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This work reviews the literature on an alleged global warming 'pause' in global mean surface temperature (GMST) to determine how it has been defined, what time intervals are used to characterise it, what data are used to measure it, and what methods used to assess it. We test for 'pauses', both in the normally understood meaning of the term to mean no warming trend, as well as for a 'pause' defined as a substantially slower trend in GMST. The tests are carried out with the historical versions of GMST that existed for each pause-interval tested, and with current versions of each of the GMST datasets. The tests are conducted following the common (but questionable) practice of breaking the linear fit at the start of the trend interval ('broken' trends), and also with trends that are continuous with the data bordering the trend interval. We also compare results when appropriate allowance is made for the selection bias problem. The results show that there is little or no statistical evidence for a lack of trend or slower trend in GMST using either the historical data or the current data. The perception that there was a 'pause' in GMST was bolstered by earlier biases in the data in combination with incomplete statistical testing.

Structural biases, which are intrinsic in the social structures in which we function, play a key role in maintaining boundaries between traditionally privileged and underprivileged groups; however, they are particularly difficult to identify from within those societies. Two instances are highlighted in which the social structures of science appear to have discouraged collaboration, to the disadvantage of software and data users. Possible links are suggested to the strongly hierarchical structure of science and other factors which may in turn also serve to maintain sex and/or gender disparities in participation in the scientific endeavour.

This work focuses on the use of the existing protein-model-building software Buccaneer to provide structural interpretation of electron cryo-microscopy (cryo-EM) maps. Originally developed for application to X-ray crystallography, the necessary steps to optimise the usage of Buccaneer with cryo-EM maps are shown. This approach has been applied to the data sets of 208 cryo-EM maps with resolutions of better than 4 Å. The results obtained also show an evident improvement in the sequencing step when the initial reference map and model used for crystallographic cases are replaced by a cryo-EM reference. All other necessary changes to settings in Buccaneer are implemented in the model-building pipeline from within the CCP-EM interface (as of version 1.4.0).

A comparison of four protein model-building pipelines (ARP/wARP, Buccaneer, PHENIX AutoBuild and SHELXE) was performed using data sets from 202 experimentally phased cases, both with the data as observed and truncated to simulate lower resolutions. All pipelines were run using default parameters. Additionally, an ARP/wARP run was completed using models from Buccaneer. All pipelines achieved nearly complete protein structures and low Rwork/Rfree at resolutions between 1.2 and 1.9 Å, with PHENIX AutoBuild and ARP/wARP producing slightly lower R factors. At lower resolutions, Buccaneer leads to significantly more complete models.

A method is described for the refinement of an electron-density model against a set of structure-factor observations which does not rely on atomic parameters. The effective level of detail in the parameterization can be varied to ensure that the refinement is well determined at any resolution supported by the data., Refinement is a critical step in the determination of a model which explains the crystallographic observations and thus best accounts for the missing phase components. The scattering density is usually described in terms of atomic parameters; however, in macromolecular crystallography the resolution of the data is generally insufficient to determine the values of these parameters for individual atoms. Stereochemical and geometric restraints are used to provide additional information, but produce interrelationships between parameters which slow convergence, resulting in longer refinement times. An alternative approach is proposed in which parameters are not attached to atoms, but to regions of the electron-density map. These parameters can move the density or change the local temperature factor to better explain the structure factors. Varying the size of the region which determines the parameters at a particular position in the map allows the method to be applied at different resolutions without the use of restraints. Potential applications include initial refinement of molecular-replacement models with domain motions, and potentially the use of electron density from other sources such as electron cryo-microscopy (cryo-EM) as the refinement model.

