Constraining DALEC v2 using multiple data streams and ecological constraints: analysis and application.

We use a variational method to assimilate multiple data streams into the terrestrial ecosystem carbon cycle model DALECv2. Ecological and dynamical constraints have recently been introduced to constrain unresolved components of this otherwise ill-posed problem. Here we recast these constraints as a multivariate Gaussian distribution to incorporate them into the variational framework and we demonstrate their benefit through a linear analysis. Using an adjoint method we study a linear approximation of the inverse problem: firstly we perform a sensitivity analysis of the different outputs under consideration, and secondly we use the concept of resolution matrices to diagnose the nature of the ill-posedness and evaluate regularisation strategies.We then study the non linear problem with an application to real data. Finally, we propose a modification to the model: introducing a spin-up period provides us with a built-in formulation of some ecological constraints which facilitates the variational approach

Beyond Gaussian Statistical Modeling in Geophysical Data Assimilation

International audienceThis review discusses recent advances in geophysical data assimilation beyond Gaussian statistical modeling, in the fields of meteorology, oceanography, as well as atmospheric chemistry. The non-Gaussian features are stressed rather than the nonlinearity of the dynamical models, although both aspects are entangled. Ideas recently proposed to deal with these non-Gaussian issues, in order to improve the state or parameter estimation, are emphasized. The general Bayesian solution to the estimation problem and the techniques to solve it are first presented, as well as the obstacles that hinder their use in high-dimensional and complex systems. Approximations to the Bayesian solution relying on Gaussian, or on second-order moment closure, have been wholly adopted in geophysical data assimilation (e.g., Kalman filters and quadratic variational solutions). Yet, nonlinear and non-Gaussian effects remain. They essentially originate in the nonlinear models and in the non-Gaussian priors. How these effects are handled within algorithms based on Gaussian assumptions is then described. Statistical tools that can diagnose them and measure deviations from Gaussianity are recalled. The following advanced techniques that seek to handle the estimation problem beyond Gaussianity are reviewed: maximum entropy filter, Gaussian anamorphosis, non-Gaussian priors, particle filter with an ensemble Kalman filter as a proposal distribution, maximum entropy on the mean, or strictly Bayesian inferences for large linear models, etc. Several ideas are illustrated with recent or original examples that possess some features of high-dimensional systems. Many of the new approaches are well understood only in special cases and have difficulties that remain to be circumvented. Some of the suggested approaches are quite promising, and sometimes already successful for moderately large though specific geophysical applications. Hints are given as to where progress might come from

Variational multiscale stabilization of finite and spectral elements for dry and moist atmospheric problems

