Internal climate variability (ICV) represents the intrinsic fluctuations within the climate system. Understanding how ICV varies across regions, seasons, and climatic conditions is crucial for detecting externally forced climate change and assessing future climate risks. It can also provide a useful benchmark for assessing climate model performance and the emergence of anthropogenically forced climate change. This study aims to compare ICV estimates obtained using different methods and data types. We focus on seasonal mean near-surface air temperature over Europe, utilizing data from 15 CMIP6 models, a 100-member MPI-ESM large ensemble, and three observational datasets. The ICV estimates are based on pre-industrial control simulations (Pidata), the Hawkins and Sutton (2009) residual-based method (HS09), regression-based removal of external forcings, and a single-model large-ensemble framework. The magnitude of ICV estimates generally decreases from northeastern to southwestern Europe on both annual and seasonal timescales, except during JJA, when the highest estimates shift southward. The highest ICV estimates are observed in DJF and the lowest at the annual timescale. Among all the methods, Pidata generally yields the highest ICV estimates across all seasonal and annual timescales, except during JJA. Based on the HS09 method, the similarity between the model-based estimates and the observations is most pronounced during DJF and at the annual timescale. However, this similarity is weaker when the regression-based method is applied, with the model-based estimates generally exceeding the observation-based ones. The projected changes in ICV show a tendency for reduced (enhanced) values in DJF (JJA). Although a north-south contrast emerges in other seasons, with declining (increasing) ICV in northern (southern) Europe, the overall spatial pattern indicates a reduction in projected ICV estimates across large parts of Europe, except during JJA.
Groundwater stores a third of all global freshwater and supports water supply, irrigation and ecosystems across the world. As such, groundwater drought can have wide-reaching financial, social and environmental impacts, particularly when drought events are prolonged or multi-year. Although recent work has made significant progress in understanding the drivers and patterns of multi-year meteorological droughts, we do not know how this signal translates into multi-year groundwater drought, where subsurface processes, anthropogenic influences and abstractions can alter the meteorological signal. This is particularly true at the global-scale, where a major barrier to understanding large-scale groundwater drought dynamics is the difficulty of obtaining consistent and comprehensive groundwater data. In this research, we use a new global hyper-resolution (∼1 km) groundwater dataset to investigate the global patterns and drivers of groundwater drought from 1960–2019, with a specific focus on multi-year events. We start by characterizing the propagation of meteorological (represented by SPEI-12) to groundwater drought, evaluating how and to what extent the sub-surface plays a role in modulating the meteorological drought signal. Subsequently, we define three global groundwater response types that provide a framework for understanding the processes and geo-physical drivers of normal versus multi-year groundwater droughts. We find that 35 % of global groundwater grid cells have an average groundwater drought duration which is multi-year. In 83 % of these locations, the subsurface extends the meteorological drought signal, whereas in the remaining 17 %, groundwater drought duration appears to be primarily driven by meteorological anomalies. Our analysis offers new insights into global-scale drought exposure by identifying regions which have been most vulnerable to multi-year groundwater drought in the past, as well as those which might be more vulnerable in the future. Importantly, our typology also highlights areas where multi-year groundwater droughts can be anticipated based on meteorological drought anomalies and can therefore inform strategies for managing and mitigating future water scarcity risks.
Nuclear conflicts could ignite intense fires that inject considerable amounts of black carbon (BC) into the upper troposphere and lower stratosphere, with the potential to disrupt global climate. While uncertainties in the BC injection remain large, relatively few modeling studies have explored the climatic response to more limited nuclear exchange between major nuclear powers. Here, we investigate the climate response to a set of relatively limited-forcing scenarios (12 to 24 Tg BC) for nuclear exchanges between the United States and Russia. This corresponds to one-tenth to one-fifth of the forcing strength of typically considered. We use the Canadian Earth System Model version 5, an independent contributor to the worldwide set of Earth System Models that has not been explored previously in this application. Consistent with previous work, decreased downwelling shortwave radiation at the surface drives prolonged surface cooling. Unexpectedly, however, a transient surface warming develops in the first boreal summer following a boreal-winter injection, linked to reduced cooling via net longwave and turbulent fluxes. The transient warming is most pronounced for the lowest forcing cases. Precipitation, which decreases even while the surface warms, is found to be controlled by stabilizing, top-heavy warming. Multi-model assessment of these relatively limited-forcing cases is warranted given their potential impacts.
It is not really questioned today that climate can be described in theory by an ensemble of trajectories differing in their initial conditions, which is then translated to numerical ensembles in climate models. It is also widely accepted that any evolution observed within a few decades after initialization is not yet relevant to climate. Evolution at a later stage, instead, is then used to characterize climate and its change, under the implicit assumption that slower processes do not considerably contribute to differences between ensemble members, allowing internal variability of climate to be identified with these differences. However, a careful justification for this practice is as yet lacking. In particular, a definition of climate in support of this practice is outstanding, including the identification of the kind of time scales at play through providing an argumentation for their relevance. Our study aims at filling this gap. After pointing out that the most important criterion for a definition of climate is the uniqueness of the probability measure on which the definition relies, we first recall the naive proposal to represent such a probability measure by the distribution of ensemble members that has, loosely speaking, converged to the natural probability measure of the so-called snapshot or pullback attractor of the dynamics. We then consider the time scales of convergence and refine the proposal by taking a probability measure that is conditional on the (possibly time-evolving) state of modes characterized by convergence time scales longer than the horizon of a particular study. We design an ensemble simulation initialization scheme for studying convergence time scales and uniqueness of ensembles in Earth system models.
One potentially scalable method to remove CO2 from the air is ocean alkalinity enhancement (OAE), which works to lower the ocean's CO2 partial pressure (pCO2) and accelerate CO2 sequestration and durable storage. This study explores how OAE might affect the seasonal carbon cycle, which plays a key role in the ocean's annual CO2 uptake. By analysing Earth System Model simulations of OAE implemented continuously at the European coastline under low and high emissions, it was found that: (a) due to surface alkalinity retention, OAE reduces ocean pCO2 most strongly in summer, turning the region into a year-round carbon sink; (b) the strongest air-sea CO2 uptake enhancement takes place in winter; (c) the ocean's carbon sink is increased more strongly in SSP3-7.0 than in SSP1-2.6 due to a lower buffering capacity.
