Cold Season Precipitation Research Articles

Cold-Frontal Rainbands over the United Kingdom: Sensitivity of Precipitation Processes to Microphysics Schemes in WRF Simulations

Abstract A narrow cold-frontal rainband (NCFR) and wide cold-frontal rainband (WCFR) over the United Kingdom on 24 January 2018 are selected to evaluate the sensitivity of cold-season precipitation to three bulk cloud microphysics schemes in the Weather Research and Forecasting (WRF) Model. The three simulations tend to overpredict the intensity and the areal coverage of NCFRs but underpredict those of WCFRs against the Met Office radar imagery. Nonetheless, the WRF double-moment 6-class (WDM6) simulation produces the most realistic NCFR and WCFR. More specifically, the intensity of the NCFR in the WDM6 simulation is overpredicted in the developing stage and underpredicted in the dissipating stage when compared to the Thompson and Morrison simulations. This phenomenon is mainly attributed to the rain mixing ratio at 1 km. The more intense and larger area of the WCFR in the WDM6 simulation is associated with more low-level snow, graupel, and rain than the other two simulations. In the WDM6 simulation, the larger low-level snow mixing ratios are due to the higher rates of aggregation between cloud ice and snow, whereas the larger rain mixing ratio is due to higher melting rates. Thus, differences in low-level ice-phase processes between microphysics schemes are large, strongly influencing the intensity of WCFRs, but the differences in warm-rain processes are smaller. Additionally, the small size of raindrops in the WDM6 simulation leads to wider and more intense WCFR as these raindrops are preferentially sent rearward relative to the cold front. Significance Statement Models are useful tools to predict weather but have errors in predicting different weather types. In the United Kingdom, precipitation is often predicted to be too strong at cold fronts and too weak behind the fronts compared to radar observations. Choosing different options for how precipitation is represented in one model produced different precipitation forecasts, with WDM6 being the best. One reason why WDM6 was the best was because it produced more and smaller raindrops, which would be transported behind the front, and it had higher melting rates at low levels, resulting in more precipitation behind the fronts than other options. Our results help identify why different options produce different forecasts, leading to possible future improvements in these options.

X-Band Radar and Surface-Based Observations of Cold-Season Precipitation in Western Colorado’s Complex Terrain

Abstract Hydrologic processes associated with intermountain cold-season precipitation in the Upper Colorado River basin have important impacts on avalanche forecasting and water resource management. However, traditional weather radar networks struggle with observations in this complex terrain. Data collected during the Study of Precipitation, the Lower Atmosphere, and the Surface for Hydrometeorology (SPLASH) and its sister campaign, Surface Atmosphere Integrated Field Laboratory (SAIL) in the East River watershed of western Colorado, are used to examine a multistorm period from 23 December 2021 to 1 January 2022 that contributed 35% of the total winter precipitation in this watershed. Dual-polarization X-band radar and disdrometer measurements show ∼30-mm differences in precipitation amount at two sites in proximity over four distinct storm events within the period. Wind patterns, synoptic forcings, microphysical characteristics of precipitation, and surface meteorology are analyzed to explain the observed spatial variability of cold-season precipitation in complex mountainous terrain. Analysis shows that differences over time within this event are mainly accounted for by synoptic forcings, such as frontal passages; differences between sites are accounted for by the impact of variations in local wind patterns on precipitation microphysics. Patterns of surface precipitation intensity are compared and found to be correlated with X-band radar signatures; a relationship between a strong dendritic growth stage and intense low-density surface precipitation is reinforced by this study. This relationship demonstrates the importance of particle growth mechanisms on surface snowfall patterns in high-altitude complex terrain, underscoring the importance of realistic microphysical parameterizations. Significance Statement The amount and density of snowpack from western Colorado winter storms have significant impacts on water resources in the Upper Colorado River basin. Snowpack characteristics are affected by small-scale differences in how snow forms in the atmosphere. These differences are hard to study in the complex terrain of the Rockies, but data from the SPLASH and SAIL field campaigns allows us to investigate how snow crystal formation and mountain-driven wind patterns affect snow near the surface. Our study finds that snow crystal growth varies over small space and time scales and is likely controlled by the terrain beneath a given location and resultant local wind patterns. These results imply that predicting snowpack in the Rockies requires properly representing local wind patterns and crystal growth processes in models.

