Understanding surface temperature is important for habitability. Recent work on Mars has found that the dependence of surface temperature on elevation (surface lapse rate) converges to zero in the limit of a thin $\mathrm{CO_2}$ atmosphere. However, the mechanisms that control the surface lapse rate are still not fully understood. It remains unclear how the surface lapse rate depends on both greenhouse effect and surface pressure. Here, we use climate models to study when and why “mountaintops are cold”. We find the tropical surface lapse rate increases with the greenhouse effect and with surface pressure. The greenhouse effect dominates the surface lapse rate transition and is robust across latitudes. The pressure effect is important at low latitudes in moderately opaque ($\tau \sim 0.1$) atmospheres. A simple model provides insights into the mechanisms of the transition. Our results suggest that topographic cold-trapping may be important for the climate of arid planets.

In the context of space weather forecasting, solar EUV irradiance specification is needed on multiple time scales, with associated uncertainty quantification for determining the accuracy of downstream parameters. Empirical models of irradiance often rely on parametric fits between irradiance in several bands and various solar indices. We build upon these empirical models by using Generalized Additive Models (GAMs) to represent solar irradiance. We apply the GAM approach in two steps: (1) A GAM is between FISM2 irradiance and solar indices F10.7, Revised Sunspot Number, and the Lyman-alpha solar index. (2) A second GAM is fit to model the residuals of the first GAM with respect to TIMED/SEE irradiance. We evaluate the performance of this approach during Solar Cycle 24 and compare it with FISM2, and show that mean relative irradiance error for integrated solar EUV irradiance is decreased from -3.459% for FISM2 to 0.04% using GAMs driven by known solar indices with a small increase in variance. We demonstrate negligible dependence of performance on solar cycle and season, and we assess the efficacy of the GAM approach across different wavelengths.

The influence of climate feedbacks on regional hydrological changes under warming is poorly understood. Here, a moist energy balance model (MEBM) with a Hadley Cell parameterization is used to isolate the influence of climate feedbacks on changes in zonal-mean precipitation-minus-evaporation (P-E) under greenhouse-gas forcing. It is shown that cloud feedbacks act to narrow bands of tropical P-E and increase P-E in the deep tropics. The surface-albedo feedback shifts the location of maximum tropical P-E and increases P-E in the polar regions. The intermodel spread in the P-E changes associated with feedbacks arises mainly from cloud feedbacks, with the lapse-rate and surface-albedo feedbacks playing important roles in the polar regions. The P-E change associated with cloud feedback locking in the MEBM is similar to that of a climate model with inactive cloud feedbacks. This work highlights the unique role that climate feedbacks play in causing deviations from the “wet-gets-wetter, dry-gets-drier” paradigm.

A document by Shelley Stall. Click on the document to view its contents.

Atmospheric gravity waves (GWs) impact the circulation and variability of the atmosphere. Sub-grid scale GWs, which are too small to be resolved, are parameterized in weather and climate models. However, some models are now available at resolutions at which these waves must be resolved and it is important to test whether these models do this correctly. In this study, a GW resolving run of the ECMWF (European Centre for Medium-Range Weather Forecasts) IFS (Integrated Forecasting System), run with a 1.4 km average grid spacing (TCo7999 resolution), was compared to observations from the Atmospheric Infrared Sounder (AIRS) instrument, on NASA’s Aqua satellite, to test how well the model resolves these waves. In this analysis, nighttime data were used from the first 10 days of November 2018 over Asia and surrounding regions. The IFS run is resampled with AIRS’s observational filter using two different methods for comparison. The ECMWF ERA5 reanalysis is also resampled as AIRS, to allow for comparison of how the high resolution IFS run resolves GWs compared to a lower resolution model that uses GW drag parametrizations. Wave properties are found in AIRS and the resampled models using a multi-dimensional S-Transform method. Orographic GWs can be seen in similar locations at similar times in all three data sets. However, wave amplitudes and momentum fluxes in the resampled IFS run were found to be significantly lower than in the observations. This could be a result of horizontal and vertical wavelengths in the IFS run being underestimated.

