Search

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.

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.

Tropical Pacific decadal variability (TPDV), though not the totality of Pacific decadal variability, has wide-ranging climatic impacts. It is currently unclear whether this phenomenon is predictable. In this study, we reconstruct the attractor of the tropical Pacific system in long, unforced simulations from an intermediate-complexity model, two general circulation models (GCMs), and the observations with the aim of assessing the predictability of TPDV in these systems. We find that in the intermediate-complexity model, positive (high variance, El Niño–like) and negative (low variance, La Niña–like) phases of TPDV emerge as a pair of regime-like states. The observed system bears resemblance to this behavior, as does one GCM, while the other GCM does not display this structure. However, these last three time series are too short to confidently characterize the full distribution of interdecadal variability. The intermediate-complexity model is shown to lie in highly predictable parts of its attractor 37% of the time, during which most transitions between TPDV regimes occur. The similarities between the observations and this system suggest that the tropical Pacific may be somewhat predictable on interdecadal time scales.

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.

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.

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.

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.

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.

Storm tracks play a major role in regulating the precipitation and hydrological cycle in midlatitudes. The changes in the location and amplitude of the storm tracks in response to global warming will have significant impacts on the poleward transport of heat, momentum and moisture and on the hydrological cycle. Recent studies have indicated a poleward shift of the storm tracks and the midlatitude precipitation zone in the warming world that will lead to subtropical drying and higher latitude moistening. This study agrees with this key feature for not only the annual mean but also different seasons and for the zonal mean as well as horizontal structures based on the analysis of Geophysical Fluid Dynamics Laboratory (GFDL) CM2.1 model simulations. Further analyses show that the meridional sensible and latent heat fluxes associated with the storm tracks shift poleward and intensify in both boreal summer and winter in the late twenty-first century (years 2081-2100) relative to the latter half of the twentieth century (years 1961-2000). The maximum dry Eady growth rate is examined to determine the effect of global warming on the time mean state and associated available potential energy for transient growth. The trend in maximum Eady growth rate is generally consistent with the poleward shift and intensification of the storm tracks in the middle latitudes of both hemispheres in both seasons. However, in the lower troposphere in northern winter, increased meridional eddy transfer within the storm tracks is more associated with increased eddy velocity, stronger correlation between eddy velocity and eddy moist static energy, and longer eddy length scale. The changing characteristics of baroclinic instability are, therefore, needed to explain the storm track response as climate warms. Diagnosis of the latitude-by-latitude energy budget for the current and future climate demonstrates how the coupling between radiative and surface heat fluxes and eddy heat and moisture transport influences the midlatitude storm track response to global warming. Through radiative forcing by increased atmospheric carbon dioxide and water vapor, more energy is gained within the tropics and subtropics, while in the middle and high latitudes energy is reduced through increased outgoing terrestrial radiation in the Northern Hemisphere and increased ocean heat uptake in the Southern Hemisphere. This enhanced energy imbalance in the future climate requires larger atmospheric energy transports in the midlatitudes which are partially accomplished by intensified storm tracks. Finally a sequence of cause and effect for the storm track response in the warming world is proposed that combines energy budget constraints with baroclinic instability theory.

Decadal variations of very small amplitude [∼0.3°C in sea surface temperature (SST)] in the tropical Pacific Ocean, the genesis region of the interannual El Niño–Southern Oscillation (ENSO) phenomenon, have been shown to have powerful impacts on global climate. Future projections from different climate models do not agree on how this critical feature will change under the influence of anthropogenic forcing. A number of attempts have been made to resolve this issue by examining observed trends from the 1880s to the present, a period of rising atmospheric concentrations of greenhouse gases. A recent attempt concluded that the three major datasets disagreed on the trend in the equatorial gradient of SST. Using a corrected version of one of these datasets, and extending the analysis to the seasonal cycle, it is shown here that all agree that the equatorial Pacific zonal SST gradient has strengthened from 1880 to 2005 during the boreal fall when this gradient is normally strongest. This result appears to favor a theory for future changes based on ocean dynamics over one based on atmospheric energy considerations. Both theories incorporate the expectation, based on ENSO theory, that the zonal sea level pressure (SLP) gradient in the tropical Pacific is coupled to SST and should therefore strengthen along with the SST gradient. While the SLP gradient has not strengthened, it is found that it appears to have weakened only during boreal spring, consistent with the SST seasonal trends. Most of the coupled models included in the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report underestimate the strengthening SST gradient in boreal fall, and show almost no change in the SLP gradient in any season. The observational analyses herein suggest that both theories are at work but with relative strengths that vary seasonally, and that the two theories need not be inconsistent with each other.

