The high geographical latitude of the Arctic creates unique hydrological conditions as a result of a low thermal energy state and, therefore, annual average air temperatures are low and winters long. Consequently, winter snow accumulation and snowmelt are the most important features of the hydrological cycle in cold regions. Snow accumulation may last for nine months and is then released in a relatively short time, typically 10 to 14 days just before summer solstice (Kane and Hinzman, 1988). Each year, for six months or more, Arctic water bodies are affected by ice because of the long duration of cold temperatures. The presence of ice affects overall streamflow, making it intensely seasonal or even ephemeral. Spring melting of snow and ice, when soils are frozen and infiltration is reduced, accentuates the amount of runoff and peak flow. The total water content of the snowpack within the AlaskaArctic at winter’s end comprises 40% or more of the region’s annual precipitation (Kane et al., 1991; Kane et al., 2008b); on average, about two-thirds of the snow water equivalent (SWE) leaves catchments as runoff (Kane et al., 2008a; Kane et al., 2000; Kane et al., 2004). On larger rivers in the AlaskaArctic, the breakup flood can account for about 40% of the annual discharge (Arnborg et al., 1967); on smaller streams, for as much as 90% (McCann et al., 1972).
CHAPTER 3: CONCLUSIONS
Results of this research indicate that projected increases in winter precipitation over Alaska’s North Slope may indirectly increase the quality of caribou forage. Deeper snow insulates the soil and allows microbial mineralization to continue throughout the winter, increasing soil nitrogen available for plant uptake in early spring. Snow depth may not have as large an impact on dry matter digestibility as leaf-level nutrients, but even the small increases seen in this study may influence forage intake, with subsequent multiplier effects on survival and fecundity. Direct changes in N availability and indirect changes in vegetation community structure, though, may have a stronger influence on overall caribou nutrition in the Arctic than species-specific changes in forage
Abstract We analyze daily precipitation extremes from simulations of a polar-optimized version of the Weather Research and Forecasting (WRF) model. Simulations cover 19 years and use the Regional Arctic System Model (RASM) domain. We focus on Alaska because of its proximity to the Paci ﬁc and Arctic oceans; both provide large moisture fetch inland. Alaska ’s topography also has important impacts on orographically forced precipitation. We use self-organizing maps (SOMs) to understand circulation characteristics conducive for extreme precipitation events. The SOM algorithm employs an arti ﬁcial neural network that uses an unsupervised training process, which results in ﬁnding general patterns of circulation behavior. The SOM is trained with mean sea level pressure (MSLP) anomalies. Widespread extreme events, de ﬁned as at least 25 grid points experiencing 99th percentile precipitation, are examined using SOMs. Widespread extreme days are mapped onto the SOM of MSLP anomalies, indicating circulation patterns. SOMs aid in determining high-frequency nodes, and hence, circulations are conducive to extremes. Multiple circulation patterns are responsible for extreme days, which are differentiated by where extreme events occur in Alaska. Additionally, several meteorological ﬁelds are composited for nodes accessed by extreme and nonextreme events to determine speci ﬁc conditions necessary for a widespread extreme event. Individual and adjacent node composites produce more physically reasonable circulations as opposed to composites of all extremes, which include multiple synoptic regimes. Temporal evolution of extreme events is also traced through SOM space. Thus, this analysis lays the groundwork for diagnosing differences in atmospheric circulations and their associated widespread, extreme precipitation events.
