Bianchi Model CMB Polarization and its Implications for CMB Anomalies

We derive the CMB radiative transfer equation in the form of a multipole hierarchy in the nearly-Friedmann-Robertson-Walker limit of homogeneous, but anisotropic, universes classified via their Bianchi type. Compared with previous calculations, this allows a more sophisticated treatment of recombination, produces predictions for the polarization of the radiation, and allows for reionization. Our derivation is independent of any assumptions about the dynamical behaviour of the field equations, except that it requires anisotropies to be small back to recombination; this is already demanded by observations. We calculate the polarization signal in the Bianchi VIIh case, with the parameters recently advocated to mimic the several large-angle anomalous features observed in the CMB. We find that the peak polarization signal is ~ 1.2 micro K for the best-fit model to the temperature anisotropies, and is mostly confined to multipoles l<10. Remarkably, the predicted large-angle EE and TE power spectra in the Bianchi model are consistent with WMAP observations that are usually interpreted as evidence of early reionization. However, the power in B-mode polarisation is predicted to be similar to the E-mode power and parity-violating correlations are also predicted by the model; the WMAP non-detection of either of these signals casts further strong doubts on the veracity of attempts to explain the large-angle anomalies with global anisotropy. On the other hand, given that there exist further dynamical degrees of freedom in the VIIh universes that are yet to be compared with CMB observations, we cannot at this time definitively reject the anisotropy explanation.Comment: Accepted for publication in MNRAS. Minor grammatical and typographical changes to reflect version in press. 13 pages, 6 figure

Assessing uncertainty and complexity in regional-scale crop model simulations

Crop models are imperfect approximations to real world interactions between biotic and abiotic factors. In some situations, the uncertainties associated with choices in model structure, model inputs and parameters can exceed the spatiotemporal variability of simulated yields, thus limiting predictability. For Indian groundnut, we used the General Large Area Model for annual crops (GLAM) with an existing framework to decompose uncertainty, to first understand how skill changes with added model complexity, and then to determine the relevant uncertainty sources in yield and other prognostic variables (total biomass, leaf area index and harvest index). We developed an ensemble of simulations by perturbing GLAM parameters using two different input meteorology datasets, and two model versions that differ in the complexity with which they account for assimilation. We found that added complexity improved model skill, as measured by changes in the root mean squared error (RMSE), by 5-10% in specific pockets of western, central and southern India, but that 85% of the groundnut growing area either did not show improved skill or showed decreased skill from such added complexity. Thus, adding complexity or using overly complex models at regional or global scales should be exercised with caution. Uncertainty analysis indicated that, in situations where soil and air moisture dynamics are the major determinants of productivity, predictability in yield is high. Where uncertainty for yield is high, the choice of weather input data was found critical for reducing uncertainty. However, for other prognostic variables (including leaf area index, total biomass and the harvest index) parametric uncertainty was generally the most important source, with a contribution of up to 90% in some cases, suggesting that regional-scale data additional to yield to constrain model parameters is needed. Our study provides further evidence that regional-scale studies should explicitly quantify multiple uncertainty sources

Agrometeorological forecasting

Agrometeorological forecasting covers all aspects of forecasting in agrometeorology. Therefore, the scope of agrometeorological forecasting very largely coincides with the scope of agrometeorology itself. All on-farm and regional agrometeorological planning implies some form of impact forecasting, at least implicitly, so that decision-support tools and forecasting tools largely overlap. In the current chapter, the focus is on crops, but attention is also be paid to sectors that are often neglected by the agrometeorologist, such as those occurring in plant and animal protection. In addition, the borders between meteorological forecasts for agriculture and agrometeorological forecasts are not always clear. Examples include the use of weather forecasts for farm operations such as spraying pesticides or deciding on trafficability in relation to adverse weather. Many forecast issues by various national institutions (weather, but also commodity prices or flood warnings) are vital to the farming community, but they do not constitute agrometeorological forecasts. (Modified From the introduction of the chapter: Scope of agrometeorological forecasting)JRC.H.4-Monitoring Agricultural Resource

