Changes In Snow Depth Research Articles

Investigating Extreme Snowfall Changes in China Based on an Ensemble of High-Resolution Regional Climate Models

Anthropogenically induced global warming intensifies the water cycle around the world. As a critical sector of the water cycle, snow depth and its related extremes greatly impact agriculture, animal husbandry, and food security, yet lack investigation. In this study, five high-resolution climate models are selected to simulate and project snow depth and its extremes over China. The simulation capabilities of models in reproducing the basic climate variables in winter are gauged in terms of spatial and temporal patterns over nine subregions. It is found that the driving global climate model (GCM) can contribute to similar patterns, while the different regional climate model (RCM) schemes lead to large variations in the snowfall accumulating on the land surface. The warming magnitude is larger under a higher representative concentration pathway (RCP) scenario (2.5 °C greater under RCP8.5 than RCP4.5). The distribution of ensemble mean winter precipitation changes is more fragmented because of the relatively low skill in reproducing water-related content in the climate system. The projected precipitation change is larger under RCP8.5 than under RCP4.5 due to the amplification of the hydrological cycle by temperature warming. The projected changes in the ensemble mean snow depth mainly occur over the Tibetan Plateau with a decreasing trend. Only several grids over the Himalayas Mountains and the upper stream of the Yarlung Zangbo River are projected with a slight increase in snow depth. Both the intensity and frequency of extreme snow events are projected to increase in Northeast China and Inner Mongolia, which are important agricultural and animal husbandry production areas in China. The reason behind this projection can be explained by the fact that the hydrological cycle intensified by temperature warming leads to excessive snowfall stacking up during winter. The changes in extreme snowfall events in the future will have a significant impact on China’s agricultural and animal husbandry production and threaten food security.

Winter Arctic sea ice thickness from ICESat-2: upgrades to freeboard and snow loading estimates and an assessment of the first three winters of data collection

Abstract. NASA's ICESat-2 mission has provided near-continuous, high-resolution estimates of sea ice freeboard across both hemispheres since data collection started in October 2018. This study provides an impact assessment of upgrades to both the ICESat-2 freeboard data (ATL10) and NASA Eulerian Snow On Sea Ice Model (NESOSIM) snow loading on estimates of winter Arctic sea ice thickness. Misclassified leads were removed from the freeboard algorithm in the third release (rel003) of ATL10, which generally results in an increase in freeboards compared to rel002 data. The thickness increases due to increased freeboards in ATL10 improved comparisons of Inner Arctic Ocean sea ice thickness with thickness estimates from ESA's CryoSat-2. The upgrade from NESOSIM v1.0 to v1.1 results in only small changes in snow depth and density which have a less significant impact on thickness compared to the rel002 to rel003 ATL10 freeboard changes. The updated monthly gridded thickness data are validated against ice draft measurements obtained by upward-looking sonar moorings deployed in the Beaufort Sea, showing strong agreement (r2 of 0.87, differences of 11 ± 20 cm). The seasonal cycle in winter monthly mean Arctic sea ice thickness shows good agreement with various CryoSat-2 products (and a merged ICESat-2–CryoSat-2 product) and PIOMAS (Pan-Arctic Ice-Ocean Modeling and Assimilation System). Finally, changes in Arctic sea ice conditions over the past three winter seasons of data collection (November 2018–April 2021) are presented and discussed, including a 50 cm decline in multiyear ice thickness and negligible interannual differences in first-year ice. Interannual changes in snow depth provide a notable impact on the thickness retrievals on regional and seasonal scales. Our monthly gridded thickness analysis is provided online in a Jupyter Book format to increase transparency and user engagement with our ICESat-2 winter Arctic sea ice thickness data.

