* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download 2.3 Climate Scenarios
Myron Ebell wikipedia , lookup
2009 United Nations Climate Change Conference wikipedia , lookup
Instrumental temperature record wikipedia , lookup
Soon and Baliunas controversy wikipedia , lookup
German Climate Action Plan 2050 wikipedia , lookup
Heaven and Earth (book) wikipedia , lookup
Global warming controversy wikipedia , lookup
ExxonMobil climate change controversy wikipedia , lookup
Michael E. Mann wikipedia , lookup
Climatic Research Unit email controversy wikipedia , lookup
Effects of global warming on human health wikipedia , lookup
Fred Singer wikipedia , lookup
Politics of global warming wikipedia , lookup
Numerical weather prediction wikipedia , lookup
Global warming wikipedia , lookup
Climate change feedback wikipedia , lookup
Climate change denial wikipedia , lookup
Climate resilience wikipedia , lookup
Climate change adaptation wikipedia , lookup
Climate change in Saskatchewan wikipedia , lookup
Carbon Pollution Reduction Scheme wikipedia , lookup
Atmospheric model wikipedia , lookup
Climate change in Tuvalu wikipedia , lookup
Climatic Research Unit documents wikipedia , lookup
Climate engineering wikipedia , lookup
Climate change and agriculture wikipedia , lookup
Climate sensitivity wikipedia , lookup
Media coverage of global warming wikipedia , lookup
Solar radiation management wikipedia , lookup
Economics of global warming wikipedia , lookup
Climate governance wikipedia , lookup
Effects of global warming wikipedia , lookup
Public opinion on global warming wikipedia , lookup
Citizens' Climate Lobby wikipedia , lookup
Scientific opinion on climate change wikipedia , lookup
Climate change in the United States wikipedia , lookup
Attribution of recent climate change wikipedia , lookup
Climate change and poverty wikipedia , lookup
Global Energy and Water Cycle Experiment wikipedia , lookup
Effects of global warming on humans wikipedia , lookup
Surveys of scientists' views on climate change wikipedia , lookup
Climate change, industry and society wikipedia , lookup
Chapter 2 Projection of Future Climate Scenarios Contents 2.1 General Circulation Model 2.2 Downscaling 2.3 Climate Scenarios 2.4 Uncertainty of Climate Scenarios Purposes • What are climate scenarios? • Why do we need climate scenarios? • How can we setup our climate scenarios? – Current climate scenario? – Future climate scenario? What are climate scenarios? • Climate scenarios represent possible weather statistics, which may consist of – monthly mean rainfall, – Probability of wet day (rainy day) – monthly mean temperature, – Standard deviation of temperature for each month … Why? • Those studies related to climate require weather data as inputs. • However, future weather data are not available, unless you have a time machine. • Thus, we need to project possible future climate scenarios. Then, possible future weather can be generated based on these scenarios. Model Daily Rainfall Q=ϕ✕R Q: Daily stream flow R: Daily rainfall ϕ : stream flow index Daily Stream flow How? • Future