Evidence for a climate signal in trends of global crop yield variability over the past 50 years

Low variability of crop production from year to year is desirable for many reasons, including reduced income risk and stability of supplies. Therefore, it is important to understand the nature of yield variability, whether it is changing through time, and how it varies between crops and regions. Previous studies have shown that national crop yield variability has changed in the past, with the direction and magnitude dependent on crop type and location. Whilst such studies acknowledge the importance of climate variability in determining yield variability, it has been assumed that its magnitude and its effect on crop production have not changed through time and, hence, that changes to yield variability have been due to non-climatic factors. We address this assumption by jointly examining yield and climate variability for three major crops (rice, wheat and maize) over the past 50 years. National yield time series and growing season temperature and precipitation were de-trended and related using multiple linear regression. Yield variability changed significantly in half of the crop–country combinations examined. For several crop–country combinations, changes in yield variability were related to changes in climate variability.

Effects of absorbing aerosols in cloudy skies: a satellite study over the Atlantic Ocean

We present a method for deriving the radiative effects of absorbing aerosols in cloudy scenes from satellite retrievals only. We use data of 2005–2007 from various passive sensors aboard satellites of the “A-Train” constellation. The study area is restricted to the tropical- and subtropical Atlantic Ocean. To identify the dependence of the local planetary albedo in cloudy scenes on cloud liquid water path and aerosol optical depth (AOD), we perform a multiple linear regression. The OMI UV-Aerosolindex serves as an indicator for absorbing-aerosol presence. In our method, the aerosol influences the local planetary albedo through direct- (scattering and absorption) and indirect (Twomey) aerosol effects. We find an increase of the local planetary albedo (LPA) with increasing AOD of mostly scattering aerosol and a decrease of the LPA with increasing AOD of mostly absorbing aerosol. These results allow us to derive the direct aerosol effect of absorbing aerosols in cloudy scenes, with the effect of cloudy-scene aerosol absorption in the tropical- and subtropical Atlantic contributing (+21.2±11.1)×10−3 Wm−2 to the global top of the atmosphere radiative forcing.

Measurements of stratospheric ozone at a mid-latitude observing station Valentia, Ireland (51.94° N, 10.25° W), using ground-based and ozonesonde observations from 1994 to 2009

Sixteen years (1994 – 2009) of ozone profiling by ozonesondes at Valentia Meteorological and Geophysical Observatory, Ireland (51.94° N, 10.23° W) along with a co-located MkIV Brewer spectrophotometer for the period 1993–2009 are analyzed. Simple and multiple linear regression methods are used to infer the recent trend, if any, in stratospheric column ozone over the station. The decadal trend from 1994 to 2010 is also calculated from the monthly mean data of Brewer and column ozone data derived from satellite observations. Both of these show a 1.5 % increase per decade during this period with an uncertainty of about ±0.25 %. Monthly mean data for March show a much stronger trend of ~ 4.8 % increase per decade for both ozonesonde and Brewer data. The ozone profile is divided between three vertical slots of 0–15 km, 15–26 km, and 26 km to the top of the atmosphere and a 11-year running average is calculated. Ozone values for the month of March only are observed to increase at each level with a maximum change of +9.2 ± 3.2 % per decade (between years 1994 and 2009) being observed in the vertical region from 15 to 26 km. In the tropospheric region from 0 to 15 km, the trend is positive but with a poor statistical significance. However, for the top level of above 26 km the trend is significantly positive at about 4 % per decade. The March integrated ozonesonde column ozone during this period is found to increase at a rate of ~6.6 % per decade compared with the Brewer and satellite positive trends of ~5 % per decade.

Association between Mediterranean and Nordic diet scores and changes in weight and waist circumference: influence of FTO and TCF7L2 loci

BACKGROUND: Several studies have shown that adherence to the Mediterranean Diet measured by using the Mediterranean diet score (MDS) is associated with lower obesity risk. The newly proposed Nordic Diet could hold similar beneficial effects. Because of the increasing focus on the interaction between diet and genetic predisposition to adiposity, studies should consider both diet and genetics. OBJECTIVE: We investigated whether FTO rs9939609 and TCF7L2 rs7903146 modified the association between the MDS and Nordic diet score (NDS) and changes in weight (Δweight), waist circumference (ΔWC), and waist circumference adjusted for body mass index (BMI) (ΔWCBMI). DESIGN: We conducted a case-cohort study with a median follow-up of 6.8 y that included 11,048 participants from 5 European countries; 5552 of these subjects were cases defined as individuals with the greatest degree of unexplained weight gain during follow-up. A randomly selected subcohort included 6548 participants, including 5496 noncases. Cases and noncases were compared in analyses by using logistic regression. Continuous traits (ie, Δweight, ΔWC, and ΔWCBMI) were analyzed by using linear regression models in the random subcohort. Interactions were tested by including interaction terms in models. RESULTS: A higher MDS was significantly inversely associated with case status (OR: 0.98; 95% CI: 0.96, 1.00), ΔWC (β = -0.010 cm/y; 95% CI: -0.020, -0.001 cm/y), and ΔWCBMI (β = -0.008; 95% CI:-0.015, -0.001) per 1-point increment but not Δweight (P = 0.53). The NDS was not significantly associated with any outcome. There was a borderline significant interaction between the MDS and TCF7L2 rs7903146 on weight gain (P = 0.05), which suggested a beneficial effect of the MDS only in subjects who carried 1 or 2 risk alleles. FTO did not modify observed associations. CONCLUSIONS: A high MDS is associated with a lower ΔWC and ΔWCBMI, regardless of FTO and TCF7L2 risk alleles. For Δweight, findings were less clear, but the effect may depend on the TCF7L2 rs7903146 variant. The NDS was not associated with anthropometric changes during follow-up.

