ALBERT

Description: Recent research promotes implementing next-generation wind plant control methods to mitigate turbine-to-turbine wake effects. Numerical simulation and wind tunnel experiments have previously demonstrated the potential benefit of wind plant control for wind plant optimization, but full-scale validation of the wake-mitigating control strategies remains limited. As part of this study, the yaw and blade pitch of a utility-scale wind turbine were strategically modified for a limited time period to examine wind turbine wake response to first-order turbine control changes. Wind turbine wake response was measured using Texas Tech University's Ka-band Doppler radars and dual-Doppler scanning strategies. Results highlight some of the complexities associated with executing and analyzing wind plant control at full scale using brief experimental control periods. Some difficulties include (1) the ability to accurately implement the desired control changes, (2) identifying reliable data sources and methods to allow these control changes to be accurately quantified, and (3) attributing variations in wake structure to turbine control changes rather than a response to the underlying atmospheric conditions (e.g., boundary layer streak orientation, atmospheric stability). To better understand wake sensitivity to the underlying atmospheric conditions, wake evolution within the early-evening transition was also examined using a single-Doppler data collection approach. Analysis of both wake length and meandering during this period of transitioning atmospheric stability indicates the potential benefit and feasibility of wind plant control should be enhanced when the atmosphere is stable.

Description: The role of polar regions is increasing in terms of megatrends such as globalization, new transport routes, demography, and the use of natural resources with consequent effects on regional and transported pollutant concentrations. We set up the ERA-PLANET Strand 4 project “iCUPE – integrative and Comprehensive Understanding on Polar Environments” to provide novel insights and observational data on global grand challenges with an Arctic focus. We utilize an integrated approach combining in situ observations, satellite remote sensing Earth observations (EOs), and multi-scale modeling to synthesize data from comprehensive long-term measurements, intensive campaigns, and satellites to deliver data products, metrics, and indicators to stakeholders concerning the environmental status, availability, and extraction of natural resources in the polar areas. The iCUPE work consists of thematic state-of-the-art research and the provision of novel data in atmospheric pollution, local sources and transboundary transport, the characterization of arctic surfaces and their changes, an assessment of the concentrations and impacts of heavy metals and persistent organic pollutants and their cycling, the quantification of emissions from natural resource extraction, and the validation and optimization of satellite Earth observation (EO) data streams. In this paper we introduce the iCUPE project and summarize initial results arising out of the integration of comprehensive in situ observations, satellite remote sensing, and multi-scale modeling in the Arctic context.

Description: Many studies have shown a decrease in Arctic sea ice extent. It does not logically follow, however, that the extent of the marginal ice zone (MIZ), here defined as the area of the ocean with ice concentrations from 15 % to 80 %, is also changing. Changes in the MIZ extent has implications for the level of atmospheric and ocean heat and gas exchange in the area of partially ice-covered ocean and for the extent of habitat for organisms that rely on the MIZ, from primary producers like sea ice algae to seals and birds. Here, we present, for the first time, an analysis of satellite observations of pan-Arctic averaged MIZ extent. We find no trend in the MIZ extent over the last 40 years from observations. Our results indicate that the constancy of the MIZ extent is the result of an observed increase in width of the MIZ being compensated for by a decrease in the perimeter of the MIZ as it moves further north. We present simulations from a coupled sea ice–ocean mixed layer model using a prognostic floe size distribution, which we find is consistent with, but poorly constrained by, existing satellite observations of pan-Arctic MIZ extent. We provide seasonal upper and lower bounds on MIZ extent based on the four satellite-derived sea ice concentration datasets used. We find a large and significant increase (〉50 %) in the August and September MIZ fraction (MIZ extent divided by sea ice extent) for the Bootstrap and OSI-450 observational datasets, which can be attributed to the reduction in total sea ice extent. Given the results of this study, we suggest that references to “rapid changes” in the MIZ should remain cautious and provide a specific and clear definition of both the MIZ itself and also the property of the MIZ that is changing.

