Responses of compound daytime and nighttime warm-dry and warm-humid events to individual anthropogenic forcings

Daytime heat is often associated with reduced soil moisture and cloud cover, while nighttime heat is connected to high humidity and increased cloud cover. Due to these differing mechanisms, compound daytime and nighttime heat events may respond differently to major anthropogenic forcings (greenhouse gases, anthropogenic aerosols, land-use and land-cover change). Here, we use GISS ModelE2.1-G historical single-forcing runs from 1955 to 2014 to examine how individual anthropogenic forcings affect compound heat events—specifically warm daytime and nighttime temperatures compounded with dry precipitation or high humidity conditions. We show that greenhouse gases alone amplify the natural frequency of warm-dry events by 1.5–5 times and warm-humid events by 2–9 times in tropical and extratropical latitudes. Conversely, aerosols and land-use/land-cover change reduce the frequency of these events, resulting in more modest increases and in some regions, declines, in the historical ‘all-forcings’ scenario. Individually, aerosol effects are stronger and more widespread compared to land-use, oftentimes reducing the natural frequency of these events by 60%–100%. The responses of these compound events are primarily driven by changes in daytime and nighttime temperatures through large-scale warming via greenhouse gases and cooling from aerosols and land-use/land-cover change. However, changes in warm-dry events are amplified in regions with concurrent precipitation declines (e.g. Central America, Mediterranean regions) and warm-humid events are amplified by global concurrent humidity increases. Additionally, we find differences between daytime and nighttime compound responses in the historical experiment that can be traced back to the individual forcings. In particular, aerosols produce a greater cooling effect on daytime relative to nighttime temperatures, which notably results in a historical reduction of Northern Hemisphere daytime warm-dry events relative to natural conditions. Our analysis provides a more comprehensive understanding of the significant impacts of different anthropogenic climate forcings on daytime and nighttime warm-dry and warm-humid events, informing future risk and impact assessments.


Introduction
Human activities have caused regional and largescale changes in the Earth's climate through several anthropogenic forcings (IPCC 2021). Rising greenhouse gas concentrations have driven significant global temperature increases, changes in precipitation patterns, and the increasing frequency and intensity of extreme events, including heatwaves, extreme rainfall, and droughts (Hegerl et al 1997, Christidis et al 2005, Seneviratne et al 2012, Easterling et al 2016, Marvel et al 2019. Anthropogenic aerosols have cooled temperatures by increasing regional albedo levels (Charlson et al 1992). Aerosols have also been hypothesized to influence large-scale circulation changes through asymmetrical surface temperature changes, resulting in late 20th century drying in Northern Hemisphere monsoonal regions (Bollasina et al 2011, Polson et al 2014. Moreover, land cover and land management practices have driven changes in regional climate conditions. Irrigation has moderated temperatures through latent heat flux changes (Cook et al 2015, Thiery et al 2017, Singh et al 2018, while deforestation has been linked to contrasting local and non-local climate changes (Christidis et al 2013, Findell et al 2017, Winckler et al 2019. Of the many changes attributed to anthropogenic climate change, changes in compound events will contribute to some of the most severe societal and environmental impacts, but are still not well understood (Zscheischler et al 2020, Zhang et al 2021. Compound events can be defined as the combination of multiple climate processes associated with negative impacts (Zscheischler et al 2018), and those associated with extreme heat are especially significant (Mazdiyasni andAghaKouchak 2015, Li et al 2020). Compound hot and dry precipitation events have been shown to produce amplified impacts on ecosystem health and food production, and can lead to severe wildfires and power grid failures (Allen et al 2015, AghaKouchak et al 2020, Ribeiro et al 2020. Compound hot and humid conditions are also extremely dangerous for human health and can amplify heat-related morbidity and mortality rates (Li et al 2020, Raymond et al 2020. With future warming, compound hot events are expected to become more frequent and intense due to changing temperature distributions alongside changes in the dependencies of interrelated variables (Zscheischler et al 2020). Improving our understanding of compound hot events is therefore relevant for climate change preparedness. Critically, while daytime and nighttime temperatures are generally governed by the same overlying atmospheric conditions (Perkins 2015), different mechanisms may affect how these events respond to climate forcings. Daytime heat is often associated with dry soil moisture conditions, reduced cloud cover, and can be sensitive to land-atmosphere interactions; conversely, nighttime heat is typically associated with high humidity and increased cloud cover (Thomas et al 2020). It is therefore possible that daytime and nighttime compound heat responses may differ across anthropogenic forcings.
To study the individual contributions of each major anthropogenic forcing to changes in compound heat event occurrences, we used data from Goddard Institute for Space Studies (GISS) ModelE2.1-G Coupled Model Intercomparison Project: Phase 6 (CMIP6) Detection and Attribution Model Intercomparison Project (DAMIP) singleforcing runs (Gillett et al 2016). Using monthly maximum and minimum temperatures to represent daytime and nighttime temperatures, we examined four compound events representing 'warm-dry' (warm maximum/minimum temperature co-occurring with low precipitation) and 'warm-humid' (warm maximum/minimum temperature co-occurring with high specific humidity) conditions. We focused on meteorological drought and high humidity due to the severe environmental and agricultural impacts associated with these events (Madadgar et al 2017, Harpold and Brooks 2018, Matthews 2018. With these event pairs, we investigated how individual anthropogenic greenhouse gas, aerosol, and landuse/land-cover forcings affect the frequency of these compound events.

