In order to explore the sensitivity of the climate impact of volcanic eruptions to eruption season and latitude, we simulate volcanic eruptions at different latitudes and in different seasons with the Max Planck Institute Earth System Model (MPI-ESM). We use the same configuration of the MPI-ESM model as used for the historical simulation of CMIP6. An initial run is performed firstly (PINArst). Then we perform 23 and 10 control runs without any volcanic eruption (PINAref and PINAwRef). Two groups of three different latitudinal volcanic eruptions in boreal summer and winter are simulated. We perform 10-member simulations for each eruption case. 9 Tg of total sulfur injection magnitude is prescribed. The eruption latitudes are set to be 0° for the equatorial eruptions (PINAeq and PINAwEQ) and 30° N and 30° S for the northern and southern hemispheric eruptions (PINAnh, PINAwNH, PINAsh and PINAwNH), respectively. For the summer eruptions, the date is set to be the same as the 1991 Pinatubo eruption on June 15, 1991; for the winter eruptions, the date is set to be December 15, 1991.

In order to explore the sensitivity of the climate impact of volcanic eruptions to eruption season and latitude, we simulate volcanic eruptions at different latitudes and in different seasons with the Max Planck Institute Earth System Model (MPI-ESM). We use the same configuration of the MPI-ESM model as used for the historical simulation of CMIP6. An initial run is performed firstly (PINArst). Then we perform 23 and 10 control runs without any volcanic eruption (PINAref and PINAwRef). Two groups of three different latitudinal volcanic eruptions in boreal summer and winter are simulated. We perform 10-member simulations for each eruption case. 9 Tg of total sulfur injection magnitude is prescribed. The eruption latitudes are set to be 0° for the equatorial eruptions (PINAeq and PINAwEQ) and 30° N and 30° S for the northern and southern hemispheric eruptions (PINAnh, PINAwNH, PINAsh and PINAwNH), respectively. For the summer eruptions, the date is set to be the same as the 1991 Pinatubo eruption on June 15, 1991; for the winter eruptions, the date is set to be December 15, 1991.

Given the importance of sub-daily extreme precipitation events for the occurrence of pluvial floods, it is a key component in climate change adaptation to quantify the likelihood of such extreme events under current and future climate conditions. Such assessments are usually limited by a lack of sufficiently dense and sub-daily precipitation observations, (ii) high-resolution convection-permitting regional climate model (CPM) simulations that realistically represent sub-daily precipitation extremes, and (iii) statistical methods that allow us to extrapolate extreme precipitation return levels under limited data availability and non-stationary conditions (i.e., climate change) based on the main governing physical processes. We overcome these constraints through the utilization of kilometer-scale hourly radar precipitation estimates (RADKLIM) and spatially disaggregated observed daily temperature data (HYRAS-DE-TAS), and the implementation of a novel CPM ensemble covering the entirety of Germany, obtained from the NUKLEUS project within the BMBF-funded RegIKlim (Regionale Information zum Klimahandeln) initiative. Additionally, we introduce the Temperature-dependent Non-Asymptotic statistical model for eXtreme return levels (TENAX) model, a new approach that integrates daily temperature as a covariate, aligning with observed Clausius-Clapeyron scaling rates. This innovation results in a groundbreaking dataset of hourly extreme precipitation for Germany, marking the first instance of accounting for non-stationary climate conditions, i.e., in a +2K and +3K warmer world. The new dataset contains kilometer-scale hourly precipitation extremes for the return level of a 100-year event. Due to the inherent biases of radar-based estimates compared to ground observations, the precipitation extremes have been bias-adjusted on return level basis using KOSTRA.

We develop a self-consistent, large ensemble, high-resolution, bias-corrected global dataset of future climates for a set of four possible 21st century scenarios, which is suitable for assessing local-scale climate change impacts and climate policy benefits from a risk-based perspective across different applications. Four emission scenarios represent the existing energy and environmental policies and commitments of potential future pathways, namely, Reference, Paris Forever, Paris 2°C and Paris 1.5°C. We employ the MIT Integrated Global System Modeling (IGSM) framework, which consists of the MIT Earth System Model (MESM) of intermediate complexity and the Economic Projections and Policy Analysis model (EPPA). The EPPA characterizes detailed economic activities to track inter-sectoral and inter-regional links, while the MESM represents key physical, chemical, and biological components of the Earth system that are impacted by human activity. Such integrated framework ensures consistent treatment of interactions among population growth, economic development, energy and land system changes and physical climate responses, which can provide improved assessments of climate impacts and climate policy benefits across multiple sectors. The MESM contains a two-dimensional (zonally averaged) atmospheric model with interactive chemistry coupled to the zonally averaged version of Global Land System model and an anomaly-diffusing ocean model. This architecture allows for conducting a large ensemble of climate simulations for robust uncertainty analyses at significantly less computational cost than state-of-the-art climate models. In addition, we apply a combined spatial disaggregation (SD) – bias correction (BC) delta method with SD for achieving the high resolution and BC for correcting the biases inherent in the MESM future climate projections. The delta method adds the anomalies or deltas (future climate trends) onto a historical, detrended climate that is based on the third phase of the Global Soil Wetness Project (GSWP3, http://hydro.iis.u-tokyo.ac.jp/GSWP3/). The anomalies or deltas are derived by spatially disaggregating the IGSM zonal climate projections based on regional hydroclimate change patterns from the 18 Coupled Model Intercomparison Project Phase 6 (CMIP6) climate models. For each emission scenario, a distribution of plausible trajectories is provided by a 50-member ensemble to represent the uncertainty in the Earth system (e.g., the climate sensitivity, rate of heat uptake by the ocean, uncertainty in carbon cycle), allowing for constructing a 900-member ensemble of regional climate outcomes. The dataset contains nine key meteorological variables on a monthly scale from 2021 to 2100 at a spatial resolution of 0.5°x 0.5°, including precipitation, air temperature (mean, minimum and maximum), near-surface wind speed, shortwave and longwave radiation, specific humidity, and relative humidity. A technical evaluation indicates the dataset well represents the expected large-scale climate features across various regions of the globe and can meet various needs associated with climate impact assessments, including uncertainty analyses, risk quantification, climate policy mitigation, and driving climate impact models which require monthly data inputs, on both global and regional scales. There is no model version. But all the developed models are available online (https://globalchange.mit.edu/research/research-tools/earth-system-model) and have relevant licenses. On the website you could find the following information: The source code of the MESM is publicly available for non-commercial research and educational purposes via github (i.e. github.com:mit-jp/igsm.git). Under this open source protocol, we have also established a software license through the MIT Technology Licensing Office. As the MESM has embedded models developed at three other institutions, appropriate copyright clearances for the third-party code are required.