Regime-Dependent Characteristics and Predictability of Cold-Season Precipitation Events in the St. Lawrence River Valley

Abstract The St. Lawrence River Valley experiences a variety of precipitation types (p-types) during the cold season, such as rain, freezing rain, ice pellets, and snow. These varied precipitation types exert considerable impacts on aviation, road transportation, power generation and distribution, and winter recreation and are shaped by diverse multiscale processes that interact with the region’s complex topography. This study utilizes ERA5 reanalysis data, surface cyclone climatology, and hourly station observations from Montréal, Québec, and Burlington, Vermont, during October–April 2000–18 to investigate the spectrum of synoptic-scale weather regimes that induce cold-season precipitation across the St. Lawrence River Valley. In particular, k-means clustering and self-organizing maps (SOMs) are used to classify cyclone tracks passing near the St. Lawrence River Valley, and their accompanying thermodynamic profiles, into a set of event types that include a U.S. East Coast track, a central U.S. track, and two Canadian clipper tracks. Composite analyses are subsequently performed to reveal the synoptic-scale environments and the characteristic p-types that most frequently accompany each event type. Global Ensemble Forecast System version 12 (GEFSv12) reforecasts are then used to examine the relative predictability of cyclone characteristics and the local thermodynamic profile associated with each event type at 0–5-day forecast lead times. The analysis suggests that forecasted cyclones near the St. Lawrence River Valley develop too quickly and are located left-of-track relative to the reanalysis on average, which has implications for forecasts of the local thermodynamic profile and p-type across the region when the temperature is near 0°C. Significance Statement Diverse precipitation types are observed when near-surface temperatures approach 0°C during the cold season, especially across the St. Lawrence River Valley in southern Québec. This study classifies cold-season precipitation events impacting the St. Lawrence River Valley based on the track of storm systems across the region and quantifies the average meteorological characteristics and predictability of each track. Our analysis reveals that forecasted low pressure systems develop too quickly and are left of their observed track 0–5 days prior to an event on average, which has implications for forecasted temperatures and the type of precipitation observed across the region. Our results can inform future operational forecasts of cold-season precipitation events by providing a storm-focused perspective on forecast errors during these impactful events.

Holocene insolation and sea surface temperature influences on the polar front jet stream and precipitation in the midcontinental United States

Variability in the source and seasonality of precipitation in the midcontinental United States during the Holocene was investigated using isotopic and sedimentological data from Martin Lake, northeastern Indiana, USA. Between 7100 and 4000 years before present (yr BP; present = 1950 CE), high δ18Ocal and δ13Ccal values with low variability indicate that moisture was predominantly derived from subtropical, southerly sources and delivered primarily during the warm season. Mean state shifts toward lower δ18Ocal and δ13Ccal occurred at ca. 4000 and 2550 yr BP, respectively, indicating an increase in northerly-sourced cold-season precipitation during the Late Holocene (i.e., the past 4200 years) and a subsequent reduction in warm season duration after 2550 yr BP. Record low %lithics from ca. 5000 to 4000 yr BP indicates major reductions in warm-season rain storms, consistent with regional evidence of drought at this time. An increase in the amplitude of centennial-scale variability in δ18Ocal, δ13Ccal, and %lithics after 1900 yr BP indicates greater precipitation source variability during the Common Era. During this interval, precipitation fluctuated between southerly-sourced, convective rainstorms when the Northern Hemisphere (NH) was warm (e.g., during the Medieval Climate Anomaly; 700–1000 yr BP) and northerly-sourced rain and snow when the NH was cool (e.g., during the Little Ice Age; 150–550 yr BP). These trends, especially the change at ca. 4000 yr BP, are consistent with other North American paleoclimate records that collectively suggest a continental-scale shift in precipitation seasonality during the Middle to Late Holocene transition as the tropical Pacific Ocean transitioned from La Niña-like conditions to a more El Niño-like mean state. Concurrent NH cooling and persistent El Niño-like conditions during the Late Holocene would have favored a southerly polar front jet stream with enhanced ridge and trough atmospheric circulation over North America – conditions resembling the positive mode of the Pacific-North American teleconnection (PNA). This would have increased interactions between high-latitude and subtropical airmasses over the midcontinent, increasing the proportion of northerly precipitation with low δ18O delivered during the cold season (i.e., snowfall) and during extended periods with +PNA-like atmospheric circulation (e.g., the Little Ice Age).

