Interannual Climate Variability Research Articles

Stable isotope composition in tree rings of Fagus sylvatica L. saplings reflects environmental variation induced by silviculture and microsite factors

Natural regeneration of tree species is sensitive to silvicultural interventions. This study aimed to investigate the effects of different cutting intensities and local topographic and soil conditions on the composition of stable carbon (δ13C) and oxygen (δ18O) isotopes in wood of young beech (Fagus sylvatica L.) trees. Beech saplings in the regeneration layer were sampled in summer 2018 at three study sites in Dinaric fir-beech forests in the karst area of Slovenia. Three different cutting intensities were performed in 2012: i) no cutting (control), ii) 50% cutting of the stand’s growing stock creating thinned stands, and iii) 100% cutting of the stand’s growing stock creating 0.4 ha canopy gaps. We show that δ13C increased along the gradient of cutting intensity. On average, δ13C values in the tree rings were ∼ 2‰ increased in trees from canopy gaps than from closed control stands. Furthermore, δ13C was higher on south-facing slopes characterized by higher air temperatures and lower relative humidity compared to north-facing slopes of karst sinkholes. Additionally, the results suggest a dependence of δ18O on interannual and cross-site climatic variations, particularly in the case of summer precipitation amount. δ18O also responded to soil depth, with beech individuals exhibiting lower values on deeper soils, presumably characterized by higher soil water availability compared to shallow soils. The results are discussed in the context of future climate change, as many beech-dominated forests on karst terrain in the Dinaric Mountains are particularly affected by climate warming and drying due to prolonged and reoccurring summer droughts, intensified large-scale disturbances, and often shallow soils with low water storage capacity.

Indian Ocean variability changes in the Paleoclimate Modelling Intercomparison Project

Abstract. The Indian Ocean exhibits multiple modes of interannual climate variability, whose future behaviour is uncertain. Recent analysis of glacial climates has uncovered an additional El Niño-like equatorial mode in the Indian Ocean, which could also emerge in future warm states. Here we explore changes in the tropical Indian Ocean simulated by the Paleoclimate Model Intercomparison Project (PMIP4). These simulations are performed by an ensemble of models contributing to the Coupled Model Intercomparison Project 6 and over four coordinated experiments: three past periods – the mid-Holocene (6000 years ago), the Last Glacial Maximum (21 000 years ago), the last interglacial (127 000 years ago) – and an idealized forcing scenario to examine the impact of greenhouse forcing. The two interglacial experiments are used to characterize the role of orbital variations in the seasonal cycle, whilst the other pair focus on responses to large changes in global temperature. The Indian Ocean Basin Mode (IOBM) is damped in both the mid-Holocene and last interglacial, with the amount related to the damping of the El Niño–Southern Oscillation in the Pacific. No coherent changes in the strength of the IOBM are seen with global temperature changes; neither are changes in the Indian Ocean Dipole (IOD) nor the Niño-like mode. Under orbital forcing, the IOD robustly weakens during the mid-Holocene experiment, with only minor reductions in amplitude during the last interglacial. Orbital changes do impact the SST pattern of the Indian Ocean Dipole, with the cold pole reaching up to the Equator and extending along it. Induced changes in the regional seasonality are hypothesized to be an important control on changes in the Indian Ocean variability.

On the role of wave climate temporal variability in bias correction of GCM-RCM wave simulations

This work presents the performance analysis of a multi-model ensemble of wave climate projections in the Mediterranean Sea against hindcast data. The wave projections were developed with the numerical model Wavewatch III forced by surface wind fields of 17 EURO-CORDEX GCM-RCMs providing time series of the main wave parameters on a 3-h and 10-km resolution. The performance of the wave GCM-RCM simulations during the baseline period (1979–2005) was assessed by means of the deterministic metrics RMSE and Bias. Different bias correction methodologies were analyzed by means of the application of the widespread Empirical Quantile Mapping method considering different time periods of significant wave height in order to analyze the ability of the bias-correcting methods to capture the different wave climate temporal scales ranging from storm events, monthly, seasonal and interannual variability. The results show that the use of the EQM method for the full-time series without taking into account other timescales, can lead to increased biases in some regions and seasons and that the use of time-dependent bias-correction techniques leads to an improved accurate characterization of biases considering the interannual temporal variability of significant wave height. More specifically the use of the EQM method for monthly data provides a good performance in capturing the correlation and interannual temporal variability of wave climate.

