Abstract Global drought variability of past centuries remains poorly understood due to sparse instrumental records and limited paleoclimate reconstructions. Yet, quantifying long‐term forced and unforced variability is key for assessing the extremeness of modern (2000–2024) drought events. Here, we combine 20 ensemble members from a 600‐year climate simulation (ModE‐Sim, 1420–2009) and modern reanalysis data (ERA5‐Land, 1950–2024) to place recent droughts in a multi‐century context. Drought magnitude and timing are measured using the annual maximum potential cumulative water deficit (PCWD), which integrates atmospheric moisture demand and supply, the daily timing of precipitation events, snow melt, and radiation. We analyze the deviation of modern annual, multi‐year mean, and 30‐year extreme PCWD from alternative reference periods that sample forced and unforced variability to a variable extent. We find a strong decline in the number of regions with unprecedented modern (2000–2024) single‐year and 25‐year mean PCWD as the reference is extended into the past and with the additional consideration of unforced atmospheric variability. This is not the case for extreme droughts. 30‐year extreme PCWD has increased beyond the range of variability of the last 600 years in over half of all regions globally. This suggests that drought extremes are intensifying more rapidly beyond the range experienced in the past than mean conditions. While individual droughts in the modern era may not appear unprecedented when viewed against a century‐long record, the shift in their frequency distribution indicates a rising risk of damaging impacts and an increasing likelihood of droughts reaching unprecedented magnitudes in the near future.

Abstract We use a unique combination of (paleo-)reanalysis (ModE-RA, ERA5) and model simulations (ModE-Sim) to examine extreme summer heat events in Central Europe over the past 600 years (1421-2008) and compare their occurrences to CMIP6 future climate projections. Using a common, fixed climatology, 2003 was identified as the hottest summer of the past 600 years. However, using a moving climatology approach, we identify 1540 (April–September, +2.2 $$^\circ$$ C) and 1590 (June–August, +2.8 $$^\circ$$ C) as the most extreme summer anomalies. The two summers differ in their temporal development, their impacts, and partly their atmospheric circulation patterns. We then analyze similarly anomalous summers in two sets of CMIP6 projections and the ModE-Sim ensemble and connect their occurrence to sea surface temperature anomalies over the North Atlantic. ModE-Sim, which comprises 11,760 model years, produces 0.14% (April-September) and 0.24% (June-August) Central European summers with temperature anomalies from a moving climatology surpassing 1540 and 1590. In CMIP6 projections such anomalies are more frequent, but none reaches the magnitude of the largest June-August anomaly in ModE-Sim which exceeds 4 $$^\circ$$ $$^\circ$$ C. A similarly extreme anomaly in a future climate could dramatically affect ecosystems and societies. Overall, the combination of reanalysis and model simulations provides a unique and comprehensive framework for understanding the drivers of past and future summer heat extremes.

In contrast to the radiative cooling that dominates atmospheric response to volcanoes in most regions, Northern Eurasia shows a warming signal when averaging the observed signal over several eruptions. Up to the current understanding this warming is likely caused by a positive NAO response leading to compensation of the radiative cooling through enhanced advection of mild air from the North Atlantic towards the continent. However, individual eruptions show remarkable differences when computing the response as the difference between the pre and post eruption states for each eruption separately. When only analyzing observations it is difficult to quantify the contributions of internal variability on the one hand and differences in the volcanic forcing on the other hand. Also the response mechanisms are potentially influenced by many different factors. e.g. the strength and the location of the eruption, the ocean state at the time of eruption and internal variability causing different pre-eruption states of the atmosphere. Therefore generalized statements on the volcanic response are difficult to make given the limited number of well-observed major eruptions. Ensemble climate model simulations can help to better understand the related processes by providing multiple realizations of the same historic eruptions and thereby providing a way to separate internal variability from forced signals. Here, we use ModE-Sim, a medium-size atmospheric model ensemble, and its companion dataset ModE-RA, a reanalysis product that uses ModE-Sim as an a-priori state before assimilating historic climate data from different sources. Our first results show that the commonly used practice of averaging about 15 observed eruptions may inherit high uncertainties when interpreting the volcanic winter response.

