Low-frequency Climate Variability Research Articles

Modelling multidecadal variability in flood frequency using the Two-Component Extreme Value distribution

The Two-Component Extreme Value (TCEV) distribution is traditionally known as the exact distribution of extremes arising from Poissonian occurrence of a mixture of two exponential exceedances. In some regions, flood frequency is affected by low-frequency (decadal) climate fluctuations resulting in wet and dry epochs. We extend the exact distribution of extremes approach to such regions to show that the TCEV arises as the distribution of annual maximum floods for Poissonian occurrences and (at least two) exponential exceedances. A case study using coastal basins in Queensland and New South Wales (Australia) affected by low-frequency climate variability, shows that the TCEV produces good fits to the marginal distribution over the entire range of observed values without the explicit need to resort to climate covariates and removal of potentially influential low values. Moreover, the TCEV reproduces the observed dog-leg, a key signature of different flood generation processes. A literature review shows that the assumptions underpinning the TCEV are conceptually consistent with available evidence on climate and flood mechanisms in these basins. We provide an extended domain of the TCEV distribution in the L-moment ratio diagram to account for the wider range of parameter values encountered in the case study and show that for all basins, L-skew and L-kurtosis fall within the extended domain of the TCEV.

Is Recent Warming Exceeding the Range of the Past 125,000 Years?

The Intergovernmental Panel on Climate Change (IPCC) concluded that the latest decade was warmer than any multi-century period over the past 125,000 years. This statement rests on a comparison of modern instrumental measurements against the course of past temperatures reconstructed from natural proxy archives, such as lake and marine sediments, and peat bogs. Here, we evaluate this comparison with a focus on the hundreds of proxy records developed by paleoclimatologists across the globe to reconstruct climate variability over the Holocene (12,000 years) and preceded by the Last Glacial Period (125,000 years). Although the existing proxy data provide a unique opportunity to reconstruct low-frequency climate variability on centennial timescales, they lack temporal resolution and dating precision for contextualizing the most recent temperature extremes. While the IPCC’s conclusion on the uniqueness of latest-decade warming is thus not supported by comparison with these smoothed paleotemperatures, it is still likely correct as ice core-derived forcing timeseries show that greenhouse gases were not elevated during any pre-instrumental period of the Holocene.

Tropical Pacific Quasi-Decadal Variability Suppressed by Submesoscale Eddies

Abstract Tropical Pacific quasi-decadal (TPQD) climate variability is characterized by quasi-decadal sea surface temperature (SST) variations in the central Pacific (CP). This low-frequency climate variability is suggested to influence extreme regional weather and substantially impact global climate patterns and associated socioeconomics through teleconnections. Previous studies mostly attributed the TPQD climate variability to basin-scale air–sea coupling processes. However, due to the coarse resolution of the majority of the observations and climate models, the role of subbasin-scale processes in modulating the TPQD climate variability is still unclear. Using a long-term high-resolution global climate model, we find that energetic small-scale motions with horizontal scales from tens to hundreds of kilometers (loosely referred to as equatorial submesoscale eddies) act as an important damping effect to retard the TPQD variability. During the positive TPQD events, compound increasing precipitation and warming SST in the equatorial Pacific intensifies the upper ocean stratification and weakens the temperature fronts along the Pacific cold tongue. This suppresses submesoscale eddy growth as well as their associated upward vertical heat transport by inhibiting baroclinic instability (BCI) and frontogenesis; conversely, during the negative TPQD events, the opposite is true. Using a series of coupled global climate models that participated in phase 6 of the Coupled Model Intercomparison Project with different oceanic resolutions, we show that the amplitude of the TPQD variability becomes smaller as the oceanic resolution becomes finer, providing evidence for the impacts of submesoscale eddies on damping the TPQD variability. Our study suggests that explicitly simulating equatorial submesoscale eddies is necessary for gaining a more robust understanding of low-frequency tropical climate variability. Significance Statement Submesoscale ocean eddies inhibit the development of quasi-decadal climate variability in the equatorial central Pacific, according to a high-resolution global climate simulation.

