Two-centre Convergent Close-Coupling Approach to Positron and Positronium Scattering

The two-centre convergent close-coupling (CCC) approach is used to calculate antihydrogen formation cross sections via positronium scattering on antiprotons. These cross sections have been expressed in terms of simple algebraic functions in accordance with their predicted threshold behaviours. The two-centre CCC method has also been upgraded to incorporate positron scattering on charged targets. This allowed for the cross sections to be determined for various transitions of positron scattering on a positive helium ion

Increasing freshwater and dissolved organic carbon flows to Northwest Alaska\u27s Elson lagoon

Manifestations of climate change in the Arctic are numerous and include hydrological cycle intensification and permafrost thaw, both expected as a result of atmospheric and surface warming. Across the terrestrial Arctic dissolved organic carbon (DOC) entrained in arctic rivers may be providing a carbon subsidy to coastal food webs. Yet, data from field sampling is too often of limited duration to confidently ascertain impacts of climate change on freshwater and DOC flows to coastal waters. This study applies numerical modeling to investigate trends in freshwater and DOC exports from land to Elson Lagoon in Northwest Alaska over the period 1981–2020. While the modeling approach has limitations, the results point to significant increases in freshwater and DOC exports to the lagoon over the past four decades. The model simulation reveals significant increases in surface, subsurface (suprapermafrost), and total freshwater exports. Significant increases are also noted in surface and subsurface DOC production and export, influenced by warming soils and associated active-layer thickening. The largest changes in subsurface components are noted in September, which has experienced a ∼50% increase in DOC export emanating from suprapermafrost flow. Direct coastal suprapermafrost freshwater and DOC exports in late summer more than doubled between the first and last five years of the simulation period, with a large anomaly in September 2019 representing a more than fourfold increase over September direct coastal export during the early 1980s. These trends highlight the need for dedicated measurement programs that will enable improved understanding of climate change impacts on coastal zone processes in this data sparse region of Northwest Alaska

Characterization of the spatial and temporal variability in pan-Arctic, terrestrial hydrology

Arctic hydrology represents an important component of the larger global climate system, and there are signs that significant water-cycle changes, involving complex feedbacks, have occurred. This dissertation explores the methods to estimate components of the arctic hydrological cycle, the numerous biases and uncertainties associated with the techniques, and suggestions for future research needs. The studies described here focus on quantitative models and methods for predicting the spatial and temporal variability in pan-Arctic hydrology. This dissertation discusses pan-Arctic water budgets drawn from a hydrological model which is appropriate for applications across the terrestrial Arctic. Including effects from soil-water phase changes results in increases in simulated annual runoff of 7% to 27%. A sensitivity analysis reveals that simulated runoff is far more sensitive to the time-varying climate drivers than to parameterization of the landscape. When appropriate climate data are used, the Pan-Arctic Water Balance Model (PWBM) is able to capture well the variability in seasonal river discharge at the scale of arctic sea basins. This dissertation also demonstrated a method to estimate snowpack thaw timing from radar data. Discrepancies between thaw timing inferred from the microwave backscatter data and the hydrological model are less than one week. The backscatter signal-to-noise values are highest in areas of higher seasonal snow accumulation, low to moderate tree cover and low topographic complexity. An evaluation of snow water equivalent (SWE) estimates drawn from land surface models and microwave remote sensing data suggests that simulated SWE from a hydrological model like PWBM, when forced with appropriate climate data, is far superior to current snow mass estimate derived from passive microwave data. Biases arising from interpolations from sparse, uneven networks can be significant. A bias of well over +10 mm yr-1 was found in the early network representations of spatial precipitation across Eurasia. When examining linkages between precipitation and river discharge, these biases limit our confidence in the accuracy of historical precipitation reconstructions. This dissertation assess our current capabilities in estimating components of arctic water cycle and reducing the uncertainties in predictions of arctic climate change

Effects of Uncertainty in Climate Inputs on Simulated Evapotranspiration and Runoff in the Western Arctic

