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Internally and externally caused climate change
A numerical climate model is used to simulate climate change forced only by random fluctuations of the atmospheric heat transport. This short-term natural variability of the atmosphere is shown to be a possible 'cause' not only of the variability of the annual world average temperature about its mean, but also long-term excursions from the mean. Various external causes of climate change are also tested with the model and the results compared with observations for the past 100 years. Volcanic dust is shown to have been an important cause of climate change, while the effects of sunspot-related solar constant variation and anthropogenic forcing are not evident.
Quantifying the impact of climate change on erosion - 20397
The New York State Energy Research and Development Authority (NYSERDA) is the owner of the Western New York Nuclear Service Center (WNYNSC), a 1,351-ha (3,338-ac) site located approximately 48 km (30 mi) south of Buffalo, New York. In 1962, Nuclear Fuel Services, Inc. (NFS) entered into Agreements with the Atomic Energy Commission and New York State to construct the first commercial reprocessing plant of nuclear fuel in the United States. NFS, a private company, built and operated the spent fuel reprocessing plant and waste disposal facilities, processing 640 Mg (metric tons, or 705 short tons) of spent nuclear fuel from 1966 to 1972 under an Atomic Energy Commission license. Nuclear fuel reprocessing operations ended in 1972 and never reopened, leaving behind radioactive and chemical wastes. Erosion can play an important role in the fate and transport of waste at sites where disposal of long-lived waste is anticipated. The statistical characterization of key processes related to erosion is essential to the understanding of site stability through time. One of the key processes governing erosion is extreme precipitation and it is critical that trends in the distribution of extreme precipitation events through time be represented. The evidence of climate change is increasingly well documented and projected impacts on extreme precipitation events should be incorporated in performance assessment studies when relevant. Not evaluating future climate states in a performance assessment is contradictory to good modeling practice. Specifically, excluding climate change limits development of modeling information that could aid in effective decision making. The current climate literature provides both observational evidence and climate model projections of climate trends and/or climate change in the late 20. and early 21. centuries for North America and the northeast United States. In this work, this information was used to assess the impacts of potential changes in climate on erosion processes. The goal was to understand how projections of future climate relate to the performance of the WNYNSC through time. In the first stage of this work multi-temporal historical aerial images were analyzed in conjunction with orthophotography and Lidar data to develop probability distributions for variables representing important erosion processes. The information from these analyses was then used in conjunction with simulated data from the West Valley Erosion Working Group (EWG). Analysis of EWG simulations provides estimates for the change in erosion rates through time that is driven by changes in climate. The time-varying rates of erosion change were applied to the historical aerial imagery data in order to inform time-varying rates of erosion that are driven by changes in climate. Ultimately, this process resulted in the identification of locations for features like gully heads using both the Lidar dataset and projection of the estimated location from the historical aerial photo under consideration back to the Lidar dataset. The distance between the estimate of the location from the historical image and that of the Lidar dataset was the estimated distance the feature has moved. This distance was then divided by the number of years between the Lidar dataset and the year of the historical aerial image of interest to get a rate of movement through time. This was done for several dozen points on each historical aerial image. The analyses from the EWG were used to characterize the relative impact of climate change on the erosion rates. This relative impact was quantified by comparing the LEM based estimates of erosion that were derived using historical climate data with LEM based estimates of erosion that were derived using projections of future climate. The relative increase in the erosion rates was then applied to the historical estimates of erosion derived from the analysis of the historical aerial imagery. This approach was used since the LEM-based estimated of the historical erosion rates from the past century have a bias