Hyperspectral field measurements to determine dry matter yield and nutritive value of legume-grass swards

Summary: Productivity, botanical composition and forage quality of legume-grass swards are important factors for successful arable farming in both organic and conventional farming systems. As these attributes can vary considerably within a field, a non-destructive method of detection while doing other tasks would facilitate a more targeted management of crops, forage and nutrients in the soil-plant-animal system. This study was undertaken to explore the potential of field spectral measurements for a non destructive prediction of dry matter (DM) yield, legume proportion in the sward, metabolizable energy (ME), ash content, crude protein (CP) and acid detergent fiber (ADF) of legume-grass mixtures. Two experiments were conducted in a greenhouse under controlled conditions which allowed collecting spectral measurements which were free from interferences such as wind, passing clouds and changing angles of solar irradiation. In a second step this initial investigation was evaluated in the field by a two year experiment with the same legume-grass swards. Several techniques for analysis of the hyperspectral data set were examined in this study: four vegetation indices (VIs): simple ratio (SR), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and red edge position (REP), two-waveband reflectance ratios, modified partial least squares (MPLS) regression and stepwise multiple linear regression (SMLR). The results showed the potential of field spectroscopy and proved its usefulness for the prediction of DM yield, ash content and CP across a wide range of legume proportion and growth stage. In all investigations prediction accuracy of DM yield, ash content and CP could be improved by legume-specific calibrations which included mixtures and pure swards of perennial ryegrass and of the respective legume species. The comparison between the greenhouse and the field experiments showed that the interaction between spectral reflectance and weather conditions as well as incidence angle of light interfered with an accurate determination of DM yield. Further research is hence needed to improve the validity of spectral measurements in the field. Furthermore, the developed models should be tested on varying sites and vegetation periods to enhance the robustness and portability of the models to other environmental conditions.