With the introduction of intuitive graphical software, structural biologists who are not experts in crystallography are now able to build complete protein or nucleic acid models rapidly. In contrast, carbohydrates are in a wholly different situation: scant automation exists, with manual building attempts being sometimes toppled by incorrect dictionaries or refinement problems. Sugars are the most stereochemically complex family of biomolecules and, as pyranose rings, have clear conformational preferences. Despite this, all refinement programs may produce high-energy conformations at medium to low resolution, without any support from the electron density. This problem renders the affected structures unusable in glyco-chemical terms. Bringing structural glycobiology up to ‘protein standards’ will require a total overhaul of the methodology. Time is of the essence, as the community is steadily increasing the production rate of glycoproteins, and electron cryo-microscopy has just started to image them in precisely that resolution range where crystallographic methods falter most.

The early 20th century warming (EW; 1910-1945) and the mid-20th century cooling (MC; 1950-1980) have been linked to both, internal variability of the climate system and changes in external radiative forcing. The degree to which either of the two factors contributed to EW and MC, or both, is still debated. Using a two-box impulse response model, we demonstrate that multidecadal ocean variability was unlikely to be the driver of observed changes in global mean surface temperature (GMST) after 1850 A.D. Instead, virtually all (97-98%) of the global low-frequency variability (> 30 years) can be explained by external forcing. We find similarly high percentages of explained variance for inter-hemispheric and land-ocean temperature evolution. Three key aspects are identified which underpin the conclusion of this new study: inhomogeneous anthropogenic aerosol forcing (AER), biases in the instrumental sea surface temperature (SST) datasets, and inadequate representation of the response to varying forcing factors. Once the spatially heterogeneous nature of AER is accounted for, the MC period is reconcilable with external drivers. SST biases and imprecise forcing responses explain the putative disagreement between models and observations during the EW period. As a consequence, Atlantic Multidecadal Variability (AMV) is found to be primarily controlled by external forcing too. Future attribution studies should account for these important factors when discriminating between externally-forced and internallygenerated influences on climate. We argue that AMV must not be used as a regressor and suggest a revised AMV index instead (North Atlantic Variability Index; NAVI). Our associated best estimate for the transient climate response (TCR) is 1.57 K (±0.70 at the 5-95% confidence level).

Figure 9 mistakenly duplicated figure 10 in the published article. This reflected a technical error during production. The correct figure 9 should be as follows: (Figure Presented).

This work reviews the literature on an alleged global warming 'pause' in global mean surface temperature (GMST) to determine how it has been defined, what time intervals are used to characterise it, what data are used to measure it, and what methods used to assess it. We test for 'pauses', both in the normally understood meaning of the term to mean no warming trend, as well as for a 'pause' defined as a substantially slower trend in GMST. The tests are carried out with the historical versions of GMST that existed for each pause-interval tested, and with current versions of each of the GMST datasets. The tests are conducted following the common (but questionable) practice of breaking the linear fit at the start of the trend interval ('broken' trends), and also with trends that are continuous with the data bordering the trend interval. We also compare results when appropriate allowance is made for the selection bias problem. The results show that there is little or no statistical evidence for a lack of trend or slower trend in GMST using either the historical data or the current data. The perception that there was a 'pause' in GMST was bolstered by earlier biases in the data in combination with incomplete statistical testing., Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe; 1023

We review the evidence for a putative early 21st-century divergence between global mean surface temperature (GMST) and Coupled Model Intercomparison Project Phase 5 (CMIP5) projections. We provide a systematic comparison between temperatures and projections using historical versions of GMST products and historical versions of model projections that existed at the times when claims about a divergence were made. The comparisons are conducted with a variety of statistical techniques that correct for problems in previous work, including using continuous trends and a Monte Carlo approach to simulate internal variability. The results show that there is no robust statistical evidence for a divergence between models and observations. The impression of a divergence early in the 21st century was caused by various biases in model interpretation and in the observations, and was unsupported by robust statistics.