The jet stream over the Atlantic-European sector is relevant for weather and climate in Europe. It generates temperature extremes, steers moisture and flood-propelling weather systems to Europe or allows blocks to develop and persist leading to drought. Climate change might alter the jet characteristics affecting weather extremes. However, little is known about its interannual-to-decadal variability in the past. In this contribution we present an analysis of strength, tilt, and latitude of the Atlantic-European jet during the past 600 years in a comprehensive monthly climate reconstruction and compare their variability with drought and flood reconstructions in Europe. Summer drought is enhanced in Central Europe in periods with a poleward-shifted jet. An analysis of decadal flood variability shows that flood-rich periods in the warm season in the Alps coincide with an equatorward-shifted jet. In the cold season, a strong jet increases precipitation in Northern Europe, whereas an equatorward-shifted jet leads to frequent floods in Western Europe. Jet position, tilt, and strength are significantly influenced by El Niño and volcanic eruptions, but overall, the forced component is weak. The jet characteristics provide both a mechanism and a diagnostic to analyse decadal hydroclimate variability in Europe. Our 600-year perspective shows that recent changes in the jet are still within the past variability when considering ensemble members separately.

The jet stream over the Atlantic-European sector is relevant for weather and climate in Europe. It generates temperature extremes and steers moisture and flood-propelling weather systems to Europe or facilitates the development of atmospheric blocks, which can lead to drought. Ongoing climate change may alter the jet characteristics, affecting weather extremes. However, little is known about the past interannual-to-decadal variability of the jet stream. Here we analyse the strength, tilt and latitude of the Atlantic-European jet from 1421 to 2023 in an ensemble of monthly and daily reconstructions of atmospheric fields. We compare the variability of these jet indices with blocking frequency and cyclonic activity data and with drought and flood reconstructions in Europe. Summer drought is enhanced in Central Europe in periods with a poleward-shifted jet. An equatorward-shifted jet associated with decreased blocking leads to frequent floods in Western Europe and the Alps, particularly in winter. Recurrent weather patterns causing floods often characterize an entire season, such that an association between peak discharge and jet indices is seen on seasonal or even annual scales. Jet strength and tilt are significantly influenced by volcanic eruptions. Our 600-year perspective shows that recent changes in the jet indices are within the past variability and cannot be drivers of increasing flood and drought frequency.

ClimeApp is a newly developed web-based processing tool for the state-of-the-art ModE-RA palaeo-climate reanalysis. It presents temperature, precipitation and pressure reconstructions with global coverage and monthly resolution for the period 1422 to 2008 CE. These can be visualized as maps or time series and are compared with historical or other climate-related information through composite, correlation and regression functions. ClimeApp allows access to three data sets: (1) ModE-RA, a reanalysis that is created by assimilating early instrumental documentary and proxy data into an ensemble of climate model simulations; (2) ModE-Sim, the native version of the underlying ensemble simulations, i.e. prior to data assimilation; and (3) ModE-RAclim, an alternative version of the reanalysis product. Together, these allow researchers to separate the effects of model simulations and observations on the reanalysis. The app is designed to allow quick data processing for climatologists and easy use for non-climatologists. Specifically, it aims to help bring climate into the humanities, where climatological data still have huge potential to advance research. This paper outlines the development, processing and applications of ClimeApp and presents an updated analysis of the calamitous Tambora volcanic eruption and the 1816 “year without a summer” in Europe, using the new ModE data sets. ClimeApp is available at https://mode-ra.unibe.ch/climeapp/ (last access: 25 October 2024).

Abstract 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.

