GOES,Meteosat,andMTSAThttps://www.ssec.wisc.edu/data/comp/wv/wvmoll.mpg
Totalcolumnwatervapor
Waterintheatmosphere
H2O EquivalentinmetersLiquidorsolidwaterintheatmosphere 0.0001m
Vaporintheatmosphere 0.025m
Waterinsoil,lakes,riversandglaciers 50to75m
Oceans 2800m
Stevens&Bony,PhysicsToday2013
Imageby:RobertA.Rohde,GlobalWarmingArt.
Majorgreenhousegases
WaterVapor
CarbonDioxide
Ozone
Methane,NitrousOxide
Water Vapor 60%
Carbon Dioxide 26%
O3 8%
CH4 N20 6%
CloudsalsohaveagreenhouseeffectKiehlandTrenberth1997
TheNaturalGreenhouseEffect:clearsky
https://www.nasa.gov
Stevens&Bony,PhysicsToday2013
Latentheat
Waterintheclimatesystem
Itsphysicalpropertiesdetermine• Howstronggreenhouseeffectis;• Planetaryalbedo;• Thermodynamicstructureofthetroposphere;• Largescalecirculation;• Hydrologicalcycle;• Aerosolshygroscopicgrowth
Austral Summer Precipitation 79-06
GPCP,mm/day
Nov-Mar
Precipita;on,PWVandVaportransportGPCP+ERA401989-2009
Arrautetal,J.Clim,2012mm/day
Nov-Mar
Aerialriversandlakes
mm
SouthAmericanMonsoon
MoistureTrans.&PrecipNov-Mar,ERA40
ITCZtothesouthMarengoetal2012
é gradTocean/contZhou&Lau1998
é MoistureTrans.Ocean=>Cont.Rodwell&Hoskins,2001
SubtropicsNicolineetal.2002
AndesbarrierByerle&Paegle2002
MoisturerecyclingEltahir&Bras,1994
ConvergencezoneCarvalhoetal2004
Prec(m
m/day)
Literature:LLJandprecipatLPB(e.g.Nicolinietal.,2002)LLJandMCS(Salioetal.,2007)
Amazonandmoisturerecycling?
Deforestationandmoistureflux?
Moisturefluxandprecipitation?
RiverofSmoke
Rela;onshipbetweenaerosolsandprecipita;onintheLaPlataBasin
AERONET(Aerossols)+TRMM(Precipita;on)+BRAMS(simula;ons)Reduc;oninprecipita;onwithincreaseinaerosols
SilvaDiasetal.,2014BRAMS:Simula;onswithcloudmicrophysicsconfirmthemeasurements
Considerthefluxacrossthisline
Correla^onmaps
Nov-Mar Jul-Aug
Correla^onmaps
Arrautetal,J.Clim,2012
Could we do that for every point?
Nov-Mar
Correla^onmaps
ρi, j,k,l =covt (Pi, j,MTk,l )
stdt (Pi, j )stdt (MTk,l )
Whatifbothvariablesare2Dxt?Createlinksonlywhenρi,j,k,l>thresholdρi, j,k,l =
covt (Pi, j,MTk,l )stdt (Pi, j )std
t (MTk,l )
ComplexNetworks� Inthecontextofnetworktheory,acomplexnetworkisagraph(network)withnon-trivialtopologicalfeatures
Proper^esofRealNetworks� Scale-freenetworks� Mostverticeshavefewneighbors(degree),butsomehave
veryhighdegree(power-lawdegreedistribution)
� Highclusteringcoefficient� Ifxisconnectedtoyandz,thenyandzare
likelytobeconnected
� Smallworldnetworks� Mostverticesarejustafew
edgesawayonaverage.
Examples:� peoplethatarefriendsonfacebook� computersthatareinterconnected� webpagesthatpointtoeachother� proteinsthatinteract� braincellstransmittinginformation� phone-callnetworks� transportationnetworks� transmissiongrids
MotterandYang,PhysicsToday70,1,32(2017)
Theunfoldingandcontrolofnetworkcascades
Ourintenttodayis…GiveexamplesofhowweappliedComplexNetworksforunderstandingmoisturetransportoverSouthAmerica1. Propagationofextremeevents2. Cascadingmoisturerecycling3. Climatechange&Deforestation
DJFPrecip&Moist.Flux
Cannetworksdetectmoisturebeingtransported
southandcausingprecipitationontheway?
Prec(m
m/day)
Synchroniza^onofextremeprecipita^onevents
network divergence = Δstrength(i) = in-strength(i) - out-strength(i):
0.20.3
0.9
0.40.6
0.2
0.1
0.4
represent strongest synchronizations (2%) as directed and weighted network links:
in-strength(i) = sum of all weights at links pointing to i:
out-strength(i) = sum of all weights at links pointing from i:
definition:A = (Aij)1i,jN
IS(i) =NX
j=1
Aij
OS(i) =NX
j=1
Aji
�S(i) = IS(i)�OS(i)
network construction
Directedweightednetwork
Boersetal,NatureComm.2014
Boersetal,NatureComm.2014
OUTstrenghINstrengh
In(i) = A(ij)j=1
N
∑ Out(i) = A( ji)j=1
N
∑
OUTstrenghINstrengh
Boersetal,NatureComm.2014
~80km/hcontraryto
moistureflux!Boersetal,NatureComm.2014
NetworkDivergence
ΔS(i) = In(i)−Out(i)
http://floodlist.com/america/nasa-satellites-measure-flooding-rain-in-peru-and-bolivia
Canwepredict?
