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The variations of large-scale climatic controls and surface responses through the annual cycle during strong positive (El Niño) and negative (La Niña) phase ENSO events are analyzed to assess the within-year and among-year variations of climate anomalies. Data from the NCEP–NCAR reanalysis project are presented as small-multiple maps to illustrate the spatial and temporal variability in North American climate associated with extreme phases of ENSO. Temperature, mean sea level pressure, 500-mb geopotential heights, and 850-mb specific humidity have composite-anomaly patterns that exhibit the greatest degree of spatial and temporal coherence. In general, the composite-anomaly patterns for El Niño and La Niña events are of opposite sign, with stronger, more spatially coherent anomalies occurring during El Niño events than during La Niña events. However, the strength and coherency of the precipitation anomaly patterns are reduced in the interior intermountain west during both positive and negative phase of ENSO. The variations in precipitation anomalies are compared to the 500-mb omega and 850-mb specific humidity composite-anomaly patterns, which provide information on the controls of precipitation by large-scale vertical motions and moisture availability thus providing information on the specific mechanisms associated with precipitation variability during ENSO events. [ABSTRACT FROM AUTHOR]

The European Centre for Medium-Range Weather Forecasts (ECMWF) recently released its most advanced reanalysis product, the ERA5 dataset. It was designed and generated with methods giving it multiple advantages over the previous release, the ERA-Interim reanalysis product. Notably, it has a finer spatial resolution, is archived at the hourly time step, uses a more advanced assimilation system and includes more sources of data. This paper aims to evaluate the ERA5 reanalysis as a potential reference dataset for hydrological modelling by considering the ERA5 precipitation and temperatures as proxies for observations in the hydrological modelling process, using two lumped hydrological models over 3138 North American catchments. This study shows that ERA5-based hydrological modelling performance is equivalent to using observations over most of North America, with the exception of the eastern half of the US, where observations lead to consistently better performance. ERA5 temperature and precipitation biases are consistently reduced compared to ERA-Interim and systematically more accurate for hydrological modelling. Differences between ERA5, ERA-Interim and observation datasets are mostly linked to precipitation, as temperature only marginally influences the hydrological simulation outcomes. [ABSTRACT FROM AUTHOR]

The atmospheric moisture transport processes are of great importance to the occurrence and intensity of precipitation extremes. In this paper, we review the linkage between processes, including the large‐scale atmospheric circulation, atmospheric moisture transport, and extreme precipitation events. We first summarize the thermodynamic and dynamic processes and moisture transport trackings for historical precipitation extremes. We then focus on the contribution of three major atmospheric moisture transport pathways, that is, atmospheric rivers, low‐level jets, and tropical cyclones, to the occurrence and intensity of regional precipitation extremes. Studies on large‐scale atmospheric circulation driving water vapor transport for precipitation extremes over East Asia and North America were specifically reviewed for the understanding of physical mechanisms and predictability of moisture transport and extreme precipitation events. We then pay more attention to the effects of global warming on atmospheric moisture transport, and thus regional precipitation extremes from the perspectives of thermodynamic and dynamic changes of the atmosphere. In the end, we summarize future research challenges on the physical mechanisms of atmospheric moisture transport that are associated with regional precipitation extremes, especially under a warming climate. This article is categorized under: Science of Water > Water ExtremesScience of Water > Hydrological Processes [ABSTRACT FROM AUTHOR]

This paper surveys the current state of knowledge regarding large-scale meteorological patterns (LSMPs) associated with short-duration (less than 1 week) extreme precipitation events over North America. In contrast to teleconnections, which are typically defined based on the characteristic spatial variations of a meteorological field or on the remote circulation response to a known forcing, LSMPs are defined relative to the occurrence of a specific phenomenon—here, extreme precipitation—and with an emphasis on the synoptic scales that have a primary influence in individual events, have medium-range weather predictability, and are well-resolved in both weather and climate models. For the LSMP relationship with extreme precipitation, we consider the previous literature with respect to definitions and data, dynamical mechanisms, model representation, and climate change trends. There is considerable uncertainty in identifying extremes based on existing observational precipitation data and some limitations in analyzing the associated LSMPs in reanalysis data. Many different definitions of "extreme" are in use, making it difficult to directly compare different studies. Dynamically, several types of meteorological systems—extratropical cyclones, tropical cyclones, mesoscale convective systems, and mesohighs—and several mechanisms—fronts, atmospheric rivers, and orographic ascent—have been shown to be important aspects of extreme precipitation LSMPs. The extreme precipitation is often realized through mesoscale processes organized, enhanced, or triggered by the LSMP. Understanding of model representation, trends, and projections for LSMPs is at an early stage, although some promising analysis techniques have been identified and the LSMP perspective is useful for evaluating the model dynamics associated with extremes. [ABSTRACT FROM AUTHOR]

The wintertime Pacific–North American (PNA) teleconnection pattern has previously been shown to influence springtime snow conditions over portions of North America. This paper develops a more complete physical understanding of this linkage across the continent, using a recently released long-term, continental-scale gridded North American snow depth dataset and the 40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis data. An empirical orthogonal function–based filtering process is used to identify and isolate the interannual snow depth variations associated with PNA. Then linear and partial correlations are employed to investigate the physical mechanisms that link winter PNA with spring snow depth. In the positive phase of PNA, the enhanced PNA pressure centers lead to warmer temperatures over northwestern North America and less precipitation at midlatitudes. The temperature and precipitation pathways act independently and in distinct geographical regions, and together they serve to reduce winter snow depth across much of North America. Winter anomalies in the snow depth field then tend to persist into spring. Dynamic mechanisms responsible for the PNA-influenced North American precipitation and temperature anomalies, involving moisture transport and cold air intrusions, are confirmed in this study and also extended to continental snow depth anomalies. [ABSTRACT FROM AUTHOR]

An important goal of the Climate Variability and Predictability (CLIVAR) research on the American monsoon systems is to determine the sources and limits of predictability of warm season precipitation, with emphasis on weekly to interannual time scales. This paper reviews recent progress in the understanding of the American monsoon systems and identifies some of the future challenges that remain to improve warm season climate prediction. Much of the recent progress is derived from complementary international programs in North and South America, namely, the North American Monsoon Experiment (NAME) and the Monsoon Experiment South America (MESA), with the following common objectives: 1) to understand the key components of the American monsoon systems and their variability, 2) to determine the role of these systems in the global water cycle, 3) to improve observational datasets, and 4) to improve simulation and monthly-to-seasonal prediction of the monsoons and regional water resources. Among the recent observational advances highlighted in this paper are new insights into moisture transport processes, description of the structure and variability of the South American low-level jet, and resolution of the diurnal cycle of precipitation in the core monsoon regions. NAME and MESA are also driving major efforts in model development and hydrologic applications. Incorporated into the postfield phases of these projects are assessments of atmosphere–land surface interactions and model-based climate predictability experiments. As CLIVAR research on American monsoon systems evolves, a unified view of the climatic processes modulating continental warm season precipitation is beginning to emerge. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