Accounting of marine and coastal ecosystems at the Ramsar Site, Estuarine Delta System of the Magdalena River, Ciénaga Grande de Santa Marta, Colombia

The Cienaga Grande de Santa Marta Ramsar Site (CGSM) is the most important Caribbean estuarine wetland in Colombia. The site represents a strategically important ecosystem supporting the national and local economy. However, their ability to provide ecosystem services has been seriously affected mainly due to changes in land use, disturbances of water flows, man-made climate change and interannual climatic variability. These circumstances led to its inclusion in the Montreux Record, a register of wetland sites on the ‘List of Wetlands of International Importance’ where variations in ecological character have happened, are happening or are likely to happen as an outcome of technological developments, pollution or other human interference. This paper presents the first account of marine and coastal ecosystems developed in Colombia at the Cienaga Grande de Santa Marta. Following the principles for ecosystem accounting of the System of Environmental-Economic Accounting—Ecosystem Accounting (SEEA EA), we developed accounting tables of extent and condition of ecosystems, biophysical and monetary flows of climate regulation and fishing supply and the monetary account of ecosystem assets. Results of the ecosystem account allow visualising the importance of ecosystem services of the wetland, its capacity to provide economic benefits, social welfare and livelihoods at local and national level. Finally, we identify main gaps of information, highlight the applicability of ecosystem accounting to policy- and decision-making on economic and environmental issues and evaluate the challenges to implement it.

Persistent decadal differences in plant communities assembled under contrasting climate conditions.

Plant community assembly outcomes can be contingent upon establishment year (year effects) due to variations in the environment. Stochastic events such as interannual variability in climate, particularly in the first year of community assembly, contribute to unpredictable community outcomes over the short term, but less is known about whether year effects produce transient or persistent states on a decadal timescale. To test for short-term (5-year) and persistent (decadal) effects of establishment year climate on community assembly outcomes, we restored prairie in an agricultural field using the same methods in four different years (2010, 2012, 2014, and 2016) that captured a wide range of initial (planting) year climate conditions. Species composition was measured for 5 years in all four restored prairies and for 9 and 11 years in the two oldest restored prairies established under average precipitation and extreme drought conditions. The composition of the four assembled communities showed large and significant differences in the first year of restoration, followed by dynamic change over time along a similar trajectory due to a temporary flush of annual volunteer species. Sown perennial species eventually came to dominate all communities, but communities remained distinct from each other in year five. Precipitation in June and July of the establishment year explained short-term coarse community metrics (i.e., species richness and grass/forb cover), with wet establishment years resulting in a higher cover of grasses and dry establishment years resulting in a higher cover of forbs in restored communities. Short-term differences in community composition, species richness, and grass/forb cover in restorations established under average precipitation and drought conditions persisted for 9-11 years, with low interannual variability in the composition of each prairie over the long term, indicating persistently different states on a decadal timescale. Thus, year effects resulting from stochastic variation in climate can have decadal effects on community assembly outcomes.

The Impacts of Interannual Climate Variability on the Declining Trend in Terrestrial Water Storage over the Tigris–Euphrates River Basin

