Potential Climatic Impacts and Reliability of Very Large-Scale Wind Farms

Abstract and PDF report are also available on the MIT Joint Program on the Science and Policy of Global Change website (http://globalchange.mit.edu/).Meeting future world energy needs while addressing climate change requires large-scale deployment of low or zero greenhouse gas (GHG) emission technologies such as wind energy. The widespread availability of wind power has fueled legitimate interest in this renewable energy source as one of the needed technologies. For very large-scale utilization of this resource, there are however potential environmental impacts, and also problems arising from its inherent intermittency, in addition to the present need to lower unit costs. To explore some of these issues, we use a threedimensional climate model to simulate the potential climate effects associated with installation of wind-powered generators over vast areas of land or coastal ocean. Using windmills to meet 10% or more of global energy demand in 2100, could cause surface warming exceeding 1oC over land installations. In contrast, surface cooling exceeding 1oC is computed over ocean installations, but the validity of simulating the impacts of windmills by simply increasing the ocean surface drag needs further study. Significant warming or cooling remote from both the land and ocean installations, and alterations of the global distributions of rainfall and clouds also occur. These results are influenced by the competing effects of increases in roughness and decreases in wind speed on near-surface turbulent heat fluxes, the differing nature of land and ocean surface friction, and the dimensions of the installations parallel and perpendicular to the prevailing winds. These results are also dependent on the accuracy of the model used, and the realism of the methods applied to simulate windmills. Additional theory and new field observations will be required for their ultimate validation. Intermittency of wind power on daily, monthly and longer time scales as computed in these simulations and inferred from meteorological observations, poses a demand for one or more options to ensure reliability, including backup generation capacity, very long distance power transmission lines, and onsite energy storage, each with specific economic and/or technological challenges.This study received support from the MIT Joint Program on the Science and Policy of Global Change, which is funded by a consortium of government, industry and foundation sponsors

Senior Thesis ST 2011-02

Agriculture in the Arctic is often limited by the low receipt of heat energy, which is often measured in growing degree days (GDD). With the advent of increasingly powerful climate modeling, projection and downscaling techniques, it is becoming possible to examine future climates in high resolution. Recent availability in Alaska has prompted interest in examining the distribution of current and the potential future of local agriculture. The goal of this study was to utilize Scenarios Network for Alaska Planning (SNAP) downscaled, ensemble projections to examine this in terms of GDDs in the Fairbanks North Star Borough of Alaska. Historic and projected monthly mean temperatures were utilized to calculate GDDs and then map the borough at a 4 km2 scale. Additionally, local agriculturalists were interviewed in order to put these theoretical calculations into context. Ultimately, projections of the examined agricultural locations showed an average of a 2% increase in GDD per decade and a 26% increase in GDDs from 1949 to 2099. This project indicated that the North Star Borough will receive increased heat energy due to climate change over the next century that may further enable increased yields and varieties of crops

Sensitivity of Climate Change Projections to Uncertainties in the Estimates of Observed Changes in Deep-Ocean Heat Content

Abstract and PDF report are also available on the MIT Joint Program on the Science and Policy of Global Change website (http://globalchange.mit.edu/).The MIT 2D climate model is used to make probabilistic projections for changes in global mean surface temperature and for thermosteric sea level rise under a variety of forcing scenarios. The uncertainties in climate sensitivity and rate of heat uptake by the deep ocean are quantified by using the probability distributions derived from observed 20th century temperature changes. The impact on climate change projections of using the smallest and largest estimates of 20th century deep ocean warming is explored. The impact is large in the case of global mean thermosteric sea level rise. In the MIT reference ("business as usual") scenario the median rise by 2100 is 27 and 43 cm in the respective cases. The impact on increases in global mean surface air temperature is more modest, 4.9 C and 3.9 C in the two respective cases, because of the correlation between climate sensitivity and ocean heat uptake required by 20th century surface and upper air temperature changes. The results are also compared with the projections made by the IPCC AR4's multi-model ensemble for several of the SRES scenarios. The multi-model projections are more consistent with the MIT projections based on the largest estimate of ocean warming. However the range for the rate of heat uptake by the ocean suggested by the lowest estimate of ocean warming is more consistent with the range suggested by the 20th century changes in surface and upper air temperatures, combined with expert prior for climate sensitivity.This work was supported in part by the Office of Science (BER), U.S. Dept. of Energy Grant No. DE-FG02-93ER61677, NSF, and by the MIT Joint Program on the Science and Policy of Global Change

Oceanus.

v. 39, no. 2 (1996

The effect of climate change on the population of sycamore lace bug (Corythuca ciliata, Say) based on a simulation model with phenological response

Climate change affects on insect populations in many ways: it can cause a shift in geographical spread, abundance, or diversity, it can change the location, the timing and the magnitude of outbreaks of pests and it can define the phenological or even the genetic properties of the species. Long-time investigations of special insect populations, simulation models and scenario studies give us very important information about the response of the insects far away and near to our century. Getting to know the potential responses of insect populations to climate change makes us possible to evaluate the adaptation of pest management alternatives as well as to formulate our future management policy. In this paper we apply two simple models, in order to introduce a complex case study for a Sycamore lace bug population. We test how the model works in case the whether conditions are very different from those in our days. Thus, besides we can understand the processes that happen in present, we can analyze the effects of a possible climate change, as well

Tools for Assessing Climate Impacts on Fish and Wildlife

Climate change is already affecting many fish and wildlife populations. Managing these populations requires an understanding of the nature, magnitude, and distribution of current and future climate impacts. Scientists and managers have at their disposal a wide array of models for projecting climate impacts that can be used to build such an understanding. Here, we provide a broad overview of the types of models available for forecasting the effects of climate change on key processes that affect fish and wildlife habitat (hydrology, fire, and vegetation), as well as on individual species distributions and populations. We present a framework for how climate-impacts modeling can be used to address management concerns, providing examples of model-based assessments of climate impacts on salmon populations in the Pacific Northwest, fire regimes in the boreal region of Canada, prairies and savannas in the Willamette Valley-Puget Sound Trough-Georgia Basin ecoregion, and marten Martes americana populations in the northeastern United States and southeastern Canada. We also highlight some key limitations of these models and discuss how such limitations should be managed. We conclude with a general discussion of how these models can be integrated into fish and wildlife management