1. Seasonal-to-Interannual Climate Forecasts Lisa Goddard International Research Institute for Climate & Society The Earth Institute of Columbia Universitygoddard@iri.columbia.edu

2. Seasonal-to-Interannual Variability What is it? How do we model it? Can we predict it? What are the uncertainties? Where do they come from?

3. Main Points Central role of ENSO in seasonal-to-interannual (SI) climate variability Tropical air-sea system is coupled - ocean affects atmosphere, atmosphere affects ocean - linear system (behavior of anomalies ≈ behavior of means) Seasonal climate is necessarily probabilistic  The probabilistic “uncertainty” comes from 1) Uncertainty in initial conditions, both for atmosphere & ocean 2) Imperfections of models

4. Seasonal-to-Interannual Variability What is it? How do we model it? Can we predict it? What are the uncertainties? Where do they come from?

5. CLIMATOLOGY Climatological Average: Average monthly/seasonal climate over many years. In seasonal prediction community, typically 30 (e.g. 1971- 2000). Climatological Probability: Expected frequency of ‘events’ defined over many years (e.g. 30). Can either define the ‘event’ and look for the climatological probabilities, or define the probabilities and look for the ‘event threshold’.

6. Temperature Variability Mid-latitudes: Movement of air masses (e.g. shift of “polar front”) Changes in radiative heating (e.g. more/less clouds, increased/decreased albedo due to changes in surface conditions) Climatological Average – Jan. (ºC) Climatological Standard Deviation – Jan.

7. Temperature Variability Tropics: Changes in heating of tropical atmosphere (i.e. changes in latent heating in mid- troposphere) Area and intensity of convection typically increases during El Nino, leading to more latent heating of tropical atmosphere.

8. Tropospheric Temperature Anomalies (From Yulaeva & Wallace, 1994, J. Climate) North-South Structure of Temperature Anomalies Time Series of Zonally-Averaged Temperature Anomalies

9. Precipitation Variability Tropics: Changes in position and/or strength of convective patterns (e.g. inter-tropical convergence zones). Sub-tropics & Mid-latitudes: Change in strength/position of jet stream and associated storm tracks.

10. Circulation Changes & Associated Climate Anomalies over US during ENSO events http://www.cpc.ncep.noaa.gov/products/analysis_monitoring/ensocycle/nawinter.shtml

11. Seasonal-to-Interannual Variability What is it? How do we model it? Can we predict it? What are the uncertainties? Where do they come from?

12. Seasonal-to-Interannual Variability How do we model it? On seasonal time scales much of the climate variability is a result of changes in boundary conditions to the atmosphere (e.g. patterns of SST).

13. Q: What’s so special about the Pacific?

14. A: Equatorial Pacific spans nearly ½ of Earth’s circumference Long time delay for negative feedback due to adjustment of off-equatorial perturbations  Magnitude of coupled growth  Potential predictability of future evolution Large longitudinal shift in western Pacific convection  Shifts in tropical rainfall and subsidence  Shifts in mid-latitude storm tracks

15. Influence of SST on tropical atmosphere

16. November 1997 : peak El Niño Low-level wind anomalies (925mb) Upper-level wind anomalies (200mb) Outgoing Longwave Radiation (OLR) Anom.SST Anomaly

17. November 1998 : peak La Niña Low-level wind anomalies (925mb) Upper-level wind anomalies (200mb) SST AnomalyOutgoing Longwave Radiation (OLR) Anom.

18. Anomalous SST [gradients] Anomalous low-level winds Anomalous convergence/rainfall Anomalous upper-level winds Anomalous subsidence Schematic of Tropical Ocean-Atmosphere Interaction

19. Teleconnection of El Niño to other tropical ocean basins Indian Ocean (~ 1/3 size of Pacific) - dynamical forcing from tropical Pacific  potential for coupled ocean-atmos. growth - thermo-dynamical forcing Atlantic Ocean (<1/3 size of Pacific) - N.Atlantic variability related to Pacific variability - Coupled growth possible in eastern equatorial Atlantic, but not explicitly related to Pacific variability

20. Dynamical Modeling

21. General Circulation Models Atmospheric GCMs: Specify boundary conditions (e.g. SSTs, soil moisture). Ocean effects atmosphere, but not vice-versa. Coupled Ocean-atmosphere GCMs: Specify initial [observed] ocean state. Ocean and atmosphere evolve together and can influence each other.

