UNCERTAINTY IN WEATHER AND CLIMATE PREDICTION

Transcription

1 one flap of a sea-gull s wing may forever change the future course of the weather Edward Lorenz 1963 UNCERTAINTY IN WEATHER AND CLIMATE PREDICTION Julia Slingo (Met Office) With thanks to Tim Palmer, James Murphy, Ken Mylne, David Sexton and Glenn Shutts

2 Outline Ed Lorenz and the concept of chaos Initial condition uncertainty in weather forecasting Stochastic processes and forecast uncertainty Predictability on climate timescales Reducing uncertainty in climate change Future directions: moving from uncertainty to probabilities

3 The Lorenz Attractor: The prototype chaotic model.. X Y Z = σx + σy = XZ + rx Y = XY bz Edward Lorenz ( )

4 In a nonlinear system, predictability is flow dependent and predictable Climate of the Lorenz model Z X Evolution of three different ensembles: Underlying equations are non-linear; Growth of initial uncertainty is strongly dependent on the starting conditions; Predictability of forecasts is variable.

5 Basics of Ensemble Forecasting Time Initial condition uncertainty Forecast uncertainty Analysis Possible States

6 Dependence of accuracy on flow and forecast variable 10-day forecasts of London temperatures ( 0 C) 26 th June th June 1994 y y UK UK Forecast day Forecast day Individual member forecast Ensemble mean forecast Actual observations

7 Great Storm of 15/16 October 1987.a woman rang the BBC and said she had heard that there was a hurricane on the way. Well if you are watching, don't worry there isn't. Mike Fish, BBC Weather Forecaster

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10 Benefits of ensemble prediction systems Ensembles are used to capture forecasting uncertainties and estimate probability. Ensemble forecasts provide a range of equally probable forecast solutions which allows forecasters to: assess possible outcomes. estimate risks and probabilities. gauge confidence. Probability forecasts help end-users to assess and manage risk in an uncertain world.

11 Problem of under-dispersion of the ensemble system Northern Hemisphere 500hPa Height Forecasts Forecast Error Forecast Spread Buizza et al., MWR, 2004 RMS error grows faster than the spread Ensemble is underdispersive. Ensemble forecast is overconfident. Ensemble spread increased by identifying initial perturbations (singular vectors) that give maximum error growth.

12 Stochastic parameterizations: Reducing model error and enhancing internal variability Potential Stochastic parameterizations can change mean and variance of PDF: PDF Weak noise Strong noise Impacts variability of model and increases ensemble spread Impacts systematic error (e.g. blocking, precipitation error) Unimodal Multi-modal

13 Stochastic physics in Ensemble Prediction Systems Met Office employs three schemes to address different sources of model error: Random Parameters (RP) Error due to approximations in parameterisation Stochastic Convective Vorticity (SCV) Unresolved impact of organised convection (MCSs) Stochastic Kinetic Energy Backscatter (SKEB) Excess dissipation of energy at small scales

14 Kinetic energy spectra from aircraft Unresolved processes and upscale energy transports in models Traditionally assume that effects of unresolved scales can be represented through parametrizations based on bulk formulae. Inherently assumes that there is no coupling between dynamics and physics on these unresolved scales. Essentially ignores upscale energy cascades Nastrom and Gage, 1985

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16 Phase changes of water drive weather and climate Moist processes in a complex, multi-scale system Process model Weather model Climate model Crown copyright Met Office

17 Stochastic Physics: Spectral Backscatter Scheme Rationale: A fraction of the dissipated energy (D) is scattered upscale and acts as forcing for the resolved-scale flow, Ψ Smoothed Total Dissipation ψ * D F ψ Total dissipation rate, D, from numerical dissipation, convection, gravity and mountain wave drag. F ψ Forcing pattern,, describes spatial/temporal correlations of the backscattered energy

18 Model Uncertainty and Stochastic Physics: Essential elements in probabilistic predictions Temperature at 850 hpa ( N) +Forecast Error Initial condition uncertainty only Including stochastic physics backscatter Forecast Spread Forecast Day Number

19 Basics of Ensemble Forecasting Time Initial condition uncertainty Forecast uncertainty Analysis Model Uncertainty Model uncertainty arises from stochastic, unresolved processes Possible States

20 Predictability in the midst of chaos If we can't predict the weather beyond the next week or so, why is it possible to make seasonal forecasts? What is the impact of boundary forcing, f, on Lorenz Attractor? X = σx + σy + f Y = XZ + rx Y + f Z = XY bz

21 Adding external steady forcing f to the Lorenz (1963) equations f=0 f=2 f=3 f=4

22 The influence of external forcing, f, on the state vector probability function is itself predictable. Weak forcing: Number and spatial patterns of regimes remain the same, but their frequency of occurrence is changed ( Lorenz model paradigm ) Strong forcing: Number and patterns of regimes are modified as the atmospheric system goes through bifurcation points In other words, it s not possible to say what the weather will be like at any particular place on any particular day, but it should be possible to say what the statistics of the weather might be over the coming season or decades.

