Impact of derived global weather data on simulated crop yields

Crop simulation models can be used to estimate impact of current and future climates on crop yields and food security, but require long-term historical daily weather data to obtain robust simulations. In many regions where crops are grown, daily weather data are not available. Alternatively, gridded weather databases (GWD) with complete terrestrial coverage are available, typically derived from: (i) global circulation computer models; (ii) interpolated weather station data; or (iii) remotely sensed surface data from satellites. The present study’s objective is to evaluate capacity of GWDs to simulate crop yield potential (Yp) or water-limited yield potential (Yw), which can serve as benchmarks to assess impact of climate change scenarios on crop productivity and land use change. Three GWDs (CRU, NCEP/DOE, and NASA POWER data) were evaluated for their ability to simulate Yp and Yw of rice in China, USA maize, and wheat in Germany. Simulations of Yp and Yw based on recorded daily data from well-maintained weather stations were taken as the control weather data (CWD). Agreement between simulations of Yp or Yw based on CWD and those based on GWD was poor with the latter having strong bias and large root mean square errors (RMSEs) that were 26–72% of absolute mean yield across locations and years. In contrast, simulated Yp or Yw using observed daily weather data from stations in the NOAA database combined with solar radiation from the NASAPOWER database were in much better agreement with Yp and Yw simulated with CWD (i.e. little bias and an RMSE of 12–19% of the absolute mean). We conclude that results from studies that rely on GWD to simulate agricultural productivity in current and future climates are highly uncertain. An alternative approach would impose a climate scenario on location-specific observed daily weather databases combined with an appropriate upscaling method

A dataset of future daily weather data for crop modelling over Europe derived from climate change scenarios

Coupled atmosphere-ocean general circulation models (AOGCMs, or just GCMs for short) simulate different realizations of possible future climates at global scale under contrasting scenarios of greenhouse gases emissions. While these datasets provide several meteorological variables as output, but two of the most important ones are air temperature at the Earth's surface and daily precipitation. GCMs outputs are spatially downscaled using different methodologies, but it is accepted that such data require further processing to be used in impact models, and particularly for crop simulation models. Daily values of solar radiation, wind, air humidity, and, at times, rainfall may have values which are not realistic, and/or the daily record of data may contain values of meteorological variables which are totally uncorrelated. Crop models are deterministic, but they are typicallyrun in a stochastic fashion by using a sample of possible weather time series that can be generated using stochastic weather generators. With their random variability, these multiple years of weather data can represent the time horizon of interest. GCMs estimate climate dynamics, hence providing unique time series for a given emission scenario; the multiplicity of years to evaluate a given time horizon is consequently not available from such outputs. Furthermore, if the time horizons of interest are very close (e.g. 2020 and 2030), averaging only the non-overlapping years of the GCM weather variables time series may not adequately represent the time horizon; this may lead to apparent inversions of trends, creating artefacts also in the impact model simulations. This paper presents a database of consolidated and coherent future daily weather data covering Europe with a 25 km grid, which is adequate for crop modelling in the near-future. Climate data are derived from the ENSEMBLES downscaling of the HadCM3, ECHAM5, and ETHZ realizations of the IPCC A1B emission scenario, using for HadCM3 two different regional models for downscaling. Solar radiation, wind and relative air humidity weather variables where either estimated or collected from historical series, and derived variables reference evapotranspiration and vapour pressure deficit were estimated from other variables, ensuring consistency within daily records. Synthetic time series data were also generated using the weather generator ClimGen. All data are made available upon request to the European Commission Joint Research Centre's MARS unit.JRC.H.7-Climate Risk Managemen

Climate Forcing Datasets for Agricultural Modeling: Merged Products for Gap-Filling and Historical Climate Series Estimation

