The Koren Lab

The prediction of climate has been a long-standing problem in contemporary science. One of the reasons stems from a gap in the ability to obtain 3D mapping of clouds, especially shallow scattered clouds. These clouds are strongly affected by mixing processes with their surroundings, rendering their internal volumetric structure highly heterogeneous. These heterogeneous clouds modulate the incoming solar energy and the outgoing long-wave radiation, thereby having a crucial role in the climate system. However, their 3D internal mapping is a major challenge. Here, we combine machine learning and space engineering to enable, for the first time, 3D mapping of scatterers in clouds. We employ ten nano-satellites in formation to simultaneously view the same clouds per scene from different angles and recover the 3D internal structure of shallow scattered clouds, from which we derive statistics, including uncertainty. We demonstrate this on real-world data. The results provide key features for predicting precipitation and renewable energy.

Shallow, sparse, non-precipitating convective clouds forming over the ocean are considered among the least organized cloud fields. The formation mechanism of these clouds is associated with random, local perturbations that create buoyant parcels. Their sparseness suggests no or very weak interactions between clouds. Here, we show that such clouds form within a well-organized, stable, dense mesh of convective cells that operate continuously, independent of the presence of visible clouds.

While a rich history of patchiness research has explored spatial structure in the ocean, there is no consensus over the controls on biological patchiness and how physical-ecological-biogeochemical processes and patchiness relate. The prevailing thought is that physics structures biology, but this has not been tested at basin scale with consistent in situ measurements. Here we use the slope of the relationship between variance vs spatial scale to quantify patchiness and ~650,000 nearly continuous (dx ~ 200 m) measurements - representing the Atlantic, Pacific, and Southern Oceans - and find that patchiness of biological parameters and physical parameters are uncorrelated. We show variance slope is an emergent property with unique patterns in biogeochemical properties distinct from physical tracers, yet correlated with other biological tracers. These results provide context for decades of observations with different interpretations, suggest the use of spatial tests of biogeochemical model parameterizations, and open the way for studies into processes regulating the observed patterns.