Regional Climate Projections Research Articles

Assessing the contribution of different factors in regional climate model projections using the factor separation method

AbstractThis study applies the factor separation (FS) method to investigate the contributions of different factors, along with their synergy, on a set of regional climate model (RCM) projections for the Mediterranean region. The FS method is applied to six projections for the period 1970–2100 performed with the regional model RegCM4 over the Med‐CORDEX domain. Two different sets of factors are intercompared, namely the driving global climate model (GCM) boundary conditions against two model physics settings (convection scheme and irrigation). We demonstrate the usefulness of the FS method to assess different sources of uncertainty in RCM‐based regional climate projections.

Are different facets of plant diversity well protected against climate and land cover changes? A test study in the French Alps.

Climate and land cover changes are important drivers of the plant species distributions and diversity patterns in mountainous regions. Although the need for a multifaceted view of diversity based on taxonomic, functional and phylogenetic dimensions is now commonly recognized, there are no complete risk assessments concerning their expected changes. In this paper, we used a range of species distribution models in an ensemble-forecasting framework together with regional climate and land cover projections by 2080 to analyze the potential threat for more than 2,500 plant species at high resolution (2.5 km × 2.5 km) in the French Alps. We also decomposed taxonomic, functional and phylogenetic diversity facets into α and β components and analyzed their expected changes by 2080. Overall, plant species threats from climate and land cover changes in the French Alps were expected to vary depending on the species' preferred altitudinal vegetation zone, rarity, and conservation status. Indeed, rare species and species of conservation concern were the ones projected to experience less severe change, and also the ones being the most efficiently preserved by the current network of protected areas. Conversely, the three facets of plant diversity were also projected to experience drastic spatial re-shuffling by 2080. In general, the mean α-diversity of the three facets was projected to increase to the detriment of regional β-diversity, although the latter was projected to remain high at the montane-alpine transition zones. Our results show that, due to a high-altitude distribution, the current protection network is efficient for rare species, and species predicted to migrate upward. Although our modeling framework may not capture all possible mechanisms of species range shifts, our work illustrates that a comprehensive risk assessment on an entire floristic region combined with functional and phylogenetic information can help delimitate future scenarios of biodiversity and better design its protection.

Assessing reliability of regional climate projections: the case of Indian monsoon

Projections of climate change are emerging to play major roles in many applications. However, assessing reliability of climate change projections, especially at regional scales, remains a major challenge. An important question is the degree of progress made since the earlier IPCC simulations (CMIP3) to the latest, recently completed CMIP5. We consider the continental Indian monsoon as an example and apply a hierarchical approach for assessing reliability, using the accuracy in simulating the historical trend as the primary criterion. While the scope has increased in CMIP5, there is essentially no improvement in skill in projections since CMIP3 in terms of reliability (confidence). Thus, it may be necessary to consider acceptable models for specific assessment rather than simple ensemble. Analysis of climate indices shows that in both CMIP5 and CMIP3 certain common processes at large and regional scales as well as slow timescales are associated with successful simulation of trend and mean.

Assessment of the CMIP5 global climate model simulations of the western tropical Pacific climate system and comparison to CMIP3

ABSTRACTA set of 27 global climate models from the Coupled Model Inter‐comparison Project Phase 5 (CMIP5) ensemble are assessed for their performance for the purpose of making future climate projection studies in the western tropical Pacific and differences to Coupled Model Inter‐comparison Project Phase 3 (CMIP3) are assessed. The CMIP5 models show some improvements upon CMIP3 in the simulation of the climate in the western tropical Pacific in the late 20th century. There are fewer CMIP5 models with very poor skill scores than in CMIP3 for some measures and a small group of the well‐performing models in CMIP5 have lower biases than in an equivalent group from CMIP3. These best‐performing models could be particularly informative for studying certain climate sensitivities and feedbacks in the region. There is evidence to reject one model as unsuitable for making regional climate projections in the region, and another two models unsuitable for analysis of the South Pacific Convergence Zone (SPCZ). However, while there have been improvements, many of the systematic model biases in the mean climate in CMIP3 are also present in the CMIP5 models. They are primarily related to the shape of the transition between the Indo‐Pacific warm pool and equatorial cold tongue, and the associated biases in the position and orientation of the SPCZ and Inter‐Tropical Convergence Zone, as well as in the spatial pattern, variability and teleconnections of the West Pacific monsoon, and the simulation of El Niño Southern Oscillation. Overall, the results show that careful interpretation and consideration of biases is required when using CMIP5 outputs for generating regional climate projections for the western tropical Pacific, particularly at the country scale, just as there was with CMIP3.

