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In Part II, two classes of vertical motions, fixed (associated with vertically propagating gravity waves tied to flow over topography) and transient (associated primarily with vertical wind shear and conditional instability within passing weather systems), were diagnosed over the Payette River basin of Idaho during the Seeded and Natural Orographic Wintertime Clouds: The Idaho Experiment (SNOWIE). This paper compares vertical motions retrieved from airborne Doppler radial velocity measurements with those from a 900-m-resolution model simulation to determine the impact of transient vertical motions on trajectories of ice particles initiated by airborne cloud seeding. An orographic forcing index, developed to compare vertical motion fields retrieved from the radar with the model, showed that fixed vertical motions were well resolved by the model while transient vertical motions were not. Particle trajectories were calculated for 75 cross-sectional pairs, each differing only by the observed and modeled vertical motion field. Wind fields and particle terminal velocities were otherwise identical in both trajectories so that the impact of transient vertical circulations on particle trajectories could be isolated. In 66.7% of flight-leg pairs, the distance traveled by particles in the model and observations differed by less than 5 km with transient features having minimal impact. In 9.3% of the pairs, model and observation trajectories landed within the ideal target seeding elevation range (>2000 m), whereas, in 77.3% of the pairs, both trajectories landed below the ideal target elevation. Particles in the observations and model descended into valleys on the mountains’ lee sides in 94.2% of cases in which particles traveled less than 37 km.

Freezing precipitation has major consequences for ground and air transportation, the health of citizens, and power networks. Previous studies using coarse resolution climate models have shown a northward migration of freezing rain in the future. Increased model resolution can better define local topography leading to improved representation of conditions that are favorable for freezing rain. The goal of this study is to examine the climatology and characteristics of future freezing rain events using very-high resolution climate simulations. Historical and pseudo-global warming simulations with a 4-km horizontal grid length were used and compared with available observations. Simulations revealed a northerly shift of freezing rain occurrence, and an increase in the winter. Freezing rain was still shown to occur in the Saint-Lawrence River Valley in a warmer climate, primarily due to stronger wind channeling. Up to 50% of the future freezing rain events also occurred in present day climate within 12 h of each other. In northern Maine, they are typically shorter than 6 h in current climate and longer than 6 h in warmer conditions due to the onset of precipitation during low-pressure systems occurrences. The occurrence of freezing rain also locally increases slightly north of Québec City in a warmer climate because of freezing rain that is produced by warm rain processes. Overall, the study shows that high-resolution regional climate simulations are needed to study freezing rain events in warmer climate conditions, because high horizontal resolutions better define small-scale topographic features and local physical mechanisms that have an influence on these events.

Methods to improve the representation of hail in the Thompson-Eidhammer microphysics scheme are explored. A new two-moment and predicted density graupel category is implemented into the Thompson-Eidhammer scheme. Additionally, the one-moment graupel category’s intercept parameter is modified, based on hail observations, to shift the properties of the graupel category to become more hail-like since the category is designed to represent both graupel and hail. Finally, methods to diagnose maximum expected hail size at the surface and aloft are implemented. The original Thompson-Eidhammer version, the newly implemented two-moment and predicted density graupel version, and the modified (to be more hail-like) one-moment version are evaluated using a case that occurred during the Plains Elevated Convection at Night (PECAN) field campaign, during which hail-producing storms merged into a strong mesoscale convective system. The three versions of the scheme are evaluated for their ability to predict hail sizes compared to observed hail sizes from storm reports and estimated from radar, their ability to predict radar reflectivity signatures at various altitudes, and their ability to predict cold-pool features like temperature and wind speed. One key benefit of using the two-moment and predicted density graupel category is that the simulated reflectivity values in the upper-levels of discrete storms are clearly improved. This improvement coincides with a significant reduction in the areal extent of graupel aloft, also seen when using the updated one-moment scheme. The two-moment and predicted density graupel scheme is also better able to predict a wide variety of hail sizes at the surface, including large (> 2-inch diameter) hail that was observed during this case.

In this study, a state-of-the-art microphysical model using a Lagrangian-particle-based direct numerical simulation framework is presented to examine the growth of ice particles in turbulent mixed-phase clouds. By tracking the interactions between individual ice, droplets, and turbulence at the native scales, the model offers new insights into the microphysical processes taking place in mixed-phase clouds at sub-meter-length scales. This paper examines the conditions that favor effective ice growth in the cloud-top generating cells (GCs), which are small regions of enhanced radar reflectivity near cloud tops. GCs are commonly observed in many types of mixed-phase clouds and play a critical role in producing precipitation from rain or snow. Investigations over a range of environmental (macrophysical and turbulent) and microphysical conditions (ice number concentrations) that distinguish GCs from their surrounding cloudy air were conducted. Results show that high liquid water content (LWC) or high relative humidity (RH) is critical for effective ice growth and the maintenance of mixed-phase conditions. As a result, GCs with high LWC and high RH provide favorable conditions for rapid ice growth. When the ice number concentration is below 1 cm−3, which is typical in mixed-phase clouds, a high LWC is needed for the formation of large ice particles. The study also found that supersaturation fluctuations induced by small-scale turbulent mixing have a negligible effect on the mean particle radius, but they can substantially broaden the size spectra, affecting the subsequent collection process.