The response of the equatorial Indian Ocean climate to global warming is investigated using model outputs submitted to the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report. In all of the analyzed climate models, the SSTs in the western equatorial Indian Ocean warm more than the SSTs in the eastern equatorial Indian Ocean under global warming; the mean SST gradient across the equatorial Indian Ocean is anomalously positive to the west in a warmer twenty-first-century climate compared to the twentieth-century climate, and it is dynamically consistent with the anomalous westward zonal wind stress and anomalous positive zonal sea level pressure (SLP) gradient to the east at the equator. This change in the zonal SST gradient in the equatorial Indian Ocean is detected even in the lowest-emission scenario, and the size of the change is not necessarily larger in the higher-emission scenario. With respect to the change over the equatorial Pacific in climate projections, the subsurface central Pacific displays the strongest cooling or weakest warming around the thermocline depth compared to that above and below in all of the climate models, whereas changes in the zonal SST gradient and zonal wind stress around the equator are model dependent and not straightforward.

Could the Dust Bowl drought of the 1930s have been predicted in advance if the SST anomalies of the 1930s had been foreknown? Ensembles of model simulations forced with historical observed SSTs in the global ocean, and also separately in the tropical Pacific and Atlantic Oceans, are compared with an ensemble begun in January 1929 with modeled atmosphere and land initial conditions and integrated through the 1930s with climatological SSTs. The ensemble with climatological SSTs produces values for the precipitation averaged over 1932–39 that are not statistically different from model climatology. In contrast, the ensembles with global SST forcing produce a drought centered in the central plains and southwestern North America that is clearly separated from the model climatology. Both the tropical Pacific and northern tropical Atlantic SST anomalies produce a statistically significant model drought in this region. The modeled drought has a spatial pattern that is different from the observed drought, which was instead centered in the central and northern plains and also impacted the northern Rocky Mountain states but not northeastern Mexico. The model error in extending the Dust Bowl drought too far south is attributed to an incorrect response of the model to warm subtropical North Atlantic SST anomalies. The model error in the northern states cannot be attributed to an incorrect response to tropical SST anomalies. The model also fails to reproduce the strong surface air warming across most of the continent during the 1930s. In contrast, the modeled patterns of precipitation reduction and surface air temperature warming during the 1950s drought are more realistic. Tree-ring records show that the Dust Bowl pattern of drought has occurred before, suggesting that while the extensive human-induced land surface degradation and dust aerosol loading of the 1930s drought may have played an important role in generating the observed drought pattern, natural processes, possibly including land interactions, are capable of generating droughts centered to the north of the main ENSO teleconnection region. Despite this caveat, advance knowledge of tropical SSTs alone would have allowed a high-confidence prediction of a multiyear and severe drought, but one centered too far south and without strong cross-continental warming.

The relationship between all-India summer monsoon rainfall (ISMR) and the timing of (El Niño–Southern Oscillation) ENSO-related warming/cooling is investigated, using observational data during the period from 1881 to 1998. The analysis of the evolutions of Indo-Pacific sea surface temperature (SST) anomalies suggests that when ISMR is not below normal despite the co-occurrence of an El Niño event, warming over the eastern equatorial Pacific starts from boreal winter and evolves early so that the western-central Pacific and Indian Ocean are warmer than normal during the summer monsoon season. In contrast, when the more usual El Niño–dry ISMR relationship holds, the eastern equatorial Pacific starts warming rapidly only about a season before the reference summer so that the western-central Pacific and Indian Oceans remain cold during the monsoon season.

El Nino-Southern Oscillation (ENSO) is by far the most energetic, and at present also the most predictable, short-term fluctuation in the Earth's climate system, though the limits of its predictability are still a subject of considerable debate. As a result of over two-decades of intensive observational, theoretical and modeling efforts, ENSO's basic dynamics is now well understood and its prediction has become a routine practice at application centers all over the world. The predictability of ENSO largely stems from the ocean-atmosphere interaction in the tropical Pacific and the low-dimensional nature of this coupled system. Present ENSO forecast models, in spite of their vast differences in complexity, exhibit comparable predictive skills, which seem to have hit a plateau at moderate level. However, mounting evidence suggests that there is still room for improvement. In particular, better model initialization and data assimilation, better simulation of surface heat and freshwater fluxes, and better representation of the relevant processes outside of the tropical Pacific, could all lead to improved ENSO forecasts.

