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The climate system and its spatio-temporal changes are strongly affected by modes of long-term internal variability, like the Pacific Decadal Varibility (PDV) and the Atlantic Multidecadal Variability (AMV). As they alternate between warm and cold phases, the interplay between PDV and AMV varies over decadal to multidecadal timescales. Here, we use a causal discovery method to derive fingerprints in the Atlantic-Pacific interactions and investigate their phase-dependent changes. Dependent on the phases of PDV and AMV, different regimes with characteristic causal fingerprints are identified in reanalyses in a first step. In a second step, a regime-oriented causal model evaluation is performed to evaluate the ability of models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) in representing the observed changing interactions between PDV, AMV and their extra-tropical teleconnections. The causal graphs obtained from reanalyses detect a direct opposite-sign response from AMV on PDV when analysing the complete 1900–2014 period, and during several defined regimes within that period, for example, when AMV is going through its negative (cold) phase. Reanalyses also demonstrate a same-sign response from PDV on AMV during the cold phase of PDV. Historical CMIP6 simulations exhibit varying skill in simulating the observed causal patterns. Generally, Large Ensemble (LE) simulations showed better network similarity when PDV and AMV are out of phase compared to other regimes. Also, the two largest ensembles (in terms of number of members) were found to contain realizations with similar causal fingerprints to observations. For most regimes, these same models showed higher network similarity when compared to each other. This work shows how causal discovery on LEs complements the available diagnostics and statistics metrics of climate variability to provide a powerful tool for climate model evaluation.

Disentangling the effects of internal variability and anthropogenic forcing on regional climate trends remains a key challenge with far-reaching implications. Due to its largely unpredictable nature on timescales longer than a decade, internal climate variability limits the accuracy of climate model projections, introduces challenges in attributing past climate changes, and complicates climate model evaluation. Here, we highlight recent advances in climate modeling and physical understanding that have led to novel insights about these key issues. In particular, we synthesize new findings from large-ensemble simulations with Earth system models, observational large ensembles, and dynamical adjustment methodologies, with a focus on European climate.

Disentangling the effects of internal variability and anthropogenic forcing on regional climate change remains a key challenge with far-reaching implications. Due to its largely unpredictable nature on timescales longer than a decade, internal climate variability limits the accuracy of climate model projections, introduces challenges in attributing past climate changes, and complicates climate model evaluation. Here, we highlight recent advances in climate modeling and physical understanding that have led to novel insights on these key issues. In particular, we synthesize new findings from Large Ensemble simulations with Earth System Models, Observational Large Ensembles, and “dynamical adjustment” methodologies, with a focus on European climate.

A large set of deterministic and ensemble forecasts was produced to identify the optimal spacing for forecasting U.S. East Coast snowstorms. WRF forecasts were produced on cloud-allowing (~1-km grid spacing) and convection-allowing (3–4 km) grids, and compared against forecasts with parameterized convection (>~10 km). Performance diagrams were used to evaluate 19 deterministic forecasts from the winter of 2013–14. Ensemble forecasts of five disruptive snowstorms spanning the years 2015–18 were evaluated using various methods to evaluate probabilistic forecasts. While deterministic forecasts using cloud-allowing grids were not better than convection-allowing forecasts, both had lower bias and higher success ratios than forecasts with parameterized convection. All forecasts were underdispersive. Nevertheless, forecasts on the higher-resolution grids were more reliable than those with parameterized convection. Forecasts on the cloud-allowing grid were best able to discriminate areas that received heavy snow and those that did not, while the forecasts with parameterized convection were least able to do so. It is recommended to use convection-resolving and (if computationally possible) to use cloud-allowing forecast grids when predicting East Coast winter storms.

The evolving roles of anthropogenic aerosols (AER) and greenhouse gases (GHG) in driving large-scale patterns of precipitation and SST trends during 1920–2080 are studied using a new set of “all-but-one-forcing” initial-condition large ensembles (LEs) with the Community Earth System Model version 1 (CESM1), which complement the original “all-forcing” CESM1 LE (ALL). The large number of ensemble members (15–20) in each of the new LEs enables regional impacts of AER and GHG to be isolated from the noise of the model’s internal variability. Our analysis approach, based on running 50-yr trends, accommodates geographical and temporal changes in patterns of forcing and response. AER are shown to be the primary driver of large-scale patterns of externally forced trends in ALL before the late 1970s, and GHG to dominate thereafter. The AER and GHG forced trends are spatially distinct except during the 1970s transition phase when aerosol changes are mainly confined to lower latitudes. The transition phase is also characterized by a relative minimum in the amplitude of forced trend patterns in ALL, due to a combination of reduced AER and partially offsetting effects of AER and GHG. Internal variability greatly limits the detectability of AER- and GHG-forced trend patterns in individual realizations based on pattern correlation metrics, especially during the historical period, highlighting the need for LEs. We estimate that

