Is greenhouse gas forcing a plausible explanation for the observed warming in the Baltic Sea catchment area?

We investigated whether anthropogenic forcing is a plausible explanation for the observed warming in the Baltic Sea catchment area. Therefore, we compared the most recent trends in the surface temperature over land with anthropogenic climate change projections from regional climate model simulations. We analyzed patterns of change with different spatio-temporal resolutions. The observed annual area-mean change in the daily-mean temperature was consistent with the anthropogenic climate change signal. This finding was robust to the removal of the signal of the North Atlantic Oscillation. In contrast to the annual area-mean change, we found little consistency in both annual cycle and spatial variability of the observed and projected changes

Area-covering postprocessing of ensemble precipitation forecasts using topographical and seasonal conditions

Probabilistic weather forecasts from ensemble systems require statistical postprocessing to yield calibrated and sharp predictive distributions. This paper presents an area-covering postprocessing method for ensemble precipitation predictions. We rely on the ensemble model output statistics (EMOS) approach, which generates probabilistic forecasts with a parametric distribution whose parameters depend on (statistics of) the ensemble prediction. A case study with daily precipitation predictions across Switzerland highlights that postprocessing at observation locations indeed improves high-resolution ensemble forecasts, with 4.5% CRPS reduction on average in the case of a lead time of 1 day. Our main aim is to achieve such an improvement without binding the model to stations, by leveraging topographical covariates. Specifically, regression coefficients are estimated by weighting the training data in relation to the topographical similarity between their station of origin and the prediction location. In our case study, this approach is found to reproduce the performance of the local model without using local historical data for calibration. We further identify that one key difficulty is that postprocessing often degrades the performance of the ensemble forecast during summer and early autumn. To mitigate, we additionally estimate on the training set whether postprocessing at a specific location is expected to improve the prediction. If not, the direct model output is used. This extension reduces the CRPS of the topographical model by up to another 1.7% on average at the price of a slight degradation in calibration. In this case, the highest improvement is achieved for a lead time of 4 days

Weighted verification tools to evaluate univariate and multivariate probabilistic forecasts for high-impact weather events

To mitigate the impacts associated with adverse weather conditions, meteorological services issue weather warnings to the general public. These warnings rely heavily on forecasts issued by underlying prediction systems. When deciding which prediction system(s) to utilise to construct warnings, it is important to compare systems in their ability to forecast the occurrence and severity of extreme weather events. However, evaluating forecasts for extreme events is known to be a challenging task. This is exacerbated further by the fact that high-impact weather often manifests as a result of several confounding features, a realisation that has led to considerable research on so-called compound weather events. Both univariate and multivariate methods are therefore required to evaluate forecasts for high-impact weather. In this paper, we discuss weighted verification tools, which allow particular outcomes to be emphasised during forecast evaluation. We review and compare different approaches to construct weighted scoring rules, both in a univariate and multivariate setting, and we leverage existing results on weighted scores to introduce weighted probability integral transform (PIT) histograms, allowing forecast calibration to be assessed conditionally on particular outcomes having occurred. To illustrate the practical benefit afforded by these weighted verification tools, they are employed in a case study to evaluate forecasts for extreme heat events issued by the Swiss Federal Office of Meteorology and Climatology (MeteoSwiss)

Statistical adjustment, calibration and downscaling of seasonal forecasts: a case-study for Southeast Asia

