UM collaboration meeting, 21 -22 November 2011, KMA Task: (ECSK 06) Regional downscaling Regional modelling with Had. GEM 3 -RA driven by Had. GEM 2 -AO projections National Institute of Meteorological Research (NIMR)/KMA
Outline § Introduction § 50 km-res CORDEX-East Asia experiment § Evaluation of current climate simulation § Projection of future climate change § 12. 5 km-res Korea experiment § Evaluation of current climate simulation § Projection of future climate change § Summary and future plan
Introduction v Task: (ECSK 06) Regional downscaling §Objective: • Build UM-regional model over the East Asian region and perform experimental runs for simulation of regional climate. §Deliverables: • Report on the installation of the UM-regional model for the East Asian region and its evaluation using perfect boundary conditions on seasonal simulations of East Asian monsoon activity (2008 -2010) • Report on evaluation of Had. GEM 3 -RA with a focus on climate variability in long-term integrations using ECMWF interim reanalysis data, associated with CORDEX participation (Dec 2011) • Report on assessment of East Asian climate downscaled by Had. GEM 3 RA using global climate change projections, associated with CORDEX participation (Dec 2011)
Introduction v Strategy for generating high resolution climate change scenarios under IPCC AR 5 New IPCC Scenarios RCP 4. 5/8. 5/2. 6/6. 0 Anthropogenic forcing GCM projection Had. GEM 2 -AO : ~135 km CMIP 5 Dynamical downscaling RCM projection Had. GEM 3 -RA : ~12. 5/50 km Period (years) Scenario Model Grid spacing Global Projection 1850 ~ 2300 RCP 4. 5/8. 5/2. 6/6. 0 Had. GEM 2 -AO ~135 km (1. 875°x 1. 25°) CORDEX Regional Projection 1950 ~ 2100 RCP 4. 5/8. 5/2. 6/6. 0 Had. GEM 3 -RA ~12. 5/50 km (0. 11/0. 44°) • Had. GEM 2 -AO: Atmosphere-Ocean coupled model of Hadley Centre Global Environment Model version 2 • Had. GEM 3 -RA: Atmospheric regional climate model of Hadley Centre Global Environment Model version 3
Plan of generating regional climate change scenarios v Experiments and progress (GA 3. 0 version) § Simulations of Current Climate (to evaluate the performance of RCMs) - Experiments using reanalysis boundary conditions (1989 -2008) - done * Forcing: ERA-Interim atmospheric field & Daily Reynolds SST - Experiments using GCM boundary conditions (1950 -2005) - done * Forcing: Had. GEM 2 -AO atmospheric field & daily SST § Simulations of Climate Change (to project future climate) - Experiment using GCM RCP 8. 5/4. 5 runs (2006 -2100) - done * Forcing: Had. GEM 2 -AO atmospheric field & daily SST CORDEX 50 km domain Korea 12. 5 km domain
Evaluation of current climate simulation in GCM forcing run (50 km-res) - surface climate
Climatology (1971 -200): Precipitation Observation Winter Summer Annual CRU GCM RCM Ø RCM could resolve small-scale features related with topography and coastlines.
Climatology (1971 -200): Temperature Observation Winter Summer Annual CRU GCM RCM Ø RCM could resolve small-scale features related with topography and coastlines.
Bias: Precipitation and temperature Summer RCM Winter GCM v Temperature Annual v Precipitation GCM RCM
Statistics: Precipitation & Temperature (Land) v Mean, bias, Root-mean-squared error (RMSE) and pattern correlation coefficient of precipitation and temperature. (Ref. CRU) Variables Mean (CRU) ANN 4. 37 1. 59 ANN 10. 87 JJA 20. 50 DJF Temp. (°C) JJA DJF Precip. (mm/day ) 2. 70 -0. 11 Mean (Model) Bias RMSE Pattern Corr. GCM 2. 87 0. 17 0. 99 0. 837 RCM 2. 76 0. 06 0. 92 0. 821 GCM 4. 45 0. 08 1. 67 0. 754 RCM 4. 51 0. 15 1. 64 0. 752 GCM 1. 84 0. 25 0. 66 0. 907 RCM 1. 53 -0. 06 0. 63 0. 894 GCM 9. 73 -1. 15 2. 11 0. 977 RCM 8. 91 -1. 96 2. 13 0. 984 GCM 21. 14 0. 64 1. 80 0. 942 RCM 20. 36 -0. 14 1. 31 0. 970 GCM -3. 12 -3. 01 3. 77 0. 980 RCM -3. 65 -3. 54 3. 74 0. 980 Ø Overall, both GCM and RCM show similar performance and wet/cold biases.