The disproportionate impact of enhanced evaporation from melting arctic sea ice on cold-season land precipitation trends

Diminishing Arctic sea ice has led to enhanced evaporation from the Arctic marginal seas (AMS), which is expected to alter precipitation over land. In this work, AMS evaporation is numerically tracked to quantify its contribution to cold-season (October–March) precipitation over land in the Northern Hemisphere during 1980–2021. Results show a significant 32% increase in AMS moisture contribution to land precipitation, corresponding to a 16% increase per million square km loss of sea ice area. Especially over the high-latitude land, despite the fractional contribution of AMS to precipitation being relatively low (8%), the augmented AMS evaporation contributed disproportionately (42%) to the observed upward trend in precipitation. Notably, northern East Siberia exhibited a substantial rise in both the amount and fraction of extreme snowfall sourced from the AMS. Our findings underscore the importance of the progressively ice-free Arctic as an important contributor to the escalating levels of cold-season precipitation and snowfall over northern high-latitude land.

Climate deterioration and subsistence economy in prehistoric Southern Iberia: an evaluation of potential links based on regional trajectories

The potential impact of climatic deterioration on societal breakdowns in prehistory is often based on the mere coincidence of archaeological and palaeoclimatological proxies. For a more profound discussion, climate-sensitive archaeological parameters need to be identified. As agriculture and livestock are significantly affected by the recent climate crisis, the analysis of archaeobotanical and archaeozoological remains can deepen our understanding of this topic. Here, we analyze regional trajectories in subsistence and seasonal precipitation variability across southern Iberia focusing on well-known prehistoric breakdowns around 2200 and 1600 BCE. Results suggest that the ratios of the importance of sheep/goat versus swine and sheep/goat versus cattle, respectively, may serve as a proxy for prehistoric mobility. The importance of hunting deer represents a proxy for societal turnover. While no direct link is evident between climate deterioration and the archaeozoological data, archaeobotanical data reveals a potential relation to precipitation variability. In particular, a close connection to reductions in cold-season precipitation in south-east Iberia appears likely for the ratio between barley and naked wheat. The increased importance of drought-tolerant barley correlates with a trend to reduced cold-season precipitation levels after ca. 2700 BCE. We hypothesize that prehistoric people in south-east Iberia cultivated more barley in order to adapt to periods of drier cold-season climate.

Glacier Changes from 1990 to 2022 in the Aksu River Basin, Western Tien Shan

Mountain glaciers are considered natural indicators of warming and a device for climatic change. In addition, it is also a solid reservoir of freshwater resources. Along with climate change, clarifying the dynamic changes of glacier in the Aksu River Basin (ARB) are important for hydrological processes. The study examined the variations in glacier area, elevation, and their reaction to climate change in the ARB between 1990 and 2022. The glacier melt on the runoff is explored from 2003 to 2020. This investigation utilized Landsat and Sentinal-2 images, ICESat, CryoSat, meteorological and hydrological data. The findings suggest that: (1) The glacier area in the ARB retreated by 309.40 km2 (9.37%, 0.29%·a−1) from 1990 to 2022. From 2003 to 2021, the ARB glacier surface elevation retreat rate of 0.38 ± 0.12 m·a−1 (0.32 ± 0.10 m w.e.a−1). Comparison with 2003–2009, the retreat rate is faster from 2010 to 2021. (2) From 1990 to 2022, the Toxkan and the Kumalak River Basin’s glacier area decreases between 61.28 km2 (0.28%·a−1) and 248.13 km2 (0.30%·a−1). Additionally, the rate of glacier surface elevation declined by −0.34 ± 0.11 m·a−1, −0.42 ± 0.14 m·a−1 from 2003 to 2021. (3) The mass balance sensitivities to cold season precipitation and ablation-phase accumulated temperatures are +0.27 ± 0.08 m w.e.a−1(10%)−1 and −0.33 ± 0.10 m w.e.a−1 °C−1, respectively. The mass loss is (962.55 ± 0.57) × 106 m3 w.e.a−1, (1087.50 ± 0.68) × 106 m3 w.e.a−1 during 2003–2009, 2010–2021 respectively. Warmer ablation-phase accumulated temperatures dominate glacier retreat in the ARB. (4) Glacier meltwater accounted for 34.57% and 41.56% of the Aksu River’s runoff during the ablation-phase of 2003–2009 and 2010–2020, respectively. The research has important implications for maintaining the stability of water resource systems based on glacier meltwater.

