Abstract
Abstract. Short-term glacier variations can be important for water supplies or hydropower production, and glaciers are important indicators of climate change. This is why the interest in near-real-time mass balance nowcasting is considerable. Here, we address this interest and provide an evaluation of continuous observations of point mass balance based on online cameras transmitting images every 20 min. The cameras were installed on three Swiss glaciers during summer 2019, provided 352 near-real-time point mass balances in total, and revealed melt rates of up to 0.12 m water equivalent per day (mw.e.d-1) and of more than 5 mw.e. in 81 d. By means of a particle filter, these observations are assimilated into an ensemble of three TI (temperature index) and one simplified energy-balance mass balance models. State augmentation with model parameters is used to assign temporally varying weights to individual models. We analyze model performance over the observation period and find that the probability for a given model to be preferred by our procedure is 39 % for an enhanced TI model, 24 % for a simple TI model, 23 %, for a simplified energy balance model, and 14 % for a model employing both air temperature and potential solar irradiation. When compared to reference forecasts produced with both mean model parameters and parameters tuned on single mass balance observations, the particle filter performs about equally well on the daily scale but outperforms predictions of cumulative mass balance by 95 %–96 %. A leave-one-out cross-validation on the individual glaciers shows that the particle filter is also able to reproduce point observations at locations not used for model calibration. Indeed, the predicted mass balances is always within 9 % of the observations. A comparison with glacier-wide annual mass balances involving additional measurements distributed over the entire glacier mostly shows very good agreement, with deviations of 0.02, 0.07, and 0.24 mw.e.
Highlights
Switzerland has lost already more than a third of its glacier volume since the 1970s (Fischer et al, 2015), glaciers are currently melting at about −0.6 m w.e. a−1 on average (Sommer et al, 2020), and it is expected that glaciers will continue to lose mass (Jouvet et al, 2011; Salzmann et al, 2012; Beniston et al, 2018; Zekollari et al, 2019)
We are not aware of glacier mass balance studies that have applied a multi-model ensemble based on a particle filter with the resampling methods we propose, multi-model particle filters have been used for other applications (e.g., Kreucher et al, 2004; Ristic et al, 2004; Saucan et al, 2013; Wang et al, 2016)
The camera observations were assimilated into the model ensemble by using a developed particle filtering scheme
Summary
Since glaciers are important for the supply of drinking water or for irrigation and electricity production, there is high interest in near-real-time glacier mass balance information. Such information has become important in the context of public outreach, e.g., for demonstrating the consequences of climate change (e.g., Euronews, 2019; Science Magazine, 2019). A glacier mass balance nowcasting framework assimilating relevant observations could deliver these near-realtime mass balances whenever required. For example, for the mass balance of the Greenland Ice Sheet (Fettweis et al, 2013; NSIDC, 2020a), for snow (NSIDC, 2020b; SLF, 2020) or for hydrological
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