Development of the WRF-CO2 4D-Var assimilation system v1.0

Regional atmospheric CO2 inversions commonly use Lagrangian particle trajectory model simulations to calculate the required influence function, which quantifies the sensitivity of a receptor to flux sources. In this paper, an adjoint-based four-dimensional variational (4D-Var) assimilation system, WRF-CO2 4D-Var, is developed to provide an alternative approach. This system is developed based on the Weather Research and Forecasting (WRF) modeling system, including the system coupled to chemistry (WRF-Chem), with tangent linear and adjoint codes (WRFPLUS), and with data assimilation (WRFDA), all in version 3.6. In WRF-CO2 4D-Var, CO2 is modeled as a tracer and its feedback to meteorology is ignored. This configuration allows most WRF physical parameterizations to be used in the assimilation system without incurring a large amount of code development. WRF-CO2 4D-Var solves for the optimized CO2 flux scaling factors in a Bayesian framework. Two variational optimization schemes are implemented for the system: the first uses the limited memory Broyden-Fletcher-Goldfarb-Shanno (BFGS) minimization algorithm (L-BFGS-B) and the second uses the Lanczos conjugate gradient (CG) in an incremental approach. WRFPLUS forward, tangent linear, and adjoint models are modified to include the physical and dynamical processes involved in the atmospheric transport of CO2. The system is tested by simulations over a domain covering the continental United States at 48 km × 48 km grid spacing. The accuracy of the tangent linear and adjoint models is assessed by comparing against finite difference sensitivity. The system\u27s effectiveness for CO2 inverse modeling is tested using pseudo-observation data. The results of the sensitivity and inverse modeling tests demonstrate the potential usefulness of WRF-CO2 4D-Var for regional CO2 inversions

Fire disturbance effects on land surface albedo in Alaskan tundra

The study uses satellite Moderate Resolution Imaging Spectroradiometer albedo products (MCD43A3) to assess changes in albedo at two sites in the treeless tundra region of Alaska, both within the foothills region of the Brooks Range, the 2007 Anaktuvuk River Fire (ARF) and 2012 Kucher Creek Fire (KCF). Results are compared to each other and other studies to assess the magnitude of albedo change and the longevity of impact of fire on land surface albedo. In both sites there was a marked decrease of albedo in the year following the fire. In the ARF, albedo slowly increased until 4 years after the fire, when it returned to albedo values prior to the fire. For the year immediately after the fire, a threefold difference in the shortwave albedo decrease was found between the two sites. ARF showed a 45.3% decrease, while the KCF showed a 14.1% decrease in shortwave albedo, and albedo is more variable in the KCF site than ARF site 1 year after the fire. These differences are possibly the result of differences in burn severity of the two fires, wherein the ARF burned more completely with more contiguous patches of complete burn than KCF. The impact of fire on average growing season (April–September) surface shortwave forcing in the year following fire is estimated to be 13.24 ± 6.52 W m−2 at the ARF site, a forcing comparable to studies in other treeless ecosystems. Comparison to boreal studies and the implications to energy flux are discussed in the context of future increases in fire occurrence and severity in a warming climate

Quantifying surface severity of the 2014 and 2015 fires in the Great Slave Lake area of Canada

The focus of this paper was the development of surface organic layer severity maps for the 2014 and 2015 fires in the Great Slave Lake area of the Northwest Territories and Alberta, Canada, using multiple linear regression models generated from pairing field data with Landsat 8 data. Field severity data were collected at 90 sites across the region, together with other site metrics, in order to develop a mapping approach for surface severity, an important metric for assessing carbon loss from fire. The approach utilised a combination of remote sensing indices to build a predictive model of severity that was applied within burn perimeters. Separate models were created for burns in the Shield and Plain ecoregions using spectral data from Landsat 8. The final Shield and Plain models resulted in estimates of surface severity with 0.74 variance explained (R2) for the Plain ecoregions and 0.67 for the Shield. The 2014 fires in the Plain ecoregion were more severe than the 2015 fires and fires in both years in the Shield ecoregion. In further analysis of the field data, an assessment of relationships between surface severity and other site-level severity metrics found mixed results

The Fire and Smoke Model Evaluation Experiment - A plan for integrated, large fire-atmosphere field campaigns

The Fire and Smoke Model Evaluation Experiment (FASMEE) is designed to collect integrated observations from large wildland fires and provide evaluation datasets for new models and operational systems. Wildland fire, smoke dispersion, and atmospheric chemistry models have become more sophisticated, and next-generation operational models will require evaluation datasets that are coordinated and comprehensive for their evaluation and advancement. Integrated measurements are required, including ground-based observations of fuels and fire behavior, estimates of fire-emitted heat and emissions fluxes, and observations of near-source micrometeorology, plume properties, smoke dispersion, and atmospheric chemistry. To address these requirements the FASMEE campaign design includes a study plan to guide the suite of required measurements in forested sites representative of many prescribed burning programs in the southeastern United States and increasingly common high-intensity fires in the western United States. Here we provide an overview of the proposed experiment and recommendations for key measurements. The FASMEE study provides a template for additional large-scale experimental campaigns to advance fire science and operational fire and smoke models

