GOES, Meteosat, and MTSAT ...hbarbosa/uploads/Teaching/IntroFisAtmos2019/… · Water in the...
Transcript of GOES, Meteosat, and MTSAT ...hbarbosa/uploads/Teaching/IntroFisAtmos2019/… · Water in the...
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GOES,Meteosat,andMTSAThttps://www.ssec.wisc.edu/data/comp/wv/wvmoll.mpg
Totalcolumnwatervapor
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Waterintheatmosphere
H2O EquivalentinmetersLiquidorsolidwaterintheatmosphere 0.0001m
Vaporintheatmosphere 0.025m
Waterinsoil,lakes,riversandglaciers 50to75m
Oceans 2800m
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Stevens&Bony,PhysicsToday2013
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Imageby:RobertA.Rohde,GlobalWarmingArt.
Majorgreenhousegases
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WaterVapor
CarbonDioxide
Ozone
Methane,NitrousOxide
Water Vapor 60%
Carbon Dioxide 26%
O3 8%
CH4 N20 6%
CloudsalsohaveagreenhouseeffectKiehlandTrenberth1997
TheNaturalGreenhouseEffect:clearsky
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https://www.nasa.gov
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Stevens&Bony,PhysicsToday2013
Latentheat
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Waterintheclimatesystem
Itsphysicalpropertiesdetermine• Howstronggreenhouseeffectis;• Planetaryalbedo;• Thermodynamicstructureofthetroposphere;• Largescalecirculation;• Hydrologicalcycle;• Aerosolshygroscopicgrowth
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Austral Summer Precipitation 79-06
GPCP,mm/day
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Nov-Mar
Precipita;on,PWVandVaportransportGPCP+ERA401989-2009
Arrautetal,J.Clim,2012mm/day
Nov-Mar
Aerialriversandlakes
mm
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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)
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Amazonandmoisturerecycling?
Deforestationandmoistureflux?
Moisturefluxandprecipitation?
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RiverofSmoke
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Rela;onshipbetweenaerosolsandprecipita;onintheLaPlataBasin
AERONET(Aerossols)+TRMM(Precipita;on)+BRAMS(simula;ons)Reduc;oninprecipita;onwithincreaseinaerosols
SilvaDiasetal.,2014BRAMS:Simula;onswithcloudmicrophysicsconfirmthemeasurements
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Considerthefluxacrossthisline
Correla^onmaps
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Nov-Mar Jul-Aug
Correla^onmaps
Arrautetal,J.Clim,2012
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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 )
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Whatifbothvariablesare2Dxt?Createlinksonlywhenρi,j,k,l>thresholdρi, j,k,l =
covt (Pi, j,MTk,l )stdt (Pi, j )std
t (MTk,l )
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ComplexNetworks� Inthecontextofnetworktheory,acomplexnetworkisagraph(network)withnon-trivialtopologicalfeatures
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Proper^esofRealNetworks� Scale-freenetworks� Mostverticeshavefewneighbors(degree),butsomehave
veryhighdegree(power-lawdegreedistribution)
� Highclusteringcoefficient� Ifxisconnectedtoyandz,thenyandzare
likelytobeconnected
� Smallworldnetworks� Mostverticesarejustafew
edgesawayonaverage.
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Examples:� peoplethatarefriendsonfacebook� computersthatareinterconnected� webpagesthatpointtoeachother� proteinsthatinteract� braincellstransmittinginformation� phone-callnetworks� transportationnetworks� transmissiongrids
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MotterandYang,PhysicsToday70,1,32(2017)
Theunfoldingandcontrolofnetworkcascades
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Ourintenttodayis…GiveexamplesofhowweappliedComplexNetworksforunderstandingmoisturetransportoverSouthAmerica1. Propagationofextremeevents2. Cascadingmoisturerecycling3. Climatechange&Deforestation
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DJFPrecip&Moist.Flux
Cannetworksdetectmoisturebeingtransported
southandcausingprecipitationontheway?
Prec(m
m/day)
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Synchroniza^onofextremeprecipita^onevents
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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
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Boersetal,NatureComm.2014
OUTstrenghINstrengh
In(i) = A(ij)j=1
N
∑ Out(i) = A( ji)j=1
N
∑
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OUTstrenghINstrengh
Boersetal,NatureComm.2014
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~80km/hcontraryto
moistureflux!Boersetal,NatureComm.2014
NetworkDivergence
ΔS(i) = In(i)−Out(i)
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http://floodlist.com/america/nasa-satellites-measure-flooding-rain-in-peru-and-bolivia
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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
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WhatistheroleofAmazon?
PrecLPB?
PrecSE?
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2-LayerMoistureTransportModel
ObservedET
ObservedPrecipObservedWinds
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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
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Cascading
Differentpathsforwater,andpossiblecascadingbeforegettingto
“final”destination!Zempetal,ACP2014
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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
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DRY
WET
%ofAmazonETonPrecip
Cascading
20-24%ofprecipitationoverLPBcomesfrom
ETAmazon
Cascadingtransport=+6%
[0 – 1]
Cascadingtransport=+10%
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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
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Probabilityoffindingforest
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Non-linearresponse
One-waycouplingPèVegFullycoupledsystemPçèVeg Zempetal.,NatureComm.(2017)
Self-amplifiedlossincreasesnon-linearly
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Zempetal.,NatureComm.(2017)
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êevapotranspirationé=> êmoisturetransport
êprecipitationForest=>Cerrado
Deforesta^on
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1ddynamicalsystem
atmos:soil:
1. Run until equilibrium2. Deforest N-th box3. Run until equilibrium4. Deforest N+1 box5. etc..
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Deforesta^onshowshysteresis
Boersetal.,NatureScientificReports(2017)
Oritnevercomeback,ifcouplingisverystrong
Hysteresisresponseduringreforesta^on
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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
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https://www.nees.uni-bonn.de/research-/systematics-evolution-ecology/biogeography-and-macroecology-biomaps/worldmaps/worldmaps-of-plant-diversity
Biodiversity
Amazon~10%ofallknownbiodiversity
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Biomass=>Carbonstorage
Saatchietal.,PNAS2011
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Balaetal.,PNAS2007
Global temperature additional increase of +0.7 oC with removal of all tropical forests
CO2 in a world without forestsAbout +200 ppm
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D.Lawrence&K.Vandecar,NatureClimateChange(2015)
Worldwithouttropicalrainforests
DecreaseIncrease
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AmazonTallTowerObservatory(ATTO),325m
Aerosollifecycle
Cloudlifecycle
Aerosol-cloudinteractions
Carbonbalance
Evenmorenon-linearcomplex
system!
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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
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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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