Climate change impacts on precipitation and water resources in Northwestern China

A statistical analysis is conducted on the hourly precipitation at 105 national meteorological stations in the Xinjiang region of China from 2011 to 2020. The purpose of this analysis is to determine the total quantity of precipitation during the warm and cold seasons, as well as the precipitation frequency at each station. The results of the analysis indicate that there is a more pronounced inter-annual variation in precipitation during the warm and cold seasons in Xinjiang, where the total amount of precipitation during the warm season is significantly higher than that during the cold season, with the warm season experiencing at least twice as much precipitation. In terms of the inter-annual incremental amount of precipitation, both the warm and cold seasons exhibit a trend of increasing and then decreasing over the years. However, the curvature change is more prominent in the cold season compared to the warm season, suggesting that the inter-annual variation in precipitation during the warm season remains relatively stable. The spatial distribution characteristics of the total precipitation in Xinjiang indicate that warm-season precipitation is more scattered, while cold-season precipitation is mainly concentrated in the northern region of the territory. This pattern holds true at low, medium, medium–high, and high altitudes. The cumulative precipitation during the warm season is always higher than that in the cold season, and this difference becomes more prominent with increasing altitude. This indicates that warm-season precipitation in Xinjiang is closely related to its complex topographic structure, particularly the convective weather generated by topographic clouds. On the other hand, precipitation in the cold season is mainly concentrated in the low- and medium-altitude stations, and there is not much correlation between precipitation and altitude. Precipitation is mainly affected by the mesoscale and the direct influence of large-scale weather systems. Analysis of precipitation in extreme moments reveals that during the warm season, extreme precipitation events occur mainly from the evening to early morning (19:00–03:00 the next day), accounting for 75.24% of the 105 stations. In contrast, during the cold season, extreme precipitation events occur between 03:00 and 18:00, corresponding to 74.29% of the stations. The relationship between precipitation and altitude differs in the warm and cold seasons. In the warm season, there is a linear relationship between precipitation and altitude, with a slope of 0.72 and a correlation coefficient of 0.52, indicating that precipitation increases with increasing altitude. However, in the cold season, the linear relationship between precipitation and altitude is very inconspicuous, with a slope of −0.05 and a correlation coefficient of −0.05, suggesting that there is no significant relationship between precipitation and altitude during this season. These findings provide valuable insights into the changing patterns of precipitation gradients at different altitudes and seasons in Xinjiang. This information can be used for the site selection of smokestack operations in high-altitude areas and for ground and air weather modification operations in low-altitude areas. Additionally, these data contribute to the understanding of precipitation patterns in Xinjiang, further improving the effectiveness of weather modification efforts and increasing the utilization of airborne water resources.

Evaluation of Wintertime Precipitation Estimates and Forecasts in the Mountains of Colorado

Abstract Wintertime precipitation poses many observational and forecasting challenges, especially in the complex topography of the western United States where radar beam blockage and difficulty siting in situ observations yields more sparse observations than in the eastern United States. Uncertainty in western U.S. winter precipitation is known to be high, so much so that some studies have found model simulated precipitation to produce similar or better large-scale estimates of annual precipitation than gridded observational products during climatologically anomalous years. This study evaluates high-resolution gridded precipitation estimates from Multi-Radar Multi-Sensor (MRMS) and Stage IV as well as forecasts from NOAA’s High-Resolution Rapid Refresh (HRRR) model in the Colorado Rocky Mountains. Gridded precipitation estimates and forecasts are compared with in situ SNOTEL measurements for two seasons of wintertime precipitation. The influence of forecast length, lead time, and model elevation on seasonal precipitation predictions from the HRRR are investigated. Additional comparisons are made with the relatively dense network of observations deployed in Colorado’s East River Watershed during the Study of Precipitation, the Lower Atmosphere and Surface for Hydrometeorology (SPLASH) campaign. Gridded products and forecasts are found to underestimate cold-season precipitation by 25%–65% relative to in situ and aircraft measurements, with longer forecast periods and lead times (6–24 h) having smaller biases (25%–30%) than shorter forecast periods and lead times (55%–65%). The assessment of multiple years of observations indicates that these biases are related more to the data and methods used to create the gridded products and forecasts than to precipitation characteristics. Significance Statement In the mountainous western United States, it is very challenging to both observe and forecast wintertime precipitation, yet snowfall plays an important role in providing the region’s annual water supply. This study aims to increase our understanding of the biases in observations and forecasts of snowfall in the Colorado Rocky Mountains, which can in turn impact forecasts of water availability for the ensuing warm season. In this study we find high-resolution gridded precipitation estimates and forecasts to underestimate cold-season precipitation when compared with in situ observing stations, with longer-range forecasts (e.g., daily) being the least biased. These findings were consistent over two years of study and have broad implications for the hydrologic modeling and water management communities.

