Reliable Climate Projections: The Final Frontier for Stochastic Parametrisation
Lead Research Organisation:
University of Oxford
Department Name: Oxford Physics
Abstract
Reliable climate projections are needed by governments and industry to make decisions in response to climate change. For example, the governmental National Flood Resilience Review, published in September 2016, used risks calculated from climate projections to evaluate how to best protect the country from extreme weather and flood events. By weighing up the cost of investment against the predicted risks, the government decided to invest in temporary protective flood barriers.
However, it is likely that our current climate projections do not accurately represent the possibility of different climate outcomes. On seasonal timescales, where we can test the skilfulness of our predictions, we can see that our current models are unable to reliably predict the likelihood of different events. The shortcomings observed in seasonal forecasts will also infect climate prediction. This limits the usefulness of current climate models for decision-making.
I propose to develop a new technique for representing uncertainty in climate models. This will improve our ability to predict the likelihood of different climate outcomes. The key aim is to improve the reliability of climate projections, and therefore their usefulness to decision-makers and end users.
In particular, I will target uncertainty in climate prediction due to the limitations of the climate model. The process of representing the atmosphere, oceans and land-surface and their many interactions in a piece of computer code is a large source of uncertainty. Some processes in the atmosphere take place on too small a scale to be explicitly represented on the grid used by a computer model, where grid points are typically 10-100 km apart. Instead, these processes are accounted for using "parametrisation schemes".
In the case of weather and seasonal forecasting, random numbers are widely used as a tool to help represent these small-scale processes in the model. These so called "stochastic" parametrisation schemes improve the consistency of the model with the underlying physical processes. The introduction of stochastic parametrisation schemes produced a step-change improvement in weather and seasonal forecasts.
However, current stochastic schemes do not directly target the physical source of uncertainty. Instead they simply target the impact of the uncertainty on the forecast. While this is sufficient for short-range applications, these schemes are not mathematically rigorous, and do not necessarily conserve moisture, mass or energy over longer time periods. This is very problematic for climate prediction.
My research will provide a set of new, physically motivated stochastic parametrisation schemes. By targeting uncertainty at the source, these schemes will be useful for climate projections as well as on weather and seasonal timescales.
I will develop this set of new stochastic schemes by comparing a typical forecast model to a high-resolution atmospheric simulation. My project partners across Europe will provide these state-of-the-art simulations in which small-scale processes are explicitly modelled. In the forecast model, these small-scales must be represented through parametrisation schemes. By comparing the two, I will identify processes that are better represented in a probabilistic manner instead of in the traditional deterministic manner.
I will initially evaluate my new schemes in weather and seasonal forecasts. This provides a way to validate the representation of fast timescale processes in the forecast model. Accurate representation of these processes is necessary for realistic climate simulation, due to the non-linear nature of the atmosphere. Having validated my new approach in this way, I will produce a probabilistic climate change projection where, for the first time, model uncertainty is accounted for in a complete and physically consistent manner.
However, it is likely that our current climate projections do not accurately represent the possibility of different climate outcomes. On seasonal timescales, where we can test the skilfulness of our predictions, we can see that our current models are unable to reliably predict the likelihood of different events. The shortcomings observed in seasonal forecasts will also infect climate prediction. This limits the usefulness of current climate models for decision-making.
I propose to develop a new technique for representing uncertainty in climate models. This will improve our ability to predict the likelihood of different climate outcomes. The key aim is to improve the reliability of climate projections, and therefore their usefulness to decision-makers and end users.
In particular, I will target uncertainty in climate prediction due to the limitations of the climate model. The process of representing the atmosphere, oceans and land-surface and their many interactions in a piece of computer code is a large source of uncertainty. Some processes in the atmosphere take place on too small a scale to be explicitly represented on the grid used by a computer model, where grid points are typically 10-100 km apart. Instead, these processes are accounted for using "parametrisation schemes".
In the case of weather and seasonal forecasting, random numbers are widely used as a tool to help represent these small-scale processes in the model. These so called "stochastic" parametrisation schemes improve the consistency of the model with the underlying physical processes. The introduction of stochastic parametrisation schemes produced a step-change improvement in weather and seasonal forecasts.
However, current stochastic schemes do not directly target the physical source of uncertainty. Instead they simply target the impact of the uncertainty on the forecast. While this is sufficient for short-range applications, these schemes are not mathematically rigorous, and do not necessarily conserve moisture, mass or energy over longer time periods. This is very problematic for climate prediction.
My research will provide a set of new, physically motivated stochastic parametrisation schemes. By targeting uncertainty at the source, these schemes will be useful for climate projections as well as on weather and seasonal timescales.
I will develop this set of new stochastic schemes by comparing a typical forecast model to a high-resolution atmospheric simulation. My project partners across Europe will provide these state-of-the-art simulations in which small-scale processes are explicitly modelled. In the forecast model, these small-scales must be represented through parametrisation schemes. By comparing the two, I will identify processes that are better represented in a probabilistic manner instead of in the traditional deterministic manner.
