Given the consequences and global significance of El Niño–Southern Oscillation (ENSO) events it is essential to understand the representation of El Niño diversity in climate models for the present ...day and the future. In recent decades, El Niño events have occurred more frequently in the central Pacific (CP). Eastern Pacific (EP) El Niño events have increased in intensity. However, the processes and future implications of these observed changes in El Niño are not well understood. Here, the frequency and intensity of El Niño events are assessed in models from phases 5 and 6 of the Coupled Model Intercomparison Project (CMIP5 and CMIP6), and results are compared to extended instrumental and multicentury paleoclimate records. Future changes of El Niño are stronger for CP events than for EP events and differ between models. Models with a projected La Niña–like mean-state warming pattern show a tendency toward more EP but fewer CP events compared to models with an El Niño–like warming pattern. Among the models with more El Niño–like warming, differences in future El Niño can be partially explained by Pacific decadal variability (PDV). During positive PDV phases, more El Niño events occur, so future frequency changes are mainly determined by projected changes during positive PDV phases. Similarly, the intensity of El Niño is strongest during positive PDV phases. Future changes to El Niño may thus depend on both mean-state warming and decadal-scale natural variability.
Compound wind and precipitation extremes (CWPEs) can severely impact natural and socioeconomic systems. However, our understanding of CWPE future changes, drivers, and uncertainties under a warmer ...climate is limited. Here, by analyzing the event both on oceans and landmasses via state‐of‐the‐art climate model simulations, we reveal a poleward shift of CWPE occurrences by the late 21st century, with notable increases at latitudes exceeding 50° in both hemispheres and decreases in the subtropics around 25°. CWPE intensification occurs across approximately 90% of global landmasses, and is most pronounced under a high‐emission scenario. Most changes in CWPE frequency and intensity (about 70% and 80%, respectively) stem from changes in precipitation extremes. We further identify large uncertainties in CWPE changes, which can be understood at the regional level by considering climate model differences in trends of CWPE drivers. These results provide insights into understanding CWPE changes under a warmer climate, aiding robust regional adaptation strategy development.
Plain Language Summary
Concurrent wind and precipitation extremes (CWPEs), a typical case of compound weather events, can cause flooding and strong winds that can paralyze public transportation, trigger power outages, and destroy houses and shelters. Furthermore, CWPE over the ocean can endanger the shipment of goods and its logistics. Yet, our understanding of the projected changes, underlying drivers, and uncertainties under a warmer climate is limited. Here, analyzing for the first time CWPEs both on global oceans and landmasses allows us to reveal a poleward shift of CWPEs at the global scale in response to climate change. We show that changes in precipitation extremes play a dominant role in determining the future changes in the frequency and intensity of CWPEs. Furthermore, at the regional level, we reveal substantial uncertainties in projections due to differences between the used climate models. We illustrate that these uncertainties are due to model differences in trends of CWPE drivers and argue that they should be addressed explicitly in impact assessments for guiding the development of robust adaptation strategies.
Key Points
An intensification and poleward shift of compound wind and precipitation extremes (CWPEs) will occur in a warmer climate
Most changes in frequency and intensity of CWPEs stem from changes in precipitation extremes
Substantial uncertainties at the regional level in projections of CWPEs are due to structural model differences
Abstract
In recent decades, Europe has experienced more frequent flood and drought events. However, little is known about the long-term, spatiotemporal hydroclimatic changes across Europe. Here we ...present a climate field reconstruction spanning the entire European continent based on tree-ring stable isotopes. A pronounced seasonal consistency in climate response across Europe leads to a unique, well-verified spatial field reconstruction of European summer hydroclimate back to AD 1600. We find three distinct phases of European hydroclimate variability as possible fingerprints of solar activity (coinciding with the Maunder Minimum and the end of the Little Ice Age) and pronounced decadal variability superimposed by a long-term drying trend from the mid-20th century. We show that the recent European summer drought (2015–2018) is highly unusual in a multi-century context and unprecedented for large parts of central and western Europe. The reconstruction provides further evidence of European summer droughts potentially being influenced by anthropogenic warming and draws attention to regional differences.
Accelerated warming and hiatus periods in the long-term rise of Global Mean Surface Temperature (GMST) have, in recent decades, been associated with the Interdecadal Pacific Oscillation (IPO). ...Critically, decadal climate prediction relies on the skill of state-of-the-art climate models to reliably represent these low-frequency climate variations. We undertake a systematic evaluation of the simulation of the IPO in the suite of Coupled Model Intercomparison Project 5 (CMIP5) models. We track the IPO in pre-industrial (control) and all-forcings (historical) experiments using the IPO tripole index (TPI). The TPI is explicitly aligned with the observed spatial pattern of the IPO, and circumvents assumptions about the nature of global warming. We find that many models underestimate the ratio of decadal-to-total variance in sea surface temperatures (SSTs). However, the basin-wide spatial pattern of positive and negative phases of the IPO are simulated reasonably well, with spatial pattern correlation coefficients between observations and models spanning the range 0.4-0.8. Deficiencies are mainly in the extratropical Pacific. Models that better capture the spatial pattern of the IPO also tend to more realistically simulate the ratio of decadal to total variance. Of the 13% of model centuries that have a fractional bias in the decadal-to-total TPI variance of 0.2 or less, 84% also have a spatial pattern correlation coefficient with the observed pattern exceeding 0.5. This result is highly consistent across both IPO positive and negative phases. This is evidence that the IPO is related to one or more inherent dynamical mechanisms of the climate system.
