Influence of Eurasian spring snowmelt on surface air temperature in late spring and early summer

The effect of Eurasian spring snowmelt on surface air temperature (SAT) in late spring (April–May) and early summer (June–July) and the relevant physical mechanisms during 1981–2016 are investigated. Results show that the first mode of the inter-annual Eurasian spring snowmelt represents an east–west dipole anomaly pattern, with an intense center over Siberia and another moderate center around eastern Europe. The European spring snowmelt shows an insignificant relation to the local SAT, whereas the Siberian spring snowmelt has a significant impact on the SAT in late spring and early summer. More Siberian spring snowmelt contributes to higher SAT in late spring and lower SAT in early summer via different mechanisms. In late spring, increased Siberian spring snowmelt corresponds to less local surface albedo and cloud cover, leading to the surface absorbing more shortwave radiation and thereby higher SAT. The sub-surface and deep soil moisture anomalies generated from Siberian spring snowmelt can persist into early summer. Excessive Siberian spring snowmelt corresponds to positive soil moisture anomalies, contributing to decreased sensible heat and increased cloud cover in early summer. Increased cloud cover leads to the surface receiving less shortwave radiation. Thus, lower SAT appears over Siberia in early summer due to reduced sensible heat and shortwave radiation. However, the simulation of Eurasian spring snowmelt variability and its influences on SAT via the snow hydrological effect is still a challenge for the climate models that participated in the Coupled Model Intercomparison Project phase 6.

Influence of the Eurasian spring snowmelt on summer land surface warming over Northeast Asia and its associated mechanism

Under the background of global warming, the Eurasian warming features evident spatial heterogeneity, and Northeast Asia (NEA) is one of the regions with the most significant summer warming. Based on reanalysis data and the CESM1.2.2 model, we investigated the possible impacts of spring Eurasian snowmelt on recent NEA summer warming and the relevant mechanisms. Results show that increased (decreased) spring snowmelt over East Europe to West Siberia (EEWS) is closely linked to NEA summer warming (cooling). Increased spring snowmelt can wet the soil, weakening surface sensible heating to the atmosphere and cooling the atmosphere. The persistent anomalous soil moisture and surface sensible heat induce geopotential height decrease over EEWS and strengthen the eastward-propagating wave train. Furthermore, positive geopotential height anomalies appear in downstream NEA in summer via the adjustment of the atmospheric circulation. Controlled by the anomalous high-pressure system, the west part of NEA is affected by the southerly warm advection, while the east is affected by adiabatic warming induced by the dominant descending motion. Meanwhile, decreased cloud and increased incident solar radiation over NEA favor summer land surface warming. Model results suggest that CESM1.2.2 can basically reproduce the positive correlation between NEA summer land surface temperature and EEWS spring snowmelt. With the positive spring snowmelt forcing, the simulated positive soil moisture and negative sensible heat anomalies persist from spring to summer over EEWS. Consequently, negative geopotential height anomalies appear over the snowmelt region while positive anomalies occur around Lake Baikal, resulting in evident NEA land surface warming.

Effects of snowmelt on the runoff dynamics in two catchments with different forest stands

<p>The forest stand can significantly affect the snow deposition and consequently the runoff during the melt period. This study focuses on water and element fluxes from snowpack in two Czech boreal headwater lake catchments with different forest stands (mature vs. regenerating after bark beetle tree dieback) using isotopic and hydrochemical tools. Sampling and analysis of the surface water, precipitation and snowpack throughout one  hydrological year enabled us to estimate the isotopic balance and chemical snowpack evolution, but also the snowmelt contribution in lakes inlets and outlets.</p><p>Isotopic signatures of the snowpack were seasonal, with δ<sup>2</sup>H amplitudes of -25‰ in the mature and -17‰ in the regenerating forest catchments. The mature forest had a ~1 month longer duration of snow cover and higher concentration of solutes in the precipitation and snowpack. In both catchments, heavier isotopes (<sup>18</sup>O and <sup>2</sup>H) preferentially left the snowpack, which was saturated with rainwater. This resulted in the final spring snowmelt being enriched with lighter isotopes (<sup>16</sup>O and <sup>1</sup>H). Ions were also eluted from the snowpack during rain-on-snow events and partial snow melting throughout the winter, causing fluxes of diluted water at the end of the snowmelt. Our results demonstrate the hydrological and hydrochemical variability of the snowpack, which in the future may even increase with rising temperatures and changes of precipitation patterns.</p>

