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Aquatic invasive species (AIS) present major ecological and economic challenges globally, endangering ecosystems and human livelihoods. Managers and policy makers thus need tools to predict invasion risk and prioritize species and areas of concern, and they often use native range climate matching to determine whether a species could persist in a new location. However, climate matching for AIS often relies on air temperature rather than water temperature due to a lack of global water temperature data layers, and predictive power of models is seldom evaluated. We developed 12 global lake (water) temperature‐derived "BioLake" bioclimatic layers for distribution modeling of aquatic species and compared "climatch" climate matching predictions (from climatchR package) from BioLake with those based on BioClim temperature layers and with a null model. We did this for 73 established AIS in the United States, training the models on their ranges outside of the United States and Canada. Models using either set of climate layers outperformed the null expectation by a similar (but modest) amount on average, but some species were occasionally found in locations with low climatch scores. Mean US climatch scores were higher for most species when using air temperature. Including additional climate layers in models reduced mean climatch scores, indicating that commonly used climatch score thresholds are not absolute but can be context specific and may require calibration based upon climate data used. Although finer resolution global lake temperature data would likely improve predictions, our BioLake layers provide a starting point for aquatic species distribution modeling. Climate matching was most effective for some species that originated at low latitudes or had small ranges. Climatch scores remain useful but limited for predicting AIS risk, perhaps because current ranges seldom fully reflect climatic tolerances (fundamental niches). Managers could consider climate matching as one of a suite of tools that can be used in AIS prioritization. [ABSTRACT FROM AUTHOR]

In mountains, the precipitation phase greatly varies in space and time and affects the evolution of the snow cover. Snowpack models usually rely on precipitation‐phase partitioning methods (PPMs) that use near‐surface variables. These PPMs ignore conditions above the surface thus limiting their ability to predict the precipitation phase at the surface. In this study, the impact on snowpack simulations of atmospheric‐based PPMs, incorporating upper atmospheric information, is tested using the snowpack scheme Crocus. Crocus is run at 2.5‐km grid spacing over the mountains of southwestern Canada and northwestern United States and is driven by meteorological fields from an atmospheric model at the same resolution. Two atmospheric‐based PPMs were considered from the atmospheric model: the output from a detailed microphysics scheme and a post‐processing algorithm determining the snow level and the associated precipitation phase. Two ground‐based PPMs were also included as lower and upper benchmarks: a single air temperature threshold at 0°C and a PPM using wet‐bulb temperature. Compared to the upper benchmark, the snow‐level based PPM improved the estimation of snowfall occurrence by 5% and the simulation of snow water equivalent (SWE) by 9% during the snow melting season. In contrast, due to missing processes, the microphysics scheme decreased performances in phase estimate and SWE simulations compared to the upper benchmark. These results highlight the need for detailed evaluation of the precipitation phase from atmospheric models and the benefit for mountain snow hydrology of the post‐processed snow level. The limitations to drive snowpack models at slope scale are also discussed. Plain Language Summary: The partitioning of precipitation between rainfall and snowfall is a crucial component of the evolution of the snowpack in mountains. Most snowpack models use the air temperature and humidity near the surface to derive the precipitation phase. However, the phase at the surface is strongly influenced by processes such as melting and refreezing of falling hydrometeors that occur above the surface. Atmospheric models simulate these processes and the corresponding phase at the surface. However, snowpack models rarely use this information. In this study, we considered two estimates of precipitation phase from an atmospheric model and tested them with a physically‐based snow model over the mountains of southwestern Canada and northwestern United States. The results were compared with traditional approaches using the air temperature and humidity near the surface to derive the precipitation phase. Our results showed that the precipitation phase associated with the snow level obtained from the atmospheric model improved snowfall estimate and snowpack prediction compared to the traditional approaches. In contrast, the cloud/precipitation scheme of the atmospheric model decreased performance in phase estimate and snow simulations due to missing physical processes. Our study highlights that snowpack predictions in the mountains can be improved if valuable information is obtained from atmospheric models. Key Points: Estimates of precipitation phase from an atmospheric model were used to drive snow simulations with a detailed snowpack modelSnowfall prediction and snowpack modeling are improved by using the snow level from post‐processing of the atmospheric modelDirect precipitation phase from the microphysics scheme does not improve snow simulations compared to simpler rain‐snow partitioning schemes [ABSTRACT FROM AUTHOR]

