ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus GmbHGöttingen, Germany10.5194/acp-15-6535-2015Impact of interannual variations in sources of insoluble aerosol
species on orographic precipitation over California's central Sierra NevadaCreameanJ. M.https://orcid.org/0000-0003-3819-5600AultA. P.https://orcid.org/0000-0002-7313-8559WhiteA. B.NeimanP. J.RalphF. M.MinnisP.https://orcid.org/0000-0002-4733-6148PratherK. A.kprather@ucsd.eduhttps://orcid.org/0000-0003-3048-9890NOAA Earth System Research Laboratory, Physical Sciences Division, 325
Broadway St., Boulder, CO 80304, USADepartment of Chemistry and Biochemistry, University of California,
San Diego, 9500 Gilman Dr., La Jolla, CA 92093, USAScripps Institution of Oceanography, University of California, San
Diego, 9500 Gilman Dr., La Jolla, CA 92093, USANASA Langley Research Center, 21 Langley Blvd., Hampton, VA 23681, USAnow at: Cooperative Institute for Research in Environmental
Sciences, University of Colorado at Boulder, Box 216 UCB, Boulder, CO 80309, USAnow at: Department of Environmental Health Sciences and
Department of Chemistry, University of Michigan, 500 S State St., Ann Arbor,
MI 48109, USAnow at: Scripps Institution of Oceanography, University
of California, San Diego, La Jolla, CA, USAK. A. Prather (kprather@ucsd.edu)15June20151511653565488December201412January201518May201527May2015This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/15/6535/2015/acp-15-6535-2015.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/15/6535/2015/acp-15-6535-2015.pdf
Aerosols that serve as cloud condensation nuclei (CCN) and ice nuclei (IN)
have the potential to profoundly influence precipitation processes.
Furthermore, changes in orographic precipitation have broad implications for
reservoir storage and flood risks. As part of the CalWater field campaign
(2009–2011), the variability and associated impacts of different aerosol
sources on precipitation were investigated in the California Sierra Nevada
using an aerosol time-of-flight mass spectrometer for precipitation
chemistry, S-band profiling radar for precipitation classification, remote
sensing measurements of cloud properties, and surface meteorological
measurements. The composition of insoluble residues in precipitation samples
collected at a surface site contained mostly local biomass burning and
long-range-transported dust and biological particles (2009), local sources
of biomass burning and pollution (2010), and long-range transport (2011).
Although differences in the sources of insoluble residues were observed from
year to year, the most consistent source of dust and biological residues
were associated with storms consisting of deep convective cloud systems with
significant quantities of precipitation initiated in the ice phase. Further,
biological residues were dominant (up to 40 %) during storms with
relatively warm cloud temperatures (up to -15∘C), supporting the
important role bioparticles can play as ice nucleating particles. On the
other hand, lower percentages of residues from local biomass burning and
pollution were observed over the three winter seasons (on average 31 and
9 %, respectively). When precipitation quantities were relatively low,
these insoluble residues most likely served as CCN, forming smaller more
numerous cloud droplets at the base of shallow cloud systems, and resulting
in less efficient riming processes. Ultimately, the goal is to use such
observations to improve the mechanistic linkages between aerosol sources and
precipitation processes to produce more accurate predictive weather forecast
models and improve water resource management.
Introduction
Aerosol particles serve as nuclei upon which cloud droplets and ice crystals
form and thus can have profound impacts on climate. In particular, pollution
aerosols in high number concentrations have been suggested to slow down
cloud drop coalescence and accretion by creating large populations of
small-sized cloud droplets that delay the conversion of cloud water into
precipitation (Borys et al., 2000; Rosenfeld et al., 2008). In contrast,
aerosols that form ice nuclei (IN), such as mineral dust and biological
aerosols, have been shown to enhance precipitation via secondary ice
formation and aggregation (Bergeron, 1935; Hosler et al., 1957; DeMott et
al., 2003; Morris et al., 2004; Tobo et al., 2013). Once formed, crystals
can develop rime after colliding with supercooled cloud droplets (≥ 10 µm; Yuter and Houze, 2003), particularly in more turbulent clouds
(Pinsky et al., 1998). In regions with orographically enhanced cloud
formation such as California's Sierra Nevada (Pandey et al.,
1999), IN are theorized to become incorporated into the top of high-altitude
clouds to form ice crystals (Meyers et al., 1992), whereas cloud
condensation nuclei (CCN) have been hypothesized to enhance cloud droplet
formation at the base of orographic clouds (Rosenfeld et al., 2008).
Under subfreezing conditions, a precipitating ice cloud overlaying a
pristine marine liquid water cloud enables growth of precipitation particles
through riming via the seeder–feeder process (Choularton and Perry, 1986;
Saleeby et al., 2009). However, if the lower cloud contains high
concentrations of CCN, such as those from pollution (Rosenfeld, 2000),
ice crystal riming efficiency is reduced and snow growth rates and
deposition location are altered (Saleeby et al., 2009). Although the
effects of CCN on precipitation suppression in the Sierra Nevada are
well documented (Colle and Zeng, 2004; Givati and Rosenfeld, 2004;
Rosenfeld and Givati, 2006), the combined effects of CCN and IN
simultaneously on precipitation in mixed-phase clouds are not well
established (Muhlbauer et al., 2010). It is plausible that these effects
can offset one another to some degree, and thus past measurement campaigns
that addressed one or the other could not account for the combined effects.
Map showing potential aerosol sources and the topography in the
region surrounding Sugar Pine Dam (SPD), where precipitation sample
collection and meteorological measurements occurred during CalWater.
The Sierra Nevada region is influenced by numerous sources of CCN, including
regional transport from biomass burning, urban, agricultural, and industrial
emissions from the Central Valley (Collett et al., 1990; Guan et al.,
2010) in addition to in situ formation of particles that act as CCN from
transported gas-phase species (Lunden et al., 2006; Creamean et al.,
2011) (see Fig. 1). In contrast, IN populations
have been shown to be influenced by dust transported over long distances
from arid regions in Africa and Asia (McKendry et al., 2007; Ault et al.,
2011; Uno et al., 2011; Creamean et al., 2013, 2014b).
