What causes solar active region loops to exist at transition region temperatures?

High-lying, dynamic loops have been observed at transition region temperatures since Skylab observations. The nature of these loops has been debated for many years with several explanations having been put forward. These include that the loops are merely cooling from hotter coronal loops, that they are produced from siphon flows, or that they are loops heated only to transition region temperatures. In this paper we will make use of combined SOHO-MDI (Michelson-Doppler Imager), SOHO-CDS (Coronal Diagnostic Spectrometer) and Yohkoh SXT (Soft X-ray Telescope) datasets in order to determine whether the appearance of transition region loops is related to small-scale flaring in the corona, and to estimate the magnetic configuration of the loops. The latter allows us to determine the direction of plasma flows in the transition region loops. We find that the appearance of the transition region loops is often related to small-scale flaring in the corona and in this case the transition region loops appear to be cooling with material draining down from the loop top.     

On the long-term context of the 1997–2009 ‘Big Dry’ in South-Eastern Australia: insights from a 206-year multi-proxy rainfall reconstruction

This study presents the first multi-proxy reconstruction of rainfall variability from the mid-latitude region of south-eastern Australia (SEA). A skilful rainfall reconstruction for the 1783–1988 period was possible using twelve annually-resolved palaeoclimate records from the Australasian region. An innovative Monte Carlo calibration and verification technique is introduced to provide the robust uncertainty estimates needed for reliable climate reconstructions. Our ensemble median reconstruction captures 33% of inter-annual and 72% of decadal variations in instrumental SEA rainfall observations. We investigate the stability of regional SEA rainfall with large-scale circulation associated with El Niño–Southern Oscillation (ENSO) and the Inter-decadal Pacific Oscillation (IPO) over the past 206 years. We find evidence for a robust relationship with high SEA rainfall, ENSO and the IPO over the 1840–1988 period. These relationships break down in the late 18th–early 19th century, coinciding with a known period of equatorial Pacific Sea Surface Temperature (SST) cooling during one of the most severe periods of the Little Ice Age. In comparison to a markedly wetter late 18th/early 19th century containing 75% of sustained wet years, 70% of all reconstructed sustained dry years in SEA occur during the 20th century. In the context of the rainfall estimates introduced here, there is a 97.1% probability that the decadal rainfall anomaly recorded during the 1998–2008 ‘Big Dry’ is the worst experienced since the first European settlement of Australia.     

Observations of photometric profiles of two planetary nebulae

It is noted that the Abel integral of a Gaussian function is a Gaussian function. This special property is used in order to develop an analytic method for studying photometric profile in Planetary Nebulae. Monochromatic observations have been made at the Haute-Provence Observatory using the RCA-CCD (320×512 pixels) mounted at theF/6 Newtonian focus of the 1.2 m telescope. Among the observed nebulae, two of them (NGC 6778 and NGC 7354) are regular enough to test the photometric profile method. The nebular emissivity per volume unit is then obtained for H⁺ and O⁺⁺ ions.     

Inference and uncertainty of snow depth spatial distribution at the kilometre scale in the Colorado Rocky Mountains: the effects of sample size,random sampling,predictor quality,and validation procedures

Historically, observing snow depth over large areas has been difficult. When snow depth observations are sparse, regression models can be used to infer the snow depth over a given area. Data sparsity has also left many important questions about such inference unexamined. Improved inference, or estimation, of snow depth and its spatial distribution from a given set of observations can benefit a wide range of applications from water resource management, to ecological studies, to validation of satellite estimates of snow pack. The development of Light Detection and Ranging (LiDAR) technology has provided non‐sparse snow depth measurements, which we use in this study, to address fundamental questions about snow depth inference using both sparse and non‐sparse observations. For example, when are more data needed and when are data redundant? Results apply to both traditional and manual snow depth measurements and to LiDAR observations. Through sampling experiments on high‐resolution LiDAR snow depth observations at six separate 1.17‐km² sites in the Colorado Rocky Mountains, we provide novel perspectives on a variety of issues affecting the regression estimation of snow depth from sparse observations. We measure the effects of observation count, random selection of observations, quality of predictor variables, and cross‐validation procedures using three skill metrics: percent error in total snow volume, root mean squared error (RMSE), and R². Extremes of predictor quality are used to understand the range of its effect; how do predictors downloaded from internet perform against more accurate predictors measured by LiDAR? Whereas cross validation remains the only option for validating inference from sparse observations, in our experiments, the full set of LiDAR‐measured snow depths can be considered the ‘true’ spatial distribution and used to understand cross‐validation bias at the spatial scale of inference. We model at the 30‐m resolution of readily available predictors, which is a popular spatial resolution in the literature. Three regression models are also compared, and we briefly examine how sampling design affects model skill. Results quantify the primary dependence of each skill metric on observation count that ranges over three orders of magnitude, doubling at each step from 25 up to 3200. Whereas uncertainty (resulting from random selection of observations) in percent error of true total snow volume is typically well constrained by 100–200 observations, there is considerable uncertainty in the inferred spatial distribution (R²) even at medium observation counts (200–800). We show that percent error in total snow volume is not sensitive to predictor quality, although RMSE and R² (measures of spatial distribution) often depend critically on it. Inaccuracies of downloaded predictors (most often the vegetation predictors) can easily require a quadrupling of observation count to match RMSE and R² scores obtained by LiDAR‐measured predictors. Under cross validation, the RMSE and R² skill measures are consistently biased towards poorer results than their true validations. This is primarily a result of greater variance at the spatial scales of point observations used for cross validation than at the 30‐m resolution of the model. The magnitude of this bias depends on individual site characteristics, observation count (for our experimental design), and sampling design. Sampling designs that maximize independent information maximize cross‐validation bias but also maximize true R². The bagging tree model is found to generally outperform the other regression models in the study on several criteria. Finally, we discuss and recommend use of LiDAR in conjunction with regression modelling to advance understanding of snow depth spatial distribution at spatial scales of thousands of square kilometres. Copyright © 2012 John Wiley & Sons, Ltd.     

