Dynamical Seasonal Prediction

Ankur Srivastava Scientist

Monsoon Mission Indian Institute of Tropical Meteorology, Pune

ankur.cat@tropmet.res.in

Capacity Building Training Workshop South Asian Climate Outlook Forum (SASCOF-12)

India Meteorological Department (IMD) Pune, India

17th April 2017

Why is seasonal prediction so important?

• Agriculture • Policy • Water resources • Economy • Manufacturing

Sea Ice

Oceans

The Climate System

Biosphere

Soil Moisture Run-off

Atmosphere

Precipitation Evaporation

Scales might be different but we all are trying to predict……..

Tommasi et al (2017)

We are trying to predict…

• Radiative processes

• Surface processes

• Cloud development and microphysics

• Advection and mixing

• Convergence and divergence

• Ascent and subsidence

• Sea-breezes, cold and warm fronts

• Cyclones, anticyclones, troughs, ridges

Predictability

• If we claim to understand the climate system to some extent, surely we should be able to predict it!

• If we cannot predict the atmosphere is this because our understanding is inadequate or are there deeper reasons?

The state of any physical system can be uniquely defined by giving the values of some set of variables e.g. pendulum is defined uniquely by its angle from the vertical and its angular velocity.

Pendulum is an example of a predictable system.

Even if there is a small error in the knowledge of initial state, there will be a small error in prediction.

Predictability

• To make weather forecasts we need at least two things:

(a) Knowledge of the state of the atmosphere now

(b) A model that will produce a prediction of the future state

• For the time being, assume that we have a PERFECT model.

• Analyses have errors (big gaps in observing network)

• Hence there are many possible initial states consistent with our observations

Ensemble Forecasting • No one state is an accurate representation of the current state

of weather.

• Rather than a single forecast we make many forecasts each from slightly different initial conditions.

• Spread of forecasts gives us an idea of confidence in forecasts

Limit of Predictability

• So everything looks good…

• We have a PERFECT computer model

• A big supercomputer

• We can predict the climate system indefinitely

BUT………

Prof. Ed Lorenz

• MIT professor

• Landmark 1962 paper “Deterministic Nonperiodic Flow”

• Led to “chaos theory” and dynamical systems

• Coined “butterfly effect”

Slide Courtesy: Robert Fovell Atmospheric and Oceanic Sciences University of California, Los Angeles

The Lorenz Experiment

• Lorenz’ model wasn’t a weather model, and didn’t even have grid points

• 3 simple equations, which can describe fluid flow in a cylinder with heated bottom and cooled top

• He called his variables X, Y and Z

Slide Courtesy: Robert Fovell

The Lorenz Experiment

• X indicated the magnitude and direction of the overturning motion

• As X changed sign, the fluid circulation reversed

Slide Courtesy: Robert Fovell

The Lorenz Experiment

• Y was proportional to the horizontal T gradient

Slide Courtesy: Robert Fovell

The Lorenz Experiment

• And Z revealed the fluid’s stability

The Lorenz Model

• Three simple equations

• But in important ways they were like the equations we use in weather forecasting – They are coupled

– They are nonlinear

Slide Courtesy: Robert Fovell

Simulation similar to Lorenz’ original experiment. X = circulation strength & magnitude.

The model was started with unbalanced initial values for X, Y and Z, creating a shock. The model was seeking a suitable balance.

Slide Courtesy: Robert Fovell

Next, a spin-up period with swings that grow until…

Slide Courtesy: Robert Fovell

The fluid chaotically shifts from CW to CCW circulations in an nonperiodic fashion

Slide Courtesy: Robert Fovell

This was Lorenz’ discovery… sensitive dependence on initial conditions Slide Courtesy: Robert Fovell

Dependence on initial conditions

• Caused by nonlinear terms

• Even if model is perfect, any error in initial conditions means forecast skill decreases with time

• Reality: models are far from perfect

• Long range weather prediction is impossible

• Lorenz: “We certainly had been successful at doing that anyway and now we had an excuse.”

