The infrared window region (780-1250 cm-1, 12.8 to 8.0 µm) is of great importance to Earth’s climate due to its high transparency and thermal energy. We present here a new investigation of the transparency of this spectral region based on observations by interferometers of downwelling surface radiance at two DOE Atmospheric Radiation Measurement program sites. We focus on the dominant source of absorption in this region, the water vapor continuum, and derive updated values of spectral absorption coefficients for both the self and foreign continua. Our results show that the self continuum is too strong in the previous version of Mlawer-Tobin_Clough-Kneizys-Davies (MT_CKD) water vapor continuum model, a result that is consistent with other recent analyses, while the foreign continuum is too weak in MT_CKD. In general, the weaker self continuum derived in this study results in an overall increase in atmospheric transparency in the window, although in atmospheres with low amounts of water vapor the transparency may slightly decrease due to the increase in foreign continuum absorption. These continuum changes lead to a significant decrease in downwelling longwave flux at the surface for moist atmospheres and a modest increase in outgoing longwave radiation. The increased fraction of surface-leaving radiation that escapes to space leads to a notable increase (~5-10%) in climate feedback, implying that climate simulations that use the new infrared window continuum will show somewhat less warming than before. This study also points out the possibly important role that aerosol absorption may play in the longwave radiative budget.

A document by Dion Jia Xu Ho. Click on the document to view its contents.

This study investigates the impact of weak sea surface temperature (SST) warm anomalies on trade cumulus cloudiness in an idealized and ensemble framework with large-eddy simulations. The control experiment uses a spatially uniform, time-invariant SST and mean large-scale conditions and atmospheric forcings derived from the Atlantic Tradewind Ocean-Atmosphere Mesoscale Interaction Campaign (ATOMIC). The perturbed experiment adds a Gaussian warm SST anomaly (SSTA) with a 12.5 km radius and 0.5 K magnitude. The ensemble-averaged differences between perturbation and control experiments show that cloud fraction is enhanced over the downwind half of the prescribed warm SSTA, with the enhancement peaking slightly above the environmental lifting condensation level (LCL) and then decaying with height. Furthermore, the low-level cloud response (<1 km) to the warm SSTA is stronger and occurs more systematically across different ensemble members. This near-LCL cloud response is driven by enhanced surface buoyancy flux and turbulence over the warm SSTA as opposed to SSTA-induced anomalous surface convergence and mesoscale upward motions. Process denial experiments indicate that the locally enhanced surface sensible and latent heat fluxes contribute almost equally to increase the near-LCL cloudiness, even though the locally enhanced surface sensible heat flux plays a dominant role in enhancing surface buoyancy flux. These results corroborate recent satellite composite results (Chen et al., 2023), suggesting that the observed increase of daily cloud fraction above warm SSTAs is due to more frequent turbulence-driven formation of shallow cumuli near the cloud base.

The radiation parameterization is one of the computationally most expensive components of Earth system models (ESMs). To reduce computational cost, radiation is often calculated on coarser spatial or temporal scales, or both, than other physical processes in ESMs, leading to uncertainties in cloud-radiation interactions and thereby in radiative temperature tendencies. One way around this issue is the emulation of the radiation parameterization using machine learning which is usually faster and has good accuracy in a high dimensional parameter space. This study investigates the development and interpretation of a machine learning based radiation emulator using the ICOsahedral Non-hydrostatic (ICON) model with the RTE-RRTMGP radiation code which calculates radiative fluxes based on the atmospheric state and its optical properties. With a Bidirectional Long Short-Term Memory (Bi-LSTM) architecture, which can account for vertical bidirectional auto-correlation, we can accurately emulate shortwave and longwave heating rates with a mean absolute error of $0.049~K/d\,(2.50\%)$ and $0.069~K/d\,(5.14\%)$ respectively. Further, we analyse the trained neural networks using Shapley Additive exPlanations (SHAP) and confirm that the networks have learned physical meaningful relationships among the inputs and outputs. Notably, we observe that the local temperature is used as a predictive source for the longwave heating, consistent with physical models of radiation. For shortwave heating, we find that clouds reflect radiation, leading to reduced heating below the cloud.

