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Sudden changes in energy input from the magnetosphere during geomagnetic storms could drive extreme variability in the ionosphere‐thermosphere system, which in turn affect satellite operations and other modern infrastructure. Joule heating is the main form of magnetospheric energy dissipation in the ionosphere‐thermosphere system, so it is important to know when and where Joule heating will occur. While Joule heating occurs all the time, it can increase rapidly during geomagnetic storms. We investigated the Joule heating profile of the 2013 St Patrick's day storm using the University of Michigan Global Ionosphere‐Thermosphere Model (GITM). Using empirical and data‐assimilated drivers we analyzed when and where intense Joule heating occurred. The timing, location, and sources of interhemispheric asymmetry during this geomagnetic storm are of key interest due to near equinox conditions. Hemispheric comparisons are made between parameters, including solar insolation, total electron content profiles, and Pedersen and Hall conductance profiles, obtained from GITM driven with empirical driven input, versus those driven with data‐assimilated patterns. Further comparisons are made during periods of peak hemispheric Joule heating asymmetry in an effort to investigate their potential sources. Additionally, we compare the consistency of the interhemispheric asymmetry between empirical‐ and data‐assimilated driven simulations to further analyze the role of data‐assimilated drivers on the IT system.

Paleoclimate observations constitute the only constraint on climate behavior prior to the instrumental era. However, such observations only provide indirect (proxy) constraints on physical variables. Proxy system models aim to improve the interpretation of such observations and better quantify their inherent uncertainties. However, existing models are currently scattered in the literature, making their integration difficult. Here, we present a comprehensive modeling framework for proxy systems, named PRYSM. For this initial iteration, we focus on water‐isotope based climate proxies in ice cores, corals, tree ring cellulose, and speleothem calcite. We review modeling approaches for each proxy class, and pair them with an isotope‐enabled climate simulation to illustrate the new scientific insights that may be gained from this framework. Applications include parameter sensitivity analysis, the quantification of archive‐specific processes on the recorded climate signal, and the quantification of how chronological uncertainties affect signal detection, demonstrating the utility of PRYSM for a broad array of climate studies. Key Points: A new modeling framework for paleoclimate proxies is proposed (PRYSM) PRYSM bridges the gap between GCMs and paleoclimate observations PRYSM may improve interpretation and uncertainty quantification of paleodata

The Geospace Environment Modeling (GEM) program regularly issues “community challenges” in which researchers examine a particular space physics phenomenon or geomagnetic activity event, often running numerical models to assess dominant processes and understand the timing and relationship of observed signatures. The GEM Methods and Validation Resource Group helps those GEM focus group leaders running challenges to maximize participation and optimize scientific return from the significant time investment of these endeavors. This article gives a brief history of GEM community challenges and details those best practices that lead to an inclusive and valuable experience. Plain Language Summary Over 30 years ago, a group of space scientists set out to coordinate efforts toward the creation of a community‐wide numerical modeling resource. This led to the formation of the Geospace Environment Modeling program, and one of the regular activities of this program is the instigation of “community challenges.” These challenges typically select a particular geospace activity interval or a physical process and then rally the research community to participate in the analysis of this phenomenon. The practice has led to substantial new knowledge of Earth's space environment and significant advancements in numerical modeling capabilities of this region. Here, we describe the history of these community challenges, highlight the lessons learned, and collect the best practices that maximize participation and optimize scientific return. Key Points A history is presented of the “community challenges” conducted over the past 3 decades within the Geospace Environment Modeling Program Key recommendations and lessons learned from past challenge leaders, as well as suggestions from the research community, are presented Additional resources that might aid in the successful running of a community challenge are given, including a summary of metrics options