Abstract The Tigris–Euphrates dryland river basin has experienced a declining trend in terrestrial water storage (TWS) from April 2002 to June 2017. Using satellite observations and a process-based land surface model, we find that climate variations and direct human interventions explain ∼61% (−0.57 mm month−1) and ∼39% (−0.36 mm month−1) of the negative trend, respectively. We further disaggregate the effects of climate variations and find that interannual climate variability contributes substantially (−0.27 mm month−1) to the negative TWS trend, slightly greater than the decadal climate change (−0.25 mm month−1). Interannual climate variability affects TWS mainly through the nonlinear relationship between monthly TWS dynamics and aridity. Slow recovery of TWS during short wetting periods does not compensate for rapid depletion of TWS through transpiration during prolonged drying periods. Despite enhanced water stress, the dryland ecosystems show slightly enhanced resilience to water stress through greater partitioning of evapotranspiration into transpiration and weak surface “greening” effects. However, the dryland ecosystems are vulnerable to drought impacts. The basin shows straining ecosystem functioning after experiencing a severe drought event. In addition, after the onset of the drought, the dryland ecosystem becomes more sensitive to variations in climate conditions. Significance Statement The purpose of the research is to better understand climate impacts on terrestrial water storage over dryland regions with declining water storage. In our study, we disaggregate three components of climate impacts, namely, decadal climate change, interannual variability, and intra-annual variability. We then use observational datasets and a process-based model to quantify their individual effects on water storage. We find that interannual variability is the most significant climatic contributor to the declining water storage, mainly caused by prolonged drought periods and corresponding quick drying rates due to plant transpiration. We also find that the dryland ecosystem is sensitive and vulnerable to severe drought events. This study is important because 1) it provides a framework to investigate climate impacts on water fluxes and storages, 2) it highlights the importance of vegetation dynamics on dryland hydrology, and 3) it emphasizes the negative impacts of extreme hydroclimatological events on ecosystem functioning.

Distinct impacts of two types of El Niño events on northern winter high-latitude temperatures simulated by CMIP6 climate models

The interannual variability of the Northern Hemisphere winter climate is strongly linked to the El Niño-southern oscillation (ENSO). While North Pacific and North America often exhibit a robust response to ENSO, so-called the Pacific–North America pattern, the Arctic and Eurasian climate responses to ENSO remain elusive. This study examines 40 different climate models from the coupled model intercomparison project phase 6 (CMIP6) to find the distinct Arctic and Eurasian temperature response to two types of El Niño events. Specifically, Central Pacific El Niño events are accompanied by significant pan-Arctic warming, whereas Eastern Pacific (EP) El Niño events are accompanied by cooling over the Barents-Kara Seas and Eurasian continent. During the EP El Niño events, pan-Arctic sea-level pressure (SLP) effectively strengthens, leading to weaker westerlies and surface air cooling over the northern Eurasian continent. These distinct Arctic and Eurasian winter temperature responses to two types of El Niño do not appear clearly in reanalysis data, spanning 1979–2021, probably because of the small sample size of El Niño events since the satellite era.

Interannual, sub-seasonal and spatial variations in growing season surface heat fluxes for the U.S. Corn Belt

Spatiotemporal variations in surface latent (LE) and sensible (H) heat fluxes from croplands and patches of remnant deciduous forest in the Midwest U.S. Corn Belt have been proposed to trigger “nonclassical mesoscale circulations” (NCMCs) in the atmosphere's boundary layer and moist convection, under suitable synoptic conditions. However, prior to evaluating the link between land use and land cover (LULC), NCMCs, and precipitation for the Corn Belt, it is necessary to better understand the interannual, sub-seasonal, and spatial variations in surface heat fluxes. Here, a nine-year warm season “climatology” (1999–2007) of daily afternoon and early evening averages of LE and H from two flux towers, representative of cropland (Bondville, IL) and deciduous forest (Morgan Monroe State Forest, IN), is analyzed for sub-seasonal and interannual variations in the convective fluxes. Wilcoxon signed rank tests show significant growing season and sub-seasonal anomalies in LE and H at both flux tower sites. Structural equation modeling relates these inter-growing season anomalies in LE and H to variations in net radiation, vapor pressure deficit, and temperature, while MODIS Normalized Difference Vegetation Index data link some of the sub-seasonal variations in the surface energy balance to phenology at the start of the growing season. Additional composite analysis of LE and H from the North American Regional Reanalysis shows characteristic spatial heterogeneities and significant differences in LE and H for different synoptic circulation patterns related to the variations in net radiation and LULC. These results highlight the importance of considering the sub-seasonal and spatial variations in surface heat fluxes of the Corn Belt for the range of synoptic conditions, within the context of this region's recent interannual climate variations and will help inform the search for LULC-influenced moist convection.

