Melt ponds forming on Arctic sea ice in summer significantly reduce the surface albedo and impact the heat and mass balance of the sea ice. Their seasonal development features fast and local changes in fractions of surface types demonstrating the necessity of improving melt pond fraction (MPF) products. We present a renewed method to extract MPF from Sentinel-2 satellite imagery, which is evaluated by MPF products from higher resolution satellite and helicopter-borne imagery. The analysis of melt pond evolution during the MOSAiC campaign in summer 2020, shows a split of the Central Observatory (CO) into a level ice and a highly deformed part, the latter of which exhibits exceptional early melt pond formation compared to the vicinity. Average CO MPFs amount to 17 % before and 23 % after the major drainage. Arctic-wide analysis of MPF for years 2017-2021 shows a consistent seasonal cycle in all regions and years.

Convective cold pools (CPs) are known to mediate the interaction between convective rain cells and thereby help organize thunderstorm clusters, in particular mesoscale convective systems and extreme rainfall events. Unfortunately, the observational detection of CPs on a large scale has so far been hampered by the lack of relevant large-scale nearsurface data. Unlike numerical studies, where high-resolution near-surface fields of relevant quantities such as virtual temperature and winds are available and frequently used to detect cold pools, observational studies mainly identify CPs based on surface time series. Since research vessels or weather stations measure these time series locally, the characterization of cold pools from observations is limited to regional or station-based studies. To eventually enable studies on a global scale, we here develop and evaluate a methodology for the detection of CPs that relies only on data that (i) is globally available and (ii) has high spatio-temporal resolution. We trained convolutional neural networks to segment CPs in cloud and rainfall fields from high-resolution cloud resolving simulation output. Such data is not only available from simulations, but also from geostationary satellites that fulfill both (i) and (ii). The networks make use of a U-Net architecture, a common choice for image segmentation due to its strength in learning spatial correlations at different scales. Based on cloud and rainfall fields only, the trained networks systematically identify CP pixels in the simulation output. Our methodology may thus open for reliable global CP detection from space-borne sensors. As it also provides information on the spatial extent and the relative positioning of CPs over time, our method may offer new insight into the role of CPs in convective organization.

The imprint of marine atmospheric boundary layer (MABL) dynamical structures on sea surface roughness, as seen from Sentinel-1 Synthetic Aperture Radar (SAR) acquisitions, is investigated. We focus on February 13th, 2020, a case study of the EUREC4A (Elucidating the role of clouds-circulation coupling in climate) field campaign. For suppressed conditions, convective rolls imprint on sea surface roughness is confirmed through the intercomparison with MABL turbulent organization deduced from airborne measurements. A discretization of the SAR wide swath into 25 x 25 km$^2$ tiles then allows us to capture the spatial variability of the turbulence organization varying from rolls to cells. Secondly, we objectively detect cold pools within the SAR image and combine them with geostationary brightness temperature. The geometrical or physically-based metrics of cold pools are correlated to cloud properties. This provides a promising methodology to analyze the dynamics of convective systems as seen from below and above.

A month-long data assimilation experiment is carried out to assess the impact of CrIS and IASI Transformed Retrievals (TRs) on the accuracy of analyses and forecasts from a 3-h Weather Research and Forecasting (WRF) cycling system implemented over the central North Pacific Ocean. Conventional observations and satellite MicroWave (MW) radiance data are assimilated along with TRs in comparative experiments. Both the NCEP Global Forecasting System (GFS) and the European Centre for Medium-Range Weather Forecasts (ECMWF) analyses are used in the evaluation process. The results show that the assimilation of TRs, both alone, and in combination with MW radiance assimilation, have the greatest impact on the characterization of the moisture field in the middle atmospheric levels (800 to 300 hPa), and particularly in the lower portion (800 to 600 hPa). The latter improvement is likely due to a refinement in the vertical definition of the trade-wind inversion.