A dry-air intrusion induced by the tropopause folding split the deep cloud into two layers resulting in a shallow orographic cloud with a supercooled liquid cloud top at around −15°C and an ice cloud above it on 19 January 2017 during the Seeded and Natural Orographic Wintertime Clouds: The Idaho Experiment (SNOWIE). The airborne AgI seeding of this case was simulated by the WRF Weather Modification (WRF-WxMod) Model with different configurations. Simulations at different grid spacing, driven by different reanalysis data, using different model physics were conducted to explore the ability of WRF-WxMod to capture the properties of natural and seeded clouds. The detailed model–observation comparisons show that the simulation driven by ERA5 data, using Thompson–Eidhammer microphysics with 30% of the CCN climatology, best captured the observed cloud structure and supercooled liquid water properties. The ability of the model to correctly capture the wind field was critical for successful simulation of the seeding plume locations. The seeding plume features and ice number concentrations within them from the large-eddy simulations (LES) are in better agreement with observations than non-LES runs mostly due to weaker AgI dispersion associated with the finer grid spacing. Seeding effects on precipitation amount and impacted areas from LES seeding simulations agreed well with radar-derived values. This study shows that WRF-WxMod is able to simulate and quantify observed features of natural and seeded clouds given that critical observations are available to validate the model. Observation-constrained seeding ensemble simulations are proposed to quantify the AgI seeding impacts on wintertime orographic clouds.Significance StatementRecent observational work has demonstrated that the impact of airborne glaciogenic seeding of orographic supercooled liquid clouds is detectable and can be quantified in terms of the extra ground precipitation. This study aims, for the first time, to simulate this seeding impact for one well-observed case. The stakes are high: if the model performs well in this case, then seasonal simulations can be conducted with appropriate configurations after validations against observations, to determine the impact of a seeding program on the seasonal mountain snowpack and runoff, with more fidelity than ever. High–resolution weather simulations inherently carry uncertainty. Within the envelope of this uncertainty, the model compares very well to the field observations.

Drylines are atmospheric boundaries separating dry from moist air that can initiate convection. Potential changes in the location, frequency, and characteristics of drylines in future climates are unknown. This study applies a multi-parametric algorithm to objectively identify and characterize the dryline in North America using convection-permitting regional climate model simulations with 4-km horizontal grid spacing for 13-years under a historical and a pseudo-global warming climate projection by the end of the century. The dryline identification is successfully achieved with a set of standardized algorithm parameters across the lee side of the Rocky Mountains from the Canadian Rockies to the Sierra Madres in Mexico. The dryline is present 27% of the days at 00 UTC between April and September in the current climate, with a mean humidity gradient magnitude of 0.16 g−1 kg−1 km−1. The seasonal cycle of drylines peak around April and May in the southern Plains, and in June and July in the northern Plains. In the future climate, the magnitude and frequency of drylines increase 5% and 13%, correspondingly, with a stronger intensification southward. Future drylines strengthen during their peak intensity in the afternoon in the Southern U.S. and Northeast Mexico. Drylines also show increasing intensities in the morning with future magnitudes that are comparable to peak intensities found in the afternoon in the historical climate. Furthermore, an extension of the seasonality of intense drylines could produce end-of-summer drylines that are as strong as mid-summer drylines in the current climate. This might affect the seasonality and the diurnal cycle of convective activity in future climates, challenging weather forecasting and agricultural planning.

This study examines current and future western U.S. snowfall and snowpack through current and future climate simulations with a 4-km horizontal grid spacing cloud permitting regional climate model over the entire CONtinental U.S. for a 13-year period between 2001 and 2013. At this horizontal resolution, the spatiotemporal distribution of the orographic snowfall and snowpack is well captured partly due to the ability of the model to realistically simulate mesoscale and microphysical features such as orographically induced updrafts driving clouds and precipitation. The historical simulation well captures the observed snowfall and snowpack amounts and pattern in the western U.S. The future climate simulation uses the Pseudo-Global Warming approach, taking the climate change signal from CMIP5 multi-model ensemble-mean difference between 2070–2099 and 1976–2005. The results show that the thermodynamic impacts of climate change in the western U.S. can be characterized considering mountain ranges in two distinct geographic regions: the mountain ranges close to the Pacific Ocean (coastal ranges) and those in the inter-mountain west. Climate change out to 2100 significantly impacts all aspects of the water cycle, with pronounced climate change response in the coastal ranges. A notable result is that the snowpack in the Pacific Northwest is predicted to decrease by ~ 70% by 2100. Trends of this magnitude have already been observed in the historical data and in previous studies. The current Pseudo Global Warming future climate simulation and previous global climate simulations all suggest that these trends will continue to the point that most snowpack will be gone by 2100 in the Pacific Northwest for the most aggressive RCP8.5 climate scenario, even if annual precipitation increases by 10%. Future work will focus on extending the current convective permitting results to a full climate change simulation allowing for dynamical changes in the flow.

To better understand the historical and projected future climatology and hydrologic impacts of rainfall over the Hawaiian Islands, regional climate simulations over the main Hawaiian Islands were conducted for two 10-year periods using the Weather Research and Forecasting (WRF) model in two nested domains. The first (historical) simulation was driven by the ERA-Interim global reanalysis data and observed sea surface temperature from October 2002 to September 2012. The second simulation used the Pseudo Global Warming (PGW) method to perturb historical conditions with climate change signals taken from global climate model averages. Eighty-one vertical layers were used to better resolve the trade wind inversion (TWI). Results show that the historical simulation reproduces the mean surface temperature, relative humidity, and winds with low biases and high spatial correlations. Additionally, for the historical simulation, the aggregated daily and hourly rainfall probability density functions (PDFs) and rainfall spatial-temporal distributions were well simulated, likely because WRF captured the TWI properties well. For the PGW (future) simulation, the mean rainfall increased by 2.2% in the dry season and 12.5% in the wet season; the average annual increase was 7.8%. The TWI base height decreases in future dry seasons leading to reduced rainfall on the upwind sides of high mountains. Interactions between the complex terrain and the TWI led to heterogeneous changes in patterns of future rainfall across Hawai‘i, including more than a 20% increase in extreme rates in the dry season and a ~ 10% increase during the wet season.

