Download Impacts of global warming on hydrological Introduction of multi-model ensembles and

Impacts of global warming on hydrological
cycles in the Asian monsoon region and
Introduction of multi-model ensembles and
downscaling for regional risk assessment
Koji Dairaku
Cooperating with
Seita Emori, Toru Nozawa (NIES),
Satoshi Iizuka (NIED), Wataru Sasaki (JAMSTEC),
Roger A. Pielke Sr., Adriana Beltran（Univ. of Colorado）
Storm, Flood, and Landslide Research Department
National Research Institute for Earth Science and Disaster Prevention
ATOC seminar in CU
13 Feb 2009, Boulder, CO
CONTENTS
Ⅰ. Introduction
Ⅱ. Hydrological Change under the Global
Warming in Asia
－Regional Climate Simulations in Asia nested in
CCSR/NIES AGCM by a RCM having a “physics
compatibility” with the host GCM.
Ⅲ. Multi-model ensembles and downscaling
project in Japan (S5-3)
－Verification of simulation in river basins in Japan
spectral nudging scheme, domain size
Introduction
‹ Climate change and the threats of extremes to
human life and natural ecosystems constitute a
fundamental concern.
‹ Reliable climate change scenarios and improved
impact assessment are increasingly required by the
policy community.
‹ Insurance companies are taking into account of
climate change (the rate of long-term fire insurance
will be risen)
‹ Regional climate scenarios sufficient for the
application to impact assessment and adaptation
studies
Changes in the 200-year quantile
precipitation in Tokyo using 12 GCMs
1.09 - 1.20
1.03 - 1.07
A1B
B1
(Higashi, 2007; Dairaku et al., 2008)
Impacts of Global Warming on Tama river basin
Planned high
water discharge
457mm/2-day(2000)
523mm/2-day(2050)
519mm/2-day(2100)
491mm/2-day(2200)
548mm/2-day(2300)
Current maintenance plan
200-year quantiles
Ratio: 1.09~1.20
High water
discharge
Ratio: 1.10~1.26
457mm/2-day(2000)
523mm/2-day(2050)
519mm/2-day(2100)
491mm/2-day(2200)
548mm/2-day(2300)
2000
Flood volume
Ratio: 1.46~2.31
2300
Changes in hydrograph, discharge, and flood
volume in the A1B scenario
Maximum flood depth
(Higashi, 2007; Dairaku et al., 2008)
Downscaling
‹ A lot of regional research activities (PRUDENCE,
ENSEMBLES, PIRCS, NARCCAP, MRED, RMIP, S5-3,
CCSN, etc.)
‹ Dynamical downscaling
+ Based on physics, Output variables are physically consistent,
physical interpretation.
- relatively large bias, computationally expensive.
‹ Statistical downscaling
+ relatively accurate, simple, computationally economical.
- Not based on physics, assumption of stable climate, less
consistency between different variables.
Dynamical and statistical downscaling methods are
complementary.
Ⅱ. Regional Climate Model
zLateral boundaries strongly control RCM
-accurate boundary conditions
-synoptic circulations of RCM should not be greatly different
from those of the GCM (or Reanalysis).
zSurface boundary forcing
-dominant factor for generating small-scale atmospheric
variability (e.g., topography)
-expected advantage of higher accuracy of higher spatial
resolution (“add value” in small-scale features not well
represented by the GCM (or Reanalysis)).
zSynoptically induced mesoscale systems
-not necessarily improved by higher spatial resolution.
-accurate boundary conditions
-adequate representation of physical processes
Ⅱ. Regional Climate Model
‹Model
zBased on non-hydrostatic Regional Atmospheric
Modeling System (RAMS) ver.4.3 developed primarily
by Colorado State University and ATMET (Pielke et al., 1992).
zVectorised for NEC SX-6.
z“Physics compatibility” with CCSR/NIES AGCM
(Emori et al., 2001).
- Arakawa-Schubert cumulus parameterization
(Arakawa and Schubert, 1974)
- Large scale condensation scheme.
