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We show that ModE‐Sim, a global ensemble of atmospheric model simulations that uses observed ocean boundary conditions and radiative forcings providing 36 members with daily climate information can be used to in‐depth analyze the known spatial and temporal variability of heatwaves in the Northern Hemisphere and Australia during the past 160 years. It can also be used to study actual past extreme events like heatwaves during the El Nino 1877/1878. To analyze past heatwaves we use a novel approach of a transient baseline climatology and compare to different observational data sets. Furthermore, we analyze sea surface temperature anomalies during the most extreme heatwave summers in North America, Europe and Australia and identify the most prominent anomaly patterns over the Subpolar North Atlantic and in the Central Pacific. Using a large ensemble of forced simulations, like ModE‐Sim can consequently contribute to a better understanding of preindustrial heatwaves, their decadal variability and their driving mechanisms. Plain Language Summary: In this study we use an atmospheric model to look at historical heatwaves during the past 160 years. In several regions of the Northern Hemisphere and in Australia we can successfully reproduce heatwave variability that we see in observations with our model. One of the main reasons that we reproduce the evolution and distribution of past heatwaves with our model is that our simulations use observed Sea Surface Temperatures. We show that deviations from the mean Sea Surface Temperatures over the Central Pacific and the Subpolar North Atlantic are connected to more summer heatwaves over Europe and North America. We also look at the big El Nino event in 1877/1878 and show that there was global increase in heatwaves during this event, even though heatwaves in parts of Europe and in North America got less frequent. Overall, we show that this model is very useful to better understand extreme events like heatwaves during the preindustrial era where we only have very little or no climate observations. Key Points: Using a forced AGCM ensemble can largely reproduce heatwave variability of the past 160 years throughout the Northern Hemisphere and AustraliaSea surface temperature anomalies over the Central Pacific and Subpolar North Atlantic are connected to strong heatwave summers in North America and EuropeDuring the El Nino 1877/1878 there was a global increase in heatwave days, while heatwaves in North America and parts of Europe decreased [ABSTRACT FROM AUTHOR]

We introduce ModE-Sim, a medium size ensemble of simulations with the atmospheric general circulation model ECHAM6 in its LR version (T63/approx. 1.8°horizontal with 47 vertical levels) that covers the period from 1420 to 2009. With 60 ensemble members between 1420 and 1850 and 36 ensemble members from 1850 to 2009 ModE-Sim consists of 31620 simulated years in total. The dataset forms the input for a data assimilation procedure that combines historical climate informations with additional constraints from a climate model to produce a novel gridded 3-dimensional dataset of the modern era. Additionally, ModE-Sim on its own is also suitable for many other applications as its various subsets can be used as initial condition and boundary condition ensemble to study climate variability. The main intention of this paper is to give a comprehensive description of the experimental setup of ModE-Sim and to provide an evaluation of the two key variables 2m-temperature and precipitation. We demonstrate ModE-Sim's ability to represent their mean state, to produce a reasonable response to external forcings and to sample internal variability. At the example of heat waves we show that the ensemble is even capable of capturing extreme events. [ABSTRACT FROM AUTHOR]

Although collaborative efforts have been made to retrieve climate data from instrumental observations and paleoclimate records, there is still a large amount of valuable information in historical archives that has not been utilized for climate reconstruction. Due to the qualitative nature of these datasets, historical texts have been compiled and studied by historians aiming to describe the climate impact in socioeconomic aspects of human societies, but the inclusion of this information in past climate reconstructions remains fairly unexplored. Within this context, we present a novel approach to assimilate climate information contained in chronicles and annals from the 15th century to generate robust temperature and precipitation reconstructions of the Burgundian Low Countries, taking into account uncertainties associated with the descriptions of narrative sources. After data assimilation, our reconstructions present a high seasonal temperature correlation of ∼0.8 independently of the climate model employed to estimate the background state of the atmosphere. Our study aims to be a first step towards a more quantitative use of available information contained in historical texts, showing how Bayesian inference can help the climate community with this endeavor. [ABSTRACT FROM AUTHOR]