Cloud seeding has been used as one water management strategy to overcome the increasing demand for water despite decades of inconclusive results on the efficacy of cloud seeding. In this study snowfall accumulation from glaciogenic cloud seeding is quantified based on snow gauge and radar observations from three days in January 2017, when orographic clouds in the absent of natural precipitation were seeded with silver iodide (AgI) in the Payette basin of Idaho during the Seeded and Natural Orographic Wintertime Clouds: The Idaho Experiment (SNOWIE). On each day, a seeding aircraft equipped with AgI flares flew back and forth on a straight-line flight track producing a zig-zag pattern representing two to eight lines of clouds visible through enhancements in radar reflectivity. As these seeding lines started to form precipitation, they passed over several snow gauges and through the radar observational domain. For the three cases presented here, precipitation gauges measured increases between 0.05-0.3 mm as precipitation generated by cloud seeding pass over the instruments. A variety of relationships between radar reflectivity factor and liquid equivalent snowfall rate were used to quantify snowfall within the radar observation domain. For the three cases, snowfall occurred within the radar observational domain between 25 -160 min producing a total amount of water generated by cloud seeding ranging from 123,220 to 339,540 m3 using the best-match Ze-S relationship. Uncertainties in radar reflectivity estimated snowfall are provided by considering not only the best-match Ze-S relationship but also an ensemble of Ze-S relationships based on the range of coefficients published from previous studies and then examining the percentile of snowfall estimates based on all of the Ze-S relationships within the ensemble. Considering the interquartile range and 5th/95th percentiles, uncertainties in total amount of water generated by cloud seeding can range between 20-45% compared to the best-math estimates. These results provide new insights towards understanding how cloud seeding impacts precipitation and its distribution across a region.

Takeoff and landing maneuvers can be particularly hazardous at airports surrounded by complex terrain. To address this, the Federal Aviation Administration has developed a Precipitous Terrain classification, as a way to impose more restrictive terrain clearances in the vicinity of complex terrain and to mitigate possible altimeter errors and pilot control problems experienced while executing instrument approach procedures. The current Precipitous Point Value (PPV) algorithm relies on the terrain characteristics within a local area of 2 NM, and is therefore static in time. In this work, we investigate the role of meteorological effects leading to potential aviation hazards over complex terrain, namely turbulence, altimeter setting errors and density altitude deviations. To that end, we combine observations with high-resolution numerical weather forecasts within a 2° × 2° region over the Rocky Mountains in Colorado, containing three airports that are surrounded by Precipitous Terrain. Both available turbulence reports and model’s turbulence forecasts show little correlation with the PPV algorithm for the region analyzed, indicating that the static terrain characteristics cannot generally be used to reliably capture hazardous low-level turbulence events. Altimeter setting errors and density altitude effects are also found to be only very weakly correlated with the PPV algorithm. Altimeter setting errors contribute to hazardous conditions mainly during cold seasons, driven by synoptic weather systems, while density altitude effects are on the contrary predominantly present during the spring and summer months, and follow a very well-marked diurnal evolution modulated by surface radiative effects. These findings demonstrate the effectiveness of high-resolution weather forecast information in determining aviation-relevant hazardous conditions over complex terrain.

Reductive soil disinfestation (RSD) and soil solarization (SS) were evaluated based on environmental factors, microbiome, and suppression of Fusarium oxysporum in a tomato field soil. Soil environmental factors (moisture content, electric conductivity, pH, and redox potential (RP)) were measured during soil disinfestations. All factors were more strongly influenced by RSD than SS. 16S rRNA amplicon sequencing of RSD- and SS-treated soils was performed. The bacterial communities were taxonomically and functionally distinct depending on treatment methods and periods and significantly correlated with pH and RP. Fifty-four pathways predicted by PICRUSt2 (third level in MetaCyc hierarchy) were significantly different between RSD and SS. Quantitative polymerase chain reaction demonstrated that both treatments equally suppressed F. oxysporum. The growth and yield of tomato cultivated after treatments were similar between RSD and SS. RSD and SS shaped different soil bacterial communities, although the effects on pathogen suppression and tomato plant growth were comparable between treatments. The existence of pathogen-suppressive microbes, other than Clostridia previously reported to have an effect, was suggested. Comparison between RSD and SS provides new aspects of unknown disinfestation patterns and the usefulness of SS as an alternative to RSD.

Nodal positive lung adenocarcinoma includes wide range of survival. Several methods for the classification of nodal-positive lung cancer have been proposed. However, classification considering the impact of targetable genetic variants are lacking. The possibility of genetic variants for the better stratification of nodal positive lung adenocarcinoma was estimated. Mutations of 36 genes between primary sites and metastatic lymph nodes (LNs) were compared using next-generation sequencing. Subsequently, mutations in EGFR and BRAF, rearrangements in ALK and ROS1 were evaluated in 69 resected pN1–2M0 adenocarcinoma cases. Recurrence-free survival (RFS), post-recurrence survival (PRS), and overall survival (OS) were evaluated with respect to targetable variants and tyrosine kinase inhibitor (TKI) therapy after recurrence. About 90% of variants were shared and allele frequencies were similar between primary and metastatic sites. In 69 pN1–2M0 cases, EGFR/ALK were positive in primary sites of 39 cases and same EGFR/ALK variants were confirmed in metastatic LNs of 96.7% tissue-available cases. Multivariate analyses indicated positive EGFR/ALK status was associated with worse RFS (HR 2.366; 95% CI 1.244–4.500; P = 0.009), and PRS was prolonged in cases receiving TKI therapy (no post-recurrence TKI therapies, HR 3.740; 95% CI 1.449–9.650; P = 0.006). OS did not differ with respect to targetable variants or TKI therapy. Cases harbouring targetable genetic variants had a higher risk of recurrence, but PRS was prolonged by TKI therapy. Classification according to the targetable genetic status provides a basis for predicting recurrence and determining treatment strategies after recurrence.

