We describe the new developments in version 4 of the public computer code HiggsBounds. HiggsBounds is a tool to test models with arbitrary Higgs sectors, containing both neutral and charged Higgs ...bosons, against the published exclusion bounds from Higgs searches at the LEP, Tevatron and LHC experiments. From the model predictions for the Higgs masses, branching ratios, production cross sections and total decay widths—which are specified by the user in the input for the program—the code calculates the predicted signal rates for the search channels considered in the experimental data. The signal rates are compared to the expected and observed cross section limits from the Higgs searches to determine whether a point in the model parameter space is excluded at 95 % confidence level. In this paper we present a modification of the HiggsBounds main algorithm that extends the exclusion test in order to ensure that it provides useful results in the presence of one or more significant excesses in the data, corresponding to potential Higgs signals. We also describe a new method to test whether the limits from an experimental search performed under certain model assumptions can be applied to a different theoretical model. Further developments discussed here include a framework to take into account theoretical uncertainties on the Higgs mass predictions, and the possibility to obtain the
χ
2
likelihood of Higgs exclusion limits from LEP. Extensions to the user subroutines from earlier versions of HiggsBounds are described. The new features are demonstrated by additional example programs.
We discuss Higgs boson decays in the CP-violating MSSM, and examine their phenomenological impact using cross section limits from the LEP Higgs searches. This includes a discussion of the full 1-loop ...results for the partial decay widths of neutral Higgs bosons into lighter neutral Higgs bosons (
h
a
→
h
b
h
c
) and of neutral Higgs bosons into fermions (
). In calculating the genuine vertex corrections, we take into account the full spectrum of supersymmetric particles and all complex phases of the supersymmetric parameters. These genuine vertex corrections are supplemented with Higgs propagator corrections incorporating the full 1-loop and the dominant 2-loop contributions, and we illustrate a method of consistently treating diagrams involving mixing with Goldstone and Z bosons. In particular, the genuine vertex corrections to the process
h
a
→
h
b
h
c
are found to be very large and, where this process is kinematically allowed, can have a significant effect on the regions of the CPX benchmark scenario which can be excluded by the results of the Higgs searches at LEP. However, there remains an unexcluded region of CPX parameter space at a lightest neutral Higgs boson mass of ∼45 GeV. In the analysis, we pay particular attention to the conversion between parameters defined in different renormalisation schemes and are therefore able to make a comparison to the results found using renormalisation group improved/effective potential calculations.
Fluctuations in positive and negative caregiving experiences remain only partially explained as the significant variability over time of potential predictive factors themselves is understudied. The ...current study aims to gain considerable insight into caregiving experiences and perceptions over time by using photovoice methodology to support semi-structured interviews. A case study, longitudinal design is taken with three female caregivers who provide detailed insight into their caregivers' experiences over a 12 month period. The interview transcripts were analyzed using IPA- Interpretative Phenomenological Analysis. This innovative combination of methods resulted in the emergence of three related themes which included consuming the role, feeling consumed by the role, and letting go of the role. The idiographic approach taken allowed both within case differences to be examined over time, and also between carer differences to be highlighted. Implications of illness type and its characteristics, and of attachment and relationship quality with the care recipient were seen in terms of how and when the caregivers moved between the themes identified. The use of others' support or respite care is examined vis-a vis caregiver's own beliefs, emotions, relationship attachment and motivations to care. Caregivers self-efficacy beliefs also shifted over time and were influential in caregiver experience as the care recipient condition or needs changed. No previous studies have found that negative caregiving consequences are, in part, under volitional control and yet our data on the underlying reasons for consuming caregiving or allowing themselves to consume, would suggest this may in part be true. This is important because it suggests that interventions to support caregivers should address relational and motivational factors more fully.
Concerns over climate change are motivated in large part because of their impact on human
society. Assessing the effect of that uncertainty on specific potential impacts is demanding, since it ...requires a systematic survey over both climate and impacts models. We provide a comprehensive evaluation of uncertainty in projected crop yields for maize, spring and winter wheat, rice, and soybean, using a suite of nine crop models and up to 45 CMIP5 and 34 CMIP6 climate projections for three different forcing scenarios. To make this task computationally tractable, we use a new set of statistical crop model emulators. We find that climate and crop models contribute about equally to overall uncertainty. While the ranges of yield uncertainties under CMIP5 and CMIP6 projections are similar, median impact in aggregate total caloric production is typically more negative for the CMIP6 projections (+1% to −19%) than for CMIP5 (+5% to −13%). In the first half of the 21st century and for individual crops is the spread across crop models typically wider than that across climate models, but we find distinct differences between crops: globally, wheat and maize uncertainties are dominated by the crop models, but soybean and rice are more sensitive to the climate projections. Climate models with very similar global mean warming can lead to very different aggregate impacts so that climate model uncertainties remain a significant contributor to agricultural impacts uncertainty. These results show the utility of large-ensemble methods that allow comprehensively evaluating factors affecting crop yields or other impacts under climate change. The crop model ensemble used here is unbalanced and pulls the assumption that all projections are equally plausible into question. Better methods for consistent model testing, also at the level of individual processes, will have to be developed and applied by the crop modeling community.
