Emulating Fast Processes in Climate Models

Cloud microphysical parameterizations in atmospheric models describe the formation and evolution of clouds and precipitation, a central weather and climate process. Cloud-associated latent heating is a primary driver of large and small-scale circulations throughout the global atmosphere, and clouds have important interactions with atmospheric radiation. Clouds are ubiquitous, diverse, and can change rapidly. In this work, we build the first emulator of an entire cloud microphysical parameterization, including fast phase changes. The emulator performs well in offline and online (i.e. when coupled to the rest of the atmospheric model) tests, but shows some developing biases in Antarctica. Sensitivity tests demonstrate that these successes require careful modeling of the mixed discrete-continuous output as well as the input-output structure of the underlying code and physical process.Comment: Accepted at the Machine Learning and the Physical Sciences Workshop at the 36th conference on Neural Information Processing Systems (NeurIPS) December 3, 202

Machine-learned climate model corrections from a global storm-resolving model

Due to computational constraints, running global climate models (GCMs) for many years requires a lower spatial grid resolution (${\gtrsim}50$ km) than is optimal for accurately resolving important physical processes. Such processes are approximated in GCMs via subgrid parameterizations, which contribute significantly to the uncertainty in GCM predictions. One approach to improving the accuracy of a coarse-grid global climate model is to add machine-learned state-dependent corrections at each simulation timestep, such that the climate model evolves more like a high-resolution global storm-resolving model (GSRM). We train neural networks to learn the state-dependent temperature, humidity, and radiative flux corrections needed to nudge a 200 km coarse-grid climate model to the evolution of a 3~km fine-grid GSRM. When these corrective ML models are coupled to a year-long coarse-grid climate simulation, the time-mean spatial pattern errors are reduced by 6-25% for land surface temperature and 9-25% for land surface precipitation with respect to a no-ML baseline simulation. The ML-corrected simulations develop other biases in climate and circulation that differ from, but have comparable amplitude to, the baseline simulation

Social Status Affects the Degree of Sex Difference in the Songbird Brain

It is thought that neural sex differences are functionally related to sex differences in the behaviour of vertebrates. A prominent example is the song control system of songbirds. Inter-specific comparisons have led to the hypothesis that sex differences in song nuclei size correlate with sex differences in song behaviour. However, only few species with similar song behaviour in both sexes have been investigated and not all data fit the hypothesis. We investigated the proposed structure – function relationship in a cooperatively breeding and duetting songbird, the white-browed sparrow weaver (Plocepasser mahali). This species lives in groups of 2–10 individuals, with a dominant breeding pair and male and female subordinates. While all male and female group members sing duet and chorus song, a male, once it has reached the dominant position in the group, sings an additional type of song that comprises a distinct and large syllable repertoire. Here we show for both types of male – female comparisons a male-biased sex difference in neuroanatomy of areas of the song production pathway (HVC and RA) that does not correlate with the observed polymorphism in song behaviour. In contrast, in situ hybridisation of mRNA of selected genes expressed in the song nucleus HVC reveals a gene expression pattern that is either similar between sexes in female – subordinate male comparisons or female-biased in female – dominant male comparisons. Thus, the polymorphic gene expression pattern would fit the sex- and status-related song behaviour. However, this implies that once a male has become dominant it produces the duetting song with a different neural phenotype than subordinate males

RNA molecules with conserved catalytic cores but variable peripheries fold along unique energetically optimized pathways

Functional and kinetic constraints must be efficiently balanced during the folding process of all biopolymers. To understand how homologous RNA molecules with different global architectures fold into a common core structure we determined, under identical conditions, the folding mechanisms of three phylogenetically divergent group I intron ribozymes. These ribozymes share a conserved functional core defined by topologically equivalent tertiary motifs but differ in their primary sequence, size, and structural complexity. Time-resolved hydroxyl radical probing of the backbone solvent accessible surface and catalytic activity measurements integrated with structural-kinetic modeling reveal that each ribozyme adopts a unique strategy to attain the conserved functional fold. The folding rates are not dictated by the size or the overall structural complexity, but rather by the strength of the constituent tertiary motifs which, in turn, govern the structure, stability, and lifetime of the folding intermediates. A fundamental general principle of RNA folding emerges from this study: The dominant folding flux always proceeds through an optimally structured kinetic intermediate that has sufficient stability to act as a nucleating scaffold while retaining enough conformational freedom to avoid kinetic trapping. Our results also suggest a potential role of naturally selected peripheral A-minor interactions in balancing RNA structural stability with folding efficiency

The Songbird Neurogenomics (SoNG) Initiative: Community-based tools and strategies for study of brain gene function and evolution

