New Seasonal Prediction System JMA/MRI-CPS2 (JMA/MRI-CGCM2)

Slides:



Advertisements
Similar presentations
Climate Prediction Division Japan Meteorological Agency Developments for Climate Services at Japan Meteorological Agency 1.
Advertisements

LRF Training, Belgrade 13 th - 16 th November 2013 © ECMWF Sources of predictability and error in ECMWF long range forecasts Tim Stockdale European Centre.
© Crown copyright Met Office The Met Office high resolution seasonal prediction system Anca Brookshaw – Monthly to Decadal Variability and Prediction,
1 Trend and Year-to-year Variability of Land-Surface Air Temperature and Land-only Precipitation Simulated by the JMA AGCM By Shoji KUSUNOKI, Keiichi MATSUMARU,
INPE Activities on Seasonal Climate Predictions Paulo Nobre INPE-CCST-CPTEC WGSIP-12, Miami, January 2009.
Verification of NCEP SFM seasonal climate prediction during Jae-Kyung E. Schemm Climate Prediction Center NCEP/NWS/NOAA.
1 Seasonal Forecasts and Predictability Masato Sugi Climate Prediction Division/JMA.
Task: (ECSK06) Regional downscaling Regional modelling with HadGEM3-RA driven by HadGEM2-AO projections National Institute of Meteorological Research (NIMR)/KMA.
Workshop on Weather and Seasonal Climate Modeling at INPE - 9DEC2008 INPE-CPTEC’s effort on Coupled Ocean-Atmosphere Modeling Paulo Nobre INPE-CPTEC Apoio:
Outline Further Reading: Detailed Notes Posted on Class Web Sites Natural Environments: The Atmosphere GG 101 – Spring 2005 Boston University Myneni L31:
Seasonal outlook of the East Asian Summer in 2015 Motoaki Takekawa Tokyo Climate Center Japan Meteorological Agency May th FOCRAII 1.
Exeter 1-3 December 2010 Monthly Forecasting with Ensembles Frédéric Vitart European Centre for Medium-Range Weather Forecasts.
1 Assessment of the CFSv2 real-time seasonal forecasts for Wanqiu Wang, Mingyue Chen, and Arun Kumar CPC/NCEP/NOAA.
JMA Monthly and Seasonal Forecast Systems
Towards Improving Coupled Climate Model Using EnKF Parameter Optimization Towards Improving Coupled Climate Model Using EnKF Parameter Optimization Zhengyu.
Background of Symposium/Workshop 1 Yuhei Takaya Climate Prediction Division Japan Meteorological Agency International Workshop.
EUROBRISA Workshop – Beyond seasonal forecastingBarcelona, 14 December 2010 INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA Beyond seasonal forecasting F. J. Doblas-Reyes,
Air-sea interaction over the Indian Ocean after El Nino in JMA/MRI-CGCM seasonal forecast experiment Tamaki Yasuda Meteorological.
Multi-Perturbation Methods for Ensemble Prediction of the MJO Multi-Perturbation Methods for Ensemble Prediction of the MJO Seoul National University A.
Shuhei Maeda Climate Prediction Division
Improved ensemble-mean forecast skills of ENSO events by a zero-mean stochastic model-error model of an intermediate coupled model Jiang Zhu and Fei Zheng.
ENSO Variability in SODA: SULAGNA RAY BENJAMIN GIESE TEXAS A&M UNIVERSITY WCRP 2010, Paris, Nov
Ben Kirtman University of Miami-RSMAS Disentangling the Link Between Weather and Climate.
1 JRA-55 the Japanese 55-year reanalysis project - status and plan - Climate Prediction Division Japan Meteorological Agency.
3. Products of the EPS for three-month outlook 1) Outline of the EPS 2) Examples of products 3) Performance of the system.
Y. Fujii 1, S. Matsumoto 1, T. Yasuda 1, M. Kamachi 1, K. Ando 2 ( 1 MRI/JMA, 2 JAMSTEC ) OSE Experiments Using the JMA-MRI ENSO Forecasting System 2nd.
Toward Seasonal Climate Forecasting and Climate Projections in Future Akio KITOH Meteorological Research Institute, Tsukuba, Japan FOCRAII, 6-8 April 2010,
2010/ 11/ 16 Speaker/ Pei-Ning Kirsten Feng Advisor/ Yu-Heng Tseng
