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The skill of NOAA’s official monthly U.S. precipitation forecasts (issued in the middle of the prior month) has historically been low, having shown modest skill over the southern United States, but little or no skill over large portions of the central United States. The goal of this study is to explain the seasonal and regional variations of the North American subseasonal (weeks 3–6) precipitation skill, specifically the reasons for its successes and its limitations. The performances of multiple recent-generation model reforecasts over 1999–2015 in predicting precipitation are compared to uninitialized simulation skill using the atmospheric component of the forecast systems. This parallel analysis permits attribution of precipitation skill to two distinct sources: one due to slowly evolving ocean surface boundary states and the other to faster time-scale initial atmospheric weather states. A strong regionality and seasonality in precipitation forecast performance is shown to be analogous to skill patterns dictated by boundary forcing constraints alone. The correspondence is found to be especially high for the North American pattern of the maximum monthly skill that is achieved in the reforecast. The boundary forcing of most importance originates from tropical Pacific SST influences, especially those related to El Niño–Southern Oscillation. We discuss physical constraints that may limit monthly precipitation skill and interpret the performance of existing models in the context of plausible upper limits. Significance Statement Skillful subseasonal precipitation predictions have societal benefits. Over the United States, however, NOAA’s official U.S. monthly precipitation forecast skill has been historically low. Here we explore origins for skill of North American week-3 to week-6 precipitation predictions. Skill arising from initial weather states is compared to that arising from ocean surface boundary states alone. The monthly and seasonally varying pattern of U.S. monthly precipitation skill is appreciably derived from boundary constraints, linked especially with El Niño–Southern Oscillation. Forecasts of opportunity are identified, despite the low skill of monthly precipitation forecasts on average. Potential limits of monthly precipitation skill are explored that provide insight on the juxtaposition of “skill deserts” over the central United States with high skill regions over western North America.

As anthropogenic activities warm the Earth, the fundamental solution of reducing greenhouse gas emissions remains elusive. Given this mitigation gap, global warming may lead to intolerable climate changes as adaptive capacity is exceeded. Thus, there is emerging interest in solar radiation modification, which is the process of deliberately increasing Earth’s albedo to cool the planet. Stratospheric aerosol injection (SAI)—the theoretical deployment of particles in the stratosphere to enhance reflection of incoming solar radiation—is one strategy to slow, pause, or reverse global warming. If SAI is ever pursued, it will likely be for a specific aim, such as affording time to implement mitigation strategies, lessening extremes, or reducing the odds of reaching a biogeophysical tipping point. Using an ensemble climate model experiment that simulates the deployment of SAI in the context of an intermediate greenhouse gas trajectory, we quantified the probability that internal climate variability masks the effectiveness of SAI deployment on regional temperatures. We found that while global temperature was stabilized, substantial land areas continued to experience warming. For example, in the SAI scenario we explored, up to 55% of the global population experienced rising temperatures over the decade following SAI deployment and large areas exhibited high probability of extremely hot years. These conditions could cause SAI to be perceived as a failure. Countries with the largest economies experienced some of the largest probabilities of this perceived failure. The potential for perceived failure could therefore have major implications for policy decisions in the years immediately following SAI deployment.

Stratospheric aerosol injection is a proposed form of solar climate invention (SCI) that could potentially reduce the amount of future warming from externally-forced climate change. However, more research is needed, as there are significant uncertainties surrounding the possible impacts of SCI, including unforeseen effects on regional climate patterns. In this study, we consider a climate model simulation of the deployment of stratospheric aerosols to maintain the global mean surface temperature at 1.5 ∘C above pre-industrial levels (ARISE-SAI-1.5). Leveraging two different machine learning methods, we evaluate when the effects of SCI would be detectable at regional scales. Specifically, we train a logistic regression model to classify whether an annual mean map of near-surface temperature or total precipitation is from future climate change under the influence of SCI or not. We then design an artificial neural network to predict how many years it has been since the deployment of SCI by inputting the regional maps from the climate intervention scenario. In both detection methods, we use feature attribution methods to spatially understand the forced climate patterns that are important for the machine learning model predictions. The differences in regional temperature signals are detectable in under a decade for most regions in the SCI scenario compared to greenhouse gas warming. However, the influence of SCI on regional precipitation patterns is more difficult to distinguish due to the presence of internal climate variability.