This study quantifies the effects of stressful weather and elevated air pollution levels on cause-specific mortality in Santiago, Chile, during the austral winters from 1988 to 1996. A temporal synoptic index (TSI) is used to form weather classes and air pollution classes. Prior applications of the TSI have formed classes solely on the basis of weather and may have systematically underestimated the impact of air pollution levels on daily mortality. In Santiago, the attribution of increased mortality risk was found to be largely dependent on the type of class formed (weather or pollution). High-mortality weather classes were associated with cold, dry and high-pressure conditions, while high-mortality pollution classes were associated with elevated NO2 and PM10–2.5 concentrations. Cardiovascular disease mortality was more sensitive to weather conditions, and respiratory mortality was more sensitive to pollution levels. Respiratory mortality was most sensitive to stressful conditions at longer lag times (3–6 days), while cardiovascular mortality was most sensitive at shorter lag times (0–2 days). By understanding the relative magnitudes of health risks associated with stressful weather and air pollution conditions we can improve existing air pollution/weather watch systems and better anticipate future risks associated with global climate change. Copyright © 2007 Royal Meteorological Society

We examine the relationship between the state of the equatorial Indian Ocean, ENSO, and the Indian summer monsoon rainfall using data from 1881 to 1998. The zonal wind anomalies and SST anomaly gradient over the equatorial Indian Ocean are used as indices that represent the condition of the Indian Ocean. Although the index defined by the zonal wind anomalies correlates poorly with Indian summer monsoon rainfall, the linear reconstruction of Indian summer monsoon rainfall on the basis of a multiple regression from the NINO3 and this wind index better specifies the Indian summer monsoon rainfall than the regression with only NINO3. Using contingency tables, we find that the negative association between the categories of Indian summer monsoon rainfall and the wind index is significant during warm years (El Nino) but not during cold years (La Nina). Composite maps of land precipitation also indicate that this relationship is significant during El Nino events. We conclude that there is a significant negative association between Indian summer monsoon rainfall and the zonal wind anomalies over the equatorial Indian Ocean during El Nino events. A similar investigation of the relationship between the SST index and Indian summer monsoon rainfall does not reveal a significant association. Copyright © 2006 Royal Meteorological Society.

Ocean circulation changes not needed What causes the pattern of sea surface temperature change that is seen in the North Atlantic Ocean? This naturally occurring quasi-cyclical variation, known as the Atlantic Multidecadal Oscillation (AMO), affects weather and climate. Some have suggested that the AMO is a consequence of variable large-scale ocean circulation. Clement et al. suggest otherwise. They find that the pattern of AMO variability can be produced in a model that does not include ocean circulation changes, but only the effects of changes in air temperatures and winds. Science , this issue p. 320

The seasonal and interannual predictability of ENSO variability in a version of the Zebiak–Cane coupled model is examined in a perturbation experiment. Instead of assuming that the model is “perfect,” it is assumed that a set of optimal initial conditions exists for the model. These states, obtained through a nonlinear minimization of the misfit between model trajectories and the observations, initiate model forecasts that correlate well with the observations. Realistic estimates of the observational error magnitudes and covariance structures of sea surface temperatures, zonal wind stress, and thermocline depth are used to generate ensembles of perturbations around these optimal initial states, and the error growth is examined. The error growth in response to subseasonal stochastic wind forcing is presented for comparison.In general, from 1975 to 2002, the large-scale uncertainty in initial conditions leads to larger error growth than continuous stochastic forcing of the zonal wind stress fields. Forecast ensemble spread is shown to depend most on the calendar month at the end of the forecast rather than the initialization month, with the seasons of greatest spread corresponding to the seasons of greatest anomaly variance. It is also demonstrated that during years with negative (and rapidly decaying) Niño-3 SST anomalies (such as the time period following an El Niño event), there is a suppression of error growth. In years with large warm ENSO events, the ensemble spread is no larger than in moderately warm years. As a result, periods with high ENSO variance have greater potential prediction utility.In the realistic range of observational error, the ensemble spread has more sensitivity to the initial error in the thermocline depth than to the sea surface temperature or wind stress errors. The thermocline depth uncertainty is the principal reason why initial condition uncertainties are more important than wind noise for ensemble spread.

The high‐latitude ice/snow‐albedo feedback is a principal element in many paleoclimate theories and global warming scenarios. The strength of this feedback is determined by the ice/snow effects on the top‐of atmosphere (TOA) albedo, which is also strongly affected by clouds. Using currently available satellite observations, we estimate the radiative effectiveness (RE) of ice and snow with regards to the TOA albedo, which we define as the change in the TOA albedo corresponding to changes of 0% to 100% in the ice or snow cover. The REs of the northern hemisphere (NH) sea ice, land snow, and southern hemisphere (SH) sea ice are found to be 0.22, 0.23 and 0.16, respectively. This means that, for an incident solar flux of about 400 W m−2 reaching the TOA in the polar latitudes in summer, local reduction in ice/snow concentrations from 100% to 0% will result in a decrease in reflected short wave radiation of approximately 80 W m−2. These changes in the TOA albedo are significant, yet smaller than the a...

