Скачать презентацию Medium Range Weather Monthly and Seasonal forecasts Climate Скачать презентацию Medium Range Weather Monthly and Seasonal forecasts Climate

f07b7201f746416134478927499e686a.ppt

  • Количество слайдов: 21

Medium Range Weather/ Monthly and Seasonal forecasts Climate & Seasonal Forecasts Magdalena A. Balmaseda Medium Range Weather/ Monthly and Seasonal forecasts Climate & Seasonal Forecasts Magdalena A. Balmaseda My Ocean WP 18 , Athens 29 -30 September 2009 ECMWF (UK) 1

WP 18: Task on Climate Seasonal Forecast • Participants: Ø Ø Ø Met Office WP 18: Task on Climate Seasonal Forecast • Participants: Ø Ø Ø Met Office Meteo France CMCC ECMWF Met. No (? ) • First suggestion: generalize a and split the task to 1. Seamless weather to seasonal forecasting 2. Climate Impacts My Ocean WP 18 , Athens 29 -30 September 2009 2

Overview • Forecasts at different time scales: SEAMLESS prediction • Seasonal Forecasts needs Ø Overview • Forecasts at different time scales: SEAMLESS prediction • Seasonal Forecasts needs Ø Ø Why do we want forecast at seasonal time scales? End To End Seasonal Forecasting Systems Initialization Calibration • Monthly Forecasts needs • Weather Forecasts needs • Climate Reanalysis (atmosphere/ocean/ice) needs My Ocean WP 18 , Athens 29 -30 September 2009 3

 • There is a clear demand for reliable seasonal forecasts: Ø Forecasts of • There is a clear demand for reliable seasonal forecasts: Ø Forecasts of anomalous rainfall and temperature at 3 -6 months ahead • For a range of societal, governmental, economic applications: Ø Ø Ø Agriculture Heath (malaria, dengue, …) Energy management Markets, insurance Water resource management, • Huge progress in the last decade: Ø Operational seasonal forecasts in several centres Ø Pilot/Research progress for demonstrating applicability (DEMETER, IRI, EUROBRISA, …) Ø Build-up of community infrastructure (at WMO level) My Ocean WP 18 , Athens 29 -30 September 2009 4

The basis for extended range forecasts • Forcing by boundary conditions changes the atmospheric The basis for extended range forecasts • Forcing by boundary conditions changes the atmospheric circulation, modifying the large scale patterns of temperature and rainfall, so that the probability of occurrence of certain events deviates significantly from climatology. Ø Important to bear in mind the probabilistic nature of climate forecasts Ø How long in advance? : from seasons to decades Ø The possibility of seasonal forecasting has clearly been demonstrated Ø Decadal forecasting activities are now starting. • The boundary conditions have longer memory, thus contributing to the predictability. Important boundary forcing: Ø Ø SST: ENSO, Indian Ocean Dipole, Atlantic SST Land: snow depth, soil moisture Atmospheric composition: green house gases, aerosols, … Ice? My Ocean WP 18 , Athens 29 -30 September 2009 5

End-To-End Seasonal forecasting System Forward Integration Initialization , Athens 29 -30 September 2009 My End-To-End Seasonal forecasting System Forward Integration Initialization , Athens 29 -30 September 2009 My Ocean WP 18 Forecast PRODUCTS PROBABILISTIC CALIBRATED FORECAST OCEAN ENSEMBLE GENERATION COUPLED MODEL Forecast Calibration 6

Importance of Initialization • Atmospheric point of view: Boundary condition problem Ø Forcing by Importance of Initialization • Atmospheric point of view: Boundary condition problem Ø Forcing by lower boundary conditions changes the atmospheric circulation. “Loaded dice” • Oceanic point of view: Initial value problem Ø Prediction of tropical SST: need to initialize the ocean subsurface. o Emphasis on thermal structure of the upper ocean o Predictability is due to higher heat capacity and predictable dynamics Ø A simple way: ocean model + surface fluxes. o But uncertainty in the fluxes is too large to constrain the solution. Ø Alternative : ocean model + surface fluxes + ocean observations o Using a data assimilation system. o The challenge is to initialize thermal structure – without disrupting the dynamical balances (wave propagation is important) – While preserving the water-mass characteristics My Ocean WP 18 , Athens 29 -30 September 2009 7

