Physiochemical properties of nanomaterials and its nanotoxicity.pptx
shukla_jnwp50.ppt
1. Center for Ocean-Land-
Atmosphere Studies
Dynamical Season Prediction: A
Personal Retrospective of the Past 30
Years (1975-2004), and Conjectures
about the Future
J. Shukla
George Mason University (GMU)
Center for Ocean-Land-Atmosphere Studies (COLA)
with contributions from:
J. Kinter (COLA)
Symposium on the 50th Anniversary of Operational Numerical
Weather Prediction
University of Maryland, June 14-17, 2004
2. Center for Ocean-Land-
Atmosphere Studies
Outline
• Historical Overview: The 50 years Preceding JNWP50
• International Contributions to NWP
• The First 90-day Integration of the NMC Forecast Model
– DERF: NMC-COLA Collaboration (1983-1984)
• From NWP to DSP to Coupled Model Prediction
• Dynamical Seasonal Prediction: The Current Status
• Dynamical Seasonal Prediction: Future Prospects
• Conclusions, Conjectures and Suggestions
3. Center for Ocean-Land-
Atmosphere Studies
The 50 Years Preceding JNWP50
• V. Bjerknes (1904) Equations of Motion
– Father of J. Bjerknes, son and research assistant of C. Bjerknes (Hertz, Helmholtz)
• L. F. Richardson (1922) Manual Numerical Weather Prediction
– Military background, later a pacifist, estimated death toll in wars
• C. G. Rossby (1939) Barotropic Vorticity Equation
– First “Synoptic and Dynamic” Meteorologist; Founder of Meteorology Programs at
MIT, Chicago, Stockholm
• J. Charney (1949) Filtered Dynamical Equations for NWP
– First Ph.D. student at UCLA; Chicago, Oslo, Institute for Advanced Study, MIT
• N. A. Phillips (1956) General Circulation Model
– Father of Climate Modeling; Chicago, Institute for Advanced Study, MIT
4. Center for Ocean-Land-
Atmosphere Studies
Global Contributions Towards Research on
Predictability and Prediction of Weather
– USA: Predictability: Charney et al., Lorenz;
NWP: Cressman, Phillips, Miyakoda
– Canada: Numerical methods: Robert; Data assimilation: Daley
– Australia: Spectral model: Bourke
– France: Data assimilation: Talagrand
– U.K.: Theory: Eady; NWP: Sutcliff, Sawyer
– Germany:Theory: Ertel; NWP: Hinkelmann
– Norway: Theory: Eliassen
– Russia: Theory: Obukhov, Monin, Kibel; Adjoint: Marchuk
Data assimilation: Gandin
– Japan: NWP: Fujiwara, Syono, Gambo
– Sweden: Initialization: Machenauer; NWP: Bengtsson
5. Center for Ocean-Land-
Atmosphere Studies
Weather Predictability and Prediction
• Predictability and theory: Charney et al., Lorenz, Eady; Ertel, Eliassen,
Obukhov, Monin, Kibel
• NWP: Cressman, Phillips, Miyakoda, Hinkelmann, Sutcliff, Sawyer, Syono,
Gambo, Bengtsson
• Numerical methods: Robert, Bourke, Marchuk
• Data assimilation: Daley, Talagrand, Gandin
• Initialization: Machenauer, Baer and Tribbia
• Physical parameterizations - Convection, Radiation, Boundary Layer,
Clouds, etc.
