Climate Change in New York State Updating the 2011 ClimAID Climate Risk Information Supplement to NYSERDA Report 11-18 (Responding to Climate Change in New York State)

In its 2013-2014 Fifth Assessment Report (AR5), the Intergovernmental Panel on Climate Change (IPCC) states that there is a greater than 95 percent chance that rising global average temperatures, observed since the mid-20th century, are primarily due to human activities. As had been predicted in the 1800s, the principal driver of climate change over the past century has been increasing levels of atmospheric greenhouse gases associated with fossil-fuel combustion, changing land-use practices, and other human activities. Atmospheric concentrations of the greenhouse gas carbon dioxide are now approximately 40 percent higher than in preindustrial times. Concentrations of other important greenhouse gases, including methane and nitrous oxide, have increased rapidly as well

Climate Indicators for Agriculture

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)

Future projections of extreme precipitation intensity-duration-frequency curves for climate adaptation planning in New York State

A set of future extreme precipitation probabilities are developed for New York State based on different downscaling approaches and climate model projections. Based on nearly 50 downscaling method-climate model combinations, percent differences are computed between simulated extreme precipitation amounts for one historical (1970–1999) and three future (2010–2039, 2040–2069, and 2070–2099) time periods. These percent change factors are then applied to the observed extremes to estimate future precipitation extremes. The results are presented to users via an interactive website (http://ny-idf-projections.nrcc.cornell.edu). As the engineering community is the primary user, the website displays intensity-duration-frequency (IDF) graphs depicting the: 1) mean projected extreme precipitation intensity, 2) range of future model projections, 3) distribution of observed extreme precipitation intensities, 4) confidence intervals about the observed values. One-hundred-year recurrence interval precipitation amounts exhibit a median increase of between 5 and 10% across the state in the 2010–2039 period regardless of greenhouse gas concentration. By the 2040–2069 period, the median increase is on the order of 10–20% for the high concentration case (RCP 8.5), but remains below 10% if concentrations are lower (RCP 4.5). At the end of the century, all downscaling method climate model combinations indicate increases, with a median change of between 20 and 30% in the case of high concentrations

Climate-smart farming tools

Quickly changing weather conditions and increased variations in weather conditions both within a growing season and from one year to the next make having quick and easy access to the most recent weather data and tools that can assist in applying these weather data to on-farm decisions more important. Cornell’s Climate Smart Farming Program (CSF) Decision Tools (climatesmartfarming.org/) were developed through a partnership between the CSF program and the Northeast Regional Climate Center (NRCC). The NRCC archives and supplies daily temperature observations from the National Weather Service (NWS) and daily precipitation from NWS radar data. These data are interpolated to a 4-km-by-4-km grid allowing farmers in the region to access accurate information for their farm even without a weather station on their site

Bimodality in ensemble forecasts of 2 m temperature: identification

International audienceBimodality and other types of non-Gaussianity arise in ensemble forecasts of the atmosphere as a result of nonlinear spread across ensemble members. In this paper, bimodality in 50-member ECMWF ENS-extended ensemble forecasts is identified and characterized. Forecasts of 2 m temperature are found to exhibit widespread bimodality well over a derived false-positive rate. In some regions bimodality occurs in excess of 30 % of forecasts, with the largest rates occurring during lead times of 2 to 3 weeks. Bimodality occurs more frequently in the winter hemisphere with indications of baroclinicity being a factor to its development. Additionally, bimodality is more common over the ocean, especially the polar oceans, which may indicate development caused by boundary conditions (such as sea ice). Near the equatorial region, bimodality remains common during either season and follows similar patterns to the Intertropical Convergence Zone (ITCZ), suggesting convection as a possible source for its development. Over some continental regions the modes of the forecasts are separated by up to 15 °C. The probability density for the modes can be up to 4 times greater than at the minimum between the modes, which lies near the ensemble mean. The widespread presence of such bimodality has potentially important implications for decision makers acting on these forecasts. Bimodality also has implications for assessing forecast skill and for statistical postprocessing: several commonly used skill-scoring methods and ensemble dressing methods are found to perform poorly in the presence of bimodality, suggesting the need for improvements in how non-Gaussian ensemble forecasts are evaluated

Coherent Bimodal Events in Ensemble Forecasts of 2-m Temperature

International audienceA previous study has shown that a large portion of subseasonal-to-seasonal European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble forecasts for 2-m temperature exhibit properties of univariate bimodality, in some locations occurring in over 30% of forecasts. This study introduces a novel methodology to identify “bimodal events,” meteorological events that trigger the development of spatially and temporally correlated bimodality in forecasts. Understanding such events not only provides insight into the dynamics of the meteorological phenomena causing bimodal events, but also indicates when Gaussian interpretations of forecasts are detrimental. The methodology that is developed allows one to systematically characterize the spatial and temporal scales of the derived bimodal events, and thus uncover the flow states that lead to them. Three distinct regions that exhibit high occurrence rates of bimodality are studied: one in South America, one in the Southern Ocean, and one in the North Atlantic. It is found that bimodal events in each region appear to be triggered by synoptic processes interacting with geographically specific processes: in South America, bimodality is often related to Andes blocking events; in the Southern Ocean, bimodality is often related to an atmospheric Rossby wave interacting with sea ice; and in the North Atlantic, bimodality is often connected to the displacement of a persistent subtropical high. This common pattern of large-scale circulation anomalies interacting with local boundary conditions suggests that any deeper dynamical understanding of these events should incorporate such interactions

Toward Regional Climate Services The Role of NOAA’s Regional Climate Centers

A comprehensive national climate services strategy requires the infrastructure, operational services, and applied research activities that have characterized the Regional Climate Center Program since its inception

Climate change effects on wildland fire risk in the Northeastern and Great Lakes states predicted by a downscaled multi-model ensemble

This study is among the first to investigate wildland fire risk in the Northeastern and the Great Lakes states under a changing climate. We use a multi-model ensemble (MME) of regional climate models from the Coordinated Regional Downscaling Experiment (CORDEX) together with the Canadian Forest Fire Weather Index System (CFFWIS) to understand changes in wildland fire risk through differences between historical simulations and future projections. Our results are relatively homogeneous across the focus region and indicate modest increases in the magnitude of fire weather indices (FWIs) during northern hemisphere summer. The most pronounced changes occur in the date of the initialization of CFFWIS and peak of the wildland fire season, which in the future are trending earlier in the year, and in the significant increases in the length of high-risk episodes, defined by the number of consecutive days with FWIs above the current 95th percentile. Further analyses show that these changes are most closely linked to expected changes in the focus region’s temperature and precipitation. These findings relate to the current understanding of particulate matter vis-à-vis wildfires and have implications for human health and local and regional changes in radiative forcings. When considering current fire management strategies which could be challenged by increasing wildland fire risk, fire management agencies could adapt new strategies to improve awareness, prevention, and resilience to mitigate potential impacts to critical infrastructure and population