Impacts of Land Use and Biofuels Policy on Climate: Temperature and Localized Impacts

http://globalchange.mit.edu/research/publications/reportsThe impact on climate of future land use and energy policy scenarios is explored using two landuse frameworks: (i) Pure Cost Conversion Response (PCCR), or 'extensification', where the price of land is the only constraint to convert land to agricultural production, including growing biofuels, and (ii) Observed Land Supply Response (OLSR), or 'intensification', where legal, environmental and other constraints encourage more intense use of existing managed land. These two land-use frameworks, involving different economic assumptions, were used to explore how the large-scale plantation of cellulosic biofuels to meet global energy demand impacts the future climate. The land cover of the Community Atmospheric Model Version 3.0 (CAM3.0) was manipulated to reflect these two different land use and energy scenarios (i.e. biofuels and no biofuels). Using these landscapes, present and future climate conditions were simulated to assess the land cover impact. In both the intensification and extensification scenarios, the biofuel energy policy increases the land reflectivity of many areas of the globe, indicating that biofuel cropland is replacing darker land-vegetation, which directly leads to cooling. Moreover, the extensification framework—which involves more deforestation than the intensification framework—leads to larger increases in the reflectivity of the Earth's surface and thus a stronger cooling of the land surface in the extratropics. However, the deforestation which occurred in the tropics produced an increase in temperature due to a decrease in evaporative cooling and cloud cover, and an increase in insolation and sensible heating of the near surface. Nevertheless, these surface-air temperature changes associated with land use are smaller than the effect from changes in the trace-gas forcing (i.e. the enhanced greenhouse effect), although over some regions the land-use change can be large enough to counteract the human-induced, radiatively forced warming. A comparison of these biogeophysical impacts on climate of the land use and biofuel policies with the previously published biogeochemical impact of biofuels indicates the dominance of biogeophysical impacts at 2050.This research is funded by a grant from the USA Department of Energy. The authors gratefully acknowledge the financial support for this work provided by the MIT Joint Program on the Science and Policy of Global Change through a number of Federal agencies and industrial sponsors (for the complete list see http://globalchange.mit.edu/sponsors/current.html)

Taxonomic distribution of large DNA viruses in the sea

Phylogenetic mapping of metagenomics data reveals the taxonomic distribution of large DNA viruses in the sea, including giant viruses of the Mimiviridae family

Horizontal gene transfer and nucleotide compositional anomaly in large DNA viruses

<p>Abstract</p> <p>Background</p> <p>DNA viruses have a wide range of genome sizes (5 kb up to 1.2 Mb, compared to 0.16 Mb to 1.5 Mb for obligate parasitic bacteria) that do not correlate with their virulence or the taxonomic distribution of their hosts. The reasons for such large variation are unclear. According to the traditional view of viruses as gifted "gene pickpockets", large viral genome sizes could originate from numerous gene acquisitions from their hosts. We investigated this hypothesis by studying 67 large DNA viruses with genome sizes larger than 150 kb, including the recently characterized giant mimivirus. Given that horizontally transferred DNA often have anomalous nucleotide compositions differing from the rest of the genome, we conducted a detailed analysis of the inter- and intra-genome compositional properties of these viruses. We then interpreted their compositional heterogeneity in terms of possible causes, including strand asymmetry, gene function/expression, and horizontal transfer.</p> <p>Results</p> <p>We first show that the global nucleotide composition and nucleotide word usage of viral genomes are species-specific and distinct from those of their hosts. Next, we identified compositionally anomalous (cA) genes in viral genomes, using a method based on Bayesian inference. The proportion of cA genes is highly variable across viruses and does not exhibit a significant correlation with genome size. The vast majority of the cA genes were of unknown function, lacking homologs in the databases. For genes with known homologs, we found a substantial enrichment of cA genes in specific functional classes for some of the viruses. No significant association was found between cA genes and compositional strand asymmetry. A possible exogenous origin for a small fraction of the cA genes could be confirmed by phylogenetic reconstruction.</p> <p>Conclusion</p> <p>At odds with the traditional dogma, our results argue against frequent genetic transfers to large DNA viruses from their modern hosts. The large genome sizes of these viruses are not simply explained by an increased propensity to acquire foreign genes. This study also confirms that the anomalous nucleotide compositions of the cA genes is sometimes linked to particular biological functions or expression patterns, possibly leading to an overestimation of recent horizontal gene transfers.</p

