On the link between Amazonian forest properties and shallow cumulus cloud fields

12 Pages·2014·4.34 MB·English

Atmos.Chem.Phys.,14,6063–6074,2014
www.atmos-chem-phys.net/14/6063/2014/
doi:10.5194/acp-14-6063-2014
©Author(s)2014.CCAttribution3.0License.
On the link between Amazonian forest properties and shallow
cumulus cloud fields
R.H.Heiblum1,I.Koren1,andG.Feingold2
1DepartmentofEarthandPlanetarySciences,WeizmannInstitute,Rehovot76100,Israel
2NOAAEarthSystemResearchLaboratory(ESRL),ChemicalSciencesDivision,Boulder,Colorado80305,USA
Correspondenceto:I.Koren([email protected])
Received:15October2013–PublishedinAtmos.Chem.Phys.Discuss.:15November2013
Revised:28April2014–Accepted:7May2014–Published:20June2014
Abstract. During the dry season the Amazon forest is fre- humidity near the canopy is high enough. The formation of
quently covered by shallow cumulus clouds fields, referred thesecloudshasacleardiurnalcyclewithamaximumduring
tohereasforestcumulus(FCu).Thesecloudsareshownto theafternoon.
besensitivetolandcoverand exhibitahighlevelofspatial Atypicalsatelliteimageofthesecloudfieldscanbeseen
organization. In this study we use satellite data to perform inFig.1,takenon1September2011,13:30LST.Theclouds
amorphologicalclassificationandexaminethelinkbetween form almost exclusively over land areas; in other words,
FCucloudfieldoccurrenceandtheenhancedvegetationin- cloudsareabsentabovetheAmazonRiveranditstributaries.
dex(EVI),whichiscommonlyusedasameasureforforest One of the noticeable, although not exclusive, properties of
density and productivity. Although weaker than first-order these clouds is their tendency to organize in linear patterns
effects of meteorology, a clear positive linear relation be- (Fig. 2). They can often be considered analogous to cloud
tweenEVI(i.e.,surfaceproperties)andFCufieldoccurrence streets(RamosdaSilvaetal.,2011),whicharetypicallyob-
isseenoverforestlandcover,implyingastrongcouplingbe- servedduringcoldairoutbreaksoverwarmeroceanicwaters
tweenforestsurfacefluxesandthecloudorganizationabove. (Brümmer,1999).Inthisworkthesecloudfieldsareshown
Overnon-forestlandcovertherelationshipbetweenEVIand to be very sensitive to changes in the environmental condi-
FCuoccurrenceisnonlinear,showingareductionofFCufor tionsandthereforetheycanserveasa“laboratory”forstudy-
highEVIvalues.Wefindthatforesttonon-foresttransition ingtheconditionsinwhichtheyform.Theyarehereafterre-
zonesdisplayasuperpositionofthetwodifferentlandcover ferredtoasforestcumulus(FCu)fields(moredetailsonthe
dependencies. FCu are given in Sect. 2). Daytime FCu cloud fields simi-
lartothoseseeninFigs.1and2canbeobservedinseveral
otherlocationsaroundtheglobe,suchascentralAfricadur-
ingmostoftheyearornortheastAmericaandSiberiaduring
1 Introduction the boreal summer. The common denominator in all cases
is their preferred formation over dense, large-scale forests
DuringtheAmazondryseason(australwintermonths,June– during stable meteorological conditions, when formation of
September), the Intertropical Convergence Zone (ITCZ) moredevelopedcloudsissuppressed.
moves northward (reaches ∼10◦N in mid-August), while Land cover change effects on clouds can be divided by
large-scale subsidence associated with the South Atlantic temporal and spatial scales into short- (immediate) or long-
SubtropicalHigh(SASH)dominatestheregion(Nobreetal., term effects, or local to global spatial scales (Pielke Sr
1998)andrelativelystablemeteorologicalconditionsprevail. et al., 2011); here we will focus on the short-term ones.
Undertheseconditions,organizedfieldsofshallowcumulus The immediate effects of land cover changes are attributed
(Cu)cloudsformoverandneartheforestduringthedaytime to changes in the surface radiation budget (Betts, 2009).
hourswhensurfacetriggeredconvectionispossibleandthe Different land cover types exhibit different albedo, surface
PublishedbyCopernicusPublicationsonbehalfoftheEuropeanGeosciencesUnion.
6064 R.H.Heiblumetal.:Amazonianforestpropertiesandshallowcumuluscloudfields
Figure1.Left:topographicmapofSouthAmerica(notethatthecolorbariscappedat1000m);studyregionindicatedbycross-hatchedbox.
MapbasedonETOPO11arcminglobalreliefmodeldataset(AmanteandEakins,2009).Right:typicalforestcumulus(FCu)fieldsover
theAmazonBasinstudyregion.GeoTIFFimagetakenfromMODISRapidResponseUSDAForeignAgriculturalService(FAS)subsets.
