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results)</h2></header><section class="c-scholworks"><div class="c-scholworks__main-column"><ul class="c-scholworks__tag-list"><li class="c-scholworks__tag-thesis">Thesis</li><li class="c-scholworks__tag-peer">Peer Reviewed</li></ul><div><h3 class="c-scholworks__heading"><a href="/uc/item/91w9z4sp"><div class="c-clientmarkup">The Variability of Atmospheric Rivers on Different Time Scales and Their Representation in Reanalyses and Observations</div></a></h3></div><div class="c-authorlist"><ul class="c-authorlist__list"><li class="c-authorlist__begin"><a href="/search/?q=author%3AMa%2C%20Weiming">Ma, Weiming</a> </li><li class="c-authorlist__begin"><span class="c-authorlist__heading">Advisor(s):</span> <a href="/search/?q=author%3AChen%2C%20Gang">Chen, Gang</a> </li></ul></div><div class="c-scholworks__publication"><a href="/uc/ucla_etd">UCLA Electronic Theses and Dissertations</a> (2022)</div><div class="c-scholworks__abstract"><div class="c-clientmarkup"><p>Atmospheric rivers (ARs) are filaments of enhanced water vapor transport in the atmosphere. Globally, ARs play a key role in the meridional moisture transport. Regionally,ARs can either serve as freshwater suppliers or culprits behind many of the weather hazards. ARs and their impacts have been studied extensively. However, what controls the variability of ARs on different time scales remains largely unknown. In particular, further studies are especially needed to better understand the relative role of circulation (dynamic) variability versus moisture (thermodynamic) variability, internal variability versus sea surface temperature (SST)/ sea ice variability and anthropogenic forcing versus internal variability originated from SST/sea ice variability in controlling the variability of ARs and their associated precipitation on different time scales. In addition, reanalyses have long been used as proxies of observations in AR studies. Yet, the representation of ARs and their associated precipitation in reanalyses remain unknown. Although satellite observations have also been used to study ARs, previous satellite-based AR studies used only the moisture component (integrated water vapor or IWV) to detect ARs. While ARs have been defined as filaments of enhanced moisture transport in the atmosphere, detecting ARs with only the moisture field would inevitably run the risk of detecting those filamentary features with high moisture content, but relatively weak transport component. In this dissertation, we will address the research gaps above from five different angles. First, we investigate the relative role of SST/sea ice versus internal variability in driving the interannual variability of winter AR activities over the North Hemisphere. We show that, while both SST/sea ice and internal variability play roles in driving the interannual AR variability, their roles differ across ocean basins. Over the North Pacific, SST/sea ice variability exerts substantially stronger control on the AR variability compared to interval variability. However, both SST/sea ice and internal variability play comparable roles in modulating the AR variability over the North Atlantic. Second, on longer time scale, we discover that ARs over the Southern Hemisphere have been shifting poleward in the past four decades. Using a simple scaling method, we find that this poleward shift in the ARs is mostly driven by the poleward shift of the westerly jet (dynamic) while the contribution from the changes in the moisture field is relatively minor. Using two ensembles from the Community Earth System Model (CESM), one with fully coupled oceans and another one driven by observed SST/sea ice, we show that anthropogenic forcing is mainly responsible for the observed poleward shift. However, the negative phase of Interdecadal Pacific Oscillation (IPO) in recent decades also further drives the poleward shift in ARs. Third, ARs are expected to change under a warmer climate. However, the AR response to warming is determined by numerous factors. Two of the most prominent factors are the warming of the tropical upper troposphere, which can drive the poleward shift of the westerly jet, and the amplified warming of the polar regions, which can drive the equatorward shift of the westerly jet. Using nine models participated in the Polar Amplification Model Intercomparison Project (PAMIP), we investigate how Arctic Amplification and its associated sea ice loss would affect the boreal winter AR activities over the Northern Hemisphere. We find that, in response to Arctic sea ice loss, ARs extend northeastward over North Pacific and shift equatorward over North Atlantic. We further demonstrate that these response patterns are mostly determined by the responses in the circulation. Fourth, using a moisture budget approach, the relative contribution of dynamic change versus thermodynamic change to the intensification of extreme precipitation along the North American West Coast (predominantly driven by ARs) is also quantified. We show the intensification of the extreme precipitation along the North American West Coast is mostly driven by the increase in moisture while the contribution from the dynamic change is minor. Lastly, we develop a novel method to detect ARs in satellite observations using both IWV and wind information based on the geostrophic winds. Using this method, we create satellite-based AR statistics and use these statistics to evaluate the performance of seven commonly used reanalyses in representing ARs and their associated precipitation. We show that both satellite observation and reanalyses show high agreement with each other in representing the AR frequency distribution. In terms of AR precipitation, ARs in reanalyses tend to precipitate too lightly and too often. Our studies shed light on the mechanisms driving the variability of ARs across