Sea surface temperatures form a vital part of global mean surface temperature records. Historical observation methods have changed substantially over time from buckets to engine room intake sensors, hull sensors and drifting buoys, rendering their use for climatological studies problematic. There are substantial uncertainties in the relative biases of different observations which may impact the global temperature record. Island and coastal weather stations can be compared to coastal sea surface temperature observations to obtain an assessment of changes in bias over time. The process is made more challenging by differences in the rate of warming between air temperatures and sea surface temperatures, and differences across coastal boundaries. A preliminary sea surface temperature reconstruction homogenized using coastal weather station data suggests significant changes to the sea surface temperature record, although there are substantial uncertainties of which only some can be quantified. A large warm excursion in versions 4 and 5 of the NOAA Extended Reconstructed Sea Surface Temperature during World War 2 is rejected, as is a cool excursion around 1910 present in all existing records. The mid-century plateau is cooler than in existing reconstructions.

We review the evidence for a putative early 21st-century divergence between global mean surface temperature (GMST) and Coupled Model Intercomparison Project Phase 5 (CMIP5) projections. We provide a systematic comparison between temperatures and projections using historical versions of GMST products and historical versions of model projections that existed at the times when claims about a divergence were made. The comparisons are conducted with a variety of statistical techniques that correct for problems in previous work, including using continuous trends and a Monte Carlo approach to simulate internal variability. The results show that there is no robust statistical evidence for a divergence between models and observations. The impression of a divergence early in the 21st century was caused by various biases in model interpretation and in the observations, and was unsupported by robust statistics.

This work reviews the literature on an alleged global warming 'pause' in global mean surface temperature (GMST) to determine how it has been defined, what time intervals are used to characterise it, what data are used to measure it, and what methods used to assess it. We test for 'pauses', both in the normally understood meaning of the term to mean no warming trend, as well as for a 'pause' defined as a substantially slower trend in GMST. The tests are carried out with the historical versions of GMST that existed for each pause-interval tested, and with current versions of each of the GMST datasets. The tests are conducted following the common (but questionable) practice of breaking the linear fit at the start of the trend interval ('broken' trends), and also with trends that are continuous with the data bordering the trend interval. We also compare results when appropriate allowance is made for the selection bias problem. The results show that there is little or no statistical evidence for a lack of trend or slower trend in GMST using either the historical data or the current data. The perception that there was a 'pause' in GMST was bolstered by earlier biases in the data in combination with incomplete statistical testing.

A method is presented for the automatic building of nucleotide chains into electron density which is fast enough to be used in interactive model-building software. Likely nucleotides lying in the vicinity of the current view are located and then grown into connected chains in a fraction of a second. When this development is combined with existing tools, assisted manual model building is as simple as or simpler than for proteins., The crystallographic structure solution of nucleotides and nucleotide complexes is now commonplace. The resulting electron-density maps are often poorer than for proteins, and as a result interpretation in terms of an atomic model can require significant effort, particularly in the case of large structures. While model building can be performed automatically, as with proteins, the process is time-consuming, taking minutes to days depending on the software and the size of the structure. A method is presented for the automatic building of nucleotide chains into electron density which is fast enough to be used in interactive model-building software, with extended chain fragments built around the current view position in a fraction of a second. The speed of the method arises from the determination of the ‘fingerprint’ of the sugar and phosphate groups in terms of conserved high-density and low-density features, coupled with a highly efficient scoring algorithm. Use cases include the rapid evaluation of an initial electron-density map, addition of nucleotide fragments to prebuilt protein structures, and in favourable cases the completion of the structure while automated model-building software is still running. The method has been incorporated into the Coot software package.

Scripting programming languages provide the fastest means of prototyping complex functionality. Those with a syntax and grammar resembling human language also greatly enhance the maintainability of the produced source code. Furthermore, the combination of a powerful, machine-independent scripting language with binary libraries tailored for each computer architecture allows programs to break free from the tight boundaries of efficiency traditionally associated with scripts. In the present work, we describe how an efficient C++ crystallographic library such as Clipper can be wrapped, adapted and generalised for use in both crystallographic and electron cryo-microscopy applications, scripted with the Python language. We shall also place an emphasis on best practices in automation, illustrating how this can be achieved with this new Python module. This article is protected by copyright. All rights reserved.