Furthermore, biological species (e.g., bacteria) have been shown to be more
effective IN (Despres et al., 2012; Murray et al., 2012; O'Sullivan et
al., 2014) since they activate at temperatures as warm as -1∘C
(Morris et al., 2004) compared to dust (∼-38 to -17∘C)
(Field et al., 2006; Marcolli et al., 2007). Conen et al. (2011) demonstrated that even biological fragments such as proteins can
largely determine ice nucleation properties of soil dust in a laboratory
setting, while Pratt et al. (2009) observed biological IN in ice residues
from one orographic cloud via in situ aircraft measurements. Precipitation
events in the Sierra Nevada are influenced largely by the combined effects
of transient synoptic-scale dynamics and terrain-locked orographic lift.
Ralph et al. (2013a) demonstrated that precipitation totals in
land-falling atmospheric rivers (Ralph et al., 2004) depend
considerably on orographic lift associated with water vapor transport during
storms that move across the California Coast Ranges. Their study showed
that differences in storm-total water vapor transport directed up the
mountain slope contributed 74 % of the variance in storm-total rainfall
across 91 storms from 2004 to 2010. One hypothesis is that the remaining 26 %
variance results from influences by other processes, including aerosol
impacts on precipitation, as well as convection, synoptic and frontally
forced precipitation and static stability. Aircraft and ground-based cloud
seeding experiments in the Sierra Nevada suggest aerosols serving as IN are
more frequently removed by forming ice crystals versus scavenging during
snowfall and increase precipitation rates by 0.1–1.0 mm h-1 (Reynolds and
Dennis, 1986; Deshler and Reynolds, 1990; Warburton et al., 1995). Frozen
winter precipitation in the Sierra Nevada produces a deep snowpack which
gradually feeds reservoirs in the spring (Dettinger et al.,
2011). However, the presence of CCN may also influence the snowpack by
creating smaller cloud droplets that are scavenged less efficiently by
falling cloud ice crystals in the riming process, leading to reduced
snowfall and thus significant implications for water resources (Borys et
al., 2000; Saleeby et al., 2009). In short, the interplay between CCN and IN
activity of aerosols and their impacts on precipitation in this region will
influence the depth of the Sierra Nevada snowpack and, thus, the water
resources available to California.
CalWater (http://www.esrl.noaa.gov/psd/calwater/overview/calwater1.html) was
a field campaign designed to study aerosol–cloud–precipitation interactions
in California during winter storms, as well as the dynamics of the inland
penetration of atmospheric rivers from the coast. A unique combination of
radar technology, ground-based aerosol measurements, and hydrometeorological
sensors were stationed in the Sierra Nevada and nearby for up to 6 weeks
during each of the three winter seasons from 2009 to 2011. This study focuses
on identifying cloud seeds, interstitial aerosol, and scavenged aerosols in
Sierra Nevada precipitation by examining individual particles as insoluble
residues in precipitation samples collected at a ground-based site
co-located with a precipitation radar and other meteorological sensors. Key
elements of the unique hydrometeorological measurement network were obtained
as part of the National Oceanic and Atmospheric Administration's (NOAA)
Hydrometeorology Testbed (Ralph et al., 2013b). Precipitation composition
studies regarding the insoluble components were employed for a number of
CalWater events by Ault et al. (2011) and Creamean et
al. (2013), providing valuable insight into the potential sources of aerosols acting as CCN and IN.
This study probes two unresolved questions from the previous 2009 and 2011
studies by Ault et al. (2011) and Creamean et al. (2013), respectively: (1) how do both local
pollution (i.e., from Sierra Nevada and Central Valley) and long-range-transported sources of the insoluble components of aerosols vary between
winter seasons? (2) How do these sources impact precipitation processes? This
study focuses on measurements from the 2010 winter season in addition to
demonstrating the large interannual variability in sources of insoluble
residues in the Sierra Nevada during all three winter field seasons,
including both long-range-transported and local emissions. Further, we
evaluate how these sources impact precipitation formation through comparing
the comprehensive set of cases and relating these to radar-observed
precipitation characteristics. The links obtained here between sources of
the insoluble components of aerosols and precipitation outcomes will
ultimately be used as inputs into regional climate models to develop a
longer-term mechanistic picture for how different aerosol sources influence
clouds and precipitation processes in California.
Statistics for precipitation sample collection during storms from 2009 to 2011
at SPD. The start and end dates reflect when the beakers were placed outside;
they do not always correspond to the exact start and end of falling precipitation.
The percentages of each insoluble residue type per sample are provided (bolded percentages show dominant type).