Runoff generation processes in permafrost-influenced area of the Heihe River Headwater

<p>Permafrost degradation under global warming may change the hydrological regime of the headwater catchments in alpine area such as the Tibetan Plateau (TP). In this study, he runoff generation processes in permafrost-influenced area of the Heihe River Headwater were investigated with the following results: 1) The observed stable isotope values of various water types on average was roughly in the order of snowfall and snowmelt < bulk soil water (BSW) < rainfall , stream water, mobile soil water (MSW) , and lateral subsurface flow. The depleted spring snowmelt and enriched summer rainfall formed tightly bound soil water and MSW, respectively. The dynamic mixing between tightly bound soil water and MSW resuted in BSW with more depleted and variable stable isotopic feature than MSW. 2) Along with the thawing of the frozen soil, surface runoff and shallowsubsurface flow (SSF) at 30−60 cm was the major flow pathway in the permafrost influenced alpine meadow hillslope during spring snowmelt and summer rainfall period, reapectively, with the frozen soil maintaining supra-permafrost water level. 3) Comparison between two neighouring catchments under similar precipitation conditions indicated that streamflow of the lower catchment with less permafrost proportion and earlier thawing time has larger SSF and higher based flow component, indicating the potential changes of hydrological regims subject to future warming.</p>

Long-term forecast of changes in soil erosion losses during spring snowmelt caused by climate within the plain part of Ukraine

The paper deals with the forecast of changes in erosion soil losses during the spring snowmelt due to climate change in the regions of Ukraine in the middle of the 21st century (during 2031–2050) and at its end (during 2081–2100) compared with the values of the baseline period (1961–1990). The forecast is based on the use of the so-called “hydrometeorological factor of spring soil loss”. This factor is a part of the physical-statistical mathematical model of soil erosion lossduring spring snowmelt, developed at the Department of Physical Geography of Odesa I. I. Mechnikov State (since 2000 — National) University during the 1980s – 1990s. The long-term average value of the hydrometeorological factor is linearly related to the long-term average value of spring erosion soil loss. Therefore, the relative change in the hydrometeorological factor corresponds to the relative change in soil erosion losses. The developed methodology for assessing climate-induced changes in soil erosion losses in five regions of Ukraine (North, West, Center, East and South) takes into account the change in water equivalent of snow cover at the beginning of snow melting, the change in surface runoff and its turbidity, and changes in soil erodibility. The forecast of changes in erosion soil loss was carried out using projections of annual and monthly average air temperatures and precipitation for 2031–2050 and 2081–2100 in accordance with scenario A1B from AR4 of the IPCC. As a result of the research, it was found that both in the middle and at the end of the 21st century a decrease in the rate of soil erosion during the period of spring snowmelt is expected. During 2031–2050, the expected soil losses will be less than corresponding baseline period values within the West region by 79%, within the North and East regions by 81%, and within the Center region by 85%. In the South region, the spring soil losses will be zero due to the lack of snow cover. During 2081–2100 snow cover will be absent not only in the South region, but also in the Center and East regions. In the regions North and West snow cover will remain, but the spring soil erosion losses will decrease by dozens of times and will be so small that they can also be ignored.