climate scenarios can be derived based on the outputs of GCMs and current climate scenarios. • Past and current weather data are recorded by weather stations, and thus current climate scenarios can be determined. • Current climate scenarios are modified based on the climate projections of GCMs to form future climate scenarios. • General Circulation Models (GCMs) can produce daily weather data, but the daily data are not used directly. Instead, they are used to setup climate scenarios. • GCMs have better resolutions in recent years, but there is still space for further improvement on the ability of climate projection for a local area. TO11 SiChou # KY05 KY06 #TouChen TO09 KY03 KY04 TO10 TO08 TO07 KY02 KY01 ChuLin # TO05 TO04 # RuiFeng TO03 N TO06 TO02 W E S 0 10 全球環流模式 GCM Projections Risk Information for Decision Making 降尺度分析 Downscaling 評估模式 Assessment Model 氣候情境 Climate Scenarios 流量增減模擬個數 The Risk Assessment Procedure 6 5 4 3 2 1 0 -1 短期 中期 長期 -2 -3 -4 -5 -6 氣候預設情境 氣象資料合成 Weather Generation TO01 20 Km 9 How to Set up Climate Scenarios • The most import step on an impact study is to setup climate change scenarios. There are four methods to define climate scenarios. 1. Based on GCMs’ projections 2. Assumptions • T=+2 oC; +4 oC • P= 0%, 10%, 20% 3. Spatial Analog • Such as future climate in Taiwan may be similar to current climate in Philippines. 4. Temporal Analog • Assuming future climate may be repeated as climate in a specified period in the past. • Only the first type of scenario can reflect the physical characteristics of enhanced greenhouse effects and man-induced global warming . • Different seasons may have different changes in climate. Moreover, there is difference between night and day time. Such difference can only be reasonably provided by GCMs. 2.1 General Circulation Models General Circulation Model • A general circulation model (GCM) is a mathematical model of the general circulation of a planetary atmosphere or ocean and based on the Navier–Stokes equations on a rotating sphere with thermodynamic terms for various energy sources (radiation, latent heat). These equations are the basis for complex computer programs commonly used for simulating the atmosphere or ocean of the Earth. Atmospheric and oceanic GCMs (AGCM and OGCM) are key components of global climate models along with sea ice and land-surface components. GCMs and global climate models are widely applied for weather forecasting, understanding the climate, and projecting climate change. ---From Wikipedia Global Atmospheric Model • Climate models are systems of differential equations based on the basic laws of physics, fluid motion, and chemistry. To “run” a model, scientists divide the planet into a 3dimensional grid, apply the basic equations, and evaluate the results. Atmospheric models calculate winds, heat transfer, radiation, relative humidity, and surface hydrology within each grid and evaluate interactions with neighboring points. From Wikipedia How GCM works? GCM Grids GCM Resolutions Model Country AGCM OGCM CCCM Canada T32L10 1.8o 1.8o L29 GFDL USA R30L14 