Robust uncertainty assessment of the spatio-temporal transferability of glacier mass and energy balance models

Energy and mass-balance modelling of glaciers is a key tool for climate impact studies of future glacier behaviour. By incorporating many of the physical processes responsible for surface accumulation and ablation, they offer more insight than simpler statistical models and are believed to suffer less from problems of stationarity when applied under changing climate conditions. However, this view is challenged by the widespread use of parameterizations for some physical processes which introduces a statistical calibration step. We argue that the reported uncertainty in modelled mass balance (and associated energy flux components) are likely to be understated in modelling studies that do not use spatio-temporal cross-validation and use a single performance measure for model optimization. To demonstrate the importance of these principles, we present a rigorous sensitivity and uncertainty assessment workflow applied to a modelling study of two glaciers in the European Alps, extending classical best guess approaches. The procedure begins with a reduction of the model parameter space using a global sensitivity assessment that identifies the parameters to which the model responds most sensitively. We find that the model sensitivity to individual parameters varies considerably in space and time, indicating that a single stated model sensitivity value is unlikely to be realistic. The model is most sensitive to parameters related to snow albedo and vertical gradients of the meteorological forcing data. We then apply a Monte Carlo multi-objective optimization based on three performance measures: model bias and mean absolute deviation in the upper and lower glacier parts, with glaciological mass balance data measured at individual stake locations used as reference. This procedure generates an ensemble of optimal parameter solutions which are equally valid. The range of parameters associated with these ensemble members are used to estimate the cross-validated uncertainty of the model output and computed energy components. The parameter values for the optimal solutions vary widely, and considering longer calibration periods does not systematically result in better constrained parameter choices. The resulting mass balance uncertainties reach up to 1300 kg m−2, with the spatial and temporal transfer errors having the same order of magnitude. The uncertainty of surface energy flux components over the ensemble at the point scale reached up to 50 % of the computed flux. The largest absolute uncertainties originate from the short-wave radiation and the albedo parameterizations, followed by the turbulent fluxes. Our study highlights the need for due caution and realistic error quantification when applying such models to regional glacier modelling efforts, or for projections of glacier mass balance in climate settings that are substantially different from the conditions in which the model was optimized.publishedVersio

Widespread greening suggests increased dry-season plant water availability in the Rio Santa valley, Peruvian Andes

In the semi-arid Peruvian Andes, the growing season is mostly determined by the timing of the onset and retreat of the wet season, to which annual crop yields are highly sensitive. Recently, local farmers in the Rio Santa basin (RSB) reported more erratic rainy season onsets and further challenges related to changes in rainfall characteristics. Previous studies based on local rain gauges, however, did not find any significant long-term rainfall changes, potentially linked to the scarce data basis and inherent difficulties in capturing the highly variable rainfall distribution typical for complex mountain terrain. To date, there remains considerable uncertainty in the RSB regarding changes in plant-available water over the last decades. In this study, we exploit satellite-derived information of high-resolution vegetation greenness as an integrated proxy to derive variability and trends of plant water availability. By combining MODIS Aqua and Terra vegetation indices (VIs), datasets of precipitation (both for 2000–2020) and soil moisture (since 2015), we explore recent spatio-temporal changes in the vegetation growing season. We find the Normalized Difference Vegetation Index (NDVI) to be coupled to soil moisture on a sub-seasonal basis, while NDVI and rainfall only coincide on interannual timescales. Over 20 years, we find significant greening in the RSB, particularly pronounced during the dry season (austral winter), indicating an overall increase in plant-available water over the past 2 decades. The start of the growing season (SOS) exhibits high interannual variability of up to 2 months compared to the end of the growing season (EOS), which varies by up to 1 month, therefore dominating the variability of the growing season length (LOS). The EOS becomes significantly delayed over the analysis period, matching the observed dry-season greening. While both in situ and gridded rainfall datasets show incoherent changes in annual rainfall for the region, Climate Hazards InfraRed Precipitation with Station data (CHIRPS) rainfall suggests significant positive dry-season trends for 2 months coinciding with the most pronounced greening. As the greening signal is strongly seasonal and reaches high altitudes on unglaciated valley slopes, we cannot link this signal to water storage changes on timescales beyond one rainy season, making interannual rainfall variability the most likely driver. Exploring El Niño–Southern Oscillation (ENSO) control on greening, we find an overall increased LOS linked to an earlier SOS in El Niño years, which however cannot explain the observed greening and delayed EOS. While our study could not corroborate anecdotal evidence of recent changes, we confirm that the SOS is highly variable and conclude that rainfed farming in the RSB would profit from future efforts being directed towards improving medium-range forecasts of the rainy season onset

The International Mountain Conference, Innsbruck, Austria, September 2019 (IMC2019): A Synthesis with Recommendations for Research

This paper presents a synthesis of the outcomes of sessions and recommendations for future research in mountain areas from the International Mountain Conference (IMC), held in Innsbruck, Austria, in September 2019. The thematic sections of the paper consider: first, the paleosciences, particularly archaeology; second, (bio)physical systems—the climate system, the cryo- and hydrosphere, and the biosphere—and their relationships with human systems; third, natural hazards and risks; and fourth, demographic and sociocultural trends, globalization (energy and transport networks, tourism, food supplies), policymaking, development, and research. Each section includes key literature relating to its theme, together with recommendations from the respective sessions. The paper concludes with a discussion and conclusions on the process of producing the synthesis, and its value for preparation and synthesis strategies for future conferences

Can we use TRMM-PR bright band heights to estimate the snow-rain transition in high mountain regions?