Pasture Quality Monitoring Based on Proximal and Remote Optical Sensors: A Case Study in the Montado Mediterranean Ecosystem

Permanent dryland pastures are the basis of animal feed in extensive grazing systems. Seasonality and inter-annual climatic variability, associated with shallow, acidic, and not very fertile soils, result in low productivity and rapid degradation of pasture quality, which requires the supplementation of animal feed. In this study, carried out in a biodiverse pasture field in the Mediterranean region of southern Portugal, the vegetation index (NDVI, Normalized Difference Vegetation Index) obtained from measurements performed by a proximal optical sensor (PS) and satellite images (RS) was used to assess pasture quality parameters (pasture moisture content, PMC, crude protein, CP, and neutral detergent fiber, NDF). The monitoring was carried out throughout the 2021/2022 pasture growing season. Significant correlations were obtained between the NDVI obtained by PS and RS (R2 of 0.84) and the reference values of pasture parameters obtained in laboratory protocols: PMC (R2 of 0.88 and 0.78, respectively), CP (R2 of 0.67 and 0.63, respectively), and NDF (R2 of 0.50 and 0.46, respectively). This case study also demonstrated the spatial and temporal variability of vegetative vigour and, consequently, of pasture quality in the Montado, the characteristic Mediterranean ecosystem. These results show the pertinence of these technologies in supporting the decision-making process of the farm manager, namely, to estimate the supplementation needs of animals in critical phases, especially after the spring production peak and before the autumn production peak.

The Atlantic Meridional Mode and Associated Wind–SST Relationship in the CMIP6 Models

The Atlantic Meridional Mode (AMM) is the dominant mode of interannual climate variability in the tropical Atlantic, maintained primarily by the positive wind–evaporation–sea surface temperature (SST) feedback in which the wind anomalies lead the SST anomalies by ~2 months. A previous study revealed that climate models from Coupled Model Intercomparison Project Phase 5 (CMIP5) show poor performance in simulating the AMM-related wind–SST relationship, but the possible causes remain unclear. This study assesses the representation of the AMM and associated wind–SST relationship in the climate models from CMIP6. Results show that most of the CMIP6 models can reasonably reproduce the observed spatial pattern of the AMM, with significant SST and wind anomalies in the northern tropical Atlantic and weak anomalies in the equatorial–southern oceans. However, the simulated wind–SST relationship associated with the AMM varies among the models. In particular, several models fail to capture the observed wind–SST relationship; that is, the simulated wind anomalies peak in boreal spring as in the observations, but no obvious peak occurs in the corresponding SST anomalies. Further analysis suggests the models that fail to capture the observed wind–SST relationship tend to simulate a stronger mean trade wind and a thicker mixed layer in the northern tropical Atlantic, leading to a weaker ocean–atmosphere coupling and, thus, a weaker SST response to the wind forcing. Moreover, there exists a significant out-of-phase relationship between the strength of ocean–atmosphere coupling and mean mixed layer depth among the models, supporting the impact of mean state biases on the AMM variability in the models.

Effects of Climate Variability on Two Commercial Tuna Species Abundance in the Indian Ocean

Oceanic temperature fluctuations are one of the leading factors affecting marine fish populations. Indian Ocean Dipole (IOD), characterized as the sea surface temperature (SST) anomaly change, is an ocean–atmosphere interactive process causing interannual climate variability in the Indian Ocean. Influences of the IOD on the tuna catch rates are supported by previous research. Yet, there remains limited information about the impacts on the abundance of tuna stocks. In this study, we used the standardized Catch Per Unit Effort (CPUE) index to present the stock abundance and compared the effects of the IOD on the bigeye tuna (Thunnus obesus) and yellowfin tuna (Thunnus albacares) among different management areas of the Indian Ocean Tuna Commission (IOTC). Results show significant correlations between IOD events on both species’ abundance in the tropical western Indian Ocean. However, in the tropical eastern Indian Ocean and the southern Indian Ocean, neither bigeye nor yellowfin tuna abundances were significantly correlated by the IOD. For the whole Indian Ocean, IOD was significantly correlated uniquely with the yellowfin tuna abundance. Our results emphasized the importance of evaluating the climate variability effects over fisheries abundance species by species and per fishing areas analyses.

Ambient precipitation determines the sensitivity of soil respiration to precipitation treatments in a marsh.

