Trotechaud, Sandrine, Bruno Tremblay, James Williams, Joy Romanski, Anastasia Romanou, Mitchell Bushuk, William J Merryfield, and Rym Msadek, April 2024: Predictability of the minimum sea ice extent from winter Fram Strait Ice Area Export: Model versus observations. Journal of Climate, 37(7), DOI:10.1175/JCLI-D-22-0931.12361-2377. Abstract
Observations show predictive skill of the minimum sea ice extent (Min SIE) from late winter anomalous offshore ice drift along the Eurasian coastline, leading to local ice thickness anomalies at the onset of the melt season—a signal then amplified by the ice–albedo feedback. We assess whether the observed seasonal predictability of September sea ice extent (Sept SIE) from Fram Strait Ice Area Export (FSIAE; a proxy for Eurasian coastal divergence) is present in global climate model (GCM) large ensembles, namely the CESM2-LE, GISS-E2.1-G, FLOR-LE, CNRM-CM6-1, and CanESM5. All models show distinct periods where winter FSIAE anomalies are negatively correlated with the May sea ice thickness (May SIT) anomalies along the Eurasian coastline, and the following Sept Arctic SIE, as in observations. Counterintuitively, several models show occasional periods where winter FSIAE anomalies are positively correlated with the following Sept SIE anomalies when the mean ice thickness is large, or late in the simulation when the sea ice is thin, and/or when internal variability increases. More important, periods with weak correlation between winter FSIAE and the following Sept SIE dominate, suggesting that summer melt processes generally dominate over late-winter preconditioning and May SIT anomalies. In general, we find that the coupling between the winter FSIAE and ice thickness anomalies along the Eurasian coastline at the onset of the melt season is a ubiquitous feature of GCMs and that the relationship with the following Sept SIE is dependent on the mean Arctic sea ice thickness.
Seasonal predictions of Arctic sea ice on regional spatial scales are a pressing need for a broad group of stakeholders, however, most assessments of predictability and forecast skill to date have focused on pan-Arctic sea–ice extent (SIE). In this work, we present the first direct comparison of perfect model (PM) and operational (OP) seasonal prediction skill for regional Arctic SIE within a common dynamical prediction system. This assessment is based on two complementary suites of seasonal prediction ensemble experiments performed with a global coupled climate model. First, we present a suite of PM predictability experiments with start dates spanning the calendar year, which are used to quantify the potential regional SIE prediction skill of this system. Second, we assess the system’s OP prediction skill for detrended regional SIE using a suite of retrospective initialized seasonal forecasts spanning 1981–2016. In nearly all Arctic regions and for all target months, we find a substantial skill gap between PM and OP predictions of regional SIE. The PM experiments reveal that regional winter SIE is potentially predictable at lead times beyond 12 months, substantially longer than the skill of their OP counterparts. Both the OP and PM predictions display a spring prediction skill barrier for regional summer SIE forecasts, indicating a fundamental predictability limit for summer regional predictions. We find that a similar barrier exists for pan-Arctic sea–ice volume predictions, but is not present for predictions of pan-Arctic SIE. The skill gap identified in this work indicates a promising potential for future improvements in regional SIE predictions.
Dynamical prediction systems have shown potential to meet the emerging need for seasonal forecasts of regional Arctic sea ice. Observationally constrained initial conditions are a key source of skill for these predictions, but the direct influence of different observation types on prediction skill has not yet been systematically investigated. In this work, we perform a hierarchy of Observing System Experiments with a coupled global data assimilation and prediction system to assess the value of different classes of oceanic and atmospheric observations for seasonal sea-ice predictions in the Barents Sea. We find notable skill improvements due to the inclusion of both sea-surface temperature (SST) satellite observations and subsurface conductivity-temperature-depth (CTD) measurements. The SST data is found to provide the crucial source of interannual variability, whereas the CTD data primarily provide climatological and trend improvements. Analysis of the Barents Sea ocean heat budget suggests that ocean heat content anomalies in this region are driven by surface heat fluxes on seasonal timescales.
Castruccio, Frederic, Yohan Ruprich-Robert, Stephen G Yeager, Gokhan Danabasoglu, Rym Msadek, and Thomas L Delworth, March 2019: Modulation of Arctic Sea Ice Loss by Atmospheric Teleconnections from Atlantic Multi-Decadal Variability. Journal of Climate, 32(5), DOI:10.1175/JCLI-D-18-0307.1. Abstract
Observed September Arctic sea ice has declined sharply over the satellite era. While most climate models forced by observed external forcing simulate a decline, few show trends matching the observations, suggesting either model deficiencies or significant contributions from internal variability. Using a set of perturbed climate model experiments, we provide evidence that atmospheric teleconnections associated with the Atlantic Multi-Decadal Variability (AMV) can drive low-frequency Arctic sea ice fluctuations. Even without AMV–related changes in ocean heat transport, AMV–like surface temperature anomalies lead to adjustments in atmospheric circulation patterns that produce similar Arctic sea ice changes in three different climate models. Positive AMV anomalies induce a decrease in the frequency of winter polar anticyclones, which is reflected both in the sea level pressure as a weakening of the Beaufort Sea High and in the surface temperature as warm anomalies in response to increased low-cloud cover. Positive AMV anomalies are also shown to favor an increased prevalence of an Arctic Dipole–like sea level pressure pattern in late winter / early spring. The resulting anomalous winds drive anomalous ice motions (dynamic effect). Combined with the reduced winter sea ice formation (thermodynamic effect), the Arctic sea ice becomes thinner, younger, and more prone to melt in summer. Following a phase shift to positive AMV, the resulting atmospheric teleconnections can lead to a decadal ice thinning trend in the Arctic Ocean of the order of 8-16% of the reconstructed long-term trend, and decadal trend (decline) in September Arctic sea ice area of up to 21% of the observed long-term trend.
