A climatological analysis of emergency homeless shelter openings in Long Beach, California, USA

Applied Geography 37 (2013) 168e175

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A climatological analysis of emergency homeless shelter openings in Long Beach, California, USA David A. Pepper*, Christine L. Jocoy Department of Geography, California State University, Long Beach, 1250 Bellflower Blvd., Long Beach, CA 90840, USA

a b s t r a c t Keywords: Climate Cold weather El Niño-Southern Oscillation (ENSO) Emergency shelter Homelessness Vulnerability

Exposure to cold presents a serious risk to homeless persons, particularly on cold, rainy winter nights. Emergency homeless shelters operate in cities throughout the United States to provide residents refuge from dangerous weather; however, few of these shelters appear to have incorporated rigorous climatological analysis into their scheduling procedures. We have analyzed historical climate data for Long Beach, California, where limited funding and highly variable winter weather present serious challenges to shelter operators. Our analysis indicates that the existing schedule of shelter availability, between December 1 and March 15, is fairly effective in incorporating a large number of days where the temperature is below 4.4
C (40 F) or precipitation occurs. On the other hand, we suggest that the schedule should be adjusted somewhat by accounting for both human and climatological factors. Human factors include the seasonal lag in cultural adaptation to cold and occasional unexpected delays in resource availability that prevents shelters from opening by December 1. Climatological factors include the significant variation in dangerous winter weather caused by El Niño-Southern Oscillation cycles, and perhaps, the long-term impact of climate change. Specifically, opening shelters a week or more earlier, particularly during La Niña years, would likely decrease the risk of cold-exposure to the affected homeless population. We feel that our approach could be applied effectively to aid emergency-shelter operators throughout the country, given their need to maximize access during the most dangerous weather conditions despite limited resources. Ó 2012 Elsevier Ltd. All rights reserved.

Introduction Exposure to cold is a serious environmental risk that can cause a variety of health problems and even death. Prolonged exposure to moderate cold can lead to painful skin conditions such as “trench foot”, while exposure to intense cold for even a short period of time can cause frostbite, which may result in permanent tissue injury and possible amputation of affected body parts. Significant lowering of core body temperature, known as hypothermia, can cause permanent neurological damage or death. According to a study conducted by the Centers for Disease Control (2006), 4,607 people in the United States, or more than 1,100 per year, died from hypothermia or related conditions between 1999 and 2002. Many risk factors for cold-exposure are related directly to weather conditions, including low air temperature, wind, and precipitation. Integrating these factors to estimate exposure risk,

* Corresponding author. Tel.: þ1 562 985 8432; fax: þ1 562 985 8993. E-mail addresses: [email protected] (D.A. Pepper), Christine.Jocoy@ csulb.edu (C.L. Jocoy). 0143-6228/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.apgeog.2012.10.010

however, is problematic. The wind-chill factor is a well-known index that accounts for temperature and wind speed, but it accurately models heat loss only on directly exposed skin and only when sunlight is not a factor (NWS, 2009). No comparable index exists for precipitation, although the effect of moisture on bodyheat loss has been well documented under tightly-controlled conditions (e.g. Castellani et al., 2007; Thompson & Hayward, 1996). Saturated clothing may cause a 20-fold increase in bodyheat loss (O’Connell, Petrella, & Regan, 2004), but this effect is extremely difficult to quantify on the basis of daily or hourly precipitation levels among a diverse population. Many risk factors for cold exposure are closely related to an individual’s health, habits, and circumstances. Inadequate clothing and shelter, chronic disease, old age, reduced mobility, or ingestion of alcohol or drugs may drastically increase risk (New York City Departments of Health and Mental Hygiene and Homeless Services, 2005). Conversely, appropriate adjustments in clothing, shelter, and behavior among communities and individuals, known as cultural adaptation, along with a very limited ability of the body to adapt physiologically, can reduce risk (Kaciuba-Uscilco & Greenleaf, 1989). Cultural adaptation is important over the long

