Temperature, precipitation, and related observational records provide a century or more of data on climate variability and change in the Northeastern and Midwestern United States. Paleoclimate data (tree rings, sediments record, and other sources) offer an even longer perspective that can be used to evaluate modern climate shifts as well as characterize past baselines of earth-system behavior in the region. The combination of different temporal data (decadal to million year scales) types reveals the strong role of natural variability in the region, and the fact that human activities are shifting climate statistics. For example, extreme events such as heat waves are often associated with natural circulation pattern anomalies (such as El Niños or the North Atlantic Oscillation) and an understanding of how the region is affected by such patterns is useful information for resource managers. Superimposed on these complex patterns of variability, climate change is leading to more frequent extreme heat events, an increasing number of frost- free days, and more frequent intense storm and precipitation events. Since future climate may have few analogs in the past, reliable projections should combine instrumental and paleoclimate data analysis with climate model simulations, the basic tools used to assess how climate may change under different scenarios of anthropogenic forcing.
General circulation models (GCMs) are used to forecast the impact of current and future greenhouse gas emissions on the climate across the globe (IPCC, 2007). In addition, regional climate models (RCMs), with substantially increased spatial resolution (grid spacing of 25-50 kilometers) are becoming available. These are embedded or nested within GCMs and can provide a more highly resolved picture of how climates vary at local and regional levels. However, both GCMs and RCMs are far from perfect, even in simulating current climate conditions. A first step is therefore to assess the biases in these models with respect to the climate of the northeast region and identify regional variations in climate across the domain of the NE CSC.
Another approach to obtain highly resolved spatial data from GCMs is to use statistical methods to establish the existing relationships between the large-scale atmospheric circulation features (which global climate models are able to reproduce) and climate at a particular location (which is beyond the spatial scale of GCMs) based on historical relationships. This approach, typically denoted as statistical downscaling, uses GCMs to forecast general trends and past spatial correlations and to apply these to fine scale locations. Both statistical downscaling and the application of RCMs (i.e., dynamical downscaling) are widely used in climate studies that have resource management implications. Evaluations need to be conducted to determine which of these approaches offers the most promise to natural resource managers in both the near and long-term. Decision-making and adaptation relies on the best available information, yet it is crucial that stakeholders also understand the limitations and uncertainties associated with these data. The possibility of climate changes that fall outside the range suggested by climate models and standard anthropogenic forcing scenarios should be assessed as well. The NE CSC will seek guidance from stakeholders on the information generated by climate models (e.g., specific variables and spatial and temporal resolution) that is of most value to their communities.
The Center will build on existing climate models that are specifically tailored to the geographical domain of stakeholders in the NE CSC region. Generation of climate scenarios is an objective that interacts with all subsequent themes and science needs. Areas of climate data generation that are particularly important to resource managers include 1) projections of climate extremes, primarily temperature and rainfall extremes; 2) spatial distribution of climate data at various scales, ranging from local to national scales; 3) information on how hydrological systems will change as temperature, precipitation, and extreme events change, and 4) projections of sea level rise and changes in the frequency and intensity of coastal flooding.
The NE CSC recognizes that conducting regional downscaling requires significant resources and support. Therefore the NE CSC will rely on the leadership and guidance of the NCCWSC on how best to approach this area of research and deliver products to our stakeholders. In addition, the NE CSC will work strategically with partners to address the climate change projections and assessments needs of the region by conducting the following activities:
- Provide a critical assessment of available climate projections (e.g., GCMs, RCMs, and statistical downscaling models) including a) their resolution, extent, time horizon, climate endpoints, b) information on limitations, strengths, confidence, and uncertainties, and c) the possibility of climate changes outside the ranges projected by GCMs and emission scenarios (e.g., Representative Concentration Pathways) (Moss et al. 2010).
- Assess major needs for climate projections in terms of impacts on ecosystems and human communities, including seasonal conditions, extreme events, and the degree to which available products (e.g., data and models) meet these needs.
- As gaps in regional data and modeling efforts are identified, work with partners to develop complementary tools and activities, and determine what information is needed to conduct future evaluations, best inform decision making and conduct the activities in NE CSC Science Themes 2- 7.
- Work with regional partners (e.g., NOAA, Northeast Regional Climate Center (NRCC)) to better understand how climate variability responds to different modes of circulation and patterns of natural climate variability (e.g., the Arctic Oscillation, the El Nino Southern Oscillation, and the Atlantic Multidecadal Oscillation).
- Provide improved modeling of seasonal conditions, precipitation, hydrological regimes, and extreme events that can be used to identify where the greatest probability of impacts and change will occur, and that can be used to inform partner/stakeholder planning processes (e.g., State Wildlife Action Plan (SWAPS; Association of Fish and Wildlife Agencies; LCC strategic plans for ecologically sustainable landscapes; the National Fish, Wildlife and Plants Climate Adaptation Strategy)) and adaptation strategies in the region.
- Develop and support partner decision support tools, guides, and directories of experts that translate information on the uncertainties and confidence in available approaches (e.g., modeling historical and future changes) and applicability across various scales to non-climate experts and the public.