The Caribbean low-level jet (CLLJ) is an important component of the atmospheric circulation over the Intra-Americas Sea (IAS) which impacts the weather and climate both locally and remotely. It influences the rainfall variability in the Caribbean, Central America, northern South America, the tropical Pacific and the continental Unites States through the transport of moisture. We make use of high-resolution coupled and uncoupled models from the Geophysical Fluid Dynamics Laboratory (GFDL) to investigate the simulation of the CLLJ and its teleconnections and further compare with low-resolution models. The high-resolution coupled model FLOR shows improvements in the simulation of the CLLJ and its teleconnections with rainfall and SST over the IAS compared to the low-resolution coupled model CM2.1. The CLLJ is better represented in uncoupled models (AM2.1 and AM2.5) forced with observed sea-surface temperatures (SSTs), emphasizing the role of SSTs in the simulation of the CLLJ. Further, we determine the forecast skill for observed rainfall using both high- and low-resolution predictions of rainfall and SSTs for the July–August–September season. We determine the role of statistical correction of model biases, coupling and horizontal resolution on the forecast skill. Statistical correction dramatically improves area-averaged forecast skill. But the analysis of spatial distribution in skill indicates that the improvement in skill after statistical correction is region dependent. Forecast skill is sensitive to coupling in parts of the Caribbean, Central and northern South America, and it is mostly insensitive over North America. Comparison of forecast skill between high and low-resolution coupled models does not show any dramatic difference. However, uncoupled models show improvement in the area-averaged skill in the high-resolution atmospheric model compared to lower resolution model. Understanding and improving the forecast skill over the IAS has important implications for highly vulnerable nations in the region.
Ruprich-Robert, Yohan, Thomas L Delworth, and Rym Msadek, et al., May 2018: Impacts of the Atlantic Multidecadal Variability on North American Summer Climate and Heat Waves. Journal of Climate, 31(9), DOI:10.1175/JCLI-D-17-0270.1. Abstract
The impacts of the Atlantic Multidecadal Variability (AMV) on summertime North American climate are investigated using three Coupled Global Climate Models (CGCMs) in which North Atlantic sea surface temperatures (SSTs) are restored to observed AMV anomalies. Large ensemble simulations are performed to estimate how AMV can modulate the occurrence of extreme weather like heat waves. We show that, in response to an AMV warming, all models simulate a precipitation deficit and a warming over northern Mexico and southern US that lead to an increased number of heat wave days by about 30% compared to an AMV cooling. The physical mechanisms associated with these impacts are discussed. The positive tropical Atlantic SST anomalies associated with the warm AMV drive a Matsuno-Gill-like atmospheric response that favors subsidence over northern Mexico and southern US. This leads to a warming of the whole tropospheric column, and to a decrease in relative humidity, cloud cover, and precipitation. Soil moisture response to AMV also plays a role in the modulation of heat wave occurrence. An AMV warming favors dry soil conditions over northern Mexico and southern US by driving year-round precipitation deficit through atmospheric teleconnections coming both directly from the North Atlantic SST forcing and indirectly from the Pacific. The indirect AMV teleconnections highlight the importance of using CGCMs to fully assess the AMV impacts on North America. Given the potential predictability of the AMV, the teleconnections discussed here suggest a source of predictability for the North American climate variability and in particular for the occurrence of heat waves at multi-year timescales.
Due to its persistence on seasonal timescales, Arctic sea-ice thickness (SIT) is a potential source of predictability for summer sea-ice extent (SIE). New satellite observations of SIT represent an opportunity to harness this potential predictability via improved thickness initialization in seasonal forecast systems. In this work, the evolution of Arctic sea-ice volume anomalies is studied using a 700-year control integration and a suite of initialized ensemble forecasts from a fully-coupled global climate model. Our analysis is focused on the September sea-ice zone, as this is the region where thickness anomalies have the potential to impact the SIE minimum. The primary finding of this paper is that, in addition to a general decay with time, sea-ice volume anomalies display a summer enhancement, in which anomalies tend to grow between the months of May and July. This summer enhancement is relatively symmetric for positive and negative volume anomalies and peaks in July regardless of the initial month. Analysis of the surface energy budget reveals that the summer volume anomaly enhancement is driven by a positive feedback between the SIT state and the surface albedo. The SIT state affects surface albedo through changes in the sea-ice concentration field, melt-onset date, snow coverage, and ice-thickness distribution, yielding an anomaly in the total absorbed shortwave radiation between May and August, which enhances the existing SIT anomaly. This phenomenon highlights the crucial importance of accurate SIT initialization and representation of ice-albedo feedback processes in seasonal forecast systems.
Recent Arctic sea ice seasonal prediction efforts and forecast skill assessments have primarily focused on pan-Arctic sea-ice extent (SIE). In this work, we move towards stakeholder-relevant spatial scales, investigating the regional forecast skill of Arctic sea ice in a Geophysical Fluid Dynamics Laboratory (GFDL) seasonal prediction system. Using a suite of retrospective initialized forecasts spanning 1981–2015 made with a coupled atmosphere-ocean-sea ice-land model, we show that predictions of detrended regional SIE are skillful at lead times up to 11 months. Regional prediction skill is highly region and target month dependent, and generically exceeds the skill of an anomaly persistence forecast. We show for the first time that initializing the ocean subsurface in a seasonal prediction system can yield significant regional skill for winter SIE. Similarly, as suggested by previous work, we find that sea-ice thickness initial conditions provide a crucial source of skill for regional summer SIE.
García-Serrano, J, C Frankignoul, M P King, A Arribas, Y Gao, Virginie Guemas, D Matei, and Rym Msadek, et al., October 2017: Multi-model assessment of linkages between eastern Arctic sea-ice variability and the Euro-Atlantic atmospheric circulation in current climate. Climate Dynamics, 49(7-8), DOI:10.1007/s00382-016-3454-3. Abstract
A set of ensemble integrations from the Coupled Model Intercomparison Project phase 5, with historical forcing plus RCP4.5 scenario, are used to explore if state-of-the-art climate models are able to simulate previously reported linkages between sea-ice concentration (SIC) anomalies over the eastern Arctic, namely in the Greenland–Barents–Kara Seas, and lagged atmospheric circulation that projects on the North Atlantic Oscillation (NAO)/Arctic Oscillation (AO). The study is focused on variability around the long-term trends, so that all anomalies are detrended prior to analysis; the period of study is 1979–2013. The model linkages are detected by applying maximum covariance analysis. As also found in observational data, all the models considered here show a statistically significant link with sea-ice reduction over the eastern Arctic followed by a negative NAO/AO-like pattern. If the simulated relationship is found at a lag of one month, the results suggest that a stratospheric pathway could be at play as the driving mechanism; in observations this is preferentially shown for SIC in November. The interference of a wave-like anomaly over Eurasia, accompanying SIC changes, with the climatological wave pattern appears to be key in setting the mediating role of the stratosphere. On the other hand, if the simulated relationship is found at a lag of two months, the results suggest that tropospheric dynamics are dominant, presumably due to transient eddy feedback; in observations this is preferentially shown for SIC in December. The results shown here and previous evidence from atmosphere-only experiments emphasize that there could be a detectable influence of eastern Arctic SIC variability on mid-latitude atmospheric circulation anomalies. Even if the mechanisms are robust among the models, the timing of the simulated linkages strongly depends on the model and does not generally mimic the observational ones. This implies that the atmospheric sensitivity to sea-ice changes largely depends on the mean-flow and parameterizations, which could lead to misleading conclusions elsewhere if a multi-model ensemble-mean approach is adopted. It might also represent an important source of uncertainty in climate prediction and projection. Modelling efforts are hence further required to improve representation of the background atmospheric circulation and reduce biases, in order to attain more accurate covariability.
