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Current feedbacks between human activities and the integrity of climatic, hydrologic, and biotic functioning of the Amazon system: Modeling challenges.

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Presentation on theme: "Current feedbacks between human activities and the integrity of climatic, hydrologic, and biotic functioning of the Amazon system: Modeling challenges."— Presentation transcript:

1 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, November 2008

2 Bull. American Met. Soc

3 atmosphere ocean+hydrology soil vegetation chemical species Modelling the Earth Atmosphere System

4 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) Atmosphere Ocean, Biosphere…

5 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)

6 Numerical evidences: Misra et al (Mon. Wea. Rev. 2005) -> models with strongest diurnal cycle show strongest intraseasonal cycle (Raupp and Silva Dias, JAS ) Need better description of diurnal convective cycle -> Closely coupled with surface processes From Model Intercomparison Page - and

7 ● 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

8 Multiscaling

9 Aerosol, radiation and cloud microphysics interactions

10 ● Dynamic Vegetation Model coupled to GCMs & used for climate change prediction: IBIS, TEM, CENTURY, BiomeBGC, GEMTM …

11 Coupled climate— vegetation models project dramatically different futures (CO 2, vegetation, T) using different ecosystem models. (Cox et al. 2000; Friedlingstein et al. 2001); ~ 2º K in 2100  T=5  T=3

12 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, 2004

13 Contribuição de cada fator N fatores => 2**N experimentos Freitas e Silva Dias, 2007 ● Challenge 2: How can we identify interactions in complex models?

14 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

15 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

16 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

17 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:

18 Challenge 5: Introduction of human feedbacks in the climate models

19 Integrated models: Climate and Economy GHG emissions GHG concentrations Global warming Adaptatio n Economic activities Climate changes

20 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 Agentes para projetarem cenários Regras Adaptativas 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

21 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


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