The post-1980 regional climate change increased dust AOD by +12.5 ± 15.0% and +43.6 ± 31.2% over the source and downstream areas respectively during a record-breaking dust storm in March 2021 in North China.
AbstractExtreme precipitation events can cause significant impacts to life, property, and the economy. As forecasting capabilities increase, the subseasonal-to-seasonal (S2S) time scale provides an opportunity for advanced notice of impactful precipitation events. Building on a previous workshop, the Prediction of Rainfall Extremes at Subseasonal to Seasonal Periods (PRES2iP) project team conducted a second workshop virtually in the fall of 2021. The workshop engaged a variety of practitioners, including emergency managers, water managers, tribal environmental professionals, and National Weather Service meteorologists. While the team’s first workshop examined the “big picture” in how practitioners define “extreme precipitation” and how precipitation events impact their jobs, this workshop focused on details of S2S precipitation products, both current and potential future decision tools. Discussions and activities in this workshop assessed how practitioners use existing forecast products to make decisions about extreme precipitation, how they interpret newly developed educational tools from the PRES2iP team, and how they manage uncertainty in forecasts. By collaborating with practitioners, the PRES2iP team plans to use knowledge gained going forward to create more educational and operational tools related to S2S extreme precipitation event prediction, helping practitioners to make more informed decisions.
AbstractAs the abundance of weather forecast guidance continues to grow, communicators will have to prioritize what types of information to pass on to decision-makers. This work aims to evaluate how members of the public prioritize weather forecast attributes (including information about location, timing, chance, severity, impacts, and protective actions) on average and across event timelines in the severe, tropical, and winter weather domains. Data from three demographically representative surveys of U.S. adults indicate that members of the public generally prioritize information about event location, timing, and severity when evaluating the importance of forecast attributes. This pattern is largely consistent across hazard domains but varies across event timelines. In early stages of a forecast (such as the outlook time scale), people generally prioritize information about chance and location. In middle stages (watch time scale), event timing and severity become more important. In late stages (warning time scale), information about protective actions is a higher priority, especially for people with less exposure to a hazard.
Dinnat, Emmanuel; English, Stephen; Prigent, Catherine; Kilic, Lise; Anguelova, Magdalena; Newman, Stuart; Meissner, Thomas; Boutin, Jacqueline; Stoffelen, Ad; Yueh, Simon; Johnson, Ben; Weng, Fuzhong; Jimenez, Carlos
AbstractDeveloping local climate adaptation strategies that respond to weather and climate extremes is increasingly salient. Coproducing knowledge and climate adaptation strategies can be an important approach to ensuring that they are context specific, meet community needs, and are deemed usable by local decision-makers. Most of the guidance for coproduction has focused on important, overarching themes and ethical considerations like trust, iteration, and flexibility; these are incredibly valuable, but little attention has been focused on specific, highly consequential research decisions that researchers must make that shape project outcomes. Here, we reflect on our experience in a pilot project coproducing climate adaptation knowledge and strategies in six rural communities. We identify eight questions that researchers coproducing science with communities will need to grapple with when designing and conducting research and discuss some of the related trade-offs of each. Topics include community recruitment, champion selection, participant makeup, geography, clarifying expectations, timing, prioritization, and next steps. The questions are broadly applicable to knowledge coproduction and important especially as greater attention is being given to the ethics of doing this work, the power relations, and the potential risk associated with it. We hope that these questions can guide a dialogue for others and motivate explicit discussions of trade-offs involved in planning research that is coproduced with communities. We call for more of this type of self-reflection and sharing across our research community to deepen our knowledge and hopefully lead to a more rapid improvement in outcomes across the many efforts underway today to cocreate climate knowledge for adaptation.
Valmassoi, Arianna; Keller, Jan D.; Kleist, Daryl T.; English, Stephen; Ahrens, Bodo; Ďurán, Ivan Bašták; Bauernschubert, Elisabeth; Bosilovich, Michael G.; Fujiwara, Masatomo; Hersbach, Hans; Lei, Lili; Löhnert, Ulrich; Mamnun, Nabir; Martin, Cory R.; Moore, Andrew; Niermann, Deborah; Ruiz, Juan José; Scheck, Leonhard
According to our model simulations, anthropogenic climate change enhanced the total precipitation amount during the heavy rainfall event of 19–21 July 2021 in central China by 21.3% (95% confidence interval: 16.5%–29.5%).
AbstractThe establishment of the Great Lakes wave forecast system is an early success story inspiring the introduction of open-innovation practices at the U.S. National Oceanic and Atmospheric Administration (NOAA). It shows the power of community modeling to accelerate the transition of scientific innovations to operational environmental forecasting. This paper presents an overview of wave modeling in the Great Lakes from the perspective of its societal benefits. NOAA’s operational wave modeling systems and development practices are examined, emphasizing the importance of community- and stakeholder-driven collaborative efforts to introduce innovations such as using advanced spatial grid types and physics parameterizations, leading to improved predictive skill. The success of the open-innovation approach, set in motion at NOAA by initiatives such as the Great Lakes wave forecasting system, accelerated the transition of innovations to operations. The culture change to operational modeling efforts became part of the foundation for establishing the Unified Forecast System and, more recently, the Earth Prediction Innovation Center. Open-innovation initiatives will improve operational weather and climate forecast systems through scientific and technical innovation, reducing the devastating impacts of hazardous weather and supporting NOAA’s mission of protecting life and property and enhancing the national economy.
AbstractThe Madden–Julian oscillation (MJO) is the dominant intraseasonal wave phenomenon influencing extreme weather and climate worldwide. Realistic simulations and accurate predictions of MJO genesis are the cornerstones for successfully monitoring, forecasting, and managing meteorological disasters 3–4 weeks in advance. Nevertheless, the genesis processes and emerging precursor signals of an eastward-propagating MJO event remain largely uncertain. Here, we find that the MJO genesis processes observed in the past four decades exhibit remarkable diversity with different seasonality and can be classified objectively into four types, namely, a novel downstream origin from the westward-propagating intraseasonal oscillation (WPISO; 20.4%), localized breeding from the Indian Ocean suppressed convection (IOSC; 15.4%), an upstream succession of the preceding weakly dispersive (WD; 25.9%), and strongly dispersive (SD; 38.3%) MJO. These four types are associated with different oceanic background states, characterized by central Pacific cooling, southern Maritime Continent warming, eastern Pacific cooling, and central Pacific warming for the WPISO, IOSC, WD, and SD types, respectively. The SD type is also favored during the easterly phase of the stratospheric quasi-biennial oscillation. Diverse convective initiations possibly imply various kinds of propagations of MJO. The subseasonal reforecasts indicate robustly distinct prediction skills for the diverse MJO genesis. A window of opportunity for skillful week 3–4 prediction probably opens with the aid of the WPISO-type MJO precursor, which has increased the predictability of primary MJO onset by 1 week. These findings suggest that the diversified MJO genesis can be skillfully foreseen by monitoring unique precursor signals and can also serve as benchmarks for evaluating contemporary models’ modeling and predicting capabilities.