* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download Impacts of global warming on hydrological Introduction of multi-model ensembles and
Climate change denial wikipedia , lookup
Global warming hiatus wikipedia , lookup
Climate engineering wikipedia , lookup
Economics of global warming wikipedia , lookup
Climate change adaptation wikipedia , lookup
Citizens' Climate Lobby wikipedia , lookup
Fred Singer wikipedia , lookup
Climatic Research Unit documents wikipedia , lookup
Numerical weather prediction wikipedia , lookup
Climate governance wikipedia , lookup
Global warming wikipedia , lookup
Effects of global warming on human health wikipedia , lookup
Climate change in Tuvalu wikipedia , lookup
Climate change and agriculture wikipedia , lookup
Climate sensitivity wikipedia , lookup
Politics of global warming wikipedia , lookup
Instrumental temperature record wikipedia , lookup
Climate change feedback wikipedia , lookup
Media coverage of global warming wikipedia , lookup
Climate change in the United States wikipedia , lookup
Effects of global warming wikipedia , lookup
Attribution of recent climate change wikipedia , lookup
Solar radiation management wikipedia , lookup
Scientific opinion on climate change wikipedia , lookup
Carbon Pollution Reduction Scheme wikipedia , lookup
Climate change and poverty wikipedia , lookup
Atmospheric model wikipedia , lookup
Effects of global warming on humans wikipedia , lookup
Public opinion on global warming wikipedia , lookup
Climate change, industry and society wikipedia , lookup
Surveys of scientists' views on climate change wikipedia , lookup
Years of Living Dangerously wikipedia , lookup
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