YearStormPrecip.SampleStartEnd# ofDustBiologicalBiomassPollutionOthertotalID(UTC)(UTC)residuesburning(mm)2009184S122 Feb 19:3023 Feb 18:4539911 %17 %70 %2 %0 %S223 Feb 18:4524 Feb 19:207038 %19 %31 %11 %0 %214S326 Feb 00:0026 Feb 19:4523616 %5 %76 %3 %0 %3158S41 Mar 16:002 Mar 01:3062526 %0 %79 %15 %1 %S52 Mar 01:302 Mar 04:3050523 %0 %77 %0 %0 %S62 Mar 05:202 Mar 20:2074946 %1 %46 %0 %7 %S72 Mar 20:203 Mar 01:4525149 %2 %45 %0 %3 %S83 Mar 05:203 Mar 18:2054772 %4 %19 %2 %4 %S93 Mar 18:454 Mar 01:0025379 %4 %8 %0 %9 %S104 Mar 01:004 Mar 12:008280 %9 %0 %6 %5 %2010423S1127 Jan 01:0031 Jan 01:0015321 %44 %20 %14 %1 %537S123 Feb 03:003 Feb 21:0013431 %22 %26 %19 %2 %S134 Feb 19:155 Feb 17:4511911 %29 %45 %13 %2 %S145 Feb 17:456 Feb 23:00293 %17 %41 %38 %0 %627S1520 Feb 02:4520 Feb 17:4546013 %19 %37 %29 %2 %S1621 Feb 03:2521 Feb 17:1564312 %25 %53 %8 %2 %S1721 Feb 17:1522 Feb 18:0640519 %30 %37 %12 %2 %756S1823 Feb 22:3024 Feb 17:157910 %20 %61 %5 %4 %860S1926 Feb 18:4527 Feb 00:0022523 %31 %32 %10 %4 %S2027 Feb 00:0027 Feb 06:153514 %34 %54 %5 %3 %S2127 Feb 06:1527 Feb 17:204633 %26 %33 %4 %4 %956S222 Mar 14:453 Mar 03:0019021 %25 %40 %12 %3 %S233 Mar 03:003 Mar 19:0044420 %20 %51 %8 %1 %S243 Mar 19:004 Mar 02:0024529 %29 %35 %3 %4 %S254 Mar 02:004 Mar 19:0048711 %55 %29 %4 %1 %1024S268 Mar 16:009 Mar 00:404979 %36 %18 %34 %3 %S279 Mar 00:409 Mar 16:0025316 %51 %24 %6 %2 %S289 Mar 16:0010 Mar 20:3046111 %33 %43 %13 %0 %1137S2912 Mar 18:1512 Mar 23:1523933 %28 %30 %10 %0 %S3012 Mar 23:1513 Mar 05:0037630 %16 %45 %8 %0 %S3113 Mar 05:0013 Mar 17:3029921 %27 %35 %17 %0 %20111241S3230 Jan 02:5330 Jan 20:0013055 %21 %15 %5 %4 %1384S3314 Feb 18:4015 Feb 17:0036044 %8 %16 %6 %26 %S3415 Feb 17:0516 Feb 18:0026666 %7 %10 %1 %17 %1483S3516 Feb 19:4517 Feb 17:3023394 %6 %1 %0 %0 %S3617 Feb 17:3018 Feb 18:4020878 %20 %1 %0 %1 %S3718 Feb 19:1519 Feb 18:4016371 %12 %1 %3 %14 %1577S3824 Feb 20:3026 Feb 21:009412 %83 %1 %4 %0 %1630S391 Mar 23:002 Mar 23:002673 %15 %0 %8 %4 %S402 Mar 23:003 Mar 19:0039827 %37 %18 %18 %0 %1752S415 Mar 21:006 Mar 18:1535138 %50 %5 %6 %1 %S426 Mar 18:157 Mar 18:0020429 %40 %15 %13 %2 %MeasurementsCalWater field campaign
The CalWater study centered at Sugar Pine Dam (SPD; 1064 m a.s.l.;
39.13∘ N, 120.80∘ W; shown in Fig. 1) involved a unique combination of
meteorological (NOAA) and atmospheric measurements (University of
California, San Diego; UCSD) to deconvolute how different factors affect
precipitation quantity and type. Simultaneous atmospheric and meteorological
measurements were made from 22 February to 11 March 2009, 27 January to 15 March 2010,
and 28 January to 8 March 2011. Dates, times, and analysis statistics for each of
the precipitation samples collected during the storms from 2009 to 2011 at SPD
are provided in Table . Multi-year measurements
provide an extensive data set to determine the impact different aerosol
sources have during winter storms in California.
Surface meteorology and cloud properties above SPD
Hourly precipitation rates (mm h-1) and 2 min temperature
(∘C) at SPD were acquired from NOAA's Hydrometeorological Testbed
Network (NOAA HMT-West). Storm-total precipitation represents the total
accumulated precipitation per storm throughout the CalWater winter sampling
season (provided in Table ). NOAA's S-band
profiling radar (S-PROF; White et al., 2000), a fixed
dish antenna, was operated at 2875 MHz and directed vertically to study the
backscatter of energy from hydrometeors and cloud droplets and to monitor
the radar brightband melting layer (White et al., 2003). The
S-PROF radar can distinguish between different precipitation process types
by detecting a “brightband”, where the phase of falling precipitation
changes from solid to liquid (White et al., 2002). The
accumulation and percentages of precipitation process type including
non-brightband rain (NBB rain), brightband rain (BB rain), and
snow/graupel/hail (herein simply referred to as “snow”) were estimated
using the rainfall process-partitioning algorithm developed by White et al. (2003, 2010),
which was applied to the S-PROF profiles. These
measurements represent the types of precipitation aloft, not just at the
surface level. Both snow and BB rain were formed in the ice phase; however,
BB rain reached the surface by passing through a melting layer. NBB rain is
precipitation that likely originated as liquid droplets and is characterized
by a larger number of small drops than BB rain (White et al., 2003;
Neiman et al., 2005; Martner et al., 2008). Echo top heights (km a.m.s.l) were
also estimated using S-PROF radar data using methods employed by Neiman et
al. (2005) and Martner et al. (2008) and used to
determine the depth of the clouds above SPD. Analysis was performed on all
30 min periods when the precipitation rate exceeded ∼ 1 mm h-1.
Data from the 11th Geostationary Operational Environmental Satellite
(GOES-11) were used to define effective cloud temperature, which is close to
the cloud-top temperature, and the cloud-top phase over SPD. GOES-11 was
centered at 135∘ W over the eastern Pacific Ocean. Cloud
properties from 22 February to 4 March 2009, 27 January to 13 March 2010, and
28 January to 8 March 2011 were retrieved for CalWater. The five channels on the GOES-11 imager
include a visible channel (0.65 µm), which was calibrated to the Aqua
MODIS 0.64 µm channel, as well as four infrared channels. The 4 km
pixel GOES-11 data were analyzed each hour for a domain bounded by
30–42.5∘ N latitude and 112.5–130∘ W longitude using the methods described by Minnis et
al. (2008, 2011). Data from all parallax-corrected pixels within a 10 km
radius of the SPD were used to compute mean effective cloud temperature and
percentage of cloud ice.
Analysis of insoluble precipitation residue particles and ambient aerosols
Methods for collection and analysis of insoluble precipitation residues are
described elsewhere (Holecek et al., 2007; Ault et al., 2011; Creamean et
al., 2013, 2014a). Briefly, precipitation samples were
manually collected using beakers cleaned with ultrapure Milli-Q water (18 MΩ cm-1)
and methanol. Most samples were analyzed immediately after
collection, while others were transferred to 500 mL glass bottles, frozen,
and stored for 6–10 days before chemical analysis. Insoluble residues in the
precipitation samples were resuspended using a Collison atomizer, dried
using two silica gel diffusion driers, and sampled by an aerosol
time-of-flight mass spectrometer (ATOFMS) (Gard et al., 1997).
