an abrupt and sustained decrease inGPP (DOY 249) and seasonal shift in NEE to a general CO2 source,even though Tav is still well above freezing and NDVI is near the season-al peak. The satellite FT record shows several fall transitional events andthe initial occurrence of frozen conditions in mid-October (~DOY 300)coinciding with Tav freezing and an abrupt and sustained NDVIdecrease. A brief return of non-frozen conditions occurs, but is boundedby frequent FT transitional events, while primary onset of winter frozenconditions occurs in mid-November (DOY 323).

At the boreal ENF site (Fig. 4b), considerable FT transitional frostevents occur in early spring (~DOY 90) coincident with large Tavfreeze/thaw fluctuations around 0.0 °C, and prior to the primary springthaw event (DOY 111) defined from the satellite record. The primarythaw event coincides with rapid and sustained increases in NDVI andGPP onset (DOY 123), and persistent non-frozen Tav conditions. In fall,

image of Fig.�3

Fig. 4. Daily time series of CO2 eddy flux towermeasurement based GPP and NEE carbon fluxes and air temperatures (Tav) for 2005 from selected tower sites spanning a large latitudinalgradient and representing major ecoregion types within the NH domain. Coincident satellite derived NDVI and FT time series from overlying grid cells at each tower location are alsopresented. The tower sites represent tundra, boreal evergreen needleleaf coniferous forest (ENF), grassland, and temperate ENF ecoregions. The FT results represent discrete classifications(0= frozen; 1 = non-frozen; 2 = transitional; 3= inverse transitional). Positive (+) and negative (−) NEE fluxes denote respective source and sink activity for atmospheric CO2. Solidarrows denote primary thaw timing and dotted arrows denote the primary freeze event as determined from the daily FT record.


Fig. 4 (continued).


the occurrence of several FT transitional events coincides with sub-zeroTav conditions, seasonal decrease in NDVI and sustained low GPP levels(DOY285). These events occur approximately 2 months prior to the pri-mary freeze event (DOY=343). NEE for this site shows frequent sourceactivity throughout the non-frozen season and generally transitions tosustained seasonal source activity in early October (~DOY 280) beforethe seasonal decline in NDVI and onset of freezing.

The grassland site (Fig. 4c) shows generally warmer temperatures,a longer non-frozen period and more frequent FT transitional eventsthan the tundra and boreal sites. A large number of transitional eventsoccur from mid-January to mid-May, and from early Septemberthrough December. The NDVI pattern for this site generally lacks astrong seasonal signal and shows significant canopy greenness overmost of the year (~DOY 30–330), well beyond the growing season in-ferred from tower GPP and NEE conditions and the satellite FT record.The primary spring thaw event occurs in mid-April (DOY 118), coin-cides with a gradual NDVI increase and precedes the seasonal rise inGPP (DOY 160) and onset of NEE sink activity by approximately onemonth. A brief FT transitional period occurs in early June (after DOY160) and coincides with an NDVI plateau, abrupt GPP decrease andNEE source activity, followed by a return of non-frozen conditions, in-creasing NDVI and GPP, and NEE sink activity. A return of persistenttransitional conditions in the fall (~DOY 250) coincides with a gradu-al decrease in NDVI, seasonal decrease in GPP (DOY 263) and returnof NEE winter source activity. The primary freeze event occurs inmid-November (DOY 323) and coincides with a drop in NDVI andonset of frozen Tav conditions.

At the temperate ENF site (Fig. 4d), a substantial mid-winter(~DOY 30) thaw period coincides with increased NDVI, but minimalGPP or NEE activity. The NDVI pattern shows a sustained green-up peri-od after ~DOY 70 that coincides with the seasonal rise in GPP (DOY 82)and NEE sink activity, and persistent non-frozen Tav conditions. Theseevents follow the end of a sustained frozen period, but occur approxi-mately 2 weeks prior to the primary spring thaw event (DOY 93). Infall, the end of the non-frozen period and subsequent initiation of per-sistent FT transitional conditions coincide with the seasonal decline inNDVI and return of the GPP (DOY 263) and NEEwinter pattern. The pri-mary freeze event (DOY 350) occurs several weeks after the end of thegrowing season indicated by the NDVI record and FT non-frozen periodparameter, and tower carbon fluxes.

