Increasing cereal yield is needed to meet the projected increased demand for world food supply of about 70% by 2050. Sirius, a process-based model for wheat, was used to estimate yield potential for ...wheat ideotypes optimized for future climatic projections for ten wheat growing areas of Europe. It was predicted that the detrimental effect of drought stress on yield would be decreased due to enhanced tailoring of phenology to future weather patterns, and due to genetic improvements in the response of photosynthesis and green leaf duration to water shortage. Yield advances could be made through extending maturation and thereby improve resource capture and partitioning. However the model predicted an increase in frequency of heat stress at meiosis and anthesis. Controlled environment experiments quantify the effects of heat and drought at booting and flowering on grain numbers and potential grain size. A current adaptation of wheat to areas of Europe with hotter and drier summers is a quicker maturation which helps to escape from excessive stress, but results in lower yields. To increase yield potential and to respond to climate change, increased tolerance to heat and drought stress should remain priorities for the genetic improvement of wheat.
•Wheat ideotypes for climate scenarios in Europe can be predicted by modifying cultivar parameters in process-based models.•Growth of higher yielding genotypes will need to be better tailored to the temporal distribution of water availability.•Further drought tolerance will need to be combined with increased harvest index associated with extended maturation.•Increased frequency of extreme weather events during meiosis and anthesis requires that heat stress tolerance is improved.
Projections of climate change impacts on crop yields are inherently uncertain1. Uncertainty is often quantified when projecting future greenhouse gas emissions and their influence on climate2. ...However, multi-model uncertainty analysis of crop responses to climate change is rare because systematic and objective comparisons among process-based crop simulation models1, 3 are difficult4. Here we present the largest standardized model intercomparison for climate change impacts so far. We found that individual crop models are able to simulate measured wheat grain yields accurately under a range of environments, particularly if the input information is sufficient. However, simulated climate change impacts vary across models owing to differences in model structures and parameter values. A greater proportion of the uncertainty in climate change impact projections was due to variations among crop models than to variations among downscaled general circulation models. Uncertainties in simulated impacts increased with CO2 concentrations and associated warming. These impact uncertainties can be reduced by improving temperature and CO2 relationships in models and better quantified through use of multi-model ensembles. Less uncertainty in describing how climate change may affect agricultural productivity will aid adaptation strategy development and policymaking.
The complexity of risks posed by climate change and possible adaptations for crop production has called for integrated assessment and modelling (IAM) approaches linking biophysical and economic ...models. This paper attempts to provide an overview of the present state of crop modelling to assess climate change risks to food production and to which extent crop models comply with IAM demands. Considerable progress has been made in modelling effects of climate variables, where crop models best satisfy IAM demands. Demands are partly satisfied for simulating commonly required assessment variables. However, progress on the number of simulated crops, uncertainty propagation related to model parameters and structure, adaptations and scaling are less advanced and lagging behind IAM demands. The limitations are considered substantial and apply to a different extent to all crop models. Overcoming these limitations will require joint efforts, and consideration of novel modelling approaches.
•Extreme events and future climate uncertainty represent risk for food production.•Crop models are largely able to simulate crop response to climate factors.•Adaptations are best evaluated in integrated assessment models (IAM).•Key limitations for crop models in IAM are low data availability and integration.•Cross-scale nature of IAM suggests novel modelling approaches are needed.
•Crop model ensemble size and composition affect the ensemble outputs.•Recommendations on adaptation are sensitive to model ensemble composition and size.•The new EOA index effectively measures the ...confidence level of recommendations.•Effective adaptation of wheat in the Mediterranean is feasible with high confidence.•The EOA index can be applied to assess confidence in many other contexts.