The Seeded and Natural Orographic Wintertime Clouds: The Idaho Experiment (SNOWIE) project aims to study the impacts of cloud seeding on winter orographic clouds. The field campaign took place in Idaho between 7 January and 17 March 2017 and employed a comprehensive suite of instrumentation, including ground-based radars and airborne sensors, to collect in situ and remotely sensed data in and around clouds containing supercooled liquid water before and after seeding with silver iodide aerosol particles. The seeding material was released primarily by an aircraft. It was hypothesized that the dispersal of the seeding material from aircraft would produce zigzag lines of silver iodide as it dispersed downwind. In several cases, unambiguous zigzag lines of reflectivity were detected by radar, and in situ measurements within these lines have been examined to determine the microphysical response of the cloud to seeding. The measurements from SNOWIE aim to address long-standing questions about the efficacy of cloud seeding, starting with documenting the physical chain of events following seeding. The data will also be used to evaluate and improve computer modeling parameterizations, including a new cloud-seeding parameterization designed to further evaluate and quantify the impacts of cloud seeding.

The Wyoming Weather Modification Pilot Project randomized cloud seeding experiment was a crossover statistical experiment conducted over two mountain ranges in eastern Wyoming and lasted for 6 years (2008–13). The goal of the experiment was to determine if cloud seeding of orographic barriers could increase snowfall and snowpack. The experimental design included triply redundant snow gauges deployed in a target–control configuration, covariate snow gauges to account for precipitation variability, and ground-based seeding with silver iodide (AgI). The outcomes of this experiment are evaluated with the statistical–physical experiment design and with ensemble modeling. The root regression ratio (RRR) applied to 118 experimental units provided insufficient statistical evidence (p value of 0.28) to reject the null hypothesis that there was no effect from ground-based cloud seeding. Ensemble modeling estimates of the impact of ground-based seeding provide an alternate evaluation of the 6-yr experiment. The results of the model ensemble approach with and without seeding estimated a mean enhancement of precipitation of 5%, with an inner-quartile range of 3%–7%. Estimating the impact on annual precipitation over these mountain ranges requires results from another study that indicated that approximately 30% of the annual precipitation results from clouds identified as seedable within the seeding experiment. Thus the seeding impact is on the order of 1.5% of the annual precipitation, compared to 1% for the statistical–physical experiment, which was not sufficient to reject the null hypothesis. These results provide an estimate of the impact of ground-based cloud seeding in the Sierra Madre and Medicine Bow Mountains in Wyoming that accounts for uncertainties in both initial conditions and model physics.

The Arctic has been warming faster than the global average during recent decades, and trends are projected to continue through the twenty-first century. Analysis of climate change impacts across the Arctic using dynamical models has almost exclusively been limited to outputs from global climate models or coarser regional climate models. Coarse resolution simulations limit the representation of physical processes, particularly in areas of complex topography and high land-surface heterogeneity. Here, current climate reference and future regional climate model simulations based on the RCP8.5 scenario over Alaska at 4 km grid spacing are compared to identify changes in snowfall and snowpack. In general, results show increases in total precipitation, large decreases in snowfall fractional contribution over 30% in some areas, decreases in snowpack season length by 50–100 days in lower elevations and along the southern Alaskan coastline, and decreases in snow water equivalent. However, increases in snowfall and snowpack of sometimes greater than 20% are evident for some colder northern areas and at the highest elevations in southern Alaska. The most significant changes in snow cover and snowfall fractional contributions occur during the spring and fall seasons. Finally, the spatial pattern of winter temperatures above freezing has small-scale spatial features tied to the topography. Such areas would not be resolved with coarser resolution regional or global climate model simulations.

The NCAR Water System Program has been striving to improve the representation of the water cycle and its future changes in both regional and global models during the past decade. One of our efforts is conducting continental-scale convection-permitting simulations of the current and future climate of North America using the WRF model based atmospheric-hydrological coupling system. The major science objectives of these simulations are: 1) to evaluate the capability of convection-permitting WRF model in capturing orographic precipitation and snow mass balance over the western mountains of North America and convective precipitation in the eastern part of the continent; 2) to assess future changes in seasonal snowfall and snowpack and associated surface hydrological cycles under the CMIP5-projected global warming; 3) to investigate water cycle changes in response to climate warming, including the summertime convective precipitation and associated mesoscale convective storm tracks; and 4) to examine the impact of climate change on severe weather over North America. As such, two phases of convection-permitting climate modeling have been undertaken using 4-km horizontal grid spacing covering most of North America.The phase-one effort involves two 13-year simulations as reported in Liu et al. (2017): 1) a historical simulation with initial and boundary conditions from ERA-interim, and 2) a future climate sensitivity simulation, called pseudo-global warming (PGW), with modified reanalysis-derived initial and boundary conditions by adding the CMIP5 ensemble-mean projected climate change. These WRF-downscaled climate change simulations provide a unique high-resolution dataset to the community for studying one possible scenario of regional climate changes and impacts.Recognizing that only the thermodynamic future climate impacts can be adequately addressed in the PGW approach, the NCAR Water System team has started conducting a second set (phase II) of current and future simulations at 4-km grid spacing over North America. In these simulations, the WRF model is forced using the weather perturbations derived from the NCAR CESM model 6-hourly output plus the reanalysis-based bias-corrected CMIP5 ensemble mean climate as detailed in Dai et al. (2017). The model domain is also expanded northward to include Canada and the Canadian Arctic. Because storm track changes are permitted, these simulations complement the previous PGW simulations, allowing us to address the impact of dynamic changes in the future warmer climate. We will present some preliminary analysis results of these simulations, with focus on the evaluation of the historical simulation and the added value of convection-permitting resolution and mean climate bias corrections.

The year-to-year variability of the blooming phenology of cherry trees is important as a proxy climate indicator and strongly affects cultural ecosystem services. Observation of blooming phenology at multiple points requires a simple and flexible approach. We examined changes in the canopy gap fraction extracted from binarized upward images taken periodically beneath a Cerasus × yedoensis 'Somei-yoshino' tree. The gap fraction decreased rapidly after the start of bloom, reached a minimum value at full bloom, and began to increase again, but then decreased rapidly during leaf flush. These changes reflect the phenology of blooming and leaf flush after flower drop of 'Somei-yoshino'. These characteristics allow detection of the year-to-year variability of the bloom and leaf-flush phenology of cherry and other deciduous tree species that show the same patterns.

