Quantifying spread in spatio-temporal changes of upper-ocean heat content estimates: an internationally coordinated comparison

The Earth system is accumulating energy due to human-induced activities. More than 90% of this energy has been stored in the ocean as heat since 1970, with ~64% of that in the upper 700 m. Differences in upper ocean heat content anomaly (OHCA) estimates, however, exist. Here, we use a dataset protocol for 1970–2008 – with six instrumental bias adjustments applied to expendable bathythermograph (XBT) data, and mapped by six research groups – to evaluate the spatio-temporal spread in upper OHCA estimates arising from two choices: firstly, those arising from instrumental bias adjustments; and secondly those arising from mathematical (i.e. mapping) techniques to interpolate and extrapolate data in space and time. We also examined the effect of a common ocean mask, which reveals that exclusion of shallow seas can reduce global OHCA estimates up to 13%. Spread due to mapping method is largest in the Indian Ocean and in the eddy-rich and frontal regions of all basins. Spread due to XBT bias adjustment is largest in the Pacific Ocean within 30°N–30°S. In both mapping and XBT cases, spread is higher for 1990–2004. Statistically different trends among mapping methods are not only found in the poorly-observed Southern Ocean but also on the well-observed Northwest Atlantic. Our results cannot determine the best mapping or bias adjustment schemes but they identify where important sensitivities exist, and thus where further understanding will help to refine OHCA estimates. These results highlight the need for further coordinated OHCA studies to evaluate the performance of existing mapping methods along with comprehensive assessment of uncertainty estimates.

Measurement inter-comparison of bulk snow density and water equivalent of snow cover with snow core samplers

<p>Manually collected snow data can be considered as ground truth for many applications, such as climatological or hydrological studies. Water equivalent of snow cover (SWE) can be manually measured by using a snow tube or snow cylinder to extract a snow core and measure the bulk density of the core by weighing it. Different snow core samplers and scales are used, but they all use the same measurement principle. However, there are various sources of uncertainty that have not been quantified in detail. To increase the understanding of these errors, different manual SWE measurement devices used across Europe were evaluated within the framework of the COST Action ES1404 HarmoSnow. Two field campaigns were organized in different environments to quantify uncertainties when measuring snow depth, snow bulk density and SWE with core samplers. The 1<sup>st</sup> field campaign in 2017 in Iceland focused on measurement differences attributed to different instrumentation compared with the natural variability in the snowpack, and the 2<sup>nd</sup> field campaign in 2018 in Finland focused on device comparison and on the separation of the different sources of variability. To our knowledge, such a comparison has not previously been conducted in terms of the number of device and different environments.</p><p>During the 1<sup>st</sup> campaign, repeated measurements were taken along two 20 m long snow trenches to distinguish snow variability measured at the plot and at the point scale. The results revealed a much higher variability of SWE at the plot scale, resulting from both natural variability and instrument bias, compared to repeated measurements at the same spot, resulting mostly from error induced by observers or a high variability in the snow depth. Snow Micro Pen sampling showed that the snowpack was very homogeneous for the 2<sup>nd</sup> campaign, which allowed for the disregarding of the natural variability of the snowpack properties and the focus to be on separating between instrumental bias and error induced by observers. Results confirmed that instrumental bias exceeded both the natural variability and the error induced by observers, even when observers performed measurements with snow core samplers they were not formally trained on. Under such measurement conditions, the uncertainty in bulk snow density estimation is about 5% for an individual instrument and is close to 10% among different instruments. The results showed that the devices provided slightly different uncertainties since they were designed for different snow conditions. The aim of this comparison was not to provide a definitive estimation of uncertainty for manual SWE measurements, but to illustrate the role of the different uncertainty sources.</p>

Evaluation of Tropospheric & Clock Errors for Precision GPS Positioning & Navigation

Positional inaccuracies in GPS are caused by severalerrors such as Ionospheric, Tropospheric, Satellite Clock, Receiver Clock etc., Instantaneous correction of these error aids in precise navigation. In the present work Original Hopfield model is considered for the tropospheric correction. The instantaneous tropospheric correction results in more precise position using GPS. The decreasing order of components on basis of effect are Ionospheric delay, Tropospheric delay, Clock error, satellite bias error, Receiver error, multipath error, Ephymeris error, random errors etc. It is a time taken process to calculate the individual error separately.so in this paper we only concentrated on simulation and analysis the tropospheric delay, clock error, ephemeris error. We used Modified Hopfield model to analysis Tropospheric delay, receiver instrumental bias for analysis Clock error in between we eliminate Ephymeris error, after obtained results are compared with and without time correction in original Hopfield model

Dorsal striatal dopamine D1 receptor availability predicts an instrumental bias in action learning

Learning to act to obtain reward and inhibit to avoid punishment is easier compared with learning the opposite contingencies. This coupling of action and valence is often thought of as a Pavlovian bias, although recent research has shown it may also emerge through instrumental mechanisms. We measured this learning bias with a rewarded go/no-go task in 60 adults of different ages. Using computational modeling, we characterized the bias as being instrumental. To assess the role of endogenous dopamine (DA) in the expression of this bias, we quantified DA D1 receptor availability using positron emission tomography (PET) with the radioligand [11C]SCH23390. Using principal-component analysis on the binding potentials in a number of cortical and striatal regions of interest, we demonstrated that cortical, dorsal striatal, and ventral striatal areas provide independent sources of variance in DA D1 receptor availability. Interindividual variation in the dorsal striatal component was related to the strength of the instrumental bias during learning. These data suggest at least three anatomical sources of variance in DA D1 receptor availability separable using PET in humans, and we provide evidence that human dorsal striatal DA D1 receptors are involved in the modulation of instrumental learning biases.

Absolute Velocity Estimates from Autonomous Underwater Gliders Equipped with Doppler Current Profilers

Doppler current profilers on autonomous underwater gliders measure water velocity relative to the moving glider over vertical ranges of O(10) m. Measurements obtained with 1-MHz Nortek acoustic Doppler dual current profilers (AD2CPs) on Spray gliders deployed off Southern California, west of the Galápagos Archipelago, and in the Gulf Stream are used to demonstrate methods of estimating absolute horizontal velocities in the upper 1000 m of the ocean. Relative velocity measurements nearest to a glider are used to infer dive-dependent flight parameters, which are then used to correct estimates of absolute vertically averaged currents to account for the accumulation of biofouling during months-long glider missions. The inverse method for combining Doppler profiler measurements of relative velocity with absolute references to estimate profiles of absolute horizontal velocity is reviewed and expanded to include additional constraints on the velocity solutions. Errors arising from both instrumental bias and decreased abundance of acoustic scatterers at depth are considered. Though demonstrated with measurements from a particular combination of platform and instrument, these techniques should be applicable to other combinations of gliders and Doppler current profilers.