More Like this

1. Climatological representation of mesoscale convective systems in a dynamically downscaled climate simulation

   https://doi.org/10.1002/joc.5880  

   Haberlie, Alex M.; Ashley, Walker S. (November 2018, International Journal of Climatology)

   This research assesses the utility and validity of using simulated radar reflectivity to detect potential changes in linear and nonlinear mesoscale convective system (MCS) occurrence in the Midwest United States between the early and late 21st century using convection‐permitting climate simulation output. These data include a control run and a pseudo‐global warming (PGW) run that is based on RCP 8.5. First, using a novel segmentation, classification, and tracking procedure, MCS tracks are extracted from observed and simulated radar reflectivity. Next, a comparison between observed and the control run MCS statistics is performed, which finds a negative summertime bias that agrees with previous work. Using a convolutional neural network to perform probabilistic predictions, the MCS dataset is further stratified into highly organized, quasi‐linear convective systems (QLCSs)—which can include bow echoes, squall lines, and line echo wave patterns—and generally less‐organized, non‐QLCS events. The morphologically stratified data reveal that the negative MCS bias in this region is largely driven by too few QLCSs. Although comparisons between the control run and a PGW run suggest that all MCS events are less common in the future (including QLCS and non‐QLCS events), these changes are not spatially significant, whereas the biases between the control run and observations are spatially significant. A discussion on the importance and challenges of simulating QLCSs in convection‐permitting climate model runs is provided. Finally, potential avenues of exploration are suggested related to the aforementioned issues. 

   more » « less
2. Surface Salinity Under Transitioning Ice Cover in the Canada Basin: Climate Model Biases Linked to Vertical Distribution of Fresh Water

   https://doi.org/10.1029/2021GL094739  

   Rosenblum, E.; Fajber, R.; Stroeve, J. C.; Gille, S. T.; Tremblay, L. B.; Carmack, E. C. (November 2021, Geophysical Research Letters)

   Abstract The Canada Basin has exhibited a significant trend toward a fresher surface layer and thus a more stratified upper‐ocean over the past three decades. State‐of‐the‐art ice‐ocean models, by contrast, tend to simulate a surface layer that is saltier and less stratified than observed. Here, we examine decadal changes to seasonal processes that may contribute to this wide‐reaching model bias using climate model simulations from the Community Earth System Model and below‐ice observations from the Arctic Ice Dynamics Joint Experiment in 1975 and Ice Tethered Profilers in 2006–2012. In contrast to the observations, the models simulate salinity profiles that show relatively little variation between 1975 and 2012. We demonstrate that this bias can be mainly attributed to unrealistically deep vertical mixing in the model, creating a surface layer that is saltier than observed. The results provide insight for climate model improvement with broad implications for Arctic sea ice and ecosystem dynamics. 

   more » « less
3. Precise Statistical Analysis of Classification Accuracies for Adversarial Training

   Javanmard, Adel; Soltanolkotabi, Mahdi (January 2022, The annals of statistics)

   Despite the wide empirical success of modern machine learning algorithms and models in a multitude of applications, they are known to be highly susceptible to seemingly small indiscernible perturbations to the input data known as \emph{adversarial attacks}. A variety of recent adversarial training procedures have been proposed to remedy this issue. Despite the success of such procedures at increasing accuracy on adversarially perturbed inputs or \emph{robust accuracy}, these techniques often reduce accuracy on natural unperturbed inputs or \emph{standard accuracy}. Complicating matters further, the effect and trend of adversarial training procedures on standard and robust accuracy is rather counter intuitive and radically dependent on a variety of factors including the perceived form of the perturbation during training, size/quality of data, model overparameterization, etc. In this paper we focus on binary classification problems where the data is generated according to the mixture of two Gaussians with general anisotropic covariance matrices and derive a precise characterization of the standard and robust accuracy for a class of minimax adversarially trained models. We consider a general norm-based adversarial model, where the adversary can add perturbations of bounded ellp norm to each input data, for an arbitrary p greater than one. Our comprehensive analysis allows us to theoretically explain several intriguing empirical phenomena and provide a precise understanding of the role of different problem parameters on standard and robust accuracies. 

   more » « less
4. Climate predicts geographic and temporal variation in mosquito-borne disease dynamics on two continents

   https://doi.org/10.1038/s41467-021-21496-7  

   Caldwell, Jamie M.; LaBeaud, A. Desiree; Lambin, Eric F.; Stewart-Ibarra, Anna M.; Ndenga, Bryson A.; Mutuku, Francis M.; Krystosik, Amy R.; Ayala, Efraín Beltrán; Anyamba, Assaf; Borbor-Cordova, Mercy J.; et al (December 2021, Nature Communications)

   null (Ed.)

   Abstract Climate drives population dynamics through multiple mechanisms, which can lead to seemingly context-dependent effects of climate on natural populations. For climate-sensitive diseases, such as dengue, chikungunya, and Zika, climate appears to have opposing effects in different contexts. Here we show that a model, parameterized with laboratory measured climate-driven mosquito physiology, captures three key epidemic characteristics across ecologically and culturally distinct settings in Ecuador and Kenya: the number, timing, and duration of outbreaks. The model generates a range of disease dynamics consistent with observed Aedes aegypti abundances and laboratory-confirmed arboviral incidence with variable accuracy (28–85% for vectors, 44–88% for incidence). The model predicted vector dynamics better in sites with a smaller proportion of young children in the population, lower mean temperature, and homes with piped water and made of cement. Models with limited calibration that robustly capture climate-virus relationships can help guide intervention efforts and climate change disease projections. 

   more » « less
5. Has past climate change affected cold‐specialized species differentially through space and time?

   https://doi.org/10.1111/syen.12341  

   Schoville, Sean D.; Bougie, Tierney C.; Dudko, Roman Y.; Medeiros, Matthew J. (December 2018, Systematic Entomology)

   Abstract Quaternary climate change has been strongly linked to distributional shifts and recent species diversification. Montane species, in particular, have experienced enhanced isolation and rapid genetic divergence during glacial fluctuations, and these processes have resulted in a disproportionate number of neo‐endemic species forming in high‐elevation habitats. In temperate montane environments, a general model of alpine population history is well supported, where cold‐specialized species track favourable climate conditions downslope during glacial episodes and upslope during warmer interglacial periods, which leads to a climate‐driven population or species diversification pump. However, it remains unclear how geography mediates distributional changes and whether certain episodes of glacial history have differentially impacted rates of diversification. We address these questions by examining phylogenomic data in a North American clade of flightless, cold‐specialized insects, the ice crawlers (Insecta: Grylloblattodea: Grylloblattidae:Grylloblatta). These low‐vagility organisms have the potential to reveal highly localized refugia and patterns of spatial recolonization, as well as a longer history of in situ diversification. Using continuous phylogeographic analysis of species groups, we show that all species tend to retreat to nearby low‐elevation habitats across western North America during episodes of glaciation, but species at high latitude exhibit larger distributional shifts. Lineage diversification was examined over the course of the Neogene and Quaternary periods, with statistical analysis supporting a direct association between climate variation and diversification rate. Major increases in lineage diversification appear to be correlated with warm and dry periods, rather than with extreme glacial events. Finally, we identify substantial cryptic diversity among ice crawlers, leading to high endemism across their range. This diversity provides new insights into highly localized glacial refugia for cold‐specialized species across western North America. 

   more » « less