162 research outputs found

    Modeled contrast in the response of the surface energy balance to heat waves for forest and grassland

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    Observations have shown that differences in surface energy fluxes over grasslands and forests are amplified during heat waves. The role of land-atmosphere feedbacks in this process is still uncertain. In this study, a single-column model (SCM) is used to investigate the difference between forest and grassland in their energy response to heat waves. Three simulations for the period 2005-11 were carried out: a control run using vegetation characteristics for Cabauw (the Netherlands), a run where the vegetation is changed to 100% forest, and a run with 100% short grass as vegetation. A surface evaporation tendency equation is used to analyze the impact of the land-atmosphere feedbacks on evapotranspiration and sensible heat release under normal summer and heat wave conditions with excessive shortwave radiation. Land-atmosphere feedbacks modify the contrast in surface energy fluxes between forest and grass, particularly during heat wave conditions. The surface resistance feedback has the largest positive impact, while boundary layer feedbacks generally tend to reduce the contrast. Overall, forests give higher air temperatures and drier atmospheres during heat waves. In offline land surface model simulations, the difference between forest and grassland during heat waves cannot be diagnosed adequately owing to the absence of boundary layer feedbacks

    Ten years of 1 Hz solar irradiance observations at Cabauw, the Netherlands, with cloud observations, variability classifications, and statistics

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    Surface solar irradiance varies on scales down to seconds, and detailed long-term observational datasets of this variable are rare but in high demand. Here, we present an observational dataset of global, direct, and diffuse solar irradiance sampled at 1 Hz as well as fully resolved variability until at least 0.1 Hz over a period of 10 years from the Baseline Surface Radiation Network (BSRN) station at Cabauw, the Netherlands. The dataset is complemented with irradiance variability classifications, clear-sky irradiance and aerosol reanalysis, information about the solar position, observations of clouds and sky type, and wind measurements up to 200 m above ground level. Statistics of variability derived from all time series include approximately 185 000 detected events of both cloud enhancement and cloud shadows. Additional observations from the Cabauw measurement site are freely available from the open-data platform of the Royal Netherlands Meteorological Institute. This paper describes the observational site, quality control, classification algorithm with validation, and the processing method of complementary products. Additionally, we discuss and showcase (potential) applications, including limitations due to sensor response time. These observations and derived statistics provide detailed information to aid research into how clouds and atmospheric composition influence solar irradiance variability as well as information to help validate models that are starting to resolve variability at higher fidelity. The main datasets are available at https://doi.org/10.5281/zenodo.7093164 (Knap and Mol, 2022) and https://doi.org/10.5281/zenodo.7462362 (Mol et al., 2022); the reader is referred to the “Code and data availability” section of this paper for the complete list.</p

    Record high solar irradiance in Western Europe during first COVID-19 lockdown largely due to unusual weather

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    Spring 2020 broke sunshine duration records across western Europe. The Netherlands recorded the highest surface irradiance since 1928, exceeding the previous extreme of 2011 by 13 %, and the diffuse fraction of the irradiance measured a record low percentage (38 %). The coinciding irradiance extreme and a reduction in anthropogenic pollution due to COVID-19 measures triggered the hypothesis that cleaner-than-usual air contributed to the record. Based on analyses of ground-based and satellite observations and experiments with a radiative transfer model, we estimate a 1.3 % (2.3 W m2^{-2}) increase in surface irradiance with respect to the 2010-2019 mean due to a low median aerosol optical depth, and a 17.6 % (30.7 W m2^{-2}) increase due to several exceptionally dry days and a very low cloud fraction overall. Our analyses show that the reduced aerosols and contrails due to the COVID-19 measures are far less important in the irradiance record than the dry and particularly cloud-free weather.Comment: 21 pages, 12 figures, submitted to Communications Earth and Environmen

    Major histocompatibility genes in the Lake Tana African large barb species flock: evidence for complete partitioning of class II B, but nog class I, genes among different species

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    The 16 African large barb fish species of Lake Tana inhabit different ecological niches, exploit different food webs and have different temporal and spatial spawning patterns within the lake. This unique fish species flock is thought to be the result of adaptive radiation within the past 5 million years. Previous analyses of major histocompatibility class II B exon 2 sequences in four Lake Tana African large barb species revealed that these sequences are indeed under selection. No sharing of class II B alleles was observed among the four Lake Tana African large barb species. In this study we analysed the class II B exon 2 sequences of seven additional Lake Tana African large barb species and African large barbs from the Blue Nile and its tributaries. In addition, the presence and variability of major histocompatibility complex class I UA exon 3 sequences in six Lake Tana and Blue Nile African large barb species was analysed. Phylogenetic lineages are maintained by purifying or neutral selection on non-peptide binding regions. Class II B intron 1 and exon 2 sequences were not shared among the different Lake Tana African large barb species or with the riverine barb species. In contrast, identical class I UA exon 3 sequences were found both in the lacustrine and riverine barb species. Our analyses demonstrate complete partitioning of class II B alleles among Lake Tana African large barb species. In contrast, class I alleles remain for the large part shared among species. These different modes of evolution probably reflect the unlinked nature of major histocompatibility genes in teleost fishes

    A stable isotope assay with 13C-labeled polyethylene to investigate plastic mineralization mediated by Rhodococcus ruber