In this thesis the finite and spectral element methods (FEM and SEM, respectively) applied to problems in atmospheric simulations are explored through the common thread of Variational Multiscale Stabilization (VMS). This effort is justified by three main reasons. (i) the recognized need for new solvers that can efficiently execute on massively parallel architectures ¿a spreading framework in most fields of computational physics in which numerical weather prediction (NWP) occupies a prominent position. Element-based methods (e.g. FEM, SEM, discontinuous Galerkin) have important advantages in parallel code development; (ii) the inherent flexibility of these methods with respect to the geometry of the grid makes them a great candidate for dynamically adaptive atmospheric codes; and (iii) the localized diffusion provided by VMS represents an improvement in the accurate solution of multi-physics problems where artificial diffusion may fail. Its application to atmospheric simulations is a novel approach within a field of research that is still open. First, FEM and VMS are described and derived for the solution of stratified low Mach number flows in the context of dry atmospheric dynamics. The validity of the method to simulate stratified flows is assessed using standard two- and three-dimensional benchmarks accepted by NWP practitioners. The problems include thermal and gravity driven simulations. It will be shown that stability is retained in the regimes of interest and a numerical comparison against results from the the literature will be discussed. Second, the ability of VMS to stabilize the FEM solution of advection-dominated problems (i.e. Euler and transport equations) is taken further by the implementation of VMS as a stabilizing tool for high-order spectral elements with advection-diffusion problems. To the author¿s knowledge, this is an original contribution to the literature of high order spectral elements involved with transport in the atmosphere. The problem of monotonicity-preserving high order methods is addressed by combining VMS-stabilized SEM with a discontinuity capturing technique. This is an alternative to classical filters to treat the Gibbs oscillations that characterize high-order schemes. To conclude, a microphysics scheme is implemented within the finite element Euler solver, as a first step toward realistic atmospheric simulations. Kessler microphysics is used to simulate the formation of warm, precipitating clouds. This last part combines the solution of the Euler equations for stratified flows with the solution of a system of transport equations for three classes of water: water vapor, cloud water, and rain. The method is verified using idealized two- and three-dimensional storm simulations.En esta tesis los métodos de elementos finitos y espectrales (FEM - finite element method y SEM- spectral element method, respectivamente), aplicados a los problemas de simulaciones atmosféricas, se exploran a través del método de estabilización conocidocomo Variational Multiscale Stabilization (VMS). Tres razones fundamentales justifican este esfuerzo: (i) la necesidad de tener nuevos métodos de solución de las ecuaciones diferenciales a las derivadas parciales usando máquinas paralelas de gran escala –un entorno en expansión en muchos campos de la mecánica computacional, dentro de la cual la predicción numérica de la dinámica atmosférica (NWP-numerical weather prediction)representa una aplicación importante. Métodos del tipo basado en elementos(por ejemplo, FEM, SEM, Galerkin discontinuo) presentan grandes ventajas en el desarrollo de códigos paralelos; (ii) la flexibilidad intrínseca de tales métodos respecto a lageometría de la malla computacional hace que esos métodos sean los candidatos ideales para códigos atmosféricos con mallas adaptativas; y (iii) la difusión localizada que VMSintroduce representa una mejora en las soluciones de problemas con física compleja en los cuales la difusión artificial clásica no funcionaría. La aplicación de FEM o SEM con VMS a problemas de simulaciones atmosféricas es una estrategia innovadora en un campo de investigación abierto. En primera instancia, FEM y VMS vienen descritos y derivados para la solución de flujos estratificados a bajo número de Mach en el contexto de la dinámica atmosférica. La validez del método para simular flujos estratificados es verificada por medio de test estándar aceptado por la comunidad dentro del campo deNWP. Los test incluyen simulaciones de flujos térmicos con efectos de gravedad. Se demostrará que la estabilidad del método numérico se preserva dentro de los regímenesde interés y se discutirá una comparación numérica de los resultados frente a aquellos hallados en la literatura. En segunda instancia, la capacidad de VMS para estabilizarmétodos FEM en problemas de advección dominante (i.e. ecuaciones de Euler y ecuaciones de transporte) se implementa además en la solución a elementos espectrales de alto orden en problemas de advección-difusión. Hasta donde el autor sabe, esta es una contribución original a la literatura de métodos basados en elementos espectrales en problemas de transporte atmosférico. El problema de monotonicidad con métodos de alto orden es tratado mediante la combinación de SEM+VMS con una técnica de shockcapturing para un mejor tratamiento de las discontinuidades. Esta es una alternativa a los filtros que normalmente se aplican a SEM para eilminar las oscilaciones de Gibbsque caracterizan las soluciones de alto orden. Como último punto, se implementa un esquema de humedad acoplado con el núcleo en elementos finitos; este es un primer paso hacia simulaciones atmosféricas más realistas. La microfísica de Kessler se emplea para simular la formación de nubes y tormentas cálidas (warm clouds: no permite la formación de hielo). Esta última parte combina la solución de las ecuaciones de Eulerpara atmósferas estratificadas con la solución de un sistema de ecuaciones de transporte de tres estados de agua: vapor, nubes y lluvia. La calidad del método es verificadautilizando simulaciones de tormenta en dos y tres dimensiones

Tipping Points and Early Warning Signals in the Climate-Carbon System

This is a thesis about tipping points and early warning signals. The tipping points investigated are related to various components of the climate-carbon system. In contrast, the work on early warning signals has more generic applications, however in this thesis they are analysed in the context of the climate-carbon system. The thesis begins with an introduction to the climate-carbon system as well as a discussion of tipping points in the Earth system. Then a more mathematical summary of tipping points and early warning signals is given. An investigation into the ‘compost bomb’ is undertaken, in which the spatial structure of soils is accounted for. It is found that a hot summer could cause a compost bomb. The effect of biogeochemical heating on the stability of the global carbon cycle is investigated and it is found to play only a small role. The potential for instabilities in the climate-carbon cycle is further investigated when the dynamic behaviour of the ocean carbon cycle is accounted for. It is found that some CMIP6 models may be close to having an unstable carbon cycle. Spatial early warning signals are investigated in the context of more rapidly forced systems. It is found that spatial early warning signals perform better when the system is rapidly forced compared with time series based early warning signals. The typical assumptions about white noise made when using early warning signals are also studied. It is found that time correlated noise may mask the early warning signal. It is shown that a spectral analysis can avoid this problem.European Commissio

Optimal adjustment of the atmospheric forcing parameters of ocean models using sea surface temperature data assimilation