Bioenergy from sugarcane production is considered a key mitigation strategy for global warming. Improving its representation in land surface models is important to further understand the interactions ...between climate and bioenergy production, supporting accurate climate projections and decision‐making. This study aimed to calibrate and evaluate the Joint UK Land Environment Simulator (JULES) for climate impact assessments in sugarcane. A dataset composed of soil moisture, water and carbon fluxes and biomass measurements from field experiments across Brazil was used to calibrate and evaluate JULES‐crop and JULES‐BE parametrizations. The ability to predict the spatiotemporal variability of sugarcane yields and the impact of climate change was also tested at five Brazilian microregions. Parameters related to sugarcane allometry, crop growth and development were derived and tested for JULES‐crop and JULES‐BE, together with the response to atmospheric carbon dioxide, temperature and low‐water availability (CTW‐response). Both parametrizations showed comparable performance to other sugarcane dynamic models, with a root mean squared error of 6.75 and 6.05 t ha−1 for stalk dry matter for JULES‐crop and JULES‐BE, respectively. The parametrizations were also able to replicate the average yield patterns observed in the five microregions over 30 years when the yield gap factors were taken into account, with the correlation (r) between simulated and observed interannual variability of yields ranging from 0.33 to 0.56. An overall small positive trend was found in future yield projections of sugarcane using the new calibrations, with exception of the Jataí mesoregion which showed a clear negative trend for the SSP5 scenario from the years 2070 to 2100. Our simulations showed that an abrupt negative impact on sugarcane yields is expected if daytime temperatures above 35°C become more frequent. The newly calibrated version of JULES can be applied regionally and globally to help understand the interactions between climate and bioenergy production.
In this study, we aimed to calibrate and evaluate the Joint UK Land Environment Simulator (JULES) for climate impact assessments in sugarcane. Parameters related to sugarcane allometry, crop growth and development were derived and tested for the model, together with the crop response to atmospheric carbon dioxide, temperature and drought (CTW‐response). The model showed good agreement against highly monitored experiments and represented spatiotemporal pattern of sugarcane production across five Brazilian regions. We found an overall small positive trend in future yield projections for sugarcane, but an abrupt negative impact is expected if daytime temperatures above 35°C become more frequent.
We investigate the skill of the GloSea5 seasonal forecasting system for two carbon cycle processes, which are strong contributors to global CO2 variability: the impact of meteorological conditions on ...CO2 uptake by vegetation (characterised by net primary productivity, NPP), and on fire occurrences (characterised by fire risk indices). Current seasonal forecasts of global CO2 concentrations rely on the relationship with the El Niño-Southern Oscillation (ENSO), combined with estimated anthropogenic emissions. NPP and fire are key processes underlying that global CO2-ENSO relationship: In the tropics, during El Niño events, CO2 uptake by vegetation is reduced and fires occur more frequently, leading to higher global CO2 levels. Our study assesses the skill of these processes in the forecast model for the first time. We use the McArthur forest fire index, calculated from daily data from several meteorological variables. We also assess a simpler fire index, based solely on seasonal mean temperature and relative humidity, to test the need for additional complexity. For NPP, the skill is high in regions that respond strongly to ENSO, such as equatorial South America in boreal winter, and northeast Brazil in boreal summer. There is also skill in some regions without a strong ENSO response. The fire risk indices show significant skill across much of the tropics, including Indonesia, southern and eastern Africa, and parts of the Amazon. We relate this skill to the underlying meteorological variables, finding that fire risk in particular follows similar patterns to relative humidity. On the seasonal-mean timescale, the McArthur index offers no benefits over the simpler fire index: they show the same relationship to burnt area and response to ENSO, and the same levels of skill, in almost all cases. Our results highlight potentially useful prediction skill, as well as important limitations, for seasonal forecasts of land-surface impacts of climate variability.
The First International Satellite Land Surface
Climatology Project (ISLSCP) Field Experiment (FIFE), Kansas,
US, 1987–1989, made important contributions to the understanding of energy
and CO2 ...exchanges between the land surface and the atmosphere, which
heavily influenced the development of numerical land-surface modelling.