Statistical Relations among Solid Precipitation, Atmospheric Moisture and Cloud Parameters in the Arctic

Observations collected during cold-season precipitation periods at Utquagvik, Alaska and at the multidisciplinary drifting observatory for the study of Arctic climate (MOSAiC) are used to statistically analyze the relations among the atmospheric water cycle parameters including the columnar supercooled liquid and ice amounts (expressed as liquid-water and ice-water paths, i.e., LWP and IWP), the integrated water vapor (IWV) and the near-surface snowfall rate. Data come from radar and radiometer-based retrievals and from optical precipitation sensors. While the correlation between snowfall rate and LWP is rather weak, correlation coefficients between radar-derived snowfall rate and IWP are high (~0.8), which is explained, in part, by the generally low LWP/IWP ratios during significant precipitation. Correlation coefficients between snowfall rate and IWV are moderate (~0.45). Correlations are generally weaker if snowfall is estimated by optical sensors, which is, in part, due to blowing snow. Correlation coefficients between near-surface temperature and snowfall rates are low (r < 0.3). The results from the Alaska and MOSAiC sites are generally similar. These results are not very sensitive to the amount of time averaging (e.g., 15 min averaging versus daily averages). Observationally based relations among the water cycle parameters are informative about atmospheric moisture conversion processes and can be used for model evaluations.

Assessment of snow simulation using Noah-MP land surface model forced by various precipitation sources in the Central Tianshan Mountains, Central Asia

Accurate mountainous snow estimation is paramount for hydrological processes and water resources estimation in arid regions. Using Land surface models (LSMs) is a practical numerical approach for snow estimation in a complex orography region such as the Tianshan Mountains. However, the bias of precipitation forcing is a major source of uncertainty for snow simulation over scarce-data regions. This study evaluated the performance of 4 km snow simulations using the Noah-MP LSM driven by eight precipitation sources. They are retrieved from non-model products and climate model simulations during the 2018-2019 cold season in the Central Tianshan Mountains (CTS) region. Multi-source validation datasets (in-situ observation, field snow pits, and remote sensing products) have been used for evaluation and uncertainty estimation. The results showed that the difference in precipitation amount significantly affected the snowpack simulation performance. Compared with non-model products (Global Land Data Assimilation System (GLDAS), Global Precipitation Measurement (GPM), and Gauge-adjusted Global Satellite Mapping of Precipitation (GSMaP)), the cold season precipitation from climate model simulations exhibited a better performance overall in the high-elevation regions (elevation > 1000 m) evaluated by in-situ observations. In addition, the 4 km convection-permitting modeling (CPM) precipitation (WRF-Morrison and WRF-WSM6) showed higher accuracy (RMSE: 23.48 mm/season and 18.94 mm/season, respectively) than gray-zone resolution simulations and ERA5-land driven runs in the high-elevation regions. The snow depth simulation driven by WRF-Morrison precipitation had the second smallest RMSE (4.67 cm/day) and lowest bias (-0.74 cm/day) value in the high-elevation regions compared with in-situ observation. Meanwhile, CPM precipitation-driven runs exhibited the smallest RMSE value based on the assessment of snow field pits. In addition, the CPM-driven simulation achieved the closest match to the elevation-based distribution of snow cover days in regions with elevation over 1000 m and duration over 60 days compared to the MODIS product. The findings of this study highlight that CPM with proper parameterization configurations has added values in producing realistic topographic precipitation for snow modeling using LSMs over data-scarcity mountainous areas.