Destructive and costly flooding can occur when warm storm systems deposit substantial rain on extensive snowcover1–6, as observed in February 2017 with the Oroville Dam crisis in California7. However, decision-makers lack guidance on how such rain-on-snow (ROS) flood risk may respond to climate change. Here, daily ROS events with flood-generating potential8 are simulated over western North America for a historical (2000–2013) and future (forced under Representative Concentration Pathway 8.59) period with the Weather Research and Forecasting model; 4 km resolution allows the basin-scale ROS flood risk to be assessed. In the warmer climate, we show that ROS becomes less frequent at lower elevations due to snowpack declines, particularly in warmer areas (for example, the Pacific maritime region). By contrast, at higher elevations where seasonal snowcover persists, ROS becomes more frequent due to a shift from snowfall to rain. Accordingly, the water available for runoff10 increases for 55% of western North American river basins, with corresponding increases in flood risk of 20–200%, the greatest changes of which are projected for the Sierra Nevada, the Colorado River headwaters and the Canadian Rocky Mountains. Thus, flood control and water resource planning must consider ROS to fully quantify changes in flood risk with anthropogenic warming.

Tropical cyclones have enormous costs to society through both loss of life and damage to infrastructure. There is good reason to believe that such storms will change in the future as a result of changes in the global climate system and that such changes may have important socioeconomic implications. Here a high-resolution regional climate modeling experiment is presented using the Weather Research and Forecasting (WRF) Model to investigate possible changes in tropical cyclones. These simulations were performed for the period 2001–13 using the ERA-Interim product for the boundary conditions, thus enabling a direct comparison between modeled and observed cyclone characteristics. The WRF simulation reproduced 30 of the 32 named storms that entered the model domain during this period. The model simulates the tropical cyclone tracks, storm radii, and translation speeds well, but the maximum wind speeds simulated were less than observed and the minimum central pressures were too large. This experiment is then repeated after imposing a future climate signal by adding changes in temperature, humidity, pressure, and wind speeds derived from phase 5 of the Coupled Model Intercomparison Project (CMIP5). In the current climate, 22 tracks were well simulated with little changes in future track locations. These simulations produced tropical cyclones with faster maximum winds, slower storm translation speeds, lower central pressures, and higher precipitation rates. Importantly, while these signals were statistically significant averaged across all 22 storms studied, changes varied substantially between individual storms. This illustrates the importance of using a large ensemble of storms to understand mean changes.

Novel high-resolution convection-permitting regional climate simulations over the US employing the pseudo-global warming approach are used to investigate changes in the convective population and thermodynamic environments in a future climate. Two continuous 13-year simulations were conducted using (1) ERA-Interim reanalysis and (2) ERA-Interim reanalysis plus a climate perturbation for the RCP8.5 scenario. The simulations adequately reproduce the observed precipitation diurnal cycle, indicating that they capture organized and propagating convection that most climate models cannot adequately represent. This study shows that weak to moderate convection will decrease and strong convection will increase in frequency in a future climate. Analysis of the thermodynamic environments supporting convection shows that both convective available potential energy (CAPE) and convective inhibition (CIN) increase downstream of the Rockies in a future climate. Previous studies suggest that CAPE will increase in a warming climate, however a corresponding increase in CIN acts as a balancing force to shift the convective population by suppressing weak to moderate convection and provides an environment where CAPE can build to extreme levels that may result in more frequent severe convection. An idealized investigation of fundamental changes in the thermodynamic environment was conducted by shifting a standard atmospheric profile by ± 5 °C. When temperature is increased, both CAPE and CIN increase in magnitude, while the opposite is true for decreased temperatures. Thus, even in the absence of synoptic and mesoscale variations, a warmer climate will provide more CAPE and CIN that will shift the convective population, likely impacting water and energy budgets on Earth.

Mesoscale convective system (MCS)-organized convective storms with a size of ~100 km have increased in frequency and intensity in the USA over the past 35 years 1 , causing fatalities and economic losses 2 . However, their poor representation in traditional climate models hampers the understanding of their change in the future 3 . Here, a North American-scale convection-permitting model which is able to realistically simulate MSCs 4 is used to investigate their change by the end-of-century under RCP8.5 (ref. 5 ). A storm-tracking algorithm 6 indicates that intense summertime MCS frequency will more than triple in North America. Furthermore, the combined effect of a 15–40% increase in maximum precipitation rates and a significant spreading of regions impacted by heavy precipitation results in up to 80% increases in the total MCS precipitation volume, focussed in a 40 km radius around the storm centre. These typically neglected increases substantially raise future flood risk. Current investments in long-lived infrastructures, such as flood protection and water management systems, need to take these changes into account to improve climate-adaptation practices. Limitations with climate models have previously prevented accurate diagnosis of future changes in mesoscale convective systems (MCSs). A convection-permitting model now indicates that summer MCSs will triple by 2100 in the United States, with a corresponding increase in rainfall rates and areal extent.

Deep convection is a key process in the climate system and the main source of precipitation in the tropics, subtropics, and mid-latitudes during summer. Furthermore, it is related to high impact weather causing floods, hail, tornadoes, landslides, and other hazards. State-of-the-art climate models have to parameterize deep convection due to their coarse grid spacing. These parameterizations are a major source of uncertainty and long-standing model biases. We present a North American scale convection-permitting climate simulation that is able to explicitly simulate deep convection due to its 4-km grid spacing. We apply a feature-tracking algorithm to detect hourly precipitation from Mesoscale Convective Systems (MCSs) in the model and compare it with radar-based precipitation estimates east of the US Continental Divide. The simulation is able to capture the main characteristics of the observed MCSs such as their size, precipitation rate, propagation speed, and lifetime within observational uncertainties. In particular, the model is able to produce realistically propagating MCSs, which was a long-standing challenge in climate modeling. However, the MCS frequency is significantly underestimated in the central US during late summer. We discuss the origin of this frequency biases and suggest strategies for model improvements.