Ruprich-Robert, Yohan, Rym Msadek, Frederic Castruccio, Stephen G Yeager, Thomas L Delworth, and Gokhan Danabasoglu, April 2017: Assessing the Climate impacts of the observed Atlantic Mulitdecadal Variability using the GFDL CM2.1 and NCAR CESM1 Global Coupled Models. Journal of Climate, 30(8), DOI:10.1175/JCLI-D-16-0127.1. Abstract
The climate impacts of the observed Atlantic Multidecadal Variability (AMV) are investigated using the GFDL-CM2.1 and the NCAR-CESM1 coupled climate models. The model North Atlantic sea surface temperatures are restored to fixed anomalies corresponding to an estimate of the internally driven component of the observed AMV. Both models show that during boreal summer the AMV alters the Walker Circulation and generates precipitation anomalies over the whole tropical belt. A warm phase of the AMV yields reduced precipitation over western US, drier conditions over the Mediterranean basin, and wetter conditions over Northern Europe. During boreal winter, the AMV modulates by a factor of ~2 the frequency of occurrence of El Niño/La Niña events. This response is associated with anomalies over the Pacific that project onto the Interdecadal Pacific Oscillation pattern, i.e., Pacific Decadal Oscillation-like anomalies in the Northern hemisphere and a symmetrical pattern in the Southern Hemisphere. This winter response is a lagged adjustment of the Pacific Ocean to the AMV forcing in summer. Most of the simulated global-scale impacts are driven by the tropical part of the AMV, except for the winter North Atlantic Oscillation-like response over the North Atlantic/European region, which is driven by both the subpolar and the tropical parts of the AMV. The teleconnections between the Pacific and Atlantic basins alter the direct North Atlantic local response to the AMV, which highlights the importance of using a global coupled framework to investigate the climate impacts of the AMV. The similarity of the two model responses gives confidence that impacts described in this paper are robust.
Tommasi, Desiree, Charles A Stock, A J Hobday, R Methot, Isaac C Kaplan, J P Eveson, Kirstin Holsman, Timothy J Miller, Sarah K Gaichas, Marion Gehlen, A Pershing, Gabriel A Vecchi, Rym Msadek, Thomas L Delworth, C M Eakin, Melissa A Haltuch, Roland Séférian, C M Spillman, J R Hartog, Samantha A Siedlecki, Jameal F Samhouri, Barbara A Muhling, R G Asch, Malin L Pinsky, Vincent S Saba, Sarah B Kapnick, and Carlos F Gaitán, et al., March 2017: Managing living marine resources in a dynamic environment: The role of seasonal to decadal climate forecasts. Progress in Oceanography, 152, DOI:10.1016/j.pocean.2016.12.011. Abstract
Recent developments in global dynamical climate prediction systems have allowed for skillful predictions of climate variables relevant to living marine resources (LMRs) at a scale useful to understanding and managing LMRs. Such predictions present opportunities for improved LMR management and industry operations, as well as new research avenues in fisheries science. LMRs respond to climate variability via changes in physiology and behavior. For species and systems where climate-fisheries links are well established, forecasted LMR responses can lead to anticipatory and more effective decisions, benefitting both managers and stakeholders. Here, we provide an overview of climate prediction systems and advances in seasonal to decadal prediction of marine-resource relevant environmental variables. We then describe a range of climate-sensitive LMR decisions that can be taken at lead-times of months to decades, before highlighting a range of pioneering case studies using climate predictions to inform LMR decisions. The success of these case studies suggests that many additional applications are possible. Progress, however, is limited by observational and modeling challenges. Priority developments include strengthening of the mechanistic linkages between climate and marine resource responses, development of LMR models able to explicitly represent such responses, integration of climate driven LMR dynamics in the multi-driver context within which marine resources exist, and improved prediction of ecosystem-relevant variables at the fine regional scales at which most marine resource decisions are made. While there are fundamental limits to predictability, continued advances in these areas have considerable potential to make LMR managers and industry decision more resilient to climate variability and help sustain valuable resources. Concerted dialog between scientists, LMR managers and industry is essential to realizing this potential.
Boer, G J., D M Smith, Christophe Cassou, Francisco J Doblas-Reyes, Gokhan Danabasoglu, Ben P Kirtman, Y Kushnir, M Kimoto, Gerald A Meehl, and Rym Msadek, et al., October 2016: The Decadal Climate Prediction Project. Geoscientific Model Development Discussion, 9(10), DOI:10.5194/gmd-9-3751-2016. Abstract
The Decadal Climate Prediction Project (DCPP) is a coordinated multi-model investigation into decadal climate prediction, predictability, and variability. The DCPP makes use of past experience in simulating and predicting decadal variability and forced climate change gained from CMIP5 and elsewhere. It builds on recent improvements in models, in the reanalysis of climate data, in methods of initialization and ensemble generation, and in data treatment and analysis to propose an extended comprehensive decadal prediction investigation as part of CMIP6. The DCPP consists of three Components. Component A comprises the production and analysis of an extensive archive of retrospective forecasts to be used to assess and understand historical decadal prediction skill, as a basis for improvements in all aspects of end-to-end decadal prediction, and as a basis for forecasting on annual to decadal timescales. Component B undertakes ongoing production, dissemination and analysis of experimental quasi-real-time multi-model forecasts as a basis for potential operational forecast production. Component C involves the organization and coordination of case studies of particular climate shifts and variations, both natural and naturally forced (e.g. the "hiatus", volcanoes), including the study of the mechanisms that determine these behaviours. Groups are invited to participate in as many or as few of the Components of the DCPP, each of which are separately prioritized, as are of interest to them.