Cold-Season Precipitation Sensitivity to Microphysical Parameterizations: Hydrologic Evaluations Leveraging Snow Lidar Datasets

Abstract Cloud microphysical processes are an important facet of atmospheric modeling, as they can control the initiation and rates of snowfall. Thus, parameterizations of these processes have important implications for modeling seasonal snow accumulation. We conduct experiments with the Weather Research and Forecasting (WRF V4.3.3) Model using three different microphysics parameterizations, including a sophisticated new scheme (ISHMAEL). Simulations are conducted for two cold seasons (2018 and 2019) centered on the Colorado Rockies’ ∼750-km2 East River watershed. Precipitation efficiencies are quantified using a drying-ratio mass budget approach and point evaluations are performed against three NRCS SNOTEL stations. Precipitation and meteorological outputs from each are used to force a land surface model (Noah-MP) so that peak snow accumulation can be compared against airborne snow lidar products. We find that microphysical parameterization choice alone has a modest impact on total precipitation on the order of ±3% watershed-wide, and as high as 15% for certain regions, similar to other studies comparing the same parameterizations. Precipitation biases evaluated against SNOTEL are 15% ± 13%. WRF Noah-MP configurations produced snow water equivalents with good correlations with airborne lidar products at a 1-km spatial resolution: Pearson’s r values of 0.9, RMSEs between 8 and 17 cm, and percent biases of 3%–15%. Noah-MP with precipitation from the PRISM geostatistical precipitation product leads to a peak SWE underestimation of 32% in both years examined, and a weaker spatial correlation than the WRF configurations. We fall short of identifying a clearly superior microphysical parameterization but conclude that snow lidar is a valuable nontraditional indicator of model performance.

Atmospheric and surface observations during the Saint John River Experiment on Cold Season Storms (SAJESS)

Abstract. The amount and the phase of cold-season precipitation accumulating in the upper Saint John River (SJR) basin are critical factors in determining spring runoff, ice jams, and flooding. To study the impact of winter and spring storms on the snowpack in the upper SJR basin, the Saint John River Experiment on Cold Season Storms (SAJESS) was conducted during winter–spring 2020–2021. Here, we provide an overview of the SAJESS study area, field campaign, and data collected. The upper SJR basin represents 41 % of the entire SJR watershed and encompasses parts of the US state of Maine and the Canadian provinces of Quebec and New Brunswick. In early December 2020, meteorological instruments were co-located with an Environment and Climate Change Canada station near Edmundston, New Brunswick. This included a separate weather station for measuring standard meteorological variables, an optical disdrometer, and a micro rain radar. This instrumentation was augmented during an intensive observation period that also included upper-air soundings, surface weather observations, a multi-angle snowflake camera, and macrophotography of solid hydrometeors throughout March and April 2021. During the study, the region experienced a lower-than-average snowpack that peaked at ∼ 65 cm, with a total of 287 mm of precipitation (liquid-equivalent) falling between December 2020 and April 2021, a 21 % lower amount of precipitation than the climatological normal. Observers were present for 13 storms during which they conducted 183 h of precipitation observations and took more than 4000 images of hydrometeors. The inclusion of local volunteers and schools provided an additional 1700 measurements of precipitation amounts across the area. The resulting datasets are publicly available from the Federated Research Data Repository at https://doi.org/10.20383/103.0591 (Thompson et al., 2023). We also include a synopsis of the data management plan and a brief assessment of the rewards and challenges of conducting the field campaign and utilizing community volunteers for citizen science.

Contrasting patterns of Last-deglacial and Holocene precipitation between the southern and northern portions of Arid Central Asia

The north-south migration of prevailing westerly system shapes the seasonality of modern precipitation in Arid Central Asia (ACA). The temperate continental climate with warm-season precipitation dominates the northern ACA and the Mediterranean climate with cold-season precipitation dominates the southern ACA. However, the north-south contrasts of precipitation/moisture for longer time scales remain unknown. In this study, we quantitatively reconstructed the precipitation sequence covering the past ~14.4 cal. kyr BP based on pollen data obtained from Shayan loess section in southern ACA using MAT (Modern Analogue Technique) method. Our reconstruction shows that the precipitation had an increasing trend from ~14.4 to ~8.2 cal. kyr BP and a decreasing trend from ~8.2 to ~1.5 cal. kyr BP with the past 1500 years being characterized by an averagely high precipitation. The precipitation trend in southern ACA was rather different from that in northern ACA where the precipitation has increased more or less constantly since the late deglaciation. The contrast between southern and northern ACA appears to have been associated with the south-north migration of westerlies that was accompanied with alterations of NAO (North Atlantic Oscillation) phases and also with changes in strengths of Siberian High and ENSO (El Nino-southern Oscillation).

