ERA-‐20C: the ECMWF reanalysis of the 20th century (1899-‐2010) using surface observaCons only Paul Poli, Hans Hersbach, Dick Dee, Adrian Simmons, Paul Berrisford, David Tan, and Carole Peubey Slide 1 © ECMWF MoCvaCons ● Can a century-‐long dataset bridge weather and climate scales? – Inspired by Gil Compo’s 20th Century Reanalysis – Mandated by call for proposals from EU FP7 ERA-‐CLIM project ● Challenges – ScienCfic ● How to handle a changing observing system (great increase in the global number of observaTons over the course of the 20th century) ● How to detect automaTcally data issues (breaks in staTon Tme-‐series, staTonary …) ● How to handle greater unknowns in the Earth System components as we go back in Tme ● Can we really produce meaningful maps (by today’s standards) for 1900? – Technical ● ProducTon speed and throughput ● Assembling large datasets prior to and aXer producTon Slide 2 © ECMWF SimplisCc view of the reanalysis system ● Input – Invariant forcings – Time-‐varying forcings – ObservaTons ● AssimilaTon and model – Atmosphere – Land-‐surface – Ocean waves ● Output – – – – Monitoring DocumentaTon Budgets Products Slide 3 Integration works forward in time Dataset is built from the past into the present Started on 1 January 1899 Production used several parallel ‘streams’ © ECMWF At the core of it: EsCmaCng and using uncertainCes in an ensemble of 24-‐hour 4DVAR data assimilaCons 10 members One can see this as a “Monte-Carlo” equivalent to the reanalysis estimation problem: The ensemble is supposed to contain all sources of uncertainties (nay, it doesn’t…) so that product spread is a measure of product uncertainty Slide 4 © ECMWF Great Blizzard of February 1899 (i.e., only 1 month aQer spin-‐up) Kocin et al., Weather and Forecasting, 1988 doi: http://dx.doi.org/10.1175/1520-0434(1988)003<0305:TGAOAE>2.0.CO;2 Slide 5 © ECMWF Surface pressure (top) and wind (boRom) data counts/year Slide 6 © ECMWF Total uncertainty verificaCon for observaCons assimilated: error budget closure Showing only observaTons assimilated in the first 90 minutes of the 24-‐hour 4DVAR window in ERA-‐20C ensemble – hence not an independent verifica1on Assumed Slide 7 © ECMWF Actual Temperature analysis mean global increments (Jan 1979-‐Dec 2010) [K] ERA-Interim ERA-20C ensemble ERA-20C deterministic Slide 8 © ECMWF Longwave downwelling radiaCon at NDBC Buoy#44008 (hourly) Observations (in red) come from a NDBC buoy augmented by sensors funded by project "New England Shelf Fluxes“ sponsored by JAII: Massachusetts Technology Collaborative's John Adams Innovation Institute. Data retrieved from Woods Hole Oceanographic Institution website on 26 April 2014 Slide 9 © ECMWF Total column water vapor over oceans, laCtudes 20oS-‐20oN Slide 10 © ECMWF SSM/I CM-‐SAF FCDR 22 GHz channel (comparison using NWP-‐SAF RTTOV-‐11), over oceans, laCtudes 20oS-‐20oN, non-‐rainy pixels Average brightness temperatures at obs. date/time/location/sensor/satellite Slide 11 © ECMWF Top: Total column water vapor BoRom: SSM/I CM-‐SAF FCDR 22 GHz brightness temperatures over oceans, laCtudes 20oS-‐20oN (boRom non-‐rainy pixels only) Obs4MIPs-type of evaluation: “bringing observations to the modellers” Reverse: “bringing model outputs to the observers” … MIPs4Obs? Slide 12 © ECMWF Comparison with surface temperatures measured by Russian ice staCons over the ArcCc (intersec1on of observa1ons available at ECMWF and in ICOADS 2.5.1 to guarantee data source/origin) ERA-Interim Submitted to QJRMS Slide 13 © ECMWF ERA-20C Unpublished work Conclusions & outlook ● Our first century ensemble reanalysis producTon taught us many lessons – Applied in a determinisTc run (couldn’t afford a full ensemble re-‐run) ● Stepping stone to adding now upper-‐air observaTons – And later satellite data ● Next century-‐long reanalysis will use the CERA system (Coupled atmosphere/ ocean), developed by Laloyaux et al. – Surely many more issues will appear then – But we’ll learn a great deal about discrepancies between atmosphere & ocean models & observaTons ● Reanalysis is an iteraTve work – Serving users in the mean Tme ● We hope to have ERA-‐20C data out on the public data server ‘soon’ – IniTal evaluaTon was essenTal to avoid issuing raw incorrect ensemble data – Data copying proves more Tme-‐consuming than producTon itself! ● Fair assessment of ERA-‐20C with a few observaTonal, independent datasets Slide 14 © ECMWF ERA-‐20C fine print ● Horizontal resoluTon T159 (approx. 125 km, as in ERA-‐40), 91 model levels up to 0.01 hPa ● Analysis increments at T95 (approx. 210 km) ● Model version IFS CY38R1, with added Tme-‐varying forcings: HadISST2.1.0.0 (sea-‐surface temperature and ice ● ● ● ● ● ● ● ● ● ● fracTon), greenhouse gases (O3, CO2, CH4, N2O, CFC-‐11, CFC-‐12, CFC-‐22, CCL4), solar cycle, aerosols opTcal depth, as described in model-‐only integraTon documentaTon, ERA Report Series 16, Hersbach et al., 2013 www.ecmwf.int ObservaTons source: ISPD v3.2.6 and ICOADS v2.5.1 Two producTons so far: a 10-‐member ensemble (~200 days in 2013) and a determinisTc re-‐run (~50 days in 2014) Data assimilaTon method different from ECMWF NWP operaTons in 2013/14: – 24-‐hour 4DVAR, 3-‐hourly output for analyses – Ensemble updates of background error correlaTons and global variances every 10 days – DeterminisTc uses constant (current) background error correlaTons, and global variances scaled to the ensemble – ModulaTon of background errors in vorTcity by daily maps of ensemble spread to reduce linear model instabiliTes – Digital filtering of increments in vorTcity and temperature acTvated VariaTonal bias correcTon of surface pressure observaTons – Using prior detecTon of Tme-‐series breaks based on SNHT homogeneity test using NOAA-‐CIRES 20CR departures – DeterminisTc also includes detecTon and rejecTon of staTonary observed Tme-‐series (quite a few in the 1990s…) AssimilaTon of marine surface wind observaTons from ICOADS2.5.1 Data produced in 6x 20-‐year streams for the ensemble, and 22x 5-‐year streams for the determinisTc Data volume generated about 700 Tb for ensemble, 75 Tb for determinisTc Model Tme-‐step of 60 minutes for ensemble, 30 minutes for determinisTc ( beoer atmospheric Tdes) Only ensemble documentaTon available so far: ERA Report Series 14, Poli et al., 2013 www.ecmwf.int – Documents several issues found with ensemble producTon, all fixed in determinisTc producTon Data currently being copied to a public data server for release Slide 15 © ECMWF
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