served among mammalian hepadnaviruses (11). The same de- letions were also found in a partial ASHV sequence derived from a second infected animal (data not shown), indicating that they represent an intrinsic feature of the ASHV genome. The smallest ORF of mammalian hepadnavirus genomes, named X, encodes a promiscuous transcriptional activator, and it is required to establish a productive viral infection in vivo (4, 7, 71). The ASHV X gene is identical in size to the GSHV X gene (414 bp), from which it differs at 75 (18%) nucleotide positions. It is smaller than the WHV X gene (423 bp), but the two sequences show only 15% nucleotide exchanges. Com- pared with the GSHV and WHV X proteins (GSHx and WHx), the predicted X protein of ASHV (ASHx) has more amino acid variations than any other viral gene product (Table 1). As shown in Fig. 5, alignment of the mammalian hepadna- virus X proteins confirms and extends previous observations that highly conserved regions are located in two hydrophobic sequences encompassing amino-terminal residues 1 to 20, car- boxy-terminal residues 124 to 136, and a moderately con- served, charged region in the central part. Internal start codons which might serve for translation of two small polypeptides (residues 81 and 98), and the regions suggested to be homol- ogous to the Kunitz domain of Kunitz-type protease inhibitors (residues 67 to 69 and 128 to 136) (64) are also remarkably conserved in ASHx.
scaled as discussed earlier. All analysis regions display nearly identical behavior. We de ﬁne “widespread events” as those occurring simultaneously on 25 or more model grid points (or a comparable number of scaled observation points). This choice balances a goal of having a moderately large number of samples to analyze against an assumption that widespread extremes are governed by resolved ﬁelds in the simulations. The curves for each of the ensemble members tend to group together for N up to about 50, for three of the regions. The Alaska North box shows greater spread among ensemble members, with separation of curves from individual members occurring at around N = 10 grid points. More importantly, the simulation curves show fair agreement with the observation curves; the slopes of the model and observation curves in Figure 4 differ by less than 10% on the log linear plot. This suggests that the spatial scale for simulated extreme events is roughly the same as the observed scale, despite the weaker precipitation extremes in the simulations.
The salient result in Table 1 is that the probabilities of exceedances of preindustrial extremes of the SPEI increase later in the century, reaching 13.2% in June and 4.9% in the summer season average, indicating that the wildfire risk will increase in the coming dec- ades. For June, the projected increase ranges among ensemble members from 0 to 27%, while the corresponding range for the summer season average is 0 to 17%. The projected increases for July and August are 8.9% (range: 0–12%) and 6.5% (range: 2.5–9.8%), respec- tively. It is noteworthy that the projected change dampens between June to August, per- haps in the form on increased season-ending rains. These projected changes are all sub- stantially larger than the corresponding changes from the PI to the recent decades (1979– 2019), which are 0.5% for the summer average, 2.1% for June, 1.9% for July and 2.4% for August. The fact that the recent changes are positive but much smaller than the projected future changes leads to the conclusion that the signal of anthropogenic forcing is emergent but not yet a major contributor to the wildfire risk in Southcentral Alaska. In contrast, Partain et al.  found that anthropogenic forcing has increased the risk of an event like the 2015 Interior Alaska fire season by 34–64% in an attribution analysis using BUI. South- centralAlaska has climatologically cooler and wetter summers than Interior Alaska (i.e., compare Cook Inlet to Central Interior climate divisions in ), therefore it is not surpris- ing that there is a weaker anthropogenic signal than in the Interior.
address the inconsistency of precipitation records across the national borders. This is an important issue, since most re- gional precipitation data and products have been compiled and derived from the combination of various data sources, as- suming these data and observations were compatible across the borders and among the national observational networks. Simpson et al. (2005) studied temperature and precipitation distributions over the state of Alaska (AK) and west Yukon (YK), and documented precipitation increase from north to south. They also report differences in mean monthly precip- itation across the Alaska–Yukon border, i.e. about 5–15 mm in central-east Alaska and 15–40 mm in central-west Yukon. Jones and Fahl (1994) found a weak gradient in annual pre- cipitation across the AK–YK border, including the headwa- ters of the Yukon River. Other studies also discuss precipita- tion distribution and changes over the Arctic regions (Legates and Willmott, 1990; Serreze and Hurst, 2000; Yang et al., 2005).
