Low-frequency Internal Variability Research Articles

Decadal to centennial extreme precipitation disaster gaps — Long-term variability and implications for extreme value modelling

A material consequence of climate change is the intensification of extreme precipitation in most regions across the globe. The respective trend signal is already detectable at global to regional scales, but long-term variability still dominates local observational records, which are the basis for extreme precipitation risk assessment. Whether the frequency of extreme events is purely random or subject to a low-frequency internal variability forcing is therefore highly relevant for modelling the expected number of extreme events in a typical observational record. Based on millennial climate simulations, we show that long-term variability is largely random, with no clear indication of low-frequency decadal to multidecadal variability. Nevertheless, extreme precipitation events occur highly irregularly, with potential clustering (11% probability of five or more 100-year events in 250 years) or long disaster gaps with no events (8% probability for no 100-year events in 250 years). Even for decadal precipitation records, a complete absence of any tail events is not unlikely, as, for example, in typical 70-year observational or reanalysis data, the probability is almost 50%. This generally causes return levels – a key metric for infrastructure codes or insurance pricing – to be underestimated. We also evaluate the potential of employing information across neighbouring locations, which substantially improves the estimation of return levels by increasing the robustness against potential adverse effects of long-term internal variability. The irregular occurrence of events makes it challenging to estimate return periods for planning and for extreme event attribution.

Internal multi-centennial variability of the Atlantic Meridional Overturning Circulation simulated by EC-Earth3

We report a multi-centennial oscillation of the Atlantic Meridional Overturning Circulation (AMOC) simulated by the EC-Earth3 climate model under the pre-industrial climate. This oscillation has an amplitude of ~ 6 Sv and a period of ~ 150 years and significantly impacts the atmosphere. We find that it is a self-sustained low-frequency internal variability, driven by the accumulation of salinity anomalies in the Arctic and their release into the North Atlantic, affecting the water column stability and the deep convection. Sea ice plays a major role in creating the salinity anomaly in the Arctic, while the anomalous Arctic oceanic circulation, which drives the exchange of liquid freshwater between the Arctic and the open ocean, is the main responsible for its southward propagation. Interestingly, EC-Earth3 simulations with increased greenhouse concentrations, and therefore under a warmer climate, do not exhibit these strong AMOC fluctuations. We hypothesize that in a quasi-equilibrium climate with a global air surface temperature 4.5° higher than the pre-industrial period, the low amount of sea ice in the high latitudes of the North Atlantic is no longer able to trigger the mechanism.

Slow Modes of Global Temperature Variability and Their Impact on Climate Sensitivity Estimates

AbstractInternal climate variability confounds estimates of the climate response to forcing but offers an opportunity to examine the dynamics controlling Earth’s energy budget. This study analyzes the time-evolving impact of modes of low-frequency internal variability on global-mean surface temperature (GMST) and top-of-atmosphere (TOA) radiation in preindustrial control simulations from phase 6 of the Coupled Model Intercomparison Project (CMIP6). The results show that the slow modes of variability with the largest impact on decadal GMST anomalies are focused in high-latitude ocean regions, where they have a minimal impact on global TOA radiation. When these regions warm, positive shortwave cloud and sea ice–albedo feedbacks largely cancel the negative feedback of outgoing longwave radiation, resulting in a weak net radiative feedback. As a consequence of the weak net radiative feedback, less energy is required to sustain these long-lived temperature anomalies. In contrast to these weakly radiating high-latitude modes, El Niño–Southern Oscillation (ENSO) has a large impact on the global energy budget, such that it remains the dominant influence on global TOA radiation out to decadal and longer time scales, despite its primarily interannual time scale. These results show that on decadal and longer time scales, different processes control internal variability in GMST than control internal variability in global TOA radiation. The results are used to quantify the impact of low-frequency internal variability and ENSO on estimates of climate sensitivity from historical GMST and TOA-radiative-imbalance anomalies.