The present paper is a follow-on of the work presented in Manzanas et al. (Clim Dyn 53(3–4):1287–1305, 2019) which provides a comprehensive intercomparison of alternatives for the post-processing (statistical adjustment, calibration and downscaling) of seasonal forecasts for a particularly interesting region, Southeast Asia. To answer the questions that were raised in the preceding work, apart from Bias Adjustment (BA) and ensemble Re-Calibration (RC) methods—which transform directly the variable of interest,—we include here more complex Perfect Prognosis (PP) and Model Outputs Statistics (MOS) downscaling techniques—which operate on a selection of large-scale model circulation variables linked to the local observed variable of interest. Moreover, we test the suitability of BA and PP methods for the post-processing of daily—not only seasonal—time-series, which are often needed in a variety of sectoral applications (crop, hydrology, etc.) or to compute specific climate indices (heat waves, fire weather index, etc.). In addition, we also undertake an assessment of the effect that observational uncertainty may have for statistical post-processing. Our results indicate that PP methods (and to a lesser extent MOS) are highly case-dependent and their application must be carefully analyzed for the region/season/application of interest, since they can either improve or degrade the raw model outputs. Therefore, for those cases for which the use of these methods cannot be carefully tested by experts, our overall recommendation would be the use of BA methods, which seem to be a safe, easy to implement alternative that provide competitive results in most situations. Nevertheless, all methods (including BA ones) seem to be sensitive to observational uncertainty, especially regarding the reproduction of extremes and spells. For MOS and PP methods, this issue can even lead to important regional differences in interannual skill. The lessons learnt from this work can substantially benefit a wide range of end-users in different socio-economic sectors, and can also have important implications for the development of high-quality climate services.This work has been funded by the C3S activity on Evaluation and Quality Control for seasonal forecasts and the EU project AfriCultuReS (H2020-EU.3.5.5, GA 774652). JMG was partially supported by the Project MULTI-SDM (CGL2015-66583-R, MINECO/FEDER). FJDR was partially funded by the H2020 EUCP project (GA 776613). The authors also acknowledge the SA-OBS dataset and the data providers in the SACA&D Project ( http://saca-bmkg.knmi.nl )

Volcanic Influence on European Summer Precipitation through Monsoons: Possible Cause for “Years without Summer”*

Strong tropical volcanic eruptions have significant effects on global and regional temperatures. Their effects on precipitation, however, are less well understood. Analyzing hydroclimatic anomalies after 14 strong eruptions during the last 400 years in climate reconstructions and model simulations, a reduction of the Asian and African summer monsoons and an increase of south-central European summer precipitation in the year following the eruption was found. The simulations provide evidence for a dynamical link between these phenomena. The weaker monsoon circulations weaken the northern branch of the Hadley circulation, alter the atmospheric circulation over the Atlantic–European sector, and increase precipitation over Europe. This mechanism is able to explain, for instance, the wet summer in parts of Europe during the “year without a summer” of 1816, which up to now has not been explained. This study underlines the importance of atmospheric teleconnections between the tropics and midlatitudes to better understand the regional climate response to stratospheric volcanic aerosols

Comparison of various climate change projections of eastern Australian rainfall

The Australian eastern seaboard is a distinct climate entity from the interior of the continent, with different climatic influences on each side of the Great Dividing Range. Therefore, it is plausible that downscaling of global climate models could reveal meaningful regional detail, or ‘added value’, in the climate change signal of mean rainfall change in eastern Australia un-der future scenarios. However, because downscaling is typically done using a limited set of global climate models and downscaling methods, the results from a downscaling study may not represent the range of uncertainty in plausible projected change for a region suggested by the ensemble of host global climate models. A complete and unbiased representation of the plausible changes in the climate is essential in producing climate projections useful for future planning. As part of this aim it is important to quantify any differences in the change signal between global climate models and downscaling, and understand the cause of these differ-ences in terms of plausible added regional detail in the climate change signal, the impact of sub-sampling global climate models and the effect of the downscaling models themselves. Here we examine rainfall projections in eastern Australia under a high emissions scenario by late in the century from ensembles of global climate models, two dynamical downscaling models and one statistical downscaling model. We find no cases where all three downscaling methods show the same clear regional spatial detail in the change signal that is distinct from the host models. However, some downscaled projections suggest that the eastern seaboard could see little change in spring rainfall, in contrast to the substantial rainfall decrease inland. The change signal in the downscaled outputs is broadly similar at the large scale in the various model outputs, with a few notable exceptions. For example, the model median from dynamical downscaling projects a rainfall increase over the entirety of eastern Australia in autumn that is greater than the global models. Also, there are some instances where a downscaling method produces changes outside the range of host models over eastern Australia as a whole, thus ex-panding the projected range of uncertainty. Results are particularly uncertain for summer, where no two downscaling studies clearly agree. There are also some confounding factors from the model configuration used in downscaling, where the particular zones used for statis-tical models and the model components used in dynamical models have an influence on results and produce additional uncertainty