ALBERT

Description: For comparison with measurements from the TIMED satellite and coordinated ground based observations, we present results from our Numerical Spectral Model (NSM) that incorporates the Doppler Spread Parameterization (Hines, 1997) for small-scale gravity waves (GWs). We discuss the planetary waves (PWs) that are purely generated by dynamical interactions, i.e., without explicitly specifying excitation sources related for example to tropospheric convection or topography. With tropospheric heating that reproduces the observed zonal jets near the tropopause and the accompanying reversal in the latitudinal temperature variation, which is conducive to baroclinic instability, long period PWs are produced that propagate up into the stratosphere to affect the wave driven equatorial oscillations (QBO and SAO) extending into the upper mesosphere. The PWs in the model that dominate higher up in the MLT region, however, are to a large extent produced by instabilities under the influence of the zonal circulation and temperature variations in the middle atmosphere and they are amplified by GW interactions. Three classes of PWs are generated there. (1) Rossby waves that slowly propagate westward but are carried by the zonal mean (m = 0) winds to produce eastward and westward propagating PWs respectively in the winter and summer hemispheres below 80 km. Depending on the zonal wave number and magnitudes of the zonal winds under the influence of the equatorial oscillations, the PWs typically have periods between 2 and 20 days and their horizontal wind amplitudes can exceed 40 m/s in the lower mesosphere. (2) Rossby gravity waves that propagate westward at low latitudes, having periods around 2 days for zonal wave numbers m = 2 to 4. (3) Eastward propagating equatorial Kelvin waves generated in the upper mesosphere with periods between 2 and 3 days for m = 1 & 2. The seasonal variations of the PWs reveal that the largest wind amplitudes tend to occur below 80 km in the winter hemisphere, but above that altitude in the summer hemisphere to approach magnitudes as large as 50 m/s.

Description: Climate change is estimated to exacerbate existing challenges faced by smallholder farmers in Sub-Sahara Africa. However, limited studies quantify the extent of variation in climate change impact under these systems at the local scale. The Decision Support System for Agro-technological Transfer (DSSAT) was used to quantify variation in climate change impacts on maize yield under current agricultural practices in semi-arid regions of Senegal (Nioro du Rip) and Ghana (Navrongo and Tamale). Multi-benchmark climate models (Mid-Century, 20402069 for two Representative Concentration Pathways, RCP4.5 and RCP8.5), and multiple soil and management information from agronomic surveys were used as input for DSSAT. The average impact of climate scenarios on grain yield among farms ranged between 9% and 39% across sites. Substantial variation in climate response exists across farms in the same farming zone with relative standard deviations from 8% to 117% at Nioro du Rip, 13% to 64% in Navrongo and 9% to 37% in Tamale across climate models. Variations in fertilizer application, planting dates and soil types explained the variation in the impact among farms. This study provides insight into the complexities of the impact of climate scenarios on maize yield and the need for better representation of heterogeneous farming systems for optimized outcomes in adaptation and resilience planning in smallholder systems.

Description: We report here the results from a modeling study with our Numerical Spectral Model (NSM) that extends from the ground into thermosphere. The NSM incorporates Hines Doppler Spread Parameterization for small-scale gravity waves (GWs) and describes the major dynamical features of the atmosphere, including the wave driven equatorial oscillations (QBO and SAO), and the seasonal variations of tides and planetary waves. Accounting solely for the solar migrating tidal excitation sources, the NSM generates through dynamical interactions also non-migrating tides in the mesosphere that have amplitudes comparable to those observed. The model produces the diurnal (and semidiurnal) oscillations of the zonal mean (m = 0), and eastward and westward propagating tides for zonal wave numbers m = 1 to 4. To identify the mechanism of excitation for these tides, a numerical experiment is performed. The NSM is run without the heat source for the zonal-mean circulation and temperature variation, and the amplitudes of the resulting nonmigrating tides are then negligibly small. This leads to the conclusion that the planetary waves, which normally are excited in the NSM by instabilities but are suppressed in this case, generate the nonmigrating tides through nonlinear interactions with the migrating tides.