This aerosolization method can produce single soluble and insoluble
particles, agglomerates of different particle types, and coatings of soluble
species on insoluble residues. Thus, the composition is likely somewhat
altered from how the particles would have existed in the atmosphere
(Holecek et al., 2007). Even with the caveats associated with the
aerosolization process as discussed in Creamean et al. (2013, 2014a), this method
provides useful information on chemical differences in the aerosols seeding
clouds.
Insoluble precipitation residues between 0.2 and 3.0 µm in diameter were
individually sized and chemically analyzed by the ATOFMS. In this
instrument, single particles traverse between and scatter the light from two
continuous wave lasers (532 nm) at a set distance apart from which particle
size is calculated based on particle velocity upon calibration using known
size polystyrene latex spheres. A third pulsed Nd:YAG laser (266 nm) is then
triggered and simultaneously desorbs and ionizes each sized particle,
generating positive and negative ions which are analyzed using a
dual-polarity time-of-flight mass spectrometer. The mass spectra from
individual particles were classified into different types based on
combinations of characteristic ion peaks as discussed in detail by Creamean
et al. (2014a). Peak identifications correspond to the
most probable ions for a given mass-to-charge (m/z) ratio based on previous
ATOFMS precipitation studies (Holecek et al., 2007; Ault et al., 2011;
Creamean et al., 2013, 2014a).
Ambient aerosols were analyzed using ATOFMS simultaneous to precipitation
sample collection time periods. The instrument operates in the same manner
as with the insoluble residues; however, ambient air was drawn into the inlet
instead of resuspended particles from atomized precipitation samples. Due to
the sheer number of ambient aerosols analyzed by ATOFMS, particles were
classified via a clustering algorithm as opposed to hand classification.
Single-particle mass spectra were imported into YAADA (Allen, 2004), a
software toolkit in MATLAB (The MathWorks Inc.), for detailed analysis of
particle size and chemistry. ART-2a, an adaptive resonance theory-based
clustering algorithm (Song et al., 1999), was then used to
classify particles into separate groups depending on the presence and
intensity of ion peaks within an individual particle's mass spectra. The
most populated 50–70 clusters accounted for > 90 % of the total
ART-2a classified particles and are considered representative of the overall
aerosol composition. Peak identifications within this paper correspond to
the most probable ions for a given mass-to-charge ratio. The same ART-2a
algorithm was applied to the precipitation residues; the residue percentages
per sample for each particle type were within 6–10 % of the manually
classified results. Thus, the ambient and precipitation residues, although
classified by different methods, are comparable to each other.
Results and discussionInterannual variability of precipitation residue composition measured by
ATOFMS
The insoluble residue chemical composition during the three winter sampling
seasons was mainly composed of dust, biological material, and organic carbon
(OC). The OC residues were predominantly from biomass burning
(Ault et al., 2011; Creamean et al., 2014a), with minor contributions from agricultural and pollution aerosols
from the Central Valley (hereafter referred to simply as “pollution”)
(McGregor and Anastasio, 2001; Gaston et al., 2013). Mass spectra for
each of these types are shown by Creamean et al. (2013, 2014a) and Ault et al. (2011).
Other types contributed to ≤ 8 % of the
total residues each year. Control experiments of specific mixtures and
solutions – including dust, leaf litter, smoke, and sea salt – were
conducted using ATOFMS to accurately identify residue types observed in
precipitation samples. These are discussed in detail by Creamean et al. (2014a),
in addition to the chemical speciation of the
major residue types from precipitation samples. The ATOFMS is less sensitive
to soluble species, such as sea salt, as they form residues that are too
small to detect and chemically analyze when concentrations are low due to
dilution that occurs in precipitation samples (Creamean
et al., 2014a). Briefly, in ATOFMS analysis, dust particles typically
contain a combination of different metal and metal oxides, including but not
limited to aluminosilicates, iron, and titanium. Biological residues
typically contain a combination of sodium, magnesium, potassium, calcium,
organic nitrogen markers, and/or phosphate. In many cases, dust residues
were mixed with biological material as indicated by the combination of ion
markers. The mixed nature of the dust with biological material is likely a
result of soil dust (Conen et al., 2011) or other sources such as dust
interacting with marine biomaterial during transport (Prather et al.,
2013), and to a lesser extent agglomerates produced during the analysis
resuspension process (Creamean et al., 2014a). Thus
these mixed particles were grouped into the “dust” category. Biomass burning
residues varied in composition but typically contain sodium, potassium,
aged organic carbon fragments, high-mass organic carbon markers, and/or
polycyclic aromatic hydrocarbon markers. Pollution residues contained aged
organic carbon and/or amine markers, with a dearth of common biomass burning
markers. Ault et al. (2011) illustrated the ubiquitous
presence of local biomass burning in precipitation at SPD during the 2009
winter sampling and highlighted the potential importance of these aerosols
as CCN (Holecek et al., 2007). In particular, biomass burning aerosols
containing potassium and sodium have been shown to be hygroscopic in CCN
measurements (Carrico et al., 2010; Engelhart et al., 2012). Ault et al. (2011)
also suggested the source of the dust in 2009 was
from high-altitude, long-range transport as opposed to local or regional
sources. Further, Creamean et al. (2013)
demonstrated that dust and biological aerosols during the 2011 measurements
were long-range-transported particles which became incorporated into the
tops of high-altitude clouds. Dust from Asia has been shown to reach the
US west coast consistently throughout the late winter/early spring
(Husar et al., 2001; VanCuren and Cahill, 2002; Liu et al., 2003; Jaffe
et al., 2005; Creamean et al., 2014b).
