Seasonal-to- Interannual Climate Forecasts

1. Seasonal-to-InterannualClimate Forecasts Lisa Goddard International Research Institute for Climate & SocietyThe Earth Institute of Columbia University goddard@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 from1) 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. Climatological Average – Jan. (ºC) Climatological Standard Deviation – Jan. 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)

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

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

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

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 RainfallCategorical Precipitation Probabilities Associated with El Niño OND : Eastern Africa “Short Rains” Wet Season (see Mason & Goddard, BAMS, 2001)

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

25. Example: Indian Ocean & East African RainfallZonal Overturning (“Walker”) Circulation : El Nino – La Nina East-west flow (shading)and zonal over-turningcirculation (arrows) forEl Niño – La Niña conditions AGCM: Indian Ocean forcing only • Rising motion over relatively warmer watersand sinking motion overrelatively cooler waters. •  When both Pacific and • Indian Ocean are warm, thereis competition over Indian Ocean basin between risingmotion (forced by IO) andsinking motion (forced by PO) AGCM: Pacific Ocean forcing only (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. ClimateChange Decadal Initial & ProjectedAtmospheric Composition Weather & Climate Prediction Initial & ProjectedState of Ocean Initial & ProjectedState of Atmosphere CurrentObservedState Uncertainty Time Scale, Spatial Scale

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 SSTsin 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 Predictabilityis not a fixed quantity.It depends very much on the model/techniquebeing used.

34. Model “Skill”Correlation … and on the regionand season underconsideration.

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 • 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/ERRORSImperfect 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. 1 1 5 1 2 6 2 3 7 4 8 Why probabilistic?AGCM Forecasts: Common SSTs, different atmos. ICs Model Forecast (SON 2004), Made Aug 2004 Observed RainfallSep-Oct-Nov 2004(CAMS-OPI) Seasonal climate is a combinationof boundary-forced SIGNAL, and chaotic NOISE from internaldynamics of the atmosphere.

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

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

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

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 notpredicted perfectly, they introduce additional uncertainty in the climate forecast.

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

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

46. Systematic error in locationof mean rainfall, leads tospatial error in interannualrainfall variability, and thusa resulting lack of skilllocally. 2. Errors & Biases in GCMsExample: Systematic Spatial Errors MODEL

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

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