A comprehensive benchmarking system for evaluating global vegetation models.

We present a benchmark system for global vegetation models. This system provides a quantitative evaluation of multiple simulated vegetation properties, including primary production; seasonal net ecosystem production; vegetation cover, composition and 5 height; fire regime; and runoff. The benchmarks are derived from remotely sensed gridded datasets and site-based observations. The datasets allow comparisons of annual average conditions and seasonal and inter-annual variability, and they allow the impact of spatial and temporal biases in means and variability to be assessed separately. Specifically designed metrics quantify model performance for each process, 10 and are compared to scores based on the temporal or spatial mean value of the observations and a “random” model produced by bootstrap resampling of the observations. The benchmark system is applied to three models: a simple light-use efficiency and water-balance model (the Simple Diagnostic Biosphere Model: SDBM), and the Lund-Potsdam-Jena (LPJ) and Land Processes and eXchanges (LPX) dynamic global 15 vegetation models (DGVMs). SDBM reproduces observed CO2 seasonal cycles, but its simulation of independent measurements of net primary production (NPP) is too high. The two DGVMs show little difference for most benchmarks (including the interannual variability in the growth rate and seasonal cycle of atmospheric CO2), but LPX represents burnt fraction demonstrably more accurately. Benchmarking also identified 20 several weaknesses common to both DGVMs. The benchmarking system provides a quantitative approach for evaluating how adequately processes are represented in a model, identifying errors and biases, tracking improvements in performance through model development, and discriminating among models. Adoption of such a system would do much to improve confidence in terrestrial model predictions of climate change 25 impacts and feedbacks.

Programmable data acquisition system for research measurements from meteorological radiosondes

A programmable data acquisition system to allow novel use of meteorological radiosondes for atmospheric science measurements is described. In its basic form it supports four analogue inputs at 16 bit resolution, and up to two further inputs at lower resolution configurable instead for digital instruments. It also provides multiple instrument power supplies (+8V, +16V, +5V and -8V) from the 9V radiosonde battery. During a balloon flight encountering air temperatures from +17°C to -66°C, the worst case voltage drift in the 5V unipolar digitisation circuitry was 20mV. The system liberates a new range of low cost atmospheric research measurements, by utilising radiosondes routinely launched internationally for weather forecasting purposes. No additional receiving equipment is required. Comparisons between the specially instrumented and standard meteorological radiosondes show negligible effect of the additional instrumentation on the standard meteorological data.

Coassembly in binary mixtures of peptide amphiphiles containing oppositely charged residues

The self-assembly in water of designed peptide amphiphile (PA) C16-ETTES containing two anionic residues and its mixtures with C16-KTTKS containing two cationic residues has been investigated. Multiple spectroscopy, microscopy, and scattering techniques are used to examine ordering extending from the β-sheet structures up to the fibrillar aggregate structure. The peptide amphiphiles both comprise a hexadecyl alkyl chain and a charged pentapeptide headgroup containing two charged residues. For C16-ETTES, the critical aggregation concentration was determined by fluorescence experiments. FTIR and CD spectroscopy were used to examine β-sheet formation. TEM revealed highly extended tape nanostructures with some striped regions corresponding to bilayer structures viewed edge-on. Small-angle X-ray scattering showed a main 5.3 nm bilayer spacing along with a 3 nm spacing. These spacings are assigned respectively to predominant hydrated bilayers and a fraction of dehydrated bilayers. Signs of cooperative self-assembly are observed in the mixtures, including reduced bundling of peptide amphiphile aggregates (extended tape structures) and enhanced β-sheet formation.

