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Tropical rainfall variations are of direct societal relevance and drive climate variations worldwide via teleconnections. The convective rainfall tends to occur when sea surface temperature (SST) exceeds a threshold, SSTthr, usually taken to be constant in time and space. We analyze 40-year monthly observations and find that SSTthr varies by up to 4°C in space and with season. Based on local convective instability, we develop a quantitative theory that largely explains the SSTthr variations using the climatological state of the tropical atmosphere. Although it is often assumed that spatial variations of tropical upper tropospheric temperature are small and can be neglected, it is shown that lower climatological values favor a lower SSTthr. Similarly, a small increase in climatological surface relative humidity also leads to a decrease in SSTthr as does a lower climatological air-sea temperature difference. Consequently, efforts to understand and predict natural or forced variations in tropical rainfall must account for, in addition to SST, the temperatures aloft and the near surface humidity and temperature and requires improved understanding of what controls their distribution in space and time.

Atlantic multidecadal variability (AMV) impacts temperature, precipitation, and extreme events on both sides of the Atlantic Ocean basin. Previous studies with climate models have suggested that when external radiative forcing is held constant, the large-scale ocean and atmosphere circulation are associated with sea surface temperature (SST) anomalies that have similar characteristics to the observed AMV. However, there is an active debate as to whether these internal fluctuations driven by coupled atmosphere–ocean variability remain influential to the AMV on multidecadal time scales in our modern, anthropogenically forced climate. Here we provide evidence from multiple large ensembles of climate models, paleoreconstructions, and instrumental observations of a growing role for external forcing in the AMV. Prior to 1850, external forcing, primarily from volcanoes, explains about one-third of AMV variance. Between 1850 and 1950, there is a transitional period, where external forcing explains one-half of AMV variance, but volcanic forcing only accounts for about 10% of that. After 1950, external forcing explains three-quarters of AMV variance. That is, the role for external forcing in the AMV grows as the variations in external forcing grow, even if the forcing is from different sources. When forcing is relatively stable, as in earlier modeling studies, a higher percentage of AMV variations are internally generated.

This paper attempts to enhance our understanding of the causes of Atlantic multidecadal variability (AMV). Following the literature, we define the AMV as the SST averaged over the North Atlantic basin, linearly detrended and low-pass filtered. There is an ongoing debate about the drivers of the AMV, which include internal variability generated from the ocean or atmosphere (or both) and external radiative forcing. We test the role of these factors in explaining the time history, variance, and spatial pattern of the AMV using a 41-member ensemble from a fully coupled version of CESM and a 10-member ensemble of the CESM atmosphere coupled to a slab ocean. The large ensemble allows us to isolate the role of external forcing versus internal variability, and the model differences allow us to isolate the role of coupled ocean circulation. Both with and without coupled ocean circulation, external forcing explains more than half of the variance of the observed AMV time series, indicating its important role in simulating the twentieth-century AMV phases. In this model the net effect of ocean processes is to reduce the variance of the AMV. Dynamical ocean coupling also reduces the ability of the model to simulate the characteristic spatial pattern of the AMV, but forcing has little impact on the pattern. Historical forcing improves the time history and variance of the AMV simulation, while the more realistic ocean representation reduces the variance below that observed and lowers the correlation with observations.

Persistent multiyear cold states of the tropical Pacific Ocean drive hydroclimate anomalies worldwide, including persistent droughts in the extratropical Americas. Here, the atmosphere and ocean dynamics and thermodynamics of multiyear cold states of the tropical Pacific Ocean are investigated using European Centre for Medium-Range Weather Forecasts reanalyses and simplified models of the ocean and atmosphere. The cold states are maintained by anomalous ocean heat flux divergence and damped by increased surface heat flux from the atmosphere to ocean. The anomalous ocean heat flux divergence is contributed to by both changes in the ocean circulation and thermal structure. The keys are an anomalously shallow thermocline that enhances cooling by upwelling and anomalous westward equatorial currents that enhance cold advection. The thermocline depth anomalies are shown to be a response to equatorial wind stress anomalies. The wind stress anomalies are shown to be a simple dynamical response to equatorial SST anomalies as mediated by precipitation anomalies. The cold states are fundamentally maintained by atmosphere-ocean coupling in the equatorial Pacific. The physical processes that maintain the cold states are well approximated by linear dynamics for ocean and atmosphere and simple thermodynamics.

The North Atlantic Oscillation (NAO) is predictable in climate models at near-decadal timescales. Predictive skill derives from ocean initialization, which can capture variability internal to the climate system, and from external radiative forcing. Herein, we show that predictive skill for the NAO in a very large uninitialized multi-model ensemble is commensurate with previously reported skill from a state-of-the-art initialized prediction system. The uninitialized ensemble and initialized prediction system produce similar levels of skill for northern European precipitation and North Atlantic SSTs. Identifying these predictable components becomes possible in a very large ensemble, confirming the erroneously low signal-to-noise ratio previously identified in both initialized and uninitialized climate models. Though the results here imply that external radiative forcing is a major source of predictive skill for the NAO, they also indicate that ocean initialization may be important for particular NAO events (the mid-1990s strong positive NAO), and, as previously suggested, in certain ocean regions such as the subpolar North Atlantic ocean. Overall, we suggest that improving climate models’ response to external radiative forcing may help resolve the known signal-to-noise error in climate models.

