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Compound extremes such as cooccurring soil drought (low soil moisture) and atmospheric aridity (high vapor pressure deficit) can be disastrous for natural and societal systems. Soil drought and atmospheric aridity are 2 main physiological stressors driving widespread vegetation mortality and reduced terrestrial carbon uptake. Here, we empirically demonstrate that strong negative coupling between soil moisture and vapor pressure deficit occurs globally, indicating high probability of cooccurring soil drought and atmospheric aridity. Using the Global Land Atmosphere Coupling Experiment (GLACE)-CMIP5 experiment, we further show that concurrent soil drought and atmospheric aridity are greatly exacerbated by land-atmosphere feedbacks. The feedback of soil drought on the atmosphere is largely responsible for enabling atmospheric aridity extremes. In addition, the soil moisture-precipitation feedback acts to amplify precipitation and soil moisture deficits in most regions. CMIP5 models further show that the frequency of concurrent soil drought and atmospheric aridity enhanced by land-atmosphere feedbacks is projected to increase in the 21st century. Importantly, land-atmosphere feedbacks will greatly increase the intensity of both soil drought and atmospheric aridity beyond that expected from changes in mean climate alone.

Remote Sensing, 11 (8), ISSN:2072-4292

Artificial neural-networks have the potential to emulate cloud processes with higher accuracy than the semi-empirical emulators currently used in climate models. However, neural-network models do not intrinsically conserve energy and mass, which is an obstacle to using them for long-term climate predictions. Here, we propose two methods to enforce linear conservation laws in neural-network emulators of physical models: Constraining (1) the loss function or (2) the architecture of the network itself. Applied to the emulation of explicitly-resolved cloud processes in a prototype multi-scale climate model, we show that architecture constraints can enforce conservation laws to satisfactory numerical precision, while all constraints help the neural-network better generalize to conditions outside of its training set, such as global warming., Comment: ICML 2019 Workshop. Climate Change: How Can AI Help? 3 pages, 3 figures, 1 table

Droughts and heatwaves cause agricultural loss, forest mortality, and drinking water scarcity, especially when they occur simultaneously as combined events. Their predicted increase in recurrence and intensity poses serious threats to future food security. Still today, the knowledge of how droughts and heatwaves start and evolve remains limited, and so does our understanding of how climate change may affect them. Droughts and heatwaves have been suggested to intensify and propagate via land–atmosphere feedbacks. However, a global capacity to observe these processes is still lacking, and climate and forecast models are immature when it comes to representing the influences of land on temperature and rainfall. Key open questions remain in our goal to uncover the real importance of these feedbacks: What is the impact of the extreme meteorological conditions on ecosystem evaporation? How do these anomalies regulate the atmospheric boundary layer state (event self‐intensification) and contribute to the inflow of heat and moisture to other regions (event self‐propagation)? Can this knowledge on the role of land feedbacks, when available, be exploited to develop geo‐engineering mitigation strategies that prevent these events from aggravating during their early stages? The goal of our perspective is not to present a convincing answer to these questions, but to assess the scientific progress to date, while highlighting new and innovative avenues to keep advancing our understanding in the future. ISSN:0077-8923

In large-eddy simulations, subgrid-scale (SGS) processes are parameterized as a function of filtered grid-scale variables. First-order, algebraic SGS models are based on the eddy-viscosity assumption, which does not always hold for turbulence. Here we apply supervised deep neural networks (DNNs) to learn SGS stresses from a set of neighboring coarse-grained velocity from direct numerical simulations (DNSs) of the atmospheric boundary layer at friction Reynolds numbers Re_{\tau} up to 1243 without invoking the eddy-viscosity assumption. The DNN model was found to produce higher correlation of SGS stresses compared to the Smagorinsky model and the Smagorinsky-Bardina mixed model in the surface and mixed layers and can be applied to different grid resolutions and various stability conditions ranging from near neutral to very unstable. The additional information on potential temperature and pressure were found not to be useful for SGS modeling. Deep learning thus demonstrates great potential for LESs of geophysical turbulence., Comment: 33 pages, 11 figures, 3 tables

Climate projections suffer from uncertain equilibrium climate sensitivity. The reason behind this uncertainty is the resolution of global climate models, which is too coarse to resolve key processes such as clouds and convection. These processes are approximated using heuristics in a process called parameterization. The selection of these parameters can be subjective, leading to significant uncertainties in the way clouds are represented in global climate models. Here, we explore three deep network algorithms to infer these parameters in an objective and data-driven way. We compare the performance of a fully-connected network, a one-dimensional and, a two-dimensional convolutional networks to recover the underlying parameters of the Lorenz-96 model, a non-linear dynamical system that has similar behavior to the climate system., Comment: ICML 2019 workshop on climate change