Characteristics of ringed seal Pusa hispida (‘natchiq’) denning habitat in Kotzebue Sound, Alaska, during a year of limited sea ice and snow

Sea ice and snow are essential to Arctic ecosystems, playing key roles in the lives of Arctic marine mammals and the Indigenous Peoples who rely on them. Ringed seals Pusa hispida (‘natchiq’ in Iñupiaq) use snow-covered dens on sea ice for pupping, but quantitative information on denning habitat requirements is limited, and it is unknown how changes in snow depth and sea-ice extent will impact ringed seals. Here, an Indigenous Elder Advisory Council and a multidisciplinary group of scientists used knowledge co-production to quantify fine-scale ringed seal habitat selection patterns in Kotzebue Sound, Alaska (USA), during a year of unprecedentedly limited snow and sea-ice availability. Together, we conducted unoccupied aerial vehicle-based surveys during spring 2019 and related seal counts to survey date, bathymetry, and novel proxies for snow depth and surface roughness that we derived from Landsat 8 surface reflectance and validated with on-ice measurements. Generalized additive models showed that counts of seal groups (all age classes) and pups were associated with later survey dates, deeper water, and habitat with bright Landsat 8 pixel values and intermediate pixel variability, which in turn were correlated with deep snow and surface roughness. We observed shallow snow depths, early sea-ice breakup, and high seal densities consistent with the extreme lack of ice available in 2019. Indigenous Knowledge, intentionally woven with scientific data, provided novel and more nuanced understandings of snow and sea-ice conditions for seals. Our results may give a glimpse at future ringed seal habitat and selection in a warming Arctic.

Spatiotemporal changes in snow depth and the influence factors in China from 1979 to 2019.

Snow depth is an important parameter to characterize the characteristics of snow cover, and it is also one of the most sensitive response factors to regional climate change. However, the extent of snow depth variability and its driving mechanisms are still unknown in China. Therefore, in this study, we used the regression analysis, root-mean-square error analysis, anomalous year analysis, and correlation analysis methods to explore the spatiotemporal variation characteristics of snow depth in China from 1979 to 2019 based on the reanalysis snow depth dataset. The results show that (1) the snow distribution in China is obviously spatially heterogeneous, and the southeastern, western, and southern regions of the Qinghai-Tibet Plateau, northern Xinjiang, and northeastern China have high values of snow depth; (2) the high-value regions are also the sensitive regions for anomalous variations in snow depth in China; (3) in the past 41years, the interannual variability of snow depth in China has shown a significantly decreasing trend, and the linear tendency of snow depth is - 0.093cm/10 a (p < 0.01) and the snow depth in four seasons showed a decreasing trend (p < 0.05); and (4) the driving factors of snow heterogeneity are dissimilar in different regions and seasons. In temperate zones, average air temperature is the main factor affecting snow depth in cold temperature, mid temperature, and warm temperature zones; the maximum air temperature is the main factor affecting snow depth in mid temperate and warm temperate zones. Both the minimum air temperature and the average land-surface temperature are important factors affecting the snow depth in the cold temperate, mid temperate and warm temperate zones, and all passed the significance test of 0.01.

Rapid and Gradual Permafrost Thaw: A Tale of Two Sites

AbstractWarming temperatures and increasing disturbance by wildfire and extreme weather events is driving permafrost change across northern latitudes. The state of permafrost varies widely in space and time, depending on landscape, climate, hydrologic, and ecological factors. Despite its importance, few approaches commonly measure and monitor the changes in deep (&gt;1 m) permafrost conditions with high spatial resolution. Here, we use electrical resistivity tomography surveys along two transects in interior Alaska previously disturbed by wildfire and more recently by warming temperatures and extreme precipitation. Long‐term point observations of permafrost depth, temperature, and water content inform geophysical measurements which, in turn, are used to extrapolate interpretations over larger areas and with high spatial fidelity. We contrast gradual loss of recently formed permafrost driven by warmer temperatures and increased snowfall, with rapid permafrost loss driven by changes in air temperature, snow depth, and extreme summer precipitation in 2014.