ThedashedredboxindicatestheareamagnifiedinFig.2.Imagecorrespondsto1September2011,13:30LST.TheAmazonRiverandits
tributariesinhibitalltypesofcloudformation.
roughness, moisture content, etc. (Bastable et al., 1993). levelconvergencepatternsandcloudformation(Rabinetal.,
Suchchangesaffecttheenergyfluxestotheatmosphere,the 1990;Souzaetal.,2000).
partition of this energy to sensible and latent heat, and the GenerallythereexistsapreferenceforshallowCuforma-
turbulent transfer of those fluxes to the atmosphere. Even- tion over densely forested rather than deforested areas (lo-
tually, these changes influence the diurnal evolution of the catedaroundthesouthernboundariesoftheAmazonBasin;
atmospheric boundary layer (Betts, 2000). The latter study see Fig. 1) (Cutrim et al., 1995). However, most observa-
showed how vegetation resistance controls the boundary tionalstudies,includingthoselistedabove,havefocusedex-
layer depth (with lowest resistances corresponding to the clusivelyondeforestedpocketswithinforestedareas,show-
oceanic limit) and the partition between latent and sensible ing a clear preference for shallow Cu formation over defor-
heat fluxes. Hence, the evapotranspiration properties of the estedareas(Cutrimetal.,1995;Chagnonetal.,2004;Wang
land cover vegetation are tightly linked to the dynamics of et al., 2009) and for deep convective cloud formation over
the boundary layer and the shallow Cu clouds, which com- thesurroundingforestedareas(Wangetal.,2009).
monlycaptheboundarylayer. ThesedifferencesinshallowCupreferencecouldarisedue
Deforested areas in the Amazon (either pasture or crop- to emphasis put on mesoscale circulations (driven by land
land) usually display higher sensible heat and lower latent cover transitions) rather than more subtle changes within a
heat fluxes in comparison with the forested areas, which in specific land cover type. Moreover, studies showing prefer-
turn can enhance the growth of the boundary layer during enceforshallowCuoverdeforestedlandcoverareconfined
the day and favor the formation of larger convective clouds mainly to the southwest regions of the Amazon, which are
(Fischetal.,2004).Moreover,surfaceheterogeneitiesoften highlydeforestedandexperienceverystablemeteorological
resultinlocalmesoscalebreezeswhichcanalsoaffectlow- conditionsduringthedryseason.Studiesinotherregionsof
the world such as southwestern Australia (Ray et al., 2003)
Atmos.Chem.Phys.,14,6063–6074,2014 www.atmos-chem-phys.net/14/6063/2014/
R.H.Heiblumetal.:Amazonianforestpropertiesandshallowcumuluscloudfields 6065
2 Methods
We combine satellite data in the visible for cloud morphol-
ogy and aerosol optical depth (AOD) analyses, land cover
typeandvegetationindicesforsurfacecharacterization,and
analysis data for specification of the meteorological con-
ditions. True color data at 0.5km resolution, AOD data at
1◦ resolution, and land surface properties derived from the
MODerate-resolution Imaging Spectroradiometer (MODIS)
onboardtheAquasatelliteareused(Salomonsonetal.,1989;
Friedletal.,2010;Runningetal.,1994;Remeretal.,2005).
TheAquaoverpassisat13:30LST,atthetimewhenenergy
fluxesfromthesurfacearemaximum(Fischetal.,1996)and
theFCufieldsovertheAmazonarealreadyestablished.The
study region of interest is seen in Fig. 1, and spans from
58.54◦W, 5.69◦N (northwest corner) to 49.45◦W, 13.19◦S
(southeastcorner),anareaof2100km×950km.Thetopog-
raphyofthestudyregionislow,anddevoidoflargegradients
except for some patches in the northern part and a gradual
risetohighertopographyinthesouthernpart.Analysesshow
thattheFCufieldshadnoclearcorrelationwithtopography.
Therefore,weexcludeanalysesoftopographyeffectsinthis
work. Data were collected for the dry season months, July-
August-September(J-A-S),duringtheyears2008–2011.
Figure2.Magnifiedviewofforestcumulus(FCu)cloudfields(sub- Our analyses of the FCu cloud field properties were fo-
setofFig.1).NotethelinearpatternsinwhichtheFCuorganize. cused on the statistical properties of the cloud distribution
Thescaleoftheboxis150km×175km.
within the field. Measures such as cloud area, average dis-
tancesbetweencloudcenters,andleveloforganizationwere
testedtooptimizetheclassification.Unlikethecaseofasin-
andCostaRica(Nairetal.,2003)showapreferenceforshal-
gle cloud analysis when the sensitivity to the exact cloudy
low cumulus formation over native vegetation and forested
pixeliscrucialandoneneedstoeitheravoidcloudcontam-
areasratherthanadjacentdeforestedareas.
ination of the cloud-free atmosphere (Martins et al., 2002)
Much of the Amazon deforestation is a result of massive
or, in the case of cloud retrievals, make sure that the cloud
biomass burning events which occur during the dry season
mask is free of non-cloudy pixels (Ackerman et al., 1998),
(Korenetal.,2004).Biomassburningemitshighconcentra-
our spatial–statistical measures (summarized in Fig. 3) ex-
tions of absorbing aerosols into the atmosphere, which can
hibitlesssensitivitytotheexactmethodbywhichcloudsare
interactwithradiation(i.e.,scatterandabsorbshortwavera-
maskedinthefield.
diation)andaffectthetemperatureprofileandthereforestatic
Thefirststageofprocessingistheconstructionofabasic
stability,orserveascloudcondensationnuclei(CCN)andaf-
cloudmask,achievedbyapplyingathreshold(>0.58)tothe
fectthemicrophysicalprocessesandevolutionofcloudsand
reflectanceoftheRGBchannels(bands1,3,and4,respec-
precipitation(Korenetal.,2008).Hence,itisessentialtoad-
tively).Unlikeclouds,mostbrightpixelsthatarenotclouds
dressaerosoleffectsoncloudsintheregionaswell.