22. Importance of regional SST forcing to regional atmospheric response EXAMPLE : The Indian Ocean and eastern Africa

23. Example: Indian Ocean & East African Rainfall Categorical Precipitation Probabilities Associated with El Niño OND : Eastern Africa “Short Rains” Wet Season (see Mason & Goddard, BAMS, 2001)

24. Isolated Basin Expts. Example: Indian Ocean & East African Rainfall Importance of Indian Ocean for Simulating East African Rainfall AGCM: Global Ocean-Global Atm AGCM: Pacific Ocean-Global Atm AGCM: Indian Ocean-Global Atm AGCM: Global Ocean-Global Atm (a1) (a2) (b2) (c2) (b1) (c1) (Goddard & Graham., JGR-Atmos, 1999)

25. Example: Indian Ocean & East African Rainfall Zonal Overturning (“Walker”) Circulation : El Nino – La Nina AGCM: Indian Ocean forcing only AGCM: Pacific Ocean forcing only East-west flow (shading) and zonal over-turning circulation (arrows) for El Niño – La Niña conditions Rising motion over relatively warmer waters and sinking motion over relatively cooler waters.  When both Pacific and Indian Ocean are warm, there is competition over Indian Ocean basin between rising motion (forced by IO) and sinking motion (forced by PO) (Goddard & Graham, JGR-Atmos, 1999)

26. Conclusions I Coupled ocean-atmosphere interaction occurs in all tropical ocean basins. Tropical Pacific is central to coupled climate system because its large size allows for: - relatively long timescales, leading to potential predictability of El Niño; - large amplitude growth of coupled anomalies; - potential for sustained oscillations (El Niño/La Niña); - large spatial shifts in convection, and thus atmospheric heating, impacting global circulation.

27. Conclusions I (cont.) Atmospheric circulation changes induced by El Niño / La Niña often modify SST in other tropical ocean basins. SST anomalies in the Indian and tropical Atlantic Oceans can play significant role in effecting climate variability of neighboring regions, that may be modified by the atmospheric response to SST anomalies in the tropical Pacific.

28. Seasonal-to-Interannual Variability What is it? How do we model it? Can we predict it? What are the uncertainties? Where do they come from?

29. Basis for Seasonal Climate Prediction Changes in patterns of SSTs lead to thermally direct changes in atmospheric circulation in the tropics.  This changes location of convection, which changes location of mid-tropospheric heating, impacting both tropical circulation and mid-latitude storm tracks. Known patterns of SST anomalies (e.g. El Nino/La Nina) often lead to repeatable seasonal climate anomalies for particular regions during particular seasons.

30. Weather & Climate Prediction Climate Change Uncertainty Time Scale, Spatial Scale Current Observed State Initial & Projected State of Atmosphere Initial & Projected Atmospheric Composition Decadal Initial & Projected State of Ocean

31. Initial Conditions vs. Boundary Conditions Seasonal climate is experienced as a sequence of ‘weather events’  Initial conditions are the conditions of the climate system at the start of the particular forecast. They lead to prognosis of the evolution of the weather  Boundary conditions are the imposed conditions that influence changes in the climate (such as SSTs in an atmospheric model). They lead to prognosis of the “statistics” of the weather BCs aren’t necessarily responsible for individual weather events, but may be responsible for the persistence or absence or change in intensity of the weather events.

32. “Potential Predictability” Could be empirical or dynamical  Can methodology simulate the observed variability? For AGCMs: Can model simulate observed variability given observed SSTs? Note: More esoteric approaches to estimating “potential predictability” exist, such as signal-to-noise ratios, that are even more model-centric.

33. Model “Skill” Correlation Potential Predictability is not a fixed quantity. It depends very much on the model/technique being used.

34. Model “Skill” Correlation … and on the region and season under consideration.

35. Example of seasonal rainfall forecast Regional 3-month average Probabilistic

36. Seasonal-to-Interannual Variability What is it? How do we model it? Can we predict it? What are the uncertainties? Where do they come from?