The effects in field manipulation experiments are strongly influenced by amplified interannual variation in ambient climate as the experimental duration increases. Soil respiration (SR), as an important part of the carbon cycle in terrestrial ecosystems, is sensitive to climate changes such as temperature and precipitation changes. A growing body of evidence has indicated that ambient climate affects the temperature sensitivity of SR, which benchmarks the strength of terrestrial soil carbon-climate feedbacks. However, whether SR sensitivity to precipitation changes is influenced by ambient climate is still not clear. In addition, the mechanism driving the above phenomenon is still poorly understood. Here, a long-term field manipulation experiment with five precipitation treatments (-60%, -40%, +0%, +40%, and +60% of annual precipitation) was conducted in a marsh in the Yellow River Delta, China, which is sensitive to soil drying-wetting cycle caused by precipitation changes. Results showed that SR increased exponentially along the experimental precipitation gradient each year and the sensitivity of SR (standardized by per 100 mm change in precipitation under precipitation treatments) exhibited significant interannual variation from 2016 to 2021. In addition, temperature, net radiation, and ambient precipitation all exhibited dramatic interannual variability; however, only ambient precipitation had a significant negative correlation with SR sensitivity. Moreover, the sensitivity of SR was significantly positively related to the sensitivity of belowground biomass (BGB) across 6 years. Structural equation modeling and regression analysis also showed that precipitation treatments significantly affected SR and its autotrophic and heterotrophic components by altering BGB. Our study demonstrated that ambient precipitation determines the sensitivity of SR to precipitation treatments in marshes. The findings underscore the importance of ambient climate in regulating ecosystem responses in long-term field manipulation experiments.

Long-term, large-scale experiment reveals the effects of seed limitation, climate, and anthropogenic disturbance on restoration of plant communities in a biodiversity hotspot

Ecological restoration is essential for maintaining biodiversity in the face of dynamic, global changes in climate, human land use, and disturbance regimes. Effective restoration requires understanding bottlenecks in plant community recovery that exist today, while recognizing that these bottlenecks may relate to complex histories of environmental change. Such understanding has been a challenge because few long-term, well-replicated experiments exist to decipher the demographic processes influencing recovery for numerous species against the backdrop of multiyear variation in climate and management. We address this challenge through a long-term and geographically expansive experiment in longleaf pine savannas, an imperiled ecosystem and biodiversity hotspot in the southeastern United States. Using 48 sites at three locations spanning 480 km, the 8-y experiment manipulated initial seed arrival for 24 herbaceous plant species and presence of competitors to evaluate the impacts of climate variability and management actions (e.g., prescribed burning) on plant establishment and persistence. Adding seeds increased plant establishment of many species. Cool and wet climatic conditions, low tree density, and reduced litter depth also promoted establishment. Once established, most species persisted for the duration of the 8-y experiment. Plant traits were most predictive when tightly coupled to the process of establishment. Our results illustrate how seed additions can restore plant diversity and how interannual climatic variation affects the dynamics of plant communities across a large region. The significant effects of temperature and precipitation inform how future climate may affect restoration and conservation via large-scale changes in the fundamental processes of establishment and persistence.

Pacific shoreline erosion and accretion patterns controlled by El Niño/Southern Oscillation

In the Pacific Basin, the El Niño/Southern Oscillation (ENSO) is the dominant mode of interannual climate variability, driving substantial changes in oceanographic forcing and impacting Pacific coastlines. Yet, how sandy coasts respond to these basin-scale changes has to date been limited to a few long-term beach monitoring sites, predominantly on developed coasts. Here we use 38 years of Landsat imagery to map shoreline variability around the Pacific Rim and identify coherent patterns of beach erosion and accretion controlled by ENSO. On the basis of more than 83,000 beach transects covering 8,300 km of sandy coastline, we find that approximately one-third of all transects experience significant erosion during El Niño phases. The Eastern Pacific is particularly vulnerable to widespread erosion, most notably during the large 1997/1998 El Niño event. By contrast, La Niña events coincide with significant accretion for approximately one-quarter of all transects, although substantial erosion is observed in southeast Australia and other localized regions. The observed regional variability in the coastal response to ENSO has important implications for coastal planning and adaptation measures across the Pacific, particularly in light of projected future changes in ENSO amplitude and flavour. The El Niño/Southern Oscillation drives coherent patterns of beach erosion and accretion around the Pacific Rim, according to analysis of satellite imagery covering over 8,300 km of sandy coastline.