The GEOS-5 AOGCM List of co-authors Yury Vikhliaev Max Suarez Michele Rienecker Jelena Marshak, Bin Zhao, Robin Kovack, Yehui Chang, Jossy Jacob, Larry.
Modes of variability and teleconnections: Part II Hai Lin Meteorological Research Division, Environment Canada Advanced School and Workshop on S2S ICTP,
The Centre for Australian Weather and Climate Research A partnership between CSIRO and the Bureau of Meteorology Sub-Seasonal Prediction Activities and.
Application of T382 CFS Forecasts for Dynamic Hurricane Season Prediction J. Schemm, L. Long, S. Saha and S. Moorthi NOAA/NWS/NCEP October 21, 2008 The.
Dynamic Hurricane Season Prediction Experiment with the NCEP CFS Jae-Kyung E. Schemm January 21, 2009 COLA CTB Seminar Acknowledgements: Lindsey Long Suru.
Mukougawa, Hitoshi 1, Yuhji Kuroda 2 and Toshihiko Hirooka 3 1 Disaster Prevention Research Institute, Kyoto University, JAPAN 2 Meteorological Research.
Future Projections of Precipitation Characteristics in Asia.
One-year re-forecast ensembles with CCSM3.0 using initial states for 1 January and 1 July in Model: CCSM3 is a coupled climate model with state-of-the-art.
ECMWF Training course 26/4/2006 DRD meeting, 2 July 2004 Frederic Vitart 1 Predictability on the Monthly Timescale Frederic Vitart ECMWF, Reading, UK.
11 th WGSIP Workshop, 7-8 June 2007, Barcelona, Spain Seasonal Climate Predictions at CPTEC-INPE Paulo Nobre CPTEC/INPE.
WCC-3, Geneva, 31 Aug-4 Sep 2009 Advancing Climate Prediction Science – Decadal Prediction Mojib Latif Leibniz Institute of Marine Sciences, Kiel University,
© Crown copyright Met Office Predictability and systematic error growth in Met Office MJO predictions Ann Shelly, Nick Savage & Sean Milton, UK Met Office.
1 An Assessment of the CFS real-time forecasts for Wanqiu Wang, Mingyue Chen, and Arun Kumar CPC/NCEP/NOAA.
Status / Progress Report for GPC Tokyo Takayuki Tokuhiro Climate Prediction Division, JMA
Fifth Session of the South Asian Climate Outlook Forum (SASCOF-5) JMA Seasonal Prediction of South Asian Climate for Summer 2014 Hitoshi Sato Climate Prediction.
The impact of lower boundary forcings (sea surface temperature) on inter-annual variability of climate K.-T. Cheng and R.-Y. Tzeng Dept. of Atmos. Sci.
1/39 Seasonal Prediction of Asian Monsoon: Predictability Issues and Limitations Arun Kumar Climate Prediction Center
Equatorial Atlantic Variability: Dynamics, ENSO Impact, and Implications for Model Development M. Latif 1, N. S. Keenlyside 2, and H. Ding 1 1 Leibniz.
and Decadal Prediction Systems
GPC-Montreal - Status Report - March 2014
Canadian Seasonal to Interannual Prediction System (CanSIPS)
Climate Change Climate change scenarios of the
JMA Seasonal Prediction of South Asian Climate for OND 2017
Seasonal outlook for summer 2017 over Japan
JMA Seasonal Prediction of South Asian Climate for OND 2017
GPC-Seoul: Status and future plans
Climate and Global Dynamics Laboratory, NCAR
Challenges of Seasonal Forecasting: El Niño, La Niña, and La Nada
WCRP Workshop on Seasonal Prediction
seasonal prediction for Myanmar
Climate prediction activities at Météo-France
Seasonal-to-interannual climate prediction using a fully coupled OAGCM
ATMS790: Graduate Seminar, Yuta Tomii
Operational MJO prediction at ECMWF
Sub-seasonal prediction at ECMWF
Extratropical stratoshere-troposphere exchange in a 20-km-mesh AGCM
Seasonal Predictions for South Asia
1 GFDL-NOAA, 2 Princeton University, 3 BSC, 4 Cerfacs, 5 UCAR
T. Ose, T. Yasuda (MRI/JMA), Y. Takaya, S. Maeda, C. Kobayashi
GloSea4: the Met Office Seasonal Forecasting System
Decadal Climate Prediction at BSC
Extratropical Climate and Variability in CCSM3
Presentation transcript:

New Seasonal Prediction System JMA/MRI-CPS2 (JMA/MRI-CGCM2) Tamaki Yasuda (tyasuda@met.kishou.go.jp) Japan Meteorological Agency (GPC Tokyo) WGSIP17, 13-14 September 2015, Norkörrping, Sweden

History of JMA Coupled Prediction System 2015 JMA/MRI-CPS2 Atm: TL159 (~110 km, 1.125 deg.) L60 (~0.1hPa) Ocn: 1x0.5-0.3 L52+BBL                                  2010 JMA/MRI-CPS1 for Seasonal Prediction 2008 JMA/MRI-CPS1 for ENSO Outlook Atm: TL95 (~180 km, 1.875 deg.) L40 (~0.4hPa) Ocn: 1x1-0.3 (30N-30S) L50 (top: ~1m) 2003 JMA-CGCM02 (GSM0103) for ENSO Outlook Atm: T63 (~180 km, 1.875 deg.) L40 (~0.4hPa) Ocn: 2.5x2-0.5(10N-10S) L20 (top: ~10 m) 1999 JMA-CGCM01 (GSM8911) for ENSO Outlook Atm: T42 (~250 km) L21 (~10 hPa) Ocn: 2.5x2-0.5(10N-10S) L20 Takaya et al., in prep. Takaya et al.: Japan Meteorological Agency/Meteorological Research Institute-Coupled Prediction System version 1 (JMA/MRI-CPS1) for operational seasonal forecasting, to be submitted to Climate Dynamics. WGSIP17, 13-14 September 2015, Norkörrping, Sweden

Updated Seasonal Ensemble Prediction System JMA/MRI-CPS1 (Previous) JMA/MRI-CPS2 (New) Atmosphere (JMA-AGCM) TL95L40, ~180km, Up to 0.4hPa TL159L60 , ~110km, Up to 0.1hPa Stochastic Tendency Perturbation GHG forcing in RCP4.5 scenario Ocean (MRI.COM) (Tsujino et al 2010) 1.0º (lon) x 0.3-1º (lat) L50 75ºS-75ºN Ocean Sea-ice climatology 1.0º (lon) x 0.3-0.5º (lat) L52+BBL Global Ocean with Tripolar Grids Sea-ice model Coupler (Scup) (Yoshimura and Yukimoto 2008) 1-hour coupling interval Momentum and heat flux adjustments No flux adjustment Initial Condition Atmosphere: JRA-25 Land: Climatology with ERA-15 forcing Ocean: MOVE/MRI.COM-G T, S&SSH (Usui et al. 2006) Atmosphere: JRA-55 Land: JRA-55 land analysis Ocean: MOVE/MRI.COM-G2 T, S & SSH Ensemble Size 51 (9 BGMs, 6 days with 5-day LAF) (13 BGMs, 4 days with 5-day LAF) *Hindcast: 10 member ensembles (5 BGMs, twice a month, 1979-2014) WGSIP17, 13-14 September 2015, Norkörrping, Sweden 3