ABSTRACT Historically, management of coastal dune systems has often involved artificial stabilization of active sand surfaces in order for coastal areas to be more easily controlled and modified for human benefit. In North America, the introduction of invasive grasses, namely European and American beach (marram) grasses ( Ammophila spp.) has been one of the most successful strategies used for stabilizing active coastal dune sands. Recent research has demonstrated, however, that stabilization of coastal dunes often leads to reduced landform complexity and resilience, as well as declines in species diversity. More 'dynamic' restoration efforts have emerged over the past 20 years that encourage dune mobility and aeolian activity in order to provide a more resilient biogeomorphic system. In North America, there is generally little research relating restoration methods and outcomes to geomorphic responses despite the fundamental importance of sedimentary processes and dune morphodynamics in broader ecosystem function. This paper aims to better situate dynamic dune restoration within current geomorphic understanding. A brief review of key terms and concepts used in the emerging field of dynamic dune restoration is provided and expanded upon with respect to geomorphologic considerations. A case study of a recent dynamic restoration effort in Pacific Rim National Park Reserve, British Columbia, Canada is provide to demonstrate how these concepts are applied. Introduction of European marram at this site, coupled with a warming climate and increased precipitation in recent decades at this site, is thought to be associated with a rapid decline in aeolian activity, system stabilization and accelerated ecological succession. Preliminary results on the response of the dune system to mechanical removal of Ammophila are presented to provide the foundation for a research framework to guide the broader restoration project. Recommendations for improving treatment methodologies and monitoring protocols are provided to aid future restoration projects of this nature. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The paper presents the incremental improvement of the prediction of the Great Lakes net basin supply (NBS) with the hydrometeorological model Modélisation Environmentale-Surface et Hydrologie (MESH) by increasing the accuracy of the simulated NBS components (overlake precipitation, lake evaporation, and runoff into the lake). This was achieved through a series of experiments with MESH and its parent numerical weather prediction model [the Canadian Global Environmental Multiscale model in its regional configuration (GEM Regional)]. With forcing extracted from operational GEM Regional forecasts, MESH underestimated the NBS in fall and winter. The underestimation increased when the GEM precipitation was replaced with its corrected version provided by the Canadian Precipitation Analysis. This pointed to overestimated lake evaporation and prompted the revision of the parameterization of the surface turbulent fluxes over water used both in MESH and GEM. The revised parameterization was validated against turbulent fluxes measured at a point on Lake Superior. Its use in MESH reduced the lake evaporation and largely corrected the NBS underestimation. However, the Lake Superior NBS became overestimated, signaling an inconsistency between the reduced lake evaporation and the prescribed precipitation. To remove the inconsistency, a new forcing dataset (including precipitation) was generated with the GEM model using the revised flux parameterization. A major NBS simulation improvement was obtained with the new atmospheric forcing reflecting the atmospheric response to the modified surface fluxes over the lakes. Additional improvements resulted by correcting the runoff with a modified snowmelt rate and by insertion of observed streamflows. The study shows that accurate lake evaporation simulation is crucial for accurate NBS prediction. [ABSTRACT FROM AUTHOR]

The U.S. Climate Reference Network (USCRN) was deployed between 2001 and 2008 for the purpose of yielding high-quality and temporally stable in situ climate observations in pristine environments over the twenty-first century. Given this mission, USCRN stations are engineered to operate largely autonomously with great reliability and accuracy. A triplicate approach is used to provide redundant measurements of temperature and precipitation at each location, allowing for observations at a specific time to be compared for quality control. This approach has proven to be robust in the most extreme environments, from extreme cold (−49°C) to extreme heat (++52°C), in areas of heavy precipitation (4700 mm yr−1), and in locations impacted by strong winds, freezing rain, and other hazards. In addition to a number of stations enduring extreme winter environments in Alaska and the northern United States, seven of the USCRN stations are located at elevations over 2000 m, including stations on Mauna Loa, Hawaii (3407 m) and on Niwot Ridge above Boulder, Colorado (2996 m). The USCRN temperature instruments and radiation shield have also been installed and run successfully at a station on the Quelccaya Ice Cap in Peru (5670 m). This paper reviews the performance of the USCRN station network during its brief lifetime and the potential utility of its triplicate temperature instrument configuration for measuring climate change at elevation. [ABSTRACT FROM AUTHOR]

In northwestern North America, which is a large area with complex physiography, Climatic Research Unit (CRU) Time Series, version 2.1, (TS 2.1) gridded monthly mean 2-m temperatures are systematically lower than interpolated monthly averaged North American Regional Reanalysis (NARR) pressure-level temperatures----in particular, in the winter. Quantification of these differences based on CRU gridded observations can be used to estimate pressure-level temperatures from CRU 2-m temperatures (1901--2002) that predate the NARR period (since 1979). Such twentieth-century pressure-level temperature fields can be used in glacier mass-balance modeling and as an alternative to calibrating general circulation model control runs, avoiding the need for accurate boundary layer parameterization. In this paper, an approach is presented that is transferable to moisture, wind, and other 3D fields with potential applications in wind power generation, ecology, and air quality. At each CRU grid point, the difference between CRU and NARR is regressed against seven predictors in CRU (mean temperature, daily temperature range, precipitation, vapor pressure, cloud cover, and number of wet and frost days) for the period of overlap between CRU and NARR (1979--2002). Bayesian model averaging (BMA) is used to avoid overfitting the CRU--NARR differences and underestimating uncertainties. In cross validations, BMA provides reliable posterior predictions of the CRU--NARR differences and outperforms predictions from three alternative models: the constant model (24-yr mean), the regression model of highest Bayesian model probability, and the full model retaining all seven predictors in CRU. [ABSTRACT FROM AUTHOR]

Heavy precipitation and the resulting flooding are the most serious weather-related hazards over the west coast of North America. This paper analyzes the trends in heavy precipitation for the period 1950--2009 by examining the decadal distributions of the top 60, 40, and 20 two-day precipitation events for a collection of stations along the coastal zone of the United States and British Columbia, as well as the decadal distribution of maximum daily discharge for unregulated rivers from northern California to Washington State. During the past 60 years there has been a modest increase in heavy precipitation events over southern and central coastal California, a decline in heavy events from northern California through the central Oregon coast, a substantial increase in major events over Washington, and a modest increase over coastal British Columbia. Most of these trends are not significantly different from zero at the 95%% level. The trends in maximum daily discharge of unregulated rivers are consistent with the above pattern, with increasing discharges over the past three decades over Washington and northern Oregon and declines over the remainder of Oregon and northern California. Finally, the above trends in heavy rainfall and daily discharge are compared to the future patterns indicated by general circulation models under various global warming scenarios. [ABSTRACT FROM AUTHOR]

The pre-cold-frontal low-level jet within oceanic extratropical cyclones represents the lower-tropospheric component of a deeper corridor of concentrated water vapor transport in the cyclone warm sector. These corridors are referred to as atmospheric rivers (ARs) because they are narrow relative to their length scale and are responsible for most of the poleward water vapor transport at midlatitudes. This paper investigates landfalling ARs along adjacent north- and south-coast regions of western North America. Special Sensor Microwave Imager (SSM/I) satellite observations of long, narrow plumes of enhanced integrated water vapor (IWV) were used to detect ARs just offshore over the eastern Pacific from 1997 to 2005. The north coast experienced 301 AR days, while the south coast had only 115. Most ARs occurred during the warm season in the north and cool season in the south, despite the fact that the cool season is climatologically wettest for both regions. Composite SSM/I IWV analyses showed landfalling wintertime ARs extending northeastward from the tropical eastern Pacific, whereas the summertime composites were zonally oriented and, thus, did not originate from this region of the tropics. Companion SSM/I composites of daily rainfall showed significant orographic enhancement during the landfall of winter (but not summer) ARs. The NCEP–NCAR global reanalysis dataset and regional precipitation networks were used to assess composite synoptic characteristics and overland impacts of landfalling ARs. The ARs possess strong vertically integrated horizontal water vapor fluxes that, on average, impinge on the West Coast in the pre-cold-frontal environment in winter and post-cold-frontal environment in summer. Even though the IWV in the ARs is greater in summer, the vapor flux is stronger in winter due to much stronger flows associated with more intense storms. The landfall of ARs in winter and north-coast summer coincides with anomalous warmth, a trough offshore, and ridging over the Intermountain West, whereas the south-coast summer ARs coincide with relatively cold conditions and a near-coast trough. ARs have a much more profound impact on near-coast precipitation in winter than summer, because the terrain-normal vapor flux is stronger and the air more nearly saturated in winter. During winter, ARs produce roughly twice as much precipitation as all storms. In addition, wintertime ARs with the largest SSM/I IWV are tied to more intense storms with stronger flows and vapor fluxes, and more precipitation. ARs generally increase snow water equivalent (SWE) in autumn/winter and decrease SWE in spring. On average, wintertime SWE exhibits normal gains during north-coast AR storms and above-normal gains during the south-coast AR storms. The north-coast sites are mostly lower in altitude, where warmer-than-normal conditions more frequently yield rain. During those events when heavy rain from a warm AR storm falls on a preexisting snowpack, flooding is more likely to occur. [ABSTRACT FROM AUTHOR]

