Transcript Влияние глобальных климатических измен

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
Моделирование процессов поверхности суши с детальным
описанием процессов в биосфере и гидрологии в рамках
модели климатической системы
В.Крупчатников
e-mail: [email protected]
Web: http://sibnigmi.ru
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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
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Modelling of surface processes and vegetation
SiB , LSM, ISBA, CLM, CLM2, CLM3.5, NOAH,..., LSM/INMRAS
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?)
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Prognostic Variables for Canopy Layer
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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)
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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)
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The Planet Simulator run
Variations in vegetation-cover parameters for the two scenarios vs. integration decade
number (0–2000, 10–2100) for Siberia.
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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.
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Summary, Research Questions and…
The dynamics of vegetation in Siberia is in agreement with the dynamics of surface
hydrology and with surface heat sources.
At the end of the integration time for scenario A2, significant variations in the structure
of vegetation occur in Siberia:
• the portion of the land surface 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 control experiment, at the end of the integration time, the portions of forest and
herb amount to 22 and 24%, respectively.
In this case, albedo increased from 0.3 to 0.4, and evapotranspiration decreased by more
than two times due to the decrease of the forest portion.
.
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The southward shift of the forest boundary and the rapid increase in the depth of
snow cover in fall during the last decade of the 21st century resulted in an
increase of surface albedo in Siberia (especially in winter) and in surface
cooling in this region
Comparing the data obtained from a simulation of vegetation dynamics with a
more complex model (LPJ), we obtained similar results for the evolution of the
basic types of vegetation 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.
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• 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?
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• 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!!
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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) .
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Modeling of ecosystem dynamics and carbon cycling with coupled climate
model - LPJ-DGV model.
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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
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Coupled atmospheric - ocean model (INM/RAS):
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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
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Land surface model(ICM&MG/SB RAS):
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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
Leaf area index
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Grid structure in land surface model
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Global Net CO2 fluxes(mmol CO2/m^2 c), (coupled simulation)
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CO2 emissions from terrestrial vegetation (photosynthesis, respiration)
1
Ae
 m s Pa  b
rs
cs ei
A  min(wc , wj , we )
Rm  [ LRl f ( N )  l  B sR s  Br Rr ]e
Tv 25
am
10
Rg  0.25( A sun Lsun  Asha Lsha )
NPP   ( AsunLsun  AshaLsha  Rm  Rg )  t

CH4 emissions from natural wetlands
T T mean
10
10
R prod  R0  f ( NPP )  f (T )  Q
Roxid  
TTmean
Vmax  CCH 4
Q10 10 Q  f (C )  (C
e
CH 4
CH 4  Cthresh )
K m  CCH 4
F  Fdiff  Fe  Fplant ,
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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
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Seasonal variation of CH4 fluxes for Western Siberia and Michigan
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Dynamic global vegetation model
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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)
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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
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Plant functional type
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Simulated dominant PFT
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Model were driven by CM3.0(INM)
(scenario A2)
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A2 Storyline and Scenario Family
The 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 scenario families.
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.
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INM-simulation cloudiness, 2010,
dcld = cld(2040) – cld(2010) and dcld = cld(2080) – cld(2040)
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INM-simulation of temperature 2010,
dTemp = T(2040) - T(2010) and dTemp = T(2080) – T(2040)
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INM-simulation of precipitation 2010,
dPrec = Prec(2040) - Prec(2010) and dPrec = Prec(2080) – Prec(2040)
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Increasing of CO2 concentration (ppm) according to scenario A2
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Table of PFT
• TrBE -
tropical broad-leaved evergreen
• TrBR - tropical broad-leaved raingreen
• TeNE – temperate needle – leaved evergreen
• TeBE - temperate broad – leaved evergreen
• TeBS - temperate broad – leaved summergreen
• BoNE – boreal needle – leaved evergreen
• BoNS - boreal needle – leaved summergreen
• BoBS - boreal broad – leaved summergreen
• TeH - temperate herbaceous
• TrH - tropical herbaceous
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LPJ/INM(RAS)-simulation (scenario A2)
Temperate(TeBS) (black), Boreal(BoNE) (green) forest dynamics for grid
cell(60E,60N)
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LPJ/INM(RAS)-simulation (scenario A2)
Boreal(BoBS) forest (green) and Temperate(TeH) herbaceous (yellow) dynamics for
grid cell(60E,60N)
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LPJ/INM(RAS)-simulation (scenario A2) of carbon pools, (60E,60N)
vegc – green, soilc – black, litterc - yellow
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LPJ/INM(RAS)-simulation (scenario A2) of carbon pools(left)),(90E,60N)
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LPJ/INM(RAS)-simulation (scenario A2)
Boreal(BoNE, BoBS) forest dynamics for grid cell(90E,60N)
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LPJ/INM(RAS)-simulation (scenario A2)
Temperate(TeH) herbaceous dynamics for grid cell(90E,60N)
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Conclusion
LPJ model includes
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explicit representation of vegetation structure, dynamics and competition
between PFT over greed cell, soil biogeochemistry;
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LPJ run of modern climate are in agreement with other models;
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The study showed that simulated PFT after 2050 year (at ~ 500ppm)
begin to lose stability and reaches new balance.
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“Those who have knowledge, do not predict,
Those who predict, do not have knowledge.”
(Lao Tzu)
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