Votre recherche

Manual hydrometeor macro photographs were collected during the Winter Precipitation Type Research Multi-Scale Experiment (WINTRE-MIX) between 01 Feb – 15 March 2022. The macro photographs were collected by manual ground observation teams from the University at Albany (UAlbany), University of Colorado Boulder (CU), Université du Québec à Montréal (UQAM), and McGill University (McGill). Sections in the readme provide information on the camera setup, protocol, and dataset file formats, as well as limitations associated with the data.

Abstract Freezing rain and ice pellets are particularly difficult to forecast when solid precipitation is completely melted aloft. This study addresses this issue by investigating the processes that led to a long-duration ice pellet event in Montreal, Québec, Canada, on 11–12 January 2020. To do so, a benchmark model initialized with ERA5 data is used to show that solid precipitation was completely melted below the melting layer, which discards partial melting from the possible ice pellet formation processes. Macro photography of precipitation reveals that small columnar crystals (∼200 μ m) and ice pellets occurred simultaneously for more than 10 h. The estimation of ice crystal number concentration using macro photographs and laser-optical disdrometer data suggests that all supercooled drops could have refrozen by contact freezing with ice crystals. Rimed ice pellets also indicate ice supersaturation in the subfreezing layer. Given these observations, the formation of ice pellets and ice crystals was probably promoted by secondary ice production and the horizontal advection of ice crystals below the melting layer, as we illustrate using a conceptual model. Overall, these findings demonstrate how ice nucleation processes at temperatures near 0°C can drastically change the precipitation phase and the impact of a storm. Significance Statement Ice pellets are generally formed when snow particles partially melt while falling through a warm layer aloft before completely refreezing in a cold layer closer to the surface. Ice pellets can also be formed when snow particles completely melt aloft, but freezing rain is often produced in such conditions. On 11–12 January 2020, ice pellets were produced during more than 10 h in Montreal, Quebec, Canada. Macro photographs of the precipitation particles show that ice pellets occurred simultaneously with small ice crystals. Most of the ice pellets were produced while snow particles were completely melted aloft. The supercooled drops probably refroze due to collisions with the ice crystals that could have been advected by the northeasterly winds near the surface.

Abstract Global rivers and streams are important carbon transport pathways from land to the ocean. However, few studies have quantified terrigenous carbon dynamics in river ecosystems and its variations due to climate change and anthropogenic perturbations. Therefore, our study analysed fluvial particulate organic carbon (POC) and developed a processed‐based model (TRIPLEX‐HYDRA) to simulate the production, transport and removal (i.e., deposition, degradation and dam retention) processes of fluvial POC along the land–ocean aquatic continuum (LOAC). Based on our results, approximately 0.29 Pg of POC is exported from land to the ocean through rivers each year. More specifically, we found that rivers at low latitudes (30°S–30°N, 0.18 Pg yr −1 ) and high northern latitudes (60°N–90°N, 0.05 Pg yr −1 ) had higher POC fluxes compared to rivers in other regions. This high POC flux is related to strong erosion rates and high soil organic carbon storage. Additionally, our model simulation revealed that total POC flux from global river has not significantly changed from 1983 to 2015 but displays markedly decreased or increased trend at regional scale. These regional variations in POC export are affected by climate warming and dam construction. Moreover, approximately 0.46 Pg of POC is deposited or trapped by dams along the LOAC system, which plays a vital role in the global river carbon budget. Although some limitations and uncertainties remain, this study establishes a theoretical and methodological basis for quantifying riverine POC dynamics in the LOAC system.

The Sentinel Radiation MetData provided in situ measurements of radiation fluxes for the Winter Precip Type Research Multi-scale Experiment (WINTRE-MIX) are included in this dataset. Pyranometer and radiation data are provided in netCDF format. Data were collected from the following 4 sites: Gault, Arboretum, UQAM-PK and Trois-Rivières.