The Modern Era Reanalysis (ModE-RA) is a global monthly paleo-reanalysis covering the period between 1421 and 2008. To reconstruct past climate fields an offline data assimilation approach is used, blending together information from an ensemble of transient atmospheric model simulations and observations. In the early period, ModE-RA utilizes natural proxies and documentary data, while from the 17th century onward instrumental measurements are also assimilated. The impact of each observation on the reconstruction is stored in the observation feedback archive, which provides additional information on the input data such as preprocessing steps and the regression-based forward models. The monthly resolved reconstructions include estimates of the most important climate fields. Furthermore, we provide a reconstruction, ModE-RAclim, which together with ModE-RA and the model simulations allows to disentangle the role of observations and model forcings. ModE-RA is best suited to study intra-annual to multi-decadal climate variability and to analyze the causes and mechanisms of past extreme climate events.

Abstract. ClimeApp is a newly developed web-based processing tool for the state-of-the-art ModE-RA paleo-climate reanalysis. It presents temperature, precipitation and pressure reconstructions with global coverage and monthly resolution for the period 1422 to 2008 C.E. These can be visualized as maps or timeseries and compared with historical or other climate-related information through composite, correlation and regression functions. Alongside ModE-RA, ClimeApp allows access to the ModE-Sim climate simulation, which is the basis of ModE-RA before assimilating early instrumental, documentary and proxy data. Together with the sensitivity experiment ModE-RAclim, these three data sets allow researchers to separate the effects of external forcing from internal climate variability. The app is designed to allow quick data processing for climatologists and easy use for non-climatologists. Specifically, it aims to help bring climate into the humanities, where climatological data still has huge potential to advance research. This paper outlines the development, processing and applications of ClimeApp, and presents an updated analysis of the calamitous Tambora volcanic eruption and the 1816 ‘year without a summer’ in Europe, using the new ModE datasets. ClimeApp is available at https://mode-ra.unibe.ch/climeapp/.

The Modern Era Reanalysis (ModE-RA) extends current re-analysis back until the year 1420 CE at monthly resolution. It combines our understanding of physics coming from an ensemble of atmospheric model simulations with all available direct and indirect climate observations of monthly to annual resolution. A 20-member ensemble of atmospheric model simulations (ModE-Sim) driven by an ensemble of external forcings and an ensemble of new sea surface temperature reconstructions serves as a prior estimate of the possible climate states at each assimilation time step. After the entire simulations were completed, we assimilated multiple data sources using an offline Kalman filtering technique. We include up to ~100000 monthly to annual observations per year. These consist of thousands of existing and newly digitised instrumental measurements of temperature, precipitation, wet days per months, and pressure, including measurements made on ships over the ocean and in harbours. Earliest instrumental station data go back to the year 1658. Additionally, we collected and digitised climate information from historical documents, including phenological data. These are especially valuable for the autumn, winter and spring season and go back to the year 1420. Finally, we assimilate annually resolved climate proxies. The vast majority are tree-ring observations, which represent growing season conditions on the continents. In otherwise data sparse regions, we supplemented ice and coral data at high latitudes and the tropical oceans, respectively. ModE-RA offers especially insides into interannual to multidecadal variability such as phases of accelerated warming, monsoon strength or subtropical droughts as well as rare events such as volcanic eruptions.

The ongoing discussion about the AMOC slowdown over the 21st century requires a detailed understanding of preindustrial AMOC variability. Here, we present a surface nudging technique to reconstruct the AMOC variability during the Little Ice Age from 1450–1780 CE. The AMOC reconstruction is based on a 10-member ensemble ocean model simulation nudged to proxy-reconstructed sea surface temperature. This approach validates and improves existing knowledge of the AMOC variability, showing that the AMOC slowdown under stable atmospheric CO2 conditions is mainly driven by a 4 to 7 year lagged effect of surface heat flux associated with the North Atlantic Oscillation.