Increasing influence of heat stress on French maize yields from the 1960s to the 2030s

Improved crop yield forecasts could enable more effective adaptation to climate variability and change. Here, we explore how to combine historical observations of crop yields and weather with climate model simulations to produce crop yield projections for decision relevant timescales. Firstly, the effects on historical crop yields of improved technology, precipitation and daily maximum temperatures are modelled empirically, accounting for a nonlinear technology trend and interactions between temperature and precipitation, and applied specifically for a case study of maize in France. The relative importance of precipitation variability for maize yields in France has decreased significantly since the 1960s, likely due to increased irrigation. In addition, heat stress is found to be as important for yield as precipitation since around 2000. A significant reduction in maize yield is found for each day with a maximum temperature above 32 °C, in broad agreement with previous estimates. The recent increase in such hot days has likely contributed to the observed yield stagnation. Furthermore, a general method for producing near-term crop yield projections, based on climate model simulations, is developed and utilized. We use projections of future daily maximum temperatures to assess the likely change in yields due to variations in climate. Importantly, we calibrate the climate model projections using observed data to ensure both reliable temperature mean and daily variability characteristics, and demonstrate that these methods work using retrospective predictions. We conclude that, to offset the projected increased daily maximum temperatures over France, improved technology will need to increase base level yields by 12% to be confident about maintaining current levels of yield for the period 2016–2035; the current rate of yield technology increase is not sufficient to meet this target

Effects of combined abiotic stresses on nutrient content of European wheat and implications for nutritional security under climate change

Climate change is causing problems for agriculture, but the effect of combined abiotic stresses on crop nutritional quality is not clear. Here we studied the effect of 10 combinations of climatic conditions (temperature, CO2, O3 and drought) under controlled growth chamber conditions on the grain yield, protein, and mineral content of 3 wheat varieties. Results show that wheat plants under O3 exposure alone concentrated + 15 to + 31% more grain N, Fe, Mg, Mn P and Zn, reduced K by − 5%, and C did not change. Ozone in the presence of elevated CO2 and higher temperature enhanced the content of Fe, Mn, P and Zn by 2–18%. Water-limited chronic O3 exposure resulted in + 9 to + 46% higher concentrations of all the minerals, except K. The effect of climate abiotic factors could increase the ability of wheat to meet adult daily dietary requirements by + 6% to + 12% for protein, Zn and Fe, but decrease those of Mg, Mn and P by − 3% to − 6%, and K by − 62%. The role of wheat in future nutrition security is discussed

TAMSAT-ALERT v1: a new framework for agricultural decision support

Early warning of weather-related hazards enables farmers, policy makers and aid agencies to mitigate their exposure to risk. We present a new operational framework, Tropical Applications of Meteorology using SATellite data and ground based measurements-AgricuLtural EaRly warning sysTem (TAMSAT-ALERT), which aims to provide early warning for meteorological risk to agriculture. TAMSAT-ALERT combines information on land-surface properties, seasonal forecasts and historical weather to quantitatively assess the likelihood of adverse weather-related outcomes, such as low yield. This article describes the modular TAMSAT-ALERT framework and demonstrates its application to risk assessment for low maize yield in northern Ghana (Tamale). The modular design of TAMSAT-ALERT enables it to accommodate any impact or land-surface model driven with meteorological data. The implementation described here uses the well-established General Large Area Model (GLAM) for annual crops to provide probabilistic assessments of the meteorological hazard for maize yield in northern Ghana (Tamale) throughout the growing season. The results show that climatic risk to yield is poorly constrained in the beginning of the season, but as the season progresses, the uncertainty is rapidly reduced. Based on the assessment for the period 2002–2011, we show that TAMSAT-ALERT can estimate the meteorological risk on maize yield 6 to 8 weeks in advance of harvest. The TAMSAT-ALERT methodology implicitly weights forecast and observational inputs according to their relevance to the metric being assessed. A secondary application of TAMSAT-ALERT is thus an evaluation of the usefulness of meteorological forecast products for impact assessment. Here, we show that in northern Ghana (Tamale), the tercile seasonal forecasts of seasonal cumulative rainfall and mean temperature, which are routinely issued to farmers, are of limited value because regional and seasonal temperature and rainfall are poorly correlated with yield. This finding speaks to the pressing need for meteorological forecast products that are tailored for individual user applications