(e.g.,brightroadsorsandpatches)arenotwhite(e.g.,have
The goal of this work is to evaluate the morphological
spectral dependence in the visible spectrum); therefore an-
characteristics of the Amazon FCu fields, and to use a sta-
otherthreshold(<0.08)isappliedtotheabsolutedifferences
tistical approach to study the effects of land cover change
inthereflectancebetweentheredandblue.Themorpholog-
on the FCu fields over the Amazon region. Specifically we
ical characteristics of the cloud field were calculated for a
use the EVI (enhanced vegetation index) as a measure of
movingwindowof51×51pixels(25.5km×25.5km).Five
the well-being of the forest. It has been shown that this in-
basic characteristics were shown to contain most of the in-
dexcorrelateswellwithforestproductivityandcanopyden-
formation: cloud fraction, mean and standard deviation of
sity(Glennetal.,2008;Sjöströmetal.,2011),andcanbea
distances between cloud centroids, and mean and standard
goodpredictorforevapotranspirationandmoisturefluxesto
deviationofcloudareas.
the lower atmosphere (Mu et al., 2007; Juárez et al., 2008),
Based on the above criteria we classified the cloud fields
which in turn drive the formation of dry season Amazonian
intothreeclasses:FCu,deeperconvectiveclouds,andsparse
clouds(BettsandSilvaDias,2010).
tonoclouds(seeTable1).Theclassificationwastunedand
validatedvisuallyusingdatafromhundredsofboxesofcloud
www.atmos-chem-phys.net/14/6063/2014/ Atmos.Chem.Phys.,14,6063–6074,2014
6066 R.H.Heiblumetal.:Amazonianforestpropertiesandshallowcumuluscloudfields
Figure3.SummaryofFCufielddetectionalgorithmfor2011.(a)CorrespondingtruecolorgeoTIFFimagefromFig.1.(b)Cloudmaskof
truecolorimage;theredboxrepresentsa25.5km×25.5kmmovingwindow.(c)Close-upofthemovingwindow.Blueasterisksindicate
thecentroidsofindividualclouds.Selectedstatisticsofwindowsarelistedbelowthepanel.Inthiscasethecloudfieldwithinthewindow
passedtheFCufieldthresholdslistedinTable1.(d)Finalproductofalgorithm,probability(0–1)ofobservingFCufield(pFCu)during
J-A-S2011.
Table1.MovingwindowthresholdsforforestCu(FCu),deepconvectiveCu,andsparsetonoCucloudfields.Thresholdsincludecloud
fraction (CF), mean cloud area (A), standard deviation of cloud areas (σA), mean distance between cloud centroids (D), and standard
deviationofdistances(σD).Missingdatarepresentthresholdsthatarenotrelevanttotheanalysis.
Parameter→ CF[%] A[km2] σA[km2] D[km] σD [km]
Fieldtype↓ Low High Low High Low High Low High Low High
Forestcumulus 0.15 0.4 1.5 8 1.5 12.5 1.8 3.2 0.6 1.3
Sparse 0 0.1 0 3 0 3 – – – –
Deepconvective 0.6 1 50 – – – – – – –
fields (see Fig. 3c). The boxes were used to calculate the (i.e.,allforesttypesincludingmixed);(ii)non-forest,classes
mean statistics of the FCu clouds fields. The typical val- 6 through 10 (woodlands, grasses, shrublands), 12 (crop-
uesforthemorphologicalcharacteristicsofthethreedefined lands),13(urban),and16(barren);and(iii)water,class17.
fields types are shown in Table 2. The narrow distributions Anexampleofsuchaclassificationmapfor2011isseenin
andinterannualconsistencyofthesekeycloudpropertiesal- Fig.4a.
lowedforarobustdetectionofthefields. SpectraldefinitionsforMODISvegetationindices:NDVI
After performing the analysis above for all days during (normalizeddifferencevegetationindex)andEVI,andtheir
J-A-S for a specific year (a total of 75–80 overpasses), the validationsovernumeroussitescanbefoundinpreviouspa-
probabilityforanFCufieldtoexist(hereafternamedpFCu) pers(Hueteetal.,2002;Muetal.,2007).Bothvegetationin-
wascalculatedforeachpixel(seeFig.3d).Similarmethod- dicescanbeusedtoassessthesurfaceenergybudgetcompo-
ologies have been used in other studies of shallow cumulus nents(latentheat,Bowenratio)andplantphysiologycompo-
clouds(Rayetal.,2003;Cutrimetal.,1995).Itisimportant nents such as evapotranspiration, leaf area index, fractional
tonotethattheclassificationresultswereshowntoberobust vegetationcover,canopyarchitecture,andmore(Glennetal.,
and not sensitive to small variations in the selected thresh- 2008).SinceNDVItendstosaturateinareasofhighbiomass
olds. (Huete et al., 2002), and is more sensitive to atmospheric
Yearlyclassificationofdifferentlandcovertypeswasdone aerosol contamination (Xiao et al., 2003), EVI is preferred
usingtheMODIScollection5MCD12Q1product(Friedlet inourstudy.
al.,2010).Theproductincludesfivetypesoflandcoverclas- Furthermore, studies have shown EVI to be better corre-
sifications,outofwhichthe14-classUniversityofMaryland lated with evapotranspiration than NDVI, with linear cor-
(UMD)classificationwaschosen(Hansenetal.,2000).For relation coefficients (r2) usually ranging between 0.7 and
the purposes of this study, we divided the UMD land cover 0.9 (Glenn et al., 2010; Nagler et al., 2005). Henceforth we
classificationintothreetypes:(i)forest,classes1through5 shalluseEVIasageneralmeasureofvegetationdensityand
Atmos.Chem.Phys.,14,6063–6074,2014 www.atmos-chem-phys.net/14/6063/2014/
R.H.Heiblumetal.:Amazonianforestpropertiesandshallowcumuluscloudfields 6067
Table2.Averagestatisticsforthecloudfields(andtheirspatialparameters)asdefinedinTable1,years2008–2011.Missingdatarepresent
irrelevantstatistics(forexample,distancebetweencloudsismeaninglessforsparseanddeepconvectivefieldssincewecommonlyobserve
onlyonecloudwithinthe25kmmovingwindow).
