TMS can be used to evoke twitches during submaximal contractions of the human calf muscle and, along with MNS, used to assess fatigue during submaximal exercise.

<p>Exposure biases are non-climatic changes in the surface air temperature record which were introduced due to changes in the way thermometers were protected from solar radiation. The possible presence of the exposure bias in early instrumental temperature datasets is a well-known issue, and the impact of changing thermometer exposures, particularly the transition between historic exposures and the Stevenson screen, has been explored by previous studies. However, despite this, very few adjustments have been made to account for the bias, with the exception of a handful of localised studies. As a result, the exposure bias still accounts for significant uncertainty in global surface air temperature compilations, such as HadCRUT5.</p><p>In this work we report an attempt to address the exposure bias for extratropical weather stations in a version of CRUTEM5 that has been extended back in time to 1781 (CRUTEM5_ext). We developed statistical models to predict the bias introduced by transitions from four main categories of historic exposure – open, wall-mounted, intermediate and closed – to the Stevenson screen. The models are based on an empirical analysis of the characteristics of the exposure bias observed in 20 parallel measurement studies, together with the temperature and radiation variables that were <em>a priori</em> expected to influence the magnitude of the bias in mean temperatures on a monthly timescale. Separately, we have compiled a database detailing the historic exposures in use at stations and the timing (or approximate timing when a precise time is not known) of the transition to the Stevenson screen. The statistical models, where robust, are then applied to the individual stations within CRUTEM5_ext to make adjustments for the exposure changes according to the database of historic exposures.</p><p>This presentation will outline the model development, give a brief overview of the evolution of thermometer exposures in use in the early instrumental period for extratropical stations, and will illustrate the impact the exposure bias adjustments have on the CRUTEM5_ext data. This work forms part of the NERC-funded GloSAT project (https://www.glosat.org/) which is developing a global surface air temperature dataset starting in 1781. Where appropriate, stations used to create the GloSAT dataset will be adjusted for the exposure bias using the models presented here. </p>

<p>We present a new data set of air temperature change across land and ocean extending back to the late-18<sup>th</sup> century. This new data set uses marine air temperature observations rather than the sea surface temperature measurements typically used by pre-existing data sets. This allows the new data set to extend further into the past than existing instrumental temperature records, which typically have start dates in the mid-to-late 19<sup>th</sup> century. The new data set brings together advances in understanding of measurement biases affecting all-day marine air temperature observations with a new assessment of the effects of non-standard thermometer enclosures used at land meteorological stations in the early instrumental record. A further innovation is the use of kriging to obtain localised temperature estimates that allow land air temperature records to be converted into anomalies even for stations without observations during the baseline period. Global and hemispheric series show close agreement with those based on sea-surface temperature for much of the overlapping period of their records, some of the interesting differences will be presented. This data set has been developed under the GloSAT project (https://www.glosat.org/).</p>

<p>Long observational records of land surface air temperature are vital to our understanding of climate variability and change, as well as for testing predictions of climatic trends. However, of the relatively few long observational records which exist, many contain inhomogeneities or biases resulting from changing instrumentation, station location/surroundings and/or observing practises. One of the most significant issues is the exposure bias. Prior to the widespread adoption of louvered Stevenson-type screens in the late-19<sup>th</sup> century, various (often insufficient) approaches were used to shield thermometers. Each approach exposed the thermometer to differing levels of solar radiation, thus introducing inhomogeneities into individual station records and biases across regions, if similar approaches were used. Poorly shielded thermometers, for example, tended to read higher during the summer half year than those in Stevenson-type screens. Despite a number of studies documenting the presence of the exposure bias in early instrumental data, relatively few corrections have been applied or incorporated into global temperature datasets. This is largely due to the pervasive nature of the bias and a lack of observational metadata impeding bias identification or estimation of the appropriate correction.</p><p>In this work we explore a range of datasets to identify the potential for exposure bias in early instrumental data. We analyse historical data, corrections applied to homogenized datasets, as well as the small number of parallel measurements from differentially-shielded thermometers, in order to better define the characteristics of the exposure bias. These characteristics are then used to identify potential instances of exposure bias in early instrumental temperature records. We consider differences in seasonal anomalies, which is a key feature of many exposure biases, as well as their geographical variation (focussing mostly, but not solely, on Europe). We analyse how these behave at stations where it is known that exposure bias has already been adjusted for (though perhaps not completely) versus those that have not been. We also make comparisons with proxy reconstructions of temperature as an independent reference that is not susceptible to the same biases as the early instrumental data.</p><p>This work forms part of the NERC-funded GloSAT project which is developing a global surface air temperature dataset starting in 1781. The ultimate aim of the work reported here is to refine the error associated with these biases, in order to improve the representation of the exposure bias in error models used for gridded instrumental temperature datasets.</p>

Spotted lanternfly (SLF) (Lycorma delicatula (White)), an invasive planthopper discovered in Pennsylvania, USA in 2014, continues to spread and is now present in 14 states with substantial infestations present in seven states. Population projections using adult SLF trapping or visual counts are not reliable due to the transient, migratory behavior of the adults which make population forecasts difficult. Another approach to population monitoring is utilization of the stationary egg mass stage, but counting small cryptic egg masses throughout the canopy of large trees in dense woodlots is arduous and prone to error. After several field seasons testing various trapping configurations and materials, we have identified an efficient, simple, low-cost trap termed a ‘lamp shade trap’ that is attached to the lower trunk area of an SLF host tree. SLF females readily enter the trap and lay eggs on the thin, flexible trap surface. A vertical trap orientation was superior, and the most productive woodlots yielded an average of 47 and 54 egg masses per trap, and several traps had over 100 egg masses. There were 1,943 egg masses tallied from 105 traps placed at six locations in two states. Egg mass counts in the area above and below the traps and on nearby control trees yielded very few egg masses in comparison. Selection of trees 15 to 20 cm in diameter for trap placement is most efficient, yielding good egg mass abundance while minimizing the amount of trap material used. The lamp shade trap has potential as an effective tool to identify SLF in new areas, gauge SLF population levels in woodlots and can also be used to collect and monitor egg masses for research purposes.