In the southwestern United States, California (CA) is one of the most climatically sensitive regions given its low (≤250 mm/year) seasonal precipitation and its inherently variable hydroclimate, subject to large magnitude modulation. To reconstruct past climate change in CA, cave calcite deposits (stalagmites) have been utilized as an archive for environmentally sensitive proxies, such as stable isotope compositions (δ18O, δ13C) and trace element concentrations (e.g., Mg, Ba, Sr). Monitoring the cave and associated surface environments, the chemical evolution of cave drip-water, the calcite precipitated from the drip-water, and the response of these systems to seasonal variability in precipitation and temperature is imperative for interpreting stalagmite proxies. Here we present monitored drip-water and physical parameters at Lilburn Cave, Sequoia Kings Canyon National Park (Southern Sierra Nevada), CA, and measured trace element concentrations (Mg, Sr, Ba, Cu, Fe, Mn) and stable isotopic compositions (δ18O, δ2H) of drip-water and for calcite (δ18O) precipitated on glass substrates over a two-year period (November 2018 to February 2021) to better understand how chemical variability at this site is influenced by local and regional precipitation and temperature variability. Despite large variability in surface temperatures and precipitation amount and source region (North Pacific vs. subtropical Pacific), Lilburn Cave exhibits a constant cave environment year-round. At two of the three sites within the cave, drip-water δ18O and δ2H are influenced seasonally by evaporative enrichment. At a third collection site in the cave, the drip-water δ18O responds solely to precipitation δ18O variability. The Mg/Ca, Ba/Ca, and Sr/Ca ratios are seasonally responsive to prior calcite precipitation at all sites but minimally to water-rock interaction. Lastly, we examine the potential of trace metals (e.g., Mn2+and Cu2+as a geochemical proxy of recharge and find that variability in their concentrations has high potential to denote the onset of the rainy season in the study region. The drip-water composition is recorded in the calcite, demonstrating that stalagmites from Lilburn Cave, and potentially more regionally, could record seasonal variability in weather even during periods of substantially reduced rainfall.
California’s water resources rely heavily on cool‐season (November–March) precipitation in the Sierra Nevada. Interannual variability is highly volatile and seasonal forecasting has little to no skill, making water management particularly challenging. Over 1902–2020, Sierra Nevada cool‐season precipitation totals exhibited significant 2.2‐ and 13–15‐year cycles, accounting for approximately 40% of total variability and perhaps signifying potential as seasonal forecasting tools. However, the underlying climate dynamics are not well understood and it is unclear whether these cycles are stable over the long term. We use tree rings to reconstruct Sierra Nevada cool‐season precipitation back to 1400. The reconstruction is skillful, accounting for 55%–74% of observed variability and capturing the 20th‐century 2.2‐ and 13–15‐year cycles. Prior to 1900, the reconstruction indicates no other century‐long periods of significant spectral power in the 2.2‐ or 13–15‐year bands. The reconstruction does indicate significant cyclicity over other extended periods of several decades or longer, however, with dominant periodicities in the ranges of 2.1–2.7 and 3.5–8 years. The late 1700s through 1800s exhibited the highest‐amplitude cycles in the reconstruction, with periodicities of 2.4 and 5.7–7.4 years. The reconstruction should serve to caution against extrapolating the observed 2.2‐ and 13–15‐year cycles to guide future expectations. On the other hand, observations and the reconstruction suggest that interannual variability of Sierra Nevada cool‐season precipitation is not a purely white noise process and research should aim to diagnose the dynamical drivers of extended periods of cyclicity in this critical natural resource.
