Potential Predictability of Tropical Pacific Ocean - PowerPoint Presentation

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Potential Predictability of Tropical Pacific Ocean

Lauren Stevens and Matt ChamberlainOutline:Set-up of the experimentPotential predictability of ENSOPotential predictability of air-sea CO2 fluxes and Net Primary ProductivityFuture Work

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Coupled Model – MOM4 (

ACCESSo

grid and SIS) with AM2 (v1 control with 500 years of simulations)

Forecasts from January 1

st

going for 6 years with 10 ensemble members

First set is from years 315 – 319 (5 years)Small random SST perturbation in the tropicsSecond set is from years 305, 315, 320, 335, 350, 365,…,440 (11 years)Small random SST perturbation at one point in the tropics

Forecasts

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NINO3.4 Variability

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15 January forecasts

Example Forecast

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NINO3.4 Variability

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15 January forecasts

Example Forecast

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Year 305 – Red Observed; Black and grey forecast

Potential Predictability: NINO3.4

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11 January forecasts

Example Forecast

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Potential Predictability: NINO3.4

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11 January forecasts

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Potential Predictability: Tropical Pacific

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Seferian

et al., 2013, PNAS

1 year lead

2-5 year lead

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Potential Predictability: CO2 Flux

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12-month lead: Correlation Coefficient

forecasts

Persistence

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Potential Predictability: CO2 Flux

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24-month lead: Correlation Coefficient

forecasts

Persistence

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Potential Predictability: Net Primary Productivity

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12-month lead: Correlation Coefficient

forecasts

Persistence

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Potential Predictability: Net Primary Productivity

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24-month lead: Correlation Coefficient

forecasts

Persistence

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Future work

Potential predictability experiments provide a convenient way to assess different forecasting strategiesEnsemble generation methodsImpact of different climate regimesNovel forecasting productsothers

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Potential Predictability: NINO3.4

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11 January forecasts

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Potential Predictability: NINO3.4

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11 January forecasts

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Potential Predictability: NINO3.4

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11 January forecasts

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Potential Predictability: NINO3.4

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11 January forecasts

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Potential Predictability: Soil Moisture

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15 January forecasts

20-month lead: Correlation Coefficient

forecasts

Persistence

South Australia: (south of 33°S)

Anomaly Correlation Coefficient

Lead Time (months)

Blue- forecast

Orange - persistence

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Potential Predictability: NINO3.4

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15 January forecasts

Example Forecast

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ENSO

–

Anomaly Correlation

Zheng 2010

v0

v1

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Nino4 Discrimination Plot:

v0

v1

3 months

6 months

9 months

Assess the ability to forecast an event (El Nino or La Nina) and no event

Not able to reliably forecast

an event

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Nino4 Discrimination Plot:

v1

v1

3 months

6 months

9 months

Not enough events

In the forecast

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CAFE System

Climate Model – MOM5 (SISE,WOMBAT) with AM2 ->MOM5 with AM3 and ACCESS-ESM 1Data Assimilation – EnOI with ocean observations ->EnKF (96 members) with ocean, sea ice and atmospheric dataEnsemble generation with Bred Vectors -> several different sets of BVs targeting different time-scales

Forecasts – new dataset of monthly forecasts to follow the couple DA development (6 months)

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https://jobs.csiro.au

Postdoctoral Fellowship Sea Ice Modeller (56719)Postdoctoral Fellowship - Climate regimes (56508)Postdoctoral Fellowship - Ocean-Atmosphere dynamics (56622)CSIRO Postdoctoral Fellowship – Atmospheric DynamicsCSIRO Postdoctoral Fellowship - Ocean-Atmosphere Carbon Fluxes (49441)

Post Doctorial Positions

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ROC:

4 months

6 months

9 months

v0

v1

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Potential Predictability: Rainfall

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15 January forecasts

South Australia (south of 33°S):

Anomaly Correlation Coefficient

Blue- forecast

Orange - persistence

Lead Time (months)

20-month lead: Correlation Coefficient

forecasts

Persistence

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Forecasts:

DA vs Reanalysis

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for initial state

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January forecasts (2005

to 2016)

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Decadal Climate Forecasting Project

Initial GoalsBuild the Climate Analysis Forecast Ensemble (CAFE) system and deliver multi-year to decadal climate forecasts (probabilistic problem and we will provide ensemble forecasts) Apply diagnostics tools, including ensemble verification metrics, to accurately assess the skill of the forecastsAdvance fundamental research into: where does the predictability of the climate system resides, the processes that give rise to that predictability, and the key observations that help us to realise the potential climate predictability Explore the utility of our climate forecasts for a select group of external clients (e.g. Digiscape) 27 |

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Data Assimilation, Climate Modelling and Ensemble Generation

Develop and run a coupled ocean-atmosphere-sea ice climate model data assimilation scheme to incorporate observations into the climate model to characterise the climate stateEnsemble climate forecasting system initiated from the climate stateThis is the core of the Climate Analysis Forecast Ensemble (CAFE) system

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Processes and Observations

Climate Processes that drive potential predictabilityPredictability StudiesObserving System Experiments and Observing System Simulation Experiments New observation for data assimilation (e.g. sea ice, ocean colour) and assessment of their impact on the climate forecasts

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Application and Verification

• need process-based skill assessment• understand mechanisms underlying forecasts• outline deficient process representations in model • provide narrative for forecast use• document skill in public archives and over time• no magic Strong overlap with all components of CAFE System

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Forecast Dataset - 2002 -2016

every month of length 2 years with 11 ensemble membersevery 6 months of length 6 years with 11 ensemble membersTo apply a forecast need to understand what the forecast is need to know how to use it need to evaluate how good it isneed to understand its limitationsneed to support the close collaboration between the generation of the forecast and the users

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