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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 variability (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 to 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 to PDV when analyzing 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 to 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 were 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 statistical metrics of climate variability to provide a powerful tool for climate model evaluation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The canonical index of "Atlantic Multidecadal Variability" (AMV) is the low‐pass filtered timeseries of sea surface temperature anomalies (SSTA) averaged over the North Atlantic. This index and its associated SSTA spatial pattern confound externally forced climate change and internally generated climate variability. The internal component of AMV is commonly isolated by either subtracting the global‐mean SSTA or removing the pattern of SSTA associated with the global‐mean. This study evaluates the skill of each method with regard to the spatial pattern of internal AMV, using nine coupled model Large Ensembles over the period 1940–2100 as a testbed in which the true internal AMV is known a priori. The first method aliases the structure of forced climate change onto internal AMV, while the second method is generally robust to climate change. The models simulate realistic patterns of internal AMV, although such an assessment is hampered by the brevity of the observational record. Plain Language Summary: As anthropogenic climate change escalates, conventional methods aimed at isolating modes of natural climate variability from forced changes may be inadequate. This study considers the "Atlantic Multidecadal Oscillation" (also known as "Atlantic Multidecadal Variability"), a well‐known phenomenon canonically defined from the timeseries of sea surface temperatures averaged over the North Atlantic basin. A simple and widely used approach for removing the anthropogenic signal from this index and its associated spatial pattern is to subtract the global‐mean temperature at each location and time. However, this method aliases the pattern of forced climate change onto the pattern of natural AMV. A simple alternative approach based on subtracting the pattern of anthropogenic climate change associated with global‐mean temperature is much more successful at isolating the natural component of AMV within a background changing climate. The conclusions are based on evidence from nine different state‐of‐the‐art coupled climate model "large ensembles," which serve as methodological testbeds by providing robust estimates of the true structure of the models' natural of modes of variability under human‐induced climate change. The results have general implications for how modes of natural variability are defined in a future warming world. Key Points: A common index of Atlantic Multidecadal Variability (AMV) is low‐pass filtered North Atlantic minus global‐mean sea surface temperature (SST)This index aliases the structure of forced climate change onto the pattern of internal AMV in the futureAn alternative index based on removing the forced pattern of SST associated with the global‐mean is generally robust to climate change [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]