Silvia Terzago

Changing Arctic snow cover: A review of recent developments and assessment of future needs for observations, modelling, and impacts

Snow is a critically important and rapidly changing feature of the Arctic. However, snow-cover and snowpack conditions change through time pose challenges for measuring and prediction of snow. Plausible scenarios of how Arctic snow cover will respond to changing Arctic climate are important for impact assessments and adaptation strategies. Although much progress has been made in understanding and predicting snow-cover changes and their multiple consequences, many uncertainties remain. In this paper, we review advances in snow monitoring and modelling, and the impact of snow changes on ecosystems and society in Arctic regions. Interdisciplinary activities are required to resolve the current limitations on measuring and modelling snow characteristics through the cold season and at different spatial scales to assure human well-being, economic stability, and improve the ability to predict manage and adapt to natural hazards in the Arctic region.

Precipitation in the Karakoram-Himalaya: a CMIP5 view

This work analyzes the properties of precipitation in the Hindu-Kush Karakoram Himalaya region as simulated by thirty-two state-of-the-art global climate models participating in the Coupled Model Intercomparison Project phase 5 (CMIP5). We separately consider the Hindu-Kush Karakoram (HKK) in the west and the Himalaya in the east. These two regions are characterized by different precipitation climatologies, which are associated with different circulation patterns. Historical model simulations are compared with the Climate Research Unit (CRU) and Global Precipitation Climatology Centre (GPCC) precipitation data in the period 1901–2005. Future precipitation is analyzed for the two representative concentration pathways (RCP) RCP 4.5 and RCP 8.5 scenarios. We find that the multi-model ensemble mean and most individual models exhibit a wet bias with respect to CRU and GPCC observations in both regions and for all seasons. The models differ greatly in the seasonal climatology of precipitation which they reproduce in the HKK. The CMIP5 models predict wetter future conditions in the Himalaya in summer, with a gradual precipitation increase throughout the 21st century. Wetter summer future conditions are also predicted by most models in the RCP 8.5 scenario for the HKK, while on average no significant change can be detected in winter precipitation for both regions. In general, no single model (or group of models) emerges as that providing the best results for all the statistics considered, and the large spread in the behavior of individual models suggests to consider multi-model ensemble means with extreme care.

Snowpack Changes in the Hindu Kush–Karakoram–Himalaya from CMIP5 Global Climate Models

AbstractThe Hindu Kush, Karakoram, and Himalaya (HKKH) mountain ranges feed the most important Asian river systems, providing water to about 1.5 billion people. As a consequence, changes in snow dynamics in this area could severely impact water availability for downstream populations. Despite their importance, the amount, spatial distribution, and seasonality of snow in the HKKH region are still poorly known, owing to the limited availability of surface observations in this remote and high-elevation area. This work considers global climate models (GCM) participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5) and analyzes how they represent current and future snowpack in the HKKH region in terms of snow depth and snow water equivalent. It is found that models with high spatial resolution (up to 1.25°) simulate a spatial pattern of the winter snowpack in greater agreement with each other, with observations, with reanalysis datasets, and with the orographic features of the region, compar...

Integration of satellite remote sensing data in ecosystem modelling at local scales: Practices and trends

Spatiotemporal ecological modelling of terrestrial ecosystems relies on climatological and biophysical Earth observations. Due to their increasing availability, global coverage, frequent acquisition and high spatial resolution, satellite remote sensing (SRS) products are frequently integrated to in situ data in the development of ecosystem models (EMs) quantifying the interaction among the vegetation component and the hydrological, energy and nutrient cycles. This review highlights the main advances achieved in the last decade in combining SRS data with EMs, with particular attention to the challenges modellers face for applications at local scales (e.g. small watersheds). We critically review the literature on progress made towards integration of SRS data into terrestrial EMs: (1) as input to define model drivers; (2) as reference to validate model results; and (3) as a tool to sequentially update the state variables, and to quantify and reduce model uncertainty. The number of applications provided in the literature shows that EMs may profit greatly from the inclusion of spatial parameters and forcings provided by vegetation and climatic-related SRS products. Limiting factors for the application of such models to local scales are: (1) mismatch between the resolution of SRS products and model grid; (2) unavailability of specific products in free and public online repositories; (3) temporal gaps in SRS data; and (4) quantification of model and measurement uncertainties. This review provides examples of possible solutions adopted in recent literature, with particular reference to the spatiotemporal scales of analysis and data accuracy. We propose that analysis methods such as stochastic downscaling techniques and multi-sensor/multi-platform fusion approaches are necessary to improve the quality of SRS data for local applications. Moreover, we suggest coupling models with data assimilation techniques to improve their forecast abilities. This review encourages the use of SRS data in EMs for local applications, and underlines the necessity for a closer collaboration among EM developers and remote sensing scientists. With more upcoming satellite missions, especially the Sentinel platforms, concerted efforts to further integrate SRS into modelling are in great demand and these types of applications will certainly proliferate.

Elevation-dependent warming in global climate model simulations at high spatial resolution

The enhancement of warming rates with elevation, so-called elevation-dependent warming (EDW), is one of the regional, still not completely understood, expressions of global warming. Sentinels of climate and environmental changes, mountains have experienced more rapid and intense warming trends in the recent decades, leading to serious impacts on mountain ecosystems and downstream. In this paper we use a state-of-the-art Global Climate Model (EC-Earth) to investigate the impact of model spatial resolution on the representation of this phenomenon and to highlight possible differences in EDW and its causes in different mountain regions of the Northern Hemisphere. To this end we use EC-Earth climate simulations at five different spatial resolutions, from

The European mountain cryosphere: a review of its current state, trends, and future challenges

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Snow water equivalent in the Alps as seen by gridded data sets, CMIP5 and CORDEX climate models

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