Evaluation of hydrological effect of stakeholder prioritized climate change adaptation options based on multi-model regional climate projections

An integrated process involving participatory and modelling approaches for prioritizing and evaluating climate change adaptation options for the Kangsabati reservoir catchment is presented here. We assess the potential effects of climate change on water resources and evaluate the ability of stakeholder prioritized adaptation options to address adaptation requirements using the Water Evaluation And Planning (WEAP) model. Two adaptation options, check dams and increasing forest cover, are prioritized using pair-wise comparison and scenario analysis. Future streamflow projections are generated for the mid-21st century period (2021–2050) using four high resolution (~25 km) Regional Climate Models and their ensemble mean for SRES A1B scenario. WEAP simulations indicate that, compared to a base scenario without adaptation, both adaptation options reduce streamflow. In comparison to check dams, increasing forest cover shows greater ability to address adaptation requirements as demonstrated by the temporal pattern and magnitude of streamflow reduction. Additionally, over the 30 year period, effectiveness of check dams in reducing streamflow decreases by up to 40 %, while that of forest cover increases by up to 47 %. Our study highlights the merits of a comparative assessment of adaptation options and we conclude that a combined approach involving stakeholders, scenario analysis, modelling techniques and multi-model projections may support climate change adaptation decision-making in the face of uncertainty.

Climate change in the Western Bug river basin and the impact on future hydro-climatic conditions

Within the IWRM research project IWAS, we generated regional climate projections via dynamic downscaling for the catchment area of the Western Bug river in the border area of Poland, Belarus, and Ukraine. Data sources used are WMO meteorological station data and global climate ERA40 reanalysis data for an evaluation run, as well as global simulations from the ECHAM5 model for a twentieth-century control run and for the IPCC emission scenarios A2 and B1. We evaluated the performance of the regional climate model CCLM on the basis of simulated 2m temperature and precipitation. Furthermore, we analyzed the hydro-climatic conditions of the past and their projected future changes in the catchment based on 2m temperature, precipitation, potential evaporation and climatic water balance. The latter is discussed as an indicator for potential water availability in the region. Our evaluation, like many other studies, attests that the CCLM performs well for 2m temperature. Precipitation is not modelled adequately for the Western Bug basin. Remarkably, the precipitation bias is five times higher in the ECHAM5-driven control run than in the ERA40-driven evaluation run. Despite all the uncertainties, the significance of the modelled changes clearly suggests robust model results for the last three decades of this century. Up to the end of the century, both scenarios A2 and B1 lead to highly significant warming for each month in the long-term mean, with highest warming rates in winter. Instead, precipitation does not change significantly in the long-term yearly mean, but the intra-annual distribution of monthly precipitation sums shifts, with an increase in winter and a strong decrease in summer. Combined, this leads to a changed climatic water balance with a stronger deficit in summer and a higher gain in winter. Particular in the south-eastern part, the summer deficit cannot be compensated within the annual cycle. This might have serious implications for many socio-economic sectors.

Modeling climate and fuel reduction impacts on mixed-conifer forest carbon stocks in the Sierra Nevada, California

Quantifying the impacts of changing climatic conditions on forest growth is integral to estimating future forest carbon balance. We used a growth-and-yield model, modified for climate sensitivity, to quantify the effects of altered climate on mixed-conifer forest growth in the Lake Tahoe Basin, California. Estimates of forest growth and live tree carbon stocks were made for low and high emission scenarios using four downscaled general circulation model (GCM) projections. The climate scenarios were coupled with a range of commonly-used fuels reduction treatments to quantify the combined effects of these factors on live tree carbon stocks. We compared mid- (2020–2049) and late-21st (2070–2099) century carbon stock estimates with a baseline period of 1970–1999 using common input data across time periods. Recursive partitioning analysis indicates that GCM, forest composition, and simulation period most influence live tree carbon stock changes. Comparison with the late 20th century baseline period shows mixed carbon stock responses across scenarios. Growth varied by species, often with compensatory responses among dominant species that limited changes in total live tree carbon. The influence of wildfire mitigation treatments was relatively consistent with each GCM by emission scenario combination. Treatments that included prescribed fire had greater live tree carbon gains relative to baseline under the scenarios that had overall live tree carbon gains. However, across GCMs the influence of treatments varied considerably among GCM projections, indicating that further refinement of regional climate projections will be required to improve model estimates of fuel manipulations on forest carbon stocks. Additionally, had out simulations included the effects of projected climate changes on increasing wildfire probability, the effects of management treatments on carbon stocks may have been more pronounced because of the influence of treatment on fire severity.

