The intensity and extent of transmission of arboviruses such as dengue, chikungunya, and Zika virus have increased markedly over the last decades. Autochthonous transmission of dengue and chikungunya by Aedes albopictus has been recorded in Southern Europe where the invasive mosquito was already established and viraemic travelers had imported the virus. Ae. albopictus populations are spreading northward into Germany. Here, we model the current and future climatically suitable regions for Ae. albopictus establishment in Germany, using climate data of spatially high resolution. To highlight areas where vectors and viraemic travellers are most likely to come into contact, reported dengue and chikungunya incidences are integrated at the county level. German cities with the highest likelihood of autochthonous transmission of Aedes albopictus-borne arboviruses are currently located in the western parts of the country: Freiburg im Breisgau, Speyer, and Karlsruhe, affecting about 0.5 million people. In addition, 8.8 million people live in regions considered to show elevated hazard potential assuming further spread of the mosquito: Baden-Württemberg (Upper Rhine, Lake Constance regions), southern parts of Hesse, and North Rhine-Westphalia (Lower Rhine). Overall, a more targeted and thus cost-efficient implementation of vector control measures and health surveillance will be supported by the detailed maps provided here.
Changes in marine boundary layer cloud (MBLC) radiative properties in response to aerosol perturbations are largely responsible for uncertainties in future climate predictions. In particular, the relationship between the cloud droplet number concentration (Nd, a proxy for aerosol) and the cloud liquid water path (LWP) remains challenging to quantify from observations. In this study, satellite observations from multiple polar-orbiting platforms for 2006–2011 are used in combination with atmospheric reanalysis data in a regional machine learning model to predict changes in LWP in MBLCs in the Southeast Atlantic. The impact of predictor variables on the model output is analysed using Shapley values as a technique of explainable machine learning. Within the machine learning model, precipitation fraction, cloud top height, and Nd are identified as important cloud state predictors for LWP, with dynamical proxies and sea surface temperature (SST) being the most important environmental predictors. A positive nonlinear relationship between LWP and Nd is found, with a weaker sensitivity at high cloud droplet concentrations. This relationship is found to be dependent on other predictors in the model: Nd–LWP sensitivity is higher in precipitating clouds and decreases with increasing SSTs.
Marine low clouds cool the Earth's climate, with their coverage (LCC) being controlled by their environment. Here, an observed significant decrease of LCC in the northeastern Pacific over the past two decades is linked quantitatively to changes in cloud‐controlling factors. In a comparison of different statistical and machine learning methods, a decrease in the inversion strength and near‐surface winds, and an increase in sea surface temperatures (SSTs) are unanimously shown to be the main causes of the LCC decrease. While the decreased inversion strength leads to more entrainment of dry free‐tropospheric air, the increasing SSTs are shown to lead to an increased vertical moisture gradient that enhances evaporation when entrainment takes place. While the LCC trend is likely driven by natural variability, the trend‐attribution framework developed here can be used with any method in future analyses. We find the choice of predictors is more important than the method.
<p>In this contribution, a significant reduction of low-level marine clouds (LLCs) in the northeastern Pacific is found over a 20-year period in satellite observations and attributed to increasing sea surface temperatures (SSTs).</p><p>LLCs play a key role for the Earth&#8217;s energy balance, however, their response to climatic changes is not clear, yet. Here, 20 years of Clouds and the Earth&#8217;s Radiant Energy System (CERES) cloud observations are analyzed together with reanalysis data sets in multivariate-regression and machine-learning frameworks to link an observed decrease of LLCs in the subtropical northern Pacific to changes in environmental factors. In the analyses, the observed LCC trend is explained almost exclusively by an increase in SSTs, but counteracted to some extent by increased low-level moisture availability. The influence of other factors such as estimated inversion strength, local winds and aerosols is investigated in the statistical frameworks but found to be negligible when compared to the effect of SST changes. The results provide observational evidence for the low-cloud feedback that back model findings of reduced LCC due to increased SSTs in a changing climate.</p>
<p>Satellite observations are used in regional machine learning models to quantify sensitivities of marine boundary-layer clouds (MBLC) to aerosol changes.</p><p>MBLCs make up a large part of the global cloud coverage as they are persistently present over more than 20% of the Earth&#8217;s oceans in the annual mean.They play an important role in Earth&#8217;s energy budget by reflecting solar radiation and interacting with thermal radiation from the surface, leading to a net cooling effect. Cloud properties and their radiative characteristics such as cloud albedo, horizontal and vertical extent, lifetime and precipitation susceptibility are dependent on environmental conditions. Aerosols in their role as condensation nuclei affect these cloud radiative properties through changes in the cloud droplet number concentration and subsequent cloud adjustments to this perturbation. However, the magnitude and sign of these effects remain among the largest uncertainties in future climate predictions.</p><p>In an effort to help improve these predictions a machine learning approach in combination with observational data is pursued:</p><p>Satellite observations from the collocated A-Train dataset (C3M) for 2006-2011 are used in combination with ECMWF atmospheric reanalysis data (ERA5) to train regional Gradient Boosting Regression Tree (GBRT) models to predict changes in key physical and radiative properties of MBLCs. The cloud droplet number concentration (N<sub>d</sub>) and the liquid water path (LWP) are simulated for the eastern subtropical oceans, which are characterised by a high annual coverage of MBLC due to the occurrence of semi-permanent stratocumulus sheets. Relative humidity above cloud, cloud top height and temperature below the cloud base and at the surface are identified as important predictors for both N<sub>d</sub> and LWP.&#160; The impact of each predictor variable on the GBRT model's output is analysed using Shapley values as a method of explainable machine learning, providing novel sensitivity estimates that will improve process understanding and help constrain the parameterization of MBLC processes in Global Climate Models.</p>
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