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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.

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.

Brazil experiences extreme weather and climate events that cause numerous economic and social losses, and according to climate change projections, these events will increase in intensity and frequency over this century.This study adds to the body of research on Brazil’s climate change by analyzing the historical patterns and projected changes in temperature and precipitation extremes across Brazil using the World Climate Research Program’s Expert Team on Climate Change Detection and Indices framework. This novel approach analyzes climate extreme events over the past four decades (1980–2016) using multiple gridded observation and reanalysis datasets. Furthermore, future changes in climate extremes are analyzed from 20 downscaled Earth System Models (ESMs) at high horizontal resolution (0.25° of latitude/longitude), under two representative concentration pathway scenarios (RCP4.5 and RCP8.5). Projected changes in the extreme indices are analyzed over mid-twenty-first century (2046–2065) and end-of-twenty-first century (2081–2100) relative to the reference period 1986–2005. Results show consistent warming patterns with increasing (decreasing) trends in warm (cold) extremes in the historical datasets. A similar but more intense warm pattern is projected in the mid and end of the twenty-first century. For precipitation indices, observations show an increase in consecutive dry days and a reduction of consecutive wet days over almost all Brazil. The frequency and intensity of extremely wet days over Brazil are expected to increase according to future scenarios. Designing effective adaptation and mitigation measures in response to changes in climate extremes events depends on this improved understanding of how conditions have and are likely to change in the future at regional scales.

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.

The water footprint has developed into a widely-used concept to examine water use and resulting local impacts caused during agricultural and industrial production. Building on recent advancements in the water footprint concept, it can be an effective steering instrument to support, inter alia, achieving sustainable development goals (SDGs) - SDG 6 in particular. Within the research program “Water as a Global Resource” (GRoW), an initiative of the Federal Ministry for Education and Research, a number of research projects currently apply and enhance the water footprint concept in order to identify areas where water is being used inefficiently and implement practical optimization measures (see imprint for more information). With this paper, we aim to raise awareness on the potential of the water footprint concept to inform decision-making in the public and private sectors towards improved water management and achieving the SDGs.