The North Atlantic subpolar gyre (SPG) plays a crucial role in determining the regional ocean surface temperature (SST), which has profound implications on the surrounding continental and coastal climate. Here, we analyze the Max Planck Institute-Grand Ensemble global warming experiments and show that the SPG can evolve in two distinct phases under continuous global warming. In the first phase, as the global mean surface temperature approaches 2-K warming, the eastern SPG intensifies in combination with a weakening Atlantic meridional overturning circulation (AMOC), accompanied by a cooling of subpolar North Atlantic SST, known as the warming hole. The associated oceanic fingerprint matches with the observations over the last 15 years, where an intensification and cooling of the eastern SPG is related to salinity reduction at the eastern side of the SPG. However, for further warming beyond 2 K, in spite of a continuous decline in the AMOC, a northward shift of the mean zonal wind extends the subtropical gyre northward with an associated disruption of the eastern SPG intensification, resulting in the cessation of the warming hole. Therefore, a shift from the initially dominating oceanic drivers to the atmospheric driver results into a two-phase evolution of the North Atlantic Ocean SPG circulation and the associated SST under continuous global warming.

The decade 1531-1540 was characterized by a high number of dry summer episodes making it the driest summer decade of the past 500 years in some areas of Central Europe. In addition to established climate reconstructions, we use the ModE-RA (Modern Era Reanalysis) and ModE-Sim (Modern Era Simulations) data sets which provide gridded climate information of the past 600 years to analyse the summers of 1531-1540 and compare it with other decadal dry spells over Europe.While most previous studies focus on the variability of individual drought events or multi-decadal mega droughts, our aim is to identify decadal scale dry spells similar to the 1531-1540 decade. With our three-dimensional reanalysis and the model simulations forced with observed volcanic forcings and prescribed SST we can then investigate the atmospheric and oceanic drivers of such events as well as the influence of the volcanic forcings.Our first results show that the magnitude and distribution of observed decadal dry spells in ModE-RA is realistic and comparable to other climate reconstructions. In the ModE-Sim ensemble mean the drying signal for the 1531-1540 event is less strong but still visible. Overall, with our ongoing analysis we contribute to the evaluation of past and future decadal dry spells over Europe that are driven by both natural and anthropogenic forcings.

ModE-Sim is a medium size ensemble that can be used to study climate variability of the past 600 years. It was created using the atmospheric general circulation model ECHAM6 in its LR version (T63L47). 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 was designed as an 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 ensemble to study climate variability. We show that the ensemble has a realistic response to external forcings and that it is capable of capturing internal variability on monthly to annual time scales. At the example of heat waves we show that ModE-Sim can even be useful to study extreme events.

We introduce ModE-Sim (Modern Era SIMulations), a medium-sized ensemble of simulations with the atmospheric general circulation model ECHAM6 in its LR (low-resolution) version (T63; approx. 1.8∘ horizontal grid width with 47 vertical levels). At the lower boundary we use prescribed sea surface temperatures and sea ice that reflect observed values while accounting for uncertainties in these. Furthermore we use radiative forcings that also reflect observed values while accounting for uncertainties in the timing and strength of volcanic eruptions. The simulations cover 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 31 620 simulated years in total. ModE-Sim is suitable for many applications as its various subsets can be used as initial-condition and boundary-condition ensembles 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, mainly focusing on the two key variables, 2 m 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. Through the example of heat waves, we show that the ensemble is even capable of capturing certain types of extreme events.

Annual-to-decadal variability in northern midlatitude temperature is dominated by the cold season. However, climate field reconstructions are often based on tree rings that represent the growing season. Here we present cold-season (October-to-May average) temperature field reconstructions for the northern midlatitudes, 1701-1905, based on extensive phenological data (freezing and thawing dates of rivers, plant observations). Northern midlatitude land temperatures exceeded the variability range of the 18th and 19th centuries by the 1940s, to which recent warming has added another 1.5 °C. A sequences of cold winters 1808/9-1815/6 can be explained by two volcanic eruptions and unusual atmospheric flow. Weak southwesterlies over Western Europe in early winter caused low Eurasian temperatures, which persisted into spring even though the flow pattern did not. Twentieth century data and model simulations confirm this persistence and point to increased snow cover as a cause, consistent with sparse information on Eurasian snow in the early 19th century.

