The effects of assimilating a sub-grid-scale sea ice thickness distribution in a new Arctic sea ice data assimilation system

In the past decade groundbreaking new satellite observations of the Arctic sea ice cover have been made, allowing researchers to understand the state of the Arctic sea ice system in greater detail than before. The derived estimates of sea ice thickness are useful but limited in time and space. In this study the first results of a new sea ice data assimilation system are presented. Observations assimilated (in various combinations) are monthly mean sea ice thickness and monthly mean sea ice thickness distribution from CryoSat-2 and NASA daily Bootstrap sea ice concentration. This system couples the Centre for Polar Observation and Modelling's (CPOM) version of the Los Alamos Sea Ice Model (CICE) to the localised ensemble transform Kalman filter (LETKF) from the Parallel Data Assimilation Framework (PDAF) library. The impact of assimilating a sub-grid-scale sea ice thickness distribution is of particular novelty. The sub-grid-scale sea ice thickness distribution is a fundamental component of sea ice models, playing a vital role in the dynamical and thermodynamical processes, yet very little is known of its true state in the Arctic. This study finds that assimilating CryoSat-2 products for the mean thickness and the sub-grid-scale thickness distribution can have significant consequences for the modelled distribution of the ice thickness across the Arctic and particularly in regions of thick multi-year ice. The assimilation of sea ice concentration, mean sea ice thickness and sub-grid-scale sea ice thickness distribution together performed best when compared to a subset of CryoSat-2 observations held back for validation. Regional model biases are reduced: the thickness of the thickest ice in the Canadian Arctic Archipelago (CAA) is decreased, but the thickness of the ice in the central Arctic is increased. When comparing the assimilation of mean thickness with the assimilation of sub-grid-scale thickness distribution, it is found that the latter leads to a significant change in the volume of ice in each category. Estimates of the thickest ice improve significantly with the assimilation of sub-grid-scale thickness distribution alongside mean thickness

Report on Offense Grading In New Jersey

The University of Pennsylvania Criminal Law Research Group was commissioned to do a study of offense grading in New Jersey. After an examination of New Jersey criminal law and a survey of New Jersey residents, the CLRG issued this Final Report. (For the report of a similar project for Pennsylvania, see Report on Offense Grading in Pennsylvania, http://ssrn.com/abstract=1527149, and for an article about the grading project, see The Modern Irrationalities of American Criminal Codes: An Empirical Study of Offense Grading, http://ssrn.com/abstract=1539083, Journal of Criminal Law and Criminology (forthcoming 2011).) The New Jersey study found serious conflicts between the relative grading judgments of New Jersey residents and those contained in existing New Jersey criminal law, as well as instances where mandatory minimum sentences often require sentences that exceed the maximum appropriate punishment, inconsistencies among the grading of similar offenses, overly broad offenses that impose similar grades on conduct of importantly different seriousness, and a flawed grading structure that provides too few grading categories, thereby assuring pervasive problems in failing to distinguish conduct of importantly different seriousness. These systemic failures risk undermining the criminal justice system\u27s moral credibility with the community, improperly delegate the value judgments inherent in grading decisions to individual sentencing judges ad hoc, fail to give citizens notice of the relative importance of conflicting duties, and invite application of different sentencing rules to similarly situated offenders. The Report examines how these grading problems came about, how they might be fixed, and how such grading irrationalities might be avoided in the future

Report on Offense Grading In New Jersey

The University of Pennsylvania Criminal Law Research Group was commissioned to do a study of offense grading in New Jersey. After an examination of New Jersey criminal law and a survey of New Jersey residents, the CLRG issued this Final Report. (For the report of a similar project for Pennsylvania, see Report on Offense Grading in Pennsylvania, http://ssrn.com/abstract=1527149, and for an article about the grading project, see The Modern Irrationalities of American Criminal Codes: An Empirical Study of Offense Grading, http://ssrn.com/abstract=1539083, Journal of Criminal Law and Criminology (forthcoming 2011).) The New Jersey study found serious conflicts between the relative grading judgments of New Jersey residents and those contained in existing New Jersey criminal law, as well as instances where mandatory minimum sentences often require sentences that exceed the maximum appropriate punishment, inconsistencies among the grading of similar offenses, overly broad offenses that impose similar grades on conduct of importantly different seriousness, and a flawed grading structure that provides too few grading categories, thereby assuring pervasive problems in failing to distinguish conduct of importantly different seriousness. These systemic failures risk undermining the criminal justice system\u27s moral credibility with the community, improperly delegate the value judgments inherent in grading decisions to individual sentencing judges ad hoc, fail to give citizens notice of the relative importance of conflicting duties, and invite application of different sentencing rules to similarly situated offenders. The Report examines how these grading problems came about, how they might be fixed, and how such grading irrationalities might be avoided in the future

A multi-model CMIP6-PMIP4 study of Arctic sea ice at 127 ka: sea ice data compilation and model differences