The most prominent feature of the time-mean global meridional overturning circulation (MOC) is the Atlantic MOC (AMOC). However, interannual variability of the global MOC is shown here to be dominated by Pacific MOC (PMOC) variability over the full depth of the ocean at most latitudes. This dominance of interannual PMOC variability is robust across modern climate models and an observational state estimate. PMOC interannual variability has large-scale organization, its most prominent feature being a cross-equatorial cell spanning the tropics. Idealized experiments show that this variability is almost entirely wind driven. Interannual anomalies of zonal mean zonal wind stress produce zonally integrated Ekman transport anomalies that are larger in the Pacific Ocean than in the Atlantic Ocean, simply because the Pacific is wider than the Atlantic at most latitudes. This contrast in Ekman transport variability implies greater variability in the near-surface branch of the PMOC when compared with the near-surface branch of the AMOC. These near-surface variations in turn drive compensating flow anomalies below the Ekman layer. Because the baroclinic adjustment time is longer than a year at most latitudes, these compensating flow anomalies have baroclinic structure spanning the full depth of the ocean. Additional analysis reveals that interannual PMOC variations are the dominant contribution to interannual variations of the global meridional heat transport. There is also evidence of interaction between interannual PMOC variability and El Niño–Southern Oscillation.

Significance How the magnitude of El Niño will change is of great societal concern, yet it remains largely unknown. Here we show analysis of how changing El Niño properties, due to 20th century climate change, can shed light on changes to the intensity of El Niño in the future. Since the 1970s, El Niño has changed its origination from the eastern Pacific to the western Pacific, along with increased strong El Niño events due to a background warming in the western Pacific warm pool. This suggests the controlling factors that may lead to increased extreme El Niño events in the future. If the observed background changes continue under future anthropogenic forcing, more frequent extreme El Niño events will induce profound socioeconomic consequences., El Niño’s intensity change under anthropogenic warming is of great importance to society, yet current climate models’ projections remain largely uncertain. The current classification of El Niño does not distinguish the strong from the moderate El Niño events, making it difficult to project future change of El Niño’s intensity. Here we classify 33 El Niño events from 1901 to 2017 by cluster analysis of the onset and amplification processes, and the resultant 4 types of El Niño distinguish the strong from the moderate events and the onset from successive events. The 3 categories of El Niño onset exhibit distinct development mechanisms. We find El Niño onset regime has changed from eastern Pacific origin to western Pacific origin with more frequent occurrence of extreme events since the 1970s. This regime change is hypothesized to arise from a background warming in the western Pacific and the associated increased zonal and vertical sea-surface temperature (SST) gradients in the equatorial central Pacific, which reveals a controlling factor that could lead to increased extreme El Niño events in the future. The Coupled Model Intercomparison Project phase 5 (CMIP5) models’ projections demonstrate that both the frequency and intensity of the strong El Niño events will increase significantly if the projected central Pacific zonal SST gradients become enhanced. If the currently observed background changes continue under future anthropogenic forcing, more frequent strong El Niño events are anticipated. The models’ uncertainty in the projected equatorial zonal SST gradients, however, remains a major roadblock for faithful prediction of El Niño’s future changes.

North Atlantic sea surface temperatures (SST) exhibit a lagged response to the North Atlantic Oscillation (NAO) in both models and observations, which has previously been attributed to changes in ocean heat transport. Here we examine the lagged relationship between the NAO and Atlantic multidecadal variability (AMV) in the context of the two other major components of the AMV: atmospheric noise and external forcing. In preindustrial control runs, we generally find that after accounting for spurious signals introduced by filtering, the SST response to the NAO is only statistically significant in the subpolar gyre. Further, the lagged SST response to the NAO is small in magnitude and offers a limited contribution to the AMV pattern, statistics, or predictability. When climate models include variable external forcing, the relationship between the NAO and AMV is obscured and becomes inconsistent. In these historically forced runs, knowledge of the prior NAO offers reduced predictability. The differences between the preindustrial and the historically forced ensembles suggest that we do not yet have enough observational data to surmise the true NAO–AMV relationship and add evidence that external forcing plays a substantial role in producing the AMV.

This study examines coupled relationships among clouds, atmospheric circulation, and sea ice in Antarctic winter. We find that the wave-3 pattern dominates the leading covariability mode among cloud, atmospheric circulation, and sea ice. Both horizontal transport and vertical motion contribute to cloud formation, resulting in maximum cloud anomalies spatially between maximum meridional wind and pressure anomalies in the coupled system. The radiative effect of the clouds related to the wave-3 pattern can generate sea ice anomalies up to 12 cm thick in one month in the Amundsen Sea. It also strengthens the sea ice anomalies that are directly induced by low-level atmospheric circulation anomalies. In addition, the radiative forcing of the leading cloud mode in the lower troposphere is suppressed by the dynamic and thermodynamic effects of the circulation anomalies. These discoveries provide a better understanding of Antarctica’s interactive processes, and also offer physical evidence for climate model validations.

The European Great Famine of 1315–1317 triggered one of the worst population collapses in European history and ranks as the single worst European famine in mortality as a proportion of population. Historical records point to torrential rainfall, land saturation, crop failure, and prolonged flooding as important causes of the famine. Here we use the tree-ring based Old World Drought Atlas (OWDA) to show that the average of each growing season preceding the Great Famine years (1314–1316) was the fifth wettest over Europe from 1300 to 2012 C.E. The spatial and temporal characteristics of our OWDA-estimated anomalies are in excellent agreement with available historical accounts. We also characterize a mode of European hydroclimate variability that is associated with the Great Famine, which we term the “Great Famine mode.” This mode emerges as the leading mode of European hydroclimate variability from 1300–2012 and is strongly associated with extreme wet and dry events in Europe over the last millennium. Exceptionally wet growing seasons, compared to the past 700 years, preceded the European Great Famine of 1315-1317 and are associated with a newly identified hydroclimate pattern, according to analyses of Europe-wide tree ring data.