Large variations existed between the major precipitation residue types
during the three winter seasons (Table ). The
results from 2009 were presented in detail by Ault et al. (2011), and therefore will only be briefly discussed
here. It is important to note that only two of the three 2009 storms (storms
1 and 3 here) were presented in Ault et al. (2011) due
to their meteorological similarities. As shown in
Table , during storms 1 and 2, the residues were
mainly composed of biomass burning (70 and 76 % for samples 1 and 3,
denoted “S1” and “S3”, respectively), with some dust present (up to
38 % in S2). However, during storm 3, the residue composition shifted to
predominantly dust (46–80 %, S6–S10). Even though meteorological
conditions were relatively similar during the most intense storms (storms 1
and 3), the precipitation shifted to snow during storm 3 due to colder
conditions later in that event. This storm produced 40 % more
precipitation than the first storm (Ault et al., 2011).
During the 2010 winter sampling season, high percentages of biomass burning
particles were present throughout the entire study (up to 61 %, 38 % on
average) and constituted the dominate residue type during almost all of the
storms. In contrast, in 2011, dust residues were dominant during the first
storms (44–94 %, storms 12–14), while biological percentages were highest
during most of the latter storms (37–83 %, storms 15–17). The results from
2011, presented in detail in Creamean et al. (2013), are only briefly discussed. Overall, each
winter sampling season was impacted by very different aerosol sources, which
we hypothesize impacted the type and quantity of precipitation as discussed
in the following section.
Comparison of average ambient aerosol versus precipitation residue
composition per sampling time period during CalWater 2010. Percentages
represent either the number of each type of aerosol or residue per total
number of aerosols or residues analyzed per sample. Sea salt was not
observed in precipitation and biological particles were not observed in the
ambient data.
Although we cannot determine directly, we hypothesize that the residue types
from each winter sampling season were most likely present due to nucleation
in cloud with a smaller contribution from scavenging of ambient aerosols
during rainfall/snowfall. Figure 2 shows the
composition of the precipitation residues compared to the relative abundance
of the ambient aerosols present during each sampling time period for 2010
(2009 and 2011 are shown and/or discussed in Ault et al. (2011) and
Creamean et al. (2013), respectively). Dust, biomass burning, and
pollution were present in both in the ambient aerosol as well as the
residues. Sea salt was not observed in the precipitation due to its soluble
nature, while biological particles were not observed as ambient aerosols
likely due to the fact that the majority of these particles originated from
soil dust and were separated during the resuspension process (Creamean et al., 2013, 2014a).
For all three sampling seasons, the time periods with the
highest relative amount of dust, biomass burning, or pollution residues in
the precipitation samples did not correspond to the highest relative amount of
the same type of ambient aerosol (i.e., almost all of the Spearman's
correlation coefficients (ρ) were low or negative and did not
demonstrate statistical significance as shown in
Table 2). Herein, we employ the use of ρ to
show the monotonic relationships between the residue composition and ambient
aerosol or cloud and precipitation properties, since the relationship
between aerosols and precipitation is not a linear function of two variables
and other factors play a role. The absence of correlation between similar
types of ambient aerosol versus precipitation residue particles may suggest
that the majority of the residues were from nucleation of cloud particles, with a
possible smaller contribution from scavenging during precipitation particle
descent.
Precipitation process type (30 min), residue type (per sample),
precipitation accumulation (1 h), and surface temperature (2 min) during
all storms from 2010. Time periods without precipitation process
measurements correspond to no falling precipitation or missing S-PROF data.
Each precipitation sample bar of the residue types represents one sample and
the width of the bar reflects the sample collection time period. Sample IDs
are provided above each sample bar and correspond to those in Table 1. Note
that the sample length is only shown during rain or snow and thus may not
directly correspond to times provided in Table 1. The horizontal black
dashed line represents the 40 % mark for ATOFMS.
Spearman's correlation coefficients P values, and statistical significance of
relationships between similar particle types that were found in the precipitation samples
and ambient aerosols during the same time period of precipitation collection.
YearSpearmanPSignificantDust 2009-0.430.21No20100.250.49No20110.580.08NoBiomass burning 20090.070.78No20100.210.40No20110.560.02YesPollution 20090.080.83No2010-0.080.83No20110.040.19NoLinking residue composition to precipitation type and quantity using ATOFMS
and S-PROF
As observed by Ault et al. (2011), aerosols can produce
up to 40 % more precipitation during storms in the Sierra Nevada. Fan et
al. (2014) showed the large impact that dust and biological aerosols can
have on Sierra Nevada snowpack, where they simulated these aerosols
increasing snowpack by 40 %. Further, Martin et al. (2015)
simulated storms during CalWater in 2011 and demonstrated how the storms
with more dust and biological particles incorporated into upper cloud levels
produced 23 % (but as much as 67 %) more precipitation than storms with
a greater influence from regional pollution aerosols. Variations in
meteorological forcing also play a role in the precipitation type and
quantity (Martin et al., 2015), but the rather systematic
correlations between different aerosol sources and precipitation processes
previously shown and described herein suggest the aerosol sources can still
play a vital role.
Dust and biological residues were dominant when precipitation formed as ice
Here, we demonstrate how the variability in the different sources of
insoluble residues from aerosols influence both the type and quantity of
precipitation during the CalWater storms in the Sierra Nevada. In most
cases, the sources of the ATOFMS residues were correlated with the
precipitation process type as delineated by the meteorological (S-PROF
radar) measurements. This is demonstrated by the 2010 samples in
Fig. 3 (2009 and 2011 are shown in Ault et al., 2011, and Creamean et al., 2013, respectively,
but follow similar trends to
the 2010 samples). Overall, BB rain or snow events (when surface
temperatures dropped to ∼ 0 ∘C) were typically
detected during time periods when precipitation samples contained higher
percentages of dust plus biological residues (hereafter referred to as % Dust + Bio),
particularly when Dust + Bio was > 40 % of the
total residues. Throughout this discussion, the dust and biological residues
are combined to simulate the percentage of residue types that likely served
as IN and because they are likely from a similar source
(Creamean et al., 2013). However they are shown
separately in the figures to demonstrate the relative contribution of each,
which is particularly important for the biological residues as discussed in
more detail below. Sample time periods with the most biomass burning and
pollution residues typically corresponded to the most NBB rain during 2010,
suggesting precipitation was formed as liquid due to the lesser influence
from Dust + Bio. For instance, storm 5 in 2010 corresponded to samples with
some of the lowest percentages of Dust + Bio (down to 20 %) and frequent
detection of NBB rain (5 out of 13.5 h), particularly towards the end of the
storm. BB rain was detected during the precipitation sampling at the end of
this storm as well, possibly because Dust + Bio residues were still present
and thus ice was still nucleated in the clouds above SPD. The sample from
storm 7 (S18) also contained low % Dust + Bio (30 %), and frequent
detection of NBB rain (6.5 out of 15 h). Overall, these results show that
dust and biological residues were dominant during time periods when
precipitation formed in the ice phase based on ATOFMS and S-PROF
measurements.
