Bushuk, Mitchell, Sahara Ali, David A Bailey, Qing Bao, Lauriane Batté, Uma S Bhatt, Edward Blanchard-Wrigglesworth, Ed Blockley, Gavin Cawley, Junhwa Chi, François Counillon, Philippe Goulet Coulombe, Richard I Cullather, Francis X Diebold, Arlan Dirkson, Eleftheria Exarchou, Maximilian Göbel, William Gregory, Virginie Guemas, Lawrence C Hamilton, Bian He, Sean Horvath, Monica Ionita, Jennifer E Kay, Eliot Kim, Noriaki Kimura, Dmitri Kondrashov, Zachary M Labe, Woo-Sung Lee, Younjoo J Lee, Cuihua Li, Xuewei Li, Yongcheng Lin, Yanyun Liu, Wieslaw Maslowski, François Massonnet, Walter N Meier, William J Merryfield, Hannah Myint, Juan C Acosta Navarro, Alek Petty, Fangli Qiao, David Schröder, Axel Schweiger, Qi Shu, Michael Sigmond, Michael Steele, Julienne Stroeve, Nico Sun, Steffen Tietsche, Michel Tsamados, Keguang Wang, Jianwu Wang, Wanqui Wang, Yiguo Wang, Yun Wang, James Williams, Qinghua Yang, Xiaojun Yuan, Jinlun Zhang, and Yongfei Zhang, July 2024: Predicting September Arctic sea ice: A multi-model seasonal skill comparison. Bulletin of the American Meteorological Society, 105(7), DOI:10.1175/BAMS-D-23-0163.1. Abstract
This study quantifies the state-of-the-art in the rapidly growing field of seasonal Arctic sea ice prediction. A novel multi-model dataset of retrospective seasonal predictions of September Arctic sea ice is created and analyzed, consisting of community contributions from 17 statistical models and 17 dynamical models. Prediction skill is compared over the period 2001–2020 for predictions of Pan-Arctic sea ice extent (SIE), regional SIE, and local sea ice concentration (SIC) initialized on June 1, July 1, August 1, and September 1. This diverse set of statistical and dynamical models can individually predict linearly detrended Pan-Arctic SIE anomalies with skill, and a multi-model median prediction has correlation coefficients of 0.79, 0.86, 0.92, and 0.99 at these respective initialization times. Regional SIE predictions have similar skill to Pan-Arctic predictions in the Alaskan and Siberian regions, whereas regional skill is lower in the Canadian, Atlantic, and Central Arctic sectors. The skill of dynamical and statistical models is generally comparable for Pan-Arctic SIE, whereas dynamical models outperform their statistical counterparts for regional and local predictions. The prediction systems are found to provide the most value added relative to basic reference forecasts in the extreme SIE years of 1996, 2007, and 2012. SIE prediction errors do not show clear trends over time, suggesting that there has been minimal change in inherent sea ice predictability over the satellite era. Overall, this study demonstrates that there are bright prospects for skillful operational predictions of September sea ice at least three months in advance.
Druckenmiller, M L., R L Thoman, T A Moon, Liss Marie Andreassen, Thomas J Ballinger, Logan T Berner, Germar H Bernhard, Uma S Bhatt, Siiri Bigalke, Jarle W Bjerke, Jason E Box, Brian Brettschneider, Mike Brubaker, David Burgess, Amy Butler, Hanne H Christiansen, Bertrand Decharme, Chris Derksen, Dmitry Divine, Caroline Drost Jensen, Alesksandra Elias Chereque, Howard E Epstein, Sinead Farrell, Robert S Fausto, Xavier Fettweis, Vitali E Fioletov, Caitlyn Florentine, Bruce C Forbes, Gerald V Frost, Sebastian Gerland, Jens-Uwe Grooß, Edward Hanna, Inger Hanssen-Bauer, Máret J Heatta, Stefan Hendricks, Iolanda Ialongo, Ketil Isaksen, Jelmer Jeuring, Gensuo Jia, Bjørn Johnsen, Lars Kaleschke, Seong-Joong Kim, Jack Kohler, and Zachary M Labe, et al., August 2024: The Arctic. Bulletin of the American Meteorological Society, 105(8), DOI:10.1175/BAMS-D-24-0101.1S277–S330.