The Last Interglacial period (LIG) is a period with increased summer insolation at high northern latitudes, which results in strong changes in the terrestrial and marine cryosphere. Understanding the mechanisms for this response via climate modelling and comparing the models' representation of climate reconstructions is one of the objectives set up by the Paleoclimate Modelling Intercomparison Project for its contribution to the sixth phase of the Coupled Model Intercomparison Project. Here we analyse the results from 16 climate models in terms of Arctic sea ice. The multi-model mean reduction in minimum sea ice area from the pre industrial period (PI) to the LIG reaches 50 % (multi-model mean LIG area is 3.20×106 km2, compared to 6.46×106 km2 for the PI). On the other hand, there is little change for the maximum sea ice area (which is 15–16×106 km2 for both the PI and the LIG. To evaluate the model results we synthesise LIG sea ice data from marine cores collected in the Arctic Ocean, Nordic Seas and northern North Atlantic. The reconstructions for the northern North Atlantic show year-round ice-free conditions, and most models yield results in agreement with these reconstructions. Model–data disagreement appear for the sites in the Nordic Seas close to Greenland and at the edge of the Arctic Ocean. The northernmost site with good chronology, for which a sea ice concentration larger than 75 % is reconstructed even in summer, discriminates those models which simulate too little sea ice. However, the remaining models appear to simulate too much sea ice over the two sites south of the northernmost one, for which the reconstructed sea ice cover is seasonal. Hence models either underestimate or overestimate sea ice cover for the LIG, and their bias does not appear to be related to their bias for the pre-industrial period. Drivers for the inter-model differences are different phasing of the up and down short-wave anomalies over the Arctic Ocean, which are associated with differences in model albedo; possible cloud property differences, in terms of optical depth; and LIG ocean circulation changes which occur for some, but not all, LIG simulations. Finally, we note that inter-comparisons between the LIG simulations and simulations for future climate with moderate (1 % yr−1) CO2 increase show a relationship between LIG sea ice and sea ice simulated under CO2 increase around the years of doubling CO2. The LIG may therefore yield insight into likely 21st century Arctic sea ice changes using these LIG simulations

The effects of assimilating a sub-grid scale sea ice thickness distribution in a new Arctic sea ice data assimilation system

&amp;lt;div&amp;gt;&amp;lt;span&amp;gt;A modified, standalone version of the Los Alamos Sea Ice Model (CICE) has been coupled to the Parallelized Data Assimilation Framework (PDAF) to produce a new Arctic sea ice data assimilation system CICE-PDAF, with routines for assimilating many types of recently developed sea ice observations. In this study we explore the effects of assimilating a sub-grid scale sea ice thickness distribution derived from Cryosat-2 Arctic sea ice estimates into CICE-PDAF. The true state of the sub-grid scale ice thickness distribution is not well established, and yet it plays a key role in large scale sea ice models and is vital to the dynamical and thermodynamical processes necessary to produce a good representation of the Arctic sea ice state. We examine how assimilating sub-grid scale sea ice thickness distributions can affect the evolution of the sea ice state in CICE-PDAF and better our understanding of the Arctic sea ice system.&amp;lt;/span&amp;gt;&amp;lt;/div&amp;gt;</jats:p

Utilising Cryosat-2 observations of the Arctic sea ice cover to produce a new Arctic sea ice reanalysis

&amp;lt;p&amp;gt;In this work we present results from a new sea ice reanalysis over the satellite era. We use a newly created sea ice data assimilation system CICE-PDAF, combining the&amp;amp;#160;Los Alamos Sea Ice Model (CICE) and the&amp;amp;#160;Parallelized Data Assimilation Framework (PDAF), to take advantage of the new observations of the sea ice cover produced in the last decade by Cryosat-2. Sea ice thickness and sea ice thickness distribution observations from Cryosat-2, alongside sea ice concentration observations, are assimilated to explore their effects on our current estimates of the Arctic sea ice cover. In particular we look at its effects on the sea ice thickness distribution.&amp;amp;#160;The true state of the Arctic sub-grid scale thickness distribution system is not well known, and yet it plays a key role in the dynamic and thermodynamic processes present in the model to produce a good estimate of the Arctic sea ice state. Thus by combining knowledge from state-of-the-art sea ice models with knowledge from newly developed observations we hope to produce a clearer picture of the Arctic sea ice and its thickness distribution.&amp;lt;/p&amp;gt;</jats:p

Towards Langmuir-Blodgett films of magnetically interesting materials: solution equilibria in amphiphilic iron(II) complexes of a triazole-containing ligand