Current impacts and escalating risks of climate change require strong and decisive action to reduce greenhouse gas (GHG) emissions. They also highlight the urgency of research to enhance safety for human and natural systems, especially for those most vulnerable. This is reflected in two recent US National Academies of Science, Engineering, and Medicine studies that recommended a national focus on advancing our understanding of how to manage urgent current and future climate risks, and the study of approaches for increasing the reflection of sunlight from the atmosphere to reduce global warming, a process referred to as sunlight reflection modification (SRM). Here, we build on these recommendations by proposing a roadmap approach for the planning, coordination, and delivery of research to support a robust scientific assessment of SRM to reduce near-term climate risks in a defined timeframe. This approach is designed to support the evaluation of SRM as a possible rapid, temporary, additive measure to reduce catastrophic impacts from anthropogenic climate change, not as a substitute for aggressive GHG mitigation. Assessing SRM is proposed to be undertaken in the context of climate hazard risks through 2050, weighing the impacts associated with likely climate change trajectories against scenarios of possible SRM implementations. Provided that research is undertaken openly and that scientific resources are made widely available, the transparency of the process and the evidence generated would contribute to the democratization of information, participation by diverse stakeholders, more informed decision-making, and better opportunities for all people to weigh SRM options against climate change risks.

Predicting Pacific Decadal Oscillation (PDO) transitions and understanding the associated mechanisms has proven a critical but challenging task in climate science. As a form of decadal variability,...

We show that explainable neural networks can identify regions of oceanic variability that contribute predictability on decadal timescales in a fully coupled Earth system model. The neural networks ...

The climate is changing from human activities. This has major implications for the future and for human society and ecosystems, including birds. However, it is often masked by natural variability and there is great weather-related variability. This chapter reviews observed changes in climate, with a focus on changes in surface climate including variations in major patterns (modes) of climate variability and teleconnections. Of particular importance are changes in extremes. It describes how natural and anthropogenic drivers of climate change are assessed using climate models and concludes with a brief summary of future projected changes in climate and their impacts.

This study highlights the expected range of projected winter air temperature and precipitation trends over the next 30–50 years due to unpredictable fluctuations of the North Atlantic Oscillation (NAO) superimposed upon forced anthropogenic climate change. The findings are based on a 40-member initial-condition ensemble of simulations covering the period 1920–2100 conducted with the Community Earth System Model version 1 (CESM1) at 1° spatial resolution. The magnitude (and in some regions, even the sign) of the projected temperature and precipitation trends over Europe, Russia and parts of the Middle East vary considerably across the ensemble depending on the evolution of the NAO in each individual member. Thus, internal variability of the NAO imparts substantial uncertainty to future changes in regional climate over the coming decades. To validate the model results, we apply a simple scaling approach that relates the margin-of-error on a trend to the statistics of the interannual variability. In this way, we can obtain the expected range of projected climate trends using the interannual statistics of the observed NAO record in combination with the model’s radiatively-forced response (given by the ensemble-mean of the 40 simulations). The results of this observationally-based estimate are similar to those obtained directly from the CESM ensemble, attesting to the fidelity of the model’s representation of the NAO and the utility of this approach. Finally, we note that the interannual statistics of the NAO and associated surface climate impacts are subject to uncertainty due to sampling fluctuations, even when based on a century of data.

Warmer winters alter the dynamics of a local bird population and reduce immigration rate due to density-dependent feedback., Climate change will affect the population dynamics of many species, yet the consequences for the long-term persistence of populations are poorly understood. A major reason for this is that density-dependent feedback effects caused by fluctuations in population size are considered independent of stochastic variation in the environment. We show that an interplay between winter temperature and population density can influence the persistence of a small passerine population under global warming. Although warmer winters favor an increased mean population size, density-dependent feedback can cause the local population to be less buffered against occasional poor environmental conditions (cold winters). This shows that it is essential to go beyond the population size and explore climate effects on the full dynamics to elaborate targeted management actions.

Although differences exist between seasonal- and decadal-scale climate variability, predictability, and prediction, investment in observations, prediction systems, and decision systems for either time scale can benefit both. Fil: Goddard, Lisa. Columbia University; Estados Unidos Fil: Hurrell, James W.. National Center for Atmospheric Research; Estados Unidos Fil: Kirtman, Benjamin P.. University of Miami; Estados Unidos Fil: Murphy, James. Hadley Centre for Climate Prediction and Research; Reino Unido Fil: Stockdale, Timothy. European Centre for Medium-Range Weather Forecasts; Reino Unido Fil: Vera, Carolina Susana. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina

