current feedbacks between human activities and the integrity of climatic, hydrologic, and biotic...
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Current feedbacks between human activities and the integrity of climatic, hydrologic, and biotic functioning of the
Amazon system:
Modeling challenges
Pedro Leite da Silva Dias (*)National Laboratory of Scientific Computing - LNCC/MCT
Institute of Astronomy, Geophysics and Atmospheric Sciences IAG/USP
Amazonia em Perspectiva: Ciência Integrada para um Futuro Sustentável
LBA/GEOMA/PPBio Conference - Manaus, 17-20 November 2008
Bull. American Met. Soc. - 2008
X a
t+ L a X a = N a X
a, X
o, X
v, X
c, X
s+F
aX a ,X o ,X v ,X c ,X s
X o
t+ L o X o = N o X
a, X
o, X
v, X
c, X
s+F
oX a ,X o ,X v ,X c ,X s
X v
t+ L v X v = N v X
a, X
o, X
v, X
c, X
s+F
vX a ,X o ,X v ,X c ,X s
X c
t+ L c X c = N c X
a, X
o, X
v, X
c, X
s+F
cX a ,X o ,X v ,X c ,X s
X s
t+ L s X s = N s X
a, X
o, X
v, X
c, X
s+F
sX a ,X o ,X v ,X c ,X s
X a u , v , w , T , qv
, ql
, qr
, qi
, . . . . X o u , v , w , T , sv
, . . .
X v l a ii
, s i gi v
, r o o ti d
, s t o mi c
, V O Ci
, Ci
, Ni
, . . . .
X c C O2
, C H4
, O3
, N Ox
, V O C ' s , S O2
, . . .
X s Ti s
, Wi s
, Ni n
, . . . .
atmosphere
ocean+hydrology
soil
vegetation
chemical species
Modelling the Earth Atmosphere System
Challenge 1: Theoretical studies on the non-linear nature of the coupling between time and spatial scales of Xa, Xo, Xv, Xs, Xc etc. E.g., coupling of slow and fast manifolds using toy-models such as (Pena and Kalnay, 2003)
11 1 1 2
11 1 1 1 1 2
11 1 1 1 2
Fast equations
( ) ( )
( )
( )
dxy x C Sx O
dtdy
rx y x z C Sy Odtdz
x y bz C Szdt
σ= − − +
= − − + +
= − +
AtmosphereAtmosphere Ocean, Biosphere…Ocean, Biosphere…
Non linear character of N,F operator => interaction among scales
•Very simple models show evidence of non-linear energy transfer: shallow water model (Raupp & Silva Dias 2006);
•Conceptual baroclinic/barotropic models:
• strong diurnal heating -> energy in gravity waves (divergence) <-> interaction with synoptic scale waves (Rossby waves) <-> interaction with basic state (long Rossby waves) => intraseasonal scale (Raupp & Silva Dias 2008a,b)
•Numerical evidences: Misra et al (Mon. Wea. Rev. 2005) -> models with strongest diurnal cycle show strongest intraseasonal cycle (Raupp and Silva Dias, JAS - 2008)
•Need better description of diurnal convective cycle ->
•Closely coupled with surface processes
From Model Intercomparison Page - www.master.iag.usp.br/intercomp and http://www6.cptec.inpe.br/iodopweb/intercomparacao/phps/
● Understanding this loop and the role of the tropical forests are critical questions in the FAR (IPCC - Forth Assessment Report) and for the next assessment
Multiscaling
Aerosol, radiation and cloud microphysics interactions
● Dynamic Vegetation Model coupled to GCMs & used for climate change prediction:IBIS, TEM, CENTURY, BiomeBGC, GEMTM…
• Coupled climate—vegetation models project dramatically different futures (CO2, vegetation, T) using different ecosystem models.(Cox et al. 2000; Friedlingstein et al. 2001);
Carbon Flux: Land to Air
-10-8-6-4-202468
10
1850 1900 1950 2000 2050 2100
PgC/yr
Global Mean Temperature
13
14
15
16
17
18
19
20
1850 1900 1950 2000 2050 2100
Celsius
~ 2º Kin 2100 T=5T=3
Aerosol and dynamic vegetation (veg. param. respond to climate)
Without aerosols LAI continuously decreases during transition
Experiment reflects role of diffuse radiation in C dynamics during transition from dry to wet season in Rondonia
Moreira and Silva Dias, 2004Moreira and Silva Dias, 2004
Contribuição de cada fator
N fatores => 2**N experimentosN fatores => 2**N experimentos
Freitas e Silva Freitas e Silva Dias, 2007Dias, 2007
●Challenge 2: How can we identify interactions in complex models?
Among major challenges for modelling
atmosphere/surface interactions:
● Challenge 3: Data assimilation for IC e BC in the complex models
● Remote sensing of the atmosphere (temp, wind, moisture, trace gases….)
● Remote sensing of the surface (land/ocean)
●Integration among prognostic models and observing system – large progress in weather forecasting in the last 10 years came from improvements in data assimilation
Challenge 4: Improvement of integrated regional and global modelling
• Although substantial progress has been attained in combining regional and global models most studies still suffers from some type of mismatch between the global and regional model dynamics/physics. Actions needed:• To enable regional simulations to be run on the model’s own global grid (or
spectral space), without the need for one-way nesting inside another large-scale model;
• To enable global simulations that incorporate regional model-type parameterizations;
• To combine water and energy cycles of the global ocean-land-atmosphere system into a unified climate modelling system;
• To enable climate studies that depend on two-way interactions between global, regional, and micro-scales
• Requirements for weather, climate and environmental quality models:
– Highly efficient– Robust (numericaly stable) for:
• Weather forecasting (hours, days)• Long integrations (climate) (months to thousands of
years) – Precision
• Second order or higher • Equilibrium between truncation errors in space and
time
•Avoid use of different models for each spatial scale;
•Multiscaling modeling;
•Numerical challenge: efficiency/precision
•Example:Global grid structure in OLAM - successor of RAMS/BRAMS
• Where are we going:
Challenge 5: Introduction of human feedbacks in the climate models
Integrated models: Climate and Economy
GHG emissions
GHG concentrations Global warming
AdaptationEconomic
activities
Climate changes
Modelo do Clima GlobalModelo Econômico e de Emissões
Crescimento do PIB
Emissões
Perdas Sociais Totais
Modelo dos Custos de Abatimento
Modelo de Danos Climáticos
Ciclo do Carbono
Balanço Energético
Modelo Usado pelos Agentespara projetarem cenários
RegrasAdaptativas
1. Mudança do Horizonte de Antecipação
2. Mudança do Horizonte de Planejamento
3. Mudança das Opções de Abatimento
Jogo Não Cooperativo
Regime Político do Clima
Meio Ambiente de Base
Modelo do Clima
Modelo das Economias
Modelos Integrados Clima – Economia Baseados em Múltiplos Agentes
Luis Aimola 2006
Conclusion:
Need for coordinated effort among multidisciplinary teams:
• Multiscaling treatment of the dynamics => efficiency and accuracy
• Physical processes : theoretical challenges due to multiscaling interactions (e.g. : thermodynamics of adjustment processes in the atmosphere - radiation/clouds/surface)
• Data assimilation: optimization process
• Module integration: theoretical analysis of non-linearities; introduction of human controls and feedbacks
• Model Validation: observational work