Air-pollution monitoring is sparse across most of the United States, so geostatistical models are important for reconstructing concentrations of fine particulate air pollution (PM2.5) for use in health studies. We present XGBoost-IDW Synthesis (XIS), a daily high-resolution PM2.5 machine-learning model covering the contiguous US from 2003 through 2021. XIS uses aerosol optical depth from satellites and a parsimonious set of additional predictors to make predictions at arbitrary points, capturing near-roadway gradients and allowing the estimation of address-level exposures. We built XIS with a computationally tractable workflow for extensibility to future years, and we used weighted evaluation to fairly assess performance in sparsely monitored regions. Averaging across all years in site-level cross-validation, the weighted mean absolute error of predictions (MAE) was 2.13 μg/m3, a substantial improvement over the mean absolute deviation from the median, which was 4.23 μg/m3. Comparing XIS to a leading product from the US Environmental Protection Agency, the Fused Air Quality Surface Using Downscaling (FAQSD), we obtained a 22% reduction in MAE. We also found a stronger relationship between PM2.5 and social vulnerability with XIS than with the FAQSD. Thus, XIS has potential for reconstructing environmental exposures, and its predictions have applications in environmental justice and human health.

Wildfires expose populations to increased morbidity and mortality due to increased air pollutant concentrations. Data included burned area, particulate matter (PM10, PM2.5), carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), temperature, relative humidity, wind-speed, aerosol optical depth (AOD) and mortality rates due to Circulatory System Disease (CSD), Respiratory System Disease (RSD), Pneumonia (PNEU), Chronic Obstructive Pulmonary Disease (COPD), and Asthma (ASMA). Only the months of the 2011-2020 wildfire season (June-July-August-September-October) with burned area greater than 1000 ha were considered. Multivariate statistical methods were used to reduce the dimensionality of the data to create two fire-pollution-meteorology indices (PBI, API), which allow us to understand how the combination of these variables affect cardio-respiratory mortality. Cluster analysis applied to PBI-API-Mortality divided the data into two Clusters. Cluster 1 included the months with lower temperatures, higher relative humidity, and high PM10, PM2.5, and NO2 concentrations. Cluster 2 included the months with more extreme weather conditions such as higher temperatures, lower relative humidity, larger forest fires, high PM10, PM2.5, O3, and CO concentrations, and high AOD. The two clusters were subjected to linear regression analysis to better understand the relationship between mortality and the PBI and API indices. The results showed statistically significant (p-value < 0.05) correlation (r) in Cluster 1 between RSDxPBI (rRSD = 0.539), PNEUxPBI (rPNEU = 0.644). Cluster 2 showed statistically significant correlations between RSDxPBI (rRSD = 0.464), PNEUxPBI (rPNEU = 0.442), COPDxPBI (rCOPD = 0.456), CSDxAPI (rCSD = 0.705), RSDxAPI (rCSD = 0.716), PNEUxAPI (rPNEU = 0.493), COPDxAPI (rPNEU = 0.619).

It has been widely recognized that tropical cyclone (TC) genesis requires favorable large-scale environmental conditions. Based on these linkages, numerous efforts have been made to establish an empirical relationship between seasonal TC activities and large-scale environmental favorabilities in a quantitative way, which lead to conceptual functions such as the TC genesis index. However, due to the limited amount of reliable TC observations and complexity of the climate system, a simple analytic function may not be an accurate portrait of the empirical relation between TCs and their ambiences. In this research, we use convolution neural networks (CNNs) to disentangle this complex relationship. To circumvent the limited amount of seasonal TC observation records, we implement transfer-learning technique to train ensembles of CNNs first on suites of high-resolution climate simulations with realistic seasonal TC activities and large-scale environmental conditions, and then subsequently on the state-of-the-art reanalysis from 1950 to 2019. Our CNNs can remarkably reproduce the historical TC records, and yields significant seasonal prediction skills when the large-scale environmental inputs are provided by operational climate forecasts. Furthermore, by forcing the ensemble CNNs with 20th century reanalysis products and phase 6 of the Coupled Model Intercomparison Project (CMIP6) experiments, we attempted to investigate TC variabilities and their changes in the past and future climates. Specifically, our ensemble CNNs project a decreasing trend of global mean TC activity in the future warming scenario, which is consistent with our dynamic projections using TC-permitting high-resolution coupled climate model.