The adequate simulation of internal climate variability is key for our understanding of climate as it underpins efforts to attribute historical events, predict on seasonal and decadal time scales, and isolate the effects of climate change. Here the skill of models in reproducing observed modes of climate variability is assessed, both across and within the CMIP3, CMIP5, and CMIP6 archives, in order to document model capabilities, progress across ensembles, and persisting biases. A focus is given to the well-observed tropical and extratropical modes that exhibit small intrinsic variability relative to model structural uncertainty. These include El Niño–Southern Oscillation (ENSO), the Pacific decadal oscillation (PDO), the North Atlantic Oscillation (NAO), and the northern and southern annular modes (NAM and SAM). Significant improvements are identified in models’ representation of many modes. Canonical biases, which involve both amplitudes and patterns, are generally reduced across model generations. For example, biases in ENSO-related equatorial Pacific sea surface temperature, which extend too far westward, and associated atmospheric teleconnections, which are too weak, are reduced. Stronger tropical expression of the PDO in successive CMIP generations has characterized their improvement, with some CMIP6 models generating patterns that lie within the range of observed estimates. For the NAO, NAM, and SAM, pattern correlations with observations are generally higher than for other modes and slight improvements are identified across successive model generations. For ENSO and PDO spectra and extratropical modes, changes are small compared to internal variability, precluding definitive statements regarding improvement.

The Whole Atmosphere Community Climate Model version 6 (WACCM6) is a major update of the whole atmosphere modeling capability in the Community Earth System Model (CESM), featuring enhanced physical, chemical and aerosol parameterizations. This work describes WACCM6 and some of the important features of the model. WACCM6 can reproduce many modes of variability and trends in the middle atmosphere, including the Quasi‐Biennial Oscillation, Stratospheric Sudden Warmings and the evolution of Southern Hemisphere springtime ozone depletion over the 20th century. WACCM6 can also reproduce the climate and temperature trends of the 20th century throughout the atmospheric column. The representation of the climate has improved in WACCM6, relative to WACCM4. In addition, there are improvements in high latitude climate variability at the surface and sea ice extent in WACCM6 over the lower top version of the model (CAM6) that come from the extended vertical domain and expanded aerosol chemistry in WACCM6, highlighting the importance of the stratosphere and tropospheric chemistry for high latitude climate variability.

This study presents a description of the El Niño–Southern Oscillation (ENSO) and Pacific Decadal Variability (PDV) in a multicentury preindustrial simulation of the Community Earth System Model Version 2 (CESM2). The model simulates several aspects of ENSO relatively well, including dominant timescale, tropical and extratropical precursors, composite evolution of El Niño and La Niña events, and ENSO teleconnections. The good model representation of ENSO spectral characteristics is consistent with the spatial pattern of the anomalous equatorial zonal wind stress in the model, which results in the correct adjustment timescale of the equatorial thermocline according to the delayed/recharge oscillator paradigms, as also reflected in the realistic time evolution of the equatorial Warm Water Volume. PDV in the model exhibits a pattern that is very similar to the observed, with realistic tropical and South Pacific signatures which were much weaker in some of the CESM2 predecessor models. The tropical component of PDV also shows an association with ENSO decadal modulation which is similar to that found in observations. However, the ENSO amplitude is about 30% larger than observed in the preindustrial CESM2 simulation, and even larger in the historical ensemble, perhaps as a result of anthropogenic influences. In contrast to observations, the largest variability is found in the central Pacific rather than in the eastern Pacific, a discrepancy that somewhat hinders the model's ability to represent a full diversity in El Niño spatial patterns and appears to be associated with an unrealistic confinement of the precipitation anomalies to the western Pacific.

An overview of the Community Earth System Model Version 2 (CESM2) is provided, including a discussion of the challenges encountered during its development and how they were addressed. In addition, an evaluation of a pair of CESM2 long preindustrial control and historical ensemble simulations is presented. These simulations were performed using the nominal 1° horizontal resolution configuration of the coupled model with both the “low‐top” (40 km, with limited chemistry) and “high‐top” (130 km, with comprehensive chemistry) versions of the atmospheric component. CESM2 contains many substantial science and infrastructure improvements and new capabilities since its previous major release, CESM1, resulting in improved historical simulations in comparison to CESM1 and available observations. These include major reductions in low‐latitude precipitation and shortwave cloud forcing biases; better representation of the Madden‐Julian Oscillation; better El Niño‐Southern Oscillation‐related teleconnections; and a global land carbon accumulation trend that agrees well with observationally based estimates. Most tropospheric and surface features of the low‐ and high‐top simulations are very similar to each other, so these improvements are present in both configurations. CESM2 has an equilibrium climate sensitivity of 5.1–5.3 °C, larger than in CESM1, primarily due to a combination of relatively small changes to cloud microphysics and boundary layer parameters. In contrast, CESM2's transient climate response of 1.9–2.0 °C is comparable to that of CESM1. The model outputs from these and many other simulations are available to the research community, and they represent CESM2's contributions to the Coupled Model Intercomparison Project Phase 6.