Description: Since the mid-1990s, Australia's Commonwealth Science Industry and Research Organisation (CSIRO) has been developing a biogeochemical (BGC) model for coupling with a hydrodynamic and sediment model for application in estuaries, coastal waters and shelf seas. The suite of coupled models is referred to as the CSIRO Environmental Modelling Suite (EMS) and has been applied at tens of locations around the Australian continent. At a mature point in the BGC model's development, this paper presents a full mathematical description, as well as links to the freely available code and user guide. The mathematical description is structured into processes so that the details of new parameterisations can be easily identified, along with their derivation. In EMS, the underwater light field is simulated by a spectrally resolved optical model that calculates vertical light attenuation from the scattering and absorption of 20+ optically active constituents. The BGC model itself cycles carbon, nitrogen, phosphorous and oxygen through multiple phytoplankton, zooplankton, detritus and dissolved organic and inorganic forms in multiple water column and sediment layers. The water column is dynamically coupled to the sediment to resolve deposition, resuspension and benthic–pelagic biogeochemical fluxes. With a focus on shallow waters, the model also includes detailed representations of benthic plants such as seagrass, macroalgae and coral polyps. A second focus has been on, where possible, the use of geometric derivations of physical limits to constrain ecological rates. This geometric approach generally requires population-based rates to be derived from initially considering the size and shape of individuals. For example, zooplankton grazing considers encounter rates of one predator on a prey field based on summing relative motion of the predator with the prey individuals and the search area; chlorophyll synthesis includes a geometrically derived self-shading term; and the bottom coverage of benthic plants is calculated from their biomass using an exponential form derived from geometric arguments. This geometric approach has led to a more algebraically complicated set of equations when compared to empirical biogeochemical model formulations based on populations. But while being algebraically complicated, the model has fewer unconstrained parameters and is therefore simpler to move between applications than it would otherwise be. The version of EMS described here is implemented in the eReefs project that delivers a near-real-time coupled hydrodynamic, sediment and biogeochemical simulation of the Great Barrier Reef, northeast Australia, and its formulation provides an example of the application of geometric reasoning in the formulation of aquatic ecological processes.

Description: In order to ensure structural reliability, wind turbine design is typically based on the assumption of gradual degradation of material properties (fatigue loading). Nevertheless, the relation between the wake-induced load exposure of turbines and the reliability of their major components has not been sufficiently well defined and demonstrated. This study suggests a methodology that makes it possible to correlate loads with reliability of turbines in wind farms in a computationally efficient way by combining physical modeling with machine learning. It can be used for estimating the current health state of a turbine and enables a more precise prediction of the “load budget”, i.e., the effect of load-induced degradation and faults on the operating costs of wind farms. The suggested approach is demonstrated on an offshore wind farm for comparing performance, loads and lifetime estimations against recorded main bearing failures from maintenance reports. The validation of the estimated power against the 10 min supervisory control and data acquisition (SCADA) power signals shows that the surrogate model is able to capture the power performance relatively well with a 1.5 % average error in the prediction of the annual energy production (AEP). It is found that turbines positioned at the border of the wind farm with a higher expected AEP are estimated to experience earlier main bearing failures. However, a clear connection between the load estimations and failure observations could not be confirmed in this study. Finally, the analysis stresses that more failure data are required in future work to enable statistically significant associations of the observed main bearing lifetimes with load exposures across the wind farm and to validate and generalize the suggested approach and its associated findings.

Description: Long-term time series are a fundamental prerequisite to understanding and detecting climate shifts and trends. Understanding the complex interplay of changing ocean variables and the biological implication for marine ecosystems requires extensive data collection for monitoring, hypothesis testing, and validation of modelling products. In marginal seas, such as the Mediterranean Sea, there are still monitoring gaps, both in time and in space. To contribute to filling these gaps, an extensive dataset of dissolved inorganic nutrient observations (nitrate, phosphate, and silicate) was collected between 2004 and 2017 in the western Mediterranean Sea and subjected to rigorous quality control techniques to provide to the scientific community a publicly available, long-term, quality-controlled, internally consistent biogeochemical data product. The data product includes 870 stations of dissolved inorganic nutrients, including temperature and salinity, sampled during 24 cruises. Details of the quality control (primary and secondary quality control) applied are reported. The data are available in PANGAEA (https://doi.org/10.1594/PANGAEA.904172, Belgacem et al., 2019).

Description: Butanetriol and pentanetriol dialkyl glycerol tetraethers (BDGTs and PDGTs) are membrane lipids, recently discovered in sedimentary environments and in the methanogenic archaeon Methanomassiliicoccus luminyensis. They possess an unusual structure, which challenges fundamental assumptions in lipid biochemistry. Indeed, they bear a butanetriol or a pentanetriol backbone instead of a glycerol at one end of their core structure. In this study, we unambiguously located the additional methyl group of the BDGT compound on the C3 carbon of the lipid backbone via high-field nuclear magnetic resonance (NMR) experiments. We further systematically explored the abundance, distribution and isotopic composition of BDGTs and PDGTs as both intact polar and core lipid forms in marine sediments collected in contrasting environments of the Mediterranean Sea and Black Sea. High proportions of intact polar BDGTs and PDGTs in the deeper methane-laden sedimentary layers and relatively 13C-depleted BDGTs, especially in the Rhone Delta and in the Black Sea, are in agreement with a probable methanogenic source for these lipids. However, contributions from heterotrophic Archaea to BDGTs (and PDGTs) cannot be excluded, particularly in the eastern Mediterranean Sea, and contrasting BDGT and PDGT headgroup distribution patterns were observed between the different sites studied. This points to additional, non-methanogenic, archaeal sources for these lipids.

