Spring Snow Depth Research Articles

Interdecadal variability of winter and spring Eurasian snow depth and its impact on Eastern China precipitation

Empirical orthogonal function (EOF) and correlation analyses were employed to investigate the winter and spring snow depth in Eurasia and its relationship with Eastern China precipitation based on the observed and reanalyzed data from 1980 to 2016. The results show that the winter and spring snow cover in Eurasia not only highlights a decreasing trend due to global warming (the first EOF mode, its variance accounted for 24.4% and 22.6% of the total variance) but also exhibits notable interdecadal variation (the second EOF mode, its variance accounted for 10.2% and 11.5% of the total variance). The second EOF mode of winter snow depth in Eurasia is characterized by a west-east dipole pattern. It was observed that the spatial correlation pattern between the EOF2 of Eurasian snow depth and summer precipitation in China closely resembles the meridional quadrupole structure of the third EOF mode of summer precipitation in China. This pattern is characterized by excessive rainfall in Northeast China and the lower-middle reaches of the Yangtze River, and less rainfall over the Yellow River basin and southern China. The EOF mode of spring snow depth not only reflects the declining trend but also regulates precipitation in Eastern China. The possible mechanisms by which snow depth causes changes in soil moisture and subsequently affects atmospheric circulation are then explored from the perspective of the hydrological effects of snow cover. Decreased (Increased) snow depth in Eurasia during the winter and spring directly leads to diminished (increased) soil moisture while increasing (decreasing) net radiation and sensible heat flux at the surface. The meridional distribution of surface temperature also exhibits a dipole pattern, leading to enhanced subtropical westerly jet in the upper troposphere. The Eurasian snow cover anomalies pattern triggered an anomalous mid-latitude Eurasian wave train, which strengthened significantly in the Western Siberian Plain. It then splits into two branches, one continuing to propagate eastward at high latitudes and the other shifting towards East Asia, thereby impacting precipitation in Eastern China. This work indicates that the second EOF mode of Eurasian snow cover can impact the precipitation variability in Eastern China during the same period and in summer on an interdecadal scale.

Multi-Source Dataset Assessment and Variation Characteristics of Snow Depth in Eurasia from 1980 to 2018

Snow is an indicator of climate change. Its variation can affect surface energy, water balance, and atmospheric circulation, providing important feedback on climate change. There is a lack of assessment of the spatial characteristics of multi-source snow data in Eurasia, and these data exhibit high spatial variability and other differences. Therefore, using data obtained from the Global Historical Climatology Network Daily (GHCND) from 1980 to 2018, snow depth information from ERA5, MERRA2, and GlobSnow is assessed in this study. The spatiotemporal variation characteristics and the primary spatial modes of seasonal variations in snow depth are analyzed. The results show that the snow depth, according to GlobSnow data, is closer to that of the measured site data, while the ERA5_Land and MERRA2 data are overestimated. The annual variations in snow depth are consistent with seasonal variations in winter and spring, with an increasing trend in the mountains of Central Asia and Siberia and a decreasing trend in most of the rest of Eurasia. The dominant patterns of snow depth in late autumn, winter, and spring are all north–south dipole patterns, and there is overall consistency in summer.

Annual growth of brown trout in alpine lakes is highly influenced by spring snow depth and ice-out day

Growth of annular zones in otoliths of brown trout from the alpine lakes Litlosvatn and Kollsvatn on western part of the Hardangervidda mountain plateau have been studied during the period 2004–2021, concurrent with recordings of accumulated snow in spring and dates of ice-out. Unlike the conditions in lowland areas, years with much accumulated snow have not decreased during the last decades, and years with delayed ice break-up are still frequent, opposite the trend observed elsewhere in Europe and in North America. The annual growth of the brown trout otoliths is significantly reduced in years with much snow in April and late ice-out dates, irrespective of age of the fish, indicating that somatic growth of brown trout is considerably reduced in such years. Accumulated snow in spring and ice-out day may thus be useful parameters in predictions of fish production and potential yield in such alpine lakes.

