Janne Hakkarainen

Direct space-based observations of anthropogenic CO2 emission areas from OCO-2

Anthropogenic CO2 emissions from fossil fuel combustion have large impacts on climate. In order to monitor the increasing CO2 concentrations in the atmosphere, accurate spaceborne observations—as available from the Orbiting Carbon Observatory-2 (OCO-2)—are needed. This work provides the first direct observation of anthropogenic CO2 from OCO-2 over the main pollution regions: eastern USA, central Europe, and East Asia. This is achieved by deseasonalizing and detrending OCO-2 CO2 observations to derive CO2 anomalies. Several small isolated emission areas (such as large cities) are detectable from the anomaly maps. The spatial distribution of the CO2 anomaly matches the features observed in the maps of the Ozone Monitoring Instrument NO2 tropospheric columns, used as an indicator of atmospheric pollution. The results of a cluster analysis confirm the spatial correlation between CO2 and NO2 data over areas with different amounts of pollution. We found positive correlation between CO2 anomalies and emission inventories. The results demonstrate the power of spaceborne data for monitoring anthropogenic CO2 emissions.

A dilemma of the uniqueness of weather and climate model closure parameters

Parameterisation schemes of subgrid-scale physical processes in atmospheric models contain so-called closure parameters. Their precise values are not generally known; thus, they are subject to fine-tuning for achieving optimal model performance. In this article, we show that there is a dilemma concerning the optimal parameter values: an identical prediction model formulation can have two different optimal closure parameter value settings depending on the level of approximations made in the data assimilation component of the prediction system. This result tends to indicate that the prediction model re-tuning in large-scale systems is not only needed when the prediction model undergoes a major change, but also when the data assimilation component is updated. Moreover, we advocate an accurate albeit expensive method based on so-called filter likelihood for the closure parameter estimation that is applicable in fine-tuning of both prediction model and data assimilation system parameters. In this article, we use a modified Lorenz-95 system as a prediction model and extended Kalman filter and ensemble adjustment Kalman filter for data assimilation. With this setup, we can compute the filter likelihood for the chosen parameters using the output of the two versions of the Kalman filter and apply a Markov chain Monte Carlo algorithm to explore the parameter posterior distributions.

Generalized correlation integral vectors: A distance concept for chaotic dynamical systems

Several concepts of fractal dimension have been developed to characterise properties of attractors of chaotic dynamical systems. Numerical approximations of them must be calculated by finite samples of simulated trajectories. In principle, the quantities should not depend on the choice of the trajectory, as long as it provides properly distributed samples of the underlying attractor. In practice, however, the trajectories are sensitive with respect to varying initial values, small changes of the model parameters, to the choice of a solver, numeric tolerances, etc. The purpose of this paper is to present a statistically sound approach to quantify this variability. We modify the concept of correlation integral to produce a vector that summarises the variability at all selected scales. The distribution of this stochastic vector can be estimated, and it provides a statistical distance concept between trajectories. Here, we demonstrate the use of the distance for the purpose of estimating model parameters of a chaotic dynamic model. The methodology is illustrated using computational examples for the Lorenz 63 and Lorenz 95 systems, together with a framework for Markov chain Monte Carlo sampling to produce posterior distributions of model parameters.

Retrieval of atmospheric CH4 profiles from Fourier transform infrared data using dimension reduction and MCMC

We introduce an inversion method that uses dimension reduction for the retrieval of atmospheric methane (CH4) profiles. Uncertainty analysis is performed using the Markov chain Monte Carlo (MCMC) statistical estimation. These techniques are used to retrieve CH4 profiles from the ground-based spectral measurements by the Fourier Transform Spectrometer (FTS) instrument at Sodankyla (67.4 degrees N, 26.6 degrees E), Northern Finland. The Sodankyla FTS is part of the Total Carbon Column Observing Network (TCCON), a global network that observes solar spectra in near-infrared wavelengths. The high spectral resolution of the data provides approximately 3 degrees of freedom about the vertical structure of CH4 between around 0 and 40km. We reduce the dimension of the inverse problem by using principal component analysis. Smooth and realistic profiles are sought by estimating three parameters for the profile shape. We use Bayesian framework with adaptive MCMC to better characterize the full posterior distribution of the solution and uncertainties related to the retrieval. The retrieved profiles are validated against in situ AirCore soundings which provide an accurate reference up to 20-30km. The method is presented in a general form, so that it can easily be adapted for other applications, such as different trace gases or satellite-borne measurements where more accurate profile information and better analysis of the uncertainties would be highly valuable.

An Assessment of the Emission and Dispersion of Volcanic Ash and Sulphur Dioxide in the Recent Eruptions in Iceland

The chapter presents an assessment of the continental scale atmospheric dispersion of volcanic ash and sulphur dioxide from the recent eruptions of the Eyjafjallajokull (2010) and Grimsvotn (2011) volcanoes. The study is based on a combination of modelling, remote-sensing, and in-situ observations: while the release height is obtained from in-situ observations, the emitted mass flux of SO2 and particulate matter is calibrated using the satellite-based instruments. The analysed features include the split of the Grimsvotn plume to high-altitude SO2 and middle-troposphere ash clouds and the temporal variation of the Eyjafjallajokull emission composition.

On a Contribution of Wild-Land Fires on Atmospheric Composition in Arid Regions

The study presents the Fire Assimilation System (FAS) based on Level MODIS Collection 5 Active Fire Products. The FAS estimates the emission fluxes originated from the wild-land fires and provides this information to the atmospheric composition modelling system SILAM. Presently, the FAS incorporates the emission factors for three main land-use types (grass and agriculture, forest and mixed), which values were verified for several European episodes. Current work is a part of the FAS calibration for totally different geographical regions, Portugal and Australia, which nonetheless have several common environmental features.