Parameter→
Fieldtype↓ Year CF[%] A[km2] σA[km2] D[km] σD [km]
2008 0.23±0.02 2.93±0.37 5.79±1.08 2.21±0.12 0.91±0.07
2009 0.23±0.02 3.00±0.31 5.64±0.95 2.26±0.10 0.90±0.06
Forestcumulus(FCu) 2010 0.24±0.02 3.03±0.41 5.80±1.13 2.25±0.13 0.88±0.07
2011 0.24±0.02 3.13±0.37 5.65±1.00 2.31±0.13 0.87±0.07
2008 0.03±0.02 1.31±0.43 1.33±0.43 – –
2009 0.04±0.01 1.24±0.38 1.24±0.42 – –
Sparse 2010 0.03±0.02 1.55±0.62 1.54±0.63 – –
2011 0.03±0.02 1.39±0.47 1.33±0.50 – –
2008 0.83±0.06 144.0±58.2 – – –
2009 0.82±0.06 139.7±56.7 – – –
Deepconvective 2010 0.83±0.06 143.5±56.4 – – –
2011 0.83±0.06 143.9±59.8 – – –
to best reflect the spatial variance of the FCu fields are the
geopotential height (HGT) at 700hPa (see Fig. 5a) and the
relative humidity (RH) at 850hPa (see Fig. 5b). These pa-
rameters can also be seen as physically tightly linked to
FCu formation. High geopotential height at 700hPa (pres-
surelevelsof850–500hPagivesimilarresults)indicatesup-
per level subsidence, adiabatic warming and drying, and is
associatedwiththeSASH(FigueroaandNobre,1990).Rel-
ativehumidityat850hPacorrespondstothemeancloudbase
height(basedonceilometermeasurements),andisessential
tocumulusformation.
3 Results
3.1 Forestcumulus(FCu)dependenceonmeteorology
ThemeteorologicalcontextoftheFCuprobabilityfor2011
Figure4. Landcover classificationmap(a)andmean J-A-SEVI
isshowninFig.5candd.Thenorth–southpFCudifferences
(b)fortheyear2011.
overthelandarebestcapturedbytheHGTat700hPafield.
ThevalueofHGT=3157m(indicatedbythedashedlinein
Fig. 5a and solid line in Fig. 5c) was chosen as the bound-
productivity overthe forest andnon-forest landcovers. The ary between the northern part of the Amazon region (NA
mean J-A-S EVI for 2011 is shown in Fig. 4b. The domain in Fig. 5a, c) that shows little meteorological variance and
shows high EVI values over forest land cover in compari- the southern part of the Amazon region (SA in Fig. 5a, c)
son with non-forest land cover, with the latter also showing that shows high coupling between pFCu and meteorology.
much larger EVI variance. Very similar EVI maps are seen Hence, in order to minimize meteorological influence, only
foryears2008–2010. thenorthernregion(NA)isusedtotestlarge-scalevegetation
Focusing on the southern half of the domain, Fig. 3d indexeffectsonFCufields.
clearlyshowsreductionintheprobabilityforFCufields.The PatternsofRHat850hPamayaffectsomeofthefeatures
same pattern exists for the year 2008–2010. Using NOAA- seen in Fig. 3d as well. The southeast-to-northwest positive
NCEP Global Data Assimilation System (GDAS) analysis gradient in the southern Amazon (SA) pFCu seems to cor-
data(Sahaetal.,2006;ParrishandDerber,1992),weexam- respondtoanequivalentRHgradientseeninFig.5b.Addi-
ined the spatial patterns of various meteorological parame- tionally,fromthedependenceofpFCuonRHinFig.5d,we
ters (J-A-S averages). The two parameters that were found canseethatareaswithhighRH(RH>80%,indicatedbythe
www.atmos-chem-phys.net/14/6063/2014/ Atmos.Chem.Phys.,14,6063–6074,2014
6068 R.H.Heiblumetal.:Amazonianforestpropertiesandshallowcumuluscloudfields
Figure 5. Meteorological setting (based on GDAS analysis data) in the Amazon during J-A-S and the effects on pFCu over forest and
non-forestlandcovers.(a)geopotentialheight(HGT)[m]at700hPapressurelevel;thedashedlinerepresentstheborderbetweenNAand
SAregions.(b)Relativehumidity(RH)[%]at850hPa;high-RHareasenclosedwithinthedashedlineareexcludedfromfurtheranalyses.
(c)ChanceofobservingFCufieldasafunctionofHGTat700hPa.Dataaresortedinto500bins,with11484and2808countsperbinfor
forestandnon-forest,respectively.TheblacklinerepresentsHGTseparationbetweenNAandSAregions.(d)Sameas(c),butforRHat
850hPa.DataaboveRH=80%(blacklineinpanel)areexcludedfromanalysesinthiswork.
dashedlineinFig.5b)arelessfavorableforFCuformation. 3.2 InterannualcomparisonofcloudfieldsandAOD
ThehighRHareasaremainlycoastalregions,andthereduc-
tioninpFCuthereisprobablyduetoincreasedcloudactivity
TheaverageAODtogetherwiththecloudfielddetectional-
(i.e.,thecloudfraction(CF)/meanarea(σ )incoastalareas
A
gorithm results for the years 2008–2011 are summarized in
isfrequentlyabovetheupperthresholdsinTable1)asare-
Fig.6.PreviouslydiscussedcloudfeaturessuchasFCufield
sultofmesoscalecoastalbreezesobservedinmanyprevious
preference over the northern part of region, and preference
works,e.g.,Heiblumetal.(2011)andMaldaetal.(2007).
