seasonal forecasting from demeter to ensembles21 july 2009 seasonal forecasting from demeter to...

Seasonal forecasting from DEMETER to ENSEMBLES 21 July 2009

Seasonal Forecasting From

DEMETER to ENSEMBLES

Francisco J. Doblas-ReyesECMWF


● Coupled ocean-atmosphere systems without assimilation of sub-surface ocean observations.

● Multi-model (ECMWF, GloSea, Météo-France, IfM-Kiel, CERFACS, INGV, LODYC) ensemble re-forecasts.

● Re-forecast period 1959-2001, seasonal (6 months, February, May, August and November start date), 9-member ensembles, ERA40 initialization in most cases.

DEMETER


● Model uncertainty is a major source of forecast error. Three approaches to deal with model uncertainty are being investigated in ENSEMBLES: multi-model (ECMWF, GloSea, DePreSys, Météo-France, IfM-Kiel, CERFACS, INGV), stochastic physics (ECMWF) and perturbed parameters (DePreSys).

● Hindcasts in two streams:o Stream 1: hindcast period 1991-2001, seasonal (7 months, May

and November start date), annual (14 months, November start date), 9-member ensembles, ERA40 initialization in most cases; DePreSys (IC and PP ensembles) 10-year runs in every instance.

o Stream 2: As in Stream 1 but over 1960-2005, with 4 start dates for seasonal hindcasts, at least 1 for annual and at least one 3-member decadal hindcast every 5 years; DePreSys 10-year runs once a year and 30-year runs every 5 years.

ENSEMBLES


Feb 87 May 87 Aug 87 Nov 87 Feb 88 ...

K models x M ensemble members

M*K-member ensemble

Assume a multi-model ensemble system with coupled initialized GCMs

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6

Ensemble climate forecast systems

Lead time = 7


Feb 87 May 87 Aug 87 Nov 87 Feb 88 ...

Ensemble climate forecast systems

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6

Assume a multi-model ensemble system with coupled initialized GCMs

Lead time = 4


Main systematic errors in dynamical climate forecasts:o Differences between the model climatological pdf (computed for a

lead time from all start dates and ensemble members) and the reference climatological pdf (for the corresponding times of the reference dataset): systematic errors in mean and variability.

o Conditional biases in the forecast pdf: errors in conditional probabilities implying that probability forecasts are not trustworthy. This type of systematic error is best assessed using the reliability diagram.

Temperature

Differences in climatological pdfs

Reference pdf Model pdf

Systematic error in ensemble forecasts

Threshold

Forecast PDF

t=1

t=2

t=3

Mean bias Different variabilities

Actual occurrences


ENSEMBLES Stream 2 T2m mean bias wrt ERA40/OPS, 1960-2005

First month

May

Months 2-4

JJA

Months 5-7

SON

Systematic error in seasonal forecasts

ECMWF Météo-France


Attributes diagrams for 1-month lead seasonal (JJA) precipitation above the upper tercile over the tropical band for the ENSEMBLES Stream 1 multi-model (left, 45 members), stochastic physics (centre, 9 members) and perturbed parameters (right, 9 members) hindcasts started in May over

the period 1991-2001 verified against GPCP. The Brier and ROC skill scores, along with 95% confidence intervals (in brackets) computed using

a bootstrap method, are shown on top of each panel.

Multi-model0.129 (0.082,0.178)0.441 (0.378,0.502)

Stochastic physics0.059 (0.005,0.105)

0.391 (0.322,0.453)

Perturbed parameters0.050 (0.002,0.105)0.381 (0.329,0.443)

Attributes diagram


Reliability diagrams for 1-month lead seasonal (JJA) precipitation above the upper tercile over the tropical band for the ENSEMBLES Stream 1 multi-model (left, 45 members), stochastic physics (centre, 9 members) and

perturbed parameters (right, 9 members) hindcasts over the period 1991-2001 verified against GPCP.

Direct model output (no bias correction) and threshold (upper tercile) computed from the reference climatology

Multi-model Stochastic physics

Perturbed parameters

Direct model output


Scores for southern South America precipitation from Stochastic Physics, Perturbed Parameters (both with 9-

member ensembles) and Multi-model (5 models, 45 members). Sample values are shown with black dots along with 95% confidence intervals obtained using a bootstrap

method (verified against GPCP over 1991-2001).

Anomaly correlation coefficient Ratio between spread and RMSEROCSS for anomalies

above the upper tercile

Stream 1 seasonal hindcasts


Stream 2 seasonal hindcasts

3

237

64

Brier skill score for several regions (Northern Hemisphere, Tropics, Southern Hemisphere), events (anomalies above/below the upper/lower

tercile), lead times (2-4, 5-7 months), start dates (Feb, May, Aug and Nov) and variables (near-surface temperature, precipitation, Z500, T850 and MSLP) computed over the period 1960-2005. The inset numbers indicate

the number of cases where a system is superior.

176


DEMETER vs ENSEMBLES

Brier skill score for Niño3 SST re-forecasts in DEMETER, ENSEMBLES and DEMETER+ENSEMBLES using all start dates over the period 1980-2001.

Forecast period 2-4 months

Forecast period 4-6 months

x’>upper tercilex’<lower tercile


DEMETER vs ENSEMBLES

Brier skill score for re-forecasts of near-surface temperature and precipitation for different land regions over the period 1980-2001.

DEMETER ENSEMBLES

DEMETER+ENSEMBLES


EUROBRISA at IC3

● The Catalan Institute for Climate Sciences (IC3, Barcelona, Spain) will start this winter a new group on seasonal and interannual climate forecasting.

● The main goals are to develop a capability to perform research on climate forecasting and to work on methods that provide useful climate information; the target regions are the Mediterranean area, South America and Africa.

● A solid link to EUROBRISA is expected at IC3. Two members of the group will work on seasonal forecasting calibration and combination with a focus on the Mediterranean region and the calibration of forecasts using non-stationary series (in the presence of trends and low-frequency variability).


Summary

● Substantial systematic error, including lack of reliability, is still a fundamental problem in dynamical seasonal and interannual forecasting and forces a posteriori corrections to obtain useful predictions.

● Comprehensive assessments of the forecast quality measures (including estimates of their standard error) are indispensable in forecast system comparisons.

● Perturbed-parameter ensembles are competitive with multi-model ensembles.

● The ENSEMBLES multi-model is marginally better than the DEMETER multi-model; much is still to be gained from robust calibration and combination.

seasonal forecasting from demeter to ensembles21 july 2009 seasonal forecasting from demeter to...

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