We develop a swamp water mosquito population model that is forced solely by environmental variability. Measured temperature and land surface wetness conditions are used to simulate Anopheles walkeri population dynamics in a northern New Jersey habitat. Land surface wetness conditions, which represent oviposition habitat availability, are derived from simulations using a dynamic hydrology model. Using only these two density-independent effects, population model simulations of biting Anoph. walkeri correlate significantly with light trap collections. These results suggest that prediction of mosquito populations and the diseases they transmit could be better constrained by inclusion of environmental variability.

Using satellite estimates of snow depth, we examine the interannual variability of the monsoon rains of Bangladesh, an area greatly affected by land surface hydrological processes including Himalayan snowpack size, snowmelt river flooding, and Bay of Bengal storm surge. For the twentieth century, we found Bangladesh monsoon rainfall (BMR) to be uncorrelated with the All-Indian Monsoon Index. This result is consistent with previous findings for shorter time records. We next used a short 9-year record of satellite estimates of April snow

The Zebiak–Cane (ZC) model for simulation of the El Nino–Southern Oscillation is shown to be capable of producing sequences of variability that exhibit shifts in the time-mean state of the eastern equatorial Pacific that resemble observations of tropical Pacific decadal variability. The model's performance in predicting these shifts is compared to two naive forecasting strategies. It is found that the ZC model consistently outperforms the two naive forecasts that serve as a null hypothesis in assessing the significance of results. Forecasts initialized during anomalously warm and anomalously cold decades are shown to have the highest predictability. These modeling results suggest that, to a moderate extent, the state of the tropical Pacific in one decade can predetermine its time-mean state in the following decade. However, even in this idealized context decadal forecasting skill is modest. Results are discussed in the context of their implications for the ongoing debate over the origin of decada...

Pacific Ocean oceanic heat transport is studied in an ocean model coupled to an atmospheric mixed-layer model. The shallow meridional overturning circulation cells in the Tropics and subtropics transport heat away from the equator. The heat transport by the horizontal gyre circulation in the Tropics is smaller and directed toward the equator. The response of the Pacific oceanic heat transport to El Nino-like winds, extratropical winds, and variations in the Indonesian Throughflow is studied. Large, opposing changes are found in the heat transport by the meridional overturning and the horizontal gyres in response to El Nino-like winds. Consequently, the change in total heat transport is relatively small. The overturning transport decreases and the gyres spin down when the winds decrease in the Tropics. This compensation breaks down when the Indonesian Throughflow is allowed to vary in the model. A reduced Indonesian Throughflow, as observed during El Nino-like conditions, causes a large reduction of poleward heat transport in the South Pacific and affects the ocean heat transport in the southern tropical Pacific. Extratropical atmospheric anomalies can affect tropical ocean heat transport as the tropical thermocline is ventilated from the extratropics. The authors find that changes in the heat loss in the midlatitudes affect tropical ocean heat transport by driving an enhanced buoyancy-driven overturning that reaches into the Tropics. The results are related to observed changes in the overturning circulation in the Pacific in the 1990s, sea surface temperarture changes, and changes in atmospheric circulation. The results imply that the ratio of heat transport in the ocean to that in the atmosphere can change.

An ensemble local hydrologic forecast derived from the seasonal forecasts of the International Research Institute for Climate Prediction (IRI) is presented. Three-month seasonal forecasts were used to resample historical meteorological conditions and generate ensemble forcing datasets for a TOPMODEL-based hydrology model. Eleven retrospective forecasts were run at Florida and New York sites. Forecast skill was assessed for mean area modeled water table depth (WTD) and compared with WTD simulated with observed data. Hydrology model forecast skill was evident at the Florida site. Persistence of initial hydrologic conditions and local skill of the IRI seasonal forecast contributed to this local hydrologic forecast skill. At the New York site, there was no persistence of initial hydrologic conditions and local skill of the IRI seasonal forecast was poor; these factors precluded local hydrologic forecast skill at this site.