Snow depth variability across the Qinghai Plateau and its influencing factors during 1980–2018

AbstractAs a crucial component of the climate system, snow cover plays an important role on surface energy budgets, hydrological cycles and socioeconomic development. This paper investigated the spatiotemporal patterns of snow depth across the Qinghai Plateau (QP) during 1980–2018 based on passive microwave (PMW) satellite observation and reanalysis products. The study identified the relationships between the changes in PMW snow depth and topographic features and revealed the impact of climatic variables (air temperature, precipitation and wind speed) and large‐scale atmospheric circulations on observed snow depth variability. The results show that the spatial pattern of snow depth climatology is similar in all datasets, except in terms of magnitude, with high values in the southern and southeastern parts of the QP and low values in the eastern and northwestern parts. Average snow depth correlates positively with precipitation, elevation and slope, and negatively with air temperature and wind speed. The long‐term trends in snow depth vary with the season and the datasets. The PMW snow depth across the QP shows a significant annual (−0.125 cm·decades−1, p &lt; .10) and spring (−0.184 cm·decades−1, p &lt; .05) negative trend, while snow depth for ERA5 and MERRA2 does not present significant trends. Air temperature dominates total snow depth variation over the QP, explaining 43.85, 24.88 and 47.28% of annual, winter and spring PMW snow depth variations, which significantly affects snow depth variations in most parts of the QP, yet the effects of precipitation and wind speed on snow depth variation exhibit significant regional differences, and atmospheric circulations (e.g., AMO) also have a remarkable controlling effect on some localized areas.

Long-term snow alters the sensitivity of nonstructural carbohydrates of Syntrichia caninervis to snow cover: Based on a 7-year experiment.

The dynamics of nonstructural carbohydrates (NSC) profoundly affect productivity and ecological adaptability to adversity in plants. Global warming induced the frequent occurrence of extreme precipitation events that altered the winter snow pattern in deserts. However, there is a lack of understanding of how desert mosses respond to long-term snow cover change at the NSC level. Therefore, in this study, long-term (7-years) winter snow removal (-S), ambient snow (CK), and double snow (+S) experiments were set in the field to investigate the content of NSC and its component in Syntrichia Caninervis. Our results showed that changes in snow depth, snow years, and their interaction significantly affected NSC and its component of Syntrichia caninervis. Compared to snow removal, NSC, soluble sugar, and starch significantly decreased with the increasing snow depth. The ratio of soluble sugar to starch significantly increased, while NSC and soluble sugar gradually returned to the normal level with an increase in snow years. It is worth mentioning that snow removal significantly reduced the soluble sugar to starch ratio compared to ambient snow depth, whereas the double snow experiment significantly increased the ratio of soluble sugar to starch during winter. This indicated an obvious trade-off between carbon utilization and carbon storage in Syntrichia caninervis. Snow removal stimulated Syntrichia caninervis to store sufficient carbon sources by starch accumulation for its future growth, while double snow promoted its current growth by soluble sugar accumulation. The variance in decomposition showed that soil physical and chemical properties, snow cover, and their interaction explained 83% of the variation in NSC and its components, with soil and plant water content, pH, and electrical conductivity (P-WC, S-WC, S-pH, and S-EC) as significant predictors. This highlights that snow indirectly affected NSC and its component contents by changing soil physical and chemical properties; however, long-term changes in snow cover could slow down its sensitivity to snow.

Characteristics of Snow Depth and Snow Phenology in the High Latitudes and High Altitudes of the Northern Hemisphere from 1988 to 2018

Snow cover is an important part of the Earth’s surface and its changes affect local and even global climates due to the high albedo and heat insulation. However, it is difficult to directly compare the results of previous studies on changes in snow cover in the Northern Hemisphere mainland (NH) due to the use of different datasets, research methods, or study periods, and a lack comparison in terms of the differences and similarities at high latitudes and high altitudes. By using snow depth datasets, we analyzed the spatio-temporal distributions and variations in snow depth (SD) and snow phenology (SP) in the NH and nine typical areas. This study revealed that SD in the NH generally decreased significantly (p &lt; 0.01) from 1988 to 2018, with a rate of −0.55 cm/decade. Changes in SD were insignificant at high altitudes, but significant decreases were found at high latitudes. With regard to SP, the snow cover onset day (SCOD) advanced in 31.57% of the NH and was delayed in 21.10% of the NH. In typical areas such as the Rocky Mountains, the West Siberian Plain, and the Central Siberian Plateau, the SCOD presented significant advancing trends, while a significant delay was the trend observed in the Eastern European Plain. The snow cover end day (SCED) advanced in 37.29% of the NH and was delayed in 14.77% of the NH. Negative SCED trends were found in most typical areas. The snow cover duration (SCD) and snow season length (SSL) showed significant positive trends in the Rocky Mountains, while significant negative trends were found in the Qinghai–Tibet Plateau. The results of this comprehensive comparison showed that most typical areas were characterized by decreased SD, advanced SCOD and SCED, and insignificantly increasing SCD and SSL trends. The SCD and SSL values were similar at high latitudes, while the SSL value was larger than the SCD value at high altitudes. The SD exhibited similar interannual fluctuation characteristics as the SCD and SSL in each typical area. The SCD and SSL increased (decreased) with advanced (delayed) SCODs.