New Sources of Predictability Global ocean domain Dynamical sea ice simulation Fully covered stratosphere (Top: 0.1 hPa) Land initialization with JRA-55 More sophisticated description of GHGs (6 gases prescribed with RCP4.5 scenario) The new system is capable of incorporating a wide range of potential sources of the predictability. Atmo-sphere Strato-sphere Sea Ice Ocean Land WGSIP17, 13-14 September 2015, Norkörrping, Sweden 4

SST Standard Deviation (DJF) Observation (COBE-SST) - Too large amplitude of SST interannual variability is reduced. JMA/MRI-CPS2 ; LT=1 month (initial: Nov) JMA/MRI-CPS1 ; LT=1 month (initial: Nov) JMA/MRI-CPS2 ; LT=4 months (initial: Aug) JMA/MRI-CPS1 ; LT=4 months (initial: Aug) [ K ] WGSIP17, 13-14 September 2015, Norkörrping, Sweden

WGSIP17, 13-14 September 2015, Norkörrping, Sweden ENSO Prediction ACC of NINO.3 SST RMSE of NINO.3 SST - Better prediction skill of ENSO for longer lead time in the new system WGSIP17, 13-14 September 2015, Norkörrping, Sweden

ACC for 3-month forecast 2-m temperature averaged in the Northern Hemisphere (20-90N) Precipitation averaged in the Tropical Region (20S-20N) WGSIP17, 13-14 September 2015, Norkörrping, Sweden

Forecast of Arctic Sea Ice Arctic sea-ice extent (Initial: May) Arctic sea-ice extent in September Observation (COBE-ICE) Lead time of 2 months ACC=0.73 Lead time of 5 months ACC=0.68 Color: Individual forecast Black: Observed climatology (COBE-ICE) (Grey shaded) minimum and maximum - Seasonal variation of Arctic sea ice - Interannual variability and reduction trend of Arctic sea-ice extent WGSIP17, 13-14 September 2015, Norkörrping, Sweden 8

Improvement with Land Initialization Anomaly correlation of monthly 2-m temperature over land (lead time: 0 month) Land Initialization (JMA/MRI-CPS1: Climatology with ERA-15 forcing) JMA/MRI-CPS2: JRA-55 land analysis (Land A) Additional experiment: Climatology (1981-2010) of JRA-55 land analysis (Land C) Global Northern Hemisphere Eurasia ACC Initial Month Initial Month Initial Month Land A: slightly better predictions than Land C Global: 90S-90N,0E-360E NH: 20N-90N,0E-360E Eurasia: 20N-90N,0E-180E WGSIP17, 13-14 September 2015, Norkörrping, Sweden

Improvement with GHGs Trend 2-m temperature Trend over Land (JJA) (Initial: May) VarCO2 - JMA/MRI-CPS1 - CO2 Trend VarGHG - JMA/MRI-CPS2: - CO2, CH4, N2O, CHC-11, CHF-12, HCFC-22 (GHGs) Trend ConstGHG (Additional experiment) - JMA/MRI-CPS2 - Constant GHGs VarCO2 ConstGHG VarGHG JRA-55 * The land-sea mask is different between JRA-55 and model Linear trend of 2-m temperature: VARCO2 < ConstGHG < VarGHG < JRA-55 WGSIP17, 13-14 September 2015, Norkörrping, Sweden

Summary (JMA/MRI-CPS2) JMA’s seasonal EPS has been updated in June 2015. 1. Improvement of JMA/MRI-CPS2: - Enhanced horizontal / vertical resolution - New sources of predictability: such as global ocean, stratosphere, sea ice and GHGs - New initial conditions: JRA-55 for atmosphere and land surface MRI.COM/MOVE-G2 for ocean 2. Improvement of prediction skill: - ENSO amplitude of interannual variability and prediction skill - 3-month forecast (such as 2-m temperature, precipitation) - Sea-ice interannual variability and reduction trend - Warming trend of 2-m temperature over land - MJO amplitude (not shown) WGSIP17, 13-14 September 2015, Norkörrping, Sweden

New Ensemble Initialization System for Seamless Climate Predictions For WGSIP Sep, 2015 Activity of MIROC group New Ensemble Initialization System for Seamless Climate Predictions Masayoshi Ishii Meterological Research Institute (MRI), Japan Meteorological Agency (JMA) with colleagues of AORI, NEIS, JAMSTEC, and MRI (This system is used in MRI and MIROC groups.)