Quantifying burned area for North American forests: Implications for direct reduction of carbon stocks

A synthesis was carried out to analyze information available to quantify fire activity and burned area across North America, including a comparison of different data sources and an assessment of how variations in burned area estimate impact carbon emissions from fires. Data sets maintained by fire management agencies provide the longest record of burned area information. Canada and Alaska have the most well developed data sets consisting of the perimeters of large fires (\u3e200 ha) going back to 1959 and 1950, respectively. A similar data set back to 1980 exists for the Conterminous U.S., but contains data only from federal land management agencies. During the early half of the 20th century, average burned area across North America ranged between 10 and 20 × 106 ha yr−1, largely because of frequent surface fires in the southeastern U.S. Over the past two decades, an average of 5 × 106 ha yr−1 has burned. Moderate-resolution (500–1000 m) satellite burned area products information products appear to either underestimate burned area (GFED3 and MCD45A1) or significantly overestimate burned area (L3JRC and GLOBCARBON). Of all the satellite data products, the GFED3 data set provides the most consistent source of burned area when compared to fire management data. Because they do not suitably reflect actual fire activity, the L3JRC and GLOBCARBON burned area data sets are not suitable for use in carbon cycle studies in North America. The MCD45A1 data set appears to map a higher fraction of burned area in low biomass areas compared to the GFED3 data set

Mapping modeled exposure of wildland fire smoke for human health studies in California

Wildland fire smoke exposure affects a broad proportion of the U.S. population and is increasing due to climate change, settlement patterns and fire seclusion. Significant public health questions surrounding its effects remain, including the impact on cardiovascular disease and maternal health. Using atmospheric chemical transport modeling, we examined general air quality with and without wildland fire smoke PM2.5. The 24-h average concentration of PM2.5 from all sources in 12-km gridded output from all sources in California (2007–2013) was 4.91 μg/m3. The average concentration of fire-PM2.5 in California by year was 1.22 μg/m3 (~25% of total PM2.5). The fire-PM2.5 daily mean was estimated at 4.40 μg/m3 in a high fire year (2008). Based on the model-derived fire-PM2.5 data, 97.4% of California’s population lived in a county that experienced at least one episode of high smoke exposure (“smokewave”) from 2007–2013. Photochemical model predictions of wildfire impacts on daily average PM2.5 carbon (organic and elemental) compared to rural monitors in California compared well for most years but tended to over-estimate wildfire impacts for 2008 (2.0 µg/m3 bias) and 2013 (1.6 µg/m3 bias) while underestimating for 2009 (−2.1 µg/m3 bias). The modeling system isolated wildfire and PM2.5 from other sources at monitored and unmonitored locations, which is important for understanding population exposure in health studies. Further work is needed to refine model predictions of wildland fire impacts on air quality in order to increase confidence in the model for future assessments. Atmospheric modeling can be a useful tool to assess broad geographic scale exposure for epidemiologic studies and to examine scenario-based health impacts

Modeling acute respiratory illness during the 2007 San Diego wildland fires using a coupled emissions-transport system and general additive modeling

Background A study of the impacts on respiratory health of the 2007 wildland fires in and around San Diego County, California is presented. This study helps to address the impact of fire emissions on human health by modeling the exposure potential of proximate populations to atmospheric particulate matter (PM) from vegetation fires. Currently, there is no standard methodology to model and forecast the potential respiratory health effects of PM plumes from wildland fires, and in part this is due to a lack of methodology for rigorously relating the two. The contribution in this research specifically targets that absence by modeling explicitly the emission, transmission, and distribution of PM following a wildland fire in both space and time. Methods Coupled empirical and deterministic models describing particulate matter (PM) emissions and atmospheric dispersion were linked to spatially explicit syndromic surveillance health data records collected through the San Diego Aberration Detection and Incident Characterization (SDADIC) system using a Generalized Additive Modeling (GAM) statistical approach. Two levels of geographic aggregation were modeled, a county-wide regional level and division of the county into six sub regions. Selected health syndromes within SDADIC from 16 emergency departments within San Diego County relevant for respiratory health were identified for inclusion in the model. Results The model captured the variability in emergency department visits due to several factors by including nine ancillary variables in addition to wildfire PM concentration. The model coefficients and nonlinear function plots indicate that at peak fire PM concentrations the odds of a person seeking emergency care is increased by approximately 50% compared to non-fire conditions (40% for the regional case, 70% for a geographically specific case). The sub-regional analyses show that demographic variables also influence respiratory health outcomes from smoke. Conclusions The model developed in this study allows a quantitative assessment and prediction of respiratory health outcomes as it relates to the location and timing of wildland fire emissions relevant for application to future wildfire scenarios. An important aspect of the resulting model is its generality thus allowing its ready use for geospatial assessments of respiratory health impacts under possible future wildfire conditions in the San Diego region. The coupled statistical and process-based modeling demonstrates an end-to-end methodology for generating reasonable estimates of wildland fire PM concentrations and health effects at resolutions compatible with syndromic surveillance data