This study evaluates projected changes to rain-on-snow (ROS) characteristics (i.e., frequency, rainfall amount, and runoff) for the future 2041-2070 period with respect to the current 1976-2005 period over North America using six simulations, based on two Canadian RCMs, driven by two driving GCMs for RCP4.5 and 8.5 emission pathways. Prior to assessing projected changes, the two RCMs are evaluated by comparing ERA-Interim driven RCM simulations with available observations, and results indicate that both models reproduce reasonably well the observed spatial patterns of ROS event frequency and other related features. Analysis of current and future simulations suggest general increases in ROS characteristics during the November-March period for most regions of Canada and for northwestern US for the future period, due to an increase in the rainfall frequency with warmer air temperatures in future. Future ROS runoff is often projected to increase more than future ROS rainfall amounts, particularly for northeastern North America, during snowmelt months, as ROS events usually accelerate snowmelt. The simulations show that ROS event is a primary flood generating mechanism over most of Canada and north-western and -central US for the January-May period for the current period and this is projected to continue in the future period. More focused analysis over selected basins shows decreases in future spring runoff due to decreases in both snow cover and ROS runoff. The above results highlight the need to take into consideration ROS events in water resources management adaptation strategies for future climate. [ABSTRACT FROM AUTHOR]

The article highlights the weather conditions for April 2008 in the U.S., Alaska and Canada. The author stated that wet conditions persisted in most areas from eastern Plains to the Mississippi Valley with below-normal temperatures. Meanwhile, precipitation in Alaska is above normal with two to six times greater than normal. On the other hand, snow in Canada is not so remarkable and the eastern part has a reprieve from snowstorms.

Nonlinear projections of the Arctic Oscillation (AO) index onto North American winter (December–March) 500-mb geopotential height (Z500) and surface air temperature (SAT) anomalies reveal a pronounced asymmetry in the atmospheric patterns associated with positive and negative phases of the AO. In a linear view, the Z500 anomaly field associated with positive AO resembles a positive North Atlantic Oscillation pattern with statistically significant positive and negative anomalies stretching zonally into central-eastern USA and Canada, respectively, resulting in a cold climate anomaly over northeastern and eastern Canada, Alaska and the west coast of USA, and a warm climate anomaly over the rest of the continent. By contrast, the nonlinear behavior, mainly a quadratic association with AO, which is most apparent when the amplitude of the AO index is large, has the same spatial pattern and sign for both positive and negative values of the index. The nonlinear pattern reveals negative Z500 anomalies over the west coast of USA and the North Atlantic and positive Z500 anomalies at higher latitudes centered over the Gulf of Alaska and northeastern Canada accompanied by cooler than normal climate over the USA and southwestern Canada and warmer than normal climate over other regions of the continent. A similar analysis is conducted on the data from the Canadian Center for Climate Modelling and Analysis second generation coupled general circulation model. The nonlinear patterns of North American Z500 and SAT anomalies associated with the AO in the model simulation are generally consistent with the observational results, thereby confirming the robustness of the nonlinear behavior of North American winter climate with respect to the AO in a climate simulation that is completely independent of the observations. [ABSTRACT FROM AUTHOR]

The formula used in the U.S. and Canada to express the combined effect of wind and low temperature on how cold it feels was changed in November 2001. Many had felt that the old formula for equivalent temperature, derived in the 1960s from Siple and Passel's flawed but quite useful Wind Chill Index, unnecessarily exaggerated the severity of the weather. The new formula is based on a mathematical model of heat flow from the upwind side of a head-sized cylinder moving at walking speed into the wind. The paper details the assumptions that were made in generating the new wind chill charts. It also points out weaknesses in the concept of wind chill equivalent temperature, including its steady-state character and a seemingly paradoxical effect of the internal thermal resistance of the cylinder on comfort and equivalent temperature. Some improvements and alternatives are suggested. [ABSTRACT FROM AUTHOR]

Discusses several weather events experienced in the U.S. and Canada in August 1985. Hurricanes; Cloudburst; Percentage of normal precipitation; Departure of average temperature from normal; Highest and lowest temperature; Greatest number of sunshine hours.

Presents guidelines for displaying the normal atmospheric temperature in the almanac programs. Determination of the normal daily maximum, minimum, and average temperatures; Lines to be added to the main almanac program to display the normal temperature of the date; Constants for representative locations in the U.S. and Canada; Calculation of normal temperatures.