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Abstract The Amazon rainforest (ARF) is threatened by deforestation and climate change, which could trigger a regime shift to a savanna‐like state. Whilst previous work has suggested that forest resilience has declined in recent decades, that work was based only on local resilience indicators, and moreover was potentially biased by the employed multi‐sensor and optical satellite data and undetected anthropogenic land‐use change. Here, we show that the average correlation between neighboring grid cells' vegetation time series, which is referred to as spatial correlation, provides a more robust resilience indicator than local estimations. We employ it to measure resilience changes in the ARF, based on single‐sensor Vegetation Optical Depth data under conservative exclusion of human activity. Our results show an overall loss of resilience until around 2019, which is especially pronounced in the southwestern and northern Amazon for the time period from 2002 to 2011. The results from the reliable spatial correlation indicator suggest that in particular the southwest of the ARF has experienced pronounced resilience loss over the last two decades.

Abstract Potential climate tipping points pose a growing risk for societies, and policy is calling for improved anticipation of them. Satellite remote sensing can play a unique role in identifying and anticipating tipping phenomena across scales. Where satellite records are too short for temporal early warning of tipping points, complementary spatial indicators can leverage the exceptional spatial-temporal coverage of remotely sensed data to detect changing resilience of vulnerable systems. Combining Earth observation with Earth system models can improve process-based understanding of tipping points, their interactions, and potential tipping cascades. Such fine-resolution sensing can support climate tipping point risk management across scales.

Abstract Observations are increasingly used to detect critical slowing down (CSD) to measure stability changes in key Earth system components. However, most datasets have non-stationary missing-data distributions, biases and uncertainties. Here we show that, together with the pre-processing steps used to deal with them, these can bias the CSD analysis. We present an uncertainty quantification method to address such issues. We show how to propagate uncertainties provided with the datasets to the CSD analysis and develop conservative, surrogate-based significance tests on the CSD indicators. We apply our method to three observational sea-surface temperature and salinity datasets and to fingerprints of the Atlantic Meridional Overturning Circulation derived from them. We find that the properties of these datasets and especially the specific gap filling procedures can in some cases indeed cause false indication of CSD. However, CSD indicators in the North Atlantic are still present and significant when accounting for dataset uncertainties and non-stationary observational coverage.

Abstract Observations and models suggest that the Amazon rainforest might transition to a savanna-like state in response to anthropogenic climate and land use change. Here, we combine observations of precipitation, temperature and tree cover with high-resolution comprehensive climate model simulations to investigate the combined effect of global warming and a potential Atlantic Meridional Overturning Circulation collapse on the Amazon. Our results show that, while strong warming lead to forest dieback, an Atlantic Meridional Overturning Circulation collapse would stabilize the Amazon by increasing rainfall and decreasing temperature in most parts. Although an Atlantic Meridional Overturning Circulation collapse would have devastating impacts globally, our results suggest that it may delay or even prevent parts of the Amazon rainforest from dieback. Besides the many negative consequences of its collapse, the interactions we identify here make a tipping cascade, i.e., that an Atlantic Meridional Overturning Circulation collapse would trigger Amazon dieback, appear less plausible.

In the context of the repeated droughts that have affected central Europe over the last years (2018–2020, 2022), climate-resilient management of water resources, based on timely information about the current state of the terrestrial water cycle and forecasts of its evolution, has gained an increasing importance. To achieve this, we propose a new setup for simulations of the terrestrial water cycle using the integrated hydrological model ParFlow/CLM at high spatial and temporal resolution (i.e., 0.611 km, hourly time step) over Germany and the neighboring regions. We show that this setup can be used as a basis for a monitoring and forecasting system that aims to provide stakeholders from many sectors, but especially agriculture, with diagnostics and indicators highlighting different aspects of subsurface water states and fluxes, such as subsurface water storage, seepage water, capillary rise, or fraction of plant available water for different (root-)depths. The validation of the new simulation setup with observation-based data monthly over the period 2011–2020 yields good results for all major components of the terrestrial water cycle analyzed here, i.e., volumetric soil moisture, evapotranspiration, water table depth, and river discharge. As this setup relies on a standardized grid definition and recent globally available static fields and parameters (e.g., topography, soil hydraulic properties, land cover), the workflow could easily be transferred to many regions of the Earth, including sparsely gauged regions, since ParFlow/CLM does not require calibration.

Climate change has increasingly adverse effects on global crop yields through the occurrence of heat waves, water stress, and other weather-related extremes. Besides losses of average yields, a decrease in yield stability—i.e. an increase in variability of yields from year to year—poses economic risks and threatens food security. Here we investigate a number of climate indices related to adverse weather events during the flowering of wheat, maize and rapeseed, in the current cultivation areas as well as the main European producer countries. In 52 projections from regional climate models, we identify robust increases in the interannual variability of temperature, precipitation and soil moisture by ∼+20% in standard deviation in the model median. We find that winter wheat is most exposed to variability increases, whereas rapeseed flowering escapes the largest increases due to the early flowering time and the northern locations of cultivation areas, while the opposite (escape due to southern locations and late flowering) is true for maize to some extent. Considering the timing of crop development stages, we also find a robust increase in the variability of the temporal occurrence of flowering, which suggests a decreased reliability in the timing of crop stages, hampering management steps like fertilization, irrigation or harvesting. Our study raises concerns for European crop yield stability in a warmer climate and highlights the need for risk diversification strategies in agricultural adaptation.

Questions of causality are ubiquitous in Earth system sciences and beyond, yet correlation techniques still prevail. This Perspective provides an overview of causal inference methods, identifies promising applications and methodological challenges, and initiates a causality benchmark platform.

Abstract Climate variability is critically important for nature and society, especially if it increases in amplitude and/or fluctuations become more persistent. However, the issues of whether climate variability is changing, and if so, whether this is due to anthropogenic forcing, are subjects of ongoing debate. Increases in the amplitude and persistence of temperature fluctuations have been detected in some regions, e.g. the North Pacific, but there is no agreed global signal. Here we systematically scan monthly surface temperature indices and spatial datasets to look for trends in variance and autocorrelation (persistence). We show that monthly temperature variability and autocorrelation increased over 1957–2002 across large parts of the North Pacific, North Atlantic, North America and the Mediterranean. Furthermore, (multi)decadal internal climate variability appears to influence trends in monthly temperature variability and autocorrelation. Historically-forced climate models do not reproduce the observed trends in temperature variance and autocorrelation, consistent with the models poorly capturing (multi)decadal internal climate variability. Based on a review of established spatial correlations and corresponding mechanistic ‘teleconnections’ we hypothesise that observed slowing down of sea surface temperature variability contributed to observed increases in land temperature variability and autocorrelation, which in turn contributed to persistent droughts in North America and the Mediterranean.