VEGETATION PHENOLOGY IN GLOBAL CHANGE STUDIESbiomet/KU_Biometeorology_Lab/... · 454 Phenology: An Integrative Environmental Science that on average, the global climate warmed by

From Schwartz (ed.), Phenology: an integrative environmental science, 453-466. 2003 Kluwer Academic Publishers.

Chapter 7.1

VEGETATION PHENOLOGY IN GLOBALCHANGE STUDIES

Michael A. White1, Nathaniel Brunsell2, and Mark D. Schwartz3

1Department of Aquatic, Watershed, and Earth Resources, Utah State University, Logan, UT,USA; 2Duke Univeristy, Research Triangle, NC, USA; 3Department of Geography, Universityof Wisconsin-Milwaukee, Milwaukee, WI, USA

Key words: Global Change, Growing season length, Land-atmosphere interactions,Wavelets, AVHRR

1. INTRODUCTION

Global change, encompassing natural and anthropogenic changes to theEarth system at sub-annual to geologic time scales, has strong interactionswith vegetation phenology. In this chapter we will refer to global change asalterations to the Earth system that are certainly or probably influenced byhuman activity, primarily since the industrial revolution. This form of globalchange includes irrefutable anthropogenic alterations to terrestrial land coverand alterations to the global climate that are probably anthropogenicallyinfluenced. Within this context we discuss three aspects of vegetationphenology: the influence of vegetation phenology on general circulationmodels (GCMs); a wavelet analysis of phenological patterns and associatedevidence of likely phenological responses to direct human-induced landcover alteration; and third, serious challenges regarding the use ofphenological data and concepts in global change research.

2. GLOBAL CHANGE

Global change is most controversially associated with climate change.The Intergovernmental Panel on Climate Change (IPCC WG I 2001) found

454 Phenology: An Integrative Environmental Science

that on average, the global climate warmed by approximately 0.5°C in the20th century. This overall trend, though, masked localized trends that wereoften much higher and/or confined to increases in seasonal or nighttimetemperatures. Despite large uncertainty relating to cloud physics, aerosols,and the influence of land cover changes, most GCMs predict a continuedtrend of increasing temperatures throughout the 21st century, even ifgreenhouse gas emissions are reduced or stabilized (IPCC WG I 2001). Dueto the lack of an experimental Earth, the precise influence of anthropogenicactivity on climate cannot be predicted with absolute accuracy, but thelikelihood that humans are at least partially responsible for current climaticchanges is extremely high. Regardless of origin, climate change has occurredwithin the 20th century, the pace of the change is increasing, and it is verylikely that the climate will continue to warm.

Other forms of global change are not subject to debate. In particular,humans have extensively modified the land use and land cover (LULC) ofthe terrestrial biosphere. Through conversion of natural habitats toagricultural or urban use, fire suppression, military activity, logging, mining,power generation, accidental or intentional transport of invasive species, etc.,the anthropogenic fingerprint is visible and pervasive across the Earth’ssurface. Human use of global landscapes for agricultural use is alreadyextensive (DeFries et al. 2002). Of the remainder, much has experiencedprior anthropogenic LULC change, the impacts of which can be evident longafter the initial disturbance (Foster et al. 1998). While humans have modifiedother elements of the Earth system such as nutrient cycles (Vitousek 1994)and soils (Imhoff et al. 1997), climate and LULC change are most relevantfor phenological issues.

Three major areas of phenological responses by both plants and animalsto variation in climatic signals are discussed in other chapters: evidence fromfield datasets showing changes in phenology, phenological responses tourbanization, and implications of phenological variation for the terrestrialcarbon cycle. See Menzel (2002) for a general discussion of phenology andglobal change. In the following sections, we present a focused analysis intwo areas in which vegetation phenology may respond to or influence globalchange. First, we illustrate the importance of vegetation phenology in GCMapplications. Second, we present a wavelet analysis showing the influence ofedaphic conditions, climate, and LULC on the spatial patterns ofphenological variability and discuss likely global change impacts on existingpatterns. Finally, by showing four valid definitions of growing season length,we highlight the potential for confusion in phenological research.