Sampling Trace-Level Organic Solutes with Polymeric Tubing Part 2. Dynamic Studies

This is the second part of a study conducted to determine whether polymeric sampling tubing can affect organic analyte concentrations during a sampling event. In this part of the study, we looked for sorption and desorption of tricholoroethylene (TCE) and leaching of organic constituents in water pumped through five types of polymeric tubing. The materials tested were a rigid fluoropolymer, a flexible fluoropolymer, low-density polyethylene (LDPE), and two plasticized polypropylene tubings. The effects of tubing length and flow rate were examined.
The least sorptive tubings, both initially and at equilibrium, were the fluoropolymers. However, in some instances the LDPE tubing had little effect on TCE concentrations. This was when a slow flow rate was used to sample relatively shallow wells (50 feet [15 m] or less) or when a faster flow rate (1 L/min) was used to sample wells that are less than 500 feet (152 m). Further testing is recommended using more sorptive analytes.
Using high performance liquid chromatography (HPLC), we were unable to detect any constituents leaching from any of the tubings used in these studies, even when a slow flow rate was used. However, desorption of sorbed analytes is a concern for all the tubings tested, including the rigid fluoropolymer.     

Crop modelling: towards locally relevant and climate-informed adaptation

A gap between the potential and practical realisation of adaptation exists: adaptation strategies need to be both climate-informed and locally relevant to be viable. Place-based approaches study local and contemporary dynamics of the agricultural system, whereas climate impact modelling simulates climate-crop interactions across temporal and spatial scales. Crop-climate modelling and place-based research on adaptation were strategically reviewed and analysed to identify areas of commonality, differences, and potential learning opportunities to enhance the relevance of both disciplines through interdisciplinary approaches. Crop-modelling studies have projected a 7–15% mean yield change with adaptation compared to a non-adaptation baseline (Nature Climate Change 4:1–5, 2014). Of the 17 types of adaptation strategy identified in this study as place-based adaptations occurring within Central America, only five were represented in crop-climate modelling literature, and these were as follows: fertiliser, irrigation, change in planting date, change in cultivar and area cultivated. The breath and agency of real-life adaptation compared to its representation in modelling studies is a source of error in climate impact simulations. Conversely, adaptation research that omits assessment of future climate variability and impact does not enable to provide sustainable adaptation strategies to local communities so risk maladaptation. Integrated and participatory methods can identify and reduce these sources of uncertainty, for example, stakeholder’s engagement can identify locally relevant adaptation pathways. We propose a research agenda that uses methodological approaches from both the modelling and place-based approaches to work towards climate-informed locally relevant adaptation.     

Precise temperature monitoring in boreholes: evidence for oscillatory convection? Part II: theory and interpretation

In the previous part of this work (Cermak, Safanda and Bodri, this volume p.MMM) we have described experimental data and quantified the heterogeneity features of the microtemperature time series. The spectral analysis and the local growth of the second moment technique revealed scaling structure of all observed time series generally similar and suggested the presence of two temperature forming processes. The longer-scale part can be attributed to the heat conduction in compositional and structural heterogeneous solid rocks, further affected by various local conditions. Short-scale temperature oscillations are produced by the intra-hole fluid convection due to inherent instability of water column filling the hole. Here we present how the observational evidence is supported by the results of the computer simulations. The exact modes of intra-hole convection may be different, ranging from quasi-periodic (“quiescent”) state to close of turbulence. As demonstrated by numerical modeling and referred on laboratory experiments, at higher Rayleigh numbers the periodic character of oscillation characteristic for “quiescent” regime is superseded by stochastic features. This so called “oscillatory” convection occurs due to instability within the horizontal boundary layers between the individual convectional cells. In spite of the fact that the basic convective cell motion is maintained and convection is characterized by slow motion, the oscillatory intra-hole flow and corresponding temperature patterns exhibit typical features of turbulence. The idea of boundary layer instability as a source of stochastic temperature fluctuations could explain many distinct features of borehole temperatures that previously cannot be interpreted.