The predictability of North American climate is diagnosed by taking into account both forced climate change and natural decadal-scale climate variability over the next decade. In particular, the “signal” in North American surface air temperature and precipitation over 2011–20 associated with the expected change in boundary conditions related to future anthropogenic greenhouse gas (GHG) forcing, as well as the “noise” around that signal due to internally generated ocean–atmosphere variability, is estimated. The structural uncertainty in the estimate of decadal predictability is diagnosed by examining the sensitivity to plausible scenarios for the GHG-induced change in boundary forcing, the model dependency of the forced signals, and the dependency on methods for estimating internal decadal noise. The signal-to-noise analysis by the authors is thus different from other published decadal prediction studies, in that this study does not follow a trajectory from a particular initial state but rather considers the statistics of internal variability in comparison with the GHG signal. The 2011–20 decadal signal is characterized by surface warming over the entire North American continent, precipitation decreases over the contiguous United States, and precipitation increases over Canada relative to 1971–2000 climatological conditions. The signs of these forced responses are robust across different sea surface temperature (SST) scenarios and the different models employed, though the amplitude of the response differs. The North American decadal noise is considerably smaller than the signal associated with boundary forcing, implying a potential for high forecast skill for 2011–20 North American climate even for prediction methods that do not attempt to initialize climate models. However, the results do suggest that initialized decadal predictions, which seek to forecast externally forced signals and also constrain the internal variability, could potentially improve upon uninitialized methods in regions where the external signal is small relative to internal variability.

This paper discusses large-scale climate variability for several marine ecosystems and suggests types of ecosystem responses to climate change. Our analysis of observations and model results for the Pacific and Atlantic Oceans concludes that most climate variability is accounted for by the combination of intermittent 1–2 year duration events, e.g. the cumulative effect of monthly weather anomalies or the more organized El Nino/La Nina, plus broad-band “red noise” intrinsic variability operating at decadal and longer timescales. While ocean processes such as heat storage and lags due to ocean circulation provide some multi-year memory to the climate system, basic understanding of the mechanisms resulting in observed large decadal variability is lacking and forces the adoption of a “stochastic or red noise” conceptual model of low frequency variability at the present time. Thus we conclude that decadal events with rapid shifts and major departures from climatic means will occur, but their timing cannot be forecast. The responses to climate by biological systems are diverse in character because intervening processes introduce a variety of amplifications, time lags, feedbacks, and non-linearities. Decadal ecosystem variability can involve a variety of climate to ecosystem transfer functions. These can be expected to convert red noise of the physical system to redder (lower frequency) noise of the biological response, but can also convert climatic red noise to more abrupt and discontinuous biological shifts, transient climatic disturbance to prolonged ecosystem recovery, and perhaps transient disturbance to sustained ecosystem regimes. All of these ecosystem response characteristics are likely to be active for at least some locations and time periods, leading to a mix of slow fluctuations, prolonged trends, and step-like changes in ecosystems and fish populations in response to climate change. Climate variables such as temperatures and winds can have strong teleconnections (large spatial covariability) within individual ocean basins, but between-basin teleconnections, and potential climate-driven biological synchrony over several decades, are usually much weaker and a highly intermittent function of the conditions prevailing at the time within the adjoining basins. As noted in the recent IPCC 4th Assessment Report, a warming trend of ocean surface layers and loss of regional sea ice is likely before 2030, due to addition of greenhouse gases. Combined with large continuing natural climate variability, this will stress ecosystems in ways that they have not encountered for at least 100s of years.

There is a new perspective of a continuum of prediction problems, with a blurring of the distinction between short-term predictions and long-term climate projections. At the heart of this new persp...

Marine ecosystems are undergoing rapid change at local and global scales. To understand these changes, including the relative roles of natural variability and anthropogenic effects, and to predict the future state of marine ecosystems requires quantitative understanding of the physics, biogeochemistry and ecology of oceanic systems at mechanistic levels. Central to this understanding is the role played by dominant patterns or “modes” of atmospheric and oceanic variability, which orchestrate coherent variations in climate over large regions with profound impacts on ecosystems. We review the spatial structure of extratropical climate variability over the Northern Hemisphere and, specifically, focus on modes of climate variability over the extratropical North Atlantic. A leading pattern of weather and climate variability over the Northern Hemisphere is the North Atlantic Oscillation (NAO). The NAO refers to a redistribution of atmospheric mass between the Arctic and the subtropical Atlantic, and swings from one phase to another producing large changes in surface air temperature, winds, storminess and precipitation over the Atlantic as well as the adjacent continents. The NAO also affects the ocean through changes in heat content, gyre circulations, mixed layer depth, salinity, high latitude deep water formation and sea ice cover. Thus, indices of the NAO have become widely used to document and understand how this mode of variability alters the structure and functioning of marine ecosystems. There is no unique way, however, to define the NAO. Several approaches are discussed including both linear (e.g., principal component analysis) and nonlinear (e.g., cluster analysis) techniques. The former, which have been most widely used, assume preferred atmospheric circulation states come in pairs, in which anomalies of opposite polarity have the same spatial structure. In contrast, nonlinear techniques search for recurrent patterns of a specific amplitude and sign. They reveal, for instance, spatial asymmetries between different phases of the NAO that are likely important for ecological studies. It also follows that there is no universally accepted index to describe the temporal evolution of the NAO. Several of the most common measures are presented and compared. All reveal that there is no preferred time scale of variability for the NAO: large changes occur from one winter to the next and from one decade to the next. There is also a large amount of within-season variability in the patterns of atmospheric circulation of the North Atlantic, so that most winters cannot be characterized solely by a canonical NAO structure. A better understanding of how the NAO responds to external forcing, including sea surface temperature changes in the tropics, stratospheric influences, and increasing greenhouse gas concentrations, is crucial to the current debate on climate variability and change.