From 1875 to 1878, concurrent multiyear droughts in Asia, Brazil, and Africa, referred to as the Great Drought, caused widespread crop failures, catalyzing the so-called Global Famine, which had fatalities exceeding 50 million people and long-lasting societal consequences. Observations, paleoclimate reconstructions, and climate model simulations are used 1) to demonstrate the severity and characterize the evolution of drought across different regions, and 2) to investigate the underlying mechanisms driving its multiyear persistence. Severe or record-setting droughts occurred on continents in both hemispheres and in multiple seasons, with the “Monsoon Asia” region being the hardest hit, experiencing the single most intense and the second most expansive drought in the last 800 years. The extreme severity, duration, and extent of this global event is associated with an extraordinary combination of preceding cool tropical Pacific conditions (1870–76), a record-breaking El Niño (1877–78), a record strong Indian Ocean dipole (1877), and record warm North Atlantic Ocean (1878) conditions. Composites of historical analogs and two sets of ensemble simulations—one forced with global sea surface temperatures (SSTs) and another forced with tropical Pacific SSTs—were used to distinguish the role of the extreme conditions in different ocean basins. While the drought in most regions was largely driven by the tropical Pacific SST conditions, an extreme positive phase of the Indian Ocean dipole and warm North Atlantic SSTs, both likely aided by the strong El Niño in 1877–78, intensified and prolonged droughts in Australia and Brazil, respectively, and extended the impact to northern and southeastern Africa. Climatic conditions that caused the Great Drought and Global Famine arose from natural variability, and their recurrence, with hydrological impacts intensified by global warming, could again potentially undermine global food security.

The El Nino Southern Oscillation (ENSO) is a major source of interannual climate variability. ENSO life cycles and the associated teleconnections evolve over multiple years at a global scale. This analysis is the first attempt to characterize the structure of the risk posed by trans-Pacific ENSO teleconnections to crop production in the greater Pacific Basin region. In this analysis we identify the large-scale atmospheric dynamics of ENSO teleconnections that affect heat and moisture stress during the growing seasons of maize, wheat and soy. We propose a coherent framework for understanding how trans-Pacific ENSO teleconnections pose a correlated risk to crop yields in major agricultural belts of the Americas, Australia and China over the course of an ENSO life cycle by using observations and a multi-model ensemble of climate anomalies during crop flowering seasons. Trans-Pacific ENSO teleconnections are often (but not always) offsetting between major producing regions in the Americas and those in northern China or Australia. El Ninos tend to create good maize and soybean growing conditions in the US and southeast South America, but poor growing conditions in northern China, southern Mexico and the Cerrado in Brazil. The opposite is true during La Nina. Wheat growing conditions in southeast South America generally have the opposite sign of those in Australia. Furthermore, multi-year La Ninas can force multi-year growing season anomalies in Argentina and Australia. Most ENSO teleconnections relevant for crop flowering seasons are the result of a single trans-Pacific circulation anomaly that develops in boreal summer and persists through the following spring. During the late summer and early fall of a developing ENSO event, the tropical Pacific forces an atmospheric anomaly in the northern midlatitudes that spans the Pacific from northern China to North America and in the southern midlatitudes from Australia to southeast South America. This anomaly directly links the soybean and maize growing seasons of the US, Mexico and China and the wheat growing seasons of Argentina, southern Brazil and Australia. The ENSO event peaks in boreal winter, when the atmospheric circulation anomalies intensify and affect maize and soybeans in southeast South America. As the event decays, the ENSO-induced circulation anomalies persist through the wheat flowering seasons in China and the US.

This paper summarizes advances in research on tropical–polar teleconnections, made roughly over the last decade. Elucidating El Niño–Southern Oscillation (ENSO) impacts on high latitudes has remained an important focus along different lines of inquiry. Tropical to polar connections have also been discovered at the intraseasonal time scale, associated with Madden–Julian oscillations (MJOs). On the time scale of decades, changes in MJO phases can result in temperature and sea ice changes in the polar regions of both hemispheres. Moreover, the long-term changes in SST of the western tropical Pacific, tropical Atlantic, and North Atlantic Ocean have been linked to the rapid winter warming around the Antarctic Peninsula, while SST changes in the central tropical Pacific have been linked to the warming in West Antarctica. Rossby wave trains emanating from the tropics remain the key mechanism for tropical and polar teleconnections from intraseasonal to decadal time scales. ENSO-related tropical SST anomalies affect higher-latitude annular modes by modulating mean zonal winds in both the subtropics and midlatitudes. Recent studies have also revealed the details of the interactions between the Rossby wave and atmospheric circulations in high latitudes. We also review some of the hypothesized connections between the tropics and poles in the past, including times when the climate was fundamentally different from present day especially given a larger-than-present-day global cryosphere. In addition to atmospheric Rossby waves forced from the tropics, large polar temperature changes and amplification, in part associated with variability in orbital configuration and solar irradiance, affected the low–high-latitude connections.

Predictions of changes of the land monsoon rainfall (LMR) in the coming decades are of vital importance for successful sustainable economic development. Current dynamic models, though, have shown little skill in the decadal prediction of the Northern Hemisphere (NH) LMR (NHLMR). The physical basis and predictability for such predictions remain largely unexplored. Decadal change of the NHLMR reflects changes in the total NH continental precipitation, tropical general circulation, and regional land monsoon rainfall over northern Africa, India, East Asia, and North America. Using observations from 1901 to 2014 and numerical experiments, it is shown that the decadal variability of the NHLMR is rooted primarily in (i) the north–south hemispheric thermal contrast in the Atlantic–Indian Ocean sector measured by the North Atlantic–south Indian Ocean dipole (NAID) sea surface temperature (SST) index and (ii) an east–west thermal contrast in the Pacific measured by an extended El Niño–Southern Oscillation (XEN) index. Results from a 500-yr preindustrial control experiment demonstrate that the leading mode of decadal NHLMR and the associated NAID and XEN SST anomalies may be largely an internal mode of Earth’s climate system, although possibly modified by natural and anthropogenic external forcing. A 51-yr, independent forward-rolling decadal hindcast was made with a hybrid dynamic conceptual model and using the NAID index predicted by a multiclimate model ensemble. The results demonstrate that the decadal changes in the NHLMR can be predicted approximately a decade in advance with significant skills, opening a promising way forward for decadal predictions of regional land monsoon rainfall worldwide.

