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The Climate Indicators for Agriculture report presents 20 indicators of climate change, carefully selected across multiple agricultural production types and food system elements in the United States. Together, they represent an overall view of how climate change is influencing U.S. agriculture and food systems. Individually, they provide useful information to support management decisions for a variety of crop and livestock production systems. The report includes multiple categories of indicators, including physical indicators (e.g., temperature, precipitation), crop and livestock (e.g., animal heat stress), biological indicators (e.g., pests), phenological indicators (e.g. seasonality), and socioeconomic indicators (e.g., total factor productivity)., This document was produced through a collaboration between the U.S. Department of Agriculture, Colorado State University, and the National Center for Atmospheric Research under an Interagency Agreement with the National Science Foundation and Cooperative Agreement #58-0111-18-015.

Climate Indicators for Agriculture identifies 20 climate indicators that are important for agricultural management in a changing climate. The same indicators will allow scientists to identify the types and magnitude of change faced by agricultural producers and land managers across the United States. Five categories of indicator are described. Physical indicators show changes in climate variables that have direct effects on agricultural production and food systems. Crop and Livestock indicators show climate-induced changes in agricultural systems. Biological Indicators show how climate change is altering the range and infestation intensities of weeds, pests, disease and disease vectors that affect crop production and animal agriculture. Phenological Indicators show how climate change is affecting the timing of seasonal activities in agriculture, such as the date of flowering and pollination for a given crop. Socioeconomic Indicators show how human and economic factors in agriculture are affected by climate change.

This article points out through climate simulations from the National Center for Atmospheric Research (NCAR) that global warming will be increased by the end of the 21st century 3.3°C and in some areas as 7-8°C. This change will have an impact on agricultural production and human mortality. A new emphasis on the integration of social sciences with physical sciences will be fundamental to address the problem of climate adaptation; likewise, it will be important for decision-making to overcome the vulnerability and the challenges that brings a climate change En este artículo se señala a través de las simulaciones climáticas del centro nacional de la investigación atmosférica (NCAR) que, para finales del s. XXI, habrá un incremento de la temperatura del 3.3°C y en algunas áreas hasta de 7-8°C. Este cambio repercutirá sobre la producción agrícola y la mortalidad de los seres humanos. El nuevo enfoque que integra las ciencias sociales con las ciencias físicas busca manejar el problema de adaptación al al cambio climático y, asimismo, influir en la toma de decisiones para afrontar la vulnerabilidad y los retos que impone la adaptación al cambio climático.

Terrestrial vegetation engages in important feedbacks with the carbon cycle and the climate system. The National Center for Atmospheric Research’s (NCAR) Community Land Model version 3.5 coupled to carbon/nitrogen cycling (CLM-CN 3.5) was compared to satellite observations of Normalized Difference Vegetation Index (NDVI) to investigate its ability to simulate temporal and spatial variability in vegetation density. High-resolution AVHRR satellite-derived NDVI data obtained from the University of Maryland Global Inventory Modeling and Mapping Studies (GIMMS) project was regridded and temporally averaged to match the monthly, 2.5° by 1.875° resolution of CLM-CN 3.5 NDVI values. In CLM-CN3.5 the NDVI was calculated from near-infrared and visible spectrum reflectances. Gridded monthly and annual climatological time series were produced to determine model accuracy at different spatial and temporal scales. Interannual variability simulation accuracy was examined using NDVI-anomaly plots for historically-documented droughts. Multidecadal trends in vegetation were investigated using time series of CLM-CN 3.5 NDVI data for 1870-2004 to see if the model captured the recent greening trend documented by previous research. Our findings indicate CLM-CN 3.5 tended to overestimate NDVI when values were less than 0.15, and underestimated otherwise. Modeled NDVI peaked a month sooner than observed values. Regionally, CLM-CN 3.5 did not capture the monsoon-induced vegetation response in the Sahel. Drought-vegetation response simulation varied in accuracy. CLM-CN 3.5 captured recent (1960-2004) multidecadal greening trends with a high degree of certainty (R=0.96). These results will form a foundation for future research to determine the causes of error and to address biases in future model development.

