Anne Stoner

Assessing general circulation model simulations of atmospheric teleconnection patterns

Abstract The ability of coupled atmosphere–ocean general circulation models (AOGCMs) to simulate variability in regional and global atmospheric dynamics is an important aspect of model evaluation. This is particularly true for recurring large-scale patterns known to be correlated with surface climate anomalies. Here, the authors evaluate the ability of all Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) historical Twentieth-Century Climate in Coupled Models (20C3M) AOGCM simulations for which the required output fields are available to simulate three patterns of large-scale atmospheric internal variability in the North Atlantic region: the Arctic Oscillation (AO), the North Atlantic Oscillation (NAO), and the Atlantic multidecadal oscillation (AMO); and three in the North Pacific region: the El Nino–Southern Oscillation (ENSO), the Pacific decadal oscillation (PDO), and the Pacific–North American Oscillation (PNA). These patterns are evaluated in two ways: first, in terms o...

Evaluating the stationarity assumption in statistically downscaled climate projections: is past performance an indicator of future results?

Empirical statistical downscaling (ESD) methods seek to refine global climate model (GCM) outputs via processes that glean information from a combination of observations and GCM simulations. They aim to create value-added climate projections by reducing biases and adding finer spatial detail. Analysis techniques, such as cross-validation, allow assessments of how well ESD methods meet these goals during observational periods. However, the extent to which an ESD method’s skill might differ when applied to future climate projections cannot be assessed readily in the same manner. Here we present a “perfect model” experimental design that quantifies aspects of ESD method performance for both historical and late 21st century time periods. The experimental design tests a key stationarity assumption inherent to ESD methods – namely, that ESD performance when applied to future projections is similar to that during the observational training period. Case study results employing a single ESD method (an Asynchronous Regional Regression Model variant) and climate variable (daily maximum temperature) demonstrate that violations of the stationarity assumption can vary geographically, seasonally, and with the amount of projected climate change. For the ESD method tested, the greatest challenges in downscaling daily maximum temperature projections are revealed to occur along coasts, in summer, and under conditions of greater projected warming. We conclude with a discussion of the potential use and expansion of the perfect model experimental design, both to inform the development of improved ESD methods and to provide guidance on the use of ESD products in climate impacts analyses and decision-support applications.

The effects of climate downscaling technique and observational data set on modeled ecological responses

Assessments of future climate change impacts on ecosystems typically rely on multiple climate model projections, but often utilize only one downscaling approach trained on one set of observations. Here, we explore the extent to which modeled biogeochemical responses to changing climate are affected by the selection of the climate downscaling method and training observations used at the montane landscape of the Hubbard Brook Experimental Forest, New Hampshire, USA. We evaluated three downscaling methods: the delta method (or the change factor method), monthly quantile mapping (Bias Correction-Spatial Disaggregation, or BCSD), and daily quantile regression (Asynchronous Regional Regression Model, or ARRM). Additionally, we trained outputs from four atmosphere-ocean general circulation models (AOGCMs) (CCSM3, HadCM3, PCM, and GFDL-CM2.1) driven by higher (A1fi) and lower (B1) future emissions scenarios on two sets of observations (1/8º resolution grid vs. individual weather station) to generate the high-resolution climate input for the forest biogeochemical model PnET-BGC (eight ensembles of six runs).The choice of downscaling approach and spatial resolution of the observations used to train the downscaling model impacted modeled soil moisture and streamflow, which in turn affected forest growth, net N mineralization, net soil nitrification, and stream chemistry. All three downscaling methods were highly sensitive to the observations used, resulting in projections that were significantly different between station-based and grid-based observations. The choice of downscaling method also slightly affected the results, however not as much as the choice of observations. Using spatially smoothed gridded observations and/or methods that do not resolve sub-monthly shifts in the distribution of temperature and/or precipitation can produce biased results in model applications run at greater temporal and/or spatial resolutions. These results underscore the importance of carefully considering field observations used for training, as well as the downscaling method used to generate climate change projections, for smaller-scale modeling studies. Different sources of variability including selection of AOGCM, emissions scenario, downscaling technique, and data used for training downscaling models, result in a wide range of projected forest ecosystem responses to future climate change.

Modeled ecohydrological responses to climate change at seven small watersheds in the northeastern United States

A cross-site analysis was conducted on seven diverse, forested watersheds in the northeastern United States to evaluate hydrological responses (evapotranspiration, soil moisture, seasonal and annual streamflow, and water stress) to projections of future climate. We used output from four atmosphere-ocean general circulation models (AOGCMs; CCSM4, HadGEM2-CC, MIROC5, and MRI-CGCM3) included in Phase 5 of the Coupled Model Intercomparison Project, coupled with two Representative Concentration Pathways (RCP 8.5 and 4.5). The coarse resolution AOGCMs outputs were statistically downscaled using an asynchronous regional regression model to provide finer resolution future climate projections as inputs to the deterministic dynamic ecosystem model PnET-BGC. Simulation results indicated that projected warmer temperatures and longer growing seasons in the northeastern United States are anticipated to increase evapotranspiration across all sites, although invoking CO2 effects on vegetation (growth enhancement and increases in water use efficiency (WUE)) diminish this response. The model showed enhanced evapotranspiration resulted in drier growing season conditions across all sites and all scenarios in the future. Spruce-fir conifer forests have a lower optimum temperature for photosynthesis, making them more susceptible to temperature stress than more tolerant hardwood species, potentially giving hardwoods a competitive advantage in the future. However, some hardwood forests are projected to experience seasonal water stress, despite anticipated increases in precipitation, due to the higher temperatures, earlier loss of snow packs, longer growing seasons, and associated water deficits. Considering future CO2 effects on WUE in the model alleviated water stress across all sites. Modeled streamflow responses were highly variable, with some sites showing significant increases in annual water yield, while others showed decreases. This variability in streamflow responses poses a challenge to water resource management in the northeastern United States. Our analyses suggest that dominant vegetation type and soil type are important attributes in determining future hydrological responses to climate change.