Fingerprinting Low-Frequency Last Millennium Temperature Variability in Forced and Unforced Climate Models

Abstract Constraining unforced and forced climate variability impacts interpretations of past climate variations and predictions of future warming. However, comparing general circulation models (GCMs) and last millennium Holocene hydroclimate proxies reveals significant mismatches between simulated and reconstructed low-frequency variability at multidecadal and longer time scales. This mismatch suggests that existing simulations underestimate either external or internal drivers of climate variability. In addition, large differences arise across GCMs in both the magnitude and spatial pattern of low-frequency climate variability. Dynamical understanding of forced and unforced variability is expected to contribute to improved interpretations of paleoclimate variability. To that end, we develop a framework for fingerprinting spatiotemporal patterns of temperature variability in forced and unforced simulations. This framework relies on two frequency-dependent metrics: 1) degrees of freedom (≡N) and 2) spatial coherence. First, we use N and spatial coherence to characterize variability across a suite of both preindustrial control (unforced) and last-millennium (forced) GCM simulations. Overall, we find that, at low frequencies and when forcings are added, regional independence in the climate system decreases, reflected in fewer N and higher coherence between local and global mean surface temperature. We then present a simple three-box moist-static-energy-balance model for temperature variability, which is able to emulate key frequency-dependent behavior in the GCMs. This suggests that temperature variability in the GCM ensemble can be understood through Earth’s energy budget and downgradient energy transport, and allows us to identify sources of polar-amplified variability. Finally, we discuss insights the three-box model can provide into model-to-model GCM differences. Significance Statement Forced and unforced temperature variability are poorly constrained and understood, particularly that at time scales longer than a decade. Here, we identify key differences in the time scale–dependent behavior of forced and unforced temperature variability using a combination of numerical climate models and principles of downgradient energy transport. This work, and the spatiotemporal characterizations of forced and unforced temperature variability that we generate, will aid in interpretations of proxy-based paleoclimate reconstructions and improve mechanistic understanding of variability.

Impacts of low-frequency climate variability on tropical cyclone activity in the North Indian Ocean basin

Impacts of low-frequency climate variability on tropical cyclone activity in the North Indian Ocean basin

Compound marine heatwaves and low sea surface salinity extremes over the tropical Pacific Ocean

Marine heatwaves (MHWs) and low sea surface salinity (SSS) events can significantly impact marine ecosystems and dynamic systems, respectively. Compound marine extreme events can cause more significant damage than individual extreme events. However, the spatiotemporal patterns of compound MHW-low SSS extremes are not well understood. Daily reanalysis data were used to identify the basic patterns of compound extreme events and their drivers. These events mainly occur over the central tropical Pacific Ocean during record-breaking El Niño events. This analysis revealed that extreme sea surface warming associated with El Niño drives increased convection, which subsequently leads to increased rainfall. It ultimately causes extreme sea surface freshening. This analysis highlights the significance of air-sea interactions and low-frequency climate variability in shaping compound extreme events.

On the Spatial Scale Dependence of Long‐Term Persistence in Global Annual Precipitation Data and the Hurst Phenomenon

AbstractPrecipitation deficits are the main physical drivers of droughts across the globe, and their level of persistence can be characterized by the Hurst coefficient H (0.5 < H < 1), with high H indicating strong long‐term persistence (LTP). Previous analyses of point and gridded annual global precipitation datasets have concluded that LTP in precipitation is weak (H ∼ 0.6) which is inconsistent with higher values of H for large river basins, for example, the Nile. Based on an analysis of gridded annual precipitation data for eight selected regions distributed across the globe, an important new finding is that H increases with the spatial scale of averaging, with mean H values at the grid and regional scale of 0.66 and 0.83, respectively. The discovery of enhanced LTP at the regional scale of averaging of precipitation has important implications for characterizing the severity of regional droughts, as well as LTP in the annual flows of large rivers and recharge to major aquifers. Teleconnections with known modes of low frequency variability in the global climate system are demonstrated using correlation analysis and stepwise regression. Despite having several constituent regions exhibiting LTP, the Northern Hemisphere surprisingly has no LTP; this is shown to result from different modes of low frequency climatic variability canceling each other out. LTP for the Southern Hemisphere is moderate, and weak for Global average precipitation. LTP in Blue Nile basin scale precipitation is shown to explain the Hurst Phenomenon in naturalized annual flows for the River Nile, more than 70 years after its discovery by Hurst.