Snow depth alteration and its relation with climate variability in China

AbstractVarious climatic factors and their interactions affect China's snow depth alterations. However, a measure of the relative importance of climatic factors regarding the variance in snow depth is lacking. Here, we use a heretofore largest dataset, quality‐controlled daily observations from 1,665 meteorological stations, and statistical analyses to identify proxy drivers and their contributions regarding regional variability and trends in China's snow depth and snow depth‐climate relationships from 1951 to 2019. Results show that seasonal snow depth alterations are substantial in China and caused by various climatic drivers. In winter, observed positive trends in the northwest and northeast of China mainly respond to augmentation in solid precipitation and snow accumulation in fall. For the Tibetan Plateau, the negative snow depth anomaly seems to respond to the rising air temperature below the freezing point along with some secular changes in solid precipitation and shallower fall snowpacks. All these trends in winter snow depth over the northwest, northeast, and Tibetan Plateau of China account approximately for 30% of the total interannual variance in spring snow depth in these regions. In the milder central north and south of China, snow accumulation is not evident. Besides solid precipitation and rising air temperature below the freezing point, atmospheric humidity is also notable for its effect on the interannual variance in winter snow depth in these regions. In spring and fall, however, the negative effect of warming climate on snow depth becomes remarkably more important. It seems that about 13–33% (12–50%) of the total interannual variance in the widespread decrease (insignificant perturbation), except for some weak increases in the northeast region (decreases in the Tibetan Plateau), of China's spring (fall) snow depth are caused by the rising air temperature below the freezing point.

Simulating the extent and depth of spring snow cover for medieval settlements in Iceland and Greenland

Medieval settlements in Iceland and Greenland were vulnerable to changes in spring (April-June) snow cover duration and depth. These would have adversely affected the viability of their pastoral farming systems, but the impact would have been spatially variable. We use a physical-based model of snow distribution and melt to model spring snow cover and depth at a scale relevant to human activities across four sites: southern and northern Iceland, and inner and middle fjord sites in South Greenland, using both present day and simulated climate data from the HadCM3 GCM-model. Our climate scenarios cover the period CE 1000–1500, encompassing a climate shift to cooler conditions. We find that under average present climate conditions the inner fjord site in Greenland has similar spring snow conditions to sites in Iceland, but that the middle fjord site has notably greater snow cover, and as climate cools spring snow cover at this site becomes extensive (>60 days). The largest increase in snow cover duration between current average climate conditions and the coolest climate scenarios (47 days increase) is experienced at our Iceland sites. Inner and middle fjord sites in Greenland diverge in terms of snow cover under all scenarios, a potential driver of the growing importance of marine wild resources and the end of the Norse Greenland settlement.

Impact of Solar Activity on Snow Cover Variation Over the Tibetan Plateau and Linkage to the Summer Precipitation in China

Solar activity is one of the main external forcing factors driving the Earth’s climate system to change. The snow cover over the Tibetan Plateau is an important physical factor affecting the East Asian climate. At present, insufficient research on the connection between solar activity and snow cover over the Tibetan Plateau has been carried out. Using Solar Radio Flux (SRF), Solar Sunspot Number (SSN), and Total Solar Irradiance (TSI) data, this paper calculated the correlation coefficients with snow indices over the Tibetan Plateau, such as winter and spring snow depth (WSD/SSD) and snow day number (WSDN/SSDN). These snow indices are obtained from the daily gauge snow data in the Tibetan Plateau. Through correlation analyses, it is found that there are significant synchronous or lag correlations between snow indices and solar parameters on multi-time scales. In particular, the Spring Snow Day Number (SSDN) is of significant synchronous or lag correlation with SRF, SSN, and TSI on multi-time scales. It is further found that SSDN over the Tibetan Plateau has more stable positive correlations with SRF by using the 21-year running mean and cross spectrum analyses. Therefore, SSDN can be ascertained to be the most sensitive snow index to the solar activity compared with other snow indices. Moreover, its influence on summer precipitation of China is strongly regulated by solar activity. In high solar activity years (HSAY), the significant correlated area of summer precipitation in China to SSDN is located further north than that in low solar activity years (LSAY). Such impact by solar activity is also remarkable after excluding the impact of ENSO (i.e., El Niño–Southern Oscillation) events. These results provide support for the application of snow indices in summer rainfall prediction in China.

