An Air-to-Soil Transition Model for Discrete Scattering-Emission Modelling at L-Band

Topsoil structures and inhomogeneous distribution of moisture in the soil volume will induce dielectric discontinuities from air to bulk soil, which in turn may induce multiple and volume scattering and affect the microwave surface emission. In situ ELBARA-III L-band radiometer observations of brightness temperature TBp (p =H or V polarization) at the Maqu site on the Eastern Tibetan Plateau are exploited to understand the effect of surface roughness on coherent and incoherent emission processes. Assisted with in situ soil moisture (SM) and temperature profile measurements, this study develops an air-to-soil transition (ATS) model that incorporates the dielectric roughness (i.e., resulted from fine-scale topsoil structures and the soil volume) characterized by SM and geometric roughness effects, and demonstrates the necessity of the ATS model for modelling L-band TBp. The Wilheit (1978) coherent and Lv et al. (2014) incoherent models are compared for determining the dielectric constant of bulk soil in the ATS zone and for calculating soil effective temperature Teff. The Tor Vergata discrete scattering model (TVG) integrated with the advanced integral equation model (AIEM) is used as the baseline model configuration for simulating L-band TBp. Whereafter, the ATS model is integrated with the foregoing model for assessing its performance. Results show the ATS-based models reduce the underestimation of TBp (≈20-50 K) by the baseline simulations. Being dynamic in nature, the proposed dielectric roughness parameterization in the ATS model significantly improves the ability in interpreting TBp dynamics, which is important for improving SM retrieval at the global scale.

Using emissions derived from atmospheric observations to inform the reported UK inventory

<p align="justify">Verification of the nationally reported greenhouse gas (GHG) inventories using inverse modelling and atmospheric observations is considered to be best practice by the United Nations Framework Convention on Climate Change (UNFCCC). It allows for an independent assessment of the nationally reported GHG emissions using a comprehensively different approach to the inventory methods. Significant differences in the emissions estimated using the two approaches are a means of identifying areas worthy of further investigation.</p><p align="justify"> </p><p align="justify"><span>An inversion methodology called Inversion Technique for Emission Modelling (InTEM) has been developed that uses a non-negative least squares minimisation technique to determine the emission magnitude and distribution that most accurately reproduces the observations. By estimating the underlying </span><span><em>baseline</em></span><span> time series, atmospheric concentrations where the short-term impact of regional pollution has been removed, and by modelling where the air has passed over on route to the observation stations on a regional scale, estimates of UK emissions are made. </span>In this study we use an extensive network of observations with six stations across the UK and six more in neighbouring countries<span>. InTEM uses information from a</span> Lagrangian dispersion model NAME (Numerical Atmospheric dispersion Modelling Environment), driven by three-dimensional, modelled meteorology, to understand how the air mixes during transport from the emission sources to observation points. <span>The InTEM inversion results are submitted annually by the UK as part of their National Inventory Report to the UNFCCC. They are used within the UK inventory team to highlight areas for investigation and have led to significant improvements to the submitted UK inventory. The latest UK comparisons will be shown along with examples of how the inversion results have informed the inventory.</span></p>

Updated land use and land cover change (LULCC) emission estimates based on new high-resolution LULCC forcing data

<p>Land use and land cover change (LULCC) has a significant role for the global carbon cycle. Despite big improvements in LULCC emission modelling, related uncertainties remain relatively high. Major uncertainties in quantifying LULCC emissions stem from uncertainties in underlying LULCC datasets. The novel, high-resolution (~1 km×1 km) LULCC dataset HIstoric Land Dynamics Assessment+ (HILDA+) reflects gross transitions derived from multiple remote sensing products and offers an alternative to existing land use change datasets, serving as forcing for process-based and bookkeeping models. By incorporating HILDA+ in the “bookkeeping of land use emissions” (BLUE) model, which is one of the three bookkeeping models used in the Global Carbon Budget 2020, we gain a different temporal and spatial perspective on LULCC estimates and related sources of uncertainty. </p><p>We compare our results to emission estimates based on LUH2, which is broadly used as LULCC forcing for process-based and bookkeeping models. First results of our analysis show overall lower LULCC emissions for the estimates based on HILDA+. For the time period 1990-2019, mean yearly emission estimates based on HILDA+ are 0.595 GtC yr−1 compared to 1.368 GtC yr−1 based on LUH2. Reasons are lower emissions from cropland expansion and less carbon uptake from vegetation regrowth after abandonment of managed land (i.e. a smaller carbon sink). Furthermore, fewer discontinuities in the BLUE runs with the HILDA+ forcing compared to the LUH2-based estimates suggests a better representation of land use dynamics and their effects. Overall, the simulations based on HILDA+ capture spatial heterogeneity to a greater degree and provide a more detailed picture of local sources and sinks, which is crucial for (1) a better representation of component fluxes (e.g. deforestation, degradation) and (2) effective mitigation and adaptation policies.</p>