I will initially evaluate my new schemes in weather and seasonal forecasts. This provides a way to validate the representation of fast timescale processes in the forecast model. Accurate representation of these processes is necessary for realistic climate simulation, due to the non-linear nature of the atmosphere. Having validated my new approach in this way, I will produce a probabilistic climate change projection where, for the first time, model uncertainty is accounted for in a complete and physically consistent manner.
Planned Impact
The following groups will benefit from this research
Operational forecasting centres
The new stochastic schemes I produce will be included into two operational models during the project: the European Centre for Medium Range Weather Forecasts and the UK Met Office. The new suite of stochastic parametrisation schemes will improve the skill of forecasts on a range of timescales. In particular, the reliability of weather and seasonal forecasts will improve, which will make them more useful for end users. This will directly benefit the modelling centres, as it increases the economic value of their forecasts. The schemes will also be suitable for use in climate models, where they have the potential to improve probabilistic climate projection. It is anticipated that other modelling groups worldwide will readily adopt the schemes.
Policy makers
Using a complete set of physically motivated stochastic parametrisation schemes to represent model uncertainty has the potential to improve probabilistic climate projection compared to the perturbed parameter or multi-model approaches currently used. The new schemes will improve the reliability of the projection for a given forcing scenario. This will directly benefit policy makers, who can make better-informed decisions. This will increase the effectiveness of policy, and facilitate planning to mitigate and adapt to climate change.
Forecast users
Improving operational probabilistic weather and seasonal forecasts improves our ability as a society to prepare, both for severe weather events such as storms, and for seasonal warnings of drought or flooding. A better-calibrated early warning system allows both companies and members of the public to plan and react. This research will therefore benefit society and especially those sectors of society that use weather and climate models for quantitative decision-making. Examples include:
- Industry
Forecast users in industry will benefit economically from the improved forecasts on a range of timescales. For example, the UK rail network can decide whether to invest in new 'slab tracks' that are very costly, but more resistant to buckling in hot weather, given that the number of hot days is predicted to increase due to changes in climate. On a shorter timescale, forecasts of extreme wind could inform a wind farm manager of the need to shut down the wind turbines to prevent damage in a storm. In both cases, if the forecasts used to inform these decisions are unreliable, businesses may make decisions that are costly to them (e.g. shutting down a turbine), while the benefits of making that decision are never realised (e.g. the storm does not materialise). Similarly, improved forecasts will directly increase the economic performance of these industries, and thereby improve the economic competitiveness of the United Kingdom.
- Charity sector
Improved forecasts on seasonal timescales have many important humanitarian applications, including the likelihood of floods or droughts for agricultural planning, and the projections of the prevalence of infectious diseases in a given season. The charity sector is showing an interest in using seasonal forecast information to plan relief programs in regions at risk: improved reliability of seasonal forecasts will improve the efficacy of these projects.
- Public
Improved weather forecasts benefit the public, as the information can be integrated into personal plans for hours or days ahead. This increased confidence in forecasts will in return be beneficial to the forecasting centres. The public will also benefit through improved decision-making by industries that provide public services, such as water companies, transport providers, and local government. Longer term, the health and life of the general public will benefit from an increased resilience to climate change, enabled by good policy decisions made today.
Operational forecasting centres
The new stochastic schemes I produce will be included into two operational models during the project: the European Centre for Medium Range Weather Forecasts and the UK Met Office. The new suite of stochastic parametrisation schemes will improve the skill of forecasts on a range of timescales. In particular, the reliability of weather and seasonal forecasts will improve, which will make them more useful for end users. This will directly benefit the modelling centres, as it increases the economic value of their forecasts. The schemes will also be suitable for use in climate models, where they have the potential to improve probabilistic climate projection. It is anticipated that other modelling groups worldwide will readily adopt the schemes.
Policy makers
Using a complete set of physically motivated stochastic parametrisation schemes to represent model uncertainty has the potential to improve probabilistic climate projection compared to the perturbed parameter or multi-model approaches currently used. The new schemes will improve the reliability of the projection for a given forcing scenario. This will directly benefit policy makers, who can make better-informed decisions. This will increase the effectiveness of policy, and facilitate planning to mitigate and adapt to climate change.
Forecast users
Improving operational probabilistic weather and seasonal forecasts improves our ability as a society to prepare, both for severe weather events such as storms, and for seasonal warnings of drought or flooding. A better-calibrated early warning system allows both companies and members of the public to plan and react. This research will therefore benefit society and especially those sectors of society that use weather and climate models for quantitative decision-making. Examples include:
- Industry
Forecast users in industry will benefit economically from the improved forecasts on a range of timescales. For example, the UK rail network can decide whether to invest in new 'slab tracks' that are very costly, but more resistant to buckling in hot weather, given that the number of hot days is predicted to increase due to changes in climate. On a shorter timescale, forecasts of extreme wind could inform a wind farm manager of the need to shut down the wind turbines to prevent damage in a storm. In both cases, if the forecasts used to inform these decisions are unreliable, businesses may make decisions that are costly to them (e.g. shutting down a turbine), while the benefits of making that decision are never realised (e.g. the storm does not materialise). Similarly, improved forecasts will directly increase the economic performance of these industries, and thereby improve the economic competitiveness of the United Kingdom.
