Seasonal Climate Prediction Li Xu Department of Meteorology University of Utah

An ability to anticipate the climate fluctuations one or more seasons in advance would have measurable benefits for decision making in hydrology, agriculture, energy,health and other sectors of society.

Seasonal Climate Attribution Consortium –Climate Diagnostics Center (CDC) – NASA Seasonal-to-Interannual Prediction Project (NSIPP) – International Research Institute for Climate Prediction (IRI) – National Centers for Environmental Prediction (NCEP) – Center for Ocean-Land-Atmosphere (COLA) – Scripps Institute for Oceanography (SIO) – Geophysical Fluid Dynamics Laboratory (GFDL)

Outline Scientific Basis Two approaches to inferring atmospheric responses to boundary forcing Role of initial and boundary condition Downscaling Signal and noise Summary

Scientific basis Slow variations in the earth’s boundary condition can influence global atmospheric circulation and thus global surface climate ( Barnston et al 2005) –Sea surface temp (SST) –Soil moisture –Snow & ice cover –Vegetations If future evolution in the boundary conditions can be anticipated, then from the knowledge of their influence on global atmosphere circulation, skillful seasonal predictions for atmospheric climate anomaly are possible. Boundary Forcing

Empirical/Statistical Approach Involves analysis of historically observed boundary forcing and accompanying global circulation and surface climate –Correlation/Lag Correlation –Analogue/Composite –multiple linear regression – Canonical Correlation Analysis (CCA) Predictor Atmospheric Response Seasonal Prediction Expected Future value of the predictor Provide little understanding of physical processes Less robustness due to insufficient sample

Dynamic Approach -Ji et al (1995) first use an atmospheric model coupled with a pacific basin ocean model to produce a successful operational wintertime seasonal prediction in NCEP/NOAA -Typical climate model consist of an Atmospheric General Circulation Model (AGCM) coupled with Ocean, Land and Sea ice model Flux coupler –Each component model can be separate coded –free to choose own spatial resolution and time step. –Individual components can be created, modified, or replaced without necessitating code –Coupler insuring the conservation of fluxed quantities.

Hydrosphere Atmosphere Biosphere Cryosphere Energy flux: L: Latent Heat H: Sensible Heat S: Net Solar Radiation Mass flux: E: Evaporation P: Precipitation Moment flux: τ : surface drag/stress UCAR CCSM3.0 Flux Coupler (

Atmosphere Predictability First kind: Associated with the information within initial conditions (IC) –Lorenz(1969) showed that in chaotic nature atmosphere, small error in initial condition will grow progressively and ultimately affect the largest scale. –employed Ensemble techniques: extend to 2-3 weeks(Kalnay,2003) Second kind: Associated with the information contained in the slowly evolving boundary conditions (BC) After 10 days from initial, second kind predictability leads to measurable forecast skill at very long lead times. (Reichler and Roads,2003)

Experiment nameInitial ConditionsBoundary Conditions ICobservedclimatology BCClimatology randomlyobserved ICBCobserved iBCICBC simulation 1yrobservedInitial well adjusted to the boundary forcing The role of boundary and initial conditions for dynamic seasonal predictability (Reichler and Roads,2003) Crossover of IC and BC Day AC

(Reichler and Roads,2003)

operational seasonal forecast AC scores of NCEP dynamic Seasonal forecast system 2000 (Kanamitsu et al,2002)

reduce forecast uncertainty Ensemble –Slight different in initial condition to reduce uncertainty due to initial condition error Superensemble –Using different models and physical processes to reduce the model uncertainty Consensus forecast –combine the Empirical/Statistical and dynamic approach to produce optical forecast

Downscaling AGCM resolution: km One-way nesting: AGCM runs provide initial and time- dependent meteorological lateral boundary conditions (LBC) for high resolution RCM, without feedback to the drived AGCM improved the spatial as well as the temporal detail distribution of climate anomaly,Also presenting the minimum spread among the ensemble members( Nobre et al,2001) ownscale.html

Signal-noise ratio (S/N) Signal: –relative proportion of the climate variability that is potential predictable given perfect knowledge of external forcing (BC) Noise: –reminder of climate variability that is relative to the fluctuations internal to the atmosphere, which are unpredictable beyond the first 2 weeks (IC). S/N >1: predictable S/N <1: unpredictable Signal-to-noise ratio (S/N): Caveat: some long-lived intra- atmospheric phenomena (MJO, QBO) also Contribute some extent of signal

Ensemble averaged anomaly: Internal variability (IV): Mean internal variability (MIV): External variability (EV): Signal-to-noise ratio: Phelps et al, 2004 i: particular ensemble number a: particular year n: total ensemble number m; total hindcast years

Phelps et al, 2004

CPC/NCEP operational seasonal forecast for Mar 2006

summary From 1980s, advances in understanding the wide array of processes that contribute to the climate variability have improved dramatically in the seasonal climate prediction. Most of the current seasonal predictive skill over many regions of the world comes from the strong influence of ENSO. Owing to the chaotic nature of day-to-day weather fluctuations, climate forecasts will always remain probabilistic and be subject to considerable uncertainty. Seasonal climate prediction is still a basic research problem, because our understanding of atmosphere’s response to the external forcing remains still insufficient

Thanks for attention!

Seasonal Climate Attribution Consortium (Barnston et al 2005) Six-way agreement plot including the Obs and five AGCM simulation T2m Obs in DJF