Description: When the same weather or climate simulation is run on different high-performance computing (HPC) platforms, model outputs may not be identical for a given initial condition. While the role of HPC platforms in delivering better climate projections is to some extent discussed in the literature, attention is mainly focused on scalability and performance rather than on the impact of machine-dependent processes on the numerical solution. Here we investigate the behaviour of the Preindustrial (PI) simulation prepared by the UK Met Office for the forthcoming CMIP6 (Coupled Model Intercomparison Project Phase 6) under different computing environments. Discrepancies between the means of key climate variables were analysed at different timescales, from decadal to centennial. We found that for the two simulations to be statistically indistinguishable, a 200-year averaging period must be used for the analysis of the results. Thus, constant-forcing climate simulations using the HadGEM3-GC3.1 model are reproducible on different HPC platforms provided that a sufficiently long duration of simulation is used. In regions where El Niño–Southern Oscillation (ENSO) teleconnection patterns were detected, we found large sea surface temperature and sea ice concentration differences on centennial timescales. This indicates that a 100-year constant-forcing climate simulation may not be long enough to adequately capture the internal variability of the HadGEM3-GC3.1 model, despite this being the minimum simulation length recommended by CMIP6 protocols for many MIP (Model Intercomparison Project) experiments. On the basis of our findings, we recommend a minimum simulation length of 200 years whenever possible.

Description: Glacial isostatic adjustment (GIA) is a major source of uncertainty for ice and ocean mass balance estimates derived from satellite gravimetry. In Antarctica the gravimetric effect of cryospheric mass change and GIA are of the same order of magnitude. Inverse estimates from geodetic observations hold some promise for mass signal separation. Here, we investigate the combination of satellite gravimetry and altimetry and demonstrate that the choice of input data sets and processing methods will influence the resultant GIA inverse estimate. This includes the combination that spans the full GRACE record (April 2002–August 2016). Additionally, we show the variations that arise from combining the actual time series of the differing data sets. Using the inferred trends, we assess the spread of GIA solutions owing to (1) the choice of different degree-1 and C20 products, (2) viable candidate surface-elevation-change products derived from different altimetry missions corresponding to different time intervals, and (3) the uncertainties associated with firn process models. Decomposing the total-mass signal into the ice mass and the GIA components is strongly dependent on properly correcting for an apparent bias in regions of small signal. Here our ab initio solutions force the mean GIA and GRACE trend over the low precipitation zone of East Antarctica to be zero. Without applying this bias correction, the overall spread of total-mass change and GIA-related mass change using differing degree-1 and C20 products is 68 and 72 Gt a−1, respectively, for the same time period (March 2003–October 2009). The bias correction method collapses this spread to 6 and 5 Gt a−1, respectively. We characterize the firn process model uncertainty empirically by analysing differences between two alternative surface mass balance products. The differences propagate to a 10 Gt a−1 spread in debiased GIA-related mass change estimates. The choice of the altimetry product poses the largest uncertainty on debiased mass change estimates. The spread of debiased GIA-related mass change amounts to 15 Gt a−1 for the period from March 2003 to October 2009. We found a spread of 49 Gt a−1 comparing results for the periods April 2002–August 2016 and July 2010–August 2016. Our findings point out limitations associated with data quality, data processing, and correction for apparent biases.

Description: Recent years have seen a rapid reduction in the summer Arctic sea ice extent. To both understand this trend and project the future evolution of the summer Arctic sea ice, a better understanding of the physical processes that drive the seasonal loss of sea ice is required. The marginal ice zone, here defined as regions with between 15 % and 80 % sea ice cover, is the region separating pack ice from the open ocean. Accurate modelling of this region is important to understand the dominant mechanisms involved in seasonal sea ice loss. Evolution of the marginal ice zone is determined by complex interactions between the atmosphere, sea ice, ocean, and ocean surface waves. Therefore, this region presents a significant modelling challenge. Sea ice floes span a range of sizes but sea ice models within climate models assume they adopt a constant size. Floe size influences the lateral melt rate of sea ice and momentum transfer between atmosphere, sea ice, and ocean, all important processes within the marginal ice zone. In this study, the floe size distribution is represented as a power law defined by an upper floe size cut-off, lower floe size cut-off, and power-law exponent. This distribution is also defined by a new tracer that varies in response to lateral melting, wave-induced break-up, freezing conditions, and advection. This distribution is implemented within a sea ice model coupled to a prognostic ocean mixed-layer model. We present results to show that the use of a power-law floe size distribution has a spatially and temporally dependent impact on the sea ice, in particular increasing the role of the marginal ice zone in seasonal sea ice loss. This feature is important in correcting existing biases within sea ice models. In addition, we show a much stronger model sensitivity to floe size distribution parameters than other parameters used to calculate lateral melt, justifying the focus on floe size distribution in model development. We also find that the attenuation rate of waves propagating under the sea ice cover modulates the impact of wave break-up on the floe size distribution. It is finally concluded that the model approach presented here is a flexible tool for assessing the importance of a floe size distribution in the evolution of sea ice and is a useful stepping stone for future development of floe size modelling.