Atmospheric aerosols influence the radiation budget, cloud amount, cloud properties, and surface albedos of sea ice and snow over the Arctic. In spite of their climatic importance, Arctic aerosol contains large uncertainties due to limited observations. This study evaluates the Arctic aerosol variability in three reanalyses, JRAero, CAMSRA, and MERRA2, in terms of the aerosol optical depth (AOD), and its relationship to the atmospheric disturbances on synoptic timescales. The AOD becomes highest in July–August over most of the Arctic regions, except for the North Atlantic and Greenland, where monthly variability is rather small. The three reanalyses show a general consistency in the horizontal distribution and temporal variability of the total AOD in summer. In contrast, the contributions of individual aerosol species to the total AOD are quite different among the reanalyses. Compared with observations, the AOD variability is represented well in all reanalyses in summer with high correlation coefficients, albeit exhibiting errors as large as the average AOD. The composite analysis shows that large aerosol emissions in Northern Eurasia and Alaska and transport by a typical atmospheric circulation pattern contribute to the high aerosol loading events in each area of the Arctic. Meanwhile, the empirical orthogonal function analysis depicts that the first- and second-largest AOD variabilities on the synoptic timescales appear over Northern Eurasia. Our results indicate that these summertime AOD variabilities mainly result from aerosol transportation and deposition due to the atmospheric disturbances on synoptic scales, suggesting an essential role played by Arctic cyclones.

The wintertime Kuroshio sea surface temperature (SST) front have the significant climate effects on southern China. The study demonstrates a close relationship between heavy precipitation over Southern China and Kuroshio SST front in winter. More than half winter heavy rainfall events in Southern China are proved to be resulted from strong High-frequency Variability events of the sea surface Wind Coupled with Precipitation (HV-WCP) over Kuroshio SST front. One day before strong HV-WCP events, the initial precipitation appears over Middle-lower Yangtze River due to the significantly enhanced frontal intensity. Then the precipitation generates low level cyclone and southeasterly wind anomalies, after it moving into Kuroshio front area because of the winter monsoon. The significant marine atmospheric boundary layer (MABL) height gradient over Kuroshio leads to plentiful moisture transporting from MABL into free atmosphere and enhances the local precipitation again. This process further causes the large-scale stratus rainband extending to Southern China and enhancing the heavy rainfall locally. Especially in 2008 winter, several processes of a strong HV-WCP event followed by continuous weak ones are conducive to the low-temperature-precipitation disaster in Southern China

A document by Xun Zou. Click on the document to view its contents.

Climate models still need to be improved in their capability of reproducing the present climate at both global and regional scale. The assessment of their performance depends on the datasets used as comparators. Reanalysis and gridded (homogenized or not homogenized) observational datasets have been frequently used for this purpose. However, none of these can be considered a reference dataset. Here, for the first time, using in-situ measurements from NOAA U.S. Climate Reference Network (USCRN), a network of 139 stations with high-quality instruments deployed across the continental U.S, daily temperature, and precipitation from a suite of dynamically downscaled regional climate models (RCMs; driven by ERA-Interim) involved in NA-CORDEX are assessed. The assessment is extended also to the most recent and modern widely used reanalysis (ERA5, ERA-Interim, MERRA2, NARR) and gridded observational datasets (Daymet, PRISM, Livneh, CPC). Results show that biases for the different datasets are mainly seasonal and subregional dependent. On average, reanalysis and in-situ-based datasets are generally warmer than USCRN year-round, while models are colder (warmer) in winter (summer). In-situ-based datasets provide the best performance in most of the CONUS regions compared to reanalysis and models, but still have biases in regions such as the Midwest mountains and the Northwestern Pacific. Results also highlight that reanalysis does not outperform RCMs in most of the U.S. subregions. Likewise, for both reanalysis and models, temperature and precipitation biases are also significantly depending on the orography, with larger temperature biases for coarser model resolutions and precipitation biases for reanalysis.

During the last ice age, the western United States was covered by large lakes, sustained partly by higher levels of precipitation. Increased rainfall was driven by the atmospheric circulation associated with the presence of large North American ice sheets, yet Pleistocene lakes generally reached their highstands not at glacial maximum but during deglaciation. Prior modeling studies, however, showed nearly monotonic drying since the last glacial maximum. Here I show that iTraCE, a new transient climate simulation of the last deglaciation, reproduces a robust peak in winter rainfall over the Great Basin near 16 ka. The simulated peak is driven by a transient strengthening and southward shift of the midlatitude jet. While meltwater forcing is an important driver of changes to the North Pacific Jet, changing orbital conditions and rising atmospheric CO2 also shift the jet south and contribute to wetter conditions over the western US during deglaciation.