The Effect of Greenhouse Gas–Induced Warming on the Impact of El Niño and La Niña Events on Daily Precipitation Extremes in the Boreal Cold Season

Abstract El Niño–Southern Oscillation (ENSO) has a profound influence on the occurrence of extreme precipitation events at local and regional scales in the present-day climate, and thus it is important to understand how that influence may change under future global warming. We consider this question using the large-ensemble simulations of CESM2, which simulates ENSO well historically. CESM2 projects that the influence of ENSO on extreme precipitation will strengthen further under the SSP3–7.0 scenario in most regions whose extreme precipitation regimes are strongly affected by ENSO in the boreal cold season. Extreme precipitation in the boreal cold season that exceeds historical thresholds is projected to become more common throughout the ENSO cycle. The difference in the intensity of extreme precipitation events that occur under El Niño and La Niña conditions will increase, resulting in “more extreme and more variable hydroclimate extremes.” We also consider the processes that affect the future intensity of extreme precipitation and how it varies with the ENSO cycle by partitioning changes into thermodynamic and dynamic components. The thermodynamic component, which reflects increases in atmospheric moisture content, results in a relatively uniform intensification of ENSO-driven extreme precipitation variation. In contrast, the dynamic component, which reflects changes in vertical motion, produces a strong regional difference in the response to forcing. In some regions, this component amplifies the thermodynamic-induced changes, while in others, it offsets them or even results in reduction in extreme precipitation variation.

Evaluation of Seasonal Differences among Three NOAA Climate Data Records of Precipitation

Abstract Three satellite precipitation datasets—CMORPH, PERSIANN-CDR, and GPCP—from the NOAA/Climate Data Record program were evaluated in their ability to capture seasonal differences in precipitation for the period 2007–18 over the conterminous United States. Data from the in situ U.S. Climate Reference Network (USCRN) provided reference precipitation measurements and collocated atmospheric conditions (temperature) at the daily scale. Satellite precipitation products’ (SPP) performance with respect to cold season precipitation was compared to warm season and full-year analysis for benchmarking purposes. Considering an ensemble of typical performance metrics including accuracy, false alarm ratio, probability of detection, probability of false detection, and the Kling–Gupta efficiency (KGE) that combines correlation, bias, and variability, we found that the three SPPs displayed better performances during the warm season than during the cold season. Among the three datasets, CMORPH displayed better performance—smaller bias, higher correlation, and a better KGE score—than the two other SPPs on an annual basis and during the warm season. During the cold season, CMORPH showed the worst performance at higher latitudes over areas experiencing recurring snow or frozen and mixed precipitation. CMORPH’s performances were further degraded compared to PERSIANN-CDR and GPCP when considering freezing temperatures (T < 0°C) due to the inability to microwave sensors to retrieve precipitation over snow-covered surface. However, for cold rainfall events detected simultaneously by the satellite and the USCRN stations (i.e., conditional case), CMORPH performance noticeably improved but remained inferior to the two other datasets. The quantification of seasonal precipitation errors and biases for three satellite precipitation datasets presented in this work provides an objective basis for the improvement of rainfall retrieval algorithms of the next generation of satellite precipitation products.

Analysis of atmosphere–ocean interaction on cold season precipitation in Iran

Analysis of atmosphere–ocean interaction on cold season precipitation in Iran

A Numerical Evaluation of the Impact of Operational Ground-Based Glaciogenic Cloud Seeding on Precipitation over the Wind River Range, Wyoming