Increased risk of near term global warming due to a recent AMOC weakening

Some of the new generation CMIP6 models are characterised by a strong temperature increase in response to increasing greenhouse gases concentration1. At first glance, these models seem less consistent with the temperature warming observed over the last decades. Here, we investigate this issue through the prism of low-frequency internal variability by comparing with observations an ensemble of 32 historical simulations performed with the IPSL-CM6A-LR model, characterized by a rather large climate sensitivity. We show that members with the smallest rates of global warming over the past 6-7 decades are also those with a large internally-driven weakening of the Atlantic Meridional Overturning Circulation (AMOC). This subset of members also matches several AMOC observational fingerprints, which are in line with such a weakening. This suggests that internal variability from the Atlantic Ocean may have dampened the magnitude of global warming over the historical era. Taking into account this AMOC weakening over the past decades means that it will be harder to avoid crossing the 2 °C warming threshold.

Internal Variability Role on Estimating Sea Level Acceleration in Fremantle Tide Gauge Station

Low frequency internal signals bring challenges to signify the role of anthropogenic factors in sea level rise and to attain a certain accuracy in trend and acceleration estimations. Due to both spatially and temporally poor coverage of the relevant data sets, identification of internal variability patterns is not straightforward. In this study, the identification and the role of low frequency internal variability (decadal and multidecadal) in sea level change of Fremantle tide gauge station is analyzed using two climate indices, Pacific Decadal Oscillation (PDO) and Tripole Interdecadal Pacific Oscillation (TPI). It is shown that the multidecadal sea level variability is anticorrelated with corresponding components of climate indices in the Pacific Ocean, with correlation coefficients of −0.9 and −0.76 for TPI and PDO, respectively. The correlations are comparatively low on decadal time scale, −0.5 for both indices. This shows that internal variability on decadal and multidecadal scales affects the sea level variation in Fremantle unequally and thus, separate terms are required in trajectory models. To estimate trend and acceleration in Fremantle, three trajectory models are tested. The first model is a simple second-degree polynomial comprising trend and acceleration terms. Low passed PDO, representing decadal and interdecadal variabilities in Pacific Ocean, added to the first model to form the second model. For the third model, decomposed signals of decadal and multidecadal variability of TPI are added to the first model. In overall, TPI represents the low frequency internal variability slightly better than PDO for sea level variation in Fremantle. Although the estimated trends do not change significantly, the estimated accelerations varies for the three models. The accelerations estimated from the first and second models are statistically insignificant, 0.006 ± 0.012 mm yr−2 and 0.01 ± 0.01 mm yr−2, respectively, while this figure for the third model is 0.018 ± 0.011 mm yr−2. The outcome exemplifies the importance of modelling low frequency internal variability in acceleration estimations for sea level rise in regional scale.

Characterizing and attributing the warming trend in sea and land surface temperatures

Because of low-frequency internal variability, the observed and underlying warming trends in temperature series can be markedly different. Important differences in the observed nonlinear trends in hemispheric temperature series suggest that the northern and southern hemispheres have responded differently to the changes in the radiative forcing. Using recent econometric techniques, we can reconcile such differences and show that all sea and land temperatures share similar time series properties and a common underlying warming trend having a dominant anthropogenic origin. We also investigate the interhemispheric temperature asymmetry (ITA) and show that the differences in warming between hemispheres are in part driven by anthropogenic forcing but that most of the observed rapid changes is likely due to natural variability. The attribution of changes in ITA is relevant since increases in the temperature contrast between hemispheres could potentially produce a shift in the Intertropical Convergence Zone and alter rainfall patterns. The existence of a current slowdown in the warming and its causes are also investigated. The results suggest that the slowdown is a common feature in global and hemispheric sea and land temperatures that can, at least partly, be attributed to changes in anthropogenic forcing.