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term; for example, studies by Analitis et al. (2008), Curriero et al. (2002), and Oven et al. (2012) have shown that the rate of coldrelated mortality actually tends to be higher in regions with fairly mild climates, such as the Southern United States and Southern Europe, than it is in persistently colder regions. Over the short term, daily temperature range, as opposed to absolute temperature, can be quite significant. The risk of hypothermia appears to be highest not when the temperature is coldest, but when nighttime temperatures are below 4.4
C (40 F) and contrast markedly with warm daytime temperatures (NCH, 2010). As a result, the spring and fall seasons, when such conditions predominate in many areas of the United States, may be particularly dangerous. The homeless, as a group, experience an elevated risk from cold exposure, although the level of risk is difficult to quantify from official statistics. Nervous system depression, heart arrhythmia, or renal failure may be the proximate cause of death even in cases where cold is a key factor (CDC, 2006). While a study of the causes of homeless deaths in Los Angeles County between 2000 and 2007 revealed very few deaths attributed to hypothermia or environmental exposure (LACEHH, 2007), even a few exposure-related deaths trigger humanitarian concern and added pressure to implement policies that prevent them. Government agencies, community groups, and other advocates throughout the country recognize the risk and attempt to mitigate it by operating emergency homeless shelters. These operate under a wide variety of constraints, including limited funding and staff, insufficient space, lack of appropriate resources to deal with medical or psychological issues, and at times, resistance from local communities. Therefore, it is crucial that shelter operators maximize limited resources by ensuring that openings coincide closely with the most dangerous environmental conditions. Shelter operators generally have a very good sense of local climate and consult 1- and 5-day local weather forecasts, which they incorporate into scheduling protocols (Gary Shelton, LBACH, personal communication). However, we are unaware of any cases where operators have used detailed climatological data in a methodical fashion to design optimal schedules.

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between 23.3
C (10 F) for Nome, Alaska, and 4.4
C (40 F) for four shelters. Lower temperature thresholds were generally used in colder climates, owing to financial constraints and cultural adaptation. In some cases, shelters had both temperature activation thresholds and date restrictions, limiting openings to the late fall, winter, and early spring. Interestingly, other weather variables, most notably precipitation, were not usually incorporated as formal criteria for shelter opening, although the report noted that operators often opened shelters at higher temperatures during wet weather. According to the report from the NCH (2010), the ideal schedule for homeless shelter operation in the United States would involve remaining open 24-h per day between October and April, and on other days during the year when temperature falls below 4.4
C (40 F). This recommendation is well-intentioned, but is of limited value since few shelters have the resources to remain open for such a long duration. Moreover, given the immense variation in climate throughout the country, this broad generalization could certainly be improved by using local climate data to identify the periods of greatest risk. In this paper, we have used 40-years of temperature and precipitation data to identify an optimal schedule for shelter openings in Long Beach, California. We use the results to suggest improvements to the current strategy and to provide future guidance. We hope our methods provide a framework for the incorporation of climate data to create optimal schedules for shelter openings that may be applied elsewhere. Emergency winter shelters for homeless people in Long Beach, California Long Beach is a city of 462,000 people (US Census 2010 data) located within Los Angeles County (Fig. 1). Based on the most recent counts, Long Beach has a homeless population of 4,290 on any given night (City of Long Beach, 2011). The County, which includes the cities of Los Angeles, Long Beach, Glendale, 85 smaller cities, and unincorporated areas, has a total population of 9.8 million (US

Emergency homeless shelters in the United States Extreme weather tends to be only one of many factors considered when developing schedules for cold-weather emergency homeless shelters in the United States. According to a report from the National Coalition for the Homeless (NCH, 2010), there is considerable variation throughout the country regarding when shelters are either open or are available to be opened in case of dangerous weather. The report, which incorporates information from 94 organizations in 60 communities throughout the country, notes that shelter operators use a variety of activation criteria, including specific beginning and ending dates, minimum temperature cut-offs, the judgment of shelter officials, perceived demand, or in some cases, a combination of these factors. In some instances, shelters remained open 24 h a day during periods when the chosen activation criteria were met, while others operated on a nightly basis only. Beginning and ending dates were used at twenty-seven shelters included in the study, meaning that these shelters were open for a predetermined period of time over the winter. Of these shelters, all were open during January, February and March, and all but one was open in December. Two-thirds of the shelters were open in November, but only four were open in October, and only seven remained open through April. Thirty-seven shelters reported using a minimum temperature threshold to determine the need for shelter openings, but the temperature criterion varied considerably. More than one-third of the shelters (14 total) used the freezing point (0
C or 32 F), while among the others, the cut-off ranged

Fig. 1. The study area.