Trends in tree cover change over three decades related to interannual climate variability and wildfire in California

The U.S. State of California has experienced frequent drought events, hotter temperatures and other disruptions to the climate system whose effects on ecosystems have been widely reported in recent decades. Studies primarily confined to specific vegetation communities or species, individual drought incidents, or analysis over a relatively short intervals, has limited our understanding of the broad-scale effects on tree cover and the spatiotemporal variability of effects across broader regions. We focused analysis on multi-annual land cover and land surface change to assess patterns and trends in tree cover loss in tree-dominated Californian ecoregions from 1986 to 2019. The top three years of total tree cover loss for the state were 2018 (1901 km2), 2015 (1556 km2), and 2008 (1549 km2). Overall, annual tree cover loss had upward trends. Tree cover loss rapidly surged later in the study period and was apparently driven by climate stress and wildfires. Underlying geographic variability was apparent in both non-fire and fire-related tree cover loss that sharply increased during hotter multi-year droughts. The increasingly hotter and drier climate conditions were associated with significant increases in fire-induced mortality. Our findings indicate that a possible effect of future hotter and drier climate would lead to further tree cover loss, thereby endangering California’s ecosystem goods and services. Geographic variability in tree cover trends indicates that ecoregion-specific mitigation and adaptation strategies would be useful to conserve the region’s forest resources. Such strategies may benefit from consideration of historical disturbances, ecoregion’s sensitivity to disturbance types, as well as potential ecoregion-specific climate-vegetation-fire feedbacks.

Habitat heterogeneity and biotic interactions mediate climate influences on seedling survival in a temperate forest

Seedling stage has long been recognized as the bottleneck of forest regeneration, and the biotic and abiotic processes that dominate at seedling stage largely affect the dynamics of forest. Seedlings might be particularly vulnerable to climate stress, so elucidating the role of interannual climate variation in fostering community dynamics is crucial to understanding the response of forest to climate change. Using seedling survival data of 69 woody species collected for five consecutive years from a 25-ha permanent plot in a temperate deciduous forest, we identified the effects of biotic interactions and habitat factors on seedling survival, and examined how those effects changed over time. We found that interannual climate variations, followed by biotic interactions and habitat conditions, were the most significant predictors of seedling survival. Understory light showed a positive impact on seedling mortality, and seedling survival responded differently to soil and air temperature. Effects of conspecific neighbor density were significantly strengthened with the increase of maximum air temperature and vapor pressure deficits in the growing season, but were weakened by increased maximum soil temperature and precipitation in the non-growing season. Surprisingly, seedling survival was strongly correlated with interannual climate variability at all life stages, and the strength of the correlation increased with seedling age. In addition, the importance of biotic and abiotic factors on seedling survival differed significantly among species-trait groups. Thus, the neighborhood-mediated effects on mortality might be significantly contributing or even inverting the direct effects of varying abiotic conditions on seedling survival, and density-dependent effects could not be the only important factor influencing seedling survival at an early stage.

Predicting crop yields in Senegal using machine learning methods

AbstractAgriculture plays an important role in Senegalese economy and annual early warning predictions of crop yields are highly relevant in the context of climate change. In this study, we used three main machine learning methods (support vector machine, random forest, neural network) and one multiple linear regression method, namely Least Absolute Shrinkage and Selection Operator (LASSO), to predict yields of the main food staple crops (peanut, maize, millet and sorghum) in 24 departments of Senegal. Three combination of predictors (climate data, vegetation data or a combination of both) are used to compare the respective contribution of statistical methods and inputs in the predictive skill. Our results showed that the combination of climate and vegetation with the machine learning methods gives the best performance. The best prediction skill is obtained for peanut yield likely due to its high sensitivity to interannual climate variability. Although more research is needed to integrate the results of this study into an operational framework, this paper provides evidence of the promising performance machine learning methods. The development and operationalization of such prediction and their integration into operational early warning systems could increase resilience of Senegal to climate change and contribute to food security.

The El Niño Southern Oscillation drives multidirectional inter-reef larval connectivity in the Great Barrier Reef.