Hydrological models require accurate precipitation and air temperature inputs in order to adequately depict water fluxes and storages across Arctic regions. Biases such as gauge undercatch, as well as uncertainties in numerical weather prediction reanalysis data that propagate through water budget models, limit the ability to accurately model the terrestrial arctic water cycle. A hydrological model forced with three climate datasets and three methods of estimating potential evapotranspiration (PET) was used to better understand the impact of these processes on simulated water fluxes across the Western Arctic Linkage Experiment (WALE) domain. Climate data were drawn from the NCEP–NCAR reanalysis (NNR) (NCEP1), a modified version of the NNR (NCEP2), and the Willmott–Matsuura (WM) dataset. PET methods applied in the model were Hamon, Penman–Monteith, and Penman–Monteith using adjusted vapor pressure data. High vapor pressures in the NNR lead to low simulated evapotranspiration (ET) in model runs using the Penman–Monteith PET method, resulting in increased runoff. Annual ET derived from simulations using Penman–Monteith PET was half the magnitude of ET simulated when the Hamon method was used. Adjustments made to the reanalysis vapor pressure data increased the simulated ET flux, reducing simulated runoff. Using the NCEP2 or WM climate data, along with the Penman–Monteith PET function, results in agreement to within 7% between the simulated and observed runoff across the Yukon River basin. The results reveal the high degree of uncertainty present in climate data and the range of water fluxes generated from common model drivers. This suggests the need for thorough evaluations of model requirements and potential biases in forcing data, as well as corroborations with observed data, in all efforts to simulate arctic water balances

Deep learning estimation of northern hemisphere soil freeze-thaw dynamics using satellite multi-frequency microwave brightness temperature observations

Satellite microwave sensors are well suited for monitoring landscape freeze-thaw (FT) transitions owing to the strong brightness temperature (TB) or backscatter response to changes in liquid water abundance between predominantly frozen and thawed conditions. The FT retrieval is also a sensitive climate indicator with strong biophysical importance. However, retrieval algorithms can have difficulty distinguishing the FT status of soils from that of overlying features such as snow and vegetation, while variable land conditions can also degrade performance. Here, we applied a deep learning model using a multilayer convolutional neural network driven by AMSR2 and SMAP TB records, and trained on surface (~0–5 cm depth) soil temperature FT observations. Soil FT states were classified for the local morning (6 a.m.) and evening (6 p.m.) conditions corresponding to SMAP descending and ascending orbital overpasses, mapped to a 9 km polar grid spanning a five-year (2016–2020) record and Northern Hemisphere domain. Continuous variable estimates of the probability of frozen or thawed conditions were derived using a model cost function optimized against FT observational training data. Model results derived using combined multi-frequency (1.4, 18.7, 36.5 GHz) TBs produced the highest soil FT accuracy over other models derived using only single sensor or single frequency TB inputs. Moreover, SMAP L-band (1.4 GHz) TBs provided enhanced soil FT information and performance gain over model results derived using only AMSR2 TB inputs. The resulting soil FT classification showed favorable and consistent performance against soil FT observations from ERA5 reanalysis (mean percent accuracy, MPA: 92.7%) and in situ weather stations (MPA: 91.0%). The soil FT accuracy was generally consistent between morning and afternoon predictions and across different land covers and seasons. The model also showed better FT accuracy than ERA5 against regional weather station measurements (91.0% vs. 86.1% MPA). However, model confidence was lower in complex terrain where FT spatial heterogeneity was likely beneath the effective model grain size. Our results provide a high level of precision in mapping soil FT dynamics to improve understanding of complex seasonal transitions and their influence on ecological processes and climate feedbacks, with the potential to inform Earth system model predictions

Divergence in seasonal hydrology across northern Eurasia: Emerging trends and water cycle linkages

Discharge from large Eurasia rivers increased during the 20th century, yet much remains unknown regarding details of this increasing freshwater flux. Here, for the three largest Eurasian basins (the Ob, Yenisei, and Lena) we examine the nature of annual and seasonal discharge trends by investigating the flow changes along with those for precipitation, snow depth, and snow water equivalent. On the basis of a multiperiod trend analysis and examination of station data, we propose two characteristic regimes to explain the long‐term discharge increase from these large Eurasian rivers. Over the early decades from approximately 1936 to 1965, annual precipitation correlates well with annual discharge, and positive discharge trends are concurrent with summer/fall discharge increases. The latter decades were marked by a divergence between winter/spring flows, which increased, amid summer/fall discharge declines. A comparison of cold season precipitation (CSP) and spring discharge trends across subbasins of the Ob, Yenisei, and Lena shows limited agreement with one precipitation data set but good agreement (R2 \u3e 0.90) when a second is used. While natural variability in the Arctic system tends to mask these emerging trends, spatial and temporal changes can generally be characterized by increased solid precipitation, primarily to the north, along with a drier hydrography during the warm season