Compound Climate Risk: Diagnosing Clustered Regional Flooding at Inter-Annual and Longer Time Scales

The potential for extreme climate events to cluster in space and time has driven increased interest in understanding and predicting compound climate risks. Through a case study on floods in the Ohio River Basin, we demonstrated that low-frequency climate variability could drive spatial and temporal clustering of the risk of regional climate extremes. Long records of annual maximum streamflow from 24 USGS gauges were used to explore the regional spatiotemporal patterns of flooding and their associated large-scale climate modes. We found that the dominant time scales of flood risk in this basin were in the interannual (6–7 years), decadal (11–13 years), and secular bands and that different sub-regions within the Ohio River Basin responded differently to large-scale forcing. We showed that the leading modes of streamflow variability were associated with ENSO and secular trends. The low-frequency climate modes translated into epochs of increased and decreased flood risk with multiple extreme floods or the absence of extreme floods, thus informing the nature of compound climate-induced flood risk. A notable finding is that the secular trend was associated with an east-to-west shift in the flood incidence and the associated storm track. This is consistent with some expectations of climate change projections.

Climate Autobiography Timeline: Adapting Timeline Research Methods to the Study of Climate Perceptions

Abstract Climate perception is a growing area of study in the social sciences and one that has implications on the tools and strategies we use to communicate climate change risk information. However, the range of climate perception studies remains limited, focused primarily on perceptions of day-to-day weather, sudden-onset severe events, or long-term permanent change. Phenomena situated between these extremes (e.g., annual- to decadal-scale variability) are largely missing from social science of climate research. Whether this is due to limited perception by research participants, is due to limited research attention, or is a reflection of the methods commonly applied to human dimensions of climate research, this gap precludes analysis of the full range of complex climate experiences and their influence on climate perception and understanding. In this paper, we offer a proof of concept for the climate autobiography timeline (CAT), a visual timeline tool developed to assess climate perception while prompting an ordered consideration of time, with the goal of eliciting insights into complex and long-term climate experiences such as low-frequency climate variability. Results are based off a preliminary application of the CAT across focus groups conducted in Newfoundland and Labrador, a province of Canada that is subject to low-frequency climate variability and frequent high-impact weather. Results reveal three key findings: 1) weather and climate narratives are commonly anchored to two time periods, potentially obscuring perceptions of variability; 2) narratives focus on socially important weather and climate phenomena; and 3) the social and visual coconstruction of weather and climate narratives may yield more holistic representations of local climate knowledge. Significance Statement The purpose of this work is to highlight the utility of timeline research methods to the study of climate perception research. Specifically, the climate autobiography timeline (CAT) serves as a tool that can address limitations of research tools commonly applied to the study of climate perceptions, notably the inability for current methods to elicit and organize complex climate experiences. Failure to capture these experiences may prevent a holistic and socially grounded understanding of climate perceptions. Drawing from a preliminary application of CATs in the province of Newfoundland and Labrador in Canada, we highlight how the tool can provide information complementary to, but distinct from, data collected through more commonly used methods such as interviews or surveys. This approach holds promise for analyses of long-term climate history, impacts of historical severe events, and cultural impact of weather and climate.