fordeepconvectivecloudsinhighRHareas(i.e.,coastaland
AsseeninFig.5candd,thelarge-scalepFCudependence
northwesternareas),areseenforallfouryears.Therelatively
onthetwometeorologicalparametersissimilarforbothfor-
highchancefordeepconvectivecloudsinthenorthwestpart
estandnon-forestlandcovertypes.
ofthestudyregionmaybeduetovariableterrainandcom-
MeteorologicaldependenciesofEVIwerecheckedaswell
plex topography in that region as well (see Fig. 1). Water
forbothforestandnon-forestlandcovers.Fortheforestland
bodiessuchastheAmazonRiver,AtlanticOcean,andlakes
cover (as can be seen in Fig. 4), EVI is relatively constant
clearlyinhibitalltypesofcloudformation.Thesouthernpart
for all meteorology, indicating that EVI reflects an inherent
of the region experiences little or no clouds at all, due to
forest property. However, the non-forest land cover EVI is
the dry and stable meteorology in that area. The year 2009
tightlylinkedtothemeteorology,andthereforeanycorrela-
shows the highest occurrence of FCu formation throughout
tions between EVI and pFCu over non-forest may actually
thedomain.Meteorologicalconditionsduringthatyearshow
correspondtometeorologicalvariance.
amorehomogeneouspatternofRHat850hPacomparedto
other years (i.e., higher RH at the southern part (SA) and
Atmos.Chem.Phys.,14,6063–6074,2014 www.atmos-chem-phys.net/14/6063/2014/
R.H.Heiblumetal.:Amazonianforestpropertiesandshallowcumuluscloudfields 6069
SinceareasofhighAOD(whitecontoursinFig.6)donot
overlapwithareasofrelativelyconstantmeteorologicalcon-
ditionsthatarefavorableforFCuformation(blackcontours
inFig.6),andduetoambiguoussignals(bothtemporaland
spatial) of aerosol effects on clouds for the whole domain,
weassumetheseeffects(describedinSect.1)areunlikelyto
beastrongforcingforthespatialdistributionofpFCu.
3.3 Enhancedvegetationindex(EVI)effectonforest
cumulus(FCu)fields
Tominimizeinfluencesofmeteorology(andpotentiallyalso
aerosol)onthedata,welimitthecurrentanalysisofEVIef-
fects on FCu fields to the NA region excluding RH>80%
areas during 2011 (area enclosed by dashed black contour,
Fig. 6), taken as a representative example. The J-A-S pFCu
data (Fig. 3d) were sorted as a function of the mean J-A-S
EVIdata(Fig.4b)forforest(bluedots)andnon-forest(ma-
gentadots)landcoversseparately(seeFig.7a).Binstatistics
areincludedinthefigurelegend.Fortheforestlandcover,we
seeapositivedependenceofpFCuonEVI.Thisdependence
isespeciallystrongforthelowerEVIvalues.Theincreasein
pFCuthensaturatesatamoderatevalueofaboutEVI=0.54.
For the non-forest land cover, the dependence of pFCu on
EVIissomewhatdifferent.ForlowEVIvalues(EVI<0.48),
thereisastrongpositivedependence(similartothatseenin
forestlandcover),butforhighervaluesofEVI>0.48there
isacleardecreaseofpFCuwithEVI.Itisimportanttonote
Figure 6. Cloud field statistics and AOD during J-A-S for 2008 that for all EVI values, there is a higher chance of observ-
(left panels) to 2011 (right panels). Panels include (from top to inganFCufieldaboveforestlandcoverthannon-forestland
bottom)chanceforFCufield,chancefordeepconvectivecumulus
cover.
field,chanceforsparsetonocumulusfield,andmeanAODtaken
Untilnow,wehavefocusedonlimitingtheeffectsofmete-
from the Giovanni online data system. White dashed contours in
orologicalandaerosolvarianceonpFCu,buthaveyettocon-
2008(2010)panelsindicatetheregionofAOD>0.25(0.5).Black
sider the effects of mesoscale circulations that may form at
dashedcontoursinupperpanelsrepresentareaswithminimizedme-
theboundariesandtransitionareasbetweenlandcovertypes.
teorologicalvariance(seeSect.3.1),foreachyear,usedinSect.3.3
for EVI vs. pFCu analyses. High-AOD pixels along the mouth of Sincethesecirculationsarelocal,theirintensitycanberep-
theAmazonRiverwerediscardedastheycorrespondtoaMODIS resentedbythedistancefromthelandcoverboundaries.As
AODalgorithmartifactinthatareaduetothesediment-ladenwaters seeninFig.7bfortheNAregion,pFCuoverforestlandcover
there. islowestclosetotheboundarieswithotherlandcovertypes,
thenincreasessharplywithlargevarianceatshortdistances
(<5km), and is relatively constant at distances larger than
lowerRHinthenorthernpart(NA)),whichcouldberespon-
∼10km.Thevarianceseenfordistanceshigherthan80km
siblefortheincreaseinpFCu.
may be due to local topography and forest changes. To try
A striking difference in AOD between the years can be
toeliminatethemesoscaleeffectsfromtheEVIanalysis,we
seen, with average values reaching above 0.4 (0.8) in the
dividedthedataintotwosubsets:(i)forestdatawithin10km
southwestpartoftheregionduring2008(2010)butlimited
of other land cover types (Fig. 7c), which we assume in-
to0.25during2009and2011.Moreover,thespatialvariance
cludesthebulkofmesoscalecirculationeffects,and(ii)for-
of AOD is much smaller during 2009 and 2011, and is un-
estdatafurtherthan10kmawayfromotherlandcovertypes
likelytobeamajorfactorinpFCuvariability.Theextremely
(Fig. 7d), which we consider to be free of mesoscale circu-
highAODduring2010canbeexplainedbyboththeextreme
lationeffects.Therewasnopointindoingthesameexercise
drought(Lewisetal.,2011)andfrequentfires(TenHoeveet
for non-forest land cover since more than 95% of that data
al.,2012)(i.e.,abundanceofbiomassburningaerosol)inthe
arecloserthan10kmtootherlandcovertypes.