Data
We used monthly maximum and minimum temperatures, precipitation, and specific humidity data from five ensemble members (r[1-5]i1p1f2) from GISS ModelE2.1-G CMIP6 DAMIP historical singleforcing runs from the most recent 60 years of available data, 1955-2014(NASA/GISS 2018. The GISS Model E2.1-G is one of the latest configurations of the GISS ModelE for CMIP6, coupling the atmospheric general circulation model, ModelE, to the GISS ocean model to produce 2-by-2.5 degree resolution output. GISS ModelE2.1-G simulations have been shown to accurately simulate observed climate conditions and realistically respond to natural and anthropogenic climate forcings (Miller et al 2021, NASA/GISS 2018. We used the historical natural-only (hist-nat) experiment, which includes only natural (solar, orbital, and volcanic aerosol) forcings, to find baseline estimates of compound event occurrences due to natural variability (Gillett et al 2016). We then investigated the effects of anthropogenic forcings on compound events by comparing hist-nat frequencies to four alternative experiments. The first three are single-forcing experiments: historical greenhouse gas-only (hist-GHG), anthropogenic aerosolonly (hist-aer), and historical land use-only (landhist), which includes land cover (e.g. agricultural expansion) and irrigation changes (Cook et al 2015). The fourth experiment is the historical 'all-forcing' experiment, which includes natural forcings, ozone depletion, and all anthropogenic forcings from the three single-forcing experiments. We used the histnat experiment in lieu of the pre-industrial control (piControl) experiment to directly examine the contributions of anthropogenic forcings to the recent historical climate.