Chapter 7.1: Vegetation Phenology in Global Change Studies 455

3. PHENOLOGY AND CLIMATE MODELS

In the early 1990s much GCM research focused on coupling global land,ocean, and atmospheric models to simulate the complete Earth system.Around that time, many models of the land surface (Dorman and Sellers1989; Bonan et al. 2002a) began to incorporate the concept of phenologyinto coarse resolution modeling. However, for most of these efforts, theglobal distribution of a plant functional type (PFT) would have identicalseasonality profiles. In other words, annual leaf area index (LAI) profiles ofgrasslands in the central United States and Mongolia would be identical. Inthe late 20th and early 21st century multiple decades of consistently processedsatellite-derived LAI became available, allowing for a consideration of theimpacts of actual phenology in a variety of climate modeling activities.

Two complementary articles, both using a land surface model (LSM,Bonan 1996) coupled with the Community Climate Model (Kiehl et al.1998), illustrate the growing acceptance of phenological concepts inmodeling activities. In the first, Buermann et al. (2001) used 0.25° LAI datato construct 1981-1991 average, minimum, and maximum LAI profiles for13 LSM PFTs. Similar to the prescribed LSM condition, each PFT had thesame LAI profile; the profile in this case was based on satellite-measuredLAI rather than estimated values. The authors compared climatic simulationswith the realistic range of satellite-measured LAI and the prescribed LAIfrom the LSM and found that in general, satellite LAIs were lower than theprescribed LSM LAIs. Consequently, a known cold bias in CCM waspartially corrected through an increase in sensible heat fluxes, more thancompensating for decreased radiation absorption caused by higher albedo.Second, using 1992-1993 global 1km LAI data, Bonan et al. (2002a)simulated the impact of spatially variable LAI profiles in comparison to theprescribed LSM seasonality. They found that use of the realistic LAIstrongly affected model simulations of ground temperature and evaporation,albedo, and soil moisture. Radiation partitioning into sensible and latentfluxes was also impacted. The studies are complementary: one shows theimpact of interannual variability (Buermann et al. 2001) while the othershows the impact of spatial variability (Bonan et al. 2002a). In combination,the studies show that failure to incorporate phenological variability in timeand space is likely to cause serious errors in climate simulations. Regionalsimulations demonstrate similar impacts of vegetation phenology on climate.Using the RAMS model, Lu and Shuttleworth (2002) showed that dependingon the phenological parameterization used, regional climate in the US GreatPlains and Rocky Mountain region could be colder and wetter or hotter anddrier than in a “default” simulation and argued for the realistic depiction ofthe timing and magnitude of vegetation activity in climate studies.


Vegetation phenology is also an important factor for an emergentresearch field: coupling climate models with dynamic global vegetationmodels (DGVMs). In a DGVM, plant biogeography is coupled withbiogeochemical concepts to simultaneously simulate climate and ecosystemcarbon and water cycles in a world of shifting PFTs. Phenology is alreadyincluded in most DGVMs run off-line (uncoupled) from GCMs (Cramer etal. 2001); late 20th and early 21st century work focused on executing DGVMscoupled to GCMs (Levis et al. 1999; Foley et al. 2000; Bonan et al. 2002b).Within coupled DGVM-GCMs, phenology affects the following processes:(1) the absolute value and seasonal timing of land surface albedo; (2) thepartitioning of net radiation to sensible and latent fluxes; (3) the timing ofphotosynthesis; (4) the timing and amount of litterfall. Such studies havedemonstrated numerous climate-vegetation interactions including anapparent stability of boreal forest area under climatic perturbation (Levis etal. 1999) and shifts in western Africa from desert to grassland (Claussen andGayler 1997).

In cases where coupled LSM-GCMs or DGVM-GCMs are run for thenear past, remotely sensed estimates of LAI may be used. For future or farpast climates, a prognostic phenology scheme must be used. Most DGVMsand coarse resolution LSMs predict phenological variation based on broadrules relating climatic variation to seasonality (Bonan et al. 2002b) or aproductivity index (see models in Cramer et al. 2001). Most, though, usebasic and untested climatic triggers to regulate the timing of growth.Compared to other model components, relatively little effort has beendevoted to prognostic phenology modeling. Given the importance ofphenology within coupled climate models, expansion, implementation, andtesting of earlier prognostic schemes (White et al. 1997: Botta et al. 2000) iscrucial. Further, an understanding of the scale at which phenologicalvariation occurs and the likely global change impacts on such patterns isimportant for modeling efforts; we explore these topics in the next section.

