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A recently launched project under the auspices of the World Climate Research Program’s (WCRP) Coordinated Regional Downscaling Experiments Flagship Pilot Studies program (CORDEX-FPS) is presented. This initiative aims to build first-of-its-kind ensemble climate experiments of convection permitting models to investigate present and future convective processes and related extremes over Europe and the Mediterranean. In this manuscript the rationale, scientific aims and approaches are presented along with some preliminary results from the testing phase of the project. Three test cases were selected in order to obtain a first look at the ensemble performance. The test cases covered a summertime extreme precipitation event over Austria, a fall Foehn event over the Swiss Alps and an intensively documented fall event along the Mediterranean coast. The test cases were run in both “weather-like” (WL, initialized just before the event in question) and “climate” (CM, initialized 1 month before the event) modes. Ensembles of 18–21 members, representing six different modeling systems with different physics and modelling chain options, was generated for the test cases (27 modeling teams have committed to perform the longer climate simulations). Results indicate that, when run in WL mode, the ensemble captures all three events quite well with ensemble correlation skill scores of 0.67, 0.82 and 0.91. They suggest that the more the event is driven by large-scale conditions, the closer the agreement between the ensemble members. Even in climate mode the large-scale driven events over the Swiss Alps and the Mediterranean coasts are still captured (ensemble correlation skill scores of 0.90 and 0.62, respectively), but the inter-model spread increases as expected. In the case over Mediterranean the effects of local-scale interactions between flow and orography and land–ocean contrasts are readily apparent. However, there is a much larger, though not surprising, increase in the spread for the Austrian event, which was weakly forced by the large-scale flow. Though the ensemble correlation skill score is still quite high (0.80). The preliminary results illustrate both the promise and the challenges that convection permitting modeling faces and make a strong argument for an ensemble-based approach to investigating high impact convective processes.

\"Through direct exploration of the subseafloor, U.S.-supported scientific ocean drilling programs have significantly contributed to a broad range of scientific accomplishments in Earth science disciplines, shaping understanding of Earth systems and enabling new fields of inquiry. Scientific Ocean Drilling: Accomplishments and Challenges reviews the scientific accomplishments of U.S.-supported scientific ocean drilling over the past four decades. The book evaluates how the programs (Deep Sea Drilling Project [DSDP], 1968-1983, Ocean Drilling Program [ODP], 1984-2003, and Integrated Ocean Drilling Program [IODP], 2003-2013) have shaped understanding of Earth systems and Earth history and assessed the role of scientific ocean drilling in enabling new fields of inquiry. This book also assesses the potential for transformative discoveries for the next proposed phase of scientific ocean drilling, which is scheduled to run from 2013 to 2023. The programs' technological innovations have played a strong role in these accomplishments. The science plan for the proposed 2013-2023 program presents a strong case for the continuation of scientific ocean drilling. Each of the plan's four themes identifies compelling challenges with potential for transformative science that could only be addressed through scientific ocean drilling, although some challenges appear to have greater potential than others. Prioritizing science plan challenges and integrating multiple objectives into single expeditions would help use resources more effectively, while encouraging technological innovations would continue to increase the potential for groundbreaking science.\"--Publisher's description.

Weather and climate variations on subseasonal to decadal time scales can have enormous social, economic, and environmental impacts, making skillful predictions on these time scales a valuable tool for decision-makers. As such, there is a growing interest in the scientific, operational, and applications communities in developing forecasts to improve our foreknowledge of extreme events. On subseasonal to seasonal (S2S) time scales, these include high-impact meteorological events such as tropical cyclones, extratropical storms, floods, droughts, and heat and cold waves. On seasonal to decadal (S2D) time scales, while the focus broadly remains similar (e.g., on precipitation, surface and upper-ocean temperatures, and their effects on the probabilities of high-impact meteorological events), understanding the roles of internal variability and externally forced variability such as anthropogenic warming in forecasts also becomes important. The S2S and S2D communities share common scientific and technical challenges. These include forecast initialization and ensemble generation; initialization shock and drift; understanding the onset of model systematic errors; bias correction, calibration, and forecast quality assessment; model resolution; atmosphere–ocean coupling; sources and expectations for predictability; and linking research, operational forecasting, and end-user needs. In September 2018 a coordinated pair of international conferences, framed by the above challenges, was organized jointly by the World Climate Research Programme (WCRP) and the World Weather Research Programme (WWRP). These conferences surveyed the state of S2S and S2D prediction, ongoing research, and future needs, providing an ideal basis for synthesizing current and emerging developments in these areas that promise to enhance future operational services. This article provides such a synthesis.

\"Pandas is a popular Python package used for practical, real-world data analysis. It provides efficient, fast, high-performance data structures that make data exploration and analysis very easy. This learner's guide will help you through a comprehensive set of features provided by the pandas library to perform efficient data manipulation and analysis. You will learn how to use pandas to perform data anaylsis in Python.\"--Page 4 of cover.

