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Glacial-state greenhouse gas concentrations and Southern Hemisphere climate conditions persisted until ∼17.7 ka, when a nearly synchronous acceleration in deglaciation was recorded in paleoclimate proxies in large parts of the Southern Hemisphere, with many changes ascribed to a sudden poleward shift in the Southern Hemisphere westerlies and subsequent climate impacts. We used high-resolution chemical measurements in the West Antarctic Ice Sheet Divide, Byrd, and other ice cores to document a unique, ∼192-y series of halogen-rich volcanic eruptions exactly at the start of accelerated deglaciation, with tephra identifying the nearby Mount Takahe volcano as the source. Extensive fallout from these massive eruptions has been found >2,800 km from Mount Takahe. Sulfur isotope anomalies and marked decreases in ice core bromine consistent with increased surface UV radiation indicate that the eruptions led to stratospheric ozone depletion. Rather than a highly improbable coincidence, circulation and climate changes extending from the Antarctic Peninsula to the subtropics-similar to those associated with modern stratospheric ozone depletion over Antarctica-plausibly link the Mount Takahe eruptions to the onset of accelerated Southern Hemisphere deglaciation ∼17.7 ka.

An updated accumulation map for Greenland is presented on the basis of 39 new ice core estimates of accumulation, 256 ice sheet estimates from ice cores and snow pits used in previous maps, and reanalysis of time series data from 20 coastal weather stations. The period 1950-2000 is better represented by the data than are earlier periods. Ice-sheetwide accumulation was estimated based on kriging. The average accumulation (95% confidence interval, or ±2 times standard error) over the Greenland ice sheet is 30.0 ± 2.4 g cm -2 a-1, with the average accumulation above 2000-m elevation being essentially the same, 29.9 ± 2.2 g cm-2 a -1. At higher elevations the new accumulation map maintains the main features shown in previous maps. However, there are five coastal areas with obvious differences: southwest, northwest, and eastern regions, where the accumulation values are 20-50% lower than previously estimated, and southeast and northeast regions, where the accumulation values are 20-50% higher than previously estimated. These differences are almost entirely due to new coastal data. The much lower accumulation in the southwest and the much higher accumulation in the southeast indicated by the current map mean that long-term mass balance in both catchments is closer to steady state than previously estimated. However, uncertainty in these areas remains high owing to strong gradients in precipitation from the coast inland. A significant and sustained precipitation measurement program will be needed to resolve this uncertainty. Copyright 2009 by the American Geophysical Union.

An analysis of 96 snow pit and ice cores distributed over the Greenland ice sheet is used to determine the main drivers of variability in the preserved records of nitrate concentration. The data set provides samples from spatially distributed locations, allowing us to investigate the effect of snow accumulation rate, temperature, and sublimation on nitrate concentration. The mean ice sheet concentration in the dry snow zone (2000 ≥ mean annual sea level (masl)) is 132 ng g-1, ranging between 47 and 265 ng g -1 with a standard deviation of ±37 ng g-1. Nitrate flux varies between 1.1 and 14.7 μg cm-2 a-1 with a mean of 4 ± 2 μg cm-2 a-1. Large-scale spatial variability exists as a result of accumulation gradients, with concentration 5% greater in the northern plateau, yet flux over the northern plateau is 30% lower than the dry snow zone as a whole. While spatially, flux appears to be more dependent on accumulation, preservation of flux shows increasing dependence on concentration with increasing accumulation. The relationship between concentration and accumulation is nonlinear, showing less dependence in the low-accumulation regions versus high-accumulation regions. Accumulation alone is insufficient to account for the observed variability in nitrate flux in the low-accumulation regions, and evidence supports the need for additional components to a transfer function model for nitrate that includes photochemistry, temperature, and sublimation. Spatial variability across the ice sheet is nonuniform, and changes in nitrate concentration have occurred in some regions at a greater rate than others. While the data support that overall the ice sheet acts as an archive of paleoatmospheric concentration despite the effects of postdepositional processing, one needs to consider spatial variables to properly account for trends and variability in the records. This is tested by evaluating past spatial relationships and yields the result that the significant geographic shifts with respect to reactive N concentrations have occurred over the ice sheet in the past century. Copyright 2009 by the American Geophysical Union.