Description: This chapter describes methods developed by the Agricultural Model Intercomparison and Improvement Project (AgMIP) to implement a transdisciplinary, systems-based approach for regional-scale (local to national) integrated assessment of agricultural systems under future climate, biophysical, and socio-economic conditions. These methods were used by the AgMIP regional research teams in Sub-Saharan Africa and South Asia to implement the analyses reported in their respective chapters of this book. Additional technical details are provided in Appendix 1.The principal goal that motivates AgMIP's regional integrated assessment (RIA) methodology is to provide scientifically rigorous information needed to support improved decision-making by various stakeholders, ranging from local to national and international non-governmental and governmental organizations.

Description: High-latitude delta(exp 18)O archives deriving from meteoric water (e.g., tree-rings and ice-cores) can provide valuable information on past temperature variability, but stationarity of temperature signals in these archives depends on the stability of moisture source/trajectory and precipitation seasonality, both of which can be affected by atmospheric circulation changes. A tree-ring delta(exp 18)O record (AD 1780-2003) from the Mackenzie Delta is evaluated as a temperature proxy based on linear regression diagnostics. The primary source of moisture for this region is the North Pacific and, thus, North Pacific atmospheric circulation variability could potentially affect the tree-ring delta(exp 18)O-temperature signal. Over the instrumental period (AD 1892-2003), tree-ring delta(exp 18)O explained 29% of interannual variability in April-July minimum temperatures, and the explained variability increases substantially at lower-frequencies. A split-period calibration/verification analysis found the delta(exp 18)O-temperature relation was time-stable, which supported a temperature reconstruction back to AD 1780. The stability of the delta(exp 18)O-temperature signal indirectly implies the study region is insensitive to North Pacific circulation effects, since North Pacific circulation was not constant over the calibration period. Simulations from the NASA-GISS ModelE isotope-enabled general circulation model confirm that meteoric delta(exp 18)O and precipitation seasonality in the study region are likely insensitive to North Pacific circulation effects, highlighting the paleoclimatic value of tree-ring and possibly other delta(exp 18)O records from this region. Our delta(exp 18)O-based temperature reconstruction is the first of its kind in northwestern North America, and one of few worldwide, and provides a long-term context for evaluating recent climate warming in the Mackenzie Delta region.

Description: Efforts to limit global warming to below 2C in relation to the preindustrial level are under way, in accordance with the 2015 Paris Agreement. However, most impact research on agriculture to date has focused on impacts of warming 〉2C on mean crop yields, and many previous studies did not focus sufficiently on extreme events and yield interannual variability. Here, with the latest climate scenarios from the Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) project, we evaluated the impacts of the 2015 Paris Agreement range of global warming (1.5 and 2.0C warming above the preindustrial period) on global wheat production and local yield variability. A multicrop and multiclimate model ensemble over a global network of sites developed by the Agricultural Model Intercomparison and Improvement Project (AgMIP) for Wheat was used to represent major rainfed and irrigated wheat cropping systems. Results show that projected global wheat production will change by 2.3% to 7.0% under the 1.5C scenario and 2.4% to 10.5% under the 2.0C scenario, compared to a baseline of 19802010, when considering changes in local temperature, rainfall, and global atmospheric CO2 concentration, but no changes in management or wheat cultivars. The projected impact on wheat production varies spatially; a larger increase is projected for temperate high rainfall regions than for moderate hot low rainfall and irrigated regions. Grain yields in warmer regions are more likely to be reduced than in cooler regions. Despite mostly positive impacts on global average grain yields, the frequency of extremely low yields (bottom 5 percentile of baseline distribution) and yield interannual variability will increase under both warming scenarios for some of the hot growing locations, including locations from the second largest global wheat producerIndia, which supplies more than 14% of global wheat. The projected global impact of warming 〈2C on wheat production is therefore not evenly distributed and will affect regional food security across the globe as well as food prices and trade.