Real Time Ocean Observations Moorings ARGO floats XBT (e. Xpandable Bathi. Thermograph) Satellite SST Real Time Ocean Observations Moorings ARGO floats XBT (e. Xpandable Bathi. Thermograph) Satellite SST Sea Level My Ocean WP 18 , Athens 29 -30 September 2009 8

Re-forecasting needs an ocean re-analysis Models have errors and direct model output needs calibration Re-forecasting needs an ocean re-analysis Models have errors and direct model output needs calibration Ocean reanalysis Real time Probabilistic Coupled Forecast time Coupled Hindcasts, needed to estimate model climatological PDF, require a historical ocean reanalysis • Quality of reanalysis affects the climatological PDF, and therefore the real-time forecast • Consistency between historical and real-time initial conditions is required My Ocean WP 18 , Athens 29 -30 September 2009 9

Forecast Skill Ocean data assimilation improves the forecast skill (Alves et al 2003) No Forecast Skill Ocean data assimilation improves the forecast skill (Alves et al 2003) No Data Assimilation Impact of Data Assimilation No Data Assimilation My Ocean WP 18 , Athens 29 -30 September 2009 10

A decade of progress on ENSO prediction S 1 S 2 S 3 • A decade of progress on ENSO prediction S 1 S 2 S 3 • Steady progress: ~1 month/decade skill gain • How much is due to the initialization, how much to model development? My Ocean WP 18 , Athens 29 -30 September 2009 Half of the gain on forecast skill is due to improved ocean initialization 11

Ocean Observation & Reliable forecast products Forecast Systems are generally not reliable (RMS > Ocean Observation & Reliable forecast products Forecast Systems are generally not reliable (RMS > Spread) RMS Error Ensemble Spread A. B. Can we reduce the error? How much? (Predictability limit) Can we increase the spread by improving the ensemble generation and calibration? Calibration and multi-model can increase the skill and reliability of forecasts. In a general case, even the multi-model needs calibration. Long records are needed for robust calibration and downscaling My Ocean WP 18 , Athens 29 -30 September 2009 12

Requirements for Seasonal • Data for initialization Ø Ø Ø In situ QC data Requirements for Seasonal • Data for initialization Ø Ø Ø In situ QC data (historical and real time) SST, high resolution (historical and real time) Sea Level Anomalies (historical and real time) MDT Bottom pressure (? ) Sea ice (in the future) • Independent data for validation/ comparison Ø Sea level data (gauges) Ø Ocean currents (blended product) Ø Ocean reanalysis from other centres (including those without model) • Direct 3 D ocean states: depending on centre Ø Met-Office, INGV (OK), since they produce My. Ocean reanalysis with their own systems Ø Meteo-France will try the ORCA 2 product from Mercator Ø ECMWF needs ORCA 1 resolution o Can not use the ¼ degree product directly The longer the record, the better My Ocean WP 18 , Athens 29 -30 September 2009 13

ECMWF and 3 D ocean reanalysis from My Ocean • The time scales of ECMWF and 3 D ocean reanalysis from My Ocean • The time scales of the project precludes the direct output of the ¼ ocean re-analysis for seasonal forecasts Ø Quite expensive assessment. • Alternative Ø Ø Interpolate ¼ into 1 deg Use the interpolated data. Directly: not very interesting experiment (? ) Anomaly initialization. • Other alternatives: Ø Use My Ocean re-analysis of sea ice. Ø Consider other time scales (weather /monthly forecasting). Ø Start work on the implementation of the ¼ of degree model from My. Ocean to be ready for future use of My Ocean products. My Ocean WP 18 , Athens 29 -30 September 2009 14

