On the impact of transport model errors for the estimation of CO2 surface fluxes from GOSAT observations

A series of observing system simulation experiments is presented in which column averaged dry air mole fractions of CO2 (XCO2) from the Greenhouse gases Observing SATellite (GOSAT) are made consistent or not with the transport model embedded in a flux inversion system. The GOSAT observations improve the random errors of the surface carbon budget despite the inconsistency. However, we find biases in the inferred surface CO2 budget of a few hundred MtC/a at the subcontinental scale, that are caused by differences of only a few tenths of a ppm between the simulations of the individual XCO2 soundings. The accuracy and precision of the inverted fluxes are little sensitive to an 8-fold reduction in the data density. This issue is critical for any future satellite constellation to monitor XCO2 and should be pragmatically addressed by explicitly accounting for transport errors in flux inversion systems

Improved quantification of Chinese carbon fluxes using CO2/CO correlations in Asian outflow

[1] We use observed CO2:CO correlations in Asian outflow from the TRACE-P aircraft campaign (February–April 2001), together with a three-dimensional global chemical transport model (GEOS-CHEM), to constrain specific components of the east Asian CO2 budget including, in particular, Chinese emissions. The CO2/CO emission ratio varies with the source of CO2 (different combustion types versus the terrestrial biosphere) and provides a characteristic signature of source regions and source type. Observed CO2/CO correlation slopes in east Asian boundary layer outflow display distinct regional signatures ranging from 10–20 mol/mol (outflow from northeast China) to 80 mol/mol (over Japan). Model simulations using best a priori estimates of regional CO2 and CO sources from Streets et al. [2003] (anthropogenic), the CASA model (biospheric), and Duncan et al. [2003] (biomass burning) overestimate CO2 concentrations and CO2/CO slopes in the boundary layer outflow. Constraints from the CO2/CO slopes indicate that this must arise from an overestimate of the modeled regional net biospheric CO2 flux. Our corrected best estimate of the net biospheric source of CO2 from China for March–April 2001 is 3200 Gg C/d, which represents a 45 % reduction of the net flux from the CASA model. Previous analyses of the TRACE-P data had found that anthropogenic Chinese C

Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High-Resolution Simulations

Climate projections continue to be marred by large uncertainties, which originate in processes that need to be parameterized, such as clouds, convection, and ecosystems. But rapid progress is now within reach. New computational tools and methods from data assimilation and machine learning make it possible to integrate global observations and local high-resolution simulations in an Earth system model (ESM) that systematically learns from both. Here we propose a blueprint for such an ESM. We outline how parameterization schemes can learn from global observations and targeted high-resolution simulations, for example, of clouds and convection, through matching low-order statistics between ESMs, observations, and high-resolution simulations. We illustrate learning algorithms for ESMs with a simple dynamical system that shares characteristics of the climate system; and we discuss the opportunities the proposed framework presents and the challenges that remain to realize it.Comment: 32 pages, 3 figure

Investigating Sources of Variability and Error in Simulations of Carbon Dioxide in an Urban Region

Greenhouse gas (GHG) emissions estimation methods that use atmospheric trace gas observations, including inverse modeling techniques, perform better when carbon dioxide (CO2) fluxes are more accurately transported and dispersed in the atmosphere by a numerical model. In urban areas, transport and dispersion is particularly difficult to simulate using current mesoscale meteorological models due, in part, to added complexity from surface heterogeneity and fine spatial/temporal scales. It is generally assumed that the errors in GHG estimation methods in urban areas are dominated by errors in transport and dispersion. Other significant errors include, but are not limited to, those from assumed emissions magnitude and spatial distribution. To assess the predictability of simulated trace gas mole fractions in urban observing systems using a numerical weather prediction model, we employ an Eulerian model that combines traditional meteorological variables with multiple passive tracers of atmospheric CO2 from anthropogenic inventories and a biospheric model. The predictability of the Eulerian model is assessed by comparing simulated atmospheric CO2 mole fractions to observations from four in situ tower sites (three urban and one rural) in the Washington DC/Baltimore, MD area for February 2016. Four different gridded fossil fuel emissions inventories along with a biospheric flux model are used to create an ensemble of simulated atmospheric CO2 observations within the model. These ensembles help to evaluate whether the modeled observations are impacted more by the underlying emissions or transport. The spread of modeled observations using the four emission fields indicates the model's ability to distinguish between the different inventories under various meteorological conditions. Overall, the Eulerian model performs well; simulated and observed average CO2 mole fractions agree within 1% when averaged at the three urban sites across the month. However, there can be differences greater than 10% at any given hour, which are attributed to complex meteorological conditions rather than differences in the inventories themselves. On average, the mean absolute error of the simulated compared to actual observations is generally twice as large as the standard deviation of the modeled mole fractions across the four emission inventories. This result supports the assumption, in urban domains, that the predicted mole fraction error relative to observations is dominated by errors in model meteorology rather than errors in the underlying fluxes in winter months. As such, minimizing errors associated with atmospheric transport and dispersion may help improve the performance of GHG estimation models more so than improving flux priors in the winter months. We also find that the errors associated with atmospheric transport in urban domains are not restricted to certain times of day. This suggests that atmospheric inversions should use CO2 observations that have been filtered using meteorological observations rather than assuming that meteorological modeling is most accurate at certain times of day (such as using only mid-afternoon observations)

