Numerous studies have reported in situ monitoring and source analysis in the Tibetan Plateau (TP), a region crucial for climate systems. However, a gap remains in understanding the comprehensive distribution of atmospheric pollutants in the TP and their transboundary pollution transport. Here, we analyzed the high-resolution satellite TROPOMI observations from 2018 to 2023 in Tibet and its surrounding areas. Our result reveals that, contrary to the results from in situ surface CO monitoring, Tibet exhibits a distinct seasonality in atmospheric carbon monoxide total column average mixing ratio (XCO), with higher levels in summer and lower levels in winter. This distinctive seasonal pattern may be related to the TP’s ‘air pump’ effect and the Asia summer monsoon. Before 2022, the annual growth rate of XCO in Tibet was 1.63 %·year^‑1

The sensitivity of cloud microphysics to aerosol loading, quantified by the aerosol-cloud interactions (ACI) index, plays a crucial role in calculating the radiative forcing due to ACI (RF_aci). However, the dependence of the ACI index on liquid water content (LWC) and its impact on RF_aci are often overlooked. This study aims to investigate this dependence and evaluate its implications for RF_aci, based on ground in-situ aerosol-cloud observations on Mt. Lu in eastern China. The results demonstrate that the ACI index exhibits an initial increase, followed by a decline with increasing LWC. In the unconstrained LWC scenario, the ACI index, calculated based on cloud droplet number concentration (ACI_n of 0.13), is found to be lower than the lower bound of ACI_n (0.17 to 0.35) obtained from the constrained LWC scenario. Neglecting this LWC-dependence leads to a significant underestimation of the mean RF_aci by 53%. Furthermore, when calculating RF_aci using the ACI index from droplet effective diameter (ACI_d), implying the neglect of the dispersion effect by assuming a fixed droplet spectrum width, it results in an overestimation of RF_aci by 22% compared to using ACI_n. These findings shed new light on the assessment of RF_aci and help reconcile differences between observed and simulated RF_aci. Aerosol-cloud interactions are the major source of uncertainty in understanding human-induced climate change. This study focused on the relationship between aerosol and clouds, specifically investigating how the sensitivity of cloud properties to aerosol loading associates with the amount of liquid water present in the clouds. Using ground in-situ observations of clouds and aerosols on Mt. Lu in eastern China, this study showed that the sensitivity of cloud properties to aerosol loading initially increases and then decreases as the cloud water content increases. Neglecting this dependence can lead to a significant underestimation of the impact of aerosol-cloud interactions on climate. These findings provide valuable insights for improving our understanding of how aerosols and clouds interact and contribute to climate change.

Satellites have been providing spaceborne observations of the total column of CO2 (denoted XCO2) for over two decades now, and, with the need for independent verification of Paris Agreement objectives, many new satellite concepts are currently planned or being studied to complement or extend the instruments that already exist. Depending on whether they are targeting natural and/or anthropogenic fluxes of CO2, the designs of these future concepts vary greatly. The characteristics of their shortwave infrared (SWIR) observations notably explore several orders of magnitude in spectral resolution (from λ/Δλ ∼ 400 for Carbon Mapper to λ/Δλ ∼ 25 000 for MicroCarb) and include different selections of spectral bands (from one to four bands, among which there are the CO2-sensitive 1.6 µm and/or 2.05 µm bands). The very nature of the spaceborne measurements is also explored: for instance, the NanoCarb imaging concept proposes to measure CO2-sensitive truncated interferograms, instead of infrared spectra like other concepts, in order to significantly reduce the instrument size. This study synthetically explores the impact of three different design parameters on the XCO2 retrieval performance obtained through optimal estimation: (1) the spectral resolution, (2) the signal-to-noise ratio (SNR) and (3) the spectral band selection. Similar performance assessments are completed for the exactly defined OCO-2, MicroCarb, Copernicus CO2 Monitoring (CO2M) and NanoCarb concepts. We show that improving the SNR is more efficient than improving the spectral resolution to increase XCO2 precision when perturbing these parameters across 2 orders of magnitude, and we find that a low SNR and/or a low spectral resolution yield XCO2 with vertical sensitivities that give more weight to atmospheric layers close to the surface. The exploration of various spectral band combinations illustrates, especially for lower spectral resolutions, how including an O2-sensitive band helps to increase the optical path length information and how the 2.05 µm CO2-sensitive band contains more geophysical information than the 1.6 µm band. With very different characteristics, MicroCarb shows a CO2 information content that is only slightly higher than that of CO2M, which translates into XCO2 random errors that are lower by a factor ranging from 1.1 to 1.9, depending on the observational situation. The performance of NanoCarb for a single pixel of its imager is comparable to those of concepts that measure spectra at low SNR and low spectral resolution, but, as this novel concept would observe a given target several times during a single overpass, its performance improves when combining all the observations. Overall, the broad range of results obtained through this synthetic XCO2 performance mapping hint at the future intercomparison challenges that the wide variety of upcoming CO2-observing concepts will pose.