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Data Assimilation at the Met Office

Data Assimilation at the Met Office. Chris Jones. Hadley Centre, Met Office, Exeter. CTCD Workshop. 8 th Nov, 2005. Outline. Intro 2 interpretations of DA Current DA at the Met Office Plans/hopes for future. Met Office Data Assimilation.

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Data Assimilation at the Met Office

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  1. Data Assimilation at the Met Office Chris Jones Hadley Centre, Met Office, Exeter. CTCD Workshop. 8th Nov, 2005

  2. Outline • Intro • 2 interpretations of DA • Current DA at the Met Office • Plans/hopes for future

  3. Met Office Data Assimilation • The Met Office has a long history of data assimilation in an operational (NWP) framework • Currently running operational 4D-Var scheme. • DA now appealing to a much wider audience • Ocean forecasting • Seasonal/decadal forecasting • Carbon cycle/ecosystem research • Keen to make most of existing expertise internal and external to Met Office • Central part of CarboEurope • Not my area of expertise, despite a chequered past…

  4. What does DA mean? • 2 different interpretations of the phrase “data assimilation” • i. Conventional, “NWP” style: • Model formulation is fixed • Uses “current” or “new” data • Constrains model prognostic variables • Product is • Analysis – best estimate of snapshot of reality • Initialsed state from which to produce best forecast • ii. Parameter optimisation: • A form of what used to be called “tuning” (which was highly subjective) • Generally uses “historical” data or climatology • Multiple model runs to constrain internal parameters • Product is the model itself (optimised)

  5. Data Assimilation applications at the Met Office • Climate/Carbon cycle related data assimilation does already exist at the Met Office. • 1. “DePreSys” (Decadal Prediction System) • Run climate model, nudging sea surface temperature and salinity to observations • Assimilate atmospheric variables too (better first season, better surface fluxes into ocean) • Run climate model for next 10 years as a forecast • Hindcasts show some skill relative to persistence

  6. Data Assimilation applications at the Met Office • Climate/Carbon cycle related data assimilation does already exist at the Met Office. • 2. NCOF/CASIX ocean biogeochemistry modelling • Run ocean carbon cycle model (HadOCC) in operational (“FOAM”) hi-res ocean model. • Assimilate physical variables (in-situ + satellite) • Drive with atmos fluxes from NWP model • produces realistic ocean state • Allows simulation of ocean pCO2, and air-sea CO2 flux • Aim to also assimilate ocean colour obs from satellite (as a proxy for chlorophyll concentration) • Better constraint on biological variables

  7. pCO2 in North Atlantic Climate model (no DA) FOAM (HadOCC + physical DA) obs Better ocean simulation (through DA) improves C-cycle simulation

  8. Attribution of mechanisms • Given confidence in simulation we can learn from the mechanisms in the model • Spring draw-down of pCO2 is biologically driven. • Rest of year is physically driven (mainly response to SSTs)

  9. Data Assimilation applications at the Met Office • Climate/Carbon cycle related data assimilation does already exist at the Met Office. • 3. CAMELS (Carbon Assimilation and Modelling of the European Land Surface) • Optimisation of terrestrial carbon cycle models using observed carbon cycle data • Results promising to date • Will feed into integration component of CarboEurope

  10. Prior and optimised diurnal cycle at Bray in Orchidee:means over 1997 Growing Season (days 195-216) Black: data with uncertainties Green: prior Red: optimized

  11. BETHY simulated carbon flux

  12. A success… and a caveat • TRIFFID modelled NEP saturates very quickly with light levels • Can’t represent diurnal cycle of productivity • Requires better treatment of light penetration into canopy

  13. A success… and a caveat • “light mod” allows better treatment • Parameters optimally determined using observed data from Loobos • Original model could be optimised too • Get decent looking performance • Parameters outside “physical” range • Danger of optimising deficient model…

  14. Future Plans: CarboEurope-IP • CarboEurope aims to, “understand and quantify the present terrestrial carbon balance of Europe” • 4 components. • “continental integration” • Make use of all different data streams to constrain models • Make use of models to give full coverage of European land surface Essentially a Data Assimilation problem

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