4. WAVELET ANALYSIS

We used the biweekly composite normalized difference vegetation index(NDVI) values from the Earth Resources Observation Systems Data Center(EDC) 1990-1999 conterminous United States (CONUS) 1km AdvancedVery High Resolution Radiometer (AVHRR) dataset to calculate the start ofseason (SOS) and end of season (EOS). NDVI, while potentially confoundedby viewing geometry and atmospheric contamination, is related to thefraction of photosynthetically active radiation absorbed by vegetationcanopies (Myneni and Williams 1994). NDVI is commonly used in


remotely-sensed plant productivity models (Coops 1999; Potter et al. 2001),and has a long history in phenological research (Lloyd 1990; Reed et al.1994; Moulin et al. 1997; Asner et al. 2000). Our method uses a pixel-specific seasonal midpoint NDVI threshold (SMN) based on the followingsteps: (1) the annual minimum and maximum NDVI is extracted from cloud-and snow-screened data; (2) the midpoint between the minimum andmaximum is calculated; (3) the procedure is repeated for each year except1994 (satellite failure); and (4) SMN is assigned as the average of the ninevalues. The SMN is thus sensitive to each pixel’s seasonal NDVI cycle. Adiscussion of the methodology and comparisons with observation isavailable elsewhere (White et al. 1997; White et al. 1999; Schwartz et al.2002; White et al. 2002). Using a daily NDVI time series created fromcloud-screened and spline-fit biweekly NDVI, we defined SOS as the date atwhich the SMN was exceeded and EOS as the date at which the NDVI fellbelow the SMN. We calculated annual growing season length (GSL) asEOS-SOS and then calculated the 1990-1999 average GSL from the nineindividual years. We implemented wavelet analysis as described below.

Wavelet transformations (see Csillag and Kabos (2002) for detaileddescription) are conceptually similar to Fourier transformations. In theFourier approach, sine and cosine functions are used to recreate a signal. Thewavelet approach relies on the dilation and translation of a wavelet functionwherein the function is non-zero for a finite distance. Wavelet analysisallows determination of the extent to which the signal or image matches thewavelet function at particular locations and resolutions. In a classic example,Torrence and Compo (1998) showed how wavelet analysis could be used todetermine the structure and timing of the El Niño Southern Oscillation. Forimage processing, a dyadic decomposition is used in which the spatialresolution (pixel size) is increased by powers of two. Images should besquare with dimensions equal to powers of two.

Wavelet transforms allow the determination of the relative contributionof each resolution to the observed data. This is done with wavelet variance:

€

S(a) =1N

ΨD (a,x)2

x∑ (1)

where S(a) is the wavelet variance at resolution a (2, 4, 8, 16, etc.), ΨD(a,x)are the detailed wavelet coefficients resulting from a two dimensionalwavelet transform of the original signal f(x) as a function of location x (inthis case a vector in two dimensional space), and N is the number of datapoints in the original signal (Percival 1995). A higher value of S(a) indicatesthat at the given resolution, the data show a high correspondence to the


wavelet function. The plot of wavelet variance vs. the resolution is termedthe scalogram (Kumar and Foufoula-Georgiou 1993), and is used todetermine the dominant length scale (DLS) observed in the data. The DLS isdefined as the scale at which the maximum wavelet variance occurs in thescalogram (Kumar and Foufoula-Georgiou 1997) and represents the lengthscale of the highest correspondence between the wavelet function and theinput dataset. For the purposes of this work, the primary focus is on thedetermination of the DLS, not on the orientation of that scaling behavior.Therefore, only the detail components in the horizontal and verticaldirections of the wavelet transform are used. This results in the inability toanalyze anisotropy within the signal (especially within the cospectra wherethe DLS of the different signals may be occurring in orthogonal directions).However, this was not deemed to be important since the primary purpose ofthis research is to examine the variability in length scales across regions, andnot the directional aspects of the length scales.

The wavelet cospectra can be developed in an analogous way. In thiscase, the wavelet decomposition of two signals f(x) and g(x) of length N areused to compute the cross-scalogram:

€

C(a) =1N

ΨfD (a,x)ΨgD (a,x)x∑ (2)

where the overbar designates the complex conjugate. The wavelet cospectraare conceptually similar to regression-based covariance.