The interactive responses of water quality and hydrology to changes in multiple stressors, and implications for the long-term effective management of phosphorus

Soluble reactive phosphorus (SRP) plays a key role in eutrophication, a global problem decreasing habitat quality and in-stream biodiversity. Mitigation strategies are required to prevent SRP fluxes from exceeding critical levels, and must be robust in the face of potential changes in climate, land use and a myriad of other influences. To establish the longevity of these strategies it is therefore crucial to consider the sensitivity of catchments to multiple future stressors. This study evaluates how the water quality and hydrology of a major river system in the UK (the River Thames) respond to alterations in climate, land use and water resource allocations, and investigates how these changes impact the relative performance of management strategies over an 80-year period. In the River Thames, the relative contributions of SRP from diffuse and point sources vary seasonally. Diffuse sources of SRP from agriculture dominate during periods of high runoff, and point sources during low flow periods. SRP concentrations rose under any future scenario which either increased a) surface runoff or b) the area of cultivated land. Under these conditions, SRP was sourced from agriculture, and the most effective single mitigation measures were those which addressed diffuse SRP sources. Conversely, where future scenarios reduced flow e.g. during winters of reservoir construction, the significance of point source inputs increased, and mitigation measures addressing these issues became more effective. In catchments with multiple point and diffuse sources of SRP, an all-encompassing effective mitigation approach is difficult to achieve with a single strategy. In order to attain maximum efficiency, multiple strategies might therefore be employed at different times and locations, to target the variable nature of dominant SRP sources and pathways.

Climate determinism revisited: multiple equilibria in a complex climate model

Multiple equilibria in a coupled ocean–atmosphere–sea ice general circulation model (GCM) of an aquaplanet with many degrees of freedom are studied. Three different stable states are found for exactly the same set of parameters and external forcings: a cold state in which a polar sea ice cap extends into the midlatitudes; a warm state, which is ice free; and a completely sea ice–covered “snowball” state. Although low-order energy balance models of the climate are known to exhibit intransitivity (i.e., more than one climate state for a given set of governing equations), the results reported here are the first to demonstrate that this is a property of a complex coupled climate model with a consistent set of equations representing the 3D dynamics of the ocean and atmosphere. The coupled model notably includes atmospheric synoptic systems, large-scale circulation of the ocean, a fully active hydrological cycle, sea ice, and a seasonal cycle. There are no flux adjustments, with the system being solely forced by incoming solar radiation at the top of the atmosphere. It is demonstrated that the multiple equilibria owe their existence to the presence of meridional structure in ocean heat transport: namely, a large heat transport out of the tropics and a relatively weak high-latitude transport. The associated large midlatitude convergence of ocean heat transport leads to a preferred latitude at which the sea ice edge can rest. The mechanism operates in two very different ocean circulation regimes, suggesting that the stabilization of the large ice cap could be a robust feature of the climate system. Finally, the role of ocean heat convergence in permitting multiple equilibria is further explored in simpler models: an atmospheric GCM coupled to a slab mixed layer ocean and an energy balance model

Joint compensation of multiple RF impairments in MIMO STBC systems

In this paper, we propose a compensation method for the joint effect of high-power amplifier (HPA) nonlinearity, in-phase/quadrature-phase (I/Q) imbalance and crosstalk in multiple-input multiple-output (MIMO) orthogonal space-time block coding (OSTBC) systems. The performance of the MIMO OSTBC equipped with the proposed compensation mechanism is evaluated in terms of average symbol error probability and system capacity, in Rayleigh fading channels. Numerical results are provided and show the effects on performance of several system parameters, namely, the HPA parameters, image-leakage ratio, crosstalk, numbers of antennas, and phase-shift keying modulation order.

Development and evaluation of an Earth-System model – HadGEM2

We describe here the development and evaluation of an Earth system model suitable for centennial-scale climate prediction. The principal new components added to the physical climate model are the terrestrial and ocean ecosystems and gas-phase tropospheric chemistry, along with their coupled interactions. The individual Earth system components are described briefly and the relevant interactions between the components are explained. Because the multiple interactions could lead to unstable feedbacks, we go through a careful process of model spin up to ensure that all components are stable and the interactions balanced. This spun-up configuration is evaluated against observed data for the Earth system components and is generally found to perform very satisfactorily. The reason for the evaluation phase is that the model is to be used for the core climate simulations carried out by the Met Office Hadley Centre for the Coupled Model Intercomparison Project (CMIP5), so it is essential that addition of the extra complexity does not detract substantially from its climate performance. Localised changes in some specific meteorological variables can be identified, but the impacts on the overall simulation of present day climate are slight. This model is proving valuable both for climate predictions, and for investigating the strengths of biogeochemical feedbacks.