In support of the 2004 North American Monsoon Experiment (NAME) field campaign, NOAA established and maintained a field site about 100 km north of Mazatlán, Mexico, consisting of wind profilers, precipitation profilers, surface upward–downward-looking radiometers, and a 10-m meteorological tower to observe the environment within the North American monsoon. Three objectives of this NOAA project are discussed in this paper: 1) to observe the vertical structure of precipitating cloud systems as they passed over the NOAA profiler site, 2) to estimate the vertical air motion and the raindrop size distribution from near the surface to just below the melting layer, and 3) to better understand the microphysical processes associated with stratiform rain containing well-defined radar bright bands. To provide a climatological context for the profiler observations at the field site, the profiler reflectivity distributions were compared with Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) reflectivity distributions from the 2004 season over the NAME domain as well as from the 1998–2005 seasons. This analysis places the NAME 2004 observations into the context of other monsoon seasons. It also provides a basis for evaluating the representativeness of the structure of the precipitation systems sampled at this location. The number of rain events observed by the TRMM PR is dependent on geography; the land region, which includes portions of the Sierra Madre Occidental, has more events than the coast and gulf regions. Conversely, from this study it is found that the frequencies of occurrence of stratiform rain and reflectivity profiles with radar bright bands are mostly independent of region. The analysis also revealed that the reflectivity distribution at each height has more year-to-year variability than region-to-region variability. These findings suggest that in cases with a well-defined bright band, the vertical profile of the reflectivity relative to the height of the bright band is similar over the gulf, coast, and land regions. [ABSTRACT FROM AUTHOR]

This paper documents the sensitivity of the modeled evolution of the North American monsoon system (NAMS) to convective parameterization in terms of thermodynamic and circulation characteristics, stability profiles, and precipitation. The convective parameterization schemes (CPSs) of Betts–Miller–Janjic, Kain–Fritsch, and Grell were tested using version 3.4 of the PSU–NCAR fifth-generation Mesoscale Model (MM5) running in a pseudoclimate mode. Model results for the initial phase of the 1999 NAM are compared with surface climate station observations and seven radiosonde sites in Mexico and the southwestern United States. The results show substantial differences in modeled precipitation, surface climate, and atmospheric stability occuring between the different model simulations, which are attributable to the representation of convection in the model. Moreover, large intersimulation differences in the low-level circulation fields are found. While none of the CPSs tested gave perfect simulation of observations everywhere in the model domain, the Kain–Fritsch scheme generally gave significantly superior estimates of surface and upper air verification error statistics. [ABSTRACT FROM AUTHOR]

Little guidance is available to assist wetland managers in developing climate adaptation plans. To facilitate development of recommendations for adaptation strategies, it is essential to first determine if or how wetland managers are addressing these challenges. We used an online survey to solicit feedback from wetland managers and biologists in the Southern Great Plains of North America to gain information on perceptions of wetland managers regarding climate change; assess how the effects of climate change are being addressed through management; and identify barriers to implementing climate change adaptation. The majority of wetland managers (63%) agreed they are currently experiencing effects of climate change in wetlands, and most respondents (76%) reported that changes in the timing of water availability throughout the year was the most likely impact. Managers reported using a diversity of approaches in managing for changing precipitation, with management of native and invasive plant species being the two most common practices. Lack of funding and personnel were the most commonly identified factors limiting manager's response to changing precipitation patterns. In addition, >50% of managers indicated uncertainty about the effects of climate change on wetlands as a barrier to management, which may relate to limited access to peer-reviewed science. While most of the management practices reported were short-term measures and may not reflect long-term adaptation for climate change, the fact that many managers are considering climate change in their management suggests that there is considerable opportunities to continue developing capacity for climate change adaptation in the region. [ABSTRACT FROM AUTHOR]

The influence of Rossby waves emitted in the northeastern Pacific Ocean on the Northern Hemisphere's atmosphere during summer is analysed using ERA5 reanalysis and a new large ensemble performed with the EC-Earth3 model. The Rossby Wave Sources (RWS) trigger wave-like patterns arising from the upper troposphere of the north-eastern Pacific region, causing a response around the Northern Hemisphere with alternating regions of positive and negative correlation values between RWS and geopotential height at 500 hPa. Increased RWS intensity during summer is related to negative temperature anomalies over western North America, and positive temperature anomalies over eastern North America, concurrently with increased precipitation over the western subtropical Atlantic and Northern Europe during summer. Colder than normal conditions on the North Pacific Ocean intensify the RWS and its impact on the global atmospheric circulation. Different warm or cold states in the Pacific and Atlantic Oceans modify the atmospheric response to RWS, showing a change in the middle troposphere (500 hPa) towards a more-wavy structure with cold Pacific conditions, and towards a less-wavy structure with a warm Pacific Ocean. Furthermore, the North Atlantic plays a very important role in hindering (in the case of warm water) or permitting (cold water) that Rossby waves generated in the Pacific modulate the atmospheric conditions over Europe. [ABSTRACT FROM AUTHOR]

ABSTRACT Aim The network structure of food webs plays an important role in the maintenance of diversity and ecosystem functioning in ecological communities. Previous research has found that ecosystem size, resource availability, assembly history and biotic interactions can potentially drive food web structure. However, the relative influence of climatic variables that drive broad-scale biogeographic patterns of species richness and composition has not been explored for food web structure. In this study, we assess the influence of broad-scale climatic variables in addition to known drivers of food web structure on replicate observations of a single aquatic food web, sampled from the leaves of the pitcher plant ( Sarracenia purpurea), at different geographic sites across a broad latitudinal and climatic range. Location Using standardized sampling methods, we conducted an extensive 'snapshot' survey of 780 replicated aquatic food webs collected from the leaves of the pitcher plant S. purpurea at 39 sites from northern Florida to Newfoundland and westward to eastern British Columbia. Methods We examined correlations of 15 measures of food web structure at the pitcher and site scales with geographic variation in temperature and precipitation, concentrations of nutrients from atmospheric nitrogen deposition, resource availability, ecosystem size and the abundance of the pitcher plant mosquito ( Wyeomyia smithii), a potential keystone species. Results At the scale of a single pitcher plant leaf, linkage density, species richness, measures of chain length and the proportion of omnivores in a web all increased with pitcher volume. Linkage density and species richness were greater at high-latitude sites, which experience low mean temperatures and precipitation and high annual variation in both of these variables. At the site scale, variation in 8 of the 15 food web metrics decreased at higher latitudes, and variation in measures of chain length increased with the abundance of mosquitoes. Main conclusions Ecosystem size and climatic variables related to latitude were most strongly correlated with network structure of the Sarracenia food web. However, in spite of large sample sizes, thorough standardized sampling and the large geographic extent of the survey, even the best-fitting models explained less than 40% of the variation in food web structure. In contrast to biogeographic patterns of species richness, food web structure was largely independent of broad-scale climatic variables. The large proportion of unexplained variance in our analyses suggests that stochastic assembly may be an important determinant of local food web structure. [ABSTRACT FROM AUTHOR]

Abstract: Climate models provide estimates of climatic change over periods of time in the ancient past. Macrophysical climate models (MCM) differ from the more widely used general circulation models (GCM), in that MCMs provide temporally high-resolution (~100years) and site-specific estimations of monthly values of climate variables such as temperature and precipitation. In this paper, seasonal changes in climate variables are modeled for six 14C-dated fossil localities in North America. Five of these localities represent the time of maximum extent of ice during the most recent glacial episode, the Full Glacial (25+–15ka), including one at the peak of the Last Glacial Maximum (17–15ka). The other locality represents the time as the ice began to recede, the Late Glacial (15–11ka). Seasonal variations in temperature and precipitation modeled by MCM are herein compared with interpretations of seasonal variation based upon oxygen isotopes from serially sampled hypsodont teeth (mostly Equus and Bison) collected from each of these localities. Additionally, the MCM-modeled seasonal variations are used to predict the expected abundances of different plant functional groups (PFG) during those times, especially C3 and C4 functional groups, using modern relationships. These predictions are compared with carbon isotopic values from the same teeth. The importance of atmospheric pCO2 for the relative abundance of plants utilizing the C4 metabolic pathway is discussed, given that glacial episodes are known to have been times of lower atmospheric pCO2. Interpretations of seasonal variability and the relative abundance of C3 versus C4 vegetation based upon isotopes from tooth enamel are in broad agreement with predictions using the MCM and the modern distribution of PFGs with climate variables. The influence of pCO2 on the distribution of C4 vegetation during glacial times appears to be negligible. [Copyright &y& Elsevier]