The Decadal Climate Prediction Project addresses a range of scientific issues involving the ability of the climate system to be predicted on annual to decadal timescales, the skill that is currently and potentially available, the mechanisms involved in long timescale variability, and the production of forecasts of benefit to both science and society.
Day, J J., Steffen Tietsche, Matthew Collins, William J Hurlin, Masao Ishii, S P E Keeley, D Matei, and Rym Msadek, et al., June 2016: The Arctic Predictability and Prediction on Seasonal-to-Interannual TimEscales (APPOSITE) data set. Geoscientific Model Development, DOI:10.5194/gmd-9-2255-2016. Abstract
Recent decades have seen significant developments in seasonal-to-interannual timescale climate prediction capabilities. However, until recently the potential of such systems to predict Arctic climate had not been assessed. This paper describes a multi-model predictability experiment which was run as part of the Arctic Predictability and Prediction On Seasonal to Inter-annual Timescales (APPOSITE) project. The main goal of APPOSITE was to quantify the timescales on which Arctic climate is predictable. In order to achieve this, a coordinated set of idealised initial-value predictability experiments, with seven general circulation models, was conducted. This was the first model intercomparison project designed to quantify the predictability of Arctic climate on seasonal to inter-annual timescales. Here we present a description of the archived data set (which is available at the British Atmospheric Data Centre) and an update of the project's results. Although designed to address Arctic predictability, this data set could also be used to assess the predictability of other
Tropical cyclone (TC) activity in the North Pacific and North Atlantic Oceans is known to be affected by the El Niño Southern Oscillation (ENSO). This study uses GFDL FLOR model, which has relatively high-resolution in the atmosphere, as a tool to investigate the sensitivity of TC activity to the strength of ENSO events. We show that TCs exhibit a non-linear response to the strength of ENSO in the tropical eastern North Pacific (ENP) but a quasi-linear response in the tropical western North Pacific (WNP) and tropical North Atlantic. Specifically, stronger El Niño results in disproportionate inhibition of TCs in the ENP and North Atlantic, and leads to an eastward shift in the location of TCs in the southeast of the WNP. However, the character of the response of TCs in the Pacific is insensitive to the amplitude of La Niña events. The eastward shift of TCs in the southeast of the WNP in response to a strong El Niño is due to an eastward shift of the convection and of the associated environmental conditions favorable for TCs. The inhibition of TC activity in the ENP and Atlantic during El Niño is attributed to the increase in the number of days with strong vertical wind shear during stronger El Niño events. These results are further substantiated with coupled model experiments. Understanding of the impact of strong ENSO on TC activity is important for present and future climate as the frequency of occurrence of extreme ENSO events is projected to increase in future.
This study demonstrates skillful seasonal prediction of 2m air temperature and precipitation over land in a new high-resolution climate model developed by Geophysical Fluid Dynamics Laboratory, and explores the possible sources of the skill. We employ a statistical optimization approach to identify the most predictable components of seasonal mean temperature and precipitation over land, and demonstrate the predictive skill of these components. First, we show improved skill of the high-resolution model over the previous lower-resolution model in seasonal prediction of NINO3.4 index and other aspects of interest. Then we measure the skill of temperature and precipitation in the high-resolution model for boreal winter and summer, and diagnose the sources of the skill. Lastly, we reconstruct predictions using a few most predictable components to yield more skillful predictions than the raw model predictions. Over three decades of hindcasts, we find that the two most predictable components of temperature are characterized by a component that is likely due to changes in external radiative forcing in boreal winter and summer, and an ENSO-related pattern in boreal winter. The most predictable components of precipitation in both seasons are very likely ENSO-related. These components of temperature and precipitation can be predicted with significant correlation skill at least 9 months in advance. The reconstructed predictions using only the first few predictable components from the model show considerably better skill relative to observations than raw model predictions. This study shows that the use of refined statistical analysis and a high-resolution dynamical model leads to significant skill in seasonal predictions of 2m air temperature and precipitation over land.
Keenlyside, N, Jin Ba, J Mecking, N-E Omrani, M Latif, Rong Zhang, and Rym Msadek, October 2015: North Atlantic Multi-Decadal Variability — Mechanisms and Predictability In Climate Change: Multidecadal and Beyond, DOI:10.1142/9789814579933_0009. Abstract
The North Atlantic Ocean undergoes pronounced basin-wide, multi-decadal variations. The corresponding fluctuations in sea surface temperature (SST) have become known as the Atlantic Multidecadal Oscillation (AMO) or Atlantic multidecadal variability (AMV). AMV is receiving increasing attention for three key reasons: (1) it has been linked to climate impacts of major socio-economic importance, such as Sahel rainfall; (2) it may temporarily mask anthropogenic global warming not only in the North Atlantic Sector, but over the Northern Hemisphere (NH); and (3) it appears to be predictable on decadal timescales. This chapter provides an overview of current understanding of AMV, summarizing proposed mechanisms, our ability to simulate and predict it, as well as challenges for future research.
This study investigates the seasonality of the relationship between the Great Plains low-level jet (GPLLJ) and the Pacific Ocean from spring to summer, using observational analysis and coupled model experiments. The observed GPLLJ and El Niño-Southern Oscillation (ENSO) relation undergoes seasonal changes with a stronger GPLLJ associated with La Niña in boreal spring and El Niño in boreal summer. The ability of the GFDL FLOR global coupled climate model, which has the high-resolution atmospheric and land components, to simulate the observed seasonality in the GPLLJ-ENSO relationship is assessed. The importance of simulating the magnitude and phase-locking of ENSO accurately in order to better simulate its seasonal teleconnections with the Intra-Americas Seas (IAS) is demonstrated. This study explores the mechanisms for seasonal changes in the GPLLJ-ENSO relation in model and observations. It is hypothesized that ENSO affects the GPLLJ variability through the Caribbean low-level jet (CLLJ) during the summer and spring seasons. These results suggest that climate models with improved ENSO variability would advance our ability to simulate and predict seasonal variations of the GPLLJ and their associated impacts on the United States.