BACKGROUND: Songbirds hold great promise for biomedical, environmental and evolutionary research. A complete draft sequence of the zebra finch genome is imminent, yet a need remains for application of genomic resources within a research community traditionally focused on ethology and neurobiological methods. In response, we developed a core set of genomic tools and a novel collaborative strategy to probe gene expression in diverse songbird species and natural contexts. RESULTS: We end-sequenced cDNAs from zebra finch brain and incorporated additional sequences from community sources into a database of 86,784 high quality reads. These assembled into 31,658 non-redundant contigs and singletons, which we annotated via BLAST search of chicken and human databases. The results are publicly available in the ESTIMA:Songbird database. We produced a spotted cDNA microarray with 20,160 addresses representing 17,214 non-redundant products of an estimated 11,500–15,000 genes, validating it by analysis of immediate-early gene (zenk) gene activation following song exposure and by demonstrating effective cross hybridization to genomic DNAs of other songbird species in the Passerida Parvorder. Our assembly was also used in the design of the "Lund-zfa" Affymetrix array representing ~22,000 non-redundant sequences. When the two arrays were hybridized to cDNAs from the same set of male and female zebra finch brain samples, both arrays detected a common set of regulated transcripts with a Pearson correlation coefficient of 0.895. To stimulate use of these resources by the songbird research community and to maintain consistent technical standards, we devised a "Community Collaboration" mechanism whereby individual birdsong researchers develop experiments and provide tissues, but a single individual in the community is responsible for all RNA extractions, labelling and microarray hybridizations. CONCLUSION: Immediately, these results set the foundation for a coordinated set of 25 planned experiments by 16 research groups probing fundamental links between genome, brain, evolution and behavior in songbirds. Energetic application of genomic resources to research using songbirds should help illuminate how complex neural and behavioral traits emerge and evolve

Modulation by steroid hormones of a "sexy" acoustic signal in an Oscine species, the Common Canary Serinus canaria

The respective influence of testosterone and estradiol on the structure of the Common Canary Serinus canaria song was studied by experimentally controlling blood levels of steroid hormones in males and analyzing the consequent effects on acoustic parameters. A detailed acoustic analysis of the songs produced before and after hormonal manipulation revealed that testosterone and estradiol seem to control distinct song parameters independently. The presence of receptors for testosterone and estradiol in the brain neural pathway controlling song production strongly suggests that the observed effects are mediated by a steroid action at the neuronal level.<br>A influência da testosterona e do estradiol, respectivamente, na estrutura do canto do Canário-do-reino Serinus canaria foi estudada analisando o efeito da manipulação dos níveis sanguíneos de hormônios esteróides em machos nos parâmetros acústicos do canto. Uma analise detalhada dos cantos produzidos antes e depois da manipulação hormonal revelou que testosterona e estradiol parecem controlar independentemente parâmetros acústicos distintos. A presença de receptores para esses hormônios no circuito neuronal para controle da produção do canto sugere fortemente que os efeitos observados são mediados pela ação de esteróides a nivel neuronal

Seasonal changes in patterns of gene expression in avian song control brain regions.

This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Photoperiod and hormonal cues drive dramatic seasonal changes in structure and function of the avian song control system. Little is known, however, about the patterns of gene expression associated with seasonal changes. Here we address this issue by altering the hormonal and photoperiodic conditions in seasonally-breeding Gambel's white-crowned sparrows and extracting RNA from the telencephalic song control nuclei HVC and RA across multiple time points that capture different stages of growth and regression. We chose HVC and RA because while both nuclei change in volume across seasons, the cellular mechanisms underlying these changes differ. We thus hypothesized that different genes would be expressed between HVC and RA. We tested this by using the extracted RNA to perform a cDNA microarray hybridization developed by the SoNG initiative. We then validated these results using qRT-PCR. We found that 363 genes varied by more than 1.5 fold (>log(2) 0.585) in expression in HVC and/or RA. Supporting our hypothesis, only 59 of these 363 genes were found to vary in both nuclei, while 132 gene expression changes were HVC specific and 172 were RA specific. We then assigned many of these genes to functional categories relevant to the different mechanisms underlying seasonal change in HVC and RA, including neurogenesis, apoptosis, cell growth, dendrite arborization and axonal growth, angiogenesis, endocrinology, growth factors, and electrophysiology. This revealed categorical differences in the kinds of genes regulated in HVC and RA. These results show that different molecular programs underlie seasonal changes in HVC and RA, and that gene expression is time specific across different reproductive conditions. Our results provide insights into the complex molecular pathways that underlie adult neural plasticity