An Analogue Approach to Identify Heavy Precipitation Events: Evaluation and Application to CMIP5 Climate Models in the United States

An analogue method is presented to detect the occurrence of heavy precipitation events without relying on modeled precipitation. The approach is based on using composites to identify distinct large-scale atmospheric conditions associated with widespread heavy precipitation events across local scales. These composites, exemplified in the south-central, midwestern, and western United States, are derived through the analysis of 27-yr (1979–2005) Climate Prediction Center (CPC) gridded station data and the NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA). Circulation features and moisture plumes associated with heavy precipitation events are examined. The analogues are evaluated against the relevant daily meteorological fields from the MERRA reanalysis and achieve a success rate of around 80% in detecting observed heavy events within one or two days. The method also captures the observed interannual variations of seasonal heavy events with higher correlation and smaller RMSE than MERRA precipitation. When applied to the same 27-yr twentieth-century climate model simulations from Phase 5 of the Coupled Model Intercomparison Project (CMIP5), the analogue method produces a more consistent and less uncertain number of seasonal heavy precipitation events with observation as opposed to using model-simulated precipitation. The analogue method also performs better than model-based precipitation in characterizing the statistics (minimum, lower and upper quartile, median, and maximum) of year-to-year seasonal heavy precipitation days. These results indicate the capability of CMIP5 models to realistically simulate large-scale atmospheric conditions associated with widespread local-scale heavy precipitation events with a credible frequency. Overall, the presented analyses highlight the improved diagnoses of the analogue method against an evaluation that considers modeled precipitation alone to assess heavy precipitation frequency.United States. National Aeronautics and Space Administration. Energy and Water Cycle Study Research Announcement (NNH07ZDA001N)National Science Foundation (U.S.). MacroSystems Biology Program (NSF-AES EF 1137306

A Framework for Modeling Uncertainty in Regional Climate Change

In this study, we present a new modeling framework and a large ensemble of climate projections to investigate the uncertainty in regional climate change over the US associated with four dimensions of uncertainty. The sources of uncertainty considered in this framework are the emissions projections (using different climate policies), climate system parameters (represented by different values of climate sensitivity and net aerosol forcing), natural variability (by perturbing initial conditions) and structural uncertainty (using different climate models). The modeling framework revolves around the Massachusetts Institute of Technology (MIT) Integrated Global System Model (IGSM), an integrated assessment model with an intermediate complexity earth system model (with a two-dimensional zonal-mean atmosphere). Regional climate change over the US is obtained through a two-pronged approach. First, we use the IGSM-CAM framework which links the IGSM to the National Center for Atmospheric Research (NCAR) Community Atmosphere Model (CAM). Secondly, we use a pattern-scaling method that extends the IGSM zonal mean based on climate change patterns from various climate models. Results show that uncertainty in temperature changes are mainly driven by policy choices and the range of climate sensitivity considered. Meanwhile, the four sources of uncertainty contribute more equally to precipitation changes, with natural variability having a large impact in the first part of the 21st century. Overall, the choice of policy is the largest driver of uncertainty in future projections of climate change over the US.This work was partially funded by the US Environmental Protection Agency under Cooperative Agreement #XA-83600001. The Joint Program on the Science and Policy of Global Change is funded by a number of federal agencies and a consortium of 40 industrial and foundation sponsors. For a complete list of sponsors, see: http://globalchange.mit.edu. This research used the Evergreen computing cluster at the Pacific Northwest National Laboratory. Evergreen is supported by the Office of Science of the US Department of Energy under Contract No. DE-AC05-76RL01830. The 20th Century Reanalysis V2 data was provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their Web site at http://www.esrl.noaa.gov/psd/

Twenty-First-Century Changes in U.S. Regional Heavy Precipitation Frequency Based on Resolved Atmospheric Patterns

