Presentation on theme: "Page 1 Penehuro F. Lefale Scientific Officer World Climate Programme (WCP) World Meteorological Organization (WMO)."— Presentation transcript:
Page 1 Penehuro F. Lefale Scientific Officer World Climate Programme (WCP) World Meteorological Organization (WMO). Website: Presentation to the Regional Technical Meeting on CLIPS and Agro meteorological Applications for the Mercosur Countries Campinas, Brazil, July Current Advances in the Science of Climate Forecasting: Prospects and Limitations Credits to the following Institutions; UK Met Office Hadley Centre (Dr. R. Graham), ECMWF (Prof. D. Anderson, Dr A. Troccoli), IRI (Dr S. Mason), Beijing Climate Center (BCC), Chinese Meteorological Administration (CMA) (Prof. Chen) for some of the slides used in this presentation.
Page 2 Outline of Presentation I : Definitions II : The Climate System III : Seasonal to Inter-annual Forecasting IV : Prospects and Limitations
Page 3 “Current predictive capability for the onset of El Niño is still relatively modest, particularly for the onset of weak events, due to a number of factors related to an El Niño’s initiation” Lyon and Barnston, IRI US CLIVAR VARIATIONS, Spring 2005, Vol. 3. No. 2 Preamble “All models are wrong…” G. Box ‘…but some models are useful..’ Although they may need a bit of help…
Page 4 * A climate model is a very complex system, with many components…. * Models must be tested at system level, i.e. by running the model and comparing the results with observations. Such tests can reveal problems, but their source is often hidden by the model’s complexity… * It is also important to test the model at the component level, i.e. by isolating particular components and testing them outside the framework of the complete model…. * Here, we can make an analogy with the testing of a new aircraft. Flight tests are needed to evaluate the entire aircraft as a system, but component tests are also essential… IPCC AR4, 2007 (in prep)
Page 5 Seamless ForecastsMinutes Hours Days 1 Week 2 Week Months Seasons Years TransportationTransportation Forecast Lead Time Warnings & Alert Coordination Watches Forecasts Threats Assessments Guidance OutlookPrediction Protection of Life & Property Space Operation RecreationRecreation EcosystemEcosystem State/Local Planning EnvironmentEnvironment Flood Mitigation & Navigation AgricultureAgriculture Reservoir Control EnergyEnergy CommerceCommerce Benefits HydropowerHydropower Fire Weather HealthHealth ForecastUncertaintyForecastUncertainty Weather: State of the atmosphere at a given time and place Climate: statistics of the atmosphere (average conditions of the atmosphere) Climate Change: change in climate over time, whether due to natural variability or as a result of human activity (IPCC). Climate Change: change of climate that is attributed directly or indirectly to human activity that alters the composition of the global atmosphere and that is in addition to natural variability observed over comparable time periods (UNFCCC). Initial Conditions Boundary Conditions GHG Concentrations Climate Change Scenarios The corollary is that: the components of climate change should be manifest on all time and space scales Climate is traditionally viewed as the integration of discrete weather events and variables over time and space Global Warming
Page 6 Part II: The Climate System * Key Components of the Climate System * Predictability of the Climate System * Advances in Modeling * Model Development – the UK Met office GloSea and ECMWF DEMETER examples
Page 7 Schematic view of the components of the global climate system (bold), their processes (thin arrows) and some aspects that may change (bold arrows). (IPCC TAR, WG I, 2001, p.88)
Page 8 Predictability of the Climate System The feature that gives longer potential predictability is the ocean (and maybe slow boundary changes associated with snow cover, soil moisture, sea ice..). The ocean has a large heat capacity and slow adjustment times relative to the atmosphere. If the ocean forces the atmosphere on these timescales, then there can be longer predictability. On the other hand if the atmosphere forces the ocean with little or no feedback, there might be little predictability. Latif et al., 2002, Timmermann 2005, Hasselmann, 1976, Anderson., D., 2005, ECMWF, June 2005.
Page 9 This plot shows where the skill in predicting SST is highest. The tropical Pacific is highest but the tropical Atlantic and Indian ocean could have skill as well. The skill currently realised by models is lower.