more » « less- NSF-PAR ID:
- 10374632
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Water Resources Research
- Volume:
- 57
- Issue:
- 3
- ISSN:
- 0043-1397
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Excessive phosphorus (P) applications to croplands can contribute to eutrophication of surface waters through surface runoff and subsurface (leaching) losses. We analyzed leaching losses of total dissolved P (TDP) from no-till corn, hybrid poplar (Populus nigra X P. maximowiczii), switchgrass (Panicum virgatum), miscanthus (Miscanthus giganteus), native grasses, and restored prairie, all planted in 2008 on former cropland in Michigan, USA. All crops except corn (13 kg P ha−1 year−1) were grown without P fertilization. Biomass was harvested at the end of each growing season except for poplar. Soil water at 1.2 m depth was sampled weekly to biweekly for TDP determination during March–November 2009–2016 using tension lysimeters. Soil test P (0–25 cm depth) was measured every autumn. Soil water TDP concentrations were usually below levels where eutrophication of surface waters is frequently observed (> 0.02 mg L−1) but often higher than in deep groundwater or nearby streams and lakes. Rates of P leaching, estimated from measured concentrations and modeled drainage, did not differ statistically among cropping systems across years; 7-year cropping system means ranged from 0.035 to 0.072 kg P ha−1 year−1 with large interannual variation. Leached P was positively related to STP, which decreased over the 7 years in all systems. These results indicate that both P-fertilized and unfertilized cropping systems may leach legacy P from past cropland management. Experimental details The Biofuel Cropping System Experiment (BCSE) is located at the W.K. Kellogg Biological Station (KBS) (42.3956° N, 85.3749° W; elevation 288 m asl) in southwestern Michigan, USA. This site is a part of the Great Lakes Bioenergy Research Center (www.glbrc.org) and is a Long-term Ecological Research site (www.lter.kbs.msu.edu). Soils are mesic Typic Hapludalfs developed on glacial outwash54 with high sand content (76% in the upper 150 cm) intermixed with silt-rich loess in the upper 50 cm55. The water table lies approximately 12–14 m below the surface. The climate is humid temperate with a mean annual air temperature of 9.1 °C and annual precipitation of 1005 mm, 511 mm of which falls between May and September (1981–2010)56,57. The BCSE was established as a randomized complete block design in 2008 on preexisting farmland. Prior to BCSE establishment, the field was used for grain crop and alfalfa (Medicago sativa L.) production for several decades. Between 2003 and 2007, the field received a total of ~ 300 kg P ha−1 as manure, and the southern half, which contains one of four replicate plots, received an additional 206 kg P ha−1 as inorganic fertilizer. The experimental design consists of five randomized blocks each containing one replicate plot (28 by 40 m) of 10 cropping systems (treatments) (Supplementary Fig. S1; also see Sanford et al.58). Block 5 is not included in the present study. Details on experimental design and site history are provided in Robertson and Hamilton57 and Gelfand et al.59. Leaching of P is analyzed in six of the cropping systems: (i) continuous no-till corn, (ii) switchgrass, (iii) miscanthus, (iv) a mixture of five species of native grasses, (v) a restored native prairie containing 18 plant species (Supplementary Table S1), and (vi) hybrid poplar. Agronomic management Phenological cameras and field observations indicated that the perennial herbaceous crops emerged each year between mid-April and mid-May. Corn was planted each year in early May. Herbaceous crops were harvested at the end of each growing season with the timing depending on weather: between October and November for corn and between November and December for herbaceous perennial crops. Corn stover was harvested shortly after corn grain, leaving approximately 10 cm height of stubble above the ground. The poplar was harvested only once, as the culmination of a 6-year rotation, in the winter of 2013–2014. Leaf emergence and senescence based on daily phenological images indicated the beginning and end of the poplar growing season, respectively, in each year. Application of inorganic fertilizers to the different crops followed a management approach typical for the region (Table 1). Corn was fertilized with 13 kg P ha−1 year−1 as starter fertilizer (N-P-K of 19-17-0) at the time of planting and an additional 33 kg P ha−1 year−1 was added as superphosphate in spring 2015. Corn also received N fertilizer around the time of planting and in mid-June at typical rates for the region (Table 1). No P fertilizer was applied to the perennial grassland or poplar systems (Table 1). All perennial grasses (except restored prairie) were provided 56 kg N ha−1 year−1 of N fertilizer in early summer between 2010 and 2016; an additional 77 kg N ha−1 was applied to miscanthus in 2009. Poplar was fertilized once with 157 kg N ha−1 in 2010 after the canopy had closed. Sampling of subsurface soil water and soil for P determination Subsurface soil water samples were collected beneath the root zone (1.2 