The Arctic zone is characterized generally by average annual precipitation of less than 50 cm and an average annual temperature of −6˚C or less . The arctic region of Alaska was delineated for this study as a total area of 317,380 km 2 within the Major Land Resource Areas  covering the Arctic Coastal Plain, Arctic Foothills, and Brooks Range Mountains (Figure 1). According to MODIS 1-km land cover mapping (Figure 2), this Alaskan arctic region is predominantly (99%) open tundra cover, 0.1% evergreen (coniferous) forest cover, and 0.7% barren land cover. Wetlands, as identified by Whitcomb et al. , covered 28% (89,180 km 2 ) of the re- gion, located mainly on the coastal plain, with 82% of these ecosystems classified as herbaceous and 18% as shrub covered wetlands across the region (Figure 2). About 85% of the region was located on continuous per- mafrost. Elevation across the region ranges from sea level on the coastal plain to 2500 m in the highest portions of Brooks Range Mountains. The AICC wildfire boundary record showed that only 1.4% (4563 km 2 ) of the Alaskan arctic region was burned by fires between 1940 and 2010. During the period between 2000 and 2010 alone, an area of 1707 km 2 of the region was burned by wildfire, more than any other decade since before 1940.
4.1 Evaluation of recent climate change in Alaska We compared the δD values and annual accumulation rates estimated from the ice core with air temperatures and an- nual precipitation, respectively, observed at weather sta- tions located in Alaska (Table 2 and Fig. 1; climatologi- cal data provided by the Alaska Climate Research Center, http://climate.gi.alaska.edu/index.html, and the U.S. Geo- logical Survey, http://ak.water.usgs.gov/glaciology/gulkana/ index.html). Figure 5 shows the calculated correlation coef- ficients between the 7-year running averages of δD in the ice core and the air temperatures observed at weather stations in Alaska, and between the annual accumulation rates esti- mated from the ice core and annual precipitation observed at the weather stations. The δD values and air temperatures were highly correlated in both the central and southern areas (coastal area of the Gulf of Alaska), and the annual accu- mulation rates and precipitation were also correlated in the southern area. Our results suggest that δD values reflect the air temperatures of both centralAlaska and the coastal area of the Gulf of Alaska, and that the annual accumulation rates
To illustrate how the synoptic scale weather developed over time during ASCOS in response to the mean flow- field displayed in Fig. 4, a select set of ECMWF surface- pressure and 10-m wind analyses are shown in Fig. 8; also included are 12-hourly storm tracks for the most signifi- cant weather systems derived from ECMWF analyses. Dur- ing the first part of the expedition several significant storms passed the ASCOS track moving from east to west; the op- posite of the usual direction of travel but consistent with the MSLP anomalies (Fig. 4d). Figure 8a shows the first, start- ing on 4 August (DoY 217) in the Canada basin and moving clockwise around the pole, reaching the Kara Sea by 7 and 8 August (DoY 220 and 221) then crossing over Svalbard on 10 August and passing south of ASCOS on 12 August (DoY 225), the first day of the ASCOS ice drift. This weather system brought strong winds, precipitation and generally ad- verse conditions for working on the ice and thus slowed down the initial deployment of instrumentation on the ice.
