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  1. Abstract. Ice growth from vapor deposition is an important process for the evolution of cirrus clouds, but the physics of depositional ice growth at the low temperatures (<235 K) characteristic of the upper troposphere and lower stratosphere is not well understood. Surface attachment kinetics, generally parameterized as a deposition coefficient αD, control ice crystal habit and also may limit growth rates in certain cases, but significant discrepancies between experimental measurements have not been satisfactorily explained. Experiments on single ice crystals have previously indicated the deposition coefficient is a function of temperature and supersaturation, consistent with growth mechanisms controlled by the crystal's surface characteristics. Here we use observations from cloud chamber experiments in the Aerosol Interactions and Dynamics in theAtmosphere (AIDA) aerosol and cloud chamber to evaluate surface kinetic models in realistic cirrus conditions. These experiments have rapidly changing temperature, pressure, and ice supersaturation such that depositional ice growth may evolve from diffusion limited to surface kinetics limited over the course of a single experiment. In Part 1, we describe the adaptation of a Lagrangian parcel model with the Diffusion Surface Kinetics Ice Crystal Evolution (DiSKICE) model (Zhang and Harrington, 2014) to the AIDA chamber experiments. We compare the observed ice water content and saturation ratios to that derived under varying assumptions for ice surface growth mechanisms for experiments simulating ice clouds between 180 and 235 K and pressures between 150 and 300 hPa. We found that both heterogeneous and homogeneous nucleation experiments at higher temperatures (>205 K) could generally be modeled consistently with either a constant deposition coefficient or the DiSKICE model assuming growth on isometric crystals via abundant surface dislocations. Lower-temperature experiments showed more significant deviations from any depositional growth model, with different ice growth rates for heterogeneous and homogeneous nucleation experiments. 
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  2. Abstract

    Convective available potential energy (CAPE), a metric associated with severe weather, is expected to increase with warming, but we have lacked a framework that describes its changes in the populated midlatitudes. In the tropics, theory suggests mean CAPE should rise following the Clausius–Clapeyron (C–C) relationship at ∼6%/K. In the heterogeneous midlatitudes, where the mean change is less relevant, we show that CAPE changes are larger and can be well‐described by a simple framework based on moist static energy surplus, which is robust across climate states. This effect is highly general and holds across both high‐resolution nudged regional simulations and free‐running global climate models. The simplicity of this framework means that complex distributional changes in future CAPE can be well‐captured by a simple scaling of present‐day data using only three parameters.

     
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  3. Abstract Convective available potential energy (CAPE) is of strong interest in climate modeling because of its role in both severe weather and in model construction. Extreme levels of CAPE (>2000 J kg −1 ) are associated with high-impact weather events, and CAPE is widely used in convective parameterizations to help determine the strength and timing of convection. However, to date few studies have systematically evaluated CAPE biases in models in a climatological context, and none have addressed bias in the high tail of CAPE distributions. This work compares CAPE distributions in ~200 000 summertime proximity soundings from four sources: the observational radiosonde network [Integrated Global Radiosonde Archive (IGRA)], 0.125° reanalyses (ERA-Interim and ERA5), and a 4-km convection-permitting regional WRF simulation driven by ERA-Interim. Both reanalyses and the WRF Model consistently show too-narrow distributions of CAPE, with the high tail (>90th percentile) systematically biased low by up to 10% in surface-based CAPE and even more in alternate CAPE definitions. This “missing tail” corresponds to the most impacts-relevant conditions. CAPE bias in all datasets is driven by surface temperature and humidity: reanalyses and the WRF Model underpredict observed cases of extreme heat and moisture. These results suggest that reducing inaccuracies in land surface and boundary layer models is critical for accurately reproducing CAPE. 