3.5. Northern Hemisphere FT and NDVI relationships

Relationships between the FT parameter series and MODIS NDVIseasonal anomalies are summarized for the four major NH ecoregionsin Table 2. The number of cells analyzed is relatively low for tundradue to a large number of NDVI retrieval gaps at higher latitudes, espe-cially in spring, and a relatively small number of available high quality(QC) NDVI retrievals. For the 9-year (2000–2008)MODIS record, the re-lationship between non-frozen period (Jan–Aug) and mean summerNDVI (NDVIJJA) anomalies was generally positive for tundra and borealforest, and negative for grassland and temperate forest areas. A positivecorrelation indicates that years with a longer non-frozen period are as-sociated with greater vegetation canopy growth, whereas a negativecorrelation indicates that shorter non-frozen periods promote less can-opy growth. The basis for this relationship is that the non-frozen periodis an effective surrogate for the potential growing seasonwhere season-al frozen temperatures are a major constraint to vegetation productivi-ty; where plant-available water supply is limiting to productivity, alonger non-frozen period may have less impact or promote reducedNDVI due to greater potential for evaporative water loss and drought-induced productivity decline. The correlation analysis indicates thatthe non-frozen period defined by the satellite FT record is an effectivemeasure of MODIS NDVI based vegetation growth anomalies for higherlatitude boreal forest and tundra biomes. These results also indicate thatnon-frozen season variability has less impact on NDVI productivity forlower latitude temperate forests and grasslands where additionalfactors, including available water supply, influence vegetation growth.

The timing of the primary thaw day in spring is inversely propor-tional to NDVIMAM for the four ecoregions (Table 2). These results areconsistent with previous studies indicating that years with relativelyearly (late) spring thaw conditions promote general increases(decreases) in vegetation growth (Kimball et al., 2006; Menzel et al.,2006; Wang et al., 2011). The relationship between spring thaw timingand NDVIMAM is stronger for boreal forest, grassland and temperateforest areas and weaker for tundra. However, these relationships areinfluenced by artifacts that are independent of canopy growth changes,including reduced NDVI sample size at higher latitudes (e.g. tundra),especially in the spring, and generally positive impact of earlier snow-melt on MODIS NDVIMAM retrievals. The primary spring thaw eventalso showed a stronger correlation to NDVIMAM than NDVIJJA, except


for Tundra, indicating greater benefit of earlier thawing on spring vege-tation growth, while the relative benefit of an earlier and longernon-frozen season for summer vegetation growth is reduced for borealforest, grassland and temperate forest areas. Spring thaw timing waspositively correlated with NDVIJJA for warmer temperate forestswhere vegetation growth is less impacted by seasonal frozen tempera-tures relative to other factors, including water supply and photoperiod(Korner & Basler, 2010).

The spatial pattern (Fig. 5a,b) of correlations between NDVIJJA andsatellite FT based non-frozen period (Jan to Aug) anomalies for the2000–2008 MODIS record shows generally low correspondence belowapproximately 45 °N, and increasing correspondence at higher latitudeswhere the long-term (2000–2008) mean annual non-frozen period isless than approximately 6 months. The corresponding latitudinal varia-tions show the positive mean correlation and slope values above ap-proximately 50 °N and increasing trend patterns up to approximately70 °N. Widespread, positive correlations occur at higher latitudes, in-cluding Alaska, western and northern Canada, western Europe andnorthern Eurasia. These results are consistent with previous studiesshowing that seasonal frozen temperatures are a major constraint tovegetation growth in northern boreal and Arctic biomes (Nemaniet al., 2003). Positive correlations between non-frozen period andNDVIJJA anomalies also occur at lower latitudes, including higher eleva-tion areas ofwesternNorth America, Europe andAsia, though the corre-lation pattern ismore heterogeneous.Widespreadnegative correlationsalso occur, including central Asia and eastern North America, resultingin lower average correspondence.

