Atmospheric three-dimensional inverse modeling of regional industrial emissions and global oceanic uptake of carbon tetrachloride

Carbon tetrachloride (CCl4) has substantial stratospheric ozone depletion potential and its consumption is controlled under the Montreal Protocol and its amendments. We implement a Kalman filter using atmospheric CCl4 measurements and a 3-dimensional chemical transport model to estimate the interannual regional industrial emissions and seasonal global oceanic uptake of CCl4 for the period of 1996–2004. The Model of Atmospheric Transport and Chemistry (MATCH), driven by offline National Center for Environmental Prediction (NCEP) reanalysis meteorological fields, is used to simulate CCl4 mole fractions and calculate their sensitivities to regional sources and sinks using a finite difference approach. High frequency observations from the Advanced Global Atmospheric Gases Experiment (AGAGE) and the Earth System Research Laboratory (ESRL) of the National Oceanic and Atmospheric Administration (NOAA) and low frequency flask observations are together used to constrain the source and sink magnitudes, estimated as factors that multiply the a priori fluxes. Although industry data imply that the global industrial emissions were substantially declining with large interannual variations, the optimized results show only small interannual variations and a small decreasing trend. The global surface CCl4 mole fractions were declining in this period because the CCl4 oceanic and stratospheric sinks exceeded the industrial emissions. Compared to the a priori values, the inversion results indicate substantial increases in industrial emissions originating from the South Asian/Indian and Southeast Asian regions, and significant decreases in emissions from the European and North American regions.United States. National Aeronautics and Space Administration (Grant NNX07AE89G)United States. National Aeronautics and Space Administration (Grant NAG5-12669)United States. National Aeronautics and Space Administration (Grant NAG5-12099)National Science Foundation (U.S.) (grant ATM-0120468)United States. National Aeronautics and Space Administration (Grant NNX07AF09G)United States. National Aeronautics and Space Administration (Grant NNX07AE87G)Great Britain. Department for Environment, Food and Rural Affairs (grants EPG 1/1/159)Great Britain. Department for Environment, Food and Rural Affairs (grant CPEG 24)Great Britain. Department for Environment, Food and Rural Affairs (grants GA01081)Australia. Bureau of MeteorologyCSIRO Marine and Atmospheric Researc

Chemical Source Inversion using Assimilated Constituent Observations in an Idealized Two-dimensional System

We present a source inversion technique for chemical constituents that uses assimilated constituent observations rather than directly using the observations. The method is tested with a simple model problem, which is a two-dimensional Fourier-Galerkin transport model combined with a Kalman filter for data assimilation. Inversion is carried out using a Green's function method and observations are simulated from a true state with added Gaussian noise. The forecast state uses the same spectral spectral model, but differs by an unbiased Gaussian model error, and emissions models with constant errors. The numerical experiments employ both simulated in situ and satellite observation networks. Source inversion was carried out by either direct use of synthetically generated observations with added noise, or by first assimilating the observations and using the analyses to extract observations. We have conducted 20 identical twin experiments for each set of source and observation configurations, and find that in the limiting cases of a very few localized observations, or an extremely large observation network there is little advantage to carrying out assimilation first. However, in intermediate observation densities, there decreases in source inversion error standard deviation using the Kalman filter algorithm followed by Green's function inversion by 50% to 95%

Biomass burning emissions estimated with a global fire assimilation system based on observed fire radiative power

The Global Fire Assimilation System (GFASv1.0) calculates biomass burning emissions by assimilating Fire Radiative Power (FRP) observations from the MODIS instruments onboard the Terra and Aqua satellites. It corrects for gaps in the observations, which are mostly due to cloud cover, and filters spurious FRP observations of volcanoes, gas flares and other industrial activity. The combustion rate is subsequently calculated with land cover-specific conversion factors. Emission factors for 40 gas-phase and aerosol trace species have been compiled from a literature survey. The corresponding daily emissions have been calculated on a global 0.5° × 0.5° grid from 2003 to the present. General consistency with the Global Fire Emission Database version 3.1 (GFED3.1) within its accuracy is achieved while maintaining the advantages of an FRP-based approach: GFASv1.0 makes use of the quantitative information on the combustion rate that is contained in the FRP observations, and it detects fires in real time at high spatial and temporal resolution. GFASv1.0 indicates omission errors in GFED3.1 due to undetected small fires. It also exhibits slightly longer fire seasons in South America and North Africa and a slightly shorter fire season in Southeast Asia. GFASv1.0 has already been used for atmospheric reactive gas simulations in an independent study, which found good agreement with atmospheric observations. We have performed simulations of the atmospheric aerosol distribution with and without the assimilation of MODIS aerosol optical depth (AOD). They indicate that the emissions of particulate matter need to be boosted by a factor of 2–4 to reproduce the global distribution of organic matter and black carbon. This discrepancy is also evident in the comparison of previously published top-down and bottom-up estimates. For the time being, a global enhancement of the particulate matter emissions by 3.4 is recommended. Validation with independent AOD and PM10 observations recorded during the Russian fires in summer 2010 show that the global Monitoring Atmospheric Composition and Change (MACC) aerosol model with GFASv1.0 aerosol emissions captures the smoke plume evolution well when organic matter and black carbon are enhanced by the recommended factor. In conjunction with the assimilation of MODIS AOD, the use of GFASv1.0 with enhanced emission factors quantitatively improves the forecast of the aerosol load near the surface sufficiently to allow air quality warnings with a lead time of up to four days

