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Task: (ECSK06) Regional downscaling Regional modelling with HadGEM3-RA driven by HadGEM2-AO projections National Institute of Meteorological Research (NIMR)/KMA.

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Presentation on theme: "Task: (ECSK06) Regional downscaling Regional modelling with HadGEM3-RA driven by HadGEM2-AO projections National Institute of Meteorological Research (NIMR)/KMA."— Presentation transcript:

1 Task: (ECSK06) Regional downscaling Regional modelling with HadGEM3-RA driven by HadGEM2-AO projections National Institute of Meteorological Research (NIMR)/KMA UM collaboration meeting, 21-22 November 2011, KMA

2 Outline  Introduction  50km-res CORDEX-East Asia experiment  Evaluation of current climate simulation  Projection of future climate change  12.5km-res Korea experiment  Evaluation of current climate simulation  Projection of future climate change  Summary and future plan

3 Introduction  Task: (ECSK06) 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 HadGEM3-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 HadGEM3- RA using global climate change projections, associated with CORDEX participation (Dec 2011)

4 Introduction Global ProjectionRegional Projection Period (years)1850 ~ 23001950 ~ 2100 ScenarioRCP 4.5/8.5/2.6/6.0 ModelHadGEM2-AOHadGEM3-RA Grid spacing~135km (1.875°x1.25°)~12.5/50km (0.11/0.44°) New IPCC Scenarios RCP 4.5/8.5/2.6/6.0 New IPCC Scenarios RCP 4.5/8.5/2.6/6.0 GCM projection HadGEM2-AO : ~135 km GCM projection HadGEM2-AO : ~135 km RCM projection HadGEM3-RA : ~12.5/50 km RCM projection HadGEM3-RA : ~12.5/50 km Anthropogenic forcing Dynamical downscaling HadGEM2-AO: Atmosphere-Ocean coupled model of Hadley Centre Global Environment Model version 2 HadGEM3-RA: Atmospheric regional climate model of Hadley Centre Global Environment Model version 3  Strategy for generating high resolution climate change scenarios under IPCC AR5 CMIP5 CORDEX

5 Plan of generating regional climate change scenarios Korea 12.5km domain CORDEX 50km domain  Experiments and progress (GA3.0 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: HadGEM2-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: HadGEM2-AO atmospheric field & daily SST

6 Evaluation of current climate simulation in GCM forcing run (50km-res) in GCM forcing run (50km-res) - surface climate - surface climate

7 Climatology (1971-200): Precipitation CRURCMGCM ObservationGCMRCM Annual Summer Winter  RCM could resolve small-scale features related with topography and coastlines.

8 Climatology (1971-200): Temperature CRURCMGCM ObservationGCMRCM Annual Summer Winter  RCM could resolve small-scale features related with topography and coastlines.

9 Bias: Precipitation and temperature RCM GCM RCMGCM  Precipitation  Temperature Annual Summer Winter

10 Statistics: Precipitation & Temperature (Land) Variables Mean (CRU) Mean (Model) BiasRMSE Pattern Corr. Precip. (mm/day) ANN2.70 GCM2.870.170.990.837 RCM2.760.060.920.821 JJA4.37 GCM4.450.081.670.754 RCM4.510.151.640.752 DJF1.59 GCM1.840.250.660.907 RCM1.53-0.060.630.894 Temp. (°C) ANN10.87 GCM9.73-1.152.110.977 RCM8.91-1.962.130.984 JJA20.50 GCM21.140.641.800.942 RCM20.36-0.141.310.970 DJF-0.11 GCM-3.12-3.013.770.980 RCM-3.65-3.543.740.980  Mean, bias, Root-mean-squared error (RMSE) and pattern correlation coefficient of precipitation and temperature. (Ref.CRU)  Overall, both GCM and RCM show similar performance and wet/cold biases.