The Charney-Shukla hypothesis

• Error growth due to imperfectness of numerical prediction ⇒Improvement of numerical model

• Growth of initial condition error ⇒ Improvement of objective analysis

However………... • There remains finite (non zero) error in initial condition,

• We cannot know the ‘true’ initial condition

• Small initial error(difference) grows fast(exponentially) as time progresses and the magnitude of error becomes the same order as natural variability after a certain time ⇒Chaos

Predictability of the first kind

In seasonal time scale, forecast based on initial condition is impossible at least generally

The forecast is based on the influences of boundary conditions such as SSTs or soil wetness or snow cover, etc.

The Charney-Shukla hypothesis

Predictability of the second kind

Correlation of rainfall over India with SST

(1981-2017)

Complexities involved in a Climate Modelling System

Discretization Methods The atmosphere and the ocean are divided in computational cells: (Temperature, Wind, Rain, Sea Ice, SST, Salinity, … ) both horizontally and vertically.

Dimension of the cell (resolution) 200-300 km

The dimensions of the cell are usually not the same in the atmosphere and ocean component because of the different dynamics.

Oceans -- Sea Ice

Atmosphere

Wind Stress Precipitation Solar

Radiation

Atmospheric Radiation

Air Temperature

Sea Surface

Temperature Sensible Heat Flux Latent Heat Flux

Wind Stress Fresh Water Flux

Surface Temperature

COUPLER: (1) Interpolate from the atmospheric grid to the ocean grid and viceversa. (2) Compute fluxes

Very Large Computers are needed

The main problem is how to synchronize the time evolution of the atmosphere with the evolution of the ocean. The most natural choice is to have a complete synchronization (synchronous coupling):

This choice would require to have similar time steps for both models, for instance 30min for the atmospheric model and 2 hours for the ocean model. Computationally very expensive

Atmosphere

Ocean

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Dt Dt

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Dt Dt

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Another possibility is to exploit the different time scales using the fact that the ocean changes much more slowly than the atmosphere (asynchronous coupling):

Atmosphere

Ocean

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Integrate for a long time

This choice save computational time at the expense of accuracy, but for very long simulations (thousands of years) may be the only choice.

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Co

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Integrate for a long time

• Atmospheric variation is not fully controlled by variation of boundary condition such as SSTs but there internal variability also exists.

• Examples of internal variations are cyclones, Madden-Julian oscillation, intra-seasonal oscillations, etc.

• These can be predicted as initial value problem in short time scale but they are

unpredictable at seasonal time scale.

Goswami et al. (2003)

Goswami et al. (2012)

Reduction of noise Since the internal variation can be reduced by time mean but the signal is not reduced,

Time-mean is taken in seasonal forecast.

inent XXX

predictable Unpredictable

reduced by time mean

This time mean is effective especially in tropics.

In addition, main SST signals such as ENSO are in tropics.

Seasonal forecast in tropics is easier than in mid-latitude.

The noise reduction effect of time mean

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• Though time mean is effective to reduce noise, the noise is not removed completely.

• Therefore there remains uncertainty from internal variation and again probabilistic forecast is necessary.

Predictability of the coupled Ocean-Atmosphere System

• Weather forecasts – little use after a week or so

• 1-2 weeks = “predictability horizon” for weather

• Seasonal Forecasts – exploits the fact that atmosphere is influenced by surface conditions that vary slowly (e.g. SSTs)

• Possible because “predictability horizon” for the ocean is much longer: months, years and even decades

• Also possible because the atmosphere strongly responds to SST anomalies – especially in the tropics

• Predictability can arise in association with persistent soil moisture and snow cover (important for Asian monsoon) too

Recipe for a seasonal forecast

A coupled ocean-atmosphere-land model

Ensembles of Initial State

Potential Predictability VS Actual Prediction Skill of ISMR

37

IMD Operational Model Prediction Skill of ISMR

Wang et al., (2015; Nature Communications)

Important events & Dates Sr. no Imp events Dates

1.) IMD meeting: Stake Holders Meeting on National Mission on Monsoon Dynamical Prediction

16th Jan - 2010

2.) Setup CFS v1 at IITM 2010

3.) Monsoon Desk at NCEP June, 2011

4.) IITM Meeting: Monsoon Mission Initiation Meeting

24th January - 2012

5.) Administrative Sanction order 26th July,-2012

6.) International Consultancy Meeting 11-12 September -2012

7.) International Review Meeting February, 2015 and February 2017

8.) Conclusion of Monsoon Mission Phase I March, 31st 2017

Status of Indian Prediction System Prior to Monsoon Mission

• No coupled dynamical modelling System to make seasonal Forecasts

• No extended Range Dynamical Prediction System for predicting Active/break Cycles of Monsoon