Permafrost warming, degradation, and thaw subsidence can have significant implications for ecosystems, infrastruc- ture, and climate at local, regional, and global scales (Jor- genson et al., 2001; Nelson et al., 2001; Schuur et al., 2008). In general, permafrost in Alaska has warmed between 0.3 and 6.0 ◦ C since ground temperature measurements began between the 1950 and 1980s (Lachenbruch and Marshall, 1986; Romanovsky and Osterkamp, 1995; Romanovsky et al., 2002, 2010; Osterkamp, 2007). Warming and thawing of near-surface permafrost may lead to widespread terrain in- stability in ice-rich permafrost in the Arctic (Jorgenson et al., 2006; Lantz and Kokelj, 2008; Gooseff et al., 2009; Jones et al., 2015; Liljedahl et al., 2016) and the subArctic (Os- terkamp et al., 2000; Jorgenson and Osterkamp, 2005; Lara et al., 2016). Such land surface changes can impact vegeta- tion, hydrology, aquatic ecosystems, and soil-carbon dynam- ics (Grosse et al., 2011; Jorgenson et al., 2013; Kokelj et al., 2015; O’Donnell et al., 2011; Schuur et al., 2008; Vonk et al., 2015). For example, in boreal peatlands, thaw of ice-rich per- mafrost often converts forested permafrost plateaus into lake and wetland bog and fen complexes (Camill, 1999; Jorgen- son et al., 2001; Payette et al., 2004; Sannel and Kuhry, 2008, 2011; Quinton et al., 2011; Jorgenson et al., 2012; Kanevskiy et al., 2014; Swindles et al., 2015; Lara et al., 2016). Fur- thermore, the transition from permafrost peatlands to thawed or only seasonally frozen peatlands can have a positive or a negative feedback on regional and global carbon cycles de- pending on permafrost conditions and differential effects of thaw on net primary productivity and heterotrophic respira- tion (Turetsky et al., 2007; Swindles et al., 2015), as well as
Figure 5 shows the time series of winter seasonal frequency of widespread extreme events for Alaska South, as an example. The plots for each analysis region show that ensemble members agree most with each other in years with either very many or very few simulated extreme events; individual members showed greater disagreement in the interim years. Although there can be substantial spread among the ensemble members, the simulation is able to capture the observational variability well; the magnitude of interannual variability in the model is approximately the same as observed. Other regions also show similar variability behavior. The analysis regions are in the higher latitudes, suggesting possible in ﬂuence of the Arctic Oscillation (AO) on interannual variability. The AO involves ﬂuctuations in surface pressure that affect storm tracks in the higher midlatitudes [Thompson and Wallace, 1998, 2001]. A positive (negative) phase indicates negative (positive) pressure anomalies over the Arctic region, with the opposite pressure anomaly equatorward. We compared the simulated and observed interannual variability to the Arctic Oscillation index. We found that the most positive AO index tended to coincide with years of high occurrences of precipitation extremes and years with negative AO index tended to be years with relatively few extremes. Our plots show a connection between years of increased precipitation extremes (1992, 1995, 1998, 2001, and 2003) and the positive phase of the AO; the positive phase suggests a poleward movement of storm tracks. Matsuo and Heki  show that a positive phase of the AO produced increased precipitation in the high latitude. Sea level pressure composites for widespread extreme days show systematic low pressure over the eastern Arctic Ocean for all analysis regions.
consequently only operated from approximately May to early November. In 2013, a wind turbine and additional batteries were added to the tussock tundra site, thereby permitting year-round operations.
The eddy covariance system for measuring the fluxes of CO 2 , water, and energy was placed on a 3 m high tripod in the center of each site. The instrumentation consisted of a 3-D sonic anemometer (CSAT-3; Campbell Scientific Instruments, Logan, Utah, USA) mounted at a height of 2.5 m at all three sites. An open-path infrared gas analyzer (LI-7500 IRGA; LI-COR, 2004; Lincoln, Nebraska, USA) was used at all three sites until 2012. At this time, the LI-7500A IRGA (LI-COR, 2009), which is not subject to sensor heating, as can be the case with the LI- 7500 IRGA, (discussed below), was installed at the wet sedge site. The LI-7500 IRGA remained at the heath tundra site, but with the addition in 2013 of the enclosed path LI-7200 IRGA (LI- COR, 2010). The main axes of the IRGAs were tilted by 20º with respect to the horizontal to aid in draining condensation and precipitation from the optical windows. The IRGAs and the CSAT- 3 sonic anemometers were both mounted on a shared horizontal bar and were laterally separated by 20 cm to reduce flux loss and flow distortion. The differing time delays in signals were taken into account by shifting the CSAT-3 data by one scan (at 10 Hz) to match the fixed 302.369 ms delay (or 3 scans at 10 Hz) that is programmed into the LI-7500 or LI-7500A. This