37. SOURCES OF UNCERTAINTY IN SEASONAL CLIMATE FORECASTS 1) INITIAL CONDITIONS of Atmosphere & Ocean = Inherent uncertainty in climate system (internal dynamics, or chaos, of the system)  Sensitivity of ocean to initial conditions impacts Boundary Conditions for atmosphere 2) MODEL BIASES/ERRORS Imperfect models of the climate (small scale processes not resolved; physical processes/interactions not included; topography not resolved)

38. 1a Uncertainty in [Atmospheric] Initial Conditions (Chaos or Internal Variability of Atmosphere) The final state of the atmosphere, and its evolution in getting there depends on the initial condition of the atmosphere. However, we can not measure that exactly or with sufficient temporal and spatial resolution. Even if two initial states are nearly indistinguishable, their differences will give rise to different evolutions in a matter of days to weeks. Initial Final

39. 11 2 Why probabilistic? AGCM Forecasts: Common SSTs, different atmos. ICs Observed Rainfall Sep-Oct-Nov 2004 (CAMS-OPI) 1 2 3 4 5 6 7 8 Seasonal climate is a combination of boundary-forced SIGNAL, and chaotic NOISE from internal dynamics of the atmosphere. Model Forecast (SON 2004), Made Aug 2004

40. Why probabilistic? Observed Rainfall Sep-Oct-Nov 2004 (CAMS-OPI) Model Forecast (SON 2004), Made Aug 2004 ENSEMBLE MEAN Average model response, or SIGNAL, due to prescribed SSTs was for normal to below-normal rainfall over southern US/ northern Mexico in this season. Need to also communicate fact that some of the ensemble member predictions were actually wet in this region. Thus, there may be a ‘most likely outcome’, but there are also a ‘range of possibilities’ that must be quantified.

41. What probabilistic forecasts represent “SIGNAL” The SIGNAL represents the ‘most likely’ outcome. The NOISE represents internal atmospheric chaos, and random errors in the models. “NOISE” Historical distribution Climatological Average Forecast Mean Forecast distribution Below Normal Above Normal Near-Normal

42. Reliability! Forecasts should “mean what they say”. A Major Goal of Probabilistic Forecasts

43. 2. Uncertainty in Boundary Conditions (error/uncertainty in predicted SSTs or estimated land surface) The predictable part of SI climate variability is primarily due to changes at the Earth’s surface, in particular changes in SST patterns. Thus the ability to predict seasonal climate variations rests on the ability to predict the relevant SST anomalies. The ENSO phenomenon of the tropical Pacific exerts the largest influence on SI climate variability, globally. It is also the most predictable feature of SST variability in the global oceans. We need ENSO forecasts to be as accurate as possible. Of course, accurate SST predictions in the tropical Indian and Atlantic are important also. To the extent that the SSTs are not predicted perfectly, they introduce additional uncertainty in the climate forecast.

44. Loss of skill in AGCM due to imperfect predictions of SST Dominant pattern of precipitation error associated with dominant pattern of SST prediction error (Goddard & Mason, 2002) 1b. Uncertainty in Boundary Conditions

45. Loss of skill in AGCM due to imperfect predictions of SST Dominant pattern of precipitation error associated with dominant pattern of SST prediction error 1b. Uncertainty in Boundary Conditions

46. Systematic error in location of mean rainfall, leads to spatial error in interannual rainfall variability, and thus a resulting lack of skill locally. MODEL 2. Errors & Biases in GCMs Example: Systematic Spatial Errors

47. 2. Errors & Biases in GCMs Example: Using Multiple Models (AGCMs) to Reduce Random Errors Combining models reduces deficiencies of individual models

48. Conclusions II Seasonal predictions can be based on empirical or dynamical models – both try to capture the robust responses to changes in boundary conditions (e.g. SSTs) The 2 main sources of uncertainty in seasonal climate forecasts are: –Initial Conditions in atmosphere & ocean (and land, etc.) –Model Biases/Errors Seasonal forecasts are necessarily probabilistic –Want to minimize “bad” uncertainty by identifying and correcting systematic biases –Want to quantify “good” uncertainty inherent in the climate system –Multi-model ensembles lead to more reliable forecasts by reducing random errors The possibility exists to enhance information to higher spatial and temporal scales –Requires research! Results are often region and season specific. Successful application of seasonal climate forecasts may require creativity to address users’ needs