Page 10 Predictability of the Climate System Estimating anthropogenic climate change on times much longer than the predictability time-scale of natural climate fluctuations does not, by definition, depend on the initial state. Predicting climate change is one of estimating changes in the probability distribution of climatic states (e.g., cyclonic/anticyclones weather, El Niño, global temperature, etc) as atmospheric composition is altered in some prescribed manner. IPCC TAR, 2001; IPCC AR4, 2007 (in prep)
Page 11 Part III: Climate Forecasting * The basis for seasonal forecasting * Atmospheric Modeling * Ocean Modeling * Coupled Modeling
Page 12 Demand driven Climate Forecasting Courtesy: IRI with modification by Lefale. Statistical/Empirical Models: univariate univariate multivariate multivariate Traditional Methods: Bio-indicators Bio-indicators The Farmer’s Almanac The Farmer’s Almanac Coupled Models: AOGCM AOGCM the ocean (memory) the ocean (memory) SSTs (ENSO) SSTs (ENSO)
Page 13 Sources of Seasonal Prediction – Known to be important: – El Nino variability (biggest single signal) – Other tropical ocean SST ( important, but multifarious) – Climate change (all forms) (especially important in mid-latitudes) – Local land surface conditions (e.g. soil moisture in spring) – Other possible factors: – Mid-latitude ocean temperatures (always controversial) – Remote soil moisture/ snow/ice cover (not well established) – Volcanic eruptions (important for large events) – Stratospheric - possible tropospheric impact – Dynamic memory of atmosphere - most likely on one or two month – Solar cycle, stratosphere - questionable statistical connections
Page 14 Methods of Seasonal Prediction Empirical/Statistical method –Use past observational record and statistical methods + –Works with reality instead of error-prone numerical models + –Limited number of past cases means that it works best when observed variability is dominated by a single source of predictability - –A non-stationary climate is problematic - Dynamical method (Single-tier GCM forecasts) –Include comprehensive range of sources of predictability + –Predict joint evolution of SST and atmosphere flow + –Includes indeterminacy of future SST, important for prob. forecasts + –Model errors are an issue! - Combination method (Two-tier forecast systems) –First predict SST anomalies (ENSO or global; dynamical or statistical) –Use ensemble of atmosphereice GCMs to predict global response –Use El Nino index to statistically predict a local variable of interest
Page 15 IPCC TAR, 2001.
Page 16 Pin table analogy Small differences at the start are amplified by chaos effects Individual “plays” are unpredictable Probability of where a ball ends up is given by “climate” statistics Normal seasons Extreme seasons Frequency Hindering Prediction Courtesy: Graham, R., 2005, UK Met Office.
Page 17 Courtesy: Graham, R., 2005, UK Met Office.
Page 19 Example: Glosea product (SSTs)
Page 20 EU research project DEMETER –multi-models represent forecast uncertainties due to model formulation, as well as initial condition uncertainty –7 coupled CGCMs from European institutes –retrospective period, up to 43 years ( ) Result: multi-models improve skill and reliability Real-time operational European multi-model prediction system –Currently Met Office (GloSea) and ECMWF (system2) –Meteo-France system will soon join operations –Other European models may join European multi-model: research and operations
Page 21 Single Model against multi-model comparison
Page 22 Single Model against multi-model comparison GloSea GloSea+ECMWF multi-model P(well above) P(well below) probability of well-above temperatures, Feb-Mar-Apr
Page 23 Probabilities for 3 categories Risk of ‘extreme’ Reference climatological data dry avge wet Example combined dynamical/statistical products: North East Brazil: March-May 2005 rainy season
Page 27 Routine weather forecasting* “AMIP”-style runs: seasonal or interannual with specified SSTs and sea ice Comparison of processes and phenomena with observed/analysed data Evaluation of the performance of atmospheric models * Initial value prediction is dependent on several factors beyond the numerical model itself – e.g. data assimilation techniques, ensemble size, ensemble generation method)
Page 28 Typical data use to evaluate climate models Re-analyses of the global circulation Synthesized climatologies e.g. Precipitation Satellite Observations In situ Measurements
Page 29 Part III: The Calibration and Verification * Observations * Data Assimilation * Downscaling * Errors in the Models
Page 30 Observations (new data) A wide range of remote and in situ systems being used to expand the observational data flow from the oceans Signals from the tropical subsurface (e.g. TAO array) are good indicators of where process is tending Considerable improvements from satellite systems New observations add additional information
Page 31 The Observational network Mooring (TAO, PIRATA AND TRITON) (T) XBTs (dropped from ships of opportunity (T) CTDs (High quality but very few-contained by research ships) T & S SST from satellite (IR, MW), ship and buoys. SSS from a few ships, from a few moorings, from satellite in future (e-g- SMOS Aquarius) Sea level from altimetry (ERS, TOPEX, Jason) Current meters (very few ~5 along the equatorial pacific) Subsurface temperature and salinity from ARGO
Page 32 The Observational network Atlas moorings are the backbone of the equatorial ocean observing system. They measure T at 10 depths from the surface to 500m. The data are transmitted via satellite and are on the GTS within a few hours.