Summer climate in the North Atlantic–European sector possesses a principal pattern of year-to-year variability that is the parallel to the well-known North Atlantic Oscillation in winter. This summer North Atlantic Oscillation (SNAO) is defined here as the first empirical orthogonal function (EOF) of observed summertime extratropical North Atlantic pressure at mean sea level. It is shown to be characterized by a more northerly location and smaller spatial scale than its winter counterpart. The SNAO is also detected by cluster analysis and has a near-equivalent barotropic structure on daily and monthly time scales. Although of lesser amplitude than its wintertime counterpart, the SNAO exerts a strong influence on northern European rainfall, temperature, and cloudiness through changes in the position of the North Atlantic storm track. It is, therefore, of key importance in generating summer climate extremes, including flooding, drought, and heat stress in northwestern Europe. The El Niño–Southern Oscillation (ENSO) phenomenon is known to influence summertime European climate; however, interannual variations of the SNAO are only weakly influenced by ENSO. On interdecadal time scales, both modeling and observational results indicate that SNAO variations are partly related to the Atlantic multidecadal oscillation. It is shown that SNAO variations extend far back in time, as evidenced by reconstructions of SNAO variations back to 1706 using tree-ring records. Very long instrumental records, such as central England temperature, are used to validate the reconstruction. Finally, two climate models are shown to simulate the present-day SNAO and predict a trend toward a more positive index phase in the future under increasing greenhouse gas concentrations. This implies the long-term likelihood of increased summer drought for northwestern Europe.

A new surface boundary forcing dataset for uncoupled simulations with the Community Atmosphere Model is described. It is a merged product based on the monthly mean Hadley Centre sea ice and SST dataset version 1 (HadISST1) and version 2 of the National Oceanic and Atmospheric Administration (NOAA) weekly optimum interpolation (OI) SST analysis. These two source datasets were also used to supply ocean surface information to the 40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-40). The merged product provides monthly mean sea surface temperature and sea ice concentration data from 1870 to the present: it is updated monthly, and it is freely available for community use. The merging procedure was designed to take full advantage of the higher-resolution SST information inherent in the NOAA OI.v2 analysis.

Three interrelated climate phenomena are at the center of the Climate Variability and Predictability (CLIVAR) Atlantic research: tropical Atlantic variability (TAV), the North Atlantic Oscillation (NAO), and the Atlantic meridional overturning circulation (MOC). These phenomena produce a myriad of impacts on society and the environment on seasonal, interannual, and longer time scales through variability manifest as coherent fluctuations in ocean and land temperature, rainfall, and extreme events. Improved understanding of this variability is essential for assessing the likely range of future climate fluctuations and the extent to which they may be predictable, as well as understanding the potential impact of human-induced climate change. CLIVAR is addressing these issues through prioritized and integrated plans for short-term and sustained observations, basin-scale reanalysis, and modeling and theoretical investigations of the coupled Atlantic climate system and its links to remote regions. In this paper, a brief review of the state of understanding of Atlantic climate variability and achievements to date is provided. Considerable discussion is given to future challenges related to building and sustaining observing systems, developing synthesis strategies to support understanding and attribution of observed change, understanding sources of predictability, and developing prediction systems in order to meet the scientific objectives of the CLIVAR Atlantic program.

The spatial patterns, time history, and seasonality of African rainfall trends since 1950 are found to be deducible from the atmosphere’s response to the known variations of global sea surface temperatures (SSTs). The robustness of the oceanic impact is confirmed through the diagnosis of 80 separate 50-yr climate simulations across a suite of atmospheric general circulation models. Drying over the Sahel during boreal summer is shown to be a response to warming of the South Atlantic relative to North Atlantic SST, with the ensuing anomalous interhemispheric SST contrast favoring a more southern position of the Atlantic intertropical convergence zone. Southern African drying during austral summer is shown to be a response to Indian Ocean warming, with enhanced atmospheric convection over those warm waters driving subsidence drying over Africa.The ensemble of greenhouse-gas-forced experiments, conducted as part of the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, fails to simulate the pattern or amplitude of the twentieth-century African drying, indicating that the drought conditions were likely of natural origin. For the period 2000–49, the ensemble mean of the forced experiments yields a wet signal over the Sahel and a dry signal over southern Africa. These rainfall changes are physically consistent with a projected warming of the North Atlantic Ocean compared with the South Atlantic Ocean, and a further warming of the Indian Ocean. However, considerable spread exists among the individual members of the multimodel ensemble.