The Sentinel Non-Radiation MetData provided in situ measurements of meteorological variables (such as 2-meter temperature, relative humidity, wind speed and direction, and precipitation) collected for the the Winter Precip Type Research Multi-scale Experiment (WINTRE-MIX) are included in this dataset. Data are provided in netCDF format. Data were collected from the following 4 sites: Gault, Arboretum, UQAM-PK and Trois-Rivières.

Abstract Given their potentially severe impacts, understanding how freezing rain events may change as the climate changes is of great importance to stakeholders including electrical utility companies and local governments. Identification of freezing rain in climate models requires the use of precipitation-type algorithms, and differences between algorithms may lead to differences in the types of precipitation identified for a given thermodynamic profile. We explore the uncertainty associated with algorithm selection by applying four algorithms (Cantin and Bachand, Baldwin, Ramer, and Bourgouin) offline to an ensemble of simulations of the fifth-generation Canadian Regional Climate Model (CRCM5) at 0.22° grid spacing. First, we examine results for the CRCM5 driven by ERA-Interim reanalysis to analyze how well the algorithms reproduce the recent climatology of freezing rain and how results vary depending on algorithm parameters and the characteristics of available model output. We find that while the Ramer and Baldwin algorithms tend to be better correlated with observations than Cantin and Bachand or Bourgouin, their results are highly sensitive to algorithm parameters and to the number of pressure levels used. We also apply the algorithms to four CRCM5 simulations driven by different global climate models (GCMs) and find that the uncertainty associated with algorithm selection is generally similar to or greater than that associated with choice of driving GCM for the recent past climate. Our results provide guidance for future studies on freezing rain in climate simulations and demonstrate the importance of accounting for uncertainty between algorithms when identifying precipitation type from climate model output. Significance Statement Freezing rain events and ice storms can have major consequences, including power outages and dangerous road conditions. It is therefore important to understand how climate change might affect the frequency and severity of these events. One source of uncertainty in climate studies of these events is related to the choice of algorithm used to detect freezing rain in model output. We compare the frequency of freezing rain identified using four different algorithms and find sometimes large differences depending on the algorithm chosen over some regions. Our findings highlight the importance of taking this source of uncertainty into account and will provide researchers with guidance as to which algorithms are best suited for climate studies of freezing rain.

Abstract Reconstructions of ocean primary productivity (PP) help to explain past and present biogeochemical cycles and climate changes in the oceans. We document PP variations over the last 50 kyr in a currently oligotrophic subtropical region, the Gulf of Cadiz. Data combine refined results from previous investigations on dinocyst assemblages, alkenones, and stable isotopes ( 18 O, 13 C) in planktonic ( Globigerina bulloides ) and endobenthic ( Uvigerina mediterranea ) foraminifera from cores MD04‐2805 CQ and MD99‐2339, with new isotopic measurements on epibenthic ( Cibicides pachyderma ‐ Cibicidoides wuellerstorfi ) foraminifera and dinocyst‐based estimates of PP using the new n  = 1,968 modern database. We constrain PP variations and export production by integrating qualitative information from bioindicators with dinocyst‐based quantitative reconstructions such as PP and seasonal sea surface temperature and information about remineralization from the benthic Δδ 13 C (difference between epibenthic and endobenthic foraminiferal δ 13 C signatures). This study also includes new information on alkenone‐based SST and total organic carbon which provides insights into the relationship between past regional hydrological activity and PP regime change. We show that PP, carbon export, and remineralization were generally high in the NE subtropical Atlantic Ocean during the last glacial period and that the Last Glacial Maximum (LGM) had lower Δδ 13 C than the Heinrich Stadials with sustained high PP, likely allowing enhanced carbon sequestration. We link these PP periods to the dynamics of upwelling, active almost year‐round during sadials, but restricted to spring‐summer during interstadials and LGM, like today. During interstadials, nutrient advection through freshwater inputs during autumn‐winter needs also to be considered to fully understand PP regimes. , Key Points Productivity (PP) in the Gulf of Cadiz is dependent on the seasonality control for both upwelling and nutrient‐enriched freshwater inputs We show generally high PP, carbon export, and remineralization during the last glacial period at the study site The Last Glacial Maximum had lower Δδ 13 C than the Heinrich Stadials with sustained high PP likely allowing enhanced carbon sequestration