Kretschmer, Marlene, Aglaé Jézéquel, Zachary M Labe, and Danielle Touma, September 2024: A shifting climate: New paradigms and challenges for (early career) scientists in extreme weather research. Atmospheric Science Letters, 25(11), DOI:10.1002/asl.1268. Abstract
Research on weather and climate extremes has become integral to climate science due to their increasing societal relevance and impacts in the context of anthropogenic climate change. In this perspective we examine recent changes and evolving paradigms in the study of extreme events, emphasizing the increasingly interdisciplinary nature of research and their societal implications. We discuss the importance of understanding the physical basis of extreme events and its linkages to climate impacts, highlighting the need for collaboration across multiple disciplines. Furthermore, we explore the challenge of big climate data analysis and the application of novel statistical methods, such as machine learning, in enhancing our understanding of extreme events. Additionally, we address the engagement with different stakeholder groups and the evolving landscape of climate services and private-sector involvement. We conclude with reflections on the risks and opportunities for early career researchers in navigating these interdisciplinary and societal demands, stressing the importance of meaningful scientific engagement, and removing barriers to inclusivity and collaboration in climate research.
To better understand the regional changes in summertime temperatures across the conterminous United States (CONUS), we adopt a recently developed machine learning framework that can be used to reveal the timing of emergence of forced climate signals from the noise of internal climate variability. Specifically, we train an artificial neural network (ANN) on seasonally averaged temperatures across the CONUS and then task the ANN to output the year associated with an individual map. In order to correctly identify the year, the ANN must therefore learn time-evolving patterns of climate change amidst the noise of internal climate variability. The ANNs are first trained and tested on data from large ensembles and then evaluated using observations from a station-based data set. To understand how the ANN is making its predictions, we leverage a collection of ad hoc feature attribution methods from explainable artificial intelligence (XAI). We find that anthropogenic signals in seasonal mean minimum temperature have emerged by the early 2000s for the CONUS, which occurred earliest in the Eastern United States. While our observational timing of emergence estimates are not as sensitive to the spatial resolution of the training data, we find a notable improvement in ANN skill using a higher resolution climate model, especially for its early twentieth century predictions. Composites of XAI maps reveal that this improvement is linked to temperatures around higher topography. We find that increases in spatial resolution of the ANN training data may yield benefits for machine learning applications in climate science.
A key consideration for evaluating climate projections is uncertainty in future radiative forcing scenarios. Although it is straightforward to monitor greenhouse gas concentrations and compare observations with specified climate scenarios, it remains less obvious how to detect and attribute regional pattern changes with plausible future mitigation scenarios. Here we introduce a machine learning approach for linking patterns of climate change with radiative forcing scenarios and use a feature attribution method to understand how these linkages are made. We train a neural network using output from the SPEAR Large Ensemble to classify whether temperature or precipitation maps are most likely to originate from one of several potential radiative forcing scenarios. Despite substantial atmospheric internal variability, the neural network learns to identify “fingerprint” patterns, including significant localized regions of change, that associate specific patterns of climate change with radiative forcing scenarios in each year of the simulations. We illustrate this using output from additional ensembles with sharp reductions in future greenhouse gases and highlight specific regions (in this example, the subpolar North Atlantic and Central Africa) that are critical for associating the new simulations with changes in radiative forcing scenarios. Overall, this framework suggests that explainable machine learning could provide one strategy for detecting a regional climate response to future mitigation efforts.