As a first step towards ambiphilic SCO systems where the hydrophobic part of the system is introduced by a non-coordinating anion (i.e. where no modification of the ligands to introduce hydrophobic substituents is required), [FeII(OH2)2(C16SO3)2] and [CoII(OH2)2(C16SO3)2] have been reacted with the triazole-containing ligands adpt and pldpt (C16SO¬3 = hexadecanesulfonate anion, adpt = 4-amino-3,5-bis(2-pyridyl)-1,2,4-triazole, pldpt = 4-pyrrolyl-3,5-bis(2-pyridyl)-1,2,4-triazole). In the solid state, HS complexes of the form [FeII(Rdpt)2(C16SO3)2] and [CoII(Rdpt)2(CH3OH)2](C16SO3)2 are observed, even when excess ligand is used (Rdpt = adpt or pldpt). In solution, the cobalt complexes remain in this form as evidenced by colour, Visible/NIR and IR spectroscopy. For the iron complexes, there is an equilibrium in solution between the neutral high-spin form of the complex [FeII(Rdpt)2(C16SO3)2] and the dicationic low-spin form [FeII(Rdpt)3](C16SO3)2. Polar solvents favour the dicationic form, while less polar solvents favour the neutral form (as evidenced by solution colour and solution IR spectroscopy). Visible/NIR spectroscopy and Evans’ method NMR spectroscopy show the equilibrium can be shifted towards the [FeII(Rdpt)3](C16SO3) form by adding additional ligand to the solution. The X-ray crystal structures of [FeII(adpt)2(C16SO3)2] and [CoII(adpt)2(CH3OH)2](C16SO3)2·1.33CH3OH are presented. [FeII(adpt)2(C16SO3)2] has a 2D bilayer structure with alternating layers of polar Fe(adpt)2 centres, and hydrophobic alkyl chains. The complex cations in [CoII(adpt)2(CH3OH)2](C16SO3)2·1.33CH3OH form 1-D columns in the solid state. The capacity of the amphiphilic complexes [FeII(pldpt)2(C16SO3)2] and [FeII(adpt)2(C16SO3)2] to self-assemble has been probed at the air-water interface using Langmuir techniques. The pertinent pressure-area isotherms reveal only a low tendency of the complexes to form films.Other funderInternational Science Technology Linkages FundRoyal Society of New ZealandERA-net ChemistryAlfred Werner Foundatio

Solvent Polarity Predictably Tunes Spin Crossover T-1/2 in Isomeric Iron(II) Pyrimidine Triazoles

Two isomeric pyrimidine-based Rdpt-type triazole ligands were made: 4-(4-methylphenyl)-3-(2-pyrimidyl)-5-phenyl-4 H-1,2,4-triazole (L-2pyrimidine) and 4-(4-methylphenyl)-3-(4-pyrimidyl)-5-phenyl-4 H-1,2,4-triazole (L-4pyrimidine). When reacted with [FeII(pyridine)(4)(NCE)2], where E = S, Se, or BH3, two families of mononuclear iron(II) complexes are obtained, including six solvatomorphs, giving a total of 12 compounds: [Fe-II(L-2pyrimidine)(2)(NCS)2] (1), [Fe-II(L-2pyrimidine)(2)(NCSe)(2)] (2), 2 1.5H(2)O, [Fe-II(L-2pyrimidine)(2)(NCBH3)(2)]2CHCl3 (32CHCl3), 3 and 32H(2)O, [Fe-II(L-4pyrimidine)(2)(NCS)2] (4), 4H(2)O, [Fe-II(L-4pyrimidine)(2)(NCSe)(2)] (5), 52CH(3)OH, 51.5H(2)O, and [Fe-II(L-4pyrimidine)(2)(NCBH3)(2)] 2.5H(2)O (6 2.5H(2)O). Single-crystal X-ray diffraction reveals that the N-6-coordinated iron(II) centers in 1, 2, 3 2CHCl(3), 4, 5, and 52CH(3)OH have two bidentate triazole ligands equatorially bound and two axial NCE co-ligands trans-coordinated. All structures are high spin (HS) at 100 K, except 3 2CHCl(3), which is low spin (LS). Solid-state magnetic measurements show that only 3 2CHCl(3) ( T1/2 above 400 K) and 5 1.5H(2)O ( T1/2 = 110 K) undergo spin crossover (SCO); the others remain HS at 300-50 K. When 3 2CHCl3 is heated at 400 K it desorbs CHCl3 becoming 3, which remains HS at 400-50 K. UV-Vis studies in CH2Cl2, CHCl3, (CH3)(2)CO, CH3CN, and CH3NO2 solutions for the BH3 analogues 3 and 6 led to a 6:1 ratio of L (npyrimidine)/Fe(II) being employed for the solution studies. These revealed SCO activity in all five solvents, with T-1/2 values for the 2-pyrimidine complex (247-396 K) that were consistently higher than for the 4-pyrimidine complex (216-367 K), regardless of solvent choice, consistent with the 2-pyrimidine ring providing a stronger ligand field than the 4-pyrimidine ring. Strong correlations of solvent polarity index with the T1/2 values in those solvents are observed for each complex, enabling predictable T1/2 tuning by up to 150 K. While this correlation is tantalizing, here it may also be reflecting solvent-dependent speciation-so future tests of this concept should employ more stable complexes. Differences between solid-state (ligand field; crystal packing; solvent content) and solution (ligand field; solvation; speciation) effects on SCO are highlighted