Abstract. Global changes in the climate, especially the warming trend in mean temperature, have received increasing public and scientific attention. Improving the understanding of changes in the mean and variability of climate variables as well as their interrelation is crucial for reliable climate change projections. Comparisons between general circulation models and paleoclimate archives using indirect proxies for temperature and/or precipitation have been used to test and validate the capability of climate models to represent climate changes. The oxygen isotopic ratio δ18O is routinely measured in speleothem samples at decadal or higher resolution and single specimens can cover full Glacial-Interglacial cycles. The calcium carbonate cave deposits are precisely dateable and provide well preserved (semi-) continuous, albeit multivariate climate signals in the lower and mid-latitudes, where the measured δ18O in the mineral does not directly represent temperature or precipitation. Therefore, speleothems represent suitable archives to assess simulated climate model abilities for the simulation of climate variability beyond the timescales covered by meteorological observations (10–100 yr). Here, we present three transient isotope enabled simulations from the Hadley Center Climate Model version 3 (iHadCM3) covering the last millennium (850–1850 CE) and compare these to a large global dataset of speleothem δ18O records from the Speleothem Isotopes Synthesis and AnaLysis (SISAL) database version 2 (Comas-Bru et al., 2020). We evaluate systematically offsets in mean and variance of simulated δ18O and test for the main climate drivers for individual records or regions. The time-mean spatial offsets between the simulated δ18O and the speleothem data are fairly small. However, using robust filters and spectral analysis, we show that the observed proxy-based variability of δ18O is lower (higher) than simulated by iHadCM3 on decadal (centennial) timescales. Most of this difference can likely be attributed to the records' lower temporal resolution and averaging processes affecting the δ18O signal. Using cross-correlation analyses at site-level and modeled gridbox level, we find evidence for highly variable but generally low signal-to-noise ratios in the proxy data. This points at a high influence of cave-internal processes and regional climate particularities and could suggest low regional representativity of individual sites. Long-range strong positive correlations dominate the speleothem correlation network but are much weaker in the simulation. One reason for this could lie in a lack of longterm internal climate variability in these model simulations, which could be tested by repeating similar comparisons with other isotope-enabled climate models and paleoclimate databases.

Abstract. Improving the understanding of changes in the mean and variability of climate variables as well as their interrelation is crucial for reliable climate change projections. Comparisons between general circulation models and paleoclimate archives using indirect proxies for temperature or precipitation have been used to test and validate the capability of climate models to represent climate changes. The oxygen isotopic ratio δ18O, a proxy for many different climate variables, is routinely measured in speleothem samples at decadal or higher resolution, and single specimens can cover full glacial–interglacial cycles. The calcium carbonate cave deposits are precisely dateable and provide well preserved (semi-)continuous albeit multivariate climate signals in the lower and mid-latitudes, where the measured δ18O in the mineral does not directly represent temperature or precipitation. Therefore, speleothems represent suitable archives to assess climate model abilities to simulate climate variability beyond the timescales covered by meteorological observations (101–102 years). Here, we present three transient isotope-enabled simulations from the Hadley Center Climate Model version 3 (iHadCM3) covering the last millennium (850–1850 CE) and compare them to a large global dataset of speleothem δ18O records from the Speleothem Isotopes Synthesis and AnaLysis (SISAL) database version 2 (Comas-Bru et al., 2020b). We systematically evaluate offsets in mean and variance of simulated δ18O and test for the main climate drivers recorded in δ18O for individual records or regions. The time-mean spatial offsets between the simulated δ18O and the speleothem data are fairly small. However, using robust filters and spectral analysis, we show that the observed archive-based variability of δ18O is lower than simulated by iHadCM3 on decadal and higher on centennial timescales. Most of this difference can likely be attributed to the records' lower temporal resolution and averaging or smoothing processes affecting the δ18O signal, e.g., through soil water residence times. Using cross-correlation analyses at site level and modeled grid-box level, we find evidence for highly variable but generally low signal-to-noise ratios in the proxy data. This points to a high influence of cave-internal processes and regional climate particularities and could suggest low regional representativity of individual sites. Long-range strong positive correlations dominate the speleothem correlation network but are much weaker in the simulation. One reason for this could lie in a lack of long-term internal climate variability in these model simulations, which could be tested by repeating similar comparisons with other isotope-enabled climate models and paleoclimate databases.