European flood frequency and intensity change on a multidecadal scale. Floods were more frequent in the 19th (central Europe) and early 20th century (western Europe) than during the mid-20th century and again more frequent since the 1970s. The causes of this variability are not well understood and the relation to climate change is unclear. Palaeoclimate studies from the northern Alps suggest that past flood-rich periods coincided with cold periods. In contrast, some studies suggest that more floods might occur in a future, warming world. Here we address the contribution of atmospheric circulation and of warming to multidecadal flood variability. For this, we use long series of annual peak streamflow, daily weather data, reanalyses, and reconstructions. We show that both changes in atmospheric circulation and moisture content affected multidecadal changes of annual peak streamflow in central and western Europe over the past two centuries. We find that during the 19th and early 20th century, atmospheric circulation changes led to high peak values of moisture flux convergence. The circulation was more conducive to strong and long-lasting precipitation events than in the mid-20th century. These changes are also partly reflected in the seasonal mean circulation and reproduced in atmospheric model simulations, pointing to a possible role of oceanic variability. For the period after 1980, increasing moisture content in a warming atmosphere led to extremely high moisture flux convergence. Thus, the main atmospheric driver of flood variability changed from atmospheric circulation variability to water vapour increase. [ABSTRACT FROM AUTHOR]

This paper describes a global monthly gridded Sea Surface Temperature (SST) and Sea Ice Concentration (SIC) dataset for the period 1000–1849, which can be used as boundary conditions for atmospheric model simulations. The reconstruction is based on existing coarse-resolution annual temperature ensemble reconstructions, which are then augmented with intra-annual and sub-grid scale variability. The intra-annual component of HadISST.2.0 and oceanic indices estimated from the reconstructed annual mean are used to develop grid-based linear regressions in a monthly stratified approach. Similarly, we reconstruct SIC using analog resampling of HadISST.2.0 SIC (1941–2000), for both hemispheres. Analogs are pooled in four seasons, comprising of 3-months each. The best analogs are selected based on the correlation between each member of the reconstructed SST and its target. For the period 1780 to 1849, We assimilate historical observations of SST and night-time marine air temperature from the ICOADS dataset into our reconstruction using an offline Ensemble Kalman Filter approach. The resulting dataset is physically consistent with information from models, proxies, and observations. Measurement(s) temperature of sea surface • sea ice concentration Technology Type(s) computational modeling technique Sample Characteristic - Environment ocean Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.16592657 [ABSTRACT FROM AUTHOR]

The question of whether ocean dynamics are relevant for basin-scale North Atlantic decadal temperature variability is the subject of ongoing discussions. Here, we analyze a set of simulations with a single climate model consisting of a 2000-yr preindustrial control experiment, a 100-member historical ensemble, and a 100-member ensemble forced with an incremental CO2 increase by 1% yr−1. Compared to previous approaches, our setup offers the following advantages: First, the large ensemble size allows us to robustly separate internally and externally forced variability and to robustly detect statistical links between different quantities. Second, the availability of different scenarios allows us to investigate the role of the background state for drivers of the variability. We find strong evidence that ocean dynamics, particularly ocean heat transport variations, form an important contribution to generate the Atlantic multidecadal variability (AMV) in the Max Planck Institute Earth System Model (MPI-ESM). Particularly the northwest North Atlantic is substantially affected by ocean circulation for the historical and preindustrial simulations. Anomalies of the Labrador Sea deep ocean density precede a change of the Atlantic meridional overturning circulation (AMOC) and heat advection to the region south of Greenland. Under strong CO2 forcing, the AMV–SST regression pattern shows crucial changes: SST variability in the northwestern part of the North Atlantic is strongly reduced, so that the AMV pattern in this scenario is dominated by the low-latitude branch. We found a connection to changes in the deep-water formation that cause a strong reduction of the mean AMOC and its variability. Consequently, ocean heat transport convergence becomes less important for the SST variability south of Greenland. [ABSTRACT FROM AUTHOR]