towards underestimating erosion. This underestimation is hypothesized to be a consequence of an inability of the LEMs to account for the impacts of land-use change (i.e. deforestation) which had been shown to significantly increase erosion in similar Northern hardwood forest ecosystems. In summary, gully head retreat rate and gully widening rate were characterized using statistical probability distributions from the historical aerial image analysis. Simulated data from the EWG was used to estimate the climatically-driven changes in erosion rates through time. The changes through time from the EWG were applied to the gully head retreat rate and gully widening rate characterized using the historical aerial image analysis. This information can be used to inform PPA models to project future risks from a given site. (authors)
Significant impact of forcing uncertainty in a large ensemble of climate model simulations
Forcing due to solar and volcanic variability, on the natural side, and greenhouse gas and aerosol emissions, on the anthropogenic side, are the main inputs to climate models. Reliable climate model simulations of past and future climate change depend crucially upon them. Here we analyze large ensembles of simulations using a comprehensive Earth System Model to quantify uncertainties in global climate change attributable to differences in prescribed forcings. The different forcings considered here are those used in the two most recent phases of the Coupled Model Intercomparison Project (CMIP), namely CMIP5 and CMIP6. We show significant differences in simulated global surface air temperature due to volcanic aerosol forcing in the second half of the 19th century and in the early 21st century. The latter arise from small-to-moderate eruptions incorporated in CMIP6 simulations but not in CMIP5 simulations. We also find significant differences in global surface air temperature and Arctic sea ice area due to anthropogenic aerosol forcing in the second half of the 20th century and early 21st century. These differences are as large as those obtained in different versions of an Earth System Model employing identical forcings. In simulations from 2015 to 2100, we find significant differences in the rates of projected global warming arising from CMIP5 and CMIP6 concentration pathways that differ slightly but are equivalent in terms of their nominal radiative forcing levels in 2100. Our results highlight the influence of assumptions about natural and anthropogenic aerosol loadings on carbon budgets, the likelihood of meeting Paris targets, and the equivalence of future forcing scenarios.
Projecting spatiotemporally explicit effects of climate change on stream temperature: A model comparison and implications for coldwater fishes
Conservation planners and resource managers seek information about how the availability and locations of cold-water habitats will change in the future and how these predictions vary among models. In this work, we used a physical process-based model to demonstrate the implications of climate change for streamflow and water temperature in two watersheds with distinctive flow regimes: the Snoqualmie watershed (WA) and Siletz watershed (OR), USA. Our model incorporated a downscaled ensemble of global climate model outputs and was calibrated with in situ and remotely sensed water temperatures. Furthermore, we compared predictions from our processed-based model to those from a publicly available and widely used statistical model. The process-based model projected greater changes in summer maximum water temperatures for the mixed-rain-snow Snoqualmie watershed than for the rain-dominated Siletz watershed as a result of the near-complete loss of winter snowpack and significant reduction in summer flow in the Snoqualmie watershed expected by the 2080s. Both models projected generally similar future spatial patterns of maximum water temperature in the two rivers, with cool reaches distributed farther upstream and fewer in number. However, the process-based model projected higher spatial heterogeneity in water temperature due to our spatially explicit simulation of streamflow and because we calibrated the model with spatially continuous remotely sensed water temperature data. We used stream temperature projections to assess the vulnerability of Pacific salmon and trout to changes in the spatial distribution of cold-water habitats during August by the 2080s. Results suggest that salmonids may have fewer summertime cold-water habitats in both watersheds. Projected stream warming may further limit particular species and life stages, especially in the Snoqualmie watershed. Our comparison of models highlights the importance of considering what might be gained by using a process-based model for evaluating and prioritizing management actions that mitigate climate impacts on cold-water habitats for stream fishes.