East Africa has two rainy seasons: the long rains [March–May (MAM)] and the short rains [October–December (OND)]. Most CMIP3/5 coupled models overestimate the short rains while underestimating the long rains. In this study, the East African rainfall bias is investigated by comparing the coupled historical simulations from CMIP5 to the corresponding SST-forced AMIP simulations. Much of the investigation is focused on the MRI-CGCM3 model, which successfully reproduces the observed rainfall annual cycle in East Africa in the AMIP experiment but its coupled historical simulation has a similar but stronger bias as the coupled multimodel mean. The historical–AMIP monthly climatology rainfall bias in East Africa can be explained by the bias in the convective instability (CI), which is dominated by the near-surface moisture static energy (MSE) and ultimately by the MSE’s moisture component. The near-surface MSE bias is modulated by the sea surface temperature (SST) over the western Indian Ocean. The warm SST bias in OND can be explained by both insufficient ocean dynamical cooling and latent flux, while the insufficient shortwave radiation and excess latent heat flux mainly contribute to the cool SST bias in MAM.

A number of studies have attempted to explain the cause of decadal variability in the tropical Pacific and explore its possible link to decadal variability in the midlatitude Pacific. To investigate some of the current theories of Pacific decadal variability, a linear, wind-driven model, designed to simulate only baroclinic wave dynamics, was forced with wind stress anomalies in the Pacific Ocean basin from 1945 through 1992. An analysis technique designed to isolate the decadal/interdecadal scale variability from interannual ENSO variability was performed on the model’s thermocline depth anomaly (TDA). It was found that the temporal and spatial patterns of the observed tropical decadal sea surface temperatures are consistent with our modeled TDA. Furthermore, restricting the wind forcing to within 58 of the equator does not substantially alter the decadal/interdecadal variability of the equatorial region. The authors conclude that the observed decadal variability in the low-latitude Pacific is primarily a linear dynamical response to tropical wind forcing and does not directly require an oceanic link to the midlatitudes. The question of how tropical wind anomalies are generated is not addressed. In addition, it is shown that in model scenarios where the wind forcing is restricted to the western equatorial Pacific, the 1976‐77 climate shift is still clearly visible as a dominant feature of tropical decadal variability. The temporal decadal signal of the model-generated TDA is more pronounced during the eastern equatorial upwelling season (July‐September) than in the boreal winter. This is consistent with the observed seasonal bias in tracer and SST data from the eastern equatorial Pacific.

This project addressed area (1) of the FOA, “Interaction of Climate Change and Low Frequency Modes of Natural Climate Variability”. Our overarching objective is to detect, describe and understand the changes in low frequency variability between model simulations of the preindustrial climate and simulations of a doubled CO2 climate. The deliverables are a set of papers providing a dynamical characterization of interannual, decadal, and multidecadal variability in coupled models with attention to the changes in this low frequency variability between pre-industrial concentrations of greenhouse gases and a doubling of atmospheric concentrations of CO2. The principle mode of analysis, singular vector decomposition, is designed to advance our physical, mechanistic understanding. This study will include external natural variability due to solar and volcanic aerosol variations as well as variability internal to the climate system. An important byproduct is a set of analysis tools for estimating global singular vector structures from the archived output of model simulations.

Recent developments in Tropical Atlantic Variability (TAV) identify the El Nino-Southern Oscillation (ENSO) as one of the leading factors in the interannual climate variability of the basin. An ENSO event results in Tropic- wide anomalies in the atmospheric circulation that have a direct effect on precipitation variability, as well as an indirect effect, that is, one mediated by sea surface temperature (SST) anomalies generated in the remote ocean basins. In order to separate the relative contributions of the atmospheric and oceanic components of the ENSO teleconnection to the tropical Atlantic Ocean, results from two ensembles of atmospheric general cir- culation model (AGCM) experiments, differing in oceanic boundary conditions, are compared. AGCM integra- tions performed with the Community Climate Model version 3 (CCM3), forced by global, observed SST during 1950-94 reproduce the observed ENSO-related rainfall anomalies over the tropical Americas and adjacent Atlantic. A parallel ensemble of integrations, forced with observed SST in the tropical Atlantic only, and climatology elsewhere, is used to separate the effect of the direct atmospheric teleconnection from the atmo- sphere's response to the ENSO-forced SST anomalies in the Atlantic basin. It is found that ENSO-related atmospheric and oceanic anomalies force rainfall anomalies of the same sign in northeast Brazil, of opposite sign in the Caribbean basin. The direct atmospheric influence of a warm ENSO event reduces model rainfall as a whole over the tropical Atlantic basin. This observation is consistent with the hypothesis that an ENSO-related Tropic-wide warming of the free troposphere forces the vertical stabilization of the tropical atmosphere. ENSO-related atmospheric anomalies are also known to force a delayed (relative to the mature phase of ENSO) warming of tropical North Atlantic SST through the weakening of the northeasterly trade winds and consequent reduction of surface fluxes. It is found that this delayed oceanic component forces a northward displacement of the Atlantic intertropical convergence zone, resulting in increased precipitation over the Caribbean and reduced precipitation over northeast Brazil during the boreal spring following the mature phase of ENSO.

