Do we model what we measure?

Abstract/Description

Mountain regions are essential for understanding Earth’s climatic history, as their cycles of glacial advance and retreat have shaped landscapes, ecosystems, and regional climates during the Quaternary, leaving behind palaeoglacier records that reveal past climate dynamics. These glacier records are particularly important for understanding regional and local climate variations, as mountain glaciers respond sensitively to climatic changes, highlighting the importance of studying their past glaciation. This higher sensitivity is evident in the timing of maximum glacial extent (i.e. local Last Glacial Maximum, LLGM) in mountains, which often occurred outside the global LGM (26–19 kyr BP). However, existing global palaeoglacier databases (e.g., Ehlers and Gibbard, 2004; Ehlers et al., 2011) have not been updated to incorporate glacier extensions reconstructed in the last decade.

To address this gap, we present a new open-access global geodatabase of mountain glacier extents for the LGM. This synthesis integrates ice-extent reconstructions from 213 studies across 271 mountain ranges globally, standardising over 16,331 individual glacier reconstructions into a digital geodatabase covering the period 57-14 kyr BP. We implemented a hierarchical mountain range classification system, compiled metadata from each publication, and linked each reconstruction to its original sources. This effort has updated the state of knowledge in 157 mountain ranges, added over 9,450 new glacier reconstructions, and identified a gap in research in 114 mountain ranges where no updated reconstructions appear to have been produced in at least 13 years.

Our geodatabase is a powerful resource for investigating regional past climate variability, mountain landscape evolution, and ecological impacts of glaciations. It provides glacier masks for validating and refining climate-glacier modelling and offers spatial boundaries for palaeoecological reconstructions of mountain ecosystems. Furthermore, it identifies research gaps and understudied regions, guiding future work in Quaternary science. We anticipate releasing the database soon with the corresponding publication and website, along with detailed methodology and guidelines for further use.

Abstract/Description

Cold deserts, characterized by rugged terrain and extreme temperatures, often lack proper on-ground meteorological stations. As a result, climate studies in these regions predominantly rely on satellite data, which frequently overestimate actual conditions on the ground. However, satellite obtained climate data models must be validated against ground-based observations to ensure accuracy and reliability. This study utilizes long-term meteorological data of approximately 30 years, from an on-ground station in the Indian cold desert, with high accuracy and reliability, to analyze significant climatic trends. The results indicate a rise in temperature, a decline in snowfall, and erratic rainfall patterns. Through time series analysis of climatic parameters such as temperature, wind speed, and snowfall, this study examines whether residents’ observed climate changes align with the actual climate trends. Moreover, the procurement of authentic ground-based data is expensive and often unavailable, posing a significant challenge for climate research in cold desert regions. This underscores the need for reliable field data to validate climate models and assess if they accurately reflect real-life conditions in these vulnerable regions. The findings emphasize the need for improved climate models that integrate climate drivers to ensure accurate climate projections for these extreme condition areas.

Abstract/Description

Glaciers are crucial components of the Earth’s climate system and serve as indicators of climate change. Their substantial mass loss due to global warming significantly contributes to sea-level rise and impacts regional hydrology, downstream ecosystems and settlements. Despite considerable advancements in observational and modelling techniques, accurate quantification of glacier responses to climate change and prediction of their future behaviour remains challenging, particularly in regions characterized by rapidly changing glaciers and complex topography. Remote sensing data is widely used for direct glacial mass change estimation and as input for mountain glacier models, offering global observations with relatively regular time steps and can be helpful for the representation of spatial variability within individual glaciers due to multiple observational points. However, remote sensing-derived datasets often suffer from scale mismatches, uncertainties, and significant errors in regions with steep topography and spatially heterogeneous glacial dynamics, limiting their direct use for model forcing and validation. Therefore, one of the key limitations in this field remains the availability of high-resolution regional datasets that can be used for glacial models. In this study, we investigate the application of remote sensing data downscaling techniques to improve spatial and temporal resolution of glacial mass balance estimates. We focus on the integration of relatively high-resolution surface elevation changes derived from satellite altimetry with coarse-resolution mass changes inferred from satellite gravimetry data to localize mass changes. This study mainly focuses on the glaciers of the Patagonia region. This region, characterized by rapid glacier retreat and complex climatic influences, serves as an ideal case study for integrating multiple satellite datasets and regional models. Preliminary results highlight the potential of enhanced data integration techniques to resolve sub-regional mass changes and improve model-data comparisons in glaciological studies. The potential outcomes of this work aim to benefit the glacial model forcing, parameterization, and data assimilation by providing a refined glacial mass balance dataset and a transferable framework for application in other regions.

Abstract/Description

Research on water resources in high mountain regions often faces the challenge of integrating scarce and diverse data sets. For my PhD, I combine field observations with global and regional model-based and remote sensing datasets to simulate glacio-hydrological processes in Central Asia from the 1980s to 2100. My glacio-hydrological model is forced with reanalysis data and downscaled GCM projections, while calibration relies on a variety of glaciological and hydrological datasets, including integrated point measurements, UAV- and satellite-based geodetic mass balances, and discharge estimates from different methods.
The available data come from a variety of sources, including Soviet-era records, fragmented post-independence datasets, and measurements from my own field campaigns in Kyrgyzstan, Uzbekistan, and Mongolia. These datasets vary widely in temporal coverage, measurement techniques, and technical standards, leading to significant uncertainties.
This talk will highlight the importance of field observations for reducing uncertainties in large-scale model inputs, while also discussing how meso- and macro-scale models are sensitive to uncertainties in observational data. Examples from several study sites in the Tian Shan will illustrate these challenges and provide a basis for discussion on how to effectively integrate field data in modeling high mountain hydrology.