Arctic avian predators synchronise their spring migration with the northern progression of snowmelt

Migratory species display a range of migration patterns between irruptive (facultative) to regular (obligate), as a response to different predictability of resources. In the Arctic, snow directly influences resource availability. The causes and consequences of different migration patterns of migratory species as a response to the snow conditions remains however unexplored. Birds migrating to the Arctic are expected to follow the spring snowmelt to optimise their arrival time and select for snow-free areas to maximise prey encounter en-route. Based on large-scale movement data, we compared the migration patterns of three top predator species of the tundra in relation to the spatio-temporal dynamics of snow cover. The snowy owl, an irruptive migrant, the rough-legged buzzard, with an intermediary migration pattern, and the peregrine falcon as a regular migrant, all followed, as expected, the spring snowmelt during their migrations. However, the owl stayed ahead, the buzzard stayed on, and the falcon stayed behind the spatio-temporal peak in snowmelt. Although none of the species avoided snow-covered areas, they presumably used snow presence as a cue to time their arrival at their breeding grounds. We show the importance of environmental cues for species with different migration patterns.

Features of the regime of drainage of longterm seasonally permafrost peat soils of Western Siberia

As a result of perennial (1980–2018) field and lysimetric studies, it was found that soil moisture in the presence of permafrost is determined by the amount of moisture consumed by soil and plants for evaporation, on the one hand, and the amount of precipitation in the spring and summer period thicker, on the other. The optimal depth of the groundwater table for the south of the Tyumen region lies within: annual grasses – 0,9–1,1, perennial grasses – 0,7–0,8 m. In order to eliminate the accumulation of excess moisture in the groundwater level from the end of the effective period in the frozen layer before the beginning of freezing, air temperatures are reduced to 1,6–2 m and maintained at this depth until spring snowmelt. During the period of snowmelt, they do not dump the melt water, but use it to raise groundwater to a depth of 0,8–1,1 m and maintain this level until the end of the effective air temperature period. The required depth of the groundwater table is ensured by using drainage in the marshes of low floodplain terraces and open channels on the peatlands of the watersheds.

Uncertainty of Vegetation Green-Up Date Estimated from Vegetation Indices Due to Snowmelt at Northern Middle and High Latitudes

Vegetation green-up date (GUD), an important phenological characteristic, is usually estimated from time-series of satellite-based normalized difference vegetation index (NDVI) data at regional and global scales. However, GUD estimates in seasonally snow-covered areas suffer from the effect of spring snowmelt on the NDVI signal, hampering our realistic understanding of phenological responses to climate change. Recently, two snow-free vegetation indices were developed for GUD detection: the normalized difference phenology index (NDPI) and normalized difference greenness index (NDGI). Both were found to improve GUD detection in the presence of spring snowmelt. However, these indices were tested at several field phenological camera sites and carbon flux sites, and a detailed evaluation on their performances at the large spatial scale is still lacking, which limits their applications globally. In this study, we employed NDVI, NDPI, and NDGI to estimate GUD at northern middle and high latitudes (north of 40° N) and quantified the snowmelt-induced uncertainty of GUD estimations from the three vegetation indices (VIs) by considering the changes in VI values caused by snowmelt. Results showed that compared with NDVI, both NDPI and NDGI improve the accuracy of GUD estimation with smaller GUD uncertainty in the areas below 55° N, but at higher latitudes (55°N-70° N), all three indices exhibit substantially larger GUD uncertainty. Furthermore, selecting which vegetation index to use for GUD estimation depends on vegetation types. All three indices performed much better for deciduous forests, and NDPI performed especially well (5.1 days for GUD uncertainty). In the arid and semi-arid grasslands, GUD estimations from NDGI are more reliable (i.e., smaller uncertainty) than NDP-based GUD (e.g., GUD uncertainty values for NDGI vs. NDPI are 4.3 d vs. 7.2 d in Mongolia grassland and 6.7 d vs. 9.8 d in Central Asia grassland), whereas in American prairie, NDPI performs slightly better than NDGI (GUD uncertainty for NDPI vs. NDGI is 3.8 d vs. 4.7 d). In central and western Europe, reliable GUD estimations from NDPI and NDGI were acquired only in those years without snowfall before green-up. This study provides important insights into the application of, and uncertainty in, snow-free vegetation indices for GUD estimation at large spatial scales, particularly in areas with seasonal snow cover.