3.6. Northern Hemisphere FT trends

The satellite FT results show a strong, positive NH trend(0.189 day yr−1; pb0.001) in mean annual non-frozen period that iscorrelated (r=0.712) with a 0.033 °C yr−1 (pb0.001) warming trendin mean annual surface air temperatures (Fig. 6). There were no signif-icant relationships between the FT defined non-frozen period and NNRannual precipitation (not shown), implying that water availability wasnot a major influence on landscape FT status. However, the non-significant relationshipmay also reflect documented large, regional un-certainty in NNR precipitation (Drobot et al., 2006). Strong, positive andnegative MEI values represent respective warm (El Niño) and cool (LaNiña) ENSO phases. Positive MEI and associated strong El Niño condi-tions during 1982–1983 and 1997–1998 coincide with longer non-frozen periods and warmer surface air temperatures relative to thelong-term record. The 1991 Mt. Pinatubo eruption was followed by ashort-term cooling in global air temperatures (Lucht et al., 2002) andcoincides with a temporary decrease in the mean annual non-frozenperiod.

The lengtheningnon-frozenperiod trend is largely drivenby an earliertrend in spring thawonset (−0.149 days yr−1; pb0.001) as summarizedin Table 3. The FT results show a smaller, insignificant trend toward laterarrival of the seasonal frozen period (0.034 days yr−1). A strong, positivetrend in the annual FT transitional period (0.198 days yr−1; pb0.001)

Table 2Correlation analysis between seasonal MODIS NDVI anomalies and non-frozen period (Jan–Au(CO) daily FT record for the 2000–2008 period; NDVI seasonal anomalies are presented for sumare summarized for major ecoregions within the NH domain; the proportion (%) of cells analyzwhere this value was identified as an outlier. Values enclosed in parentheses represent the prSignificance levels of the mean ecoregion correlations are denoted by asterisks as: *pb0.1; **p

aEcoregion Proportions of cells analyzed (%) Period of record Non-frozen period (J

Tundra 36.0 [25.4] 2000–2008 0.643* (65.5; 34.5)Boreal Forest 88.7 [85.2] 2000–2008 0.793* (65.9; 34.1)Grassland 98.6 [97.8] 2000–2008 −0.177 (48.4; 51.6)Temperate Forest 63.8 [63.4] 2000–2008 −0.354 (45.5; 63.7)

a Olson et al. (2001).

increases at a faster rate in fall (0.149 days yr−1; pb0.001) than in spring(−0.046 days yr−1; pb0.1). The total number of non-frozen and transi-tional days in winter also shows a strong increasing trend of 0.159 -days yr−1 (pb0.001) for the 30-year record. The 1987 values for non-frozen period, primary thaw and freeze dates, and spring/fall transitionalperiodswere identified as outliers relative to the 30-year record; this wasattributed to large gaps in the SMMR and SSM/I Tb records for 1987 andassociated greater uncertainty in FT classification accuracy relative toother years of record. NH trends for these parameters were thereforecomputed with and without 1987 values included (Table 3), but showedminimal differences in the regional trends.

The FT parameter trends for major NH ecoregions (Table 3) showsimilar positive non-frozen period trends for tundra, grassland and tem-perate forest areas, but a smaller boreal forest trend, due to relativelylarger increase in the boreal FT transitional period relative to other ecor-egions. The FT results show generally significant trends in mean annualtransitional period, primary freeze day and fall transitional period met-rics for the four ecoregions. The spring transitional period trend is in-creasing for tundra and boreal forest, but decreasing in grassland andtemperate forest areas. Negative primary thaw day trends (pb0.01)occur in all four ecoregions; a general advance in the primary springthaw day and relatively weak delay in the primary fall freeze eventare also shown for the four ecoregions, resulting in a lengthening non-frozen period consistent with the larger NH domain. All four ecoregionsshow strong and significant increasing trends in the fall transitionalperiod, whereas regional trends in cumulative winter non-frozen andtransitional events are increasing overall, but with weaker trends intundra and boreal forest areas.