Investigation of age trends in tree-ring stable carbon and oxygen isotopes from northern Fennoscandia over the past millennium

Although tree-ring stable carbon (δ13C) and oxygen (δ18O) isotopes are increasingly used for climate reconstructions, it remains unclear whether isotopic ratios from the two chemical elements and different tree species exhibit age-related trends that require removal prior to any paleoclimatic interpretation. Here, we present 2,355 δ13C and 2,237 δ18O decadal measurements of living and relict Scots pines (Pinus sylvestris L.) from northern Fennoscandia to investigate the occurrence of isotope-specific age trends at both the individual tree and chronology level between 941 and 2010 CE, together with total-ring width and maximum density data. We show that δ13C values increase by ∼0.035‰ per 10 years of tree age, and therefore require detrending, which is not the case for δ18O that only contains minor changes related to age. This study provides independent evidence for the unique paleoclimatic value of stable δ18O isotopic ratios from the cellulose of living and relict pine wood to reconstruct high-to low-frequency climate variability. Conversely, caution is advised when information from diverse tree-ring parameters, species and regions is combined in multi-proxy climate reconstructions.

Assessing the large-scale drivers of precipitation in the northeastern United States via linear orthogonal decomposition

This study examines the linear orthogonal modes associated with monthly precipitation in the northeastern United States, from CESM1 LENS (35 ensemble members, 1979–2005) and two reanalysis datasets (ERA5, 1950–2018 and NOAA-CIRES-DOE 20CRv3, 1950–2015). Calendar months are aggregated together, and any linear trends in data are removed. Using region-averaged precipitation anomaly time series and monthly anomalies for several global 2D atmospheric fields, a linear orthogonal decomposition method is implemented to iteratively extract time series (based on field and geographic location) of absolute maximum correlation. Linear modes associated with this method are then projected onto the full set of 2D fields to provide physical insight into the mechanisms involved in generating precipitation. In this region, the first mode is associated with vapor transport from the Atlantic seaboard, the second mode is characterized by westward vapor transport associated with extratropical cyclones, and the third mode captures vapor transport from the Gulf of Mexico during the fall and winter. However, the third mode is less robust in the spring and summer. Results are generally consistent across the datasets, and applying multiple linear regression with the linear modes to predict the precipitation anomalies produces R-squared values of around 0.54–0.65 for CESM1 LENS, and around 0.58–0.88 for reanalysis, with the lowest values generally in the spring and late summer. The influence of low-frequency climate variability on the modes is considered for CESM1 LENS, and the modes in late winter can be predicted with some success via a combination of several, prominent large-scale teleconnection patterns.

A Simple Coupled Model of the Wind–Evaporation–SST Feedback with a Role for Stability

Abstract The wind–evaporation–SST (WES) feedback describes a coupled mechanism by which an anomalous meridional sea surface temperature (SST) gradient in the tropics evolves over time. As commonly posed, the (positive) WES feedback depends critically on the atmospheric response to SST anomalies being governed by a process akin to that argued by Lindzen and Nigam in 1987, and omits an alternative process by which SST anomalies modulate surface wind speed through vertical momentum mixing as proposed by Wallace et al. and Hayes et al. in 1989. A simple model is developed that captures the essential coupled dynamics of the WES feedback as commonly posed, while also allowing for momentum entrainment in response to evolving SST anomalies. The evolution of the coupled system depends strongly on which effects are enabled in the model. When both effects are accounted for in idealized cases near the equator, the initial anomalous meridional SST gradient grows over a time scale of a few months but is damped within one year. The sign and magnitude of the WES feedback depend on latitude within the tropics and exhibit hemispheric asymmetry. When constrained by realistic profiles of prevailing zonal wind, the model predicts that the WES feedback near the equator is stronger during boreal winter, while the domain over which it is positive is broader during boreal summer, and that low-frequency climate variability can also modulate the strength and structure of the WES feedback. These insights may aid in the interpretation of coupled climate behavior in observations and more complex models. Significance Statement Regional climate variability on time scales from months to decades, including El Niño, relies heavily on feedbacks between the atmosphere and the ocean in which some initial change in the environment is either amplified or damped over time. Several conceptual models for such feedbacks have been devised over the years to explain the coupled climate behavior seen in observations and computer simulations. A rather ubiquitous one is called the wind–evaporation–SST (WES) feedback, but the typical phrasing of it does not incorporate a potentially important influence of ocean temperature changes on the stability of the atmosphere above it. This study adds that effect to the WES feedback framework and examines climate variability through the lens of the augmented conceptual model.