Quantifizierung der Fress- und Wiederkäuaktivitäten von Milchkühen

Niedrige Milchpreise setzen die deutschen Milchbauern unter Kostendruck und veranlassen sie, die Laktationsleistung der Kühe zu erhöhen. Im Zusammenhang mit weiteren Milchleistungssteigerungen kommt einer wiederkäuergerechten Fütterung im Hinblick auf die Vermeidung gesundheitlicher Risiken eine besondere Bedeutung zu. Ziel des Forschungsvorhabens war es, eine Messmethode zur Bestimmung von Fress- und Wiederkäuaktivitäten zu entwickeln, welche die bisherigen methodischen Unzulänglichkeiten überwindet und ermöglicht den qualitativen und quantitativen Einfluss tierindividueller, fütterungsbedingter und leistungsbezogener Faktoren zu erfassen. Hierzu wurde der Prototyp eines Messsystem entwickelt, das in einem Messhalfter integriert wurde. Das Messsystem beinhaltet einen Sensor zur Erfassung der Kaubewegungen, einen Datenlogger für die Speicherung der Messdaten, ein Akkupack zur Energieversorgung, und eine Auswertungssoftware für die automatische Analyse der erfassten Messdaten. Zwecks Überprüfung des Prototypen unter Praxisbedingungen wurden im Rahmen eines Fütterungsversuches Messungen der Fress- und Wiederkäuaktivität durchgeführt. Die Anwendung des Prototypen auf dem Versuchsbetrieb deckte die folgenden methodischen Unzulänglichkeiten auf: - elektromagnetische Störfelder, hervorgerufen durch die auf den Versuchsbetrieb verwendeten Wiegetröge zur Erfassung der Futteraufnahme, - Fehlmessungen aufgrund verrutschender Halfter, bedingt durch die große Variation in den Schädelmaßen der Versuchstiere, - keine hinreichende Differenzierung der einzelnen Verhaltenstypen bei schnell aufeinanderfolgenden unterschiedlichen Verhaltensmustern. Die aufgetretenen elektrischen Probleme konnten mittels einer verbesserten Abschirmung gegen elektromagnetische Felder behoben werden. Fehlmessungen aufgrund sich verschiebender Halfter konnten durch eine Änderung des Halfterdesigns verringert werden. Es war jedoch nicht möglich, diese Störgröße gänzlich zu beseitigen. Ebenso war es nicht möglich, die Differenzierung der einzelnen Verhaltenstypen bei schnell aufeinanderfolgenden unterschiedlichen Verhaltensmustern mittels einer Änderung im Auswertealgorithmus zu verbessern. Um diesen beiden Problemen Rechnung zu tragen, wurden die Ergebnisse der Auswertungssoftware mittels einer Sichtkontrolle der Messwertkurven validiert. Nach der Validierung verblieben für die statistische Auswertung folgende Messergebnisse: - Anzahl auswertbarer Einzeltiere: 9 - Anzahl auswertbarer Messtage: 73 - Anzahl auswertbarer Wiederkäuphasen: 512 - Anzahl auswertbarer Fressphasen: 676 - Anzahl auswertbarer Einzelboli: 11.347 In der statistischen Auswertung wurden Korrelation der Charakteristika der Wiederkäuboli: Länge des Bolus in Sekunden, Anzahl der Kauschläge pro Bolus und der Frequenz der Kauschläge pro Sekunde und Bolus, der Wiederkäuphasen: Länge der Wiederkäuphasen in Minuten, Anzahl Boli in der Wiederkäuphase und die Anzahl der Kauschläge in der Wiederkäuphase und der Wiederkäudauer pro Tag (in Minuten) mit den erfassten Einflussfaktoren: tierindividuelle Parameter, Milchparameter und Fütterungsparameter berechnet. Um wechselseitige Beziehungen der Einflussfaktoren untereinander besser darstellen zu können, wurde im nächsten Schritt eine multiple lineare Regression durchgeführt. Zur Erfassung der Gewichtung der Einflussfaktoren wurde als dritte statistische Methode eine Regressionsbaumanalyse berechnet. Die Charakteristika der Wiederkäuboli wiesen eine große tierindividuelle Variation zwischen den Einzeltieren auf. Die Milchkühe käuten im Mittel 46 ± 12 Sekunden wieder und vollzogen in dieser Zeit 60 ± 25 Kauschläge bei einer mittleren Kauschlagfrequenz von 1,3 ± 0,4 Kauschlägen pro Sekunde. Die größte tierindividuelle Variation mit 53,1 % wies der Parameter Anzahl der Kauschläge pro Bolus auf. Die Frequenz der Kauschläge pro Bolus wurde in hohem Maße von der Anzahl der Kauschläge pro Bolus beeinflusst (R² = 0,71) und nur in geringem Maße von der Länge des Bolus (R² = 0,21). Das geringe Bestimmtheitsmaß der multiplen Regression der Länge des Bolus (R² = 0,18) deutet auf eine sehr geringe Beeinflussung durch alle erfassten Einflussfaktoren hin. Ebenso wie die Charakteristika der Wiederkäuboli wiesen die Charakteristika der Wiederkäuphasen eine hohe inter- und intraindividuelle Variation (>45%) und bei allen Versuchstieren eine hohe Korrelation der Charakteristika untereinander auf (r = 0,74 bis r =0,98). In der Regressionsanalyse konnte keiner der geprüften Einflussfaktoren einen nennenswerten Erklärungswert liefern. Die Variationen in der Länge der Wiederkäuphasen, Anzahl der Boli pro Phase und der Anzahl der Kauschläge pro Phase wurden weder von der Fütterung, noch vom Tier oder der Milchleistung in nennenswerter Weise geprägt. Eine Beurteilung dieser Einflussfaktoren anhand der Beobachtung einzelner Wiederkäuphasen ist somit nicht möglich. Die Fress- und Wiederkäudauer pro Tag wiesen eine große tierindividuelle Variation auf (37,9 % bzw. 29,0 %). Bei beiden Verhaltensweisen ist die intraindividuelle Variation geringer als die interindividuelle Variation (Fressdauer: 37,9 zu 23,6 %; Wiederkäudauer: 29,0 zu 9%). Der geringe Wert des intraindividuellen Variationskoeffizienten der Wiederkäudauer pro Tag legt eine starke tierindividuelle Determinierung der Wiederkäuaktivität nahe.

The prediction of seasonal and inter-annual climate variations and their impacts on the water resources in the eastern seaboard of Thailand

