Research

We study different aspects of the predictability of weather and climate as complex physical systems.

Model Uncertainty

Link to Hannah's presentation

Assessing model uncertainty in weather and climate forecasts

All weather and climate forecasts are subject to uncertainties arising from imperfect knowledge of initial and boundary conditions, and from model errors associated with missing or poorly resolved processes. Handling the uncertainty that arises from model errors is very challenging and no widely accepted methodology exists. Here we develop and test different strategies to address model uncertainty in GCM-based weather and climate forecasts. We aim to identify, understand and constrain the physical processes that dominate forecast uncertainty. We particularly focus on uncertainties that are relevant to both weather and climate forecasting, e.g. by taking a seamless prediction approach. Our ultimate goal is to improve the prospects for providing more reliable probabilistic weather and climate forecasts to users.

Publications:

Development of stochastic parametrisations

Description.

Publications:

  • Shutts, G., M. Leutbecher, A. Weisheimer, T. Stockdale, L. Isaksen and M. Bonavita (2011). Representing model uncertainty: stochastic parametrizations at ECMWF. ECMWF Newsletter, 129, 19-24. [pdf]
  • Palmer, T.N., R. Buizza, F. Doblas-Reyes, T. Jung, M. Leutbecher, G.J. Shutts, M. Steinheimer and A. Weisheimer (2009b). Stochastic parametrization and model uncertainty. ECMWF Tech. Memo. 598, 42pp. [pdf]
  • Berner, J., F.J. Doblas-Reyes, T.N. Palmer, G. Shutts and A. Weisheimer (2009). Impact of a quasi-stochastic cellular automaton backscatter scheme on the systematic error and seasonal prediction skill of a global climate model. In "Stochastic Physics and Climate Modelling", by T.N. Palmer and P. Williams (Eds.), Chapter 15, 375-395, Cambridge University Press.

Inexact computing

We study the use of inexact hardware in numerical weather and climate models. Inexact hardware is promising a reduction of computational cost and power consumption of supercomputers and could be a shortcut to higher resolution forecasts with higher forecast accuracy. Please find a presentation that provides a summary of our work on inexact computing here.

An imprecise land surface model

Using the ECMWF land surface model HTESSEL, we are exploring the impact of reduced precision arithmetic on simulation of land surface processes.

People involved:

Dave MacLeod , Andrew Dawson, Tim Palmer

Inexact computing on multiple spatial scales

Inexact techniques might make our models more efficient, but not if they are applied too strongly or in the wrong places. We emulate these techniques in multiscale models to investigate whether using different, optimal levels of 'inexactness' on different spatial scales can improve overall forecast accuracy. We are also developing and testing a multi-scale atmospheric model with reduced precision superparameterisation and double/single precision for atmospheric dynamics for modelling atmospheric convection.

People involved:

Tobias Thornes , Peter Dueben, Aneesh Subramanian, Andrew Dawson, Tim Palmer

Relevant publication:

Dueben et al., 2014, Monthly Weather Review

Field-Programmable Gate Arrays (FPGAs) in Earth System modelling.

Field Programmable Gate Arrays promise a significant increase in computational performance for simulations in geophysical fluid dynamics compared with CPUs of similar power consumption and allow to adjust the precision of individual floating point numbers to specific application needs. Together with collaborators at Imperial College and Maxeler technologies, we have shown significant performance and energy efficiency gains by using FPGA's to implement low and medium complexity climate models.

People involved:

Stephen Jeffress, Peter Dueben, Tim Palmer

Relevant publication:

Dueben et al., 2015, JAMES

Pruned and bit-width truncated chips in Earth System modelling.

In close colaboration with hardware developers, we investigate the use of new hardware setups that allow to trade numerical precision against an increase in performance and a reduction in power consumption.

People involved:

Peter Dueben, Tim Palmer

Relevant publication:

Dueben et al., 2014, Phil. Trans. R. Soc. A

Interactions between rounding errors and model uncertainty.

We investigate the use of rounding errors to represent sub-grid-scale variability and compare rounding error patterns with random forcings of stochastic parametrisation schemes.

People involved:

Peter Dueben, Tim Palmer

Relevant publication:

Dueben et al., 2015, Theor. Comput. Fluid Dyn.

Reduced numerical precision in atmosphere models.

We study the use of reduced precision hardware in three-dimensional models of the atmosphere. We started with tests in the spectral dynamical core IGCM and proceed now to the OpenIFS model. We also studied the use of single precision in the Integrated Forecast System of ECMWF. The use of single precision allows a significant cost reduction with almost no impact on model results.

People involved:

Peter Dueben, Tim Palmer

Relevant publication:

Dueben et al., 2014, Monthly Weather Review

Reduced precision hardware in data-assimilation.

We study how rounding errors will influence ensemble data assimilation. We will also investigate if data storage can be improved using the optimal level of numerical precision.

People involved:

Sam Hatfield, Peter Dueben, Aneesh Subramanian, Tim Palmer

Predictability

High-resolution modelling

It is now understood that many small-scale processes have a profound influence on large-scale climate and climate variability. Including smaller scale processes in our numerical models of the weather and climate allows us to better understand the contribution of the smaller-scales to large-scale circulation. Smaller scale processes can be explicitly included in these models by increasing the horizontal resolution. High horizontal resolution is very expensive in terms of computation time and resources, and it is therefore important that we develop a good understanding of the impact of increased horizontal resolution in order to justify its use in operational models.

Horizontal Resolution: The four horizontal resolutions of the ECMWF IFS integrated under the Athena Project.Horizontal Resolution: The four horizontal resolutions of the ECMWF IFS integrated under the Athena Project.

Predictability on seasonal time scales

Atmospheric predictability on seasonal time scales arises from the slowly varying boundary conditions like the ocean or the land surface. The most prominent example of a coupled ocean-atmosphere process on these time scales is the El Nino Southern Oscillation (ENSO). ENSO is the single largest source for seasonal predictability in many regions of the world.

Our research interests include:

  • skill assessment of ECMWF's dynamical seasonal forecasting system S4
  • multi-model seasonal forecasts
  • forecast quality assessment of the ENSEMBLES seasonal-to-interannual forecasts
  • case studies

Publications:

Predictability on decadal time scales

The climate system exhibits variability on a variety of timescales. Decadal predictability is a relatively new area of research trying to explore impacts from the atmospheric and oceanic initial conditions as well as from the boundary forcings on near-term climate predictions. The next Intergovernmental Panel on Climate Change (IPCC) 5th Assessment Report (AR5), due in 2013, will have in its Working Group 1 volume on the Physical Science Basis a dedicated chapter on "Near-Term Climate Change: Projections and Predictability".

Our latest study by Corti et al. (2012) we assess the reliability of decadal predictions of SST and 2m temperature based on a 54-member ensemble of the ECMWF coupled model. It was shown that the reliability from the ensemble system is good over global land areas, Europe and Africa and for the North Atlantic, Indian Ocean and, to a lesser extent, North Pacific basins for lead times up to 6–9 years. North Atlantic SSTs are reliably predicted even when the climate trend is removed, consistent with the known predictability for this region. By contrast, reliability in the Indian Ocean, where external forcing accounts for most of the variability, deteriorates severely after de-trending. More conventional measures of forecast quality, such as the anomaly correlation coefficient of the ensemble mean, are also considered, showing that the ensemble has significant skill in predicting multi-annual temperature averages.

Publications:

Seamless prediction of weather and climate

Publications:

Climate change

Description.

Publications:

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