Predictability of weather and climate, seasonal prediction, … · 2019. 3. 1. · Reto Knutti / David Bresch, ETH Zurich. Predictability of the second kind (Palmer, 1998) Experiment

Predictability of weather and climate, seasonal prediction, seamless prediction

Reto Knutti, IAC ETH(with material from Andreas Weigel, Meteoswiss/SwissRE)

ETH Zurich | Reto Knutti

Reto Knutti / David Bresch, ETH Zurich

Weather prediction has a value

Prediction of storm Joachim 16.12.2011

«COSMO» Prediction of probability for wind gusts >90km/h (Forecast lead time 36h).

(Blic

k am

Abe

nd 1

6.12

.11)

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Timescales of forecasts

Question 1: Why do we have weather forecast and climateprojections but nothing in between?

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Quality of forecasts

Question 2: Why is the forecast sometimes completelyunclear, and sometimes almost certain for many days?

Early March 2016 Early July 2015

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Predictability of the first kind The sensitivity to initial

conditions can be shown with the conceptual three component Lorenz model (Lorenz 1963)

3 coupled differential equations

Sensitivity to initial conditions, i.e. predictability depends on the state of the system.

Adapted from M. Liniger & T. Palmer

x

z

y

5


Predictability of the first kind

high predictability

medium predictability

low predictability


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Predictability of the first kind


The memory of the atmosphere to initial conditions is limited to approximately 10 days

The memory of the oceans to initial conditions can range from months to years

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Weather prediction works, and improves

Bauer et al., Nature 2015

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Summary

Two kinds of predictability:Predictability of first kind (initial conditions) and predictability of second kind (boundary conditions)

Weather forecasting relies on initial conditions and exploits predictability of first kind

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Predictability of the second kind

(Palmer, 1998)

Experiment 1: coincidence

Experiment 2: With boundary cond.

Even though individual weather events are not predictable beyond 10 days, the average weather behavior (=climate) may be influenced by predictable boundary conditions for several months and longer.

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Sectors affected by seasonal climate variability

Tourism Water resources

management Energy Agriculture Infrastructure Consumer goods industry Insurance …

Switzerland, winter 2001/02

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The Böögg

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The Böögg

Böögg

Time until head explodes (min)

Mea

n JJ

A te

mpe

ratu

re

R2 = 0.007p = 0.60

heat summer 2003

Schmuki & Weigel, 2006

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El Nino

SST anomalies April 2016Source: NOAA

normal

El Niño

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El Nino El Niño is felt worldwide Being able to predict El Niño

implies being able to predict climate anomalies around the globe (in certain regions and certain seasons)

Other sources of seasonal predictability: SST anomalies in Indian and

Atlantic oceans Anomalies in soil moisture (e.g.

European summer) Anomalies in continental snow

cover (e.g. European spring)

El Niño

La Niña

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El Nino provides seasonal predictability

http://www.cpc.ncep.noaa.gov/products/predictions/long_range/seasonal.php?lead=2

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El Nino links to weather

Weather Channel forecasts on March 6, 2006; https://weather.com/storms/tornado/news/severe-flood-forecast-march-7-12-2016

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El Nino

Dec 27, 2015

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2016 Forecast and verification

https://www.ncdc.noaa.gov/temp-and-precip/us-maps/3/201605#us-maps-select

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Ensembles

Look at distribution rather than

single values:

PROBABILITYFORECASTS

To account for initial condition uncertainty, the analysis is perturbed and ensembles are generated which sample the distribution of possible initial conditions, given the observations available.

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Calibration

Observed climatology

Prob

abilit

y

Temperature

ModelClimatology

Prob

abilit

y

Temperature

How can we obtain these transfer functions?

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Calibration

Hindcast 1 Observation 1

Hindcast 2 Observation 2

Hindcast N Observation N

2009

2008

1981

Mod

el c

limat

olog

y

Obs

erve

d cl

imat

olog

y Forecasts of past cases (so-called hindcasts) are used to derive

correction terms for systematic biases in mean and variance. This procedure is called calibration.

At MeteoSwiss, hindcasts are made back to 1981.

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Summary

Using all observations available, a best guess of the initial conditions of the ocean is obtained by data assimilation (the analysis).

To sample the uncertainty distribution of possible initial conditions, given the observations, ensembles are generated by perturbing the analysis.

Dynamical coupled atmosphere-ocean general circulation models are applied to calculate the evolution of each ensemble member.

Typically 20 to 40 years of hindcasts are calculated to derive correction functions to remove systematic biases in mean and variance (calibration).

Forecast skill depends on the lead time, variable, the region, the initial state, etc. Poor skill may be due to a poor model, but can also simply be a consequence of limited predictability.

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Ranked probability score

The RPS is defined as the squared area enclosed by the forecast CDF and the observation CDF.

Width and location matter.

Perfect forecast: RPS = 0

Imperfect forecast:RPS > 0

Cum

ulat

ive

prob

abilit

y

CDF variable

66%

100%

33%

variable

Prob

abilit

y de

nsity

PDF

Cum

ulat

ive

prob

abilit

y

CDF variable

66%

100%

33%

variable

Prob

abilit

y de

nsity

PDF

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Ranked probability skill score (RPSS)

Often, one wants to know how much added value a forecast provides with respect to climatology: Ranked Probability Skill Score (RPSS):

Quantifies deviation of climatologic forecasts from observation

Quantifies deviation of ensemble forecasts from observation

Perfect: RPSS = 1Skill: RPSS > 0No skill: RPSS ≤ 0

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Skill of System 3 for temperature

prediction climate prediction

climate

Winter1981-2007

RPSS

0.4

-0.4

0

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Overconfidence in real forecasts

Fcst 1 Nov 2007 Fcst 1 Aug 2008

Overconfidence can imply negative skill.27


Skill of System 3 for temperaturespring summer

autumn winter

?