The dynamical simulation of the latest version of the Community Atmosphere Model (CAM3) is examined, including the seasonal variation of its mean state and its interannual variability. An ensemble of integrations forced with observed monthly varying sea surface temperatures and sea ice concentrations is compared to coexisting observations. The most significant differences from the previous version of the model [Community Climate Model version 3 (CCM3)] are associated with changes to the parameterized physics package. Results show that these changes have resulted in a modest improvement in the overall simulated climate; however, CAM3 continues to share many of the same biases exhibited by CCM3. At sea level, CAM3 reproduces the basic observed patterns of the pressure field. Simulated surface pressures are higher than observed over the subtropics, however, an error consistent with an easterly bias in the simulated trade winds and low-latitude surface wind stress. The largest regional differences over the Northern Hemisphere (NH) occur where the simulated highs over the eastern Pacific and Atlantic Oceans are too strong during boreal winter, and erroneously low pressures at higher latitudes are most notable over Europe and Eurasia. Over the Southern Hemisphere (SH), the circumpolar Antarctic trough is too deep throughout the year. The zonal wind structure in CAM3 is close to that observed, although the middle-latitude westerlies are too strong in both hemispheres throughout the year, consistent with errors in the simulated pressure field and the transient momentum fluxes. The observed patterns and magnitudes of upper-level divergent outflow are also well simulated by CAM3, a finding consistent with an improved and overall realistic simulation of tropical precipitation. There is, however, a tendency for the tropical precipitation maxima to remain in the NH throughout the year, while precipitation tends to be less than indicated by satellite estimates along the equator. The CAM3 simulation of tropical intraseasonal variability is quite poor. In contrast, observed changes in tropical and subtropical precipitation and the atmospheric circulation changes associated with tropical interannual variability are well simulated. Similarly, principal modes of extratropical variability bear considerable resemblance to those observed, although biases in the mean state degrade the simulated structure of the leading mode of NH atmospheric variability.

he potential relationships between tropical cyclones and global climate change are scientifically and socially complex, with great implications for society. The exceptional nature of the 2005 North Atlantic hurricane season alone provides great incentives for better understanding the full range of interactions and causes and effects thereof. The 2005 season saw the largest number (27) of named storms (sustained winds over 17 m s–1) and the largest number (14) of hurricanes (sustained winds over 33 m s–1), and it was the only year with three category 5 storms (maximum sustained winds over 67 m s–1). Also recorded was the most intense storm on record (Wilma, minimum pressure 882 hPa), the most intense storm ever in the Gulf of Mexico (Rita, 897 hPa), and the most costly storm on record (Katrina; some estimates put the cost of Katrina at as high as $200 billion; see information online at www.rms.com/Publications/ KatrinaReport_LessonsandImplications.pdf). (Please see www.ncdc.noaa.gov/oa/climate/research/2005/ hurricanes05.html for additional details.) The climate is in the process of a rapid warming, caused in part by human activities, including emission of greenhouse gases (GHGs) and aerosols, and changes in land use (Houghton et al. 2001; Karl and Trenberth 2003). These climate changes may well be changing the properties of tropical cyclones, yet the potential relationships between climate change and tropical cyclones and the consequences for humans have been downplayed or dismissed by a number of recent articles, testimonies, and press releases (e.g., Michaels et al. 2005;1 Pielke et al. 2005; Mayfield 2005). For example, the recent article with the allencompassing title “Hurricanes and global warming” by Pielke et al. (2005) raises several important points, yet it is incomplete and misleading because it 1) omits any mention of several of the most important aspects of the potential relationships between hurricanes and global warming, including rainfall, sea level, and storm surge; 2) leaves the impression that there is no significant connection between recent climate change caused by human activities and hurricane characteristics and impacts; and 3) does not take full account of the significance of recently identified trends and variations in tropical storms in causing impacts as compared to increasing societal vulnerability. In a similar vein, public statements and testimonies by prominent hurricane forecasters have denied or minimized important connections between global warming and tropical cyclones, attributing interannual variations in tropical cyclones only to natural variability (e.g., Mayfield 2005). It is true that society’s vulnerability to hurricane damage is increasing rapidly, predominantly because of the rise in population and property values near