Nitrate records from six Greenland ice cores covering the period 1789 to 1995 show a significant correlation in concentration for averaging periods greater than 10 years, as well as an approximately 60% increase in average concentration during the last 75 years. Annual nitrate fluxes contain low-frequency trends driven primarily by changes in concentration, while higher-frequency variability is driven by changes in snow accumulation. Increases in concentration yield nearly 30% higher nitrate flux (2.5 to 3.2 μg m-2 yr-1) and an 11% increase in variability during the 1895 to 1994 period versus the prior 100 years. Nitrate trends in the cores during the last 100 years are also correlated with global nitrate emissions, with a highly significant average r value of 0.93 for the six cores. During the period of anthropogenic influence, nitrate is positively correlated with the North Atlantic Oscillation, while prior to that the correlation is negative, and less significant, suggesting a link between transport of anthropogenic emissions and the North Atlantic Oscillation. Significant preanthropogenic periodicities identified through singular spectrum analysis show decadal variability in the nitrate record leading to shifts as great as 30% from the mean state but none as great as the anthropogenic-driven deviation. Copyright 2006 by the American Geophysical Union.

In a yearlong investigation of the air-snow transfer function for nitrate (NO3-) at the Greenland Environmental Observatory, Summit (3203 m above sea level), surface snow concentrations measured every other day were compared with levels measured in 10 snow pits dug adjacent to accumulation stakes. Concentrations in the surface snow ranged from 0.4 to 34 μM with a mean of 2.9 ± 1.9 μM. Measured firn profiles in the snow pits had a maximum NO3- concentration of 12 μM and a mean of 2.7 ± 0.5 μM. Reconstructed profiles from surface snow observations and accumulation data closely matched the observed profiles. The small difference in preserved concentrations from observed surface snow concentrations gives evidence of only 7% postdepositional loss at this site (mean annual accumulation ∼23 g cm-2 yr-1). Removing the three highest outliers (which may originate from local sources) of surface snow concentration drops the mean to 2.7 μM, further demonstrating preservation. Results indicate that at this site accumulation is the most significant process affecting preservation of nitrate in the firm. Other rapid postdepositional processes may impact surface snow concentrations, but do not appear to significantly change the preserved record. The inverse analysis of converting preserved records to surface snow concentrations provides equivalent evidence of the same preservation. Copyright 2004 by the American Geophysical Union.

This paper presents Shyft, a novel hydrologic modelling software for streamflow forecasting targeted for use in hydropower production environments and research. The software enables the rapid development and implementation in operational settings, the capability to perform distributed hydrologic modelling with multiple model and forcing configurations. Multiple models may be built up through the creation of hydrologic algorithms from a library of well known routines or through the creation of new routines, each defined for processes such as: evapotranspiration, snow accumulation and melt, and soil water response. Key to the design of Shyft is an Application Programming Interface (api) that provides access to all components of the framework (including the individual hydrologic routines) via Python, while maintaining high computational performance as the algorithms are implemented in modern C++. The api allows for rapid exploration of different model configurations and selection of an optimal forecast model. Several different methods may be aggregated and composed, allowing direct intercomparison of models and algorithms. In order to provide an enterprise level software, strong focus is given to computational efficiency, code quality, documentation and test coverage. Shyft is released Open Source under the GNU Lesser General Public License v3.0 and available at https://gitlab.com/shyft-os, facilitating effective cooperation between core developers, industry, and research institutions. [ABSTRACT FROM AUTHOR]