Climate, habitat interactions, and mule deer resource selection on winter landscapes

AbstractMule deer (Odocoileus hemionus) are a valued game species across their range in North America. At higher latitudes mule deer inhabit the Northern Forests ecoregion, where the winter season poses challenging conditions. We studied resource selection during winter by mule deer in northwest Montana, USA, focused on the effects of snow and canopy cover on deer behavior. Specifically, we assessed the effects of land cover type, terrain characteristics, snow accumulation, percent canopy cover, solar radiation, and forage biomass on resource selection by 126 mule deer captured during 2017–2019 in 3 partially migratory subpopulations. We collected pellet samples and conducted diet analyses to assess forage species by individual and study area. Deer displayed a shift from nitrogen‐rich forbs and shrubs in summer to nutrient‐poor evergreens like conifers in winter. Deer avoided areas with deeper snow and sought more closed canopy forests for their snow‐intercept advantages. Weather and vegetative variation among and within study areas resulted in disparities in selection for attributes such as land cover. Similarly, mule deer diets varied spatiotemporally as a function of snow, with some deer favoring coniferous species in deep snow, whereas others less constrained by snow had more diverse diets with more shrub species. Additional analyses revealed annual individual survival correlated with changes in snow depth, suggesting fitness consequences as snow accumulated. These differences suggest locally adapted behaviors as mule deer reflect energetic constraints of winter landscapes.

Using snow depth observations to provide insight into the quality of snowpack simulations for regional-scale avalanche forecasting

Abstract. The combination of numerical weather prediction and snowpack models has potential to provide valuable information about snow avalanche conditions in remote areas. However, the output of snowpack models is sensitive to precipitation inputs, which can be difficult to verify in mountainous regions. To examine how existing observation networks can help interpret the accuracy of snowpack models, we compared snow depths predicted by a weather–snowpack model chain with data from automated weather stations and manual observations. Data from the 2020–2021 winter were compiled for 21 avalanche forecast regions across western Canada covering a range of climates and observation networks. To perform regional-scale comparisons, SNOWPACK model simulations were run at select grid points from the High-Resolution Deterministic Prediction System (HRDPS) numerical weather prediction model to represent conditions at treeline elevations, and observed snow depths were upscaled to the same locations. Snow depths in the Coast Mountain range were systematically overpredicted by the model, while snow depths in many parts of the interior Rocky Mountain range were underpredicted. These discrepancies had a greater impact on simulated snowpack conditions in the interior ranges, where faceting was more sensitive to snow depth. To put the comparisons in context, the quality of the upscaled observations was assessed by checking whether snow depth changes during stormy periods were consistent with the forecast avalanche hazard. While some regions had high-quality observations, other regions were poorly represented by available observations, suggesting in some situations modelled snow depths could be more reliable than observations. The analysis provides insights into the potential for validating weather and snowpack models with readily available observations, as well as for how avalanche forecasters can better interpret the accuracy of snowpack simulations.

