Isotopic measurements showed that N ₂ O production during drought is unexpectedly dominated by denitrification of organic nitrogen.

Glacio-hydrological models combine both glacier and catchment hydrology modeling and are used to assess the hydrological response of high-mountain glacierized catchments to climate change. To capture the uncertainties from these model combinations, it is essential to compare the outcomes of several model entities forced with the same climate projections. For the first time, we compare the results of two completely independent glacio-hydrological models: (i) HQsim-GEM and (ii) AMUNDSEN. In contrast to prevailing studies, we use distinct glacier models and glacier initialization times. At first glance, the results achieved for future glacier states and hydrological characteristics in the Rofenache catchment in Ötztal Alps (Austria) appear to be similar and consistent, but a closer look reveals clear differences. What can be learned from this study is that low-complexity models can achieve higher accuracy in the calibration period. This is advantageous especially when data availability is weak, and priority is given to efficient computation time. Furthermore, the time and method of glacier initialization play an important role due to different data requirements. In essence, it is not possible to make conclusions about the model performance outside of the calibration period or more specifically in the future. Hence, similar to climate modeling, we suggest considering different modeling approaches when assessing future catchment discharge or glacier evolution. Especially when transferring the results to stakeholders, it is vital to transparently communicate the bandwidth of future states that come with all model results.

This article presents analyses of retrospective seasonal forecasts of snow accumulation. Re-forecasts with 4 months' lead time from two coupled atmosphere–ocean general circulation models (NCEP CFSv2 and MetOffice GloSea5) drive the Alpine Water balance and Runoff Estimation model (AWARE) in order to predict mid-winter snow accumulation in the Inn headwaters. As snowpack is hydrological storage that evolves during the winter season, it is strongly dependent on precipitation totals of the previous months. Climate model (CM) predictions of precipitation totals integrated from November to February (NDJF) compare reasonably well with observations. Even though predictions for precipitation may not be significantly more skilful than for temperature, the predictive skill achieved for precipitation is retained in subsequent water balance simulations when snow water equivalent (SWE) in February is considered. Given the AWARE simulations driven by observed meteorological fields as a benchmark for SWE analyses, the correlation achieved using GloSea5-AWARE SWE predictions is r = 0.57. The tendency of SWE anomalies (i.e. the sign of anomalies) is correctly predicted in 11 of 13 years. For CFSv2-AWARE, the corresponding values are r = 0.28 and 7 of 13 years. The results suggest that some seasonal prediction of hydrological model storage tendencies in parts of Europe is possible.

Biogeochemical processes in soils largely control the atmospheric mixing ratio of nitrous oxide (N2O). The growing use of nitrogen (N) fertilizer in agriculture drives anthropogenic N2O emissions, which currently surpass projections with some of the highest emissions. In order to adapt mitigation strategies and to model the future N cycle it is crucial to fully understand N2O emission pathways in a changing climate. The underlying processes, attributed to microbial transformation of N, primarily occur via the oxic nitrification and anoxic denitrification pathways. These processes depend greatly on soil, plant and ecosystem properties, which in turn rely on meteorological drivers (e.g. air temperature and precipitation). This means that the many environmental factors that drive microbial activity and N2O emissions in soils are vulnerable to climate change, including extreme events such as droughts. Consequently, the rates of nitrification and denitrification are expected to be strongly impacted by changing climatic conditions, which could also alter the N2O production and consumption dynamics across the soil profile. This study aims to understand how N2O production and consumption pathways respond to the individual and combined effects of warming, elevated atmospheric CO2 concentration, and drought-rewetting events in managed mountain grassland. For the first time, we use online, in-situ stable isotopic measurements of both surface N2O emissions and of N2O across the soil profile to distinguish pathways for N2O production and consumption. Different modeling approaches will be used to reconstruct production and consumption dynamics from soil gas isotopic measurements, and to upscale results to examine global relevance.

Soils are the dominant global source of the important greenhouse gas nitrous oxide (N2O). The anthropogenic input of nitrogen (N) into soil ecosystems increases the rate of soil N cycling, and thus enhances soil N2O emissions. N2O is produced during microbial N transformation processes, mainly via oxic nitrification and anoxic denitrification processes. These predominant pathways depend heavily on soil environmental conditions, such as soil moisture, aeration and substrate availability, which are modulated by weather and climate conditions, atmospheric composition and land use. Consequently, N2O emission rates and pathways are likely to be affected by future global changes in climate and atmospheric composition. However, the combined effects of elevated carbon dioxide (eCO2) and elevated air temperature on both N2O emission rates and pathways are unclear, as the effects can be synergistic, antagonistic or additive, and they can be further influenced by additional interacting disturbances (e.g. summer drought).Here we test how soil N2O fluxes and emission pathways respond to environmental changes in a multifactorial climate manipulation experiment, combining warming and eCO2, as well as precipitation manipulation to simulate an extreme drought during the growing season in a managed montane grassland. For the first time, we combine in-situ surface N2O flux measurements with online high-time resolution isotopic measurements, soil N2O isotope depth profiles, molecular microbial ecology, and complementary soil and microclimate measurements. Under future global change conditions, we expect increasing N2O emission rates, as well as an increasing importance of denitrification, due to the effect of large emission pulses following rewetting. In addition, we hypothesize that drought effects overrule other environmental change factors. Our results will provide an unprecedented insight into the effects of global changes on soil N dynamics and soil N2O emissions in managed montane grasslands. Furthermore, these findings will help to improve the modelling of N dynamics at the atmosphere-biosphere interface, which will be used to derive soil N2O production and consumption pathways, based on soil N2O isotope measurements, and to upscale the results to examine their potential global relevance.

This article presents analyses of retrospective seasonal forecasts of snow accumulation. Re-forecasts with 4 months lead time from two coupled atmosphere–ocean general circulation models (NCEP CFSv2 and MetOffice GloSea5) drive the Alpine Water balance and Runoff Estimation model (AWARE) in order to predict mid-winter snow accumulation in the Inn headwaters. As the snowpack is a hydrological storage that evolves during the winter season, it is strongly dependent on precipitation totals of the previous months. Climate model (CM) predictions of precipitation totals integrated from November to February (NDJF) compare reasonably well with observations. This predictive skill is retained in subsequent water balance simulations when snow water equivalent (SWE) in February is considered. Given the AWARE simulations driven by observed meteorological fields as a benchmark for SWE analyses, the correlation achieved using GloSea5-AWARE SWE predictions is r = 0.57. The tendency of SWE anomalies (i.e. the sign of anomalies) is correctly predicted in 11 of 13 years. For CFSv2, the corresponding values are r = 0.28 and 7 of 13 years. The results suggest that some seasonal predictions may be capable of predicting tendencies of hydrological model storages in parts of Europe.