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The influence of mineralogy on the assembly of microbial communities in glacial environments has been difficult to assess due to complications in isolating mineralogy from other variables. Here we assess the abundance and composition of microbial communities that colonized defined minerals incubated for 12 months in two meltwater streams (N and S) emanating from Kaldalónsjökull (Kal), a basalt-hosted glacier in Iceland. The two streams shared similar meltwater geochemistry as well as bedrock and proglacial sediment elemental compositions. Yet genomic DNA and PCR-amplifiable 16S rRNA genes were detected only in Kal S. The amount of recoverable DNA was highest for hematite incubated in Kal S and the composition of 16S rRNA genes recovered from Kal S sediments was most like those recovered from hematite and magnetite, an effect driven largely by similarities in the relative abundance of the putative hydrogenotrophic iron reducer Rhodoferax. We suggest this is attributable to comminution and weathering reactions involving exposed iron silicate minerals that generate and release hydrogen and Fe(III) that can be coupled to support microbial metabolism in Kaldalónsjökull, and possibly other basaltic habitats. The low abundance of cells in Kal N could be due to low availability of Fe(III) or another substrate.

 

Abstract An effective method to understand cloud processes and to assess the fidelity with which they are represented in climate models is the cloud controlling factor framework, in which cloud properties are linked with variations in large-scale dynamical and thermodynamical variables. This study examines how midlatitude cloud radiative effects (CRE) over oceans co-vary with four cloud controlling factors: mid-tropospheric vertical velocity, estimated inversion strength (EIS), near-surface temperature advection, and sea surface temperature (SST), and assesses their representation in CMIP6 models with respect to observations and CMIP5 models. CMIP5 and CMIP6 models overestimate the sensitivity of midlatitude CRE to perturbations in vertical velocity, and underestimate the sensitivity of midlatitude shortwave CRE to perturbations in EIS and temperature advection. The largest improvement in CMIP6 models is a reduced sensitivity of CRE to vertical velocity perturbations. As in CMIP5 models, many CMIP6 models simulate a shortwave cloud radiative warming effect associated with a poleward shift in the Southern Hemisphere (SH) midlatitude jet stream, an effect not present in observations. This bias arises because most models’ shortwave CRE are too sensitive to vertical velocity perturbations and not sensitive enough to EIS perturbations, and because most models overestimate the SST anomalies associated with SH jet shifts. The presence of this bias directly impacts the transient surface temperature response to increasing greenhouse gases over the Southern Ocean, but not the global-mean surface temperature. Instead, the models’ climate sensitivity is correlated with their shortwave CRE sensitivity to surface temperature advection perturbations near 40°S, with models with more realistic values of temperature advection sensitivity generally having higher climate sensitivity. 

Changes in midlatitude clouds as a result of shifts in general circulation patterns are widely thought to be a potential source of radiative feedbacks onto the climate system. Previous work has suggested that two general circulation shifts anticipated to occur in a warming climate, poleward shifts in the midlatitude jet streams and a poleward expansion of the Hadley circulation, are associated with differing effects on midlatitude clouds. This study examines two dynamical cloud‐controlling factors, mid‐tropospheric vertical velocity, and the estimated inversion strength (EIS) of the marine boundary layer temperature inversion, to explain why poleward shifts in the Southern Hemisphere midlatitude jet and Hadley cell edge have varying shortwave cloud‐radiative responses at midlatitudes. Changes in vertical velocity and EIS occur further equatorward for poleward shifts in the Hadley cell edge than they do for poleward shifts of the midlatitude jet. Because the sensitivity of shortwave cloud radiative effects (SWCRE) to variations in vertical velocity and EIS is a function of latitude, the SWCRE anomalies associated with jet and Hadley cell shifts differ. The dynamical changes associated with a poleward jet shift occur further poleward in a regime where the sensitivities of SWCRE to changes in vertical velocity and EIS balance, leading to a near‐net zero change in SWCRE in midlatitudes with a poleward jet shift. Conversely, the dynamical changes associated with Hadley cell expansion occur further equatorward at a latitude where the sensitivity of SWCRE is more strongly associated with changes in mid‐tropospheric vertical velocity, leading to a net shortwave cloud radiative warming effect in midlatitudes.

 

This paper presents an iterative learning approach for optimizing course geometry in repetitive path following applications. In particular, we focus on airborne wind energy (AWE) systems. Our proposed algorithm consists of two key features: First, a recursive least squares fit is used to construct an estimate of the behavior of the performance index. Second, an iteration-to-iteration path adaptation law is used to adjust the path shape in the direction of optimal performance. We propose two candidate update laws, both of which parallel the mathematical structure of common iterative learning control (ILC) update laws but replace the tracking-dependent terms with terms based on the performance index.We apply our formulation to the iterative crosswind path optimization of an AWE system, where the goal is to maximize the average power output over a figure-8 path. Using a physics based AWE system model, we demonstrate that the proposed adaptation strategy successfully achieves convergence to near-optimal figure-8 paths for a variety of initial conditions under both constant and real wind profiles. 

Clouds and their associated radiative effects are a large source of uncertainty in global climate models. One region with particularly large model biases in shortwave cloud radiative effects (CRE) is the Southern Ocean. Previous research has shown that many dynamical “cloud controlling factors” influence shortwave CRE on monthly time scales and that two important cloud controlling factors over the Southern Ocean are midtropospheric vertical velocity and estimated inversion strength (EIS). Model errors may thus arise from biases in representing cloud controlling factors (atmospheric dynamics) or in representing how clouds respond to those cloud controlling factors (cloud parameterizations), or some combination thereof. This study extends previous work by examining cloud controlling factors over the Southern Ocean on daily time scales in both observations and global climate models. This allows the cloud controlling factors to be examined in the context of transient weather systems. Composites of EIS and midtropospheric vertical velocity are constructed around extratropical cyclones and anticyclones to examine how the different dynamical cloud controlling factors influence shortwave CRE around midlatitude weather systems and to assess how models compare to observations. On average, models tend to produce a realistic cyclone and anticyclone, when compared to observations, in terms of the dynamical cloud controlling factors. The difference between observations and models instead lies in how the models’ shortwave CRE respond to the dynamics. In particular, the models’ shortwave CRE are too sensitive to perturbations in midtropospheric vertical velocity and, thus, they tend to produce clouds that excessively brighten in the frontal region of the cyclone and excessively dim in the center of the anticyclone.

 

The Gene Ontology (GO) is a comprehensive resource of computable knowledge regarding the functions of genes and gene products. As such, it is extensively used by the biomedical research community for the analysis of -omics and related data. Our continued focus is on improving the quality and utility of the GO resources, and we welcome and encourage input from researchers in all areas of biology. In this update, we summarize the current contents of the GO knowledgebase, and present several new features and improvements that have been made to the ontology, the annotations and the tools. Among the highlights are 1) developments that facilitate access to, and application of, the GO knowledgebase, and 2) extensions to the resource as well as increasing support for descriptions of causal models of biological systems and network biology. To learn more, visit http://geneontology.org/.