A D-InSAR method to improve snow depth estimation accuracy

<p indent="0mm">Snow depth is an important parameter that reflects the law of spatiotemporal variation in snow cover and is an indispensable observation variable for studying global and regional climate change and the hydrological cycle. Traditional manual field measurements and ground station observations have limited scopes and cannot fully reflect regional-scale snow conditions. Satellite remote sensing can simultaneously observe surface information over a large area and has been widely used in snow monitoring since the 1970s. Optical remote sensing has difficulty obtaining snow depth information directly through the visible light band and is limited by atmospheric conditions. Microwave remote sensing can penetrate clouds and fog and can effectively capture changes in snow depth. Differential interferometric synthetic aperture radar (D-InSAR) technology uses the differential interferometric phase formed by microwave penetration through the snow layer before and after snowfall to establish a geometric function relationship with the snow depth; it is widely used in regional small-scale snow depth estimation research. However, its estimation accuracy is affected by many factors, such as interferometric image pair coherence, local topography, and snow dielect capability at microwave wavelengths. Based on high-resolution Sentinel-1 SAR data, this study optimizes the snow depth differential interferometric phase unwrapping accuracy by introducing Sentinel-2 optical images and high coherence coefficient regions to select ground control points, which are phase correction benchmarks. Furthermore, introducing field-measured snow parameters such as snow density and satellite local terrain incidence angles based on digital elevation model (DEM) data reduces the error of the differential interferometric phase-slope distance relationship model, thereby enabling estimations of the spatiotemporal distribution of snow depth in the Babao River Basin in the northeastern Qinghai-Tibet Plateau during the 2021 ablation period. Simultaneously, the accuracy of the snow depth estimation ability is evaluated based on the synchronous field measurement data of snow cover satellites. The main factors affecting the accuracy of snow depth estimation are discussed and analysed. Data from 122 ground snow depth measurements (meteorological stations + field measurements) are used to verify the results. The results show that the optimized D-InSAR differential interferometry can improve the snow depth estimation accuracy. The RMSE is <sc>3.9 cm,</sc> the<italic> </italic>MAPE is 20.03%, and the <italic>R</italic>² is 0.92. However, the estimated snow depth is generally underestimated (MBE%=−16.8%), and the maximum underestimation error of the snow depth is <sc>9.1 cm.</sc> In addition to the influencing factors of D-InSAR system interferometric decorrelation, the estimations are affected by the penetration ability of microwaves in the snow and snow parameters such as stratigraphy structure, temperature and humidity. This limits the ability of differential interferometry to estimate snow depth and is more suitable for dry and homogeneous snow cover. This method allows for more precise and faster monitoring of centimetre-level snow depth changes; at the same time, the underlying surface of the study is relatively simple, and the influence of forest and other vegetation coverage is not considered. In follow-up research, snow cover should be optimized with multi-property and multi-environment interferometric models. An InSAR scattering model with a layered factor is introduced to improve the propagation information inside the snow layer then combined with the multiangle radar measurement of the ascending and descending orbits; experiments are carried out using the penetration ability of more bands in microwaves to reduce interferometric error sources and improve the accuracy of snow depth estimation. This study can provide a scientific reference for D-InSAR snow depth estimation research.

Evaluation of CYGNSS Observations for Snow Properties, a Case Study in Tibetan Plateau, China

Snow plays an important role in the water cycle and global climate change, and the accurate monitoring of changes in snow depth is an important task. However, monitoring snow properties is still challenging and unclear, particularly in the Tibetan Plateau, which has rough land and uneven terrain. The traditional monitoring methods have some limitations in monitoring snow depth changes, and the Global Navigation Satellite System-Reflectometry (GNSS-R) provides a new opportunity for snow monitoring. This paper employed data from the Cyclone Global Navigation Satellite System (CYGNSS) to discover the effect of snow properties. Firstly, the observations of CYGNSS were used to find the sensitive to snow properties, and the relationships between signal to noise ratio (SNR), leading edge slope (LES), surface reflectivity (SR), and snow depth were studied and analyzed, respectively. It is found that the correlation between the first two parameters and snow depth is poor, while SR can indicate the changes in snow depth, and is proposed as an indicator of SR change, namely, surface reflectivity–difference ratio factor (SR–DR factor). Furthermore, the long-time series data in the Tibetan Plateau (2018–2019) are used to analyze its effects on the time series of the SR–DR factor, while the influences of the soil freeze/thaw (F/T) process and soil moisture are excluded during the analysis. The results indicate that the SR–DR factor can be a good indicator and discriminator for snow depth. Our work shows that space-borne GNSS-R has the potential for the monitoring of snow properties.

