Predictability of weather and climate What have we learned from comprehensive modeling studies?

Predictability of weather and climateWhat have we learned from comprehensive modeling studies? Lennart Bengtsson MPI for Met. Hamburg ESSC, Uni. Reading Many thanks to J. Shukla Multiscale modeling and simulation in Science

Laplace Essai philosophique sur les probabilités We may regard the present state of the universe as the effect of its past and the cause of its future. An intellect which at a certain momentwould know all forces that set nature in motion, and all positions ofall items of which nature is composed, if this intellect were also vastenough to submit these data to analysis, it would embrace in a singleformula the movements of the greatest bodies of the universe and thoseof the tiniest atom; for such an intellect nothing would be uncertainand the future just like the past would be present before its eyes. Multiscale modeling and simulation in Science

CLIMATE PREDICTIONAND CHAOS • “ For want of a nail, the shoe was lost; • For want of a shoe, the horse was lost; • For want of a horse, the rider was lost; • For want of a rider, the battle was lost; • For want of a battle, the kingdom was lost “ Multiscale modeling and simulation in Science

Predictability of weather Multiscale modeling and simulation in Science

St. Petersburg prediction 10.1 2006 17th onward ca -30 C Multiscale modeling and simulation in Science

Example of ultra-high predictabilityObserved and simulated QBONote the marked changes in wind direction at 10-30 hPa every 26-28 month Multiscale modeling and simulation in Science

Predictability of weather and climate.What have we learned from comprehensive modeling studies? • Predictability of atmospheric flow (from mid latitude weather prediction to the quasi-biennal oscillation) Predictability of weather (how close are we to the limit?) Coupled ocean atmosphere modes (El Nino-Southern Oscillation) What do we mean by climate predictability? (what is predictable?) Climate and climate change predictability Concluding remarks Multiscale modeling and simulation in Science

A Fundamental Question in Weather Predictability A Dynamical System is : TYPE 1 – characterized by an infinite range of predictability TYPE 2 – the range of predictability is finite, but can be increased indefinitely by decreasing the size of the initial error TYPE 3 – the range of predictability is finite and intrinsically limited Does the Weather Constitute a Type 2 or a Type 3 System? Multiscale modeling and simulation in Science

The Growth of Very Small Errors • Basic Idea – Reduce the Size of the Initial Error by putting it on smaller and smaller scales • Ultimate Predictability controlled bythe predictability time T= time necessary for the error to propagate “upscale” from very, very small initial scale to a finite, pre-chosen scale • How does T behave as the initial error gets infinitely small? This tells us if we haveTYPE 2 orTYPE 3 behavior! • For a Spectrum E(k) ~ k -3 or steeper: • T becomes infinite (thus TYPE 2) • For a Spectrum E(k) less steep than k -3: • T is finite (thus TYPE 3) Multiscale modeling and simulation in Science

Nastrom and Gage, 1985: A climatology of atmospheric wavenumber spectra of wind and temperature observed by commercial aircraft. J. Atmos. Sci., 42, 950–960.

Does the Observed -5/3 Spectrum Imply that the Range of Predictability Cannot be Lengthened by Reducing Initial Error? • Perhaps the eddies associated with the observed -5/3 spectrum do not interact with the large scale Multiscale modeling and simulation in Science

Improvements in NWP from Miyakoda (1972) to 2002. Courtesy ECMWF How long to get to D+10 in winter? Multiscale modeling and simulation in Science

Multiscale modeling and simulation in Science

The principle of error reduction in data assimilation Multiscale modeling and simulation in Science

Improvement in medium-range forecast skill 12-month running mean of anomaly correlation (%) of 500hPa height forecasts Multiscale modeling and simulation in Science

Lorenz, E. N., 1982: Atmospheric Predictability Experiments with a Large Numerical Model. Tellus, 34, 505-513 • Estimates of the lower and upper bounds of predictability of instantaneous weather patterns for ECMWF forecast system • Lower bound: skill of “current” operational forecasting procedures • Upper bound: Growth of initial error, defined as the difference between two forecasts valid at the same time (Lorenz curves) “Additional improvements at extended range may be realized if the one-day forecast is capable of being improved significantly.” Multiscale modeling and simulation in Science

Predictive skill ( Z 500 hPa) for the NHand predictability estimates ( for 6 ( red) and 24 hr (blue) increments) Multiscale modeling and simulation in Science