Parametric bivariate standardized index
For our study, we generated standardized precipitation indices (SPIs), standardized humidity indices (SHIs), and standardized maximum and minimum temperature indices (STIs), to calculate bivariate standardized indices and identify warm-dry and warm-humid events. We used the entire calendar year to fully represent these events, as warm-dry and warm-humid conditions produce significant impacts in all seasons (Dierauer et al 2019, Ribeiro et al 2020. To construct the indices, we used a parametric approach and six month moving windows to capture medium-term moisture and heat events. For each grid cell, we pooled data from 1955 to 2014 from all hist-nat ensemble members to serve as the reference dataset for determining the appropriate distribution parameters so the standardized values are comparable across experiments. For SPI, we calculated hist-nat six month moving sums and fit the pooled precipitation sums from each month to the appropriate gamma distribution (McKee et al 1993). We used the non-parametric Kolmogorov-Smirnov test to evaluate the goodness of fits of the selected distributions and found that more than 97% of months in all grid cells achieved adequate fits (Massey 1951). We then used each month's fitted distribution to approximate the cumulative probabilities associated with the precipitation sums from each experiment (historical, hist-nat, hist-GHG, hist-aer, and land-hist). We transformed the probabilities into standardized values with: where p is the cumulative probability of precipitation and ϕ is the standard normal distribution function. For SHI and STI, we calculated six month moving averages for specific humidity, maximum and minimum temperature. We used appropriate beta distributions to construct the SHI index (Price 2001) and normal distributions to construct the STI index (Hansen et al 2012, Zscheischler et al 2014. For these indices, we found that greater than 98% of distributions achieved adequate fits. Then, as was done for SPI, we associated cumulative probabilities with the raw humidity and temperature values from each experiment and transformed the probabilities into standardized values with equation (1).
Following similar approaches used in the literature, we constructed the bivariate index with copula theory (Hao and AghaKouchak 2013, Li et al 2021). The copula is a multivariate distribution function which can model the dependence between multiple variables and can be used to study hydrometeorological multivariate dependencies (Michele and Salvadori 2003). With the VineCopula R package, we selected the copula with the lowest Bayesian information criterion and verified goodness-of-fit with White's information matrix equality to represent the dependence between the reference indices for each month of each grid cell (White 1982, Nagler et al 2019. We used Gaussian, Clayton, Gumbel, Frank, and Joe copulas, which have previously been used to represent the monthly bivariate dependencies (Sadegh et al 2017, Seneviratne 2017, Li et al 2021). For months where the variables are statistically independent, we employed the Independence copula (Genest and Favre 2007). Based on Sklar's theorem, the joint cumulative distribution function F of STI (X) and SPI (Y) can be expressed in terms of a copula and its marginals: where C is the fitted copula's cumulative distribution function, and F X (x) and F Y (y) represent the marginal cumulative distribution functions of STI and SPI where F X (x) = P (X ⩽ x) and F Y (x) = P (Y ⩽ y) (Sklar 1959).
The joint cumulative probability of warm and dry conditions can be calculated with: where u and v are the univariate temperature and precipitation probabilities.
The joint cumulative probability of warm and humid conditions can be calculated with: where u and v are the temperature and humidity probabilities. Finally, to generate our bivariate indices, we transformed the joint cumulative probabilities into standardized values with equation (1).

Compound event analysis
Using our bivariate indices, we identified compound warm-dry and warm-humid events as consecutive months exceeding the 90th percentile of the cumulative joint probability. Our primary analysis examined the frequency of compound events during 1955-2014. In the supplemental materials, we compare ModelE2.1-G historical output to the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) reanalysis data to demonstrate that ModelE2.1-G adequately represents the frequencies of these compound events (SM figure 1). In addition, we examined how the compound events are zonally distributed by pooling land grid cells from all ensemble members in each latitude band. We additionally pooled each SREX region's land grid cells from all ensemble members and evaluated median differences from the hist-nat baseline with Mood's median test using an alpha of 0.05 (Mood 1950, Field et al 2012. In the supplemental materials, we include plots of compound event duration and intensity, defining duration as the number of months and intensity as the sum of standardized values associated with each event (SM figures 2-7) and results using 3and 12 month moving windows as well as 80th and 95th percentile thresholds (SM figures 8-15). We also examined how anthropogenic forcings have impacted the background climate conditions during 1955-2014 to provide additional context for our results.

Global evaluation of compound warm events
Here, we first examine the global frequency of compound warm-dry and warm-humid events in the hist-nat experiment in 1955-2014 (figure 1). In the absence of anthropogenic forcings, warm-dry events are mostly found in tropical and extratropical regions, while warm-humid events occur more frequently in the higher latitudes. These distributions reflect the strengths of the dependencies between warm and dry conditions and warm and humid conditions due to natural variability, and have been shown in observations (Dai 2006, Zscheischler and and in MERRA-2 (SM figure 1). In hist-nat conditions, daytime (Tmax) warm-dry events occur more frequently relative to nighttime (Tmin) warm-dry events in most regions. In contrast, nighttime warmhumid events are more frequent relative to daytime warm-humid events across the globe. These dissimilarities may stem from underlying differences in the drivers of daytime and nighttime high temperatures. As previously mentioned, daytime heat is generally associated with dry land surface conditions and low cloud cover, while nighttime heat has been connected to high humidity and cloud cover (Thomas et al 2020).
In general, daytime and nighttime warm-dry events have similar responses to the individual forcings (figure 2). Compared to hist-nat conditions, greenhouse gases (figures 2(e) and (f)) drive largescale increases in event frequency, while anthropogenic aerosols and land-use/land-cover changes (figures 2(i), (j), (m) and (n)) cause broad decreases. In the hist-GHG experiment, tropical and extratropical regions display the largest increases in daytime and nighttime warm-dry events, amplifying hist-nat baseline frequency patterns. In the hist-aer experiment, the hist-nat frequencies of daytime and nighttime warm-dry events are suppressed by aerosols. Meanwhile, most regions do not show significant warm-dry changes in land-hist. In the historical experiment (figures 2(a) and (b)), the combination of these individual forcings strikingly results in modest decreases in daytime warm-dry events relative to histnat conditions in the Northern Hemisphere, as well as substantial increases in the Southern Hemisphere for both event types.
For daytime and nighttime warm-humid events, we also see general event frequency increases in the hist-GHG experiment (figures 2(g) and (h)) and decreases in the hist-aer and land-hist experiments (figures 2(k), (l), (o) and (p)). The presence of greenhouse gases amplifies the hist-nat frequency of warmhumid events, resulting in large increases in the Northern polar regions, as well as increases in the tropics and extratropics (e.g. North America, Sub-Saharan Africa, Europe, and East Asia) where high frequencies of nighttime warm-humid events occur under hist-nat baseline conditions. However, the presence of aerosols and land-use/land-cover change also substantially impacts the Northern polar regions. Consequentially, in the historical experiment, we see increases concentrated in the tropics and extratropics relative to the baseline (figures 2(c) and (d)). Overall, we see a greater absolute increase in nighttime warmhumid events in the historical experiment, especially in tropical and polar latitudes, reflecting differences in the responses of daytime and nighttime events to the combination of anthropogenic forcings.

