NUTRItion and CLIMate (NUTRICLIM): investigating the relationship between climate variables and childhood malnutrition through agriculture, an exploratory study in Burkina Faso

Malnutrition remains a leading cause of death in children in low- and middle-income countries; this will be aggravated by climate change. Annually, 6.9 million deaths of children under 5 were attributable directly or indirectly to malnutrition. Although these figures have recently decreased, evidence shows that a world with a medium climate (local warming up to 3–4 °C) will create an additional 25.2 million malnourished children. This proof of concept study explores the relationships between childhood malnutrition (more specifically stunting), regional agricultural yields, and climate variable through the use of remote sensing (RS) satellite imaging along with algorithms to predict the effect of climate variability on agricultural yields and on malnutrition of children under 5. The success of this proof of purpose study, NUTRItion and CLIMate (NUTRICLIM), should encourage researchers to apply both concept and tools to study of the link between weather variability, crop yield, and malnutrition on a larger scale. It would also allow for linking such micro-level data to climate models and address the challenge of projecting the additional impact of childhood malnutrition from climate change to various policy relevant time horizons

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

Fast Plasma Investigation for MMS: Simulation of the Burst Triggering System

The Magnetospheric Multiscale (MMS) mission will study small-scale reconnection structures and their rapid motions from closely spaced platforms using instruments capable of high angular, energy, and time resolution measurements. To meet these requirements, the Fast Plasma Instrument (FPI) consists of eight (8) identical half top-hat electron sensors and eight (8) identical ion sensors and an Instrument Data Processing Unit (IDPU). The sensors (electron or ion) are grouped into pairs whose 6 degree x 180 degree fields-of-view (FOV) are set 90 degrees apart. Each sensor is equipped with electrostatic aperture steering to allow the sensor to scan a 45 degree x 180 degree fan about the its nominal viewing (0 deflection) direction. Each pair of sensors, known as the Dual Electron Spectrometer (DES) and the Dual Ion Spectrometer (DIS), occupies a quadrant on the MMS spacecraft and the combination of the eight electron/ion sensors, employing aperture steering, image the full-sky every 30-ms (electrons) and 150-ms (ions), respectively. To probe the diffusion regions of reconnection, the highest temporal/spatial resolution mode of FPI results in the DES complement of a given spacecraft generating 6.5-Mb (raised dot) per second of electron data while the DIS generates 1.1-Mb (raised dot) per second of ion data yielding an FPI total data rate of 6.6-Mb (raised dot) per second. The FPI electron/ion data is collected by the IDPU then transmitted to the Central Data Instrument Processor (CIDP) on the spacecraft for science interest ranking. Only data sequences that contain the greatest amount of temporal/spatial structure will be intelligently down-linked by the spacecraft. This requires a data ranking process known as the burst trigger system. The burst trigger system uses pseudo physical quantities to approximate the local plasma environments. As each pseudo quantity will have a different value, a set of two scaling factors is employed for each pseudo term. These pseudo quantities are then combined at the instrument, spacecraft, and observatory level leading to a final ranking of data based on expected scientific interest. Here, we present simulations of the fixed point burst trigger system for the FPI. A variety of data sets based on previous mission data as well as analytical formulations are tested. Comparisons of floating point calculations versus the fixed point hardware simulation are shown. Analysis of the potential sources of error from overflows, quantization, etc. are examined and mitigation methods are presented. Finally a series of calibration curves are presented, showing the expected error in pseudo quantities based solely on the scale parameters chosen and the expected data range. We conclude with a presentation of the current base-lined FPI burst trigger approach

Seasonal cycles enhance disparities between low- and high-income countries in exposure to monthly temperature emergence with future warming

A common proxy for the adaptive capacity of a community to the impacts of future climate change is the range of climate variability which they have experienced in the recent past. This study presents an interpretation of such a framework for monthly temperatures. Our results demonstrate that emergence into genuinely 'unfamiliar' climates will occur across nearly all months of the year for low-income nations by the second half of the 21st century under an RCP8.5 warming scenario. However, high income countries commonly experience a large seasonal cycle, owing to their position in the middle latitudes: as a consequence, temperature emergence for transitional months translates only to more-frequent occurrences of heat historically associated with the summertime. Projections beyond 2050 also show low-income countries will experience 2–10 months per year warmer than the hottest month experienced in recent memory, while high-income countries will witness between 1–4 months per year hotter than any month previously experienced. While both results represent significant departures that may bring substantive societal impacts if greenhouse gas emissions continue unabated, they also demonstrate that spatial patterns of emergence will compound existing differences between high and low income populations, in terms of their capacity to adapt to unprecedented future temperatures

Atmospheric Climate Model Experiments Performed at Multiple Horizontal Resolutions