Exploring qualitative regional climate projections: a case study for Nauru

Coupled General Circulation Models simulate broad-scale climate patterns and are standard tools for understanding potential changes to the climate system. Climate change projection information is required at the local scale for adaptation planning. Model skill can be limited at this scale due to model biases and uncertainties. Pacific Islands are one such example, particularly Nauru which lies at the intersection of the South Pacific Convergence Zone (SPCZ), the Western Pacific Monsoon, the oceanic warm pool and the ENSO zone of influence. In light of such constraints, a qualitative climate projection approach is presented whereby projections are based on expected changes to the broad-scale climate features, drawing on a review of current literature and new research. Nauru sits in the region subject to seasonal and interannual migrations of the SPCZ. Climate models simulate an overly zonal SPCZ with Nauru lying too close to the dry cold tongue zone. This sets an erroneous base state for Nauru by locating it in a very different climate zone. The climate model changes in the SPCZ can be used to argue how the climate of Nauru would change were it and the SPCZ correctly collocated. Similar approaches are applied to the set of climate features influencing Nauru to generate modified climate projections.

Projected 21st century snowfall changes over the French Alps and related uncertainties

Snowfall changes in mountain areas in response to anthropogenic forcing could have widespread hydrological, ecological and economic impacts. In this paper, the robustness of snowfall changes over the French Alps projected during the 21st century and the associated uncertainties are studied. In particular, the role of temperature changes on snowfall changes is investigated. Those issues are tackled through the analysis of the results of a very large ensemble of high-resolution regional climate projections, obtained either through dynamical or statistical downscaling. We find that, at the beginning and at the end of the cold season extending from November to March (included), temperature change is an important source of spread in snowfall changes. However, no link is found between temperature and snowfall changes in January and February. At the beginning and at the end of the cold season, the rate of change in snowfall per Kelvin does not depend much on the bias correction step, the period or the greenhouse gas scenario but mostly on the downscaling method and the climate models, the latter uncertainty source being dominant.

Regional Climate Projections of Extreme Heat Events in Nine Pilot Canadian Communities for Public Health Planning

AbstractPublic health planning needs the support of evidence-based information on current and future climate, which could be used by health professionals and decision makers to better understand and respond to the health impacts of extreme heat. Climate models provide information regarding the expected increase in temperatures and extreme heat events with climate change and can help predict the severity of future health impacts, which can be used in the public health sector for the development of adaptation strategies to reduce heat-related morbidity and mortality. This study analyzes the evolution of extreme temperature indices specifically defined to characterize heat events associated with health risks, in the context of a changing climate. The analysis is performed by using temperature projections from the Canadian Regional Climate Model. A quantile-based statistical correction is applied to the projected temperatures, in order to reduce model biases and account for the representativeness error. Moreover, generalized Pareto distributions are used to extend the temperature distribution upper tails and extrapolate the statistical correction to extremes that are not observed in the present but that might occur in the future. The largest increase in extreme daytime temperatures occurs in southern Manitoba, Canada, where the already overly dry climate and lack of soil moisture can lead to an uncontrolled enhancement of hot extremes. The occurrence of warm nights and heat waves, on the other hand, is already large and will increase substantially in the communities of the Great Lakes region, characterized by a humid climate. Impact and adaptation studies need to account for the temperature variability due to local effects, since it can be considerably larger than the model natural variability.

Forest fire danger projections in the Mediterranean using ENSEMBLES regional climate change scenarios

We present future fire danger scenarios for the countries bordering the Mediterranean areas of Europe and north Africa building on a multi-model ensemble of state-of-the-art regional climate projections from the EU-funded project ENSEMBLES. Fire danger is estimated using the Canadian Forest Fire Weather Index (FWI) System and a related set of indices. To overcome some of the limitations of ENSEMBLES data for their application on the FWI System—recently highlighted in a previous study by Herrera et al. (Clim Chang 118:827–840, 2013)—we used an optimal proxy variable combination. A robust assessment of future fire danger projections is undertaken by disentangling the climate change signal from the uncertainty derived from the multi-model ensemble, unveiling a positive signal of fire danger potential over large areas of the Mediterranean. The increase in the fire danger signal is accentuated towards the latest part of the transient period, thus pointing to an elevated fire potential in the region with time. The fire-climate links under present and future conditions are further discussed building upon observed climate data and burned area records along a representative climatic gradient within the study region.