11 Annual cycle of Precipitation and temperature  30-yr mean annual cycle of area-averaged precipitation and surface air temperature (1951~1980): East Asia monsoon region(100E-150E,20N-50N) Precip. Temp  Black: Observation (CRU)  Red: GCM  Blue: RCM

12 Climate change projection (50km-res) Change in surface air temperature and precipitationChange in surface air temperature and precipitation

13 Climate change Projection: Temperature  Time series of annual mean surface air temperature averaged over model domain OBS (CRU) GCM-Historical GCM –RCP4.5 GCM –RCP8.5 RCM -Historical RCM –RCP4.5 RCM – RCP8.5 Difference-Historical Difference –RCP4.5 Difference –RCP8.5  RCM tends to underestimate warming trend

14 Time series of CO2 concentration in RCP scenarios  RCM are using constant value of CO2 concentration with concentration for 1985  Underestimation of warming trend is seems to be due to lack of increase of green house gases.

15 Climate change Projection: Precipitation  Time series of annual mean precipitation averaged over model domain OBS (CRU) GCM-Historical GCM –RCP4.5 GCM –RCP8.5 RCM -Historical RCM –RCP4.5 RCM – RCP8.5 Difference-Historical Difference –RCP4.5 Difference –RCP8.5  Inter-annual variability of both GCM RCM is weak.

16 Climate change Projection: Anomalies  Reference period: 1971-2000 OBS (CRU) GCM-Historical GCM –RCP4.5 GCM –RCP8.5 RCM -Historical RCM –RCP4.5 RCM – RCP8.5  It is clear that RCM tends to underestimate warming trend.

17 Climate change projection: Temperature Current climate (1971-2000) Change (2070-2099) RCP4.5RCP8.5 GCM 18.51 ℃ 2.80 ℃ 4.87 ℃ RCM 18.13 ℃ 2.69 ℃ 4.62 ℃ CurrentChange (RCP4.5)Change (RCP8.5) GCM RCM

18 Climate change projection: Precipitation Current climate (1970-2000) Change (2070-2099) RCP4.5RCP8.5 GCM4.84 mm/day8.29 %9.27 % RCM5.24 mm/day6.24 %7.43 % CurrentChange (RCP4.5)Change (RCP8.5) GCM RCM

19 Summary 1  Overall, performance of HadGEM3-RA on current climate simulation is similar to HadGEM2-AO.  However, HadGEM3-RA could resolve small-scale features related with topography and coastline.  General patterns of regional climate change projection by HadGEM3-RA is similar to projection by HadGEM2-AO.  But, HadGEM3-RA tends to underestimate warming trend due to lack of increase of green house gases.

20 Evaluation of current climate simulation in GCM forcing run (12.5km res) in GCM forcing run (12.5km res) - surface climate - surface climate

21 Climatology (1971-200): Precipitation ObservationGCMRCM Annual Summer Winter  RCM could resolve small-scale features related with topography and coastlines.  RCM of 12.5km-res is better than not only GCM but also RCM of 50km-res.

22 Climatology (1971-200): Temperature ObservationGCMRCM Annual Summer Winter  RCM could resolve small-scale features related with topography and coastlines.  RCM of 12.5km-res is better than not only GCM but also RCM of 50km-res.

23 Bias: Precipitation and Temperature Annual Summer Winter RCMGCMRCMGCM  Precipitation  Temperature

24 Statistics: Precipitation & Temperature (Land) Variables Mean (OBS) Mean (Model) BiasRMSE Pattern Corr. Precip. (mm/day) ANN2.28 GCM2.480.200.420.936 RCM2.580.300.460.938 JJA4.50 GCM4.27-0.230.840.830 RCM4.780.281.120.723 DJF0.83 GCM1.230.400.450.894 RCM1.130.300.360.916 Temp. (°C) ANN8.68 GCM8.04-0.641.380.954 RCM7.95-0.730.910.986 JJA21.93 GCM22.870.941.400.904 RCM22.730.801.200.935 DJF-5.90 GCM-8.93-3.033.800.948 RCM-8.93-3.033.040.986  Mean, bias, Root-mean-squared error (RMSE) and pattern correlation coefficient of precipitation and temperature. (Ref. APHRO and CRU)  Overall, RCM show better performance than GCM. But, RCM shows wet/cold biases.

25 Annual cycle of Precipitation and temperature  30-yr mean annual cycle of area-averaged precipitation and surface air temperature (1971~2000) Precip. Temp  Black: OBS  Red: GCM  Blue: RCM Corr.PrecipTemp GCM0.9510.999 RCM0.9630.999

26 Probability of daily precipitation  The probability of daily precipitation with thresholds up to 50 mm/day Thresholds (mm/day) Probability (%)  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.

