Annotation of the results obtained in 2023
At this stage of the project, the development of parameterization of mountain glaciation was carried out, intended for calculating the glacial component of runoff into the Global Ocean within the framework of the Earth System Model of the INM RAS. The core of the parameterization is the so-called “minimal model” of Oerlemans [Oerlemans, 2008; Toropov et al., 2023], and the “periphery” is a software block that allows you to calculate the components of the mass balance of a mountain glacier using meteorological data (observations, reanalyses, or data from the global climate models). This block includes an algorithm for calculating the orographic component of precipitation, a procedure for recalculating incoming short-wave radiation on the glacier surface, a scheme for calculating turbulent fluxes of heat and moisture based on the Monin-Obukhov similarity theory, and a snow cover model developed for high-mountain conditions. During the reporting year, based on the first version of the developed parameterization, calculations of the components of the mass balance and dynamics of the test glaciers of the Central Caucasus (Dzhankuat and Garabashi) were performed. In this framework, the total amount of precipitation on the surfaces of the Dzhankuat and Garabashi glaciers was calculated for the period 1981 – 2020 using a previously created orographic precipitation model (Toropov et. al., 2023). It is shown that the orographic precipitation model captures the main features of the spatio-temporal distribution of precipitation in the Caucasus using the example of Elbrus, as well as the area of the Dzhankuat glacier. These data became the basis for a fairly successful assessment of the accumulation layer on the tested glaciers. The correlation coefficient between the model and actually measured annual accumulation layer is 0.68. The magnitude of the negative accumulation trend, according to observational data, was 10 mm.w.e./year. It is associated with a general trend of decreasing annual precipitation in the Central Caucasus and is confirmed both by meteorological observations at surrounding weather stations and by the results of an analysis of the Elbrus ice core [Toropov et al., 2022]. On average, over the period under consideration, the model error is only 10 mm, which can be considered a good result. Another task was to improve the glacier ablation calculation block. This block was supplemented with an algorithm for the distribution of incoming solar radiation depending on the slope exposure, its steepness and horizon closure based on the well-known Kondratiev formula, with some modifications used in [Muller M. D., Scherer D., 2005]. To calculate turbulent fuxes on the glacial surface, the standard approximation of the surface layer and the Monin-Obukhov similarity theory were used [Monin A, Yaglom A., 1971], while the standard Businger-Dyer formulation was used for stability functions [Businger et. al., 1971; Dyer, 1974]. The model reproduced a statistically significant positive ablation trend on both glaciers; the correlation coefficient between the model and field data series is 0.65. However, according to observational data, the growth rate is 20 mm/year, while according to the modeling results it is half as much (~10 mm/year). On average, the model error in ablation is 350±150 mm w. e., i.e., close to the value of interannual variability (Toropov et al., 2018). The increase in ablation is determined by an increase in the radiation balance over the surfaces of the Garabashi and Dzhankuat glaciers, which, in turn, is associated with a trend towards a decrease in the cloudiness associated with an increase in the frequency of anticyclones in warm season (Toropov et. al., 2019). Ablation errors, which were identified at this stage of development of the proposed parameterization, are apparently associated with underestimation of the blocking effect of glacial moraines [Rybak et al., 2022]. Based on mass balance calculations, the dynamics of the length of the Dzhankuat and Garabashi glaciers was modeled for the period 1985–2020 using the Oerlemans' "minimal model" and methods described above. For the same period, calculations were performed using measured mass balance values. It is shown that the average annual retreat of the glacier tongue for this period was 13 m/year, which is in good agreement with field data and satellite information. During the period of maximum melting (2000–2020), the rate of retreat of the Dzhankuat and Garabashi glaciers increased to 20 m/year. These results show that the minimal Oerlemans model as a basis of parameterization of mountain glaciation is well suited to the task of modeling mountain glaciation within Earth System Models. Also, during the reporting year, a snow cover model of intermediate complexity was developed, based on the numerical solution of the one-dimensional heat equation. A distinctive feature of the created snow cover model for mountainous areas is an algorithm for taking into account the rate of sublimation of ice crystals during snowstorms based on the approach (Bintanja, 2001), according to which this process is determined by the moisture deficit, as well as the degree of turbulence of the atmosphere in the surface layer. Comparison of numerical experiments with observational data on Elbrus showed that in the situations with a combination of low humidity and high wind speed, the contribution of snowstorm sublimation to both the heat balance and the mass balance of the snow cover can be decisive, reaching 230 W/m2, and exceeds other components of the heat balance. In such cases, underestimation of heat consumption on snow sublimation can lead to an underestimation of the snow surface temperature by 4-5 °C. The quality of snow surface temperature simulation was assessed based on comparison with radiation temperature obtained from the outgoing long-wave radiation flux (with emissivity coefficient of 0.98), as well as with the results of the SPONSOR model of a higher level of complexity. The average temperature error was ±1°C, and the correlation coefficients between the tested model series and observations were 0.87 and 0.92 for the SPONSOR model and the developed model, respectively. An analysis of unique meteorological observations which were carried out in the high-mountainous part of the Elbrus Garabashi glacier and covered the entire accumulation season of 2021–2022, was performed. Detailed data on temperature, humidity, wind, snowstorm and radiation regimes at an altitude of more than 4700 m above sea level were obtained. Typical values and basic statistics of radiation fluxes and basic meteorological quantities were obtained. These data will be used to validate the meteorological block of the developed parameterization of mountain glaciation. It is also shown that the ERA5 reanalysis successfully reproduces the main characteristics of the meteorological regime in high mountain conditions, which generally allows its data to be used as input for the developed parameterization. An automatic weather station (AWS) which operated from June 17 to August 24, 2023 in wireless data transfer mode was installed for the first time on the Mikelcheran glacier (northern slope of Elbrus) in the altitude range of 3800-4300 meters during the expedition carried out on June 8 - 17, 2023. It is important to note that on the Garabashi glacier, meteorological measurements were carried out at approximately the same altitude (3950 m above sea level) and synchronously (from June 21 to August 31, 2023). Both AWSs operated with a time interval of 1 minute and included measurements of the components of the radiation balance at a height of 1 - 1.5 meters above the surface of the glacier, basic meteorological parameters using the Hobo AWS on the Michelcheran glacier and Campbell on the Garabashi glacier, the melting layer using the Sonic Ranger acoustic sensor on Garabashi glacier, as well as the temperature profile in a two-meter snow layer with a vertical interval of 0.1 m using a GeoPrecission thermocouple. All information received will be used to test the developed snow cover model as field data. At the moment, there are no analogues of such high-mountain observations in Russia.

 

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