The AgMERRA and AgCFSR climate forcing datasets provide daily, high-resolution, continuous, meteorological series over the 1980-2010 period designed for applications examining the agricultural impacts of climate variability and climate change. These datasets combine daily resolution data from retrospective analyses (the Modern-Era Retrospective Analysis for Research and Applications, MERRA, and the Climate Forecast System Reanalysis, CFSR) with in situ and remotely-sensed observational datasets for temperature, precipitation, and solar radiation, leading to substantial reductions in bias in comparison to a network of 2324 agricultural-region stations from the Hadley Integrated Surface Dataset (HadISD). Results compare favorably against the original reanalyses as well as the leading climate forcing datasets (Princeton, WFD, WFD-EI, and GRASP), and AgMERRA distinguishes itself with substantially improved representation of daily precipitation distributions and extreme events owing to its use of the MERRA-Land dataset. These datasets also peg relative humidity to the maximum temperature time of day, allowing for more accurate representation of the diurnal cycle of near-surface moisture in agricultural models. AgMERRA and AgCFSR enable a number of ongoing investigations in the Agricultural Model Intercomparison and Improvement Project (AgMIP) and related research networks, and may be used to fill gaps in historical observations as well as a basis for the generation of future climate scenarios

Collinsville solar thermal project: yield forecasting (draft report)

The final report has been published and is available here. Executive Summary 1        Introduction This report’s primary aim is to provide yield projections for the proposed Linear Fresnel Reflector (LFR) technology plant at Collinsville, Queensland, Australia.  However, the techniques developed in this report to overcome inadequate datasets at Collinsville to produce the yield projections are of interest to a wider audience because inadequate datasets for renewable energy projects are commonplace.  The subsequent report called ‘Energy economics and dispatch forecasting’ (Bell, Wild & Foster 2014a) uses the yield projections from this report to produce long-term wholesale market price and dispatch forecasts for the plant.  2        Literature review The literature review discusses the four drivers for yield for LFR technology: DNI (Direct Normal Irradiance) Temperature Humidity Pressure Collinsville lacks complete historical datasets of the four drivers to develop yield projects but its three nearby neighbours do possess complete datasets, so could act as proxies for Collinsville.  However, analysing the four drivers for Collinsville and its three nearby sites shows that there is considerable difference in their climates.  This difference makes them unsuitable to act as proxies for yield calculations.  Therefore, the review investigates modelling the four drivers for Collinsville. We introduce the term “effective” DNI to help clarify and ameliorate concerns over the dust and dew effects on terrestrial DNI measurement and LFR technology. We also introduce a modified TMY technique to overcome technology specific Typical Metrological Year (TMY).  We discuss the effect of climate change and the El Nino Southern Oscillation (ENSO) on yield and their implications for a TMY. 2.1     Research questions Research question arising from the literature review include: The overarching research question: Can modelling the weather with limited datasets produce greater yield predictive power than using the historically more complete datasets from nearby sites? This overarching question has a number of smaller supporting research questions: Is BoM’s DNI satellite dataset adequately adjusted for cloud cover at Collinsville? Given the dust and dew effects, is using raw satellite data sufficient to model yield? Does elevation between Collinsville and nearby sites affect yield? How does the ENSO affect yield? Given the 2007-2012 constraint, will the TMY process provide a “Typical” year over the ENSO cycle? How does climate change affect yield? A further research question arises in the methodology but is included here for completeness. What is the expected frequency of oversupply from the Linear Fresnel Novatec Solar Boiler? 3        Methodology In the methodology section, we discuss the data preparation and the model selection process for the four drivers of yield. 4        Results and analysis In the results section we present the four driver models selected and the process that was undertaken to arrive at the models. 5        Discussion We analyse the extent to which the research questions are informed by the results. 6        Conclusion In this report, we have identified the key research questions and established a methodology to address these questions.  The models for the four drivers have been established allowing the calculation of the yield projections for Collinsville

The detection of climate change due to the enhanced greenhouse effect

The greenhouse effect is accepted as an undisputed fact from both theoretical and observational considerations. In Earth's atmosphere, the primary greenhouse gas is water vapor. The specific concern today is that increasing concentrations of anthropogenically introduced greenhouse gases will, sooner or later, irreversibly alter the climate of Earth. Detecting climate change has been complicated by uncertainties in historical observations and measurements. Thus, the primary concern for the GEDEX project is how can climate change and enhanced greenhouse effects be unambiguously detected and quantified. Specifically examined are the areas of: Earth surface temperature; the free atmosphere (850 millibars and above); space-based measurements; measurement uncertainties; and modeling the observed temperature record