• Ensembles: Farrell, Kalnay, Palmer, Toth
6. Center for Ocean-Land-
Atmosphere Studies
The First 90-day Integration of the
NMC Forecast Model
DERF: NMC-COLA Collaboration (1983-1984)
• Meeting with Bonner, Rasmusson, Phillips and Brown (3 Oct 1983)
• Statement of Intent for NMC-COLA Work on DERF (14 Feb 1984)
• Acronym “DERF” created by Gerrity (24 Aug 1984)
• NMC Committee on DERF created
• Tracton Named CAC DERF Project Leader (11 Jun 1985)
• Large Number of NMC Scientists Involved in DERF
• Major Logistical Arrangements Required to Make 90-day Run
First 90-day Run of NMC Model Approved by Brown (30 Sep 1985)
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11. Center for Ocean-Land-
Atmosphere Studies
Monthly and Seasonal
Predictability and Prediction
• Dynamical Predictability: Shukla (1981, 1984), Miyakoda,
Gordon, Caverly, Stern, Sirutis, and Bourke (1983)
• Boundary-Forced Predictability: Charney and Shukla (1977,
1981), Shukla (1984)
• Theory: Hoskins and Karoly (1981), Webster (1972, 1981)
• Programs: PROVOST (Europe); DSP (USA); SMIP (WCRP)
12. Center for Ocean-Land-
Atmosphere Studies
Simulation of (Uncoupled) Boundary-Forced
Response: Ocean, Land and Atmosphere
INFLUENCE OF OCEAN
ON ATMOSPHERE
– Tropical Pacific SST
– Arabian Sea SST
– North Pacific SST
– Tropical Atlantic SST
– North Atlantic SST
– Sea Ice
– Global SST (MIPs)
INFLUENCE OF LAND
ON ATMOSPHERE
– Mountain / No-Mountain
– Forest / No-Forest (Deforestation)
– Surface Albedo (Desertification)
– Soil Wetness
– Surface Roughness
– Vegetation
– Snow Cover
13. Center for Ocean-Land-
Atmosphere Studies
From Numerical Weather Prediction (NWP)
To Dynamical Seasonal Prediction (DSP) (1975-2004)
• Operational Short-Range NWP: was already in place
• 15-day & 30-day Mean Forecasts: demonstrated by Miyakoda (basis for creating
ECMWF-10 days)
• Dynamical Predictability of Monthly Means: demonstrated by analysis of variance
• Boundary Forcing: predictability of monthly & seasonal means (Charney & Shukla)
• AGCM Experiments: prescribed SST, soil wetness, & snow to explain observed
atmospheric circulation anomalies
• OGCM Experiments: prescribed observed surface wind to simulate tropical Pacific sea
level & SST (Busalacchi & O’Brien; Philander & Seigel)
• Prediction of ENSO: simple coupled ocean-atmosphere model (Cane, Zebiak)
• Coupled Ocean-Land-Atmosphere Models: predict short-term climate fluctuations
22. Standard Deviation of Monthly Equatorial Pacific SSTA
COLA Predictions
(1980-1999)
COLA Coupled Simulation
(250 years)
GFDL MOM3 ODA
(1980-1999)
Observations Forecast (JUL ICs) Simulation
25. 20 Years: 1980-1999
4 Times per Year: Jan., Apr., Jul., Oct.
6 Member Ensembles
Kirtman, 2003
Current Limit of Predictability of ENSO (Nino3.4)
Potential Limit of Predictability of ENSO
28. Center for Ocean-Land-
Atmosphere Studies
Challenges
Conceptual/Theoretical
Modeling
Observational
Computational
Institutional
Applications for Benefit to Society
29. Center for Ocean-Land-
Atmosphere Studies
Challenges
Conceptual/Theoretical
ENSO: unstable oscillator?
ENSO: stochastically forced, damped linear system?
(The past 50 years of observations support both theories)
– Role of weather noise?
Modeling
• Systematic errors of coupled models - too large
• Uncoupled models not appropriate to simulate Nature in some
regions/seasons: CLIMATE IS A COUPLED PROCESS
• Atmospheric response to warm and cold ENSO events is nonlinear
(SST, rainfall and circulation)
• Distinction between ENSO-forced and internal dynamics variability
30. Center for Ocean-Land-
Atmosphere Studies
Challenges
Observational
• Observations of ocean variability
• Initialization of coupled models
Computational
• Very high resolution models of climate system need million fold
increases in computing
• Storage, retrieval and analysis of huge model outputs
• Power (cooling) and space requirements-too large
31. Center for Ocean-Land-
Atmosphere Studies
Challenges
Institutional
• Development of accurate climate (O-L-A) models, assimilation and
initialization techniques, require a dedicated team with a critical mass of
scientists (~200) and resources (~$100 million per year: $50M
computing; $30M research; $20M experiments)
• Climate modeling and prediction efforts should be 10 times NWP but is
currently only ~10% of NWP
Applications for Benefit to Society
• Educate the consumers about the limits of predictability (uncertainty
and unreliability)
• Decision making and risk management using probabilistic predictions
37. Center for Ocean-Land-
Atmosphere Studies
Conclusions, Conjectures and Suggestions
• The estimates of the growth rate of initial errors in NWP models is well
known, and the current limits of predictability of weather are well
documented. The most promising way to improve forecasts for days 2-15
is to improve the forecast at day 1.
• The limits of predictability for short-term climate predictions (seasons 1-
4), are not well known, because the estimates of predictability remain
model-dependent. Our ability to make more accurate seasonal predictions
is limited by:
– Inadequate understanding of coupled dynamics
– Insufficient observations
– Inaccurate models
– Insufficient computing
– Inefficient institutional arrangements
38. Center for Ocean-Land-
Atmosphere Studies
• During the past 25 years, the weather forecast error
at day 1 has been reduced by more than 50%. At
present, forecasts for day 4 are, in general, as good
as forecasts for day 2 made 25 years ago.