In ocean general circulation models, near-surface atmospheric variables used to specify the atmospheric boundary condition remain one of the main sources of error. The objective of this research is to constrain the surface forcing function of an ocean model by sea surface temperature (SST) data assimilation. For that purpose, a set of corrections for ERAinterim (hereafter ERAi) reanalysis data is estimated for the period of 1989–2007, using a sequential assimilation method, with ensemble experiments to evaluate the impact of uncertain atmospheric forcing on the ocean state. The control vector of the assimilation method is extended to atmospheric variables to obtain monthly mean parameter corrections by assimilating monthly SST and sea surface salinity (SSS) climatological data in a low resolution global configuration of the NEMO model. In this context, the careful determination of the prior probability distribution of the parameters is an important matter. This paper demonstrates the importance of isolating the impact of forcing errors in the model to perform relevant ensemble experiments. <br><br> The results obtained for every month of the period between 1989 and 2007 show that the estimated parameters produce the same kind of impact on the SST as the analysis itself. The objective is then to evaluate the long-term time series of the forcing parameters focusing on trends and mean error corrections of air–sea fluxes. Our corrections tend to equilibrate the net heat-flux balance at the global scale (highly positive in ERAi database), and to remove the potentially unrealistic negative trend (leading to ocean cooling) in the ERAi net heat flux over the whole time period. More specifically in the intertropical band, we reduce the warm bias of ERAi data by mostly modifying the latent heat flux by wind speed intensification. Consistently, when used to force the model, the corrected parameters lead to a better agreement between the mean SST produced by the model and mean SST observations over the period of 1989–2007 in the intertropical band

Methods for assimilating remotelysensed water storage changes into hydrological models