Key Points: • A U-Net is refined to forecast seven atmospheric variables on global scale, falling behind the state-of-the-art by only one day. • Forecasts are generated on the HEALPix mesh, facilitating the development of location invariant convolution kernels. • Without converging to climatology, the model produces stable and realistic states 13 of the atmosphere in 365-days rollouts. Abstract: We present a parsimonious deep learning weather prediction model on the Hierarchical Equal Area isoLatitude Pixelization (HEALPix) to forecast seven atmospheric variables for arbitrarily long lead times on a global approximately 110 km mesh at 3h time resolution. In comparison to state-of-the-art machine learning weather forecast models, such as Pangu-Weather and GraphCast, our DLWP-HPX model uses coarser resolution and far fewer prognostic variables. Yet, at one-week lead times its skill is only about one day behind the state-of-the-art numerical weather prediction model from the European Centre for Medium-Range Weather Forecasts. We report successive forecast improvements resulting from model design and data-related decisions, such as switching from the cubed sphere to the HEALPix mesh, inverting the channel depth of the U-Net, and introducing gated recurrent units (GRU) on each level of the U-Net hierarchy. The consistent east-west orientation of all cells on the HEALPix mesh facilitates the development of location-invariant convolution kernels that are successfully applied to propagate global weather patterns across our planet. Without any loss of spectral power after two days, the model can be unrolled autoregressively for hundreds of steps into the future to generate stable and realistic states of the atmosphere that respect seasonal trends, as showcased in one year simulations. Our parsimonious DLWP-HPX model is research-friendly and potentially well-suited for sub-seasonal and seasonal forecasting. Plain Language Summary: Weather forecasting is traditionally realized by numerical weather prediction models that solve physical equations to simulate the progression of the atmosphere. Numerical methods are compute intense and their performance is increasingly challenged by less compute demanding and highly sophisticated machine learning approaches. Yet, a downside of these new models is their reliability: They are not guaranteed to generate physically plausible states, which often prevents them from generating stable and realistic forecasts beyond two weeks into the future. Here, a parsimonious machine learning model is developed to forecast just seven variables of the atmosphere (compared to more than 800 in numerical models and 69 or 218 in competitive machine learning models) over an entire year. Despite the small number of variables, our model generates forecasts that only fall behind expensive state-of-the-art predictions by a single day. That is, our error in a seven-days forecast matches that of a state-of-the-art forecast at day eight. Advancing weather forecasts with research friendly and parsimonious machine learning models beyond two weeks promises to extend horizons for planning in various fields that impact environment, economy, and society. 

Channel meandering is ubiquitous in tidal marshes, yet it is routinely omitted in morphodynamic models. Here we propose a novel numerical method to simulate channel meandering in tidal marshes on a Cartesian grid. The method calculates a first-order flow by considering the balance between pressure gradient and bed friction. To account for flow momentum shift towards meander outer banks, the flow is empirically modified. Unlike previous simplified methods that relied on the curvature of the bank, this modification is based on the curvature of the flow, making the model suitable for use in dendritic channel networks. The modified flow intrinsically accounts for the topographic steering effect, which tends to deflect the momentum toward the outer bank. As a result, the outer bank becomes steeper and erodes due to soil creep. Additionally, the outer bank experiences erosion proportional to the flow curvature. This erosion mechanism parameterizes the direct erosion caused by flow impacting the bank through a proportionality coefficient, which modulates the rate of lateral channel migration. Deposition on the inner bank is automatically simulated by the model, triggered by reduced bed shear stress in that area. The model accurately reproduces meander sinuosity, migration rates, and associated processes such as cutoffs, channel piracies, and network reorganizations. The model provides an efficient tool for predicting marsh landscape evolution from decades to millennia, which will enable exploring how lateral migration and meandering of tidal channels affect marsh ecomorphodynamics, carbon and nutrient cycling, drainage efficiency, and pond dynamics.

Under a warming climate, it is unclear how environments associated with US tornado outbreaks are changing. This work narrows this gap by using maximum covariance analysis (MCA) to find multivariate relationships between 500-hPa geopotential height anomalies and a severe thunderstorm proxy – WMAXSHEAR (a product of convective available potential energy and 0–6 km vertical wind shear) associated with past (1950-2019) May major tornado outbreaks. Results highlight three main patterns that explain the majority of covariance between tornado outbreaks and the large-scale atmospheric environment. Tornado outbreaks occurring under the dominant pattern (MCA1) initiate at different hours of the day and tend to last for many hours. Tornado outbreaks associated with the second (MCA2) and third (MCA3) patterns are shorter in duration and tend to initiate during the warmest daytime hours. Moreover, an increase in the magnitude and the spatial extent of the patterns conducive to tornado outbreaks was observed after 1980.

