Assessment of an atmospheric transport model for annual inverse estimates of California greenhouse gas emissions

Abstract

Atmospheric inverse estimates of gas emissions depend on transport model predictions, hence driving a need to assess uncertainties in the transport model. In this study we assess the uncertainty in WRF‐STILT (Weather Research and Forecasting and Stochastic Time‐Inverted Lagrangian Transport) model predictions using a combination of meteorological and carbon monoxide (CO) measurements. WRF configurations were selected to minimize meteorological biases using meteorological measurements of winds and boundary layer depths from surface stations and radar wind profiler sites across California. We compare model predictions with CO measurements from four tower sites in California from June 2013 through May 2014 to assess the seasonal biases and random errors in predicted CO mixing ratios. In general, the seasonal mean biases in boundary layer wind speed (< ~ 0.5 m/s), direction (< ~ 15°), and boundary layer height (< ~ 200 m) were small. However, random errors were large (~1.5–3.0 m/s for wind speed, ~ 40–60° for wind direction, and ~ 300–500 m for boundary layer height). Regression analysis of predicted and measured CO yielded near‐unity slopes (i.e., within 1.0 ± 0.20) for the majority of sites and seasons, though a subset of sites and seasons exhibit larger (~30%) uncertainty, particularly when weak winds combined with complex terrain in the South Central Valley of California. Looking across sites and seasons, these results suggest that WRF‐STILT simulations are sufficient to estimate emissions of CO to up to 15% on annual time scales across California.

Inverse Estimation of an Annual Cycle of California's Nitrous Oxide Emissions

“…We simulated meteorology for four different horizontal resolutions (vertical levels = 50) of 36, 12, 4, and 1.3 km (two inner domains for SFBA and SoCAB) using initial and boundary meteorological conditions provided by the North American Regional Reanalysis data set (Mesinger et al, ). For surface physics, we used two different land surface models (LSMs) depending on the location of each site (Bagley et al, ; Jeong et al, , ; see Table S1 for details). For the Central Valley, we use the five‐layer thermal diffusion LSM (5‐L LSM) to account for irrigation in the land surface process during summer (Jeong et al, ) while using the Noah LSM (Chen & Dudhia, ) for other seasons.…”

“…For the Central Valley we also use the MYNN2 PBL scheme except for summer for which we use the MYJ (Mellor‐Yamada‐Janjić) scheme (Janjić, ; Mellor & Yamada, ) coupled with the 5‐L LSM (Jeong et al, , ). Based on the transport evaluation using predicted and measured CO data (Bagley et al, ), we apply the YSU (Yonsei University) PBL scheme (Hong et al, ) for a few cases (e.g., winter season in the southern San Joaquin Valley) to use an improved representation of topographic influences on boundary layer meteorology (Jiménez & Dudhia, ).…”

Assessing fossil fuel CO 2 emissions in California using atmospheric observations and models

“…±10%), likely reflecting the somewhat poorer data coverage achieved in the field campaigns as compared to the simulation experiments and, potentially, uncertainties in the model-data system that were not explored in the simulation experiments. Simulated atmospheric transport in the WRF-STILT model setup used here has been evaluated and refined based on meteorological data [17] and model-data analysis of carbon monoxide [30], but further studies on regional atmospheric transport incorporating more models and observational metrics would improve the characterization of uncertainty in model transport.…”

Accounting for the vertical distribution of emissions in atmospheric CO&lt;sub&gt;2&lt;/sub&gt; simulations

“…Again, higher percentiles tend to show larger differences. The impact on observations from tall tower networks measuring CO 2 some 100 to 300 m above the surface (Bakwin et al, 1995;Andrews et al, 2014) will likely be somewhat smaller than suggested by the numbers above, especially in winter when the atmosphere is less well mixed.…”

“…Our results indeed confirm a strong sensitivity of nearsurface afternoon concentrations: a regional CO 2 tracer released in the model domain only at the surface was on average 43 % higher in winter and 14 % higher in summer than a tracer released according to realistic vertical profiles. Since measurements of CO 2 are often taken from towers some 100 to 300 m above the surface (Bakwin et al, 1995), the impact on actual ground-based observations will likely be somewhat smaller.…”