www.bsc.es barcelona, 2015 climate prediction and climate services virginie guemas and the climate...

www.bsc.es

Barcelona, 2015

Climate Prediction and Climate Services

http://ic3.cat/wikicfu

Virginie Guemas and the Climate Forecasting Unit 9 February 2015

Climate timescales and climate prediction

Meehl et al. (2009)

Focus on sub-seasonal, seasonal, interannual and decadal timescales

Climate system predictability

Memory on interannual to centennial timescales in the ocean

Memory on seasonal to interannual timescales in the sea ice and land surface

External radiative forcings (solar activity, greenhouse gases, aerosols)

Decadal climate prediction exercise

Nov 2000 Nov 2001 Nov 2002 Nov 2003 Nov 2004 Nov 2005 Nov 2006

Forecast time 5 years

Core

Tier 1

Forecast time 1 year

Methodology

Observations 1960 2005

… until 2009

5-member prediction

started 1 Nov 1960

Experimental setup : 1 grid-point

Methodology


5-member prediction

started 1 Nov 19655-member

prediction started 1 Nov

1960


Methodology


… until 2009

5-member prediction

started 1 Nov 1970

5-member prediction



1960


Methodology


5-member prediction

started 1 Nov 2005

… until 2009

5-member prediction

started 1 Nov 1970

5-member prediction



1960


… every 5 years …

Methodology


5-member prediction

started 1 Nov 2005

… until 2009

5-member prediction

started 1 Nov 1970

5-member prediction



1960


Focus on averages over forecast years 2 to 5


Methodology


5-member prediction

started 1 Nov 2005


… until 2009

5-member prediction

started 1 Nov 1970

5-member prediction

started 1 Nov 1965


Focus on averages over forecast years 2 to 5Ensemble-mean

5-member prediction

started 1 Nov 1960

Methodology

1960 2005

… until 2009


As many values as hindcasts for both the model and the observations to compute skill scores. Ex : correlations

Typical decadal forecast skill – IPCC AR5

Doblas-Reyes et al. (2013) Nature Communications

(Top row) Root mean square skill score (RMSSS) of the ensemble mean of the initialised predictions and (bottom row) ratio of the root mean square error (RMSE) of the initialised and uninitialised predictions for the near-surface temperature from the multi-model CMIP5 experiment (1960-2005) for (left) 2-5 and (right) 6-9 forecast years. Five-year start date interval.

Added-value from initialisation

Skill

Typical seasonal forecast skillCorrelation of the ensemble mean for the ENSEMBLES multi-model (45 members) wrt ERA40-ERAInt (T2m over 1960-2005) and GPCP (precip over 1980-2005) with 1-month lead

T2m JJA T2m DJF

Prec JJA Prec DJF

Some open fronts Work on initialisation: generate initial conditions (e.g. for sea ice, ocean). Compare different initialisation techniques (e.g. full field versus anomaly initialisation)

Improving model processes: Inclusion and/or testing of model components (biogeochemistry, vegetation, aerosols, sea ice) or new parameterizations, model parameter calibration, increase in resolution

Calibration and combination: empirical prediction (better use of current benchmarks), local knowledge.

Forecast quality assessment: scores closer to the user, reliability as a main target, process-based verification, attribution of climate events with successful predictions, diagnostics of model weaknesses with failing predictions

More sensitivity to the users’ needs: going beyond downscaling, better documentation (e.g. use the IPCC language), demonstration of value and outreach.

Initialization : in-house sea ice reconstructions

NEMO3.2 ocean model + LIM2 sea ice model

Forcings : 1958-2006 DFS4.3 or 1979-2013 ERA-interim

Nudging : T and S toward ORAS4, timescales = 360 days below 800m, and 10 days above except in the mixed layer, except at the equator (1°S-1°N), SST & SSS restoring (-40W/m2, -150 mm/day/psu)

Wind perturbations + 5-member ORAS4 - - - > 5 members for sea ice reconstruction

5 member sea ice reconstruction for 1958-present consistent with ocean and atmosphere states used for initialization

Guemas et al (2014) Climate Dynamics

Initialization : in-house sea ice reconstructions

Reconstruction IceSat

Too much ice in central Arctic, too few in the Chukchi and East Siberian Seas

2003-2007 October-November Arctic sea ice thickness

Guemas et al (2014) Climate Dynamics

Sea ice reconstruction – extraction of variability modes

Clustering methods more robust than EOF analysis + account for nonlinearities.

