Climate Dynamics最新文献

"Single Model initial-condition Large Ensembles" (SMILEs) conducted with Earth system models have transformed our ability to quantify internal climate variability and forced climate change at local and regional scales. An important consideration in their experimental design is the choice of initialization procedure as this influences the duration of initial-condition memory, with implications for interpreting the temporal evolution of both the ensemble-mean and ensemble-spread. Here we leverage the strategic design of the 100-member Community Earth System Model version 2 (CESM2) SMILE to investigate the dependence of ensemble spread on the method of initialization (micro- vs. macro- perturbations) and the effects of ocean initial-condition memory. We find that the evolution of ensemble spread in 10-year low-pass filtered data is relatively insensitive to the method of initialization beyond the second decade, with the notable exception of the tropical Indo-Pacific in the 4th decade, when macro-initialization significantly enhances ensemble spread, possibly as a result of a state-dependent response to major volcanic activity. Initial-condition memory associated with the chosen Atlantic Meridional Overturning Circulation (AMOC) states unfolds in two stages: First, in the North Atlantic lasting 4-5 decades, and subsequently, in the Indo-Pacific sector of the Southern Ocean appearing 35-years after initialization and lasting 3-4 decades. Known AMOC dynamics explain the first stage, but the role of AMOC and the mechanisms responsible for the delayed appearance of initial-condition memory in the Southern Ocean remain to be fully elucidated. Implications and recommendations for the design of future SMILEs are provided.

Supplementary information: The online version contains supplementary material available at 10.1007/s00382-024-07553-z.

Heatwaves and summer droughts across Europe are likely to intensify under anthropogenic global warming thereby affecting ecological and societal systems. To place modern trends and extremes in the context of past natural variability, annually resolved and absolutely dated climate reconstructions are needed. Here, we present a network of 153 yew (Taxus baccata L.) tree-ring width (TRW) series from 22 sites in southern England that cover the past 310 years. Significant positive correlations were found between TRW chronologies and both April-July precipitation totals (r > 0.7) and July drought indices (r > 0.59) back to 1901 CE (p < 0.05). We used a suite of residual and standard TRW chronologies to reconstruct interannual to multi-decadal spring-summer precipitation and mid-summer drought variability over western Europe, respectively. Our yew hydroclimate reconstructions capture the majority of reported summer droughts and pluvials back to 1710 CE. Clusters of severe drought spells occurred in the second half of the 18th and mid-twentieth century. Our study suggests that the frequency and intensity of recent hydroclimate extremes over western Europe are likely still within the range of past natural variability.

Supplementary information: The online version contains supplementary material available at 10.1007/s00382-025-07601-2.

Some machine learning models, in particular deep neural networks (DNNs), are not very well understood; nevertheless, they are frequently used in science. Does this lack of understanding pose a problem for using DNNs to understand empirical phenomena? Emily Sullivan has recently argued that understanding with DNNs is not limited by our lack of understanding of DNNs themselves. In the present paper, we will argue, contra Sullivan, that our current lack of understanding of DNNs does limit our ability to understand with DNNs. Sullivan's claim hinges on which notion of understanding is at play. If we employ a weak notion of understanding, then her claim is tenable, but rather weak. If, however, we employ a strong notion of understanding, particularly explanatory understanding, then her claim is not tenable.

Climate science has long explored whether higher resolution regional climate models (RCMs) provide improved simulation of regional climates over global climate models (GCMs). The advent of convective-permitting RCMs (CPRCMs), where sufficiently fine-scale grids allow explicitly resolving rather than parametrising convection, has created a clear distinction between RCM and GCM formulations. This study investigates the simulation of tropical-extratropical (TE) cloud bands in a suite of pan-South America convective-permitting Met Office Unified Model (UM) and Weather Research and Forecasting (WRF) climate simulations. All simulations produce annual cycles in TE cloud band frequency within 10-30% of observed climatology. However, too few cloud band days are simulated during the early summer (Nov-Dec) and too many during the core summer (Jan-Feb). Compared with their parent forcing, CPRCMs simulate more dry days but systematically higher daily rainfall rates, keeping the total rain biases low. During cloud band systems, the CPRCMs correctly reproduced the observed changes in tropical rain rates and their importance to climatology. Circulation analysis suggests that simulated lower subtropical rain rates during cloud bands systems, in contrast to the higher rates in the tropics, are associated with weaker northwesterly moisture flux from the Amazon towards southeast South America, more evident in the CPRCMs. Taken together, the results suggest that CPRCMs tend to be more effective at producing heavy daily rainfall rates than parametrised simulations for a given level of near-surface moist energy. The extent to which this improves or degrades biases present in the parent simulations is strongly region-dependent.