Predicting atmospheric optical properties for radiative transfer computations using neural networks.

The radiative transfer equations are well known, but radiation parametrizations in atmospheric models are computationally expensive. A promising tool for accelerating parametrizations is the use of machine learning techniques. In this study, we develop a machine learning-based parametrization for the gaseous optical properties by training neural networks to emulate a modern radiation parametrization (RRTMGP). To minimize computa- tional costs, we reduce the range of atmospheric conditions for which the neural networks are applicable and use machine-specific optimized BLAS functions to accelerate matrix computations. To generate training data, we use a set of randomly perturbed atmospheric profiles and calculate optical properties using RRTMGP. Predicted optical properties are highly accurate and the resulting radiative fluxes have average errors within 0.5 W m-2 compared to RRTMGP. Our neural network-based gas optics parametrization is up to four times faster than RRTMGP, depending on the size of the neural networks. We further test the trade-off between speed and accuracy by training neural networks for the narrow range of atmospheric conditions of a single large-eddy simulation, so smaller and therefore faster networks can achieve a desired accuracy. We conclude that our machine learning-based parametrization can speed-up radiative transfer computations while retaining high accuracy. This article is part of the theme issue 'Machine learning for weather and climate modelling'.

Far-infrared surface emissivity and climate.

Presently, there are no global measurement constraints on the surface emissivity at wavelengths longer than 15 µm, even though this surface property in this far-IR region has a direct impact on the outgoing longwave radiation (OLR) and infrared cooling rates where the column precipitable water vapor (PWV) is less than 1 mm. Such dry conditions are common for high-altitude and high-latitude locations, with the potential for modeled climate to be impacted by uncertain surface characteristics. This paper explores the sensitivity of instantaneous OLR and cooling rates to changes in far-IR surface emissivity and how this unconstrained property impacts climate model projections. At high latitudes and altitudes, a 0.05 change in emissivity due to mineralogy and snow grain size can cause a 1.8-2.0 W m(-2) difference in the instantaneous clear-sky OLR. A variety of radiative transfer techniques have been used to model the far-IR spectral emissivities of surface types defined by the International Geosphere-Biosphere Program. Incorporating these far-IR surface emissivities into the Representative Concentration Pathway (RCP) 8.5 scenario of the Community Earth System Model leads to discernible changes in the spatial patterns of surface temperature, OLR, and frozen surface extent. The model results differ at high latitudes by as much as 2°K, 10 W m(-2), and 15%, respectively, after only 25 y of integration. Additionally, the calculated difference in far-IR emissivity between ocean and sea ice of between 0.1 and 0.2, suggests the potential for a far-IR positive feedback for polar climate change.

Radiative flux and forcing parameterization error in aerosol-free clear skies.

Radiation parameterizations in GCMs are more accurate than their predecessorsErrors in estimates of 4 ×CO2 forcing are large, especially for solar radiationErrors depend on atmospheric state, so global mean error is unknown.

Balancing Accuracy, Efficiency, and Flexibility in Radiation Calculations for Dynamical Models.

This paper describes the initial implementation of a new toolbox that seeks to balance accuracy, efficiency, and flexibility in radiation calculations for dynamical models. The toolbox consists of two related code bases: Radiative Transfer for Energetics (RTE), which computes fluxes given a radiative transfer problem defined in terms of optical properties, boundary conditions, and source functions; and RRTM for General circulation model applications-Parallel (RRTMGP), which combines data and algorithms to map a physical description of the gaseous atmosphere into such a radiative transfer problem. The toolbox is an implementation of well-established ideas, including the use of a k-distribution to represent the spectral variation of absorption by gases and the use of two-stream, plane-parallel methods for solving the radiative transfer equation. The focus is instead on accuracy, by basing the k-distribution on state-of-the-art spectroscopy and on the sometimes-conflicting goals of flexibility and efficiency. Flexibility is facilitated by making extensive use of computational objects encompassing code and data, the latter provisioned at runtime and potentially tailored to specific problems. The computational objects provide robust access to a set of high-efficiency computational kernels that can be adapted to new computational environments. Accuracy is obtained by careful choice of algorithms and through tuning and validation of the k-distribution against benchmark calculations. Flexibility with respect to the host model implies user responsibility for maps between clouds and aerosols and the radiative transfer problem, although comprehensive examples are provided for clouds.

Developments in the MPI-M Earth System Model version 1.2 (MPI-ESM1.2) and Its Response to Increasing CO2.

A new release of the Max Planck Institute for Meteorology Earth System Model version 1.2 (MPI-ESM1.2) is presented. The development focused on correcting errors in and improving the physical processes representation, as well as improving the computational performance, versatility, and overall user friendliness. In addition to new radiation and aerosol parameterizations of the atmosphere, several relatively large, but partly compensating, coding errors in the model's cloud, convection, and turbulence parameterizations were corrected. The representation of land processes was refined by introducing a multilayer soil hydrology scheme, extending the land biogeochemistry to include the nitrogen cycle, replacing the soil and litter decomposition model and improving the representation of wildfires. The ocean biogeochemistry now represents cyanobacteria prognostically in order to capture the response of nitrogen fixation to changing climate conditions and further includes improved detritus settling and numerous other refinements. As something new, in addition to limiting drift and minimizing certain biases, the instrumental record warming was explicitly taken into account during the tuning process. To this end, a very high climate sensitivity of around 7 K caused by low-level clouds in the tropics as found in an intermediate model version was addressed, as it was not deemed possible to match observed warming otherwise. As a result, the model has a climate sensitivity to a doubling of CO2 over preindustrial conditions of 2.77 K, maintaining the previously identified highly nonlinear global mean response to increasing CO2 forcing, which nonetheless can be represented by a simple two-layer model.

An Observational View of Relationships Between Moisture Aggregation, Cloud, and Radiative Heating Profiles.

Data from several coincident satellite sensors are analyzed to determine the dependence of cloud and precipitation characteristics of tropical regions on the variance in the water vapor field. Increased vapor variance is associated with decreased high cloud fraction and an enhancement of low-level radiative cooling in dry regions of the domain. The result is found across a range of sea surface temperatures and rain rates. This suggests the possibility of an enhanced low-level circulation feeding the moist convecting areas when vapor variance is large. These findings are consistent with idealized models of self-aggregation, in which the aggregation of convection is maintained by a combination of low-level radiative cooling in dry regions and mid-to-upper-level radiative warming in cloudy regions.

A study of the link between gastric reflux and chronic sinusitis in adults.

Much discussion is taking place regarding the role of gastric reflux disease in the development and maintenance of chronic sinus disease. We studied 31 patients in a large urban private practice who had recalcitrant chronic sinusitis despite aggressive medical and surgical therapy. After we obtained information on the severity of each patient's sinus disease, we performed either double- or triple-catheter probe pH testing to assess the presence of reflux disease. Of the 30 patients who were successfully tested (1 patient did not tolerate probe testing), 25 demonstrated reflux disease, including 2 whose reflux reached the level of the nasopharynx. These 25 patients were placed on a proton-pump inhibitor (PPI) regimen and reassessed at least 1 month later. At follow-up, 14 of 15 evaluable patients demonstrated at least some improvement in their sinus symptoms, including 7 who experienced either a complete or almost-complete resolution of symptoms. The improvements in sinus symptoms corresponded with improvements in reflux symptoms. These findings suggest that antireflux therapy might play a role in the treatment of recalcitrant chronic sinus disease.