We describe the coordination of ORCESTRA, a field campaign designed to advance understanding of how mesoscale convective processes influence Earth's climate. ORCESTRA harmonized the execution of eight sub-campaigns that spanned the tropical North Atlantic from 10 August to 30 September 2024. The ORCESTRA sub-campaigns performed measurements from three research aircraft, a research vessel, two ground stations, and EarthCARE, a new Earth Explorer Satellite. These platforms performed thousands of atmospheric soundings, supported a rich constellation of active and passive remote sensing, conducted a wide variety of in situ measurements, and deployed autonomous sensors in the air and sea. Together with its measurements, ORCESTRA is being accompanied by extensive numerical experimentation to advance the development of a new generation of more physical climate models.
Despite the urgency to mitigate climate change, global greenhouse gas concentrations are still increasing, and even global emissions are still growing. Furthermore, the global sinks-both natural and technical-are not improving rapidly enough. Therefore, discussion and investigations to utilize solar radiation modification (SRM) have resurfaced. Stratospheric Aerosol Injection (SAI) is one of the most studied methods, particularly using sulfate aerosols. However, sulfate SAI includes several risks, such as deposition of acidic aerosol particles on the Earth's surface, increasing acidification and ozone depletion, and changing weather patterns. Here we propose an alternative approach, namely Organic Stratospheric Aerosol Injection (OSAI). The OSAI is initiated by an injection of isoprene or monoterpenes, which, according to our conceptual investigations and global model simulations, seem to be plausible candidates for this purpose. The organic SAI is likely to avoid some of the problems inherent to sulfate SAI, even though further research is needed in terms of its potential influences on stratospheric heating and the ozone layer. While SRM is still preferably avoided, our results suggest that organic aerosols may be an alternative to sulfate for further consideration if it is deemed a viable and necessary option.
Radiance observations from microwave sounders have become one of the most valuable sources of information that is used in Numerical Weather Prediction (NWP). However, uncertainties in the land (and sea-ice) surface modelization do not permit optimal use of channels that receive a mixed contribution from the surface and the troposphere (i.e., low-peaking channels). In the context of the preparation of the Arctic Weather Satellite (AWS) mission, the HARMONIE-AROME model was run to evaluate the impact of assimilating the low-peaking channels of existing instruments using the so-called “dynamic emissivity method.” The aim of this work was also to build the best possible version of HARMONIE-AROME data assimilation system (3D-Var and 4D-Var) before the launch of the AWS instrument that happened in August 2024 and to be ready to assimilate the data as soon as the instrument was declared operational in June 2025.
Storylines are physically plausible scenarios of future climate change, statistically derived from an ensemble of climate model projections and organized according to the magnitude of projected changes in two or more remote drivers that strongly influence the spatial pattern of the climate response. Here, we provide novel insights into the Arctic storylines identified by Levine et al. (2024), where Barents-Kara Sea warming and lower-tropospheric Arctic warming during the extended summer season (May-October) were remote drivers, as we identify a set of models from the Coupled Model Intercomparison Project phase 6 to represent the storylines. We do this by first identifying models that are similar to these storylines in terms of each remote driver response and quantifying this similarity. Second, we evaluate the model's performance in terms of a simple performance score based on the mean normalized root-mean-square error for multiple climate variables of importance for the storylines. The normalized values vary between 0 and 1 for all variables, allowing them to exert a comparable influence on the score. The advantage of the score is that it provides an easily implementable and interpretable way of identifying models that are characterized by large errors relative to the rest of the ensemble. Finally, we combine the similarity estimate and the score to select models to represent the storylines. We focus on the Arctic during the extended summer season for which the storylines were designed, but also consider other seasons and regions. Through this exercise, we also document the methodology, benefits, and limitations of the score.
Modern data assimilation schemes typically use the same discrete dynamical model not only to evolve the state estimate in time but also to approximate the evolution, or propagation, of the estimation error covariance. Ensemble-based methods, such as the ensemble Kalman filter, approximate the evolution of the covariance through the propagation of individual ensemble members. Thus, it is tacitly assumed that if the discrete state propagation and resulting mean state estimates are accurate, then the ensemble-based discrete covariance propagation will be accurate as well, apart from sampling errors due to limited ensemble size. Through a series of numerical experiments supported by analytical results, we demonstrate that this assumption is false when correlation length scales approach grid resolution. We show that for states satisfying advective dynamics, although the discrete state propagation and ensemble mean state estimates are accurate, the corresponding ensemble covariances can be remarkably inaccurate. The inaccuracy of the ensemble covariances is well beyond that expected of typical sampling errors or numerical discretization errors. The underlying problem is a fundamental discrepancy between discrete covariance propagation and the continuum covariance dynamics, which we can identify because the exact continuum covariance dynamics are known. Errors in the ensemble covariances, which can be at least one order of magnitude larger than those of the mean state when correlation lengths begin to approach grid scale, cannot be rectified by covariance inflation and localization. This work brings to light a fundamental problem in data assimilation schemes that propagate covariances using the same discrete dynamical model used to propagate the state.