- Two-stream k-distribution radiation scheme (Nakajima et al.,
2000)
- Mellor-Yamada level 2.0 turbulence scheme
(Mellor and Yamada, 1974, 1982)
- Soil and vegetation model (MATSIRO, Takata et al., 2003)
Hydrological Change under the Global Warming in Asia
“Water Resources and Variability in Asia in the 21st Century” funded by MEXT (2001-2004)
zModel: Three-dimensional nonhydrostatic compressible dynamicequations model (NIES-RAMS)
zHorizontal grid: 60km grid space with
161×119 grids (9,700km×7,100km)
zVertical grid: z = 22 (～28km)
Grid space stretched 250 ～ 2,000m
zInitial and Boundary condition:
CCSR/NIES AGCM (T42)
zLateral Boundary condition; Nudging
10 grids, 6 hourly
zIntegrated periods; 11 years
1981-1990, 2041-2050 (1 year spin-up)
zTopography (USGS 10min), Land Use
（GLCC）, LAI (ISLSCPⅠ), SST (GISST,
GISST + ”Warming Pattern” by
CCSR/NIES CGCM(T21))
zSoil Moisture: simulated by MATSIRO
Dynamical Downscaling
GCM
Downscaling
RCM
(RAMS)
+
LSM
(MATSIRO)
Dairaku and Emori (2007), Dairaku et al. (2008)
Obs. vs. CCSR/NIES GCM vs. RAMS
CMAP
TRMM
GCM
RCM
Obs. vs. CCSR/NIES GCM vs. RAMS
CMAP
GPCP
GCM
RCM
Projected future climate change
T2m
q2m
uv200
uv850
Projected future climate change
Vapor Flux & Convergence
P
Evaporation
Runoff
‹Seasonal Changes of the Terrestrial Hydrological
Cycles under Climate Change
MON
JPN
TIB
IND
CHI
SEA
Seasonal Changes of Hydrological Cycles
SEA (Present)
Precipitation
1
mm/h
0.8
Total Precipitation
Convective Precipitation
Large-scale Precipitation
Snowfall
0.6
0.4
0.2
0
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
W/m^2
100
80
60
1.6
1.2
0.8
40
0.4
20
0
0
-20
1
-0.4
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
Precipitation
Evaporation
Snowfall
Runoff
Snowmelt
Soil Moisture
0.8
mm/h
120
2
Net Radiation
Sensible Heat
Latent Heat
Bowen Ratio
Bowen Ratio (H/lE)
140
Water Flux
0.6
0.5
0.6
0.4
0.4
0.3
0.2
0
0.2
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
m^3/m^3
Heat Flux
Seasonal Changes of Hydrological Cycles
SEA (Change in Future )
Precipitation
Temperature & Humidity at 2m
310
2m Temperature (present)
2m Specific Humidity (present)
2m Temperature (future)
2m Specific Humidity (future)
25
0.2
20
290
10
mm/h
K
15
g/kg
300
280
Total Precipitation
Convective Precipitation
Large-scale Precipitation
Snowfall
0.1
0
270
5
260
0
-0.1
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
Heat Flux
50
40
Net Radiation
Net Downward Longwave
Latent Heat
Water Flux
Net Downward Shortwave
Sensible Heat
0.2
Snowfall
Evaporation
Soil Moisture
0.05
0.1
20
mm/h
W/m^2
30
Precipitation
Snowmelt
Runoff
10
0
0
m^3/m^3
320
0
-0.1
-10
-20
-0.2
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
-0.05
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
Ⅱ. Summary
‹An attempt of dynamical downscaling by the physics
compatible RCM with the host GCM was conducted to
investigate regional responses of hydrologic changes
under the global warming.
‹RCM could capture general features of AGCM and
some regional spatial structures such as orographic
precipitation were added.
‹Physics compatibility between RCM and GCM
enables the interpretation relatively simple.
physical parameterization such as a cumulus convection
scheme can have a significant sensitivity to horizontal resolution.
Physics schemes tuned for the GCM may not be adequate for the
RCM. Presumed statistical equilibrium of the cumulus
parameterization in a coarse grid may not be properly established
with finer grid spacing.
Ⅱ. Summary (cont.)
‹Increased water vapor enhances precipitation
especially over the mountainous regions. Increases of
annual mean precipitation and surface runoff were
projected in a lot of regions.
‹Seasonal hydrological cycles change
Convection, cloud cover, evaporation, snow melting, soil
moisture, etc.
‹Both positive and negative changes of seasonal
surface runoff were projected in some regions that
may increase flood risk and cause mismatch between
water demand and water availability in agricultural
season.
‹Toward RMIP phase Ⅲ(multi-model ensemble in Asia)
Ⅲ. Multi-model ensembles and downscaling
for regional risk assessment of climate change
„
„
„
„
„
„
„
MRI (Meteorological Research
Institute)
NIED (National Research
Institute for Earth Science and
Disaster Prevention)
University of Tsukuba,
(Meteorology and Climatology)
DPRI (Disaster Prevention
Research Institute, Kyoto
University)
NIAES (National Institute for
Agro-Environmental Sciences)
IIS (Institute of Industrial
Science, University of Tokyo)
Hokkaido University
S-5 Project
‹ S-5 project (MOE: FY2007-2011)
(4.4 million$/year)
1. Present future climate figured by integrating the climate
change scenario studies
2. Evaluation of the credibility of climate change scenarios
by the analyses of model results
3. “Downscaling” study in Japan/Asian region
4. Develop the scenarios of hi-resolution emission and
LULC
S-4 （FY2004-2009）
Kakushin project
Integrated impact studies
(Agriculture, coastal disasters,
fishery, epidemics etc.)