The Practitioner's Dilemma: How to Assess the Credibility of Downscaled Climate Projections

Suppose you are a city planner, regional water manager, or wildlife conservation specialist who is asked to include the potential impacts of climate variability and change in your risk management and planning efforts. What climate information would you use? The choice is often regional or local climate projections downscaled from global climate models (GCMs; also known as general circulation models) to include detail at spatial and temporal scales that align with those of the decision problem. A few years ago this information was hard to come by. Now there is Web‐based access to a proliferation of high‐resolution climate projections derived with differing downscaling methods.

Vulnerability of hydropower generation to climate change in China: Results based on Grey forecasting model

This paper analyzes the long-term relationships between hydropower generation and climate factors (precipitation), hydropower generation capacity (installed capacity of hydropower station) to quantify the vulnerability of renewable energy production in China for the case of hydropower generation. Furthermore, this study applies Grey forecasting model to forecast precipitation in different provinces, and then sets up different scenarios for precipitation based on the IPCC Special Report on Emission Scenarios and results from PRECIS (Providing Regional Climate projections for Impacts Studies) model. The most important result found in this research is the increasing hydropower vulnerability of the poorest regions and the main hydropower generation provinces of China to climate change. Other main empirical results reveal that the impacts of climate change on the supply of hydropower generation in China will be noteworthy for the society. Different scenarios have different effects on hydropower generation, of which A2 scenario (pessimistic, high emission) has the largest. Meanwhile, the impacts of climate change on hydropower generation of every province are distinctly different, of which the Southwest part has the higher vulnerability than the average level while the central part lower.

Assessing the robustness of projected precipitation changes over central Africa on the basis of a multitude of global and regional climate projections

It is well accepted within the scientific community that a large ensemble of different projections is required to achieve robust climate change information for a specific region. For this purpose we have compiled a state-of-the-art multi-model multi-scenario ensemble of global and regional precipitation projections. This ensemble combines several global projections from the CMIP3 and CMIP5 databases, along with some recently downscaled regional CORDEX-Africa projections. Altogether daily precipitation data from 77 different climate change projections is analysed; separated into 31 projections for a high and 46 for a low emission scenario. We find a robust indication that, independent of the underlying emission scenario, annual total precipitation amounts over the central African region are not likely to change severely in the future. However some robust changes in precipitation characteristics, like the intensification of heavy rainfall events as well as an increase in the number of dry spells during the rainy season are projected for the future. Further analysis shows that over some regions the results of the climate change assessment clearly depend on the size of the analyzed ensemble. This indicates the need of a “large-enough” ensemble of independent climate projections to allow for a reliable climate change assessment.

Similar spatial patterns of climate responses to aerosol and greenhouse gas changes

Anthropogenic aerosols are highly spatially variable, whereas greenhouse gases are largely well-mixed at the global scale, but both affect climate. Nevertheless, climate simulations suggest that regional changes in sea surface temperature and precipitation to changes in greenhouse gas and aerosol forcings are similar. Spatial variations in ocean warming have been linked to regional changes in tropical cyclones1, precipitation2,3 and monsoons4. But development of reliable regional climate projections for climate change mitigation and adaptation remains challenging5. The presence of anthropogenic aerosols, which are highly variable in space and time, is thought to induce spatial patterns of climate response that are distinct from those of well-mixed greenhouse gases4,6,7,8,9. Using CMIP5 climate simulations that consider aerosols and greenhouse gases separately, we show that regional responses to changes in greenhouse gases and aerosols are similar over the ocean, as reflected in similar spatial patterns of ocean temperature and precipitation. This similarity suggests that the climate response to radiative changes is relatively insensitive to the spatial distribution of these changes. Although anthropogenic aerosols are largely confined to the Northern Hemisphere, simulations that include aerosol forcing predict decreases in temperature and westerly wind speed that reach the pristine Southern Hemisphere oceans. Over land, the climate response to aerosol forcing is more localized, but larger scale spatial patterns are also evident. We suggest that the climate responses induced by greenhouse gases and aerosols share key ocean–atmosphere feedbacks, leading to a qualitative resemblance in spatial distribution.