ECMWF: Weather and Climate Dynamical Forecasts 10 -Day Medium-Range Forecasts Monthly Forecasts Seasonal Forecasts ECMWF: Weather and Climate Dynamical Forecasts 10 -Day Medium-Range Forecasts Monthly Forecasts Seasonal Forecasts Atmospheric model Wave model Ocean model Real Time Ocean Analysis ~Real time My Ocean WP 18 , Athens 29 -30 September 2009 Ocean model Delayed Ocean Analysis ~12 days 15

Monthly forecasts • Mixed layer processes are important • Currently, monthly forecasts use the Monthly forecasts • Mixed layer processes are important • Currently, monthly forecasts use the same ocean model configuration that seasonal • Ideally, one would use a dynamical ocean model with Ø Higher horizontal/vertical resolution • Alternative, an ocean mixed layer model can be used: Ø High horizontal/vertical resolution but not dynamics Ø High resolution initial conditions for this ML model will be needed My Ocean WP 18 , Athens 29 -30 September 2009 16

Requirements for Monthly Forecast • High resolution 3 D ocean re-analysis to initialize ML Requirements for Monthly Forecast • High resolution 3 D ocean re-analysis to initialize ML model Ø Temperature and Salinity. Velocities Ø Long Reanalysis and timely real-timi (within 12 -24 h) • Possibility 1: Ø Get data for a long term reanalysis. Exact period to be discussed. (1993 -2005) is a possibility) Ø Use it to initialize the ML model and conduct a series of monthly hindcasts. Ø Assess skill respect initial conditions from a low resolution ocean model. Ø Problem: human resources are scarce • Possibility 2: Ø Start work on the implementation of the ¼ of degree model from My. Ocean to be ready for future use of My Ocean products. My Ocean WP 18 , Athens 29 -30 September 2009 17

Medium Range Weather forecasts • They will benefit from better representation of air-sea interaction: Medium Range Weather forecasts • They will benefit from better representation of air-sea interaction: Ø Currents coupled to the wave model Ø High resolution SST with diurnal cycle. Sea-ice • Currently, persisted SST anomalies are used • Interest in testing coupling to currents. Or at least, initialization of currents Ø And use persisted currents during the forecast • Ideally, one would use a dynamical ocean/sea-ice model with Ø Higher horizontal resolution Ø Higher vertical resolution Ø But software does not exist yet • Alternative, ML model as in monthly. My Ocean WP 18 , Athens 29 -30 September 2009 18

Requirements for Medium Range • Possibility 1: Ø Use ocean currents. It involves other Requirements for Medium Range • Possibility 1: Ø Use ocean currents. It involves other groups at ECMWF. Ø Need for skill assessment of the product. • Possibility 2: Ø Start work on the implementation of the ¼ of degree model from My. Ocean to be ready for future use of My Ocean products. My Ocean WP 18 , Athens 29 -30 September 2009 19

Summary • Extend the name of the task to seamless prediction, to include medium Summary • Extend the name of the task to seamless prediction, to include medium range and seasonal. Separate task on climate impacts. • Direct products from TACS will be used by the different partners. Ready to provide assessment for different time scales • URD Ø From ECMWF can include Atmospheric Re-analysis, Medium Range weather forecast, monthly and seasonal • Use of 3 D reanalysis Ø Not prescriptive. Depending of group. Ø Assessment of seasonal from CMCC, Met. Office and Meteo-France Ø ECMWF may not provide assessment of 3 D products for seasonal. It could (needs further discussion/iterations. Resource dependent) o o Use Sea-ice in seasonal Initialization of ML model for monthly (resources) Use of velocity data for the wave model in medium range. Start implementing My. Ocean software (1/4 degree ocean model/sea ice) My Ocean WP 18 , Athens 29 -30 September 2009 20

THE END My Ocean WP 18 , Athens 29 -30 September 2009 21 THE END My Ocean WP 18 , Athens 29 -30 September 2009 21