Laux, P.

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High-resolution climate information of SE Asia: Data for impacts modelers and decision makers Patrick Laux Karlsruhe Institute of Technology (KIT)  Institute of Meteorology and Climate Research Department of Atmospheric Environmental Research (IMK-IFU)  Outline • Background & Motivation • LUCCi Project • Study Region • Dynamical Downscaling • Regional Climate Simulations • WRF Parameterization Experiments • Long-term Simulations: Trends and Expected Climate Change • Summary & Outlook   BMBF Project LUCCi … more information at:                 http://www.lucci-vietnam.info/ Problem: Data availability Souvignet et al., 2013 Vu Gia Thu Bon River Basin Data availability in VGTB basin • Sparse observation network of hydro-meteorological data • Few hydrometeorological stations (located in lowlands) • Low sampling rates (daily)   • Stakeholders demand for scientific sound CC adaptation strategies, e.g.:  • Flood protection measures (adaptation of infrastructure) • Future hydropower potential (low flows) • Water availability for agriculture  → High-resolution hydro-meteorological data (past and future) required to feed hydrological and agricultural impact models! GCMs Atmosphere & Ocean Δx ≈ 300…100km RCMs Δx ≈ 50…10km P, T, RH, u, v … Dynamical Downscaling Population Growth, Economic Development & Technological Progress Emission Scenarios of GHG Concentrations Global Climate Models Global Climate Scenarios Downscaling Methods Regional Climate Scenarios Weather Research and Forecast Model (WRF) Horizontal exchange between columns (momentum, heat and moisture) Vertical exchange between layers Atmospheric compartment  • Next generation atmospheric modeling system • Developed at NCAR • Successor of the Mesoscale Model 5 (MM5) • Various applications at different scales: • Weather forecasts • (Long-term) climate simulations  • Atmospheric and (sub)surface       compartments:     Atmosphere – Explicit Calculation of … Gas law Precipitation Energy conservation at land surface (Sub)surface Compartment  • Lower boundary: SVAT-model for surface and subsurface water budgets  • Joint atmospheric-terrestrial water budget calculations Surface and subsurface compartment Land (sub)surface – Model Equations   zTKztTC iv )(bs a ts a t )()( )()()(FzKzDzt  )()( KD32)()(bsatsatKKbs a ts a t)()(  elsePorPIfeKffPtemptf ]01.5[420174.0)7.2(,)()536.01(43.6)(0864.0)(EpoteevRsatanwil trefpotwi l tfE)()1(E1dirrs a tqhcrs a tqcRCRRB11]log[]log[²00zhzhkCaan)( ,avs f cha i rvmo i stTTCcH WRF Setup • Domain D01 • horizontal: 99 x 99 grid points with a resolution of 45 km • vertical: 50 layers up to 50 hPa • time step: 180 s • Domain D02 • horizontal: 142 x 145 grid points with a resolution of 15 km • vertical: 50 layers up to 50 hPa • time step: 120 s • Domain D03 • horizontal: 66 x 75 grid points with a resolution of 5 km • vertical: 50 layers up to 5000 Pa • time step: 30 s  D01 D02 D03 → GOAL: Transient WRF simulation from 1960 - 2050 Outline • Background & Motivation • LUCCi Project • Study Region • Dynamical Downscaling • Regional Climate Simulations • WRF Parameterization Experiments • Long-term Simulations: Trends and Expected Climate Change • Summary & Outlook Parameterization Experiments Run Microphysic schemes PBL physic schemes Cumulus physic schemes B Lin et al.  Hong et al. Betts-Miller-Janjic  C Lin et al. Nakanishi and Niino Betts-Miller-Janjic D Lin et al.  Nakanishi and Niino New SAS E Lin et al. Hong et al.  New SAS F WRF Single-Moment 3-class  Hong et al.  Betts-Miller-Janjic G WRF Single-Moment 3-class  Nakanishi and Niino Betts-Miller-Janjic H WRF Single-Moment 3-class  Hong et al.  New SAS I WRF Single-Moment 3-class  Nakanishi and Niino New SAS J WRF Double-Moment 6-class  Hong et al.  Betts-Miller-Janjic K WRF Double-Moment 6-class Nakanishi and Niino Betts-Miller-Janjic L WRF Double-Moment 6-class  Nakanishi and Niino New SAS M WRF Double-Moment 6-class Hong et al.  New SAS - 12 Combinations using 3 MP, 2 PBL and 2 CU schemes - 2 Combinations using NCEP & ERA40 Reanalyses → 2 x 12 = 24 WRF simulation for 2000 performed WRF Parameterization Experiments: Run B Run D Run K Run M Simulated annual precipitation for 2000 [mm] [mm] [mm] [mm] Validation ERA40-WRF: Daily Precipitation Annually Autumn Summer Spring Winter A: APHRODITE data = Reference   Pearson Correlation Coefficient Root Mean Squared Error Standard Deviation Final Decision (simulated T, P) Run Microphysic schemes PBL physic schemes Cumulus physic schemes B Lin et al.  Hong et al. Betts-Miller-Janjic  C Lin et al. Nakanishi and Niino Betts-Miller-Janjic D Lin et al.  Nakanishi and Niino New SAS E Lin et al. Hong et al.  New SAS F WRF Single-Moment 3-class  Hong et al.  