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CLIVAR Climate of the Century Project Adam Scaife, Chris Folland, Jim Kinter, David Fereday January 2009 © Crown copyright Met Office th 20 • Aim • Characterize and understand variability and predictability of climate over the past ~130 years associated with slowly varying forcing functions including SST • History • Initiated by Hadley Centre in 1993 • Now jointly lead by Hadley Centre (Folland) and COLA Center for Ocean Land Atmosphere studies (Kinter) • CLIVAR project & reports to WMO/CAS/WGNE • Workshops: Hadley 1994, COLA 2002, ICTP 2004, Hadley 2007 © Crown copyright Met Office • Experimental Design • Initially focused on ensembles of AGCM simulations, at least 4 members • All forced with same HadISST sea surface temperature and sea ice analysis • Longer timescale than other intercomparisons such as AMIP: 1871 onwards • Focus is on climate variability and predictability rather than model evaluation © Crown copyright Met Office • Experimental Design cont…. • Expanded to include other forcing data sets, including greenhouse gases, ozone, volcanic aerosols and solar variability • Recent extensions: • “Pacemaker” experiments with coupled models in order to more accurately simulate variability that is inherently coupled • Land surface forcing, interaction with LUCID Land Use and Climate – IDentification of robust impacts (De Noblet et al) • More highly resolved SST to be available later this year: HadISST2 © Crown copyright Met Office Reproducing climate variability Land Surface Temperature Southern Oscillation © Crown copyright Met Office Sahel Rainfall North Atlantic Oscillation Multi-Model Comparisons e.g. Evaluation of Climate Events: Potentially predictable, “forced” and well modelled YES 20th Century Climate Event (e.g. surface T trend) Consistent with ensemble means? YES Unpredictable internal variation but well modelled NO Consistent with ensemble members? NO © Crown copyright Met Office Poorly modelled in this experiment: missing process/forcing Predictable interdecadal trends? Land Surface T: 1970-2000 Sahel Rainfall: 1950-1980 NAO: 1965-1995 Ensemble Means Sahel Rainfall: 1950-1980 Ensemble Members © Crown copyright Met Office NAO: 1965-1995 Selected results: Increase in predictability of boreal winter land temperature, using two models mainly caused by decadal changes in ENSO variability Kang et al, 2006, GRL, highlighted © Crown copyright Met Office Selected results: Winter NAO and the stratosphere Change in NAO index Change in surface pressure Model also forced with HadISST and all known major forcings in C20C mode. Full NAO and surface climate change 1965-95 reproduced Scaife et al, 2005, GRL © Crown copyright Met Office North Atlantic Oscillation in Summer • Biggest single atmospheric circulation influence on summer climate in N W Europe/UK. • Related to summer storm track – like 2007/8 flood or 1976 drought in UK. • Related to ENSO SSTs, West African Monsoon and climate worldwide on decadal time scales, perhaps via AMO SNAO pattern • Current phase: (a) better understand mechanisms of SNAO links to atmospheric circulation and forcings, e.g. W. African summer monsoon. (b) Investigate SNAO seasonal predictability. Rainfall correlations 1900-1998 © Crown copyright Office Folland et al.Met 2008 CF Selected results: Simulating Indian Monsoon Rainfall (IMR) and causes of its decadal variations Interannual ensemble means, ENSL (1902-1999; black) and CRU (red), mm/day Decadal IMR of CRU (red) and the ensemble means of C20C (black), mm/day © Crown copyright Kucharski et Met al.Office 2008 Selected results: Simulating Dust Bowl era drought © Crown copyright Met Office Schubert et al. 2004 Atlantic hurricanes in C20C simulations Storm counts (obs, model) © Crown copyright Met Office Pacemaker Experiments • Motivation: • On seasonal time scales, there is large-scale atmosphere-ocean covariability (e.g. ENSO-monsoon) • There is also local atmosphere-ocean coupling • • • Latent heat flux – SST • Rainfall – SST • Lag-lead relationships None of these processes are well represented (often wrong sign) in typical AGCM simulations with global prescribed SST CGCM Pacemaker Strategy • Specify SST only where it drives the atmosphere, and model the ocean (slab or dynamic) elsewhere • Main example: prescribe SST in tropical eastern Pacific (Lau and Nath, 2003) • Allow for coupled feedbacks outside region of specified SST • Test importance of thermodynamic vs. dynamic coupling • Some experiments with mixed-layer (slab) ocean models • Some experiments with dynamic ocean models © Crown copyright Met Office Pacemaker Strategy: Overcoming Shortcomings of AGCMS and Coupled Models Observed Observed Pacemaker JJA Rainfall Composite (El Nino - La Nina) Pacemaker design: specified SST regions Pacemaker DJF SST Composite (El Nino - La Nina) The “Pacemaker” strategy permits a consistent air-sea energy balance while simultaneously including the time sequence of climate-driver events, such as ENSO. Teleconnections from the eastern tropical Pacific to remote tropical and extratropical regions are well represented in pacemaker runs, e.g., phenomena that are at once driven by and independent of ENSO, like the Asian monsoon. © Crown copyright Met Office Cash et al. 2007 Evolving C20C Experimental Design • • Pacemaker • Specified SST in limited region (e.g. eastern tropical Pacific or north Atlantic) • Thermodynamic ocean (slab or mixed layer formulation with Q-flux) • Dynamical ocean models in some basins Land Use and Change • • Coordination with LUCID Phenomena-Focused Experiments • subsets of C20C group • West African Monsoon Modeling and Evaluation (WAMME) • Asian monsoon • Influence of the stratosphere on seasonal predictability © Crown copyright Met Office Some Lessons Learned • Collaborative data analysis sometimes works better than large on-line databases • Beware of normalised indices Absolute Sahel Rainfall Normalised Sahel Rainfall • Normalised, ensemble mean anomalies can give the impression of reproducible and potentially predictable anomalies, when members do not even span the observations. © Crown copyright Met Office Available Diagnostics • Large selection of data available: • PMSL, T, RH, Z, precip, U, V, w, cloud, heat flux, wind stress, soil moisture • All monthly and some daily diagnostics • Data available on line from COLA, HADLEY, SNU, GSFC • http://www.iges.org/c20c/sharing_data.html © Crown copyright Met Office Link with WGSIP on potential predictability? • C20C forcing datasets available • Use C20C data as a limit to predictability? • Decadal climate events • Earlier hindcasts? • Pre-1979 • Atm. analyses from 1891(Compo et al) © Crown copyright Met Office