In global applications and data sparse regions, which comprise most of the earth, hydrologic model-based flood monitoring relies on precipitation data from satellite multisensor precipitation products or numerical weather forecasts. However, these products often exhibit substantial errors during the meteorological conditions that lead to flooding, including extreme rainfall. The propagation of precipitation forcing errors to predicted runoff and streamflow is scale-dependent and requires an understanding of the autocorrelation structure of precipitation errors, since error autocorrelation impacts the accumulation of precipitation errors over space and time in hydrologic models. Previous efforts to account for satellite precipitation uncertainty in hydrologic models have demonstrated the potential for improving streamflow estimates; however, these efforts use satellite precipitation error models that rely heavily on ground reference data such as rain gages or weather radar and do not characterize the nonstationarity of precipitation error autocorrelation structures. This work evaluates a new method, the Space-Time Rainfall Error and Autocorrelation Model (STREAM), which stochastically generates possible true precipitation fields, as input to the Hillslope Link Model to generate ensemble streamflow estimates. Unlike previous error models, STREAM represents the nonstationary and anisotropic autocorrelation structure of satellite 2 precipitation error and does not use any ground reference to do so. Ensemble streamflow predictions are compared with streamflow generated using satellite precipitation fields as well as a radar-gage precipitation dataset during peak flow events. Results demonstrate that this approach to accounting for precipitation uncertainty effectively characterizes the uncertainty in streamflow estimates and reduces the error of predicted streamflow. Streamflow ensembles forced by STREAM improve streamflow prediction nearly to the level obtained using ground-reference forcing data across basin sizes.

Many chemical processes depend non-linearly on temperature. Gravity-wave-induced temperature perturbations have been previously shown to affect atmospheric chemistry, but accounting for this process in chemistry-climate models has been a challenge because many gravity waves have scales smaller than the typical model resolution. Here, we present a method to account for subgrid-scale orographic gravity-wave-induced temperature perturbations on the global scale for the Whole Atmosphere Community Climate Model (WACCM). The method consists of deriving the temperature perturbation amplitude $\hat{T}$ consistent with the model’s subgrid-scale gravity wave parametrization, and imposing $\hat{T}$ as a sinusiodal temperature perturbation in the model’s chemistry solver. Because of limitations in the gravity wave parameterization, scaling factors may be necessary to maintain a realistic wave amplitude. We explore scaling factors between 0.6 and 1 based on comparisons to altitude-dependent $\hat{T}$ distributions in two observational datasets. We probe the impact on the chemistry from the grid-point to global scales, and show that the parametrization is able to represent mountain wave events as reported by previous literature. The gravity waves for example lead to increased surface area densities of stratospheric aerosols. This in turn increases chlorine activation, with impacts on the associated chemical composition. We obtain large local changes in some chemical species (e.g., active chlorine, NOx, N2O5) which are likely to be important for comparisons to airborne or satellite observations, but find that the changes to ozone loss are more modest. This approach enables the chemistry-climate modeling community to account for subgrid-scale gravity wave temperature perturbations in a consistent way.

Large-scale convection associated with the Madden-Julian Oscillation (MJO) initiates over the Indian Ocean and propagates eastward across the Maritime Continent (MC). Over the MC, MJO events are generally weakened due to complex interactions between the large-scale MJO and the MC landmass. The MC barrier effect is responsible for the dissipation of 40-50\% of observed MJO events and is often exaggerated in weather and climate models. We examine how MJO propagation over the MC is affected by two aspects of the MC - its land-sea contrast and its terrain. To isolate the effects of mountains and land-sea contrast on MJO propagation, we conduct three high-resolution coupled atmosphere-ocean model experiments: 1) control simulation (CTRL) of the 2011 November-December MJO event, 2) flattened terrain without MC mountains (FLAT), and 3) no-land simulation (WATER) in which the MC islands are replaced with 50 m deep ocean. CTRL captures the general properties of the diurnal cycle of precipitation and MJO propagation across the MC. The WATER simulation produces a more intense and smoother-propagating MJO compared with that of CTRL. In contrast, the FLAT simulation produces much more convection and precipitation over land (without mountains) than CTRL, which results in a stronger barrier effect on MJO propagation. The land-sea contrast induced land-locked convection weakens the MJO’s convective organization. The land-locked convective systems over land in FLAT are more intense, grow larger, and last longer, which is more detrimental to MJO propagation over the MC, than the mountains that are present in CTRL.