Identifying the full spectrum of climatic signals controlling a tree species' growth and adaptation to climate change

Identifying the key climatic parameters controlling a tree species’ growth in a given environment can improve our understanding of the species’ adaptability to climate change. However, this can be significantly affected by the detection and analytical methodologies used, at least for water-limited ecosystems. To address this issue, we tested together for the first time the five key methods widely applied for detecting climatic drivers of tree growth. The test was applied on the growth dynamics of Pinus pinea L. (the last ca. 130 years) and for evaluating its climatic sensitivity (the last ca. 60 years), under contrasting climatic conditions, from sites in Italy and Greece. As predictors, we selected a high number of seasonal, annual, and multi-annual climate integration periods (626 variables in total for precipitation and SPEI) through running correlation analysis. Methods that retain low-frequency variability and persistence of past climatic signals in radial growth (ABD, RCS, and raw BAI) revealed that long-term precipitation accumulated over 3–6 years was the key driver for P. pinea growth in the wettest sites. These signals probably appeared because of the cumulative effects of past years’ rainfall in determining water table levels. They would have been missed if low-frequency climate and growth variability were not considered, how it happens using standardized BAI and TRW. In the driest site, productivity was low and explained by 1–2 years precipitation signals, suggesting that mostly moisture stored in topsoil from more recent rainfall sustained P. pinea growth, a typical shallow-rooted species. Thus, the loss in root contact with the declining water table may explain the higher vulnerability to drought stress at its dry-end distribution range, particularly under low precipitation. A further drop in water table levels, driven by global warming, will most likely increase such vulnerability. Hence, conservation efforts will require maintaining a stable water table, prohibiting groundwater overexploitation at P. pinea habitats. Reducing competition from other deeper rooted tree species, better adapted to drought and fire, may further benefit the species’ conservation status.

A Bayesian approach to exploring the influence of climate variability modes on fire weather conditions and lightning-ignited wildfires

Understanding the relationships between large-scale, low-frequency climate variability modes, fire weather conditions and lighting-ignited wildfires has implications for fire-weather prediction, fire management and conservation. This article proposes a Bayesian network framework for quantifying the influence of climate modes on fire weather conditions and occurrence of lightning-ignited wildfires. The main objectives are to describe and demonstrate a probabilistic framework for identifying and quantifying the joint and individual relationships that comprise the climate-wildfire system; gain insight into potential causal mechanisms and pathways; gauge the influence of climate modes on fire weather and lightning-ignition relative to that of local-scale conditions alone; assess the predictive skill of the network; and motivate the use of techniques that are intuitive, flexible and for which user‐friendly software is freely available. A case study illustrates the application of the framework to a forested region in southwest Australia. Indices for six climate variability modes are considered along with two hazard variables (observed fire weather conditions and prescribed burn area), and a 41-year record of lightning-ignited wildfire counts. Using the case study data set, we demonstrate that the proposed framework: (1) is based on reasonable assumptions provided the joint density of the variables is converted to multivariate normal; (2) generates a parsimonious and interpretable network architecture; (3) identifies known or partially known relationships between the variables; (4) has potential to be used in a predictive setting for fire weather conditions; and (5) climate modes are more directly related to fire weather conditions than to lightning-ignition counts.