We review past, present and future North Atlantic hurricane activity based on analysis of observational records and models projections. When adjusted for likely missed tropical cyclones, the observational record does not show any significant increase or decrease of North Atlantic hurricane frequency. Downscaling results for most available CMIP5 models show a decrease or little change in overall frequency of tropical storms and hurricanes, although in the Atlantic basin, previous studies by other investigators report a wider range of change (+/−60%). Some model projections of late 21st century hurricane activity indicate an increase in frequency of the strongest storms (category 4–5 hurricanes). The projected increase is substantial (+100% per century) in the CMIP3 ensemble model downscaling, but much smaller (+40%) and only marginally significant in the CMIP5 ensemble model downscaling. Rainfall rates in the inner core of the hurricanes are projected to increase with potentially a substantial damage impact. The largest source of uncertainty in predicting changes in Atlantic tropical storms activity over the first half of the 21st century arises from the internal variability of the climate system. Nonetheless, some of these natural fluctuations appear to be predictable beyond seasonal time scale. We review recent predictability assessment results based on two CMIP5 models. Initializing these models with observational estimates leads to encouraging results in predicting multi-year variations in North Atlantic hurricane frequency. However the short record and the persistent character of the time series limits the ability to confidently predict North Atlantic hurricane activity for now. Remaining model biases, despite the tremendous improvement over the recent decades, and the changing observational system make it an ongoing challenge to simulate past hurricane activity and project or predict its future behavior.
Perez, R C., M O Baringer, S Dong, S L Garzoli, G J Goni, R Lumpkin, C S Meinen, Rym Msadek, and U Rivero, March 2015: Measuring the Atlantic Meridional Overturning Circulation. Marine Technology Society Journal, 49(2), 167-177. Abstract
The Atlantic meridional overturning circulation (AMOC) plays a crucial role in redistributing heat and salt throughout the global oceans. Achieving a more complete understanding of the behavior of the AMOC system requires a comprehensive observational network that spans the entire Atlantic basin. This article describes several different types of observational systems that are used by scientists of the National Oceanographic and Atmospheric Administration and their partners at other national and international institutions to study the complex nature of the AMOC. The article also highlights several emerging technologies that will aid AMOC studies in the future.
The seasonal predictability of extratropical storm tracks in Geophysical Fluid Dynamics Laboratory (GFDL)’s high-resolution climate model has been investigated using an average predictability time analysis. The leading predictable components of extratropical storm tracks are ENSO-related spatial pattern for both boreal winter and summer, and the second predictable components are mostly due to changes in external radiative forcing and multidecadal oceanic variability. These two predictable components for both seasons show significant correlation skill for all leads from 0 to 9 months, while the skill of predicting the boreal winter storm track is consistently higher than that of the austral winter. The predictable components of extratropical storm tracks are dynamically consistent with the predictable components of the upper troposphere jet flow for both seasons. Over the region with strong storm track signals in North America, the model is able to predict the changes in statistics of extremes connected to storm track changes (e.g., extreme low and high sea level pressure and extreme 2m air temperature) in response to different ENSO phases. These results point towards the possibility of providing skillful seasonal predictions of the statistics of extratropical extremes over land using high-resolution coupled models.
Carton, J A., A Cunningham, Eleanor E Frajka-Williams, Young-Oh Kwon, David Marshall, and Rym Msadek, August 2014: The Atlantic Overturning Circulation: More Evidence of Variability and Links to Climate. Bulletin of the American Meteorological Society, 95(8), DOI:10.1175/BAMS-D-13-00234.1.
Deshayes, Julie, R G Curry, and Rym Msadek, May 2014: CMIP5 model inter-comparison of freshwater budget and circulation in the North Atlantic. Journal of Climate, 27(9), DOI:10.1175/JCLI-D-12-00700.1. Abstract
The subpolar North Atlantic is a center of variability of ocean properties, wind stress curl and air-sea exchanges. Observations and hindcast simulations suggest that from the early 1970s to the mid 1990s the subpolar gyre became fresher while the gyre and meridional circulations intensified. This is opposite to the relationship of freshening causing a weakened circulation, most often reproduced by climate models. We hypothesize that both these configurations exist but dominate on different timescales: (A) a fresher subpolar gyre when the circulation is more intense, at interannual frequencies, and (B) a saltier subpolar gyre when the circulation is more intense, at longer periods. Rather than going into the detail of the mechanisms sustaining each configuration, our objective is to identify which configuration dominates and to test whether this depends on frequency, in pre-industrial control runs of 5 climate models from the Coupled Model Inter-comparison Project phase 5 (CMIP5). To this end, we have developed a novel inter-comparison method that enables analysis of freshwater budget and circulation changes in a physical perspective that overcomes model specificities. Lag-correlations and a cross-spectral analysis between freshwater content changes and circulation indices validate our hypothesis, as configuration (A) is only visible at interannual frequencies, while configuration (B) is mostly visible at decadal and longer periods, suggesting that the driving role of salinity on the circulation depends on frequency. Overall, this analysis underscores the large differences among state-of-the-art climate models in their representations of the North Atlantic freshwater budget.
Gnanadesikan, Anand, John P Dunne, and Rym Msadek, May 2014: Connecting Atlantic Temperature Variability and Biological Cycling in two Earth System Models. Journal of Marine Systems, 133, DOI:10.1016/j.jmarsys.2013.10.003. Abstract
Connections between the interdecadal variability in North Atlantic temperatures and biological cycling have been widely hypothesized. However, it is unclear whether such connections are due to small changes in basin-averaged temperatures indicated by the Atlantic Multidecadal Oscillation (AMO) Index, or whether both biological cycling and the AMO index are causally linked to changes in the Atlantic Meridional Overturning Circulation (AMOC). We examine interdecadal variability in the annual and month-by-month diatom biomass in two Earth System Models with the same formulations of atmospheric, land, sea ice and ocean biogeochemical dynamics but different formulations of ocean physics and thus different AMOC structure and variability. In the isopycnal-layered ESM2G, strong interdecadal changes in surface salinity associated with changes in AMOC produce spatially heterogeneous variability in convection, nutrient supply and thus diatom biomass. These changes also produce changes in ice cover, shortwave absorption and temperature and hence the AMO Index. Off West Greenland, these changes are consistent with observed changes in fisheries and support climate as a causal driver.. In the level-coordinate ESM2M, nutrient supply is much higher and interdecadal changes in diatom biomass are much smaller in amplitude and not strongly linked to the AMO index.
