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This study assessed the climate conditions that caused the tropical Zongo Glacier (16° S, Bolivia) to reach its Little Ice Age (LIA) maximum extent in the late 17th century. We carried out sensitivity analyses of the annual surface mass balance to different physically coherent climate scenarios constrained by information taken from paleoclimate proxies and sensitivity studies of past glacier advances. These scenarios were constrained by a 1.1 K cooling and a 20% increase in annual precipitation compared to the current climate. Seasonal precipitation changes were constructed using shuffled input data for the model: measurements of air temperature and relative humidity, precipitation, wind speed, incoming short and longwave radiation fluxes, and assessed using a distributed energy balance model. They were considered plausible if conditions close to equilibrium glacier-wide mass balance were obtained. Results suggest that on top of a 1.1 K cooling and ${\sim }$20% increase in annual precipitation, only two seasonal precipitation patterns allow LIA equilibrium: evenly distributed precipitation events across the year and an early wet season onset.

Knowledge of the ice thickness distribution of glaciers and ice caps is an important prerequisite for many glaciological and hydrological investigations. A wealth of approaches has recently been presented for inferring ice thickness from characteristics of the surface. With the Ice Thickness Models Intercomparison eXperiment (ITMIX) we performed the first coordinated assessment quantifying individual model performance. A set of 17 different models showed that individual ice thickness estimates can differ considerably - locally by a spread comparable to the observed thickness. Averaging the results of multiple models, however, significantly improved the results: on average over the 21 considered test cases, comparison against direct ice thickness measurements revealed deviations on the order of 10 ± 24% of the mean ice thickness (1σ estimate). Models relying on multiple data sets - such as surface ice velocity fields, surface mass balance, or rates of ice thickness change - showed high sensitivity to input data quality. Together with the requirement of being able to handle large regions in an automated fashion, the capacity of better accounting for uncertainties in the input data will be a key for an improved next generation of ice thickness estimation approaches.

Andean glaciers are losing mass rapidly but a centennial‐scale context to those rates is lacking. Here we show the extent of >5,500 glaciers during the Little Ice Age chronozone (LIA; c. 1,400 to c. 1,850) and compute an overall area change of −25% from then to year 2000 at an average rate of −36.5 km2 yr−1 or −0.11% yr−1. Glaciers in the Tropical Andes (Peru, Bolivia) have depleted the most; median −56% of LIA area, and the fastest; median −0.16% yr−1. Up to 10 × acceleration in glacier area loss has occurred in Tropical mountain sub‐regions comparing LIA to 2,000 rates to post‐2000 rates. Regional climate controls inter‐regional variability, whereas local factors affect intra‐region glacier response time. Analyzing glacier area change by river basins and by protected areas leads us to suggest that conservation and environmental management strategies should be re‐visited as proglacial areas expand. Plain Language Summary: Andean glaciers are melting fast but how that rate compares in a longer‐term context is unknown. In this study we mapped the extent of >5,500 glaciers during the Little Ice Age, which was the last major glacial advance culminating about c. 150 years ago. We analyzed the change in glacier size and computed overall area change of −25% from the LIA to year 2000 at a rate of −36.5 km2 per year or −0.11% per year. Glaciers within Peru and Bolivia have shrunk the most by median −56% of LIA area, and the fastest by median −0.16% per year. We discuss that these glaciers are depleting and retreating due to climate change but that response is compounded by glacier size, shape and terminus environment effects. As glaciers melt they reveal proglacial landscapes that tend to be highly unstable, impacting water resources, natural hazards and terrestrial and aquatic ecology. Key Points: Little Ice Age (LIA) chronozone extent of >5,500 glaciers mapped from geomorphological evidenceOverall glacier area change of −25% to year 2000 at a rate of −36.5 km2 yr−1 or −0.11% yr−1Up to 10 × acceleration in glacier area loss for Tropical sub‐regions comparing LIA to 2,000 with post‐2000 rates [ABSTRACT FROM AUTHOR]