2.0o2.0o L18 GISS USA 4o5o L9 4o5o L13 UKMO UK 2.5o3.8o L19 2.5o3.8o L20 Spatial Scale of Taiwan Main Island 基隆25008 121.44 Taiwan:3o1.2o 梧棲 GISS :4o5o 花蓮121.36 高雄 120.18 恆春 220 (台灣大學吳明進教授提供) • GCMs provide projections for each grid point, which represent average values for a grid. • The risk studies for water resources or ecosystems often require climate projections for a smaller area and may need daily weather data. Thus, as mentioned, spatial and temporal downscaling processes are necessary. 2.2 Downscaling Definition of Downscaling • Downscaling is a technique to obtain information for a finer scale from information for a larger scale. • For example, temporal downscaling can provide daily data based on monthly means. Weather generation is kind of temporal downscaling. • On the other hand, spatial downscaling can produce data for a local area, such as a watershed, from data for a regional area, such as an island or a state. Known areal average to find local characteristics 250 km 25 km 25 km 250 km • GCM Scale – 250 km250km • Upstream Watershed Scale – 25 km25km • Ecosystem – 1 km1 km Downscaling How to find its climate? How to Apply GCMs’ Outputs? Grids that GCMs provide projections Spatial Downscaling Methods • Simple Downscaling (Delta Method) – Climate changes of a local area are assumed the same as the nearest grid point • Modifying recorded weather data by imposing the predicted climate changes of the nearest grid. • Modifying historic weather statistics based on climate change forecasts of the nearest grid. • Statistical Downscaling – Finding the statistical relationships between regional climate and local climate. • Physical Downscaling – Taking GCMs’ forecasts as boundary conditions for a regional climate model. Method 1.1 • Modifying recorded weather data by imposing the predicted climate changes – It assumes there is uniform climate changes within a grid and observed weather sequence is repeatable. – The days in the same month are modified by the same changes even though they are in different years. Tt,m Tt ,m Tm t m & m 1 to 12 Pt,m Pt ,m RPm t m & m 1 to 12 1. T1ocal = Tregional 2. RPprecip-1ocal = RPprecip- regional Method 1.2 • Modifying historic weather statistics based on climate change projections and then generating weather data. – It also assumes there is uniform climate changes within a grid, e.g. the change of a weather station is the same as the change predicted by the nearest grid point. – Monthly changes predicted by GCMs are used to modify historical monthly weather statistics. Tm Tm m 1 to 12 Tm Pm RPm m 1 to 12 Pm – Modified monthly statistics are future climate scenarios and are applied to generate future weather data. Method 2 Statistical Downscaling • First, finding the relationships between regional climate pattern and local climate. • Then, monthly changes predicted by GCMs are projected to a local station based on the identified relationships. Tm Trans(Tm ) m 1 to 12 Tm Pm Trans( RPm ) m 1 to 12 Pm Recorded Rainfall FROM :21-OCT-2005 00:00 TO :21-OCT-2005 08:30 Rank Rainfall (mm) station Code Location 