Active Layer Thickness Variation on the Qinghai‐Tibetan Plateau: Historical and Projected Trends

AbstractAs the buffer layer between the atmosphere and permafrost, the active layer is vulnerable to climate change. The variation in the active layer thickness (ALT) has important effects on surface energy balance, ecosystem, hydrological cycle, vegetation cover, and engineering construction in permafrost regions. The goal of this study is to discuss the active layer variation under different shared socioeconomic pathways (SSPs) for specific warming levels and to reveal the potential interactions between the ALT and the associated driving factors in typical hydrological basins. We revised the Stefan solution using the edaphic factor and the thawing index calculated by multimodel data from the Coupled Model Intercomparison Project Phase 6 to estimate the variation in the ALT. During 2015 to 2100, the ALT will increase by 14 cm (SSP1‐2.6), 43 cm (SSP2‐4.5), and 1.44 m (SSP5‐8.5), with average increase rates of 2.5 cm/decade, 5.8 cm/decade, and 17.5 cm/decade, respectively. The rates of increase of the ALT in the Hexi basin, Inner basin, Mekong basin, Yangtze basin, and Yellow basin are 12.6 cm/decade, 6.7 cm/decade, 5.2 cm/decade, 8.0 cm/decade, and 5 cm/decade, respectively. These results illustrate that air temperature is the primary determinant of ALT variation and normalized difference vegetation index (NDVI) and snow depth may influence the ALT change. The most significant correlations are between the ALT and NDVI in the Yangtze basin. In different seasons, the spring snow depth has the greatest impact on the ALT in the Hexi basin.

Prediction of municipality-level winter wheat yield based on meteorological data using machine learning in Hokkaido, Japan.

This study analyzed meteorological constraints on winter wheat yield in the northern Japanese island, Hokkaido, and developed a machine learning model to predict municipality-level yields from meteorological data. Compared to most wheat producing areas, this island is characterized by wet climate owing to greater annual precipitation and abundant snowmelt water supply in spring. Based on yield statistics collected from 119 municipalities for 14 years (N = 1,516) and high-resolution surface meteorological data, correlation analyses showed that precipitation, daily minimum air temperature, and irradiance during the grain-filling period had significant effects on the yield throughout the island while the effect of snow depth in early winter and spring was dependent on sites. Using 10-d mean meteorological data within a certain period between seeding and harvest as predictor variables and one-year-leave-out cross-validation procedure, performance of machine learning models based on neural network (NN), random forest (RF), support vector machine regression (SVR), partial least squares regression (PLS), and cubist regression (CB) were compared to a multiple linear regression model (MLR) and a null model that returns an average yield of the municipality. The root mean square errors of PLS, SVR, and RF were 872, 982, and 1,024 kg ha−1 and were smaller than those of MLR (1,068 kg ha−1) and null model (1,035 kg ha−1). These models outperformed the controls in other metrics including Pearson’s correlation coefficient and Nash-Sutcliffe efficiency. Variable importance analysis on PLS indicated that minimum air temperature and precipitation during the grain-filling period had major roles in the prediction and excluding predictors in this period (i.e. yield forecast with a longer lead-time) decreased forecast performance of the models. These results were consistent with our understanding of meteorological impacts on wheat yield, suggesting usefulness of explainable machine learning in meteorological crop yield prediction under wet climate.

Spring snow depth changes and feedback to surface air temperature across the Tibetan plateau from 1961 to 2013

