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The second version of the coupled Norwegian Earth System Model (NorESM2) is presented and evaluated. NorESM2 is based on the second version of the Community Earth System Model (CESM2) and shares with CESM2 the computer code infrastructure and many Earth system model components. However, NorESM2 employs entirely different ocean and ocean biogeochemistry models. The atmosphere component of NorESM2 (CAM-Nor) includes a different module for aerosol physics and chemistry, including interactions with cloud and radiation; additionally, CAM-Nor includes improvements in the formulation of local dry and moist energy conservation, in local and global angular momentum conservation, and in the computations for deep convection and air–sea fluxes. The surface components of NorESM2 have minor changes in the albedo calculations and to land and sea-ice models.We present results from simulations with NorESM2 that were carried out for the sixth phase of the Coupled Model Intercomparison Project (CMIP6). Two versions of the model are used: one with lower (∼ 2∘) atmosphere–land resolution and one with medium (∼ 1∘) atmosphere–land resolution. The stability of the pre-industrial climate and the sensitivity of the model to abrupt and gradual quadrupling of CO2 are assessed, along with the ability of the model to simulate the historical climate under the CMIP6 forcings. Compared to observations and reanalyses, NorESM2 represents an improvement over previous versions of NorESM in most aspects. NorESM2 appears less sensitive to greenhouse gas forcing than its predecessors, with an estimated equilibrium climate sensitivity of 2.5 K in both resolutions on a 150-year time frame; however, this estimate increases with the time window and the climate sensitivity at equilibration is much higher. We also consider the model response to future scenarios as defined by selected Shared Socioeconomic Pathways (SSPs) from the Scenario Model Intercomparison Project defined under CMIP6. Under the four scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5), the warming in the period 2090–2099 compared to 1850–1879 reaches 1.3, 2.2, 3.0, and 3.9 K in NorESM2-LM, and 1.3, 2.1, 3.1, and 3.9 K in NorESM-MM, robustly similar in both resolutions. NorESM2-LM shows a rather satisfactory evolution of recent sea-ice area. In NorESM2-LM, an ice-free Arctic Ocean is only avoided in the SSP1-2.6 scenario.

The Coupled Model Intercomparison Project (CMIP) is one of the biggest international efforts aimed at better understanding the past, present, and future of climate changes in a multi-model context. A total of 21 model intercomparison projects (MIPs) were endorsed in its sixth phase (CMIP6), which included 190 different experiments that were used to simulate 40 000 years and produced around 40 PB of data in total. This paper presents the main findings obtained from the CPMIP (the Computational Performance Model Intercomparison Project), a collection of a common set of metrics, specifically designed for assessing climate model performance. These metrics were exclusively collected from the production runs of experiments used in CMIP6 and primarily from institutions within the IS-ENES3 consortium. The document presents the full set of CPMIP metrics per institution and experiment, including a detailed analysis and discussion of each of the measurements. During the analysis, we found a positive correlation between the core hours needed, the complexity of the models, and the resolution used. Likewise, we show that between 5 %–15 % of the execution cost is spent in the coupling between independent components, and it only gets worse by increasing the number of resources. From the data, it is clear that queue times have a great impact on the actual speed achieved and have a huge variability across different institutions, ranging from none to up to 78 % execution overhead. Furthermore, our evaluation shows that the estimated carbon footprint of running such big simulations within the IS-ENES3 consortium is 1692 t of CO2 equivalent.As a result of the collection, we contribute to the creation of a comprehensive database for future community reference, establishing a benchmark for evaluation and facilitating the multi-model, multi-platform comparisons crucial for understanding climate modelling performance. Given the diverse range of applications, configurations, and hardware utilised, further work is required for the standardisation and formulation of general rules. The paper concludes with recommendations for future exercises aimed at addressing the encountered challenges which will facilitate more collections of a similar nature.