This report documents salient features of version 3.3 of the Community Atmosphere Model (CAM3.3) and of three climate simulations in which the resolution of its latitude-longitude grid was systematically increased. For all these simulations of global atmospheric climate during the period 1980-1999, observed monthly ocean surface temperatures and sea ice extents were prescribed according to standard Atmospheric Model Intercomparison Project (AMIP) values. These CAM3.3 resolution experiments served as control runs for subsequent simulations of the climatic effects of agricultural irrigation, the focus of a Laboratory Directed Research and Development (LDRD) project. The CAM3.3 model was able to replicate basic features of the historical climate, although biases in a number of atmospheric variables were evident. Increasing horizontal resolution also generally failed to ameliorate the large-scale errors in most of the climate variables that could be compared with observations. A notable exception was the simulation of precipitation, which incrementally improved with increasing resolution, especially in regions where orography plays a central role in determining the local hydroclimate

The Impact of Parameterized Convection on the Simulation of Crop Processes

Global climate and weather models are a key tool for the prediction of future crop productivity, but they all rely on parameterizations of atmospheric convection, which often produce significant biases in rainfall characteristics over the tropics. The authors evaluate the impact of these biases by driving the General Large Area Model for annual crops (GLAM) with regional-scale atmospheric simulations of one cropping season over West Africa at different resolutions, with and without a parameterization of convection, and compare these with a GLAM run driven by observations. The parameterization of convection produces too light and frequent rainfall throughout the domain, as compared with the short, localized, high-intensity events in the observations and in the convection-permitting runs. Persistent light rain increases surface evaporation, and much heavier rainfall is required to trigger planting. Planting is therefore delayed in the runs with parameterized convection and occurs at a seasonally cooler time, altering the environmental conditions experienced by the crops. Even at high resolutions, runs driven by parameterized convection underpredict the small-scale variability in yields produced by realistic rainfall patterns. Correcting the distribution of rainfall frequencies and intensities before use in crop models will improve the process-based representation of the crop life cycle, increasing confidence in the predictions of crop yield. The rainfall biases described here are a common feature of parameterizations of convection, and therefore the crop-model errors described are likely to occur when using any global weather or climate model, thus remaining hidden when using climate-model intercomparisons to evaluate uncertainty

Climate and southern Africa's water-energy-food nexus

In southern Africa, the connections between climate and the water-energy-food nexus are strong. Physical and socioeconomic exposure to climate is high in many areas and in crucial economic sectors. Spatial interdependence is also high, driven for example, by the regional extent of many climate anomalies and river basins and aquifers that span national boundaries. There is now strong evidence of the effects of individual climate anomalies, but associations between national rainfall and Gross Domestic Product and crop production remain relatively weak. The majority of climate models project decreases in annual precipitation for southern Africa, typically by as much as 20% by the 2080s. Impact models suggest these changes would propagate into reduced water availability and crop yields. Recognition of spatial and sectoral interdependencies should inform policies, institutions and investments for enhancing water, energy and food security. Three key political and economic instruments could be strengthened for this purpose; the Southern African Development Community, the Southern African Power Pool, and trade of agricultural products amounting to significant transfers of embedded water

Impact of progressive global warming on the global-scale yield of maize and soybean

Global surface temperature is projected to warm over the coming decades, with regional differences expected in temperature change, rainfall and the frequency of extreme events. Temperature is a major determinant of crop growth and development, affecting planting date, growing season length and yield. We investigated the effects of increments of mean global temperature warming from 0.5 °C to 4 °C on soybean and maize development and yield, both globally and for the main producing countries, and simulated adaptation through changing planting date and variety. Increasing temperature resulted in reduced growing season lengths and ultimately reduced yields for both crops. The global yield for maize decreased as temperature increased, although the severity of the decrease was dependent on geographic region. Small temperature increases of 0.5 °C had no effect on soybean yield, although yield decreased as temperature increased. These negative effects, however, were partly compensated for by the implementation of adaptation strategies including planting earlier in the season and changing variety. The degree of compensation was dependent on geographical area and crop, with maize adaptation delaying the negative effects of temperature on yield, compared to soybean adaptation which increased yield in China, India and Korea DPR as well as delaying the effects in the remaining countries. The results of this paper indicate the degree to which farmer-controlled adaptation strategies can alleviate the negative impacts of increasing temperature on two major crop species

Evaluation of cropland maximum light use efficiency using eddy flux measurements in North America and Europe

Croplands cover 12% of the ice-free land surface and play an important role in the global carbon cycle. Light use efficiency (LUE) models have often been employed to estimate the exchange of CO2 between croplands and the atmosphere. A key parameter in these models is the maximum light use efficiency (ε*), but estimates of ε* vary by at least a factor 2. Here we used 12 agricultural eddy-flux measurement sites in North America and Europe to constrain LUE models in general and ε* in particular. We found that LUE models could explain on average about 70% of the variability in net ecosystem exchange (NEE) when we increased the ε* from 0.5 to 0.65-2.0g C per MJ Photosynthetic Active Radiation (PAR). Our results imply that croplands are more important in the global carbon budget than often thought. In addition, inverse modeling approaches that utilize LUE model outputs as a-priori input may have to be revisited in areas where croplands are an important contributor to regional carbon fluxes. Copyright 2011 by the American Geophysical Union