L
L
HH x
Boersetal,NatureComm.2014
El Nino Observed Not observed
Forecasted 7% 7%
Not forecasted 1% 85%
Heidke Skill Score = 0.57 of max 0.60
WhatistheroleofAmazon?
PrecLPB?
PrecSE?
2-LayerMoistureTransportModel
ObservedET
ObservedPrecipObservedWinds
Moisture(complex)network
Zempetal,Atmos.Chem.Phys.2014
56% Molion (1975); Recent (20-35%):
23% Brubaker (1993);30% Eltahir & Bras (1994);
34% Trenberth (1999); 24% Nobrega et al (2005);
Van der Ent (2010)
Scale effect: 4% for one grid-box
28% for entire Amazon
Cascading
Differentpathsforwater,andpossiblecascadingbeforegettingto
“final”destination!Zempetal,ACP2014
Cascading� For45%ofthewalks,thedirecttransportthemostimportant
� For55%,awalkwithatleastonestopismoreefficient! Fig. - Distribution of optimal paths
For 0 steps, local recycling For 2 steps, direct transport For 3 or more steps, path with cascading
0 2 4 6 8 Number of steps
Zempetal,ACP2014
DRY
WET
%ofAmazonETonPrecip
Cascading
20-24%ofprecipitationoverLPBcomesfrom
ETAmazon
Cascadingtransport=+6%
[0 – 1]
Cascadingtransport=+10%
Savanna
Seasonal Forest
Effects of water defining the transition forest-Savanna
Malhi et al., PNAS 2010
Nobre & Shukla, J Climate 1991Cox et al, Theo. App. Clim. 2004Sampaio et al., Geo. Res. Let. 2007Hirota et al., Science 2011Nepstad et al., Science 2014Levine et al., PNAS 2016Lapola et al., PNAS 2018
Probabilityoffindingforest
Non-linearresponse
One-waycouplingPèVegFullycoupledsystemPçèVeg Zempetal.,NatureComm.(2017)
Self-amplifiedlossincreasesnon-linearly
Zempetal.,NatureComm.(2017)
êevapotranspirationé=> êmoisturetransport
êprecipitationForest=>Cerrado
Deforesta^on
1ddynamicalsystem
atmos:soil:
1. Run until equilibrium2. Deforest N-th box3. Run until equilibrium4. Deforest N+1 box5. etc..
Deforesta^onshowshysteresis
Boersetal.,NatureScientificReports(2017)
Oritnevercomeback,ifcouplingisverystrong
Hysteresisresponseduringreforesta^on
Rainfall 1.5 m to 3 m
Average discharge of 219,000 m3/sec of water
1/6 of all fresh water that drains into the world's oceans
HydrologicalCycle
https://www.nees.uni-bonn.de/research-/systematics-evolution-ecology/biogeography-and-macroecology-biomaps/worldmaps/worldmaps-of-plant-diversity
Biodiversity
Amazon~10%ofallknownbiodiversity
Biomass=>Carbonstorage
Saatchietal.,PNAS2011
Balaetal.,PNAS2007
Global temperature additional increase of +0.7 oC with removal of all tropical forests
CO2 in a world without forestsAbout +200 ppm
D.Lawrence&K.Vandecar,NatureClimateChange(2015)
Worldwithouttropicalrainforests
DecreaseIncrease
AmazonTallTowerObservatory(ATTO),325m
Aerosollifecycle
Cloudlifecycle
Aerosol-cloudinteractions
Carbonbalance
Evenmorenon-linearcomplex
system!
A B123
D
4
E
Exis%ng
A AerosolMicrophysics• Sca7ering• Absorp;on• Concentra;on• Par;clesizeandmass• Chemicalcomposi;on• CCNspectra
B AerosolMicrophysics
C Bioaerosolsampler
D Sunphotometer
E Filtercollec;on
New
1 Ceilometer(IR)
2 Micro-pulsedlidar(VIS)
3 Ramanlidar(UV)
4 All-skyIRcamera
5 Fogsampler
5
C
Schematic view of the aerosol measurements at the ATTO site. Detailed aerosol particle microphysics measurements are already in operation at the 60 m triangular tower (A), and the tall tower (B). Filter collection for elemental chemical composition, and organic and elemental carbon concentration (E) takes place at the walkup tower, and for analysis of fungi and spores (C), at the tall tower. New instruments will allow measuring the vertical profile of aerosol and cloud properties, and collecting in-cloud water for chemical and biological analysis.
Aerosol measurements at ATTO
Aerosol-CloudPhysicsinstrumenta^onsite,containing:Joss-Waldvogeldisdrometer(JWD),RadarWindProfiler(RWP),W-bandCloudRadar(CldRad),GPSforwatervaporcolumn,MP3000,Sodaranddual-polariza^onLidar.Thissystemwillallowaninnova^veviewofprecipita^onforma^onoverCentralAmazonia
Aerosol-CloudPhysicsinstrumenta;onsite
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