WGSIP17, 13-14 September 2015, Norkörrping, Sweden Purposes To provide less uncertain information on global warming projection and prediction. To realize seasonal-to-decadal predictions seamlessly for future climate variations. To reproduce past climate changes and variations for more than 100 years for understanding past global warming and for improvement of climate prediction models. To understand mechanisms of climate variations on various spatio-temporal scales. WGSIP17, 13-14 September 2015, Norkörrping, Sweden

New Initialization System & 150-yr Reanalysis Coupled Model + EnKF From 1850 to present Ocean Obs. Quality Control Objective Analysis w/ EnKF CGCM Ensemble Run Atmos. Obs. Computational Node Reformation Spec. (ver. 1) LETKF (Hunt et al. 2007) Loosely coupled, full DA Assimilation interval: 6 hours for Atmos. and 5 days for Ocean Surface Pressure Data (ISPD v.3.2.8) Typhoon bogus (IBTrACS v03r05) SST: COBE-SST2 + SST perturbations Gridded subsurface ocean T and S (Ishii and Kimoto 2009) A new system configured specifically for long-term climate reanalysis The same initialization system is used by MRI and MIROC groups. WGSIP17, 13-14 September 2015, Norkörrping, Sweden

500hPa Geopotential height at 12UTC, February 20th, 2005 ERA-Interim EnKF Spread Pattern CC after subtracting zonal means Pattern Correlation Time Preliminary Run with MIROC3m (T42L20 AGCM and 1-deg OGCM)

Exercise on Nino-3.4 SSTA Prediction Raw predictions Training period of prediction models is now extended to the beginning of the 20th Century or more. Prediction biases removed.

Thank you for your attention. WGSIP17, 13-14 September 2015, Norkörrping, Sweden

Improved Madden-Julian Oscillation Initial: Nov-Apr Wheeler-Hendon Index: RMM1, RMM2 Correlation JMA/MRI CPS2 JMA/MRI CPS1 JMA 1-month EPS (AGCM) [day] larger amplitude Amplitude Error smaller amplitude - Forecast time of 0.5 correlation extends roughly 2 days. - Too small MJO amplitude is improved. WGSIP17, 13-14 September 2015, Norkörrping, Sweden

ACC for 3-month forecast 2-m temperature JMA/MRI-CPS2 2-m temperature averaged in the Northern Hemisphere (20-90N) (Initial: May) JJA GLB:0.414 NH:0.375 (Initial: Nov) DJF GLB:0.405 NH:0.323 WGSIP17, 13-14 September 2015, Norkörrping, Sweden

ACC for 3-month forecast Precipitation JMA/MRI-CPS2 Precipitation averaged in the Tropical Region (20S-20N) (Initial: May) JJA GLB:0.167 TRP:0.342 (Initial: Nov) DJF GLB:0.216 TRP:0.362 WGSIP17, 13-14 September 2015, Norkörrping, Sweden

T2 and 300hPa Geopotential height February, 2010 EnKF ERA-I Preliminary Run with MIROC3m (T42L20 AGCM and 1-deg OGCM) Shade: SAT Contour: Z300

Monthly Mean Precipitation July 2010 GPCP July 2010 GPCP EnKF EnKF July 2010 Internannual variability (1980-2010)

Atmosphere and Ocean Data Rescue Recently processed SLP data (1890 – 1940; Kubota et al. JAMSTEC) Upper air observations to be digitized  Expected data distribution (red) of marine met. Obs. by the Japanese imperial navy (1903-1944, ~ 1 million) Participating in and collaborating with international activities: ACRE, ICOADS, ICA&D, IQuOD Collaborating with ACRE, ICOADS, and IQuOD …