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