m depth) using samplers installed at approximately 20 cm into the unconsolidated sand of 2Bt2 and 2E/Bt horizons (soils at the site are described in Crum and Collins54). Soil water was collected from two kinds of samplers: Prenart samplers constructed of Teflon and silica (http://www.prenart.dk/soil-water-samplers/) in replicate blocks 1 and 2 and Eijkelkamp ceramic samplers (http://www.eijkelkamp.com) in blocks 3 and 4 (Supplementary Fig. S1). The samplers were installed in 2008 at an angle using a hydraulic corer, with the sampling tubes buried underground within the plots and the sampler located about 9 m from the plot edge. There were no consistent differences in TDP concentrations between the two sampler types. Beginning in the 2009 growing season, subsurface soil water was sampled at weekly to biweekly intervals during non-frozen periods (April–November) by applying 50 kPa of vacuum to each sampler for 24 h, during which the extracted water was collected in glass bottles. Samples were filtered using different filter types (all 0.45 µm pore size) depending on the volume of leachate collected: 33-mm dia. cellulose acetate membrane filters when volumes were less than 50 mL; and 47-mm dia. Supor 450 polyethersulfone membrane filters for larger volumes. Total dissolved phosphorus (TDP) in water samples was analyzed by persulfate digestion of filtered samples to convert all phosphorus forms to soluble reactive phosphorus, followed by colorimetric analysis by long-pathlength spectrophotometry (UV-1800 Shimadzu, Japan) using the molybdate blue method60, for which the method detection limit was ~ 0.005 mg P L−1. Between 2009 and 2016, soil samples (0–25 cm depth) were collected each autumn from all plots for determination of soil test P (STP) by the Bray-1 method61, using as an extractant a dilute hydrochloric acid and ammonium fluoride solution, as is recommended for neutral to slightly acidic soils. The measured STP concentration in mg P kg−1 was converted to kg P ha−1 based on soil sampling depth and soil bulk density (mean, 1.5 g cm−3). Sampling of water samples from lakes, streams and wells for P determination In addition to chemistry of soil and subsurface soil water in the BCSE, waters from lakes, streams, and residential water supply wells were also sampled during 2009–2016 for TDP analysis using Supor 450 membrane filters and the same analytical method as for soil water. These water bodies are within 15 km of the study site, within a landscape mosaic of row crops, grasslands, deciduous forest, and wetlands, with some residential development (Supplementary Fig. S2, Supplementary Table S2). Details of land use and cover change in the vicinity of KBS are given in Hamilton et al.48, and patterns in nutrient concentrations in local surface waters are further discussed in Hamilton62. Leaching estimates, modeled drainage, and data analysis Leaching was estimated at daily time steps and summarized as total leaching on a crop-year basis, defined from the date of planting or leaf emergence in a given year to the day prior to planting or emergence in the following year. TDP concentrations (mg L−1) of subsurface soil water were linearly interpolated between sampling dates during non-freezing periods (April–November) and over non-sampling periods (December–March) based on the preceding November and subsequent April samples. Daily rates of TDP leaching (kg ha−1) were calculated by multiplying concentration (mg L−1) by drainage rates (m3 ha−1 day−1) modeled by the Systems Approach for Land Use Sustainability (SALUS) model, a crop growth model that is well calibrated for KBS soil and environmental conditions. SALUS simulates yield and environmental outcomes in response to weather, soil, management (planting dates, plant population, irrigation, N fertilizer application, and tillage), and genetics63. The SALUS water balance sub-model simulates surface runoff, saturated and unsaturated water flow, drainage, root water uptake, and evapotranspiration during growing and non-growing seasons63. The SALUS model has been used in studies of evapotranspiration48,51,64 and nutrient leaching20,65,66,67 from KBS soils, and its predictions of growing-season evapotranspiration are consistent with independent measurements based on growing-season soil water drawdown53 and evapotranspiration measured by eddy covariance68. Phosphorus leaching was assumed insignificant on days when SALUS predicted no drainage. Volume-weighted mean TDP concentrations in leachate for each crop-year and for the entire 7-year study period were calculated as the total dissolved P leaching flux (kg ha−1) divided by the total drainage (m3 ha−1). One-way ANOVA with time (crop-year) as the fixed factor was conducted to compare total annual drainage rates, P leaching rates, volume-weighted mean TDP concentrations, and maximum aboveground biomass among the cropping systems over all seven crop-years as well as with TDP concentrations from local lakes, streams, and groundwater wells. When a significant (α = 0.05) difference was detected among the groups, we used the Tukey honest significant difference (HSD) post-hoc test to make pairwise comparisons among the groups. In the case of maximum