Observations in the visible and infrared spectral bands from the Imager instrument onboard Geostationary Operational Environmental Satellite (GOES) have been used to derive snow depth. The technique makes use of correlation between depth of the snow pack and satellite-derived subpixel fractional snow cover. Previous efforts to infer snow depth from satellite data with this technique were focused on grasslands and croplands, where the snow depth/snow fraction relationship is most pronounced. In this paper we improve the retrieval algorithm to extend snow depth estimates to forested areas. The enhanced algorithm accounts for the tree cover fraction and for the type of forest, deciduous or coniferous. The developed technique was used to derive maps of snow depth over mid-latitude areas of North America during winter seasons of 2003–2004 and 2004–2005. Satellite-based snow depth maps were produced daily at 4 km spatial resolution. To validate the retrievals we compared them with surface observations of snow depth and with the snow depth analysis prepared at the NOAA National Operational Hydrological Remote Sensing Center (NOHRSC). The estimated retrieval error was about 30% for snow depths below 30 cm and increased to 50% for snow depths ranging from 30 to 50 cm. Snow depth retrievals were limited to scenes with less than 80% deciduous forest cover fraction and less than 50% needle leaf forest cover. [Copyright &y& Elsevier]

This paper combines a wide‐area assessment of forecast changes in wintertime synoptic conditions over western North America with a meso‐scale alpine hydrometeorology model to evaluate the impacts of forecast climate change on snowpack conditions in an alpine watershed. The synoptic analysis was used to generate long‐term climate time series scenarios using the Canadian Centre for Climate Modelling and Analysis first‐generation coupled general circulation model (GCM). The alpine hydrometeorology model SIMGRID is used to predict changes in wintertime precipitation at the watershed scale. The SNOPAC model is a simple snow model that predicts the overall snow accumulation throughout a watershed based on the output from SIMGRID. A vapour transfer model has been incorporated into the SNOPAC model to estimate snow volumes more accurately. The model is applied to a small alpine watershed in the southern Canadian Rockies. The synoptic analysis and GCM output forecasts a modest increase in both winter precipitation and temperatures in the study area. The hypothesis herein is that the increase in winter precipitation due to synoptic conditions will not compensate for regional changes in the rain‐to‐snow ratios. The net result will be a decline in winter accumulations of precipitation as snow, and hence an expected decline in spring runoff. Copyright © 2005 Royal Meteorological Society. [ABSTRACT FROM AUTHOR]

Variations in the water vapor that atmospheric rivers (ARs) carry toward North America within Pacific storms strongly modulates the spatiotemporal distribution of west‐coast precipitation. The "AR Recon" program was established to improve forecasts of landfalling Pacific‐coast ARs and their associated precipitation. Dropsondes are deployed from weather reconnaissance aircraft and pressure sensors have been added to drifting ocean buoys to fill a major gap in standard weather observations, while research is being conducted on the potential for airborne Global Navigation Satellite System (GNSS) radio occultation (ARO) to also contribute to forecast improvement. ARO further expands the spatial coverage of the data collected during AR Recon flights. This study provides the first description of these data, which provide water vapor and temperature information typically as far as 300 km to the side of the aircraft. The first refractivity profiles from European Galileo satellites are provided and their accuracy is evaluated using the dropsondes. It is shown that spatial variations in the refractivity anomaly (difference from the climatological background) are modulated by AR features, including the low‐level jet and tropopause fold, illustrating the potential for RO measurements to represent key AR characteristics. It is demonstrated that assimilation of ARO refractivity profiles can influence the moisture used as initial conditions in a high‐resolution model. While the dropsonde measurements provide precise, in situ wind, temperature and water vapor vertical profiles beneath the aircraft, and the buoys provide surface pressure, ARO provides complementary thermodynamic information aloft in broad areas not otherwise sampled at no additional expendable cost. Plain Language Summary: Aircraft are deployed to make extraobservations and improve forecasts of the atmospheric rivers that carry moisture toward the coast of North America in northeast Pacific storms. Dropsondes are released from the aircraft to measure moisture, temperature and winds as they descend. We have increased the coverage of the aircraft measurements by making simultaneous measurements of the atmosphere using Global Positioning System (GPS) signals, and for the first time, signals from the European Galileo satellite system which provides 50% more radio occultation measurements. These airborne radio occultation (ARO) measurements of the refractive index of the atmosphere are based on travel time delays of the satellite signals, and are used to provide refractivity, moisture, and temperature profile information to the sides of the aircraft. The observations are assimilated into a mesoscale weather model to improve the initial moisture conditions used for the weather forecast. Cross sections through the updated model volume show variations in the refractivity that correspond to the atmospheric river features at low levels and cold front features at upper levels. Key Points: Flights over northeast Pacific atmospheric rivers provide dense airborne radio occultation and dropsonde data for assimilation in modelsThe first Galileo RO profiles are compared with nearby Global Positioning System (GPS) profiles to assess accuracy in the troposphereThe model refractivity anomaly distinguishes key characteristics of the atmospheric river including the low‐level jet and tropopause fold [ABSTRACT FROM AUTHOR]

Climate simulations often need to be adjusted (i.e., corrected) before any climate change impacts studies. However usual bias correction approaches do not differentiate the bias from the different uncertainties of the climate simulations: scenario uncertainty, model uncertainty and internal variability. In particular, in the case of a multi-run ensemble of simulations (i.e., multiple runs of one model), correcting, as usual, each member separately, would mix up the model biases with its internal variability. In this study, two ensemble bias correction approaches preserving the internal variability of the initial ensemble are proposed. These "Ensemble bias correction" (EnsBC) approaches are assessed and compared to the approach where each ensemble member is corrected separately, using precipitation and temperature series at two locations in North America from a multi-member regional climate ensemble. The preservation of the internal variability is assessed in terms of monthly mean and hourly quantiles. Besides, the preservation of the internal variability in a changing climate is evaluated. Results show that, contrary to the usual approach, the proposed ensemble bias correction approaches adequately preserve the internal variability even in changing climate. Moreover, the climate change signal given by the original ensemble is also conserved by both approaches. [ABSTRACT FROM AUTHOR]

• Future change in extreme droughts over 5797 North American watersheds was studied. • Two climate model large ensembles were used to assess the change of frequency. • Changes in meteorological droughts are mostly correlated to mean precipitation. • Large increases in the frequency of hydrological droughts are observed over NA. • Projected changes in future extreme droughts get worse for longer return periods. This paper describes projected frequency changes in extreme summer meteorological and hydrological droughts over North American catchments. It uses two large ensemble climate models (50-member CanESM2 and 40-member CESM1) under the RCP 8.5 scenario to robustly assess frequency changes up to the 100-year drought, relative to the 1980–2009 reference period. Meteorological droughts, linked solely to precipitation deficit, are represented by the 1-month Standardized Precipitation Index (SPI), whereas hydrological droughts are characterized by the 1-month Streamflow Drought Index (SDI), based on hydrological simulation using a lumped hydrological model. Results show widely different patterns for future changes in extreme meteorological versus hydrological droughts. Future meteorological droughts show patterns of increasing and decreasing frequency that roughly match future expected changes of mean summer precipitation, although some regions are nonetheless projected to see more frequent extreme meteorological droughts despite increases in mean summer precipitation. The frequency changes for the 100-year meteorological droughts can be quite severe, with up to a 20-fold increase observed over some watersheds. On the other hand, hydrological droughts, which combine the effect of precipitation and temperature changes, show a mostly uniform pattern of large to very large increases in drought frequency. This shows that the projected temperature increase is a main driver of future extreme hydrological droughts, enough to overcome the projected increase in mean summer precipitation for many North American catchments. Projected changes in both meteorological and hydrological droughts get consistently worse for the longer considered return periods. In other words, frequency changes for the 100-year droughts are more significant than those expected for the 2- and 20-year droughts. This gradual worsening toward larger extremes has potentially large societal and economic impacts. The large projected increases in the frequency of extreme hydrological drought frequency (up to 27 times) are likely to severely stress water management systems across North America. [ABSTRACT FROM AUTHOR]