AmazonBasinthatyear.However,itshouldbenotedthatthe
Figures 7c and d illustrate how the pFCu dependence on
drought’seffectonourstudyregionwasminimalcompared
EVI shifts as we penetrate deeper into the forested areas.
totherestofthebasin.
In Fig. 7c, forest land cover data were divided into 0–1km,
www.atmos-chem-phys.net/14/6063/2014/ Atmos.Chem.Phys.,14,6063–6074,2014
6070 R.H.Heiblumetal.:Amazonianforestpropertiesandshallowcumuluscloudfields
Figure7.pFCufieldasafunctionofEVIintheNAregion(withRH<80%)duringJ-A-S,2011.(a)Alldataaboveforestandnon-forest
landcovertypes.Countsperbin(CPB)includedinpanellegend.(b)pFCufieldoverforestlandcoverasafunctionofdistance(km)from
nearestnon-forest/waterlandcoverpixel.DatacorrespondtoNAregion(withRH<80%).(c)Sameas(a)butwithforestlandcoverdata
constrainedtowithin10kmofotherlandcovertypesandsortedinto200bins.Theforestdataweredividedintofourdistancesfromother
landcoversubsets(seelegend)toillustratethetransitionfromanon-forest-likedependencetoadeep-forest-likedependence.Theblack
dashedlineisthelinearfitfordeepforestEVIdependenceseeninpanel(d).(d)Sameas(a)butonlyforforestlandcoverdatafurtherthan
10kmfromotherlandcovertypes.PanelincludesrawEVIdata(blue)andEVIsmoothedwitha5km(red)or25km(green)diskfilter.
Linearfitsforallcasesaddedinpanellegend.
1–2.5km,2.5–5km,and5–10kmdistanceintervalsfromany
other land cover. The non-forest pFCu dependence on EVI
fromFig.7aisaddedforreference.Agradualtransitionfrom
a non-forest-like dependence on EVI to a deep-forest-like
dependence on EVI (i.e., positive linear, black dashed line
in Fig. 7c) is seen. Hence, we can assume that in transition
zones between forest land cover and water/non-forest land
covers,asuperpositionofdeepforestandnon-forestdepen-
denciesonEVItakesplace.Generally,foragivenEVIvalue
the further the distance from water/non-forest land covers,
thehigherthechanceofobservingFCufields.
For deep forest data further than 10km away from other
land cover types (blue dots, Fig. 7d) we can see a positive
linear dependence of pFCu on EVI. We applied a linear fit
tothedata(R2=0.95;fitdetailsaddedinfigurelegend)and
obtained a slope of ∂(pFCu) =0.13. Clearly, the well-being
∂(EVI)
Figure 8. Four year (2008–2011) comparison of pFCu field as a and productivity of the deep forest promotes the formation
functionofEVI,forforestlandcoverdatafurtherthan10kmfrom ofFCufields.Totesttherobustnessofthelineartrendabove
otherlandcovertypes,andsortedinto200bins.A5kmspatialdisk (Fig. 7d, blue line), we applied two low-pass disk-shaped
filterwasappliedtotheEVIdata.Countsperbin(CPB)andlinear filters to the EVI data, one with a radius of 5km (green
fitsforallyearsaddedinfigurelegend. starsandline,Fig.7d)andtheotherwitharadiusof25km
(red triangles and line, Fig. 7d). Our assumption is that us-
ing“smoothed”EVIdata,beinglesssensitivetolocalnoise,
Atmos.Chem.Phys.,14,6063–6074,2014 www.atmos-chem-phys.net/14/6063/2014/
R.H.Heiblumetal.:Amazonianforestpropertiesandshallowcumuluscloudfields 6071
revealsthemorerobustlarger-scaleeffectsofEVIonpFCu. Table3.LinearfitstatisticsforpFCuvs.EVIdependenciesusing
Theresultsofthisanalysisshowthatthepositivelineartrend no filter and 5 and 25km spatial disk filters on the EVI data, for
seenforthedeepforestisindeedrobust,withstrongerdepen- years2008–2011.Statisticsincludelinearfitslope(a),pointofin-
dencies as filter size increases. The slope of pFCu vs. EVI tersectionwithyaxis(b),andtheR2coefficient.