Climate models project increasing precipitation intensity but decreasing frequency as greenhouse gases increase. However, the exact mechanism for the frequency decrease remains unclear. Here we investigate this by analyzing hourly data from regional climate change simulations with 4 km grid spacing covering most of North America using the Weather Research and Forecasting model. The model was forced with present and future boundary conditions, with the latter being derived by adding the CMIP5 19-model ensemble mean changes to the ERA-interim reanalysis. The model reproduces well the observed seasonal and spatial variations in precipitation frequency and histograms, and the dry interval between rain events over the contiguous US. Results show that overall precipitation frequency indeed decreases during the warm season mainly due to fewer light-moderate precipitation (0.1 10 mm/h) events increase. Dry spells become longer and more frequent, together with a reduction in time-mean relative humidity (RH) in the lower troposphere during the warm season. The increased dry hours and decreased RH lead to a reduction in overall precipitation frequency and also for light-moderate precipitation events, while water vapor-induced increases in precipitation intensity and the positive latent heating feedback in intense storms may be responsible for the large increase in intense precipitation. The size of intense storms increases while their number decreases in the future climate, which helps explain the increase in local frequency of heavy precipitation. The results generally support a new hypothesis for future warm-season precipitation: each rainstorm removes ≥7% more moisture from the air per 1 K local warming, and surface evaporation and moisture advection take slightly longer than currently to replenish the depleted moisture before the next storm forms, leading to longer dry spells and a reduction in precipitation frequency, as well as decreases in time-mean RH and vertical motion.

Climate downscaling using regional climate models (RCMs) has been widely used to generate local climate change information needed for climate change impact assessments and other applications. Six-hourly data from individual simulations by global climate models (GCMs) are often used as the lateral forcing for the RCMs. However, such forcing often contains both internal variations and externally-forced changes, which complicate the interpretation of the downscaled changes. Here, we describe a new approach to construct representative forcing for RCM-based climate downscaling and discuss some related issues. The new approach combines the transient weather signal from one GCM simulation with the monthly mean climate states from the multi-model ensemble mean for the present and future periods, together with a bias correction term. It ensures that the mean climate differences in the forcing data between the present and future periods represent externally-forced changes only and are representative of the multi-model ensemble mean, while changes in transient weather patterns are also considered based on one select GCM simulation. The adjustments through the monthly fields are comparable in magnitude to the bias correction term and are small compared with the variations in 6-hourly data. Any inconsistency among the independently adjusted forcing fields is likely to be small and have little impact. For quantifying the mean response to future external forcing, this approach avoids the need to perform RCM large ensemble simulations forced by different GCM outputs, which can be very expensive. It also allows changes in transient weather patterns to be included in the lateral forcing, in contrast to the Pseudo Global Warming (PGW) approach, in which only the mean climate change is considered. However, it does not address the uncertainty associated with internal variability or inter-model spreads. The simulated transient weather changes may also be unrepresentative of other models. This new approach has been applied to construct the forcing data for the second phase of the WRF-based downscaling over much of North America with 4 km grid spacing.

Accurate prediction of mixed-phase precipitation remains challenging for numerical weather prediction models even at high resolution and with a sophisticated explicit microphysics scheme and diagnostic algorithm to designate the surface precipitation type. Since mixed-phase winter weather precipitation can damage infrastructure and produce significant disruptions to air and road travel, incorrect surface precipitation phase forecasts can have major consequences for local and statewide decision-makers as well as the general public. Building upon earlier work, this study examines the High-Resolution Rapid Refresh (HRRR) model’s ability to forecast the surface precipitation phase, with a particular focus on model-predicted vertical temperature profiles associated with mixed-phase precipitation, using upper-air sounding observations as well as the Automated Surface Observing Systems (ASOS) and Meteorological Phenomena Identification Near the Ground (mPING) observations. The analyses concentrate on regions of mixed-phase precipitation from two winter season events. The results show that when both the observational and model data indicated mixed-phase precipitation at the surface, the model represents the observed temperature profile well. Overall, cases where the model predicted rain but the observations indicated mixed-phase precipitation generally show a model surface temperature bias of

There is general consensus that projected warming will cause earlier snowmelt, but how snowmelt rates will respond to climate change is poorly known. We present snowpack observations from western North America illustrating that shallower snowpack melts earlier, and at lower rates, than deeper, later-lying snow-cover. The observations provide the context for a hypothesis of slower snowmelt in a warmer world. We test this hypothesis using climate model simulations for both a control time period and re-run with a future climate scenario, and find that the fraction of meltwater volume produced at high snowmelt rates is greatly reduced in a warmer climate. The reduction is caused by a contraction of the snowmelt season to a time of lower available energy, reducing by as much as 64% the snow-covered area exposed to energy sufficient to drive high snowmelt rates. These results have unresolved implications on soil moisture deficits, vegetation stress, and streamflow declines. Observations from western North America and model simulations are used to understand how climate change will affect snowmelt. Snowmelt is found to be slower under climate change as earlier melt means there is less energy for high melt rates.