Physiological disorders in mangoes (Mangifera indica L.) caused noticeable losses since they reduced the fruit’s quality and customer acceptability. Significant care is needed because in certain commercial varieties, there have been complaints of excessive softening of the pulp tissue around the stone or kernel (jelly seed). A broken-down mesocarp surrounding a seed (stone) is called a jelly seed (JS). The damaged portion may eventually take on an odd flavour and discolouration. The highest incidence of JS was found in late-harvested as well as in tree-ripened fruits. This problem in mango has attracted many researchers in recent years to investigate the incidence level and its possible causes, but the exact cause of the problem is still not clearly understood. Furthermore, to date, no clear-cut physiological, biochemical or molecular mechanism of JS formation has been established by the research, despite the fact that a number of mechanisms have been put forward for JS formation. Thus, this review provides all significant information regarding pre-disposing factors, causes, mechanism of excessive tissue softening, kernel-to-pulp communication and vice versa reported to date on the aforementioned physiological disorder. We also review various management options developed to control JS formation in mangoes. Overall, this is the first comprehensive review that provides complete insight into JS disorder in mangoes and the future action required for research.

The class J design space is investigated with half wave current excitation for a solid-state RF amplifier capable of delivering hundreds of watts. Unlike conventional class J designs, the present analysis aims to explore a continuous design space in order to operate a commercially available device, within its practical limits of drain voltage. This design analysis together with package effects and the inclusion of non-linear capacitor is verified experimentally by fabricating a high-power (550 W CW) high-efficiency (62.8%) solid-state amplifier operating at 505.8 MHz. This power was obtained by in-phase combining two similar continuous class J stages, each one contributing half of the total power. For high-power lateral diffused metal-oxide semiconductor devices, the class J design space is found to be more realisable than popular modes of operation in view of the large non-linear output capacitance of the device. The measured output power, efficiency, spurious response and large signal output reflection coefficients are satisfactory and as anticipated from the design analysis. Since the final application of this amplifier is for a solid-state transmitter, a study of repeatability in terms of phase and amplitude imbalances was carried out by fabricating and evaluating multiple amplifiers, each one working with the proposed design principle.

This paper presents a dual-band bandpass filter designed of »/4 split type resonator. A hook-type coupled line structure is used to create a stop band in a wide single bandpass filter. This structure provides external coupling and creates two bands at different frequencies. To justify the idea, the result of dual bandpass filter with two center frequencies at 2.4 GHz and 3.5 GHz are shown. This filter has specific applications with Wi-Max communication. There are two reflection zeros in each pass band and three Transmission zeros out of band for better out of band rejecton.

In this paper, a planar slotted microstrip antenna with rectangular slotted patch used as the single radiating element and tapered shape slot in ground has been designed and analyzed. The proposed antenna is compact in size with an 3 overall dimension of 22×24×1.6 mm . This antenna is fabricated on FR-4 epoxy substrate with relative permittivity of 4.4 and loss tangent of 0.02 and 50©. Resistance is used as the characteristics impedance for microstrip line feed, being compact in size, antenna still achieves a bandwidth of wide range from 3.6 GHz to 11.8 GHz. By properly adjusting the feed gap (h), that is the distance between the tuning stub and the slot in the ground plane, determines the coupling between them and a good impedance matching is obtained and by cutting two slots on the rectangular radiating patch, with dimensions 1.5×1 mm and 1.5×1.5 mm, the gain of the antenna is drastically improved and it is found that gain plot is above zero for frequency range of 8 GHz to 12 GHz and a maximum gain of 9.2 dB is obtained at 12 GHz frequency.