increases to 0.39 for the 5km filter and 0.71 for the 25km
filter,anincreaseof200and450%,respectively.Forlarger Parameter → a b R2
Filtersize(km)↓ Year
filters with radius >30km, the main signal decays because
ofsignificantlossofEVIspatialinformation. 2008 0.23 0.21 0.93
The same process done for 2011 above was repeated for 2009 0.03 0.38 0.33
None
the years 2008–2010. For each year, we took the area with 2010 0.16 0.22 0.94
minimummeteorologicaleffectonpFCu(indicatedbyblack 2011 0.13 0.31 0.95
dashedcontoursinFig.6),usingthemethodologydescribed
2008 0.47 0.08 0.84
in Sect. 3.1. Areas of high AOD were partially avoided 2009 0.05 0.37 0.05
this way as well, although we cannot rule out influence of 5km 2010 0.35 0.12 0.81
aerosols on the results for years 2008 and 2010 especially. 2011 0.39 0.18 0.87
Onlydeepforestdatawereconsidered.Theresultsaresum-
2008 0.74 −0.04 0.64
marizedinTable3.ThedependenceofpFCuonspatiallyfil-
2009 0.22 0.28 0.14
teredEVI(only5kmfilter)for2008–2011isshowninFig.8. 25km 2010 0.75 −0.02 0.55
Forallyears,anincreaseinpFCuwithEVIisseen,yield- 2011 0.71 0.02 0.74
ing positive slopes for all years and spatial filter sizes (Ta-
ble3).TheminorreductionsinpFCuwithEVI,seeninFig.8
forveryhighEVI(>0.55,2008)orlowEVI(<0.46,2009), experiences stable conditions during the dry season, weak
areprobablyduetolocaleffectsandsparsestatisticsatthose meteorologicalgradientscontrolwhereFCufieldscanform.
EVI ranges. Consistent with 2011, the larger the spatial fil- ThesouthernAmazon(SA)subsetisusuallytoodryandsta-
ter size, the larger the linear fit slope, showing an average bletoenableFCuformation,aboveforestandnon-forestland
increase of more than 200% for the 5km filter and 400% cover alike. In contrast, the northwestern part and coastal
forthe25kmfilter(Table3).Atthesametime,thedatabe- areas of the northern Amazon (NA) subset experience rela-
come noisier with increasing filter size, reducing the corre- tivelyunstableconditionswhicharerealizedbytheincreased
lationcoefficientvaluesfrom∼0.9withoutafilterto∼0.6 presence of deep convective clouds and reduction in pFCu.
forthe25kmfilter.Althoughconsistentwiththeotheryears, RegardingthelinkbetweensurfaceEVIandpFCu,fivemain
2009showsamuchweakerpFCudependenceonEVI.This conclusionscanbedrawn:
maybeduetothefactthattheenvironmentalconditionsdur-
ing that year were especially favorable for FCu formation 1. FCufieldsformexclusivelyoverlandareasintheAma-
throughout the domain (see Fig. 6), reducing the second- zonregion,preferablyoverforestlandcover.
orderEVIeffectontheclouds.
2. The chance of observing FCu fields over forest land
cover increases with EVI (excluding very low or very
high EVI values for specific years), and can generally
4 Discussionandconclusions
berepresentedbyalinearfit.
In this work we examine the link between the Amazon for- 3. Up to a filter size of 25km, the pFCu dependence on
estandthecloudsthatformaboveitaspartoftheeffortto- EVIincreasesasweincreasethespatialsmoothingfilter
wardsunderstandinghowanthropogenicforestdilutionmay sizeoftheEVIdata.
affectclouds.Bydefiningforestcumulus(FCu)cloudsfields,
4. ThechanceofobservingFCufieldsovernon-forestland
we have created a simple metric that is clearly tightly cou-
cover increases (decreases) for values lower (higher)
pled and highly sensitive to surface changes in the Ama-
thanEVI=0.48,andisgenerallylowerthanoverforest
zon region. Although chosen subjectively, we note that re-
landcover.However,thescatteredspatialdistributionof
sultsofthisworkareinsensitivetochangesintheupperand
non-forest land cover (see Fig. 4a) and the strong cor-
lowerthresholds.Wetestedseveralsetsofthresholdranges,
relation between non-forest EVI and meteorology cast
andeventhoughtheabsolutevaluesofpFCudochange,the
doubtonthesignificanceofthisfinding.
trendsobtainedinSect.3.3arethesame.
Asignificantpartoftheanalysisconcentratedonseparat- 5. The dependence of pFCu on EVI in transition areas
ingtheeffectsofmeteorologyandbiomassburningfromthe from non-forest/water boundaries into forested land
more subtle effect of land cover EVI. Not surprisingly, the covercanbeexpressedasasuperpositionofforestand
meteorological conditions are the main factor that sets the non-forestdependencies.
stage for FCu formation. Although the entire study region
www.atmos-chem-phys.net/14/6063/2014/ Atmos.Chem.Phys.,14,6063–6074,2014
6072 R.H.Heiblumetal.:Amazonianforestpropertiesandshallowcumuluscloudfields
Thesefindingsshowthestrongcontrolthatlandcoverand References
land cover gradients exert on FCu. Even though the depen-
denceneednotbelinear,EVIcanbeconsideredhighlycorre- Ackerman, S. A., Strabala, K. I., Menzel, W. P., Frey, R. A.,
Moeller,C.C.,andGumley,L.E.:Discriminatingclearskyfrom
latedwithevapotranspiration,implyingthathighlatentheat
clouds with MODIS, J. Geophys. Res.-Atmos., 103, 32141–
(moisture)fluxesarecrucialforthedevelopmentandorgani-
32157,doi:10.1029/1998jd200032,1998.
zationoftheFCufields.