The annual non-frozen period trend is increasing for 70.5% of theNHdomain (Fig. 7a). Annual trends in primary thaw and freeze events arepredominantly negative (earlier) and positive (later) over 90.1% and78.2% of the domain, respectively (Fig. 7b, c). An increasing transitionalperiod trend occurs over 84.9% of the domain (Fig. 7d). However, the FTresults show large annual variability so that the relative proportions ofcells with significant FT trends are reduced to 29.3% (non-frozen peri-od), 30.6% (primary thaw day), 13.8% (primary freeze day) and 33.3%(transitional period) of the domain.

The mean latitudinal distributions of fall freeze and non-frozen andtransitional period trends are predominantly positive, while springthaw trends are predominantly negative, but with large spatial variabil-ity over the NH domain (Fig. 8). The mean non-frozen period trendshows a marked decrease near 30–35 °N due to widespread negativetrend areas within the Southern Asia highlands and other mountainousareas of southern Europe and western North America. The mean non-frozen period trend is generally positive and consistent over the mid-latitudes (40–55 °N), followed by a general increasing trend rate(~0.02 days per degree of latitude) at higher latitudes above 55 °N.The transitional period trend results show a different latitudinal distri-bution, with a mean negative trend at lower latitudes near 25–30 °Nand associated with large regional decreases in south-central NorthAmerica, Southern Asia and Western Europe. The mean transitionalperiod trend increases (~0.01 days per degree of latitude) to a peak

g) and primary spring thaw date anomalies derived from the SMMR and SSM/I combinedmer (JJA) and spring (MAM) conditions. After detrending, correlations (r) for each perioded in each ecoregion is also shown. Values in brackets represent statistics excluding 1987oportion (%) of cells with positive (in bold) and negative correlations in each ecoregion.b0.01; ***pb0.001.

an–Aug) and NDVIJJA Primary thaw day and NDVIMAM Primary thaw day and NDVIJJA

−0.468 (15.5; 84.5) −0.541 (19.1; 80.9)−0.981*** (9.0; 91.0) −0.528 (25.9; 74.1)

−0.510 (37.9; 62.1) 0.087 (54.0; 46.0)−0.568 (27.8; 72.2) 0.437 (51.4; 48.6)

Fig. 5. Spatial patterns of grid cell-wise linear regression coefficients (r) between summerNDVI (NDVIJJA) and corresponding cumulative non-frozen period (Jan to Aug) anomalies for the2000–2008 MODIS NDVI record and within the NH domain (a); land areas in white were masked from the analysis. Four r-value categories are classified by significance level, includingnegative (p≤0.1), weak negative (p>0.1), weak positive (p>0.1) and positive (p≤0.1) levels. The corresponding latitudinal variations inmean r and regression slope (s) values for eachperiod are also shown (b); the bar graph denotes the mean non-frozen period within each latitudinal bin as derived from the long-term (2000–2008) FT record.


positive rate near 50–55 °N, followed by a general trend decrease(~−0.02 days per degree of latitude) from 50 °N to 80 °N. The meanthaw trend is generally positive at lower latitudes below 45 °N and neg-ative at higher latitudes above 45 °N. The fall freeze trend ismostly pos-itive at all latitudes and coincides with an increasing non-frozen periodtrend. The proportion of significant non-frozen period trends rangesbetween 18.8 and 57.5% for each latitudinal interval.

4. Discussion and conclusions

The 30-year FT record from the combined SMMR and SSM/I data-base shows an increasing (1.9 days decade−1) NH trend in mean

Fig. 6. Mean annual non-frozen period trend for the NH domain as derived from the SMMR anMERRA) derived mean annual Tav record for the domain is also represented. Grey shading deannual non-frozen period is lengthening (pb0.001) by 0.189 day yr−1 over the period of recordENSO index (MEI) is shown as vertical red and blue shading denoting respective positive (El Ni(vertical dashed line).