This paper describes the Stratospheric Aerosol Geoengineering Large Ensemble (GLENS) project, which promotes the use of a unique model dataset, performed with the Community Earth System Model, with the Whole Atmosphere Community Climate Model as its atmospheric component [CESM1(WACCM)], to investigate global and regional impacts of geoengineering. The performed simulations were designed to achieve multiple simultaneous climate goals, by strategically placing sulfur injections at four different locations in the stratosphere, unlike many earlier studies that targeted globally averaged surface temperature by placing injections in regions at or around the equator. This advanced approach reduces some of the previously found adverse effects of stratospheric aerosol geoengineering, including uneven cooling between the poles and the equator and shifts in tropical precipitation. The 20-member ensemble increases the ability to distinguish between forced changes and changes due to climate variability in global and regional climate variables in the coupled atmosphere, land, sea ice, and ocean system. We invite the broader community to perform in-depth analyses of climate-related impacts and to identify processes that lead to changes in the climate system as the result of a strategic application of stratospheric aerosol geoengineering.

The Earth System Model Evaluation Tool (ESMValTool) is a community diagnostics and performance metrics tool designed to improve comprehensive and routine evaluation of Earth System Models (ESMs) participating in the Coupled Model Intercomparison Project (CMIP). It has undergone rapid development since the first release in 2016 and is now a well-tested tool that provides end-to-end provenance tracking to ensure reproducibility. It consists of an easy-to-install, well documented Python package providing the core functionalities (ESMValCore) that performs common pre-processing operations and a diagnostic part that includes tailored diagnostics and performance metrics for specific scientific applications. Here we describe large-scale diagnostics of the second major release of the tool that supports the evaluation of ESMs participating in CMIP Phase 6 (CMIP6). ESMValTool v2.0 includes a large collection of diagnostics and performance metrics for atmospheric, oceanic, and terrestrial variables for the mean state, trends, and variability. ESMValTool v2.0 also successfully reproduces figures from the evaluation and projections chapters of the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) and incorporates updates from targeted analysis packages, such as the NCAR Climate Variability Diagnostics Package for the evaluation of modes of variability the Thermodynamic Diagnostic Tool (TheDiaTo) to evaluate the energetics of the climate system, as well as parts of AutoAssess that contains a mix of top-down performance metrics. The tool has been fully integrated into the Earth System Grid Federation (ESGF) infrastructure at the Deutsches Klima Rechenzentrum (DKRZ) to provide evaluation results from CMIP6 model simulations shortly after the output is published to the CMIP archive. A result browser has been implemented that enables advanced monitoring of the evaluation results by a broad user community at much faster timescales than what was possible in CMIP5.

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.

The Pacific decadal oscillation (PDO), the dominant year-round pattern of monthly North Pacific sea surface temperature (SST) variability, is an important target of ongoing research within the meteorological and climate dynamics communities and is central to the work of many geologists, ecologists, natural resource managers, and social scientists. Research over the last 15 years has led to an emerging consensus: the PDO is not a single phenomenon, but is instead the result of a combination of different physical processes, including both remote tropical forcing and local North Pacific atmosphere–ocean interactions, which operate on different time scales to drive similar PDO-like SST anomaly patterns. How these processes combine to generate the observed PDO evolution, including apparent regime shifts, is shown using simple autoregressive models of increasing spatial complexity. Simulations of recent climate in coupled GCMs are able to capture many aspects of the PDO, but do so based on a balance of processes often more independent of the tropics than is observed. Finally, it is suggested that the assessment of PDO-related regional climate impacts, reconstruction of PDO-related variability into the past with proxy records, and diagnosis of Pacific variability within coupled GCMs should all account for the effects of these different processes, which only partly represent the direct forcing of the atmosphere by North Pacific Ocean SSTs.

This study elucidates the physical mechanisms underlying internal and forced components of winter surface air temperature (SAT) trends over North America during the past 50 years (1963–2012) using a combined observational and modeling framework. The modeling framework consists of 30 simulations with the Community Earth System Model (CESM) at 1° latitude–longitude resolution, each of which is subject to an identical scenario of historical radiative forcing but starts from a slightly different atmospheric state. Hence, any spread within the ensemble results from unpredictable internal variability superimposed upon the forced climate change signal. Constructed atmospheric circulation analogs are used to estimate the dynamical contribution to forced and internal components of SAT trends: thermodynamic contributions are obtained as a residual. Internal circulation trends are estimated to account for approximately one-third of the observed wintertime warming trend over North America and more than half locally over parts of Canada and the United States. Removing the effects of internal atmospheric circulation variability narrows the spread of SAT trends within the CESM ensemble and brings the observed trends closer to the model’s radiatively forced response. In addition, removing internal dynamics approximately doubles the signal-to-noise ratio of the simulated SAT trends and substantially advances the “time of emergence” of the forced component of SAT anomalies. The methodological framework proposed here provides a general template for improving physical understanding and interpretation of observed and simulated climate trends worldwide and may help to reconcile the diversity of SAT trends across the models from phase 5 of the Coupled Model Intercomparison Project (CMIP5).