Tools available in s2dverification R package

Fučkar et al (2015) ClimateDynamics

k-means cluster analysis of reconstructed sea ice thickness (SIT)

Initialization : sea ice data assimilation

Observations (e.g., ice concentration only)

1. Model forecasts

2. Analysis

The ensemble Kalman filter: a multivariate data assimilation method for smoother initialization

Francois Massonnet


Francois Massonnet

OBS

OBS

Importance of multivariate initialization for seasonal sea ice prediction


Francois Massonnet

Fully-coupled sea ice data assimilation in EC-Earth: the next challenge

What are the perturbations required to generate adequate spread in EC-Earth during the forecast steps of the assimilation run ?

Should the atmosphere be updated when sea ice observations are assimilated?

Can we afford to run the EnKF with less members (CPU time is limited) ?

The climate prediction drift issue

Observed world

Retrospective prediction ( hindcast ) affected by a strong drift, need for a-posteriori bias correction

Time

Pre

dict

ed

Var

iabl

e

(ex.

Tem

per

atu

re)

BIA

S

Biased model world

Danila Volpi

Testing bias correction methods – percentile matchingBias-corrected ECMWF S4 forecasts for November with start date in November over 1981-2012. One-year-out cross-validation applied.

Method 1: Simple = Per-pair

Method 2: Percentile Matching

Bias corrected forecast

Uncorrected forecast

Observation

Veronica Torralba

Bias correction and calibrationECMWF S4 predictions of 10 m wind speed over the North Sea for DJF starting in November. Raw output (top), bias corrected (simple scaling = per-pair, left), ensemble calibration = percentile matching (right). One-year-out cross-validation applied.

Veronica Torralba

Developing a new bias correction method

IC (Initial conditions) bias correction method (green) accounts for the dependence of the climate prediction drift on the observed initial conditions through a linear regression -> lowerforecast error

Fučkar et al (2014) Geophysical Research Letters

Tools available in s2dverification R package


Issue : Distinction between climate drift and climate signal

Hypothesis : If the model climate is stable (no drift), the simulated variability is independent of the model mean state within the range of current model biases and closer to the observed variability than when mixed with the drift

Testing the hypothesis : Allowing the climate model biases but constraining the phase of the simulated variability toward the contemporaneous observed one at the initialization time : Anomaly Initialization (AI)

Danila Volpi


Observed world

Biased model world

Retrospective prediction ( hindcast ) affected by a strong drift, need for a-posteriori bias correction

Time

Pre

dict

ed

Var

iabl

e

(ex.

Tem

per

atu

re)

BIA

S

Retrospective prediction with anomaly initialization

Danila Volpi

Anomaly versus full-field initialization

EC-Earth2.3, 5 members, start dates every 2 years from 1960 to 2004

NOINI : historical simulation

FFI : Full-field initialization from ORAS4 + ERA

OSI-AI : Ocean and sea ice anomaly initialization with corrections to ensure consistency

rho-OSI-wAI : Ocean and sea ice weighted anomaly initialization to account for the different model and observed amplitudes of variability + (density, temperature) Instead of (temperature, salinity) anomaly initialisation

Volpi et al (2015) Climate Dynamics

Anomaly versus full-field initialization

RMSE AMO. Ref: ERSST RMSE PDO. Ref: ERSST

RMSE sea ice area. Ref: Guemas et al (2014)

RMSE sea ice volume. Ref: Guemas et al (2014)

NOINI

NOINI

NOINI NOINI

FFI

FFI

FFIFFI

OS

I-AI

OSI-AI

OSI-AI

OS

I-AI

rho-OSI-wAI

rho-OSI-wAI rho-OSI-wAI

rho-OSI-wAI


Anomaly versus full-field initialisation

Experiment with the minimum SST RMSE

Forecast year 1 Forecast years 2-5


Ensemble generation : Stochastic perturbationsDJF one-month lead time bias for the 10-metre zonal wind (m/s) from EC-Earth3 T255/ORCA1 hindcasts over 1993-2009 (10-member ensembles) with the standard forecast system and with SPPT. (blue = reduction in bias).

Control |SPPT|-|Control|

Lauriane Batté

Impact of initialization : CMIP5 decadal predictions

Predictions Historical simulations

Observations

Atlantic multidecadal variability (AMV)

Global mean surface atmospheric temperature

CMIP5 decadal predictions. Global-mean t2m and AMV against GHCN/ERSST3b for forecast years 2-5.

Doblas-Reyes et al. (2013) Nature Communications

Impact of sea ice initializationPredictions with EC-Earth2.3 started every November over 1979-2010 with ERAInt and ORAS4 initial conditions, and our sea-ice reconstruction. Two sets, one initialised with realistic and another one with climatological sea-ice initial conditions.