This study examines how wind shear affects precipitating marine stratocumulus clouds under different cloud droplet number concentrations (Nd). We performed a series of large eddy simulations (LES) of nocturnal marine stratocumulus clouds using Cloud Model 1 (CM1). The simulations show that Nd is the dominant factor for cloud cellular organization in this cloud regime rather than wind shear. Low Nd characterizes the open cellular structure with a high in-cloud liquid water path (LWP). When wind shear is increased, the cloud fraction tends to decrease along with LWP, suggesting the cloud top region is significantly influenced by the entrainment and mixing of dry air from the free troposphere. We also examine cold pools in open and closed cellular clouds. Open-cell clouds produce larger and deeper cold pools compared to closed-cell clouds. Interestingly, cold pools can exist without surface precipitation and are produced by evaporation of light precipitation (drizzle) below the cloud base with weak downdrafts. The evaporation of raindrops and drizzle play an important role in initiating new convection, particularly where colliding outflows occur downstream of the cloud. This secondary convection contributes to the development and maintenance of the cloud cellular organization and formation. 

Specification and forecast ionospheric parameters, such as ionospheric electron density (Ne), have been an important topic in space weather and ionosphere research. Neural networks (NNs) emerge as a powerful modeling tool for Ne prediction. However, heavy manual attention costs time to determine the optimal NN structures. In this work, we propose to use neural architecture search (NAS), an automatic machine learning method, to address this problem of NN models. NAS aims to find the optimal network structure through the alternated optimization of the hyperparameters and the corresponding network parameters. A total of 16-year data from Millstone Hill incoherent scatter radar (ISR) are used for NN models. One single-layer NN (SLNN) model and one deep NN (DNN) model are trained with NAS, namely SLNN-NAS and DNN-NAS, for Ne prediction and compared with their counterparts without NAS from previous studies, denoted as SLNN and DNN. Our results show that SLNN-NAS and DNN-NAS outperformed SLNN and DNN, respectively. NN models can reveal more finer details than the empirical ionospheric model developed using traditional data fitting approaches. DNN-NAS yields the best prediction accuracy measured by quantitative metrics and rankings of daily pattern prediction. The limited improvement of NAS is likely due to the network complexity and the limitation of fully connected NN without a memory mechanism.

A document by William Edmund Ward. Click on the document to view its contents.

The elimination of rain evaporation in the planetary boundary layer (PBL) has been found to lead to convective self-aggregation (CSA) even without radiative feedback, but the precise mechanisms underlying this phenomenon remain unclear. We conducted cloud-resolving simulations with two domain sizes and progressively reduced rain evaporation in the PBL. Surprisingly, CSA only occurred when rain evaporation was almost completely removed. The additional convective heating resulting from the reduction of evaporative cooling in the moist patch was found to be the trigger, thereafter a dry subsidence intrusion into the PBL in the dry patch takes over and sets CSA in motion. Temperature and moisture anomalies oppose each other in their buoyancy effects, hence explaining the need for almost total rain evaporation removal. We also found radiative cooling and not cold pools to be the leading cause for the comparative ease of CSA to take place in the larger domain.

The objective of this study was to examine both the climatology of the residual mean circulation, and the roles of resolved wave (RW) and unresolved wave (UW) forcings over four Mars years, based on the transformed Eulerian mean equation system using the EMARS reanalysis dataset. While RW forcing was estimated directly as Eliassen–Palm flux divergence, the forcing by UWs, including subgrid-scale gravity waves, was estimated indirectly using the zonal momentum equation. This indirect method, devised originally for study of Earth’s middle atmosphere, is applicable to latitudinal regions having angular momentum isopleths connected from the surface to the top of the atmosphere, which are usually mid- and high-latitude regions. In low latitudes of the winter hemisphere, a strong residual mean poleward flow is observed at an altitude range of 40–80 km, where the latitudinal gradient of the absolute angular momentum is small. The strong poleward flow crosses the isopleths of angular momentum in the regions of its northern and southern ends, indicating the necessity of the wave forcing. Our results suggest that the structure of the residual mean circulation at mid- and high-latitude regions is largely determined by UW forcing, particularly above the altitude of 60 km, whereas the RW contribution is also large below the altitude of 60 km.

The Yangtze River Valley (YRV) experienced an unprecedented heatwave in midsummer of 2022, but the detailed physical processes involved in the influence of anomalous large-scale atmospheric circulation on the heatwave remain unknown. Here, we show that the positive meridional gradient of anomalous atmospheric moisture at the middle-lower troposphere and associated extreme dry air advection over the YRV are key prerequisites for the formation of the 2022 YRV heatwave. The 2022 YRV heatwave is dominated by the interannual variability, which contributes 72.7% to the total temperature anomalies. Diagnosis of the surface heat budget equation indicates that the surface cloud radiative forcing is the most important process in driving the 2022 YRV heatwave, which is dominated by the positive surface short-wave cloud radiative forcing associated with the suppressed precipitation and the middle-low clouds. The suppressed precipitation is induced by the vertical dynamical processes of anomalous moisture advection caused by the anomalous descending flows over the YRV, which are driven by the negative advection of anomalous latent heat energy by climatological meridional wind (anomalous dry air advection) according to the atmospheric moist static energy equation. Simulations from the Lagrangian model FLEXPART further indicate that the moisture anomaly over the north of YRV is mainly originated from the surface evaporation in the YRV, implying that there is a positive land-air feedback during the life cycle of the YRV heatwave. Our study adds a perspective to the existing mechanism analyses of the 2022 YRV heatwave to serve accurate climate prediction and adaptation planning.

