OUTLINE

1. Моделирование процессов поверхности суши с детальным описанием процессов в биосфере и гидрологии в рамках модели климатической системы В.Крупчатников e-mail: vkrupchatnikov@yandex.ruWeb: http://sibnigmi.ru Объединенный научный семинар "Глобальные и региональные аспекты в изучении климатической системы Земли" (Рук. чл.-корр. РАН, проф. В.В. Зуев)19.12.2013, Институт мониторинга климатических и экологических систем СО РАН

2. OUTLINE Introduction - Background - Study Aim - Modelling surface processes and Vegetation - Experimental Design - Future changes in Siberian region - Results • Discussions • The new land component of ECHAM • Summary 15.11.2014

3. Modelling of surface processes and vegetation SiB , LSM, ISBA, CLM, CLM2, CLM3.5, NOAH,..., LSM/INM-RAS Exchanges between atmosphere and surface of : Heat Water Radiation Momentum Biophysical consistency Biogeochemistry, particularly as it affects atmospheric CO2 Treatment of human land use (e.g., agriculture) and land-use change Vegetation distribution changes consistently with climate Scalability (grid independence?) 15.11.2014

4. Prognostic Variables for Canopy Layer 15.11.2014

5. Dynamics Global Vegetation Models(DGVM) The BIOME4 Global Vegetation Model (Haxeltine and Prentice ,1996). - Lund – Potsdam –Jena (LPJ) – intermediate complexity model with broad rang of applications to global climate dynamics (S. Sitch et al, 2003) - The Community Land Model + Dynamic Global Vegetation Model(S.Levis, G. Bonan, M. Vertenstein, and K. Oleson, 2004) - HyLand (HYL) model (Levy et al., 2004); - ORCHIDEE (ORC) model (Krinner et al., 2005); - Sheffield -DGVM (SHE) (Woodward et al., 1995; Woodward and Lomas, 2004); - TRIFFID (TRI) (Cox 2001). - JSBACH (Raddatz et al., 2007; Brovkin et al., 2009; Reick et al., 2013) 15.11.2014

6. The Planet Simulator run Variations in vegetation-cover parameters for the two scenarios vs. integration decade number (0–2000, 10–2100) for Siberia. 15.11.2014

7. Distribution of the portions of (a) and (b) forest vegetation and (c) and (d) herb and bushes over Siberia; (a) and (c) correspond to the beginning (the first decade) of the 21st century and (b) and (d) correspond to the end (the eighth decade) of the 21st century. Scenario A2. 15.11.2014

8. Summary, Research Questions and… • The dynamics of vegetation in Siberia is in agreementwith the dynamics of surface hydrology and withsurface heat sources. • At the end of the integration timefor scenario A2, significant variations in the structureof vegetation occur in Siberia: • the portion of the landsurface occupied by vegetation decreases from ~48%to 35%, • the forest portion decreases from 20 to 10%,and the herb portion increases up to 26%. • In the controlexperiment, at the end of the integration time, theportions of forest and herb amount to 22 and 24%,respectively. • In this case, albedo increased from 0.3 to0.4, and evapotranspiration decreased by more thantwo times due to the decrease of the forest portion. • .

9. The southward shift of the forest boundary and therapid increase in the depth of snow cover in fall duringthe last decade of the 21st century resulted in anincrease of surface albedo in Siberia (especially inwinter) and in surface cooling in this region Comparing thedata obtained from a simulation of vegetation dynamicswith a more complex model (LPJ), we obtainedsimilar results for the evolution of the basic types ofvegetation by scenario A2 The presented model of methane emission coupled with LSM model yielded global estimates of CH4 fluxes from wetland soils, seasonal changes in fluxes CH4 in main areas of wetland ecosystems in northern latitudes.

10. What is the uncertainty in the future atmospheric CO2 concentration associated with choice of DGVM and SRES emission scenario? • How uncertain is the Climate-Carbon feedback? • Do DGVMs agree on their Global and Regional responses to changes in climate and atmospheric composition? • Which key ecological processes are poorly represented in the models?