Winter circulation in the Arctic is characterized by an asymmetrical winter vortex with large troughs over eastern North America and western Asia and ridges over western North America, eastern Atlantic and central Asia (Serreze and Barry, 2014). Winds at the jet stream level (250 hPa) during the winters of 2004-2006 from NCEP/CFSR (Figure 23) deviate somewhat from the general circulation pattern described previously. Figure 24 shows composite winter circulation at the 250 hPa for just 2005 from NCEP/CFSR. Both figures show changes in the winter vortex and in the placement of the eastern North American trough compared to climatology. The diverging arm of the eastern North American trough corresponds to the general location of the North Atlantic storm track, which typically runs east of the Greenland Ice sheet. Figures 24 and 25 show a westward shift in the eastern North American trough where the diverging arm is intersected by the Greenland Ice sheet (Figure 24) or completely shifted to the western side of the ice sheet (Figure 25). Enhanced penetration of southerly air into the Arctic may have shifted the placement of the eastern North American trough and increased the rate of upper- level divergence and surface level convergence, which would have created favorable conditions for cyclone formation and precipitation. The circulation during the winter of 2005 (the peak of the anomalous spike) shows a pronounced trough over the CAA, which would have favored cyclone formation in this area. Local orography may have contributed to the occurrence of extreme events via the rapid uplift of air as it
a. PAW: ERA-Interim time-average bias
We analyzed monthly spatial mean fields of MSLP, 2-m temperature, 500-hPa geopotential heights, and level temperatures for the full RACM domain so as to determine the pre- and post-nudging PAW biases versus ERA-Interim output. Table 2 shows biases in these fields for each of our target regions with no nudging and with the WRF standard nudging strength. As mentioned in section 3, the initial simulation on the RACM domain produced large, time-average bias within the North Pacific storm track. In our analysis regions, the largest biases occurred in Oceana followed by the Alaska analysis region. Spectral nudging substantially reduced the bias for nearly all fields shown in Table 2. The ex- ception was 2-m temperature over the ocean, which would already have relatively small bias because the model uses specified sea surface temperature. There also appears to be a seasonal pattern in that the January case produces much higher biases than the July case.
In 2002, the ARRC sponsored the South Central Rail Network Commuter Study and Operation Plan. In addition to service between Mat-Su and Anchorage, this study explored service between Girdwood and Anchorage. The effort’s ridership analysis relied on quantification of the universe of weekday commuter trips to Anchorage. Modal splits typical of commuter rail elsewhere in the United States were then applied to the trip total to generate an estimate of potential commuter rail ridership. The analysis was supported by findings of a telephone survey and focus groups of Mat-Su - Anchorage commuters and was aimed at understanding commuter behavior and preferences. The survey/focus group findings confirmed strong interest in commuter rail.
4.1. Climatic Erosional Ef ﬁciency, Erosion Resistance, Rock Uplift, and Equilibrium Landscapes
In an equilibrium landscape, mean hillslope gradients adjust such that catchment wide erosion rates balance rock uplift. Any decrease in climatic erosional ef ﬁciency, or increase in erosional resistance, requires higher hillslope gradients to attain the same catchment scale erosion rates (Figure 1). In transient landscapes, channels may respond to a change in base level faster than hillslopes, and as a result high inter ﬂuve relief will develop (Figure 1e). High hillslope gradients can therefore indicate low climatic erosional ef ﬁciency, high erosional resistance, high uplift rates, and/or high channel incision rates relative to hillslope erosion rates. A major challenge in the study of climate-tectonics-landscape interactions is in identifying which of these factors is the most important and under what conditions. A number of studies [Carretier et al., 2013; Aalto et al., 2006] ﬁnd high correlations between catchment-scale erosion rates and hillslope gradients, suggesting that catchment morphology is primarily responding to spatial variations in uplift rate until threshold hillslopes are attained [DiBiase et al., 2012; Larsen and Montgomery, 2012]. Catchment erosion rates have been found to both correlate with mean annual precipitation [Bookhagen and Strecker, 2012; Owen et al., 2010] and to not correlate with local climatic variables [Aalto et al., 2006; Insel et al., 2010b; Riebe et al., 2001]. In this study, we ﬁnd signiﬁcant correlations between rainfall, vegetation, and geomorphic properties at the orogen scale. The in ﬂuence of climate and vegetation on mean hillslope gradients in the central Andes may be better understood by considering the physical mechanisms underlying the observed statistical relationships. The statistical relationships between the variables considered differ between partly and fully vegetated landscapes, so these categories are considered separately below.