We used GSL for calculation of wavelet variance and the following threedatasets relating to climate, edaphic conditions, and land cover for co-spectral analysis. For climate, we used surface meteorological records from adaily CONUS 1km 1980-1997 (1990-1997 used) dataset (P.E. Thornton,unpublished manuscript developed from methods in Thornton et al. 1997,and described at www.daymet.org). From the daily records of maximumand minimum temperature and radiation we calculated annual potentialevapotranspiration (PET) using a Priestley-Taylor approach as described inKimball et al. (1997). Next, we calculated the annual water deficit (WD) asprecipitation - PET and finally calculated the average for the period ofrecord. For edaphic conditions, we derived soil percent sand, silt, and clayand soil depth from a 1km CONUS soils database (Miller and White 1998)and then calculated soil water content at field capacity (SWC) usingequations in Cosby et al. (1984). Lastly, we obtained 1992 National LandCover Data (http://edc.usgs.gov/products/landcover/nlcd.html) reclassified to1km resolution. From 21 possible classes, we used agricultural (AG,combination of five classes), forest (FO, combination of three classes),


shrubland (SH), grassland/herbaceous (GR), barren (BA, combination ofthree classes), urban (combination of three classes), and water classes. Thusfor each 1km pixel and for each of the above land cover classes, a percentcoverage was available. All data were either produced at or georectified tothe standard EDC Lambert Azimuthal Equal Area (LAZEA) projection.

As shown in Plate 1, we extracted three regions from the CONUS GSLimage roughly corresponding to west, center, and east regions of the UnitedStates where the dimension of each region was 1024×1024 pixels. For eachregion, we employed Mallat’s two-dimensional dyadic decompositionalgorithm (Mallat 1999) with Daubechie’s second order wavelet asimplemented in the Interactive Data Language (Research Systems Inc.,Boulder, CO) to calculate the GSL wavelet scalogram and wavelet cospectrafor WD, SWC, and land cover (Plate 1). To assess cospectra at a givenlength scale, examine the columns in the image portion of each panel. In thewest at 2km this shows highest cospectra for SWC. To assess cospectra forone variable at all length scales, examine the rows. In the west, SWC showshighest cospectra at 32km. Scales are different on color bars; colors amongpanels are thus not directly comparable and should be used only to assesscospectral patterns for the individual regions. All regions showed a DLS at2km, indicating that variation in GSL was highest at the shortest lengthscale. The center and east showed a general decline in wavelet variance atlonger length scales with a drop at 512km for the east. The west, though,showed higher wavelet variance at 32 to 128km. Overall wavelet variancewas dramatically higher in the west.

The images in the right side of Plate 1 show that patterns of waveletcospectra varied strongly by region (urban and water classes showed verylow cospectra and are not presented). Consider that for the DLS (2km), themaximum cospectra were SWC in the west, AG & GR in the center, and AG& FO in the east. Dominant cospectra by region were SH, SWC, and FO inthe west; AG and GR in the center; and AG & FO in the east. In spite of thepatterns of variability, several consistent trends emerged. First, theimportance of SWC tended to decline as overall precipitation increased fromwest to east. Second, for each region, the WD cospectra increased towardslonger length scales, indicating that climatic influences on GSL variationwere manifest primarily at regional, not local, scales. Third, in the center andeast regions, the human-created AG class was a dominant control in thecospectra. With the exception of SWC dominance of the 2km and 512kmscales in the west, maximum cospectra were associated with land cover andnot edaphic (SWC) or climatic (WD) controls. While we present results forWD here, we also investigated cospectra of separate climatic controlsincluding precipitation, maximum and minimum temperature, radiation, andvapor pressure (and the annual amplitude of each). For individual regions


and length scales a particular climatic variable could be more dominant thanWD, but the overall pattern of low and generally increasing cospectra atlonger length scales did not change.