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  4. Abstract. In situ measurements in the climatically important upper troposphere–lower stratosphere (UTLS) are critical for understanding controls on cloud formation, the entry of water into the stratosphere, and hydration–dehydration of the tropical tropopause layer.Accurate in situ measurement of water vapor in the UTLS however is difficult because of low water vapor concentrations (<5 ppmv) and a challenging low temperature–pressure environment.The StratoClim campaign out of Kathmandu, Nepal, in July and August 2017, which made the first high-altitude aircraft measurements in the Asian Summer Monsoon (ASM), also provided an opportunity to intercompare three in situ hygrometers mounted on the M-55 Geophysica: ChiWIS (Chicago Water Isotope Spectrometer), FISH (Fast In situ Stratospheric Hygrometer), and FLASH (Fluorescent Lyman-α Stratospheric Hygrometer).Instrument agreement was very good, suggesting no intrinsic technique-dependent biases: ChiWIS measures by mid-infrared laser absorption spectroscopy and FISH and FLASH by Lyman-α induced fluorescence.In clear-sky UTLS conditions (H2O<10 ppmv), mean and standard deviations of differences in paired observations between ChiWIS and FLASH were only (-1.4±5.9) % and those between FISH and FLASH only (-1.5±8.0) %.Agreement between ChiWIS and FLASH for in-cloud conditions is even tighter, at (+0.7±7.6) %.Estimated realized instrumental precision in UTLS conditions was 0.05, 0.2, and 0.1 ppmv for ChiWIS, FLASH, and FISH, respectively.This level of accuracy and precision allows the confident detection of fine-scale spatial structures in UTLS water vapor required for understanding the role of convection and the ASM in the stratospheric water vapor budget. 
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  5. null (Ed.)
    Abstract. High-altitude cirrus clouds are climatically important: their formationfreeze-dries air ascending to the stratosphere to its final value, and theirradiative impact is disproportionately large. However, their formation andgrowth are not fully understood, and multiple in situ aircraft campaigns haveobserved frequent and persistent apparent water vapor supersaturations of5 %–25 % in ultracold cirrus (T<205 K), even in the presence of iceparticles. A variety of explanations for these observations have been putforth, including that ultracold cirrus are dominated by metastable ice whosevapor pressure exceeds that of hexagonal ice. The 2013 IsoCloud campaign atthe Aerosol Interaction and Dynamics in the Atmosphere (AIDA) cloud andaerosol chamber allowed explicit testing of cirrus formation dynamics atthese low temperatures. A series of 28 experiments allows robust estimationof the saturation vapor pressure over ice for temperatures between 189 and235 K, with a variety of ice nucleating particles. Experiments are rapidenough (∼10 min) to allow detection of any metastable ice that mayform, as the timescale for annealing to hexagonal ice is hours or longer overthe whole experimental temperature range. We show that in all experiments,saturation vapor pressures are fully consistent with expected values forhexagonal ice and inconsistent with the highest values postulated formetastable ice, with no temperature-dependent deviations from expectedsaturation vapor pressure. If metastable ice forms in ultracold cirrusclouds, it appears to have a vapor pressure indistinguishable from that ofhexagonal ice to within about 4.5 %. 
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  6. Abstract. Concerns about food security under climate change motivate efforts to better understand future changes in crop yields.Process-based crop models, which represent plant physiological and soil processes, are necessary tools for this purpose since they allow representing future climate and management conditions not sampled in the historical record and new locations to which cultivation may shift.However, process-based crop models differ in many critical details, and their responses to different interacting factors remain only poorly understood.The Global Gridded Crop Model Intercomparison (GGCMI) Phase 2 experiment, an activity of the Agricultural Model Intercomparison and Improvement Project (AgMIP), is designed to provide a systematic parameter sweep focused on climate change factors and their interaction with overall soil fertility, to allow both evaluating model behavior and emulating model responses in impact assessment tools.In this paper we describe the GGCMI Phase 2 experimental protocol and its simulation data archive.A total of 12 crop models simulate five crops with systematic uniform perturbations of historical climate, varying CO2, temperature, water supply, and applied nitrogen (“CTWN”) for rainfed and irrigated agriculture, and a second set of simulations represents a type of adaptation by allowing the adjustment of growing season length.We present some crop yield results to illustrate general characteristics of the simulations and potential uses of the GGCMI Phase 2 archive.For example, in cases without adaptation, modeled yields show robust decreases to warmer temperatures in almost all regions, with a nonlinear dependence that means yields in warmer baseline locations have greater temperature sensitivity.Inter-model uncertainty is qualitatively similar across all the four input dimensions but is largest in high-latitude regions where crops may be grown in the future. 
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