1 40.0 冬山 C1U68 宜蘭縣冬山鄉(冬山國中) 2 28.5 竹子湖 46693 台北市陽明山(氣象站) 3 27.0 竹子湖 01A42 台北市陽明山(十河局) 4 24.5 泰平 C0A55 台北縣雙溪鄉 5 24.0 寒溪 C1U67 宜蘭縣冬山鄉(大進國小) 6 23.5 玉蘭 C0U65 宜蘭縣大同鄉 7 23.5 太平 L1A84 台北縣雙溪鄉(翡翠水庫) 8 18.0 三星 C1U66 宜蘭縣三星鄉(三星鄉運動公園) 9 13.5 牛鬥 C1U50 宜蘭縣大同鄉 10 13.0 北投國小 A1A9V 台北市北投區(養工處) Historical Weather Data Regional Climate Regional Weather Pattern GCMs’ Predicted Regional Weather Pattern Downscaling Local Weather Data Relationship Local Climate Projected Local Weather Data Similar Pattern, but different Weather 10.21.2005 Rainy Day 10.28.2005 Sunny Day Method 2 Bias Correction • Assume the probability of observed data is the same as the probability of GCM projections. X GCM GCM GCM X OBS OBS OBS XGCM - mGCM s GCM Xunbias = ( CDF = Xunbias - mOBS s OBS XGCM - mGCM s GCM ) ´ s OBS + mOBS CDF OBS GCM 37 Example of Bias Correction GFCM21_Jan 38 Bias Correction of GCM projections Xunbias,m = ( XGCM ,m - mGCM ,m Xunbias,F,m = ( s GCM ,m ) ´ s observed,m + mobserved,m XGCM ,F,m - mGCM ,m s GCM ,m ) ´ s observed,m + m observed,m munbias,m = mobserved,m mGCM ,F,m - mGCM ,m munbias,F,m = ( ) ´ s observed,m + m observed,m s GCM ,m Bias Correction for Temperature munbias,m = mobserved,m mGCM ,F,m - mGCM ,m )´ s observed,m + mobserved,m munbias,F,m = ( s GCM ,m mGCM ,F,m - mGCM,m )´ s observed,m Dmunbias,m = munbias,F,m - munbias,m = ( s GCM ,m m Local,F,m = mobserved,m + Dmunbias,m mGCM ,F,m - mGCM,m )´ s observed,m + mobserved,m m Local,F,m = ( s GCM ,m Bias Correction for Precipitation munbias,m = mobserved,m mGCM ,F,m - mGCM ,m munbias,F,m = ( ) ´ s observed,m + m observed,m s GCM ,m munbias,F,m mGCM ,F,m - mGCM ,m s observed,m RX F,m = =( )´ +1 munbias,m s GCM ,m mobserved,m m Local,F,m = mobserved,m ´ RX F,m mGCM ,F,m - mGCM ,m m Local,F,m = ( ) ´ s observed,m + m observed,m s GCM ,m Bias Correction for Climate Variable mGCM ,F,m - mGCM ,m m Local,F,m = ( )´ s observed,m + mobserved,m s GCM,m •where μLocal,F,m is the bias-corrected future mean value of month m for a local area, μGCM,F,m and μGCM,m are GCM projected future and current mean values, σGCM,m is the GCM projected standard deviation of month m under current climate condition, μobserved,m and σobserved,m are the observed mean and standard deviation of month m under current climate condition for a local area. Example of Bias Correction 43 Generalized Bias-Correction Method • Climate variable X belongs to distribution F(X, α, β) where α and β are parameters of distribution function. F(X, αGCM, βGCM) F(X, αobs, βobs) Prob Prob CDF CDF Xunbias XGCM Xunbias,m = F -1 (Prob, a observed , bobserved ) Xunbias,m = F -1 (F(XGCM , aGCM , bGCM ), aobserved , bobserved ) Method 3 physical downscaling • Taking GCMs’ forecasts as boundary conditions for a regional climate model. – Prof. Wu in AS of NTU uses the outputs from Global Spectral Model (GSM)_CCM3 as boundary conditions to drive Regional Spectral Model (RSM). – Other regional models, including Purdue Model and MM5, are used in Taiwan to produce a local climate scenario matrix. 