Dynamical Forecasting 2. 69EG3137 – Impacts & Models of Climate Change. Details for Today: DATE: 27 th January 2005 BY: Mark Cresswell FOLLOWED BY: Nothing. Lecture Topics. GARP PROVOST TOGA GOALS Impact of ENSO DEMETER The future. GARP.
Dynamical Forecasting 2
69EG3137 – Impacts & Models of Climate Change
Details for Today:
DATE:27th January 2005
Set up by the World Meteorological Organization (WMO) to examine the physical basis of climate and implications for climate modelling – published document in 1975
Key section was by Edward Lorenz dealing with climate predictability
Defined “climate” as an ensemble (collection) of all states observed during some finite time period
Climate “prediction” must therefore be seen as the process of determining how this ensemble will change at some point
As perfect measurements are impossible, the predictability of any non-periodic system decays to zero as the range of prediction becomes infinite
Unpredictability is caused by instability of small perturbations that become subject to chaos
Small errors in the representation of initial conditions would tend to double in amplitude every four days during a forecast
Eventually errors become no greater than guesswork – I.e. randomly selecting an atmospheric state as a prediction
Perfect forecasting of large-scale atmospheric features would require perfect representation and forecasting of smaller scale features – impossible as models have too crude a spatial scale to resolve such processes
The sea surface temperature (SST) is a key boundary condition likely to provide most predictability
If the ensemble of weather patterns associated with one SST pattern differs more than trivially with those associated with another SST pattern then forecasts of positive skill should be possible
A decrease (degradation) in spatial resolution of a GCM by a factor of two can speed up the model by possibly a factor of ten
PRedictability Of climate Variations On Seasonal to inter-annual Timescales
EC collaborative project – UKMO contribution was great
15-years (1979 to 1993) – sets of 4-month range, 9 member ensemble integrations from the HadAM3 AGCM
Used prescribed ideal (observed) SST data – so simulations are thus regarded as providing “potential” skill
Skill was found to be highest in the tropics
Skill in the extratropics was found to be in Spring (MAM)
Scores for precipitation are generally lower than temperature
ENSO forcing has a marked global impact on model predictability
A substantial proportion of the skill achieved using observed SSTs is retained using a persisted SST – suggesting that persisted SST anomalies from the month preceding the start of an integration could be viable for real-time forecasting
Prediction of model skill may be approached through the relationship between ensemble spread and the skill of the ensemble mean
The spread of members about the mean is a measure of the sensitivity to initial conditions
Low ensemble spread is associated with high ensemble mean skill
Tropical Ocean and Global Atmosphere research programme
Evolved from loosely coordinated research efforts in the early 1970s – began as US effort in 1983 and became international in 1985
Examined the mechanism for ENSO and the way wind stress (and hence tropical Pacific SSTAs) was coupled to trade wind strength
Key of TOGA was examination of tropical ocean – atmosphere system predictability
Impact of ENSO
The El Niño Southern Oscillation (ENSO) may have a warm phase (El Niño) or cool phase (La Nina). In both cases it represents a warm or cold SST anomaly in the Eastern Pacific.
Owen and Palmer (1986) produced the first empirical evidence for the impact of ENSO on dynamical long-range climate forecasts.
Based on two 3-member ensembles of 90-day forecasts for 1982-83 with the UKMO 11-layer GCM
Impact of ENSO
The first ensemble used observed SSTs and the second used climatological SSTs.
ENSO SST anomalies can force a realistic and statistically significant time-averaged response around the entire tropical region
In the tropics, the skill was improved with observed SSTs for all timescales. In the extratropics, skill was improved only on a 30-day timescale
The European Centre for Medium Range Weather Forecasting (ECMWF) near Reading has been leading the way in new long-range prediction research
ECMWF houses a Fujitsu VPP5000 supercomputer with a massively parallel 100-processor array.
Computer allows HOPE ocean model and EPS GCM to work together as a completely coupled ocean-atmosphere climate model. A new forecasting system is currently being developed called DEMETER
Development of a European Multimodel Ensemble system for seasonal to inTERannual prediction
The project aims to develop a well-validated European coupled multi-model ensemble forecast system for reliable seasonal to interannual prediction
Funded by the EU it brings together 6 european climate modelling groups: ECMWF, Météo-France, LODYC, UKMO, MPI, CERFACS, INGV and INM-HIRLAM. Each model is installed and run at ECMWF.
Analysis and formulation is based on evidence from PROVOST
Initialisation and validation is performed with the new ERA-40 reanalysis dataset (1957 to 2001) that replaces the older ERA-15 data - with its associated flaws.
By combining the 6 sets of ensembles (one from each European model) it is hoped that simulations with greater skill will be achieved.
Archives of forecast variables (including: air temperature, U and V velocities, specific humidity, snow depth, cloud cover, precipitation etc) will be made available for further research and model verification
Following on from DEMETER, a new project started in 2003/2004 funded by the European Framework 6
University of Reading has begun to work on a new model formulation for global climate model resolution of 40km. Once installed at the Hadley Centre, this new model will be used to examine future global climate change scenarios (up to 100 years ahead) for specific regions
Improved assimilation will be possible with new satellite remote sensing systems being planned and now on line (e.g. METEOSAT Second Generation and GPM project)