Climate Change

Anca BrookshawEduardo Penabad Ramos, Simona Briceag, Andras Horanyiand a large number of C3S contractors and collaborators

Mul t i -model seasonal forecasts and c l imate pro ject ions

C3S General Assembly, October 2019

ClimateChange

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October 2019:

ECMWF SEAS5UK Met Office GloSea5Météo-France System7CMCC SPSv3DWD GCFS2.0NCEP CFSv2

C 3 S s e a s o n a l f o r e c a s t m u l t i - s y s t e m

• 6-month forecasts issued every month on the 13th

• Large ensembles (members: ~50 forecast,25-40 hindcast)• Common reference period (1993-2016)• Common horizontal resolution (1-degree)• ~30 surface variables• 5 variables on 11 pressure levels (from 925hPa to

10hPa)

• Graphical products through C3S webpagehttps://climate.copernicus.eu/charts/c3s_seasonal/

• Data service through CDShttps://cds.climate.copernicus.eu/cdsapp#!/search?type=dataset

ClimateChange

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C 3 S s e a s o n a l – f o r e c a s t s y s t e m s

Current model configurations: ECMWF (SEAS5): IFS atmosphere (TCo319, equivalent to N320: ~30km and 91 levels), NEMO ocean

(0.25 deg, 75 levels), LIM sea ice Met Office (GloSea 5): UM atmosphere (N216, ~50 km and 85 levels), NEMO ocean (0.25 deg, 75

levels), CICE sea-ice Météo-France (System 7): ARPEGE atmosphere (TL359, ~60km, 91 levels), NEMO ocean (0.25 deg, 75

levels), GELATO sea-ice CMCC (SPS.v3): CSEM atmosphere ( 1 deg, ~100 km, 46 levels), NEMO ocean (0.25 deg, 50 levels),

CICE sea-ice DWD (GCFS 2.0): ECHAM6 atmosphere (T127, ~150 km, 95 levels), MPIOM ocean (0.4 deg, 40 levels;

includes sea ice) NCEP (CFSv2): NCEP GFS (T128, ~1 deg, 64 levels), GFDL MOM4 ocean (0.5 deg long, 0.25 deg 10S-

10N, 0.5 deg poleward of 30N and 30S, gradual transition between the two)

Ensemble generation: burst (ECMWF, CMCC, DWD) or lagged (the rest), with or without perturbations to initial conditions.See thematic fair on Wednesday

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C 3 S s e a s o n a l – f o r e c a s t s y s t e m s

https://confluence.ecmwf.int/display/COPSRV/Description+of+the+C3S+seasonal+multi-system

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E n s e m b l e d e s i g n - e x a m p l e s1 Jan 25 Jan9 Jan 17 Jan 1 Feb 9 Feb

1 Jan 25 Jan9 Jan 17 Jan 1 Feb 9 Feb

1 Jan 1 Feb

Hindcast: 25 members

Real-time forecast: 51 members

Hindcast: 25 members

Real-time forecast: 51 members

UKMO

ECMWF

Real-time forecast: 2 members each day

Hindcast:7 members

Hindcast:7 members

Hindcast:7 members

Hindcast:7 members

Hindcast:7 members

…. ….

Lagged start vs burst start– flexibility in defining the ensemble– frequency of intialisation: operational schedule, sampling of initial conditions– ‘complexity’ of bias correction (pairing forecasts and hindcasts)

ClimateChange

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C 3 S s e a s o n a l f o r e c a s t s - d a t a p r o d u c t s

• “daily data”• 6h/24h or 12h• forecasts and hindcasts

• “monthly statistics”• mean, min, max, stdev• forecasts and hindcasts

• “anomalies” (post-processed)• just real-time forecasts

NOTE: “from 2017 to present”?

For each dataset, 3 tabs: • overview (briefly describes content)• download form• documentation (essential guide to selectingrelevant data and optimising downloads)

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C 3 S s e a s o n a l f o r e c a s t s - d a t a p r o d u c t s

https://cds.climate.copernicus.eu/cdsapp#!/dataset/seasonal-original-single-levels?tab=form

ClimateChange

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C 3 S s e a s o n a l f o r e c a s t s - d o c u m e n t a t i o n

https://cds.climate.copernicus.eu/cdsapp#!/dataset/seasonal-original-single-levels?tab=doc

ClimateChange

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C 3 S s e a s o n a l f o r e c a s t s - d o c u m e n t a t i o n

ClimateChange

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C 3 S s e a s o n a l f o r e c a s t s - d o c u m e n t a t i o n

https://cds.climate.copernicus.eu/cdsapp#!/dataset/seasonal-original-single-levels?tab=doc

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C 3 S s e a s o n a l f o r e c a s t s - d o c u m e n t a t i o n

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C 3 S s e a s o n a l – g r a p h i c a l p r o d u c t s

http://climate.copernicus.eu/charts/c3s_seasonal/

Publication schedule:• monthly updates• on the 13th of each month

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• Climate projections for the Copernicus Data Store – provide the technical infrastructure to access climate prediction data from Earth System Grid Federation (ESGF); global model output (C3S_34a lot 1) regional model output (C3S_34b lot 1)

• quality control of data/metadata• increased reliability of access (copies of the data at each location and load balancing of requests,• with single point of access through CDS; metrics to monitor performance of data service)

• improved documentation for the relevant CDS catalogue entriesSee poster at thematic fair on Wednesday

• Definition and completion of a matrix of global/regional model and scenarios combinations, which allows robust assessment of the uncertainties arising from these factors in a multi-model set of regional projections (C3S_34b lot2)

See presentation and poster at thematic fair, on Wednesday

C 3 S w o r k o n c l i m a t e p r o j e c t i o n s

Autoscaling - the ability of a service to grow to meet demand. As the number of requests to the service increases, new server hosts (replicas) are provisioned to meet the demand. This is made possible using container technologies.

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S e a s o n a l f o r e c a s t ‘ d a t a ’ o r ‘ i n f o r m a t i o n ’ ?

La Niña?

Niño index

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S e a s o n a l f o r e c a s t ‘ d a t a ’ o r ‘ i n f o r m a t i o n ’ ?

El Niño!

Post-processing of raw model data is needed to produce predictions suitable for climate services.

Niño index

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D i a g n o s e s y s t e m a t i c f o r e c a s t e r r o r s

25 members, 23 years

7 members, 23 years

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• Bias adjustment• Definition of forecast products• Evaluation of forecast attributes • Calibration to account for imperfections and achieve reliable information

Graphical products produced and displayed at C3S are examples of possible post-processing, following best practice, rather than the ‘definitive word’ on methodology.

‘ U s e r - r e a d y ’ i n f o r m a t i o n : p o s t - p r o c e s s i n g

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Thank you