Field and modelling based research indicates that for tropical glaciers, variations in snow cover and the altitude of the snow line via albedo effects are among the most crucial factors to explain the differences in annual glacier mass balance variability. It is therefore essential to identify the height of the phase change during precipitation events and its coupling with glacier mass balance. This knowledge is also fundamental to assess possible future impacts of e.g. changing air temperatures on glacier mass balances at low latitudes. However, the knowledge on heights of phase changes and air temperature during precipitation events is severely limited by the small number of meteorological measurements at high altitudes in the tropics and the Himalaya. Additionally, their one-dimensional type of observation that cannot appropriately account for the variations along the vertical dimension. Remote sensing data are promising tools to fill these data gaps. Before using remote sensing products for studying surface processes, it is crucial to know their accuracies and limitations. Here, we use the the bright band (BB) calulation of the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) as provided in the product 2A23. The bright band is a horizontal layer of stronger radar reflectivity produced by the melting of precipitation at the level where solid precipitation turns into rain. It may be thus a good proxy for the snowline during precipitation events at high mountain regions. To our knowledge, the potential of this product in studies of glacier surface processes has not been further evaluated so far

Farmers’ first rain: investigating dry season rainfall characteristics in the Peruvian Andes

In the Peruvian Andes, the first light rainfalls towards the end of the dry season in August-September are known as pushpa. Softening soils and improving sowing conditions, these rains are crucial for planting dates and agricultural planning. Yet pushpa remains to date unexplored in the literature. This study uses observations and convection-permitting model simulations to describe the characteristics of pushpa in the Rio Santa valley (Peru). Comparing an observed pushpa case in August 2018 with a dry and wet event of the same season, we find pushpa to coincide with upper-level westerly winds that are otherwise characteristic for dry periods. These conditions impose an upper-level dry layer that favours small-scale, vertically-capped convection, explaining the low rainfall intensities that are reportedly typical for pushpa. Climatologically, we find 83% of pushpa-type events to occur under westerly winds, dominating in August, when 60% of the modelled spatial rainfall extent is linked to pushpa. Larger, more intense deep-convective events gradually increase alongside more easterly winds in September, causing the relative pushpa cloud coverage to drop to ̃20%. We note high inter-annual and -decadal variability in this balance between pushpa and intense convective rainfall types, with the spatial extent of pushpa rainfall being twice as high during 2000-2009 than for the 2010-2018 decade over the key sowing period. This result may explain farmers' perception in the Rio Santa valley, who recently reported increased challenges due to delayed but more intense pushpa rains before the rainy season start. We thus conclude that the sowing and germination season is crucially affected by the balance of pushpa-type and deep-convective rain, resulting in a higher probability for late first rains to be more intense

Future runoff from glacierized catchments in the Central Andes could substantially decrease

In Peru, about 50% of the energy is produced from hydropower plants. An important amount of this energy is produced with water from glaciated catchments. In these catchments river streamflow is furthermore needed for other socio-economic activities such as agriculture. However, the amount and seasonality of water from glacial melt is expected to undergo strong changes. As glaciers are projected to further decline with continued warming, runoff will become more and more sensitive to possible changes in precipitation patterns. Moreover, as stated by a recent study (Neukom et al., 2015), wet season precipitation sums in the Central Andes could decrease up to 19-33 % by the end of the 21st century compared to present-day conditions. Here, we investigate future runoff availability for selected glacierized catchments in the Peruvian Andes. In a first step, we apply a simplified energy balance and runoff model (ITGG-2.0-R) for current conditions