Genetic variation in GABRB3 is associated with Asperger syndrome and multiple endophenotypes relevant to autism.

BACKGROUND: Autism spectrum conditions (ASC) are associated with deficits in social interaction and communication, alongside repetitive, restricted, and stereotyped behavior. ASC is highly heritable. The gamma-aminobutyric acid (GABA)-ergic system has been associated consistently with atypicalities in autism, in both genetic association and expression studies. A key component of the GABA-ergic system is encoded by the GABRB3 gene, which has been previously implicated both in ASC and in individual differences in empathy. METHODS: In this study, 45 genotyped single nucleotide polymorphisms (SNPs) within GABRB3 were tested for association with Asperger syndrome (AS), and related quantitative traits measured through the following tests: the Empathy Quotient (EQ), the Autism Spectrum Quotient (AQ), the Systemizing Quotient-Revised (SQ-R), the Embedded Figures Test (EFT), the Reading the Mind in the Eyes Test (RMET), and the Mental Rotation Test (MRT). Two-loci, three-loci, four-loci haplotype analyses, and one seven-loci haplotype analysis were also performed in the AS case--control sample. RESULTS: Three SNPs (rs7180158, rs7165604, rs12593579) were significantly associated with AS, and two SNPs (rs9806546, rs11636966) were significantly associated with EQ. Two SNP-SNP pairs, rs12438141-rs1035751 and rs12438141-rs7179514, showed significant association with variation in the EFT scores. One SNP-SNP pair, rs7174437-rs1863455, was significantly associated with variation in the MRT scores. Additionally, a few haplotypes, including a 19 kb genomic region that formed a linkage disequilibrium (LD) block in our sample and contained several nominally significant SNPs, were found to be significantly associated with AS. CONCLUSION: The current study confirms the role of GABRB3 as an important candidate gene in both ASC and normative variation in related endophenotypes.

Representation of model error in a convective-scale ensemble prediction system

In this paper ensembles of forecasts (of up to six hours) are studied from a convection-permitting model with a representation of model error due to unresolved processes. The ensemble prediction system (EPS) used is an experimental convection-permitting version of the UK Met Office’s 24- member Global and Regional Ensemble Prediction System (MOGREPS). The method of representing model error variability, which perturbs parameters within the model’s parameterisation schemes, has been modified and we investigate the impact of applying this scheme in different ways. These are: a control ensemble where all ensemble members have the same parameter values; an ensemble where the parameters are different between members, but fixed in time; and ensembles where the parameters are updated randomly every 30 or 60 min. The choice of parameters and their ranges of variability have been determined from expert opinion and parameter sensitivity tests. A case of frontal rain over the southern UK has been chosen, which has a multi-banded rainfall structure. The consequences of including model error variability in the case studied are mixed and are summarised as follows. The multiple banding, evident in the radar, is not captured for any single member. However, the single band is positioned in some members where a secondary band is present in the radar. This is found for all ensembles studied. Adding model error variability with fixed parameters in time does increase the ensemble spread for near-surface variables like wind and temperature, but can actually decrease the spread of the rainfall. Perturbing the parameters periodically throughout the forecast does not further increase the spread and exhibits “jumpiness” in the spread at times when the parameters are perturbed. Adding model error variability gives an improvement in forecast skill after the first 2–3 h of the forecast for near-surface temperature and relative humidity. For precipitation skill scores, adding model error variability has the effect of improving the skill in the first 1–2 h of the forecast, but then of reducing the skill after that. Complementary experiments were performed where the only difference between members was the set of parameter values (i.e. no initial condition variability). The resulting spread was found to be significantly less than the spread from initial condition variability alone.