- Charity sector
Improved forecasts on seasonal timescales have many important humanitarian applications, including the likelihood of floods or droughts for agricultural planning, and the projections of the prevalence of infectious diseases in a given season. The charity sector is showing an interest in using seasonal forecast information to plan relief programs in regions at risk: improved reliability of seasonal forecasts will improve the efficacy of these projects.
- Public
Improved weather forecasts benefit the public, as the information can be integrated into personal plans for hours or days ahead. This increased confidence in forecasts will in return be beneficial to the forecasting centres. The public will also benefit through improved decision-making by industries that provide public services, such as water companies, transport providers, and local government. Longer term, the health and life of the general public will benefit from an increased resilience to climate change, enabled by good policy decisions made today.
Organisations
- University of Oxford (Fellow, Lead Research Organisation)
- NCAR National Center for Atmospheric Research (Collaboration)
- University of Colorado Boulder (Collaboration)
- UNIVERSITY OF READING (Collaboration)
- University of Victoria (Collaboration)
- National Center for Atmospheric Research (Collaboration)
- World Meteorological Organization (Collaboration)
- Natural Environment Research Council (Collaboration)
- Argonne National Laboratory (Collaboration)
- European Centre for Medium Range Weather Forecasting ECMWF (Collaboration)
- UNIVERSITY OF EXETER (Collaboration)
- Meteorological Office UK (Collaboration)
- National Oceanic and Atmospheric Administration (Collaboration)
- German Weather Service (Collaboration)
- Max Planck Institute for Meteorology (Collaboration)
- University of Bonn (Collaboration)
- Météo France (Collaboration)
People |
ORCID iD |
Hannah Christensen (Principal Investigator / Fellow) |
Publications
Christensen, H. M.
(2024)
Machine Learning for Stochastic Parametrisation
in arXiv
Roberts M
(2018)
The Benefits of Global High Resolution for Climate Simulation: Process Understanding and the Enabling of Stakeholder Decisions at the Regional Scale
in Bulletin of the American Meteorological Society
Fabiano F
(2020)
Euro-Atlantic weather Regimes in the PRIMAVERA coupled climate simulations: impact of resolution and mean state biases on model performance
in Climate Dynamics
Yang C
(2019)
The impact of stochastic physics on the El Niño Southern Oscillation in the EC-Earth coupled model
in Climate Dynamics
Yang C
(2019)
Correction to: The impact of stochastic physics on the El Niño Southern Oscillation in the EC-Earth coupled model
in Climate Dynamics
Huntingford C
(2019)
Machine learning and artificial intelligence to aid climate change research and preparedness
in Environmental Research Letters
Delaunay A
(2022)
Interpretable Deep Learning for Probabilistic MJO Prediction
in Geophysical Research Letters
Christensen H
(2021)
The Fractal Nature of Clouds in Global Storm-Resolving Models
in Geophysical Research Letters
Strommen K
(2023)
On the Relationship Between Reliability Diagrams and the "Signal-To-Noise Paradox"
in Geophysical Research Letters
Strommen K
(2019)
Progress towards a probabilistic Earth system model: examining the impact of stochasticity in the atmosphere and land component of EC-Earth v3.2
in Geoscientific Model Development
Description | The most significant achievements of the award so far are: 1. Demonstrating the use of a coarse graining techniques for assessing model error. I propose a new technique which combines high-resolution simulations with a low-resolution forecasting model (a 'single column model'). This enables the error in the forecast model to be measured. This is important for producing probabilistic forecasts, in which we account for the uncertainty in our prediction. In particular, stochastic parametrisations provide a way to account for errors in our model when producing the forecast. I have used these new measurements of model error to assess existing stochastic parametrisation schemes as well as motivate new approaches (Christensen et al, 2018; Christensen, submitted). I have simultaneously made use of more traditional coarse-graining approaches to characterise uncertainty in specific processes of interest in a low-resolution model (Bessac et al, 2019). 2. I have continued to demonstrate the potential for using stochastic parametrisation schemes in climate models, with a particular focus on the beneficial impact such schemes have on the El Nino Southern Oscillation (Berner et al, 2018; Yang et al, 2019). It is important that seasonal and climate forecasts can capture the behaviour of the El Nino Southern Oscillation, as it has widespread impacts on extreme weather events, including the likelihood of droughts and flooding. |
Exploitation Route | These findings will be taken forward by the PI over the remaining part of the award. In addition: - the approach of Bessac et al (2019) forms the basis for a new proposal submitted to the Natural Sciences and Engineering Research Council of Canada (results to be announced in April/May 2019) - the new approach of Christensen et al, (2018) and Christensen (submitted) forms the basis of a proposed project under the World Weather Research Programme and World Climate Research Programme for a co-ordinated 'Model Uncertainty Intercomparison Project'. Discussions are underway to confirm which international groups wish to be involved. |