Species, habitats, and ecosystems are increasingly exposed to multiple anthropogenic stressors, fueling a rapidly expanding research program to understand the cumulative impacts of these environmental modifications. Since the 1970s, a growing set of methods has been developed through two parallel, sometimes connected, streams of research within the applied and academic realms to assess cumulative effects. Past reviews of cumulative effects assessment (CEA) methods focused on approaches used by practitioners. Academic research has developed several distinct and novel approaches to conducting CEA. Understanding the suite of methods that exist will help practitioners and academics better address various ecological foci (physiological responses, population impacts, ecosystem impacts) and ecological complexities (synergistic effects, impacts across space and time). We reviewed 6 categories of methods (experimental, meta-analysis, single-species modeling, mapping, qualitative modeling, and multispecies modeling) and examined the ability of those methods to address different levels of complexity. We focused on research gaps and emerging priorities. We found that no single method assessed impacts across the 4 ecological foci and 6 ecological complexities considered. We propose that methods can be used in combination to improve understanding such that multimodel inference can provide a suite of comparable outputs, mapping methods can help prioritize localized models or experimental gaps, and future experiments can be paired from the outset with models they will inform. Las especies, los hábitats y los ecosistemas están cada vez más expuestos a múltiples estresantes antropogénicos, lo que aviva a los programas de investigación de rápida expansión a entender los impactos acumulativos de estas modificaciones ambientales. Desde la década de 1970 se ha desarrollado un conjunto creciente de métodos a partir de dos corrientes paralelas (a veces conectadas) de investigación dentro del ámbito académico y del aplicado para evaluar los efectos acumulativos. Las revisiones pasadas de los métodos de evaluación de los efectos acumulativos (CEA, en inglés) se han enfocado en las estrategias que usan los practicantes. La investigación académica ha desarrollado varias estrategias novedosas y distintivas para realizar CEA. El entendimiento del juego de métodos que existen ayudará a los practicantes y a los académicos a lidiar de mejor manera con varios focos ecológicos (respuestas fisiológicas, impactos poblacionales, impactos al ecosistema) y complejidades ecológicas (efectos sinérgicos, impactos a lo largo del tiempo y el espacio). Revisamos seis categorías de métodos (experimental, meta-análisis, modelado de especie única, mapeo, modelado cualitativo y modelado multi-especie) y examinamos la habilidad de estos métodos para lidiar con niveles diferentes de complejidad. Nos enfocamos en los vacíos de investigación y las prioridades emergentes. Encontramos que ninguno de los métodos evaluó los impactos en los cuatro focos ecológicos y en las seis complejidades ecológicas que se consideraron. Proponemos que los métodos pueden usarse de manera combinada para mejorar el entendimiento de tal manera que la inferencia multi-modelo pueda proporcionar un conjunto de resultados comparables, los métodos de mapeo puedan ayudar a priorizar los modelos localizados o los vacíos experimentales, y los futuros experimentos puedan emparejarse desde el inicio con los modelos a los que informarán. 物神、栖息地和生态系统越来越多地暴露在多重的人类活动所产生的压カ下，这ー现象推动着郅些用于 理解这些环境变化的累积影响的研究项目快速扩展。自20世纪70年代起，在应用和学术的领域，一系列方法被 通过时而平行时而交叉的方式开发出来，用于评估这种累积效应。过去关于累积效应评估方法的综述主要聚焦 于实践者使用的方法，而学术研究也发展出不少独特的新方法来评估累积效应。掌握现有的整套方法可以帮助 应用实践者和学术研究者更好地研究诠释各种生态学焦点(生理响应、种群影响、生态系统影响)以及生态复 杂性（协同作用、跨时空影响) 。我们回顾了六类方法（实验、集合分析、单物种建摸、制图、定性建模和多物 种建模 并分析了这些方法处理不同水平的生态复杂性的能力。我们重点关注ー些研究空缺和新兴的研究热 点发现没有哪种单一的方法可以同时用于包含四个生态学焦点和六类生态学复杂性的累积效应的评估。于是 我们提出可以组合使用不同方法来帮助理解这些问題 这样多模型推论可以提供一系列可以比较的結果，制图 的方法可以帮助优先考虑局部地区的模型和实验的空缺，未来的实验也可以从ー开始就与他们要建立的模型匹 配。

Plants are enormously diverse in their traits and ecological adaptation, even within given ecosystems, such as tropical rainforests. Accounting for this diversity in vegetation models poses serious challenges. Global plant functional trait databases have highlighted general trait correlations across species that have considerably advanced this research program. However, it remains unclear whether trait correlations found globally hold within communities, and whether they extend to drought tolerance traits. For 134 individual plants spanning a range of sizes and life forms (tree, liana, understorey species) within an Amazonian forest, we measured leaf drought tolerance (leaf water potential at turgor loss point, πtlp), together with 17 leaf traits related to various functions, including leaf economics traits and nutrient composition (leaf mass per area, LMA; and concentrations of C, N, P, K, Ca and Mg per leaf mass and area), leaf area, water‐use efficiency (carbon isotope ratio), and time‐integrated stomatal conductance and carbon assimilation rate per leaf mass and area. We tested trait coordination and the ability to estimate πtlp from the other traits through model selection. Performance and transferability of the best predictive model were assessed through cross‐validation. Here πtlp was positively correlated with leaf area, and with N, P and K concentrations per leaf mass, but not with LMA or any other studied trait. Five axes were needed to account for >80% of trait variation, but only three of them explained more variance than expected at random. The best model explained only 30% of the variation in πtlp, and out‐sample predictive performance was variable across life forms or canopy strata, suggesting a limited transferability of the model. Synthesis. We found a weak correlation among leaf drought tolerance and other leaf traits within a forest community. We conclude that higher trait dimensionality than assumed under the leaf economics spectrum may operate among leaves within plant communities, with important implications for species coexistence and responses to changing environmental conditions, and also for the representation of community diversity in vegetation models. We found a weak correlation among leaf drought tolerance and other leaf traits within a forest community. We conclude that higher trait dimensionality than assumed under the leaf economics spectrum may operate among leaves within plant communities, with important implications for species coexistence and responses to changing environmental conditions, and also for the representation of community diversity in vegetation models.