The research of this thesis dissertation covers developments and applications of short-and long-term climate predictions. The short-term prediction emphasizes monthly and seasonal climate, i.e. forecasting from up to the next month over a season to up to a year or so. The long-term predictions pertain to the analysis of inter-annual- and decadal climate variations over the whole 21st century. These two climate prediction methods are validated and applied in the study area, namely, Khlong Yai (KY) water basin located in the eastern seaboard of Thailand which is a major industrial zone of the country and which has been suffering from severe drought and water shortage in recent years. Since water resources are essential for the further industrial development in this region, a thorough analysis of the potential climate change with its subsequent impact on the water supply in the area is at the heart of this thesis research. The short-term forecast of the next-season climate, such as temperatures and rainfall, offers a potential general guideline for water management and reservoir operation. To that avail, statistical models based on autoregressive techniques, i.e., AR-, ARIMA- and ARIMAex-, which includes additional external regressors, and multiple linear regression- (MLR) models, are developed and applied in the study region. Teleconnections between ocean states and the local climate are investigated and used as extra external predictors in the ARIMAex- and the MLR-model and shown to enhance the accuracy of the short-term predictions significantly. However, as the ocean state – local climate teleconnective relationships provide only a one- to four-month ahead lead time, the ocean state indices can support only a one-season-ahead forecast. Hence, GCM- climate predictors are also suggested as an additional predictor-set for a more reliable and somewhat longer short-term forecast. For the preparation of “pre-warning” information for up-coming possible future climate change with potential adverse hydrological impacts in the study region, the long-term climate prediction methodology is applied. The latter is based on the downscaling of climate predictions from several single- and multi-domain GCMs, using the two well-known downscaling methods SDSM and LARS-WG and a newly developed MLR-downscaling technique that allows the incorporation of a multitude of monthly or daily climate predictors from one- or several (multi-domain) parent GCMs. The numerous downscaling experiments indicate that the MLR- method is more accurate than SDSM and LARS-WG in predicting the recent past 20th-century (1971-2000) long-term monthly climate in the region. The MLR-model is, consequently, then employed to downscale 21st-century GCM- climate predictions under SRES-scenarios A1B, A2 and B1. However, since the hydrological watershed model requires daily-scale climate input data, a new stochastic daily climate generator is developed to rescale monthly observed or predicted climate series to daily series, while adhering to the statistical and geospatial distributional attributes of observed (past) daily climate series in the calibration phase. Employing this daily climate generator, 30 realizations of future daily climate series from downscaled monthly GCM-climate predictor sets are produced and used as input in the SWAT- distributed watershed model, to simulate future streamflow and other hydrological water budget components in the study region in a multi-realization manner. In addition to a general examination of the future changes of the hydrological regime in the KY-basin, potential future changes of the water budgets of three main reservoirs in the basin are analysed, as these are a major source of water supply in the study region. The results of the long-term 21st-century downscaled climate predictions provide evidence that, compared with the past 20th-reference period, the future climate in the study area will be more extreme, particularly, for SRES A1B. Thus, the temperatures will be higher and exhibit larger fluctuations. Although the future intensity of the rainfall is nearly constant, its spatial distribution across the region is partially changing. There is further evidence that the sequential rainfall occurrence will be decreased, so that short periods of high intensities will be followed by longer dry spells. This change in the sequential rainfall pattern will also lead to seasonal reductions of the streamflow and seasonal changes (decreases) of the water storage in the reservoirs. In any case, these predicted future climate changes with their hydrological impacts should encourage water planner and policy makers to develop adaptation strategies to properly handle the future water supply in this area, following the guidelines suggested in this study.

A prediction model for establishing the disability degree in adults with spinal cord injury: results based on WHO-DAS II perspective

Objective: To establish a prediction model of the degree of disability in adults with Spinal CordInjury (SCI ) based on the use of the WHO-DAS II . Methods: The disability degree was correlatedwith three variable groups: clinical, sociodemographic and those related with rehabilitation services.A model of multiple linear regression was built to predict disability. 45 people with sci exhibitingdiverse etiology, neurological level and completeness participated. Patients were older than 18 andthey had more than a six-month post-injury. The WHO-DAS II and the ASIA impairment scale(AIS ) were used. Results: Variables that evidenced a significant relationship with disability were thefollowing: occupational situation, type of affiliation to the public health care system, injury evolutiontime, neurological level, partial preservation zone, ais motor and sensory scores and number ofclinical complications during the last year. Complications significantly associated to disability werejoint pain, urinary infections, intestinal problems and autonomic disreflexia. None of the variablesrelated to rehabilitation services showed significant association with disability. The disability degreeexhibited significant differences in favor of the groups that received the following services: assistivedevices supply and vocational, job or educational counseling. Conclusions: The best predictiondisability model in adults with sci with more than six months post-injury was built with variablesof injury evolution time, AIS sensory score and injury-related unemployment.