Sectors | Aerospace Defence and Marine Agriculture Food and Drink Energy Environment Leisure Activities including Sports Recreation and Tourism Government Democracy and Justice Retail Other |
Description | Invited expert at Bishop of Oxford's Climate Action Meetings |
Geographic Reach | Local/Municipal/Regional |
Policy Influence Type | Participation in a guidance/advisory committee |
Description | Oxford University Press John Fell Fund |
Amount | £54,208 (GBP) |
Funding ID | 0009731 |
Organisation | University of Oxford |
Sector | Academic/University |
Country | United Kingdom |
Start | 03/2021 |
End | 02/2022 |
Description | Standard Grant |
Amount | £618,745 (GBP) |
Funding ID | NE/W005530/1 |
Organisation | Natural Environment Research Council |
Sector | Public |
Country | United Kingdom |
Start | 03/2022 |
End | 03/2025 |
Title | Forcing files for the ECMWF Integrated Forecasting System (IFS) Single Column Model (SCM) over Indian Ocean/Tropical Pacific derived from 10-day high resolution CASCADE simulation |
Description | This data set consisting of initial conditions, boundary conditions and forcing profiles for the Single Column Model (SCM) version of the European Centre for Medium-range Weather Forecasts (ECMWF) model, the Integrated Forecasting System (IFS). The IFS SCM is freely available through the OpenIFS project, on application to ECMWF for a licence. The data were produced and tested for IFS CY40R1, but will be suitable for earlier model cycles, and also for future versions assuming no new boundary fields are required by a later model. The data are archived as single time-stamp maps in netCDF files. If the data are extracted at any lat-lon location and the desired timestamps concatenated (e.g. using netCDF operators), the resultant file is in the correct format for input into the IFS SCM. The data covers the Tropical Indian Ocean/Warm Pool domain spanning 20S-20N, 42-181E. The data are available every 15 minutes from 6 April 2009 0100 UTC for a period of ten days. The total number of grid points over which an SCM can be run is 480 in the longitudinal direction, and 142 latitudinally. With over 68,000 independent grid points available for evaluation of SCM simulations, robust statistics of bias can be estimated over a wide range of boundary and climatic conditions. The initial conditions and forcing profiles were derived by coarse-graining high resolution (4 km) simulations produced as part of the NERC Cascade project, dataset ID xfhfc (also available on CEDA). The Cascade dataset is archived once an hour. The dataset was linearly interpolated in time to produce the 15-minute resolution required by the SCM. The resolution of the coarse-grained data corresponds to the IFS T639 reduced gaussian grid (approx 32 km). The boundary conditions are as used in the operational IFS at resolution T639. The coarse graining procedure by which the data were produced is detailed in Christensen, H. M., Dawson, A. and Holloway, C. E., 'Forcing Single Column Models using High-resolution Model Simulations', in review, Journal of Advances in Modeling Earth Systems (JAMES). |
Type Of Material | Database/Collection of data |
Year Produced | 2018 |
Provided To Others? | Yes |
Impact | I am using this dataset to characterise the errors in a low resolution forecast model. These measurements will be used to improve the representation of model uncertainty in weather and climate models. |
URL | http://dx.doi.org/10.5285/bf4fb57ac7f9461db27dab77c8c97cf2 |
Title | Fractal Analysis of Clouds in DYAMOND Summer Simulations (revised) |
Description | Data accompanying "The Fractal Nature of Clouds in Global Storm-Resolving Models", by H. M. Christensen and O. Driver, submitted to Geophysical Research Letters. Summary We compute the fractal dimension of clouds in the DYAMOND Summer simulations: https://www.esiwace.eu/services/dyamond/summer This is compared to the dimension computed using the Himawari 8 satellite. The simulations span 1 August--10 September 2016. We use data between 25oS-25oN, 80-200oE. A binary cloud field is defined for the model simulations using outgoing long wave radiation using a given threshold. For Himawari observations we use the derived Cloud Top Temperature product, with a consistent threshold: see paper for details. Any pixel with outgoing long wave radiation or cloud top temperature below these values is defined as 'cloudy'. Available model and satellite derived data [model identifier]_clouds_230.csv Contains sets of Area-Perimeter data couplets for each selected timestamp in the DYAMOND simulation indicated by [model identifier], using the 230 K cloud top temperature threshold. [model identifier]_dims_threshold.csv Contains the fractal dimension measured for each selected timestamp in the DYAMOND simulation indicated by [model identifier], as a function of threshold. This is the Area-Perimeter fractal dimension, \(P \propto A^{D/2}\). This can be obtained as the gradient of the regression line through the logarithm of the data in the 'clouds' files, multiplied by two. Data are provided for the following thresholds: 200, 210, 220, 230, 240, 250, 260 K. Since the satellite fields are only available during daylight hours, we provide and analyse the data at 0200, 0300, and 0400 UTC for both satellite and model data (or the closest available timestamp to these times for each model). Acknowledgements H.M.C. was funded by Natural Environment Research Council grant number NE/P018238/1. DYAMOND data management was provided by the German Climate Computing Center (DKRZ) and supported through the projects ESiWACE and ESiWACE2. The projects ESiWACE and ESiWACE2 have received funding from the European Union's Horizon 2020 research and innovation programme under grant agreements No 675191 and 823988. This work used resources of the Deutsches Klimarechenzentrum (DKRZ) granted by its Scientific Steering Committee (WLA) under project IDs bk1040 and bb1153. |
