Validation of Aeolus wind products over the tropical Atlantic using radiosondes

Since its launch by the European Space Agency in 2018, the Aeolus satellite has been using the first Doppler wind lidar in space to acquire three-dimensional atmospheric wind profiles around the globe. Especially in the tropics, these observations compensate for the currently limited number of other wind observations, making an assessment of the quality of Aeolus wind products in this region crucial for numerical weather prediction. To evaluate the quality of the Aeolus L2B wind products across the tropical Atlantic Ocean, 20 radiosondes corresponding to Aeolus overpasses were launched from the islands of Sal, Saint Croix and Puerto Rico during August-September 2021 as part of the Joint Aeolus Tropical Atlantic Campaign. During this period, Aeolus sampled winds within a complex environment with a variety of cloud types in the vicinity of the Inter-tropical Convergence Zone and aerosol particles from Saharan dust outbreaks. On average, the validation for Aeolus Rayleigh-clear revealed a random error of 3.8 – 4.3 m s–1 between 2–16 km and 4.3 – 4.8 m s–1 between 16–20 km, with a systematic error of -0.5±0.2 m s–1. For Mie-cloudy, the random error between 2–16 km is 1.1 – 2.3 m s–1 and the systematic error is -0.9 ±0.3 m s–1. It is therefore concluded that Rayleigh-clear winds do not satisfy the random error requirement of the mission, whereas Mie-cloudy winds do so, when considering the standard error. Below clouds or within dust layers, the quality of Rayleigh-clear observations are degraded when the useful signal is reduced. In these conditions, we also noticed an underestimation of the L2B estimated error. Gross outliers which we define with large deviations from the radiosonde but low error estimates account for less than 5% of the data. These outliers appear at all altitudes and under all environmental conditions; however, their root-cause remains unknown. Finally, we confirm the presence of an orbital-dependent bias observed with both radiosondes and European Centre for Medium-Range Weather Forecasts model equivalents. The results of this study contribute to a better characterization of the Aeolus wind product in different atmospheric conditions and provide valuable information for further improvement of the wind retrieval algorithm

Tropical cyclones in global storm-resolving models

Recent progress in computing and model development has initiated the era of global storm-resolving modeling and with it the potential to transform weather and climate prediction. Within the general theme of vetting this new class of models, the present study evaluates nine global-storm resolving models in their ability to simulate tropical cyclones (TCs). Results show that, broadly speaking, the models produce realistic TCs and remove longstanding issues known from global models such as the deficiency to accurately simulate TC intensity. However, TCs are strongly affected by model formulation, and all models suffer from unique biases regarding the number of TCs, intensity, size, and structure. Some models simulated TCs better than others, but no single model was superior in every way. The overall results indicate that global storm-resolving models are able to open a new chapter in TC prediction, but they need to be improved to unleash their full potential

Clouds and convective self-aggregation in a multi-model ensemble of radiative-convective equilibrium simulations

The Radiative-Convective Equilibrium Model Intercomparison Project (RCEMIP) is an intercomparison of multiple types of numerical models configured in radiative-convective equilibrium (RCE). RCE is an idealization of the tropical atmosphere that has long been used to study basic questions in climate science. Here, we employ RCE to investigate the role that clouds and convective activity play in determining cloud feedbacks, climate sensitivity, the state of convective aggregation, and the equilibrium climate. RCEMIP is unique amongst intercomparisons in its inclusion of a wide range of model types, including atmospheric general circulation models (GCMs), single column models (SCMs), cloud-resolving models (CRMs), large eddy simulations (LES), and global cloud-resolving models (GCRMs). The first results are presented from the RCEMIP ensemble of more than 30 models. While there are large differences across the RCEMIP ensemble in the representation of mean profiles of temperature, humidity, and cloudiness, in a majority of models anvil clouds rise, warm, and decrease in area coverage in response to an increase in sea surface temperature (SST). Nearly all models exhibit self-aggregation in large domains and agree that self-aggregation acts to dry and warm the troposphere, reduce high cloudiness, and increase cooling to space. The degree of self-aggregation exhibits no clear tendency with warming. There is a wide range of climate sensitivities, but models with parameterized convection tend to have lower climate sensitivities than models with explicit convection. In models with parameterized convection, aggregated simulations have lower climate sensitivities than un-aggregated simulations

Advancing South American Water and Climate Science through Multidecadal Convection-Permitting Modeling

