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Abstract. Data assimilation is a promising approach to obtain climate reconstructions that are both consistent with observations of the past and with our understanding of the physics of the climate system as represented in the climate model used. Here, we investigate the use of ensemble square root filtering (EnSRF) -a technique used in weather forecasting -for climate reconstructions. We constrain an ensemble of 29 simulations from an atmosphere-only general circulation model (GCM) with 37 pseudo-proxy temperature time series. Assimilating spatially sparse information with low temporal resolution (semi-annual) improves the representation of not only temperature, but also other surface properties, such as precipitation and even upper air features such as the intensity of the northern stratospheric polar vortex or the strength of the northern subtropical jet. Given the sparsity of the assimilated information and the limited size of the ensemble used, a localisation procedure is crucial to reduce "overcorrection" of climate variables far away from the assimilated information.

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Climatic variations at decadal scales such as phases of accelerated warming or weak monsoons have profound effects on society and economy. Studying these variations requires insights from the past. However, most current reconstructions provide either time series or fields of regional surface climate, which limit our understanding of the underlying dynamics. Here, we present the first monthly paleo-reanalysis covering the period 1600 to 2005. Over land, instrumental temperature and surface pressure observations, temperature indices derived from historical documents and climate sensitive tree-ring measurements were assimilated into an atmospheric general circulation model ensemble using a Kalman filtering technique. This data set combines the advantage of traditional reconstruction methods of being as close as possible to observations with the advantage of climate models of being physically consistent and having 3-dimensional information about the state of the atmosphere for various variables and at all points in time. In contrast to most statistical reconstructions, centennial variability stems from the climate model and its forcings, no stationarity assumptions are made and error estimates are provided.

CapsuleState-of-the-Art statistical postprocessing techniques for ensemble forecasts are reviewed, together with the challenges posed by a demand for timely, high-resolution and reliable probabilistic information. Possible research avenues are also discussed.

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.

Abstract. Near-term climate predictions such as decadal climate forecasts are increasingly being used to guide adaptation measures. For near-term probabilistic predictions to be useful, systematic errors of the forecasting systems have to be corrected. While methods for the calibration of probabilistic forecasts are readily available, these have to be adapted to the specifics of decadal climate forecasts including the long time horizon of decadal climate forecasts, lead-timedependent systematic errors (drift) and the errors in the representation of long-term changes and variability. These features are compounded by small ensemble sizes to describe forecast uncertainty and a relatively short period for which typically pairs of reforecasts and observations are available to estimate calibration parameters. We introduce the Decadal Climate Forecast Recalibration Strategy (DeFoReSt), a parametric approach to recalibrate decadal ensemble forecasts that takes the above specifics into account. DeFoReSt optimizes forecast quality as measured by the continuous ranked probability score (CRPS). Using a toy model to generate synthetic forecast observation pairs, we demonstrate the positive effect on forecast quality in situations with pronounced and limited predictability. Finally, we apply DeFoReSt to decadal surface temperature forecasts from the MiKlip prototype system and find consistent, and sometimes considerable, improvements in forecast quality compared with a simple calibration of the lead-time-dependent systematic errors.

Subseasonal predictions bridge the gap between medium-range weather forecasts and seasonal climate predictions. This time horizon is of crucial importance for many planning purposes, including energy production and agriculture. The verification of such predictions is normally done for areal averages of upper-air parameters. Only few studies exist that verify the forecasts for surface parameters with observational stations, although this is crucial for real-world applications, which often require such predictions at specific surface locations. With this study we provide an extensive station-based verification of subseasonal forecasts against 1,637 ground based observational time series across Europe. Twenty years of temperature and precipitation reforecasts of the European Centre for Medium-Range Weather Forecasts Integrated Forecasting System are used to analyze the period of April 1995 to March 2014. A lead time and seasonally dependent bias correction is performed to correct the daily temperature and precipitation forecasts at all stations individually. Two bias correction techniques are compared, a mean debiasing method and a quantile mapping approach. Commonly used skill scores characterizing different aspects of forecast quality are computed for weekly aggregated forecasts with lead times of 5-32 days. Overall, promising skill is found for temperature in all seasons except spring. Temperature forecasts tend to show higher skill in Northern Europe and in particular around the Baltic Sea, and in winter. Bias correction is shown to be essential in enhancing the forecast skill in all four weeks for most of the stations and for both variables with QM generally performing better.MONHART ET AL. 7999