Exploring timescales of predictability in species distributions

Accurate forecasts of how animals respond to climate‐driven environmental change are needed to prepare for future redistributions, however, it is unclear which temporal scales of environmental variability give rise to predictability of species distributions. We examined the temporal scales of environmental variability that best predicted spatial abundance of a marine predator, swordfish Xiphias gladius, in the California Current. To understand which temporal scales of environmental variability provide biological predictability, we decomposed physical variables into three components: a monthly climatology (long‐term average), a low frequency component representing interannual variability, and a high frequency (sub‐annual) component that captures ephemeral features. We then assessed each component's contribution to predictive skill for spatially‐explicit swordfish catch. The monthly climatology was the primary source of predictability in swordfish spatial catch, reflecting the spatial distribution associated with seasonal movements in this region. Importantly, we found that the low frequency component (capturing interannual variability) provided significant skill in predicting anomalous swordfish distribution and catch, which the monthly climatology cannot. The addition of the high frequency component added only minor improvement in predictability. By examining models' ability to predict species distribution anomalies, we assess the models in a way that is consistent with the goal of distribution forecasts – to predict deviations of species distributions from their average historical locations. The critical importance of low frequency climate variability in describing anomalous swordfish distributions and catch matches the target timescales of physical climate forecasts, suggesting potential for skillful ecological forecasts of swordfish distributions across short (seasonal) and long (climate) timescales. Understanding sources of prediction skill for species environmental responses gives confidence in our ability to accurately predict species distributions and abundance, and to know which responses are likely less predictable, under future climate change. This is important as climate change continues to cause an unprecedented redistribution of life on Earth.

Long-term persistence in discharge time series of mountainous catchments in the Elbe River basin

Abstract. Statistical detection of trends in hydrometeorological time series is a crucial task when revealing how river systems react to environmental and human-induced changes. It was shown that the autocorrelation structure of a series influences the power of parametric and nonparametric trend tests. While the order of short-memory processes can be sufficiently captured by AR(I)MA models, the determination of the Hurst exponent, which describes the long memory, is still challenging, considering that the available methods partially give different results. In the Elbe River basin, Europe, several studies focusing on the detection (or description) of long-term persistence were performed. However, different lengths of series and different methods were used. The aim of the present work is to gather the results gained in various parts of the Elbe basin in Central Europe and to compare them with our estimation of the Hurst exponent using six discharge series observed in selected subbasins. Instead of the dependence of the exponent on the catchment area suggested by the theory of aggregated short-memory processes, we rather found a relationship between this parameter and the series length. As the theory is not supported by our findings, we suppose that the Hurst phenomenon is caused by a complex interplay of low-frequency climate variability and catchment processes. Experiments based on distributed water balance models should be the further research objective, ideally under the umbrella of mutual international projects.

Multivariate framework for the assessment of key forcing to Lake Malawi level variations in non-stationary frequency analysis

Lake Malawi in south eastern Africa is a very important freshwater system for the socio-economic development of the riparian countries and communities. The lake has however experienced considerable recession in the levels in recent years. Consequently, frequency analyses of the lake levels premised on time-invariance (or stationarity) in the parameters of the underlying probability distribution functions (pdfs) can no longer be assumed. In this study, the role of hydroclimate forcing factors (rainfall, lake evaporation, and inflowing discharge) and low frequency climate variability indicators (e.g., El Nino Southern Oscillation-ENSO and the Indian Ocean Dipole Mode-IODM) on lake level variations is investigated using a monthly mean lake level dataset from 1899 to 2017. Non-stationarity in the lake levels was tested and confirmed using the Mann-Kendall trend test (α = 0.05 level) for the first moment and the F test for the second moment (α = 0.05 level). Change points in the series were identified using the Mann-Whitney-Pettit test. The study also compared stationary and non-stationary lake level frequency during 1961 to 2004, the common period where data were available for all the forcing factors considered. Annual maximum series (AMS) and peak over threshold (POT) analysis were conducted by fitting various candidate extreme value distributions (EVD) and parameter fitting methods. The Akaike information criteria (AIC), Bayesian information criteria (BIC), deviance information criteria (DIC), and likelihood ratios (RL) served as model evaluation criteria. Under stationary conditions, the AMS when fitted to the generalized extreme value (GEV) distribution with maximum likelihood estimation (MLE) was found to be superior to POT analysis. For the non-stationary models, open water evaporation as a covariate of the lake levels with the GEV and MLE was found to have the most influence on the lake level variations as compared with rainfall, discharge, and the low frequency climatic forcing. The results are very critical in flood zoning especially with various planned infrastructural developments around the lakeshore.