Understanding physical processes within the water cycle is a challenging issue that requires merging information from various disciplines. The Gravity Recovery And Climate Experiment (GRACE) mission provides a unique opportunity to measure time-variable gravity fields, which can be converted to global total water storage anomalies (TWSA). These observations represent a vertical integral of all individual water compartments, which is difficult to observe by in-situ or other remote-sensing techniques. Knowledge about interactions between hydrological fluxes and terrestrial water storage compartments is reflected in large-scale hydrological models that nowadays increase in complexity to simulate all relevant physical processes within the global water cycle. Hydrological models are driven by climate forcing fields and their parameters are usually calibrated against river discharge to ensure a realistic water balance on river basin scale. However, errors in climate forcing fields, model parameters, and model structure limit the reliability of hydrological models. Therefore, it is necessary to improve model simulations by introducing measurements, which is known as data assimilation or data-model fusion. In this thesis, a novel calibration and data assimilation (C/DA) framework is developed to merge remotely-sensed large scale TWSA with hydrological models. To implement this framework, the WaterGAP Global Hydrology Model (WGHM) is chosen, which is a sophisticated 0.5°x0.5° conceptual model that simulates daily water changes in surface and sub-surface water compartments (including groundwater), and considers water consumption. In particular, a flexible approach is introduced to assimilate GRACE TWSA as (sub-)basin or gridded averages into WGHM, while for the first time, implementing the observation error correlations in the C/DA system. A sensitivity analysis is performed to identify significant parameters in the largest river basins world-wide. It is also investigated whether GRACE TWSA can be used to calibrate model parameters. To reduce sampling errors and to improve the computational efficiency, the classical ensemble Kalman filter (EnKF) technique is extended to a square root analysis (SQRA) scheme, and the singular evolutive interpolated Kalman (SEIK) filter. The relationships between these algorithms are addressed. A simple model and WGHM are used to describe the mathematical details of the data assimilation techniques. The observation error model, spatial resolution of observations, choice of filtering algorithm, and model ensemble size are assessed within a realistic synthetic experiment designed for the Mississippi River Basin, USA. Real GRACE products are also integrated into WGHM over this region. Investigations indicate that introducing GRACE TWSA constrains the water balance equation and corrects for insufficiently known climate forcing, in particular precipitation. Individual water states and fluxes are also adjusted but more improvements are expected by assimilating further in-situ and remotely-sensed observations. The processing choices represent important impacts on the final results. The C/DA framework is transferred to the Murray-Darling River Basin, Australia, to improve the simulation of hydrological changes under a long-term drought condition. GRACE C/DA introduces a negative trend to WGHM simulated TWSA. A validation with in-situ groundwater measurements indicates that the trend is correctly associated with the groundwater compartment. Thus, the C/DA helps to identify deficits in model simulations and improves the understanding of hydrological processes. The promising results provide a first step towards more complex C/DA applications on global scale and in conjunction with further terrestrial water storage observations.Methoden zur Assimilierung von satelliten-basierten Wasserspeicheränderungen in hydrologische Modelle Zum Verständnis der physikalischen Prozesse des Wasserkreislaufes ist das Zusammenführen von Kenntnissen verschiedener Disziplinen erforderlich. Die Messungen zeitabhängiger Gravitationsfelder der Gravity Recovery And Climate Experiment (GRACE) Satellitenmission liefern einzigartige Erkenntnisse über globale Gesamtwasserspeicher-änderungen (GWSA). Diese Größe repräsentiert die Summe aller einzelnen Wasserspeicherkomponenten, welche nur unzulänglich durch lokale oder andere satellitengestützte Verfahren beobachtet werden kann. Großskalige hydrologische Modelle simulieren Interaktionen zwischen terrestrischen Wasserspeicherkomponenten. Ihre Komplexität steigt heutzutage immer weiter, um alle relevanten physikalischen Prozesse im globalen Wasserkreislauf abzubilden. Sie werden durch Klimadaten angetrieben und durch Modellparameter gesteuert. Zur Gewährleistung einer realistischen Wasserbilanz in Flusseinzugsgebieten werden letztere üblicherweise gegen Durchflussmessungen kalibriert. Dennoch limitieren Unsicherheiten in den Klimadaten, in den Modellparametern und in der Modellstruktur die Zuverlässigkeit hydrologischer Prädiktionen. Um Simulationen zu verbessern ist daher die Integration von Beobachtungsdaten notwendig, welches unter dem Begriff der Datenassimilierung bekannt ist. In dieser Arbeit wird ein neuer Kalibrierungs- und Datenassimilierungsansatz (K/DA) zur Kombination von großskalig beobachteten GWSA und hydrologischen Modellen am Beispiel des WaterGAP Global Hydrology Model (WGHM) entwickelt. WGHM ist ein konzeptionelles Wasserbilanzmodell, das tägliche Wasseränderungen auf und im Boden (inklusive Grundwasser) auf einer räumlichen Skala von 0,5°x0,5° berechnet und anthropogene Wasserentnahmen berücksichtigt. Insbesondere wird ein flexibler Ansatz zur Integration gegitterter und räumlich gemittelter GWSA eingeführt, während die Korrelationen der Beobachtungsfehler zum ersten Mal in der Assimilierung berücksichtigt werden. Eine Sensitivitätsanalyse identifiziert maßgebliche Parameter für die weltweit größten Flusseinzugsgebiete. Es wird außerdem untersucht, ob GRACE-GWSA zur Parameter-kalibrierung herangezogen werden können. Um Stichprobenfehler zu reduzieren und um die rechnerische Effizienz zu steigern, wird die klassische Ensemble Kalman Filter (EnKF) Methode um das Square Root Analysis (SQRA) Schema und den Singular Evolutive Interpolated Kalman (SEIK) Filter erweitert. Die Zusammenhänge dieser Algorithmen werden dargestellt. Die mathematischen Details der Methoden werden anhand eines einfachen Modells und des WGHM beschrieben. Das Modell der Beobachtungsfehler, die Auflösung der Beobachtungen, die Auswahl der Filteralgorithmen und die Größe des Modellensembles werden in einem realistischen synthetischen Experiment für das Flusseinzugsgebiet des Mississippis (USA) analysiert. GRACE-GWSA werden ebenfalls für dieser Region in das WGHM integriert. Untersuchungen zeigen, dass die Wasserbilanz an die Daten angepasst wird und ungenaue Klimadaten, insbesondere Niederschlag, ausgeglichen werden. Wasserspeicher-komponenten werden ebenfalls angepasst, würden aber durch die Assimilierung weiterer lokaler und satellitengestützter Daten profitieren. Der K/DA Ansatz hat einen entscheidenden Einfluss auf die Ergebnisse. Der entwickelte Ansatz wird auf das Einzugsgebiet des Murray und Darling Flusses (Australien) übertragen, um die Simulation hydrologischer Änderungen während einer Trockenperiode zu verbessern. GRACE-K/DA führt einen negativen Trend in das Modell ein. Die Validierung mit lokalen Grundwasserdaten bestätigt, dass der Trend korrekt mit dem Grundwasserspeicher assoziiert wird. Die K/DA ermöglicht somit Defizite in Modellsimulationen zu identifizieren und verbessert das Verständnis hydrologischer Prozesse. Die vielversprechenden Ergebnisse bereiten einen ersten Schritt in Richtung globaler K/DA in Verbindung mit weiteren hydrologischen Beobachtungen