Climate change and waterborne diarrhoea in northern India: Impacts and adaptation strategies

Although several studies show the vulnerability of human health to climate change, a clear comprehensive quantification of the increased health risks attributable to climate change is lacking. Even more complicated are assessments of adaptation measures for this sector. We discuss the impact of climate change on diarrhoea as a representative of a waterborne infectious disease affecting human health in the Ganges basin of northern India. A conceptual framework is presented for climate exposure response relationships based on studies from different countries, as empirical studies and appropriate epidemiological data sets for India are lacking. Four climate variables are included: temperature, increased/extreme precipitation, decreased precipitation/droughts and relative humidity. Applying the conceptual framework to the latest regional climate projections for northern India shows increases between present and future (2040s), varying spatially from no change to an increase of 21% in diarrhoea incidences, with 13.1% increase on average for the Ganges basin. We discuss three types of measures against diarrhoeal disease: reactive actions, preventive actions and national policy options. Preventive actions have the potential to counterbalance this expected increase. However, given the limited progress in reducing incidences over the past decade consorted actions and effective implementation and integration of existing policies are needed.

The potential effects of climate change on malaria transmission in Africa using bias-corrected regionalised climate projections and a simple malaria seasonality model

Climatic conditions such as relatively cold temperatures and dryness are able to limit malaria transmission. Climate change is therefore expected to alter malaria spread. A previous assessment of the potential impacts of climate change on the seasonality of malaria in Africa is revisited. Bias-corrected regional climate projections with a horizontal resolution of 0.5° are used from the Regional Model (REMO), which include land use and land cover changes. The malaria model employed is the climate-driven seasonality model (MSM) from the Mapping Malaria Risk in Africa project for which a comparison with data from the Malaria Atlas Project (MAP) and the Liverpool Malaria Model (LMM), and a novel validation procedure lends more credence to results. For climate scenarios A1B and B1 and for 2001–2050, REMO projects an overall drying and warming trend in the African malaria belt, that is largely imposed by the man-made degradation of vegetation. As a result, the malaria projections of the MSM show a decreased length of the malaria season in West Africa. The northern Sahel is no more longer suitable for malaria in the projections and shorter malaria seasons are expected for various areas farther south. In East Africa, higher temperatures and nearly unchanged precipitation patterns lead to longer transmission seasons and an increase in highland malaria. Assuming constant population numbers, an overall increase in person-months of exposure of up to 6 % is found. The results of this simple seasonality model are similar to previous projections from the more complex LMM. However, a different response to the warming of highlands is found for the two models. It is concluded that the MSM is an efficient tool to assess the climate-driven malaria seasonality and that an uncertainty analysis of future malaria spread would benefit from a multi-model approach.

A multi-disciplinary assessment of the southeastern United States climate

The southeastern United States (SUS) is a region that exhibits considerable climate variability throughout the year along with high-impact weather extremes of severe weather and land-falling Atlantic tropical cyclones in the summer and fall, freeze events in the winter season, and tornadoes in the spring season. The SUS region has rapidly grown in terms of human population for last several decades that further exposes vulnerabilities to the frailties of nature. Furthermore, in the recent times, the agricultural production of the region has grown significantly with the rise in consumption and demand for bio fuels, increase in price and demand for commodity and food crops and increasing irrigation infrastructure that is subsidized by the state governments in the SUS. Similarly, the coastal development and capitalization of natural marine resource environment have also risen with the growing wealth of the population. All of these developments have led to an increasing interest in the understanding of the climate variations and change on the built and the natural environments of the SUS. This special issue is a manifestation of such an interest in the SUS with contributions from several climate-related inter-disciplinary groups including the Southeast Climate Consortium (SECC; http://seclimate. org/), the Florida Climate Institute (FCI; http://floridacli mateinstitute.org/), the University of Florida Water Institute (http://waterinstitute.ufl.edu/), and the Florida Water & Climate Alliance (FloridaWCA; http://www.floridawca.org). There are 15 research papers in this special issue covering climate science and its applications in hydrology, ecology, economy, crop science, and social science. It is quite apparent from the diverse set of papers that the demand for ‘‘reliable’’ regional-scale climate data over the SUS is of considerable interest for climate impact assessment. As a result, there are several papers in this collection which dwell on developing the regional-scale climate information from dynamic downscaling (e.g., Misra et al. 2012), or from statistical downscaling (e.g., Asefa and Adams 2013) or from a combination of statistical and dynamical downscaling (e.g., Hwang et al. 2013). Asefa and Adams (2013) introduced in their paper a new statistical bias correction technique for regional climate projections over central Florida based on a Bayesian approach that weights on the reliability of the global climate model in reproducing the observed climate. Hwang et al. (2013) highlight the merit of using dynamically downscaled and statistically bias-corrected climate data for hydrological applications over the Tampa Bay watershed. Misra et al. (2012) show the advantage and fidelity of dynamically downscaling the twentieth-century global atmospheric reanalysis (20CR) to 10 km grid resolution. For example, they demonstrate that data inhomogeneity issues over SUS in the 20CR are greatly ameliorated by the internal variations resolved by the dynamic downscaling to 10-km grid resolution. The interest in regional-scale climate information over SUS is also evidenced in a number of papers in this special issue investigating the reliability of some of the existing high-resolution regional climate datasets. LaRow (2012) analyzes the surface wind and precipitation in land-falling Atlantic tropical cyclones over the SUS from one of the existing global reanalyses downscaled to 10 km grid resolution. Similarly, Obeysekera (2013) interrogates the reliability of the meteorological variables used in the calculation of the surface evapotranspiration from the V. Misra (&) Department of Earth, Ocean and Atmospheric Science & Florida Climate Institute & Center for Ocean-Atmospheric Prediction Studies, Florida State University, Tallahassee, FL, USA e-mail: vmisra@fsu.edu