Climate variability, that is variations in the statistics of climate parameters, characterizes Earth's dynamical system and is the primary influence on extreme events (Katz & Brown, 1992). Variability arises from unforced processes, internal to the climate system, and from forced processes, caused by external natural and anthropogenic drivers. Natural drivers include volcanic and solar forcing, contributing significantly to climate variability (Crowley & Unterman, 2013b). Due to anthropogenic activities, the recent trend of global mean surface temperature (GMST) and other variables has clearly emerged beyond the range of natural variability (

<p>Rain-on-snow (ROS) floods are responsible for the overwhelming majority of floods affecting multiple major river basins simultaneously in Europe during the last century. These widespread floods have serious negative economical, social and ecological effects, and knowledge about their rate of occurrence is critical for future projections in the face of climate change.</p><p>Recent studies have shown that ROS events (with flood-inducing potential) in Europe increase and decrease based on the elevation range considered since 1950 and there appears to be a clustering pattern of flood-poor and flood-rich periods since 1900. Our goal is to analyze if these changes in frequency can be realistically described by a stationary process (or a combination thereof) or if there must be hidden time-dependent driving factors to explain the observed clustering. To test this theory we analyze a simulation for the time period 1901-2010 based on ERA-20C dynamically downscaled using a coupled RCM. We apply a method from scan statistics and confirm the existence of significant periods poor and rich in ROS events with regards to the reference condition of independent and identically distributed random events and present their position in time. The same procedure is applied to the ROS event constituents (rainfall and snowmelt), where we identify such periods in the rainfall, but not in the snowmelt time series. We construct a stochastic ROS model by modelling precipitation and snowmelt via stationary gamma distributions fitted to our data but are unable to reproduce the observed clustering behaviour using the combined signal.</p><p>This study confirms that the observed ROS floods in Central Europe are unlikely to be the result of stationary processes which hints at climate drivers for the compound rain-on-snow process in Europe.</p>

<div> <p>Climate variability is the primary influence on climate extremes and affected by natural forcing from solar irradiance and volcanic eruptions. Global warming impacts climate variability, but there is contradictory and incomplete evidence on the spatio-temporal patterns. Strong volcanic eruptions have been suggested to reduce temperatures less in warmer climate states. However, the underlying question of state-dependent effect of natural forcing on local and global variability remains open. Moreover, there are uncertainties about the role of natural forcing in the mismatch between simulated and reconstructed local, long-term variability. <span> </span></p> </div><div> <p>Using a 12-member GCM ensemble with targeted boundary conditions, we present naturally-forced and equilibrium, millennium-length simulations for the Last Glacial Maximum (LGM) and the Pre-Industrial (PI). We quantify the local and global climate response to solar and volcanic forcing in the LGM and PI, and contrast variability from forced and control simulations on annual-to-multicentennial scales. We differentiate various contributions from the atmosphere, oceans, and particularly that of sea ice using a 2D energy balance model (EBM). Spectral analysis of simulated temperatures shows that global variability is predominately determined by natural forcing. Local mean spectra are more characteristic for the mean climate state and reveal a decrease in local variability with warming. The global and local response to natural forcing is robust against changes in the mean climate. Particularly, the spatial patterns of the surface climate's response to volcanic eruptions widely agree across states. Weak local differences resulted primarily from sea ice dynamics. The sea ice contribution is the strongest on interannual scales. It remains significant on decadal scales and longer, providing a key mechanism of long-term variability. We validate the simulated variability against observational and paleoclimate data. The variance obtained from proxies is increasingly larger on longer timescales compared to that from simulated time series. The inclusion of natural forcing reduces the model-data mismatch on decadal-to-multicentennial scales and, thus, provides a more accurate representation of climate variability. </p> </div><div> <p>Consideration of natural forcing is therefore paramount for model-data comparison and future projections. The robust temperature response suggests that findings on the ability of models to simulate past variability should translate to future climates, and can thus help constrain variability.<span> </span></p> </div>