The AgI Seeding Cloud Impact Investigation (ASCII) campaign, conducted in early 2012 and 2013 over two mountain ranges in southern Wyoming, was designed to examine the impact of ground-based glaciogenic seeding on snow growth in winter orographic clouds. Part I of this study ( Pokharel and Geerts, 2016 ) describes the project design, instrumentation, as well as the ambient atmospheric conditions and macrophysical and microphysical properties of the clouds sampled in ASCII. This paper (Part II) explores how the silver iodide (AgI) seeding affects snow growth in these orographic clouds in up to 27 intensive operation periods (IOPs), depending on the instrument used. In most cases, 2 h without seeding (NOSEED) were followed by 2 h of seeding (SEED). In situ data at flight level (2D-probes) indicate higher concentrations of small snow particles during SEED in convective clouds. The double difference of radar reflectivity Z (SEED − NOSEED in the target region, compared to the same trend in the control region) indicates an increase in Z for the composite of ASCII cases, over either mountain range, and for any of the three radar systems (WCR, MRR, and DOW), each with their own control and target regions, and for an array of snow gauges. But this double difference varies significantly from case to case, which is attributed to uncertainties related to sampling representativeness and to differences in natural trends between control and target regions. We conclude that a sample much larger than ASCII's sample is needed for clear observational evidence regarding the sensitivity of seeding efficacy to atmospheric and cloud conditions.

Climate change is causing increases in extreme rainfall across the United States. This study uses observations and high-resolution modelling to show that rainfall changes related to rising temperatures depend on the available atmospheric moisture. Extreme precipitation intensities have increased in all regions of the Contiguous United States (CONUS)1 and are expected to further increase with warming at scaling rates of about 7% per degree Celsius (ref. 2), suggesting a significant increase of flash flood hazards due to climate change. However, the scaling rates between extreme precipitation and temperature are strongly dependent on the region, temperature3, and moisture availability4, which inhibits simple extrapolation of the scaling rate from past climate data into the future5. Here we study observed and simulated changes in local precipitation extremes over the CONUS by analysing a very high resolution (4 km horizontal grid spacing) current and high-end climate scenario that realistically simulates hourly precipitation extremes. We show that extreme precipitation is increasing with temperature in moist, energy-limited, environments and decreases abruptly in dry, moisture-limited, environments. This novel framework explains the large variability in the observed and modelled scaling rates and helps with understanding the significant frequency and intensity increases in future hourly extreme precipitation events and their interaction with larger scales.

We examine the effects of regional climate model (RCM) horizontal resolution and forcing scaling (i.e., spatial aggregation of meteorological datasets) on the portrayal of climate change impacts. Specifically, we assess how the above decisions affect: (i) historical simulation of signature measures of hydrologic behavior, and (ii) projected changes in terms of annual water balance and hydrologic signature measures. To this end, we conduct our study in three catchments located in the headwaters of the Colorado River basin. Meteorological forcings for current and a future climate projection are obtained at three spatial resolutions (4-, 12- and 36-km) from dynamical downscaling with the Weather Research and Forecasting (WRF) regional climate model, and hydrologic changes are computed using four different hydrologic model structures. These projected changes are compared to those obtained from running hydrologic simulations with current and future 4-km WRF climate outputs re-scaled to 12- and 36-km. The results show that the horizontal resolution of WRF simulations heavily affects basin-averaged precipitation amounts, propagating into large differences in simulated signature measures across model structures. The implications of re-scaled forcing datasets on historical performance were primarily observed on simulated runoff seasonality. We also found that the effects of WRF grid resolution on projected changes in mean annual runoff and evapotranspiration may be larger than the effects of hydrologic model choice, which surpasses the effects from re-scaled forcings. Scaling effects on projected variations in hydrologic signature measures were found to be generally smaller than those coming from WRF resolution; however, forcing aggregation in many cases reversed the direction of projected changes in hydrologic behavior.

Assessment of how the wintertime cloud seeding conditions over Utah's mountains may change provides essential information for the state to work on increasing its snowpack in the warmer climate. In this study, data from the National Center for Atmospheric Research's high-resolution, convection-permitting Weather Research and Forecasting model are used to evaluate suitable conditions for wintertime cloud seeding over Utah's mountains, and to estimate future changes to these conditions under a pseudo-global warming scenario. While previous studies have examined historical cloud seeding conditions, this is the first study that explores the impact of future climate change on these conditions. Based on two atmospheric variables commonly used in cloud-seeding operations, i.e. average temperature from the surface to 1 km above ground level (−20 °C≤T≤−6 °C) and vertically integrated supercooled liquid water (SLW > 0.01 mm), Utah's mountains are suitable for seeding more than 20% of the time during winter. A greater rate of suitability exists in the northern and Uinta Mountains. In the warmer climate under a high-emissions scenario, these seeding conditions may wane as the percentage of precipitating clouds suitable for seeding, about 60% under current conditions, would decline to about 40% across the state with significance. This projected decrease is due to rising temperatures and a decreased frequency of precipitation events. These findings imply that climate warming will narrow the window of opportunity for winter cloud seeding operation in Utah.

Orographic precipitation and snowpack provide a vital water resource for the western U.S., while convective precipitation accounts for a significant part of annual precipitation in the eastern U.S. As a result, water managers are keenly interested in their fate under climate change. However, previous studies of water cycle changes in the U.S. have been conducted with climate models of relatively coarse resolution, leading to potential misrepresentation of key physical processes. This paper presents results from a high-resolution climate change simulation that permits convection and resolves mesoscale orography at 4-km grid spacing over much of North America using the Weather Research and Forecasting (WRF) model. Two 13-year simulations were performed, consisting of a retrospective simulation (October 2000–September 2013) with initial and boundary conditions from ERA-interim and a future climate sensitivity simulation with modified reanalysis-derived initial and boundary conditions through adding the CMIP5 ensemble-mean high-end emission scenario climate change. The retrospective simulation is evaluated by validating against Snowpack Telemetry (SNOTEL) and an ensemble of gridded observational datasets. It shows overall good performance capturing the annual/seasonal/sub-seasonal precipitation and surface temperature climatology except for a summer dry and warm bias in the central U.S. In particular, the WRF seasonal precipitation agrees with SNOTEL observations within a few percent over the mountain ranges, providing confidence in the model’s estimation of western U.S. seasonal snowfall and snowpack. The future climate simulation forced with warmer and moister perturbed boundary conditions enhances annual and winter-spring-fall seasonal precipitation over most of the contiguous United States (CONUS), but suppresses summertime precipitation in the central U.S. The WRF-downscaled climate change simulations provide a high-resolution dataset (i.e., High-Resolution CONUS downscaling, HRCONUS) to the community for studying one possible scenario of regional climate changes and impacts.

