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

Creative Commons 'BY' version 4.0 license

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.

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