Climate change is expected to severely affect cropping systems and food production in many parts of the world unless local adaptation can ameliorate these impacts. Ensembles of crop simulation models can be useful tools for assessing if proposed adaptation options are capable of achieving target yields, whilst also quantifying the share of uncertainty in the simulated crop impact resulting from the crop models themselves. Although some studies have analysed the influence of ensemble size on model outcomes, the effect of ensemble composition has not yet been properly appraised. Moreover, results and derived recommendations typically rely on averaged ensemble simulation results without accounting sufficiently for the spread of model outcomes. Therefore, we developed an Ensemble Outcome Agreement (EOA) index, which analyses the effect of changes in composition and size of a multi-model ensemble (MME) to evaluate the level of agreement between MME outcomes with respect to a given hypothesis (e.g. that adaptation measures result in positive crop responses). We analysed the recommendations of a previous study performed with an ensemble of 17 crop models and testing 54 adaptation options for rainfed winter wheat (Triticum aestivum L.) at Lleida (NE Spain) under perturbed conditions of temperature, precipitation and atmospheric CO2 concentration. Our results confirmed that most adaptations recommended in the previous study have a positive effect. However, we also showed that some options did not remain recommendable in specific conditions if different ensembles were considered. Using EOA, we were able to identify the adaptation options for which there is high confidence in their effectiveness at enhancing yields, even under severe climate perturbations. These include substituting spring wheat for winter wheat combined with earlier sowing dates and standard or longer duration cultivars, or introducing supplementary irrigation, the latter increasing EOA values in all cases. There is low confidence in recovering yields to baseline levels, although this target could be attained for some adaptation options under moderate climate perturbations. Recommendations derived from such robust results may provide crucial information for stakeholders seeking to implement adaptation measures.
A successful LINAC operation at Synchrotron Radiation Facility−Siberian Circular Photon Source (SRF SKIF) is directly related to the improvement of diagnostic methods for measuring the transverse ...profile and longitudinal distribution of the beam charge, which, in turn, requires the use of new materials or methods for their production. The thermal outgassing has been measured from aerogel and electrophoretically deposited P43 phosphor used in the LINAC diagnostic system of SRF SKIF.
Abstract
Precipitation extremes are widely thought to intensify with global warming due to an exponential growth following the Clausius–Clapeyron (C–C) equation which links the atmosphere water vapor ...saturation pressure with air temperature. However, a number of recent studies based on station and reanalyzes data for the contemporary period showed that scaling rates between extreme precipitation and temperature strongly depend on temperature range, moisture availability, and a region of interest. Being performed for some regions, such estimates, however, lack for Northern Eurasia, where prominent temperature changes and rapid shift from large-scale to convective precipitation are observed. Here, we examine the scaling between daily precipitation extremes and surface air temperature (SAT) over Russia for 1966–2017 using meteorological station data and for 1979–2020 using ERA5 reanalysis. The precipitation-temperature relation is examined for total precipitation and, separately, for convective and large-scale precipitation types. In winter, we reveal a general increase in extreme precipitation of all precipitation types according to the C–C relationship. For the Russian Far East region, the stratiform precipitation extremes scale with SAT following even super C–C rates, about two times as fast as C–C. However, in summer we find a peak-like structure of the precipitation-temperature scaling, especially for the convective precipitation in the southern regions. Extreme precipitation reaches their peak values at the temperature range between 15 °C and 20 °C. At higher temperatures, the negative scaling prevails. Analyzed data show a pronounced decrease in relative humidity with increasing surface temperatures beyond the 15 °C–20 °C threshold. This indicates that moisture availability is the major factor for the peak-shaped relationship between extreme precipitation and temperature revealed by our analysis.
This study explored the utility of the impact response surface (IRS) approach for investigating model ensemble crop yield responses under a large range of changes in climate. IRSs of spring and ...winter wheat Triticum aestivum yields were constructed from a 26-member ensemble of process-based crop simulation models for sites in Finland, Germany and Spain across a latitudinal transect. The sensitivity of modelled yield to systematic increments of changes in temperature (−2 to +9°C) and precipitation (−50 to +50%) was tested by modifying values of baseline (1981 to 2010) daily weather, with CO₂ concentration fixed at 360 ppm. The IRS approach offers an effective method of portraying model behaviour under changing climate as well as advantages for analysing, comparing and presenting results from multi-model ensemble simulations. Though individual model behaviour occasionally departed markedly from the average, ensemble median responses across sites and crop varieties indicated that yields decline with higher temperatures and decreased precipitation and increase with higher precipitation. Across the uncertainty ranges defined for the IRSs, yields were more sensitive to temperature than precipitation changes at the Finnish site while sensitivities were mixed at the German and Spanish sites. Precipitation effects diminished under higher temperature changes. While the bivariate and multi-model characteristics of the analysis impose some limits to interpretation, the IRS approach nonetheless provides additional insights into sensitivities to inter-model and inter-annual variability. Taken together, these sensitivities may help to pinpoint processes such as heat stress, vernalisation or drought effects requiring refinement in future model development.