Detection, Attribution, and Projection of Regional Rainfall Changes on (Multi-) Decadal Time Scales: A Focus on Southeastern South America

Abstract Observed austral summertime (November through April) rainfall in southeastern South America (SESA)—including northern Argentina, Uruguay, southern Brazil, and Paraguay—has exhibited substantial low-frequency variations with a multidecadal moistening trend during the twentieth century and a subsequent decadal drying trend during the current century. Understanding the mechanisms responsible for these variations is essential for predicting long-term rainfall changes. Here with a suite of attribution experiments using a pair of high-resolution global climate models, GFDL CM2.5 and FLOR-FA, the authors investigate the causes of these regional rainfall variations. Both models reproduce the twentieth-century moistening trend, albeit with a weaker magnitude than observed, in response to the radiative forcing associated with increasing greenhouse gases. The increasing greenhouse gases drive tropical expansion; consequently, the subtropical dry branch of Hadley cell moves away from SESA, leading to the rainfall increase. The amplitude discrepancy between the observed and simulated rainfall changes suggests a possible underestimation by the models of the atmospheric response to the radiative forcing, as well as an important role for low-frequency internal variability in the observed moistening trend. Over the current century, increasing greenhouse gases drive a continuous SESA rainfall increase in the models. However, the observed decadal rainfall decline is largely (~60%) reproduced in response to the observed Pacific trade wind strengthening, which is likely associated with natural Pacific decadal variability. These results suggest that the recent summertime rainfall decline in SESA is temporary and that the positive trend will resume in response to both increasing greenhouse gases and a return of Pacific trade winds to normal conditions.

Separating Internal Variability from the Externally Forced Climate Response

Abstract Separating low-frequency internal variability of the climate system from the forced signal is essential to better understand anthropogenic climate change as well as internal climate variability. Here both synthetic time series and the historical simulations from phase 5 of CMIP (CMIP5) are used to examine several methods of performing this separation. Linear detrending, as is commonly used in studies of low-frequency climate variability, is found to introduce large biases in both amplitude and phase of the estimated internal variability. Using estimates of the forced signal obtained from ensembles of climate simulations can reduce these biases, particularly when the forced signal is scaled to match the historical time series of each ensemble member. These so-called scaling methods also provide estimates of model sensitivities to different types of external forcing. Applying the methods to observations of the Atlantic multidecadal oscillation leads to different estimates of the phase of this mode of variability in recent decades.

Holocene climate change and its context for the future

pose a major challenge for reconstructing annual temperatures and comparing unlike datasets (e.g. Mix 2006), particularly during the Holocene when seasonal insolation changes were strong compared to other forcings that act across the year. Model simulations are likewise challenged by initiating glacial inceptions from insolation forcing and are limited by some weakly constrained forcing inputs, such as volcanic and solar activity. r esolving the Holocene temperature conundrum is important for understanding the forcing-response mechanisms during the current interglacial and for putting present and future climate into context, as the global temperature trend dictates to what extent today’s earth system has already exited the Holocene range (Fig. 1b). Variability Temperature, precipitation, and glacier variability at sub-millennial frequencies and in multiple regions was also discussed. Given the relatively small changes in climate during the Holocene, differentiating a meaningful climate signal from proxy or local noise was highlighted as a critical goal for accurately reconstructing Holocene variability. This issue is central to comparisons between data and model results, which currently disagree over the spectrum of regional variability. Models tend to suppress the regional-scale variability seen by proxies at multi-decadal and longer periods (Fig. 1c). This discrepancy suggests that models may not generate enough low frequency internal variability, thus limiting their ability to produce accurate simulations of climate at longer time scales (Laepple and Huybers 2014).

On forced temperature changes, internal variability, and the AMO

We estimate the low-frequency internal variability of Northern Hemisphere (NH) mean temperature using observed temperature variations, which include both forced and internal variability components, and several alternative model simulations of the (natural + anthropogenic) forced component alone. We then generate an ensemble of alternative historical temperature histories based on the statistics of the estimated internal variability. Using this ensemble, we show, first, that recent NH mean temperatures fall within the range of expected multidecadal variability. Using the synthetic temperature histories, we also show that certain procedures used in past studies to estimate internal variability, and in particular, an internal multidecadal oscillation termed the “Atlantic Multidecadal Oscillation” or “AMO”, fail to isolate the true internal variability when it is a priori known. Such procedures yield an AMO signal with an inflated amplitude and biased phase, attributing some of the recent NH mean temperature rise to the AMO. The true AMO signal, instead, appears likely to have been in a cooling phase in recent decades, offsetting some of the anthropogenic warming. Claims of multidecadal “stadium wave” patterns of variation across multiple climate indices are also shown to likely be an artifact of this flawed procedure for isolating putative climate oscillations.