Abstarct In this model study the authors explore the possibility that the internal component of the Atlantic multidecadal oscillation (AMO) sea surface temperature (SST) signal is indistinguishable from the response to white noise forcing from the atmosphere and ocean. Here, complex models are compared without externally varying forcing with a one-dimensional noise-driven model for SST. General analytic expressions are obtained for both unfiltered and low-pass filtered lead–lag correlations. It is shown that this simple model reproduces many of the simulated lead–lag relationships among temperature, rate of change of temperature, and surface heat flux. It is concluded that the finding that at low frequencies the ocean loses heat to the atmosphere when the temperature is warm, which has been interpreted as showing that the ocean circulation drives the AMO, is a necessary consequence of the fact that at long periods the net heat flux (ocean plus atmosphere) is zero to a good approximation. It does not distinguish between the atmosphere and ocean as the source of the AMO and is consistent with the hypothesis that the AMO is driven by white noise heat fluxes. It is shown that some results in the literature are artifacts of low-pass filtering, which creates spurious low-frequency signals when the underlying data are white or red noise. It is concluded that in the absence of external forcing the AMO in most GCMs is consistent with being driven by white noise, primarily from the atmosphere.

In this analysis we show how globally coherent teleconnections from life-cycles of the El Nino Southern Oscillation (ENSO) lead to correlated crop production anomalies in North and South America. We estimate the magnitude of ENSO-induced Pan-American production anomalies and discuss how increasing crop harvesting frequency may affect Pan-American production variability. We find that ENSO accounts for ∼72%, 30% and 57% of Pan-American maize, soybean and wheat production variability, respectively. ENSO-induced production anomalies are greatest for maize, with median anomalies of ∼5% of Pan-American production. ENSO-induced yield anomalies for maize and soybeans tend to be of the same sign in North America and southeast South America but of an opposite sign in northeast Brazil. Teleconnections for wheat are more complicated because ENSO affects wheat yields via lagged soil moisture teleconnections in the US and an increased probability of disease in South America, but anomalies tend to be of the same sign in North America and southeast South America. After broadly characterizing ENSO-induced production anomalies, we demonstrate that they are not static in time. Increasing crop harvesting frequency has affected the correlated risks posed by ENSO. We use a soil water balance to show that in Brazil changing to a safrinha cropping cycle increases both the mean water stress and the ENSO-induced soil water content anomalies during flowering in both the maize and soybean seasons, which is a result of increasing evaporative demand during times of lower precipitation and moving the flowering seasons into months with strong ENSO teleconnections. Increasing crop harvesting frequency in Brazil has therefore increased ENSO-induced production variability of soybeans and maize.

Focusing on ENSO seasonal phase locking, diversity in peak location, and propagation direction, as well as the El Niño–La Niña asymmetry in amplitude, duration, and transition, a set of empirical probabilistic diagnostics (EPD) is introduced to investigate how the ENSO behaviors reflected in SST may change in a warming climate. EPD is first applied to estimate the natural variation of ENSO behaviors. In the observations El Niños and La Niñas mainly propagate westward and peak in boreal winter. El Niños occur more at the eastern Pacific whereas La Niñas prefer the central Pacific. In a preindustrial control simulation of the GFDL CM2.1 model, the El Niño–La Niña asymmetry is substantial. La Niña characteristics generally agree with observations but El Niño’s do not, typically propagating eastward and showing no obvious seasonal phase locking. So an alternative approach is using a stochastically forced simulation of a nonlinear data-driven model, which exhibits reasonably realistic ENSO behaviors and natural variation ranges. EPD is then applied to assess the potential changes of ENSO behaviors in the twenty-first century using CMIP5 models. Other than the increasing SST climatology, projected changes in many aspects of ENSO reflected in SST anomalies are heavily model dependent and generally within the range of natural variation. Shifts favoring eastward-propagating El Niño and La Niña are the most robust. Given various model biases for the twentieth century and lack of sufficient model agreements for the twenty-first-century projection, whether the projected changes for ENSO behaviors would actually take place remains largely uncertain.

Process-based models have been used to simulate and forecast a number of nonlinear dynamical systems, including influenza and other infectious diseases. In this work, we evaluate the effects of model initial condition error and stochastic fluctuation on forecast accuracy in a compartmental model of influenza transmission. These two types of errors are found to have qualitatively similar growth patterns during model integration, indicating that dynamic error growth, regardless of source, is a dominant component of forecast inaccuracy. We therefore examine the nonlinear growth of model initial error and compute the fastest growing directions using singular vector analysis. Using this information, we generate perturbations in an ensemble forecast system of influenza to obtain more optimal ensemble spread. In retrospective forecasts of historical outbreaks for 95 US cities from 2003 to 2014, this approach improves short-term forecast of incidence over the next one to four weeks., Author summary Mathematical models are now used to forecast infectious disease incidence at the population scale. By better understanding how errors in prediction systems are introduced, grow and impact the predictability of infectious disease, forecast accuracy could be improved. Here we explore the growth pattern of errors introduced from two major sources–model initial conditions and stochastic fluctuation–in a simple, compartmental model describing influenza transmission. We find that model initial error typically undergoes faster growth due to nonlinear amplification during model evolution. Adopting techniques used in numerical weather prediction, we leverage this growth of uncertainty and modify an ensemble forecast system to generate optimal perturbations along the fastest growing direction of initial error. This perturbation procedure increases ensemble spread, which better captures observations with large uncertainties. In retrospective forecasts for 95 US cities during the 2003 through 2014 flu seasons, this procedure leads to a substantial improvement of short-term forecast quality.