The El Niño Southern Oscillation (ENSO) is the strongest source of interannual global climate variability, and extreme ENSO events are projected to increase in frequency under climate change. Interannual variability in the Coral Sea circulation has been associated with ENSO, although uncertainty remains regarding ENSO's influence on hydrodynamics and larval dispersal in the adjacent Great Barrier Reef (GBR). We investigated larval connectivity during ENSO events from 2010 to 2017 throughout the GBR, based on biophysical modelling of a widespread predatory reef fish, Lutjanus carponotatus. Our results indicate a well-connected system over the study period with high interannual variability in inter-reef connectivity associated with ENSO. Larval connectivity patterns were highly correlated to variations in the Southern Oscillation Index (SOI). During El Niño conditions and periods of weak SOI, larval dispersal patterns were predominantly poleward in the central and southern regions, reversing to a predominant equatorward flow during very strong SOI and extreme La Niña conditions. These ENSO-linked connectivity patterns were associated with positive connectivity anomalies among reefs. Our findings identify ENSO as an important source of variation in larval dispersal and connectivity patterns in the GBR, which can influence the stability of population dynamics and patterns of biodiversity in the region.

Improving and Harmonizing El Niño Recharge Indices

AbstractEl Niño Southern Oscillation (ENSO) is the leading mode of interannual climate variability, with large socioeconomical and environmental impacts. The main conceptual model for ENSO, the Recharge Oscillator (RO), considers two independent modes: the fast zonal tilt mode in phase with central‐eastern Pacific Temperature (Te), and the slow recharge mode in phase quadrature. However, usual indices (western or equatorial sea level/thermocline depth h) do not orthogonally isolate the slow recharge mode, leaving it correlated with Te. Furthermore the optimal index is currently debated. Here, we develop an improved recharge index by objectively optimizing the RO equations fit to observations. (a) Te‐variability is regressed out, to build hind statistically‐independent from Te. Capturing the pure recharge, hind reconciles usual indices. (b) The optimum is equatorial plus southwestern Pacific hind_eq+sw (because of ENSO Ekman pumping meridional asymmetry). Using hind_eq+sw, the RO becomes more consistent with observations. hind_eq+sw is more relevant for ENSO operational diagnostics.

Drivers of intermodel uncertainty in land carbon sink projections

Abstract. Over the past decades, land ecosystems removed from the atmosphere approximately one-third of anthropogenic carbon emissions, highlighting the importance of the evolution of the land carbon sink for projected climate change. Nevertheless, the latest cumulative land carbon sink projections from 11 Earth system models participating in the Coupled Model Intercomparison Project phase 6 (CMIP6) show an intermodel spread of 150 Pg C (i.e., ∼ 15 years of current anthropogenic emissions) for a policy-relevant scenario, with mean global warming by the end of the century below 2 ∘C relative to preindustrial conditions. We hypothesize that this intermodel uncertainty originates from model differences in the sensitivities of net biome production (NBP) to atmospheric CO2 concentration (i), to air temperature (ii), and to soil moisture (iii), as well as model differences in average conditions of air temperature (iv) and soil moisture (v). Using multiple linear regression and a resampling technique, we quantify the individual contributions of these five drivers for explaining the cumulative NBP anomaly of each model relative to the multi-model mean. We find that the intermodel variability of the contributions of each driver relative to the total NBP intermodel variability is 52.4 % for the sensitivity to temperature, 44.2 % for the sensitivity to soil moisture, 44 % for the sensitivity to CO2, 26.2 % for the average temperature, and 21.9 % for the average soil moisture. Furthermore, the sensitivities of NBP to temperature and soil moisture, particularly at tropical regions, contribute to explain 34 % to 65 % of the cumulative NBP deviations from the ensemble mean of the two models with the lowest carbon sink (ACCESS-ESM1-5 and UKESM1-0-LL) and of the two models with the highest sink (CESM2 and NorESM2-LM), highlighting the primary role of the response of NBP to interannual climate variability. Overall, this study provides insights on why each Earth system model projects either a low or high land carbon sink globally and across regions relative to the ensemble mean, which can focalize efforts to identify the representation of processes that lead to intermodel uncertainty.