Observed relationships between maize yield and climate in Sri Lanka

The present study evaluated the effects of climate variability on maize (Zea mays L.) yield in Sri Lanka at different spatial scales. Biophysical data from the Department of Agriculture (DOA) in Sri Lanka for six major maize-growing districts (Ampara, Anuradhapura, Badulla, Hambantota, Moneragala, and Kurunegala) from 1990 to 2010 were analyzed. Simple linear regression models were fitted to observed climate data and detrended maize yield to identify significant correlations. The correlation between first differences of maize yield and climate (r) was further investigated at 0.50° grid scale using interpolated climate data. After 2003, significantly positive (p < 0.01) yield trends varied from 154 kg ha–1 yr–1 to 360 kg ha–1 yr–1. The correlations between maize yield and climate reported that five out of six districts were significant at 10% level. Rainfall had a consistent significant (p < 0.10) positive impact on maize yield in Anuradhapura, Hambantota, and Moneragala, where seasonal total rainfall together with high temperature (“hot-dry”) are the key limitations. Further, the seasonal mean temperature had a negative impact on maize yield in Moneragala (“hot-dry”), the only district that showed high temperatures. Badulla district (“cold-dry”) reported a significant (r = 0.38) positive correlation with mean seasonal temperature, indicating higher potential toward increasing temperatures. Each 1°C rise in seasonal mean temperature reduced maize yield by about 5% from 1990 to 2010. Overall, there was a reasonable correlation between district maize yield and seasonal climate in most of the districts within the maize belt of Sri Lanka.

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.

Changes in physical activity following a genetic-based internet-delivered personalized intervention: randomized controlled trial (Food4Me)

Background: There is evidence that physical activity (PA) can attenuate the influence of the fat mass- and obesity-associated (FTO) genotype on the risk to develop obesity. However, whether providing personalized information on FTO genotype leads to changes in PA is unknown. Objective: The purpose of this study was to determine if disclosing FTO risk had an impact on change in PA following a 6-month intervention. Methods: The single nucleotide polymorphism (SNP) rs9939609 in the FTO gene was genotyped in 1279 participants of the Food4Me study, a four-arm, Web-based randomized controlled trial (RCT) in 7 European countries on the effects of personalized advice on nutrition and PA. PA was measured objectively using a TracmorD accelerometer and was self-reported using the Baecke questionnaire at baseline and 6 months. Differences in baseline PA variables between risk (AA and AT genotypes) and nonrisk (TT genotype) carriers were tested using multiple linear regression. Impact of FTO risk disclosure on PA change at 6 months was assessed among participants with inadequate PA, by including an interaction term in the model: disclosure (yes/no) × FTO risk (yes/no). Results: At baseline, data on PA were available for 874 and 405 participants with the risk and nonrisk FTO genotypes, respectively. There were no significant differences in objectively measured or self-reported baseline PA between risk and nonrisk carriers. A total of 807 (72.05%) of the participants out of 1120 in the personalized groups were encouraged to increase PA at baseline. Knowledge of FTO risk had no impact on PA in either risk or nonrisk carriers after the 6-month intervention. Attrition was higher in nonrisk participants for whom genotype was disclosed (P=.01) compared with their at-risk counterparts. Conclusions: No association between baseline PA and FTO risk genotype was observed. There was no added benefit of disclosing FTO risk on changes in PA in this personalized intervention. Further RCT studies are warranted to confirm whether disclosure of nonrisk genetic test results has adverse effects on engagement in behavior change.