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    Methods that unambiguously prove microbial plastic degradation and allow for quantification of degradation rates are necessary to constrain the influence of microbial degradation on the marine plastic budget. We developed an assay based on stable isotope tracer techniques to determine microbial plastic mineralization rates in liquid medium on a lab scale. For the experiments, 13C-labeled polyethylene (13C-PE) particles (irradiated with UV-light to mimic exposure of floating plastic to sunlight) were incubated in liquid medium with Rhodococcus ruber as a model organism for proof of principle. The transfer of 13C from 13C-PE into the gaseous and dissolved CO2 pools translated to microbially mediated mineralization rates of up to 1.2 % yr−1 of the added PE. After incubation, we also found highly 13C-enriched membrane fatty acids of R. ruber including compounds involved in cellular stress responses. We demonstrated that isotope tracer techniques are a valuable tool to detect and quantify microbial plastic degradation

    Complex patterns of local adaptation in teosinte

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    Populations of widely distributed species often encounter and adapt to specific environmental conditions. However, comprehensive characterization of the genetic basis of adaptation is demanding, requiring genome-wide genotype data, multiple sampled populations, and a good understanding of population structure. We have used environmental and high-density genotype data to describe the genetic basis of local adaptation in 21 populations of teosinte, the wild ancestor of maize. We found that altitude, dispersal events and admixture among subspecies formed a complex hierarchical genetic structure within teosinte. Patterns of linkage disequilibrium revealed four mega-base scale inversions that segregated among populations and had altitudinal clines. Based on patterns of differentiation and correlation with environmental variation, inversions and nongenic regions play an important role in local adaptation of teosinte. Further, we note that strongly differentiated individual populations can bias the identification of adaptive loci. The role of inversions in local adaptation has been predicted by theory and requires attention as genome-wide data become available for additional plant species. These results also suggest a potentially important role for noncoding variation, especially in large plant genomes in which the gene space represents a fraction of the entire genome

    Land–Atmosphere Interactions: The LoCo Perspective

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    Land–atmosphere (L-A) interactions are a main driver of Earth’s surface water and energy budgets; as such, they modulate near-surface climate, including clouds and precipitation, and can influence the persistence of extremes such as drought. Despite their importance, the representation of L-A interactions in weather and climate models remains poorly constrained, as they involve a complex set of processes that are difficult to observe in nature. In addition, a complete understanding of L-A processes requires interdisciplinary expertise and approaches that transcend traditional research paradigms and communities. To address these issues, the international Global Energy and Water Exchanges project (GEWEX) Global Land–Atmosphere System Study (GLASS) panel has supported “L-A coupling” as one of its core themes for well over a decade. Under this initiative, several successful land surface and global climate modeling projects have identified hot spots of L-A coupling and helped quantify the role of land surface states in weather and climate predictability. GLASS formed the Local Land–Atmosphere Coupling (LoCo) project and working group to examine L-A interactions at the process level, focusing on understanding and quantifying these processes in nature and evaluating them in models. LoCo has produced an array of L-A coupling metrics for different applications and scales and has motivated a growing number of young scientists from around the world. This article provides an overview of the LoCo effort, including metric and model applications, along with scientific and programmatic developments and challenges

    Land-Atmosphere Interactions: The LoCo Perspective

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    Land-atmosphere (L-A) interactions are a main driver of Earth's surface water and energy budgets; as such, they modulate near-surface climate, including clouds and precipitation, and can influence the persistence of extremes such as drought. Despite their importance, the representation of L-A interactions in weather and climate models remains poorly constrained, as they involve a complex set of processes that are difficult to observe in nature. In addition, a complete understanding of L-A processes requires interdisciplinary expertise and approaches that transcend traditional research paradigms and communities. To address these issues, the international Global Energy and Water Exchanges project (GEWEX) Global Land-Atmosphere System Study (GLASS) panel has supported 'L-A coupling' as one of its core themes for well over a decade. Under this initiative, several successful land surface and global climate modeling projects have identified hotspots of L-A coupling and helped quantify the role of land surface states in weather and climate predictability. GLASS formed the Local L-A Coupling ('LoCo') project and working group to examine L-A interactions at the process level, focusing on understanding and quantifying these processes in nature and evaluating them in models. LoCo has produced an array of L-A coupling metrics for different applications and scales, and has motivated a growing number of young scientists from around the world. This article provides an overview of the LoCo effort, including metric and model applications, along with scientific and programmatic developments and challenges

    Consistency, variability, and predictability of on-farm nutrient responses in four grain legumes across east and west Africa

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    Open Access Article; Published online: 26 May 2023Grain legumes are key components of sustainable production systems in sub-Saharan Africa, but wide-spread nutrient deficiencies severely restrict yields. Whereas legumes can meet a large part of their nitrogen (N) requirement through symbiosis with N2-fixing bacteria, elements such as phosphorus (P), potassium (K) and secondary and micronutrients may still be limiting and require supplementation. Responses to P are generally strong but variable, while evidence for other nutrients tends to show weak or highly localised effects. Here we present the results of a joint statistical analysis of a series of on-farm nutrient addition trials, implemented across four legumes in four countries over two years. Linear mixed models were used to quantify both mean nutrient responses and their variability, followed by a random forest analysis to determine the extent to which such variability can be explained or predicted by geographic, environmental or farm survey data. Legume response to P was indeed variable, but consistently positive and we predicted application to be profitable for 67% of farms in any given year, based on prevailing input costs and grain prices. Other nutrients did not show significant mean effects, but considerable response variation was found. This response heterogeneity was mostly associated with local or temporary factors and could not be explained or predicted by spatial, biophysical or management factors. An exception was K response, which displayed appreciable spatial variation that could be partly accounted for by spatial and environmental covariables. While of apparent relevance for targeted recommendations, the minor amplitude of expected response, the large proportion of unexplained variation and the unreliability of the predicted spatial patterns suggests that such data-driven targeting is unlikely to be effective with current data
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