Description: Berlin (Germany) has one of the longest climate records in the world (Cubasch and Kadow, 2011). In the 17th century mainly the family of the astronomer Kirch started measuring the temperature and wrote down general weather patterns. In the beginning of the 18th century the measurements became more regular including multiple measures per day. Over time, more inner city stations appeared but also disappeared. Therefore, this Berlin Climate Record is a moving station, but representable for the inner city of Berlin. With these datasets we reactivate the long inner city climate record in several variables. In this study, we digitized, analyzed, corrected, reconstructed, and provide the datasets on the very rare daily time frequency.

Description: Berlin has one of the longest climate records in the world (Cubasch and Kadow, 2011). In the 17th century mainly the family of the astronomer Kirch started measuring the temperature and wrote down general weather patterns. In the beginning of the 18th century the measurements became more regular including multiple measures per day. Over time, more inner city stations appeared but also disappeared. Therefore, this Berlin Climate Record is a moving station, but representable for the inner city of Berlin. Many approaches were combining these datasets including bias correction efforts. With this dataset from 1876-01-01 to 1908-12-31 we reactivate the long inner city precipitation record, related to Kadow et al. 2016. In this study, we digitized, analyzed, and corrected the precipitation data set on the daily time frequency. The dataset has been reconstructed from: "Das Klima von Berlin (III) Niederschlag, Luftfeuchtigkeit, Dampfdruck, Bewölkung, Sonnenscheindauer, Wind, Luftdruck - Abh. des Met. Dienstes der DDR Nr. 118, Band XV, Akademie-Verlag 1976".

Description: Decadal predictions by Earth system models aim to capture the state and phase of the climate several years in advance. Atmosphere-ocean interaction plays an important role for such climate forecasts. While short-term weather forecasts represent an initial value problem and long-term climate projections represent a boundary condition problem, the decadal climate prediction falls in-between these two timescales. In recent years, more precise initialization techniques of coupled Earth system models and increased ensemble sizes have improved decadal predictions. However, climate models in general start losing the initialized signal and its predictive skill from one forecast year to the next. Here we show that the climate prediction skill of an Earth system model can be improved by a shift of the ocean state towards the ensemble mean of its individual members at seasonal intervals. We found that this procedure, called ensemble dispersion filter, results in more accurate results than the standard decadal prediction. Global mean and regional temperature, precipitation, and winter cyclone predictions show an increased skill up to 5 years ahead. Furthermore, the novel technique outperforms predictions with larger ensembles and higher resolution. Our results demonstrate how decadal climate predictions benefit from ocean ensemble dispersion filtering towards the ensemble mean.

Description: Berlin has one of the longest climate records in the world (Cubasch and Kadow, 2011). In the 17th century mainly the family of the astronomer Kirch started measuring the temperature and wrote down general weather patterns. In the beginning of the 18th century the measurements became more regular including multiple measures per day. Over time, more inner city stations appeared but also disappeared. Therefore, this Berlin Climate Record is a moving station, but representable for the inner city of Berlin. Many approaches were combining these datasets including bias correction efforts. With this dataset from 01.01.1766 to 31.12.1934 we reactivate the long inner city temperature record. In this study, we digitized, analyzed, and corrected the data set on the daily time frequency. The dataset has been reconstructed from: "Das Klima von Berlin (II) Temperatur - Abh. des Met. Dienstes der DDR Nr. 103, Band XIII, Akademie-Verlag 1971"

Description: Paleoclimate data assimilation (DA) is a promising technique to systematically combine the information from climate model simulations and proxy records. Here, we investigate the assimilation of tree-ring-width (TRW) chronologies into an atmospheric global climate model using ensemble Kalman filter (EnKF) techniques and a process-based tree-growth forward model as an observation operator. Our results, within a perfect-model experiment setting, indicate that the "online DA" approach did not outperform the "off-line" one, despite its considerable additional implementation complexity. On the other hand, it was observed that the nonlinear response of tree growth to surface temperature and soil moisture does deteriorate the operation of the time-averaged EnKF methodology. Moreover, for the first time we show that this skill loss appears significantly sensitive to the structure of the growth rate function, used to represent the principle of limiting factors (PLF) within the forward model. In general, our experiments showed that the error reduction achieved by assimilating pseudo-TRW chronologies is modulated by the magnitude of the yearly internal variability in the model. This result might help the dendrochronology community to optimize their sampling efforts.

Description: Data assimilation (DA) methods have been used recently to constrain the climate model forecasts by paleo-proxy records. Both DA and climate models are computationally very expensive. Moreover, in paleo-DA, the time step of consequence for observations is usually too long for a dynamical model to follow the previous analysis state and the chaotic behavior of the model becomes dominant. The majority of recent paleoclimate studies using DA have performed low- or intermediate-resolution global simulations along with an "off-line" DA approach. In an off-line DA, the re-initialization cycle is completely removed after the assimilation step. In this paper, we design a computationally affordable DA to assimilate yearly pseudo-observations and real observations into an ensemble of COSMO-CLM high-resolution regional climate model (RCM) simulations over Europe, for which the ensemble members slightly differ in boundary and initial conditions. Within a perfect model experiment, the performance of the applied DA scheme is evaluated with respect to its sensitivity to the noise levels of pseudo-observations. It was observed that the injected bias in the pseudo-observations linearly impacts the DA skill. Such experiments can serve as a tool for the selection of proxy records, which can potentially reduce the state estimation error when they are assimilated. Additionally, the sensitivity of COSMO-CLM to the boundary conditions is addressed. The geographical regions where the model exhibits high internal variability are identified. Two sets of experiments are conducted by averaging the observations over summer and winter. Furthermore, the effect of the spurious correlations within the observation space is studied and a optimal correlation radius, within which the observations are assumed to be correlated, is detected. Finally, the pollen-based reconstructed quantities at the mid-Holocene are assimilated into the RCM and the performance is evaluated against a test dataset. We conclude that the DA approach is a promising tool for creating high-resolution yearly analysis quantities. The affordable DA method can be applied to efficiently improve climate field reconstruction efforts by combining high-resolution paleoclimate simulations and the available proxy records.