Abstract Aim Plant biomass allocation reflects the distribution of photosynthates among different organs in response to changing environmental conditions. Global change influences plant growth across terrestrial ecosystems, but impacts of individual and combined multiple global change factors (GCFs) on plant biomass allocation at the global scale are unclear. Location Global. Time period Contemporary. Major taxa studied Plants in terrestrial ecosystems. Methods We conducted a meta‐analysis of data comprising 4,180 pairwise observations to assess individual and combined effects of nitrogen addition (N), warming (W), elevated CO 2 (C), irrigation (I), and drought (D) on plant biomass allocation based on the ‘ratio‐based optimal partitioning’ and ‘isometric allocation’ hypotheses. Results We found that (a) ratio‐based plant biomass fractions of different organs were only minimally affected by individual and combined effects of the studied GCFs; (b) combined effects of two‐factor pairs of GCFs on plant biomass allocation were commonly additive, rather than synergistic or antagonistic; (c) moderator variables influenced, but seldom changed the direction of individual and combined effects of GCFs on plant biomass allocation; and (d) neither individual nor combined effects of the studied GCFs altered allometric relationships among different organs, indicating that patterns of plant biomass allocation under the environmental stress conditions exerted by the multiple GCFs were better explained by the isometric allocation rather than the ratio‐based optimal partitioning hypothesis. Main conclusions Our results show consistent patterns of allometric plant biomass partitioning under effects of multiple GCFs and provide evidence of an isometric plant biomass allocation trajectory in response to global change perturbations. These findings improve our understanding and prediction of terrestrial vegetation responses to future global change scenarios.

Abstract Cloud and convective parameterizations strongly influence uncertainties in equilibrium climate sensitivity. We provide a proof‐of‐concept study to constrain these parameterizations in a perturbed parameter ensemble of the atmosphere‐only version of the Goddard Institute for Space Studies Model E2.1 simulations by evaluating model biases in the present‐day runs using multiple satellite climatologies and by comparing simulated δ 18 O of precipitation (δ 18 O p ), known to be sensitive to parameterization schemes, with a global database of speleothem δ 18 O records covering the Last Glacial Maximum (LGM), mid‐Holocene (MH) and pre‐industrial (PI) periods. Relative to modern interannual variability, paleoclimate simulations show greater sensitivity to parameter changes, allowing for an evaluation of model uncertainties over a broader range of climate forcing and the identification of parts of the world that are parameter sensitive. Certain simulations reproduced absolute δ 18 O p values across all time periods, along with LGM and MH δ 18 O p anomalies relative to the PI, better than the default parameterization. No single set of parameterizations worked well in all climate states, likely due to the non‐stationarity of cloud feedbacks under varying boundary conditions. Future work that involves varying multiple parameter sets simultaneously with coupled ocean feedbacks will likely provide improved constraints on cloud and convective parameterizations. , Plain Language Summary Equilibrium climate sensitivity (ECS) is a key climate metric that quantifies the rise in global mean surface temperature in response to doubling of atmospheric CO 2 . Changes in hydroclimate, temperature extremes, and other aspects of future climate projections are closely tied to a model's ECS. For decades, ECS range has remained wide despite improvements from using multiple lines of evidence. One persistent source of this spread is related to cloud and convective processes, which occur at scales too small to be explicitly resolved, and thus require parameterizations to be represented in climate models. These parameterizations directly influence water isotopes by modulating simulated clouds and atmospheric circulation, and thus can be used to constrain model processes and identify model biases. In this work, we demonstrated that paleoclimate simulations are more parameter sensitive than the modern, highlighting the potential of past climates in discriminating cloud and convective parameterizations. Using satellite‐ and proxy‐model comparisons, we identified the top performing parameterizations which differ for each time period likely due to varying cloud feedbacks under diverse climatic forcing. Overall, our results provide a framework for fine‐tuning model representations using combined paleoclimate and satellite data, offering a unique opportunity to assess model uncertainties over a broader range of climate variability. , Key Points Paleoclimate relative to modern are more parameter sensitive, allowing for an assessment of uncertainties over a variety of climate forcing Certain simulations reproduced the δ 18 O of precipitation from paleoclimate proxies better than the default parameterization No single set of parameters works well in all climate states likely due to varying boundary conditions influencing cloud feedbacks