Why Do CMIP6 Models Fail to Simulate Snow Depth in Terms of Temporal Change and High Mountain Snow of China Skillfully?

AbstractGlobal climate models are important tools for investigating historical and future responses of snow under climate change. However, snow simulations from Coupled Model Intercomparison Project Phase 6 (CMIP6) models have not been well evaluated in terms of temporal change and mountain snow, with major error sources remaining unclear. This study synthetically uses long‐term station observations, reanalysis, and remote sensing data to evaluate snow depth simulations of 31 state‐of‐the‐art CMIP6 models in both the high mountain Tibetan Plateau and other areas of China, and analyzes the sources of error. For the first‐time, the accumulated errors of precipitation and temperature of prior months are found to be crucial for explaining why CMIP6 models failed to capture trends and hotspots of snow depth change and notable time lags of the peak month. The worst snow depth performance of CMIP6 was on the Tibetan Plateau and resulted from more serious precipitation overestimates and temperature underestimates than in other regions.

Snow albedo and its sensitivity to changes in deposited light-absorbing particles estimated from ambient temperature and snow depth observations at a high-altitude site in the Himalaya

Snow darkening by deposited light-absorbing particles (LAP) accelerates snowmelt and shifts the snow melt-out date (MOD). Here, we present a simple approach to estimate the snow albedo variability due to LAP deposition and test this method with data for 2 seasons (February–May 2016 and December 2016–June 2017) at a high-altitude valley site in the Central Himalayas, India. We derive a parameterization for the snow albedo that only depends on the daily observations of average ambient temperature and change in snow depth, as well as an assumed average concentration of LAP in snow precipitation. Linear regression between observed and parameterized albedo for the base case assuming an equivalent elemental carbon concentration [ECeq] of 100 ng g–1 in snow precipitation yields a slope of 0.75 and a Pearson correlation coefficient r2 of 0.76. However, comparing the integrated amount of shortwave radiation absorbed during the winter season using observed albedo versus base case albedo resulted in rather small differences of 11% and 4% at the end of Seasons 1 and 2, respectively. The enhanced energy absorbed due to LAP at the end of the 2 seasons for the base case scenario (assuming an [ECeq] of 100 ng g–1 in snow precipitation) was 40% and 36% compared to pristine snow. A numerical evaluation with different assumed [ECeq] in snow precipitation suggests that the relative sensitivity of snow albedo to changes in [ECeq] remains rather constant for the 2 seasons. Doubling [ECeq] augments the absorption by less than 20%, highlighting that the impact on a MOD is small even for a doubling of average LAP in snow precipitation.

Quantifying the temporal stability in seasonal habitat for sage‐grouse using regression and ensemble tree approaches

AbstractIdentifying and quantifying the extent to which landscape‐level habitat variables drive the spatial distribution of individuals across a region can provide fundamental insights into a species ecology and be essential to wildlife management and conservation plans. Although the preferences for habitat resources and the resources themselves are not static over time, most research at large spatial scales does not consider seasonal effects nor quantify annual temporal variability in the spatial distribution of habitat resources. In this study, we used a machine learning (boosted regression trees [BRTs]) and generalized linear mixed model (GLMM) approach to quantify seasonal habitat selection across three life stages (nest, late brood, and winter habitat) of sage‐grouse and estimated annual stability across a 13‐year dataset in south‐central Wyoming. Generalized linear mixed models had high area under the curve (AUC) values, but were not as high as the BRT models that had mean AUC values of 0.86, 0.81, and 0.87 for nest, late brood, and winter habitat, respectively. Generalized linear mixed models and BRT result provided similar results, but because of the higher validation values of the BRT models, we assessed annual variation by predicting the BRT models across years. We found significant spatial trends in the distribution of nesting habitat, with general decreases in the relative probability of use across the core of the study area and corresponding increases in selection on the periphery. The primary temporally shifting variables for the nesting BRT models were development, Normalized Difference Vegetation Index, and topographic wetness, suggesting they were shifting out of preferable ranges for these variables as habitat suitability was decreased over the course of our study. Winter habitat appeared to have similar spatial changes in probability of selection, but these changes were likely related to changes in winter precipitation and snow depth, which were the primary contributors to the winter BRT models. The annual dynamics of habitat selection are seldom addressed in large‐scale research but can have potentially dramatic influences on our identification of preferred habitats.