Can oceanic reanalyses be used to assess recent anthropogenic changes and low-frequency internal variability of upper ocean temperature?

A multivariate analysis of the upper ocean thermal structure is used to examine the recent long-term changes and decadal variability in the upper ocean heat content as represented by model-based ocean reanalyses and a model-independent objective analysis. The three variables used are the mean temperature above the 14°C isotherm, its depth and a fixed depth mean temperature (250 m mean temperature). The mean temperature above the 14°C isotherm is a convenient, albeit simple, way to isolate thermodynamical changes by filtering out dynamical changes related to thermocline vertical displacements. The global upper ocean observations and reanalyses exhibit very similar warming trends (0.045°C per decade) over the period 1965–2005, superimposed with marked decadal variability in the 1970s and 1980s. The spatial patterns of the regression between indices (representative of anthropogenic changes and known modes of internal decadal variability), and the three variables associated with the ocean heat content are used as fingerprint to separate out the different contributions. The choice of variables provides information about the local heat absorption, vertical distribution and horizontal redistribution of heat, this latter being suggestive of changes in ocean circulation. The discrepancy between the objective analysis and the reanalyses, as well as the spread among the different reanalyses, are used as a simple estimate of ocean state uncertainties. Two robust findings result from this analysis: (1) the signature of anthropogenic changes is qualitatively different from those of the internal decadal variability associated to the Pacific Interdecadal Oscillation and the Atlantic Meridional Oscillation, and (2) the anthropogenic changes in ocean heat content do not only consist of local heat absorption, but are likely related with changes in the ocean circulation, with a clear shallowing of the tropical thermocline in the Pacific and Indian oceans.

The Influence of Ocean Convection Patterns on High-Latitude Climate Projections

Abstract The mean state and variability of deep convection in the ocean influence the North Atlantic climate. Using an ensemble experiment with a coupled atmosphere–ocean–sea ice model, it is shown that cooling and subdued warming areas can occur over the North Atlantic Ocean and adjacent landmasses under global warming. Different “present-day” convection patterns in the Greenland–Iceland–Norway (GIN) Sea result in different future surface-air temperature changes. At higher latitudes, the more effective positive sea ice feedback increases the likelihood of changes in convection causing a regional cooling that is larger than the warming brought about by the enhanced greenhouse effect. The modeled freshening of deep ocean layers in the North Atlantic in a time period preceding a reorganization of GIN Sea convection is consistent with recent observations. Low-frequency internal variability in the ocean model has relatively little impact on the response patterns.

Acoustic Detection of Greenhouse-induced Climate Changes in the Presence of Slow Fluctuations of the Thermohaline Circulation

Munk and Forbes have proposed to detect greenhouse gas-induced climate changes in the World Ocean with an array of long-range acoustic transmissions from Heard Island in the southern Indian Ocean. We estimated–assuming a continuously monitorable simplified axial ray propagation–the signal-to-noise ratio for such an experiment in an environment of slow fluctuations of the thermohaline circulation on a decadal time scale. The signal and noise are obtained from two coarse-resolution ocean general circulation model simulations. In the first, prescribed greenhouse atmospheric anomalies forced the ocean and yielded rough estimates of ocean response to greenhouse warming. In the second, some aspects of low-frequency internal variability of the ocean were obtained by stochastic forcing of the same ocean model. By this technique, no oscillations of the coupled ocean-atmosphere system Eke, for instance, El Niño-Southern Oscillation (ENSO) could be stimulated. Both signal and internal variability proved to be strongest at high latitudes, where the depth of the sound channel is small. At lower latitudes the signal is relatively weak, except for the western Atlantic. An array with an acoustic source near Heard Island would monitor primarily temperature changes in the near-surface layers of the Southern Ocean rather than in low-latitude intermediate depths. The trend detection probability for any single path came out to be weak, at least for a one-decade measuring interval. But using information from at least a two-decade interval and an array of receivers improved the detection probabilities substantially. Two different pattern detection strategies were tested: projecting the natural variability on the expected greenhouse signal and projecting the greenhouse signal onto the major components of the natural variability. Both techniques proved to give almost identical results.