Summary of IN precipitation residue composition, observed surface
meteorology at SPD, and cloud properties above SPD. (a) The percentages of
dust and biological residues and the % ice-induced precipitation (snow
plus BB rain). (b) Echo top height deviation (km) calculated from all storms
during CalWater (average: 3.51 km based on data from 43 days during
ATOFMS sample collection time periods provided in Table 1). Positive
(negative) deviations correspond to higher (lower) than average echo top
heights. Effective cloud temperature and percentage of cloud ice are also
shown. Data were removed if in the homogeneous nucleation regime (≤-36∘C).
The respective instruments in which each measurement was
acquired is provided in the axis labels.
Although 2010 samples contained very different relative contributions of
residue types when compared to 2009 and 2011, the different residue types
followed very similar relationships with cloud ice amounts, precipitation
type and quantity, and cloud depth. Figures 4 and 5 provide a summary of
observed meteorological conditions during each of the three winter sampling
seasons in addition to precipitation residue composition averaged per storm
and properties of clouds above SPD. Snow and BB rain are combined and
denoted as “ice-induced precipitation”, i.e., precipitation that was
initially formed as ice (Creamean et al., 2013).
The echo top heights and storm-total precipitation are shown as deviations
from their averages during all of CalWater storms to demonstrate the range
of their variations: the echo top height average and storm-total
precipitation averages were 3.51 km and 55.46 mm, respectively, based on
data from 43 days during sample collection time periods provided in
Table . Data from GOES-11 were removed if the
cloud effective temperature was within the homogeneous nucleation regime
(≤-36∘C; during storms 7 and 8) to enable the investigation
of heterogeneous ice nucleation processes only. It is important to note that
correlations are not statistically significant due to the low number (17) of
events; however, they still provide a useful context to the trends between
the residue composition and cloud and precipitation properties. As shown in
Fig. 4, events with more ice-induced
precipitation and cloud ice typically correspond to samples with more dust
and/or biological residues (ρ= 0.58 and 0.67, respectively, for
Dust + Bio). Correlation plots and Spearman's correlation coefficients for
Dust + Bio versus ice-induced precipitation and cloud ice are shown in the
Supplement figure S1, along with correlation plots for Dust + Bio versus
echo top height deviation. This relationship supports our hypothesis that
the majority of the residues were nucleated as opposed to scavenged. If, for
example, most of the residues were scavenged, we might not expect such
strong relationships of dust and biological residues with the amount of
cloud ice and ice-induced precipitation.
Summary of organic carbon precipitation residue composition and
storm-total precipitation deviation. Organic carbon residues are separated
into those from biomass burning and those from local pollution. Storm-total
precipitation deviation (mm) is calculated from all storms during CalWater
(average: 55.46 mm based on data from 43 days during ATOFMS sample
collection time periods provided in Table 1). Positive (negative) deviations
correspond to higher (lower) than average echo top heights.
In particular, the storms with the highest Dust + Bio (storms 14 and 15;
93 and 95 %, respectively) correspond to some of the highest values of
ice-induced precipitation (82 and 96 %, respectively). Interestingly,
these two storms had very different residue composition: storm 14 had more
dust (81 %), whereas storm 15 had more biological residues (83 %). The
effective cloud temperatures were -32 and -25∘C,
respectively, suggesting that the dust IN were more effective at colder
temperatures, while the biological IN were active at warmer temperatures.
Other interesting cases are storms 4 and 10 from 2010, where biological
residues composed 80 and 77 % of the potential IN and ice-induced
precipitation was 87 and 92 %, respectively. Cloud temperatures were
also relatively warm during these storms (-16 and
-15∘C, respectively), further demonstrating that biological IN
are active at warmer temperatures. In the cases where biological residues
were dominant during storms 3, 10, and 15 and likely served as IN at warmer
cloud temperatures, the cloud ice content was ≥ 50 % based on GOES-11
measurements. It is important to note that the purely biological residues
could be a result of the aerosolization process and thus might have originally
been components of the dust particles. Although biological particles were
not observed as ambient aerosol at the ground, they were observed as
interstitial aerosol and in individual cloud particles during the 2011
in-cloud aircraft measurements (Creamean et al., 2013). The likelihood that the majority of the biological residues are separated from the dust during the aerosolization process is supported by the following: (1) a higher abundance of purely biological residues was observed in the precipitation samples compared to the interstitial aerosol or cloud particles and (2) a higher abundance of dust mixed with biological material was observed in the aircraft measurements compared to the precipitation collected on the ground. Even considering this issue, the dust particles that
were present in cloud still contained more biological material during time
periods with warmer cloud temperatures and thus would have enabled the dust to
serve as more efficient IN as delineated by Conen et al. (2011) and
O'Sullivan et al. (2014).
The percentages of dust and biological residues were also generally in phase
with the echo top height deviation as shown in
Fig. 4 (ρ= 0.39): when the clouds were
deeper, i.e., larger positive echo top height deviation (shallower, i.e.,
larger negative echo top height deviation), the % Dust + Bio was higher
(lower) as was the relative amount of ice-induced precipitation. However,
storm 10 was atypical; the % ice-induced precipitation was high (92 %),
while % Dust + Bio was not as high (52 %), which could be a result of
the clouds being shallower. Based on these results, we suggest that when the
clouds were sufficiently deep, they were more likely to have incorporated
long-range-transported dust and biological aerosols that were present only
at higher altitudes (above ∼ 3 km), such as in the cases
documented by Ault et al. (2011) and Creamean et al. (2013), and the simulations of storms 13 and 14 by
Martin et al. (2015). These dust and biological aerosols likely
initiated ice formation and thus influenced the relative amount of
ice-induced precipitation.