A stochastic weather generator is a model capable of generating daily weather patterns that are statistically similar to the observed patterns. Weather generators are commonly used in climate change ...studies as a computationally inexpensive tool to generate high resolution climate change scenarios based on the output from global climate models. Considering that the frequency and the magnitude of extreme weather events are likely to increase under climate change, there is a growing need to investigate how well weather extremes are simulated by weather generators. The aim of this study was to test the skill of the LARS-WG stochastic weather generator to simulate extreme weather events at 20 locations with diverse climates. Yearly maxima of daily precipitation, maximum temperature and length of heat waves, and their 10 and 20 yr return values were compared for observed and synthetic data by fitting the generalized extreme value distribution and computing confidence intervals. Means of yearly maxima and return values of daily synthetic precipitation were within the 95% confidence intervals (CI⁹⁵) of observed data for all sites. Daily maximum temperature extremes were reproduced less accurately. Although the root mean squared error (RMSE) calculated for the means of maxima of maximum temperature was <1°C, synthetic means for approximately half of the sites were outside the CI⁹⁵ for observed values. This indicates that the assumption used in LARS-WG, that daily temperature could be approximated by the normal distribution, is inadequate. Means of yearly maxima for length of heat waves and 10 and 20 yr return values were within the CI⁹⁵ for all sites except 3. For those sites where LARS-WG performance was inadequate, daily maximum temperature was not normally distributed but was skewed.
Periods of extreme wet-weather elevate agricultural diffuse water pollutant loads and climate projections for the UK suggest wetter winters. Within this context, we monitored nitrate and suspended ...sediment loss using a field and landscape scale platform in SW England during the recent extreme wet-weather of 2019–2020. We compared the recent extreme wet-weather period to both the climatic baseline (1981–2010) and projected near- (2041–2060) and far- (2071–2090) future climates, using the 95th percentiles of conventional rainfall indices generated for climate scenarios downscaled by the LARS-WG weather generator from the 19 global climate models in the CMIP5 ensemble for the RCP8.5 emission scenario. Finally, we explored relationships between pollutant loss and the rainfall indices. Grassland field-scale monthly average nitrate losses increased from 0.39-1.07 kg ha−1 (2016–2019) to 0.70–1.35 kg ha−1 (2019–2020), whereas losses from grassland ploughed up for cereals, increased from 0.63-0.83 kg ha−1 to 2.34–4.09 kg ha−1. Nitrate losses at landscape scale increased during the 2019–2020 extreme wet-weather period to 2.04–4.54 kg ha−1. Field-scale grassland monthly average sediment losses increased from 92-116 kg ha−1 (2016–2019) to 281–333 kg ha−1 (2019–2020), whereas corresponding losses from grassland converted to cereal production increased from 63-80 kg ha−1 to 2124–2146 kg ha−1. Landscape scale monthly sediment losses increased from 8-37 kg ha−1 in 2018 to between 15 and 173 kg ha−1 during the 2019–2020 wet-weather period. 2019–2020 was most representative of the forecast 95th percentiles of >1 mm rainfall for near- and far-future climates and this rainfall index was related to monitored sediment, but not nitrate, loss. The elevated suspended sediment loads generated by the extreme wet-weather of 2019–2020 therefore potentially provide some insight into the responses to the projected >1 mm rainfall extremes under future climates at the study location.
•Rainfall is a key driver of agricultural externalities on water.•Field - landscape scale pollution emissions were elevated in an extreme wet period.•Rainfall indices were used to compare the wet period with projected future climates.•95th percentiles of >1 mm rainfall are comparable between the present and future.•Monitored elevated sediment loss was driven by the extremes of >1 mm rainfall.
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