Inverse Problems are encountered in many fields of geosciences. One class of inverse problems, in the context of predictability, is assimilation of observations in dynamical models of the system under study. Furthermore, objective quantification of the uncertainty on the results obtained is the object of growing concern and interest.

This session will be devoted to the presentation and discussion of methods for inverse problems, data assimilation and associated uncertainty quantification, in ocean and atmosphere dynamics, atmospheric chemistry, hydrology, climate science, solid earth geophysics and, more generally, in all fields of geosciences.

We encourage presentations on advanced methods, and related mathematical developments, suitable for situations in which local linear and Gaussian hypotheses are not valid and/or for situations in which significant model
errors are present. We also welcome contributions dealing with algorithmic aspects and numerical implementation of the solution of inverse problems and quantification of the associated uncertainty, as well as novel methodologies at the crossroad between data assimilation and purely data-driven, machine-learning-type algorithms.

Invited speakers:
Luca Cantarello (University of Leeds)
Jean-Michel Brankart (University of Grenoble)

Public information:
In the session we will encourage all participants to present their work. These brief presentations will last about 5 minutes.

Accurate predictions of geophysical fluid have enormous social and economic values but remain to have significant uncertainties at different time and spatial scales. Although some dynamical, statistical, and their combined (“scholastic”) approaches were often used to make predictions and showed their respective usefulness, there exist great limitations in improving prediction level. This session will bring together experts to jointly address new approaches to predictions of geophysical fluid and to identification and quantification of uncertainties associated with predictability, and create an exchange of ideas likely to advance the state of predictions. Papers are invited on all aspects of conventional dynamical and statistical approaches to predictions and predictability estimation, and underlying that justification of the appropriateness of the use of any of them in a particular situation is particularly welcome. Papers on techniques that combine the dynamical and statistical approaches with newly emerging techniques of machine learning are also welcome.

Public information:
Our session is scheduled for a live, text-based chat on Wed, 06 May, 08:30–10:15, a total of 105 minutes. The conveners encourage all of the authors upload the presentation materials and enjoy the discussion time of the session.

The way to proceed the session discussion is as follows.

(1) Each presenting author to present their work for about 1-2 minutes, with an introduction to contents, methods and results, then the participants will have a general idea of what it is about. After it, I'll open the floor for questions or comments.
(2) For the invited talk, it will have 8 minutes for discussion. And each of other talks will have about 3-4 minutes for discussion.
(3) We'll go through the presentations as listed in the right panel of the chat room.
(4) If there are authors to be absent and time to spare is left, we will have free discussion time interval and the participants can have questions or comments to the presentation of your interests during this time interval.

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 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.

At the same time, a lot of efforts are put in combining multiple forecasting sources in order to get reliable and seamless forecasts on time ranges from minutes to weeks. Such blending techniques are currently developed in many meteorological centers.

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, papers dealing with the problem of combining or blending different types of forecasts in order to improve reliability from very short to long time scales.

Theoretical and model studies show that the ocean is a chaotic system interacting with the atmosphere: uncertainties in ocean model initial states may grow and strongly affect the simulated variability up to multidecadal and basin scales, with or without coupling to the atmosphere. In addition, ocean simulations require both the use of subgrid-scale parameterizations that mimick crudely unresolved processes, and the calibration of the parameters associated with these parameterizations, while respecting numerical stability constraints. Oceanographers are increasingly adopting ensemble simulation strategies and probabilistic analysis methods, and developing stochastic parameterizations for modeling and understanding the ocean variability in this context of multiple uncertainties.

Presentations are solicited about the conception and analysis of ocean ensemble simulations, the characterization of ocean model uncertainties, and the development of stochastic parameterizations for ocean models. The session will also cover the dynamics and structure of the ocean chaotic variability, its relationship with the atmospheric variability, and the use of dynamical system or information theories for the investigation of the oceanic variability. We welcome as well studies about the propagation of the ocean chaotic variability towards other components of the climate system, about its consequences regarding ocean predictability, operational forecasts, detection and attribution of climate signals, climate simulations and projections.