Featured paper: See also the Editorial by McDowell et al

Bright sunshine duration (BSD) data are required for simulating biomes using process-based vegetation models. However, monthly global paleoclimate datasets that can be used in paleo data–model comparisons do not necessarily contain BSD or radiation data. Considering the theoretical and practical aspects, the scheme of Yin, X. (1999) is here recommended to estimate monthly time series of relative BSD using only monthly climate and location data. As a case study for the Carpathian Region, the efficiency of both the original and a variant of that scheme is analysed in this paper. The alternative scheme has high applicability in paleoenvironmental studies. Comparison of the estimated and observed BSD data shows that from May to August, the value of relative root mean squared error in more than 90 percent of the study area does not exceed the threshold of 20 percent, indicating an excellent performance of the original estimation scheme. It is also found that though the magnitude of overestimation for the alternative algorithm is significant in the winter period, the proposed method performs similarly well in the growing season as the original. Furthermore, concerning modelling the distribution of biomes, simulation experiments are performed to assess the effects of modifying some configuration settings: (a) the generation of relative BSD data, and (b) the algorithm used to create quasi-daily weather data from the monthly values. Under both the recent humidity conditions of the study region and the spatial resolution of the climate dataset used, the results can be considered sufficiently robust, regardless of the configuration settings tested. Thus, using monthly temperature and precipitation climatologies, the spatial distribution of biomes can be properly simulated with the configuration settings proposed here.

We assess whether magnetically and electrically self‐consistent ring current simulations can account simultaneously for in situ magnetic field and ion flux measurements in the inner magnetosphere during the large 10 August 2000 storm (min Dst = −107 nT). We use the Rice Convection Model–Equilibrium (RCM‐E) and drive it with time‐dependent magnetic field, electric field, and plasma boundary conditions that are guided by empirical and assimilative models. Comparisons of the simulated and observed magnetic field from Geostationary Operational Environmental Satellites (GOES) and observed proton differential flux spectra from Los Alamos National Laboratory (LANL) satellites are made at geosynchronous orbit (GEO). Similarly, simulated and observed magnetic field and proton density and temperature are compared along the orbit of Polar (r ∼ 1.8–9 RE) for the event. The simulated and observed magnetic field components agree reasonably well at GEO and along the orbit of Polar. However, since the effects of substorm dipolarizations are not explicitly modeled, the simulation fails to reproduce observed sawtooth fluctuations in the magnetic field. Over energies from 1 to 150 keV, the RCM‐E reproduced well the ion dispersion features in the LANL 1994‐084 ion differential flux spectra over energies at GEO and proton densities and temperatures calculated from Polar proton flux measurements. Thus, the RCM‐E simulations can account simultaneously for in situ magnetic field and ion flux measurements for the 10 August 2000 storm. This demonstrates that a self‐consistent model can produce realistic features of the storm time inner magnetosphere. Key Points The RCM‐E and in situ magnetic field agreed well during the 10 August 2000 storm The RCM‐E and in situ ion flux measurements also agreed well during storm event Self‐consistent models can produce realistic features of inner magnetosphere

A new model validation and performance assessment tool is introduced, the sliding threshold of observation for numeric evaluation (STONE) curve. It is based on the relative operating characteristic (ROC) curve technique, but instead of sorting all observations in a categorical classification, the STONE tool uses the continuous nature of the observations. Rather than defining events in the observations and then sliding the threshold only in the classifier/model data set, the threshold is changed simultaneously for both the observational and model values, with the same threshold value for both data and model. This is only possible if the observations are continuous and the model output is in the same units and scale as the observations, that is, the model is trying to exactly reproduce the data. The STONE curve has several similarities with the ROC curve—plotting probability of detection against probability of false detection, ranging from the (1,1) corner for low thresholds to the (0,0) corner for high thresholds, and values above the zero‐intercept unity‐slope line indicating better than random predictive ability. The main difference is that the STONE curve can be nonmonotonic, doubling back in both the x and y directions. These ripples reveal asymmetries in the data‐model value pairs. This new technique is applied to modeling output of a common geomagnetic activity index as well as energetic electron fluxes in the Earth's inner magnetosphere. It is not limited to space physics applications but can be used for any scientific or engineering field where numerical models are used to reproduce observations. Plain Language Summary Scientists often try to reproduce observations with a model, helping them explain the observations by adjusting known and controllable features within the model. They then use a large variety of metrics for assessing the ability of a model to reproduce the observations. One such metric is called the relative operating characteristic (ROC) curve, a tool that assesses a model's ability to predict events within the data. The ROC curve is made by sliding the event‐definition threshold in the model output, calculating certain metrics and making a graph of the results. Here, a new model assessment tool is introduced, called the sliding threshold of observation for numeric evaluation (STONE) curve. The STONE curve is created by sliding the event definition threshold not only for the model output but also simultaneously for the data values. This is applicable when the model output is trying to reproduce the exact values of a particular data set. While the ROC curve is still a highly valuable tool for optimizing the prediction of known and preclassified events, it is argued here that the STONE curve is better for assessing model prediction of a continuous‐valued data set. Key Points A new event‐detection‐based metric for model performance appraisal is given with sliding thresholds in both observational and model values The new metric is like the relative operating characteristic curve but uses continuous observational values, not just categorical status The new metric is used on real‐time model predictions of common geomagnetic activity parameters, demonstrating its features and strengths