資料來源:tccip.ncdr.nat.gov.tw 資料來源:台大吳明進教授 GSM & RSM Resolutions Model Resolutions GSM or RSM0 280km 280km RSM1 50km 50km RSM2 15km 15km GSM: Global Spectral Model RSM: Regional Spectral Model Weather Research and Forecasting (WRF) Model • The Weather Research and Forecasting (WRF) Model is a next-generation mesoscale numerical weather prediction system designed for both atmospheric research and operational forecasting needs. • TCCIP project used WRF model to downscale MRI projections to the scale of 5km. 2.3 Climate Scenarios Procedure of Risk Assessment Emission Scenarios vs Climate Scenarios Outputs: Daily & Monthly Data Stabilization at 550 ppm Emission Scenarios GCMs Climate Scenarios: • Current Scenarios • Future Scenarios Scenarios • Emission Scenarios (溫室氣體排放情境) – SRES Scenarios (Special Report on Emissions Scenarios, 2000) – RCPs Scenarios (Representative Concentration Pathways) • radiative forcing values in the year 2100 relative to preindustrial values (+2.6, +4.5, +6.0, and +8.5 W/m2, respectively) • Climate Scenarios (氣候情境) – Current Climate Scenario (Baseline) – Future Climate Scenario Experiments of GCMs • Equilibrium Experiment 1. 2. 3. 4. Setup initial atmospheric conditions Running model to reach equilibrium states Change climate parameters, e.g.2×CO2 Re-running model to reach another equilibrium states 5. Comparisons between two equilibrium states • Transition Experiment 1. 2. 3. 4. Respond to instant forcing Respond to gradual change of CO2 concentration Gradually increase CO2 concentration Simulate climate SRES Scenarios • SRES is defined based on future economic growth toward B1 – global or regional – Economic or environmental • SRES classifies four scenarios, including A1, A2, B1,and B2. IPCC AR4 Popular Scenarios • A2 • A1B • B1 A2 A1B B1 Stabilization at 550 ppm RCPs • RCP8.5 – Rising radiative forcing pathway leading to 8.5 W/m2 in 2100. • RCP6.0 – Stabilization without overshoot pathway to 6 W/m2 at stabilization after 2100 • RCP4.5 – Stabilization without overshoot pathway to 4.5 W/m2 at stabilization after 2100 • RCP2.6 – Peak in radiative forcing at ~ 3 W/m2 before 2100 and decline Database • IPCC Data Distribution Center – http://www.ipcc-data.org/index.html • TCCIP also provides scenarios for Taiwan. – http://tccip.ncdr.nat.gov.tw/NCDR/main/index.aspx AR5採用GCMs - 由CMIP5專案彙整 ↑ 來自28個單位,共計61個GCMs TCCIP Climate Scenarios • TCCIP [Taiwan Climate Change Projection and Information Platform Project: 臺灣氣候變遷推估與 資訊平台計畫] is a core project funded by National Science Council, which is in charge of preparing climate scenarios for climate change impact study in Taiwan. Climate scenarios are developed for four periods. – – – – Current Climate (Baseline, 基期) Short-term Future Climate (短期) Mid-term Future Climate (中期) Long-term Future Climate (長期) : 1986~2005 : 2020~2039 : 2050~2069 : 2070~2099 GCMs對應各RCP情境整理表(1/2) Modeling ACenter Model BCC-CSM1.1 BCC BCC-CSM1.1(m) CanCM4 CCCma CanESM2 CMCC-CESM CMCC CMCC-CM CMCC-CMS CNRM-CERFACS CNRM-CM5 CNRM-CERFACS CNRM-CM5-2 ACCESS1.0 CSIRO-BOM ACCESS1.3 CSIRO-QCCCE CSIRO-Mk3.6.0 EC-EARTH EC-EARTH FIO FIO-ESM GCESS BNU-ESM INM INM-CM4 IPSL-CM5A-LR IPSL