Page 33 The Observational network
Page 34 The Observational network Operating method of ARGO floats
Page 35 Data coverage for June 1982
Page 36 Data coverage for March 2002
Page 37 Build up of ARGO February 2005
Page 38 DD builds on physically based models for both global and regional scales and has a great potential. SD relies on GCM for large scale and statistical models for regional and/or local scales. DD still has problems with today ’ s climate! SD can deal with non-standard or difficult (e.g. Sea ice) variables. SD can handle a variety of different scales. SD is lless problematic with bias (because of data-based). SD is fast ->large number of predictions However, more risky with extrapolations! Needs extensive data! Dynamic downscaling (DD) versus statistical downscaling (SD)
Page 39 Improvement in Forecast Skills Courtesy: ECMWF
Page 40 Part IV: Prospects and Limitations
Page 41 Role of controlled sensitivity experiments in both simplified and full models Use of ultra high resolution to improve parameterizations for use in longer run lower resolutions Importance of vertical resolution and what is going on at the top of the lower boundary layer and tropopause surfaces Correct scaling of and new parameterization schemes Diversity of models Incorporation of processes such as inertia instability e.g. especially as air and water crosses the equator Random errors due to the cumulative impact of unresolved impact of unresolved processes Prospects for improving atmospheric models
Page 42 Conventional vs. Dateline El Niño Impacts Larkin and Harrison, GRL, 2005a&b (in press) The NOAA definition for El Niño was recently adopted by the WMO Region IV (May 2005) The definition identifies as El Niño many more Autumn and Winter seasons than has been conventional These additional seasons show warming in NINO 3.4 and out towards the Dateline, but not in the eastern tropical Pacific El Niño impact associations need to be re-evaluated given this new definition These additional Dateline seasons have substantially different seasonal average impact association structures compared to conventionally identified impacts
Page 43 Conventional vs. Dateline El Niño Impacts – Autumn and winter temperature Anomaly Composites Larkin and Harrison, GRL, 2005a&b (in press)
Page 44 Conventional vs. Dateline El Niño Impacts – Autumn and winter precipitation Anomaly Composites Larkin and Harrison, GRL, 2005a&b (in press)
Page 45 Conventional vs.. Dateline El Niño Impacts – Frequency of Extreme seasons (top 20%) Larkin and Harrison, GRL, 2005a&b (in press)
Page 46 Conventional vs. Dateline Global Impacts Larkin and Harrison, GRL, 2005a&b (in press)
Page 47 Conventional vs. Dateline Global Impacts Larkin and Harrison, GRL, 2005a&b (in press)
Page 48 International Climate Activities ISSC International Social Science Council ICSU International Council for Science UNESCO UN Educational Scientific and Cultural Organization WMOUN WORLD BANK FAO WHO IOCMAB UNEPUNDP IPCC UN FCCC/COPGEF CCA Coordination activities within the Climate Agenda IHDP International Human Dimensions Programme IGBP International Geosphere- Biosphere Programme WCRP (WMO, ICSU) WCDMP (WMO) WCAC (WMO) WCIRP (UNEP) GOOS (IOC, WMO, UNEP, ICSU) GCOS (WMO, IOC, UNEP, ICSU) GTOS (UNEP, FAO, UNESCO, WMO, ICSU) THE CLIMATE AGENDA WORLD CLIMATE PROGRAMME (WCP) International Non-government Organization INTERNATIONAL SCIENTIFIC PROGRAMMES Global Earth Observation Systems of System (GEOSS) – Group of Earth Observation (GEO) CCl CAgM
Page 49 Concluding Remarks Uncertainty embedded in climate forecasting - ‘chaotic’ processes inherent in the atmospheric system Skill of the SI Forecast varies by geographic region, by climate parameter and by time-scale Operational Improved Climatology 6 to 12-month forecast in every month Probability forecast Real-time monitoring of verification (WMO-SVS) Major Research Topics Model improvement climate system model (more component of climate system, ice/land/…) land surface initialization direct couple scheme/coupler sstatistical-dynamical combination (downscaling…) multi-model ensemble
Page 50 Concluding Remarks Coupled models appear to be mature enough to be used in decision making Further model developments as well as increase in model resolution, will however be beneficial in advancing science of SI Improvements in the underlying science must be matched by improvements in communications between providers and users Forecasting should be viewed as a more integrated system rather than an end to end process.