Recent extreme weather such as Hurricane Sandy and the 2012 drought demonstrate the vulnerability of the United States to climate extremes in the present and point to the potential for increased future damages under a changing climate. They also provide lessons for reducing harm and realizing any potential benefits. Preparedness measures – also referred to as adaptation – can cost-effectively increase resilience today and in the future. The upfront costs will be more than offset by reductions in property damage, lives and livelihoods lost, and expensive post-disaster recovery processes. While others have addressed use of science for adaptation in specific sectors including biodiversity (Heller and Zavaleta, 2009) and freshwater ecosystem management (Wilby et al., 2010), or by simply taking a more pragmatic approach to adaptation under uncertainty (Hallegatte, 2009), here the authors make the case that a new, comprehensive approach is needed to create and use science to inform adaptations with applicable and sound knowledge (Kerr et al., 2011).

The origin of the so-called summer North Atlantic ‘‘Horseshoe’’ (HS) sea surface temperature (SST) mode of variability, which is statistically linked to the next winter’s North Atlantic Oscillation (NAO), is investigated from data and experiments with the CCM3 atmospheric general circulation model (AGCM). Lagged observational analyses reveal a linkage between HS and anomalous rainfall in the vicinity of the Atlantic intertropical convergence zone. Prescribing the observed anomalous convection in the model generates forced atmospheric Rossby waves that propagate into the North Atlantic sector. The accompanying perturbations in the surface turbulent and radiative fluxes are consistent with forcing the SST anomalies associated with HS. It is suggested that HS can therefore be interpreted as the remote footprint of tropical atmospheric changes. The ARPEGE AGCM is then used to test if the persistence of HS SST anomalies from summer to late fall can feed back to the atmosphere and have an impact on the next winter’s North Atlantic variability. Observed HS SST patterns are imposed in the model from August to November. They generate a weak but coherent early winter response projecting onto the NAO and therefore reproduce the observed HS‐NAO relationship obtained from lagged statistics. Changes in the simulated upper-level jet are associated with the anomalous HS meridional SST gradient and interact with synoptic eddy activity from October onward. The strength and position of the transients as a function of seasons are hypothesized to be of central importance to explain the nature, timing, and sign of the model response. In summary, the present study emphasizes the importance of summer oceanic and atmospheric conditions in both the Tropics and extratropics, and their persistence into early winter for explaining part of the NAO’s lowfrequency variability.

This study examines the tropical linkages to interdecadal climate fluctuations over the North Pacific during boreal winter through a comprehensive and physically based analysis of a wide variety of observational datasets spanning the twentieth century. Simple difference maps between epochs of high sea level pressure over the North Pacific (1900-24 and 1947-76) and epochs of low pressure (1925-46 and 1977-97) are presented for numerous climate variables throughout the tropical Indo-Pacific region, including rainfall, cloudiness, sea surface temperature (SST), and sea level pressure. The results support the notion that the Tropics play a key role in North Pacific interdecadal climate variability. In particular, SST anomalies in the tropical Indian Ocean and southeast Pacific Ocean, rainfall and cloudiness anomalies in the vicinity of the South Pacific convergence zone, stratus clouds in the eastern tropical Pacific, and sea level pressure differences between the tropical southeast Pacific and Indian Oceans all exhibit prominent interdecadal fluctuations that are coherent with those in sea level pressure over the North Pacific. The spatial patterns of the interdecadal tropical climate anomalies are compared with those associated with ENSO, a predominantly interannual phenomenon; in general, the two are similar with some differences in relative spatial emphasis. Finally, a published 194-yr coral record in the western tropical Indian Ocean is shown to compare favorably with the twentieth-century instrumental records, indicating the potential for extending knowledge of tropical interdecadal climate variability to earlier time periods.

Boreal winter North Atlantic climate change since 1950 is well described by a trend in the leading spatial structure of variability, known as the North Atlantic Oscillation (NAO). Through diagnoses of ensembles of atmospheric general circulation model (AGCM) experiments, we demonstrate that this climate change is a response to the temporal history of sea surface temperatures (SSTs). Specifically, 58 of 67 multi-model ensemble members (87%), forced with observed global SSTs since 1950, simulate a positive trend in a winter index of the NAO, and the spatial pattern of the multi-model ensemble mean trend agrees with that observed. An ensemble of AGCM simulations with only tropical SST forcing further suggests that variations in these SSTs are of primary importance. The probability distribution function (PDF) of 50-year NAO index trends from the forced simulations are, moreover, appreciably different from the PDF of a control simulation with no interannual SST variability, although chaotic atmospheric variations are shown to yield substantial 50-year trends. Our results thus advance the view that the observed linear trend in the winter NAO index is a combination of a strong tropically forced signal and an appreciable “noise” component of the same phase. The changes in tropical rainfall of greatest relevance include increased rainfall over the equatorial Indian Ocean, a change that has likely occurred in nature and is physically consistent with the observed, significant warming trend of the underlying sea surface.

