1. Seasonal and decadal prediction

2. Ocean Assimilation and Reanalysis for Climate Research Head of Group : Keith Haines Maria Valdivieso da Costa (NCEO) Ocean Synthesis using Global Ocean NEMO 1  model Chunlei Liu (e-Science) HadCM3 all forcings and DePreSys initialisation Nicola Howe (NCEO) Satellite data for validation of decadal predictions Vladimir Stepanov (Rapid MOC) MOC variability in Ocean Synthesis comparisons with Rapid Array NEMO ORCA grid: Madec, 2008 “NEMO ocean engine” red - assimilated datablack - control magnt – 1988-2007 blue - RAPID-data Hao Zuo (Arctic IPY) Heat and Freshwater budgets in Arctic from Ocean reanalysis G70 control run R07 assimilation run

3. Decadal Predictability Head of Group : Rowan Sutton Buwen Dong (NCAS Climate) Impacts of ocean-atmosphere processes Dan Hodson (NCAS Climate) Impacts of decadal variability in Atlantic SST Ed Hawkins (NCAS Climate) Statistical decadal predictions and identifying optimal regions for observations Leon Hermanson (NCAS Climate) Creation of Ocean analysis fields for DePreSys Len Shaffrey (NCAS Climate) Decadal predictions for CMIP5 using HiGEM Jon Robson (VALOR) AMOC predictability and initialisation of DePrySys Jason Webster (PhD Student) Variability and predictability in the Indo- Pacific http://www.metoffice.gov.uk/science/specialist/lon g-range/global/decadal_fc.html Hawkins and Sutton, 2009 Journal of Climate

4. Extremes/hazardous weather

5. Adaptation and the business community Part 4 – Impact of and adaptation to climate change Ben Lloyd-Hughes (Deloitte Research Fellow)

6. Adaptation and the business community Who is being asked to report? Why? What will authorities include in their reports? When?

7. Utilising climate research to inform the insurance industry: Dynamically simulated storms for risk assessment Jane Strachan (Willis Research Fellow) Pier Luigi Vidale (Willis Senior Academic) Kevin Hodges (ESSC) Malcolm Roberts (UKMO) Len Shaffrey (NCAS-Climate) David Stevenson (University of Exeter) Renato Vitolo (University of Exeter) Aidan Slingsby (City University) Willis Analytics

8. Research Objectives Investigate the ability of GCMs to capture tropical storm activity in terms of location, frequency, intensity and structure, considering both model resolution and model formulation. Utilise our ability to run coupled AOGCMs for multi-century simulations to help us understand climate variability, and how this affects tropical cyclone activity Identify and extract storm tracks from these simulations to build a long-term, spatially consistent, database of dynamically simulated storms. Develop methodologies to combine the capabilities of observations and GCMs to create storm event-sets, to be used as tools to investigate the impact of long-term variability on climate- related hazards. 20 year timeslice of dynamically simulated storms Interannual variability: Atlantic tropical cyclone count Correlation with BestTrack: High res: 0.73 Mid res: 0.44 Low res: 0.13 North Atlantic annual tropical cyclone count 50 yrs observed 150 yrs simulated (HiGEM) Track density anomaly for El Nino/ La Nina storms El Nino La Nina