Neurobehavioral Development in Children With Potential Exposure to Pesticides

Children may be at higher risk than adults from pesticide exposure, due to their rapidly developing physiology, unique behavioral patterns, and interactions with the physical environment. This preliminary study conducted in Ecuador examines the association between household and environmental risk factors for pesticide exposure and neurobehavioral development. We collected data over 6 months in the rural highland region of Cayambe, Ecuador (2003–2004). Children age 24–61 months residing in 3 communities were assessed with the Ages and Stages Questionnaire and the Visual Motor Integration Test. We gathered information on maternal health and work characteristics, the home and community environment, and child characteristics. Growth measurements and a hemoglobin finger-prick blood test were obtained. Multiple linear regression analyses were conducted. Current maternal employment in the flower industry was associated with better developmental scores. Longer hours playing outdoors were associated with lower gross and fine motor and problem solving skills. Children who played with irrigation water scored lower on fine motor skills (8% decrease; 95% confidence interval 9.31 to 0.53), problem-solving skills (7% decrease; 8.40 to 0.39), and Visual Motor Integration test scores (3% decrease; 12.00 to 1.08). These results suggest that certain environmental risk factors for exposure to pesticides may affect child development, with contact with irrigation water of particular concern. However, the relationships between these risk factors and social characteristics are complex, as corporate agriculture may increase risk through pesticide exposure and environmental contamination, while indirectly promoting healthy development by providing health care, relatively higher salaries, and daycare options.

Threshold detection: matching statistical methodology to ecological questions and conservation planning objectives

Two types of ecological thresholds are now being widely used to develop conservation targets: breakpoint-based thresholds represent tipping points where system properties change dramatically, whereas classification thresholds identify groups of data points with contrasting properties. Both breakpoint-based and classification thresholds are useful tools in evidence-based conservation. However, it is critical that the type of threshold to be estimated corresponds with the question of interest and that appropriate statistical procedures are used to determine its location. On the basis of their statistical properties, we recommend using piecewise regression methods to identify breakpoint-based thresholds and discriminant analysis or classification and regression trees to identify classification thresholds.

Validation of improved TAMANN neural network for operational satellite-derived rainfall estimation in Africa

Real-time rainfall monitoring in Africa is of great practical importance for operational applications in hydrology and agriculture. Satellite data have been used in this context for many years because of the lack of surface observations. This paper describes an improved artificial neural network algorithm for operational applications. The algorithm combines numerical weather model information with the satellite data. Using this algorithm, daily rainfall estimates were derived for 4 yr of the Ethiopian and Zambian main rainy seasons and were compared with two other algorithms-a multiple linear regression making use of the same information as that of the neural network and a satellite-only method. All algorithms were validated against rain gauge data. Overall, the neural network performs best, but the extent to which it does so depends on the calibration/validation protocol. The advantages of the neural network are most evident when calibration data are numerous and close in space and time to the validation data. This result emphasizes the importance of a real-time calibration system.

Understanding electron heat flux signatures in the solar wind

Suprathermal electrons (E > 80 eV) carry heat flux away from the Sun. Processes controlling the heat flux are not well understood. To gain insight into these processes, we model heat flux as a linear dependence on two independent parameters: electron number flux and electron pitch angle anisotropy. Pitch angle anisotropy is further modeled as a linear dependence on two solar wind components: magnetic field strength and plasma density. These components show no correlation with number flux, reinforcing its independence from pitch angle anisotropy. Multiple linear regression applied to 2 years of Wind data shows good correspondence between modeled and observed heat flux and anisotropy. The results suggest that the interplay of solar wind parameters and electron number flux results in distinctive heat flux dropouts at heliospheric features like plasma sheets but that these parameters continuously modify heat flux. This is inconsistent with magnetic disconnection as the primary cause of heat flux dropouts. Analysis of fast and slow solar wind regimes separately shows that electron number flux and pitch angle anisotropy are equally correlated with heat flux in slow wind but that number flux is the dominant correlative in fast wind. Also, magnetic field strength correlates better with pitch angle anisotropy in slow wind than in fast wind. The energy dependence of the model fits suggests different scattering processes in fast and slow wind.