In this paper, we review the use of satellite-based remote sensing in combination with in situ data to inform Earth surface modelling. This involves verification and optimization methods that can handle both random and systematic errors and result in effective model improvement for both surface monitoring and prediction applications. The reasons for diverse remote sensing data and products include (i) their complementary areal and temporal coverage, (ii) their diverse and covariant information content, and (iii) their ability to complement in situ observations, which are often sparse and only locally representative. To improve our understanding of the complex behavior of the Earth system at the surface and sub-surface, we need large volumes of data from high-resolution modelling and remote sensing, since the Earth surface exhibits a high degree of heterogeneity and discontinuities in space and time. The spatial and temporal variability of the biosphere, hydrosphere, cryosphere and anthroposphere calls for an increased use of Earth observation (EO) data attaining volumes previously considered prohibitive. We review data availability and discuss recent examples where satellite remote sensing is used to infer observable surface quantities directly or indirectly, with particular emphasis on key parameters necessary for weather and climate prediction. Coordinated high-resolution remote-sensing and modelling/assimilation capabilities for the Earth surface are required to support an international application-focused effort.

Light-use efficiency (LUE), which quantifies the plants' efficiency in utilizing solar radiation for photosynthetic carbon fixation, is an important factor for gross primary production estimation. Here we use satellite-based solar-induced chlorophyll fluorescence as a proxy for photosynthetically active radiation absorbed by chlorophyll (APARchl) and derive an estimation of the fraction of APARchl (fPARchl) from four remotely sensed vegetation indicators. By comparing maximum LUE estimated at different scales from 127 eddy flux sites, we found that the maximum daily LUE based on PAR absorption by canopy chlorophyll (εchlmax), unlike other expressions of LUE, tends to converge across biome types. The photosynthetic seasonality in tropical forests can also be tracked by the change of fPARchl, suggesting the corresponding (εchlmax) to have less seasonal variation. This spatio-temporal convergence of LUE derived from fPARchl can be used to build simple but robust gross primary production models and to better constrain process-based models.

Stratification can cause turbulence spectra to deviate from Kolmogorov's isotropic -5/3 power-law scaling in the universal equilibrium range at high Reynolds numbers. However, a consensus has not been reached with regard to the exact shape of the spectra. Here we propose a theoretically-derived shape of the turbulent kinetic energy (TKE) and temperature spectra in horizontal wavenumber that consists of three regimes at small Froude number: the buoyancy subrange, a transition region and isotropic inertial subrange through derivation based on previous research. These regimes are confirmed by various observations in the atmospheric boundary layer. We also show that DNS may not apply in the study of very stable atmospheric boundary layers at very high Reynolds numbers as they cannot correctly represent the observed spectral regimes because of the lack of scale separation limited by current computational capacity. In addition, the spectrum in the transition regime explains why Monin-Obukhov similarity theory cannot entirely describe the behavior of the stable atmospheric boundary., 10 pages, 5 figures

Turbulent fluxes in the atmospheric surface layer are key input for the prediction of weather, hydrology, and carbon dioxide concentration. In numerical modelling of turbulent fluxes, a -7/3 power-law scaling in turbulence cospectra is usually assumed at high wavenumbers. In eddy-covariance (EC) measurements of turbulent fluxes, an assumed shape of turbulence cospectra is typically required for high-frequency spectral corrections, typically assuming a -7/3 power law. The derivation of -7/3 power-law scaling is based primarily on dimensional analysis, and other cospectral scaling has also been observed. Here we examine the shape of turbulence cospectra at high wavenumbers from extensive field measurements of wind velocity, temperature, water vapour and CO2 concentrations in various stably stratified atmospheric conditions. We propose a turbulence cospectral shape with -2 power law rather than -7/3 law for high wavenumber equilibrium range of the stable atmospheric boundary layer. This finding contributes to improved estimation of turbulent fluxes in both modelling and observation., Comment: 21 pages, 15 figures

Modeling and representing moist convection in coarse-scale climate models remains one of the main bottlenecks of current climate simulations. Many of the biases present with parameterized convection are strongly reduced when convection is explicitly resolved (in cloud resolving models at high spatial resolution ~ a kilometer or so). We here present a novel approach to convective parameterization based on machine learning over an aquaplanet with prescribed sea surface temperatures. The machine learning is trained over a superparameterized version of a climate model in which convection is resolved by an embedded 2D cloud resolving models. The machine learning representation of convection, called Cloud Brain (CBRAIN) replicates many of the convective features of the superparameterized climate model, yet reduces its inherent stochasticity. The approach presented here opens up a new possibility and a first step towards better representing convection in climate models and reducing uncertainties in climate predictions.