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armory opened at least 40 out of 60 days during the winter seasons of 1988 and 1989 (Rotella, 1990). Even with the advantages of a consecutive seasonal operation policy under LAHSA since 1994, annual funding fluctuations, local opposition, and administrative delays have affected openings. In Long Beach in 2009, the County’s winter shelter, by opening 17 days late, failed to provide refuge during several cold and wet weather events (Mellon, 2009a, 2009b). Delayed openings, combined with unexpected off-season rains during November and late March, led homeless advocates from the Rainy Day Shelter program in Long Beach to approach us with questions about whether and how climate change might be changing weather patterns in ways that make the County’s winter shelter opening policies inadequate for providing refuge during the coldest and wettest times of the year. Long Beach: climatology The city of Long Beach is located at sea level along the Pacific Coast of Southern California, at 33.82
N and 118.15
W. It has a Mediterranean Climate, which is characterized by moderate yearround temperatures and fairly low annual precipitation, with dry summers, and moist winters (Fig. 2). It has an annual average temperature of 18.2
C (64.7 F) and precipitation of 30.7 cm (1300 ), nearly all of which is rainfall (NCDC, 2012a). The average temperature is lowest during December, January, February, and March, with a minimum in December, when the mean nighttime low is 7.7
C (45.8 F) and the daytime high is 19.3
C (66.8 F). The lowest temperature ever recorded was 3.9
C (25 F), which occurred in January 1963. Monthly precipitation exceeds 2.5 cm (100 ) only between November and March, with a maximum of 7.7 cm (3.100 ) in February. Overall, Long Beach has a very moderate climate in comparison with most cities in the United States; however, winter temperatures are sometimes low enough, particularly in combination with rainfall, to present a risk of cold-exposure related injury to a population that is largely unaccustomed to persistent cold weather. Winter precipitation in Long Beach is caused primarily by transitory mid-latitude cyclones, often accompanied by cold fronts, which migrate eastward from the Pacific Ocean. These storms typically bring increased cloud cover, rainfall, increased winds, and a drop in temperature to the region. There is considerable variation in the frequency and intensity of these cyclonic storms from year to year. Years with few storms commonly receive less than 25 cm (1000 ) of precipitation, while years with large numbers of cyclones may receive precipitation that exceeds 75 cm (3000 ). Much of this variation is attributable to regional and global scale climate fluctuations such as El Niño-Southern Oscillation (ENSO), the Pacific Decadal Oscillation (PDO), and changes in the Pacific North America (PNA) Index. These atmospheric phenomena are interconnected, but we will concentrate on ENSO cycles in this study, both because

35

Precipitation

High Temp

Low Temp

30 25 20 15 10 5 0

Fig. 2. Climate graph for Long Beach, California.

20 18 16 14 12 10 8 6 4 2 0

Precipitation (cm)

Census 2010 data) and a homeless population of 51,340 on any given night (LAHSA, 2011). The emergency shelters that open during cold weather months are funded through the Los Angeles Homeless Services Authority (LAHSA), a joint county-city government agency formed in 1993 to coordinate funding for homeless services throughout Los Angeles City and County (LAHSA, 2012). LAHSA administers the Winter Shelter Program, started in 1994, which distributes federal funding to non-profit organizations to operate nighttime temporary shelters in each of the County’s 8 Service Planning Areas (SPAs) during the coldest and wettest times of the year. As the largest independent city in the SPA 8 area, Long Beach has been the location for the winter shelter in recent years. The policies guiding the dates for opening the winter shelters, particularly the use of weather activation criteria, have changed over time. The current program, in place annually since 2000, begins December 1 and ends March 15, following the assumption that this period includes the largest number of nights characterized by the coldest and wettest weather events of the year. In Long Beach, the County program is supplemented by the Rainy Day Shelter program, funded and operated by the Long Beach Area Coalition for the Homeless (LBACH), a volunteer-based advocacy group. When inclement weather is predicted, LBACH pays for the winter shelter to stay open during the day and opens temporary shelters within area churches when the winter shelter program is not operating (on dates that fall outside December 1eMarch 15) (LBACH, 2010). Both programs loosely define the coldest and wettest weather events as days and nights when the temperature falls below 4.4
C (40 F), or below 10
C (50 F) when the chance of rain is 50% or greater. These numbers come from past policies guiding the opening of temporary shelters, specifically state emergency policies for the use of National Guard armories for the homeless (Emmons, 1988; O’Donnell, 1992; Rae-Dupree, 1990), County hotel voucher and shelter programs implemented between 1987 and 1993 (Lu, 1989; Smith, 1993), and LAHSA’s program from 1994 to 2000 that used this same weather activation criteria to open shelters on dates in November and March, before and after the consecutively operated program dates (Monica Guthrie-Davis, LAHSA, personal communication). In the current programs, these measurements now serve only as guidelines rather than as strict requirements for shelter openings. For the County program, administrators no longer require that threshold daily weather activation criteria are met, and for the Rainy Day Shelter program, operators use the criteria only as a guideline, concentrating instead on opening as often as possible when it rains given budget allowances and prioritizing a chance of severe rainstorms and longer durations of rain in their decision to open (Gary Shelton, LBACH, personal communication). There are trade-offs involved with using these daily forecast-based decisions as a recent experience illustrates. In March and April 2012, the Rainy Day Shelter would have opened five more days than it did, but the program ran out of money. Historical records and contemporary discussions suggest that policy makers continue to grapple with the benefits and limitations of weather activation versus seasonal consecutive opening approaches. On the one hand, weather activation openings are particularly difficult for budgeting and managing operational logistics, but they are associated with state-level resources that can supplement local budgets, such as the use of armories. On the other hand, seasonal consecutive openings may provide operational and budgeting stability, but if they fail to capture the coldest and wettest weather events, shelters may operate at less than full daily capacity. Shifts in the County’s winter shelter policy over time reflect these concerns. In 1990, prior to the existence of LAHSA, the City of Los Angeles shifted from weather activation to a 60-day consecutive operation after a study found that the Van Nuys