Ensembles of atmospheric general circulation model (AGCM) experiments are used in an effort to understand the boreal winter Northern Hemisphere (NH) extratropical climate response to the observed warming of tropical sea surface temperatures (SSTs) over the last half of the twentieth Century. Specifically, we inquire about the origins of unusual, if not unprecedented, changes in the wintertime North Atlantic and European climate that are well described by a linear trend in most indices of the North Atlantic Oscillation (NAO). The simulated NH atmospheric response to the linear trend component of tropic-wide SST change since 1950 projects strongly onto the positive polarity of the NAO and is a hemispheric pattern distinguished by decreased (increased) Arctic (middle latitude) sea level pressure. Progressive warming of the Indian Ocean is the principal contributor to this wintertime extratropical response, as shown through additional AGCM ensembles forced with only the SST trend in that sector. The Indian Ocean influence is further established through the reproducibility of results across three different models forced with identical, idealized patterns of the observed warming. Examination of the transient atmospheric adjustment to a sudden “switch-on” of an Indian Ocean SST anomaly reveals that the North Atlantic response is not consistent with linear theory and most likely involves synoptic eddy feedbacks associated with changes in the North Atlantic storm track. The tropical SST control exerted over twentieth century regional climate underlies the importance of determining the future course of tropical SST for regional climate change and its uncertainty. Better understanding of the extratropical responses to different, plausible trajectories of the tropical oceans is key to such efforts.

The observed low-frequency winter atmospheric variability of the North Atlantic–European region and its relationship with global surface oceanic conditions is investigated based on the climate and weather regimes paradigm. Asymmetries between the two phases of the North Atlantic Oscillation (NAO) are found in the position of the Azores high and, to a weaker extent, the Icelandic low. There is a significant eastward displacement or expansion toward Europe for the NAO+ climate regime compared to the NAO− regime. This barotropic signal is found in different datasets and for two quasi-independent periods of record (1900–60 and 1950–2001); hence, it appears to be intrinsic to the NAO+ phase. Strong spatial similarities between weather and climate regimes suggest that the latter, representing long time scale variability, can be interpreted as the time-averaging signature of much shorter time scale processes. Model results from the ARPEGE atmospheric general circulation model are used to validate observ...

The development of human civilisations has occurred at a time of stable climate. This climate stability is now threatened by human activity. The rising global climate risk occurs at a decisive moment for world development. World nations are currently discussing a global development agenda consequent to the Millennium Development Goals (MDGs), which ends in 2015. It is increasingly possible to envisage a world where absolute poverty is largely eradicated within one generation and where ambitious goals on universal access and equal opportunities for dignified lives are adopted. These grand aspirations for a world population approaching or even exceeding nine billion in 2050 is threatened by substantial global environmental risks and by rising inequality. Research shows that development gains, in both rich and poor nations, can be undermined by social, economic and ecological problems caused by human-induced global environmental change. Climate risks, and associated changes in marine and terrestrial ecosystems that regulate the resilience of the climate system, are at the forefront of these global risks. We, as citizens with a strong engagement in Earth system science and socio-ecological dynamics, share the vision of a more equitable and prosperous future for the world, yet we also see threats to this future from shifts in climate and environmental processes. Without collaborative action now, our shared Earth system may not be able to sustainably support a large proportion of humanity in the decades ahead.

Variability of the North Atlantic Oscillation and the Tropical Atlantic dominate the climate of the North Atlantic sector, the underlying ocean and surrounding continents on interannual to decadal time scales. Here we review these phenomena, their climatic impacts and our present state of understanding of their underlying cause. Copyright © 2001 Royal Meteorological Society.

A comprehensive comparison is made between two tropospheric temperature datasets over the period 1979–98: the most recent and substantially revised (version d) microwave sounding unit (MSU) channel 2 data retrievals, and a gridded radiosonde analysis provided by the Hadley Centre of the U.K. Meteorological Office. The latter is vertically weighted to approximate the deep layer temperatures measured by the satellite data. At individual grid points, there is good overall agreement among monthly anomalies, especially over the Northern Hemisphere continents where the climate signal is large, although monthly root-mean-square (rms) differences typically exceed 0.6°C. Over the Tropics, correlations are lower and rms differences can be as large as the standard deviations of monthly anomalies. Differences in the gridpoint variances are significant at many locations, which presumably reflects sources of noise in one or both measurement systems. It is often argued for climate purposes that temperature anomalies are...

