MATHEMATICAL MODELLING CLIMATE AND CLIMATE CHANGE: REGIONAL ASPECTS - PowerPoint PPT Presentation

International Conference and Young Scientists School on Computational Information Technologies for Environmental Sciences: “CITES-2007” Tomsk, Russia, 14-25 July, 2007 MATHEMATICAL MODELLING CLIMATE AND CLIMATE CHANGE: REGIONAL ASPECTS V.N. Lykosov Russian Academy of Sciences Institute for Numerical Mathematics, Moscow E-mail: lykossov@inm.ras.ru lykossov@inm.ras.ru

Climate Change 2007: The Physical Science Basis Working Group I Contribution to the IPCC Fourth Assessment Report Presented by R.K. Pachauri, IPCC Chair and Bubu Jallow, WG 1 Vice Chair Nairobi, 6 February 2007

Global mean temperatures are rising faster with time Warmest 12 years: 1998,2005, 2003, 2002,2004, 2006, 2001,1997, 1995, 1999,1990, 2000 Period Rate 50 0.128 ± 0.026 100 0.074 ± 0.018 Years ° /decade

Land surface temperatures are rising faster than SSTs SST Land

Land precipitation is changing significantly over broad areas I ncreases Decreases Smoot hed annual anomalies f or pr ecipit at ion (%) over land f r om 1900 t o 2005; ot her r egions ar e dominat ed by var iabilit y.

Snow cover and Arctic sea ice are decreasing Spring snow cover shows 5% stepwise drop during 1980s Arctic sea ice area decreased by 2.7% per decade (Summer: -7.4%/decade)

Glaciers and Frozen Ground Area of seasonally frozen Increased Glacier retreat ground in NH has decreased since the early 1990s by 7% from 1901 to 2002

Physics of the Climate System

The climate system is characterized by a finite set of parameters whose values at a fixed time determine its state. Climate is an ensemble of states passed by the climate system during a sufficiently long time interval. In general case, the ensemble is considered to mean a set of states and a certain probability measure given on this set, determining the probability that the climate system may be located on a certain subset of the given set. The central problem of the modern theory of climate is prediction of it changes caused by anthropogenic activities. In view of specific peculiarities of the climate system, this problem cannot be solved with the use of the conventional methods repeatedly tested in natural sciences. The basic (but not single) tool to study the climate system dynamics is mathematical (numerical) modeling.

Objectives of climate modeling To reproduce both “climatology” (seasonal and monthly means) • and statistics of variability: intra-seasonal (monsoon cycle, characteristics of storm-tracks, etc.) and climatic (dominated modes of inter-annual variability such as El-Nino phenomenon or Arctic Oscillation) To estimate climate change due to anthropogenic activity • To reproduce with high degree of details regional climate: • features of hydrological cycle, extreme events, impact of global climate change on regional climate, environment and socio- economic relationships Fundamental question: what climatic parameters and in what • accuracy must by reproduced by a mathematical model of the climate system to make its sensitivity to small perturbations of external forcing close to the sensitivity of the actual climate system?

Meehl, G.A., T.F. Stocker, W.D. Collins, P. Friedlingstein, A.T. Gaye, J.M. Gregory, A. Kitoh, R. Knutti, J.M. Murphy, A. Noda, S.C.B. Raper,I.G. Watterson, A.J. Weaver and Z.-C. Zhao, 2007: Global Climate Projections. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S.,D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

CMIP – Coupled Model Intercomparison Project http://www-pcmdi.llnl.gov/cmip CMIP collects output from global coupled ocean- atmosphere general circulation models (about 30 coupled GCMs). Among other usage, such models are employed both to detect anthropogenic effects in the climate record of the past century and to project future climatic changes due to human production of greenhouse gases and aerosols.