1. Generalist invertebrate predators are sensitive to weather conditions, but the relationship between their trophic interactions and weather is poorly understood. This study investigates how weather affects the identity and frequency of spider trophic interactions as mediated by prey community structure, web characteristics and density-independent prey choice. 2. Spiders and their locally available prey were collected from barley fields in Wales, UK from April to September 2017-2018. The gut contents of 300 spiders were screened using DNA metabarcoding, analysed via multivariate models, and compared against prey availability using null models. 3. Spiders' trophic interactions changed over time and with weather conditions, primarily related to concomitant changes in their prey communities. Spiders did, however, appear to mitigate the effects of structural changes in prey communities through changing prey preferences according to prevailing weather conditions, possibly facilitated by adaptive web construction. 4. Using these findings, we demonstrate that prey choice data collected under different weather conditions can be used to refine inter-annual predictions of spider trophic interactions, although prey abundance was secondary to diversity in driving the diet of these spiders. By improving our understanding of the interaction between trophic interactions and weather, we can better predict how ecological networks are likely to change in response to variation in weather conditions and, more urgently, global climate change.

Convective cloud development during the Indian monsoon helps moisten the atmospheric environment and drive the monsoon trough northwards each year, bringing a large amount of India’s annual rainfall. Therefore, an increased understanding of how monsoon convection develops from observations will help inform model development. In this study, 139 days of India Meteorological Department Doppler weather radar data is analysed for 7 sites across India during the 2016 monsoon season. Convective cell-top heights (CTH) are objectively identified through the season, and compared with near-surface (at 2 km height) reflectivity. These variables are analysed over three time scales of variability during the monsoon: monsoon progression on a month-by-month basis, active-break periods and the diurnal cycle. We find a modal maximum in CTH around 6–8 km for all sites. Cell-averaged reflectivity increases with CTH, at first sharply, then less sharply above the freezing level. Bhopal and Mumbai exhibit lower CTH for monsoon break periods compared to active periods. A clear diurnal cycle in CTH is seen at all sites except Mumbai. For south-eastern India, the phase of the diurnal cycle depends on whether the surface is land or ocean, with the frequency of oceanic cells typically exhibiting an earlier morning peak compared to land, consistent with the diurnal cycle of precipitation. Our findings confirm that Indian monsoon convective regimes are partly regulated by the large-scale synoptic environment within which they are embedded. This demonstrates the excellent potential for weather radars to improve understanding of convection in tropical regions

An analysis of concurrent extreme events of continental precipitation and Integrated Water Vapour Transport (IVT) is crucial to our understanding of the role of the major global mechanisms of atmospheric moisture transport, including that of the landfalling Atmospheric Rivers (ARs) in extratropical regions. For this purpose, gridded data on CPC precipitation and ERA-5 IVT at a spatial resolution of 0.5º were used to analyze these concurrent events, covering the period from Winter 1980/1981 to Autumn 2017. For each season, and for each point with more than 400 non-dry days, several copula models were fitted to model the joint distribution function of the two variables. At each of the analysed points, the best copula model was used to estimate the probability of a concurrent extreme. At the same time, within the sample of observed concurrent extremes, the proportion of days with landfalling ARs was calculated for the whole period and for two 15-year sub-periods, one earlier period and one more recent (warmer) period. Three metrics based on copulas were used to analyse carefully the influence of IVT on extreme precipitation in the main regions of occurrence of AR landfall. The results show that the probability of occurrence of concurrent extremes is strongly conditioned by the dynamic component of the IVT, the wind. The occurrence of landfalling ARs accounts for most of the concurrent extreme days of IVT and continental precipitation, with percentages of concurrent extreme days close to 90% in some seasons in almost all the known regions of maximum occurrence of landfalling ARs, and with percentages greater than 75% downwind of AR landfall regions. This coincidence was lower in tropical regions, and in monsoonal areas in particular, with percentages of less than 50%. With a few exceptions, the role of landfalling ARs as drivers of concurrent extremes of IVT and continental precipitation tends to show a decrease in recent (warmer) periods. For almost all the landfalling AR regions with high or very high probabilities of achieving a concurrent extreme, there is a general trend towards a lower influence of IVT on extreme continental precipitation in recent (warmer) periods.