annual non-frozen period coincident with global warming and largelydriven by earlier (−1.5 days decade−1) spring thawing; this translatesinto a 3.9 to 7.5 day lengthening of the average non-frozen period from1979 to 2008. These results are similar to previous studies showinga −3.5 days decade−1 advance in NDVI (AVHRR) driven growing sea-son onset for northern forests from 1982 to 1999 (Zhou et al., 2001), amean −1.2 days decade−1 NH advance in spring first leaf date from1955 to 2002 (Schwartz et al., 2006) and a −2.5 days decade−1

advance in spring phenology for European temperate climate areasfrom 1971 to 2000 (Menzel et al., 2006). Burrows et al. (2011) alsoreported a similar global mean−1.46 days decade−1 advance in springthawing from the land temperature record from 1960 to 2009.

d SSM/I CO overpass based FT record. The coincident reanalysis (average NNR, NCEP2 andnotes the maximum and minimum range around the ensemble Tav mean. The observed(1979–2008) and coincides with 0.033 °C yr−1 Tavwarming (pb0.001). Themultivariate

ño) and negative (La Niña)MEI values. The Jun 1991Mt. Pinatubo eruption is also denoted

image of Fig.�6

Table 3Kendall's tau trends for mean annual non-frozen and transitional periods, and primary thaw and freeze dates (days yr−1) for the NH domain and four major NH ecoregions as de-rived from the daily CO FT record for 1979–2008. Seasonal trends are also represented for the transitional period in spring (MAM) and fall (SON), and the cumulative number oftransitional and non-frozen days in winter (DJF). Values enclosed in parentheses represent statistics excluding 1987 where this value was identified as an outlier; trend significancelevels are denoted by asterisks as: *pb0.1; **pb0.01; ***pb0.001.

Domain andaecoregions

Non-frozenperiod

Transitionalperiod

Primarythaw day

Primary freezeday

Transitional period(MAM)

Transitional period(SON)

Non-frozen andtransitional period (DJF)

NH 0.189*** (0.184**) 0.198*** −0.149*** (−0.140***) 0.034 (0.027) −0.046* (−0.043*) 0.149*** (0.141***) 0.159***Tundra 0.201* (0.173*) 0.177** (0.190**) −0.233*** (−0.224***) 0.062 0.069* 0.197*** (0.192***) 0.012 (0.014*)Boreal forest 0.085 (0.068) 0.296*** (0.309***) −0.196** (−0.181**) 0.088 (0.063) 0.037 (0.045) 0.229*** (0.221***) 0.070** (0.075**)Grassland 0.227* 0.217** −0.188** (−0.176**) 0.069 (0.056) −0.089* (−0.080*) 0.159** (0.144**) 0.268*** (0.281***)Temperate forest 0.186* 0.115 −0.029 (−0.017) 0.002 (−0.006) −0.149*** 0.073* (0.066*) 0.233**

a Olson et al. (2001).


The timing and duration of the non-frozen period defined from thesatellite FT record generally bounds the effective growing season for adiverse set of tower sites representingmajor NH ecoregions, as indicat-ed by seasonal shifts in stand-level carbon fluxes, surface air tempera-ture measurements and MODIS NDVI records. The primary fall freezeevent can follow the non-frozen period by up to several weeks and is

(c) Freeze Day Trend

(d) Transition Period Trend

(b) Thaw Day Trend

(a) Non-Frozen Period Trend

> 0.75

< -0.75

> 0.75

< -0.75

> 0.75

< -0.75

> 0.75

< -0.75

Fig. 7. Regional Kendall's tau trend patterns (days yr−1) and associated significant (pb0.1(1979–2008) for non-frozen period, primary thaw day, primary freeze day and transitionadomain that were masked from the analysis. (a) Non-frozen period trend. (b) Thaw day tre

generally preceded by a number of transitional frost events that influ-ence vegetation senescence and the end of the active growing seasonbefore the arrival of persistent, frozen conditions. The single year oftower observation data used in this investigation is insufficient to deter-mine the sensitivity of the terrestrial carbon budget (e.g. GPP and NEE)to annual FT variability defined from the satellite record. However,