Public information:
OS1.5 : CHAOTIC VARIABILITY AND MODELLING UNCERTAINTIES IN THE OCEAN: TOWARDS PROBABILISTIC OCEANOGRAPHY
WEDNESDAY : 16:15 - 18:00 : TENTATIVE SCHEDULE FOR THE CHAT (Public on EGU website)
12 minutes for hightlighted talk (Sinha et al)
7 minutes for all other talks

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16:15 - 16:18 SESSION INTRODUCTION
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16:20 - 17:00 FORCED AND CHAOTIC OCEAN VARIABILITY
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01. D2581 | EGU2020-7226 | HIGHLIGHT —> 12 min
Quantifying uncertainty in decadal ocean heat uptake due to intrinsic ocean variability.
Bablu Sinha, Alex Megann, Thierry Penduff, Jean-Marc Molines, and Sybren Drijfhout

02. D2582 | EGU2020-5689 —> 7 min
Forced and chaotic variability of interannual variability of regional sea level and its causes scale over 1993-2015.
Alice Carret, William Llovel, Thierry Penduff, Jean-Marc Molines, and Benoît Meyssignac

03. D2592 | EGU2020-2737 —> 7 min
Forced and chaotic variability of basin-scale heat budgets in the global ocean: focus on the South Atlantic crossroads.
Thierry Penduff, Fei-Er Yan, Imane Benabicha, Jean-Marc Molines, and Bernard Barnier

04. D2583 | EGU2020-19875 —> 7 min
Year-to-year meridional shifts of the Great Whirl driven by oceanic internal instabilities
Kwatra Sadhvi, Iyyappan Suresh, Izumo Takeshi, Jerome Vialard, Matthieu Lengaigne, Thierry Penduff, and Jean Marc Molines.

05. D2584 | EGU2020-20309 —> 7 min
Deconstructing the subtropical AMOC variability.
Quentin Jamet, William Dewar, Nicolas Wienders, Bruno Deremble, Sally Close, and Thierry Penduff

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17:00 - 17:25 OCEAN PROCESSES AND PARAMETERIZATIONS
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06. D2586 | EGU2020-21330 —> 7 min
Eddy-Mean flow oscillations in the Southern Ocean.
Sebastiano Roncoroni and David Ferreira

07. D2585 | EGU2020-22418 —> 7 min
On wind-driven energetics of subtropical gyres.
William K. Dewar, Quentin Jamet, Bruno Deremble, and Nicolas Wienders

08. D2587 | EGU2020-11312 —> 7 min
Stochastic Advection for eddy parameterisation in Primitive Equation Models.
Stuart Patching

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17:25 - 17:50 OCEAN MODELLING UNCERTAINTIES
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09. D2589 | EGU2020-11127 —> 7 min
Ensemble quantification of short-term predictability of the ocean fine-scale dynamics: a western mediterranean test case at kilometric-scale resolution.
Stéphanie Leroux, Jean-Michel Brankart, Aurélie Albert, Pierre Brasseur, Laurent Brodeau, Julien Le Sommer, Jean-Marc Molines, and Thierry Penduff

10. D2590 | EGU2020-6489 —> 7 min
Predictability of estuarine model using Information Theory: ROMS Ocean State Ocean Model
Aakash Sane, Baylor Fox-Kemper, David Ullman, Christopher Kincaid, and Lewis Rothstein

11. D2591 | EGU2020-6000 —> 7 min
Impact of Atmospheric and Model Physics Perturbations On a High-Resolution Ensemble Data Assimilation System of the Red Sea
Siva Reddy Sanikommu, Habib Toye, Peng Zhan, Sabique Langodan, George Krokos, Omar Knio, and Ibrahim Hoteit

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17:50 - 18:00 OPEN DISCUSSION - CLOSING THE SESSION
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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).
An increasingly important aspect for climate forecast's applications is the use of most appropriate downscaling methods, based on dynamical or statistical approaches or their combination, that are needed to generate time series and fields with an appropriate spatial or temporal resolution. This is extensively considered in the session, which therefore brings together scientists from all geoscientific disciplines working on the prediction and application problems.

Earth Systems Models aim at describing the full water- and energy cycles, i.e. from the deep ocean or groundwater across the sea or land surface to the top of the atmosphere. The objective of the session is to create a valuable opportunity for interdisciplinary exchange of ideas and experiences among members of the Earth System modeling community and especially atmospheric-hydrological modelers.
Contributions are invited dealing with approaches how to capture the complex fluxes and interactions between surface water, groundwater, land surface processes, oceans and regional climate. This includes the development and application of one-way or fully-coupled hydrometeorological prediction systems for e.g. floods, droughts and water resources at various scales. We are interested in model systems that make use of innovative upscaling and downscaling schemes for predictions across various spatial- and temporal scales. Contributions on novel one-way and fully-coupled modeling systems and combined dynamical-statistical approaches are encouraged. A particular focus of the session is on weakly and strongly coupled data assimilation across the different compartments of the Earth system for the improved prediction of states and fluxes of water and energy. Merging of different observation types and observations at different length scales is addressed as well as different data assimilation approaches for the atmosphere-land system, the land surface-subsurface system and the atmosphere-ocean system. The value of different measurement types for the predictions of states and fluxes, and the additional value of measurements to update states across compartments is of high interest to the session. We also encourage contributions on use of field experiments and testbeds equipped with complex sensors and measurement systems allowing compartment-crossing and multi-variable validation of Earth System Models.