Shallow clouds associated with aerosols from local biomass burning and
pollution produced less precipitation
In contrast, when clouds were more shallow, (1) dust and biological aerosols
likely traveled over the cloud tops, and thus did not become incorporated,
and/or (2) less dust and biological aerosols were transported into the
region. Thus a larger influence from local aerosols in the form of biomass
burning and pollution residues was observed, as shown in
Fig. 5. Local biomass burning residues composed
most of the OC residues (78 %) compared to pollution (22 %),
particularly in 2009 and 2010. On average, biomass burning (31 %) and
pollution residues (9 %) did not constitute as many of the residues as
Dust + Bio (55 %). Figure 5 also shows the
relationship between OC residues (biomass burning and pollution) and
storm-total precipitation deviation. Generally, events with a negative
storm-total precipitation deviation corresponded to precipitation samples
containing more OC residues (ρ=-0.38), i.e., the combined
percentage of biomass burning and pollution residues was out of phase with
the storm-total precipitation deviation. For instance, the highest
percentage of OC residue types (storm 2) had the largest negative
storm-total precipitation deviation. Correlation plots and Spearman's
correlation coefficients for OC versus precipitation deviation are shown in
the Fig. S1. Further, storms 13–15 in 2011 had some of the
lowest percentages of OC residues and some of the largest positive
storm-total precipitation deviations compared to the remaining 2011 storms.
The OC residues from local biomass burning and pollution likely served as
CCN and seeded the lower levels of orographic clouds, resulting in smaller
cloud droplets that are less efficiently scavenged during the riming process
(Borys et al., 2000; Rosenfeld and Givati, 2006; Saleeby et al., 2009).
Although CCN are typically thought to be soluble in nature, partially
soluble or insoluble organic-containing aerosols have been shown to serve as
CCN as well. For instance, CCN closure studies have found better agreement
between predicted and observed CCN concentrations when insoluble organic
particles were used in their simulations (Broekhuizen et al., 2006; Wang
et al., 2008; Ervens et al., 2010). Further, previous studies have shown
that relatively insoluble organic particles with small amounts of soluble
inorganic material, such as sodium chloride, can drive the CCN activity of
the organic particles (Broekhuizen et al., 2004; Ervens et al., 2010).
Even partially or slightly soluble organics have been shown to serve as CCN
(Bilde and Svenningsson, 2004), particularly if the particles were
wet instead of dry (Henning et al., 2005). For the 2009 samples, measurements of
select soluble species were acquired and presented by Creamean et al. (2014a). Results presented there showed correlations
between sodium, potassium, sulfate, chloride, nitrate, and phosphate and
insoluble OC residues, thus signifying that these insoluble OC residues were
likely cores of the original particles from biomass burning and/or
pollution. Therefore, the OC residues observed in all the CalWater samples,
although insoluble, could have potentially originated as cores with soluble
species on the surfaces or partially soluble organic particles that remained
intact while in solution, enabling them to serve as CCN and lead to the
relationships with shallow clouds and negative precipitation deviation.
Broader implications
Overall, the results from this study demonstrate the interannual variability
in the sources of aerosols seeding clouds over the Sierra Nevada as
indicated by the insoluble residue composition. The combination of dust and
biological residues, aerosols that likely served as IN, increased over time
from 2009 to 2011, whereas the organic carbon residues (including local
biomass burning and pollution residues) decreased over time. Further, the
level at which the cloud nuclei impact cloud formation is important for
resulting effects on precipitation processes: dust and biological residues
likely serve as IN at higher altitudes in cloud, while organic carbon
residues serve as CCN at cloud base. However, this study presents a limited
number of data points and thus needs to be extended by future, additional
measurements. It has been shown that dust and biological aerosols originate
from long-range transport to the Sierra Nevada, whereas biomass burning and
pollution residues are more likely from local sources (Rosenfeld and
Givati, 2006; Ault et al., 2011; Creamean et al., 2013). Dust and biological
residues were ubiquitous in the most of the samples, which induced the
formation of ice precipitation, particularly corresponding to time periods
when the samples contained a relatively high amount of biological residues.
This suggests the residues containing biological material served as more
efficient IN than dust. The two storms with the highest percentages of
either dust (storm 14) or biological (storm 15) residues demonstrate this
effect, where storm 15 produced more ice-induced precipitation and had
higher cloud temperatures, whereas much lower cloud temperatures were
observed during storm 14. Sample 35 (S35 from storm 14) contained mainly
mineral dust with little to no biological material as shown from IN
measurements and heat treatment of the sample by Creamean et al. (2014a).
Creamean et al. (2014a) also
conducted the same measurements on the sample from storm 15 (S38), which
contained IN active at high temperatures. Thus, the comparison of the
samples from storms 14 and 15 enables us to determine the IN efficiency of
dust versus biological material, both from previous laboratory measurements
and in situ observations. Storms 4 and 10 contained more biological residues
and produced substantial amounts of precipitation formed as ice under high
cloud temperatures, further corroborating the fact that biological aerosols
are more effective IN.
The source of the insoluble residues influenced whether
precipitation formed in the ice or liquid phase, and also likely affected
the quantity of precipitation that fell at SPD. Larger quantities of
precipitation in comparison to the average from all three sampling seasons
were observed during time periods when dust and biological residues were
predominant in the samples. The most plausible explanation for this, as
described previously, is that these residues likely served as IN which led
to efficient riming processes and enhanced precipitation formation (Ault
et al., 2011; Creamean et al., 2013, 2014a). In contrast,
OC residues from both biomass burning and to some extent pollution were
observed during time periods with less precipitation. One possibility is
that the local biomass burning and pollution residues served as CCN, which
enhanced cloud droplet formation after being incorporated into lower levels
of the orographic clouds and led to less precipitation (Weaver et al.,
2002; Rosenfeld and Givati, 2006; Rosenfeld et al., 2008; Saleeby et al.,
2009). A modeling study of aircraft measurements from 2011 presented by
Martin et al. (2015) shows the presence of organic carbon residues
at lower cloud levels during prefrontal storm conditions in the Sierra
Nevada, demonstrating the significance of our observations and how they
validate model results. The cloud droplets formed from biomass burning and
pollution likely decreased the riming efficiency of the ice crystals formed
at higher altitudes in the presence of dust and biological aerosols,
subsequently contributing to time periods with less ice-induced
precipitation. With fewer aerosol seeds, cloud droplets and ice crystals
form much less frequently under typical atmospheric conditions in the lower
troposphere over the Sierra Nevada, altering the quantity of precipitation.