The energy and mass balance of Peruvian glaciers

Peruvian glaciers are important contributors to dry season runoff for agriculture and hydropower, but they are at risk of disappearing due to climate change. We applied a physically based, energy balance melt model at five on-glacier sites within the Peruvian Cordilleras Blanca and Vilcanota. Net shortwave radiation dominates the energy balance, and despite this flux being higher in the dry season, melt rates are lower due to losses from net longwave radiation and the latent heat flux. The sensible heat flux is a relatively small contributor to melt energy. At three of the sites the wet season snowpack was discontinuous, forming and melting within a daily to weekly timescale, and resulting in highly variable melt rates closely related to precipitation dynamics. Cold air temperatures due to a strong La Niña year at Shallap Glacier (Cordillera Blanca) resulted in a continuous wet season snowpack, significantly reducing wet season ablation. Sublimation was most important at the highest site in the accumulation zone of the Quelccaya Ice Cap (Cordillera Vilcanota), accounting for 81% of ablation, compared to 2%–4% for the other sites. Air temperature and precipitation inputs were perturbed to investigate the climate sensitivity of the five glaciers. At the lower sites warmer air temperatures resulted in a switch from snowfall to rain, so that ablation was increased via the decrease in albedo and increase in net shortwave radiation. At the top of Quelccaya Ice Cap warming caused melting to replace sublimation so that ablation increased nonlinearly with air temperature

Quantifying the controls of Peruvian glacier response to climate

Peruvian glaciers are important contributors to dry season runoff for agriculture and hydropower, but they are at risk of disappearing due to climate warming. Their energy balance and ablation characteristics have previously been studied only for individual glaciers, with no comparisons between regions. We applied the physically-based, energy balance melt component of the model Tethys-Chloris at five on-glacier meteorological stations: three in the Cordillera Blanca near Huaraz (with glaciers above ~4300 m a.s.l.), and two in the Cordillera Vilcanota east of Cusco (with glaciers above ~ 4800 m). The climate of these regions is strongly seasonal, with an austral summer wet season and winter dry season. Our results revealed that at most sites the energy available for melt is greatest in the wet season. This is a consequence of the dry season energy losses from the latent heat flux and net longwave radiation which counter-balance the high dry season net shortwave radiation, which otherwise dominates the energy balance. The sensible heat flux is a relatively small contributor to melt energy in both seasons. Comparison of the five sites suggests that there is more energy available for melt at a given elevation in the Cordillera Vilcanota compared to the Cordillera Blanca. At three of the sites the wet season snowpack was discontinuous, forming and melting within a daily to weekly timescale. Albedo and melt are thus highly variable in the wet season and closely related to the precipitation dynamics. At the highest site, in the accumulation zone of the Quelccaya Ice Cap, 81% of ablation was from sublimation. Sublimation was less important at the lower sites, but it reduces dry season melt. Correlation of the NOAA Oceanic El Niño Index (ONI) to the outputs of the two sites with the longest records revealed that the warmer wet season temperatures characteristic of a positive ONI were associated with a decreased albedo, greater net shortwave radiation, a more positive sensible heat flux and increased melt rates. Air temperature and precipitation inputs were also perturbed at all five sites to understand their sensitivity to climate change. Enhanced mass loss was predicted with a static increase of 2°C or more, even with a +30% precipitation increase, with the lower elevation Cordillera Blanca sites at risk of the greatest mass loss due to warming

Robust uncertainty assessment of the spatio-temporal transferability of glacier mass and energy balance models

Energy and mass-balance modelling of glaciers is a key tool for climate impact studies of future glacier behaviour. By incorporating many of the physical processes responsible for surface accumulation and ablation, they offer more insight than simpler statistical models and are believed to suffer less from problems of stationarity when applied under changing climate conditions. However, this view is challenged by the widespread use of parameterizations for some physical processes which introduces a statistical calibration step. We argue that the reported uncertainty in modelled mass balance (and associated energy flux components) are likely to be understated in modelling studies that do not use spatio-temporal cross-validation and use a single performance measure for model optimization. To demonstrate the importance of these principles, we present a rigorous sensitivity and uncertainty assessment workflow applied to a modelling study of two glaciers in the European Alps, extending classical best guess approaches. The procedure begins with a reduction of the model parameter space using a global sensitivity assessment that identifies the parameters to which the model responds most sensitively. We find that the model sensitivity to individual parameters varies considerably in space and time, indicating that a single stated model sensitivity value is unlikely to be realistic. The model is most sensitive to parameters related to snow albedo and vertical gradients of the meteorological forcing data. We then apply a Monte Carlo multi-objective optimization based on three performance measures: model bias and mean absolute deviation in the upper and lower glacier parts, with glaciological mass balance data measured at individual stake locations used as reference. This procedure generates an ensemble of optimal parameter solutions which are equally valid. The range of parameters associated with these ensemble members are used to estimate the cross-validated uncertainty of the model output and computed energy components. The parameter values for the optimal solutions vary widely, and considering longer calibration periods does not systematically result in better constrained parameter choices. The resulting mass balance uncertainties reach up to 1300 kg m−2, with the spatial and temporal transfer errors having the same order of magnitude. The uncertainty of surface energy flux components over the ensemble at the point scale reached up to 50 % of the computed flux. The largest absolute uncertainties originate from the short-wave radiation and the albedo parameterizations, followed by the turbulent fluxes. Our study highlights the need for due caution and realistic error quantification when applying such models to regional glacier modelling efforts, or for projections of glacier mass balance in climate settings that are substantially different from the conditions in which the model was optimized