Climate Effects on Plant Range Distributions and Community Structure of Pacific Northwest Prairies
Pacific Northwest (PNW) prairies are an imperiled ecosystem that contain a large number of plant species with high fidelity to this habitat. The few remaining high-quality PNW prairies harbor a number of sensitive, rare, and endangered plant species that may be further at-risk with climate change. Thus, PNW prairies are an excellent model system to examine how climate change will affect the distribution of native plant species in grassland sites. Our experimental objectives were to determine: (i) how climate change will affect the range distribution of native plant species; (ii) what life history stages are most sensitive to climate change in a group of key indicator native species; (iii) the robustness of current restoration techniques and suites of species to changing climate, and in particular, the relative competitiveness of native species versus exotic invasive species; and (iv) the effects of climate change on carbon and nutrient cycling and soil-microbial-plant feedbacks. We addressed these objectives by experimentally increasing temperature 2.5 to 3.0 ºC above ambient with overhead infrared lamps and increasing wet-season precipitation by 20% above ambient in three upland prairie sites in central-western Washington, central-western Oregon, and southwestern Oregon from fall 2010 through 2012. Additional precipitation was applied within 2 weeks of when it fell so precipitation intensity was increased, particularly during the winter rainy season but with minimal additions during the summer dry season. These three sites also represent a 520-km natural climate gradient of increasing degree of severity of Mediterranean climate from north to south. After removing the extant vegetation, we planted a diverse suite of 12 native species that have their northern range limit someplace within the PNW in each experimental plot. An additional 20 more wide-spread native species were also planted into each plot. We found that recruitment of plant species within their ranges was negatively impacted by increased temperatures, but for species planted north of their current range, increased temperature was neutral. However, for surviving plants climate treatments and site-specific factors (e.g., nutrient availability) were the strongest predictors of plant growth and seed set. When recruitment and plant growth are considered together, increased temperatures are negative within a species current range but beyond this range they become positive. Germination was the most critical stage for plant response across all sites and climate treatments. Our results underscore the importance of including plant vital rates into models that are examining climate change effects on plant ranges. Warming altered plant community composition, decreased diversity, and increased total cover, with warmed northern communities over time becoming more like ambient communities further south. In particular, warming increased the cover of annual introduced species, suggesting that the observed biogeographic pattern of increasing invasion by this plant functional group in US West Coast prairies as one moves further south is at least in part due to climate. Our results suggest that with the projected increase in drought severity with climate change, Pacific Northwest prairies may face an increase of invasion by annuals, similar to what has been observed in California, resulting in novel species assemblages and shifts in functional composition, which in turn may alter ecosystem function. Warming generally increased nutrient availability and plant productivity across all sites. The seasonality of soil respiration responses to heating were strongly dependent on the Mediterranean climate gradient in the PNW, with heating responses being generally positive during periods of adequate soil moisture and becoming neutral to negative during periods of low soil moisture. The asynchrony between temperature and precipitation may make soils less sensitive to warming. Precipitation effects were minimal for all measured responses indicating the importance of increased temperatures in driving biotic responses to climate change in Mediterranean ecosystems. However, substantially increased precipitation during the dry season would almost certainly have profound effects, but the opposite is predicted by current climate change models for the PNW. A manipulative climate change experiment embedded within a natural climate gradient provides unique insights into the degree to which biotic responses to climate change are regionally consistent and site-dependent. Perhaps surprisingly, most climatic effects that we observed were either consistent in the three sites or could be readily interpreted in terms of the gradient of increasing intensity of the Mediterranean climate from north to south.
Predicting Decade-to-Century Climate Change: Prospects for Improving Models
Recent research has led to a greatly increased understanding of the uncertainties in today's climate models. In attempting to predict the climate of the 21st century, we must confront not only computer limitations on the affordable resolution of global models, but also a lack of physical realism in attempting to model key processes. Until we are able to incorporate adequate treatments of critical elements of the entire biogeophysical climate system, our models will remain subject to these uncertainties, and our scenarios of future climate change, both anthropogenic and natural, will not fully meet the requirements of either policymakers or the public. The areas of most-needed model improvements are thought to include air-sea exchanges, land surface processes, ice and snow physics, hydrologic cycle elements, and especially the role of aerosols and cloud-radiation interactions. Of these areas, cloud-radiation interactions are known to be responsible for much of the inter-model differences in sensitivity to greenhouse gases. Recently, we have diagnostically evaluated several current and proposed model cloud-radiation treatments against extensive field observations. Satellite remote sensing provides an indispensable component of the observational resources. Cloud-radiation parameterizations display a strong sensitivity to vertical resolution, and we find that vertical resolutions typically used in global models are far from convergence. We also find that newly developed advanced parameterization schemes with explicit cloud water budgets and interactive cloud radiative properties are potentially capable of matching observational data closely. However, it is difficult to evaluate the realism of model-produced fields of cloud extinction, cloud emittance, cloud liquid water content and effective cloud droplet radius until high-quality measurements of these quantities become more widely available. Thus, further progress will require a combination of theoretical and modeling research, together with intensified emphasis on both in situ and space-based remote sensing observations.