) trend areas (in purple on adjacent inset maps) derived from the 30-year FT recordl period parameters. Land areas in grey represent outliers and areas outside of the NHnd. (c) Freeze day trend. (d) Transition period trend.

image of Fig.�7

Fig. 8. Latitudinal mean Kendall's tau trends (days yr−1) in annual non-frozen period, transitional period, and seasonal thaw and freeze timing derived from the 30-year FT record(1979–2008) and NH domain; bar graphs denote the number of grid cells analyzed within each latitudinal bin. Numbers on the bar graph denote the relative proportion of cells ineach latitudinal bin with significant non-frozen period trends.


previous studies using satellite derived boreal productivity recordsshow generally positive relationships between non-frozen season andannual productivity variability, and approximately ±0.8% annual NPPsensitivity to ±1 day changes in spring thaw onset (Kimball et al.,2000, 2004, 2006; Nemani et al., 2003). In the current study, the sensi-tivity ofMODIS NDVI summer growth anomalies to annual variability inthe timing of the non-frozen season defined from the satellite FT recordprovides a surrogate measure of FT impacts to vegetation productivity;the resulting mean NDVIJJA sensitivities range from±0.02% (grassland)to ±0.2% (tundra) of the regional mean NDVIJJA values for the respec-tive ecoregions, for corresponding ±1 day changes in the non-frozenseason onset. These values are also consistent with an increasing(±0.01 to ±0.4%) northern (>50°N) latitudinal sensitivity gradient inthe mean NDVIJJA response to annual non-frozen season variability(Fig. 5).

Transitional (AM frozen, PM thawed) events have a direct, negativeimpact on vegetation growth indicated by relatively abrupt drops inGPP and NDVI, and a temporary slowdown in net photosynthesis andcarbon sink activity during the growing season. The FT record fromthis study shows a mean increase in the number of transitional frostdays (2.0 day decade−1) for the NH domain and 30-year record. TheFT transitional period anomalies also show widespread negative corre-lationswith NDVIJJA growth anomalies over 56.3% of the NH domain, in-dicating potentially larger negative impacts of these events onvegetation growth. A lengthening transitional period is a consequenceof regional warming that may be offsetting potential productivity andcarbon gains from earlier/longer non-frozen period trends. The increas-ing transitional period indicates a greater number of days with night-time freezing and mid-day thawing. Previous studies indicateincreased vegetation stress under conditions associated with greaterperiods of thaw and re-freeze (Beier et al., 2008; Bourque et al., 2005).Warming and increasing FT transitional conditions also increase therisk of premature bud burst and associated plant tissue damagefrom subsequent frost events (Gu et al., 2008; Inouye, 2008;Kreyling, 2010). Frequent FT transitional occurrences may also be inju-rious to vegetation by inducing earlier dehardening and subsequentfrost damage (Taulavuori et al., 2004), and increasing risk of xylem cav-itation and embolism in ring porous tree species (Ewers et al., 2003;Mayr et al., 2002). Vegetation sensitivity to these events likely varieswith species type and condition, and the timing, duration and severityof occurrence. These events may also benefit vegetation indirectly by

increasing available nutrients for additional growth (Euskirchen et al.,2006; Kreyling et al., 2008).

The correlation between the satellite microwave remote sensingderived non-frozen period andNDVIJJA growth anomalieswas generallypositive at higher latitudes,where the non-frozen season is shorter thanapproximately 6 months. These correlation patterns are consistentwithprevious studies showing contrasting regional patterns in NDVI trends,including general greening of northern boreal and tundra regions due toa lengthening growing season (Beck et al., 2011; Bunn & Goetz, 2006).The relative benefits of earlier/longer non-frozen seasons for vegetationproductivity aremore variable at lower latitudes, where ecosystems areless constrained by frozen temperatures and other factors includingplant-available moisture and photoperiod have a stronger influenceon vegetation growth (Nemani et al., 2003; Zhang et al., 2009). Otherfactors also influence NDVI and vegetation growth independent ofFT dynamics, including satellite optical-infrared remote sensing con-straints (Huete et al., 2002; Jones et al., 2011) and other biophysicalfactors including soil nutrient limitations, insect and disease damage,fire and changes in plant resource allocations (Goetz et al., 2007; Kurzet al., 2008; Penuelas et al., 2009).