The characteristic evolution of El Nino Southern Oscillation (ENSO) on timescales of months to years means that risks to agriculture have structure between seasons and years. The potential for consecutive ENSO-induced yield anomalies is of particular interest in major food producing areas, where modest changes in yield have significant effects on global markets. In this study, we analyse how multi-year El Nino and La Nina life cycles relate to climate sensitive portions of major crop-growing seasons in North and South America. We analyse the dynamics underlying these life cycles to illustrate which aspects of the system are most important for agriculture. In North America, the same-season teleconnections affecting soybean and maize have been well studied, but we demonstrate the importance of lagged soil moisture teleconnections for wheat in the southern Great Plains. In South America, peak ENSO sea surface temperature (SST) teleconnections are concurrent with, and therefore critical for, wheat and maize growing seasons while soil moisture memory in Argentina plays an important role during the soybean growing season. Finally, we show that ENSO teleconnection life cycles are consistent with historical yield anomalies. Both El Nino and La Nina life cycles tend to force consecutive seasons of either above or below expected yields. While the magnitude of the yield anomalies forced by ENSO is often modest, they occur in major crop-producing regions.

The Atlantic Multidecadal Oscillation (AMO) affects climate variability in the North Atlantic basin and adjacent continents with potential societal impacts. Previous studies based on model simulations and short-term satellite retrievals hypothesized an important role for cloud radiative forcing in modulating the persistence of the AMO in the tropics, but this mechanism remains to be tested with long-term observational records. Here we analyze datasets that span multiple decades and present new observational evidence for a positive feedback between total cloud amount, sea-surface temperature (SST), and atmospheric circulation that can strengthen the persistence and amplitude of the tropical branch of the AMO. In addition, we estimate cloud amount feedback from observations and quantify its impact on SST with idealized modeling experiments. From these experiments we conclude that cloud feedbacks can account for 10% to 31% of the observed SST anomalies associated with the AMO over the tropics.

In a suite of idealized experiments with the Community Atmospheric Model version 3 coupled to a slab ocean, we show that the atmospheric circulation response to CO2 increase is sensitive to extratropical cloud feedback that is potentially nonlinear. Doubling CO2 produces a poleward shift of the Southern Hemisphere (SH) midlatitude jet that is driven primarily by cloud shortwave feedback and modulated by ice albedo feedback, in agreement with earlier studies. More surprisingly, for CO2 increases smaller than ~25 %, the SH jet shifts equatorward. Nonlinearities are also apparent in the Northern Hemisphere, but with less zonal symmetry. Baroclinic instability theory and climate feedback analysis suggest that as the CO2 forcing amplitude is reduced, there is a transition from a regime in which cloud and circulation changes are largely decoupled to a regime in which they are highly coupled. In the dynamically coupled regime, there is an apparent cancellation between cloud feedback due to warming and cloud feedback due to the shifting jet, and this allows the ice albedo feedback to dominate in the high latitudes. The extent to which dynamical coupling effects exceed thermodynamic forcing effects is strongly influenced by cloud microphysics: an alternate model configuration with slightly increased cloud liquid (LIQ) produces poleward jet shifts regardless of the amplitude of CO2 forcing. Altering the cloud microphysics also produces substantial spread in the circulation response to CO2 doubling: the LIQ configuration produces a poleward SH jet shift approximately twice that produced under the default configuration. Analysis of large ensembles of the Canadian Earth System Model version 2 demonstrates that nonlinear, cloud-coupled jet shifts are also possible in comprehensive models. We still expect a poleward trend in SH jet latitude for timescales on which CO2 increases by more than ~25 %. But on shorter timescales, our results give good reason to expect significant equatorward deviations. We also discuss the implications for understanding the circulation response to small external forcings from other sources, such as the solar cycle.

A suite of empirical model experiments under the empirical model reduction framework are conducted to advance the understanding of ENSO diversity, nonlinearity, seasonality, and the memory effect in the simulation and prediction of tropical Pacific sea surface temperature (SST) anomalies. The model training and evaluation are carried out using 4000-yr preindustrial control simulation data from the coupled model GFDL CM2.1. The results show that multivariate models with tropical Pacific subsurface information and multilevel models with SST history information both improve the prediction skill dramatically. These two types of models represent the ENSO memory effect based on either the recharge oscillator or the time-delayed oscillator viewpoint. Multilevel SST models are a bit more efficient, requiring fewer model coefficients. Nonlinearity is found necessary to reproduce the ENSO diversity feature for extreme events. The nonlinear models reconstruct the skewed probability density function of SST anomalies and improve the prediction of the skewed amplitude, though the role of nonlinearity may be slightly overestimated given the strong nonlinear ENSO in GFDL CM2.1. The models with periodic terms reproduce the SST seasonal phase locking but do not improve the prediction appreciably. The models with multiple ingredients capture several ENSO characteristics simultaneously and exhibit overall better prediction skill for more diverse target patterns. In particular, they alleviate the spring/autumn prediction barrier and reduce the tendency for predicted values to lag the target month value.

Snowmelt dominated streamflow of the Western Himalayan Rivers is an important water resource during the dry pre-monsoon spring months to meet the irrigation and hydropower needs in Northern India. Here we study the seasonal prediction of melt-dominated total inflow into the Bhakra Dam in Northern India based on statistical relationships with meteorological variables during the preceding winter. Total inflow into the Bhakra dam includes the Satluj River flow together with a flow diversion from its tributary, the Beas River. Both are tributaries of the Indus River that originate from the Western Himalayas, which is an under-studied region. Average measured winter snow volume at the upper elevation stations and corresponding lower elevation rainfall and temperature of the Satluj River basin were considered as empirical predictors. Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC) were used to select the best subset of inputs from all the possible combinations of predictors for a multiple linear regression framework. To test for potential issues arising due to multi-collinearity of the predictor variables, cross-validated prediction skills of best subset were also compared with the prediction skills of Principal Component Regression (PCR) and Partial Least Squares Regression (PLSR) techniques, which yielded broadly similar results. As a whole, the forecasts of the melt season at the end of winter and as the melt season commences were shown to have potential skill for guiding the development of stochastic optimization models to manage the trade-off between irrigation and hydropower releases versus flood control during the annual fill cycle of the Bhakra reservoir, a major energy and irrigation source in the region.

