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Approaches to climate change study and neural network modelling

Approaches to climate change study and neural network modelling. Antonello Pasini CNR - Institute of Atmospheric Pollution Rome, Italy. Outline. Climate science and dynamical modelling ; neural network model; assessment on the past; about predictability in past and future scenarios;

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Approaches to climate change study and neural network modelling

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  1. Approaches to climate change study and neural network modelling Antonello Pasini CNR - Institute of Atmospheric Pollution Rome, Italy UniKore, 6-10 September 2008

  2. Outline • Climate science and dynamical modelling; • neural network model; • assessment on the past; • about predictability in past and future scenarios; • NN downscaling; • conclusions and prospects. UniKore, 6-10 September 2008

  3. Climate scientists as grown-up babies If you give a child a toy, he will eventually open it up. Let’s open it up! UniKore, 6-10 September 2008

  4. Climate scientists as grown-up babies Let’s understand how it works... … and let’s reassemble it! UniKore, 6-10 September 2008

  5. Decomposing the system UniKore, 6-10 September 2008

  6. Theoretical knowledge We possess theoretical knowledge of single sub-systems from experiments in “real laboratories” (e.g., laws from fluid-dynamics and thermodynamics of oceans and atmosphere). In order to recompose the complexity of the system, we need a “virtual laboratory”... UniKore, 6-10 September 2008

  7. Recomposing in a model UniKore, 6-10 September 2008

  8. From dynamical modelling... • Physical characterization and forecasting in the climate system is a very difficult task, if we adopt an approach with complete dynamics. • Global Climate Models (GCMs) are the standard tools for grasping this complexity. • They permit to recognize the role of some cause-effect relationships. UniKore, 6-10 September 2008

  9. From dynamical modelling... UniKore, 6-10 September 2008

  10. From dynamical modelling... • However, the results of GCMs could crucially depend on the delicate balance (fine tuning) among the relative strength of feedbacks and the various parameter-ization routines  doubtful results . • Furthermore, they show limits in reconstruction and forecasting at regional and local scales. • So, an independent (more “holystic”) analysis could be interesting. UniKore, 6-10 September 2008

  11. … to a different strategy • The simplest idea: application of a multivariate linear model to the analysis of influence/causality: forcings (which influence temperature) vs. temperature itself • Bad results: the linear model is too simple neural networks! UniKore, 6-10 September 2008

  12. … to a different strategy A biological “inspiration” UniKore, 6-10 September 2008

  13. Natural inputs Climatic behaviour Anthropogenic inputs … to a different strategy UniKore, 6-10 September 2008

  14. A neural network model UniKore, 6-10 September 2008

  15. A neural network model UniKore, 6-10 September 2008

  16. A neural network model UniKore, 6-10 September 2008

  17. A neural network model UniKore, 6-10 September 2008

  18. A neural network model • Tool for short historical time series of data (“all-frame” or “leave-one-out” procedure); • early stopping method. UniKore, 6-10 September 2008

  19. Assessment on the past Global case study; regional case study. UniKore, 6-10 September 2008

  20. Global case study Input data: • solar irradiance and stratospheric optical thickness as indices of natural forcings coming from Sun and volcanoes; • CO2 concentration and sulfate emissions as anthropogenic forcings; • SOI index (ENSO) as a circulation pattern in ocean and atmosphere which can be important for better catching the inter-annual temperature variability. UniKore, 6-10 September 2008

  21. Global case study 4 case studies: a) natural forcings only; b) anthropogenic forcings only; c) natural + anthropogenic forcings; d) natural + anthropogenic forcings + ENSO. In cases when anthropogenic forcings are considered, a strong improvement in the reconstruction performance is achieved by neural modelling (vs. linear modelling). UniKore, 6-10 September 2008

  22. Global case study UniKore, 6-10 September 2008

  23. Global case study UniKore, 6-10 September 2008

  24. Remarks • Anthropogenic forcings appear as a main probable cause of the changes in T; • the input related to ENSO acts as a 2nd-order corrector to the estimation obtained by anthropogenic and natural forcings (nevertheless, in a nonlinear system we cannot separate the single contributions to the final result); • the amount of variance not explained by our final model is low  UniKore, 6-10 September 2008

  25. Remarks  Is this low amount due to the natural variability of climate system or to some hidden dynamics coming from one or more neglected dynamical causes? Look at the residuals! • Three tests: • Fourier spectrum; • autocorrelation function; • MonteCarlo Singular Spectrum Analysis (MCSSA). UniKore, 6-10 September 2008

  26. The residuals No particular peak and periodicity; the spectrum trend is almost flat… … but, decrease in the amplitude above 3 cycles per 10 years; we cannot exclude red or pink noise. UniKore, 6-10 September 2008

  27. The residuals The auto-correlation function is almost completely confined inside the white noise limits; some oscillations are visible but more uncoupled than in previous results. UniKore, 6-10 September 2008

  28. The residuals The plots show results obtained applying MCSSA: due to some points exceeding the confidence limits provided by an AR(1) process, the presence of components different from red noise is suggested. UniKore, 6-10 September 2008