The climatically sensitive zone of the Arctic Ocean lies squarely within the domain of the North Atlantic oscillation (NAO), one of the most robust recurrent modes of atmospheric behavior. However, the specific response of the Arctic to annual and longer-period changes in the NAO is not well understood. Here that response is investigated using a wide range of datasets, but concentrating on the winter season when the forcing is maximal and on the postwar period, which includes the most comprehensive instrumental record. This period also contains the largest recorded low-frequency change in NAO activity—from its most persistent and extreme low index phase in the 1960s to its most persistent and extreme high index phase in the late 1980s/early 1990s. This longperiod shift between contrasting NAO extrema was accompanied, among other changes, by an intensifying storm track through the Nordic Seas, a radical increase in the atmospheric moisture flux convergence and winter precipitation in this sector, an increase in the amount and temperature of the Atlantic water inflow to the Arctic Ocean via both inflow branches (Barents Sea Throughflow and West Spitsbergen Current), a decrease in the late-winter extent of sea ice throughout the European subarctic, and (temporarily at least) an increase in the annual volume flux of ice from the Fram Strait.

A comprehensive comparison is made among four sea surface temperature (SST) datasets: the optimum interpolation (OI) and the empirical orthogonal function reconstructed SST analyses from the National Centers for Environmental Prediction (NCEP), the Global Sea-Ice and SST dataset (GISST, version 2.3b) from the United Kingdom Meteorological Office, and the optimal smoothing SST analysis from the Lamont-Doherty Earth Observatory (LDEO). Significant differences exist between the GISST and NCEP 1961–90 SST climatologies, especially in the marginal sea-ice zones and in regions of important small-scale features, such as the Gulf Stream, which are better resolved by the NCEP product. Significant differences also exist in the SST anomalies that relate strongly to the number of in situ observations available. In recent years, correlations between monthly anomalies are less than 0.75 south of about 10°N and are lower still over the southern oceans and parts of the tropical Pacific where root-mean-square differences ...

Climatological properties for selected aspects of the thermodynamic structure and hydrologic cycle are presented from a 15-yr numerical simulation conducted with the National Center for Atmospheric Research Community Climate Model, version 3 (CCM3), using an observed sea surface temperature climatology. In most regards, the simulated thermal structure and hydrologic cycle represent a marked improvement when compared with earlier versions of the CCM. Three major modifications to parameterized physics are primarily responsible for the more notable improvements in the simulation: modifications to the diagnosis of cloud optical properties, modifications to the diagnosis of boundary layer processes, and the incorporation of a penetrative formulation for deep cumulus convection. The various roles of these physical parameterization changes will be discussed in the context of the simulation strengths and weaknesses.

The atmospheric state simulated by the National Center for Atmospheric Research (NCAR) Community Climate Model, version 3 (CCM3), is compared to that simulated by the NCAR Climate System Model, version 1 (CSM1). CCM3 is an atmospheric general circulation model that uses specified sea surface temperatures (SSTs) for a lower boundary condition. Observed monthly mean SSTs for 1979–93 were used in the present study. CSM1 is a coupled general circulation model in which the SSTs are determined as part of the simulation and CCM3 serves as the atmospheric component. It is found that the differences between CCM3 and CSM1 are quite small in most measures of the atmospheric circulation, consistent with the accurate and drift-free simulation of the SSTs in the coupled model. There are substantial temperature differences near the surface in the Arctic and over the ocean around Antarctica, resulting from different sea-ice distributions. The tropical precipitation also has significant differences, although neit...

The energy budget of the latest version of the National Center for Atmospheric Research (NCAR) Community Climate Model (CCM3) is described. The energy budget at the top of the atmosphere and at the earth’s surface is compared to observational estimates. The annual mean, seasonal mean, and seasonal cycle of the energy budget are evaluated in comparison with earth radiation budget data at the top of the atmosphere and with the NCAR Ocean Model (NCOM) forcing data at the ocean’s surface. Individual terms in the energy budget are discussed. The transient response of the top-of-atmosphere radiative budget to anomalies in tropical sea surface temperature is also presented. In general, the CCM3 is in excellent agreement with ERBE data in terms of annual and seasonal means. The seasonal cycle of the top-of-atmosphere radiation budget is also in good (,10 Wm 22) agreement with ERBE data. At the surface, the model shortwave flux over the oceans is too large compared to data obtained by W. G. Large and colleagues (;20‐30 W m22). It is argued that this bias is related to a model underestimate of shortwave cloud absorption. The major biases in the model are related to the position of deep convection in the tropical Pacific, summertime convective activity over land regions, and the model’s inability to realistically represent marine stratus and stratocumulus clouds. Despite these deficiencies, the model’s implied ocean heat transport is in very good agreement with the explicit ocean heat transport of the NCOM uncoupled simulations. This result is a major reason for the success of the NCAR Climate System Model.