Latitudinal and regional evaluation of compound events
To summarize the global results, we provide pooled ensemble median and interquartile ranges of event frequency across latitude bands for the forced experiments relative to the hist-nat baseline (figure 3). For compound warm-dry conditions, daytime and nighttime events generally display similar patterns, with the median hist-GHG event frequency increasing by approximately 150%-500% in the tropics and extratropics (40 • N-40 • S) and the histaer event frequency experiencing declines ranging from 60% to 100% in similar latitudes (figures 3(a) and (b)). Events also substantially decline in the land-hist experiment, although aerosols provide a stronger dampening effect across almost all latitude bands. With these plots, we can also highlight the strong impact of aerosols on daytime events, with historical daytime events being noticeably reduced by 30%-50% relative to the baseline at 35 • -55 • N. The latitudinal profiles of warmhumid events also display the strong effect of greenhouse gases, with increases ranging from 200% to 900% across most latitudes between 40 • N-40 • S (figures 3(c) and (d)). Meanwhile, historical warmhumid events are strongly moderated by aerosols and land-use/land-cover change, resulting in more modest increases between 100% and 300% in similar latitudes.
Next, we use boxplots to show compound event frequency distributions in select regions. We display results from Central North America (CNA), Central Europe (CEU), South Asia (SAS), East Asia (EAS), the Amazon (AMZ), and East Africa (EAS) to present a range of climate regions across the globe. Greenhouse gases significantly increase the hist-nat baseline median of daytime warm-dry events by approximately 200%-350% for all regions presented ( figure 4(a)). Meanwhile, aerosol and land-use/ land-cover change significantly decrease daytime warm-dry event frequency, reducing the median frequency to 0 in most regions. As demonstrated with figure 3, we can see the strong moderating effect of aerosols and land-use/land-cover on the historical daytime warm-dry events in Northern Hemisphere regions (CNA, CEU, SAS, and EAS), resulting in 25%-50% median decreases relative to   the baseline. In contrast, the historical warm-dry frequency is significantly higher than the hist-nat frequency in the Southern Hemisphere (AMZ, EAF), due to the reduced contribution of aerosols and landuse/land-cover to the historical event frequency in these regions. Greenhouse gases also increase nighttime warm-dry events by approximately 300%-500% for all regions relative to the hist-nat ( figure 4(b)). In addition, aerosols and land-use/land-cover changes also provide a moderating effect on the historical event frequency in the Northern regions. However, in contrast to daytime conditions, we still see evidence of significant increases in nighttime warm-dry events under historical conditions, with increases ranging from 30% to 300% in most regions (excluding SAS). Daytime (figure 4(c)) and nighttime (figure 4(d)) warm-humid events also show consistently large event frequency increases with the hist-GHG forcing, which highlight the far-reaching impact of greenhouse gases. In the hist-aer experiment, we can see the almost complete suppression of warm-humid event frequency, highlighting the joint effect of cooling and drying from aerosols. Although land-use/land-cover changes provide more moderate reductions in warmhumid events, regional event frequencies are still significantly different from hist-nat conditions. Overall, between daytime and nighttime warm-humid events, the frequency of nighttime events is still substantially higher under historical conditions, amplifying the underlying hist-nat patterns.