Type Of Material | Database/Collection of data |
Year Produced | 2021 |
Provided To Others? | Yes |
Impact | Comparison of ultra high resolution global storm resolving models with observations |
URL | https://zenodo.org/record/5196326 |
Title | Interpretable Deep Learning for Probabilistic MJO Prediction: CNN Forecasts |
Description | This repository contains data produced for the paper "Interpretable Deep Learning for Probabilistic MJO Prediction" by A. Delaunay and H. M. Christensen (2021). >> mu_ens_XX.pt contains the mean forecasts from each ensemble member at a lead time of XX days >> cov_alea_XX.pt contains the aleatoric predictions of each ensemble member at a lead time of XX days |
Type Of Material | Database/Collection of data |
Year Produced | 2022 |
Provided To Others? | Yes |
Impact | State-of-the-art Machine Learnt MJO forecasts with uncertainty quantification for early warning |
URL | https://zenodo.org/record/5175836 |
Description | CCSP-Brazil |
Organisation | Natural Environment Research Council |
Country | United Kingdom |
Sector | Public |
PI Contribution | Development of software to assess key modes of variability relevant to Brazilian Climate. Identify whether 1) modes differ between models and observations and 2) modes in a model change in a changing climate. |
Collaborator Contribution | Use of software to bias correct modes of variability in future climate simulations, to allow climate simulations to be blended with observational data to create a seamless time series for forcing land surface models. |
Impact | Huntingford C, Jeffers E, Bonsall M, Christensen H, Lees T, Yang H. (2019). Machine learning and artificial intelligence to aid climate change research and preparedness. Environmental Research Letters, |
Start Year | 2018 |
Description | Model Uncertainty - Model Intercomparison Project (MU-MIP) |
Organisation | European Centre for Medium Range Weather Forecasting ECMWF |
Country | United Kingdom |
Sector | Public |
PI Contribution | MU-MIP: This is a project under the auspices of the World Climate Research Programme's Working Group on Numerical Experimentation and the World Weather Research Programme's Predictability, Dynamics and Ensemble Forecasting Working Group. Oxford's contribution: - Scientific lead - Co-ordinator - Producer of coarse-grained files for driving Single Column Models, derived from cloud resolving model simulations. |
Collaborator Contribution | Each partner is contributing in one or more of the following areas: - Experimental design - Producing SCM simulations - Analysis - feedthrough of results into operational centres. |
Impact | Collaboration ongoing, no outputs yet. Multi-disciplinary, including statisticians, mathematicians, physicists, and those from both academia and industry. |
Start Year | 2020 |
Description | Model Uncertainty - Model Intercomparison Project (MU-MIP) |
Organisation | German Weather Service |
Country | Germany |
Sector | Public |
PI Contribution | MU-MIP: This is a project under the auspices of the World Climate Research Programme's Working Group on Numerical Experimentation and the World Weather Research Programme's Predictability, Dynamics and Ensemble Forecasting Working Group. Oxford's contribution: - Scientific lead - Co-ordinator - Producer of coarse-grained files for driving Single Column Models, derived from cloud resolving model simulations. |
Collaborator Contribution | Each partner is contributing in one or more of the following areas: - Experimental design - Producing SCM simulations - Analysis - feedthrough of results into operational centres. |
Impact | Collaboration ongoing, no outputs yet. Multi-disciplinary, including statisticians, mathematicians, physicists, and those from both academia and industry. |
Start Year | 2020 |
Description | Model Uncertainty - Model Intercomparison Project (MU-MIP) |
Organisation | Max Planck Institute for Meteorology |
Country | Germany |
Sector | Public |
PI Contribution | MU-MIP: This is a project under the auspices of the World Climate Research Programme's Working Group on Numerical Experimentation and the World Weather Research Programme's Predictability, Dynamics and Ensemble Forecasting Working Group. Oxford's contribution: - Scientific lead - Co-ordinator - Producer of coarse-grained files for driving Single Column Models, derived from cloud resolving model simulations. |
Collaborator Contribution | Each partner is contributing in one or more of the following areas: - Experimental design - Producing SCM simulations - Analysis - feedthrough of results into operational centres. |
Impact | Collaboration ongoing, no outputs yet. Multi-disciplinary, including statisticians, mathematicians, physicists, and those from both academia and industry. |
Start Year | 2020 |
Description | Model Uncertainty - Model Intercomparison Project (MU-MIP) |