AbstractSpring snow depth (SSD) in the Tibetan Plateau (TP) plays an essential role in snow water estimation, vegetation growth, and climate projections for Southwest China and the surrounding Asian countries. Previous studies have reported a decline in SSD that is associated with increasing surface air temperature (Ts) across the TP over the past decades. However, only a few studies have explored the potential feedback of SSD to Ts, which has hindered the elucidation of the vulnerability of snow cover in the TP against the backdrop of rapid climate change. Based on data from 69 meteorological stations in the central and eastern parts of the TP, this study analyses changes in SSD and its feedback to Ts from 1961 to 2013. Overall, a slightly negative trend in SSD was observed from 1961 to 2013, during which two contrasting trends were detected using a piecewise regression approach: an initial increase from 1961 to 1981 (0.12 cm decades−1, p < .01) followed by a decrease from 1982 to 2013 (−0.36 cm decades−1, p < .01). Sensitivity analysis revealed that the SSD decline was systematically correlated with a positive Ts anomaly, with sensitivities of −0.050 cm °C−1 (p < .01) and −0.069 cm °C−1 (p < .01) for 1961–2013 and 1982–2013, respectively. However, following the accelerated SSD decline during 1982–2013, its potential feedback to Ts was attenuated, with averaged feedback values from −1.328°C cm−1 during 1961–2013 to −0.936°C cm−1 during 1982–2013 in latitudes below 3,500 m, distributed in the Qaidam and Yellow River basins, and the upper reaches of the Yangtze River basin. The decrease in feedback from SSD to Ts may lead to changes in the mechanism controlling Ts dynamics across the TP, which should be considered in climate change projections in future studies.

Trends and spatial variation in rain-on-snow events over the Arctic Ocean during the early melt season

Abstract. Rain-on-snow (ROS) events can accelerate the surface ablation of sea ice, thus greatly influencing the ice–albedo feedback. However, the variability of ROS events over the Arctic Ocean is poorly understood due to limited historical station data in this region. In this study early melt season ROS events were investigated based on four widely used reanalysis products (ERA-Interim, JRA-55, MERRA, and ERA5) in conjunction with available observations at Arctic coastal stations. The performance of the reanalysis products in representing the timing of ROS events and the phase change of precipitation was assessed. Our results show that ERA-Interim better represents the onset date of ROS events in spring, and ERA5 better represents the phase change of precipitation associated with ROS events. All reanalyses indicate that ROS event timing has shifted to earlier dates in recent decades (with maximum trends up to −4 to −6 d per decade in some regions in ERA-Interim) and that sea ice melt onset in the Pacific sector and most of the Eurasian marginal seas is correlated with this shift. There has been a clear transition from solid to liquid precipitation, leading to more ROS events in spring, although large discrepancies were found between different reanalysis products. In ERA5, the shift from solid to liquid precipitation phase during the early melt season has directly contributed to a reduction in spring snow depth on sea ice by more than −0.5 cm per decade averaged over the Arctic Ocean since 1980, with the largest contribution (about −2.0 cm per decade) in the Kara–Barents seas and Canadian Arctic Archipelago.

Inter-comparison of snow depth over Arctic sea ice from reanalysis reconstructions and satellite retrieval

Abstract. In this study, we compare eight recently developed snow depth products over Arctic sea ice, which use satellite observations, modeling, or a combination of satellite and modeling approaches. These products are further compared against various ground-truth observations, including those from ice mass balance observations and airborne measurements. Large mean snow depth discrepancies are observed over the Atlantic and Canadian Arctic sectors. The differences between climatology and the snow products early in winter could be in part a result of the delaying in Arctic ice formation that reduces early snow accumulation, leading to shallower snowpacks at the start of the freeze-up season. These differences persist through spring despite overall more winter snow accumulation in the reanalysis-based products than in the climatologies. Among the products evaluated, the University of Washington (UW) snow depth product produces the deepest spring (March–April) snowpacks, while the snow product from the Danish Meteorological Institute (DMI) provides the shallowest spring snow depths. Most snow products show significant correlation with snow depths retrieved from Operational IceBridge (OIB) while correlations are quite low against buoy measurements, with no correlation and very low variability from University of Bremen and DMI products. Inconsistencies in reconstructed snow depth among the products, as well as differences between these products and in situ and airborne observations, can be partially attributed to differences in effective footprint and spatial–temporal coverage, as well as insufficient observations for validation/bias adjustments. Our results highlight the need for more targeted Arctic surveys over different spatial and temporal scales to allow for a more systematic comparison and fusion of airborne, in situ and remote sensing observations.