We evaluate the efficacy of microphysics and planetary-boundary-layer (PBL) parametrizations of the Weather Research and Forecasting (WRF) model for simulation of the coverage and intensity (visibility) of fog during the Coastal Fog (C-FOG) Research Program. The C-FOG observations are used for model validation, particularly focusing on offshore and onshore fog events during 13–14 and 28–29 September 2018. Sensitivity experiments with high horizontal (1 km) and vertical (99 levels) resolution are conducted to elicit possible physical processes underlying the fog life-cycle. Various microphysical and PBL parametrizations, as well as empirical algorithms available for visibility calculations, are evaluated. The model coastal fog formation and characteristics strongly depend on the simulated local meteorology (e.g., temperature, relative humidity, mixing ratio, and wind field) and the microphysical parametrization employed. High space–time resolution simulations for fog coverage and visibility based on the Mansell et al. (J Atmos Sci 67(1):171–194, 2010) microphysical parametrization compare better with data vis-à-vis other microphysical parametrizations, although spatial coverage and visibility are still overestimated. The disparities are likely related to uncertainties of model fog microphysical parameters, including the liquid water content (LWC), droplet number concentration (Nd), and aerosol particle size. It is found that (i) visibility algorithms that use both the variables LWC and Nd, instead of only LWC, provide improved fog estimates; (ii) different PBL parametrizations mainly affect the fog onset and dissipation; (iii) the WRF model has improved performance over the ocean than over land, possibly due to homogeneity of the ocean-surface cover.

Since 1990, the U.S. Global Change Research Program has published five cross-sectoral National Climate Assessment (NCA) reports. Federal, state, and local governments, policymakers, and the public employ NCAs to analyze climate risks, impacts, and adaptation and mitigation options. This article surveys the NCA landscape and makes the case for centering environmental justice (EJ) to inform actionable, relevant, and accessible climate change science and responses. Case studies of NCA1 through NCA5, released during the Clinton, Obama, Trump, and Biden presidential administrations, examine the roles of EJ, the conceptual integration of transdisciplinary research efforts, and data equity considerations. The paper concludes with policy recommendations to “center” EJ into climate assessments.

Biological nitrification inhibition (BNI) has already led to several studies mainly focused on underlying molecular mechanisms and applications to agriculture. We argue that it is also important to study BNI more systematically from the ecological and evolutionary points of view to understand its implications for plants and soil nitrifiers as well as its consequences for ecosystems. Therefore, we propose here a dedicated research agenda identifying the most critical research questions: (1) How is BNI distributed across plant phylogeny and why has it been selected? (2) What are the costs-to-benefits balance of producing BNI compounds and the relative impacts on BNI evolution? (3) Can we understand the evolutionary pressures leading to BNI and identify the environmental conditions favorable to BNI plants? (4) How has BNI coevolved with plant preference for ammonium vs. nitrate? (5) Diverse BNI compounds and various inhibition mechanisms have been described, but implications of this diversity are not understood. Does it allow inhibition of various groups of nitrifiers? (6) Does this diversity of BNI compounds increase the efficiency, spatial extension, and duration of BNI effect? (7) What are the impacts of BNI compounds on other soil functions? (8) Can field experiments, coupled to scanning of the diversity of BNI capabilities within plant communities, evaluate whether BNI influences plant-plant competition and plant coexistence? (9) Can field quantification of various nitrogen (N) fluxes assess whether BNI lead to more efficient N cycling with lower losses and hence increased primary production? (10) Can the impact of BNI on N budgets and climate (through its impact on N2O emissions and its indirect impact on carbon budget) be evaluated at the regional scale? We discuss why implementing this research program is crucial both for the sake of knowledge and to develop applications of BNI for agriculture.