Organisation | Meteorological Office UK |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | MU-MIP: This is a project under the auspices of the World Climate Research Programme's Working Group on Numerical Experimentation and the World Weather Research Programme's Predictability, Dynamics and Ensemble Forecasting Working Group. Oxford's contribution: - Scientific lead - Co-ordinator - Producer of coarse-grained files for driving Single Column Models, derived from cloud resolving model simulations. |
Collaborator Contribution | Each partner is contributing in one or more of the following areas: - Experimental design - Producing SCM simulations - Analysis - feedthrough of results into operational centres. |
Impact | Collaboration ongoing, no outputs yet. Multi-disciplinary, including statisticians, mathematicians, physicists, and those from both academia and industry. |
Start Year | 2020 |
Description | Model Uncertainty - Model Intercomparison Project (MU-MIP) |
Organisation | Météo France |
Country | France |
Sector | Public |
PI Contribution | MU-MIP: This is a project under the auspices of the World Climate Research Programme's Working Group on Numerical Experimentation and the World Weather Research Programme's Predictability, Dynamics and Ensemble Forecasting Working Group. Oxford's contribution: - Scientific lead - Co-ordinator - Producer of coarse-grained files for driving Single Column Models, derived from cloud resolving model simulations. |
Collaborator Contribution | Each partner is contributing in one or more of the following areas: - Experimental design - Producing SCM simulations - Analysis - feedthrough of results into operational centres. |
Impact | Collaboration ongoing, no outputs yet. Multi-disciplinary, including statisticians, mathematicians, physicists, and those from both academia and industry. |
Start Year | 2020 |
Description | Model Uncertainty - Model Intercomparison Project (MU-MIP) |
Organisation | National Center for Atmospheric Research |
Country | United States |
Sector | Public |
PI Contribution | MU-MIP: This is a project under the auspices of the World Climate Research Programme's Working Group on Numerical Experimentation and the World Weather Research Programme's Predictability, Dynamics and Ensemble Forecasting Working Group. Oxford's contribution: - Scientific lead - Co-ordinator - Producer of coarse-grained files for driving Single Column Models, derived from cloud resolving model simulations. |
Collaborator Contribution | Each partner is contributing in one or more of the following areas: - Experimental design - Producing SCM simulations - Analysis - feedthrough of results into operational centres. |
Impact | Collaboration ongoing, no outputs yet. Multi-disciplinary, including statisticians, mathematicians, physicists, and those from both academia and industry. |
Start Year | 2020 |
Description | Model Uncertainty - Model Intercomparison Project (MU-MIP) |
Organisation | National Oceanic And Atmospheric Administration |
Country | United States |
Sector | Public |
PI Contribution | MU-MIP: This is a project under the auspices of the World Climate Research Programme's Working Group on Numerical Experimentation and the World Weather Research Programme's Predictability, Dynamics and Ensemble Forecasting Working Group. Oxford's contribution: - Scientific lead - Co-ordinator - Producer of coarse-grained files for driving Single Column Models, derived from cloud resolving model simulations. |
Collaborator Contribution | Each partner is contributing in one or more of the following areas: - Experimental design - Producing SCM simulations - Analysis - feedthrough of results into operational centres. |
Impact | Collaboration ongoing, no outputs yet. Multi-disciplinary, including statisticians, mathematicians, physicists, and those from both academia and industry. |
Start Year | 2020 |
Description | Model Uncertainty - Model Intercomparison Project (MU-MIP) |
Organisation | University of Exeter |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | MU-MIP: This is a project under the auspices of the World Climate Research Programme's Working Group on Numerical Experimentation and the World Weather Research Programme's Predictability, Dynamics and Ensemble Forecasting Working Group. Oxford's contribution: - Scientific lead - Co-ordinator - Producer of coarse-grained files for driving Single Column Models, derived from cloud resolving model simulations. |
Collaborator Contribution | Each partner is contributing in one or more of the following areas: - Experimental design - Producing SCM simulations - Analysis - feedthrough of results into operational centres. |
Impact | Collaboration ongoing, no outputs yet. Multi-disciplinary, including statisticians, mathematicians, physicists, and those from both academia and industry. |
Start Year | 2020 |
Description | Model Uncertainty - Model Intercomparison Project (MU-MIP) |
Organisation | University of Reading |
Department | Department of Meteorology |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | MU-MIP: This is a project under the auspices of the World Climate Research Programme's Working Group on Numerical Experimentation and the World Weather Research Programme's Predictability, Dynamics and Ensemble Forecasting Working Group. Oxford's contribution: - Scientific lead - Co-ordinator - Producer of coarse-grained files for driving Single Column Models, derived from cloud resolving model simulations. |
Collaborator Contribution | Each partner is contributing in one or more of the following areas: - Experimental design - Producing SCM simulations - Analysis - feedthrough of results into operational centres. |
Impact | Collaboration ongoing, no outputs yet. Multi-disciplinary, including statisticians, mathematicians, physicists, and those from both academia and industry. |