The role of sampling variability in ENSO composites of winter surface air temperature and precipitation over North America during the period 1920–2013 is assessed for observations and ensembles of coupled model simulations in which sea surface temperature anomalies in the tropical eastern Pacific are nudged to those of the real world. The individual members of each model ensemble show a surprising amount of diversity in their ENSO composites, despite being constructed from the same observed set of 18 El Niño and 14 La Niña events. For a given model, this ensemble spread can only be due to sampling variability, that is, aliasing of internal variability that is unrelated to ENSO, which in turn is shown to arise from internal atmospheric dynamics rather than coupled ocean–atmosphere processes. Analogous ensemble spread is evident in 2000 synthetic ENSO composites based on observations using random sampling techniques. These synthetic composites provide information on the range of spatial patterns and amplitudes associated with imperfect estimation of the forced ENSO signal in the observational record. In some locations, the amplitude of the estimated ENSO signal can vary by more than a factor of two. This observational uncertainty necessitates an approach to model assessment that considers not only the model’s forced response to ENSO, given by its ensemble-mean ENSO composite, but also its representation of internal variability unrelated to ENSO. Such an approach is used to reveal fidelities and shortcomings in the Community Earth System Model, version 1.

Internal variability in the climate system gives rise to large uncertainty in projections of future climate. The uncertainty in future climate due to internal climate variability can be estimated from large ensembles of climate change simulations in which the experiment setup is the same from one ensemble member to the next but for small perturbations in the initial atmospheric state. However, large ensembles are invariably computationally expensive and susceptible to model bias. Here the authors outline an alternative approach for assessing the role of internal variability in future climate based on a simple analytic model and the statistics of the unforced climate variability. The analytic model is derived from the standard error of the regression and assumes that the statistics of the internal variability are roughly Gaussian and stationary in time. When applied to the statistics of an unforced control simulation, the analytic model provides a remarkably robust estimate of the uncertainty in future climate indicated by a large ensemble of climate change simulations. To the extent that observations can be used to estimate the amplitude of internal climate variability, it is argued that the uncertainty in future climate trends due to internal variability can be robustly estimated from the statistics of the observed climate.

While internal climate variability is known to affect climate projections, its influence is often underappreciated and confused with model error. Why? In general, modeling centers contribute a small number of realizations to international climate model assessments [e.g., phase 5 of the Coupled Model Intercomparison Project (CMIP5)]. As a result, model error and internal climate variability are difficult, and at times impossible, to disentangle. In response, the Community Earth System Model (CESM) community designed the CESM Large Ensemble (CESM-LE) with the explicit goal of enabling assessment of climate change in the presence of internal climate variability. All CESM-LE simulations use a single CMIP5 model (CESM with the Community Atmosphere Model, version 5). The core simulations replay the twenty to twenty-first century (1920–2100) 30 times under historical and representative concentration pathway 8.5 external forcing with small initial condition differences. Two companion 1000+-yr-long preindustrial control simulations (fully coupled, prognostic atmosphere and land only) allow assessment of internal climate variability in the absence of climate change. Comprehensive outputs, including many daily fields, are available as single-variable time series on the Earth System Grid for anyone to use. Early results demonstrate the substantial influence of internal climate variability on twentieth- to twenty-first-century climate trajectories. Global warming hiatus decades occur, similar to those recently observed. Internal climate variability alone can produce projection spread comparable to that in CMIP5. Scientists and stakeholders can use CESM-LE outputs to help interpret the observational record, to understand projection spread and to plan for a range of possible futures influenced by both internal climate variability and forced climate change.

Application of random sampling techniques to composite differences between 18 El Niño and 14 La Niña events observed since 1920 reveals considerable uncertainty in both the pattern and amplitude of the Northern Hemisphere extratropical winter sea level pressure (SLP) response to ENSO. While the SLP responses over the North Pacific and North America are robust to sampling variability, their magnitudes can vary by a factor of 2; other regions, such as the Arctic, North Atlantic, and Europe are less robust in their SLP patterns, amplitudes, and statistical significance. The uncertainties on the observed ENSO composite are shown to arise mainly from atmospheric internal variability as opposed to ENSO diversity. These observational findings pose considerable challenges for the evaluation of ENSO teleconnections in models. An approach is proposed that incorporates both pattern and amplitude uncertainty in the observational target, allowing for discrimination between true model biases in the forced ENSO response and apparent model biases that arise from limited sampling of non-ENSO-related internal variability. Large initial-condition coupled model ensembles with realistic tropical Pacific sea surface temperature anomaly evolution during 1920–2013 show similar levels of uncertainty in their ENSO teleconnections as found in observations. Because the set of ENSO events in each of the model composites is the same (and identical to that in observations), these uncertainties are entirely attributable to sampling fluctuations arising from internal variability, which is shown to originate from atmospheric processes. The initial-condition model ensembles thus inform the interpretation of the single observed ENSO composite and vice versa.