Internal variability in multidecadal trends of surface air temperature over antarctica in austral winter in model simulations

The surface air temperature (SAT) exhibits pronounced warming over West Antarctica in recent decades, especially in austral spring and winter. Using a 30-member ensemble of simulations by Community Earth System Model (CESM), two reanalysis datasets, and observed station data, this study investigates the relative contributions of internally generated low-frequency climate variability and externally forced climate change to the austral winter SAT trend in Antarctica. Although these simulations share the same external forcing, the SAT trends during 1979–2005 show large diversity among the individual members in the CESM ensemble simulations, suggesting that internally generated variability contributes a considerable part to the multidecadal SAT change in Antarctica. Quantitatively, the total forced contribution to the SAT (1979–2005) change is about 0.53 k/27 yr, and the internal variability can be strong enough to double or cancel the externally forced warming trend. A method called “dynamical adjustment” is utilized to further divide the forced response. We find both the forced thermodynamically-induced and the forced dynamically-induced SAT trends are positive over all the regions in Antarctica, with the regional mean values of 0.20 k /27 yr and 0.33 k/27 yr, respectively. The diversity of SAT trends among the simulations is closely linked to a Southern hemisphere Annular Mode (SAM)-like atmospheric circulation multidecadal change in the Southern Hemisphere. When there exists a positive–negative seesaw of pressure trend between Antarctica and the mid-latitudes, the SAT trend is positive over most of Antarctica but negative over the Antarctic Peninsula, and vice versa. The SAM-like atmospheric circulation multidecadal change mainly arises from atmospheric internal variability rather than remote tropical Sea Surface Temperature (SST).

Predicting coral-reef futures from El Ni\xf1o and Pacific Decadal Oscillation events

El Niño Southern Oscillation (ENSO) events modulate oceanographic processes that control temperature and productivity in tropical waters, yet potential interactions with low frequency climate variability, such as the Pacific Decadal Oscillation (PDO), are poorly understood. We show that ENSO and PDO together predicted (i) maximum sea-surface temperatures (SST), which were associated with coral bleaching and declines in coral cover, and (ii) maximum chlorophyll-a concentrations, which were associated with high densities of coral-predatory Acanthaster starfish, across the tropical north Pacific Ocean since 1980. Asynchrony between the positive PDO and negative ENSO (i.e., La Niña) was associated with peaks in annual SST. By contrast, synchrony between the positive PDO and positive ENSO (i.e., El Niño) was associated with peaks in chlorophyll-a. Both conditions led to ecological disturbances and significant loss of coral cover, however, spatial models revealed where impacts to reefs were expected under varying climate scenarios. The 2015/17 ENSO event was coupled with a positive PDO and resulted in high SST and Acanthaster abundances in eastern Micronesia, while positive coral growth occurred in western Micronesia. Our novel approach for forecasting coral growth into the future may be applicable to other oceanic regions with differing oceanographic modulators.

Decline in Terrestrial Moisture Sources of the Mississippi River Basin in a Future Climate

AbstractAssessment of the impact of climate change on water resources over land requires knowledge on the origin of the precipitation and changes therein toward the future. We determine the origin of precipitation over the Mississippi River basin (MRB) using high-resolution (~25 km) climate model simulations for present and future climate (RCP4.5). Moisture resulting in precipitation over the MRB is tracked back in time using Eulerian offline moisture tracking, in order to find out from where this water originally evaporated (i.e., the moisture sources). We find that the most important continental moisture sources are the MRB itself and the area southwest of the basin. The two most relevant oceanic sources are the Gulf of Mexico/Caribbean and the Pacific. The distribution of sources varies per season, with more recycling of moisture within the basin during summer and more transport of moisture from the ocean toward the basin in winter. In future winters, we find an increase in moisture source from the oceans (related to higher sea surface temperatures), resulting in more precipitation over the MRB. In future summers, we find an approximately 5% decrease in moisture source from the basin itself, while the decrease in precipitation is smaller (i.e., lower recycling ratios). The results here are based on one climate model, and we do not study low-frequency climate variability. We conclude that Mississippi’s moisture sources will become less local in a future climate, with more water originating from the oceans.