This review paper presents an assessment of the current state of knowledge and capability in seasonal climate prediction at the end of the 20th century. The discussion covers the full range of issues involved in climate forecasting, including (1) the theory and empirical evidence for predictability; (2) predictions of surface boundary conditions, such as sea surface temperatures (SSTs) that drive the predictable part of the climate; (3) predictions of the climate; and (4) a brief consideration of the application of climate forecasts. Within this context, the research of the coming decades that seeks to address shortcomings in each area is described. Copyright © 2001 Royal Meteorological Society

Caribbean rainfall is affected by climate variability of Pacific and Atlantic origin, e.g. the El Nino-Southern Oscillation (ENSO) phenomenon, and variability in the North Atlantic High sea level pressure (SLP) center, respectively. During the lifetime of an ENSO cycle, the basin experiences dry and wet extremes. In the case of a warm event, the dry extreme precedes the mature ENSO phase, and can be explained in terms of a direct response to the atmospheric anomaly generated by the warm sea surface temperatures (SST) in the eastern equatorial Pacific. The wet extreme follows the mature phase, and is consistent with the lagged warming effect of ENSO on tropical North Atlantic SSTs. The wintertime state of the North Atlantic High is hypothesized to affect Caribbean rainfall through its effect on tropical SST. A strong North Atlantic High SLP center during the early months of the calendar year strengthens the trade winds, hence cooling SSTs in the tropical latitudes of the North Atlantic. The effect lingers on most noticeably until the start of the Caribbean rainy season, in May–June, when cool SSTs are associated with deficient rainfall in the basin.

A dynamically motivated statistical forecasting scheme for eastern Mediterranean winter rainfall is presented. The scheme is based on North Atlantic sea level pressure precursors. The resulting forecasts are robust and statistically significant at ∼13 months lead time, and improve at ∼7 months lead. It is suggested that these forecasts form a foundation for an operational early-warning system for eastern Mediterranean droughts.

Near-global 4 83 48 gridded analysis of marine sea level pressure (SLP) from the Comprehensive Ocean‐ Atmosphere Data Set for monthly averages from 1854 to 1992 was produced along with its estimated error using a reduced space optimal interpolation method. A novel procedure of covariance adjustment brought the results of the analysis to the consistency with the a priori assumptions on the signal covariance structure. Comparisons with the National Centers for Environmental Prediction‐National Center for Atmospheric Research global atmosphere reanalysis, with the National Center for Atmospheric Research historical analysis of the Northern Hemisphere SLP, and with the global historical analysis of the U.K. Meteorological Office show encouraging skill of the present product and identifies noninclusion of the land data as its main limitation. Marine SLP pressure proxies are produced for the land stations used in the definitions of the Southern Oscillation and North Atlantic Oscillation (NAO) indices. Surprisingly, they prove to be competitive in quality with the land station records. Global singular value decomposition analysis of the SLP fields versus sea surface temperature identified three major patterns of their joint large-scale and long-term variability as ‘‘trend,’’ Pacific decadal oscillation, and NAO.

A modeling study of the bomb 14C transient is presented for the Pacific Ocean. A primitive equation ocean circulation model has been configured for a high-resolution domain that accounts for the Indonesian Throughflow (ITF). Four separate runs were performed: (1) seasonal forcing with 20 Sv of ITF transport, (2) seasonal forcing with 10 Sv of ITF transport, (3) seasonal forcing with no ITF transport, and (4) interannual forcing with 15 Sv of ITF transport. This study has two main objectives. First, it is intended to describe the time evolution of the bomb 14C transient. This serves as a tool with which one can identify the physical processes controlling the evolving bomb 14C distribution in the Pacific thermocline and thus provides an interpretive framework for the database of Δ14C measurements in the Pacific. Second, transient tracers are applied to the physical oceanographic problem of intergyre exchange. This is of importance in furthering our understanding of the potential role of the upper Pacific Ocean in climate variability. We use bomb 14C as a dye tracer of intergyre exchange between the subtropical gyres and the equatorial upwelling regions of the equatorial Pacific. Observations show that while the atmospheric Δ14C signal peaked in the early to mid-1960s, the Δ14C levels in the surface water waters of the subtropical gyres peaked near 1970, and the Δ14C of surface waters in the equatorial Pacific continued to rise through the 1980s. It is shown that the model exhibits skill in representing the large-scale observed features observed for the bomb 14C transient in the Pacific Ocean. The model successfully captures the basin-scale inventories of bomb 14C in the tropics as well as in the extratropics of the North Pacific. For the equatorial Pacific this is attributed to the model's high meridional resolution. The discrepancies in the three-dimensional distribution of bomb 14C between the model and data are discussed within the context of the dynamical controls on the Δ14C distribution of bomb 14C in the Pacific.

