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International Conference on Environmental Observations, Modeling and Information Systems ENVIROMIS July 2004, Tomsk, Russia International Conference on Environmental Observations, Modeling and Information Systems ENVIROMIS July 2004, Tomsk, Russia Modeling of present and assessing of future climate variations in Siberia Modeling of present and assessing of future climate variations in Siberia V.N. Lykosov, Institute for Numerical Mathematics, Russian Academy of Sciences, Moscow

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The central direction of the climate sensitivity studies: mathematical modeling Problems: 1. The identification of models by sensitivity. 2. Is it possible to determine the sensitivity of the climate system to small external forcing using single trajectory?

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INM coupled atmosphere - ocean general circulation model

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Response to the increasing of CO 2 CMIP models (averaged) INM model

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Analysis of some extreme weather situations in Russia for the present-day climate and under CO 2 doubling E.M. Volodin Institute for Numerical Mathematics, Russian Academy of sciences, Moscow

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DJF T(2m) – NCEP observed DJF T(2m) – INM simulated JJA T(2m) – NCEP observed JJA T(2m) – INM simulated Present-day climate

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Max positive DJF T(2m) anomaly observed Max positive DJF T(2m) anomaly simulated Max negative DJF T(2m) anomaly observed Max negative DJF T(2m) anomaly simulated Present-day climate

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T(2m) climate change under CO 2 doubling DJF averaged DJF most warm DJF most cold

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T(2m) climate change under CO 2 doubling JJA averaged JJA most warm JJA most cold

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Present-day climate DJF precipitation observed DJF precipitation simulated JJA precipitation observed JJA precipitation simulated

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Present-day climate Normalized RMSD of precipitation DJF - observed Normalized RMSD of precipitation DJF - simulated Normalized RMSD of precipitation JJA - observed Normalized RMSD of precipitation JJA - simulated

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Precipitation climate change (normalized) under CO 2 doubling DJF JJA

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Preliminary conclusions The INM climate model quite well reproduces the present- day climatic distribution of themperature and precipitation as well as their seasonal variability and climatic extremes When CO 2 is increasing, winter extremes are, in general, less intensive However, summer extremes (caused by positive anomalies of the air temperature and decreased amount of precipitation) are more intensive in the southern part of Russia and less intensive in the northern Russia. The increase of northern precipitation and the decrease of southern precipitation is mainly caused by increase of the Arctic Oscillation index.

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Sensitivity of the climate system to small perturbations of external forcing (invited lecture at the World Climate Conference, Moscow, 29 September – 3 October, 2003) Sensitivity of the climate system to small perturbations of external forcing (invited lecture at the World Climate Conference, Moscow, 29 September – 3 October, 2003) V.P. Dymnikov, E.M. Volodin, V.Ya. Galin, A.S. Gritsoun, A.V. Glazunov, N.A. Diansky, V.N. Lykosov Institute of Numerical Mathematics RAS, Moscow

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The Arctic Oscillation ( constructed using NCEP/NCAR data and output of AGCM of INM RAS) AO (1EOF of surface pressure) calculated using DJF NCEP/NCAR data AO (1EOF of surface pressure) calculated using output of AGCM of INM RAS

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1. Formulation of model equations 2. Proof of the existence and uniqueness theorems 3. Attractor existence theorem, dimension estimate 4. Stability of the attractor (as set) and measure on it 5. Finite-dimensional approximations and correspondent convergence theorems Mathematical theory of climate

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(sensitivity) 6. Construction of the response operator for measure and its moments (“optimal perturbation”, inverse problems,….) 7. Methods of approximation for 8. Numerical experiments

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Response operator for 1 st moment (linear theory) Linear model for the low-frequency variability of the original system: ( A is stable, is white noise in time) Perturbed system

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Stationary response For covariance matrix we have and response operator M could be obtained as

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Numerical experiments Construction of the approximate response operator (A.S.Gritsoun,G.Branstator, V.P.Dymnikov, R.J.Numer.Analysis&M.Model, (2002), v.17,p. 399)

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Reconstruction of the CCM0 response to the sinusoidal heating anomaly

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Reconstruction of the low-level heating anomalies using the inverse response operator

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Procedure for the construction of the approximate response operator is analogues to (A.S.Gritsoun, G.Branstator, V.P.Dymnikov, R.J.Numer. Analysis&M.Model, (2002), v.17,p. 399) Optimal perturbation for AO (1EOF of PS) calculated using NCEP/NCAR reanalysis data (zonal average) Optimal perturbation for AO (1EOF of PS) calculated using output of AGCM of INM RAS (zonal average)

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Some questions on assessment of climate change impact on regional environment RAS/NASA Northern Eurasia (NEA) Earth Science Partnership Initiative (NEESPI): - are mathematical models capable of simulating observed climate changes in NEA and their feedback effects on global climate? - are mathematical models capable of simulating observed climate changes in NEA and their feedback effects on global climate? - what are the direct and feedback effects on environmental changes in NEA on the Earth climate system? - what are the direct and feedback effects on environmental changes in NEA on the Earth climate system? Permafrost changes in Siberia may have a substantial effect on the chemical deposition of the atmosphere such as carbon dioxide and methane Are stand-alone permafrost models forced by climate change scenarios produced by global climate models (which, in general, do not describe explicitly processes in the frozen ground) capable of correct assessing environmental changes in Siberia?

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Snow “Upper” ice Water Ground “Lower” ice U H,LE EsEs EaEa S Thermodynamics of shallow reservoir 1) One-dimensional approximation. 2) On the upper boundary: fluxes of momentum, sensible and latent heat, solar and long-wave radiation are calculated On the lower boundary: fluxes are prescribed 3) Water and ice: heat transport Snow and ground: heat- and moisture transport U – wind velocity H – sensible heat flux LE – latent heat flux S – shirt-wave radiation E a – incoming long-wave radiation E s – outgoing long-wave radiation

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Mathematical formulation - for water and ice:, - heat conductivity - for snow : - temperature - liquid water - for ground : - temperature - liquid water - ice

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Atmospheric forcing - air-water interface: - empirical formula for radiative fluxes - sensible and latent heat fluxes from the Monin-Obukhov similarity theory Routine observational data set on meteorological stations is used

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Simulated snow cover depth for station Valdai (February – April, 1977) Contours: snow density

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Snow cover height in Yakutsk: 1970/71 (а), 1971/72 (б), 1972/73 (в).

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Simulated (solid) and observed (Franklin Bluffs, Alaska – dotted) ground temperature at depth 40 cm and 80 cm for 1988 (top) and 1990 (bottom) time period

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Instead conclusion: THANK YOU for YOUR ATTENTION

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