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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 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.

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.

Convection-permitting models (CPM) with at least 4 km horizontal grid spacing enable the cumulus parameterization to be switched off and thus simulate convective processes more realistically than coarse resolution models. This study investigates if a North American scale CPM can reproduce the observed warm season precipitation diurnal cycle on a climate scale. Potential changes in the precipitation diurnal cycle characteristics at the end of the twenty first century are also investigated using the pseudo global warming approach under a high-end anthropogenic emission scenario (RCP8.5). Simulations are performed with the Advanced Research Weather Research and Forecasting (ARW-WRF) model with 4-km horizontal grid spacing. Results from the WRF historical run (2001–2013) are evaluated against hourly precipitation from 2903 weather stations and a gridded hourly precipitation product in the U.S. The magnitude and timing of the diurnal cycle peak are realistically simulated in most of the U.S. and southern Canada. The model also captures the transition from afternoon precipitation peaks eastward of the Rocky Mountains to night peaks in the central U.S., which is related to propagating mesoscale convective systems. However, the historical climate simulation does not capture the observed early morning peaks in the central U.S. and overestimates the magnitude of the diurnal precipitation peak in the southeast region. In the simulation of the future climate, both the precipitation amount of the diurnal cycle and precipitation intensity increase throughout the domain, along with an increase in precipitation frequency in the northern region of the domain in May. These increases indicate a clear intensification of the hydrologic cycle during the warm season with potential impacts on future water resources, agriculture, and flooding.

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, temperature 3 , and moisture availability 4 , which inhibits simple extrapolation of the scaling rate from past climate data into the future 5 . 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.

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.

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.

Hemibiotrophic fungal plant pathogens represent a group of agronomically significant disease‐causing agents that grow first on living tissue and then cause host death in later, necrotrophic growth. Among these, Colletotrichum spp. are devastating pathogens of many crops. Identifying expanded classes of genes in the genomes of phytopathogenic Colletotrichum, especially those associated with specific stages of hemibiotrophy, can provide insights on how these pathogens infect a large number of hosts. The genomes of Colletotrichum orbiculare, which infects cucurbits and Nicotiana benthamiana, and C. gloeosporioides, which infects a wide range of crops, were sequenced and analyzed, focusing on features with potential roles in pathogenicity. Regulation of C. orbiculare gene expression was investigated during infection of N. benthamiana using a custom microarray. Genes expanded in both genomes compared to other fungi included sequences encoding small, secreted proteins (SSPs), secondary metabolite synthesis genes, proteases and carbohydrate‐degrading enzymes. Many SSP and secondary metabolite synthesis genes were upregulated during initial stages of host colonization, whereas the necrotrophic stage of growth is characterized by upregulation of sequences encoding degradative enzymes. Hemibiotrophy in C. orbiculare is characterized by distinct stage‐specific gene expression profiles of expanded classes of potential pathogenicity genes.

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.

This paper presents recent efforts to understand the relative accuracies of different instrumentation and gauges with various windshield configurations to measure snowfall. Results from the National Center for Atmospheric Research (NCAR) Marshall Field Site will be highlighted. This site hosts a test bed to assess various solid precipitation measurement techniques and is a joint collaboration between the National Oceanic and Atmospheric Administration (NOAA), NCAR, the National Weather Service (NWS), and Federal Aviation Administration (FAA). The collaboration involves testing new gauges and other solid precipitation measurement techniques in comparison with World Meteorological Organization (WMO) reference snowfall measurements. This assessment is critical for any ongoing studies and applications, such as climate monitoring and aircraft deicing, that rely on accurate and consistent precipitation measurements.