On statistical approaches to generate Level 3 products from satellite remote sensing retrievals

Satellite remote sensing of trace gases such as carbon dioxide (CO$_2$) has increased our ability to observe and understand Earth's climate. However, these remote sensing data, specifically~Level 2 retrievals, tend to be irregular in space and time, and hence, spatio-temporal prediction is required to infer values at any location and time point. Such inferences are not only required to answer important questions about our climate, but they are also needed for validating the satellite instrument, since Level 2 retrievals are generally not co-located with ground-based remote sensing instruments. Here, we discuss statistical approaches to construct Level 3 products from Level 2 retrievals, placing particular emphasis on the strengths and potential pitfalls when using statistical prediction in this context. Following this discussion, we use a spatio-temporal statistical modelling framework known as fixed rank kriging (FRK) to obtain global predictions and prediction standard errors of column-averaged carbon dioxide based on Version 7r and Version 8r retrievals from the Orbiting Carbon Observatory-2 (OCO-2) satellite. The FRK predictions allow us to validate statistically the Level 2 retrievals globally even though the data are at locations and at time points that do not coincide with validation data. Importantly, the validation takes into account the prediction uncertainty, which is dependent both on the temporally-varying density of observations around the ground-based measurement sites and on the spatio-temporal high-frequency components of the trace gas field that are not explicitly modelled. Here, for validation of remotely-sensed CO$_2$ data, we use observations from the Total Carbon Column Observing Network. We demonstrate that the resulting FRK product based on Version 8r compares better with TCCON data than that based on Version 7r.Comment: 28 pages, 10 figures, 4 table

Quantifying Carbon and Water Dynamics of Terrestrial Ecosystems At High Temporal And Spatial Resolutions Using Process-Based Biogeochemistry Models And In Situ And Satellite Data

To better understand the role of terrestrial ecosystems in the global carbon cycle and their feedbacks to the global climate system, process-based ecosystem models that are used for quantifying net carbon exchanges between the terrestrial biosphere and the atmosphere need to be improved. My research objective is to improve the model from following aspects: 1) Improving parameterization and model structure for carbon and water dynamics, 2) improving regional model simulations at finer spatial resolutions (from 0.5 degree to 0.05 degree or finer), 3) developing faster spin-up algorithms, and 4) evaluating high performance model simulations using fast spin-up technique deployed on various computing platforms. I improved the leaf area index (LAI) modeling in a terrestrial ecosystem model (TEM) for North America. The evaluated TEM was used to estimate ET at site and regional scales in North America from 2000 to 2010. The estimated annual ET varies from 420 to 450 mm yr-1 with the improved model, close to MODIS monthly data with root-mean-square-error less than 10 mmmonth-1 for the study period. Alaska, Canada, and the conterminous US accounts for 33%, 6% and 61% of the regional ET, respectively. I then used new algorithm for a fast spin-up for TEM. With the new spin-up algorithm, I showed that the model reached a steady state in less than 10 years of simulation time, while the original method requires more than 200 years on average of model run. Lastly, I conducted simulations under both original resolution and high resolution in the conterminous US. The high-resolution simulation predicts slightly higher average annual gross primary production (GPP) (~2%) from 2000 to 2015 in the conterminous US than original version of TEM. From the improved TEM simulation, I estimated that regional GPP is between 7.12 and 7.69 Pg C yr-1 and NEP is between 0.09 and 0.75 Pg C yr-1