Ratio RMSE Init/Clim hindcasts 2-metre temperature (months 2-4)

RMSE Arctic sea-ice area

Guemas et al (2015) Geophysical Research Letters

Impact of sea ice initializationPredictions od NAO with EC-Earth2.3 started every November over 1979-2010 with ERAInt and ORAS4 initial conditions, and a sea-ice reconstruction. Two sets, one initialised with realistic and another one with climatological sea-ice initial conditions.

Javier Garcia-Serrano

Impact of land surface initializationDifference in the correlation of the ensemble-mean near-surface temperature (top) and precipitation (bottom) from two experiments (JJA), one using a realistic and another a climatological land-surface initialisation. Results for EC-Earth2.3 started every May over 1979-2010 with ERAInt and ORAS4 initial conditions and our sea-ice reconstruction.

Prodhomme et al (2015) Climate Dynamics

Impact of land surface initializationJJA precipitation in 2003 (top row) and near-surface temperature in 2010 (bottom row) anomalies from ERAInt (left) and experiments with a climatological (centre) and a realistic (right) land-surface initialisation. Results for EC-Earth2.3 started in May with initial conditions from ERAInt, ORAS4 and a sea-ice reconstruction over 1979-2010.

Prodhomme et al (2015) Climate Dynamics

Impact of increasing the resolutionMean SST (K) systematic error versus ERAInt for JJA one-month lead five-member predictions of EC-Earth3 T255/ORCA1 and T511/ORCA025. May start dates over 1993-2009 using ERA-Interim and GLORYS initial conditions.

EC-Earth3 T255/ORCA1 EC-Earth3 T511/ORCA025

Chloe Prodhomme

High – Low resolution

Predictions of DJF NAO with EC-Earth3 low and high resolution and ECMWF S4 started in November over 1993-2009 with ERA-Interim and GLORYS initial conditions and five-member ensembles. Correlation of the ensemble mean on top left.

EC-Earth3 T255/ORCA1

ECMWF S4

EC-Earth3 T511/ORCA025

Impact of increasing the resolution

Lauriane Batté

Hurricane frequency predictionsAverage number of hurricanes per year estimated from observations and from EC-Earth CMIP5 decadal predictions. The correlation of the ensemble mean for the initialized, uninitialized and statistical predictions are shown with the 95% confidence intervals.

Louis-Philippe Caron

CMIP5 predictions

Ec-Earth full-field initialized

Ec-Earth anomaly initialized

CMIP5 historical

Persistance

Attribution of extreme events

How has anthropogenic activity changed the odds of extreme events?

Southern African drought (2002/2003) and flood (1999/200)

Climate change has increased the risk of dry winter seasons and reduced the risk of wet winter seasons.

Fraction of attributable riskFAR=1-P

ALL/P

NAT

PALL,NAT

= Probability

of observing the event using all forcings and natural forcings only.

Omar Bellprat

Global mean Sea Surface Temperature

Predictions of the XXIst century hiatus

Forecast years 1 to 3 from climate predictions initialized from observations

Observations (ERSST)

Guemas et al (2013) Nature Climate Change

EC-Earth2.3 CMIP5 decadal climate predictions capture the hiatus


Observations

EC-Earth historical simulations starting from 1850 preindustrial control simulations

Forecast years 1 to 3 from EC-Earth climate predictions initialized from observations

Crucial role of initialization from observations in capturing the plateau



Ocean heat uptake (0-800m excluding the mixed layer) at the onset of the plateau


Plateau explained by increased ocean heat uptake

Global Framework on Climate Services

Climate services: renewable energy

Lienert and Doblas-Reyes (2013) Journal of Geophysical Research

Progress on open fronts Work on initialisation: more advanced data assimilation (ex: EnKF, coupled assimilation) to generate initial conditions, use of new observations and reanalyses, better ensemble generation.

Improving model processes: Impact of aerosols, interactive vegetation, prediction of biogeochemistry, more efficient use of computing resources, drift reduction, leverage knowledge from modelling at other times scales

Calibration and combination: estimation of uncertainty

Forecast quality assessment: attribution of climate extremes (drought, sea ice minima and maxima), analysis of ocean, sea ice and land sources of predictability, role of external forcings

More sensitivity to the users’ needs: going beyond downscaling, better documentation (e.g. use the IPCC language), demonstration of value and outreach.

www.bsc.es barcelona, 2015 climate prediction and climate services virginie guemas and the climate...

Documents

member prediction

climate prediction meehl

initialisation skill

year slide

forecast years

methodology observations

experimental setup

climate timescales