Now, 30 years on, we demonstrate how the wealth of data collected during FIFE and
its subsequent in-depth analysis in the literature continue to be a valuable
resource for the current generation of land-surface models. To illustrate, we
use the FIFE dataset to evaluate the representation of water stress on
tallgrass prairie vegetation in the Joint UK Land Environment Simulator
(JULES) and highlight areas for future development. We show that, while JULES
is able to simulate a decrease in net carbon assimilation and
evapotranspiration during a dry spell, the shape of the diurnal cycle is not
well captured. Evaluating the model parameters and results against this
dataset provides a case study on the assumptions in calibrating
“unstressed”
vegetation parameters and thresholds for water stress. In particular, the
responses to low water availability and high temperatures are calibrated
separately. We also illustrate the effect of inherent uncertainties in key
observables, such as leaf area index, soil moisture and soil properties.
Given these valuable lessons, simulations for this site will be a key
addition to a compilation of simulations covering a wide range of vegetation
types and climate regimes, which will be used to improve the way that water
stress is represented within JULES.
Crop models are often used to project future crop yield under climate and global change and typically show a broad range of outcomes. To understand differences in modeled responses, we analyzed ...modeled crop yield response types using impact response surfaces along four drivers of crop yield: carbon dioxide (C), temperature (T), water (W), and nitrogen (N). Crop yield response types help to understand differences in simulated responses per driver and their combinations rather than aggregated changes in yields as the result of simultaneous changes in various drivers. We find that models' sensitivities to the individual drivers are substantially different and often more different across models than across regions. There is some agreement across models with respect to the spatial patterns of response types but strong differences in the distribution of response types across models and their configurations suggests that models need to undergo further scrutiny. We suggest establishing standards in model evaluation based on emergent functionality not only against historical yield observations but also against dedicated experiments across different drivers to analyze emergent functional patterns of crop models.
Plain Language Summary
Crop models are widely used to compute crop yields under future climate change. Yields are determined by many interacting processes. Simulated future crop yields often show a broad uncertainty range. We investigate the sensitivity of nine different crop models to individual model inputs (carbon dioxide, temperature, water, nitrogen) in a very large simulation data set and find that there are substantial differences. We conclude that crop model evaluation needs to include analyses of functional properties to avoid that very diverse model responses to drivers are not tracked if interacting processes cancel out in the historical evaluation period but not in future scenarios, leading to large differences between models.
Key Points
Crop models show strong differences in input sensitivities
Standardized modeling experiments reveal differences in emergent functional relationships
New standards in model evaluation are needed
Concerns about food security under climate change motivate efforts to better understand future changes in crop yields.
Process-based crop models, which represent plant physiological and soil ...processes, are necessary tools for this purpose since they allow representing future climate and management conditions not sampled in the historical record and new locations to which cultivation may shift.
However, process-based crop models differ in many critical details, and their responses to different interacting factors remain only poorly understood.
The Global Gridded Crop Model Intercomparison (GGCMI) Phase 2 experiment, an activity of the Agricultural Model Intercomparison and Improvement Project (AgMIP), is designed to provide a systematic parameter sweep focused on climate change factors and their interaction with overall soil fertility, to allow both evaluating model behavior and emulating model responses in impact assessment tools.
In this paper we describe the GGCMI Phase 2 experimental protocol and its simulation data archive.
A total of 12 crop models simulate five crops with systematic uniform perturbations of historical climate, varying CO2, temperature, water supply, and applied nitrogen (“CTWN”) for rainfed and irrigated agriculture, and a second set of simulations represents a type of adaptation by allowing the adjustment of growing season length.
We present some crop yield results to illustrate general characteristics of the simulations and potential uses of the GGCMI Phase 2 archive.
For example, in cases without adaptation, modeled yields show robust decreases to warmer temperatures in almost all regions, with a nonlinear dependence that means yields in warmer baseline locations have greater temperature sensitivity.
Inter-model uncertainty is qualitatively similar across all the four input dimensions but is largest in high-latitude regions where crops may be grown in the future.
• Land surface models (LSMs) typically use empirical functions to represent vegetation responses to soil drought. These functions largely neglect recent advances in plant ecophysiology that link ...xylem hydraulic functioning with stomatal responses to climate.
• We developed an analytical stomatal optimization model based on xylem hydraulics (SOX) to predict plant responses to drought. Coupling SOX to the Joint UK Land Environment Simulator (JULES) LSM, we conducted a global evaluation of SOX against leaf- and ecosystem-level observations.
• SOX simulates leaf stomatal conductance responses to climate for woody plants more accurately and parsimoniously than the existing JULES stomatal conductance model. An ecosystem-level evaluation at 70 eddy flux sites shows that SOX decreases the sensitivity of gross primary productivity (GPP) to soil moisture, which improves the model agreement with observations and increases the predicted annual GPP by 30% in relation to JULES. SOX decreases JULES root-mean-square error in GPP by up to 45% in evergreen tropical forests, and can simulate realistic patterns of canopy water potential and soil water dynamics at the studied sites.
• SOX provides a parsimonious way to incorporate recent advances in plant hydraulics and optimality theory into LSMs, and an alternative to empirical stress factors.
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