Background climate conditions
To provide context for how anthropogenic activity has influenced these events, we examine how the forcings have impacted average climate conditions. In general, responses in average maximum and minimum temperature are similar to the compound event responses, with broad temperature increases in hist-GHG conditions and decreases in hist-aer and landhist (figure 5). In the hist-aer experiment, we see evidence that maximum temperature is more sensitive to aerosols, resulting in a greater decrease in maximum relative to minimum temperature, especially in the Northern Hemisphere. In the historical experiment, we can further highlight the differences between maximum and minimum temperature in the Northern Hemisphere, underscoring the stronger impact of aerosols on maximum temperatures. The different sensitivities of maximum and minimum temperature to aerosols are present in the compound responses to the anthropogenic forcings, and is especially noticeable for warm-dry events. In the land-hist experiment, we also see local mid-latitude and non-local upper-latitude declines in both maximum and minimum temperature stemming from native vegetation loss and irrigation activity, agreeing with previous findings (Singh et al 2018). Although land-use/ land-cover drive both local and non-local changes, these changes are not as widespread and are generally more modest compared to aerosol-driven changes, apart from the irrigation-driven cooling of the Indo-Gangetic Plain (Puma andCook 2010, Singh et al 2018).
We also examine how average precipitation, specific humidity, and cloud area fraction have responded to the individual forcings ( figure 6). Under hist-GHG conditions, there are average precipitation increases in the higher latitudes and decreases in the Mediterranean, Northern Africa, the Middle East, and Central America. Coupled with changes in average temperature, the tropical and extratropical areas of concurrent robust drying and warming correspond well with where warm-dry events increase under greenhouse gas conditions. In the hist-aer experiment, we see a general drying effect stemming from aerosols; however, due to associated significant largescale decreases in temperature, drying from aerosols does not meaningfully increase warm-dry events on an individual basis. Meanwhile, although landuse/land-cover change produces a strong regional wetting signal in parts of Northern Africa and the Middle East, this ultimately does not result in significant changes in warm-dry events.
Changes in average specific humidity are more widespread: specific humidity increases globally in the hist-GHG experiment, and decreases in the histaer and land-hist experiments. With greenhouse gases, concurrent increases in specific humidity and temperature directly increase the number of warmhumid events across the globe shown in figure 2. Meanwhile, aerosols and land-use/land-cover change generally provide both cooler and drier conditions that moderate the frequency of warm-humid events relative to warm-dry events.
Differences in average cloud area fraction strongly relate to average maximum and minimum temperatures, and contribute to the overall differences between daytime and nighttime compound events. Relative to hist-nat conditions, increases in historical cloud area fraction are most influenced by the presence of aerosols. As cloud area fraction provides a much stronger effect on daytime temperatures, maximum temperature is more sensitive to this forcing, contributing to the identified differences in daytime and nighttime event responses.
Ultimately, this analysis of average climate conditions reveals the contributing factors driving changes in compound warm-dry and warm-humid event frequency. Under hist-GHG conditions, tropical and extratropical regions experiencing concurrent warming and drying show pronounced increases in warmdry events, while aerosols and land-use/land-cover changes moderate these increases through their influences on temperature. Greenhouse gases also produce substantial increases in warm-humid events by influencing both average temperature and specific  humidity. Nevertheless, these same events are moderated strongly by the presence of aerosols and land-use/land-cover changes that cause concurrent declines in temperature and specific humidity. Differences in daytime and nighttime compound event frequencies are influenced by differences in day and night temperature responses to cloud area fraction, generally resulting in larger increases in historical nighttime compound events relative to the hist-nat baseline.

