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A bstract The identity of Dark Matter (DM) is one of the most active topics in particle physics today. Supersymmetry (SUSY) is an extension of the standard model (SM) that could describe the particle nature of DM in the form of the lightest neutralino in R-parity conserving models. We focus on SUSY models that solve the hierarchy problem with small fine tuning, and where the lightest SUSY particles $$ \left({\tilde{\upchi}}_1^0,{\tilde{\upchi}}_1^{\pm },{\tilde{\upchi}}_2^0\right) $$ χ ˜ 1 0 χ ˜ 1 ± χ ˜ 2 0 are a triplet of higgsino-like states, such that the mass difference $$ \Delta m\left({\tilde{\upchi}}_2^0,{\tilde{\upchi}}_1^0\right) $$ Δ m χ ˜ 2 0 χ ˜ 1 0 is 0.5–50 GeV. We perform a feasibility study to assess the long-term discovery potential for these compressed SUSY models with higgsino-like states, using vector boson fusion (VBF) processes in the context of proton-proton collisions at $$ \sqrt{s} $$ s = 13 TeV, at the CERN Large Hadron Collider. Assuming an integrated luminosity of 3000 fb − 1 , we find that stringent VBF requirements, combined with large missing momentum and one or two low- p T leptons, is effective at reducing the major SM backgrounds, leading to a 5 σ (3 σ ) discovery reach for $$ m\left({\tilde{\upchi}}_2^0\right) $$ m χ ˜ 2 0 < 180 (260) GeV, and a projected 95% confidence level exclusion region that covers $$ m\left({\tilde{\upchi}}_2^0\right) $$ m χ ˜ 2 0 up to 385 GeV, parameter space that is currently unconstrained by other experiments. 

Abstract A set of diagnostics based on simple, statistical relationships between precipitation and the thermodynamic environment in observations is implemented to assess phase 6 of the Coupled Model Intercomparison Project (CMIP6) model behavior with respect to precipitation. Observational data from the Atmospheric Radiation Measurement (ARM) permanent field observational sites are augmented with satellite observations of precipitation and temperature as an observational baseline. A robust relationship across observational datasets between column water vapor (CWV) and precipitation, in which conditionally averaged precipitation exhibits a sharp pickup at some critical CWV value, provides a useful convective onset diagnostic for climate model comparison. While a few models reproduce an appropriate precipitation pickup, most models begin their pickup at too low CWV and the increase in precipitation with increasing CWV is too weak. Convective transition statistics compiled in column relative humidity (CRH) partially compensate for model temperature biases—although imperfectly since the temperature dependence is more complex than that of column saturation. Significant errors remain in individual models and weak pickups are generally not improved. The conditional-average precipitation as a function of CRH can be decomposed into the product of the probability of raining and mean precipitation during raining times (conditional intensity). The pickup behavior is primarily dependent on the probability of raining near the transition and on the conditional intensity at higher CRH. Most models roughly capture the CRH dependence of these two factors. However, compensating biases often occur: model conditional intensity that is too low at a given CRH is compensated in part by excessive probability of precipitation. 

Abstract Conditional instability and the buoyancy of plumes drive moist convection but have a variety of representations in model convective schemes. Vertical thermodynamic structure information from Atmospheric Radiation Measurement (ARM) sites and reanalysis (ERA5), satellite-derived precipitation (TRMM3b42), and diagnostics relevant for plume buoyancy are used to assess climate models. Previous work has shown that CMIP6 models represent moist convective processes more accurately than their CMIP5 counterparts. However, certain biases in convective onset remain pervasive among generations of CMIP modeling efforts. We diagnose these biases in a cohort of nine CMIP6 models with subdaily output, assessing conditional instability in profiles of equivalent potential temperature,θ_e, and saturation equivalent potential temperature,θ_es, in comparison to a plume model with different mixing assumptions. Most models capture qualitative aspects of theθ_esvertical structure, including a substantial decrease with height in the lower free troposphere associated with the entrainment of subsaturated air. We define a “pseudo-entrainment” diagnostic that combines subsaturation and aθ_esmeasure of conditional instability similar to what entrainment would produce under the small-buoyancy approximation. This captures the trade-off between largerθ_eslapse rates (entrainment of dry air) and small subsaturation (permits positive buoyancy despite high entrainment). This pseudo-entrainment diagnostic is also a reasonable indicator of the critical value of integrated buoyancy for precipitation onset. Models with poorθ_e/θ_esstructure (those using variants of the Tiedtke scheme) or low entrainment runs of CAM5, and models with low subsaturation, such as NASA-GISS, lie outside the observational range in this diagnostic. 

Abstract Precipitation sustains life and supports human activities, making its prediction one of the most societally relevant challenges in weather and climate modeling. Limitations in modeling precipitation underscore the need for diagnostics and metrics to evaluate precipitation in simulations and predictions. While routine use of basic metrics is important for documenting model skill, more sophisticated diagnostics and metrics aimed at connecting model biases to their sources and revealing precipitation characteristics relevant to how model precipitation is used are critical for improving models and their uses. This paper illustrates examples of exploratory diagnostics and metrics including 1) spatiotemporal characteristics metrics such as diurnal variability, probability of extremes, duration of dry spells, spectral characteristics, and spatiotemporal coherence of precipitation; 2) process-oriented metrics based on the rainfall–moisture coupling and temperature–water vapor environments of precipitation; and 3) phenomena-based metrics focusing on precipitation associated with weather phenomena including low pressure systems, mesoscale convective systems, frontal systems, and atmospheric rivers. Together, these diagnostics and metrics delineate the multifaceted and multiscale nature of precipitation, its relations with the environments, and its generation mechanisms. The metrics are applied to historical simulations from phases 5 and 6 of the Coupled Model Intercomparison Project. Models exhibit diverse skill as measured by the suite of metrics, with very few models consistently ranked as top or bottom performers compared to other models in multiple metrics. Analysis of model skill across metrics and models suggests possible relationships among subsets of metrics, motivating the need for more systematic analysis to understand model biases for informing model development.