Start Year | 2020 |
Description | Model Uncertainty - Model Intercomparison Project (MU-MIP) |
Organisation | World Meteorological Organization |
Country | Switzerland |
Sector | Public |
PI Contribution | MU-MIP: This is a project under the auspices of the World Climate Research Programme's Working Group on Numerical Experimentation and the World Weather Research Programme's Predictability, Dynamics and Ensemble Forecasting Working Group. Oxford's contribution: - Scientific lead - Co-ordinator - Producer of coarse-grained files for driving Single Column Models, derived from cloud resolving model simulations. |
Collaborator Contribution | Each partner is contributing in one or more of the following areas: - Experimental design - Producing SCM simulations - Analysis - feedthrough of results into operational centres. |
Impact | Collaboration ongoing, no outputs yet. Multi-disciplinary, including statisticians, mathematicians, physicists, and those from both academia and industry. |
Start Year | 2020 |
Description | SAMSI Machine Learning |
Organisation | NCAR National Center for Atmospheric Research |
Country | United States |
Sector | Academic/University |
PI Contribution | Expertise in development of stochastic parametrisation schemes, analysis of ensemble weather forecasts, climate diagnostics. Contributions to experiment design, coding, performing experiments, analysis of data, writing publications. |
Collaborator Contribution | Expertise in machine learning and other advanced statistical techniques. Contributions to experiment design, coding, performing experiments, analysis of data, writing publications. Presentation of results at conferences |
Impact | Gagne D, Christensen H, Subramanian A, Monahan A. (2020). Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz '96 Model. Journal of Advances in Modeling Earth Systems, Presentations at: American Geophysical Union Fall Meeting in Washington DC, 2018 American Meteorological Society Annual Meeting in Phoenix, 2019 European Geosciences Union annual meeting in Vienna, 2019 International Union of Geodesy and Geophysics meeting in Montreal, 2019 The collaboration is multi-disciplinary: Gagne: Data science Christensen, Subramanian: Atmospheric science Monahan: Statistics |
Start Year | 2017 |
Description | SAMSI Machine Learning |
Organisation | University of Colorado Boulder |
Country | United States |
Sector | Academic/University |
PI Contribution | Expertise in development of stochastic parametrisation schemes, analysis of ensemble weather forecasts, climate diagnostics. Contributions to experiment design, coding, performing experiments, analysis of data, writing publications. |
Collaborator Contribution | Expertise in machine learning and other advanced statistical techniques. Contributions to experiment design, coding, performing experiments, analysis of data, writing publications. Presentation of results at conferences |
Impact | Gagne D, Christensen H, Subramanian A, Monahan A. (2020). Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz '96 Model. Journal of Advances in Modeling Earth Systems, Presentations at: American Geophysical Union Fall Meeting in Washington DC, 2018 American Meteorological Society Annual Meeting in Phoenix, 2019 European Geosciences Union annual meeting in Vienna, 2019 International Union of Geodesy and Geophysics meeting in Montreal, 2019 The collaboration is multi-disciplinary: Gagne: Data science Christensen, Subramanian: Atmospheric science Monahan: Statistics |
Start Year | 2017 |
Description | SAMSI Machine Learning |
Organisation | University of Victoria |
Country | Australia |
Sector | Academic/University |
PI Contribution | Expertise in development of stochastic parametrisation schemes, analysis of ensemble weather forecasts, climate diagnostics. Contributions to experiment design, coding, performing experiments, analysis of data, writing publications. |
Collaborator Contribution | Expertise in machine learning and other advanced statistical techniques. Contributions to experiment design, coding, performing experiments, analysis of data, writing publications. Presentation of results at conferences |
Impact | Gagne D, Christensen H, Subramanian A, Monahan A. (2020). Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz '96 Model. Journal of Advances in Modeling Earth Systems, Presentations at: American Geophysical Union Fall Meeting in Washington DC, 2018 American Meteorological Society Annual Meeting in Phoenix, 2019 European Geosciences Union annual meeting in Vienna, 2019 International Union of Geodesy and Geophysics meeting in Montreal, 2019 The collaboration is multi-disciplinary: Gagne: Data science Christensen, Subramanian: Atmospheric science Monahan: Statistics |
Start Year | 2017 |
Description | SAMSI stochastic Wind Fluxes |
Organisation | Argonne National Laboratory |
Country | United States |
Sector | Public |
PI Contribution | Expertise in stochastic parametrisations. Expertise in Earth-system modelling. Implementation of new stochastic parametrisation in European Centre for Medium Range Weather Forecasts Integrated Forecasting System. |
Collaborator Contribution | Expertise in statistical analysis, in particular, statistical modelling of space-time processes. Expertise in air-sea interactions and modelling of air-sea fluxes. |