The impacts of the accelerated glacier retreat in recent decades on glacier runoff changes are still unknown in most Andean catchments, increasing uncertainties in estimating water availability. This particularly affects the outer tropics and Dry Andes, heavily impacted by prolonged droughts. Current global estimates overlook climatic and morphometric disparities, which significantly influence model parameters, among Andean glaciers. Meanwhile, local studies have used different approaches to estimate glacier runoff in a few catchments. Improving 21st-century glacier runoff projections relies on calibrating and validating models using corrected historical climate inputs and calibrated parameters across diverse glaciological zones. Here, we simulate glacier evolution and related runoff changes between the periods 2000–2009 and 2010–2019 across 786 Andean catchments (11 282 km2 of glacierized area, 11° N to 55° S) using the Open Global Glacier Model (OGGM). TerraClimate atmospheric variables were corrected using in situ data, getting a mean temperature bias by up to 2.1 °C and enhanced monthly precipitation. Glacier mass balance and volume were calibrated, where melt factor and the Glen A parameter exhibited significant alignment with varying environmental conditions. Simulation outcomes were validated against in situ data in three documented catchments (with a glacierized area > 8 %) and monitored glaciers. Our results at the Andes scale reveal an average reduction of 8.3 % in glacier volume and a decrease of 2.2 % in surface area between the periods 2000–2009 and 2010–2019. Comparing these two periods, glacier and climate variations have led to a 12 % increase in mean annual glacier melt (86.5 m3s-1) and a decrease in rainfall on glaciers of - 2 % (- 7.6 m3s-1) across the Andes, with both variables comprising the glacier runoff. We confirmed the utility of our corrected regional simulations of glacier runoff contribution at the catchment scale, where our estimations align with previous studies (e.g., Maipo 34° S, Chile) as well as provide new insights on the seasonal glaciers' largest contribution (e.g., La Paz 16° S, Bolivia) and new estimates of glacier runoff contribution (e.g., Baker 47° S, Chile). [ABSTRACT FROM AUTHOR]

Modeling the short‐term (<50 years) evolution of glaciers is difficult because of issues related to model initialization and data assimilation. However, this timescale is critical, particularly for water resources, natural hazards, and ecology. Using a unique record of satellite remote‐sensing data, combined with a novel optimisation and surface‐forcing‐calculation method within the framework of the deep‐learning‐based Instructed Glacier Model, we are able to ameliorate initialization issues. We thus model the committed evolution of all glaciers in the European Alps up to 2050 using present‐day climate conditions, assuming no future climate change. We find that the resulting committed ice loss exceeds a third of the present‐day ice volume by 2050, with multi‐kilometer frontal retreats for even the largest glaciers. Our results show the importance of modeling ice dynamics to accurately retrieve the ice‐thickness distribution and to predict future mass changes. Thanks to high‐performance GPU processing, we also demonstrate our method's global potential. Plain Language Summary: Modeling glaciers is highly challenging over the next few decades because setting up models correctly is a big issue. This is unfortunate, because we really want to know what is going to happen on that timescale, as it will directly affect our lives, homes and jobs. We present a new modeling approach, taking advantage of new data and machine‐learning methods, that allows us to set our model up much more effectively. We thus work out how much ice will be lost in the European Alps between now and 2050, even if the climate does not change further, and find that a third of the ice will be lost, come what may. Even the largest glacier fronts will retreat by several kilometers. We show that modeling glaciers properly, with a well‐set‐up model, is really important to make accurate predictions. Key Points: We present a novel, fast, accurate deep‐learning method for glacier model initialization and forward simulation at a regional scaleWe calculate glacier climatic surface elevation change without needing calibration across the whole of the European Alps for the first timeWe find committed ice loss in the European Alps to be 34% by 2050, rising to 46% with linear extrapolation of 2000–2020 mass‐balance trends [ABSTRACT FROM AUTHOR]