IPSL-CM5A-MR IPSL-CM5B-LR LASG-CESS FGOALS-g2 LASG-IAP FGOALS-s2 MIROC4h MIROC AMIROC5 MIROC-ESM MIROC MIROC-ESM-CHEM HadCM3 HadGEM2-A MOHC HadGEM2-CC HadGEM2-ES RCP2.6 RCP4.5 RCP6.0 RCP8.5 Baseline GCMs對應各RCP情境整理表(2/2) Modeling ACenter Model MPI-ESM-LR MPI-M MPI-ESM-MR MPI-ESM-P MRI MRI-CGCM3 MRI-ESM1 GISS-E2-H NASA GISS GISS-E2-H-CC GISS-E2-R GISS-E2-R-CC NCAR NCC NIMR/KMA CCSM4 NorESM1-M NorESM1-ME HadGEM2-AO GFDL-CM2.1 NOAA GFDL GFDL-CM3 GFDL-ESM2G GFDL-ESM2M CESM1(BGC) CESM1(CAM5) NSF-DOE-NCAR CESM1(CAM5.1, FV2) CESM1(FASTCHEM) CESM1(WACCM) RCP2.6 RCP4.5 RCP6.0 RCP8.5 Baseline How many GCMs should be chosen? How to choose GCMs? Procedure to Choose GCMs Climate Zonation Analysis Performance of GCMs for each station Ranking GCMs Choice of GCMs Weighted performance for a zone Climate Zonation • Those which GCMs could provide reasonable baseline climate have higher priority for risk study. • However, it is not realistic that nearby weather stations choose different GCMs, especially within the same watershed. • Therefore, climate zonation is determined first. The weather stations in the same zone should use the same GCMs. CWB weather stations 站名 淡水 鞍部 臺北 竹子湖 基隆 彭佳嶼 花蓮 蘇澳 宜蘭 東吉島 澎湖 臺南 高雄 嘉義 臺中 阿里山 大武 玉山 新竹 恆春 成功 蘭嶼 日月潭 臺東 梧棲 經度 121.83 121.73 121.51 121.54 121.73 122.07 121.61 121.86 121.75 119.66 119.56 120.23 120.31 120.42 120.68 120.81 120.90 120.95 121.01 120.74 121.37 121.55 120.90 121.15 120.52 緯度 25.17 25.19 25.04 25.17 25.13 25.63 23.98 24.60 24.77 23.26 23.57 23.01 22.57 23.50 24.15 23.51 22.37 23.49 24.83 22.01 23.10 22.04 23.88 22.75 24.26 共25站 林嘉佑(2014) 劉子明(2010) 吳明進(1993) 北部 台北、淡水、新竹、 梧棲、台中、 北部 台北、淡水、新 竹 北部 台北、新竹、 彭佳嶼 北海岸 基隆 東北海岸 基隆 北海岸 基隆 東部 宜蘭、花蓮 成功、台東 東北部 宜蘭 東北部 宜蘭 東部 花蓮、成功、台 東 東部 花蓮、成功、 台東、大武、 恆春 南部 大武、恆春 恆春半島 大武、恆春 西南部 嘉義、台南、高雄 西部 台中、高雄、台 南 北部山區 鞍部、竹子湖、蘇澳 北部山區 竹子湖、鞍部 中部山區 日月潭、玉山 中部山區 日月潭、玉山 中部 台中、日月 潭 南部山區 阿里山 南部山區 阿里山 山地 阿里山 北部外島 彭佳嶼 西部外島 澎湖、東吉島 東部外島 蘭嶼 西南 澎湖、台南、 高雄 針對氣象站挑選GCMs鄰近格點 GCM 採用站點 bcc-csm1-1-m 119.25、22.991 MRI-CGCM3 119.25、24.112 120.375、21.869 120.375、22.991 120.375、24.112 121.5、21.869 121.5、22.991 121.5、24.112 121.5、25.234 bcc-csm1-1 122.625、25.234 120.938、23.72 120.938、26.511 對應測站 東吉島 澎湖 大武、恆春 台南、高雄、嘉義、阿里山 台中、日月潭、梧棲 蘭嶼 玉山、成功、台東 花蓮、蘇澳 淡水、鞍部、台北、竹子湖、基隆、宜蘭、 新竹 彭佳嶼 臺北、花蓮、蘇澳、宜蘭、東吉島、澎湖、 臺南、高雄、嘉義、臺中、阿里山、大武、 玉山、新竹、恆春、成功、蘭嶼、日月潭、 臺東、梧棲 淡水、鞍部、竹子湖、基隆、彭佳嶼 Performance Indicator • Indicator – Correlation of mean monthly rainfall(越高越好) – RMSE of mean monthly rainfall in dry season (越低越好) – RMSE of mean monthly rainfall in wet season (越低越好) • Rank based on the three indicators 1. Rank based on each indicator 2. Sum of ranks of three indicators 3. Determine final rank R Dry RMSR Wet RMSE Total Final Rank GCM A 2 1 3 6 2 GCM B 1 2 1 4 1 GCM C 3 4 2 9 3 GCM D 4 3 4 11 4 Performance for a zone • Sum of final ranks of all stations in the zone 淡水 台北 台中 新竹 梧棲 • Rank again bcc-csm1-1-m 14 12 3 11 3 bcc-csm1-1 CCSM4 CESM1-CAM5 CSIRO-Mk3-6-0 FIO-ESM GFDL-CM3 GFDL-ESM2G GFDL-ESM2M GISS-E2-H GISS-E2-R HadGEM2-AO IPSL-CM5A-LR IPSL-CM5A-MR MIROC-ESM-CHEM MIROC-ESM MIROC5 MRI-CGCM3 NorESM1-M NorESM1-ME 1 5 4 11 6 19 17 20 15 10 2 18 16 12 9 