The Hydrometeorological Sandbox - École de technologie supérieure (HYSETS) is a rich, comprehensive and large-scale database for hydrological modelling covering 14425 watersheds in North America. The database includes data covering the period 1950–2018 depending on the type and source of data. The data include a wide array of hydrometeorological data required to perform hydrological and climate change impact studies: (1) watershed properties including boundaries, area, elevation slope, land use and other physiographic information; (2) hydrometric gauging station discharge time-series; (3) precipitation, maximum and minimum daily air temperature time-series from weather station records and from (4) the SCDNA infilled gauge meteorological dataset; (5) the NRCan and Livneh gridded interpolated products' meteorological data; (6) ERA5 and ERA5-Land reanalysis data; and (7) the SNODAS and ERA5-Land snow water equivalent estimates. All data have been processed and averaged at the watershed scale, and provides a solid basis for hydrological modelling, climate change impact studies, model calibration assessment, regionalization method evaluation and essentially any study requiring access to large amounts of spatiotemporally varied hydrometeorological data. Measurement(s) elevation • temperature • hydrological precipitation process • Fluid Discharge • climate • physiographic feature Technology Type(s) digital curation Sample Characteristic - Environment watershed Sample Characteristic - Location North America Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12600281 [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Precipitation extremes have implications for many facets of both the human and natural systems, predominantly through flooding events. Observations have demonstrated increasing trends in extreme precipitation in North America, and models and theory consistently suggest continued increases with future warming. Here, we address the question of whether observed changes in annual maximum 1- and 5-d precipitation can be attributed to human influence on the climate. Although attribution has been demonstrated for global and hemispheric scales, there are few results for continental and subcontinental scales. We utilize three large ensembles, including simulations from both a fully coupled Earth system model and a regional climate model. We use two different attribution approaches and find many qualitatively consistent results across different methods, different models, and different regional scales. We conclude that external forcing, dominated by human influence, has contributed to the increase in frequency and intensity of regional precipitation extremes in North America. If human emissions continue to increase, North America will see further increases in these extremes. [ABSTRACT FROM AUTHOR]

Cool- and warm-season precipitation totals have been reconstructed on a gridded basis for North America using 439 tree-ring chronologies correlated with December–April totals and 547 different chronologies correlated with May–July totals. These discrete seasonal chronologies are not significantly correlated with the alternate season; the December–April reconstructions are skillful over most of the southern and western United States and north-central Mexico, and the May–July estimates have skill over most of the United States, southwestern Canada, and northeastern Mexico. Both the strong continent-wide El Niño–Southern Oscillation (ENSO) signal embedded in the cool-season reconstructions and the Arctic Oscillation signal registered by the warm-season estimates faithfully reproduce the sign, intensity, and spatial patterns of these ocean–atmospheric influences on North American precipitation as recorded with instrumental data. The reconstructions are included in the North American Seasonal Precipitation Atlas (NASPA) and provide insight into decadal droughts and pluvials. They indicate that the sixteenth-century megadrought, the most severe and sustained North American drought of the past 500 years, was the combined result of three distinct seasonal droughts, each bearing unique spatial patterns potentially associated with seasonal forcing from ENSO, the Arctic Oscillation, and the Atlantic multidecadal oscillation. Significant 200–500-yr-long trends toward increased precipitation have been detected in the cool- and warm-season reconstructions for eastern North America. These seasonal precipitation changes appear to be part of the positive moisture trend measured in other paleoclimate proxies for the eastern area that began as a result of natural forcing before the industrial revolution and may have recently been enhanced by anthropogenic climate change. [ABSTRACT FROM AUTHOR]

Positive precipitation biases over western North America have remained a pervasive problem in the current generation of coupled global climate models. These biases are substantially reduced, however, in a version of the Geophysical Fluid Dynamics Laboratory Forecast-Oriented Low Ocean Resolution (FLOR) coupled climate model with systematic sea surface temperature (SST) biases artificially corrected through flux adjustment. This study examines how the SST biases in the Atlantic and Pacific Oceans contribute to the North American precipitation biases. Experiments with the FLOR model in which SST biases are removed in the Atlantic and Pacific are carried out to determine the contribution of SST errors in each basin to precipitation statistics over North America. Tropical and North Pacific SST biases have a strong impact on northern North American precipitation, while tropical Atlantic SST biases have a dominant impact on precipitation biases in southern North America, including the western United States. Most notably, negative SST biases in the tropical Atlantic in boreal winter induce an anomalously strong Aleutian low and a southward bias in the North Pacific storm track. In boreal summer, the negative SST biases induce a strengthened North Atlantic subtropical high and Great Plains low-level jet. Each of these impacts contributes to positive annual mean precipitation biases over western North America. Both North Pacific and North Atlantic SST biases induce SST biases in remote basins through dynamical pathways, so a complete attribution of the effects of SST biases on precipitation must account for both the local and remote impacts. [ABSTRACT FROM AUTHOR]

In the context of climate change and projected increase in global temperature, the atmosphere's water holding capacity is expected to increase at the Clausius-Clapeyron (C-C) rate by about 7% per 1 °C warming. Such an increase may lead to more intense extreme precipitation events and thus directly affect the probable maximum precipitation (PMP), a parameter that is often used for dam safety and civil engineering purposes. We therefore use a statistically motivated approach that quantifies uncertainty and accounts for nonstationarity, which allows us to determine the rate of change of PMP per 1 °C warming. This approach, which is based on a bivariate extreme value model of precipitable water (PW) and precipitation efficiency (PE), provides interpretation of how PW and PE may evolve in a warming climate. Nonstationarity is accounted for in this approach by including temperature as a covariate in the bivariate extreme value model. The approach is demonstrated by evaluating and comparing projected changes to 6-hourly PMP from two Canadian regional climate models (RCMs), CanRCM4 and CRCM5, over North America. The main results suggest that, on the continental scale, PMP increases in these models at a rate of approximately 4% per 1 °C warming, which is somewhat lower than the C-C rate. At the continental scale, PW extremes increase on average at the rate of 5% per 1 °C near surface warming for both RCMs. Most of the PMP increase is caused by the increase in PW extremes with only a minor contribution from changes in PE extremes. Nevertheless, substantial deviations from the average rate of change in PMP rates occur in some areas, and these are mostly caused by sensitivity of PE extremes to near surface warming in these regions. [ABSTRACT FROM AUTHOR]

The characterization of extreme precipitation at fine spatiotemporal scale represents a paramount challenge in hydroclimate sciences due to large uncertainties affecting the precipitation estimation from existing datasets. Comparing the spatiotemporal structure of precipitation extremes estimated from different datasets thus represents an essential step for climate model evaluation, as it provides insight into a model's ability to simulate atmospheric processes occurring at different scales. This study compares the probability distributions and the annual and diurnal cycles of occurrence of daily and subdaily precipitation annual maxima (AM) estimated over northeastern North America from five observed and simulated datasets: meteorological station series, the bias-corrected (CRT) satellite CMORPH, version 1.0, and the Multi-Source Weighted-Ensemble Precipitation (MSWEP), version 2, gridded datasets, various Canadian RCM, version 5 (CRCM5), simulations, and a 13-yr convection-permitting WRF, version 3.4.1, simulation. ERA-Interim-driven CRCM5 and WRF simulations well reproduced subdaily extreme quantiles and the AM annual and diurnal cycles observed at stations, while CMORPH and MSWEP displayed good performance only for daily and longer extreme statistics. The spatiotemporal statistical structure of precipitation extremes is then assessed considering the variation of AM quantiles across various spatial scales and durations. The results suggest that a two-parameter analytical relationship well describes the AM spatiotemporal structure at the regional scale, allowing us to approximate some crucial properties of point precipitation extremes from gridded datasets. Averaging the estimates from various members of the initial-condition CRCM5 Large Ensemble (CRCM5-LE) also made it possible to reduce the sampling errors and robustly estimate theAMspatiotemporal structure at the local scale of eachmodel grid box. [ABSTRACT FROM AUTHOR]