Low clouds over tropical oceans reflect a great proportion of solar radiation back to space and thereby cool the Earth, yet this phenomenon has been poorly simulated in several previous generations of climate models. The principal aim of the present study is to employ satellite observations to evaluate the representation of marine tropical low clouds and their radiative effect at the top of the atmosphere in a subset of latest climate models participating in CMIP6. We strive for regime-oriented model validation and hence introduce a qualitative approach to discriminate stratocumulus (Sc) from shallow cumulus (Cu). The novel Sc-Cu categorization has a conceptual advantage of being based on cloud properties, rather than relying on a model response to a cloud-controlling factor. We find that CMIP6 models underestimate low-cloud cover in both Sc- and Cu-regions of tropical oceans. A more detailed investigation of cloud biases reveals that most CMIP6 models underestimate the relative frequency of occurrence (RFO) of Sc and overestimate RFO of Cu. We further demonstrate that tropical low cloudiness in CMIP6 models remains too bright. The regime-oriented validation represents the basis for improving parameterizations of physical processes that determine the cloud cover and radiative impact of Sc and Cu, which are still misrepresented in current climate models.

A document by Florian Elias Roemer. Click on the document to view its contents.

Radiative cooling on the lowest atmospheric levels is of strong importance for modulating atmospheric circulations and organizing convection, but detailed observations and a robust theoretical understanding are lacking. Here we use unprecedented observational constraints from subsidence regimes in the tropical Atlantic to develop a theory for the shape and magnitude of low-level longwave radiative cooling in clear-sky, showing large peaks at the top of the boundary layer. A suite of novel scaling approximations is first developed from simplified spectral theory, in close agreement with the measurements. The radiative cooling peak height is set by the maximum lapse rate in water vapor path, and its magnitude is mainly controlled by the ratio of column relative humidity above and below the peak. We emphasize how elevated intrusions of moist air can reduce low-level cooling, by sporadically shading the spectral range which effectively cools to space. The efficiency of this spectral shading depends both on water content and altitude of moist intrusions; its height dependence cannot be explained by the temperature difference between the emitting and absorbing layers, but by the decrease of water vapor extinction with altitude. This analytical work can help to narrow the search for low-level cloud patterns sensitive to radiative-convective feedbacks: the most organized patterns with largest cloud fractions tend to occur in atmospheres below 10% relative humidity and feel the strongest low-level cooling. This motivates further assessment of these favorable conditions for radiative-convective feedbacks and a robust quantification of corresponding shallow cloud dynamics in current and warmer climates.

Broadband (spectrally-integrated) radiation calculations are dominated by the expense of spectral integration, and many applications require fast parameterizations for computing radiative flux. Here we describe a novel approach using a linear weighted sum of monochromatic calculations at a small set of optimally-chosen frequencies. The empirically-optimized quadrature method is used to compute atmospheric boundary fluxes, net flux profiles throughout the atmosphere, heating rate profiles, and top-of-the-atmosphere forcing by CO2. We evaluate the method against two modern correlated k-distributions and find that we can achieve comparable errors with 32 spectral points. We also examine the effect of minimizing different cost functions, and find that in order to accurately represent heating rates and CO2 forcing, these quantities must be included in the cost function.

The article describes a new practical model for the infrared absorption of chlorofluorocarbons and other gases with dense spectra, based on HITRAN absorption cross-sections. The model is very simple, consisting of frequency-dependent polynomial coefficients describing the pressure and temperature dependence of absorption. Currently it is implemented for the halocarbon species required by the Radiative Forcing Model Intercomparison Project (RFMIP). This approach offers practical advantages compared to previously available options, and is traceable, since the polynomial coefficients follow directly from the laboratory spectra. The model is applied to the problem of computing instantaneous clear-sky halocarbon radiative efficiencies and present day radiative forcing. Results are in reasonable agreement with earlier assessments that were carried out with the less explicit Pinnock method, and thus broadly validate that method. Overall, halocarbons are responsible for a substantial share of the present-day forcing, 0.573 Watt/squaremeter (instantaneous clear-sky at the TOA), corresponding to approximately 20% of the total anthropogenic forcing, or 44% compared to anthropogenic CO2 forcing alone.

Weather forecasts made with imperfect models contain flow- and state-dependent errors. Data assimilation (DA) partially corrects these errors with new information from observations. As such, the corrections, or “analysis increments’, produced by the DA process embed information about model errors. An attempt is made here to extract that information to improve numerical weather prediction. Neural networks (NNs) are trained to predict corrections to the systematic error in the NOAA’s FV3-GFS model based on a large set of analysis increments. A simple NN focusing on an atmospheric column significantly improves the estimated model error correction relative to a linear baseline. Leveraging large-scale horizontal flow conditions using a convolutional NN, when compared to the simple column-oriented NN, does not improve skill in correcting model error. The sensitivity of model error correction to forecast inputs is highly localized by vertical level and by meteorological variable, and the error characteristics vary across vertical levels. Once trained, the NNs are used to apply an online correction to the forecast during model integration. Improvements are evaluated both within a cycled DA system and across a collection of 10-day forecasts. It is found that applying state-dependent NN-predicted corrections to the model forecast improves the overall quality of DA and improves the 10-day forecast skill at all lead times.