Rapid quality assessment of wheat cultivars registered in Poland using the 2-g mixograph and multivariate statistical analysis

Baking and 2-g mixograph analyses were performed for 55 cultivars (19 spring and 36 winter wheat) from various quality classes from the 2002 harvest in Poland. An instrumented 2-g direct-drive mixograph was used to study the mixing characteristics of the wheat cultivars. A number of parameters were extracted automatically from each mixograph trace and correlated with baking volume and flour quality parameters (protein content and high molecular weight glutenin subunit [HMW-GS] composition by SDS-PAGE) using multiple linear regression statistical analysis. Principal component analysis of the mixograph data discriminated between four flour quality classes, and predictions of baking volume were obtained using several selected mixograph parameters, chosen using a best subsets regression routine, giving R-2 values of 0.862-0.866. In particular, three new spring wheat strains (CHD 502a-c) recently registered in Poland were highly discriminated and predicted to give high baking volume on the basis of two mixograph parameters: peak bandwidth and 10-min bandwidth.

The 11-yr solar cycle in ERA-40 data: an update to 2008

Multiple linear regression is used to diagnose the signal of the 11-yr solar cycle in zonal-mean zonal wind and temperature in the 40-yr ECMWF Re-Analysis (ERA-40) dataset. The results of previous studies are extended to 2008 using data from ECMWF operational analyses. This analysis confirms that the solar signal found in previous studies is distinct from that of volcanic aerosol forcing resulting from the eruptions of El Chichón and Mount Pinatubo, but it highlights the potential for confusion of the solar signal and lower-stratospheric temperature trends. A correction to an error that is present in previous results of Crooks and Gray, stemming from the use of a single daily analysis field rather than monthly averaged data, is also presented.

European CMBS pricing: bond, mortgage and real estate characteristics

The recent global economic crisis is often associated with the development and pricing of mortgage-backed securities (i.e. MBSs) and underlying products (i.e. sub-prime mortgages). This work uses a rich database of MBS issues and represents the first attempt to price commercial MBSs (i.e. CMBSs) in the European market. Our results are consistent with research carried out in the US market and we find that bond-, mortgage-, real estate-related and multinational characteristics show different degrees of significance in explaining European CMBS spreads at issuance. Multiple linear regression analysis using a databank of CMBSs issued between 1997 and 2007 indicates a strong relationship with bond-related factors, followed by real estate and mortgage market conditions. We also find that multinational factors are significant, with country of issuance, collateral location and access to more liquid markets all being important in explaining the cost of secured funding for real estate companies. As floater coupon tranches tend to be riskier and exhibit higher spreads, we also estimate a model using this sub-set of data and results hold, hence reinforcing our findings. Finally, we estimate our model for both tranches A and B and find that real estate factors become relatively more important for the riskier investment products.

FunFOLDQA: a quality assessment tool for protein-ligand binding site residue predictions

The estimation of prediction quality is important because without quality measures, it is difficult to determine the usefulness of a prediction. Currently, methods for ligand binding site residue predictions are assessed in the function prediction category of the biennial Critical Assessment of Techniques for Protein Structure Prediction (CASP) experiment, utilizing the Matthews Correlation Coefficient (MCC) and Binding-site Distance Test (BDT) metrics. However, the assessment of ligand binding site predictions using such metrics requires the availability of solved structures with bound ligands. Thus, we have developed a ligand binding site quality assessment tool, FunFOLDQA, which utilizes protein feature analysis to predict ligand binding site quality prior to the experimental solution of the protein structures and their ligand interactions. The FunFOLDQA feature scores were combined using: simple linear combinations, multiple linear regression and a neural network. The neural network produced significantly better results for correlations to both the MCC and BDT scores, according to Kendall’s τ, Spearman’s ρ and Pearson’s r correlation coefficients, when tested on both the CASP8 and CASP9 datasets. The neural network also produced the largest Area Under the Curve score (AUC) when Receiver Operator Characteristic (ROC) analysis was undertaken for the CASP8 dataset. Furthermore, the FunFOLDQA algorithm incorporating the neural network, is shown to add value to FunFOLD, when both methods are employed in combination. This results in a statistically significant improvement over all of the best server methods, the FunFOLD method (6.43%), and one of the top manual groups (FN293) tested on the CASP8 dataset. The FunFOLDQA method was also found to be competitive with the top server methods when tested on the CASP9 dataset. To the best of our knowledge, FunFOLDQA is the first attempt to develop a method that can be used to assess ligand binding site prediction quality, in the absence of experimental data.