?

0.4-0.4 0RPSS

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Single model vs. multi-modelFcst 1 Aug 2008

ECMWFECMWF

+ UK Met Office+ Météo-France

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Skill of multi-models (summer predictions)

ECMWF

UKMO

UKMO + ECMWF

Further improvement by weighting ?

0.4-0.4 0

RPSS

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Recalibration: rescale and inflate

Rescale ensemble mean + inflate ensemble spread

r

s

(Weigel et al, 2009, Mon. Wea. Rev.)

Values of r and s can – like model weights - be estimated from verification data

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Conventional and recalibrated forecastsJJA forecasts of T2m, Initialization 1 May,1960-2001

0.4

-0.4

0Conventional

Recalibrated RPSS

Weigel et al, 2009, Mon. Wea. Rev.32


Seasonal forecast for Switzerland

http://www.meteoswiss.admin.ch/home/climate/future/seasonal-outlook.htmlhttp://www.meteoswiss.admin.ch/home/climate/present-day/climate-development.html

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Seasonal forecast for Switzerland

(http://www.meteoswiss.admin.ch/home/climate/future/seasonal-outlook/background-information-on-seasonal-climate-outlook.html)

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Summary

The verification of ensemble forecasts requires a sufficient number of verification samples and involves the application of probabilistic skill metrics.

One of the most widely used skill scores is the ranked probability skill score (RPSS), a probabilistic generalization of the mean squared error.

Seasonal forecasts show high prediction skill in the tropics, particularly the ENSO region. Predictability is low in the extratropics.

In central Europe, seasonal forecasts currently are at best only slightly better than climatology.

In some regions, negative skill is observed.

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Applications: Malaria

Red dots: Estimate of malaria incidence

Epidemics with high death rates occur in wetter and/or warmer-than-average years (Thomson et al., 2006, Nature)

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Energy trading

Nov. 2002 – Feb. 2003

EEX

pric

es(B

lock

Bas

e)

HD

D a

nom

alie

sov

er W

este

rn E

urop

e

HDD = heating degree dayEEX = European energy exchange

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Predictability in climate change

Scenarios Model structureParameters

VariabilityInitial conditions

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Sources of uncertainty

(Hawkins and Sutton, 2009)39


Model agreement in CMIPNew (CMIP5) Old (CMIP3)

Stippling: high model agreement, hatching: no significant change,white: inconsistent model projections (Knutti and Sedlacek, 2012)

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Model agreement in CMIPNew (CMIP5) Old (CMIP3)

Stippling: high model agreement, hatching: no significant change,white: inconsistent model projections (Knutti and Sedlacek, 2012)

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Lack of precipitation change explains much of the lack of model agreement

(Knutti and Sedlacek, 2012)42


Kerr Science 2011But ask researchers what’s in store for the Seattle area, the Pacific Northwest, or even the western half of the United States, and they’ll often demur. As Mass notes, “there’s tremendous uncertainty here,” and he’s not just talking about the Pacific Northwest. Switching from global models to models focusing on a single region creates a more detailed forecast, but it also “piles uncertainty on top of uncertainty,” says meteorologist David Battisti of UW Seattle. First of all, there are the uncertainties inherent in the regional model itself. Then there are the global model’s uncertainties at the regional scale, which it feeds into the regional model. As the saying goes, if the global model gives you garbage, regional modeling will only give you more detailedgarbage. And still more uncertainties are created as data are transferred from the global to the regional model.

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Variability in a the 40 member CCSM ensemble

(Deser et al., 2012)44


Variability in a the 40 member CCSM ensemble

(Deser et al., 2012)45


Seamless prediction“[…] Advances in climate prediction will require closecollaboration between the weather and climate predictionresearch communities. It is essential that decadal and multi-decadal climateprediction models accurately simulate the key modes ofnatural variability on the seasonal and sub-seasonal time scales. […] This synergy between the weather and climate predictionefforts will motivate further the development of seamlessprediction systems. […]”

Summit Statement from World Modelling Summit for Climate Prediction, 6-9 May 2008, ECMWF, Reading (UK)

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Making predictions

Damage threshold What will the nextdecadesbring?

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Making predictions Understand the system Characterize trend, mean, variability, memory Build a model, make predictions, to estimate the probability of exceeding

the damage threshold, quantify damage Quantify uncertainty

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Forced response vs. variability

Damage threshold

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Periodic forced response?

Damage threshold

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Initial condition predictability

Damage threshold

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Model error, verification

Damage threshold

Linear trend plus initialized red noise

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Making predictions Understand the system Characterize trend, mean, variability, memory Build a model, make predictions, to estimate the probability of exceeding

the damage threshold, quantify damage Quantify uncertainty and model error

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Documents

Predictability of weather and climate, seasonal prediction, … · 2019. 3. 1. · Reto Knutti / David Bresch, ETH Zurich. Predictability of the second kind (Palmer, 1998) Experiment