Observed stratocumulus to cumulus transitions (SCT) and their sensitivity to aerosols are studied using a Large-Eddy Simulation (LES) model that simulates the aerosol lifecycle, including aerosol sources and sinks. To initialize, force, and evaluate the LES, we used a combination of reanalysis, satellite, and aircraft data from the 2015 Cloud System Evolution in the Trades field campaign over the Northeast Pacific. The simulations follow two Lagrangian trajectories from initially overcast stratocumulus to the tropical shallow cumulus region near Hawaii. The first trajectory is characterized by an initially clean, well-mixed stratocumulus-topped marine boundary layer (MBL), then continuous MBL deepening and precipitation onset followed by a clear SCT and a consistent reduction of aerosols that ultimately leads to an ultra-clean layer in the upper MBL. The second trajectory is characterized by an initially polluted and decoupled MBL, weak precipitation, and a late SCT. Overall, the LES simulates the observed general MBL features. Sensitivity studies with different aerosol initial and boundary conditions reveal aerosol-induced changes in the transition, and albedo changes are decomposed into the Twomey effect and adjustments of cloud liquid water path and cloud fraction. Impacts on precipitation play a key role in the sensitivity to aerosols: for the first case, runs with enhanced aerosols exhibit distinct changes in microphysics and macrophysics such as enhanced cloud droplet number concentration, reduced precipitation, and delayed SCT. Cloud adjustments are dominant in this case. For the second case, enhancing aerosols does not affect cloud macrophysical properties significantly, and the Twomey effect dominates.

Radiation schemes are physically important but computationally expensive components of weather and climate models. This has spurred efforts to replace them with a cheap emulator based on neural networks (NN), obtaining large speed-ups, but at the expense of accuracy, energy conservation and generalization. An alternative approach which is slower but more robust than full emulation is to use NNs to predict optical properties, without abandoning the radiative transfer equations. Recently, NNs were developed to replace the RRTMGP gas optics scheme, and shown to be accurate while improving speed.However, the evaluations were based solely on offline radiation computations. In this paper, we describe the implementation and prognostic evaluation of RRTMGP-NN in the Integrated Forecasting System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF). The new gas optics scheme was incorporated into ecRad, the modular ECMWF radiation scheme. Using a hybrid loss function designed to reduce radiative forcing errors, and an early stopping method based on monitoring fluxes and heating rates with respect to a line-by-line benchmark, new NN models were trained on RRTMGP k-distributions with reduced spectral resolutions. Offline evaluation shows a very high level of accuracy for clear-sky fluxes and heating rates; for instance the RMSE in shortwave surface downwelling flux is 0.78 W m−2 for RRTMGP and 0.80 W m−2 for RRTMGP-NN in a present-day scenario, while upwelling flux errors are actually smaller for the NN. Because our approach does not affect the treatment of clouds, no additional errors will be introduced for cloudy profiles. RRTMGP-NN closely reproduces radiative forcings for 5 important greenhouse gases across a wide range of concentrations such as 8x CO2. To assess the impact of different gas optics schemes in the IFS, four 1-year coupled ocean-atmosphere simulations were performed for each configuration. The results show that RRTMGP-NN and RRTMGP produce very similar model climates, with the differences being smaller than those between existing schemes, and statistically insignificant for zonal means of single-level quantities such as surface temperature. The use of RRTMGP-NN speeds up ecRad by a factor of 1.5 compared to RRTMGP (the gas optics being almost 3 times faster), and is also faster than the older and less accurate RRTMG which is used in the current operational cycle of the IFS

The January 2022 Hunga Tonga–Hunga Haʻapai eruption was one of the most explosive volcanic events of the modern era, producing a vertical plume which peaked > 50km above the Earth. The initial explosion and subsequent plume triggered atmospheric waves which propagated around the world multiple times. A global-scale wave response of this magnitude from a single source has not previously been observed. Here we show the details of this response, using a comprehensive set of satellite and ground-based observations to quantify it from surface to ionosphere. A broad spectrum of waves was triggered by the initial explosion, including Lamb waves5,6 propagating at phase speeds of 318.2+/-6 ms-1 at surface level and between 308+/-5 to 319+/-4 ms-1 in the stratosphere, and gravity waves propagating at 238+/-3 to 269+/-3 ms-1 in the stratosphere. Gravity waves at sub-ionospheric heights have not previously been observed propagating at this speed or over the whole Earth from a single source. Latent heat release from the plume remained the most significant individual gravity wave source worldwide for >12 hours, producing circular wavefronts visible across the Pacific basin in satellite observations. A single source dominating such a large region is also unique in the observational record. The Hunga Tonga eruption represents a key natural experiment in how the atmosphere responds to a sudden point-source-driven state change, which will be of use for improving weather and climate models.