The results of this investigation are similar to previous studies indi-cating a generally positive vegetation productivity response to earlierspring thawing and lengthening non-frozen season trends over north-ern land areas, and attributed to a general relaxation of frozen temper-ature constraints to plant growth (e.g., Kimball et al., 2004, 2006;McDonald et al., 2004; Nemani et al., 2003). Our results indicate thatthe positive NDVI productivity response is more widespread in areaswhere seasonal frozen temperatures are a dominant constraint to pro-ductivity (Nemani et al., 2003).We also note previous studies indicatingthat the relative influence of these frozen temperature constraints onvegetation productivity are changing with continued warming and co-incide with an apparent increase in plant-availablemoisture limitationsto vegetation growth and changing disturbance regimes for many areas(Angert et al., 2005; Kasischke & Turetsky, 2006; Zhang et al., 2008,2009).

While a longer non-frozen periodmay benefit vegetation productiv-ity, these carbon gainsmay be offset by enhanced ecosystem respirationand additional carbon losses from increased wildfire activity (Hayeset al., 2011). In boreal and tundra ecoregions, earlier spring thawingand a longer non-frozen period have been shown to increase both veg-etation productivity and soil respiration (Euskirchen et al., 2006; Smith


et al., 2004). However, these colder ecoregions have characteristicallystrong soil nutrient (primarily nitrogen) limitations to plant growth,where respiration losses may be offset by enhanced ecosystem produc-tivity and net carbon gains driven by increased availability of plant-available nutrients from enhanced soil decomposition (Mack et al.,2004) and atmosphere nitrogen deposition (Magnani et al., 2007). Inareas with underlying permafrost, soil decomposition and heterotro-phic respiration may be depressed by higher soil moisture levels andanaerobic soil conditions (Dunn et al., 2007), whereas permafrost deg-radationmay promote widespread increases in respiration and net eco-system carbon losses with continued warming and longer non-frozenseasons.

The results of this study provide a consistent and long-term(1979–2008) global record of daily landscape FT dynamics, with docu-mented accuracy suitable for global change studies. The FT signalderived from 37 V GHz SMMR and SSM/I Tb series represents aggregateconditions within the satellite FOV and does not distinguish individual(e.g. air, soil, vegetation, snow) elements or sub-grid scale processeswithin the 25-kmgrid cell. The FT results are sensitive to frozen temper-ature constraints to vegetation growth and associated changes in thepotential growing season where frozen temperatures are a major con-straint to canopy photosynthesis. These data document a general relax-ation of cold temperature constraints to plant growth and lengtheningof the potential growing season at higher latitudes with global warm-ing. The relative benefits of these changes to vegetation productivity ap-pear spatially heterogeneous, with generally stronger benefits forvegetation growth at higher (>45 °N) latitudes. The continued benefitsof a lengthening growing season on vegetation growth will likely de-pend on adequate supplies of plant-available moisture and the abilityof vegetation to meet increasing evaporative demands of a warmerclimate.

Acknowledgments

This work was conducted at the University of Montana and JetPropulsion Laboratory, California Institute of Technology under contractto the National Aeronautics and Space Administration. This work wassupported under the NASA Making Earth Science Data Records for Usein Research Environments (MEaSUREs) program; SMMR and SSM/Idata were provided by the National Snow and Ice Data Center(NSIDC),while in situ and reanalysismeteorology datasetswere provid-ed byNCEP/NCAR and the National Climate Data Center. This work usedCO2 eddy covariance data acquired by the FLUXNET community and inparticular by the following PIs: Christian Bernhofer (DE_Tha), TorbjornJohansson (SE_Abi), Hank A. Margolis (CA_Qfo) and Lawrence B.Flanagan (CA_Let).

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satellite detection of increasing northern hemisphere non-frozen seasons from 1979 to 2008:...

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