Plants require nitrogen (N) for growth, development and defence against abiotic and biotic stresses. The extensive use of artificial N fertilizers has played an important role in the Green Revolution. N assimilation can involve a reductase series ( NO3- → NO2- → NH4+ ) followed by transamination to form amino acids. Given its widespread use, the agricultural impact of N nutrition on disease development has been extensively examined. When a pathogen first comes into contact with a host, it is usually nutrient starved such that rapid assimilation of host nutrients is essential for successful pathogenesis. Equally, the host may reallocate its nutrients to defence responses or away from the site of attempted infection. Exogenous application of N fertilizer can, therefore, shift the balance in favour of the host or pathogen. In line with this, increasing N has been reported either to increase or to decrease plant resistance to pathogens, which reflects differences in the infection strategies of discrete pathogens. Beyond considering only N content, the use of NO3- or NH4+ fertilizers affects the outcome of plant-pathogen interactions. NO3- feeding augments hypersensitive response- (HR) mediated resistance, while ammonium nutrition can compromise defence. Metabolically, NO3- enhances production of polyamines such as spermine and spermidine, which are established defence signals, with NH4+ nutrition leading to increased γ-aminobutyric acid (GABA) levels which may be a nutrient source for the pathogen. Within the defensive N economy, the roles of nitric oxide must also be considered. This is mostly generated from NO2- by nitrate reductase and is elicited by both pathogen-associated microbial patterns and gene-for-gene-mediated defences. Nitric oxide (NO) production and associated defences are therefore NO3- dependent and are compromised by NH4+ . This review demonstrates how N content and form plays an essential role in defensive primary and secondary metabolism and NO-mediated events.

The Norwegian Climate Prediction Model version 1 (NorCPM1) is a new research tool for performing climate reanalyses and seasonal-to-decadal climate predictions. It combines the Norwegian Earth System Model version 1 (NorESM1) – which features interactive aerosol–cloud schemes and an isopycnic-coordinate ocean component with biogeochemistry – with anomaly assimilation of sea surface temperature (SST) and T/S-profile observations using the ensemble Kalman filter (EnKF).We describe the Earth system component and the data assimilation (DA) scheme, highlighting implementation of new forcings, bug fixes, retuning and DA innovations. Notably, NorCPM1 uses two anomaly assimilation variants to assess the impact of sea ice initialization and climatological reference period: the first (i1) uses a 1980–2010 reference climatology for computing anomalies and the DA only updates the physical ocean state; the second (i2) uses a 1950–2010 reference climatology and additionally updates the sea ice state via strongly coupled DA of ocean observations.We assess the baseline, reanalysis and prediction performance with output contributed to the Decadal Climate Prediction Project (DCPP) as part of the sixth Coupled Model Intercomparison Project (CMIP6). The NorESM1 simulations exhibit a moderate historical global surface temperature evolution and tropical climate variability characteristics that compare favourably with observations. The climate biases of NorESM1 using CMIP6 external forcings are comparable to, or slightly larger than those of, the original NorESM1 CMIP5 model, with positive biases in Atlantic meridional overturning circulation (AMOC) strength and Arctic sea ice thickness, too-cold subtropical oceans and northern continents, and a too-warm North Atlantic and Southern Ocean. The biases in the assimilation experiments are mostly unchanged, except for a reduced sea ice thickness bias in i2 caused by the assimilation update of sea ice, generally confirming that the anomaly assimilation synchronizes variability without changing the climatology. The i1 and i2 reanalysis/hindcast products overall show comparable performance. The benefits of DA-assisted initialization are seen globally in the first year of the prediction over a range of variables, also in the atmosphere and over land. External forcings are the primary source of multiyear skills, while added benefit from initialization is demonstrated for the subpolar North Atlantic (SPNA) and its extension to the Arctic, and also for temperature over land if the forced signal is removed. Both products show limited success in constraining and predicting unforced surface ocean biogeochemistry variability. However, observational uncertainties and short temporal coverage make biogeochemistry evaluation uncertain, and potential predictability is found to be high. For physical climate prediction, i2 performs marginally better than i1 for a range of variables, especially in the SPNA and in the vicinity of sea ice, with notably improved sea level variability of the Southern Ocean. Despite similar skills, i1 and i2 feature very different drift behaviours, mainly due to their use of different climatologies in DA; i2 exhibits an anomalously strong AMOC that leads to forecast drift with unrealistic warming in the SPNA, whereas i1 exhibits a weaker AMOC that leads to unrealistic cooling. In polar regions, the reduction in climatological ice thickness in i2 causes additional forecast drift as the ice grows back. Posteriori lead-dependent drift correction removes most hindcast differences; applications should therefore benefit from combining the two products.The results confirm that the large-scale ocean circulation exerts strong control on North Atlantic temperature variability, implying predictive potential from better synchronization of circulation variability. Future development will therefore focus on improving the representation of mean state and variability of AMOC and its initialization, in addition to upgrades of the atmospheric component. Other efforts will be directed to refining the anomaly assimilation scheme – to better separate internal and forced signals, to include land and atmosphere initialization and new observational types – and improving biogeochemistry prediction capability. Combined with other systems, NorCPM1 may already contribute to skilful multiyear climate prediction that benefits society.