Alternatives to linear analysis of energy balance data from lactating dairy cows

The current energy requirements system used in the United Kingdom for lactating dairy cows utilizes key parameters such as metabolizable energy intake (MEI) at maintenance (MEm), the efficiency of utilization of MEI for 1) maintenance, 2) milk production (k(l)), 3) growth (k(g)), and the efficiency of utilization of body stores for milk production (k(t)). Traditionally, these have been determined using linear regression methods to analyze energy balance data from calorimetry experiments. Many studies have highlighted a number of concerns over current energy feeding systems particularly in relation to these key parameters, and the linear models used for analyzing. Therefore, a database containing 652 dairy cow observations was assembled from calorimetry studies in the United Kingdom. Five functions for analyzing energy balance data were considered: straight line, two diminishing returns functions, (the Mitscherlich and the rectangular hyperbola), and two sigmoidal functions (the logistic and the Gompertz). Meta-analysis of the data was conducted to estimate k(g) and k(t). Values of 0.83 to 0.86 and 0.66 to 0.69 were obtained for k(g) and k(t) using all the functions (with standard errors of 0.028 and 0.027), respectively, which were considerably different from previous reports of 0.60 to 0.75 for k(g) and 0.82 to 0.84 for k(t). Using the estimated values of k(g) and k(t), the data were corrected to allow for body tissue changes. Based on the definition of k(l) as the derivative of the ratio of milk energy derived from MEI to MEI directed towards milk production, MEm and k(l) were determined. Meta-analysis of the pooled data showed that the average k(l) ranged from 0.50 to 0.58 and MEm ranged between 0.34 and 0.64 MJ/kg of BW0.75 per day. Although the constrained Mitscherlich fitted the data as good as the straight line, more observations at high energy intakes (above 2.4 MJ/kg of BW0.75 per day) are required to determine conclusively whether milk energy is related to MEI linearly or not.

A study of the effect of heat source location in a ventilated room using multiple regression analysis

Multiple regression analysis is a statistical technique which allows to predict a dependent variable from m ore than one independent variable and also to determine influential independent variables. Using experimental data, in this study the multiple regression analysis is applied to predict the room mean velocity and determine the most influencing parameters on the velocity. More than 120 experiments for four different heat source locations were carried out in a test chamber with a high level wall mounted air supply terminal at air change rates 3-6 ach. The influence of the environmental parameters such as supply air momentum, room heat load, Archimedes number and local temperature ratio, were examined by two methods: a simple regression analysis incorporated into scatter matrix plots and multiple stepwise regression analysis. It is concluded that, when a heat source is located along the jet centre line, the supply momentum mainly influences the room mean velocity regardless of the plume strength. However, when the heat source is located outside the jet region, the local temperature ratio (the inverse of the local heat removal effectiveness) is a major influencing parameter.

Sparse modeling using orthogonal forward regression with PRESS statistic and regularization

The paper introduces an efficient construction algorithm for obtaining sparse linear-in-the-weights regression models based on an approach of directly optimizing model generalization capability. This is achieved by utilizing the delete-1 cross validation concept and the associated leave-one-out test error also known as the predicted residual sums of squares (PRESS) statistic, without resorting to any other validation data set for model evaluation in the model construction process. Computational efficiency is ensured using an orthogonal forward regression, but the algorithm incrementally minimizes the PRESS statistic instead of the usual sum of the squared training errors. A local regularization method can naturally be incorporated into the model selection procedure to further enforce model sparsity. The proposed algorithm is fully automatic, and the user is not required to specify any criterion to terminate the model construction procedure. Comparisons with some of the existing state-of-art modeling methods are given, and several examples are included to demonstrate the ability of the proposed algorithm to effectively construct sparse models that generalize well.

Validation of three new measure-correlate-predict models for the long-term prospection of the wind resource

The estimation of the long-term wind resource at a prospective site based on a relatively short on-site measurement campaign is an indispensable task in the development of a commercial wind farm. The typical industry approach is based on the measure-correlate-predict �MCP� method where a relational model between the site wind velocity data and the data obtained from a suitable reference site is built from concurrent records. In a subsequent step, a long-term prediction for the prospective site is obtained from a combination of the relational model and the historic reference data. In the present paper, a systematic study is presented where three new MCP models, together with two published reference models �a simple linear regression and the variance ratio method�, have been evaluated based on concurrent synthetic wind speed time series for two sites, simulating the prospective and the reference site. The synthetic method has the advantage of generating time series with the desired statistical properties, including Weibull scale and shape factors, required to evaluate the five methods under all plausible conditions. In this work, first a systematic discussion of the statistical fundamentals behind MCP methods is provided and three new models, one based on a nonlinear regression and two �termed kernel methods� derived from the use of conditional probability density functions, are proposed. All models are evaluated by using five metrics under a wide range of values of the correlation coefficient, the Weibull scale, and the Weibull shape factor. Only one of all models, a kernel method based on bivariate Weibull probability functions, is capable of accurately predicting all performance metrics studied.

Regularized logistic models for probabilistic forecasting and diagnostics

Logistic models are studied as a tool to convert dynamical forecast information (deterministic and ensemble) into probability forecasts. A logistic model is obtained by setting the logarithmic odds ratio equal to a linear combination of the inputs. As with any statistical model, logistic models will suffer from overfitting if the number of inputs is comparable to the number of forecast instances. Computational approaches to avoid overfitting by regularization are discussed, and efficient techniques for model assessment and selection are presented. A logit version of the lasso (originally a linear regression technique), is discussed. In lasso models, less important inputs are identified and the corresponding coefficient is set to zero, providing an efficient and automatic model reduction procedure. For the same reason, lasso models are particularly appealing for diagnostic purposes.