Betts-Miller-Janjic G WRF Single-Moment 3-class  Nakanishi and Niino Betts-Miller-Janjic H WRF Single-Moment 3-class  Hong et al.  New SAS I WRF Single-Moment 3-class  Nakanishi and Niino New SAS J WRF Double-Moment 6-class  Hong et al.  Betts-Miller-Janjic K WRF Double-Moment 6-class Nakanishi and Niino Betts-Miller-Janjic L WRF Double-Moment 6-class  Nakanishi and Niino New SAS M WRF Double-Moment 6-class Hong et al.  New SAS ERA40 Reanalysis  • Lower bias in T • Higher pattern correlation of P (summer) (Laux et al., 2013) ERA40-WRF vs. Obs (DOM2): P, T2 CRU_T2 WRF_T2 WRF_P APH_P Outline • Background & Motivation • LUCCi Project • Study Region • Dynamical Downscaling • Regional Climate Simulations • WRF Parameterization Experiments • Long-term Simulations: Trends, Expected Climate Change • Summary & Outlook Past Climate: Annual trends WRF-ERA40 T2 (1971-2000) P (1971-2000) Significant at α=0.05 Not significant at α=0.05 [° C/year] [mm/year] Past Climate: Seasonal trends WRF-ERA40  T2 (1971-2000) [° C/year] [° C/year] [° C/year] [° C/year] DJF SON MAM JJA Expected Climate Change: WRF-ECHAM5 (2001-2030) minus (1971-2000)  (2021-2050) minus (1971-2000) B1 A1B [mm] [mm] [mm] [mm] Precipitation Tailor-made Information for Stakeholders Change of Precipitation Extremes (P95) A1B: (2021-2050) minus (1971-2000) [%] Tailor-made Information for Stakeholders Change of mean Wind Speed A1B: (2021-2050) minus (1971-2000) [ms-2] Tailor-made Information for Stakeholders Change of Growing Days of 2nd cropping cycle (Rice) A1B: (2021-2050) minus (1971-2000) [days] Growing days = Number days after 1 July between the first occurrence of > 6 consecutive days with T2 > 20 °C and > 6 consec. days with T2 < 20 °C Summary & Outlook • High-resolution and reliable climate information (~1 Mio CPU hours):      WRF-ECHAM5 (1961-2050) for A1B and B1, WRF-ERA40 (1971-2000)  • Past: T2 increased up ~1.2° C & P increased ~300 mm (1971-2000),  but: strong spatio-temporal differences • Future: CC is expected to increase (decrease) water availability  for B1 (A1B) scenario, increase of precipitation extremes expected       • Addressing stakeholder’s needs and providing tailor-made information for different sectors (agriculture, hydrology, energy, etc.) → please contact me  • Further Research:   • Analysis of occurrence probabilities & return intervals of rare events (Extremes Value Theory) • Uncertainty estimation coming from forcing GCMs (Statistical Downscaling)   Acknowledgments Donors of LUCCi Project    Organization Team     Computing Resources and Support   Contact: patrick.laux@kit.edu  Open question: Bias correction? Real bias of WRF simulations or artifacts in gridded rainfall products induced by interpolation of sparse hydro-meteorological observations and lack of data in high elevations?  Mean daily precipitation bias WRF-ERA40 (1971-2000) [mm/day] Validation WRF: T2 (Year 2000) Annually Annually ERA40 NCEP A: CRU data Pearson Correlation Coefficient Root Mean Squared Error Standard Deviation Validation NCEP-WRF: Daily Precipitation Annually Autumn Summer Spring Winter A: APHRODITE data = Reference data  Pearson Correlation Coefficient Root Mean Squared Error Standard Deviation Expected Climate Change: WRF-ECHAM5 WRF Modeling System  LINUX Cluster  at  KIT, IMK-IFU LINUX Cluster  at  KIT, IMK-IFU HPC XC4000 Karls- ruhe Technical details: building and running WRF at XC4000 (KIT, SCC) • Building – WRF written in Fortran90 – MPI interface and programs for file parsing written in C – WRF build relies on Perl version 5 or later and standard UNIX utilities – Modules used • intel/12.0.5/default  • gcc/4.6.0/default • hp-mpi/2.3.1/default (distributed memory) – External libraries • netCDF library  • Running  – Job chains • 1 simulation year per job • Restart run performed for each month Performance of WRF at XC4000 (KIT, SCC) Further technical details: • CPU resources (for 1 year simulation) – Number of CPUs: 128 – Sum of CPU-time over all processors: 182-16:30:02 – Elapsed time: 1-11:39:26 – Maximum virtual memory by any process: 701.62M – Maximum number of minor page faults for any process: 40663916 – Total number of voluntary context switches for all processes: 418026354  • Approved computation time: 69125d 0h 0m 0s  – Already used: 28343d 5h 4m 38s (41.00%) Reference Data CPC 0.5° CRU 0.5° APH 0.25° GPCC 0.5° Year 2000 Trends Precip annual ERA40 

High-resolution climate information of Southeast Asia: Data for impacts modelers and decision makers

https://publikationen.bibliothek.kit.edu/230095096/3816508

High-resolution climate information of Southeast Asia: Data for impacts modelers and decision makers

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