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.

The slowdown in global warming has been identified predominately through changes in the Pacific Ocean. This study investigates the teleconnections and seasonal changes associated with the slowdown. The present forcing from the tropical Pacific is found to produce many of the changes in atmospheric circulation, for example, changes in the upper troposphere wave patterns increase the chances of cold European winters.

This study highlights the relative importance of internally generated versus externally forced climate trends over the next 50 yr (2010–60) at local and regional scales over North America in two global coupled model ensembles. Both ensembles contain large numbers of integrations (17 and 40): each of which is subject to identical anthropogenic radiative forcing (e.g., greenhouse gas increase) but begins from a slightly different initial atmospheric state. Thus, the diversity of projected climate trends within each model ensemble is due solely to intrinsic, unpredictable variability of the climate system. Both model ensembles show that natural climate variability superimposed upon forced climate change will result in a range of possible future trends for surface air temperature and precipitation over the next 50 yr. Precipitation trends are particularly subject to uncertainty as a result of internal variability, with signal-to-noise ratios less than 2. Intrinsic atmospheric circulation variability is mainly responsible for the spread in future climate trends, imparting regional coherence to the internally driven air temperature and precipitation trends. The results underscore the importance of conducting a large number of climate change projections with a given model, as each realization will contain a different superposition of unforced and forced trends. Such initial-condition ensembles are also needed to determine the anthropogenic climate response at local and regional scales and provide a new perspective on how to usefully compare climate change projections across models.

A community diagnostics and performance metrics tool for the evaluation of Earth system models (ESMs) has been developed that allows for routine comparison of single or multiple models, either against predecessor versions or against observations. The priority of the effort so far has been to target specific scientific themes focusing on selected essential climate variables (ECVs), a range of known systematic biases common to ESMs, such as coupled tropical climate variability, monsoons, Southern Ocean processes, continental dry biases, and soil hydrology–climate interactions, as well as atmospheric CO2 budgets, tropospheric and stratospheric ozone, and tropospheric aerosols. The tool is being developed in such a way that additional analyses can easily be added. A set of standard namelists for each scientific topic reproduces specific sets of diagnostics or performance metrics that have demonstrated their importance in ESM evaluation in the peer-reviewed literature. The Earth System Model Evaluation Tool (ESMValTool) is a community effort open to both users and developers encouraging open exchange of diagnostic source code and evaluation results from the Coupled Model Intercomparison Project (CMIP) ensemble. This will facilitate and improve ESM evaluation beyond the state-of-the-art and aims at supporting such activities within CMIP and at individual modelling centres. Ultimately, we envisage running the ESMValTool alongside the Earth System Grid Federation (ESGF) as part of a more routine evaluation of CMIP model simulations while utilizing observations available in standard formats (obs4MIPs) or provided by the user., Geoscientific Model Development, 9 (5), ISSN:1991-9603, ISSN:1991-959X

As climate models improve, decision-makers' expectations for accurate climate predictions are growing. Natural climate variability, however, limits climate predictability and hampers the ability to guide adaptation in many regions such as North America. Scientists, policymakers and the public need to improve communication and avoid raising expectations for accurate regional predictions everywhere.

This study presents an overview of the El Niño–Southern Oscillation (ENSO) phenomenon and Pacific decadal variability (PDV) simulated in a multicentury preindustrial control integration of the NCAR Community Climate System Model version 4 (CCSM4) at nominal 1° latitude–longitude resolution. Several aspects of ENSO are improved in CCSM4 compared to its predecessor CCSM3, including the lengthened period (3–6 yr), the larger range of amplitude and frequency of events, and the longer duration of La Niña compared to El Niño. However, the overall magnitude of ENSO in CCSM4 is overestimated by ~30%. The simulated ENSO exhibits characteristics consistent with the delayed/recharge oscillator paradigm, including correspondence between the lengthened period and increased latitudinal width of the anomalous equatorial zonal wind stress. Global seasonal atmospheric teleconnections with accompanying impacts on precipitation and temperature are generally well simulated, although the wintertime deepening of the Aleutian low erroneously persists into spring. The vertical structure of the upper-ocean temperature response to ENSO in the north and south Pacific displays a realistic seasonal evolution, with notable asymmetries between warm and cold events. The model shows evidence of atmospheric circulation precursors over the North Pacific associated with the “seasonal footprinting mechanism,” similar to observations. Simulated PDV exhibits a significant spectral peak around 15 yr, with generally realistic spatial pattern and magnitude. However, PDV linkages between the tropics and extratropics are weaker than observed.