The importance of transport model uncertainties for the estimation of CO2 sources and sinks using satellite measurements

This study presents a synthetic model intercomparison to investigate the importance of transport model errors for estimating the sources and sinks of CO2 using satellite measurements. The experiments were designed for testing the potential performance of the proposed CO2 lidar A-SCOPE, but also apply to other space borne missions that monitor total column CO2. The participating transport models IFS, LMDZ, TM3, and TM5 were run in forward and inverse mode using common a priori CO2 fluxes and initial concentrations. Forward simulations of column averaged CO2 (xCO2) mixing ratios vary between the models by s=0.5 ppm over the continents and s=0.27 ppm over the oceans. Despite the fact that the models agree on average on the sub-ppm level, these modest differences nevertheless lead to significant discrepancies in the inverted fluxes of 0.1 PgC/yr per 106 km2 over land and 0.03 PgC/yr per 106 km2 over the ocean. These transport model induced flux uncertainties exceed the target requirement that was formulated for the A-SCOPE mission of 0.02 PgC/yr per 106 km2, and could also limit the overall performance of other CO2 missions such as GOSAT. A variable, but overall encouraging agreement is found in comparison with FTS measurements at Park Falls, Darwin, Spitsbergen, and Bremen, although systematic differences are found exceeding the 0.5 ppm level. Because of this, our estimate of the impact of transport model uncerainty is likely to be conservative. It is concluded that to make use of the remote sensing technique for quantifying the sources and sinks of CO2 not only requires highly accurate satellite instruments, but also puts stringent requirements on the performance of atmospheric transport models. Improving the accuracy of these models should receive high priority, which calls for a closer collaboration between experts in atmospheric dynamics and tracer transpor

A semi-empirical representation of the temporal variation of total greenhouse gas levels expressed as equivalent levels of carbon dioxide

Abstract and PDF report are also available on the MIT Joint Program on the Science and Policy of Global Change website (http://globalchange.mit.edu/).In order to examine the underlying longer-term trends in greenhouse gases, that are driven for example by anthropogenic emissions or climate change, it is useful to remove the recurring effects of natural cycles and oscillations on the sources and/or sinks of those gases that have strong biological (e.g., CO2, CH4, N2O) and/or photochemical (e.g. CH4) influences on their global atmospheric cycles. We use global observations to calculate monthly estimates of greenhouse gas levels expressed as CO2 equivalents, and then fit these estimates to a semi-empirical model that includes the natural seasonal, QBO, and ENSO variations, as well as a second order polynomial expressing longer-term variations. We find that this model provides a reasonably accurate fit to the observation-based monthly data. We also show that this semiempirical model has some predictive capability; that is it can be used to provide a reasonably reliable estimate of CO2 equivalents at the current time using validated observations that lag real time by a few to several months.This study received support from the MIT Joint Program on the Science and Policy of Global Change, which is funded by a consortium of government, industry and foundation sponsors

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science

DADA: data assimilation for the detection and attribution of weather and climate-related events

A new nudging method for data assimilation, delay‐coordinate nudging, is presented. Delay‐coordinate nudging makes explicit use of present and past observations in the formulation of the forcing driving the model evolution at each time step. Numerical experiments with a low‐order chaotic system show that the new method systematically outperforms standard nudging in different model and observational scenarios, also when using an unoptimized formulation of the delay‐nudging coefficients. A connection between the optimal delay and the dominant Lyapunov exponent of the dynamics is found based on heuristic arguments and is confirmed by the numerical results, providing a guideline for the practical implementation of the algorithm. Delay‐coordinate nudging preserves the easiness of implementation, the intuitive functioning and the reduced computational cost of the standard nudging, making it a potential alternative especially in the field of seasonal‐to‐decadal predictions with large Earth system models that limit the use of more sophisticated data assimilation procedures