Discussion
Under warming temperatures, compound events will produce severe impacts across sectors. Previous studies have documented how compound warmdry and warm-humid events have and will continue to increase due to climate change, but have not delved into how individual anthropogenic forcings contribute to the occurrence of these events Seneviratne 2017, Li et al 2020). Additionally, the degree to which these events will change in response to anthropogenic forcings, and the underlying mechanisms driving these changes, is not well understood. Most studies have also used average or maximum temperature to represent compound hot events (Alizadeh et al 2020), and have not examined how compound events defined by nighttime temperatures may respond differently.
Here, we conducted a comprehensive analysis of the effects of greenhouse gases, aerosols, and land-use/land-cover change on daytime and nighttime warm-dry and warm-humid events. Greenhouse gases increase warm-dry event frequencies in tropical and extratropical latitudes through large-scale daytime and nighttime temperature increases, while aerosols and land-use/land-cover changes reduce warm-dry event frequencies in similar latitudes through cooling. In the historical experiment, we see net decreases in daytime warm-dry events in the Northern mid-latitudes, and net increases in both daytime and nighttime warm-dry events in the Southern Hemisphere. Meanwhile, greenhouse gases substantially amplify warm-humid event frequencies in tropical, extratropical, and polar regions, while aerosols and land-use/land-cover changes considerably dampen warm-humid events in the Northern polar regions. Overall, we see net increases in historical daytime and nighttime warm-humid events across most latitudes, although we see larger increases in the Southern Hemisphere.
Our results generally agree with established temperature, precipitation, and humidity changes associated with greenhouse gas and aerosol emissions. Previous studies have tied global temperature and specific humidity increases and regional drying patterns from dynamic and thermodynamic changes in the hydrological cycle to greenhouse gases (Seager et al 2010, Marvel andBonfils 2013). Large-scale cooling and drying from aerosols has also been well established, and GISS E2.1 G has been shown to produce similar temperature responses to aerosols relative to the CMIP6 multi-model mean; however, there are still existing model biases related to aerosols (Tokarska et al 2020). With regards to landuse/land-cover changes, our results agree with previous studies that have attributed cooling to native vegetation loss and irrigation increases ( Overall, substantial differences between daytime and nighttime compound events emerge in the historical experiment that can be traced back to the individual forcings. Due to the impacts of aerosols and associated cloud cover increases on maximum temperature, we even see historical decreases in the frequency of daytime warm-dry events in the Northern Hemisphere. The temperature responses seen here agree with previous studies investigating diurnal temperature changes (Hansen et al 1995).
Although CMIP models produce temperatureprecipitation and temperature-humidity dependence patterns similar to observations, we acknowledge that the strengths of the dependencies are overstated in most models, including GISS (Dunn et al 2017). Most CMIP6 models also underestimate the diurnal temperature range relative to observations due to aerosol and cloud model biases, which can be reflected in our results (Wang and Clow 2020). Precipitation and humidity biases in GISS ModelE2.1 may also impact our results (Kelley et al 2020). Separately, we acknowledge that the methodological choices we have made can also influence our results. For example, using alternative models with different representations of land-atmosphere coupling may impact these compound events and how sensitive they may be to the various forcings.

Conclusion
We have demonstrated how anthropogenic forcings influence daytime and nighttime warm-dry and warm-humid events at regional and global scales. By examining the single-forcing experiments, we have characterized how responses in average climate conditions contribute to differences in event frequencies. Because daytime and nighttime compound events can lead to distinct impacts, our analysis offers a more comprehensive assessment of current and future compound event risks to human health, ecosystem health, and infrastructure. As greenhouse gas emissions are expected to continue impacting our global climate, we expect to see greater frequencies of warm-dry and especially warm-humid events. Additionally, due to the expected 21st century decline in anthropogenic aerosols, we may expect to see even greater increases in daytime and nighttime warm-dry and warm-humid conditions.

Data availability statement
The data that support the findings of this study are openly available at the following URL/DOI: https://doi.org/10.22033/ESGF/CMIP6.1400.

Acknowledgments
B I Cook, K Marvel, G A Schmidt, L S Nazarenko, and M Kelly were all supported by NASA's Modeling, Analysis, and Prediction program. F Chiang was supported by an appointment to the NASA Postdoctoral Program at the NASA Goddard Institute for Space Studies, administered by Universities Space Research Association and Oak Ridge Associated Universities under contracts with NASA. Resources supporting this work were provided by the NASA High-End Computing (HEC) Program through the NASA Center for Climate Simulation (NCCS) at Goddard Space Flight Center. The views and conclusions contained in this article are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of NASA or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.