Data Assimilation, Predictability, Error Identification and Uncertainty Quantification in Geosciences
Many situations occur in Geosciences where one wants to obtain an accurate description of the present, past or future state of a particular system. Examples are prediction of weather and climate, assimilation of observations, or inversion of seismic signals for probing the interior of the planet. One important aspect is the identification of the errors affecting the various sources of information used in the estimation process, and the quantification of the ensuing uncertainty on the final estimate.
The session is devoted to the theoretical and numerical aspects of that broad class of problems. A large number of topics are dealt with in the various papers to be presented: algorithms for assimilation of observations, and associated mathematical aspects (particularly, but not only, in the context of the atmosphere and the ocean), predictability of geophysical flows, with stress on the impact of initial and model errors, inverse problems of different kinds, and also new aspects at the crossing between data assimilation and data-driven methods. Applications to specific physical problems are presented.
Olivier Pannekoucke (Météo-France, Toulouse)
Manuel Pulido (University of Reading)
Advances in statistical post-processing for deterministic and ensemble forecasts
Statistical post-processing techniques for weather, climate, and hydrological forecasts are powerful approaches to compensate for effects of errors in model structure or initial conditions, and to calibrate inaccurately dispersed ensembles. These techniques are now an integral part of many forecasting suites and are used in many end-user applications such as wind energy production or flood warning systems.
Many of these techniques are now flourishing in the statistical, meteorological, climatological, hydrological, and engineering communities. The methods range in complexity from simple bias correction up to very sophisticated distribution-adjusting techniques that take into account correlations among the prognostic variables.
In this session, we invite papers dealing with both theoretical developments in statistical post-processing and evaluation of their performances in different practical applications oriented toward environmental predictions.
Coupled modelling and data assimilation of dynamics and chemistry of the atmosphere
As the societal impacts of hazardous weather and other environmental pressures grow, the need for integrated predictions which can represent the numerous feedbacks and linkages between physical and chemical atmospheric processes is greater than ever. This has led to development of a new generation of high resolution multi-scale coupled prediction tools to represent the two-way interactions between aerosols, chemical composition, meteorological processes such as radiation and cloud microphysics.
Contributions are invited on different aspects of integrated model and data assimilation development, evaluation and understanding. A number of application areas of new integrated modelling developments are expected to be considered, including:
i) improved numerical weather prediction and chemical weather forecasting with feedbacks between aerosols, chemistry and meteorology,
ii) two-way interactions between atmospheric composition and climate variability.
This session aims to share experience and best practice in integrated prediction, including:
a) strategy and framework for online integrated meteorology-chemistry modelling;
b) progress on design and development of seamless coupled prediction systems;
c) improved parameterisation of weather-composition feedbacks;
d) data assimilation developments;
e) evaluation, validation, and applications of integrated systems.
This Section is organised in cooperation with the Copernicus Atmosphere Monitoring Service (CAMS), the "Pan-Eurasian Experiment" (PEEX) multidisciplinary program and the WMO Global Atmosphere Watch (GAW) Programme, celebrating its 30 years anniversary in 2019.
Biases in weather and climate models: representing uncertain sub-grid processes, understanding large-scale drivers, and paths to improvement
Weather and climate models used for weather forecasts, seasonal predictions and climate projections, are essential for decision making on timescales from hours to decades. However, information about future weather and climate relies on complex, though imperfect, numerical models of the Earth-system. Systematic biases and random errors have detrimental effects on predictive skill for dynamically driven fields on weather and seasonal time scales. Biases in climate models also contribute to the high levels of uncertainty in many aspects of climate change as the biases project strongly on future changes. A large source of uncertainty and error in model simulations is unresolved processes, represented through parameterization schemes. However, these errors typically materialize at large spatial scales. Our physical understanding of the mechanical and dynamical drivers of these large-scale biases is incomplete. Incomplete mechanistic understanding hinders marked improvements in models, including identification of the parameterizations most in need of improvement.
Understanding and reducing the errors in weather and climate models due to parameterizations and poorly represented mesoscale to regional scales processes is a necessary step towards improved weather and climate prediction. This session aims to bring together these two perspectives, and unite the weather and climate communities to address this common problem and accelerate progress in this area.
This session seeks submissions that aim to quantify, understand, and reduce sources of error and bias in weather and climate models. Themes covered in this session include:
- Theory and development of parameterization. Impact on errors in mean state, model variability and physical process representation;
- Improved physical understanding of the drivers of large-scale biases including the use of process studies, idealized modeling studies and studies with strong observational components;
- Growth and propagation of error and bias in models; model errors across temporal and spatial scales; dependency of errors on model resolution and the development of scale-aware parameterization schemes;
- Use of “emergent constraints” to relate present day model biases with the climate change signal;
- Understanding and representing random model error.