Abstract The Canadian Rockies are a triple-continental divide, whose high mountains are drained by major snow-fed and rain-fed rivers flowing to the Pacific, Atlantic, and Arctic Oceans. The objective of the April–June 2019 Storms and Precipitation Across the continental Divide Experiment (SPADE) was to determine the atmospheric processes producing precipitation on the eastern and western sides of the Canadian Rockies during springtime, a period when upslope events of variable phase dominate precipitation on the eastern slopes. To do so, three observing sites across the divide were instrumented with advanced meteorological sensors. During the 13 observed events, the western side recorded only 25% of the eastern side’s precipitation accumulation, rainfall occurred rather than snowfall, and skies were mainly clear. Moisture sources and amounts varied markedly between events. An atmospheric river landfall in California led to moisture flowing persistently northward and producing the longest duration of precipitation on both sides of the divide. Moisture from the continental interior always produced precipitation on the eastern side but only in specific conditions on the western side. Mainly slow-falling ice crystals, sometimes rimed, formed at higher elevations on the eastern side (>3 km MSL), were lifted, and subsequently drifted westward over the divide during nonconvective storms to produce rain at the surface on the western side. Overall, precipitation generally crossed the divide in the Canadian Rockies during specific spring-storm atmospheric conditions although amounts at the surface varied with elevation, condensate type, and local and large-scale flow fields.

Abstract Winter precipitation is the source of many inconveniences in many regions of North America, for both infrastructure and the economy. The ice storm that hit the Canadian Maritime Provinces on 24–26 January 2017 remains one of the most expensive in history for the province of New Brunswick. Up to 50 mm of freezing rain caused power outages across the province, depriving up to one-third of New Brunswick residences of electricity, with some outages lasting 2 weeks. This study aims to use high-resolution atmospheric modeling to investigate the meteorological conditions during this severe storm and their contribution to major power outages. The persistence of a deep warm layer aloft, coupled with the slow movement of the associated low pressure system, contributed to widespread ice accumulation. When combined with the strong winds observed, extensive damage to electricity networks was inevitable. A 2-m temperature cold bias was identified between the simulation and the observations, in particular during periods of freezing rain. In the northern part of New Brunswick, cold-air advection helped keep temperatures below 0°C, while in southern regions, the 2-m temperature increased rapidly to slightly above 0°C because of radiational heating. The knowledge gained in this study on the processes associated with either maintaining or stopping freezing rain will enhance the ability to forecast and, in turn, to mitigate the hazards associated with those extreme events. Significance Statement A slow-moving low pressure system produced up to 50 mm of freezing rain for 31 h along the east coast of New Brunswick, Canada, on 24–26 January 2017, causing unprecedented power outages. Warm-air advection aloft, along with a combination of higher wind speeds and large amounts of ice accumulation, created ideal conditions for severe freezing rain. The storm began with freezing rain along the entire north–south cross section of eastern New Brunswick and changed to rain only in the south, when local temperatures increased to >0°C. Near-surface cold-air advection kept temperatures below 0°C in the north. Warming from the latent heat produced by freezing contributed to persistent near-0°C conditions during freezing rain.