Impact | Bessac J, Monahan A, Christensen H, Weitzel N, (2019). Stochastic Parameterization of Subgrid-Scale Velocity Enhancement of Sea Surface Fluxes. Monthly Weather Review, Bessac J, Christensen H, Endo K, Monahan A, Weitzel N, (2021). Scale-aware space-time stochastic parameterization of subgrid-scale velocity enhancement of sea surface fluxes. Journal of Advances in Modeling Earth Systems, Presentations at: Joint Statistical Meeting in Vancouver in 2018 International Union of Geodesy and Geophysics meeting in Montreal in 2019 This collaboration is multidisciplinary. Atmospheric science: Christensen Statistics: Bessac, Monahan and Weitzel |
Start Year | 2017 |
Description | SAMSI stochastic Wind Fluxes |
Organisation | University of Bonn |
Country | Germany |
Sector | Academic/University |
PI Contribution | Expertise in stochastic parametrisations. Expertise in Earth-system modelling. Implementation of new stochastic parametrisation in European Centre for Medium Range Weather Forecasts Integrated Forecasting System. |
Collaborator Contribution | Expertise in statistical analysis, in particular, statistical modelling of space-time processes. Expertise in air-sea interactions and modelling of air-sea fluxes. |
Impact | Bessac J, Monahan A, Christensen H, Weitzel N, (2019). Stochastic Parameterization of Subgrid-Scale Velocity Enhancement of Sea Surface Fluxes. Monthly Weather Review, Bessac J, Christensen H, Endo K, Monahan A, Weitzel N, (2021). Scale-aware space-time stochastic parameterization of subgrid-scale velocity enhancement of sea surface fluxes. Journal of Advances in Modeling Earth Systems, Presentations at: Joint Statistical Meeting in Vancouver in 2018 International Union of Geodesy and Geophysics meeting in Montreal in 2019 This collaboration is multidisciplinary. Atmospheric science: Christensen Statistics: Bessac, Monahan and Weitzel |
Start Year | 2017 |
Description | SAMSI stochastic Wind Fluxes |
Organisation | University of Victoria |
Country | Australia |
Sector | Academic/University |
PI Contribution | Expertise in stochastic parametrisations. Expertise in Earth-system modelling. Implementation of new stochastic parametrisation in European Centre for Medium Range Weather Forecasts Integrated Forecasting System. |
Collaborator Contribution | Expertise in statistical analysis, in particular, statistical modelling of space-time processes. Expertise in air-sea interactions and modelling of air-sea fluxes. |
Impact | Bessac J, Monahan A, Christensen H, Weitzel N, (2019). Stochastic Parameterization of Subgrid-Scale Velocity Enhancement of Sea Surface Fluxes. Monthly Weather Review, Bessac J, Christensen H, Endo K, Monahan A, Weitzel N, (2021). Scale-aware space-time stochastic parameterization of subgrid-scale velocity enhancement of sea surface fluxes. Journal of Advances in Modeling Earth Systems, Presentations at: Joint Statistical Meeting in Vancouver in 2018 International Union of Geodesy and Geophysics meeting in Montreal in 2019 This collaboration is multidisciplinary. Atmospheric science: Christensen Statistics: Bessac, Monahan and Weitzel |
Start Year | 2017 |
Description | Stochastic Parametrisations in Met Office Limited Area Model |
Organisation | Meteorological Office UK |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | Experiments with the UKMO limited area model. Implementation of two new stochastic parametrisation and associated testing. |
Collaborator Contribution | Support with using Met Office Limited Area model. Scientific and technical input |
Impact | Two parametrisation schemes are currently being tested |
Start Year | 2021 |
Description | Lecture, Christian Rural and Environmental Studies course, Ripon College Cuddesdon, Oxford. |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Other audiences |
Results and Impact | Lecture on physical basis for climate change, with a focus on climate modelling and IPCC AR6 report. |
Year(s) Of Engagement Activity | 2021 |
URL | https://cres.org.uk |
Description | Oxford Physics YouTube channel interview |
Form Of Engagement Activity | Engagement focused website, blog or social media channel |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Public/other audiences |
Results and Impact | Interviewed for University of Oxford's physics department outreach channel "Weather: why is it so hard to predict?" |
Year(s) Of Engagement Activity | 2020 |
URL | https://www.youtube.com/watch?v=bygi5eEk1f4 |
Description | Talk at local low-carbon group |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | Local |
Primary Audience | Public/other audiences |
Results and Impact | Outreach talk at local low-carbon group. Describing climate modelling, prediction, and associated uncertainties. |
Year(s) Of Engagement Activity | 2019 |
Description | WMO WCRP WGNE and WWRP PDEF meetings |
Form Of Engagement Activity | A formal working group, expert panel or dialogue |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Industry/Business |
Results and Impact | Invited expert at WMO working group meetings. Presenting Model Uncertainty Model Intercomparison Project: pitching idea, presenting aims, experimental design, and updates on progress |
Year(s) Of Engagement Activity | 2018,2020,2021,2022 |
Description | Walter Orr Roberts Memorial Lecture |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Public/other audiences |
Results and Impact | Presented the Walter Orr Roberts Memorial Public Lecture on "Accelerating Actionable Climate Information Through Machine Learning" |
Year(s) Of Engagement Activity | 2022 |
URL | https://www.agci.org/resources/a124x000002L24AAAS/accelerating-actionable-climate-information-throug... |