Survey evidence has indicated that a significant percentage of the population does not fully embrace the scientific consensus regarding climate change. This paper assesses whether the hourly temperature data support this denial. The analysis examines the relationship between hourly CO2 concentration levels and temperature using hourly data from the NOAA-operated Barrow observatory in Alaska. At this observatory, the average annual temperature over the 2015-2020 period was about 3.37 oC higher than in 1985–1990. A time-series model to explain hourly temperature is formulated using the following explanatory variables: the hourly level of total downward solar irradiance, the CO2 value lagged by one hour, proxies for the diurnal variation in temperature, proxies for the seasonal temperature variation, and proxies for possible non-anthropomorphic drivers of temperature. The purpose of the time-series approach is to capture the data’s heteroskedastic and autoregressive nature, which would otherwise “mask” CO2’s “signal” in the data. The model is estimated using hourly data from 1985 through 2015. The results are consistent with the hypothesis that increases in CO2 concentration levels have nontrivial consequences for hourly temperature. The estimated annual contributions of factors exclusive of CO2 and downward total solar irradiance are very small. The model was evaluated using out-of-sample hourly data from 1 Jan 2016 through 31 Aug 2017. The model’s out-of-sample hourly temperature predictions are highly accurate, but this accuracy is significantly degraded if the estimated CO2 effects are ignored. In short, the results are consistent with the scientific consensus on climate change.

Concerns about water security often inform climate risk-related decisions made by environmentally focused investors (Porritt, 2001; Stern, 2006). Yet, potential liabilities for damage caused by extreme flood and drought events linked to global warming present risks that are not always reflected in share prices (Krosinsky et al., 2012). Considering the highly destructive nature of such events, we query whether companies, or specific sectors, could and should be held at least partially liable for their emission-releasing business activities. Recent articles (Rayer & Millar, 2018; Rayer et al., 2020) estimate that under a hypothetical climate liability regime, North Atlantic hurricane seasons might increasingly generate 1-2% losses on market capitalizations (or share prices) for the top seven carbon-emitting, publicly listed companies. In this paper, we extend the concept of the climate liability regime to estimate the impact of global flood- and drought-related damages on the share prices of nine fossil-fuel firms (including the seven mentioned by Rayer et al. (2020)). Following Rayer et al. (2020), we use incremental climate impacts and historical corporate emissions to estimate that climate change-related global flood and drought damages for the period of 2012 to 2016 amount to approximately 2-3% of the top nine carbon-emitting companies’ market capitalizations. We also include a discussion of moral responsibility and the proportion of obligations between producers and users. Quantifying impacts from extreme weather events increases salience and serves as an example of how science can identify and address the important business questions, pertinent to both investors and companies, that arise from a changing climate. References Krosinsky, C., Robins, N., & Viederman, S. (2012). Evolutions in sustainable investing. John Wiley & Sons. Porritt, J. (2001). The world in context. HRH The Prince of Wales’ Business and the Environment Programme, Cambridge. Rayer, Q. G., & Millar, R. J. (2018). Investing in Extreme Weather Conditions. Citywire Wealth Manager®, (429) 36. Rayer, Q., Pfleiderer, P., & Haustein, K. (2020). Global Warming and Extreme Weather Investment Risks. Palgrave Macmillan. https://doi.org/10.1007/978-3-030-38858-4_3 Stern, N. (2006). Stern Review executive summary. London.

In atmospheric modeling, superparameterization has gained popularity as a technique to improve cloud and convection representations in large scale models by coupling them locally to cloud-resolving models. We show how the different representations of cloud water in the local and the global models in superparameterization lead to a suppression of cloud advection and ultimately to a systematic underrepresentation of the cloud amount in the large scale model. We demonstrate this phenomenon in a regional superparameterization experiment with the global model OpenIFS coupled to the local model DALES (the Dutch Atmospheric Large Eddy Simulation), as well as in an idealized setup, where the large-scale model is replaced by a simple advection scheme. To mitigate the problem of suppressed cloud advection, we propose a scheme where the spatial variability of the local model’s total water content is enhanced in order to achieve the correct cloud condensate amount.