Recent advances in understanding the role of the El Nino-Southern Oscillation (ENSO) in climate variability present opportunities for improving efficiency in agricultural production. We investigated the relationships between ENSO, climate and maize yields in the U.S. cornbelt, using both observed data and crop simulations. Using a time-series of sea-surface temperature anomalies (SSTA) from the NINO3 region of the Pacific Ocean and historical records of temperature and precipitation spatially averaged across 51 mid-western climate divisions from 1950 to 1995, we ran linear correlation tests at three different lags. Northern hemisphere wintertime SSTAs were significantly correlated with air temperature at the 95% level of confidence in both the previous (r 0.32) and following (r 0.41) summer, but had opposite signs. Correlations with precipitation were significant only in the summer preceding the ENSO event (r0.31). Detrended maize yield for the same area and time period was also significantly related to SSTAs in the winter after harvest, with a correlation coefficient of 0.39, indicating that ENSO accounts for : 15% of interannual maize yield variability in the cornbelt. Crop growth simulations at seven sites across the region suggest that water stress in July and August is the primary cause of lowered corn yield in La Nina years, but shortened grainfill period due to higher temperatures is also important. The benefits of El Nino-related rainfall and cooler temperatures are less pronounced than the negative impacts of warmer and dryer La Ninas. However, advance warning of both ENSO phases may present opportunities for improved crop management in the cornbelt. Copyright © 1999 Royal Meteorological Society.

Using the NASA scatterometer (NSCAT) winds for initialization has greatly improved the Lamont-Doherty Earth Observatory model forecasts of the 1997/1998 E1 Nifio. The improvement is mostly attributed to the better resolved wind field in the southeast tropical Pacific. Because of the simplicity of the model and the short record of the NSCAT data, our model results should be taken as indicative rather than conclusive. Nevertheless, it is crucial to assimilate accurate information into the initial model state to predict the development of E1 Nifio. Satellite-derived wind products certainly have the potential to provide such information for real-time forecasting.

The effects of temporal and spatial smoothing of wind forcing were evaluated in a model simulation of the tropical Pacific Ocean variability during the onset phase of the 1997/1998 El Nino. A total of 16 experiments were performed using the NASA scatterometer wind data smoothed at time intervals from 1 to 30 days and on spatial scales from 1° to 10°. A major effect of the temporal smoothing of winds is to warm sea surface temperature (SST) by reducing the energy input for vertical turbulent mixing. When the daily wind forcing was replaced by the monthly average, the mean SST increased by 0.5° to 1° over most of the tropical Pacific. The spatial smoothing of winds is not as effective as the temporal smoothing in causing SST warming, but it has a more severe influence on dynamical ocean response for smoothing scales above 5°. The onset of the 1997/1998 El Nino can be successfully simulated using the wind forcing averaged to monthly intervals and 2° squares. For climate models the spatial smoothing of wind forcing on scales larger than the width of the equatorial waveguide is a more serious limitation than the temporal smoothing on scales up to 1 month.

Precursors for the Indian monsoon are known to be highly epoch specific in their skills in predicting the monsoon on seasonal time scales. We show that the various precursors are correlated with the monsoon, only when they are correlated with ENSO, which happened in the recent period 1951–1990, but not in the 1990's and the period 1911–50. This accounts for the skill in monsoon prediction during 1951–90. We find that ENSO and its precursors tend toward higher amplitude and 3–5 year periods in the 1951–1990 epoch, and toward decreased amplitude and 5–7 year periods in the 1911–1950 epoch. We argue that the shift to lower frequency and amplitude in the earlier epoch diminished the association between the monsoon precursors and ENSO, leading to diminished skill in predicting the monsoon in that epoch. However, the simultaneous relationship between the monsoon and ENSO has been stable over the past 140 years, suggesting that the monsoon—ENSO teleconnections are robust once ENSO is established. Changes in the frequency of ENSO have implications for statistical prediction schemes for ENSO and the monsoon.