aboveground biomass, we used the Tukey–Kramer method to make pairwise comparisons among the groups because the absence of poplar data after the 2013 harvest resulted in unequal sample sizes. We also used the Tukey–Kramer method to compare the frequency distributions of TDP concentrations in all of the soil leachate samples with concentrations in lakes, streams, and groundwater wells, since each sample category had very different numbers of measurements. Individual spreadsheets in “data table_leaching_dissolved organic carbon and nitrogen.xls” 1. annual precip_drainage 2. biomass_corn, perennial grasses 3. biomass_poplar 4. annual N leaching _vol-wtd conc 5. Summary_N leached 6. annual DOC leachin_vol-wtd conc 7. growing season length 8. correlation_nh4 VS no3 9. correlations_don VS no3_doc VS don Each spreadsheet is described below along with an explanation of variates. Note that ‘nan’ indicate data are missing or not available. First row indicates header; second row indicates units 1. Spreadsheet: annual precip_drainage Description: Precipitation measured from nearby Kellogg Biological Station (KBS) Long Term Ecological Research (LTER) Weather station, over 2009-2016 study period. Data shown in Figure 1; original data source for precipitation (https://lter.kbs.msu.edu/datatables/7). Drainage estimated from SALUS crop model. Note that drainage is percolation out of the root zone (0-125 cm). Annual precipitation and drainage values shown here are calculated for growing and non-growing crop periods. Variate Description year year of the observation crop “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” precip_G precipitation during growing period (milliMeter) precip_NG precipitation during non-growing period (milliMeter) drainage_G drainage during growing period (milliMeter) drainage_NG drainage during non-growing period (milliMeter) 2. Spreadsheet: biomass_corn, perennial grasses Description: Maximum aboveground biomass measurements from corn, switchgrass, miscanthus, native grass and restored prairie plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2015. Data shown in Figure 2. Variate Description year year of the observation date day of the observation (mm/dd/yyyy) crop “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” replicate each crop has four replicated plots, R1, R2, R3 and R4 station stations (S1, S2 and S3) of samplings within the plot. For more details, refer to link (https://data.sustainability.glbrc.org/protocols/156) species plant species that are rooted within the quadrat during the time of maximum biomass harvest. See protocol for more information, refer to link (http://lter.kbs.msu.edu/datatables/36) For maize biomass, grain and whole biomass reported in the paper (weed biomass or surface litter are excluded). Surface litter biomass not included in any crops; weed biomass not included in switchgrass and miscanthus, but included in grass mixture and prairie. fraction Fraction of biomass biomass_plot biomass per plot on dry-weight basis (Grams_Per_SquareMeter) biomass_ha biomass (megaGrams_Per_Hectare) by multiplying column biomass per plot with 0.01 3. Spreadsheet: biomass_poplar Description: Maximum aboveground biomass measurements from poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2015. Data shown in Figure 2. Note that poplar biomass was estimated from crop growth curves until the poplar was harvested in the winter of 2013-14. Variate Description year year of the observation method methods of poplar biomass sampling date day of the observation (mm/dd/yyyy) replicate each crop has four replicated plots, R1, R2, R3 and R4 diameter_at_ground poplar diameter (milliMeter) at the ground diameter_at_15cm poplar diameter (milliMeter) at 15 cm height biomass_tree biomass per plot (Grams_Per_Tree) biomass_ha biomass (megaGrams_Per_Hectare) by multiplying biomass per tree with 0.01 4. Spreadsheet: annual N leaching_vol-wtd conc Description: Annual leaching rate (kiloGrams_N_Per_Hectare) and volume-weighted mean N concentrations (milliGrams_N_Per_Liter) of nitrate (no3) and dissolved organic nitrogen (don) in the leachate samples collected from corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2016. Data for nitrogen leached and volume-wtd mean N concentration shown in Figure 3a and Figure 3b, respectively. Note that ammonium (nh4) concentration were much lower and often undetectable (<0.07 milliGrams_N_Per_Liter). Also note that in 2009 and 2010 crop-years, data from some replicates are missing. Variate Description crop “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” crop-year year of the observation replicate each crop has four replicated plots, R1, R2, R3 and R4 no3 leached annual leaching rates of nitrate (kiloGrams_N_Per_Hectare) don leached annual leaching rates of don (kiloGrams_N_Per_Hectare) vol-wtd no3 conc. Volume-weighted mean no3 concentration (milliGrams_N_Per_Liter) vol-wtd don conc. Volume-weighted mean don concentration (milliGrams_N_Per_Liter) 5. Spreadsheet: summary_N leached Description: Summary of total amount and forms of N leached (kiloGrams_N_Per_Hectare) and the percent of applied N lost to leaching over the seven years for corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2016. Data