INM climate model and experiments (Dymnikov et al., 2005, Volodin & Diansky, 2006) http://ksv.inm.ras.ru/index AGCM - Finite difference model with spatial resolution 5°x4° and 21 levels in sigma- coordinates from the surface up to 10 hPa. - In radiation absorption of water vapour, clouds, CO 2 , O 3 , CH 4 , N 2 O, O 2 and aerosol are taken into account. Solar spectrum is divided by 18 intervals, while infrared spectrum is divided by 10 intervals. - Deep convection, orographic and non-orographic gravity wave drag are considered in the model. Soil and vegetation processes are taken into account. || Non-flux-adjusted coupling || OGCM -The model is based on the primitive equations of the ocean dynamics in spherical sigma -coordinate system. It uses the splitting-up method in physical processes and spatial coordinates. Model horizontal resolution is 2.5°x2°, it has 33 unequal levels in the vertical with an exponential distribution. -An other version: 50m upper ocean layer with ice

Near-the-surface winter air temperature: simulation (top) and observations (bottom)

Annually mean precipitation (mm/day) as follows from observations (top), CMIP models averaged results (middle) and INM simulation (bottom).

Comparison of observed changes in global-average surface air temperature over the 20-th century with that from an ensemble of climate model simulations (http://www.grida.no/climate/ipcc_tar/vol4/english/022.htm)

Regional scale modeling and assessment Atmospheric modeling, e.g. using global climate model with improved • spatial resolution in the region under consideration and non-hydrostatic mesoscale models: parameterization of mesoscale variability Catchment modeling, e.g. constructing models of river dynamics: • parameterization of hydrological cycle Vegetation modeling, e.g. models of vegetation dynamics: parameterization • of biogeochemical and hydrological cycles Soil (including permafrost) modeling, e.g. models of snow and frozen • ground mechanics: parameterization of hydrological and biogeochemical cycles Coupled regional models • Air and water quality modeling • Statistical and dynamic downscaling (e.g. regional projections of global • climate change patterns)

Kattsov, V.M., J.E. Walsh, W.L. Chapman, V.A. Govorkova, T.V. Pavlova, and X. Zhang, 2007: Simulation and projection of arctic freshwater budget components by the IPCC AR4 global climate models. J.Hydrometeor . Ob: P and P-E annual means (1960-1989) 2 1,8 1,6 1,4 mm/day 1,2 P 1 P-E 0,8 0,6 0,4 0,2 0 ECHAM5/MPI-OM CNRM-CM3 GISS-AOM INM-CM3.0 MRI-CGCM2.3.2 UKMO-HadCM3 UKMO-HadGEM1 Serreze BCCR-BCM2.0 CCSM3 CGCM3.1(T47) CGCM3.1(T63) CSIRO-Mk3.0 GFDL-CM2.0 GFDL-CM2.1 IPSL-CM4 ECHO-G MIROC3.2(hires) PCM GISS-EH GISS-ER MIROC3.2(medres) mean-21 mean-19

T.J. Philips et al. (2002). Large-Scale Validation of AMIP II Land-Surface Simulations

Растительность , гидрология поверхности и речной сток Пример ячейки сетки , занятой хвойным лесом , широколиственным лесом , травой и голой поверхностью Гидрология поверхности – уравнение водного баланса поверхности и почвы : ∑ + + θ = − − − −      W W z ( q E E q q ) t can sn i i prc v g surunoff drng i Гидрология речного стока – распределенные модели ландшафтного типа (TOPMODEL, TOPOG, MPATH, MODFLOW )

The presence of different types 150 145 of surfaces (forests, lakes, hills, etc.) 135 100 125 115 105 50 95 85 75 0 65 55 Thermal contrasts: 45 35 -50 “forest – bare soil”, “land – sea” 25 15 urban “heat islands”, etc. 5 -100 -5 -150 -150 -100 -50 0 50 100 150 Local atmospheric circulations: breezes, urban breezes, slope winds

Uncertainties The precise magnitude of “climatic” change in regime of land surface is unknown (and unknowable?) • Climate uncertainty (climate scenarios, climatic sensitivity, regional climatic response, etc.) • Land surface model uncertainty • Process uncertainty