Detecting snow-related signals in radial growth of Pinus uncinata mountain forests

Climate warming is responsible for observed reduction in snowpack depth and an earlier and faster melt-out in many mountains of the Northern Hemisphere. Such changes in mountain hydroclimate could negatively affect productivity and tree growth in high-elevation forests, but few studies have investigated how and where recent warming trends and changes in snow cover influence forest growth. A network comprising 36 high-elevation Pinus uncinata forests was sampled in the NE Iberian Peninsula, mainly across the Spanish Pyrenees, using dendrochronology to relate tree radial growth to a detailed air temperature and snow depth data. Radial growth was negatively influenced by a longer winter snow season and a higher late-spring snowpack depth. Notably, the effect of snow on tree growth was found regardless the widely reported positive effect of growing-season air temperatures on P. uncinata growth. No positive influence of moisture from spring snowmelt on annual growth of P. uncinata was detected in sampled forests. Tall trees showed a lower growth responsiveness to snow than small trees. Decreasing trends in winter and spring snow depths were detected at most Pyrenean forests, suggesting that the growth of high-elevation P. uncinata forests can beneficiate for a shallower and of shorter duration snowpack associated with warmer conditions. However, water-limited sites located on steep slopes or on rocky substrates, with poor soil-water holding capacity, could experience drought stress because of early depleted snow-related soil moisture.

Atmospheric heat sinks over the western Tibetan Plateau associated with snow depth in late spring

AbstractThe atmospheric heat source/sink (AHS) and snow cover/depth over the Tibetan Plateau (TP) in late spring play important roles in modulating the evolution of Asian summer monsoons. However, quantitative estimation of the AHS over the TP is still a large challenge because of limited observational data. In this work, both data analyses and numerical simulations from the Weather Research and Forecasting model with an updated daily snow‐depth data set are conducted to explore the intrinsic connection between the AHS and snow depth over the TP in May. Data analyses indicate that only a weak negative relationship exists between snow depth and the AHS over the western TP. On the other hand, despite the overall consistency in the spatial pattern of the AHS between the reanalysis data sets and the Weather Research and Forecasting model, a strong cooling effect (about −14.4 W/m2) appears over the western TP (32°–40°N, 70°–78°E) and the Nyenchen Tanglha Mountains in the simulation, where the altitude is above 4,000 m with thick snow cover. This characteristic is opposite to that observed in reanalysis data sets. Further analysis indicates that the underestimated atmospheric net longwave radiative cooling effect that is associated with snow depth may exaggerate the atmospheric heat source in current reanalysis data sets. Large uncertainties in the AHS still exist in current state‐of‐the‐art reanalysis data sets, especially over the regions of the TP covered by snow.

Estimating Snow Depth and Leaf Area Index Based on UAV Digital Photogrammetry.

This study presents a novel approach in the application of Unmanned Aerial Vehicle (UAV) imaging for the conjoint assessment of the snow depth and winter leaf area index (LAI), a structural property of vegetation, affecting the snow accumulation and snowmelt. The snow depth estimation, based on a multi-temporal set of high-resolution digital surface models (DSMs) of snow-free and of snow-covered conditions, taken in a partially healthy to insect-induced Norway spruce forest and meadow coverage area within the Šumava National Park (Šumava NP) in the Czech Republic, was assessed over a winter season. The UAV-derived DSMs featured a resolution of 0.73–1.98 cm/pix. By subtracting the DSMs, the snow depth was determined and compared with manual snow probes taken at ground control point (GCP) positions, the root mean square error (RMSE) ranged between 0.08 m and 0.15 m. A comparative analysis of UAV-based snow depth with a denser network of arranged manual snow depth measurements yielded an RMSE between 0.16 m and 0.32 m. LAI assessment, crucial for correct interpretation of the snow depth distribution in forested areas, was based on downward-looking UAV images taken in the forest regime. To identify the canopy characteristics from downward-looking UAV images, the snow background was used instead of the sky fraction. Two conventional methods for the effective winter LAI retrieval, the LAI-2200 plant canopy analyzer, and digital hemispherical photography (DHP) were used as a reference. Apparent was the effect of canopy density and ground properties on the accuracy of DSMs assessment based on UAV imaging when compared to the field survey. The results of UAV-based LAI values provided estimates were comparable to values derived from the LAI-2200 plant canopy analyzer and DHP. Comparison with the conventional survey indicated that spring snow depth was overestimated, and spring LAI was underestimated by using UAV photogrammetry method. Since the snow depth and the LAI parameters are essential for snowpack studies, this combined method here will be of great value in the future to simplify snow depth and LAI assessment of snow dynamics.