Temperature (oC)

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they have the most recognizable influence on cyclonic activity and because their seasonal time-scale can be most effectively incorporated into scheduling protocols for shelter openings. ENSO is the term used to describe the quasi-periodic changes in ocean and atmospheric circulation in the Equatorial Pacific that tend to be most prominent in December and January. These regional changes influence weather patterns around the world, including Southern California (Ropelewski & Halpert, 1986, 1987). This oscillation has two phases, which represent the end-members on a continuum, as well as a neutral state, where neither pattern predominates. During the El Niño, or warm, phase of the cycle, increased ocean temperature and decreased atmospheric pressure in the coastal Pacific steer an increased number of storms southward from their typical trajectory and into Southern California, causing increased wintertime rainfall. During the La Niña, or cold, phase, strong high pressure pushes most mid-latitude storms in January and February farther northward, typically reducing total wintertime rainfall in Southern California. The strength of El Niño/ La Niña years varies considerably, as measured by indices of atmospheric pressure and sea surface temperature, and researchers sometimes differentiate between Eastern Pacific and Central Pacific types of events (Yu, Kao, Lee, & Kim, 2011)

Climate change Climate change is a significant global issue, and considerable research has been devoted to examining its potential impact in a variety of contexts. Its influence on cold-exposure among the population may be significant, although the specifics are difficult to determine. The IPCC (2007) states that global long-term average temperature will likely increase by 1.1e6.4
C (2.0e11.5 F) during the 21st century, and more specifically, projections by Hayhoe et al. (2004) suggest that winter temperature in Southern California will likely increase by 2.2e4.0
C (4.0e7.2 F) within the same time frame. Thus, it seems unlikely that anthropogenic climate change will cause an increase in the number of dangerously cold nights in the study area. Future precipitation changes could be significant, although predictions vary (Kiparsky & Gleick, 2003). Hayhoe et al. (2004) suggest that winter precipitation in California may decrease by 15e30% by the end of the century, although the decrease will likely be concentrated in the central and northern parts of the state. According to the California Climate Change Center, some models indicate a moderate increase in precipitation, others a moderate decrease, and still others suggest little change (Lynd Luers, Cayan, Franco, Hanneman, & Croes, 2006). Variations in the strength, frequency, or pattern of El Niño or La Niña circulation caused by climate change could be extremely significant to this study since these are primary drivers of wintertime precipitation. Some researchers such as Trenberth and Hoar (1996, 1997) have linked increased global temperature with an increased frequency and strength of El Niño circulation. If this were the case, more frequent winter storms would likely affect Southern California. On the other hand, recent research by Stevenson et al. (2012) have shown that the frequency of El Niño or La Niña events themselves is not significantly influenced by global temperature change, although individual winter storms may intensify. Models run by Kug, An, Ham, & Kang (2010) show that a doubling in CO2 could cause an eastward shift of El Niño and La Niña teleconnection patterns over North America, which could potentially cause a minor increase in winter precipitation in Southern California during El Niño years. In short, climate change is clearly an issue to consider in this study, although a review of literature suggest that its potential impact on Southern California and for the homeless population in Long Beach is uncertain.