13 8 7 3 8 2 6 4 5 18 15 19 14 7 1 20 13 16 17 11 9 10 3 20 10 16 11 17 2 15 12 14 13 1 4 5 18 19 6 7 8 9 10 3 9 4 6 14 15 16 12 5 1 13 18 20 19 2 17 7 8 19 11 12 2 17 4 16 13 15 14 1 7 6 20 18 5 8 9 10 Sum 43 58 31 47 32 51 57 78 80 70 49 6 62 58 86 82 37 49 41 33 Final 11 10 3 9 4 6 14 15 16 12 5 1 13 18 20 19 2 17 7 8 Choice of GCMs rank 1 2 3 4 5 西北部 東部 恆春半島 HadGEM2-AO CESM1-CAM5 MIROC5 CCSM4 GISS-E2-R GISS-E2-R CSIRO-Mk3-6-0 CCSM4 CCSM4 NorESM1-ME bcc-csm1-1 CSIRO-Mk3-6-0 MIROC5 CSIRO-Mk3-6-0 HadGEM2-AO 南部 HadGEM2-AO MIROC5 bcc-csm1-1-m CCSM4 CESM1-CAM5 北部山區 中部山區 bcc-csm1-1 MIROC5 CESM1-CAM5 CCSM4 NorESM1-ME HadGEM2-AO HadGEM2-AO CESM1-CAM5 MRI-CGCM3 MRI-CGCM3 西部離島 台灣 HadGEM2-AO HadGEM2-AO MIROC5 CESM1-CAM5 CESM1-CAM5 CCSM4 bcc-csm1-1-m MIROC5 CCSM4 GISS-E2-R • 部分氣候分區僅包含單一測站,排序結果與單站 分析結果相同 • 為確保區域氣象站之推薦模式亦可反映出台灣整 體之氣候特性,部分僅於分區內表現較佳之模式 已被剔除 2.4 Uncertainty of GCM projections 10.00 35.0 台北 30.0 CGCM1 CSIRO 6.00 HADCM3 4.00 CCCM 氣溫(oC) 降雨(cm) 8.00 GFDL 2.00 GISS 0.00 1 2 3 4 5 6 7 8 9 10 11 12 台北 25.0 CGCM1 20.0 CSIRO 15.0 HADCM3 10.0 CCCM 5.0 GFDL 0.0 GISS -5.0 1 2 3 4 5 6 月 6.00 4.00 2.00 0.00 5 6 7 月 11 12 CGCM1 30.0 台中 CSIRO 25.0 CGCM1 20.0 CSIRO 15.0 HADCM3 GFDL 10.0 CCCM GISS 5.0 GFDL 0.0 GISS CCCM 4 10 35.0 HADCM3 3 9 台中 8 9 10 11 12 氣溫(oC) 降雨(cm) 8.00 2 8 月 10.00 1 7 1 2 3 4 5 6 7 月 8 9 10 11 12 Relationships between records of a local weather station and predictions for the nearest grid point SRES CGCM1 CSIRO Country Study Program HADCM3 CCCM GFDL GISS Taipei Rainfall 0.68 -0.68 0.69 0.72 -0.36 0.91 Temp. 0.98 0.98 0.99 0.98 0.97 0.97 TaiChong Rainfall 0.74 -0.80 0.86 0.76 -0.21 0.87 Temp. 1.00 0.95 0.98 0.99 0.99 0.98 Tainan Rainfall 0.85 -0.81 0.59 0.86 -0.26 0.72 Temp. 1.00 0.93 0.98 0.99 1.00 0.95 TaiDong Rainfall 0.80 -0.57 0.69 0.84 -0.60 0.39 Temp. 0.99 0.97 0.99 0.99 0.99 0.94 Comparisons between different grids’Projections N24E124中 N20E120中 N24E120短 N24E124長 N20E120長 N24E120中 2.5 2.5 2.5 2 2 2 1.5 1 1.5 降雨比值 降雨比值 降雨比值 N24E124短 N20E120短 SRES-CGCM2 A2 1 N24E120長 1.5 1 0.5 0.5 0.5 0 1 2 3 4 5 6 7 8 9 10 11 12 0 1 2 3 4 5 6 7 8 9 10 11 12 N24E124中 N20E120中 N24E120短 N24E120中 5 5 5 4 4 4 1 0 -1 1 2 3 4 5 6 7 月份 8 9 10 11 12 氣溫差值 6 2 3 2 0 0 3 4 5 6 7 月份 8 5 6 7 8 9 10 11 12 9 10 11 12 N24E120長 2 1 2 4 3 1 1 3 N24E124長 N20E120長 6 3 2 月份 6 氣溫差值 氣溫差值 1 月份 月份 N24E124短 N20E120短 0 1 2 3 4 5 6 7 月份 8 9 10 11 12 Summary • Climate scenarios, in fact, are weather statistics. Current climate scenarios can be determined based on historical weather data. • Climate change scenarios may be derived from GCMs or just simple assumptions. Then, future climate scenarios can be designed. • Future weather data could be generated based on climate statistics or simply impose changes on current records. • Using more than one GCM is recommended to avoid the effects of model bias. What should you know? • • • • What are SRES and RCPs scenarios? Why should you downscale GCMs’ outputs? What are climate scenarios? How can you downscale GCMs’ outputs to setup climate change scenarios? • How can you prepare your weather data for your risk assessment model?