Abstract This study evaluates an operational glaciogenic cloud-seeding program using ground-based generators of silver iodide (AgI), with a total of 190 seeded storms over 10 cold seasons, using the Weather Research and Forecasting Weather Modification (WRF-WxMod) scheme at 900-m grid spacing. This study examines both the quantitative change in precipitation and the ambient and cloud conditions impacting seeding efficacy. An ensemble approach is used, with differing model boundary conditions, ice nucleation physics, concentrations of cloud condensation nuclei, and boundary layer schemes. This is intended to provide an envelope of uncertainty of natural clouds and seeding impacts. The simulations are validated against radiosonde, snow gauge, and microwave radiometer observations, and the seeding impact is inferred from simulations with/without AgI seeding. The seeding-induced precipitation enhancement (“yield”) varies greatly between storms. A small portion of the cases produces the majority of the yield. Overall, the precipitation in the target area (the Wind River Range in Wyoming) increased by 1.10% ± 0.13% in the 10 years of operational seeding. This rather low fractional increase is related to the frequent seeding at unsuitable times, primarily because of low-level flow blocking. The flow and cloud structure for select cases are examined to provide better insight into the variability of yield. Cases with unblocked surface flow and abundant cloud liquid water tend to be the most productive. The technique presented here can be readily adapted to evaluate the seeding impact of other long-term glaciogenic seeding operations and to improve their operational efficiency. Significance Statement In the United States and elsewhere, there are several operational programs to enhance cold-season precipitation through glaciogenic seeding of orographic clouds. The impact of such activity on seasonal precipitation has always been difficult to quantify. Recent observational and numerical modeling studies indicate that orographic cloud seeding can increase precipitation, although the amounts and optimal seeding conditions remain uncertain. Operators lack guidance about the seeding efficacy and about the most suitable environmental conditions. In recent years a model parameterization, called Weather Research and Forecasting Weather Modification (WRF-WxMod), has been tested against detailed measurements. This sets the stage for our work, a well-designed numerical evaluation of 10 years of operational cloud seeding over the Wind River Range, a mountain range in Wyoming that feeds the Colorado River basin and other watersheds. The WRF-WxMod based simulation experiment presented here, one of the most computationally expensive numerical experiments on this subject to date, quantifies seeding impact and its uncertainty. It is demonstrated with a high degree of confidence that over this 10-yr period, suitable seeding conditions were rare over this mountain range, that most seeding events were unproductive, and that, as a result, the overall yield over 10 years was a mere 1.1%.

Interdecadal Pacific Variability dominated the decadal variation of cold season precipitation in arid West Asia

This study examines the interdecadal variation of cold season precipitation in arid West Asia (AWA) from 1960 to 2019 and identifies the underlying physical mechanisms. The results indicate that the precipitation in this region has exhibited significant interdecadal variation over the past 60 years, which is closely related to Interdecadal Pacific Variability (IPV). During the positive IPV phase, which was associated with a warming equatorial Middle East Pacific Ocean, an eastward-propagating Rossby wave induced a negative geopotential height anomaly over AWA, providing the dynamic conditions for precipitation. Additionally, IPV caused the westerly jet to move southward and strengthen, providing rich water vapor. Moreover, a positive geopotential height anomaly over the Indo-West Pacific, caused by cold sea surface temperature, led to the upward transport of water vapor from the Northern Indian Ocean to AWA, further enhancing the water vapor conditions. These factors together resulted in excess precipitation in AWA during the positive IPV phase, with a 17.6 mm increase compared to the negative phase, accounting for approximately 12% of the cold season precipitation. These findings were also confirmed by the piControl simulation of CESM2.

Stable Water Isotopes as an Indicator of Surface Water Intrusion in Shallow Aquifer Wells: A Cold Climate Perspective

AbstractGroundwater in shallow aquifers is commonly used for community water supply in cold climates. Shallow groundwaters are inherently susceptible to contamination from land‐use and surface water intrusion threatening drinking water usage. We used a large‐scale snapshot data set of stable water isotopes from shallow glaciofluvial aquifers used for drinking water supply in Northern Finland to assess surface water intrusion risks and recharge conditions. This data set was supplemented by long‐term stable water isotope precipitation data, Geographic Information System proximity analysis and multivariate statistics. The isotope analysis suggest that a warm season contributes about 60% to the total annual precipitation in the region. This is reflected in the aquifers isotopic composition as it represents an approximately equal mixture of warm and cold season precipitation. Groundwater isotope data normalized to precipitation inputs by line‐conditioned excess (lc‐excess) was used to flag the water supply wells impacted by surface water intrusions. The proximity analysis showed some of the wells may be affected by intrusions from gravel pit ponds, lakes and peatland drainage. On the larger scale, the wells in coastal areas were more likely to have evaporated water (intrusion) compared to inland regions of Northern Finland due to lower water availability and the presence of man‐made structures. This application of stable water isotopes with lc‐excess is a useful approach not only for recharge studies but also within water management for supply well surface water contamination risk assessment.