Climate change: potential impacts on frost–thaw conditions and seasonal load restriction timing for low-volume roadways

Low-volume roads constitute a major percentage of roadways around the world. Many of these are located in seasonal frost areas where agencies increase and decrease the allowable weight limits based on seasonal fluctuations in the load carrying capacity of the roadway due to freeze–thaw conditions. As temperatures shift due to changing climate, the timing and duration of winter freeze and spring thaw periods are likely to change, potentially causing significant impacts to local industry and economies. In this study, an ensemble of 19 climate models were used to project future temperature changes and the impact of these changes on the frost depth and timing of seasonal load changes across five instrumented pavement sites in New England. The study shows that shifts of up to 2 weeks are projected at the end of the century and that moderate variability was observed across the study region, indicating that local conditions are important for future assessments depending on the desired level of accuracy. From 1970 to 1999, the average freezing season lasted between 9 and 13 weeks in the study region. By 2000–2029, the frozen period shortens by approximately 10 days over baseline duration (10–20% reduction). By the end of the century under RCP 4.5, frozen periods are typically shorter by 4 weeks or a 30–40% reduction. However, RCP 8.5 results indicate that four out of the five sites would have no frozen period during at least six winters from 2060 to 2089.

Climate Projections for Transportation Infrastructure Planning, Operations and Maintenance, and Design

A key assumption in planning is that weather and climate events may vary from year to year but climate remains stable over the long term and can be predicted on the basis of past return values. This assumption underpins most design standards, operations and maintenance programs, and planning for transportation projects and programs. However, climate change is altering the risk of many types of weather extremes, including the frequency, severity, or both, of high temperatures, heavy precipitation events, coastal flooding, and storms. This concept of non-stationarity (i.e., that future climate conditions and weather risks will differ from those in the past) challenges scientists and engineers to incorporate climate change into present-day planning when the future is manifestly uncertain. A general framework is presented for incorporating observed and projected future climate trends into infrastructure planning. The issues that remain to be resolved, including the challenges confronted in translating climate and weather-related extremes to information that is directly relevant to assessing potential impacts on infrastructure, are highlighted.

Progress and challenges in incorporating climate change information into transportation research and design

AbstractThe vulnerability of our nation’s transportation infrastructure to climate change and extreme weather is now well documented and the transportation community has identified numerous strateg...

Projections of water, carbon, and nitrogen dynamics under future climate change in an alpine tundra ecosystem in the southern Rocky Mountains using a biogeochemical model

Using statistically downscaled future climate scenarios and a version of the biogeochemical model (PnET-BGC) that was modified for use in the alpine tundra, we investigated changes in water, carbon, and nitrogen dynamics under the Representative Concentration Pathways at Niwot Ridge in Colorado, USA. Our simulations indicate that future hydrology will become more water-limited over the short-term due to the temperature-induced increases in leaf conductance, but remains energy-limited over the longer term because of anticipated future decreases in leaf area and increases in annual precipitation. The seasonal distribution of the water supply will become decoupled from energy inputs due to advanced snowmelt, causing soil moisture stress to plants during the growing season. Decreases in summer soil moisture are projected to not only affect leaf production, but also reduce decomposition of soil organic matter in summer despite increasing temperature. Advanced future snowmelt in spring and increasing rain to snow ratio in fall are projected to increase soil moisture and decomposition of soil organic matter. The extended growing season is projected to increase carbon sequestration by 2% under the high radiative forcing scenario, despite a 31% reduction in leaf display due to the soil moisture stress. Our analyses demonstrate that future nitrogen uptake by alpine plants is regulated by nitrogen supply from mineralization, but plant nitrogen demand may also affect plant uptake under the warmer scenario. PnET-BGC simulations also suggest that potential CO2 effects on alpine plants are projected to cause larger increases in plant carbon storage than leaf and root production.

Projected climate and agronomic implications for corn production in the Northeastern United States

Corn has been a pillar of American agriculture for decades and continues to receive much attention from the scientific community for its potential to meet the food, feed and fuel needs of a growing human population in a changing climate. By midcentury, global temperature increase is expected to exceed 2°C where local effects on heat, cold and precipitation extremes will vary. The Northeast United States is a major dairy producer, corn consumer, and is cited as the fastest warming region in the contiguous U.S. It is important to understand how key agronomic climate variables affect corn growth and development so that adaptation strategies can be tailored to local climate changes. We analyzed potential local effects of climate change on corn growth and development at three major dairy locations in the Northeast (Syracuse, New York; State College, Pennsylvania and Landisville, Pennsylvania) using downscaled projected climate data (2000–2100) from nine Global Climate Models under two emission pathways (Representative Concentration Pathways (RCP) 4.5 and 8.5). Our analysis indicates that corn near the end of the 21st century will experience fewer spring and fall freezes, faster rate of growing degree day accumulation with a reduction in time required to reach maturity, greater frequencies of daily high temperature ≥35°C during key growth stages such as silking-anthesis and greater water deficit during reproductive (R1-R6) stages. These agronomic anomalies differ between the three locations, illustrating varying impacts of climate change in the more northern regions vs. the southern regions of the Northeast. Management strategies such as shifting the planting dates based on last spring freeze and irrigation during the greatest water deficit stages (R1-R6) will partially offset the projected increase in heat and drought stress. Future research should focus on understanding the effects of global warming at local levels and determining adaptation strategies that meet local needs.