Chapter 3 Biomedicine, Maternal Health Policy, and Birth Models
3.1 Introduction: US maternal health care policy and biomedicine
Women in the predominantly Iñupiat villages of northwest Alaska have gone (much like their colonial counterparts) from a social-based, women-centered birthing system to biomedical model of birth. Unlike their counterparts, this process was aided with the development of a protocol involving air travel. This policy was originally put into effect to help address some troubling infant mortality rates of the period (Schwarzburg 2007a). An historical comparison of these rates appears in a later section. In recent history, overall infant mortality rates (death within first year of life per 1,000 live births) have fallen among Alaska Native populations, neonatal deaths (in first 28 days of life) are improving, but postneonatal death (during 1 to 12 months) figures are on the rise. While the rate of FAS (Fetal Alcohol Syndrome) among Alaska Native populations is lowering in the state (Shinohara 2010), the actual numbers are still high (Indian Health Service 2007) and maternal behaviors are associated with such postneonatal outcomes (Alaska Department of Health and Social Services 2010; Shinohara 2010).
heat, strong easterly winds, the North Atlantic Subtropical High (NASH), high sea surface temperatures (SSTs), and in- tense precipitation (Wang, 2007). Regional terrain, including topography and diverse vegetation, which is found to be of importance (Lachniet et al., 2007), is still not properly rep- resented in numerical simulations. The horizontal “seesaw” observed in Central American precipitation is often attributed to the effect of the continental divide. Drier conditions in northern Central America and the Pacific slope are in contrast with a wetter Caribbean side. The annual cycle of precipita- tion is characterised by a bimodal distribution that exhibits a minimum in July–August and maximum values in June and September–October. This bimodal distribution of pre- cipitation, known as midsummer drought (MSD; after Ma- gaña et al., 1999), is more marked on the Pacific side. On the Caribbean slope, heavy precipitation is observed during May and June, followed by a drastic reduction in late June, leading to a drier and less cloudy July–August (Taylor and Alfaro, 2005). Several authors have pointed to the seasonal migra- tion of the Intertropical Convergence Zone (ITCZ), deep con- vection, low-level moisture transport, cyclone activity, and mid-latitude air intrusions as being the main drivers of re- gional precipitation (Schulz et al., 1997; Amador et al., 2006; Durán-Quesada et al., 2010; among others). Studies on the effect of large-scale structures such as the ITCZ on regional precipitation are scarce (Hidalgo et al., 2015), and some im- portant interactions, including links with the tropical Pacific, are not fully understood. The region is influenced by deep convection and highly active stratiform precipitation. A large deep convective core is located over the Panama Bight (Zulu- aga and Houze Jr., 2015), with an extended area of stratiform precipitation that becomes relevant for overall rainfall. The effect of mid-latitude interactions is known to be mostly
As it turns out, LCT participants were least likely to experience awe and an epiphany from witnessing the Arctic landscape. This occurred despite demonstrated behavioral intentions to witness changes in the Arctic environment. LCT visitors to Kaktovik are likely highly educated about the ecosystem and know what to expect. So, this knowledge may mute experiences of awe and reduce the occurrences of epiphanies. Expectations and knowledge can diminish novelty and therefore reduce feelings of awe and epiphanies. This same effect may not have occurred for polar bears because their behavior and the uncertainties of