Invited presentations: Felix Pithan (AWI) and Bob Plant (University of Reading)
Lead Convenors: Hannah Christensen and Stefan Sobolowski
Co-convenors: Craig Bishop, Ariane Frassoni, Daniel Klocke, Erica Madonna, Isla Simpson, Keith Williams, Giuseppe Zappa
Remote Sensing and Coupled Data Assimilation for Earth System Models and their Compartments
Data assimilation is becoming more important as a method to make predictions of Earth system states. Increasingly, coupled models for different compartments of the Earth system are used. This allows for making advantage of varieties of observations, in particular remotely sensed data, in different compartments. This session focuses on weakly and strongly coupled assimilation of in situ and remotely sensed measurement data across compartments of the Earth system. Examples are data assimilation for the atmosphere-ocean system, data assimilation for the atmosphere-land system and data assimilation for the land surface-subsurface system. Optimally exploiting observations in a compartment of the terrestrial system to update also states in other compartments of the terrestrial system still has strong methodological challenges. It is not yet clear that fully coupled approaches, where data are directly used to update states in other compartments, outperform weakly coupled approaches, where states in other compartments are only updated indirectly, through the action of the model equations. Coupled data assimilation allows to determine the value of different measurement types, and the additional value of measurements to update states across compartments. Another aspect of scientific interest for weakly or fully coupled data assimilation is the software engineering related to coupling a data assimilation framework to a physical model, in order to build a computationally efficient and flexible framework.
We welcome contributions on the development and applications of coupled data assimilation systems involving models for different compartments of the Earth system like atmosphere and/or ocean and/or sea ice and/or vegetation and/or soil and/or groundwater and/or surface water bodies. Contributions could for example focus on data value with implications for monitoring network design, parameter or bias estimation or software engineering aspects. In addition, case studies which include a precise evaluation of the data assimilation performance are of high interest for the session.
Precipitation Modelling: uncertainty, variability, assimilation, ensemble simulation and downscaling
The assessment of precipitation variability and uncertainty is crucial in a variety of applications, such as flood risk forecasting, water resource assessments, evaluation of the hydrological impacts of climate change, determination of design floods, and hydrological modelling in general. Within this framework, this session aims to gather contributions on research, advanced applications, and future needs in the understanding and modelling of precipitation variability, and its sources of uncertainty.
Specifically, contributions focusing on one or more of the following issues are particularly welcome:
- Novel studies aimed at the assessment and representation of different sources of uncertainty versus natural variability of precipitation.
- Methods to account for different accuracy in precipitation time series, e.g. due to change and improvement of observation networks.
- Uncertainty and variability in spatially and temporally heterogeneous multi-source precipitation products.
- Estimation of precipitation variability and uncertainty at ungauged sites.
- Precipitation data assimilation.
- Process conceptualization and modelling approaches at different spatial and temporal scales, including model parameter identification and calibration, and sensitivity analyses to parameterization and scales of process representation.
- Modelling approaches based on ensemble simulations and methods for synthetic representation of precipitation variability and uncertainty.
- Scaling and scale invariance properties of precipitation fields in space and/or in time.
- Physically and statistically based approaches to downscale information from meteorological and climate models to spatial and temporal scales useful for hydrological modelling and applications.
Ensemble Methods for Combining Different Climate (and Weather) Models
Models of the class used in the CMIP6 experiment to make global
climate projections are imperfect representations of reality that
differ widely in regard to the overall magnitude of warming, in their
regional projections, and in their short-range predictions. While
better models of the underlying physical processes are ultimately
needed, immediate improvement may come simply from better methods to
combine existing models. Contributions are solicited on new methods to
fuse models of climate and weather ranging from output averaging techniques to methods that
dynamically combine model components in a synchronizing, interactive
ensemble. The importance (or lack thereof) of nonlinearities in
determining the sufficiency of output averaging is a topic of special
Challenges in climate prediction: multiple time-scales and the Earth system dimensions
One of the big challenges in Earth system science consists in providing reliable climate predictions on sub-seasonal, seasonal, decadal and longer timescales. The resulting data have the potential to be translated into climate information leading to a better assessment of multi-scale global and regional climate-related risks.
The latest developments and progress in climate forecasting on subseasonal-to-decadal timescales will be discussed and evaluated in this session. This will include presentations and discussions of predictions for a time horizon of up to ten years from dynamical ensemble and statistical/empirical forecast systems, as well as the aspects required for their application: forecast quality assessment, multi-model combination, bias adjustment, downscaling, etc.
Following the new WCPR strategic plan for 2019-2029, prediction enhancements are solicited from contributions embracing climate forecasting from an Earth system science perspective. This includes the study of coupled processes, impacts of coupling and feedbacks, and analysis/verification of the coupled atmosphere-ocean, atmosphere-land, atmosphere-hydrology, atmosphere-chemistry & aerosols, atmosphere-ice, ocean-hydrology, ocean-ice, ocean-chemistry and climate-biosphere (including human component). Contributions are also sought on initialization methods that optimally use observations from different Earth system components, on assessing and mitigating the impacts of model errors on skill, and on ensemble methods.
We also encourage contributions on the use of climate predictions for climate impact assessment, demonstrations of end-user value for climate risk applications and climate-change adaptation and the development of early warning systems.
A special focus will be put on the use of operational climate predictions (C3S, NMME, S2S), results from the CMIP5-CMIP6 decadal prediction experiments, and climate-prediction research and application projects (e.g. EUCP, APPLICATE, PREFACE, MIKLIP, MEDSCOPE, SECLI-FIRM, S2S4E).
Multi-year prediction of ENSO
By Jing-Jia Luo from the Institute for Climate and Application Research (ICAR), Nanjing University of Science Information and Technology, China