We introduce a 36 to 40-member ensemble of simulations with the atmospheric general circulation model ECHAM6 that is designed to form the basis for a 3-dimensional climate reconstruction dataset in the PALAEO-RA project. It covers the years 1420 to 2009, the period for which combining natural proxies such as tree rings and archives of society such as documentary data allows to perform global climate reconstructions. However, the information provided by these historical sources is usually sparse in temporal and spatial resolution. Our simulations provide the necessary background for data assimilation and thus complement the historical information by adding physical constraints implemented in the model formulation. Our experimental setup is designed to determine the range of internal climate variability under prescribed forcings. It is oriented on the PMIP4 setup with slight modifications, using realistic ocean boundary conditions (SST and sea ice cover) and radiative forcings while also accounting for uncertainties in these. Our presentation will give an overview of our experimental setup and show the results of the first applications. We present an evaluation of the ensemble, including measures on how well the ensemble can sample the internal variability of some variables of interest. Beyond this, we hope to stimulate a discussion on possible further applications.

Abstract. 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 socio-economical 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 endeavour.

Annual-to-decadal variability in northern midlatitude temperature is predominantly dominated by the cold season. However, climate field reconstructions, which are essential for understanding the underlying mechanisms, are often based on tree rings. These mainly represent the growing season and allow limited insight on cold season effects. Plant and ice phenology data, on the other hand, are a rich source of cold season information that remains largely overlooked in climate reconstructions to date and could help to fill the seasonal gap. Here, we present Northern Hemispheric temperature field reconstructions for the extended cold season (October-to-May average) for 1701-1905 based entirely on phenological data. Time series of freezing and thawing dates of rivers together with a few early-spring plant observations covering a large area of the northern midlatitudes are used in a simple data assimilation framework. The reconstructions allow a 320-yr perspective of climate variability and change of boreal cold season climate and unveil that the temperature of the northern midlatitude land areas exceeded the variability range of the 18th and 19th centuries by the 1940s, to which recent warming has added another 1.5 °C. We also find 5-10 year long sequences of cold northern midlatitude winters. The most prominent example lasted from 1808/9 to 1815/6. The conspicuously cooling during that period is associated with two volcanic eruptions (1808/9 and 1815), which caused cooling as a direct effect. The years between the eruptions are characterized by weak southwesterly atmospheric flow over the Atlantic-European sector in early winter. This lead to low Eurasian temperatures, which persisted into spring while the flow pattern did not. Twentieth century data and model simulations confirm this persistence and point to increased snow cover as a cause. This is consistent with independent information on Eurasian snow in the early 19th century.

We present a 50-member global monthly gridded Sea Surface Temperature (SST) and Sea Ice Concentration (SIC) dataset covering 850 years (1000–1849). The SST fields are based on an existing coarse-resolution ensemble of annual reconstructions and augmented with intra-annual and sub-grid scale variability, such that the annual means of the coarse resolution SST reconstructions are preserved. We utilize a large body of historical observational inputs from ICOADS (1780 – 1849) in an offline data assimilation approach. Furthermore, the best sea ice analogs are selected based on a measure of similarity between subpolar and midlatitude SSTs of our reconstruction and HadISST SIC. The resulting SST and SIC fields will reflect a spatially and temporal consistent representation of the historical state of the ocean and are reconstructed to be used as forcing for AGCM simulations. Reference: Samakinwa, E., Valler, V., Hand, R. et al. An ensemble reconstruction of global monthly sea surface temperature and sea ice concentration 1000–1849. Sci Data 8, 261 (2021). https://doi.org/10.1038/s41597-021-01043-1