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

Zonally opposing shifts of the intertropical convergence zone in response to climate change

Future changes in the location of the intertropical convergence zone (ITCZ) due to climate change are of high interest since they could substantially alter precipitation patterns in the tropics and subtropics. Although models predict a future narrowing of the ITCZ during the 21st century in response to climate warming, uncertainties remain large regarding its future position, with most past work focusing on the zonal-mean ITCZ shifts. Here we use projections from 27 state-of-the-art climate models (CMIP6) to investigate future changes in ITCZ location as a function of longitude and season, in response to climate warming. We document a robust zonally opposing response of the ITCZ, with a northward shift over eastern Africa and the Indian Ocean, and a southward shift in the eastern Pacific and Atlantic Ocean by 2100, for the SSP3-7.0 scenario. Using a two-dimensional energetics framework, we find that the revealed ITCZ response is consistent with future changes in the divergent atmospheric energy transport over the tropics, and sector-mean shifts of the energy flux equator (EFE). The changes in the EFE appear to be the result of zonally opposing imbalances in the hemispheric atmospheric heating over the two sectors, consisting of increases in atmospheric heating over Eurasia and cooling over the Southern Ocean, which contrast with atmospheric cooling over the North Atlantic Ocean due to a model-projected weakening of the Atlantic meridional overturning circulation

Links of climate variability and change with regional hydroclimate: Predictability, trends, and physical mechanisms on seasonal to decadal scales

The mechanistic understanding and reliable prediction of regional hydroclimatic variability across scales remains a challenge, with important socioeconomic and environmental implications for many regions around the world. Despite the significant advances in earth system modeling during the recent decades, deterministic models show limited predictive skill of regional hydroclimate, mainly due to imperfect physical conceptualizations and inaccurate initial conditions. Statistical schemes that are based on empirically established climate teleconnections are not reliable due to the non-stationary nature of the system under climate change. In this dissertation, to gain physical insight on precipitation variability across scales, we explore a) the physical teleconnections and predictability of winter precipitation totals over the southwestern US (SWUS), and b) the future shifts in the position of the tropical rainbelt/intertropical convergence zone (ITCZ) in response to climate change. The hydroclimatic variability in these two cases is based on fundamentally different phenomena (mid latitude dynamics versus tropical circulation) and operates at largely different temporal scales (seasonal versus multidecadal timescales), thus offering great potential for physical insight and broadening the impact of this work. We present evidence that new modes of sea surface temperature variability over the southwestern Pacific have been robustly connected to SWUS precipitation over the past four decades, providing improved prediction skill compared to traditionally used indices. We suggest that the revealed connection materializes through a western Pacific pathway whereby temperature anomalies in the proximity of New Zealand propagate from the southern to the northern hemisphere during boreal summer and early fall. The importance of the revealed teleconnection and the skill of other predictive models in predicting extreme precipitation totals in SWUS is assessed via a new probabilistic framework that is also introduced in this work. Regarding the future response of the tropical rainbelt to climate change, we propose a new multivariate approach to track its position as a function of longitude, and by using state-of-the-art climate model outputs, we report a robust, zonally contrasting shift of the ITCZ with climate change. Specifically, we document that the ITCZ will shift northward over eastern Africa and the Indian Ocean, and southward in the eastern Pacific and Atlantic Oceans by 2100, for the SSP3-7.0 scenario. We find that the revealed ITCZ response is consistent with future changes in the divergent atmospheric energy transport over the tropics, and sector-mean shifts of the energy flux equator. The shifts in the energy flux equator appear to be the result of zonally contrasting imbalances in the interhemispheric atmospheric heating over the two sectors, consisting of increases in atmospheric heating over Eurasia and cooling over the Southern Ocean, which contrast with atmospheric cooling over the North Atlantic Ocean due to a model-projected weakening of the Atlantic meridional overturning circulation.The results of this dissertation highlight the need to understand the dynamic nature of the coupled ocean-atmosphere system and exploit climate information that goes beyond the traditionally used indices for improving future prediction skill of regional precipitation in a changing climate. Future research should focus on the development of new, data-driven methodologies that aim to integrate physics and machine learning, and predict seasonal precipitation variability in a setting where the predictors are not prescribed a priori, but rather emerge from the model fit to the data. For longer timescales (i.e. decadal and multi-decadal), our results provide new insights about the mechanisms that will influence the future position of the tropical rainbelt, and may allow for more robust projections of climate change impacts