Description: Current rates of agricultural water use are unsustainable in many regions, creating an urgent need to identify improved irrigation strategies for water limited areas. Crop models can be used to quantify plant water requirements, predict the impact of water shortages on yield, and calculate water productivity (WP) to link water availability and crop yields for economic analyses. Many simulations of crop growth and development, especially in regional and global assessments, rely on automatic irrigation algorithms to estimate irrigation dates and amounts. However, these algorithms are not well suited for water limited regions because they have simplistic irrigation rules, such as a single soil-moisture based threshold, and assume unlimited water. To address this constraint, a new modeling framework to simulate agricultural production in water limited areas was developed. The framework consists of a new automatic irrigation algorithm for the simulation of growth stage based deficit irrigation under limited seasonal water availability; and optimization of growth stage specific parameters. The new automatic irrigation algorithm was used to simulate maize and soybean in Gainesville, Florida, and first used to evaluate the sensitivity of maize and soybean simulations to irrigation at different growth stages and then to test the hypothesis that water productivity calculated using simplistic irrigation rules underestimates WP. In the first experiment, the effect of irrigating at specific growth stages on yield and irrigation water use efficiency (IWUE) in maize and soybean was evaluated. In the reproductive stages, IWUE tended to be higher than in the vegetative stages (e.g. IWUE was 18% higher than the well watered treatment when irrigating only during R3 in soybean), and when rainfall events were less frequent. In the second experiment, water productivity (WP) was significantly greater with optimized irrigation schedules compared to non-optimized irrigation schedules in water restricted scenarios. For example, the mean WP across 38 years of maize production was 1.1 kg/cu m for non-optimized irrigation schedules with 50 mm of seasonal available water and 2.1 kg/cu m optimized ion schedules, a 91% improvement in WP with optimized irrigation schedules. The framework described in this work could be used to estimate WP for regional to global assessments, as well as derive location specific irrigation guidance.

Description: Wheat grain protein concentration is an important determinant of wheat quality for human nutrition that is often overlooked in efforts to improve crop production. We tested and applied a 32multimodel ensemble to simulate global wheat yield and quality in a changing climate. Potential benefits of elevated atmospheric CO2 concentration by 2050 on global wheat grain and protein yield are likely to be negated by impacts from rising temperature and changes in rainfall, but with considerable disparities between regions. Grain and protein yields are expected to be lower and more variable in most lowrainfall regions, with nitrogen availability limiting growth stimulus from elevated CO2. Introducing genotypes adapted to warmer temperatures (and also considering changes in CO2 and rainfall) could boost global wheat yield by 7% and protein yield by 2%, but grain protein concentration would be reduced by 1.1 percentage points, representing a relative change of 8.6%. Climate change adaptations that benefit grain yield are not always positive for grain quality, putting additional pressure on global wheat production.

Description: This study presents results of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Coordinated Global and Regional Assessments (CGRA) of +1.5 and +2.0 C global warming above pre-industrial conditions. This first CGRA application provides multi-discipline, multi-scale, and multi-model perspectives to elucidate major challenges for the agricultural sector caused by direct biophysical impacts of climate changes as well as ramifications of associated mitigation strategies. Agriculture in both target climate stabilizations is characterized by differential impacts across regions and farming systems, with tropical maize (Zea mays) experiencing the largest losses while soy (Glycine max) mostly benefits. The result is upward pressure on prices and area expansion for maize and wheat (Triticum), while soy prices and area decline (results for rice, Oryza sativa, are mixed). An example global mitigation strategy encouraging bioenergy expansion is more disruptive to land use and crop prices than the climate change impacts alone, even in the +2.0 C World which has a larger climate signal and lower mitigation requirement than the +1.5 C World. Coordinated assessments reveal that direct biophysical and economic impacts can be substantially larger for regional farming systems than global production changes. Regional farmers can buffer negative effects or take advantage of new opportunities via mitigation incentives and farm management technologies. Primary uncertainties in the CGRA framework include the extent of CO2 benefits for diverse agricultural systems in crop models, as simulations without CO2 benefits show widespread production losses that raise prices and expand agricultural area