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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
Observations 1960 2005
5-member prediction
started 1 Nov 19655-member
prediction started 1 Nov
1960
Experimental setup : 1 grid-point
Methodology
Observations 1960 2005
… until 2009
5-member prediction
started 1 Nov 1970
5-member prediction
started 1 Nov 19655-member
prediction started 1 Nov
1960
Experimental setup : 1 grid-point
Methodology
Observations 1960 2005
5-member prediction
started 1 Nov 2005
… until 2009
5-member prediction
started 1 Nov 1970
5-member prediction
started 1 Nov 19655-member
prediction started 1 Nov
1960
Experimental setup : 1 grid-point
… every 5 years …
Methodology
Observations 1960 2005
5-member prediction
started 1 Nov 2005
… until 2009
5-member prediction
started 1 Nov 1970
5-member prediction
started 1 Nov 19655-member
prediction started 1 Nov
1960
Experimental setup : 1 grid-point
Focus on averages over forecast years 2 to 5
… every 5 years …
Methodology
Observations 1960 2005
5-member prediction
started 1 Nov 2005
… every 5 years …
… until 2009
5-member prediction
started 1 Nov 1970
5-member prediction
started 1 Nov 1965
Experimental setup : 1 grid-point
Focus on averages over forecast years 2 to 5Ensemble-mean
5-member prediction
started 1 Nov 1960
Methodology
1960 2005
… until 2009
Experimental setup : 1 grid-point
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
Initialization : sea ice data assimilation
Francois Massonnet
OBS
OBS
Importance of multivariate initialization for seasonal sea ice prediction
Initialization : sea ice data assimilation
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
The climate prediction drift issue
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
The climate prediction drift issue
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
Volpi et al (2015) Climate Dynamics
Anomaly versus full-field initialisation
Experiment with the minimum SST RMSE
Forecast year 1 Forecast years 2-5
Volpi et al (2015) Climate Dynamics
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
Predictions of the XXIst century 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
Guemas et al (2013) Nature Climate Change
Predictions of the XXIst century hiatus
Ocean heat uptake (0-800m excluding the mixed layer) at the onset of the plateau
Guemas et al (2013) Nature Climate Change
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.