Antle, J. M.; Nelson, G. C.; Porter, C.; Janssen, S.; Basso, B.; Ewert, F.; Wallach, D.; Baigorria, G.; and Winter, J. M., "The Agricultural Model Intercomparison and Improvement Project (AgMIP): Protocols and pilot studies" (2013 a b s t r a c tThe Agricultural Model Intercomparison and Improvement Project (AgMIP) is a major international effort linking the climate, crop, and economic modeling communities with cutting-edge information technology to produce improved crop and economic models and the next generation of climate impact projections for the agricultural sector. The goals of AgMIP are to improve substantially the characterization of world food security due to climate change and to enhance adaptation capacity in both developing and developed countries. Analyses of the agricultural impacts of climate variability and change require a transdisciplinary effort to consistently link state-of-the-art climate scenarios to crop and economic models. Crop model outputs are aggregated as inputs to regional and global economic models to determine regional vulnerabilities, changes in comparative advantage, price effects, and potential adaptation strategies in the agricultural sector. Climate, Crop Modeling, Economics, and Information Technology Team Protocols are presented to guide coordinated climate, crop modeling, economics, and information technology research activities around the world, along with AgMIP Cross-Cutting Themes that address uncertainty, aggregation and scaling, and the development of Representative Agricultural Pathways (RAPs) to enable testing of climate change adaptations in the context of other regional and global trends. The organization of research activities by geographic region and specific crops is described, along with project milestones. Pilot results demonstrate AgMIP's role in assessing climate impacts with explicit representation of uncertainties in climate scenarios and simulations using crop and economic models. An intercomparison of wheat model simulations near Obregón, Mexico reveals inter-model differences in yield sensitivity to [CO 2 ] with model uncertainty holding approximately steady as concentrations rise, while uncertainty related to choice of crop model increases with rising temperatures. Wheat model simulations with midcentury climate scenarios project a slight decline in absolute yields that is more sensitive to selection of crop model than to global climate model, emissions scenario, or climate scenario downscaling method. A comparison of regional and national-scale economic simulations finds a large sensitivity of projected yield changes to the simulations' resolved scales. Finally, a global economic model intercomparison example demonstrates that improvements in the understanding of agriculture futures arise from integration of the range of uncertainty in crop, climate, and economic modeling results in multi-model assessments.

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Abstract:This study seeks to understand the spatial variability of monthly and daily rainfall in Alabama, Georgia, and Florida, USA. Monthly spatial statistics are needed to improve downscaling from climate models producing seasonal rainfall forecasts, and spatial correlation of daily rainfall is needed to inform spatial weather generators used in climate risk analysis. We first determined the historical record length that is stationary followed by an analysis of the monthly spatial characteristics of rainfall variables. Rainfall data from 523 weather stations (National Climate Data Center) were obtained for the period 1915-2004 and divided into 15-year subsets for comparisons. Differences in rainfall were found between the most recent 15-year period and all others occurring during the 90-year period of record. Thus only data from 1990 to 2004 (208 weather stations) were used to avoid the detected changes in climate in the region. Correlation, covariance and variance matrices of daily and monthly rainfall amounts were calculated at monthly steps. The same statistics were also computed for frequency of rainfall events and monthly number of rainy days. Results show different spatial patterns at different temporal scales; two spatial patterns were well established. A widely spread correlation in a northeast -southwest direction was found around weather stations during the frontal rainy season, and a concentric short distance-decay in correlations existed around weather stations during the summer convective season. Spatial correlations among daily rainfall amounts are needed for spatial weather generators in a storm-by-storm basis while monthly spatial statistics are needed to ensure the validity of downscaled data from numerical seasonal rainfall forecasts.

Climate change is projected to push the limits of cropping systems and has the potential to disrupt the agricultural sector from local to global scales. This article introduces the Coordinated Climate-Crop Modeling Project (C3MP), an initiative of the Agricultural Model Intercomparison and Improvement Project (AgMIP) to engage a global network of crop modelers to explore the impacts of climate change via an investigation of crop responses to changes in carbon dioxide concentration ([CO2 ]), temperature, and water. As a demonstration of the C3MP protocols and enabled analyses, we apply the Decision Support System for Agrotechnology Transfer (DSSAT) CROPGRO-Peanut crop model for Henry County, Alabama, to evaluate responses to the range of plausible [CO2 ], temperature changes, and precipitation changes projected by climate models out to the end of the 21st century. These sensitivity tests are used to derive crop model emulators that estimate changes in mean yield and the coefficient of variation for seasonal yields across a broad range of climate conditions, reproducing mean yields from sensitivity test simulations with deviations of ca. 2% for rain-fed conditions. We apply these statistical emulators to investigate how peanuts respond to projections from various global climate models, time periods, and emissions scenarios, finding a robust projection of modest (<10%) median yield losses in the middle of the 21st century accelerating to more severe (>20%) losses and larger uncertainty at the end of the century under the more severe representative concentration pathway (RCP8.5). This projection is not substantially altered by the selection of the AgMERRA global gridded climate dataset rather than the local historical observations, differences between the Third and Fifth Coupled Model Intercomparison Project (CMIP3 and CMIP5), or the use of the delta method of climate impacts analysis rather than the C3MP impacts response surface and emulator approach.

Weather generators are tools that create synthetic daily weather data over long periods of time. These tools have also been used for downscaling monthly to seasonal climate forecasts, from global and regional circulation models to daily values for use as inputs for crop and other environmental models. One main limitation of most weather generators is that they do not take into account the spatial structure of weather. Spatial correlation of daily rainfall is important when one aggregates, for example, simulated crop yields or hydrology in a watershed or region. A method was developed to generate realizations of daily rainfall for multiple sites in an area while preserving the spatial and temporal correlations among sites. A two-step method generates rainfall events at multiple sites followed by rainfall amounts at sites where generated rainfall events occur. The generation of rainfall events was based on a new orthogonal Markov chain for discrete distributions. For generating rainfall amounts, a vector of random numbers (from a uniform distribution), of order equal to the number of locations with rainfall events that were generated to occur in a day, was matrix-multiplied by the corresponding factorized correlation matrix to create spatially correlated random numbers. Elements from the resulting vector were transformed to a gamma distribution using cumulative probability functions for each location and rescaled to rainfall amounts. One study area was located in north-central Florida, where correlated rainfall data were generated for seven weather stations to evaluate its performance versus a widely used single-site weather generator. A second area was in North Carolina, where rainfall was generated for 25 weather stations to evaluate the effects of a larger number of stations in other regions. One thousand yearlong replications of daily rainfall data were generated for each area. Monthly spatial correlations of generated daily rainfall events and amounts among all pairs of weather stations closely matched their observed counterparts. For daily rainfall amounts the correlation coefficients between the observed pairwise correlation coefficients and the ones estimated from synthetic data among weather stations were 0.977 for Florida and 0.964 for North Carolina. The performance of the geospatial–temporal (GiST) weather generator was also analyzed by comparing the distributions of lengths of dry and wet spells, joint probabilities, Markov transitional probabilities, distance decay of correlation functions, and regionwide days without rainfall at any station. Multiannual mean and standard deviation of the number of rainy days per month and mean monthly rainfall were also calculated. All comparisons between observed and generated rainfall events and amounts using the GiST weather generator were highly correlated. The root-mean-square errors of pairwise correlation values ranged from 0.05 to 0.11 for rainfall events and from 0.03 to 0.06 for amounts.