Extreme precipitation events have major societal impacts. These events are rare and can have small spatial scale, making statistical analysis difficult; both factors are mitigated by combining events over a region. A methodology is presented to objectively define "coherent" regions wherein data points have matching annual cycles. Regions are found by training self-organizing maps (SOMs) on the annual cycle of precipitation for each grid point across the contiguous United States (CONUS). Using the annual cycle for our intended application minimizes problems caused by consecutive dry periods and localized extreme events. Multiple criteria are applied to identify useful numbers of regions for our future application. Criteria assess these properties for each region: having many more events than experienced by a single grid point, good connectedness and compactness, and robustness to changing the number of regions. Our methodology is applicable across datasets and is tested here on both reanalysis and gridded observational data. Precipitation regions obtained align with large-scale geographical features and are readily interpretable. Useful numbers of regions balance two conflicting preferences: larger regions contain more events and thereby have more robust statistics, but more compact regions allow weather patterns associated with extreme events to be aggregated with confidence. For 6-h precipitation, 12–15 regions over the CONUS optimize our metrics. The regions obtained are compared against two existing region archetypes. For example, a popular set of regions, based on nine groups of states, has less coherent regions than defining the same number of regions with our SOM methodology. [ABSTRACT FROM AUTHOR]

Recently dam managers have begun to use data produced by regional climate models to estimate how probable maximum precipitation (PMP) might evolve in the future. Before accomplishing such a task, it is essential to assess PMP estimates derived from regional climate models (RCMs). In the current study PMP over North America estimated from two Canadian RCMs, CanRCM4 and CRCM5, is compared with estimates derived from three reanalysis products: ERA-Interim, NARR, and CFSR. An additional hybrid dataset (MSWEP-ERA) produced by combining precipitation from the Multi-Source Weighted-Ensemble Precipitation (MSWEP) dataset and precipitable water (PW) from ERA-Interim is also considered to derive PMP estimates that can serve as a reference. A recently developed approach using a statistical bivariate extreme values distribution is used to provide a probabilistic description of the PMP estimates using the moisture maximization method. Such a probabilistic description naturally allows an assessment of PMP estimates that includes quantification of their uncertainty. While PMP estimates based on the two RCMs exhibit spatial patterns similar to those of MSWEP-ERA and the three sets of reanalyses on the continental scale over North America, CanRCM4 has a tendency for overestimation while CRCM5 has a tendency for modest underestimation. Generally, CRCM5 shows good agreement with ERA-Interim, while CanRCM4 is more comparable to CFSR. Overall, the good ability of the two RCMs to reproduce the major characteristics of the different components involved in the estimation of PMP suggests that they may be useful tools for PMP estimation that could serve as a basis for flood studies at the basin scale. [ABSTRACT FROM AUTHOR]

In this work, the roles of El Niño–Southern Oscillation (ENSO) in the variability and predictability of the Pacific–North American (PNA) pattern and precipitation in North America in winter are examined. It is noted that statistically about 29% of the variance of PNA is linearly linked to ENSO, while the remaining 71% of the variance of PNA might be explained by other processes, including atmospheric internal dynamics and sea surface temperature variations in the North Pacific. The ENSO impact is mainly meridional from the tropics to the mid–high latitudes, while a major fraction of the non-ENSO variability associated with PNA is confined in the zonal direction from the North Pacific to the North American continent. Such interferential connection on PNA as well as on North American climate variability may reflect a competition between local internal dynamical processes (unpredictable fraction) and remote forcing (predictable fraction). Model responses to observed sea surface temperature and model forecasts confirm that the remote forcing is mainly associated with ENSO and it is the major source of predictability of PNA and winter precipitation in North America. [ABSTRACT FROM AUTHOR]

This article examines to what extent the NCEP Climate Forecast System (CFS) weeks 3–4 reforecasts reproduce the CFS Reanalysis (CFSR) storm-track properties, and if so, whether the storm-track behavior can contribute to the prediction of related winter weather in North America. The storm tracks are described by objectively tracking isentropic potential vorticity (PV) anomalies for two periods (base, 1983–2002; validation, 2003–10) to assess their value in a more realistic forecast mode. Statistically significant positive PV biases are found in the storm-track reforecasts. Removal of systematic errors is found to improve general storm-track features. CFSR and Reforecast (CFSRR) reproduces well the observed intensity and spatial distributions of storm-track-related near-surface winds, with small yet significant biases found in the storm-track regions. Removal of the mean wind bias further reduces the error on average by 12%. The spatial distributions of the reforecast precipitation correspond well with the reanalysis, although significant positive biases are found across the contiguous United States. Removal of the precipitation bias reduces the error on average by 25%. The bias-corrected fields better depict the observed variability and exhibit additional improvements in the representation of winter weather associated with strong-storm tracks (the storms with more intense PV). Additionally, the reforecasts reproduce the characteristic intensity and frequency of hazardous strong-storm winds. The findings suggest a potential use of storm-track statistics in the advancement of subseasonal-to-seasonal weather prediction in North America. [ABSTRACT FROM AUTHOR]

An extreme precipitation categorization scheme, used to temporally and spatially visualize and track the multiscale variability of extreme precipitation climatology, is applied over the continental United States. The scheme groups 3-day precipitation totals exceeding 100 mm into one of five precipitation categories, or "P-Cats." To demonstrate the categorization scheme and assess its observational uncertainty across a range of precipitation measurement approaches, we compare the climatology of P-Cats defined using in situ station data from the Global Historical Climatology Network-Daily (GHCN-D); satellite-derived data from the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA); gridded station data from the Parameter-Elevation Regression on Independent Slopes Model (PRISM); global reanalysis from the Modern-Era Retrospective Analysis for Research and Applications, version 2; and regional reanalysis from the North American Regional Reanalysis. While all datasets capture the principal spatial patterns of P-Cat climatology, results show considerable variability across the suite in frequency, spatial extent, and magnitude. Higher-resolution datasets, PRISM and TMPA, most closely resemble GHCN-D and capture a greater frequency of high-end P-Cats relative to the lower-resolution products. When all datasets are rescaled to a common coarser grid, differences persist with datasets originally constructed at a high resolution maintaining a higher frequency and magnitude of P-Cats. Results imply that dataset choice matters when applying the P-Cat scheme to track extreme precipitation over space and time. Potential future applications of the P-Cat scheme include providing a target for climate model evaluation and a basis for characterizing future change in extreme precipitation as projected by climate model simulations. [ABSTRACT FROM AUTHOR]

The teleconnection of the El Niño/Southern Oscillation (ENSO) to instrumental precipitation and temperature during the cool season over North America is strongest and most temporally stable in the TexMex sector of northern Mexico and the borderlands of southwestern United States. The ENSO impact on North American hydroclimate expands and contracts out of this region on multidecadal timescales, possibly associated with the positive and negative phases of the Atlantic Multidecadal Oscillation. A subset of tree‐ring chronologies from the TexMex sector also has the strongest and most stable ENSO signal detected in the North American network, similar to the strong ENSO signal measured in instrumental climate data from the same region. This subset of chronologies is used to reconstruct the multivariate ENSO index (MEI) as a measure of ENSO impact on North American hydroclimate during the instrumental and preinstrumental eras. The reconstruction exhibits improved fidelity in the frequency domain and better registration of spatial changes in ENSO signal over North America when compared to an MEI reconstruction based on all ENSO‐correlated tree‐ring chronologies irrespective of temporal stability of correlation. When correlated with gridded instrumental and tree‐ring reconstructed Palmer drought indices across North America, the stable MEI estimate reproduces the changes in spatial impact of ENSO signal measured with instrumental data, and it reveals similar multidecadal changes in prehistory, potentially linked to the Atlantic Multidecadal Oscillation. The Great Plains drought of the 1850s and 1860s may have been an example of this Pacific‐Atlantic configuration. Key Points: The ENSO teleconnection to North American hydroclimate varies in magnitude and spatial impact on multidecadal timescalesThe strongest and most stable ENSO influence is observed in the TexMex sector of northern Mexico and the borderlands of southwestern United StatesTree rings reproduce these temporal and spatial changes and suggest that the changes may arise in part from North Atlantic SST variations [ABSTRACT FROM AUTHOR]

