North American Multi-Model Ensemble (NMME) Bill Merryfield Canadian Centre for Climate Modelling and Analysis (CCCma) CITES-2019 International Young Scientists School, 28 May 2019
https://www.cpc.ncep.noaa.gov/products/NMME/ • US, Canadian operational forecast systems + US research systems • Hindcasts and real time forecasts • Real time forecasting since Aug 2011 • Data openly accessible: https://iridl.ldeo.columbia.edu/SOURCES/.Models/.NMME/ • Requirements for inclusion - ensemble system, range 9 months - must provide hindcast data for 1982-2010 - commitment to provide real time forecasts by 8 th of each month (CPC operational schedule)
NMME Organization Operational Centers Research Centers ECCC GFDL NASA NCAR NCEP 8 th of each month Real-time Hindcasts Forecasts Research User/applications Community Community
Currently contributing models Model Center Ensemble size CFSv2 NCEP 24 (28) CanCM3 EC/CMC 10 CanCM4 EC/CMC 10 FLOR GFDL 24 CM2.1 GFDL 10 CCSM4 NCAR 10 GEOS-5 NASA 11 Total ensemble size 99 (103)
Deterministic and probabilistic forecasts Prate 2015 OND from 201509 (1 month lead) Deterministic Probabilistic Models weighted equally Ensemble members weighted equally * *Anomalies and tercile boundaries computed separately for each model
Individual model forecasts Individual model skills
ENSO plumes Nino3.4 forecast from June 2015 Ensemble means All ensemble members great 2015-16 El Niño well predicted at least 6 months in advance
Advantages of a multimodel ensemble Deterministic skill (anomaly correlation of ensemble means)* Nino3.4 2m temperature precipitation SST index (land 23N-75N) (land 23N-75N) (23N-75N) Becker et al. 2014 https://doi.org/10.1175/JCLI-D-13-00597.1 *for earlier set of NMME models
Advantages of a multimodel ensemble Reliability of probabilistic forecasts 2m temperature (land 23N-75N) CFSv2: 1 model, ensemble size 24 miniNMME: 6 models, ensemble size 24 (6 4) NMME: 6 models, ensemble size 75 lower is better above normal (A) below normal (B) higher is better near normal Becker et al. 2016 https://doi.org/10.1175/JCLI-D-14-00862.1
NMME for International Regions https://www.cpc.ncep.noaa.gov/products/international/nmme/nmme.shtml
NMME temperature forecast for JJA 2019 lead 1 month Deterministic (ensemble mean anomaly) Probabilistic (tercile probabilities) Deterministic skill (anomaly correlation)
NMME precipitation forecast for JJA 2019 lead 1 month Deterministic (ensemble mean anomaly) Probabilistic (tercile probabilities) Deterministic skill (anomaly correlation)
More about NMME • NMME became operational in Sep 2015 - An operational requirement is 99% on-time delivery - Operational NMME forecasts will be product based , meaning if one or more models fails to deliver, then official forecast will be based on models received • New models will be being evaluated and added as they become available (Canadian GEM-NEMO in 2019) • Subseasonal NMME experiment is “SubX” is underway
NMME SubX http://cola.gmu.edu/kpegion/subx/ • Weekly initialization Forecast length 32 days (45 days encouraged) • • Hindcast period 1999-2015 (additional years encouraged) 3 ensemble members (more encouraged) • • Hindcasts and real-time forecasts (product based, like seasonal NMME) • Data at IRI: https://doi.org/10.7916/D8PG249H • Currently 6 models, 63 ensemble members (can differ from seasonal NMME, e.g. Canadian forecasts are from GEM monthly forecast) • Experimental (not yet operational)
http://wxmaps.org/subx_custom.php
NMME and SubX for research http://www.nws.noaa.gov/ost/CTB/nmme_pub.htm http://cola.gmu.edu/kpegion/nmmeworkshop2017
NMME application to global hydrological forecasting Approach: • NMME) used to drive Variable Infiltration Capacity (VIC) land surface hydrologic model • Droughts and wet spells analysed over global major river basins • NMME-based approach evaluated against the traditional Ensemble Streamflow Prediction (ESP) based on sampling climatological distribution
# forecast months that NMME/VIC ensemble median has significantly (p<0.05) higher Equitable Threat Score than ESP/VIC Yuan et al., BAMS 2015
Seasonal prediction of atmospheric river frequency
Climatological atm river days + anomaly composites ENSO effect on DJF atmospheric river frequency in NMME models Zhou & Kim 2018 https://doi.org/10.1007/s00382-017-3973-6
All years ENSO years Anomaly correlation skill for predicting atmospheric river frequency DJF lead 1 month (note that daily hindcast data is required) Zhou & Kim 2018 https://doi.org/10.1007/s00382-017-3973-6
NMME Phase 1 Data at IRI Hindcasts + real time forecasts http://iridl.ldeo.columbia.edu/SOURCES/.Models/.NMME
NMME real time daily data (new) https://www.ncdc.noaa.gov/data-access/model-data/model-datasets/north-american-multi-model-ensemble
NMME Phase 2 Data on the ESGF https://www.earthsystemgrid.org/search.html?Project=NMME
NMME Phase 2 Data Common 1 • grid • NetCDF4
Summary • NMME is now the prime source of dynamical seasonal forecast information for North America • Occasionally new models are added, and old ones retired • Currently 7 models, Canadian GEM-NEMO to be added, CMC1/CanCM3 to be retired in 2019 • Data from hindcasts and real time forecasts is freely available for applications and research • Forecast daily data now available • NMME open data driving much climate prediction research, leading to many papers • SubX = subseasonal version of NMME is following similar principles
Recommend
More recommend