Enhanced Predictability of Rapidly Intensifying Tropical Cyclones over the Western North Pacific Associated with Snow Depth Changes over the Tibetan Plateau

Abstract This study finds an enhanced relationship in recent years between January–March eastern Tibetan Plateau snow depth (TPSD) and the frequency of rapidly intensifying tropical cyclones (RITCs) over the western North Pacific (WNP) during the following peak TC season (July–November). The correlation between TPSD and RITCs is significant during 2000–14 but was insignificant during 1979–99. During 2000–14, when TPSD increases, there is an enhanced low-level anomalous anticyclone over the subtropical eastern North Pacific mainly due to the combined effect of advection and dynamics of the climatological prevailing westerly jet. Northeasterly wind anomalies are observed on the flank of the anticyclonic circulation anomaly, favoring anomalously cool sea surface temperature (SST). These anomalies lead to an anomalous pattern similar to the Pacific meridional mode (PMM), via a wind–evaporation feedback and cold advection. A Gill-type Rossby response to the PMM-like negative phase results in an anticyclonic circulation anomaly over the WNP, suppressing RITCs during 2000–14. A nearly opposite circulation anomaly occurred when TPSD was lower during 2000–14. There is a weak relationship between TPSD and RITCs, due to the lack of a link between TPSD and the PMM-like pattern from 1979 to 1999. Decadal changes in the relationship between TPSD and RITCs are mainly due to the meridional displacement of the prevailing westerly jet, which may be in response to decadal-to-multidecadal variability of SST anomalies. These changes then result in changes in the relationship between January–March TPSD and the PMM-like pattern. Significance Statement Forecasts of tropical cyclone rapid intensification, typically defined to be when a tropical cyclone intensifies by at least 30 knots (∼15 m s−1) in 24 h, remain extremely challenging. This study finds an enhanced relationship since the start of the twenty-first century between winter–spring Tibetan Plateau snow depth and western North Pacific rapidly intensifying tropical cyclones, while the relationship between snow depth and rapidly intensifying tropical cyclones was weak from 1979 to 1999. Decadal changes in the relationship between Tibetan Plateau snow depth and western North Pacific rapidly intensifying tropical cyclones is mainly due to the north–south displacement of the prevailing westerly jet, which may be in response to a transition in a midlatitude North Pacific climate mode. This study highlights the importance of the synergetic impact of the land, air, and sea on tropical cyclone climate and provides a potential predictor for seasonal-to-decadal prediction of rapidly intensifying tropical cyclones.

Snow Depth Trends from CMIP6 Models Conflict with Observational Evidence

Abstract In this study, we compiled a high-quality, in situ observational dataset to evaluate snow depth simulations from 22 CMIP6 models across high-latitude regions of the Northern Hemisphere over the period 1955–2014. Simulated snow depths have low accuracy (RMSE = 17–36 cm) and are biased high, exceeding the observed baseline (1976–2005) on average (18 ± 16 cm) across the study area. Spatial climatological patterns based on observations are modestly reproduced by the models (normalized root-mean-square deviations of 0.77 ± 0.20). Observed snow depth during the cold season increased by about 2.0 cm over the study period, which is approximately 11% relative to the baseline. The models reproduce decreasing snow depth trends that contradict the observations, but they all indicate a precipitation increase during the cold season. The modeled snow depths are insensitive to precipitation but too sensitive to air temperature; these inaccurate sensitivities could explain the discrepancies between the observed and simulated snow depth trends. Based on our findings, we recommend caution when using and interpreting simulated changes in snow depth and associated impacts.