• No expertize in handling coupled models in forecast mode • No expertize in developmental activities involving coupled

models • No coupled data assimilation system • India’s role in motivating monsoon research elsewhere in

the globe was not significant • No access to significant HPC resources to carryout the

model development activities

The Statement

It is important to borne in mind that whereas in research projects, the criteria for success is

generally the demonstration of the potential for improvement in skill (with suggested changes in

the model or more data on the clouds, ocean etc.), the deliverable of a mission will have to

be an unequivocal demonstration of improvement in skill.

The Mission

• The Mission’s goal is to build a working partnership between the Academic R & D Organizations and the Operational Agency to leapfrog in improving monsoon forecast skill.

• Requirement :All research work must be on the Operational Modeling Framework!

This is the challenge!

Objectives

• To build a working partnership between the academic and R & D Organizations, and to improve the operational monsoon forecast skill over the country.

• To setup a state-of-the-art dynamical modeling frame work for improving prediction skill of

– Seasonal and Extended range predictions

– Short and Medium range (up to two weeks) prediction

IITM CFS Model: Seasonal Prediction

Ocean Model MOMv4 global

1/2ox1/2o (1/4o in tropics) 40 levels

Atmospheric Model GFS

T382 L64 levels

Land Model NOAH

Ice Model

COUPLER

ATMOSPHERE INITIAL CONDITIONS FROM GSI

OCEAN INITIAL CONDITIONS FROM GODAS

(Original model is adopted from NCEP)

Initial conditions for Hindcast runs are obtained from CFSR

Anomaly Correlation Coefficient (ACC): NINO 3.4

CFSv2 Prediction Skill

Anomaly Correlation Coefficient (ACC) : IOD East Box

CFSv2 Prediction Skill

Monsoon Mission ModelPerformance (PredictionSkill as well as interannualvariance) is better thanother models for IndianMonsoon.

Past to Present JJA

2005

2016

Past to

Pre

sent

Towards Monsoon Mission Model

Other Developments needs to be incorporated: • LSM • Impact of Coupled Data assimilation • Stochastic Parametrization • Convective parameterizations • Cloud microphysics • Other international PIs works

Status of Indian Prediction System

Prior to Monsoon Mission

• No coupled dynamical modelling System to make seasonal Forecasts

• No extended Range Dynamical Prediction System for predicting Active/break Cycles of Monsoon

• No expertize in handling coupled models in forecast mode

• No expertize in developmental activities involving coupled models

• No coupled data assimilation system

• India’s role in motivating monsoon research elsewhere in the globe was not significant

• No access to significant HPC resources to carryout the model development activities

Monsoon Mission

• Coupled dynamical modelling System to make seasonal Forecasts of ISMR with better prediction skill.

• First extended Range Dynamical Prediction System for predicting Active/break Cycles of Monsoon.

• High resolution ensemble based prediction system (T574) has been setup. Efforts are underway to setup T1534 ensemble prediction system soon. (

• Significant work has been done to reduce coupled model biases and incorporated new physics and modules in coupled models.

• Tightly coupled data assimilation system being setup

• Significant HPC resources were put in place

What Needs to be Achieved in Future?

Achieved

• Reduced Biases

• Bias in Indian Ocean teleconnections are improved

• All forecasts are done separately

• Running separately a high resolution short range forecast system

To be achieved

• Biases still exist (further reduction required)

• Phase of the IO Teleconnections are still opposite

• Needs to develop a seamless prediction system

• Develop unified model by incorporating regional model

Future Direction: Extremes and Applications

• Fully develop the Monsoon Mission Model by incorporating all changes carried out by various PIs of monsoon mission.

• Develop a unified model based on the above model by incorporating the regional model in the above model so that extremes can be predicted with improved accuracy and seamlessly

• Operationalize coupled ocean-atmosphere data assimilation system by carrying out vigorous tests on usability to different predictions.

• Appropriate dynamical core developments will be taken up to scale up the model at very high resolutions

• Develop Weather and Climate applications (For Agriculture, Hydrology etc.)

Evaluation of forecast information Discussions with farmers (ICRISAT)