Earth Observation Data Group

Abstract:

Abstract Earth observation data should inform decision making, but good decisions can only be made if the uncertainties in the data are taken into account. Making sense of uncertainty information can be difficult, because uncertainties represent the statistical spread in the observations (e.g., expressed as $$x \pm y$$ x ± y ), which does not relate directly to one specific use case of the data. Here, we propose a Bayesian framework to transform Earth observation product uncertainties into actionable information, i.e., estimates of how confident one can be in the occurrence of specific events of interest given the data and their uncertainty. We demonstrate this framework using two case examples: (i) monitoring drought severity based on soil moisture and (ii) estimating coral bleaching risk based on sea surface temperature. In both cases, we show that ignoring uncertainties can easily lead to misinterpretation of the data, making any decisions based on these data unlikely to be the best course of action. The proposed framework is general and can, in principle, be applied to a wide range of applications. Doing so requires a careful dialogue between data users, to formulate meaningful use cases and decision criteria, and data producers, to provide a rigorous description of their data and its uncertainties. The next step would then be to confront the uncertainty-informed estimates of event probabilities (created by the framework proposed here) with the costs and benefits of possible courses of action in order to make the best possible decisions that maximize socioeconomic merit.

Abstract:

The latest range of CMIP6 model dust simulations shows a greater diversity than previous generations of models in terms of dust emission, deposition, burden and dust optical depth (DOD) (Zhao et al., 2022). Validation of dust models is crucial for understanding the impact of dust on climate and climate change, as well as for quantifying socio-economic and health impacts of dust.While models (and reanalyses) mostly provide output in terms of mass, satellite observations used for model validation are optical measurements. Thus we require good knowledge of the dust mass extinction coefficient (MEC) to successfully validate our dust models. However, the MEC is intricately linked to the dust size distribution, fraction of coarse particles and composition, all of which may vary regionally, in the vertical and in time.This presentation will provide a perspective on some recent efforts exploring the challenges in model validation relating to dust size, composition, optical depth and dust mass loading in climate models (Zhao et al., 2024; Ratcliffe et al., 2024), reanalyses (Ryder et al., 2024), space-borne lidar, space-borne optical depth and in-situ measurements, demonstrating the critical importance and uncertainty of the dust MEC.ReferencesRatcliffe, N.G., Ryder, C.L., Bellouin, N., Woodward, S., Jones, A., Johnson, B., Wieland, L.-M., Dollner, M., Gasteiger, J., Weinzierl, B. Long range transport of coarse mineral dust: an evaluation of the Met Office Unified Model against aircraft observations, Atmos. Chem. Phys., 24, 12161–12181, https://doi.org/10.5194/acp-24-12161-2024, 2024.Ryder, C.L., Bézier, C., Dacre, H., Clarkson, R., Amiridis, V., Marinou, E., Proestakis, E., Kipling, Z., Benedetti, A., Parrington, M., Rémy, S., Vaughan, M., Aircraft Engine Dust Ingestion at Global Airports, https://doi.org/10.5194/nhess-24-2263-2024, 24, 7, Natural Hazards and Earth System Science, 2024.Zhao, A., Ryder, C.L., Wilcox, L., How well do the CMIP6 models simulate dust aerosols?, Atmos. Chem. Phys., 22, 2095–2119, https://doi.org/10.5194/acp-22-2095-2022, 2022.Zhao, A., Wilcox, L., Ryder, C.L., The key role of atmospheric absorption in the Asian Summer Monsoon response to dust emissions in CMIP6 models, Atmos. Chem. Phys., https://doi.org/10.5194/acp-24-13385-2024, 2024.

Abstract:

The Arctic is experiencing heightened precipitation, affected by aerosols impacting rainfall and snowfall. However, sparse aerosol observations in the central Arctic cryosphere contribute to uncertainties in simulating aerosol-precipitation two-way interaction. This study examines aerosol-precipitation co-variation in various climate models during the Arctic spring and summer seasons from 2003 to 2011, leveraging satellite-based aerosol data and various CMIP6 climate models. Findings reveal significant spatio-temporal biases between models and observations. Snowfall dominance occurs in models where total AOD surpasses the observation by 121% (57–186%, confidence interval), intensifying simulated snowfall by two times compared to rainfall during summer. Consequently, climate models tend to underestimate central Arctic rainfall to the total precipitation ratio, suggesting a positive bias towards snowfall dominance. This highlights the importance of constraining total AOD and associated aerosol schemes in climate models using satellite measurements, which potentially could lead to a substantial reduction in snowfall contribution to the total precipitation ratio in the central Arctic, contrary to current multi-model simulations across various spatiotemporal scales.