A series of real data experiments is performed with a general circulation model in order to ascertain the sensitivity of extended range rain forecasts over the Americas to the structure and magnitude of tropical heating anomalies. The emphasis is upon heat inputs over the tropical Atlantic which have shown particularly significant drying influences over North America in our prior simulations. The heating imposed in the prior experiments is compared to the condensation heating rates that naturally occur in the forecast model, and shown to be excessive by approximately a factor of two. Present experiments reduce the imposed anomaly by a factor of three, and also incorporate sea-surface temperature decreases over the eastern tropical Pacific Ocean. The new experimental results are in many ways consistent with our prior results. The dry North American response is statistically more significant than the South American response, and occurs at least as frequently in the different members of the experimental ensembles as in our prior experiments. The drying effect is accentuated by the presence of East Pacific cooling, but this does not appear to be the dominant influence. Over tropical South America, the Pacific and Atlantic modifications produce compensating influences, with the former dominating dominant, and allowing increased rainfall over the Amazon Basin. A series of real data experiments is performed with a general circulation model in order to ascertain the sensitivity of extended range rain forecasts over the Americas to the structure and magnitude of tropical heating anomalies. The emphasis is upon heat inputs over the tropical Atlantic which have shown particularly significant drying influences over North America in our prior simulations. The heating imposed in the prior experiments is compared to the condensation heating rates that naturally occur in the forecast model, and shown to be excessive by approximately a factor of two. Present experiments reduce the imposed anomaly by a factor of three, and also incorporate sea-surface temperature decreases over the eastern tropical Pacific Ocean. The new experimental results are in many ways consistent with our prior results. The dry North American response is statistically more significant than the South American response, and occurs at least as frequently in the different members of the experimental ensembles as in our prior experiments. The drying effect is accentuated by the presence of East Pacific cooling, but this does not appear to be the dominant influence. Over tropical South America, the Pacific and Atlantic modifications produce compensating influences, with the former dominating dominant, and allowing increased rainfall over the Amazon Basin.

The Climate and Global Dynamics Division (CGD) at the National Center for Atmospheric Research (NCAR) wants to provide the greatest possible public access to their climate model research databases. CGD recognized that the information from their climate model runs could be better organized to allow for efficient discovery, analysis, and sharing of the research metadata. A number of factors were identified, including insufficient information in the data files to adequately describe the data and the difficulty of automatically generating catalogs of the experiments and data. A web-accessible metadata database was developed to enhance climate model metadata organization and accessibility. Using the spiral software development process, an iterative database design and creation process, with rapid reviews of each design, was used to ensure that the final product satisfied the user's requirements. The database was designed to meet a specific set of requirements from CGD for storing metadata from the Community Climate System Model (CCSM). A web application created a browser-based interface to the database that acts as a catalog for users to locate and sort the metadata in an online browser. Previously, CGD developers manually created a static website containing the metadata that proved to be time consuming and difficult to maintain. This project's web-accessible database enhances CGD's ability to share their metadata with the public, making their climate model results accessible to a much wider audience.

This document is the User's Guide to version 3 of the NCAR Community Climate Model, denoted CCM3. It is a companion document to Description of the NCAR Community Climate Model (CCM3) (Kiehl et al., 1996), which describes the governing equations, numerical algorithms, and physical parameterizations used in CCM3. The CCM3 is copyrighted by the University Corporation for Atmospheric Research. This Guide was written with two goals in mind: (1) to provide detailed information that describes how to use CCM3; and (2) to furnish the user interested in modifying the CCM3 code with an in-depth description of the code organization, data structures, and other important details. The form of this report is similar to the User's Guide to NCAR CCM2 (Bath et al., 1992), but has been reorganized and updated to reflect the changes made in creating CCM3. Section II of this report, "Using CCM3", starts by delineating the steps involved in obtaining, compiling and executing CCM3. Next, the namelist input that is used to control a run is described in detail, followed by several examples. The input and output datasets are then discussed, along with a description of the Model printed output and error messages. The section concludes with a brief description of the Slab Ocean Model followed by information on how to get help with the CCM. For the user interested in making changes to CCM3 code, Section III, "CCM3 Internals," has been provided. This section illuminates the basic structure and fundamental features of the CCM3 code. Section IV, "Changing the Model," addresses issues relevant to modifying code, such as the CCM3 coding convention, and includes some specific examples.

This document is a complete description of the capabilities of the CCM Processor, including all of the information needed to run the code on the Crays at NCAR. A few aspects of Processor design are discussed where appropriate, but there is no attempt to document the details of the code. This version of the CCM Processor Users' Guide (UNICOS Version) supercedes all previous versions (NCAR/TN-290+IA, NCAR/TN-335+IA), updates and drafts of this document. The NCAR CCM Processor is the primary tool for the analysis of data output by the NCAR Community Climate Model (CCM). Its main function is to perform the processing and data reduction normally associated with Model climate runs. It is also useful for the analysis of forecast and observational data that are in history tape format. Like the CCM, the Processor is a community facility maintained by the CCM Core Group.

This technical note provides an introduction to the CCM Modular Processor, a postprocessing software package. The Processor analyzes datasets such as the history tapes (Mass Storage volumes) output by the NCAR Community Climate Model (CCM), the input and boundary datasets used to initialize CCM2 and observational datasets which are written in CCM history tape format. This document is designed as a tutorial to teach new users how to access the Processor and complete simple tasks, and as a quick reference guide for more experienced users. The user is assumed to have at least a brief knowledge of the UNIX computing environment. This introductory document is not a comprehensive description of the Modular Processor, but an introduction to assist users in the practical aspects of accessing and using the Processor. This will enable the new user to begin using the Processor and complete simple tasks. This note makes the earlier document "Introduction to the CCM Modular Processor (Version PROC02)" NCAR/TN-289+IA, obsolete.