  29. The residuals No undoubted conclusion can be reached by our analysis of the residuals (besides, it is well known how is difficult to distinguish between noise and chaotic dynamical signals in short time series). Anyway, we can be confident that the major causes of temperature change have been considered and only 2nd-order dynamics has been neglected in our study. UniKore, 6-10 September 2008

  30. Regional case study We want to analyze the fundamental elements that drive the temperature behaviour at a regional scale, with the same strategy adopted in the previous global case study. It is well known that the North Atlantic Oscillation (NAO) correlates quite well with temperatures in a period called “extended winter” (December to March). We want to assess the relative influences of global forcings and NAO on temperature in Central England. UniKore, 6-10 September 2008

  31. Regional case study NAO - NAO + UniKore, 6-10 September 2008

  32. Regional case study (CET) 3 case studies and input data: a) global (natural + anthropogenic) forcings; b) NAO only; c) global forcings + NAO. UniKore, 6-10 September 2008

  33. Regional case study (CET) UniKore, 6-10 September 2008

  34. Regional case study (CET) Global forcings have a very little influence on the behaviour of temperatures in the Central England during extended winter. NAO - driving force: when NAO is considered the values of linear correlation coefficients (estimated T vs. observed T) are about 0.72  0.75 in the two cases. These values are lower than in the analogous situations of the previous global case study (about 0.88). This is probably due to the enhanced inter-annual variability of climate at regional scale. UniKore, 6-10 September 2008

  35. Discussion A non-dynamical approach allows us to obtain simple assessments in a complex system. At a global scale we are able to reconstruct the global temperature behaviour only if we take the anthropogenic forcings into account. Furthermore, we are able to recognize the influence of ENSO in better catching the inter-annual variability of our global time series of temperature. UniKore, 6-10 September 2008

  36. Discussion At a regional scale, the recognition of the major influence of NAO on the CET time series appears very important (a further discussion in the afternoon exercise session). In general, our results can be used in order to identify the fundamental elements for obtaining both: • successful dynamical regional models • and reliable statistical downscaling of GCMs in the past and for future scenarios. UniKore, 6-10 September 2008

  37. Discussion We possess a phenomenological tool for obtaining preliminary assessments on the past in the climate system. In particular it is worthwhile: • to consider an extension to inputs related to other kinds of forcings, circulation patterns and oscillations; • to apply our method to other regions of the world; • to extend our treatment to the reconstruction of precipitation regimes. UniKore, 6-10 September 2008

  38. Impact studies (animals) How rainfall, snow cover and temperature affect them? UniKore, 6-10 September 2008

  39. Impact studies (animals) Bivariate linear and nonlinear analyses (meteo-climatic forcings vs. rodent density) From Pasini et al. (submitted) UniKore, 6-10 September 2008

  40. Impact studies (animals) Neural reconstruction of rodent density in the Apennines starting from data of meteo-climatic forcings From Pasini et al. (submitted) UniKore, 6-10 September 2008

  41. Impact studies (animals) Neural “backcast” of rodent density in the Apennines starting from data of meteo-climatic forcings From Pasini et al. (submitted) UniKore, 6-10 September 2008

  42. Predictability • Paper by Lorenz (1963) and the discovery of “deterministic chaos” in meteo-climatic systems; • predictability problem and change of perspective in the forecasting activity at medium- and long-range; • ensemble integrations for estimating the predictability horizon in different meteorological situations. UniKore, 6-10 September 2008

  43. Preliminary considerations Ensemble Deterministic UniKore, 6-10 September 2008

  44. Preliminary considerations Is the Lorenz-63 model important only for historical reasons? In the present situation, we deal with very complex meteo-climatic models (107 degrees of freedom); inside these models, their physical behaviour can be obscured and also the ensemble strategy cannot be fully followed up (because of the large amount of computer time needed). UniKore, 6-10 September 2008

  45. Preliminary considerations In this framework, the Lorenz-63 model represents a toy model which mimics some features of both the atmosphere and the climate system: for instance, their chaotic behaviour … … and the presence of preferred states or “regimes”. Furthermore, the local predictability on the Lorenz attractor resembles the predictability of single real states. UniKore, 6-10 September 2008

  46. The Lorenz system dx/dt =  (y-x) dy/dt = rx - y - xz dz/dt = xy - bz Our choice of the parameters:  = 10, b = 8/3, r = 28   chaotic solutions. UniKore, 6-10 September 2008

  47. The forced Lorenz system dx/dt =  (y-x) + f0 cos  dy/dt = rx - y - xz + f0 sin  dz/dt = xy - bz Our choice of the parameters: f0 = 2.5  5,  = /2   still chaotic solutions. Toy simulation of an increase of anthropogenic forcings in the climate system UniKore, 6-10 September 2008

  48. The Lorenz system UniKore, 6-10 September 2008

  49. Unforced vs. forced UniKore, 6-10 September 2008

  50. Predictability (dynamics) UniKore, 6-10 September 2008

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