23 Birth of Seasonal Forecasting Predictability of Monsoons J. G. Charney and J. Shukla, 1981 It is shown by numerical simulation that the variability of average pressure and rainfall for July due to short-period flow instabilities occurring in the absence of boundary anomalies can account for most of the observed variability at midlatitudes but not at low latitudes. On the basis of the available evidence it is suggested that a large part of the low-latitude variability is due to boundary anomalies in such quantities as sea surface temperature, albedo and soil moisture, which, having longer time constants, are more predictable than the flow instabilities.

24 Seasonal to decadal prediction of the coupled ocean-atmosphere system Use fully coupled models of the ocean and atmosphere circulation Initialise both the atmosphere and ocean from observations Combine initial condition and stochastic physics perturbations Systematic and model-specific errors grow more strongly in fully coupled system Multi-model ensembles often used to provide better sampling of forecast phase space

25 Seasonal predictability is highly dependent on location Global Impacts of El Nino UK and Western Europe are among the least predictable places on the planet!

26 Different phases of El Nino are more predictable than others Initiation of warm event is difficult to forecast due to stochastic forcing from the atmosphere e.g. westerly wind events. Decay of warm event is more predictable due to the role of equatorial ocean dynamics - the delayed oscillator.

27 How reliable are the forecasts? Lower tercile tropical JJA rainfall Because of systematic model errors, the distribution of probable outcomes may not reflect the observed distribution i.e. the forecasts may not be reliable. Forecast reliability has to be assessed using large sets of model hindcasts. Final probabilities can be calibrated based on assessment of reliability Use of multi-model ensembles can improve reliability For seasonal and longer term predictions this is challenging because of the limited observational base

28 Ensemble prediction in a changing climate FUTURE Climatology Time Forecast uncertainty Initial condition uncertainty Analysis Model Uncertainty Model uncertainty arises from stochastic, unresolved processes CURRENT Climatology

29 Non-stationarity of the climate UK Temperature Record, : Anomalies from mean UK Monthly Mean Temperature Anomalies

30 Uncertainties in Climate Change Projections Characterised by the spread in a multi-model ensemble of climate projections run at different international centres, and collected in a common archive. Responses of annual mean surface temperature (left) and precipitation (right) to SRES A1B emissions, in 21 coupled AOGCMs contributed to IPCC AR4.

31 Multi-model ensembles (MMEs) Strengths: Each member extensively tested credibility derived from tuning and validation against a wide range of observables Constructed from a large pool of alternative components samples different structural assumptions The source of much of our knowledge of projected future changes Limitations: Not designed to sample modelling uncertainties in a systematic fashion ( ensemble of opportunity ) Rather small. Difficult to get robust estimates of most likely changes, or associated uncertainties, in noisy quantities like regional changes in extreme events Difficult to use MMEs to assess climate risks as there is no obvious best way of determining the distribution of possible changes of which the MME is a sample.

32 Perturbed physics ensembles (PPEs): An alternative approach Relatively large ensembles designed to sample modelling uncertainties systematically within a single model framework Executed by perturbing poorly constrained model parameters within expert-specified ranges Key strength: Allows greater control over experimental design cf ensembles of opportunity Key limitation: does not sample structural modelling uncertainties, e.g. changes in resolution, or in the fundamental assumptions used in the model s parameterisation schemes.