Evolution of 1-Day Forecast Error, Lorenz Error Growth, and Forecast Skill for ECMWF Model(500 hPa NH Winter) 2007 8 1.2* 0.91 Multiscale modeling and simulation in Science From ECMW *est

ECMWF EPS: Forecast Started 8th January 2005 00UTC (GUDRUN) Multiscale modeling and simulation in Science From L Froude, ESSC

ECMWF EPS: Forecast Started 6th January 2005 00UTC Multiscale modeling and simulation in Science From L Froude, ESSC

Predictive skill and predictability of storm tracksfor different observing systems NH SH Multiscale modeling and simulation in Science From L Froude, ESSC

Conclusions Weather predictability • Predictability of actual weather is limited to a few days. • Predictability of synoptic weather systems is likely limited by ca two weeks. Improvements has come through smaller initial errors due to better observations and more advanced data-assimilation. • Predictability of the general weather type varies between different regions, for different seasons and for different situations and can be from weeks to several months. • There are atmospheric patterns such as QBO that have almost unlimited predictability. Multiscale modeling and simulation in Science

El Nino/Southern Oscillation 1998 JFM SST [oC] JFM SST Climatology [oC] 1998 JFM SST Anomaly [oC] Multiscale modeling and simulation in Science

El Nino changes precipitation patterns Multiscale modeling and simulation in Science

Evolution of Climate Models 1980-2000 Model-simulated and observed 500 hPa height anomaly (m) 1983 minus 1989

Vintage 2000 AGCM

Current Limit of Predictability of ENSO (Nino3.4) Potential Limit of Predictability of ENSO 20 Years: 1980-1999 4 Times per Year: Jan., Apr., Jul., Oct. 6 Member Ensembles Multiscale modeling and simulation in Science Kirtman, 2003

Prediction of Atlantic hurricaneswith a general circulation model integrated over 30 years ECHAM5/OM ERA-40 Multiscale modeling and simulation in Science

Predictability of large-scale climate anomaly patterns • The predictability of ENSO is likely to be from several months to a few years. There are large variations in predictability. Present predictability assessment suffers from rather poor coupled models and later work is expected to change this. • The same is true for well developed land surface patterns. • Long term anomalies over Europe (NAO) have limited predictability • The fact that very long semi-persistent pattern occur both in reality and in models suggest that predictability in some regions (such as in the Sahel region) are longer. • Here we need more active basic research. Multiscale modeling and simulation in Science

Köppen climate zones Main groups • A: Tropical rainy climate, all months > +18 C • B: Dry climate, Evaporation > Precipitation • C: Mild humid climate, coldest month +18 C - -3 C • D: Snowy - forest climate, coldest month < -3C but warmest > +10 • E: Polar climate , warmest month < +10 C • ET: Tundra climate, warmest month > 0 C • Subgroups • f : Moist, no dry seasons • w: Dry season in winter • s : Dry season in summer Multiscale modeling and simulation in Science

Köppen climate zones ECHAM5 simulated ERA40 determined from analyses. Multiscale modeling and simulation in Science

Warmest and coldest season in Europe 1500-2003 Luterbacher et al(2004), Xoplaki et al (2005) Multiscale modeling and simulation in Science

50-year trends>0.23 corresponds to 95% significance T Sea ice Z 850 P Multiscale modeling and simulation in Science

Observation and model statisticsLuterbacher et al., 2005 ( Temp. in C) Multiscale modeling and simulation in Science

Climate predictability • Internal climate modes lasting up to several decades are likely to exist in the climate system. Whether these are predictable is still an open question • Model studies suggest that such internal modes have dominated climate variations during the last several centuries • It is hardly feasible to infer any changes in external forcing from meteorological records for the period 1500 to 1900. • Models are capable to reproduce the observed climatology with considerably accuracy ( e.g.Koeppen) Multiscale modeling and simulation in Science

Delworth and Knutson, 2000 Monte-Carlo simulations with a coupled AO GCM: one out five simulations almost perfectly reproduced the observed global temperature variability. obs exp 3 Multiscale modeling and simulation in Science

Resultat från den senaste klimatutvärderingenObserverad och beräknad temperaturändring Multiscale modeling and simulation in Science

Present climate Coupled Model T63L31 Future climate Multiscale modeling and simulation in Science

Predictability of snow in Germany Multiscale modeling and simulation in Science

Predictability of weather and climate What have we learned from comprehensive modeling studies?