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.

This work advances a unified approach to process-based hydrologic modeling to enable controlled and systematic evaluation of multiple model representations (hypotheses) of hydrologic processes and scaling behavior. Our approach, which we term the Structure for Unifying Multiple Modeling Alternatives (SUMMA), formulates a general set of conservation equations, providing the flexibility to experiment with different spatial representations, different flux parameterizations, different model parameter values, and different time stepping schemes. In this paper, we introduce the general approach used in SUMMA, detailing the spatial organization and model simplifications, and how different representations of multiple physical processes can be combined within a single modeling framework. We discuss how SUMMA can be used to systematically pursue the method of multiple working hypotheses in hydrology. In particular, we discuss how SUMMA can help tackle major hydrologic modeling challenges, including defining the appropriate complexity of a model, selecting among competing flux parameterizations, representing spatial variability across a hierarchy of scales, identifying potential improvements in computational efficiency and numerical accuracy as part of the numerical solver, and improving understanding of the various sources of model uncertainty.

Refugia have long been studied from paleontological and biogeographical perspectives to understand how populations persisted during past periods of unfavorable climate. Recently, researchers have applied the idea to contemporary landscapes to identify climate change refugia, here defined as areas relatively buffered from contemporary climate change over time that enable persistence of valued physical, ecological, and socio-cultural resources. We differentiate historical and contemporary views, and characterize physical and ecological processes that create and maintain climate change refugia. We then delineate how refugia can fit into existing decision support frameworks for climate adaptation and describe seven steps for managing them. Finally, we identify challenges and opportunities for operationalizing the concept of climate change refugia. Managing climate change refugia can be an important option for conservation in the face of ongoing climate change.

[1] The typically sparse distribution of weather stations in mountainous terrain inadequately resolves temperature variability. Accordingly, high-resolution gridding of climate data (for applications such as hydrological modeling) often relies on assumptions such as a constant surface temperature lapse rate (i.e., decrease of surface temperature with altitude) of 6.5°C km. Using an example of the Cascade Mountains, we describe the temporal and spatial variability of the surface temperature lapse rate, combining data from: (1) COOP stations, (2) nearby radiosonde launches, (3) a temporary dense network of sensors, (4) forecasts from the MM5 regional model, and (5) PRISM geo-statistical analyses. On the windward side of the range, the various data sources reveal annual mean lapse rates of 3.9-5.2°C km −1 , substantially smaller than the often-assumed 6.5°C km −1 . The data sets show similar seasonal and diurnal variability, with lapse rates smallest (2.5-3.5°C km −1 ) in late-summer minimum temperatures, and largest (6.5-7.5°C km −1 ) in spring maximum temperatures. Geographic (windward versus lee side) differences in lapse rates are found to be substantial. Using a simple runoff model, we show the appreciable implications of these results for hydrological modeling.

[1] Many regions of the world are dependent on snow cover for frost protection and summer water supplies. These same regions are predominantly forested, with forests highly vulnerable to change. Here we combine a meta-analysis of observational studies across the globe with modeling to show that in regions with average December-January-February (DJF) temperatures greater than À1 C, forest cover reduces snow duration by 1-2 weeks compared to adjacent open areas. This occurs because the dominant effect of forest cover shifts from slowing snowmelt by shading the snow and blocking the wind to accelerating snowmelt from increasing longwave radiation. In many locations, midwinter melt removes forest snow before solar radiation is great enough for forest shading to matter, and with warming temperatures, midwinter melt is likely to become more widespread. This temperature-effect in forest-snow-climate interactions must be considered in representations of the combined ecohydrological system and can be used advantageously in forest management strategies.

[1] Most climate models suggest amplification of global warming in high mountains, but observations are less clear. Using comprehensive, homogeneity-adjusted temperature records from over 1000 high elevation stations across the globe, we examine the causes of changing temperature trends with elevation, assessing the roles of free atmospheric change, topography (exposure and aspect), and cryospheric feedback. The data show that observed 20th century temperature trends are most rapid near the annual 0°C isotherm due to snow-ice feedback. Mountain summit and freely draining slope sites are dominated by free-air advection and thus have consistent trend magnitudes, with reduced inter-site variance in comparison with incised valley sites where local factors are more important. Thus, while there has been no simplistic elevational increase in warming rates, some generalizations can be made. Water resources and ecosystems near the 0°C isotherm in the extratropics are at increased risk from accelerated warming. The data also suggest that exposed mountain summits, away from the effects of urbanization and topographic sheltering, may provide a relatively unbiased record of the planet's climate.

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This work advances a unified approach to process-based hydrologic modeling, which we term the ''Structure for Unifying Multiple Modeling Alternatives (SUMMA).'' The modeling framework, introduced in the companion paper, uses a general set of conservation equations with flexibility in the choice of process parameterizations (closure relationships) and spatial architecture. This second paper specifies the model equations and their spatial approximations, describes the hydrologic and biophysical process parameterizations currently supported within the framework, and illustrates how the framework can be used in conjunction with multivariate observations to identify model improvements and future research and data needs. The case studies illustrate the use of SUMMA to select among competing modeling approaches based on both observed data and theoretical considerations. Specific examples of preferable modeling approaches include the use of physiological methods to estimate stomatal resistance, careful specification of the shape of the within-canopy and below-canopy wind profile, explicitly accounting for dust concentrations within the snowpack, and explicitly representing distributed lateral flow processes. Results also demonstrate that changes in parameter values can make as much or more difference to the model predictions than changes in the process representation. This emphasizes that improvements in model fidelity require a sagacious choice of both process parameterizations and model parameters. In conclusion, we envisage that SUMMA can facilitate ongoing model development efforts, the diagnosis and correction of model structural errors, and improved characterization of model uncertainty.