Climate models are an essential tool for studying and predicting climate change. Their usefulness, however, depends on how realistically they simulate the statistics of present-day climate, including its variability, among other factors.

Uncertainty in future climate change presents a key challenge for adaptation planning. In this study, uncertainty arising from internal climate variability is investigated using a new 40-member ensemble conducted with the National Center for Atmospheric Research Community Climate System Model Version 3 (CCSM3) under the SRES A1B greenhouse gas and ozone recovery forcing scenarios during 2000–2060. The contribution of intrinsic atmospheric variability to the total uncertainty is further examined using a 10,000-year control integration of the atmospheric model component of CCSM3 under fixed boundary conditions. The global climate response is characterized in terms of air temperature, precipitation, and sea level pressure during winter and summer. The dominant source of uncertainty in the simulated climate response at middle and high latitudes is internal atmospheric variability associated with the annular modes of circulation variability. Coupled ocean-atmosphere variability plays a dominant role in the tropics, with attendant effects at higher latitudes via atmospheric teleconnections. Uncertainties in the forced response are generally larger for sea level pressure than precipitation, and smallest for air temperature. Accordingly, forced changes in air temperature can be detected earlier and with fewer ensemble members than those in atmospheric circulation and precipitation. Implications of the results for detection and attribution of observed climate change and for multi-model climate assessments are discussed. Internal variability is estimated to account for at least half of the inter-model spread in projected climate trends during 2005–2060 in the CMIP3 multi-model ensemble.

Patterns of sea surface temperature (SST) variability on interannual and longer timescales result from a combination of atmospheric and oceanic processes. These SST anomaly patterns may be due to intrinsic modes of atmospheric circulation variability that imprint themselves upon the SST field mainly via surface energy fluxes. Examples include SST fluctuations in the Southern Ocean associated with the Southern Annular Mode, a tripolar pattern of SST anomalies in the North Atlantic associated with the North Atlantic Oscillation, and a pan-Pacific mode known as the Pacific Decadal Oscillation (with additional contributions from oceanic processes). They may also result from coupled ocean-atmosphere interactions, such as the El Niño-Southern Oscillation phenomenon in the tropical Indo-Pacific, the tropical Atlantic Niño, and the cross-equatorial meridional modes in the tropical Pacific and Atlantic. Finally, patterns of SST variability may arise from intrinsic oceanic modes, notably the Atlantic Multidecadal Oscillation.

The USCLI VAR working group on drought recently initiated a series of global climate model simulations forced with idealized SST anomaly patterns, designed to address a number of uncertainties regarding the impact of SST forcing and the role of land-atmosphere feedbacks on regional drought. Specific questions that the runs are designed to address include: What are the mechanisms that maintain drought across the seasonal cycle and from one year to the next? What is the role of the leading patterns of SST variability, and what are the physical mechanisms linking the remote SST forcing to regional drought, including the role of land-atmosphere coupling? The runs were carried out with five different atmospheric general circulation models (AGCM5), and one coupled atmosphere-ocean model in which the model was continuously nudged to the imposed SST forcing. This paper provides an overview of the experiments and some initial results focusing on the responses to the leading patterns of annual mean SST variability consisting of a Pacific El Nino/Southern Oscillation (ENSO)-like pattern, a pattern that resembles the Atlantic Multi-decadal Oscillation (AMO), and a global trend pattern. One of the key findings is that all the AGCMs produce broadly similar (though different in detail) precipitation responses to the Pacific forcing pattern, with a cold Pacific leading to reduced precipitation and a warm Pacific leading to enhanced precipitation over most of the United States. While the response to the Atlantic pattern is less robust, there is general agreement among the models that the largest precipitation response over the U.S. tends to occur when the two oceans have anomalies of opposite sign. That is, a cold Pacific and warm Atlantic tend to produce the largest precipitation reductions, whereas a warm Pacific and cold Atlantic tend to produce the greatest precipitation enhancements. Further analysis of the response over the U.S. to the Pacific forcing highlights a number of noteworthy and to some extent unexpected results. These include a seasonal dependence of the precipitation response that is characterized by signal-to-noise ratios that peak in spring, and surface temperature signal-to-noise ratios that are both lower and show less agreement among the models than those found for the precipitation response. Another interesting result concerns what appears to be a substantially different character in the surface temperature response over the U.S. to the Pacific forcing by the only model examined here that was developed for use in numerical weather prediction. The response to the positive SST trend forcing pattern is an overall surface warming over the world's land areas with substantial regional variations that are in part reproduced in runs forced with a globally uniform SST trend forcing. The precipitation response to the trend forcing is weak in all the models.