Abstract In mountains, the precipitation phase greatly varies in space and time and affects the evolution of the snow cover. Snowpack models usually rely on precipitation‐phase partitioning methods (PPMs) that use near‐surface variables. These PPMs ignore conditions above the surface thus limiting their ability to predict the precipitation phase at the surface. In this study, the impact on snowpack simulations of atmospheric‐based PPMs, incorporating upper atmospheric information, is tested using the snowpack scheme Crocus. Crocus is run at 2.5‐km grid spacing over the mountains of southwestern Canada and northwestern United States and is driven by meteorological fields from an atmospheric model at the same resolution. Two atmospheric‐based PPMs were considered from the atmospheric model: the output from a detailed microphysics scheme and a post‐processing algorithm determining the snow level and the associated precipitation phase. Two ground‐based PPMs were also included as lower and upper benchmarks: a single air temperature threshold at 0°C and a PPM using wet‐bulb temperature. Compared to the upper benchmark, the snow‐level based PPM improved the estimation of snowfall occurrence by 5% and the simulation of snow water equivalent (SWE) by 9% during the snow melting season. In contrast, due to missing processes, the microphysics scheme decreased performances in phase estimate and SWE simulations compared to the upper benchmark. These results highlight the need for detailed evaluation of the precipitation phase from atmospheric models and the benefit for mountain snow hydrology of the post‐processed snow level. The limitations to drive snowpack models at slope scale are also discussed. , Plain Language Summary The partitioning of precipitation between rainfall and snowfall is a crucial component of the evolution of the snowpack in mountains. Most snowpack models use the air temperature and humidity near the surface to derive the precipitation phase. However, the phase at the surface is strongly influenced by processes such as melting and refreezing of falling hydrometeors that occur above the surface. Atmospheric models simulate these processes and the corresponding phase at the surface. However, snowpack models rarely use this information. In this study, we considered two estimates of precipitation phase from an atmospheric model and tested them with a physically‐based snow model over the mountains of southwestern Canada and northwestern United States. The results were compared with traditional approaches using the air temperature and humidity near the surface to derive the precipitation phase. Our results showed that the precipitation phase associated with the snow level obtained from the atmospheric model improved snowfall estimate and snowpack prediction compared to the traditional approaches. In contrast, the cloud/precipitation scheme of the atmospheric model decreased performance in phase estimate and snow simulations due to missing physical processes. Our study highlights that snowpack predictions in the mountains can be improved if valuable information is obtained from atmospheric models. , Key Points Estimates of precipitation phase from an atmospheric model were used to drive snow simulations with a detailed snowpack model Snowfall prediction and snowpack modeling are improved by using the snow level from post‐processing of the atmospheric model Direct precipitation phase from the microphysics scheme does not improve snow simulations compared to simpler rain‐snow partitioning schemes

Abstract Aim Compared with gradual climate change, extreme climatic events have more direct and dramatic impacts on vegetation growth. However, the influence of climate extremes on important phenological periods, such as the end of the growing season (EOS), remains unclear. Here, we investigate the temporal trends of EOS across different biomes and quantify the response of EOS to multiple climate extreme indices (CEIs). Location Northern middle and high latitudes. Time period 2000–2020. Major taxa studied Plants. Methods Three phenology extraction methods were used to compute EOS from satellite, FLUXNET and Pan European Phenology Project PEP725 phenological datasets. Different stress states of cold, hot, dry and wet extremes were represented by 12 CEIs. Partial correlation and ridge regression analysis were used to quantify the response of EOS to climate extremes across latitudinal and biome scales. Results Our study showed a delayed EOS in boreal biomes, but a significantly advanced EOS in temperate biomes. The advanced EOS induced by cold stress was observed for c . 80% of the vegetated pixels. The warm‐related CEIs delayed the EOS in high latitudes, and the delayed effect weakened or even reversed with decreasing latitude. In contrast, EOS exhibited opposite response patterns to dry days and wet‐related CEIs. Overall, EOS exhibited higher sensitivity to extreme temperature in boreal biomes than in temperate biomes. Specifically, continuous drought and high heat stress induced an earlier EOS in some temperate forest biomes, whereas moderate heat stress delayed the EOS in most study biomes. In contrast, EOS was not sensitive to extreme drought in water‐restricted biomes. Main conclusions EOS exhibited divergent responses to various climate extremes with different intensities and frequencies. Moreover, the response of EOS to extreme climate stress was dependent on the biome and latitude. These findings emphasize the importance of incorporating the divergent extreme climate effects into vegetation phenological models and Earth system models.