A comprehensive benchmarking system for evaluating global vegetation models

We present a benchmark system for global vegetation models. This system provides a quantitative evaluation of multiple simulated vegetation properties, including primary production; seasonal net ecosystem production; vegetation cover; composition and height; fire regime; and runoff. The benchmarks are derived from remotely sensed gridded datasets and site-based observations. The datasets allow comparisons of annual average conditions and seasonal and inter-annual variability, and they allow the impact of spatial and temporal biases in means and variability to be assessed separately. Specifically designed metrics quantify model performance for each process, and are compared to scores based on the temporal or spatial mean value of the observations and a "random" model produced by bootstrap resampling of the observations. The benchmark system is applied to three models: a simple light-use efficiency and water-balance model (the Simple Diagnostic Biosphere Model: SDBM), the Lund-Potsdam-Jena (LPJ) and Land Processes and eXchanges (LPX) dynamic global vegetation models (DGVMs). In general, the SDBM performs better than either of the DGVMs. It reproduces independent measurements of net primary production (NPP) but underestimates the amplitude of the observed CO2 seasonal cycle. The two DGVMs show little difference for most benchmarks (including the inter-annual variability in the growth rate and seasonal cycle of atmospheric CO2), but LPX represents burnt fraction demonstrably more accurately. Benchmarking also identified several weaknesses common to both DGVMs. The benchmarking system provides a quantitative approach for evaluating how adequately processes are represented in a model, identifying errors and biases, tracking improvements in performance through model development, and discriminating among models. Adoption of such a system would do much to improve confidence in terrestrial model predictions of climate change impacts and feedbacks.

Portable smartphone quantitation of prostate specific antigen (PSA) in a fluoropolymer microfluidic device

We present a new, power-free and flexible detection system named MCFphone for portable colorimetric and fluorescence quantitative sandwich immunoassay detection of prostate specific antigen (PSA). The MCFphone is composed by a smartphone integrated with a magnifying lens, a simple light source and a miniaturised immunoassay platform, the Microcapillary Film (MCF). The excellent transparency and flat geometry of fluoropolymer MCF allowed quantitation of PSA in the range 0.9 to 60 ng/ml with < 7 % precision in 13 minutes using enzymatic amplification and a chromogenic substrate. The lower limit of detection was further improved from 0.4 to 0.08 ng/ml in whole blood samples with the use of a fluorescence substrate. The MCFphone has shown capable of performing rapid (13 to 22 minutes total assay time) colorimetric quantitative and highly sensitive fluorescence tests with good %Recovery, which represents a major step in the integration of a new generation of inexpensive and portable microfluidic devices with commercial immunoassay reagents and off-the-shelf smartphone technology.

Engineering a plant community to deliver multiple ecosystem services

The sustainable delivery of multiple ecosystem services requires the management of functionally diverse biological communities. In an agricultural context, an emphasis on food production has often led to a loss of biodiversity to the detriment of other ecosystem services such as the maintenance of soil health and pest regulation. In scenarios where multiple species can be grown together, it may be possible to better balance environmental and agronomic services through the targeted selection of companion species. We used the case study of legume-based cover crops to engineer a plant community that delivered the optimal balance of six ecosystem services: early productivity, regrowth following mowing, weed suppression, support of invertebrates, soil fertility building (measured as yield of following crop), and conservation of nutrients in the soil. An experimental species pool of 12 cultivated legume species was screened for a range of functional traits and ecosystem services at five sites across a geographical gradient in the United Kingdom. All possible species combinations were then analyzed, using a process-based model of plant competition, to identify the community that delivered the best balance of services at each site. In our system, low to intermediate levels of species richness (one to four species) that exploited functional contrasts in growth habit and phenology were identified as being optimal. The optimal solution was determined largely by the number of species and functional diversity represented by the starting species pool, emphasizing the importance of the initial selection of species for the screening experiments. The approach of using relationships between functional traits and ecosystem services to design multifunctional biological communities has the potential to inform the design of agricultural systems that better balance agronomic and environmental services and meet the current objective of European agricultural policy to maintain viable food production in the context of the sustainable management of natural resources.