A hierarchy of El Niño-Southern Oscillation (ENSO) prediction schemes has been developed during the Tropical Ocean-Global Atmosphere (TOGA) program which includes statistical schemes and physical models. The statistical models are, in general, based on linear statistical techniques and can be classified into models which use atmospheric (sea level pressure or surface wind) or oceanic (sea surface temperature or a measure of upper ocean heat content) quantities or a combination of oceanic and atmospheric quantities as predictors. The physical models consist of coupled ocean-atmosphere models of varying degrees of complexity, ranging from simplified coupled models of the “shallow water” type to coupled general circulation models. All models, statistical and physical, perform considerably better than the persistence forecast in predicting typical indices of ENSO on lead times of 6 to 12 months. The TOGA program can be regarded as a success from this perspective. However, despite the demonstrated predictability, little is known about ENSO predictability limits and the predictability of phenomena outside the tropical Pacific. Furthermore, the predictability of anomalous features known to be associated with ENSO (e.g., Indian monsoon and Sahel rainfall, southern African drought, and off-equatorial sea surface temperature) needs to be addressed in more detail. As well, the relative importance of different physical mechanisms (in the ocean or atmosphere) has yet to be established. A seasonal dependence in predictability is seen in many models, but the processes responsible for it are not fully understood, and its meaning is still a matter of scientific discussion. Likewise, a marked decadal variation in skill is observed, and the reasons for this are still under investigation. Finally, the different prediction models yield similar skills, although they are initialized quite differently. The reasons for these differences are also unclear.

A correlation between ENSO (El Nino/Southern Oscillation) and rainfall in southern Africa has been recognized for at least a decade. This recognition has led to the expectation that ENSO-based climate predictions will have significant applications in agricultural management. This study is an analysis of the potential for using ENSO predictions to reduce risk in agricultural production associated with rainfall variability at the site level. Records of sea-surface temperatures in the equatorial Pacific during November, December and January were used to define El Nino, La Nina and neutral years. Climate data from four sites in four of the five agroecological zones (AEZ) in Zimbabwe were analyzed with respect to ENSO phases and used to drive a maize growth simulation model parameterized for soil conditions typical of each area, using two nitrogen fertilizer treatments and three planting dates. The four sites (Karoi, AEZ II; Gweru, AEZ III; Masvingo, AEZ IV; and Beitbridge, AEZ V) all showed a decrease in seasonal precipitation associated with the El Nino phase, compared to both neutral and La Nina years. At sites in zones II and III, within-season rainfall variability increased for both El Nino and La Nina years relative to neutral years. While average simulated maize yields were generally lowest in El Nino years, variability in rainfall pattern and standard deviation of yields at the site level was high within each ENSO phase, indicating that more precise seasonal climate predictions would be necessary for forecasts to be valuable in crop management decisions in Zimbabwe. However, simulation results point towards the relative importance of predicting favorable cropping seasons as opposed to poor ones with respect to better nitrogen management and yield improvement for the more marginal sites.

Decadal variability of the East African precipitation during the season of March–May (long rains) is examined and the performance of a series of models in simulating the observed features is assessed. Observational results show that the drying trend of the long rains is associated with decadal natural variability associated with sea surface temperature (SST) variations over the Pacific Ocean. Empirical orthogonal function (EOF), linear regression, and composite analyses all show the spatial pattern of the associated SST field to be La Niña like. The SST-forced International Research Institute for Climate and Society (IRI) forecast models are able to capture the East African precipitation climatology, the decadal variability of the long rains, and the associated SST anomaly pattern but are not consistent with observations from the 1970s. The multimodel mean of the SST-forced models from the Coupled Model Intercomparison Project phase 5 (CMIP5) Atmospheric Model Intercomparison Project (AMIP) experiment captures the climatology and the drying trend in recent decades. The fully coupled models from the CMIP5 historical experiment, however, have systematic errors in simulating the East African precipitation climatology by underestimating the long rains while overestimating the short rains. The multimodel mean of the historical simulations of the long rains anomalies, which is the best estimate of the radiatively forced change, shows a weak wetting trend associated with anthropogenic forcing. The SST anomaly pattern associated with the long rains has large discrepancies with the observations. The results herein suggest caution in projections of East African precipitation from CMIP5 or the relationship between the East African precipitation and the SST spatial pattern found in paleoclimate studies with coupled climate models.

The skill of a coupled ocean‐atmosphere model in predicting ENSO has recently been improved using a new initialization procedure in which initial conditions are obtained from the coupled model, nudged toward observations of wind stress. The previous procedure involved direct insertion of wind stress observations, ignoring model feedback from ocean to atmosphere. The success of the new scheme is attributed to its explicit consideration of ocean‐atmosphere coupling and the associated reduction of ‘‘initialization shock’’ and random noise. The so-called spring predictability barrier is eliminated, suggesting that such a barrier is not intrinsic to the real climate system. Initial attempts to generalize the nudging procedure to include SST were not successful; possible explanations are offered. In all experiments forecast skill is found to be much higher for the 1980s than for the 1970s and 1990s, suggesting decadal variations in predictability.