Annual cycle of Precipitation and temperature v 30 -yr mean annual cycle of area-averaged precipitation and surface air temperature (1951~1980): East Asia monsoon region(100 E-150 E, 20 N-50 N) Precip. Temp § Black: Observation (CRU) § Red: GCM § Blue: RCM
Climate change projection (50 km-res) • Change in surface air temperature and precipitation
Climate change Projection: Temperature v Time series of annual mean surface air temperature averaged over model domain OBS (CRU) GCM-Historical GCM –RCP 4. 5 GCM –RCP 8. 5 RCM -Historical RCM –RCP 4. 5 RCM – RCP 8. 5 Difference-Historical Difference –RCP 4. 5 Difference –RCP 8. 5 Ø RCM tends to underestimate warming trend
Time series of CO 2 concentration in RCP scenarios Ø RCM are using constant value of CO 2 concentration with concentration for 1985 Ø Underestimation of warming trend is seems to be due to lack of increase of green house gases.
Climate change Projection: Precipitation v Time series of annual mean precipitation averaged over model domain OBS (CRU) GCM-Historical GCM –RCP 4. 5 GCM –RCP 8. 5 RCM -Historical RCM –RCP 4. 5 RCM – RCP 8. 5 Difference-Historical Difference –RCP 4. 5 Difference –RCP 8. 5 Ø Inter-annual variability of both GCM RCM is weak.
Climate change Projection: Anomalies v Reference period: 1971 -2000 OBS (CRU) GCM-Historical GCM –RCP 4. 5 GCM –RCP 8. 5 RCM -Historical RCM –RCP 4. 5 RCM – RCP 8. 5 Ø It is clear that RCM tends to underestimate warming trend.
Climate change projection: Temperature Change (RCP 4. 5) Change (RCP 8. 5) RCM GCM Current Change (2070 -2099) Current climate (1971 -2000) RCP 4. 5 RCP 8. 5 GCM 18. 51 ℃ 2. 80 ℃ 4. 87 ℃ RCM 18. 13 ℃ 2. 69 ℃ 4. 62 ℃
Climate change projection: Precipitation Change (RCP 4. 5) Change (RCP 8. 5) RCM GCM Current Change (2070 -2099) Current climate (1970 -2000) RCP 4. 5 RCP 8. 5 GCM 4. 84 mm/day 8. 29 % 9. 27 % RCM 5. 24 mm/day 6. 24 % 7. 43 %
Summary 1 v Overall, performance of Had. GEM 3 -RA on current climate simulation is similar to Had. GEM 2 -AO. v However, Had. GEM 3 -RA could resolve small-scale features related with topography and coastline. v General patterns of regional climate change projection by Had. GEM 3 -RA is similar to projection by Had. GEM 2 -AO. v But, Had. GEM 3 -RA tends to underestimate warming trend due to lack of increase of green house gases.
Evaluation of current climate simulation in GCM forcing run (12. 5 km res) - surface climate
Climatology (1971 -200): Precipitation Annual Observation GCM RCM Ø RCM could resolve small-scale features related with topography and coastlines. Winter Summer Ø RCM of 12. 5 km-res is better than not only GCM but also RCM of 50 km-res.
Climatology (1971 -200): Temperature Annual Observation GCM RCM Ø RCM could resolve small-scale features related with topography and coastlines. Winter Summer Ø RCM of 12. 5 km-res is better than not only GCM but also RCM of 50 km-res.
Bias: Precipitation and Temperature Summer RCM Winter GCM v Temperature Annual v Precipitation GCM RCM
Statistics: Precipitation & Temperature (Land) v Mean, bias, Root-mean-squared error (RMSE) and pattern correlation coefficient of precipitation and temperature. (Ref. APHRO and CRU) Variables Mean (OBS) ANN 4. 50 0. 83 ANN 8. 68 JJA 21. 93 DJF Temp. (°C) JJA DJF Precip. (mm/day ) 2. 28 -5. 90 Mean (Model) Bias RMSE Pattern Corr. GCM 2. 48 0. 20 0. 42 0. 936 RCM 2. 58 0. 30 0. 46 0. 938 GCM 4. 27 -0. 23 0. 84 0. 830 RCM 4. 78 0. 28 1. 12 0. 723 GCM 1. 23 0. 40 0. 45 0. 894 RCM 1. 13 0. 30 0. 36 0. 916 GCM 8. 04 -0. 64 1. 38 0. 954 RCM 7. 95 -0. 73 0. 91 0. 986 GCM 22. 87 0. 94 1. 40 0. 904 RCM 22. 73 0. 80 1. 20 0. 935 GCM -8. 93 -3. 03 3. 80 0. 948 RCM -8. 93 -3. 03 3. 04 0. 986 Ø Overall, RCM show better performance than GCM. But, RCM shows wet/cold biases.