Monte Carlo (MC) methods have been widely used in uncertainty analysis and parameter identification for hydrological models. The main challenge with these approaches is, however, the prohibitive number of model runs required to get an adequate sample size which may take from days to months especially when the simulations are run in distributed mode. In the past, emulators have been used to minimize the computational burden of the MC simulation through direct estimation of the residual based response surfaces. Here, we apply emulators of MC simulation in parameter identification for a distributed conceptual hydrological model using two likelihood measures, i.e. the absolute bias of model predictions (Score) and another based on the time relaxed limits of acceptability concept (pLoA). Three machine learning models (MLMs) were built using model parameter sets and response surfaces with limited number of model realizations (4000). The developed MLMs were applied to predict pLoA and Score for a large set of model parameters (95 000). The behavioural parameter sets were identified using a time relaxed limits of acceptability approach based on the predicted pLoA values; and applied to estimate the quantile streamflow predictions weighted by their respective Score. The three MLMs were able to adequately mimic the response surfaces directly estimated from MC simulations; and the models identified using the coupled ML emulators and the limits of acceptability approach have performed very well in reproducing the median streamflow prediction both during the calibration and validation periods. [ABSTRACT FROM AUTHOR]

We estimated the black carbon (BC) concentration over the Hindu Kush Himalayan region (HKH), its impact on snow-albedo reduction and sensitivity on annual glacier runoff over the identified glaciers. These estimates were based on free-running aerosol simulations (freesimu) and constrained aerosol simulations (constrsimu) from an atmospheric general circulation model, combined with numerical simulations of glacial mass balance model. BC concentration estimated from freesimu performed better over higher altitude (HA) HKH stations than that over lower altitude (LA) stations. The estimates from constrsimu mirrored well the measurements when implemented for LA stations. Estimates of the spatial distribution of BC concentration in the snowpack (BCc) over the HKH region led to identifying a hot-spot zone located around Manora peak. Among glaciers over this zone, BCc (> 60 µg kg-1) and BC-induced snow-albedo reduction (≈ 5 %) were estimated explicitly being high during the pre-monsoon for Pindari, Poting, Chorabari, and Gangotri glaciers (which are major sources of fresh water for the Indian sub-continent). The rate of increase of BCc in recent years (i.e. over the period 1961-2010) was, however, estimated being the highest for the Zemmu glacier. Sensitivity analysis with glacial mass balance model indicated the increase in annual runoff from debris-free glacier area due to BC-induced snow albedo reduction (SAR) corresponding to BCc estimated for the HKH glaciers was 4 %-18 %, with the highest being for the Milam and Pindari glacier. The rate of increase in annual glacier runoff per unit BC-induced percentage SAR was specifically high for Milam, Pindari, and Shunkalpa glacier. The source-specific contribution to atmospheric BC aerosols by emission sources led to identifying the potential emission source being primarily from the biofuel combustion in the Indo-Gangetic plain south to 30° N, but also from open burning in a more remote region north to 30° N. [ABSTRACT FROM AUTHOR]

Parameter uncertainty estimation is one of the major challenges in hydrological modelling. Here we present parameter uncertainty analysis of a recently released distributed conceptual hydrological model applied in the Nea catchment, Norway. Two variants of the generalized likelihood uncertainty estimation (GLUE) methodologies, one based on the residuals and the other on the limits of acceptability, were employed. Streamflow and remote sensing snow cover data were used in conditioning model parameters and in model validation. When using the GLUE limit of acceptability (GLUE LOA) approach, a streamflow observation error of 25 % was assumed. Neither the original limits, nor relaxing the limits up to a physically meaningful value, yielded a behavioural model capable of predicting streamflow within the limits in 100 % of the observations. As an alternative to relaxing the limits; the requirement for percentage of model predictions falling within the original limits was relaxed. An empirical approach was introduced to define the degree of relaxation. The result shows that snow and water balance related parameters induce relatively higher streamflow uncertainty than catchment response parameters. Comparable results were obtained from behavioural models selected using the two GLUE methodologies. [ABSTRACT FROM AUTHOR]