different time scales. These findings have important implications. First, given the more important role SST/sea ice variability in controlling the AR interannual variability, ARs in the North Pacific are likely more predictable than those over the North Atlantic. Better SST forecast can thus likely lead to better AR forecast along the North American West Coast. Second, on the interdecadal time scale, internal variability related to ocean processes still plays an important role in modulating AR variability. Internal variability should thus be taken into consideration when studying future AR response under warming. Third, our results indicate that the thermodynamic aspect of the AR and AR precipitation response is quite robust. We thus need to constrain the response in circulation to reduce the AR response uncertainty. Lastly, the finding that ARs in reanalyses tend to precipitate too often and too lightly directly questions the use of reanalysis-based precipitation in AR studies.</p></div></div><div class="c-scholworks__media"><ul class="c-medialist"></ul></div></div><div class="c-scholworks__ancillary"><a class="c-scholworks__thumbnail" href="/uc/item/91w9z4sp"><img src="/cms-assets/5ba60b5ef0a2371ca67959e512b7c41a923dd17ae0e5eee2cf93d41e6e8aad70" alt="Cover page: The Variability of Atmospheric Rivers on Different Time Scales and Their Representation in Reanalyses and Observations"/></a></div></section><section class="c-scholworks"><div class="c-scholworks__main-column"><ul class="c-scholworks__tag-list"><li class="c-scholworks__tag-article">Article</li><li class="c-scholworks__tag-peer">Peer Reviewed</li></ul><div><h3 class="c-scholworks__heading"><a href="/uc/item/3410j40f"><div class="c-clientmarkup">The role of interdecadal climate oscillations in driving Arctic atmospheric river trends.</div></a></h3></div><div class="c-authorlist"><ul class="c-authorlist__list"><li class="c-authorlist__begin"><a href="/search/?q=author%3AMa%2C%20Weiming">Ma, Weiming</a>; </li><li><a href="/search/?q=author%3AWang%2C%20Hailong">Wang, Hailong</a>; </li><li><a href="/search/?q=author%3ALeung%2C%20L">Leung, L</a>; </li><li><a href="/search/?q=author%3ALu%2C%20Jian">Lu, Jian</a>; </li><li><a href="/search/?q=author%3ARasch%2C%20Philip">Rasch, Philip</a>; </li><li><a href="/search/?q=author%3AFu%2C%20Qiang">Fu, Qiang</a>; </li><li><a href="/search/?q=author%3AKravitz%2C%20Ben">Kravitz, Ben</a>; </li><li><a href="/search/?q=author%3AZou%2C%20Yufei">Zou, Yufei</a>; </li><li><a href="/search/?q=author%3ACassano%2C%20John">Cassano, John</a>; </li><li><a href="/search/?q=author%3AMaslowski%2C%20Wieslaw">Maslowski, Wieslaw</a>; </li><li class="c-authorlist__end"><a href="/search/?q=author%3AChen%2C%20Gang">Chen, Gang</a> </li></ul></div><div class="c-scholworks__publication"><a href="/uc/ucla_postprints">UCLA Previously Published Works</a> (2024)</div><div class="c-scholworks__abstract"><div class="c-clientmarkup">Atmospheric rivers (ARs), intrusions of warm and moist air, can effectively drive weather extremes over the Arctic and trigger subsequent impact on sea ice and climate. What controls the observed multi-decadal Arctic AR trends remains unclear. Here, using multiple sources of observations and model experiments, we find that, contrary to the uniform positive trend in climate simulations, the observed Arctic AR frequency increases by twice as much over the Atlantic sector compared to the Pacific sector in 1981-2021. This discrepancy can be reconciled by the observed positive-to-negative phase shift of Interdecadal Pacific Oscillation (IPO) and the negative-to-positive phase shift of Atlantic Multidecadal Oscillation (AMO), which increase and reduce Arctic ARs over the Atlantic and Pacific sectors, respectively. Removing the influence of the IPO and AMO can reduce the projection uncertainties in near-future Arctic AR trends by about 24%, which is important for constraining projection of Arctic warming and the timing of an ice-free Arctic.</div></div><div class="c-scholworks__media"><ul class="c-medialist"></ul></div></div><div class="c-scholworks__ancillary"><a class="c-scholworks__thumbnail" href="/uc/item/3410j40f"><img src="/cms-assets/b3274de56f82f798fd453a4f89ac88e5132c61d92b5b227d8c7e9a97c8d51dd9" alt="Cover page: The role of interdecadal climate oscillations in driving Arctic atmospheric river trends."/></a><a href="https://creativecommons.org/licenses/by/4.0/" class="c-scholworks__license"><img class="c-lazyimage" data-src="/images/cc-by-small.svg" alt="Creative Commons 'BY' version 4.0 license"/></a></div></section></section></main></form></div><div><div class="c-toplink"><a href="javascript:window.scrollTo(0, 0)">Top</a></div><footer class="c-footer"><nav class="c-footer__nav"><ul><li><a href="/">Home</a></li><li><a href="/aboutEschol">About eScholarship</a></li><li><a href="/campuses">Campus Sites</a></li><li><a href="/ucoapolicies">UC Open Access Policy</a></li><li><a href="/publishing">eScholarship Publishing</a></li><li><a href="https://www.cdlib.org/about/accessibility.html">Accessibility</a></li><li><a href="/privacypolicy">Privacy Statement</a></li><li><a href="/policies">Site Policies</a></li><li><a href="/terms">Terms of Use</a></li><li><a href="/login"><strong>Admin Login</strong></a></li><li><a href="https://help.escholarship.org"><strong>Help</strong></a></li></ul></nav><div class="c-footer__logo"><a href="/"><img class="c-lazyimage" data-src="/images/logo_footer-eschol.svg" alt="eScholarship, University of California"/></a></div><div class="c-footer__copyright">Powered by the<br/><a href="http://www.cdlib.org">California Digital Library</a><br/>Copyright 漏 2017<br/>The Regents of the University of California</div></footer></div></div></div></div> <script src="/js/vendors~app-bundle-2aefc956e545366a5d4e.js"></script> <script src="/js/app-bundle-3c8ebc2ec05dcc3202fd.js"></script> </body> </html>