The relative roles of direct atmospheric radiative forcing (due to observed changes in well-mixed greenhouse gases, tropospheric and stratospheric ozone, sulfate and volcanic aerosols, and solar output) and observed sea surface temperature (SST) forcing of global December–February atmospheric circulation trends during the second half of the twentieth century are investigated by means of experiments with an atmospheric general circulation model, Community Atmospheric Model, version 3 (CAM3). The model experiments are conducted by specifying the observed time-varying SSTs and atmospheric radiative quantities individually and in combination. This approach allows the authors to isolate the direct impact of each type of forcing agent as well as to evaluate their combined effect and the degree to which their impacts are additive. CAM3 realistically simulates the global patterns of sea level pressure and 500-hPa geopotential height trends when both forcings are specified. SST forcing and direct atmospheric radiative forcing drive distinctive circulation responses that contribute about equally to the global pattern of circulation trends. These distinctive circulation responses are approximately additive and partially offsetting. Atmospheric radiative changes directly drive the strengthening and poleward shift of the midlatitude westerly winds in the Southern Hemisphere (and to a lesser extent may contribute to those over the Atlantic–Eurasian sector in the Northern Hemisphere), whereas SST trends (specifically those in the tropics) are responsible for the intensification of the Aleutian low and weakening of the tropical Walker circulation. Discrepancies between the atmospheric circulation trends simulated by CAM3 and Community Climate System Model, version 3 (CCSM3), a coupled model driven by the same atmospheric radiative forcing as CAM3, are traced to differences in their tropical SST trends: in particular, a 60% weaker warming of the tropical Indo-Pacific in the CCSM3 ensemble mean than in nature.

This study examines the contribution of tropical sea surface temperature (SST) forcing to the 1976/77 climate transition of the winter atmospheric circulation over the North Pacific using a combined observational and modeling approach. The National Center for Atmospheric Research (NCAR) Community Atmospheric Model version 3 (CAM3) simulates approximately 75% of the observed 4-hPa deepening of the wintertime Aleutian low from 1950–76 to 1977–2000 when forced with the observed evolution of tropical SSTs in a 10-member ensemble average. This response is driven by precipitation increases over the western half of the equatorial Pacific Ocean. In contrast, the NCAR Community Climate Model version 3 (CCM3), the predecessor to CAM3, simulates no significant change in the strength of the Aleutian low when forced with the same tropical SSTs in a 12-member ensemble average. The lack of response in CCM3 is traced to an erroneously large precipitation increase over the tropical Indian Ocean whose dynamical impact is to weaken the Aleutian low; this, when combined with the response to rainfall increases over the western and central equatorial Pacific, results in near-zero net change in the strength of the Aleutian low. The observed distribution of tropical precipitation anomalies associated with the 1976/77 transition, estimated from a combination of direct measurements at land stations and indirect information from surface marine cloudiness and wind divergence fields, supports the models’ simulated rainfall increases over the western half of the Pacific but not the magnitude of CCM3’s rainfall increase over the Indian Ocean.

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.

Simulations of the El Niño–Southern Oscillation (ENSO) phenomenon and tropical Atlantic climate variability in the newest version of the Community Climate System Model [version 3 (CCSM3)] are examined in comparison with observations and previous versions of the model. The analyses are based upon multicentury control integrations of CCSM3 at two different horizontal resolutions (T42 and T85) under present-day CO2 concentrations. Complementary uncoupled integrations with the atmosphere and ocean component models forced by observed time-varying boundary conditions allow an assessment of the impact of air–sea coupling upon the simulated characteristics of ENSO and tropical Atlantic variability. The amplitude and zonal extent of equatorial Pacific sea surface temperature variability associated with ENSO is well simulated in CCSM3 at both resolutions and represents an improvement relative to previous versions of the model. However, the period of ENSO remains too short (2–2.5 yr in CCSM3 compared to 2.5–8 yr in observations), and the sea surface temperature, wind stress, precipitation, and thermocline depth responses are too narrowly confined about the equator. The latter shortcoming is partially overcome in the atmosphere-only and ocean-only simulations, indicating that coupling between the two model components is a contributing cause. The relationships among sea surface temperature, thermocline depth, and zonal wind stress anomalies are consistent with the delayed/recharge oscillator paradigms for ENSO. We speculate that the overly narrow meridional scale of CCSM3's ENSO simulation may contribute to its excessively high frequency. The amplitude and spatial pattern of the extratropical atmospheric circulation response to ENSO is generally well simulated in the T85 version of CCSM3, with realistic impacts upon surface air temperature and precipitation; the simulation is not as good at T42. CCSM3's simulation of interannual climate variability in the tropical Atlantic sector, including variability intrinsic to the basin and that associated with the remote influence of ENSO, exhibits similarities and differences with observations. Specifically, the observed counterpart of El Niño in the equatorial Atlantic is absent from the coupled model at both horizontal resolutions (as it was in earlier versions of the coupled model), but there are realistic (although weaker than observed) SST anomalies in the northern and southern tropical Atlantic that affect the position of the local intertropical convergence zone, and the remote influence of ENSO is similar in strength to observations, although the spatial pattern is somewhat different.