The implications of the wavelet analysis for global change science are asfollows. First, it is clear that the dominant control of GSL spatial variabilityis land cover wherein the dominant class(es) usually had the dominantcospectra. That current patterns of GSL in two regions were stronglyassociated with spatial variability of the AG class indicates that the humanpresence is already strongly apparent. Second, although our data are from alimited region, it appears that in areas of limited and spatially variedprecipitation (as in the west), SWC is an important factor for GSLvariability, especially at short and long length scales. Degradation oralteration of soil resources (Imhoff et al. 1997) could therefore causechanges in phenological patterns. Third, climatic variables are remarkablyunrelated to spatial GSL variation. Interannual climatic variation controlslong- and short-term phenological variation through time at individuallocations or regions, but compared to land cover factors, climate was muchless important to the spatial variation in GSL. Fourth and perhaps of greatestinterest to the GCM community, the DLS occurs at 2km, arguing that evenfor GCM simulations using fine-scale nested grids, the effect of sub-pixelphenological heterogeneity on climate models should be rigorouslyconsidered.

5. CHALLENGES IN GLOBAL CHANGEPHENOLOGY

Phenology is clearly an important topic within global change science.Global science in fields as diverse as remote sensing (Myneni et al. 1997a),atmospheric CO2 analysis (Keeling et al. 1996), climate modeling (above),and carbon cycle modeling (Lucht et al. 2002) has identified phenology as acrucial component of the Earth system. However, the very cross-disciplinarynature of phenology has created a serious tendency to misinterpretphenological research. The term GSL is especially open to multipleinterpretations. We identify the following four possible methods of definingGSL: (1) remote sensing (see Chapter 5.1, this book); (2) weather; (3)ecophysiology; and (4) canopy duration. Each implies a separate definitionof the growing season yet in practice the global change community oftenuses the terms interchangeably.

We used the Vegetation/Ecosystem Modeling and Analysis Project(VEMAP, Kittel et al. 1999) dataset to demonstrate differences andsimilarities in the various approaches. The CONUS VEMAP dataset


contains edaphic and daily meteorological records for 1895-1993 at a0.5°×0.5° resolution. We calculated GSL for the four categories as follows.

Remote sensing. We obtained 1981-2001 monthly observations of thefraction of photosynthetically active radiation absorbed by vegetationcanopies (FPAR, Myneni et al. 1997b) that were regridded and compositedfrom the original 8km 10-day dataset to the VEMAP resolution. For eachpixel, we used a spline function to obtain daily values and estimated GSL foreach year as the number of days with FPAR > 0.5 (FPAR-GSL). This is anarbitrary threshold and is used to illustrate the concept of a remotely-sensedestimate of GSL rather than to quantitatively predict a certain vegetativestatus. These data have been corrected for within- and between-sensorcalibration issues and may be used to assess trends and interannualvariability.

Weather . We used the VEMAP minimum temperature records tocalculate the frost-free GSL, i.e. number of days each year with minimumtemperatures > 0°C (FF-GSL).

Ecophysiology. We used ecosystem simulations from the Biome-BGCmodel (Nemani et al. 2002) to calculate the carbon uptake period (CUP).CUP indicates the number of days each year in which the assimilation ofcarbon through photosynthesis exceeded the sum of heterotrophic andautotrophic respiration (CUP-GSL). In other words, CUP refers to thenumber of days with net ecosystem carbon uptake. For each VEMAP pixel,up to five PFTs were simulated (shrubs, C3 grasses, C4 grasses, evergreenneedle leaf forest, and deciduous broad leaf forest). Values were scaled bytheir proportional land cover within each VEMAP pixel. See Nemani et al.(2002) for a discussion of Biome-BGC VEMAP simulations.

Canopy duration. Using the same Biome-BGC simulations, weextracted estimates of canopy duration. For evergreen canopies, canopyduration was defined as 365 days. For deciduous canopies, annual phenologywas controlled by the Biome-BGC phenology subroutines developed inWhite et al. (1997). Pixel canopy duration (CD-GSL) was defined as aweighted average as for CUP.

We extracted GSL estimates for the period containing both VEMAP andremote sensing data (1982-1993). Plate 2 shows the mean (left images) andstandard deviation (right images) of these estimates. FPAR-GSL was 365days for much of the southeastern and coastal western US, less than 10 daysthroughout much of the intermountain West, and highly variable in thesouth-central region. Use of constant thresholds of this type for anygeographic region with significant PFT variation will produce regions withperpetual GSL and regions with no GSL whatsoever. This supports thenotion of using variable thresholds (White et al. 2002) or informationregarding the shape of the vegetation time series (Reed et al. 1994). FF-GSL


had similarly high western and southern GSL but a much more homogenousGSL in the continental interior. Compared to other GSL measures,variability was consistently low with highest values in the Pacific Northwest.CUP-GSL was highest in the Southeast and West with low variability in theEast and high variability in the West related to interannual variation inprecipitation. CD-GSL was spatially similar to CUP-GSL but was generallylonger, especially for the evergreen needle leaf dominated regions, wherevariability was also extremely low or equal to zero.