33 Atmosphere Parameters Ice fall speed Large Scale Cloud Critical relative humidity for formation Cloud droplet to rain: conversion rate and threshold Cloud fraction calculation Entrainment rate Intensity of mass flux Shape of cloud (anvils) Convection Cloud water seen by radiation Ice particle size/shape Radiation Cloud overlap assumptions Water vapour continuum absorption Boundary layer Turbulent mixing coefficients: stabilitydependence, neutral mixing length Roughness length over sea: Charnock constant, free convective value Dynamics Diffusion: order and e-folding time Gravity wave drag: surface and trapped lee wave constants Gravity wave drag start level Root depths Land surface processes Forest roughness lengths Surface-canopy coupling CO2 dependence of stomatal conductance Sea ice Albedo dependence on temperature Ocean-ice heat transfer

34 Summer precipitation changes in response to doubled CO 2 in a perturbed physics ensemble

35 UKCP09: Moving from uncertainty to probability UKCIP02 gave a single estimate of changes Using many models in IPCC AR4 gave a range of estimated changes UKCP09 uses over 400 model projections to give the probability of estimated changes

36 Moving from uncertainty to probabilities/likelihoods UKCIP02 Single projection Very unlikely to be less than (10%) UKCP09 Central estimate (50%) Very unlikely to be more than (90%) Summer Rainfall 2080 s

37 Quantifying uncertainties Improved model physics e.g. clouds s 2080 s Winter rainfall in south east England Natural Variability Downscaling Carbon Cycle Model Uncertainty Crown copyright Met Office

38 Using weather forecasting for reducing climate model uncertainties Real time comparison of vertical cloud properties using space-borne radar on CloudSat MetUM: proto-hadgem3 CloudSat Crown copyright Met Office

39 Quantifying uncertainties Improved model physics e.g. clouds s 2080 s Winter rainfall in south east England Benefits of initialisation for near-term 35 projections Natural Variability 9 Carbon Cycle Increased understanding 20of earth system processes more uncertainty? Downscaling Model Uncertainty Crown copyright Met Office

40 Decadal Prediction system: Effects of initialisation on projections of UK temperature for the next 30 years 360m ocean T, March 2007 UK 9-year mean temperature Crown copyright Met Office

41 Probabilistic predictions of climate change FUTURE Climatology Time Forecast uncertainty Initial condition uncertainty Analysis Model Uncertainty Model uncertainty arises from stochastic, unresolved processes and parameter uncertainty CURRENT Climatology

42 Multi-Model ( ) Methods of Handling Uncertainty: Strengths and Weaknesses RMSE of anomaly persistence Ensemble mean RMSE Ensemble Spread Perturbed Parameter ( ) Stochastic Physics ( ) Courtesy Antje Weisheimer, ECMWF

43 Structural uncertainty Model resolution: Will increasing horizontal resolution reduce uncertainties at the global/regional level? What resolution is required in global models to drive regional/local downscaling? What vertical resolution is required in the atmosphere and ocean? Is it time for a coordinated study of the effects of model resolution?

44 What will happen to rainfall? Systematic patterns of change are emerging, but large uncertainty in the magnitude of those changes

45 Surface Pressure Persistent Blocking Anticyclone Potential Vorticity on 315K Climate Models typically undersimulate persistent anticyclonic blocking Slide 45

46 Blocking Index. 13 month integrations of ECMWF model (at T159 and T1259). DJFM T1259 ERA-40 T159 Slide 46 T1259 run on NSF Cray XT4 Athena (two months of dedicated usage)

47 The importance of interactive upper-ocean thermodynamics for monsoon active-break cycles Lag correlations of intra-seasonally (30-50 day) filtered July and August rainfall

48 The Real Butterfly Effect raises fundamental unanswered questions about convergence of climate simulations with increasing resolution: 1. Is there an irreducible level of uncertainty in predictions of climate chang? What is it? 2. How much will uncertainties in climate-change predictions of the large scale reduce if models are run at 20km, 2km or even 0.2km resolution rather than say 200km resolution? 3. Once we reach a certain resolution (eg 20km), is it just as good to represent small scale motions using stochastic equations, than to try to resolve ad infinitum? 4. Will the development of stochastic parametrisations give more reliable estimates of uncertainty than current ad hoc multi-model methodologies? Slide What is the most efficient way of using finite computer resources for climate prediction eg how to best partition resources between resolution, Earth-system complexity and ensemble size?

49 V. Ramaswamy, GFDL, Princeton Natural Variability

50 Concluding remarks Lorenz s theory of the atmosphere as a chaotic, non-linear system pervades all of weather and climate prediction. Estimating (and reducing) uncertainty and moving to more reliable (and confident) predictions requires: Improved representation of multi-scale physics Higher resolution and more complete models More complete observations of the climate system More comprehensive ensemble prediction systems But there will always be an irreducible level of uncertainty flap of the seagull s wings on all timescales

51 Questions