Water for electricity in India: A multi-model study of future challenges and linkages to climate change mitigation
This paper provides projections of water withdrawals and consumption for electricity generation in India through 2050. Based on the results from five energy-economic modeling teams, the paper explores the implications of economic growth, power plant cooling policies, and electricity CO2 emissions reductions on water withdrawals and consumption. To isolate modeling differences, the five teams used harmonized assumptions regarding economic and population growth, the distribution of power plants by cooling technologies, and withdrawals and consumption intensities. The results demonstrate the different but potentially complementary implications of cooling technology policies and efforts to reduce CO2 emissions. The application of closed-loop cooling technologies substantially reduces water withdrawals but increases consumption. The water implications of CO2 emissions reductions, depend critically on the approach to these reductions. Focusing on wind and solar power reduces consumption and withdrawals; a focus on nuclear power increases both; and a focus on hydroelectric power could increase consumptive losses through evaporation.
Projecting species’ vulnerability to climate change: Which uncertainty sources matter most and extrapolate best?
Species distribution models (SDMs) are commonly used to assess potential climate change impacts on biodiversity, but several critical methodological decisions are often made arbitrarily. We compare variability arising from these decisions to the uncertainty in future climate change itself. We also test whether certain choices offer improved skill for extrapolating to a changed climate and whether internal cross-validation skill indicates extrapolative skill.We compared projected vulnerability for 29 wetland-dependent bird species breeding in the climatically dynamic Prairie Pothole Region, USA. For each species we built 1,080 SDMs to represent a unique combination of: future climate, class of climate covariates, collinearity level, and thresholding procedure. We examined the variation in projected vulnerability attributed to each uncertainty source. To assess extrapolation skill under a changed climate, we compared model predictions with observations from historic drought years. Uncertainty in projected vulnerability was substantial, and the largest source was that of future climate change. Large uncertainty was also attributed to climate covariate class with hydrological covariates projecting half the range loss of bioclimatic covariates or other summaries of temperature and precipitation. We found that choices based on performance in cross-validation improved skill in extrapolation. Qualitative rankings were also highly uncertain. Given uncertainty in projected vulnerability and resulting uncertainty in rankings used for conservation prioritization, a number of considerations appear critical for using bioclimatic SDMs to inform climate change mitigation strategies. Our results emphasize explicitly selecting climate summaries that most closely represent processes likely to underlie ecological response to climate change. For example, hydrological covariates projected substantially reduced vulnerability, highlighting the importance of considering whether water availability may be a more proximal driver than precipitation. However, because cross-validation results were correlated with extrapolation results, the use of cross-validation performance metrics to guide modeling choices where knowledge is limited was supported.
What are the effects of Agro-Ecological Zones and land use region boundaries on land resource projection using the Global Change Assessment Model?
Understanding the potential impacts of climate change is complicated by mismatched spatial representations between gridded Earth System Models (ESMs) and Integrated Assessment Models (IAMs), whose regions are typically larger and defined by geopolitical and biophysical criteria. In this study we address uncertainty stemming from the construction of land use regions in an IAM, the Global Change Assessment Model (GCAM), whose regions are currently based on historical climatic conditions (1961-1990). We re-define GCAM’s regions according to projected climatic conditions (2070-2099), and investigate how this changes model outcomes for land use, agriculture, and forestry. By 2100, we find potentially large differences in projected global and regional area of biomass energy crops, fodder crops, harvested forest, and intensive pasture. These land area differences correspond with changes in agricultural commodity prices and production. These results have broader implications for understanding policy scenarios and potential impacts, and for evaluating and comparing IAM and ESM simulations.