(FY2007-2011）
Modeling and Natural
disasters
S-5(3) Downscaling
„
① FY2007-2009
…
…
„
Develop the down-scaling scheme applicable to the Japan
area.
Down-scale the AOGCM(MIROC3.2)’s results.
②FY2010-2011
…
…
Expand this scheme to the Asian area.
Down-scale the next generation AOGCM’s results and
make a contribution to IPCC AR5.
Global climate change
scenario experiments by
hi-resolution GCMs
Multi-model ensemble
(MRI, NIED, Univ. of Tsukuba)
Statistical Downscaling
（Kyoto Univ., NIAES, Tsukuba
Univ., Univ. of Tokyo)
20 km
Downscaling
A few km
S-5(3) Projects (downscaling)
Future Projection by
climate models
Multi Ensemble
MRI 20km AGCM
MRI
(Kakushin project)
Downscaling studies
Univ. of
Tsukuba
Validation
Data
Hokkaido Univ. two-
Multi-Model
Ensemble
way nesting GCM-NHM
Urban Climate Model
MRI
Univ. of
Tsukuba
Impact Study
Univ. of
Kyoto
S-5(1)
20km RCM
NIED
NIAES
MRI
MRI
NIED
JRA25
MIROC
NIED
Univ. of
Kyoto
(Agriculture)
Univ. of Kyoto
Data
Manage
Univ. of
Tsukuba
Validation
(Water Resource)
Univ. of Tokyo
(Water cycle)
MRI-CGCM
Validation & Bias Correction
Preparing downscaling data
including uncertainty
S-4
20km grid
scenario
Impact Studies ( another project)
Agriculture
Down-scaling (20km Î 1km）
Statistical down-scaling
Dynamical down-scaling
Hydrology
Regional climate change scenarios for
long-term water-related risk assessment
‹Development of atmosphere-biosphere-river coupling
regional climate model for long-term risk assessment
・Input into a flood inundation model and a wave model
・Influences and interactions of LULC and Biogeochemical cycles (by coupling
the dynamic vegetation model) (Colorado Univ.: Japan-U.S. Liaison)
・Impact of extreme winds/droughts/floods on human society (Non-Life
Insurance Rating Organization of Japan)
・Snow and ice disaster risk (Snow and Ice Research Center in NIED)
Global climate change scenario
experiments by hi-resolution GCMs
Downscaling
River model (10km, 93 river basins）
Regional Climate Model
NIED RAMS （GEMRAMS + river)
- Basic Model: Regional Atmospheric Modeling System（RAMS）Ver.4.3 (Pielke et al. , 1992)
Kain-Fritsch convection scheme， Bulk-microphysics/Dumpbucket scheme，Mellor-Yamada Level
2.5, Spectral nudging scheme
- Terrestrial biogeochemical model, GEMTM (Chen and Coughenour,1994) is coupled with land
surface model, Leaf2 （Estman et al., 2001)
Sub-grid land covers, Multi-canopy Radiative Transfer Model, Sunlit/shaded leaf Photosynthesis Model (C3 & C4
path), dynamic change LAI, Temporary Surface Water, Soil Respiration, Root Effluence, Uptake and Branching Model
- River routing model
Atmospheric Model
Ecological Model
(Matsui,2003)
River Model
(Dairaku et al., 2006)
Verification of simulation in river basins in Japan
Brief description of numerical experiments
„ Model: NIED-RAMS
…
„
Coordinates
…
…
…
…
„
…
…
…
…
Cumulus: Kain-Fritsch scheme
Bulk-microphysics (Ver.0.5) / Dumpbucket (ver.0)
Radiation: Chen & Cotton
PBL: Mellor-Yamada level 2.5
Land surface: Leaf2 + GEMTM + river routing scheme
Boundary Condition (Lateral & SST)
…
„
σz terrain-following coordinate
rotated polar-stereographic projection
128×144 grid points (20km grid spacing)
Vertical grid z = 27 grid points (～21km) + 10 soil layers
Physics:
…
„
Non-hydrostatic compressible dynamic-equations model
JRA25 / JCDAS: 6 hourly (Nudging 5 grids)
Integration
…
…