This paper provides an update on research in the relatively new and fast moving field of decadal climate prediction, and addresses the use of decadal climate predictions not only for potential users of such information but also for improving our understanding of processes in the climate system. External forcing influences the predictions throughout, but their contributions to predictive skill become dominant after most of the improved skill from initialization with observations vanishes after about six to nine years. Recent multi-model results suggest that there is relatively more decadal predictive skill in the North Atlantic, western Pacific, and Indian Oceans than in other regions of the world oceans. Aspects of decadal variability of SSTs, like the mid-1970s shift in the Pacific, the mid-1990s shift in the northern North Atlantic and western Pacific, and the early-2000s hiatus, are better represented in initialized hindcasts compared to uninitialized simulations. There is evidence of higher skill in initialized multi-model ensemble decadal hindcasts than in single model results, with multi-model initialized predictions for near term climate showing somewhat less global warming than uninitialized simulations. Some decadal hindcasts have shown statistically reliable predictions of surface temperature over various land and ocean regions for lead times of up to 6–9 years, but this needs to be investigated in a wider set of models. As in the early days of El Niño-Southern Oscillation (ENSO) prediction, improvements to models will reduce the need for bias adjustment, and increase the reliability, and thus usefulness, of decadal climate predictions in the future.
Decadal prediction experiments were conducted as part of CMIP5 using the GFDL-CM2.1 forecast system. The abrupt warming of the North Atlantic subpolar gyre (SPG) that was observed in the mid 1990s is considered as a case study to evaluate our forecast capabilities and better understand the reasons for the observed changes. Initializing the CM2.1 coupled system produces high skill in retrospectively predicting the mid-90s shift, which is not captured by the uninitialized forecasts. All the hindcasts initialized in the early 90s show a warming of the SPG, however, only the ensemble mean hindcasts initialized in 1995 and 1996 are able to reproduce the observed abrupt warming and the associated decrease and contraction of the SPG. Examination of the physical mechanisms responsible for the successful retrospective predictions indicates that initializing the ocean is key to predict the mid 90s warming. The successful initialized forecasts show an increased Atlantic Meridional Overturning Circulation and North Atlantic current transport, which drive an increased advection of warm saline subtropical waters northward, leading to a westward shift of the subpolar front and subsequently a warming and spin down of the SPG. Significant seasonal climate impacts are predicted as the SPG warms, including a reduced sea-ice concentration over the Arctic, an enhanced warming over central US during summer and fall, and a northward shift of the mean ITCZ. These climate anomalies are similar to those observed during a warm phase of the Atlantic Multidecadal Oscillation, which is encouraging for future predictions of North Atlantic climate.
We present seasonal predictions of Arctic sea ice extent (SIE) over the 1982-2013 period using two suites of retrospective forecasts initialized from a fully coupled ocean-atmosphere-sea ice assimilation system. High skill scores are found in predicting year-to-year fluctuations of Arctic SIE, with significant correlations up to 7 month ahead for September detrended anomalies. Predictions over the recent era, which coincides with an improved observational coverage, outperform the earlier period for most target months. We find, however, a degradation of skill in September during the last decade, a period of sea ice thinning in observations. The two prediction models, CM2.1 and FLOR, share very similar ocean and ice component and initialization but differ by their atmospheric component. FLOR has improved climatological atmospheric circulation and sea ice mean state but its skill is overall similar to CM2.1 for most seasons, which suggests a key role for initial conditions in predicting seasonal SIE fluctuations.
Tietsche, Steffen, J J Day, Virginie Guemas, William J Hurlin, S P E Keeley, D Matei, and Rym Msadek, et al., February 2014: Seasonal to interannual Arctic sea-ice predictability in current GCMs. Geophysical Research Letters, 41(3), DOI:10.1002/2013GL058755. Abstract
We establish the first inter-model comparison of seasonal to interannual predictability of present-day Arctic climate by performing coordinated sets of idealized ensemble predictions with four state-of-the-art global climate models. For Arctic sea-ice extent and volume, there is potential predictive skill for lead times of up to three years, and potential prediction errors have similar growth rates and magnitudes across the models. Spatial patterns of potential prediction errors differ substantially between the models, but some features are robust. Sea-ice concentration errors are largest in the marginal ice zone, and in winter they are almost zero away from the ice edge. Sea-ice thickness errors are amplified along the coasts of the Arctic Ocean, an effect that is dominated by sea-ice advection. These results give an upper bound on the ability of current global climate models to predict important aspects of Arctic climate.
In our original paper (Vecchi et al., 2013, hereafter V13) we stated “the skill in the initialized forecasts comes in large part from the persistence of the mid-1990s shift by the initialized forecasts, rather than from predicting its evolution”. Smith et al (2013, hereafter S13) challenge that assertion, contending that DePreSys was able to make a successful retrospective forecast of that shift. We stand by our original assertion, and present additional analyses using output from DePreSys retrospective forecasts to support our assessment.
Tropical cyclones (TCs) are a hazard to life and property and a prominent element of the global climate system, therefore understanding and predicting TC location, intensity and frequency is of both societal and scientific significance. Methodologies exist to predict basin-wide, seasonally-aggregated TC activity months, seasons and even years in advance. We show that a newly developed high-resolution global climate model can produce skillful forecasts of seasonal TC activity on spatial scales finer than basin-wide, from months and seasons in advance of the TC season. The climate model used here is targeted at predicting regional climate and the statistics of weather extremes on seasonal to decadal timescales, and is comprised of high-resolution (50km×50km) atmosphere and land components, and more moderate resolution (~100km) sea ice and ocean components. The simulation of TC climatology and interannual variations in this climate model is substantially improved by correcting systematic ocean biases through “flux-adjustment.” We perform a suite of 12-month duration retrospective forecasts over the 1981-2012 period, after initializing the climate model to observationally-constrained conditions at the start of each forecast period – using both the standard and flux-adjusted versions of the model. The standard and flux-adjusted forecasts exhibit equivalent skill at predicting Northern Hemisphere TC season sea surface temperature, but the flux-adjusted model exhibits substantially improved basin-wide and regional TC activity forecasts, highlighting the role of systematic biases in limiting the quality of TC forecasts. These results suggest that dynamical forecasts of seasonally-aggregated regional TC activity months in advance are feasible.