The accuracy of rain-rate estimation using polarimetric radar measurements has been improved as a result of better characterization of radar measurement quality and rain microphysics. In the literature, a variety of power-law relations between polarimetric radar measurements and rain rate are described because of the dynamic or varying nature of rain microphysics. A variational technique that concurrently takes into account radar observational error and dynamically varying rain microphysics is proposed in this study. Rain-rate estimation using the variational algorithm that uses event-based observational error and background rain climatological values is evaluated using observing system simulation experiments (OSSE), and its performance is demonstrated in the case of an epic Colorado flood event. The rain event occurred between 11 and 12 September 2013. The results from OSSE show that the variational algorithm with event-based observational error consistently estimates more accurate rain rate than does the “R(ZHH, ZDR)” power-law algorithm. On the contrary, the usage of ad hoc or improper observational error degrades the performance of the variational method. Furthermore, the variational algorithm is less sensitive to the observational error of differential reflectivity ZDR than is the R(ZHH, ZDR) algorithm. The variational quantitative precipitation estimation (QPE) retrieved more accurate rainfall estimation than did the power-law dual-polarization QPE in this particular event, despite the fact that both algorithms used the same dual-polarization radar measurements from the Next Generation Weather Radar (NEXRAD).

The impacts of various assumptions of cloud properties represented within a numerical weather prediction model's radiation scheme are demonstrated. In one approach, the model assumed the radiative effective radii of cloud water, cloud ice, and snow were represented by values assigned a priori, whereas a second, “coupled” approach utilized known cloud particle assumptions in the microphysics scheme that evolved during the simulations to diagnose the radii explicitly. This led to differences in simulated infrared (IR) brightness temperatures, radiative fluxes through clouds, and resulting surface temperatures that ultimately affect model-predicted diurnally-driven convection. The combined approach of evaluating simulated versus observed IR brightness temperatures, radiation reaching the ground, and surface temperature forecasts revealed the root model biases better than evaluating any single variable. This study found that the Weather Research and Forecasting (WRF) model predicted less overall clouds than was observed, particularly in the mid‑troposphere, but that properly connecting the assumptions of particle sizes in the microphysics scheme to the radiation scheme resulted in sensible cloud-radiation indirect effects and modest improvements in simulated IR brightness temperature, amount of solar radiation reaching the ground, and surface temperature.

Drop size distributions observed by four Particle Size Velocity (PARSIVEL) disdrometers during the 2013 Great Colorado Flood are used to diagnose rain characteristics during intensive rainfall episodes. The analysis focuses on 30 h of intense rainfall in the vicinity of Boulder, Colorado, from 2200 UTC 11 September to 0400 UTC 13 September 2013. Rainfall rates R, median volume diameters D0, reflectivity Z, drop size distributions (DSDs), and gamma DSD parameters were derived and compared between the foothills and adjacent plains locations. Rainfall throughout the entire event was characterized by a large number of small- to medium-sized raindrops (diameters smaller than 1.5 mm) resulting in small values of Z (

The hemibiotrophic pathogen Colletotrichum orbiculare preferentially expresses a necrosis and ethylene-inducing peptide 1 (Nep1)-like protein named NLP1 during the switch to necrotrophy. Here, we report that the constitutive expression of NLP1 in C. orbiculare blocks pathogen infection in multiple Cucurbitaceae cultivars via their enhanced defense responses. NLP1 has a cytotoxic activity that induces cell death in Nicotiana benthamiana. However, C. orbiculare transgenic lines constitutively expressing a mutant NLP1 lacking the cytotoxic activity still failed to infect cucumber, indicating no clear relationship between cytotoxic activity and the NLP1-dependent enhanced defense. NLP1 also possesses the microbe-associated molecular pattern (MAMP) sequence called nlp24, recognized by Arabidopsis thaliana at its central region, similar to NLPs of other pathogens. Surprisingly, inappropriate expression of a mutant NLP1 lacking the MAMP signature is also effective for blocking pathogen infection, uncoupling the infection block from the corresponding MAMP. Notably, the deletion analyses of NLP1 suggested that the C-terminal region of NLP1 is critical to enhance defense in cucumber. The expression of mCherry fused with the C-terminal 32 amino acids of NLP1 was enough to trigger the defense of cucurbits, revealing that the C-terminal region of the NLP1 protein is recognized by cucurbits and, then, terminates C. orbiculare infection.

Annual precipitation increases of 10% or more are often quoted for the impact of winter orographic cloud seeding; however, establishing the basis for such values is problematic for two reasons. First, the impact of glaciogenic seeding of candidate orographic storms has not been firmly established. Second, not all winter precipitation is produced by candidate “seedable” storms. Addressing the first question motivated the Wyoming state legislature to fund a multiyear, crossover, randomized cloud-seeding experiment in southeastern Wyoming to quantify the impact of glaciogenic seeding of wintertime orographic clouds. The crossover design requires two barriers, one randomly selected for seeding, for comparisons of seeded and nonseeded precipitation under relatively homogeneous atmospheric conditions. Addressing the second question motivated the work here. The seeding criteria—700-hPa temperatures ≤−8°C, 700-hPa winds between 210° and 315°, and the presence of supercooled liquid water—were applied to eight winters to determine the percent of winter precipitation that may fall under the seeding criteria. Since no observational datasets provide precipitation and all of the atmospheric variables required for this study, a regional climate model dynamical downscaling of historical data over 8 years was used. The accuracy of the model was tested against several measurements, and the small model biases were removed. On average, ~26% of the time between 15 November and 15 April atmospheric conditions were seedable over the barriers in southeastern Wyoming. These seedable conditions were accompanied by precipitation ~12%–14% of the time, indicating that ~27%–30% of the winter precipitation resulted from seedable clouds.