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Methodology Objectives Nighttime winter homeless shelters are scheduled to operate in Long Beach annually between December 1 and March 15, which is a period of 105 consecutive nights (in non-leap years). County shelters operate on a 14-h schedule, opening in the evening at about 5 pm and closing the next morning at about 7 am. We based our evaluation of the extent to which the current winter shelter schedule captures the most dangerous nights of the year on a temperature threshold of 4.4
C (40 F) (the forecasted temperature used as the activation criterion throughout the Los Angeles metropolitan region prior to 2000) and the occurrence of precipitation (the criterion for opening the Rainy Day Shelter program). Our overall goal is to identify the optimal weather-event based opening schedule that also accommodates the practical criteria of: 1) consecutive openings and 2) funding availability for 105 nights. Our specific objectives are as follows: a) Evaluate the overall efficacy of the present schedule of shelter openings from a climatological perspective. b) Identify the optimal schedule for shelter openings on the basis of historical climate data. c) Describe patterns related to El Niño-Southern Oscillation (ENSO) circulation that may aid in refining the schedule to better reflect the likely conditions in a particular year. d) Characterize long-term trends, including those related to global climate change, which may suggest a need to modify the present schedule. e) Recommend adjustments to the County’s Winter Shelter program and the Long Beach Area Coalition’s Rainy Day Shelter program based on the findings. Assumptions and simplifications Our goal in this study is to evaluate the schedule for shelter openings based on historical climate data. It is not to evaluate the appropriateness of the temperature or the precipitation criteria on a medical or physiological basis, nor is it our intention to critique the political, financial or social factors that constrain the current shelter program in Long Beach. As such, we have made a number of simplifications for the purposes of this study. We have assumed that: a) Temperature below 4.4
C (40 F) and/or the occurrence of even a trace of precipitation are optimal indicators of exposure risk and that the National Weather Service forecasts used by shelter operators accurately predict these conditions. For the purposes of this paper, the term “activation days” will be used to denote any day on which rain occurred or the temperature fell below 4.4
C (40 F). b) All days fall within only two general categories of exposure risk e all activation days entail an equally high risk of exposure and all non-activation days entail an equally low risk. c) The present duration of shelter availability of 105 consecutive nights is fixed by budgetary and other constraints. Shelters are thus available only between a selected opening date at the beginning of the season, and a closing date 105 nights later; the only variable that is flexible is the initial opening date. d) The goal in selecting a start date is exclusively to maximize the total number of days that meet the climatological activation criteria during the period of shelter availability. Beyond the limitation of 105 consecutive days, political and socioeconomic factors have not been considered in the analysis.

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Results Activation days per year Table 1 indicates that there were a total of 1977 days during the 40-year study period, or roughly 49 days per year, that met at least one of the activation criteria (either precipitation or a temperature below 4.4
C (40 F)). Precipitation was far more common than low temperature, occurring an average of 41.1 days per year. Temperature fell below 4.4
C (40 F) an average of 9 days per year, but this coincided with rainfall less than once per year on average. El Niño years had more activation days per year (57.9) than other years, and

Table 1 Total activation days per year by event type and reason for activation.

Activation days Temperature activation Precipitation activation Both (temp. and precip.)

Total days Days per year Total days Days per year Total days Days per year Total days Days per year

El Niño (12 years)

La Niña (9 years)

Neutral (19 years)

All (40 years)

695 57.9 100 8.3 602 50.2 7 0.6

437 48.6 106 11.8 337 37.4 6 0.7

845 44.5 154 8.1 703 37.0 12 0.6

1977 49.4 360 9.0 1642 41.1 25 0.6

Neutral

10

El Nino

8

La Nina

6 4 2 0

Fig. 3. Average annual number of activation days per month throughout the study period, based on ENSO circulation type.

La Niña years had slightly more activation days per year than neutral ENSO circulation. La Niña years had significantly more activation days associated with low temperature (11.8 per year) than either El Niño or neutral years. All of these differences were statistically significant at the 95% confidence level. Fig. 3 shows that activation days occurred throughout the year, but as expected, they were most common during the winter. During the 40-year study period, the months with the largest average annual number of activation days were: 1) January (9.63 days), 2) December (9.03 days), 3) February (7.75 days), 4) March (6.63 days), and 5) November (4.15 days). The maximum number of activation days between December and April occurred during El Niño years; in fact, more than one out of every three days between December and February met or exceeded the activation criteria during the twelve El Niño years analyzed. The maximum number of activation days during October and November occurred during La Niña years. Quite notably as well, December, rather than January, was the month during La Niña that included the highest number of activation days. In general, it appears that activation days are more common earlier in the season during La Niña years than they are during other circulation patterns. Fig. 4 shows the total number of activation days per year for the study period. Clearly, there is a great deal of variability, some of which is due to ENSO cycles. The maximum number of activation days in any year, 85, occurred during the strong El Niño event of 1982e1983. The minimum of 31 occurred in 1980, which was a neutral year. A trend line fitted to the data appears to show a decline in total activation days from approximately 52.3 days per year at the beginning of the study period, to 45.0 at the end. However, this is not a statistically-significant result, given the large interannual variation in activation days (p ¼ 0.29). The presence of El Niño or La Niña circulation patterns is a much better indicator of the likely number of activation days than the long-term trend.