Snow Depth Retrieval on Arctic Sea Ice From Passive Microwave Radiometers—Improvements and Extensions to Multiyear Ice Using Lower Frequencies

AbstractSnow on sea ice influences the Arctic energy and heat budgets and is therefore important for Arctic climate studies. Methods to derive snow depth based on satellite‐borne microwave radiometer observations have existed since the 1990s. However, in the Arctic the most widely used algorithm can only be applied over first‐year ice (FYI) and does not make use of the lower frequencies, which are available since 2002. Here we present three improvements to the current passive microwave snow depth retrieval: (a) We derive new coefficients based on a regression analysis using 5 years of Operation IceBridge airborne snow depth measurements. (b) We extend the algorithm to take advantage of the lower frequency channel at 7 GHz. (c) We consider an extension of the snow depth retrieval to multiyear ice (MYI) during spring. Our results show that the gradient ratio, GR(19/7) is most suited for deriving snow over both Arctic FYI (R =°−0.73) and MYI (R = −0.57). An evaluation of the new retrieval with Operation IceBridge snow depth measurements from March and April 2015 shows a good agreement over FYI (difference = −2.1 cm; 93% of the differences below 5 cm). Over MYI the difference is −4.0 cm and 56% of the differences are below 5 cm, that is, the root mean square distance (RMSD) is larger over MYI than over FYI. We demonstrate for the first time that spring snow depth measurements can be derived from passive microwave observations over both FYI and MYI.

Climatic drivers of tree growth at tree line in Southwest Yukon change over time and vary between landscapes

Growth of trees at their altitudinal and latitudinal range limits is expected to increase as climate warms, but trees often exhibit unexplained spatial and temporal variation in climate-growth responses, particularly in alpine regions. Until this variability is explained, predictions of future tree growth are unlikely to be accurate. We sampled Picea glauca (white spruce) growing at forest and tree line on north and south aspects in two mountain ranges of southwest Yukon to determine how and why ring-width patterns vary between topographic settings, and over time. We used multivariate statistical analysis to characterize variation in ring-width patterns between topographic factors and time periods, and calculated correlations between ring-width indices and climate variables to explain this variation. Ring-width patterns varied more between mountain ranges than elevations or aspects, particularly in recent decades when ring-widths increased in one mountain range but not the other. Growth responses to summer temperature were notably weaker during warmer time periods, but growth was not positively correlated to summer precipitation, suggesting trees may not be suffering from temperature-induced drought stress. Rather, ring-width indices began responding positively to spring snow depth after 1976. We conclude that tree growth is unlikely to increase in synchrony with rising air temperatures across subarctic tree lines in southwest Yukon. Instead, they may decline in areas that are prone to thin snowpacks or rapid spring runoff due to the negative influence warming springs will have on snow depth and, consequently, early growing season soil moisture.

Reconstruction of Snow on Arctic Sea Ice

AbstractSnow on Arctic sea ice is a poorly observed variable that plays an important role in the Arctic climate system and impacts the remote sensing systems that monitor Arctic sea ice. We present and validate a reconstruction of Arctic snow depth based on observed sea ice motion and snowfall derived from reanalysis data. Overall, the reconstruction is in good agreement with direct measurements of snow depth from Operation IceBridge, slightly underestimating mean IceBridge snow depth. At the local scale (10 km), the reconstruction is more skilled than a weighted climatology over first year ice, but underestimates deeper snow over multi‐year ice. Reconstructions of single buoy snow depths are unskilled, but the reconstruction shows skill in simulating the mean snow depth across all buoys. Spring snow depths show a low‐to‐high cross‐Arctic gradient and tend to be greatest in the Atlantic sector of the eastern Arctic. The relationship between ice type (multiyear or first‐year ice) and snow depth previously documented in the western Arctic is not evident in the eastern Arctic. Using ice type to weight snow depths for satellite ice thickness retrievals may not be justifiable in the eastern Arctic. Reconstructed snow depth across the Arctic shows significant interannual variability, suggesting that use of a fixed snow depth climatology may lead to biases in retrieved ice thickness and its variability. However, interannual variability in pan‐Arctic mean snow depth is comparable or smaller than the uncertainty in both the reconstruction and IceBridge, highlighting the need for high accuracy snow depth products and reconstructions.

