Toward Probabilistic Seasonal Prediction

Nir Krakauer,Hannah Aizenman, Michael Grossberg, Irina Gladkova

Department of Civil Engineeringand CUNY Remote Sensing of the Earth Institute,

The City College of New York.nkrakauer@ccny.cuny.edu

In this talk

Seasonal and probabilistic prediction Quantifying probabilistic forecast skill An application and future prospects

Weather forecasts degrade rapidly with lead time

Effect of atmosphere initial conditions dissipates

NRC, 2010

But there is hope for some skill at month-season lead times

Persistent initial conditions (SST, soil, snow, strat, …)

Between synoptic and climate-change timescales

Deterministic (point) forecasts

"Partly cloudy, high of …"

How much confidence should we have in this? The forecast doesn't tell us; we must rely on our intuition/experience.

Partly probabilistic forecasts

"40% chance of precipitation"

How much? When?

Fully probabilistic forecasts

Distribution functions or an ensemble of possible outcomes

If well calibrated, can be used directly in scenario modeling and optimization

Deterministic vs. probabilistic prediction

How much more would we need to be told to know the outcome?

Information theory (Shannon 1948): Suppose one of n outcomes must

happen, for which we assign probability p

i

If we learn that this outcome did happen, we've learned log(p

i) bits

Summed over possible outcomes, our expected missing information is ∑i=1

n

pi log ( pi )

Information in a probability distribution

Suppose that we learn that outcome i took place

Under our baseline ignorance (e.g. climatology), the probability of i was p

i

Suppose a forecaster had given the outcome a probability q

i instead. Intuitively,

the forecast proved useful if qi > p

i.

The information gain from the forecast is log(q

i / p

i)

How useful is a forecast?

Across multiple forecast verifications, the average information content of forecasts is given by the average log(q

i / p

i)

Best case is to assign probability 1 to something that does happen: log(1 / p

i) bits gained

Assigning zero probability to something that does happen ruins a forecaster's track record [log(0)]

Information (in bits) can be converted to a forecast skill score (1 for a perfect forecast)

A forecaster's track record

Generalization to continuous variables

If x is the outcome and q, p are probability densities, the information gain is log(q(x)/p(x))

If the forecast was Gaussian with mean m and SD σ, and the climatology had mean m

0 and SD σ

0, the information gain is

(z2 – z0

2)/2 - log(σ/σ0), where z = (x – m)/σ

Probabilistic seasonal forecasting Based on known

sources of persistence, particularly ENSO

E.g., probabilistic USA forecasts for T and P tercile issued by NOAA CPC since 1990s

Potentially valuable for agricultural, water management, etc.

Diagnosing probabilistic forecast bias Confidence is how much

skill a forecast claims to have (relative to climatology)

If the forecast is well-calibrated, this should be similar to the information gain estimated by comparing forecasts to outcomes

It turns out CPC temperature forecasts are overconfident (claim 0.014 bits, actual 0.024 bits info gain), but with geographic variability

Improving on existing forecasts It turns out that CPC's

forecasts underestimate the impact of warming and precipitation change

Naive Bayesian combination of CPC's probabilities with a trend estimate based on an exponentially weighted moving average resulted in much higher skill and more consistency across regions

Other model combination techniques being tested

Next steps

Better / more relevant observation targets

– Seasonal outlooks of extreme event (drought, flood, …) risk?

Convert GCM ensemble outputs (NMME, ECMWF …) to probabilistic forecasts – need robust bias and trend adjustment methods, information-based skill metrics

Better approaches may be needed for presenting probabilistic forecasts

Summary Probabilistic forecasts

provide explicit measures of uncertainty, necessary for management applications

More work needed to make use of existing forecast systems in a probabilistic framework

"A person with a clock always knows what time it is; a person with two clocks is never sure."

Questions?

Krakauer, N. Y.; Grossberg, M. D.; Gladkova, I. & Aizenman, H. ( 2013 ) Information Content of Seasonal Forecasts in a Changing Climate, Advances in Meteorology, 2013: 480210Krakauer, N. Y. & Fekete, B. M. ( 2014 ) Are climate model simulations useful for forecasting precipitation trends? Hindcast and synthetic-data experiments, Environmental Research Letters, 9: 024009Krakauer, N. Y. ( 2014 ) Stakeholder-driven research for climate adaptation in New York City, in Drake, J.; Kontar, Y. & Rife, G. (ed.), New Trends in Earth Science Outreach and Engagement: The Nature of Communication, 195-207

nkrakauer@ccny.cuny.edu