Goddard, L M., Rym Msadek, and Thomas L Delworth, et al., January 2013: A verification framework for interannual-to-decadal predictions experiments. Climate Dynamics, 40(1-2), DOI:10.1007/s00382-012-1481-2. Abstract
Decadal predictions have a high profile in the climate science community and beyond, yet very little is known about their skill. Nor is there any agreed protocol for estimating their skill. This paper proposes a sound and coordinated framework for verification of decadal hindcast experiments. The framework is illustrated for decadal hindcasts tailored to meet the requirements and specifications of CMIP5 (Coupled Model Intercomparison Project phase 5). The chosen metrics address key questions about the information content in initialized decadal hindcasts. These questions are: (1) Do the initial conditions in the hindcasts lead to more accurate predictions of the climate, compared to un-initialized climate change projections? and (2) Is the prediction model’s ensemble spread an appropriate representation of forecast uncertainty on average? The first question is addressed through deterministic metrics that compare the initialized and uninitialized hindcasts. The second question is addressed through a probabilistic metric applied to the initialized hindcasts and comparing different ways to ascribe forecast uncertainty. Verification is advocated at smoothed regional scales that can illuminate broad areas of predictability, as well as at the grid scale, since many users of the decadal prediction experiments who feed the climate data into applications or decision models will use the data at grid scale, or downscale it to even higher resolution. An overall statement on skill of CMIP5 decadal hindcasts is not the aim of this paper. The results presented are only illustrative of the framework, which would enable such studies. However, broad conclusions that are beginning to emerge from the CMIP5 results include (1) Most predictability at the interannual-to-decadal scale, relative to climatological averages, comes from external forcing, particularly for temperature; (2) though moderate, additional skill is added by the initial conditions over what is imparted by external forcing alone; however, the impact of initialization may result in overall worse predictions in some regions than provided by uninitialized climate change projections; (3) limited hindcast records and the dearth of climate-quality observational data impede our ability to quantify expected skill as well as model biases; and (4) as is common to seasonal-to-interannual model predictions, the spread of the ensemble members is not necessarily a good representation of forecast uncertainty. The authors recommend that this framework be adopted to serve as a starting point to compare prediction quality across prediction systems. The framework can provide a baseline against which future improvements can be quantified. The framework also provides guidance on the use of these model predictions, which differ in fundamental ways from the climate change projections that much of the community has become familiar with, including adjustment of mean and conditional biases, and consideration of how to best approach forecast uncertainty.
Msadek, Rym, W E Johns, Stephen G Yeager, Gokhan Danabasoglu, Thomas L Delworth, and Anthony Rosati, June 2013: The Atlantic Meridional Heat transport at 26.5° N and its relationship with the MOC in the RAPID array and the GFDL and NCAR coupled models. Journal of Climate, 26(12), DOI:10.1175/JCLI-D-12-00081.1. Abstract
The link at 26.5° N between the Atlantic meridional heat transport (MHT) and the Atlantic meridional overturning circulation (MOC) is investigated in two climate models, GFDL CM2.1 and NCAR CCSM4, and compared with the recent observational estimates from the RAPID-MOCHA array. Despite a stronger than observed MOC magnitude, both models underestimate the mean MHT at 26.5° N due to an overly diffuse thermocline. Biases result from errors in both overturning and gyre components of the MHT. The observed linear relationship between MHT and MOC at 26.5° N is realistically simulated by the two models and is mainly due to the overturning component of the MHT. Fluctuations in overturning MHT are dominated by Ekman transport variability in CM2.1 and CCSM4, whereas baroclinic geostrophic transport variability plays a larger role in RAPID. CCSM4 which has a parameterization of Nordic Sea overflows and thus a more realistic North Atlantic Deep Water (NADW) penetration shows smaller biases in the overturning heat transport than CM2.1 due to deeper NADW at colder temperatures. The horizontal gyre heat transport and its sensitivity to the MOC are poorly represented in both models. The wind-driven gyre heat transport is northward in observations at 26.5° N whereas it is weakly southward in both models, reducing the total MHT. We emphasize model biases that are responsible for the too weak MHT, particularly at the western boundary. The use of direct MHT observations through RAPID allows us to identify the source of the too weak MHT in the two models, a bias shared by a number of CMIP5 coupled models.
Retrospective predictions of multi-year North Atlantic hurricane frequency are explored, by applying a hybrid statistical-dynamical forecast system to initialized and non-initialized multi-year forecasts of tropical Atlantic and tropical mean sea surface temperatures (SSTs) from two global climate model forecast systems. By accounting for impacts of initialization and radiative forcing, retrospective predictions of five-year mean and nine-year mean tropical Atlantic hurricane frequency show significant correlation relative to a null hypothesis of zero correlation. The retrospective correlations are increased in a two-model average forecast and by using a lagged-ensemble approach, with the two-model ensemble decadal forecasts hurricane frequency over 1961-2011 yielding correlation coefficients that approach 0.9.
These encouraging retrospective multi-year hurricane predictions, however, should be interpreted with care: although initialized forecasts have higher nominal skill than uninitialized ones, the relatively short record and large autocorrelation of the time series limits our confidence in distinguishing between the skill due to external forcing and that added by initialization. The nominal increase in correlation in the initialized forecasts relative to the uninitialized experiments is due to improved representation of the multi-year tropical Atlantic SST anomalies. The skill in the initialized forecasts comes in large part from the persistence of a mid-1990s shift by the initialized forecasts, rather than from predicting its evolution. Predicting shifts like that observed in 1994-1995 remains a critical issue for the success of multi-year forecasts of Atlantic hurricane frequency. The retrospective forecasts highlight the possibility that changes in observing system impact forecast performance.
The decadal predictability of sea surface temperature (SST) and 2m air temperature (T2m) in Geophysical Fluid Dynamics Laboratory (GFDL)'s decadal hindcasts, which are part of the Fifth Coupled Model Intercomparison Project experiments, has been investigated using an average predictability time (APT) analysis. Comparison of retrospective forecasts initialized using the GFDL's Ensemble Coupled Data Assimilation system with uninitialized historical forcing simulations using the same model, allows identification of internal multidecadal pattern (IMP) for SST and T2m. The IMP of SST is characterized by an inter-hemisphere dipole, with warm anomalies centered in the North Atlantic subpolar gyre region and North Pacific subpolar gyre region, and cold anomalies centered in the Antarctic Circumpolar Current region. The IMP of T2m is characterized by a general bi-polar seesaw, with warm anomalies centered in Greenland, and cold anomalies centered in Antarctica. The retrospective prediction skill of the initialized system, verified against independent observations, indicates that the IMP of SST may be predictable up to 4 (10) year lead time at 95% (90%) significance level, and the IMP of T2m may be predictable up to 2 (10) years at 95% (90%) significance level. The initialization of multidecadal variations of northward oceanic heat transport in the North Atlantic significantly improves the predictive skill of the IMP. The dominant roles of oceanic internal dynamics in decadal prediction are further elucidated by fixed-forcing experiments, in which radiative forcing is returned to 1961 values. These results point towards the possibility of meaningful decadal climate outlooks using dynamical coupled models, if they are appropriately initialized from a sustained climate observing system.