Compound changes in temperature and snow depth lead to asymmetric and nonlinear responses in landscape freeze\u2013thaw

Cycles of freeze–thaw (FT) are among the key landscape processes in cold regions. Under current global warming, understanding the alterations in FT characteristics is of a great importance for advising land management strategies in northern latitudes. Using a generic statistical approach, we address the impacts of compound changes in air temperature and snow depth on FT responses across Québec, a Canadian province ~ 2.5 times larger than France. Our findings show significant and complex responses of landscape FT to compound changes in temperature and snow depth. We note a vivid spatial divide between northern and southern regions and point to the asymmetric and nonlinear nature of the FT response. In general, the response of FT characteristics is amplified under compound warming compared to cooling conditions. In addition, FT responses include nonlinearity, meaning that compounding changes in temperature and snow depth have more severe impacts compared to the cumulative response of each individually. These asymmetric and nonlinear responses have important implications for the future environment and socio-economic management in a thawing Québec and highlight the complexity of landscape responses to climatic changes in cold regions.

Evaluation of LiDAR-Derived Snow Depth Estimates From the iPhone 12 Pro

Snow is a critical contributor to the global water-energy budget with impacts on springtime flooding and water resource management practices. Laser altimetry [light detection and ranging (LiDAR)] is a remote-sensing technique that has demonstrated skill in monitoring snow depth, but the expense of purchasing and transporting traditional LiDAR equipment limits their operational use. In this work, we demonstrate that the LiDAR sensor installed on the Apple iPhone 12 Pro consumer smartphone is a real-time, handheld measurement instrument for accurately observing changes in snow depth. Two independent field experiments in Southern Ontario, Canada, found that the iPhone LiDAR was able to accurately capture daily changes in snow depth when compared to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> snow ruler measurements. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">In situ</i> and LiDAR comparisons of xs <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n=75$ </tex-math></inline-formula> days at measurement site A exhibit a correlation of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$r &gt; 0.99$ </tex-math></inline-formula> , mean absolute bias less than 1 mm, and a root mean squared error (RMSE) of approximately 6 mm. A similar positive agreement was also noted at the second field study site for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n=16$ </tex-math></inline-formula> measurements over the same period. The high accuracy of the LiDAR sensor suggests that a mobile application could be developed which allows users to quickly scan a snow-covered area before and after a snowfall event and consequently use this data to aid in filling current observational gaps through a citizen-science-based approach to measuring changes in snow depth.

GNSS-R Snow Depth Inversion Based on Variational Mode Decomposition With Multi-GNSS Constellations

Snow depth monitoring is meaningful for climate analysis, hydrological research and snow disaster prevention. Global Navigation Satellite System-Reflectometry (GNSS-R) technology uses the relationship between the modulation frequency of the signal-to-noise ratio (SNR) and reflector height to monitor snow depth. Existing research on single constellation has made good progress and is gradually developing towards multi-constellation combined inversion. Aiming at the accuracy of snow depth inversion, this paper introduces the variational mode decomposition (VMD) algorithm with the characteristics of an adaptive high-pass filter to detrend the SNR data. The experimental results of KIRU station and P351 station show that VMD algorithm is suitable for different constellations and has better signal separation effect. The snow depth inversion results for both stations are in high agreement with the in-situ snow depths provided by the Swedish Meteorological and Hydrological Institute (SMHI) and the SNOTEL network, respectively. The root mean square error (RMSE) of the inversion results is reduced by 20-40% compared to the least squares fitting (LSF) algorithm, and the correlation coefficients are also greatly improved. Moreover, considering that there is no overlap between the climate station and the inversion area, this paper introduces the maximum spectral amplitude as another reference data source and obtains basically consistent experimental conclusions. On this basis, the maximum spectral amplitude is used as the input variable of the entropy method, and the feasibility of the combination strategy is studied. The results show that the combined strategy reduces a little inversion error and improves the temporal resolution of snow depth monitoring. It is of great significance for more accurate and rapid monitoring of snow depth changes and disaster warnings, and provides an important reference for further research on GNSS-R technology.