Rotated Spectral Principal Component Analysis (rsPCA) for Identifying Dynamical Modes of Variability in Climate Systems.

Spectral PCA (sPCA), in contrast to classical PCA, offers the advantage of identifying organized spatiotemporal patterns within specific frequency bands and extracting dynamical modes. However, the unavoidable trade-off between frequency resolution and robustness of the PCs leads to high sensitivity to noise and overfitting, which limits the interpretation of the sPCA results. We propose herein a simple nonparametric implementation of sPCA using the continuous analytic Morlet wavelet as a robust estimator of the cross-spectral matrices with good frequency resolution. To improve the interpretability of the results, especially when several modes of similar amplitude exist within the same frequency band, we propose a rotation of the complex-valued eigenvectors to optimize their spatial regularity (smoothness). The developed method, called rotated spectral PCA (rsPCA), is tested on synthetic data simulating propagating waves and shows impressive performance even with high levels of noise in the data. Applied to global historical geopotential height (GPH) and sea surface temperature (SST) daily time series, the method accurately captures patterns of atmospheric Rossby waves at high frequencies (3-60-day periods) in both GPH and SST and El Niño-Southern Oscillation (ENSO) at low frequencies (2-7-yr periodicity) in SST. At high frequencies the rsPCA successfully unmixes the identified waves, revealing spatially coherent patterns with robust propagation dynamics

Climate-driven changes in the predictability of seasonal precipitation

Climate-driven changes in precipitation amounts and their seasonal variability are expected in many continental-scale regions during the remainder of the 21st century. However, much less is known about future changes in the predictability of seasonal precipitation, an important earth system property relevant for climate adaptation. Here, on the basis of CMIP6 models that capture the present-day teleconnections between seasonal precipitation and previous-season sea surface temperature (SST), we show that climate change is expected to alter the SST-precipitation relationships and thus our ability to predict seasonal precipitation by 2100. Specifically, in the tropics, seasonal precipitation predictability from SSTs is projected to increase throughout the year, except the northern Amazonia during boreal winter. Concurrently, in the extra-tropics predictability is likely to increase in central Asia during boreal spring and winter. The altered predictability, together with enhanced interannual variability of seasonal precipitation, poses new opportunities and challenges for regional water management

Approche Physico-statistique de la Désagrégation des Précipitations Satellite Dans les Tropiques

Rainfall variability involves a wide range of scales: from the millimeter-scale associated with microphysics to the synoptic scale of the global atmospheric circulation. No existing observation system is able to cover all these scales by itself. Satellite-based observation systems are currently the most efficient systems to resolve the large spatial and temporal scales: from mesoscale meteorology to the synoptic scale. This thesis is dedicated to the exploration of satellites ability to resolve spatial scales from 100km to 2km and temporal scales from 24h to 15 min (in order to resolve the diurnal cycle). The chosen approach is both physical and statistical (or deterministic and probabilistic). The idea is that the deterministic approach can resolve the large scales, but several factors limit its relevance when dealing with fine scales: -The limited resolution of the instruments. -The number of orbiting instruments that limits temporal sampling. -The dynamic nature of fine scale variability.At fines scales, most of the errors in rainfall estimation from satellite comes from not perfectly localizing the precipitating cells. The first objective of this thesis is to identify precisely the lowest limit in scale where the deterministic approach is appropriate.The implementation of the physical-statistical approach relies on an existing multisensor estimate of daily precipitation at a 1° resolution: the TAPEER algorithm developed as part of the Megha-Tropiques mission. The chosen method is a hybrid physical disaggregation and stochastic downscaling via a multiscale representation. The result is an ensemble of high-resolution probable realizations of the rain intensity field. The ensemble is constrained by a high resolution rain detection mask derived from meteosat-SG infrared images at 3km resolution (one image every 15 minutes). The uncertainty associated with the final estimation is handled through the ensemble dispersion. Every realization is generated so that its statistical properties (frequency distribution of the intensities, autocorrelation function) mimic those of the true rain field. The generated fields and the proposed technique contribute to hydrological applications for instance by improving the runoff associated to high precipitation rates in models. Using several realizations is a way to study uncertainty propagation through a modelLes précipitations sont un phénomène dont la variabilité s'étend sur une très large gamme d'échelles : de l'échelle millimétrique de la goutte d'eau (échelle microphysique) à l'échelle des circulations atmosphériques globales (échelle synoptique). Il n'existe pas de système unique capable de fournir des observations des précipitations couvrant toutes ces échelles. Les observations satellite sont celles qui actuellement résolvent le plus efficacement les grandes échelles spatiales et temporelles : de la méso-échelle à l'échelle synoptique. Dans cette thèse, nous explorons en zone tropicale les capacités des satellites à résoudre les échelles spatiales de l'ordre de 100km, jusqu'aux échelles kilométriques ; et les échelles temporelles comprises entre 24 heures et 15 minutes (afin de résoudre le cycle diurne). L'approche retenue est physico-statistique. Si les grandes échelles peuvent être résolues par des approches déterministes combinant les mesures de multiples instruments spatiaux, plusieurs facteurs limitent la pertinence des approches déterministes à fine échelle :-Les limites instrumentales en terme de résolution spatiale.-Le nombre d'instruments en orbite qui limite la fréquence d'échantillonnage des mesures.-La nature dynamique de la variabilité fine échelle. En particulier, aux fines échelles, c'est la difficulté à parfaitement localiser les structures précipitantes qui entraine les erreurs d'estimation les plus importantes. L'approche physico-statistique est ici synonyme de déterministe (pour les grandes échelles) et probabiliste (pour les fines échelles). Le premier objectif de cette thèse est de déterminer précisément la limite des échelles qui peuvent être résolues de façon déterministe. L'approche physico-statistique de l'estimation des intensités de précipitation est implémentée dans cette thèse à partir d'une méthode multicapteur déterministe pré-existante : l'algorithme TAPEER, développé dans le cadre de la mission Megha-Tropiques, qui fournit une estimation du cumul pluviométrique journalier à une résolution de 1°. C'est la génération d'ensembles désagrégés par une méthode stochastique multi-échelle qui a été retenue ici. Les ensembles sont contraints par une information fine échelle : un masque de détection des aires précipitantes dérivé des images infrarouge metosat-SG à une résolution de 3km (et avec une image toutes les 15 minutes). La génération d'ensemble permet de caractériser l'incertitude sur l'estimation à travers la dispersion des réalisations de l'ensemble. Chaque réalisation de l'ensemble est générée de façon à reproduire le plus fidèlement possible les propriétés statistiques (distribution de fréquence des intensités, autocorrélation spatiale et temporelle) des véritables champs de précipitation. Ces champs et cette technique ont un apport pour les applications hydrologiques, par exemple pour améliorer le ruissellement lié aux précipitations intenses dans les modèles. Considérer plusieurs réalisations permet de plus d'étudier la propagation des incertitudes à travers un modèle