South America’s hydroclimate sustains vibrant communities and natural ecosystems of extraordinary biodiversity including the Andes Cordillera, and the Orinoco, La Plata, and Amazon basins. Global warming and land-use change are endangering ecosystem health, exacerbating hydrometeorological extremes, and threatening water and food security for millions of people on the continent (Castellanos et al. 2022). Reductions in rainfall and streamflow have been observed in southern Amazonia, the Cerrado region, northeast Brazil, and Chile (Muñoz et al. 2020; Garreaud et al. 2020; Espinoza et al. 2019; Fu et al. 2013). The increased aridity has affected agricultural yield, water supply for reservoirs, hydropower generation and impacted tens of millions of people in the large metropolitan areas of Sao Paulo, Rio de Janeiro, and Santiago de Chile (Nobre et al. 2016). Andean glaciers, an important source of water, have lost 30% of their area in the tropics and up to 60% in the southern Andes—the highest glacier mass loss rates in the world (Braun et al. 2019; Dussaillant et al. 2019; Reinthaler et al. 2019; Masiokas et al. 2020; Fox-Kemper et al. 2021). Conversely, southeastern South America is facing increasing annual rainfall and intensification of heavy precipitation since the early twentieth century (Doyle et al. 2012; Barros et al. 2015; Pabón-Caicedo et al. 2020; Arias et al. 2021; Gutiérrez et al. 2021; Morales-Yokobori 2021; Seneviratne et al. 2021). Extreme precipitation is projected to intensify throughout the continent (Arias et al. 2021; Seneviratne et al. 2021). This poses significant risk to people and infrastructure along the Andes and other mountainous areas, particularly for lower-income communities living in informal housing (Poveda et al. 2020; Ozturk et al. 2022). The overarching goals of the SAAG community are twofold: improved physical understanding and application-relevant research. Two multidecadal convection-permitting simulations are at the heart of SAAG. The historical simulation will allow us to validate the model and better understand detailed hydroclimate features over the continent, while the future climate simulation will show the projected changes of these features in a warmer climate. Furthermore, SAAG scientists are working directly with local communities, so the information can be used for improved decision making. The specific goals and science questions are as follows; goal 1 Physical understanding: Advance insights and improve prediction of key hydroclimate processes in the region including projected changes in a changing climate and Goal 2, Provide information that can be used by local communities and stakeholders for better informed decision-making in a changing climate

Tropical Cyclones and Equatorial Waves in a Convection‐Permitting Aquaplanet Simulation With Off‐Equatorial SST Maximum

Abstract Tropical weather phenomena—including tropical cyclones (TCs) and equatorial waves—are influenced by planetary‐to‐convective‐scale processes; yet, existing data sets and tools can only capture a subset of those processes. This study introduces a convection‐permitting aquaplanet simulation that can be used as a laboratory to study TCs, equatorial waves, and their interactions. The simulation was produced with the Model for Prediction Across Scales‐Atmosphere (MPAS‐A) using a variable resolution mesh with convection‐permitting resolution (i.e., 3‐km cell spacing) between 10°S and 30°N. The underlying sea‐surface temperature is given by a zonally symmetric profile with a peak at 10°N, which allows for the formation of TCs. A comparison between the simulation and satellite, reanalysis, and airborne dropsonde data is presented to determine the realism of the simulated phenomena. The simulation captures a realistic TC intensity distribution, including major hurricanes, but their lifetime maximum intensities may be limited by the stronger vertical wind shear in the simulation compared to the observed tropical Pacific region. The simulation also captures convectively coupled equatorial waves, including Kelvin waves and easterly waves. Despite the idealization of the aquaplanet setup, the simulated three‐dimensional structure of both groups of waves is consistent with their observed structure as deduced from satellite and reanalysis data. Easterly waves, however, have peak rotation and meridional winds at a slightly higher altitude than in the reanalysis. Future studies may use this simulation to understand how convectively coupled equatorial waves influence the multi‐scale processes leading to tropical cyclogenesis

Validation of Aeolus L2B products over the tropical Atlantic using radiosondes

International audienceSince its launch by the European Space Agency in 2018, the Aeolus satellite has been using the first Doppler wind lidar in space to acquire three-dimensional atmospheric wind profiles around the globe. Especially in the tropics, these measurements compensate for the currently limited number of other wind observations, making an assessment of the quality of Aeolus wind products in this region crucial for numerical weather prediction. To evaluate the quality of the Aeolus L2B wind products across the tropical Atlantic Ocean, 20 radiosondes corresponding to Aeolus overpasses were launched from the islands of Sal, Saint Croix and Puerto Rico during August-September 2021 as part of the Joint Aeolus Tropical Atlantic Campaign. During this period, Aeolus sampled winds within a complex environment with a variety of cloud types in the vicinity of the Inter-tropical Convergence Zone and aerosol particles from Saharan dust outbreaks. On average, the validation for Aeolus Raleigh-clear revealed a random error of 3.8-4.3 ms-1 between 2-16 km and 4.3-4.8 ms-1 between 16-20 km, with a systematic error of-0.5±0.2 ms-1. For Mie-cloudy, the random error between 2-16 km is 1.1-2.3 ms-1 and the systematic error is-0.9 ±0.3 ms-1. Below clouds or within dust layers, the quality of Rayleigh-clear measurements can be degraded when the useful signal is reduced. In these conditions, we also noticed an underestimation of the L2B estimated error. Gross outliers which we define with large deviations from the radiosonde but low error estimates account for less than 5% of the data. These outliers appear at all altitudes and under all environmental conditions; however, their root-cause remains unknown. Finally, we confirm the presence of an orbital-dependent bias of up to 2.5 ms-1 observed with both radiosondes and European Centre for Medium-Range Weather Forecasts model equivalents. The results of this study contribute to a better characterization of the Aeolus wind product in different atmospheric conditions and provide valuable information for further improvement of the wind retrieval algorithm

Tropical Cyclones in Global Storm-Resolving Models

論文ID: 2021-02