• With improved observations, better models and faster
computers, it is reasonable to expect that the
forecast error at day 1 will be further reduced by
50% during the next 10-20 years. Therefore, at that
time, the forecasts at day 3 could be as good as
forecasts for day 2 are today.
Conclusions, Conjectures and Suggestions
39. Center for Ocean-Land-
Atmosphere Studies
• 25 years ago, a dynamical seasonal climate prediction was not conceivable.
• In the past 20 years, dynamical seasonal climate prediction has achieved a
level of skill that is considered useful for some societal applications. However,
such successes are limited to periods of large, persistent anomalies at the
Earth’s surface. Dynamical seasonal predictions for one month lead are not yet
superior to statistical forecasts.
• There is significant unrealized seasonal predictability. Progress in dynamical
seasonal prediction in the future depends critically on improvement of
coupled ocean-atmosphere-land models, improved observations, and the
ability to assimilate those observations.
Conclusions, Conjectures and Suggestions
40. Center for Ocean-Land-
Atmosphere Studies
• Improvements in dynamical weather prediction over the past 30 years did not
occur because of any major scientific breakthroughs in our understanding of
the physics or dynamics of the atmosphere
• Dynamical weather prediction is challenging: progress takes place slowly
and through a great deal of hard work that is not necessarily scientifically
stimulating, performed in an environment that is characterized by frequent
setbacks and constant criticism by a wide range of consumers and clients
• Nevertheless, scientists worldwide have made tremendous progress in
improving the skill of weather forecasts by advances in data assimilation,
improved parameterizations, improvements in numerical techniques and
increases in model resolution and computing power
Conclusions, Conjectures and Suggestions
41. Center for Ocean-Land-
Atmosphere Studies
• Currently, about 10 centers worldwide are making dynamical weather forecasts
every day with a lead time of 5-15 days with about 5-50 ensemble members, so
that there are about 500,000 daily weather maps that can be verified each
year
– It is this process of routine verification by a large number of scientists
worldwide, followed by attempts to improve the models and data
assimilation systems, that has been the critical element in the improvement
of dynamical weather forecasts
• In contrast, if we assume that dynamical seasonal predictions, with a lead time
of 1-3 seasons, could be made by about 10 centers worldwide every month with
about 10-20 ensemble members, there would be less than 5,000 seasonal
mean predictions worldwide that can be verified each year
– This is a factor of 100 fewer cases compared to NWP, so improvement in
dynamical seasonal prediction might proceed at a pace that is much slower
than that for NWP if we didn’t do something radically different
Conclusions, Conjectures and Suggestions
42. Center for Ocean-Land-
Atmosphere Studies
• NWP (World Wide)
– 10 Centers
– 5-15 day forecasts each day
– 5-15 ensemble size
– 500,000 daily weather maps each year
• DSP (World Wide)
– 10 Center
– 1-3 seasons predictions each month
– 10-20 ensemble size
– 5,000 seasonal maps each year
DSP is a factor of 100 fewer cases than NWP
Conclusions, Conjectures and Suggestions
43. Center for Ocean-Land-
Atmosphere Studies
Consumers could save $1 billion per
year in energy costs if the average
weather forecast could be improved by
just 1º Fahrenheit.
David S. Broder
Washington Post, 22 April 2004
Excerpt from NOAA report in interview with
Admiral Conrad Lautenbacher
Under Secretary of Commerce, NOAA
Conclusions, Conjectures and Suggestions
44. Center for Ocean-Land-
Atmosphere Studies
Suggestion for Accelerating Progress
in Modeling and Prediction of the
Physical Climate System
• There is a scientific basis for extending the
successes of NWP to climate prediction
• The problem is beyond a person, a center, a nation
…
• A multi-national collaboration is required
45. Center for Ocean-Land-
Atmosphere Studies
Suggestion for Accelerating Progress
in Dynamical Seasonal Prediction
Reanalyze and Reforecast
the seasonal variations for the past 50 years,
every year
• Exercise state-of-the-art coupled ocean-atmosphere-land models and
data assimilation systems for a large number of seasonal prediction
cases and verify them against observations
– Equivalent to producing reanalysis and 1-2 season dynamical forecasts
for each month of one year, every week
• Conduct model development experiments (sensitivity to
parameterizations, resolution, coupling strategy, etc.) with the specific
goal of reducing seasonal prediction errors