The modeling of weather and climate has been a success story. The skill of forecasts continues to improve and model biases continue to decrease. Combining the output of multiple models has further improved forecast skill and reduced biases. But are we exploiting the full capacity of state-of-the-art models in making forecasts and projections? Supermodeling is a recent step forward in the multimodel ensemble approach. Instead of combining model output after the simulations are completed, in a supermodel individual models exchange state information as they run, influencing each other’s behavior. By learning the optimal parameters that determine how models influence each other based on past observations, model errors are reduced at an early stage before they propagate into larger scales and affect other regions and variables. The models synchronize on a common solution that through learning remains closer to the observed evolution. Effectively a new dynamical system has been created, a supermodel, that optimally combines the strengths of the constituent models. The supermodel approach has the potential to rapidly improve current state-of-the-art weather forecasts and climate predictions. In this paper we introduce supermodeling, demonstrate its potential in examples of various complexity, and discuss learning strategies. We conclude with a discussion of remaining challenges for a successful application of supermodeling in the context of state-of-the-art models. The supermodeling approach is not limited to the modeling of weather and climate, but can be applied to improve the prediction capabilities of any complex system, for which a set of different models exists.

Abstract Background and Aims Nitrogen (N) levels vary between ecosystems, while the form of available N has a substantial impact on growth, development and perception of stress. Plants have the capacity to assimilate N in the form of either nitrate (NO3–) or ammonium (NH4+). Recent studies revealed that NO3– nutrition increases nitric oxide (NO) levels under hypoxia. When oxygen availability changes, plants need to generate energy to protect themselves against hypoxia-induced damage. As the effects of NO3– or NH4+ nutrition on energy production remain unresolved, this study was conducted to investigate the role of N source on group VII transcription factors, fermentative genes, energy metabolism and respiration under normoxic and hypoxic conditions. Methods We used Arabidopsis plants grown on Hoagland medium with either NO3– or NH4+ as a source of N and exposed to 0.8 % oxygen environment. In both roots and seedlings, we investigated the phytoglobin–nitric oxide cycle and the pathways of fermentation and respiration; furthermore, NO levels were tested using a combination of techniques including diaminofluorescein fluorescence, the gas phase Griess reagent assay, respiration by using an oxygen sensor and gene expression analysis by real-time quantitative reverse transcription–PCR methods. Key Results Under NO3– nutrition, hypoxic stress leads to increases in nitrate reductase activity, NO production, class 1 phytoglobin transcript abundance and metphytoglobin reductase activity. In contrast, none of these processes responded to hypoxia under NH4+ nutrition. Under NO3– nutrition, a decreased total respiratory rate and increased alternative oxidase capacity and expression were observed during hypoxia. Data correlated with decreased reactive oxygen species and lipid peroxidation levels. Moreover, increased fermentation and NAD+ recycling as well as increased ATP production concomitant with the increased expression of transcription factor genes HRE1, HRE2, RAP2.2 and RAP2.12 were observed during hypoxia under NO3– nutrition. Conclusions The results of this study collectively indicate that nitrate nutrition influences multiple factors in order to increase energy efficiency under hypoxia.