Amante, C. and Eakins, B. W.: ETOPO1 1 Arc-Minute
Nevertheless, elucidating the dynamical processes which
Global Relief Model: Procedures, Data Sources and Anal-
are responsible for the formation of FCu field require fu-
ysis, NOAA Technical Memorandum NESDIS NGDC-24,
ture work. We can speculate that the FCu fields correspond doi:10.7289/V5C8276M,2009.
to a specific solution of Rayleigh–Benard thermal convec- Bastable,H.G.,Shuttleworth,W.J.,Dallarosa,R.L.G.,Fisch,G.,
tion over land (or specifically cloud streets, as discussed in andNobre,C.A.:ObservationsofClimate,Albedo,andSurface
Sect. 1), since the basic physical settings are similar over RadiationoverClearedandUndisturbedAmazonianForest,Int.
the Amazon and ocean surfaces, namely a homogeneous J.Climatol.,13,783–796,doi:10.1002/joc.3370130706,1993.
warmsurfaceandamoistboundarylayerwithawell-defined Betts, A. K.: Idealized model for equilibrium boundary layer
over land, J. Hydrometeorol., 1, 507–523, doi:10.1175/1525-
inversion layer. Hexagonal open convection cells have al-
7541(2000)001<0507:Imfebl>2.0.Co;2,2000.
readybeensimulatedovertropicallandinthewesternPacific
Betts, A. K.: Land-Surface-Atmosphere Coupling in Observa-
(Saitoetal.,2001).Thefactthatvegetationpropertiescontrol
tions and Models, J. Adv. Model Earth Syst., 1, 18 pp.,
both surface fluxes and boundary layer depth (h) to a large
doi:10.3894/James.2009.1.4,2009.
degree,andthattheRayleighnumber(Ra)ishighlydepen-
Betts, A. K. and Silva Dias, M. A. F. S.: Progress in understand-
dent on that depth (proportional to h4), suggests a physical ing land-surface-atmosphere coupling from LBA research, J.
linkbetweenforestandthecloudfieldsformedabove. Adv.Model.EarthSyst,2,20pp.,doi:10.3894/JAMES.2010.2.6,
As for climatic trends in Amazon cloud fields, it is hard 2010.
toconcludehowlarge-scaledeforestationwouldaffecttotal Brümmer, B.: Roll and cell convection in wintertime arctic cold-
cloud cover since meteorological and land cover gradients airoutbreaks,J.Atmos.Sci.,56,2613–2636,doi:10.1175/1520-
roughlycoincideinourstudyregion.Wecanpredictareduc- 0469(1999)056<2613:RACCIW>2.0.CO;2,1999.
Chagnon,F.J.F.,Bras,R.L.,andWang,J.:Climaticshiftinpat-
tionindryseasonFCufieldsasforestlandcoverundergoes
terns of shallow clouds over the Amazon, Geophys. Res. Lett.,
transitiontonon-forestorasforestwellbeingdecreases(re-
31,L24212,doi:10.1029/2004gl021188,2004.
ductioninEVI);howevermoreextensivestudiesareneeded
Cutrim, E., Martin, D. W., and Rabin, R.: Enhancement
tounderstandthetotaleffectontheradiationbudgetandwa-
of Cumulus Clouds over Deforested Lands in Amazonia,
tercycleintheAmazonduetosuchchanges.
B. Am. Meteorol. Soc., 76, 1801–1805, doi:10.1175/1520-
0477(1995)076<1801:Eoccod>2.0.Co;2,1995.
Figueroa, S. N. and Nobre, C. A.: Precipitation distribution over
Acknowledgements. ThisresearchwassupportedbytheEuropean centralandwesterntropicalSouthAmerica,Climanalise,5,36–
ResearchCouncilundertheEuropeanUnion’sSeventhFramework 45,1990.
Programme (FP7/2007-2013)/ERC (CAPRI, grant no. 306965) Fisch, G., Culf, A. D., and Nobre, C. A.: Modelling convective
and by a NASA Interdisciplinary Science (IDS) project managed boundary layer growth in Rondonia, in: Amazonian Deforesta-
by H. Maring. We acknowledge the use of Rapid Response tionandClimate,editedby:Gash,J.H.C.,Nobre,C.A.,Roberts,
imagery(truecolorimagesinthework)fromtheLandAtmosphere J.M.,andVictoria,R.L.,JohnWiley&Sons,Chichester,UK,
Near-real time Capability for EOS (LANCE) system operated by 425–436,1996.
the NASA/GSFC/Earth Science Data and Information System Fisch, G., Tota, J., Machado, L. A. T., Dias, M. A. F. S., Lyra,
(ESDIS) with funding provided by NASA/HQ. Aerosol optical R.F.D.,Nobre,C.A.,Dolman,A.J.,andGash,J.H.C.:The
depth (AOD) data and visualizations (Fig. 6) were produced with convective boundary layer over pasture and forest in Amazo-
theGiovannionlinedatasystem,developedandmaintainedbythe nia,Theor.Appl.Climatol.,78,47–59,doi:10.1007/s00704-004-
NASAGESDISC. 0043-x,2004.
Friedl, M. A., Sulla-Menashe, D., Tan, B., Schneider, A., Ra-
Editedby:P.Stier mankutty, N., Sibley, A., and Huang, X. M.: MODIS Collec-
tion 5 global land cover: Algorithm refinements and character-
ization of new datasets, Remote Sens. Environ., 114, 168–182,
doi:10.1016/j.rse.2009.08.016,2010.
Glenn, E. P., Huete, A. R., Nagler, P. L., and Nelson, S. G.: Re-
lationship between remotely-sensed vegetation indices, canopy
attributesandplantphysiologicalprocesses:Whatvegetationin-
dicescanandcannottellusaboutthelandscape,Sensors-Basel,
8,2136–2160,doi:10.3390/S8042136,2008.
Glenn,E.P.,Nagler,P.L.,andHuete,A.R.:VegetationIndexMeth-
odsforEstimatingEvapotranspirationbyRemoteSensing,Surv.
Geophys.,31,531–555,doi:10.1007/s10712-010-9102-2,2010.
Atmos.Chem.Phys.,14,6063–6074,2014 www.atmos-chem-phys.net/14/6063/2014/