The oceanic anthropogenic CO2sink: Storage, air-sea fluxes, and transports over the industrial era

This study presents a new estimate of the oceanic anthropogenic CO2(Cant) sink over the industrial era (1780 to present), from assimilation of potential temperature, salinity, radiocarbon, and CFC-11 observations in a global steady state ocean circulation inverse model (OCIM). This study differs from previous data-based estimates of the oceanic Cant sink in that dynamical constraints on ocean circulation are accounted for, and the ocean circulation is explicitly modeled, allowing the calculation of oceanic Cant storage, air-sea fluxes, and transports in a consistent manner. The resulting uncertainty of the OCIM-estimated Cant uptake, transport, and storage is significantly smaller than the comparable uncertainty from purely data-based or model-based estimates. The OCIM-estimated oceanic Cant storage is 160–166 PgC in 2012, and the oceanic Cant uptake rate averaged over the period 2000–2010 is 2.6 PgC yr−1 or about 30% of current anthropogenic CO2 emissions. This result implies a residual (primarily terrestrial) Cant sink of about 1.6 PgC yr−1 for the same period. The Southern Ocean is the primary conduit for Cant entering the ocean, taking up about 1.1 PgC yr−1 in 2012, which represents about 40% of the contemporary oceanic Cant uptake. It is suggested that the most significant source of remaining uncertainty in the oceanic Cant sink is due to potential variability in the ocean circulation over the industrial era

Three years of greenhouse gas column-averaged dry air mole fractions retrieved from satellite – Part 2: Methane

Carbon dioxide (CO2) and methane (CH4) are the two most important anthropogenic greenhouse gases. SCIAMACHY on ENVISAT is the first satellite instrument whose measurements are sensitive to concentration changes of the two gases at all altitude levels down to the Earth's surface where the source/sink signals are largest. We have processed three years (2003–2005) of SCIAMACHY near-infrared nadir measurements to simultaneously retrieve vertical columns of CO2 (from the 1.58 µm absorption band), CH4 (1.66 µm) and oxygen (O2 A-band at 0.76 µm) using the scientific retrieval algorithm WFM-DOAS. We show that the latest version of WFM-DOAS, version 1.0, which is used for this study, has been significantly improved with respect to its accuracy compared to the previous versions while essentially maintaining its high processing speed (~1 min per orbit, corresponding to ~6000 single measurements, and per gas on a standard PC). The greenhouse gas columns are converted to dry air column-averaged mole fractions, denoted XCO2 (in ppm) and XCH4 (in ppb), by dividing the greenhouse gas columns by simultaneously retrieved dry air columns. For XCO2 dry air columns are obtained from the retrieved O2 columns. For XCH4 dry air columns are obtained from the retrieved CO2 columns because of better cancellation of light path related errors compared to using O2 columns retrieved from the spectrally distant O2 A-band. Here we focus on a discussion of the XCH4 data set. The XCO2 data set is discussed in a separate paper (Part 1). For 2003 we present detailed comparisons with the TM5 model which has been optimally matched to highly accurate but sparse methane surface observations. After accounting for a systematic low bias of ~2% agreement with TM5 is typically within 1–2%. We investigated to what extent the SCIAMACHY XCH4 is influenced by the variability of atmospheric CO2 using global CO2 fields from NOAA's CO2 assimilation system CarbonTracker. We show that the CO2 corrected and uncorrected XCH4 spatio-temporal pattern are very similar but that agreement with TM5 is better for the CarbonTracker CO2 corrected XCH4. In line with previous studies (e.g., Frankenberg et al., 2005b) we find higher methane over the tropics compared to the model. We show that tropical methane is also higher when normalizing the CH4 columns with retrieved O2 columns instead of CO2. In consistency with recent results of Frankenberg et al. (2008b) it is shown that the magnitude of the retrieved tropical methane is sensitive to the choice of the spectroscopic line parameters of water vapour. Concerning inter-annual variability we find similar methane spatio-temporal pattern for 2003 and 2004. For 2005 the retrieved methane shows significantly higher variability compared to the two previous years, most likely due to somewhat larger noise of the spectral measurement