Climate-Induced Boreal Forest Change: Predictions versus Current Observations

For about three decades, there have been many predictions of the potential ecological response in boreal regions to the currently warmer conditions. In essence, a widespread, naturally occurring experiment has been conducted over time. In this paper, we describe previously modeled predictions of ecological change in boreal Alaska, Canada and Russia, and then we investigate potential evidence of current climate-induced change. For instance, ecological models have suggested that warming will induce the northern and upslope migration of the treeline and an alteration in the current mosaic structure of boreal forests. We present evidence of the migration of keystone ecosystems in the upland and lowland treeline of mountainous regions across southern Siberia. Ecological models have also predicted a moisture-stress-related dieback in white spruce trees in Alaska, and current investigations show that as temperatures increase, white spruce tree growth is declining. Additionally, it was suggested that increases in infestation and wildfire disturbance would be catalysts that precipitate the alteration of the current mosaic forest composition. In Siberia, five of the last seven years have resulted in extreme fire seasons, and extreme fire years have also been more frequent in both Alaska and Canada. In addition, Alaska has experienced extreme and geographically expansive multi-year outbreaks of the spruce beetle, which had been previously limited by the cold, moist environment. We suggest that there is substantial evidence throughout the circumboreal region to conclude that the biosphere within the boreal terrestrial environment has already responded to the transient effects of climate change. Additionally, temperature increases and warming-induced change are progressing faster than had been predicted in some regions, suggesting a potential non-linear rapid response to changes in climate, as opposed to the predicted slow linear response to climate change

MULTI-TEMPORAL AND MULTI-PLATFORM AGRICULTURAL LAND COVER CLASSIFICATION IN SOUTHEASTERN MICHIGAN

ABSTRACT We investigated the capabilities of multi-temporal and multi-platform remotely sensed imagery to differentiate crop types in the 14,600 ha Upper Tiffin watershed in southeastern Michigan, a primarily agricultural area. We focused on extracting signatures for corn, soybeans, wheat, alfalfa, and grasses as the major crops for the area. Input data included Landsat 5 TM, Terra/MODIS, and ASTER imagery for different parts of the 2004 and 2005 agricultural growing season. MODIS was selected to address the problems with obtaining sufficient repeat coverage of Landsat data during the growing season. We used both pixel-based and object-oriented classification techniques to assess the value of different techniques in leading to more useful agricultural land cover classifications. To contrast with these classification techniques, we predicted crop distributions using MODIS NDVI time-series profiles. ASTER data were investigated to see if its additional high-resolution bands and its multi-angle instruments could provide supplementary classification information. State and Federal agencies are active in the Upper Tiffin study area because of known problems with sediment and nutrient loading in local waterways. The Natural Resource Conservation Service (NRCS), part of the United States Department of Agriculture, used the results to understand how different land uses were affecting local water quality, and how the problems could be addressed

Modeling regional-scale wildland fire emissions with the wildland fire emissions information system

As carbon modeling tools become more comprehensive, spatial data are needed to improve quantitative maps of carbon emissions from fire. The Wildland Fire Emissions Information System (WFEIS) provides mapped estimates of carbon emissions from historical forest fires in the United States through a web browser. WFEIS improves access to data and provides a consistent approach to estimating emissions at landscape, regional, and continental scales. The system taps into data and tools developed by the U.S. Forest Service to describe fuels, fuel loadings, and fuel consumption and merges information from the U.S. Geological Survey (USGS) and National Aeronautics and Space Administration on fire location and timing. Currently, WFEIS provides web access to Moderate Resolution Imaging Spectroradiometer (MODIS) burned area for North America and U.S. fire-perimeter maps from the Monitoring Trends in Burn Severity products from the USGS, overlays them on 1-km fuel maps for the United States, and calculates fuel consumption and emissions with an open-source version of the Consume model. Mapped fuel moisture is derived from daily meteorological data from remote automated weather stations. In addition to tabular output results, WFEIS produces multiple vector and raster formats. This paper provides an overview of the WFEIS system, including the web-based system functionality and datasets used for emissions estimates. WFEIS operates on the web and is built using open-source software components that work with open international standards such as keyhole markup language (KML). Examples of emissions outputs from WFEIS are presented showing that the system provides results that vary widely across the many ecosystems of North America and are consistent with previous emissions modeling estimates and products