Aim To test the hypothesis that dispersal characteristics alone can explain the past migration patterns of Fagus sylvatica and Picea abies observed in southern Scandinavia. Location Scandinavia, Europe. Methods The spreading dynamics of both species were analysed using a quantitative data–model comparison approach. Pollen data recording the arrival of the two species at 24 small forest‐hollow sites distributed across the study area were compared with simulated arrival times. The simulations were based on diffusive spread combined with long‐distance dispersal events. By systematically applying different parameter combinations yielding the desired colonization speeds we could identify values for the long‐distance dispersal component that minimized deviations from the observed arrival times. Results According to the minimization process, the optimal spreading rates were 100 m year−1 for F. sylvatica and 250 m year−1 for P. abies. Simulated dispersal alone could adequately explain the wave‐like spread of P. abies but failed to explain the scattered establishment pattern observed for F. sylvatica in Scandinavia. At the fine scale of stand establishment, local microclimatic conditions or site disturbance might be more important. The estimated spreading rates are high because the species colonized Scandinavia from different geographic directions and the rates slowed when their ranges overlapped. We present new estimates for the distance and frequency of long‐distance dispersal events for our modelled species. Main conclusions Our analyses suggest that the late Holocene spread of P. abies in Scandinavia was fairly rapid and was limited only by biological processes of dispersal, while that of F. sylvatica was limited by other factors probably controlled by site properties. Picea abies has maintained a rapid and constant rate of spread throughout at least the last 4000 years, despite significant changes in climate. There is uncertainty about the precise relationship between P. abies and climate in Scandinavia, so future distributions are not easy to forecast. For F. sylvatica in Scandinavia, site quality appears to have been a limiting factor, so future land use is likely to dictate its future distribution dynamics in combination with climatic factors.

Fire activity has varied globally and continuously since the last glacial maximum (LGM) in response to long-term changes in global climate and shorter-term regional changes in climate, vegetation, and human land use. We have synthesized sedimentary charcoal records of biomass burning since the LGM and present global maps showing changes in fire activity for time slices during the past 21,000 years (as differences in charcoal accumulation values compared to pre-industrial). There is strong broadscale coherence in fire activity after the LGM, but spatial heterogeneity in the signals increases thereafter. In North America, Europe and southern South America, charcoal records indicate less-than-present fire activity during the deglacial period, from 21,000 to *11,000 cal yr BP. In contrast, the tropical latitudes of South America and Africa show greater-than-present fire activity from *19,000 to *17,000 cal yr BP and most sites from Indochina and Australia show greater-than-present fire activity from 16,000 to *13,000 cal yr BP. Many sites indicate greaterthan- present or near-present activity during the Holocene with the exception of eastern North America and eastern Asia from 8,000 to *3,000 cal yr BP, Indonesia and Australia from 11,000 to 4,000 cal yr BP, and southern South America from 6,000 to 3,000 cal yr BP where fire activity was less than present. Regional coherence in the patterns of change in fire activity was evident throughout the post-glacial period. These complex patterns can largely be explained in terms of large-scale climate controls modulated by local changes in vegetation and fuel load.

Proxy‐based reconstructions of long‐term Atlantic tropical cyclone (TC) variability reveal low‐frequency oscillations in regional TC landfalls over the Common Era. However, the limited spatial coverage and increased uncertainty of the proxy records complicates assessments of this feature. Here we present a new multi‐ensemble set of synthetic TCs downscaled from the Last Millennium Reanalysis project, which is based on sea surface temperatures that more accurately reflect past conditions. Throughout ensemble members, there are coherent multi‐centennial shifts in landfalls with persistent intervals of increased (decreased) occurrence along the eastern US concurrent with inverse activity in the southwest Caribbean and Gulf of Mexico, associated with basin‐scale redistributions of storm tracks. The emergent TC‐dipole from modeled climate provides context and support for its presence within proxy‐reconstructions. Furthermore, dipole recurrence across ensembles demonstrates that it arises from sea surface temperature‐informed climate processes. However, timing differences between ensembles indicate that transient atmospheric variability influences dipole position.