The authors investigate a sea surface temperature anomaly (SSTA)-only vector autoregressive (VAR) model for prediction of El Niño–Southern Oscillation (ENSO). VAR generalizes the linear inverse method (LIM) framework to incorporate an extended state vector including many months of recent prior SSTA in addition to the present state. An SSTA-only VAR model implicitly captures subsurface forcing observable in the LIM residual as red noise. Optimal skill is achieved using a state vector of order 14–17 months in an exhaustive 120-yr cross-validated hindcast assessment. It is found that VAR outperforms LIM, increasing forecast skill by 3 months, in a 30-yr retrospective forecast experiment.

We use a multilevel vector autoregressive model (VAR-L), to forecast sea surface temperature anomalies (SSTAs) in the Atlantic hurricane Main Development Region (MDR). VAR-L is a linear regression model using global SSTA data from L prior months as predictors. In hindcasts for the recent 30 years, the multilevel VAR-L outperforms a state-of-the-art dynamic forecast model, as well as the commonly used linear inverse model (LIM). The multilevel VAR-L model shows skill in 6–12 month forecasts, with its greatest skill in the months of the active hurricane season. The optimized model for the best long-range skill score in the MDR, chosen by a cross-validation procedure, has 12 time levels and 12 empirical orthogonal function modes. We investigate the optimal initial conditions for MDR SSTA prediction using a generalized singular vector decomposition of the propagation matrix. We find that the added temporal degrees of freedom for the predictands in VAR12 as compared with a LIM model, which allow the model to capture both the local wind–evaporation–SST feedback in the Tropical Atlantic and the impact on the Atlantic of an improved medium-range ENSO forecast, elevate the long-range forecast skill in the MDR.

El Nino diversity and its genesis are debated. An overview of existing work along with a fuzzy clustering analysis and simulations suggest that the asymmetry, irregularity and extremes of El Nino result from westerly wind bursts.

Significance: There is evidence that the 2007−2010 drought contributed to the conflict in Syria. It was the worst drought in the instrumental record, causing widespread crop failure and a mass migration of farming families to urban centers. Century-long observed trends in precipitation, temperature, and sea-level pressure, supported by climate model results, strongly suggest that anthropogenic forcing has increased the probability of severe and persistent droughts in this region, and made the occurrence of a 3-year drought as severe as that of 2007−2010 2 to 3 times more likely than by natural variability alone. We conclude that human influences on the climate system are implicated in the current Syrian conflict. Abstract: Before the Syrian uprising that began in 2011, the greater Fertile Crescent experienced the most severe drought in the instrumental record. For Syria, a country marked by poor governance and unsustainable agricultural and environmental policies, the drought had a catalytic effect, contributing to political unrest. We show that the recent decrease in Syrian precipitation is a combination of natural variability and a long-term drying trend, and the unusual severity of the observed drought is here shown to be highly unlikely without this trend. Precipitation changes in Syria are linked to rising mean sea-level pressure in the Eastern Mediterranean, which also shows a long-term trend. There has been also a long-term warming trend in the Eastern Mediterranean, adding to the drawdown of soil moisture. No natural cause is apparent for these trends, whereas the observed drying and warming are consistent with model studies of the response to increases in greenhouse gases. Furthermore, model studies show an increasingly drier and hotter future mean climate for the Eastern Mediterranean. Analyses of observations and model simulations indicate that a drought of the severity and duration of the recent Syrian drought, which is implicated in the current conflict, has become more than twice as likely as a consequence of human interference in the climate system.

IEEE International Conference on Big Data, We describe an approach using dynamic topic modeling to model influence and predict future trends in a scientific discipline. Our study focuses on climate change and uses assessment reports of the Intergovernmental Panel on Climate Change (IPCC) and the papers they cite. Since 1990, an IPCC report has been published every five years that includes four separate volumes, each of which has many chapters. Each report cites tens of thousands of research papers, which comprise a correlated dataset of temporally grounded documents. We use a custom dynamic topic modeling algorithm to generate topics for both datasets and apply crossdomain analytics to identify the correlations between the IPCC chapters and their cited documents. The approach reveals both the influence of the cited research on the reports and how previous research citations have evolved over time. For the IPCC use case, the report topic model used 410 documents and a vocabulary of 5911 terms while the citations topic model was based on 200K research papers and a vocabulary more than 25K terms. We show that our approach can predict the importance of its extracted topics on future IPCC assessments through the use of cross domain correlations, Jensen-Shannon divergences and cluster analytics.

The recent California drought was associated with a persistent ridge at the west coast of North America that has been associated with, in part, forcing from warm SST anomalies in the tropical west Pacific. Here it is considered whether there is a role for human-induced climate change in favoring such a west coast ridge. The models from phase 5 of the Coupled Model Intercomparison Project do not support such a case either in terms of a shift in the mean circulation or in variance that would favor increased intensity or frequency of ridges. The models also do not support shifts toward a drier mean climate or more frequent or intense dry winters or to tropical SST states that would favor west coast ridges. However, reanalyses do show that over the last century there has been a trend toward circulation anomalies over the Pacific–North American domain akin to those during the height of the California drought. The trend has been associated with a trend toward preferential warming of the Indo–west Pacific, an arrangement of tropical oceans and Pacific–North American circulation similar to that during winter 2013/14, the driest winter of the California drought. These height trends, however, are not reproduced in SST-forced atmosphere model ensembles. In contrast, idealized atmosphere modeling suggests that increased tropical Indo-Pacific zonal SST gradients are optimal for forcing height trends that favor a west coast ridge. These results allow a tenuous case for human-driven climate change driving increased gradients and favoring the west coast ridge, but observational data are not sufficiently accurate to confirm or reject this case.