3 months spin-up in Jan2002
3 years from 2002 to 2004
Spectral Nudging Scheme
Model Variables
dQ
=
dt
∑∑
n ≤ N m ≤M
= L(Q ) −
dQmn ik m x ik n y
⋅e ⋅e
dt
∑
ik m x
ik n y
(
)
K
⋅
Q
−
Q
⋅
e
⋅
e
∑ mn mn omn
n ≤ N m ≤M
Model Operator
Observations
Spectral Nudging
Coefficient (depend on
height)
＋
⇒
(Kida et al., 1991)
2m T
NHM
RAMS(A5)‫‏‬
RAMS(V0)‫‏‬
RAMS(NS4-2)‫‏‬
2m T
NHM
RAMS(A5)‫‏‬
RAMS(V0)‫‏‬
RAMS(NS4-2)‫‏‬
2m T
NHM
RAMS(A5)‫‏‬
RAMS(V0)‫‏‬
RAMS(NS4-2)‫‏‬
P
NHM
RAMS(A5)‫‏‬
RAMS(V0)‫‏‬
RAMS(NS4-2)‫‏‬
P
NHM
RAMS(A5)‫‏‬
RAMS(V0)‫‏‬
RAMS(NS4-2)‫‏‬
P
NHM
RAMS(A5)‫‏‬
RAMS(V0)‫‏‬
RAMS(NS4-2)‫‏‬
Bias comparison of three RCMs
2mT
P
Takayabu (MRI）
29
29 years
years simulation
simulation
29
29 years
years simulation
simulation
Verification of simulation in river basins in Japan
for 29 years
29
29 years
years simulation
simulation
29
29 years
years simulation
simulation
Sensitivities of domain size and
nudging scheme on model mean bias
128x144
216x240
2m T
RAMS_A w/o SP
RAMS_A w/ SP‫‏‬
RAMS_B w/o SP‫‏‬
RAMS_B w/ SP
2m T
RAMS_A w/o SP
RAMS_A w/ SP‫‏‬
RAMS_B w/o SP‫‏‬
RAMS_B w/ SP
2m T
RAMS_A w/o SP
RAMS_A w/ SP‫‏‬
RAMS_B w/o SP‫‏‬
RAMS_B w/ SP
P
RAMS_A w/o SP
RAMS_A w/ SP‫‏‬
RAMS_B w/o SP‫‏‬
RAMS_B w/ SP
P
RAMS_A w/o SP
RAMS_A w/ SP‫‏‬
RAMS_B w/o SP‫‏‬
RAMS_B w/ SP
P
RAMS_A w/o SP
RAMS_A w/ SP‫‏‬
RAMS_B w/o SP‫‏‬
RAMS_B w/ SP
Ⅲ.Summary
-Multi-model ensemble downscaling project in East Asia (FY2007-FY2011)
-Improving an regional climate model's capability. e.g., Tuning, Spectral
nudging scheme.
-Models were validated over river basins in Japan. Results simulated
by the two models (MRI-NHM and improved NIED-RAMS) were
relatively in good agreement with the observation. The NIED-RAMS’
bias of 2m air temperature were less than 0.5K and the bias of
precipitation were around 10% in most of the river basins on annual
averages for three years.
-29 years long term experiment was done and in good agreement with
the observation (JRA25). The biases of 2m air temperature and
precipitation for 29 years were almost similar to those of the three
years average (2002-2004).
Ⅲ.Summary (cont.)
-Model bias of 2m air temperature (2mT) was deteriorated in larger
domain. Spatial characteristics of the bias of 2mT in larger domain is
similar to that in smaller domain.
-Model bias of precipitation (P) was NOT significantly altered in larger
domain. The bias of P in JJA was strongly influenced by the domain
size. In the period, influences of synoptic-scale disturbances are
relatively weak.
-Spectral nudging has some impacts on the mean bias of surface
variables (2mT and P) in JJA but overall, its magnitude was not large.
SST should play a significant role (nudging effect) as a boundary
condition.
*This research was supported by the Global Environment Research Project Fund (S-5-3)
of the Ministry of the Environment, Japan. Model bias detection program was provided by
Prof. Tanaka in Kyoto University.
What will I do in CU?
-Assessing the value (skill) added by dynamic
downscaling to a climate simulation over and beyond
what is achieved by interpolating global climate model
predictions (or reanalysis)
-Investigate the magnitude of the anthropogenic and
biogeochemical regional climate forcings on regional
hydrological cycles to develop more sophisticated
hazard/risk assessment (to understand uncertainty).
GPCC
Thank
you!
Ｈ