Climate Lecture 20: The Educational Global Climate Model (EdGCM)
Climate change will profoundly impact Earth's environmental health as well as the world's economic and geopolitical landscape over the coming decades. The impacts of climate change are, in fact, already beginning to be experienced and have the potential to affect every living plant and animal on Earth within decades. Given this reality, every citizen of this planet should have the right to knowledge about the Earth's climate system and have the option to adapt to, or help mitigate the profound changes that are coming. In addition, a portion of the workforce needs to be capable of interpreting and analyzing climate information because, since the impacts of climate change will be widespread, pervasive, and continue to change over time, more professions will be interacting with climate data. We are already at, or past, the point where educators and their students require access to the scientific and technological resources - computer models, data, and visualization tools - that scientists use daily in the study of climate change. Although scientists use many methods to study Earth's climate system, global climate models (GCMs) have become the primary tools for exploring the complex interactions between components of the entire system: atmosphere, oceans, and land. GCMs are used to make projections of future climate change, to simulate climates of the past, and even to help scientists look for life on other planets. Like any model, a GCM can help people evaluate actions before they are taken. Like Business Intelligence software, they are Climate Intelligence tools. Unfortunately, GCMs are black boxes to most people. A previous chapter in this book by Gary Russell, entitled Building a Climate Model, is one example of the growing body of literature aimed at the general public describing the inner workings of global climate models. This literature goes a long way toward explaining climate model fundamentals. However, it will not be enough to alleviate their black-box nature unless people are afforded hands-on, authentic learning experiences as well.
One-way coupling of an integrated assessment model and a water resources model: evaluation and implications of future changes over the US Midwest
An integrated model is being developed to advance our understanding of the interactions between human activities, terrestrial system and water cycle, and how system interactions will be affected by a changing climate at the regional scale. As a first step towards that goal, a global integrated assessment model including a waterdemand model is coupled offline with a land surface hydrology – routing – water resources management model. A spatial and temporal disaggregation approach is developed to project the annual regional water demand simulations into a daily time step and subbasin representation. The model demonstrated reasonable ability to represent the historical flow regulation and water supply over the Midwest (Missouri, Upper Mississippi and Ohio). Implications for the future flow regulation, water supply and supply deficit are investigated using a climate change projection with the B1 emission scenario which affects both natural flow and water demand. Over the Midwest, changes in flow regulation are mostly driven by the change in natural flow due to the limited storage capacity over the Ohio and Upper Mississippi river basins. The changes in flow and demand have a combined effect on the Missouri Summer regulated flow. The supply deficit tends to be driven by the change in flow over the region. Spatial analysis demonstrates the relationship between the supply deficit and the change in demand over urban areas not along a main river or with limited storage, and over areas upstream of groundwater dependent fields with therefore overestimated demand.
Land Surface Modeling 2.0 for agricultural climate change impact assessments
Focus Area: This white paper addresses DOE AI4ESP focus area #2 by providing a sketch blueprint for a next generation, hybrid AI/process-based global Land Surface Modeling (LSM) framework to improve projections of climate change impacts on the land surface system including agriculture and the hydrological cycle. Science Challenge: Climate change impacts on agriculture are highly uncertain: -50% to +150% global production changes for major grains under high-end climate change, for example. The core challenge in land surface system predictability is the large gap between the scale at which the relevant biological processes act and the scale at which the risks need to be assessed. Conventional empirical and process-based modeling approaches are insufficient. A new multi-scale modeling paradigm which employs AI/ML to learn from new streams of remote sensing data and targeted ’gene-to-global’ simulations is needed.
