# Presentation: 358199

## 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 50****th**** Anniversary of Operational Numerical Weather Prediction****University of Maryland, June 14-17, 2004**

## 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**

## 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 **

## 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**

## 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**

## 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)

**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)**

## Slide 7

## Slide 8

## Slide 9

## Slide 10

## 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) **

## 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**

## 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

## Evolution of 

**Evolution of **
**Climate Models**
**1980-2000**
**Model-simulated and observed **
**rainfall anomaly (mm day****-1****) **
**1983 minus 1989**

## Evolution of 

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

## Vintage 2000

**Vintage 2000**
**AGCM**

## Observed and Simulated Surface Temperature (°C)

**Observed and Simulated Surface Temperature (°C)**

## Cross-Validated CCA of Z500 & SST (Observed and Modeled)

**Cross-Validated CCA of Z500 & SST (Observed and Modeled)**

## Slide 19

## Variance of Model-Simulated Seasonal (JFM) Rainfall (mm2)

**Variance of Model-Simulated Seasonal (JFM) Rainfall (mm****2****)**

**Notes:**
Figure 7

## Predictability of the Coupled Climate System

## Standard Deviation of Monthly Equatorial Pacific SSTA 

**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

## “Operational” ENSO Prediction with Coupled A-O GCMs

- Courtesy of A. Barnston, IRI and B. Kirtman, GMU/COLA

## “Operational” ENSO Prediction with Coupled A-O GCMs

- Courtesy of A. Barnston, IRI and B. Kirtman, GMU/COLA

## Current Limit of Predictability of ENSO (Nino3.4)

- 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**

## Impact of Ensemble Size

## Factors Limiting Predictability:
Future Challenges

**Future Challenges**

## Challenges

**Conceptual/Theoretical**
**Modeling**
**Observational**
**Computational **
**Institutional**
**Applications for Benefit to Society****  **

## 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**

## 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**

## 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**

## Inconsistency of SST and Precip in the W. Pacific - Prescribed SST

**Inconsistency of SST and Precip in the W. Pacific - Prescribed SST**

## Climate Modeling and Computing

**Models Today**
- Weather
- T254: 5 d/hr on 144 CPUs
- T511: 2.5 d/hr on 288 CPUs
- Climate
- T85/ 1°: 2.0 yrs/d on 96 CPUs
- 2°X2.5°/1°: 	5.25 yrs/d on 180 CPUs

**Models in 2014**
- Weather
- T3800 (5 km): 4 d/hr (2,160 CPUs)
- - or -
- T825 (25 km): 4 d/hr (468 CPUs)
- Climate
- T420/ 0.5°: 2.4 yrs/d (2,500 CPUs)
- -or-
- T420/0.5°: 2 mo/d (2,500 CPUs)

- {Moore’s Law (43%/yr) -OR- 10%/yr}

## Slide 34

## Slide 35

## Conclusions, Conjectures 
and Suggestions

**and Suggestions**

## 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**

## 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. 

- 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**

## 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. 

- 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**

## 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

**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**

## 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

- 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**

## 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

**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**

## 	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

**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**

## 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**

## 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

## THANK YOU!

**ANY QUESTIONS? **