The National Summer Undergraduate Research Program (NSURP) is a mentored summer research program in biosciences for undergraduate students from underrepresented backgrounds in science, technology, engineering, and mathematics (STEM). Conducted virtually over 8 weeks every summer starting in 2020, NSURP provides accessible and flexible research experiences to meet the needs of geographically diverse and schedule-constrained students. Drawing from mentee reporting and surveys conducted within the NSURP framework involving over 350 underrepresented minority undergraduate students over three cohorts (2020–2022), matched with mentors, this paper highlights the potential benefits of students participating in virtual mentored research experiences. In addition to increased access to quality research experiences for students who face travel or academic setting constraints, we found that virtual mentoring fosters cross-cultural collaborations, generates novel research questions, and expands professional networks. Moreover, this study emphasizes the role of virtual mentorship opportunities in fostering inclusivity and support for individuals from underrepresented groups in STEM fields. By overcoming barriers to full participation in the scientific community, virtual mentorship programs can create a more equitable and inclusive environment for aspiring researchers. This research contributes to the growing body of literature on the effectiveness and the potential of virtual research programs and mentorship opportunities in broadening participation and breaking down barriers in STEM education and careers. Summer Research Experiences for Undergraduates (REUs) are established to provide platforms for interest in scientific research and as tools for eventual matriculation to scientific graduate programs. Unfortunately, the COVID-19 pandemic forced the cancellation of in-person programs for 2020 and 2021, creating the need for alternative programming. The National Summer Undergraduate Research Project (NSURP) was created to provide a virtual option to REUs in microbiology to compensate for the pandemic-initiated loss of research opportunities. Although in-person REUs have since been restored, NSURP currently remains an option for those unable to travel to in-person programs in the first place due to familial, community, and/or monetary obligations. This study examines the effects of the program's first 3 years, documenting the students’ experiences, and suggests future directions and areas of study related to the impact of virtual research experiences on expanding and diversifying science, technology, engineering, and mathematics.

\"Discerning Experts assesses the assessments that many governments rely on to help guide environmental policy and action. Through their close look at environmental assessments involving acid rain, ozone depletion, and sea level rise, the authors explore how experts deliberate and decide on the scientific facts about problems like climate change. They also seek to understand how the scientists involved make the judgments they do, how the organization and management of assessment activities affects those judgments, and how expertise is identified and constructed.\"--cover

To promote health equity, it is crucial to educate the next generation of healthcare workers about disparities early on during their education. We developed a virtual research program at a medical school in the United States of America with the goal of increasing the awareness of youth about the complexities of health inequities. The program was based on a near-peer mentorship where high school students were coached by medical student mentors under the oversight of an experienced faculty mentor. We evaluated the participants' perspectives about the program using a mixed quantitative and qualitative method. Upon completion of the program, the participants were asked to complete a survey and rate their self-perceived knowledge, efficacy skills and interest in addressing health disparities in the future. Additionally, the participants' perspectives about the program were gathered using open-ended questions and analyzed using thematic analysis. Our preliminary findings indicate that the program enhanced the participants' knowledge about the complexities of health disparities and their motivation to address them in the future. The near-peer mentorship model was valuable in success of the program. The implications to enhance intrinsic and extrinsic instincts through partnerships among educational settings, underserved communities, policy makers and healthcare agencies is discussed.

\"Get started using Python in data analysis with this compact practical guide. This book includes three exercises and a case study on getting data in and out of Python code in the right format. Learn Data Analysis with Python also helps you discover meaning in the data using analysis and shows you how to visualize it. Each lesson is, as much as possible, self-contained to allow you to dip in and out of the examples as your needs dictate. If you are already using Python for data analysis, you will find a number of things that you wish you knew how to do in Python. You can then take these techniques and apply them directly to your own projects. If you aren't using Python for data analysis, this book takes you through the basics at the beginning to give you a solid foundation in the topic. As you work your way through the book you will have a better of idea of how to use Python for data analysis when you are finished. You will: get data into and out of Python code, prepare the data and its format, find the meaning of the data, visualize the data using iPython.\"--Provided by publisher.

Robust projections and predictions of climate variability and change, particularly at regional scales, rely on the driving processes being represented with fidelity in model simulations. The role of enhanced horizontal resolution in improved process representation in all components of the climate system is of growing interest, particularly as some recent simulations suggest both the possibility of significant changes in large-scale aspects of circulation as well as improvements in small-scale processes and extremes. However, such high-resolution global simulations at climate timescales, with resolutions of at least 50km in the atmosphere and 0.25° in the ocean, have been performed at relatively few research centres and generally without overall coordination, primarily due to their computational cost. Assessing the robustness of the response of simulated climate to model resolution requires a large multi-model ensemble using a coordinated set of experiments. The Coupled Model Intercomparison Project 6 (CMIP6) is the ideal framework within which to conduct such a study, due to the strong link to models being developed for the CMIP DECK experiments and other model intercomparison projects (MIPs). Increases in high-performance computing (HPC) resources, as well as the revised experimental design for CMIP6, now enable a detailed investigation of the impact of increased resolution up to synoptic weather scales on the simulated mean climate and its variability. The High Resolution Model Intercomparison Project (HighResMIP) presented in this paper applies, for the first time, a multi-model approach to the systematic investigation of the impact of horizontal resolution. A coordinated set of experiments has been designed to assess both a standard and an enhanced horizontal-resolution simulation in the atmosphere and ocean. The set of HighResMIP experiments is divided into three tiers consisting of atmosphere-only and coupled runs and spanning the period 1950-2050, with the possibility of extending to 2100, together with some additional targeted experiments. This paper describes the experimental set-up of HighResMIP, the analysis plan, the connection with the other CMIP6 endorsed MIPs, as well as the DECK and CMIP6 historical simulations. HighResMIP thereby focuses on one of the CMIP6 broad questions, \"what are the origins and consequences of systematic model biases?\", but we also discuss how it addresses the World Climate Research Program (WCRP) grand challenges.