A large stretch of the east coast of Australia experienced unprecedented rainfall and flooding over a two‐week period in early 2022. It is difficult to reliably estimate the likelihood of such a rare ...event from the relatively short observational record, so an alternative is to use data from an ensemble prediction system (e.g., a seasonal or decadal forecast system) to obtain a much larger sample of simulated weather events. This so‐called ‘UNSEEN’ method has been successfully applied in several scientific studies, but those studies typically rely on a single prediction system. In this study, we use data from the Decadal Climate Prediction Project to explore the model uncertainty associated with the UNSEEN method by assessing 10 different hindcast ensembles. Using the 15‐day rainfall total averaged over the river catchments impacted by the 2022 east coast event, we find that the models produce a wide range of likelihood estimates. Even after excluding a number of models that fail basic fidelity tests, estimates of the event return period ranged from 320 to 1814 years. The vast majority of models suggested the event is rarer than a standard extreme value assessment of the observational record (297 years). Such large model uncertainty suggests that multi‐model analysis should become part of the standard UNSEEN procedure.
Return periods derived from observations (Australian Gridded Climate Data; AGCD) and from various decadal forecast systems. The grey dashed line indicates the observed record annual maximum 15‐day rainfall total (Rx15day) of 410 mm.
Study region Western Tasmania, southeastern Australia.
Study focus We present two new tree-ring based inflow reconstructions from western Tasmania in southeastern Australia.The warm season ...reconstruction (Dec–Feb) extends from 1030–2007 CE and explains up to 42% of the variance in instrumental flow, while the cool season (JA) extends from 1550–2007 CE and explains 27% of instrumental flow variance. Key features include an extended pluvial period in the 11th Century and a protracted dry period in ∼1500CE, neither of which are represented in the DJF instrumental record. Decreasing JA flow since the 19th Century is consistent with a local sediment-based hydroclimate record.
New hydrological insights for the region The reconstructions confirm that the instrumental data do not capture how protracted past low or high flow periods have been. It is therefore important to consider pre-instrumental flow data when planning for the future. The reconstructions provide new insights into regional variability through their association with the Subtropical Ridge (STR) and the Southern Annular Mode (SAM). Differing spatial signatures of the seasonal reconstructions, and their associations with season-specific impacts of STR and SAM, highlight the need for caution when considering the use of remote hydroclimate proxy records with strong seasonal signatures. The reconstructions suggest that extrapolation of seasonally defined reconstructions to represent annual flow for regions beyond the extent of their spatial footprint may be problematic.
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•July–August streamflow declining since the 19th Century.•There have been longer dry periods in the past than in the instrumental record.•Subtropical Ridge, Southern Annular Mode influence west Tasmanian streamflow.•Geographic signatures of the reconstructions are seasonally specific.
Using an extended 120‐year record of El Niño events, we distinguish between central Pacific (CP) and eastern Pacific (EP) types to show that the strength of CP events is a factor in the amplitude and ...sign of the impact on rainfall over southeastern Australia. Both weak and strong CP events cause widespread rainfall deficits in Australia during the onset phase from April to September. However, this relationship reverses over southeastern Australia including the Murray Darling Basin river catchment region for the strongest CP events after October, leading to positive rainfall anomalies during the mature phase of strong CP El Niños. This reversal can be explained by a change in the circulation over eastern Australia from drier, more westerly orientated flow to moister, more easterly onshore flow. These findings may help with seasonal prediction efforts to predict drought‐breaking rain such as occurred in early 2020.
Plain Language Summary
El Niño Southern Oscillation events are extremely important for many countries around the world due to their impacts on rainfall. By separating El Niño into central Pacific (CP) and eastern Pacific (EP) events, we show that the strength of a CP event controls the rainfall amount for southeastern Australia. The stronger a CP event is, the drier it is over Australia during the onset phase from April to September. But after October during the mature phase of El Niño, the strongest CP events lead to more rainfall than normal over the southeast Australian river catchment known as the Murry Darling Basin, whereas the weakest CP events lead to less rainfall than normal. This relationship is strongest in January to March around the time that the CP event is fully developed. For the strongest CP events, this can be explained by a change in the circulation from drier, more westerly flow during the onset phase to moister, more easterly onshore flow during the mature phase. This finding is important for agricultural and water resources planning efforts in the Murry Darling Basin region and may help with seasonal prediction efforts to predict drought‐breaking rain such as occurred in early 2020.
Key Points
The strength of central Pacific (CP) El Niño events has a significant impact on rainfall over southeastern Australia
Strong CP events cause Australian rainfall deficits during onset but enhanced rainfall over southeastern Australia during the mature phase
This can be explained by a circulation change over eastern Australia from drier, more westerly flow to moister, more easterly onshore flow
The El Niño Southern Oscillation (ENSO) impacts climate variability globally and can influence extreme climate and weather events. We quantify the uncertainty in ENSO's atmospheric teleconnections ...with extremes using the Twentieth Century Reanalysis, showing that uncertainty estimates vary regionally over the historical period. Uncertainty is found to be greater in regions of lower socioeconomic development. This can be linked to the limited availability of observational data in these regions as well as difficulties constraining tropical climate dynamics in global gridded atmospheric data sets. Poorer locations face greater challenges due to lack of understanding of past variability limiting confidence in regional projections.
Plain Language Summary
Extreme weather events, such as droughts and floods, can be influenced by climate phenomena such as the El Niño Southern Oscillation. El Niño and La Niña events are the biggest drivers of climate variability globally and so it is important to understand their influence on climate extremes. We demonstrate that understanding of how El Niño and La Niña influence extremes is not consistent across the globe. There is the least understanding in the poorest regions of the world. As many of these regions are especially vulnerable to climate change, this has important implications for adapting to the impacts of extremes.
Key Points
Uncertainty in historical El Niño Southern Oscillation teleconnections is examined using an ensemble reanalysis product
Regional climate responses to El Niño and La Niña are more uncertain in poorer regions
This inequality in climate information must be addressed to increase resilience to extremes
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