Current generation general circulation models (GCMs) simulate synoptic-scale climate state variables such as geopotential heights, specific humidity, and integrated vapor transport (IVT) more reliably than mesoscale precipitation. Statistical downscaling methods that condition precipitation on GCM-based, synoptic-scale climate features have shown promise in the reproduction of local precipitation. However, current approaches to climate-state-informed downscaling impose some limitations on the skill of precipitation reproduction, including hard clustering of climate modes into a discrete set of states, utilization of numerical clustering methodologies poorly suited to nonnormal data, and a tendency to focus on relationships to a limited set of large-scale climate modes. This study presents a methodology based on emerging machine learning techniques to develop global approximators of regional precipitation and discharge extremes given a suite of synoptic-scale climate state variables. Archetypal analysis is first used to define regional modes of winter and summer extreme precipitation and discharge across the eastern contiguous United States. A 2D convolution neural network (NN) is then used to predict the co-occurrence of the archetypes using 300- and 700-hPa geopotential heights, 300- and 700-hPa specific humidity, and IVT. Results suggest that 300-hPa geopotential height, 700-hPa specific humidity, and IVT yield the most reliable predictions, although with some important differences by season and region. Finally, we demonstrate that the trained activations of NN convolutional layers can be used to infer the causal pathways between synoptic-scale climate features and regional extremes. [ABSTRACT FROM AUTHOR]

Circulation regimes are identified from a cluster analysis applied to 5-day means of the combined anomalies of 500 hPa geopotential height and the 250 hPa zonal wind over the extended Pacific-North America region obtained from ERA-Interim reanalyses. Five regimes are identified: the Arctic Low (AL), Pacific wavetrain (WT), Alaskan Ridge (AR) and Pacific Trough (PT) and Arctic High (AH). Regime dependent anomalies of the frequency of atmospheric rivers and measures of the storm tracks (850 hPa covariance between high pass filtered meridional wind and temperature and 300 hPa variance of high pass filtered meridional wind) are shown to be significant. The circulation regimes provide statistically significant modulation of observed precipitation (obtained from the Multi-Source Weighted-Ensemble data set). We examine regime-specific anomalies, normalized anomalies and the occurrence of extreme values of precipitation. We identify regions in which the ratio of frequency of occurrence of extremes (5th and 95th percentile) to the climatological frequency in each regime is significantly high. Precipitation is decreased over the Pacific Northwest in the AL regime, but is increased over the central and Eastern US, with a large increase in the probability of extreme precipitation, enhanced atmospheric river occurrence and stronger storm tracks. In the WT regime, Pacific atmospheric rivers are shifted towards Alaska, accompanied by a decrease in storm track magnitude over the Eastern North Pacific. The AR regime is characterized by an increase in moisture flux into the Southwest, increases in extreme precipitation and atmospheric rivers and storm track related heat transport. Increases in precipitation, and atmospheric rivers along the Southwest coast are noted in the PT regime, along with increased precipitation over Texas and Florida. The Northeastern region experiences increases in extremely dry conditions. Significant drought conditions occur over the Midwest US. Significant increases in precipitation over a broad region of the Northwest occur during the AH regime. [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs) are narrow, elongated corridors of high water vapor content transported from tropical and/or extratropical cyclones. We characterize precipitation and snow water equivalent associated with ARs intersecting the western U.S. coast during the cold season (November– March) of water years 1949–2015. For each AR landfalling date, we retrieved the precipitation and relevant hydrometeorological variables from dynamically downscaled atmospheric reanalyses (NCEP–NCAR) using the WRF mesoscale numerical weather prediction model. Landfalling ARs resulted in higher precipitation amounts throughout the domain than did non-AR storms. ARs contributed the most extreme precipitation events during January and February. Daily snow water equivalent (SWE) changes during ARs indicate that high positive net snow accumulation conditions accompany ARs in December, January, and February. We also assess the historical impact of AR storm duration and precipitation frequency by considering the precipitation depth estimated during a 72-h window bounding the AR landfall date. More extreme precipitation amounts are produced by ARs in the South Cascades and Sierra Nevada ranges compared with ARs with landfall farther north. Most AR extreme precipitation events (and lower SWE accumulations) are produced during warm AR dates, especially toward the northern end of our domain. Analysis of ARs during dry and wet years reveals that ARs during wet years are more frequent and produce heavier precipitation and snow accumulation as compared with dry years. Such conditions are evident in drought events that are associated with a reduced frequency of ARs. [ABSTRACT FROM AUTHOR]

This study examines the primary atmospheric controls over winter precipitation variability in the Great Lakes basin and the potential for seasonal prediction. We employ partial least squares (PLS) regression to identify the primary modes of joint variability between winter precipitation over each of the Great Lakes and concurrent anomalies in midlevel atmospheric flow. We find that the first identified pattern (PLS1) is related to El Niño–Southern Oscillation (ENSO), while the other patterns represent unique anomalies in atmospheric flow that govern precipitation gradients over the basin, with limited seasonal predictability. Nonlinearities are found in the relationship between a sea surface temperature (SST)-based index for ENSO and PLS1 with respect to the phase, strength, and type of ENSO event. An examination of the ENSO-related propagating wave train that drives variability of PLS1 precipitation reveals that seasonally lagged tropical Pacific convection, as measured by remotely sensed outgoing longwave radiation (OLR), is more strongly and linearly related to Great Lakes winter precipitation than SST-based ENSO indices. Cross-validated linear regressions based on October OLR signals explain 20%–32% of the out-of-sample precipitation variability in the Great Lakes basin. We conclude with a deeper assessment of the underlying relationship between patterns of OLR anomalies in the western equatorial Pacific and Great Lakes winter precipitation. Results show that precipitation response to El Niño is similar regardless of OLR intensity in the tropical Pacific, but for La Niña events, the precipitation response is stronger under weak tropical OLR anomalies. The potential for further improvements in ENSO-based seasonal forecasts are discussed. [ABSTRACT FROM AUTHOR]

Global warming is expected to increase the amount of atmospheric moisture, resulting in heavier extreme precipitation. Various studies have used the historical relationship between extreme precipitation and temperature (temperature scaling) to provide guidance about precipitation extremes in a future warmer climate. Here we assess how much information is required to robustly identify temperature scaling relationships, and whether these relationships are equally effective at different times in the future in estimating precipitation extremes everywhere across North America. Using a large ensemble of 35 North American regional climate simulations of the period 1951–2100, we show that individual climate simulations of length comparable to that of typical instrumental records are unable to constrain temperature scaling relationships well enough to reliably estimate future extremes of local precipitation accumulation for hourly to daily durations in the model's climate. Hence, temperature scaling relationships estimated from the limited historical observations are unlikely to be able to provide reliable guidance for future adaptation planning at local spatial scales. In contrast, well‐constrained temperature scaling relations based on multiple regional climate simulations do provide a feasible basis for accurately projecting precipitation extremes of hourly to daily durations in different future periods over more than 90% of the North American land area. Plain Language Summary: Global warming is expected to increase the amount of atmospheric moisture, resulting in heavier extreme precipitation. Various studies have used the historical relationship between extreme precipitation and temperature (temperature scaling) to provide guidance about precipitation extremes in a future warmer climate. Temperature scaling is useful for this purpose only when it can be robustly identified. Using a large ensemble of 35 North American regional climate simulations of the period 1951–2100, we show that individual climate simulations of length comparable to that of typical instrumental records are unable to identify temperature scaling relationships robustly enough to reliably estimate future extremes of local precipitation accumulation for hourly to daily durations in the model's climate. Hence, temperature scaling relationships estimated from the limited historical observations are unlikely to be able to provide reliable guidance for future adaptation planning at local spatial scales. This also has broader implications for how we account for nonstationarity more generally in historical observations. In contrast, temperature scaling relations based on multiple regional climate simulations do provide a feasible basis for accurately projecting precipitation extremes of hourly to daily durations over more than 90% of the North American land area. Key Points: Estimates of the scaling of local extreme precipitation with temperature based on local precipitation observations are highly uncertainRecords with lengths that are many multiples of the length of available observations are needed to well constrain the scaling relationWell‐constrained scaling relations can reliably estimate climate modeled extreme precipitation changes over almost all of North America [ABSTRACT FROM AUTHOR]