27 Climate change projection (12.5km) Change in surface air temperature and precipitationChange in surface air temperature and precipitation

28 Climate change projection: Temperature  Time series of annual mean surface air temperature averaged over model domain OBS (CRU) GCM-Historical GCM –RCP4.5 GCM –RCP8.5 RCM -Historical RCM –RCP4.5 RCM – RCP8.5 Difference-Historical Difference –RCP4.5 Difference –RCP8.5  RCM tends to underestimate warming trend  Underestimation of warming trend is seems to be due to lack of increase of green house gases.

29 Climate change projection: Precipitation  Time series of annual mean precipitation averaged over model domain OBS (CRU) GCM-Historical GCM –RCP4.5 GCM –RCP8.5 RCM -Historical RCM –RCP4.5 RCM – RCP8.5 Difference-Historical Difference –RCP4.5 Difference –RCP8.5  Inter-annual variability of RCM is similar to observation.

30 Climate change Projection: Anomalies  Reference period: 1971-2000 OBS (CRU) GCM-Historical GCM –RCP4.5 GCM –RCP8.5 RCM -Historical RCM –RCP4.5 RCM – RCP8.5  It is clear that RCM tends to underestimate warming trend.

31 Climate change projection: Temperature Current climate (1971-2000) Change (2070-2099) RCP4.5RCP8.5 GCM11.193.516.13 RCM11.143.335.79 CurrentChange (RCP4.5)Change (RCP8.5) GCM RCM

32 Climate change projection: Precipation Current climate (1970-2000) Change RCP4.5RCP8.5 GCM3.5311.7014.96 RCM3.1711.8317.90 CurrentChange (RCP4.5)Change (RCP8.5) GCM RCM

33 Summary 2  Overall, performance of HadGEM3-RA on current climate simulation is better than HadGEM2-AO.  HadGEM3-RA could resolve small-scale features related with topography and coastline.  And, HadGEM3-RA reproduced climate extreme better than HadGEM2-AO.  General patterns of regional climate change projection by HadGEM3-RA is similar to projection by HadGEM2-AO.  But, HadGEM3-RA tends to underestimate warming trend due to lack of increase of green house gases.

34 Future plan  New downscaling experiments will be performed with all RCP scenarios (RCP2.6/4.5/6.0/8.5) including prescribed green house gases. Task: ECSK06 Regional downscalingHS Kang, S Park R Jones Key milestones: Jul 2008:agree a visit (3~6 months) of a KMA expert to set up and test the HadGEM regional model for using ERA-40 Oct 2008:initiate a visit to work on the regional model Dec 2008:joint report on implementation of UM-regional model for downscaling over the East Asian region (0.2FTE) Jun 2009:report on evaluation of the UM-regional model using perfect boundary conditions on seasonal simulations of East Asian monsoon activity (0.3FTE) Jun 2010:progress report on evaluation of HadGEM3-RA over the East Asian region in long-term simulations Dec 2011:report on evaluation of HadGEM3-RA with a focus on climate variability in long-term integrations using ECMWF interim reanalysis data, associated with CORDEX participation (0.3FTE) Dec 2011:report on assessment of East Asian climate downscaled by HadGEM3-RA using global climate change projections, associated with CORDEX participation (0.3FTE) Dec 2012:report on assessment of regional climate over Korea peninsular downscaled by HadGEM3-RA with using global climate change projections of RCP 2.6/4.5/6.0/8.5 (0.3FTE)

35 Thank you very much!

36 Precipitation: Annual mean climatology Observation RCM  Climatology of annual precipitation GCM GCM biasRCM biasRCM effect

37 Precipitation: JJA mean climatology  Climatology of summer precipitation Observation RCM GCM GCM biasRCM biasRCM effect

38 Large-scale field: 500-hPa height (JJA) ObservationGCMRCM  Both GCM and RCM enhanced upper trough. GCM bias RCM bias RCM effect

39 Low level circulation: SLP, 850-hPa wind/humidity ObservationGCMRCM  Both GCM has cyclonic anomalies over East Asian monsoon region.  And, RCM enhanced cyclonic anomalies. GCM bias RCM bias RCM effect

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