Total Activation Days

We analyzed daily weather data for Long Beach Airport, collected between July 1, 1969 and June 30, 2011 (NCDC, 2012b). Years in this study were defined as running from July 1 to June 30 to keep the colder, wetter months together, and were numbered according to the year when they begin e thus, 2009 refers to the period between July 1, 2009 and June 30, 2010. Some data for 1999 and 2000 were unavailable, so these years were not included in the analysis. As a result, forty full years were included in the data set. We assigned a value of one to all dates when rain occurred or when the temperature fell below 4.4
C (40 F) and a value of zero to all other dates. We then used a moving window to calculate the sum of these values for all 105-day periods throughout the year and identified the starting date that corresponded with the maximum value. We considered this starting date to be optimal. We followed this procedure for each year, as well as for multiple years in composite, to identify ideal starting dates, as well as the maximum numbers of activation days associated with these dates. We used a similar procedure to identify whether days were classed as activation days because of temperature or precipitation. We then tabulated the results, plotted trend lines, and conducted regression analysis to identify temporal trends (more complex time-series analysis, such as ARIMA modeling, was ultimately thought to be unnecessary on the basis of these results). We also collated statistics associated with El Niño, La Niña, and neutral circulation patterns. We identified these circulation types on the basis of consensus measurements from four sources as presented in Null (2003) and Lee, Shen, Bailey, & North (2009): the Western Regional Climate Center (WRCC); the Climate Data Center (CDC); the Climate Prediction Center (CPC); and the Multivariate ENSO Index (MEI). Our study incorporated a total of twelve El Niño years (1972, 1977, 1982, 1987, 1991, 1992, 1994, 1997, 2002, 2004, 2006, 2009), nine La Niña years (1970, 1971, 1973, 1974, 1975, 1988, 1998, 2007, 2010), nineteen years that were considered "neutral". We used analysis of variance (ANOVA) tests to establish statistically significant differences between ENSO circulation modes in the number of activation days and the ideal starting dates.

Number of Days

Analysis

y = -0.18x + 398.83 R² = 0.03 p= 0.29

100 90 80 70 60 50 40 30 20 10 0

Year

Fig. 4. Total number of days per year that met one or both of the activation criteria. The trend line indicates the line of best fit.

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Assessing the number of activation days in the shelter availability period There were 1977 days during the 40-year study period that met the climatological activation criteria, but only 1191 of these, or 60.2%, occurred during the present availability period of December 1eMarch 15. One of our goals is to identify the optimum period for shelter availability, defined as the opening date that would maximize the number of activation days within that period. To do so we generated four scenarios: Scenario 1. The hypothetical maximum In this case, we identified the optimal unique start date for 105 consecutive nights for each year. It is entirely hypothetical, since we are using historical records to choose start dates with “20/20 hindsight”. We are presenting this scenario largely for the purpose of comparison, since it can only be applied retroactively. Scenario 2. The present schedule In this case, shelters are available from December 1 to March 15. Scenario 3. Optimal consistent start date Here we identify the single start date that would have been optimal if applied throughout the 40-year study period. Assuming no long-term trend, this date would be the best consistent start date to adopt in future years. Scenario 4. Optimal start date by event type Here we identify three optimal start dates on the basis of the three types of ENSO circulation. In other words, different start dates are used for El Niño years, La Niña years, and neutral years. This approach could be adopted to determine start dates in the future, since ENSO forecasts are available up to thirteen months in advance on the El Niño/La Niña page operated by the National Weather Service’s Climate Prediction Center (NWS, 2012). The results of Scenario 1 (Table 2) indicate that the maximum number of activation days on which shelters could have possibly opened over the 40-year study period is 1337, which represents 67.6% of all potential activation days (during all months of the year). Obviously, this type of approach can only be employed on historical data since the optimal dates for shelter availability in a particular year cannot be known until after the fact. On the other hand, per Scenario 2, 60.2% of days (1191 out of 1977) that met the activation criteria fell between December 1 and March 15, which is the present period of shelter availability. November 24 through March 8 was the 105-day period that would have included the maximum number of activation days for the 40-year period (Scenario 3). However, the improvement over a December 1 start would have been minor. Starting one week earlier would have included only nine more activation days during the 40-year period, for a total of