The effect of nonlinearity in CO2 heating rates on the attribution of stratospheric ozone and temperature changes

An analysis of the attribution of past and future changes in stratospheric ozone and temperature to anthropogenic forcings is presented. The analysis is an extension of the study of Shepherd and Jonsson (2008) who analyzed chemistry-climate simulations from the Canadian Middle Atmosphere Model (CMAM) and attributed both past and future changes to changes in the external forcings, i.e. the abundances of ozone-depleting substances (ODS) and well-mixed greenhouse gases. The current study is based on a new CMAM dataset and includes two important changes. First, we account for the nonlinear radiative response to changes in CO2. It is shown that over centennial time scales the radiative response in the upper stratosphere to CO2 changes is significantly nonlinear and that failure to account for this effect leads to a significant error in the attribution. To our knowledge this nonlinearity has not been considered before in attribution analysis, including multiple linear regression studies. For the regression analysis presented here the nonlinearity was taken into account by using CO2 heating rate, rather than CO2 abundance, as the explanatory variable. This approach yields considerable corrections to the results of the previous study and can be recommended to other researchers. Second, an error in the way the CO2 forcing changes are implemented in the CMAM was corrected, which significantly affects the results for the recent past. As the radiation scheme, based on Fomichev et al. (1998), is used in several other models we provide some description of the problem and how it was fixed.

Ozone database in support of CMIP5 simulations: results and corresponding radiative forcing

A continuous tropospheric and stratospheric vertically resolved ozone time series, from 1850 to 2099, has been generated to be used as forcing in global climate models that do not include interactive chemistry. A multiple linear regression analysis of SAGE I+II satellite observations and polar ozonesonde measurements is used for the stratospheric zonal mean dataset during the well-observed period from 1979 to 2009. In addition to terms describing the mean annual cycle, the regression includes terms representing equivalent effective stratospheric chlorine (EESC) and the 11-yr solar cycle variability. The EESC regression fit coefficients, together with pre-1979 EESC values, are used to extrapolate the stratospheric ozone time series backward to 1850. While a similar procedure could be used to extrapolate into the future, coupled chemistry climate model (CCM) simulations indicate that future stratospheric ozone abundances are likely to be significantly affected by climate change, and capturing such effects through a regression model approach is not feasible. Therefore, the stratospheric ozone dataset is extended into the future (merged in 2009) with multimodel mean projections from 13 CCMs that performed a simulation until 2099 under the SRES (Special Report on Emission Scenarios) A1B greenhouse gas scenario and the A1 adjusted halogen scenario in the second round of the Chemistry-Climate Model Validation (CCMVal-2) Activity. The stratospheric zonal mean ozone time series is merged with a three-dimensional tropospheric data set extracted from simulations of the past by two CCMs (CAM3.5 and GISSPUCCINI)and of the future by one CCM (CAM3.5). The future tropospheric ozone time series continues the historical CAM3.5 simulation until 2099 following the four different Representative Concentration Pathways (RCPs). Generally good agreement is found between the historical segment of the ozone database and satellite observations, although it should be noted that total column ozone is overestimated in the southern polar latitudes during spring and tropospheric column ozone is slightly underestimated. Vertical profiles of tropospheric ozone are broadly consistent with ozonesondes and in-situ measurements, with some deviations in regions of biomass burning. The tropospheric ozone radiative forcing (RF) from the 1850s to the 2000s is 0.23Wm−2, lower than previous results. The lower value is mainly due to (i) a smaller increase in biomass burning emissions; (ii) a larger influence of stratospheric ozone depletion on upper tropospheric ozone at high southern latitudes; and possibly (iii) a larger influence of clouds (which act to reduce the net forcing) compared to previous radiative forcing calculations. Over the same period, decreases in stratospheric ozone, mainly at high latitudes, produce a RF of −0.08Wm−2, which is more negative than the central Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) value of −0.05Wm−2, but which is within the stated range of −0.15 to +0.05Wm−2. The more negative value is explained by the fact that the regression model simulates significant ozone depletion prior to 1979, in line with the increase in EESC and as confirmed by CCMs, while the AR4 assumed no change in stratospheric RF prior to 1979. A negative RF of similar magnitude persists into the future, although its location shifts from high latitudes to the tropics. This shift is due to increases in polar stratospheric ozone, but decreases in tropical lower stratospheric ozone, related to a strengthening of the Brewer-Dobson circulation, particularly through the latter half of the 21st century. Differences in trends in tropospheric ozone among the four RCPs are mainly driven by different methane concentrations, resulting in a range of tropospheric ozone RFs between 0.4 and 0.1Wm−2 by 2100. The ozone dataset described here has been released for the Coupled Model Intercomparison Project (CMIP5) model simulations in netCDF Climate and Forecast (CF) Metadata Convention at the PCMDI website (http://cmip-pcmdi.llnl.gov/).