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.

Diagnostics combining atmospheric reanalysis and station-based temperature data for 1950–2003 indicate that European heat waves can be associated with the occurrence of two specific summertime atmospheric circulation regimes. Evidence is presented that during the record warm summer of 2003, the excitation of these two regimes was significantly favored by the anomalous tropical Atlantic heating related to wetter-than-average conditions in both the Caribbean basin and the Sahel. Given the persistence of tropical Atlantic climate anomalies, their seasonality, and their associated predictability, the suggested tropical–extratropical Atlantic connection is encouraging for the prospects of long-range forecasting of extreme weather in Europe.

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 wintertime atmospheric circulation responses to observed patterns of North Atlantic sea surface temperature and sea ice cover trends in recent decades are studied by means of experiments with an atmospheric general circulation model. Here the relationship between the forced responses and the dominant pattern of internally generated atmospheric variability is focused on. The total response is partioned into a portion that projects onto the leading mode of internal variability (the indirect response) and a portion that is the residual from that projection (the direct response). This empirical decomposition yields physically meaningful patterns whose distinctive horizontal and vertical structures imply different governing mechanisms. The indirect response, which dominates the total geopotential height response, is hemispheric in scale with resemblance to the North Atlantic Oscillation or Northern Hemisphere annular mode, and equivalent barotropic in the vertical from the surface to the tropopause. In contrast. the direct response is localized to the vicinity of the surface thermal anomaly (SST or sea ice) and exhibits a baroclinic structure in the vertical, with a surface trough and upper-level ridge in the case of a positive heating anomaly, consistent with theoretical models of the linear baroclinic response to extratropical thermal forcing. Both components of the response scale linearly with respect to the amplitude of the forcing but nonlinearly with respect to the polarity of the forcing. The deeper vertical penetration of anomalous heating compared to cooling is suggested to play a role in the nonlinearity of the response to SST forcing. © 2004 American Meteorological Society.

[1] The loss of Arctic sea ice has wide-ranging impacts, some of which are readily apparent and some of which remain obscure. For example, recent observational studies suggest that terrestrial snow cover may be affected by decreasing sea ice. Here, we examine a possible causal link between Arctic sea ice and Siberian snow cover during the past 3 decades using a suite of experiments with the National Center for Atmospheric Research Community Atmospheric Model version 3. The experiments were designed to isolate the influence of surface conditions within the Arctic Ocean from other forcing agents such as low-latitude sea surface temperatures and direct radiative effects of increasing greenhouse gases. Only those experiments that include the observed evolution of Arctic sea ice and sea surface temperatures result in increased snow depth over Siberia, while those that maintain climatological values for Arctic Ocean conditions result in no snow signal over Siberia. In the former, Siberian precipitation and air temperature both increase, but because surface air temperatures remain below freezing during most months, the snowpack thickens over this region. These results suggest that Arctic Ocean surface forcing is necessary and sufficient to induce a Siberian snow signal, and that other forcings in combination can modulate the strength and geographic extent of the response.

Understanding the historical and future response of the global climate system to anthropogenic emissions of radiatively active atmospheric constituents has become a timely and compelling concern. At present, however, there are uncertainties in: the total radiative forcing associated with changes in the chemical composition of the atmosphere; the effective forcing applied to the climate system resulting from a (temporary) reduction via ocean-heat uptake; and the strength of the climate feedbacks that subsequently modify this forcing. Here a set of analyses derived from atmospheric general circulation model simulations are used to estimate the effective and total radiative forcing of the observed climate system due to anthropogenic emissions over the last 50 years of the twentieth century. They are also used to estimate the sensitivity of the observed climate system to these emissions, as well as the expected change in global surface temperatures once the climate system returns to radiative equilibrium. Results indicate that estimates of the effective radiative forcing and total radiative forcing associated with historical anthropogenic emissions differ across models. In addition estimates of the historical sensitivity of the climate to these emissions differ across models. However, results suggest that the variations in climate sensitivity and total climate forcing are not independent, and that the two vary inversely with respect to one another. As such, expected equilibrium temperature changes, which are given by the product of the total radiative forcing and the climate sensitivity, are relatively constant between models, particularly in comparison to results in which the total radiative forcing is assumed constant. Implications of these results for projected future climate forcings and subsequent responses are also discussed.

Atmospheric general circulation model experiments are conducted to quantify the contribution of internal oceanic variability in the form of tropical instability waves (TIWs) to interannual wind and rainfall variability in the tropical Pacific. It is found that in the tropical Pacific, along the equator, and near 25°N and 25°S, TIWs force a significant increase in wind and rainfall variability from interseasonal to interannual time scales. Because of the stochastic nature of TIWs, this means that climate models that do not take them into account will underestimate the strength and number of extreme events and may overestimate forecast capability.