A Bayesian structure learning approach is employed to compare and contrast interactions between the major climate teleconnections over the recent past as revealed in reanalyses and climate model simulations from leading Meteorological Centers. In a previous study, the authors demonstrated a general framework using homogeneous Dynamic Bayesian Network (DBN) models constructed from reanalyzed time series of empirical climate indices to compare probabilistic graphical models. Reversible jump Markov Chain Monte Carlo (RJMCMC) is used to provide uncertainty quantification for selecting the respective network structures. The incorporation of confidence measures in structural features provided by the Bayesian approach is key to yielding informative measures of the differences between products if network-based approaches are to be used for model evaluation, particularly as point estimates alone may understate the relevant uncertainties. Here we compare models fitted from the NCEP/NCAR and JRA-55 reanalyses and CMIP5 historical simulations in terms of associations for which there is high posterior confidence. Examination of differences in the posterior probabilities assigned to edges of the directed acyclic graph (DAG) provides a quantitative summary of departures in the CMIP5 models from reanalyses. In general terms the climate model simulations are in better agreement with reanalyses where tropical processes dominate, and autocorrelation time scales are long. Seasonal effects are shown to be important when examining tropical-extratropical interactions with the greatest discrepancies and largest uncertainties present for the Southern Hemisphere teleconnections.

We report an indirect, negative, cloud-mediated, surface radiative effect (RE) of water vapor (IWVE) in certain regions in the tropics, which may be consequential for day-to-day regional heat stress. Using reanalysis and satellite data we show that this effect is marked by a surprisingly dominant positive relationship of cloud RE with near surface and column humidity. These clouds are predominantly low level and altocumuli, previously reported to have a negative surface RE, possibly lending the net negative RE to water vapor. Also reported earlier, these clouds form in the mid-troposphere, as detrainment offshoots of deep convective towers and can be advected away to large distances, hence requiring no local convective triggering in the IWVE regions. Evidently, the IWVE are co-located with the horizontal branch of the Hadley cell, with the lowest vertical forcing in the tropics. Moreover, these are also the transition regions between the highly cloudy and the driest parts of the tropics, with a waning down occurrence of cirrus, deep convective and altostratus clouds, linked with positive RE, corroborating the hypothesis. IWVE regions also show a large temporal variability in humidity possibly providing opportunity for a large variability in cloud fractional coverage, however the mechanism controlling this covariability is not understood. The IWVE is tightly tied with the seasonal cycle of the ITCZ and hence is likely a dominant source of pre-monsoon surface temperature variability and heat stress in the current climate. The evolution of the IWVE under future climate warming needs further investigation.

Numerical simulations of weather and climate models are conventionally carried out using double-precision floating-point numbers throughout the vast majority of the code. At the same time, the urgent need of high-resolution forecasts given limited computational resources encourages development of much more efficient numerical codes. A number of recent studies has suggested the use of reduced numerical precision, including half-precision floating-point numbers increasingly supported by hardware, as a promising avenue. In this paper, the possibility of using half-precision calculations in the radiation scheme ecRad operationally used in the ECMWF's Integrated Forecasting System (IFS). By deliberately mixing half-, single- and double-precision variables, we develop a mixed-precision version of the Tripleclouds solver, the most computationally demanding part of the radiation scheme, where reduced-precision calculations are emulated by a Fortran software rpe. By employing two tools that estimate the dynamic range of model parameters and identify problematic areas of the model code using ensemble statistics, the code variables were assigned particular precision levels.It is demonstrated that heating rates computed by the mixed-precision code are reasonably close to those produced by the double-precision code. Moreover, it is shown that using the mixed-precision ecRad in OpenIFS has a very limited impact on the accuracy of a medium-range forecast in comparison to the original double-precision configuration. These results imply that mixed-precision arithmetic could successfully be used to accelerate the radiation scheme ecRad and, possibly, other parametrization schemes used in weather and climate models without harming the forecast accuracy.

A document by Yuanzhe Li. Click on the document to view its contents.