Annual cycle of Precipitation and temperature v 30 -yr mean annual cycle of area-averaged precipitation and surface air temperature (1971~2000) Precip. Temp § Black: OBS § Red: GCM § Blue: RCM Corr. Precip Temp GCM 0. 951 0. 999 RCM 0. 963 0. 999
Probability of daily precipitation v The probability of daily precipitation with thresholds up to 50 mm/day 100 Observation Probability (%) GCM RCM 10 1 0. 1 (0. 1 -10) (10 -20) (20 -30) (30 -50) (50~) Thresholds (mm/day) § RCM simulated probability is much more realistic than GCM simulation. § RCM projections of changes in extremes in the future are likely to be very different to, and much more credible than, those from GCMs.
Climate change projection (12. 5 km) • Change in surface air temperature and precipitation
Climate change projection: Temperature v Time series of annual mean surface air temperature averaged over model domain OBS (CRU) GCM-Historical GCM –RCP 4. 5 GCM –RCP 8. 5 RCM -Historical RCM –RCP 4. 5 RCM – RCP 8. 5 Difference-Historical Difference –RCP 4. 5 Difference –RCP 8. 5 Ø RCM tends to underestimate warming trend Ø Underestimation of warming trend is seems to be due to lack of increase of green house gases.
Climate change projection: Precipitation v Time series of annual mean precipitation averaged over model domain OBS (CRU) GCM-Historical GCM –RCP 4. 5 GCM –RCP 8. 5 RCM -Historical RCM –RCP 4. 5 RCM – RCP 8. 5 Difference-Historical Difference –RCP 4. 5 Difference –RCP 8. 5 Ø Inter-annual variability of RCM is similar to observation.
Climate change Projection: Anomalies v Reference period: 1971 -2000 OBS (CRU) GCM-Historical GCM –RCP 4. 5 GCM –RCP 8. 5 RCM -Historical RCM –RCP 4. 5 RCM – RCP 8. 5 Ø It is clear that RCM tends to underestimate warming trend.
Climate change projection: Temperature Change (RCP 4. 5) Change (RCP 8. 5) RCM GCM Current Change (2070 -2099) Current climate (1971 -2000) RCP 4. 5 RCP 8. 5 GCM 11. 19 3. 51 6. 13 RCM 11. 14 3. 33 5. 79
Climate change projection: Precipation Change (RCP 4. 5) Change (RCP 8. 5) RCM GCM Current Change Current climate (1970 -2000) RCP 4. 5 RCP 8. 5 GCM 3. 53 11. 70 14. 96 RCM 3. 17 11. 83 17. 90
Summary 2 v Overall, performance of Had. GEM 3 -RA on current climate simulation is better than Had. GEM 2 -AO. v Had. GEM 3 -RA could resolve small-scale features related with topography and coastline. v And, Had. GEM 3 -RA reproduced climate extreme better than Had. GEM 2 -AO. v General patterns of regional climate change projection by Had. GEM 3 -RA is similar to projection by Had. GEM 2 -AO. v But, Had. GEM 3 -RA tends to underestimate warming trend due to lack of increase of green house gases.
Future plan v New downscaling experiments will be performed with all RCP scenarios (RCP 2. 6/4. 5/6. 0/8. 5) including prescribed green house gases. Task: Regional downscaling ECSK 06 Key milestones: HS Kang, S Park R Jones Jul 2008: agree using ERA-40 Oct 2008: initiate a visit to work on the regional model Dec 2008: joint (0. 2 FTE) Jun 2009: report simulations of East Asian monsoon activity (0. 3 FTE) Jun 2010: progress report on evaluation of Had. GEM 3 -RA over the East Asian region in long-term simulations Dec 2011: report using ECMWF interim reanalysis data, associated with CORDEX participation (0. 3 FTE) Dec 2011: report change projections, associated with CORDEX participation (0. 3 FTE) Dec 2012: reportregional peninsular Had. GEM 3 -RA assessment over on ofclimate Korea downscaled by with using global climate change projections of RCP 2. 6/4. 5/6. 0/8. 5 (0. 3 FTE)
Thank you very much!
Precipitation: Annual mean climatology v Climatology of annual precipitation Observation GCM bias GCM RCM effect RCM bias
Precipitation: JJA mean climatology v Climatology of summer precipitation Observation GCM bias GCM RCM effect RCM bias
Large-scale field: 500 -h. Pa height (JJA) Observation GCM bias GCM RCM effect Ø Both GCM and RCM enhanced upper trough. RCM bias
Low level circulation: SLP, 850 -h. Pa wind/humidity Observation GCM bias GCM RCM effect Ø Both GCM has cyclonic anomalies over East Asian monsoon region. Ø And, RCM enhanced cyclonic anomalies. RCM bias
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