1/39 Seasonal Prediction of Asian Monsoon: Predictability Issues and Limitations Arun Kumar Climate Prediction Center

2/39 Outline Characteristics of seasonal prediction What makes seasonal prediction possible? Predictability limits of seasonal climate variability Predictability of Asian monsoon Summary

3/39 What is Seasonal Prediction? Seasonal mean states can be characterized by the probability density function (PDF). The PDF depends on – Season – Variable – Location Seasonal prediction entails our ability to differentiate climatological PDF from the PDF for a particular season

4/39 What is Seasonal Prediction? Climatological PDF PDF for a Season (Red)

5/39 What Determines the Spread of the PDF? Spread in the PDF of seasonal means could be due to – Atmospheric internal variability – Year to year changes in the forcings external to the atmosphere Sea surface temperature (SST) Soil moisture Snow …

6/39 Role of Atmospheric Internal Variability Outcomes of two simulations from an AGCM with identical forcings. The only difference in the small perturbations in initial conditions

7/39 Outline Characteristics of seasonal prediction What makes seasonal prediction possible? Predictability limits of seasonal climate variability Predictability of Asian monsoon Summary

8/39 Influence of External Forcing: An Example of ENSO SST Anomaly over Nino 3.4

9/39 Global Influence of ENSO

10/39 US Influence of ENSO: Precipitation Forecast Observed

11/39 Changes in the spread in the climatological PDF can be influenced by – Proximity to the initial conditions – Changes in the boundary conditions at Earth’s surface – Changes in external forcings such as increasing CO 2 Different factors influence the PDF to a different degree

12/39 Influence of Various Factors on the PDF … initial conditions … boundary conditions … external conditions

13/39 Examples of Seasonal Prediction

14/39 Examples of Seasonal Prediction

15/39 Methods for Quantifying Year-to-Year Changes in the PDF Two important characteristics of the PDF are – Mean – Spread There are several approaches to determine year- to-year changes in the “mean” and “spread” of the PDF – Empirical approaches – Dynamical approaches

16/39 Empirical Prediction Approaches Mainly estimate the change in the mean Advantages – Trained based on historical observations – Unbiased – Simple and computationally efficient Disadvantages – Limited by observational data – Mostly rely on linear relationships – Non-stationarity in climate is hard to include – Cannot handle unprecedented situations

17/39 Dynamical Prediction Approaches Can provide a full estimate of PDF, i.e., both mean and spread Advantages – Linearity and non-stationarity is not an issue – Can handle unprecedented situations Disadvantages – Computational expensive and require a large infrastructure – Forecast systems have biases that requires special attention

18/39 Outline Characteristics of seasonal prediction What makes seasonal prediction possible? Predictability limits of seasonal climate variability Predictability of Asian monsoon Summary

19/39 What Sets Limits on Seasonal Predictability? For the initial value problem – Divergence between various forecasts is due to sensitivity to initial conditions in a non-linear dynamical system – Observed evolution will be one of the traces – Limits the prediction skill

20/39 What Sets Limits on Seasonal Predictability? Climatological + Specific Year

21/39 High Predictability Scenario PDFs are well Separated X P μ σ

22/39 Low Predictability Scenario PDFs have Considerable Overlap σ μ P X

23/39 The Concept of Signal-to-Noise (SNR) Signal-to-noise ratio (SNR) is defined as the ratio of shift in the mean of the PDFs and the standard deviation of the PDF Larger (smaller) SNR implies larger (small) predictability and prediction skill

24/39 SNR and Prediction Skill Relationship between anomaly correlation (AC) and SNR AC  SNR 

25/39 For a particular season, skill of prediction (or predictability) will depend on the shift in the mean and the amplitude of the spread Shift in the mean is the predictable signal and is either due to proximity to initial conditions or is due to some external forcing Spread is the unpredictable noise and mainly due to chaotic variability

26/39 Aggregated over all the seasons the measure of predictability is ratio of variability due to external forcings and internal variability Entails separating total observed variability into a predictable component and the unpredictable component

27/39 Examples of Total Variability: 200mb Z Observed DJF Seasonal Mean Observed JJA Seasonal Mean

28/39 Examples of Total Variability: Precipitation Observed DJF Seasonal Mean Observed JJA Seasonal Mean

29/39 Decomposing Total Variability Total Variability Noise Predictable Signal DJF 200-mb Z

30/39 Signal-to-Noise Ratio: 200mb Z

31/39 Anomaly Correlation

32/39 Total Variability of JJA Seasonal Mean Rainfall

33/39 Decomposing Total Variability Total Variability Noise Predictable Signal

34/39 Signal-to-Noise Ratio: Precipitation

35/39 Precipitation Skill

36/39 Prospects Improved models; There are issues with the simulation of variability of summer monsoon in climate simulations Higher resolutions of climate models Better use of multi-model ensembles Improved communication of forecast information Skill of seasonal predictions, however, will still be limited

37/39 Summary - 1 Seasonal mean climate variability has a significant contribution from atmospheric internal variability (or the noise) This can be confirmed based on atmospheric general circulation model simulations with constant external forcing The predictable signal comes from the influence of external factors on the PDF of seasonal means

38/39 Summary - 2 The relative magnitude of the predictable signal and the unpredictable noise determines the expected value of seasonal predictions The expected value of seasonal predictions depends on the season, location, and the variable Predictability is generally higher in the tropical latitudes where unpredictable noise is smaller than in the higher latitudes

39/39 Summary - 3 Predictability is generally lower for precipitation Estimates of predictability can be obtained based on model simulations, but depend on the quality of the model