In this study, the intraseasonal variations in storm-track activity, surface air temperature, and precipitation over North America associated with the Madden–Julian oscillation (MJO) in boreal winter (November–April) are investigated. A lag composite strategy that considers different MJO phases and different lag days is developed. The results highlight regions over which the MJO has significant impacts on surface weather on intraseasonal time scales. A north–south shift of storm-track activity associated with the MJO is found over North America. The shift is consistent with the MJO-related surface air temperature anomaly over the eastern United States. In many regions over the western, central, and southeastern United States, the MJO-related precipitation signal is also consistent with nearby storm-track activity. An MJO-related north–south shift of precipitation is also found near the west coast of North America, with the precipitation over California being consistent with the MJO-related storm-track activity over the eastern Pacific. MJO-related temperature and storm-track anomalies are also found near Alaska. Further analyses of streamfunction anomalies and wave activity flux show clear signatures of Rossby wave trains excited by convection anomalies related to MJO phases 3 and 8. These wave trains propagate across the Pacific and North America, bringing an anticyclonic (cyclonic) anomaly to the eastern part of North America, shifting the westerly jet to the north (south), thereby modulating the surface air temperature and storm-track activity over the continent. Rossby waves associated with phases 2 and 6 are also found to impact the U.S. West Coast. [ABSTRACT FROM AUTHOR]

Changes in the essential climate extremes indices and surface variables for the end of the twenty-first century are assessed in this study based on two transient climate change simulations, with and without land-use and land-cover changes (LULCC), but identical atmospheric forcing. The two simulations are performed with the 5th generation of the Canadian Regional Climate Model (CRCM5) driven by the Canadian Earth System Model for the (2006-2100)-Representative Concentration Pathway 4.5 (RCP4.5) scenario. For the simulation with LULCC, land-cover data sets are taken from the global change assessment model (GCAM) representing the RCP4.5 scenario for the period 2006-2100. LULCC in RCP4.5 scenario suggest significant reduction in cultivated land (e.g. Canadian Prairies and Mississippi basin) due to afforestation. CRCM5 climate projections imply a general warming by the end of the twenty-first century, especially over the northern regions in winter. CRCM5 projects more warm spell-days per year over most areas of the continent, and implicitly more summer days and tropical nights at the expense of cold-spell, frost and ice days whose number is projected to decrease by up to 40% by the end of the twenty-first century with respect to the baseline period 1971-2000. Most land areas north of 45°N, in all seasons, as well as the southeastern United States in summer, exhibit increases in mean precipitation under the RCP4.5 scenario. In contrast, central parts of the continent in summer and much of Mexico in all seasons show reduced precipitation. In addition, large areas of North America exhibit changes of 10 to 40% (depending on the season and geographical location) in the number of heavy precipitation days. Results also suggest that the biogeophysical effects of LULCC on climate, assessed through differences between the two simulations, lead to warmer regional climates, especially in winter. The investigation of processes leading to this response shows high sensitivity of the results to changes in albedo as a response to LULCC. Overall, at the seasonal scale, results show that intense afforestation may contribute to an additional 25% of projected changes. [ABSTRACT FROM AUTHOR]

ABSTRACT: The purpose of this study was to investigate the seasonal to interannual variability of the temporal and spatial distributions of land–atmosphere coupling (LAC) at the mesoscale within the Southern Great Plains (SGP) of the United States. The North American Regional Reanalysis data set from 1979 to 2014 was used to complete this study. To further expand the relationship between soil moisture and precipitation, LAC was examined for the effects of soil moisture variability on latent heat flux (SM‐E) and the impact of latent heat flux variability on precipitation (E‐P). Results revealed that within the SGP there is a temporal and spatial seasonal evolution of the SM‐E relationship and dry boreal summer month (June, July and August, JJA) periods exhibit a stronger E‐P relationship relative to pluvial boreal summer month periods. Further, the variability of coupling was large both within‐season (i.e. JJA) as well as at the interannual scale while the interannual spatial and temporal coherence was such that no specific locations showed consistent coupling within the domain. Thus, the results indicate that while the SGP domain is sensitive to coupling, the location of preferred coupling is likely due to non‐local factors at the mesoscale embedded within synoptic conditions as well as the regional climate. [ABSTRACT FROM AUTHOR]

We have constructed a fire weather climatology over North America from 1979 to 2015 using the North American Regional Reanalysis dataset and the Canadian Fire Weather Index (FWI) System. We tested for the presence of trends in potential fire season length, based on a meteorological definition, and extreme fire weather using the non-parametric Theil-Sen slope estimator and Mann-Kendall test. Applying field significance testing (i.e. joint significance of multiple tests) allowed the identification of the locations of significant trends, taking into account spatial correlations. Fire season length was found to be increasing over large areas of North America, especially in eastern Canada and the south-western US, which is consistent with a later fire season end and an earlier fire season start. Both positive and negative trends in potential fire spread days and the 99th percentile of FWI occurred in Canada and the contiguous United States, although the trends of largest magnitude and statistical significance were mostly positive. In contrast, the proportion of trends with significant decreases in these variables were much lower, indicating an overall increase in extreme fire weather. The smaller proportion of significant positive trends found over Canada reflects the truncation of the time series, necessary because assimilation of precipitation observations over Canada ceased in the reanalysis post-2002. [ABSTRACT FROM AUTHOR]

This study investigates the future change in precipitation associated with extratropical cyclones over eastern North America and the western Atlantic during the cool season (November-March) through the twenty-first century. A cyclone-relative approach is applied to 10 models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) in order to isolate precipitation changes for different cyclone intensities and storm life cycle, as well as determine the relevant physical processes associated with these changes. The historical analysis suggests that models with better performance in predicting extratropical cyclones tend to have smaller precipitation errors, and the ensemble mean has a smaller mean absolute error than the individual models. By the late-twenty-first century, the precipitation amount associated with cyclones increases by 5%-25% over the U.S. East Coast, with about 90% of the increase from the relatively strong (<990 hPa) and moderate (990-1005 hPa) cyclones. Meanwhile, the precipitation rate increases by 15%-25% over the U.S. East Coast for the strong cyclone centers, which is larger than the moderate and weak cyclones. The relatively strong cyclones just inland of the U.S. East Coast have the largest increase (~30%) in precipitation rate, since these centers over land have the largest increase in low-level temperature (and moisture), a decrease (5%-13%) in the static stability, and an increase (~5%) in upward motion during the late-twenty-first century. This east coast region also has an increase in cyclone intensity in the future even though there is a decrease in low-level baroclinicity, which suggests that the latent heat release from heavier precipitation contributes to this storm deepening. [ABSTRACT FROM AUTHOR]

Canonical correlation analysis (CCA)-based statistical corrections are applied to seasonal mean precipitation and temperature hindcasts of the individual models from the North American Multimodel Ensemble project to correct biases in the positions and amplitudes of the predicted large-scale anomaly patterns. Corrections are applied in 15 individual regions and then merged into globally corrected forecasts. The CCA correction dramatically improves the RMS error skill score, demonstrating that model predictions contain correctable systematic biases in mean and amplitude. However, the corrections do not materially improve the anomaly correlation skills of the individual models for most regions, seasons, and lead times, with the exception of October-December precipitation in Indonesia and eastern Africa. Models with lower uncorrected correlation skill tend to benefit more from the correction, suggesting that their lower skills may be due to correctable systematic errors. Unexpectedly, corrections for the globe as a single region tend to improve the anomaly correlation at least as much as the merged corrections to the individual regions for temperature, and more so for precipitation, perhaps due to better noise filtering. The lack of overall improvement in correlation may imply relatively mild errors in large-scale anomaly patterns. Alternatively, there may be such errors, but the period of record is too short to identify them effectively but long enough to find local biases in mean and amplitude. Therefore, statistical correction methods treating individual locations (e.g., multiple regression or principal component regression) may be recommended for today's coupled climate model forecasts. The findings highlight that the performance of statistical postprocessing can be grossly overestimated without thorough cross validation or evaluation on independent data. [ABSTRACT FROM AUTHOR]

Tropical cyclone precipitation (TCP) can cause significant flooding in coastal areas around the world. This study compares multiple options of a new technique for developing a gridded daily TCP product at a spatial resolution of 0.25°. These options were evaluated using NASA's Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42 product to determine the optimal approach. Results indicate that the technique is very sensitive to changes in wind corrections, interpolation method, and gauge density. The optimal method accounts for wind-induced gauge undercatch and uses a customized interpolation approach. It significantly reduces precipitation biases associated with gauge undercatch during windy conditions. The new TCP extraction approach can be used to examine variability and long-term trends in TCP, even in regions with relatively few gauges. [ABSTRACT FROM AUTHOR]