Decadal predictability of regional-scale peak winds over Europe using the Earth System Model of the Max-Planck-Institute for Meteorology

The predictability of high impact weather events on multiple time scales is a crucial issue both in scientific and socio-economic terms. In this study, a statistical-dynamical downscaling (SDD) approach is applied to an ensemble of decadal hindcasts obtained with the Max-Planck-Institute Earth System Model (MPI-ESM) to estimate the decadal predictability of peak wind speeds (as a proxy for gusts) over Europe. Yearly initialized decadal ensemble simulations with ten members are investigated for the period 1979–2005. The SDD approach is trained with COSMO-CLM regional climate model simulations and ERA-Interim reanalysis data and applied to the MPI-ESM hindcasts. The simulations for the period 1990–1993, which was characterized by several windstorm clusters, are analyzed in detail. The anomalies of the 95 % peak wind quantile of the MPI-ESM hindcasts are in line with the positive anomalies in reanalysis data for this period. To evaluate both the skill of the decadal predictability system and the added value of the downscaling approach, quantile verification skill scores are calculated for both the MPI-ESM large-scale wind speeds and the SDD simulated regional peak winds. Skill scores are predominantly positive for the decadal predictability system, with the highest values for short lead times and for (peak) wind speeds equal or above the 75 % quantile. This provides evidence that the analyzed hindcasts and the downscaling technique are suitable for estimating wind and peak wind speeds over Central Europe on decadal time scales. The skill scores for SDD simulated peak winds are slightly lower than those for large-scale wind speeds. This behavior can be largely attributed to the fact that peak winds are a proxy for gusts, and thus have a higher variability than wind speeds. The introduced cost-efficient downscaling technique has the advantage of estimating not only wind speeds but also estimates peak winds (a proxy for gusts) and can be easily applied to large ensemble datasets like operational decadal prediction systems.

A global empirical system for probabilistic seasonal climate prediction

Preparing for episodes with risks of anomalous weather a month to a year ahead is an important challenge for governments, non-governmental organisations, and private companies and is dependent on the availability of reliable forecasts. The majority of operational seasonal forecasts are made using process-based dynamical models, which are complex, computationally challenging and prone to biases. Empirical forecast approaches built on statistical models to represent physical processes offer an alternative to dynamical systems and can provide either a benchmark for comparison or independent supplementary forecasts. Here, we present a simple empirical system based on multiple linear regression for producing probabilistic forecasts of seasonal surface air temperature and precipitation across the globe. The global CO2-equivalent concentration is taken as the primary predictor; subsequent predictors, including large-scale modes of variability in the climate system and local-scale information, are selected on the basis of their physical relationship with the predictand. The focus given to the climate change signal as a source of skill and the probabilistic nature of the forecasts produced constitute a novel approach to global empirical prediction. Hindcasts for the period 1961–2013 are validated against observations using deterministic (correlation of seasonal means) and probabilistic (continuous rank probability skill scores) metrics. Good skill is found in many regions, particularly for surface air temperature and most notably in much of Europe during the spring and summer seasons. For precipitation, skill is generally limited to regions with known El Niño–Southern Oscillation (ENSO) teleconnections. The system is used in a quasi-operational framework to generate empirical seasonal forecasts on a monthly basis.

An assessment of Indian monsoon seasonal forecasts and mechanisms underlying monsoon interannual variability in the Met Office GloSea5-GC2 system

We assess Indian summer monsoon seasonal forecasts in GloSea5-GC2, the Met Office fully coupled subseasonal to seasonal ensemble forecasting system. Using several metrics, GloSea5-GC2 shows similar skill to other state-of-the-art forecast systems. The prediction skill of the large-scale South Asian monsoon circulation is higher than that of Indian monsoon rainfall. Using multiple linear regression analysis we evaluate relationships between Indian monsoon rainfall and five possible drivers of monsoon interannual variability. Over the time period studied (1992-2011), the El Nino-Southern Oscillation (ENSO) and the Indian Ocean dipole (IOD) are the most important of these drivers in both observations and GloSea5-GC2. Our analysis indicates that ENSO and its teleconnection with the Indian rainfall are well represented in GloSea5-GC2. However, the relationship between the IOD and Indian rainfall anomalies is too weak in GloSea5-GC2, which may be limiting the prediction skill of the local monsoon circulation and Indian rainfall. We show that this weak relationship likely results from a coupled mean state bias that limits the impact of anomalous wind forcing on SST variability, resulting in erroneous IOD SST anomalies. Known difficulties in representing convective precipitation over India may also play a role. Since Indian rainfall responds weakly to the IOD, it responds more consistently to ENSO than in observations. Our assessment identifies specific coupled biases that are likely limiting GloSea5-GC2 prediction skill, providing targets for model improvement.