Can seasonal and interannual variation in landscape CO2 fluxes be detected by atmospheric observations of CO2 concentrations made at a tall tower?

Abstract

The coupled numerical weather model WRF-SPA (Weather Research and Forecasting model and Soil-Plant-Atmosphere model) has been used to investigate a 3 yr time series of observed atmospheric CO₂ concentrations from a tall tower in Scotland, UK. Ecosystem-specific tracers of net CO₂ uptake and net CO₂ release were used to investigate the contributions to the tower signal of key land covers within its footprint, and how contributions varied at seasonal and interannual timescales. In addition, WRF-SPA simulated atmospheric CO₂ concentrations were compared with two coarse global inversion models, CarbonTrackerEurope and the National Oceanic and Atmospheric Administration's CarbonTracker (CTE-CT). WRF-SPA realistically modelled both seasonal (except post harvest) and daily cycles seen in observed atmospheric CO₂ at the tall tower (<i>R</i>² = 0.67, rmse = 3.5 ppm, bias = 0.58 ppm). Atmospheric CO₂ concentrations from the tall tower were well simulated by CTE-CT, but the inverse model showed a poorer representation of diurnal variation and simulated a larger bias from observations (up to 1.9 ppm) at seasonal timescales, compared to the forward modelling of WRF-SPA. However, we have highlighted a consistent post-harvest increase in the seasonal bias between WRF-SPA and observations. Ecosystem-specific tracers of CO₂ exchange indicate that the increased bias is potentially due to the representation of agricultural processes within SPA and/or biases in land cover maps. The ecosystem-specific tracers also indicate that the majority of seasonal variation in CO₂ uptake for Scotland's dominant ecosystems (forests, cropland and managed grassland) is detectable in observations within the footprint of the tall tower; however, the amount of variation explained varies between years. The between years variation in detectability of Scotland's ecosystems is potentially due to seasonal and interannual variation in the simulated prevailing wind direction. This result highlights the importance of accurately representing atmospheric transport used within atmospheric inversion models used to estimate terrestrial source/sink distribution and magnitude