Impacts of bias correction of lateral boundary conditions on regional climate projections in West Africa

Biases existing in the lateral boundary conditions (LBCs) influence climate simulations in regional climate models (RCMs). Correcting the biases in global climate model (GCM)-produced LBCs before running RCMs was proposed in previous studies as a possible way to reduce the GCM-related model dependence of future climate projections using RCMs. In this study the ICTP Regional Climate Model Version 4 (RegCM4) is used to investigate the impact of LBC bias correction on projected future changes of regional climate in West Africa. To accomplish this, two types of present versus future simulations are conducted using RegCM4: a control type where both the present and future LBCs are derived directly from the GCM output (as is done in most regional climate downscaling studies); an experiment type where the present-day LBCs are from reanalysis data and future LBCs are derived by combining the reanalysis data and the GCM-projected LBC changes. For each type of simulations, three different sets of LBCs are experimented on: 6-hourly synoptic forcing directly from the reanalysis or GCM, 6-hourly data interpolated from monthly climatology (without diurnal cycle), and 6-hourly data interpolated from the month-specific climatology of diurnal cycles. It is found that the simulations using different LBCs produce similar present-day summer rainfall patterns, but the predicted future changes differ significantly depending on how the LBC bias correction is treated. Specifically, both the bias correction applied at the synoptic scale and the bias correction applied to the monthly interpolated LBCs without diurnal cycle produce a spurious drying signal caused by physical inconsistency in the corrected future LBCs. Interpolated monthly LBCs with diurnal cycle alleviate the problem to a large extent. These results suggest that using bias-corrected LBCs to drive regional climate models may not guarantee reliable future projections although reasonable present climate can be simulated. Physical inconsistencies may be contained in the bias-corrected LBCs, increasing the uncertainties of RCM-produced future projections.

Aerosol Transport and Source Attribution Using Sunphotometers, Models and In-Situ Chemical Composition Measurements

Understanding of chemical, physical, and radiative processes-emissions, transport, deposition, and modification of aerosol optical properties due to ageing-is of major importance to global and regional climate simulations and projections as well as health impairment. This paper presents aerosol optical properties retrieved with the Multifilter Rotating Shadowband Radiometers (MFRSRs) and the source attribution based on back trajectories and in situ aerosol chemical composition analysis obtained during the Aerosol Life Cycle Intensive Observational Period at Brookhaven National Laboratory on Long Island, NY, during July and August 2011. The aerosol optical properties retrieved with the MFRSR exhibit excellent agreement with those obtained with a colocated Cimel sunphotometer. Apportioning aerosol optical depths by size modes reveals several episodes of high loading of fine aerosol (diameter less than 2.5 μm). Analysis of optical and physical properties of aerosols as well as their chemical composition obtained by an in situ high-resolution time-of-flight aerosol mass spectrometer together with back trajectories indicates that the principal source of high concentrations of fine aerosols observed during July 18-24 was forest fires in western Canada, consistent with reports by the Canadian Forest Service and satellite observations by the Moderate Resolution Imaging Spectroradiometer (MODIS).