Global-scale multidecadal variability missing in state-of-the-art climate models
Reliability of future global warming projections depends on how well climate models reproduce the observed climate change over the twentieth century. In this regard, deviations of the model-simulated climate change from observations, such as a recent “pause” in global warming, have received considerable attention. Such decadal mismatches between model-simulated and observed climate trends are common throughout the twentieth century, and their causes are still poorly understood. Here we show that the discrepancies between the observed and simulated climate variability on decadal and longer timescale have a coherent structure suggestive of a pronounced Global Multidecadal Oscillation. Surface temperature anomalies associated with this variability originate in the North Atlantic and spread out to the Pacific and Southern oceans and Antarctica, with Arctic following suit in about 25–35 years. While climate models exhibit various levels of decadal climate variability and some regional similarities to observations, none of the model simulations considered match the observed signal in terms of its magnitude, spatial patterns and their sequential time development. These results highlight a substantial degree of uncertainty in our interpretation of the observed climate change using current generation of climate models.
Not just about sunburn – the ozone hole's profound effect on climate has significant implications for Southern Hemisphere ecosystems
Abstract Climate scientists have concluded that stratospheric ozone depletion has been a major driver of Southern Hemisphere climate processes since about 1980. The implications of these observed and modelled changes in climate are likely to be far more pervasive for both terrestrial and marine ecosystems than the increase in ultraviolet‐B radiation due to ozone depletion; however, they have been largely overlooked in the biological literature. Here, we synthesize the current understanding of how ozone depletion has impacted Southern Hemisphere climate and highlight the relatively few documented impacts on terrestrial and marine ecosystems. Reviewing the climate literature, we present examples of how ozone depletion changes atmospheric and oceanic circulation, with an emphasis on how these alterations in the physical climate system affect Southern Hemisphere weather, especially over the summer season (December–February). These potentially include increased incidence of extreme events, resulting in costly floods, drought, wildfires and serious environmental damage. The ecosystem impacts documented so far include changes to growth rates of South American and New Zealand trees, decreased growth of Antarctic mosses and changing biodiversity in Antarctic lakes. The objective of this synthesis was to stimulate the ecological community to look beyond ultraviolet‐B radiation when considering the impacts of ozone depletion. Such widespread changes in Southern Hemisphere climate are likely to have had as much or more impact on natural ecosystems and food production over the past few decades, than the increased ultraviolet radiation due to ozone depletion.
Simulated vs. empirical weather responsiveness of crop yields: US evidence and implications for the agricultural impacts of climate change
Global gridded crop models (GGCMs) are the workhorse of assessments of the agricultural impacts of climate change. Yet the changes in crop yields projected by different models in response to the same meteorological forcing can differ substantially. Through an inter-method comparison, we provide a first glimpse into the origins and implications of this divergence—both among GGCMs and between GGCMs and historical observations. We examine yields of rainfed maize, wheat, and soybeans simulated by six GGCMs as part of the Inter-Sectoral Impact Model Intercomparison Project-Fast Track (ISIMIP-FT) exercise, comparing 1981–2004 hindcast yields over the coterminous United States (US) against US Department of Agriculture (USDA) time series for about 1000 counties. Leveraging the empirical climate change impacts literature, we estimate reduced-form econometric models of crop yield responses to temperature and precipitation exposures for both GGCMs and observations. We find that up to 60% of the variance in both simulated and observed yields is attributable to weather variation. A majority of the GGCMs have difficulty reproducing the observed distribution of percentage yield anomalies, and exhibit aggregate responses that show yields to be more weather-sensitive than in the observational record over the predominant range of temperature and precipitation conditions. In conclusion, this disparity is largely attributable to heterogeneity in GGCMs' responses, as opposed to uncertainty in historical weather forcings, and is responsible for widely divergent impacts of climate on future crop yields.
EPCAPE-Dalhousie Field Campaign Report
This project supported the Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE), which aimed to characterize the extent, radiative properties, aerosol interactions, and precipitation of stratocumulus clouds in the Eastern Pacific coastal region of La Jolla, California. Given the frequent anthropogenic aerosols emitted into this region, characterization of this important coastal cloud region and the aerosol impact on the cloud characteristics will improve the understanding of aerosol indirect effects and its representation in global climate models.