Precipitation-gauge observations and atmospheric reanalysis are combined to develop an analogue method for detecting heavy precipitation events based on prevailing large-scale atmospheric conditions. Combinations of atmospheric variables for circulation (geopotential height and wind vector) and moisture (surface specific humidity, column and up to 500-hPa precipitable water) are examined to construct analogue schemes for the winter [December-February (DJF)] of the "Pacific Coast California" (PCCA) region and the summer [June-August (JJA)] of the Midwestern United States (MWST). The detection diagnostics of analogue schemes are calibrated with 1979-2005 and validated with 2006-14 NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA). All analogue schemes are found to significantly improve upon MERRA precipitation in characterizing the occurrence and interannual variations of observed heavy precipitation events in the MWST. When evaluated with the late twentieth-century climate model simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5), all analogue schemes produce model medians of heavy precipitation frequency that are more consistent with observations and have smaller intermodel discrepancies than model-based precipitation. Under the representative concentration pathways (RCP) 4.5 and 8.5 scenarios, the CMIP5-based analogue schemes produce trends in heavy precipitation occurrence through the twenty-first century that are consistent with model-based precipitation, but with smaller intermodel disparity. The median trends in heavy precipitation frequency are positive for DJF over PCCA but are slightly negative for JJA over MWST. Overall, the analyses highlight the potential of the analogue as a powerful diagnostic tool for model deficiencies and its complementarity to an evaluation of heavy precipitation frequency based on model precipitation alone.National Science Foundation (U.S.) (MacroSystems Biology Program Grant NSF-AES EF#1137306)United States. Department of Energy (Integrated Framework for Climate Change Assessment DE-FG02-94ER61937)National Science Foundation (U.S.) (NSF-AGS-1552195)United States. National Aeronautics and Space Administration (Energy and Water Cycle Study Research Announcement NNH07ZDA001N

CLM-AG: An Agriculture Module for the Community Land Model version 3.5

It is estimated that 40% of all crops grown in the world today are grown using irrigation. As a consequence, shifting precipitation patterns due to climate change are viewed as a major threat to food security. This report presents the Community Land Model-Agriculture module (CLM-AG), which models crop growth and water stress. The CLM-AG model is a global generic crop model built in the framework of the Community Land Model version 3.5. This report describes the structure and main routines of the model. Two different evaluations of the model are then considered. First, at a global level, CLM-AG is run under a historic climatology and compared to the Global Agro-Ecological Zones, an existing model of irrigation need. Second, the irrigation need computed for the United States is compared to survey data from the United States Department of Agriculture. For both evaluations, CLM-AG results are comparable to either the model results or the surveyed data.Development of the IGSM applied in this research was supported by the U.S. Department of Energy, Office of Science (DE-FG02-94ER61937); the U.S. Environmental Protection Agency, EPRI, and other U.S. government agencies and a consortium of 40 industrial and foundation sponsors. For a complete list see http://globalchange.mit.edu/sponsors/current.htm

A framework for modeling uncertainty in regional climate change

In this study, we present a new modeling framework and a large ensemble of climate projections to investigate the uncertainty in regional climate change over the United States (US) associated with four dimensions of uncertainty. The sources of uncertainty considered in this framework are the emissions projections, global climate system parameters, natural variability and model structural uncertainty. The modeling framework revolves around the Massachusetts Institute of Technology (MIT) Integrated Global System Model (IGSM), an integrated assessment model with an Earth System Model of Intermediate Complexity (EMIC) (with a two-dimensional zonal-mean atmosphere). Regional climate change over the US is obtained through a two-pronged approach. First, we use the IGSM-CAM framework, which links the IGSM to the National Center for Atmospheric Research (NCAR) Community Atmosphere Model (CAM). Second, we use a pattern-scaling method that extends the IGSM zonal mean based on climate change patterns from various climate models. Results show that the range of annual mean temperature changes are mainly driven by policy choices and the range of climate sensitivity considered. Meanwhile, the four sources of uncertainty contribute more equally to end-of-century precipitation changes, with natural variability dominating until 2050. For the set of scenarios used in this study, the choice of policy is the largest driver of uncertainty, defined as the range of warming and changes in precipitation, in future projections of climate change over the US.United States. Environmental Protection Agency. Climate Change Division (Cooperative Agreement #XA-83600001