Identifying the prime drivers of the twentieth-century multidecadal variability in the Atlantic Ocean is crucial for predicting how the Atlantic will evolve in the coming decades and the resulting broad impacts on weather and precipitation patterns around the globe. Recently Booth et al (2012) showed that the HadGEM2-ES climate model closely reproduces the observed multidecadal variations of area-averaged North Atlantic sea surface temperature in the 20th century. The multidecadal variations simulated in HadGEM2-ES are primarily driven by aerosol indirect effects that modify net surface shortwave radiation. On the basis of these results, Booth et al (2012) concluded that aerosols are a prime driver of twentieth-century North Atlantic climate variability. However, here it is shown that there are major discrepancies between the HadGEM2-ES simulations and observations in the North Atlantic upper ocean heat content, in the spatial pattern of multidecadal SST changes within and outside the North Atlantic, and in the subpolar North Atlantic sea surface salinity. These discrepancies may be strongly influenced by, and indeed in large part caused by, aerosol effects. It is also shown that the aerosol effects simulated in HadGEM2-ES cannot account for the observed anti-correlation between detrended multidecadal surface and subsurface temperature variations in the tropical North Atlantic. These discrepancies cast considerable doubt on the claim that aerosol forcing drives the bulk of this multidecadal variability.
Matei et al. (Reports, 6 January 2012, p. 76) claim to show skillful multiyear predictions of the
Atlantic Meridional Overturning Circulation (AMOC). However, these claims are not justified,
primarily because the predictions of AMOC transport do not outperform simple reference forecasts
based on climatological annual cycles. Accordingly, there is no justification for the "confident"
prediction of a stable AMOC through 2014.
Msadek, Rym, C Frankignoul, and L Z-X Li, April 2011: Mechanisms of the atmospheric response to North Atlantic multidecadal variability: a model study. Climate Dynamics, 36(7-8), DOI:10.1007/s00382-010-0958-0. Abstract
The atmospheric circulation response to decadal fluctuations of the Atlantic meridional overturning circulation (MOC) in the IPSL climate model is investigated using the associated sea surface temperature signature. A SST anomaly is prescribed in sensitivity experiments with the atmospheric component of the IPSL model coupled to a slab ocean. The prescribed SST anomaly in the North Atlantic is the surface signature of the MOC influence on the atmosphere detected in the coupled simulation. It follows a maximum of the MOC by a few years and resembles the model Atlantic multidecadal oscillation. It is mainly characterized by a warming of the North Atlantic south of Iceland, and a cooling of the Nordic Seas. There are substantial seasonal variations in the geopotential height response to the prescribed SST anomaly, with an East Atlantic Pattern-like response in summer and a North Atlantic oscillation-like signal in winter. In summer, the response of the atmosphere is global in scale, resembling the climatic impact detected in the coupled simulation, albeit with a weaker amplitude. The zonally asymmetric or eddy part of the response is characterized by a trough over warm SST associated with changes in the stationary waves. A diagnostic analysis with daily data emphasizes the role of transient-eddy forcing in shaping and maintaining the equilibrium response. We show that in response to an intensified MOC, the North Atlantic storm tracks are enhanced and shifted northward during summer, consistent with a strengthening of the westerlies. However the anomalous response is weak, which suggests a statistically significant but rather modest influence of the extratropical SST on the atmosphere. The winter response to the MOC-induced North Atlantic warming is an intensification of the subtropical jet and a southward shift of the Atlantic storm track activity, resulting in an equatorward shift of the polar jet. Although the SST anomaly is only prescribed in the Atlantic ocean, significant impacts are found globally, indicating that the Atlantic ocean can drive a large scale atmospheric variability at decadal timescales. The atmospheric response is highly non-linear in both seasons and is consistent with the strong interaction between transient eddies and the mean flow. This study emphasizes that decadal fluctuations of the MOC can affect the storm tracks in both seasons and lead to weak but significant dynamical changes in the atmosphere.
Zhang, D, Rym Msadek, Michael J McPhaden, and Thomas L Delworth, April 2011: Multidecadal variability of the North Brazil Current and its connection to the Atlantic Meridional Overturning Circulation. Journal of Geophysical Research: Oceans, 116, C04012, DOI:10.1029/2010JC006812. Abstract
The North Brazil Current (NBC) connects the North and South Atlantic and is the major pathway for the surface return flow of the Atlantic meridional overturning circulation (AMOC). Here, we calculate the NBC geostrophic transport time series based on 5 decades of observations near the western boundary off the coast of Brazil. Results reveal a multidecadal NBC variability that lags Labrador Sea deep convection by a few years. The NBC transport time series is coherent with the Atlantic Multidecadal Oscillation in sea surface temperature, which also has been widely linked to AMOC fluctuations in previous modeling studies. Our results thus suggest that the observed multidecadal NBC transport variability is a useful indicator for AMOC variations. The suggested connection between the NBC and AMOC is assessed in a 700 year control simulation of the Geophysical Fluid Dynamics Laboratory's CM2.1 coupled climate model. The model results are in agreement with observations and further demonstrate that the variability of NBC transport is a good index for tracking AMOC variations. Concerning the debate about whether a slowdown of AMOC has already occurred under global warming, the observed NBC transport time series suggests strong multidecadal variability but no significant trend.
The North Atlantic is among the few places where decadal climate variations are considered potentially predictable. The physical mechanisms of the decadal variability are hypothesized to be associated with fluctuations of the Atlantic meridional overturning circulation (AMOC). Perfect model predictability experiments using the GFDL CM2.1 climate model are analyzed to investigate the potential predictability of the AMOC. Results indicate that the AMOC is predictable up to 20 years. We further connect AMOC predictability to readily observable fields. We show that modeled surface and subsurface signatures of AMOC variations defined by characteristic patterns of sea surface height, subsurface temperature, and upper ocean heat content anomalies, have a potential predictability similar to the AMOC's. Since we have longer observational records for these quantities than for direct measurements of the AMOC, our study highlights a potentially new promising method for monitoring AMOC variations, and hence assessing the predictability of the real climate system.