for nitrogen amount leached shown in Figure 4a and percent of applied N lost shown in Figure 4b. Note the fraction of unleached N includes in harvest, accumulation in root biomass, soil organic matter or gaseous N emissions were not measured in the study. Variate Description crop “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” no3 leached annual leaching rates of nitrate (kiloGrams_N_Per_Hectare) don leached annual leaching rates of don (kiloGrams_N_Per_Hectare) N unleached N unleached (kiloGrams_N_Per_Hectare) in other sources are not studied % of N applied N lost to leaching % of N applied N lost to leaching 6. Spreadsheet: annual DOC leachin_vol-wtd conc Description: Annual leaching rate (kiloGrams_Per_Hectare) and volume-weighted mean N concentrations (milliGrams_Per_Liter) of dissolved organic carbon (DOC) in the leachate samples collected from corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2016. Data for DOC leached and volume-wtd mean DOC concentration shown in Figure 5a and Figure 5b, respectively. Note that in 2009 and 2010 crop-years, water samples were not available for DOC measurements. Variate Description crop “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” crop-year year of the observation replicate each crop has four replicated plots, R1, R2, R3 and R4 doc leached annual leaching rates of nitrate (kiloGrams_Per_Hectare) vol-wtd doc conc. volume-weighted mean doc concentration (milliGrams_Per_Liter) 7. Spreadsheet: growing season length Description: Growing season length (days) of corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in the Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2015. Date shown in Figure S2. Note that growing season is from the date of planting or emergence to the date of harvest (or leaf senescence in case of poplar). Variate Description crop “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” year year of the observation growing season length growing season length (days) 8. Spreadsheet: correlation_nh4 VS no3 Description: Correlation of ammonium (nh4+) and nitrate (no3-) concentrations (milliGrams_N_Per_Liter) in the leachate samples from corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2013-2015. Data shown in Figure S3. Note that nh4+ concentration in the leachates was very low compared to no3- and don concentration and often undetectable in three crop-years (2013-2015) when measurements are available. Variate Description crop “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” date date of the observation (mm/dd/yyyy) replicate each crop has four replicated plots, R1, R2, R3 and R4 nh4 conc nh4 concentration (milliGrams_N_Per_Liter) no3 conc no3 concentration (milliGrams_N_Per_Liter) 9. Spreadsheet: correlations_don VS no3_doc VS don Description: Correlations of don and nitrate concentrations (milliGrams_N_Per_Liter); and doc (milliGrams_Per_Liter) and don concentrations (milliGrams_N_Per_Liter) in the leachate samples of corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2013-2015. Data of correlation of don and nitrate concentrations shown in Figure S4 a and doc and don concentrations shown in Figure S4 b. Variate Description crop “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” year year of the observation don don concentration (milliGrams_N_Per_Liter) no3 no3 concentration (milliGrams_N_Per_Liter) doc doc concentration (milliGrams_Per_Liter)more » « less
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null (Ed.)Abstract. Despite clear signals of regional impacts of the recent severe drought inCalifornia, e.g., within Californian Central Valley groundwater storage and Sierra Nevada forests, our understanding of how this drought affected soil moisture and vegetation responses in lowland grasslands is limited. In order to better understand the resulting vulnerability of these landscapes to fire and ecosystem degradation, we aimed to generalize drought-induced changes in subsurface soil moisture and to explore its effects within grassland ecosystems of Southern California. We used a high-resolution in situ dataset of climate and soil moisture from two grassland sites (coastal and inland), alongside greenness (Normalized Difference Vegetation Index) data from Landsat imagery, to explore drought dynamics in environments with similar precipitation but contrasting evaporative demand over the period 2008–2019. We show that negative impacts of prolonged precipitation deficits on vegetation at the coastal site were buffered by fog and moderate temperatures. During the drought, the Santa Barbara region experienced an early onset of the dry season in mid-March instead of April, resulting in premature senescence of grasses by mid-April. We developed a parsimonious soil moisture balance model that captures dynamic vegetation–evapotranspiration feedbacks and analyzed the links between climate, soil moisture, and vegetation greenness over several years of simulated drought conditions, exploring the impacts of plausible climate change scenarios that reflect changes to precipitation amounts, their seasonal distribution, and evaporative demand. The redistribution of precipitation over a shortened rainy season highlighted