Climate change affects rainfall patterns in crop-producing regions: Findings from the study “Emergence of robust precipitation changes across crop production areas in the 21st century"

Rain-fed agriculture currently constitutes 60–95% of farmed land across the developing world. Changing rainfall patterns could have a large impact on agriculture in developing countries. Using over 20 different climate models, researchers have projected how precipitation could be affected by climate change. Key takeaways include: 1) unless emissions are curbed soon, by 2040, the rainfall patterns in many major wheat, soybean, rice and maize regions will have changed outside their natural boundaries; 2) emissions reductions in accordance with the Paris Agreement would result in far less crop-producing areas experiencing novel rainfall patterns; and 3) targeting adaptation efforts remains a major challenge, but region specific results can now enable investment and action

Comparing the effects of calibration and climate errors on a statistical crop model and a process-based crop model

Understanding the relationship between climate and crop productivity is a key component of projections of future food production, and hence assessments of food security. Climate models and crop yield datasets have errors, but the effects of these errors on regional scale crop models is not well categorized and understood. In this study we compare the effect of synthetic errors in temperature and precipitation observations on the hindcast skill of a process-based crop model and a statistical crop model. We find that errors in temperature data have a significantly stronger influence on both models than errors in precipitation. We also identify key differences in the responses of these models to different types of input data error. Statistical and process-based model responses differ depending on whether synthetic errors are overestimates or underestimates. We also investigate the impact of crop yield calibration data on model skill for both models, using datasets of yield at three different spatial scales. Whilst important for both models, the statistical model is more strongly influenced by crop yield scale than the process-based crop model. However, our results question the value of high resolution yield data for improving the skill of crop models; we find a focus on accuracy to be more likely to be valuable. For both crop models, and for all three spatial scales of yield calibration data, we found that model skill is greatest where growing area is above 10-15 %. Thus information on area harvested would appear to be a priority for data collection efforts. These results are important for three reasons. First, understanding how different crop models rely on different characteristics of temperature, precipitation and crop yield data allows us to match the model type to the available data. Second, we can prioritize where improvements in climate and crop yield data should be directed. Third, as better climate and crop yield data becomes available, we can predict how crop model skill should improve

The global and regional impacts of climate change under Representative Concentration Pathway forcings and Shared Socioeconomic Pathway socioeconomic scenarios

This paper presents an evaluation of the global and regional consequences of climate change for heat extremes, water resources, river and coastal flooding, droughts, agriculture and energy use. It presents change in hazard and resource base under different rates of climate change (Representative Concentration Pathways: RCP), and socio-economic impacts are estimated for each combination of RCP and Shared Socioeconomic Pathway. Uncertainty in the regional pattern of climate change is characterised by CMIP5 climate model projections. The analysis adopts a novel approach using relationships between level of warming and impact to rapidly estimate impacts under any climate forcing. The projections provided here can be used to inform assessments of the implications of climate change. At the global scale all the consequences of climate change considered here are adverse, with large increases under the highest rates of warming. Under the highest forcing the global average annual chance of a major heatwave increases from 5% now to 97% in 2100, the average proportion of time in drought increases from 7% to 27%, and the average chance of the current 50-year flood increases from 2% to 7%. The socio-economic impacts of these climate changes are determined by socio-economic scenario. There is variability in impact across regions, reflecting variability in projected changes in precipitation and temperature. The range in the estimated impacts can be large, due to uncertainty in future emissions and future socio-economic conditions and scientific uncertainty in how climate changes in response to future emissions. For the temperature-based indicators, the largest source of scientific uncertainty is in the estimated magnitude of equilibrium climate sensitivity, but for the indicators determined by precipitation the largest source is in the estimated spatial and seasonal pattern of changes in precipitation. By 2100 the range across socio-economic scenario is often greater than the range across the forcing levels