Schreck, Carl J., David R Easterling, Joseph J Barsugli, David A Coates, Andrew Hoell, Nathaniel C Johnson, Kenneth E Kunkel, Zachary M Labe, John Uehling, Russell S Vose, and Xiangdong Zhang, November 2024: A rapid response process for evaluating causes of extreme temperature events in the United States: The 2023 Texas/Louisiana heat wave as a prototype. Environmental Research Climate, 3(4), DOI:10.1088/2752-5295/ad8028. Abstract
As climate attribution studies have become more common, routine processes are now being established for attribution analysis following extreme events. This study describes the prototype process being developed through a collaboration across National Oceanic and Atmospheric Administration (NOAA), including monitoring tools as well as observational and model-based analysis of causal factors. The prolonged period of extreme heat in summer 2023 over Texas, Louisiana and adjacent areas provided a proving ground for this emerging capability. This event posed unique challenges to the initial process. The extreme heat lasted for most of the summer while most heat wave metrics have been designed for 3–7 d events. The eastern portion of the affected area also occurred within the so-called summer-time daytime warming hole where the warming trend in maximum temperatures has been mitigated wholly or in part by increased precipitation. The extreme temperature coincided with a strong—but not record—precipitation deficit over the region. Both observations and climate model simulations illustrate that the temperatures for a given precipitation deficit have warmed in recent decades. In other words, meteorological droughts today are hotter than their historical analogs providing a stronger attribution to anthropogenic forcing than for temperature alone. These findings were summarized in a prototype plain language report that was distributed to key stakeholders. Based on their feedback, the monitoring and assessment tools will continue to be refined, and the project is exploring other climate model large ensembles to increase the robustness of attribution for future events.
Zhang, Yating, Bilal M Ayyub, Juan F Fung, and Zachary M Labe, March 2024: Incorporating extreme event attribution into climate change adaptation for civil infrastructure: Methods, benefits, and research needs. Resilient Cities and Structures, 3(1), DOI:10.1016/j.rcns.2024.03.002103-113. Abstract
In the last decade, the detection and attribution science that links climate change to extreme weather and climate events has emerged as a growing field of research with an increasing body of literature. This paper overviews the methods for extreme event attribution (EEA) and discusses the new insights that EEA provides for infrastructure adaptation. We found that EEA can inform stakeholders about current climate risk, support vulnerability-based and hazard-based adaptations, assist in the development of cost-effective adaptation strategies, and enhance justice and equity in the allocation of adaptation resources. As engineering practice shifts from a retrospective approach to a proactive, forward-looking risk management strategy, EEA can be used together with climate projections to enhance the comprehensiveness of decision making, including planning and preparing for unprecedented extreme events. Additionally, attribution assessment can be more useful for adaptation planning when the exposure and vulnerability of communities to past events are analyzed, and future changes in the probability of extreme events are evaluated. Given large uncertainties inherent in event attribution and climate projections, future research should examine the sensitivity of engineering design to climate model uncertainties, and adapt engineering practice, including building codes, to uncertain future conditions. While this study focuses on adaptation planning, EEA can also be a useful tool for informing and enhancing decisions related to climate mitigation.
Eischeid, Jon K., Martin P Hoerling, Xiao-Wei Quan, Arun Kumar, Joseph J Barsugli, Zachary M Labe, Kenneth E Kunkel, Carl J Schreck, David R Easterling, , John Uehling, and Xiangdong Zhang, October 2023: Why has the summertime central U.S. warming hole not disappeared?Journal of Climate, 36(20), DOI:10.1175/JCLI-D-22-0716.17319-7336. Abstract
A cooling trend in summer (May–August) daytime temperatures since the mid-twentieth century over the central United States contrasts with strong warming of the western and eastern United States. Prior studies based on data through 1999 suggested that this so-called warming hole arose mainly from internal climate variability and thus would likely disappear. Yet it has prevailed for two more decades, despite accelerating global warming, compelling reexamination of causes that in addition to natural variability could include anthropogenic aerosol–induced cooling, hydrologic cycle intensification by greenhouse gas increases, and land use change impacts. Here we present evidence for the critical importance of hydrologic cycle change resulting from ocean–atmosphere drivers. Observational analysis reveals that the warming hole’s persistence is consistent with unusually high summertime rainfall over the region during the first decades of the twenty-first century. Comparative analysis of large ensembles from four different climate models demonstrates that rainfall trends since the mid-twentieth century as large as observed can arise (although with low probability) via internal atmospheric variability alone, which induce warming-hole-like patterns over the central United States. In addition, atmosphere-only model experiments reveal that observed sea surface temperature changes since the mid-twentieth century have also favored central U.S cool/wet conditions during the early twenty-first century. We argue that this latter effect is symptomatic of external radiative forcing influences, which, via constraints on ocean warming patterns, have likewise contributed to persistence of the U.S. warming hole in roughly equal proportion to contributions by internal variability. These results have important ramifications for attribution of extreme events and predicting risks of record-breaking heat waves in the region.