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Table 3 Optimal start dates associated with each of three ENSO circulation patterns. Overall dates indicate the best date over the entire study period, whereas the earliest and latest optimal dates indicate the best start date for individual years. Start date

El Niño

La Niña

Neutral

All years

Earliest Latest Overall

October 10 January 17 November 29

October 1 December 25 November 21

September 17 January 11 December 7

September 17 January 17 November 24

1200 (60.7% of all possible activation days). The most effective and realistic option would have been to base availability dates on fluctuations in ENSO (Scenario 4), which would have raised the number of activation dates included to 1218. Therefore, Scenario 4 includes 27 more activation days than the current operating protocol. This is a fairly small, but statistically-significant improvement (p ¼ 0.04) that would increase shelter availability to cover 61.6% of possible activation days. Optimal start dates for each ENSO circulation pattern are shown in Table 3, along with the earliest and latest optima for any particular year during the study period. Although the best start dates are November 29 during El Niño years, November 21 during La Niña years, and December 7 in neutral years, considerable variation is evident, even between different years that experienced the same ENSO circulation pattern. This is indicated by the large variation in optimal starting date between different years. Fig. 5 shows the optimal start date for each year of the study. The trend line indicates that the optimal start date became earlier over the 40-year study period by approximately 12 days. Specifically, the regression line and its equation indicate that the optimal average start date would have been December 5 in 1969, and November 24 by 2010. However, it is important to note that this relationship is not statistically significant as a result of the large interannual variation in the data (p ¼ 0.38). Discussion and recommendations It is unclear from our historical research whether the schedule for emergency winter homeless shelter availability in Los Angeles County was originally based on a detailed analysis of climate data. Nonetheless, the present schedule, which runs from December 1 to March 15, is very effective from a climatological standpoint. Only minor improvements could be achieved by opening and closing one week earlier on an annual basis. Adjusting the dates based on ENSO circulation patterns could be more effective, particularly by opening earlier during La Niña years, ideally on November 21. Since the U.S. National Oceanic and Atmospheric Administration maintains a website that provides ENSO forecasts more than one year in advance (NWS, 2012) there would certainly be sufficient time to adjust the shelter-opening date. Finally, there is some suggestion in

Table 2 Number of activation days that fall within actual and hypothetical shelter opening periods of 105 days. Percentages represent the proportion of activation days occurring within a given 365-day period that fall within the 105-day periods specified. For example, if 50 days met the activation criteria within a given year, 30 of which fell within the specified 105-day period, the number reported would be 60%. Scenario

Activation days

El Niño (12 years)

La Niña (9 years)

Neutral (19 years)

All (40 years)

40-year baseline 1. Hypothetical maximum

365 day total 105 day total % of 365 day total 105 day total % of 365 day total 105 day total % of 365 day total 105 day total % of 365 day total

695 467 67.2 426 61.3 428 61.6 431 62.0

437 292 66.8 262 60.0 270 61.8 275 62.9

845 578 68.4 503 59.5 502 59.4 512 60.6

1977 1337 67.6 1191 60.2 1200 60.7 1218 61.6

2. Present schedule 3. Consistent annual optimum 4. Optimized by event type

Day Relative to December 1

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y = -0.31x + 622.22 R² = 0.02 p = 0.38

60 40 20 0 -20 -20 -60 -80 -100

Year

Fig. 5. Optimal start date for each year of the study period, determined by retroactively fitting the best 105-day window to the data for each year. Zero on the graph indicates a December 1 start date, while negative values indicate the number of days prior to December 1, and positive values indicate the number of days after December 1. Thus, November 1 has a value of 30, whereas December 31 has a value of þ31. The straight line is a line of best fit showing the long-term trend.