Previous studies have shown that aerosols can have a significant impact on
precipitation quantity and type in the Sierra Nevada during strong winter
storms (Ault et al., 2011; Creamean et al., 2013; Fan et al., 2014;
Martin et al., 2015). Based on this, the results presented here are in
agreement with previous research.
Fan et al. (2014) and Martin et al. (2015) demonstrate
the reproducibility of the observations in the Weather Research and
Forecasting (WRF) model by focusing on particular case studies from CalWater
2011. Observations presented herein for all CalWater storms will be
incorporated into future modeling work to improve simulations. However,
future work is needed to better isolate the impacts of storm dynamics,
aerosol microphysics, and precipitation, particularly when incorporating
observations into regional climate models. Ultimately, the goal is to
develop a mechanistic understanding of how, when, and where different
aerosol sources influence cloud microphysics and the resulting precipitation
in the Sierra Nevada. Improvement of these models can be used as predictive
tools for future weather forecasts.
Conclusions
Observed variations of sources of the insoluble residues from aerosols
serving as CCN and IN in Sierra Nevada precipitation were documented during
three winter sampling seasons as part of the CalWater field program. These
variations were then compared with meteorological observations of
precipitation characteristics aloft during the same events. Insoluble
residues in precipitation samples were used to link aerosol sources with
trends in precipitation characteristics. The unique multi-year, multi-event,
and co-located aerosol and meteorological observations enabled the
development of the following main conclusions:
Differences in aerosol sources seeding the clouds based on the composition of
insoluble residues were observed from year to year and between storms. We present
cases with predominantly long-range-transported dust and biological residues (2011),
local biomass burning and pollution residues (2010), or a combination of these sources (2009).
Although the residues in the 2010 samples were vastly different (i.e.,
influenced more by biomass burning), the relationships between the dust and biological residues
and cloud ice, precipitation type and quantity, and cloud depth were consistent with
2009 and 2011 samples.
Dust and biological residues serve as IN, becoming incorporated into deeper
cloud systems at cloud top and subsequently influencing the formation of ice-induced
precipitation at SPD. This effect was documented in the CalWater 2011 modeling study
by Fan et al. (2014).
Our observations support the hypothesis that biomass burning and pollution
residues likely served as CCN in shallower orographic clouds, which coincided with
periods of less precipitation as simulated by Martin et al. (2015) during
two CalWater 2011 storms in the Sierra Nevada.
When dust/biological residues and pollution/biomass burning residues were both
present, orographic clouds also were typically shallow and coincided with periods of
less precipitation. This aligns with the hypothesis that IN and high concentrations
of CCN at different altitudes in the same cloud system inhibit precipitation
formation (Saleeby et al., 2009).
By building on previous case studies presented by Ault et al. (2011) and
Creamean et al. (2013), results presented herein represent a noteworthy advancement in understanding
the effects of sources of insoluble aerosol species on the type and quantity
of precipitation in the California Sierra Nevada. Aerosol impacts on
clouds and precipitation derived from insoluble residue links with cloud and
precipitation properties have important implications for the Sierra Nevada
by serving as one of the key factors that influence water supply in the
region. The relationships between insoluble precipitation residues and their
potential climate impacts could translate to a global scale, i.e., apply to
other orographic regions where such insoluble particles are found in and
impact the formation of clouds and precipitation. Thus, understanding
insoluble residue sources has implications on a global level, particularly
when modeling their impacts on clouds. However, additional studies are
needed to better quantify these relationships, which served as a major
motivation for the more recent CalWater 2 field campaign, which started in
early 2015. The findings presented here from CalWater served as the
foundation for the flight planning and execution of field measurements
during CalWater 2, demonstrating the importance of our results for not only
constraining future modeling work but also serving as a driver to continue
similar measurements to develop a longer-term record. Results from both
studies will enable improvements in models to better assess how weather
patterns and/or regional climate may change due to the effects from
different aerosol sources, particularly those from long-range transport
which have a major impact on the seeder–feeder mechanism long observed over
the Sierra Nevada. Improving our ability to model the interactions between
aerosols, clouds, and precipitation can contribute to better winter storm
preparedness, water resource management, and flood mitigation.
The Supplement related to this article is available online at doi:10.5194/acp-15-6535-2015-supplement.
J. M. Creamean collected and analyzed ATOFMS data from precipitation samples in 2009,
2010, and 2011, interpreted all data, and prepared the manuscript with
contributions from all co-authors. A. P. Ault collected and analyzed ATOFMS data
from precipitation samples in 2009. A. B. White, P. J. Neiman, and F. M. Ralph collected and
analyzed S-PROF data and surface meteorology measurements at SPD. P. Minnis
analyzed GOES-11 data. F. M. Ralph was additionally involved with experimental
design. K. A. Prather was the principal investigator of this study, involved with
experimental design as well as preparation and editing of this manuscript. All
authors reviewed and commented on the paper.
Acknowledgements
Surface meteorological measurements and S-PROF radar data were retrieved
from NOAA HMT-West (http://hmt.noaa.gov/). Funding was provided by the
California Energy Commission under contract UCOP/CIEE C-09-07 and CEC
500-09-043. J. Creamean was partially supported by the National Research
Council Research Associateship Program. P. Minnis was supported by the NASA
Modeling, Analysis, and Prediction Program and the DOE ARM Program. J. Mayer,
D. Collins, J. Cahill, M. Zauscher, E. Fitzgerald, C. Gaston, and M. Moore from
UCSD provided assistance with equipment preparation and setup at SPD. The
deployment of the NOAA and UCSD/SIO equipment at SPD involved many field
staff, particularly C. King (NOAA). The Forest Hill Power Utility District
is acknowledged for hosting the sampling site at SPD. A. Martin (UCSD), G. Wick (NOAA),
and D. Gottas (NOAA) provided insightful discussions.
Edited by: A. Huffman
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