The article “Climate change and the Syrian civil war revisited” by Selby et al., 2017 (henceforth S2017) challenges research exploring the links between anthropogenic climate change, water scarcity and drought, impacts on agricultural production and economic stability, and the initial 2011 unrest in Syria. More broadly, the authors contest any causal link between climate and conflict. This is an important, evolving area of study and we encourage improved, expanded, and updated analysis of these connections. In their criticism, the authors of S2017 single out three papers (Werrell, Femia, & Sternberg, 2015; Gleick, 2014; and Kelley, Mohtadi, Cane, Seager, & Kushnir, 2015; here after K2015). Here we comment directly on our own work in K2015, though the others are equally unscathed by S2017's criticisms. We do so by refuting S2017's claims regarding the role of climate change, summarizing the sizeable evidence of the long-term drying trend in the region, and bolstering the link between the drought, migration and subsequent unrest by providing further supporting evidence. Our response contends overall that S2017 fail to make their case. K2015 (as well as Gleick, 2014; Werrell et al., 2015) claim climate as one of many contributing factors to the unrest. Nothing in S2017 refutes this, and none of their supportable arguments even offer reason for doubting this view. While K2015 can and did make a quantitative estimate of the impact of anthropogenic effects on the drought, we cannot quantify the impact of climate or any other factor, separately, on the conflict.

A multiscale-modeling framework for daily rainfall is considered and diagnostic results are presented for an application to the winter season in Northwest India. The daily rainfall process is considered to follow a hidden Markov model (HMM), with the hidden states assumed to be an unknown random function of slowly varying climatic modulation of the winter jet stream and moisture transport dynamics. The data used are from 14 stations over Satluj River basin in winter (December–January–February–March). The period considered is 1977/78–2005/06. The HMM identifies four discrete weather states, which are used to describe daily rainfall variability over study region. Each state was found to be associated with a distinct atmospheric circulation pattern, with the driest and drier states, State 1 and 2 respectively, characterized by a lack of synoptic wave activity. In contrast, the wetter and wettest states, States 3 and 4 respectively, are characterized by a zonally oriented wave train extending across Eurasia between 20N and 40N, identified with ‘western disturbances’ (WD). The occurrence of State 4 is strongly conditioned by the El Nino and Indian Ocean Dipole (IOD) phenomena in winter, which is demonstrated using large-scale correlation maps based on mean sea level pressure and sea surface temperature. This suggests that there is a tendency of higher frequency of the wet days and intense WD activities in winter during El Nino and positive IOD years. These findings, derived from daily rainfall station records, help clarify the sequence of Northern Hemisphere mid-latitude storms bringing winter rainfall over Northwest India, and their association with potentially predictable low frequency modes on seasonal time scales and longer.

The presence of rich ENSO variability in the long unforced simulation of GFDL's CM2.1 motivates the use of tools from dynamical systems theory to study vari- ability in ENSO predictability, and its connections to ENSO magnitude, frequency, and physical evolution. Local Lyapunov exponents (LLEs) estimated from the monthly NINO3 SSTa model output are used to charac- terize periods of increased or decreased predictability. The LLEs describe the growth of infinitesimal perturbations due to internal variability, and are a measure of the immediate predictive uncertainty at any given point in the system phase-space. The LLE-derived predictability esti- mates are compared with those obtained from the error growth in a set of re-forecast experiments with CM2.1. It is shown that the LLEs underestimate the error growth for short forecast lead times (less than 8 months), while they overestimate it for longer lead times. The departure of LLE-derived error growth rates from the re-forecast rates is a linear function of forecast lead time, and is also sensitive to the length of the time series used for the LLE calcula- tion. The LLE-derived error growth rate is closer to that estimated from the re-forecasts for a lead time of 4 months. In the 2,000-year long simulation, the LLE-derived pre- dictability at the 4-month lead time varies (multi)decadally only by 9-18 %. Active ENSO periods are more predict- able than inactive ones, while epochs with regular peri- odicity and moderate magnitude are classified as the most predictable by the LLEs. Events with a deeper thermocline in the west Pacific up to five years prior to their peak, along with an earlier deepening of the thermocline in the east Pacific in the months preceding the peak, are classified as more predictable. Also, the GCM is found to be less pre- dictable than nature under this measure of predictability.

Summary Here we analyze the variability of MAM (March–April–May) and JJAS (June–July–August–September) seasonal Satluj River flow into the Bhakra dam in India through Pearson anomaly correlation and composite analyses with antecedent and concurrent seasonal climatic and atmospheric circulation patterns. The MAM seasonal inflow of Bhakra dam is significantly correlated with winter (DJF/FM) precipitation and temperature of the Satluj basin while the correlation with FM was more prominent for precipitation (snow = +0.72, rainfall = +0.60), and temperature (diurnal temperature range (DTR) = −0.76 and maximum temperature ( T max ) = −0.57). The JJAS inflow was also positively correlated with DJF/FM as well as JJAS precipitation of the Satluj basin while the correlation with basin average FM was the largest (+0.54). These suggested that both MAM and JJAS inflow anomalies are linked with DJF/FM climate over the Western Himalayas and adjoining north and central Indian plains, which were also found to be linked with the fluctuation of equatorial concurrent Sea Surface Temperature anomalies over the western Indian Ocean (max anomaly correlation was > +0.70) and mean sea level pressure over western pole of the Southern Oscillation sea-saw region (max Pearson anomaly correlation was ∼ +0.60). Low (high) MAM inflow was found to be associated with negative (positive) precipitation anomalies over the basin and north India in DJF and FM while FM precipitation anomaly is more concentrated over the Western Himalayas. In addition, low (high) JJAS inflow is also associated with negative (positive) precipitation anomalies over the basin and north India in DJF and over the Western Himalaya in FM and JJAS. Negative geopotential height anomaly at 500 h Pa (Z500) over Siberia and northwestern pacific in DJF, and positive Z500 anomaly over the northwest India in FM were noticed in low MAM inflow years. Whereas high inflow in MAM was linked with a negative Z500 anomaly between two positive Z500 anomaly regions – one over eastern Siberia stretched up to northern Pacific and second over the Eastern Europe in DJF, which gets stronger in FM. We also found southwesterly (northeasterly) wind vectors at 850 h Pa pressure level (uv850) bringing more (less) moisture to the Western Himalayas in DJF and FM in high (low) MAM/JJAS flow years.