Resolving Surface Rain from GMI High-Frequency Channels: Limits Imposed by the Three-Dimensional Structure of Precipitation.

The scattering of microwaves at frequencies between 50 and 200 GHz by ice particles in the atmosphere is an essential element in the retrieval of instantaneous surface precipitation from spaceborne passive radiometers. This paper explores how the variable distribution of solid and liquid hydrometeors in the atmospheric column over land surfaces affects the brightness temperature (TB) measured by GMI at 89 GHz through the analysis of Dual-Frequency Precipitation Radar (DPR) reflectivity profiles along the 89-GHz beam. The objective is to refine the statistical relations between observed TBs and surface precipitation over land and to define their limits. As GMI is scanning with a 53° Earth incident angle, the observed atmospheric volume is actually not a vertical column, which may lead to very heterogeneous and seemingly inconsistent distributions of the hydrometeors inside the beam. It is found that the 89-GHz TB is mostly sensitive to the presence of ice hydrometeors several kilometers above the 0°C isotherm, up to 10 km above the 0°C isotherm for the deepest convective systems, but is a modest predictor of the surface precipitation rate. To perform a precise mapping of atmospheric ice, the altitude of the individual ice clusters must be known. indeed, if variations in the altitude of ice are not accounted for, then the high incident angle of GMI causes a horizontal shift (parallax shift) between the estimated position of the ice clusters and their actual position. We show here that the altitude of ice clusters can be derived from the 89-GHz TB itself, allowing for correction of the parallax shift

Underestimated MJO variability in CMIP6 models.

The Madden-Julian Oscillation (MJO) is the leading mode of intra-seasonal climate variability, having profound impacts on a wide range of weather and climate phenomena. Here, we use a wavelet-based spectral Principal Component Analysis (wsPCA) to evaluate the skill of 20 state-of-the-art CMIP6 models in capturing the magnitude and dynamics of the MJO. By construction, wsPCA has the ability to focus on desired frequencies and capture each propagative physical mode with one principal component (PC). We show that the MJO contribution to the total intra-seasonal climate variability is substantially underestimated in most CMIP6 models. The joint distribution of the modulus and angular frequency of the wavelet PC series associated with MJO is used to rank models relatively to the observations through the Wasserstein distance. Using Hovmöller phase-longitude diagrams, we also show that precipitation variability associated with MJO is underestimated in most CMIP6 models for the Amazonia, Southwest Africa, and Maritime Continent

Underestimated MJO variability in CMIP6 models.

The Madden-Julian Oscillation (MJO) is the leading mode of intra-seasonal climate variability, having profound impacts on a wide range of weather and climate phenomena. Here, we use a wavelet-based spectral Principal Component Analysis (wsPCA) to evaluate the skill of 20 state-of-the-art CMIP6 models in capturing the magnitude and dynamics of the MJO. By construction, wsPCA has the ability to focus on desired frequencies and capture each propagative physical mode with one principal component (PC). We show that the MJO contribution to the total intra-seasonal climate variability is substantially underestimated in most CMIP6 models. The joint distribution of the modulus and angular frequency of the wavelet PC series associated with MJO is used to rank models relatively to the observations through the Wasserstein distance. Using Hovmöller phase-longitude diagrams, we also show that precipitation variability associated with MJO is underestimated in most CMIP6 models for the Amazonia, Southwest Africa, and Maritime Continent