a strong coupling of evapotranspiration to incoming precipitation at the coastal site, while the lower water-holding capacity of soils at the inland site resulted in additional drainage occurring under this scenario. The loss of spring rains due to a shortening of the rainy season also revealed a greater impact on the inland site, suggesting less resilience to low moisture at a time when plant development is about to start. The results also suggest that the coastal site would suffer disproportionally from extended dry periods, effectively driving these areas into more extreme drought than previously seen. These sensitivities suggest potential future increases in the risk of wildfires under climate change, as well as increased grassland ecosystem vulnerability.more » « less
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Abstract Growth‐increment widths of Pacific geoduck (
Panopea generosa ), a long‐lived bivalve, are used to develop the first marine‐based, multicentennial, annually resolved, and exactly dated archive of Northeast Pacific sea surface temperatures (SST). The chronology is sampled from the Tree Nob Islands, British Columbia, Canada, continuously covers 1725–2008, and also contains nine older radiocarbon‐dated segments, which together span 58% of the last 1,500 years. Age‐related growth declines were removed by aligning all increments relative to age of increment formation and fitting with a single detrending curve to preserve low‐frequency signals. The geoduck chronology was used to reconstruct local SST variability across the seasonal window of April through November. The chronology at both the concurrent (lag‐0) and following (lag+1) year are both highly significant predictors of SST in a stepwise multiple linear regression, explaining 54% of the variance in the period of instrumental overlap (1940–2001), passing strict tests of calibration‐verification. Reconstructed SSTs contained significant spectral power at periods from 3 to 64 years, suggesting that 20th century variability in these periodicities is not unusual in the longer‐term context. The period of lowest growth coincided with the Dalton minimum, an episode of reduced solar irradiance from 1790–1830, as well as the 1809 Unknown eruption, suggesting that solar and volcanic signals are present in the SST history. The most conspicuous aspect of the reconstruction is the steady and unprecedented warming trend that began in the mid‐1800s and continues through present. The post‐1976 interval includes the two warmest decades of the reconstruction. -
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Abstract Heavy orographic snowfall can disrupt transportation and threaten lives and property in mountainous regions but benefits water resources, winter sports, and tourism. Little Cottonwood Canyon (LCC) in northern Utah’s Wasatch Range is one of the snowiest locations in the interior western United States and frequently observes orographic snowfall extremes with threats to transportation, structures, and public safety due to storm-related avalanche hazards. Using manual new-snow and liquid precipitation equivalent (LPE) observations, ERA5 reanalyses, and operational radar data, this paper examines the characteristics of cool-season (October–April) 12-h snowfall extremes in upper LCC. The 12-h extremes, defined based on either 95th percentile new snow or LPE, occur for a wide range of crest-level flow directions. The distribution of LPE extremes is bimodal with maxima for south-southwest or north-northwest flow, whereas new-snow extremes occur most frequently during west-northwest flow, which features colder storms with higher snow-to-liquid ratios. Both snowfall and LPE extremes are produced by diverse synoptic patterns, including inland-penetrating or decaying atmospheric rivers from the south through northwest that avoid the southern high Sierra Nevada, frontal systems, post-cold-frontal northwesterly flow, south-southwesterly cold-core flow, and closed low pressure systems. Although often associated with heavy precipitation in other mountainous regions, the linkages between local integrated water vapor transport (IVT) and orographic precipitation extremes in LCC are relatively weak, and during post-cold-frontal northwesterly flow, highly localized and intense snowfall can occur despite low IVT. These results illustrate the remarkable diversity of storm characteristics producing orographic snowfall extremes at this interior continental mountain location.
Significance Statement Little Cottonwood Canyon in northern Utah’s central Wasatch Range frequently experiences extreme snowfall events that pose threats to lives and property. In this study, we illustrate the large diversity of storm characteristics that produce this extreme snowfall. Meteorologists commonly use the amount of water vapor transport in the atmosphere to predict heavy mountain precipitation, but that metric has limited utility in Little Cottonwood Canyon where heavy snowfall can occur with lower values of such transport. Our results can aid weather forecasting in the central Wasatch Range and have implications for understanding precipitation processes in mountain ranges throughout the world.