Interdecadal variation in Korean spring drought in the early 1990s

Statistical change-point analysis was applied to a spring time series of the Palmer Drought Severity Index (PDSI) in Korea, and this showed that the total analysis period can be divided into two periods: a wet period (1975–1990) and a dry period (1991–2014). To investigate the deepening of the spring drought in Korea, a difference in the spring means between the 1991–2014 and 1975–1990 periods was analyzed with respect to large-scale environments. In the recent spring, a typical pressure system pattern in winter, which was an anomalous west-high east-low pressure system pattern, was strengthened throughout the troposphere around Korea. Due to the anomalous pressure system pattern, Korea was affected by relatively cold and dry anomalous northerlies. Furthermore, the western North Pacific subtropical high (WNPSH) was not intensified, and thus the supply of warm and moist airs into Korea was further reduced due to the upper-level jet moving further to the south. In order to determine the reason for the development of the anomalous west-high east-low pressure system pattern in East Asia, the difference in spring snow depth between the two periods was analyzed, and the analysis results showed that positive anomalies were predominant throughout most regions in East Eurasia. As a result, in the analysis of the ground heat net flux, negative anomalies were strengthened in most regions in East Eurasia. The cooling effect in the surrounding regions due to the high snow depth in East Eurasia strengthened the anomalous pressure system pattern as a west-high east-low type.

Snow cover on Arctic sea ice in observations and an Earth System Model

AbstractSpring snow depth on Arctic sea ice is assessed in observations and an Earth System Model. In both, snow depth is mainly driven by the ice type on which the snow lies. Snow depths on multiyear ice are approximately double snow depths on first‐year ice. In the model spring snow depths are greater on multiyear ice compared to first‐year ice primarily because multiyear ice begins to accumulate snow earlier in the fall but also due to snow cover survival through the summer on multiyear ice. The model simulates a decline in spring snow depth driven mainly by a transition from a multiyear to first‐year ice regime throughout the Arctic. Agreement in the loss of multiyear ice in the model with estimates from observations suggests that declining snow depths in observations mainly result from the loss of multiyear sea ice.

The Influence of the MJO on the Intraseasonal Variability of Northern Hemisphere Spring Snow Depth

Abstract Intraseasonal variability in springtime Northern Hemisphere daily snow depth change (ΔSD) by phase of the MJO was explored in this study. Principal findings of the relationship between ΔSD and the MJO included the following: 1) Statistically significant regions of lagged ΔSD anomalies for multiple phases of the MJO were found in March, April, and May in both North America and Eurasia. 2) In each month, lagged ΔSD anomalies were physically supported by corresponding lagged anomalies of 500-hPa height (Z500) and surface air temperature (SAT). Spearman rank correlation coefficients indicated a moderate to strong relationship between both Z500 and ΔSD and SAT and ΔSD in both Eurasia and North America for phases 5 and 7 in March. In April, a moderately strong relationship between Z500 and ΔSD was found over Eurasia for phase 5, but the relationship between SAT and ΔSD was weak. In May, correlations between ΔSD and both Z500 and SAT over a hemisphere-wide latitude band from 60° to 75°N were close to −0.5 and −0.4, respectively. Given the strength of these statistical relationships, the following physical pathway is proposed for intraseasonal variability of spring snow depth changes: poleward-propagating Rossby waves in response to tropical MJO convection interact with Northern Hemisphere background flow, leading to anomalous troughing and ridging. These anomalous circulation centers then impact daily snow depth change via precipitation processes and anomalies in surface air temperature.