Palaeoproxy evidence shows that the sea-surface-temperature gradient across the tropical Pacific Ocean strengthened during the Medieval Warm Period but weakens in model-projected future warming; this is because solar radiation induces greater precipitation for the same temperature change than greenhouse gases. The east–west gradient in tropical Pacific sea-surface temperatures is a key determinant of global rainfall patterns, but prior work has shown apparently contradictory responses to warming. The gradient strengthens in response to higher solar radiation (as in the Medieval Warm Period) but weakens as greenhouse gas levels increase (as in simulations of future climates). Reconciling these results has been problematic. Mark Cane and colleagues now show that the different forcings induce warming in different parts of the atmosphere, and that for the same warming, solar increases will induce overall higher precipitation than greenhouse gasses. As a result of global warming, precipitation is likely to increase in high latitudes and the tropics and to decrease in already dry subtropical regions1. The absolute magnitude and regional details of such changes, however, remain intensely debated2,3. As is well known from El Nino studies, sea-surface-temperature gradients across the tropical Pacific Ocean can strongly influence global rainfall4,5. Palaeoproxy evidence indicates that the difference between the warm west Pacific and the colder east Pacific increased in past periods when the Earth warmed as a result of increased solar radiation6,7,8,9. In contrast, in most model projections of future greenhouse warming this gradient weakens2,10,11. It has not been clear how to reconcile these two findings. Here we show in climate model simulations that the tropical Pacific sea-surface-temperature gradient increases when the warming is due to increased solar radiation and decreases when it is due to increased greenhouse-gas forcing. For the same global surface temperature increase the latter pattern produces less rainfall, notably over tropical land, which explains why in the model the late twentieth century is warmer than in the Medieval Warm Period (around ad 1000–1250) but precipitation is less. This difference is consistent with the global tropospheric energy budget12, which requires a balance between the latent heat released in precipitation and radiative cooling. The tropospheric cooling is less for increased greenhouse gases, which add radiative absorbers to the troposphere, than for increased solar heating, which is concentrated at the Earth’s surface. Thus warming due to increased greenhouse gases produces a climate signature different from that of warming due to solar radiation changes.

Over the last two centuries, the impact of the Human System has grown dramatically, becoming strongly dominant within the Earth System in many different ways. Consumption, inequality, and population have increased extremely fast, especially since about 1950, threatening to overwhelm the many critical functions and ecosystems of the Earth System. Changes in the Earth System, in turn, have important feedback effects on the Human System, with costly and potentially serious consequences. However, current models do not incorporate these critical feedbacks. We argue that in order to understand the dynamics of either system, Earth SystemModels must be coupled with Human SystemModels through bidirectional couplings representing the positive, negative, and delayed feedbacks that exist in the real systems. In particular, key Human System variables, such as demographics, inequality, economic growth, and migration, are not coupled with the Earth System but are instead driven by exogenous estimates, such as United Nations population projections.This makes current models likely to miss important feedbacks in the real Earth-Human system, especially those that may result in unexpected or counterintuitive outcomes, and thus requiring different policy interventions from current models.The importance and imminence of sustainability challenges, the dominant role of the Human System in the Earth System, and the essential roles the Earth System plays for the Human System, all call for collaboration of natural scientists, social scientists, and engineers in multidisciplinary research and modeling to develop coupled Earth-Human system models for devising effective science-based policies and measures to benefit current and future generations.

Strong El Nino events are followed by massive summer monsoon flooding over the Yangtze River basin (YRB), home to about a third of the population in China. Although the El Nino–Southern Oscillation (ENSO) provides the main source of seasonal climate predictability for many parts of the Earth, the mechanisms of its connection to the East Asian monsoon remain largely elusive. For instance, the traditional Nino3.4 ENSO index only captures precipitation anomalies over East Asia in boreal winter but not during the summer. Here we show that there exists a robust year-round and predictable relationship between ENSO and the Asian monsoon. This connection is revealed by combining equatorial (Nino3.4) and off-equatorial Pacific sea surface temperature anomalies (Nino-A index) into a new metric that captures ENSO's various aspects, such as its interaction with the annual cycle and its different flavors. This extended view of ENSO complexity improves predictability of YRB summer flooding events.

A novel strategy is proposed for the efficient simulation of geochemical tracers in ocean models. The method captures the tracer advection and diffusion in a general circulation model (GCM) without any alteration (or even knowledge) of the GCM code. In comparison with offline tracer models, the proposed method is considerably more efficient and automatically includes all parameterizations of unresolved processes present in the most sophisticated GCMs. A comparison with a global configuration of the MIT GCM shows that the scheme can capture the complex three-dimensional transport of a state-of-the-art GCM. A key advantage of the proposed technique is the ability to directly compute steady-state solutions, a facility particularly well-suited to tracers such as natural radiocarbon. This capability is applied to develop a novel algorithm for accelerating the dynamical adjustment of ocean models. © 2004 Elsevier Ltd. All rights reserved.

Growing scientific evidence from modern climate science is loaded with implications for the environmental history of the Roman Empire and its successor societies. The written and archaeological evidence, although richer than commonly realized, is unevenly distributed over time and space. A first synthesis of what the written records and multiple natural archives (multi-proxy data) indicate about climate change and variability across western Eurasia from c. 100 b.c. to 800 a.d. confirms that the Roman Empire rose during a period of stable and favorable climatic conditions, which deteriorated during the Empire's third-century crisis. A second, briefer period of favorable conditions coincided with the Empire's recovery in the fourth century; regional differences in climate conditions parallel the diverging fates of the eastern and western Empires in subsequent centuries. Climate conditions beyond the Empire's boundaries also played an important role by affecting food production in the Nile valley, and by encouraging two major migrations and invasions of pastoral peoples from Central Asia.