the data of a long-term trend toward the occurrence of days that meet the activation criteria earlier in the season. Although this is not a statistically-significant trend and does not alone indicate the need for any adjustment, it does provide some further support for the conclusion that earlier opening dates should be considered. There are also operational and physiological considerations that would suggest that more attention does need to be focused on ensuring that shelters open sufficiently early within the season. First, program administration issues have caused shelters to be opened later than December 1. In six of the seven seasons between 2003 and 2009, the shelter serving Long Beach opened late, within a week in three cases, but not until December 25 in 2003, January 3 in 2007e2008, and December 17 in 2009 (Mellen, 2008, 2009b, 2009c; Mellen & Elysse, 2005). Second, research suggests that people are better able to cope with adverse weather conditions after a duration of periodic exposure over several weeks or months (Kaciuba-Uscilco & Greenleaf, 1989). Thus, it is likely that cold or rainy days that occur in November or December may endanger homeless persons more than those occurring in January, February, or March. Moreover, seasonal changes and large temperature fluctuations are known to be significant factors in exposure risk. In Long Beach, the largest average temperature range between the daytime high and the nighttime low occurs in November. Here again, cold and wet days that occur earlier are likely to pose an increased risk to the affected population compared to those that occur later. Ultimately, we commend shelter operators on their selection of a schedule for shelter openings that is quite appropriate from a climatological standpoint. However, we strongly recommend that increased attention be focused on ensuring that shelters open in a timely fashion. In addition, we believe that shelter operators should at least consider shifting opening dates earlier by approximately one to two weeks, particularly during La Niña years. Furthermore, we recommend local programs that supplement shelter openings, such as the Rainy Day Shelter in Long Beach, recognize the potential need to open earlier in La Niña years and stay open during the day more often during El Niño years and that they budget and manage logistics accordingly. Conclusions and future research

the number of activation days in a given year. El Niño years and La Niña years have an average of about 30% and 9% more activation days, respectively, than years in which neither circulation pattern is strong. 2. The months with the largest number of activation days, in order, were January, December, February, and March. However, La Niña circulation increased the number of activation days that occurred earlier in the season (October, November, and December) relative to other circulation patterns. 3. The current shelter-availability period between December 1 and March 15 has been quite successful from a climatological standpoint. It would have allowed shelters to be opened during about 60% of all days meeting the activation criteria during the study period. Opening and closing one week earlier would have improved this percentage only slightly. Opening on different dates depending upon ENSO circulation, most notably opening earlier during La Niña years, would have improved the percentage of annual dates covered to approximately 62%. The upper limit for coverage using annual 105-day opening periods during the study interval was only about 68%. 4. There are a number of reasons to believe that increased attention should be paid to the beginning of the winter season, particularly during La Niña years. First, a shift to an earlier opening date would create a minor improvement in the number of activation days that are covered by the availability period, as well as help adjust to the possibility of a long-term climatological trend. Perhaps more importantly, individuals tend to be more vulnerable to exposure-related maladies early in the season, and moreover, in practice, shelters actually open later than December 1 in some years. Our study suggests many areas for future research from an academic standpoint. In particular, more systematic studies of coldexposure mortality and injury among the homeless would help to accurately establish the direct link between climate and risk. In addition, greater understanding of the link between precipitation as a climatic variable and moisture as a physiological heat-loss mechanism is crucial to more accurately quantifying risk. This could involve both the identification of a threshold level of precipitation associated with clothing saturation among representative homeless populations and the joint examination of temperature and precipitation data on an hourly, rather than a daily, basis. From an applied perspective, our results indicate that emergency winter homeless shelter operators in Long Beach have been using a schedule that is appropriate to the local climate, although we have suggested some important adjustments that should be considered. Even if these adjustments do not shift the County’s program schedule, our recommendations can guide supplementary shelter providers, like the LBACH’s Rainy Day program, for budgeting and logistical planning. Future analyses of how shelter operators use both historical climate data and weather and climate forecasts in decision-making would contribute to the literature on risk management strategies (Dow, O’Connor, Yarnal, Carbone, & Jocoy, 2007; O’Connor, Yarnal, Dow, Carbone, & Jocoy, 2005). Given the increased understanding of climatic cycles such as ENSO and the potential for future variation due to climate change, we feel that it is important for agencies in Southern California and throughout the country to use historical climate analysis to evaluate and adjust their scheduling strategies to address the needs of their local homeless populations.

The following conclusions can be made on the basis of the data: Acknowledgments 1. There was considerable variation in the number of activation days from year to year, with an average of about 49 per year. ENSO circulation provides some ability to explain and predict

The authors wish to thank Gary Shelton of the Long Beach Area Coalition for the Homeless for bringing this topic to our attention

D.A. Pepper, C.L. Jocoy / Applied Geography 37 (2013) 168e175

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