In spite of dramatic differences in the absolute values of GSL estimates(Figure 1a), continental average GSL anomalies were similar (Figure 1b).With the exception of a very low FPAR-GSL in 1982-1983 (possibly relatedto the El Chichon volcanic eruption), the four GSL metrics tracked the sameinterannual patterns with a remarkable degree of coherence (Figure 1b andTable 1). Correlation among the four metrics (Table 1) was lowest forcomparisons including FPAR-GSL. FF-GSL was highly correlated with bothCUP-GSL and CD-GSL. CUP-GSL and CD-GSL had the highest r2.

Figure 7.1-1. 1982-1993 growing season length (GSL) interannual variability based on dayswith FPAR > 0.5 (dashed grey); frost-free days (solid grey); carbon uptake period (blackdashed); and canopy duration (solid black). (A) Absolute values. (B) Standardized anomaliesof (A).


In summary, the comparison of GSL metrics illustrated that (1) thedifferent methods yielded dramatically different absolute values, (2) spatialpatterns were somewhat consistent along the continental boundaries butextensively different in the western US, and (3) at a continental level, alltechniques tracked similar patterns of interannual variability. We believe thatthese results highlight the potential for confusion when using the genericterm growing season length but also the surprising ability of all GSLmeasures to track interannual variability. We argue for more explicit termssuch as carbon uptake period or canopy duration. In cases when use of theterm growing season length is most appropriate, as for many satelliteapplications, an explicit attempt should be made to explain how the satelliteobservations correspond to ground conditions (e.g. White et al. 1999).

Table 7.1-1. 1982-1993 CONUS growing season length (GSL) correlations (r2). FPAR, dayswith FPAR > 0.5; FF, days with minimum temperature > 0°C; CUP, number of days with netecosystem uptake of carbon; CD=canopy duration (number of days with 1-100% of canopypresent).

FPAR FF CUP CD

FPAR 1 0.45 0.28 0.54

FF 0.45 1 0.69 0.70

CUP 0.28 0.69 1 0.79

CD 0.54 0.70 0.79 1

6. CONCLUSIONS

Vegetation phenology is a crucial field for global change studies. Otherchapters discuss the use of vegetation phenology in monitoring, modeling,remote sensing, and climate/weather. Here, we illustrated three aspects ofvegetation phenology and global change. First, we discussed the importanceof phenology in the context of climate simulations. Second, using waveletanalysis of U.S. vegetation phenology, we showed that the dominant lengthscale of spatial variation was 2km. Spatial variation in phenology was inmost cases more strongly related to land cover variation than to edaphic orclimatic variation, illustrating the potential of humans to rapidly modifylandscape phenology patterns. Third, using a comparison of growing seasonlength calculations, we highlighted the potential for confusion whenimplementing phenological concepts in global change research.


ACKNOWLEDGEMENTS

Michael White was supported by NASA grant NAG5-11282.

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Plate 7.1-1. Wavelet analysis for west, center, and east (square regions in US map). The linegraph in the lower left of each panel shows the wavelet scalogram (log scale on x-axis anddifferent y-axis scales). Images in the right of each panel show the wavelet cospectra for thenine length scales (2km-512km) and seven variables (agriculture (AG), forest (FO), shrub(SH), grasslands & herbaceous (GR), barren (BA), soil water content at field capacity (SWC),and water deficit (WD, precipitation – potential evapotranspiration).


Plate 7.1-2. Comparison of 1982-1993 growing season length (GSL) calculations. Panels aremean (left) and standard deviation (right). GSL calculated as: (A) days with FPAR > 0.5; (B)frost-free days; (C) carbon uptake period; and (D) canopy duration.

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VEGETATION PHENOLOGY IN GLOBAL CHANGE STUDIESbiomet/KU_Biometeorology_Lab/... · 454 Phenology: An Integrative Environmental Science that on average, the global climate warmed by