11. By 2100, atmospheric CO2 concentrations differ by up to 285 ppm among DGVMs, equivalent to ~64% of the uncertainty associated with choice of SRES emission scenario (448 ppm). • Improving our understanding of and ability to model terrestrial biosphere processes (e.g. plant response to drought/ heat stress) is paramount to enhance our ability to predict the future development of the Earth system ! JSBACH ? Yes!!

12. JSBACH Christian H. Reick, Veronika Gayler, Thomas Raddatz, Reiner Schnur and Stiig Wilkenskjeld Max Planck Institute for Meteorology D-20146 Hamburg, Germany July 22, 2013 I have been instructed as visiting scientist to establish contacts with the working group for the dissemination and implementation of the surface model JSBACH with a detailed description of hydrology and processes in the biosphere, and in the soil, to obtain official permission for later inclusion that model as components of the climate system model MGO (St. Petersburg) .

35. Modeling of ecosystem dynamics and carbon cycling with coupled climate model - LPJ-DGV model.

36. Model Description • CGCM/INM RAS 5x4 horizontal resolution and 21-level vertical resolution • LSM/ICMMG SB RAS - biophysical and biochemical surface model • Dynamic global vegetation model

37. Coupled atmospheric - ocean model (INM/RAS): • Terrain-following vertical coordinate (21 σ-levels) • Semi-implicit formulation of integration in time • Energy conservation finite-difference schemes (5x 4) (Arakawa-Lamb,1981) • Convection (deep, middle, shallow) • Radiation (H2O, CO2, O3, CH4, N2O, O2; 18 spectral bands for SR and 10 spectral bands for LR) • PBL (5 σ-levels) • Gravity wave drag over irregular terrain

38. Land surface model(ICM&MG/SB RAS): • Vegetation composition, structure • Radiative fluxes • Momentum and energy fluxes • Vegetation and ground temperature • Soil and lake temperature • Surface hydrology (snow, runoff, soil water, canopy water etc.) • CO2 emissions from terrestrial vegetation • CH4 emissions from natural wetlands

39. Leaf area index

40. Grid structure in land surface model

41. Global Net CO2 fluxes(mmol CO2/m^2 c), (coupled simulation)

42. CO2 emissions from terrestrial vegetation (photosynthesis, respiration) • CH4 emissions from natural wetlands 15.11.2014 42

43. CH4 fluxes from wetlands: observations [Muller J.F., 1992]; CH4 fluxes (mmol CH4(m^2 c) coupled simulation CH4 emissions from natural wetlands (coupled framework) West Siberia 15.11.2014 43

44. Seasonal variation of CH4 fluxes for Western Siberia and Michigan 15.11.2014 44

45. Dynamic global vegetation model • Plant functional types (10 PFT) • Annual vegetation and carbon dynamics • Penology • Production - water availability - photosynthesis - respiration - reproduction - allocation - mortality • Input data (monthly mean temperature, precipitation and cloud cover)

46. LPJ dynamic global vegetation model • is able to simulate spatial distributions of soil, litter and vegetation carbon pools and NPP, runoff within their accepted ranges and agree with observed patterns • is able to reproduce global vegetation distribution in general agreement with satellite derived maps of phenology and leaf type • is able to reproduce carbon and water exchange with atmosphere on seasonal time scale; • is able to evaluate seasonal cycle of CO2

47. Plant functional type

48. Simulated dominant PFT

49. Model were driven by CM3.0(INM) (scenario A2)

50. A2 Storyline and Scenario FamilyThe A2 scenario family represents a differentiated world. Compared to the A1 storyline it is characterized by lower trade flows, relatively slow capital stock turnover, and slower technological change. The A2 world "consolidates" into a series of economic regions. Self-reliance in terms of resources and less emphasis on economic, social, and cultural interactions between regions are characteristic for this future. Economic growth is uneven and the income gap between now-industrialized and developing parts of the world does not narrow, unlike in the A1 and B1 scenario families.The A2 world has less international cooperation than the A1 or B1 worlds. People, ideas, and capital are less mobile so that technology diffuses more slowly than in the other scenariofamilies.High-income but resource-poor regions shift toward advanced post-fossil technologies (renewables or nuclear), while low-income resource-rich regions generally rely on older fossil technologies.. As in other SRES storylines, the intention in this storyline is not to imply that the underlying dynamics of A2 are either good or bad.