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Johanna Baehr

Abstract

The incorporation of local temperature and salinity observations from the Rapid Climate Change–Meridional Overturning Circulation and Heatflux Array (RAPID–MOCHA), as well as the cable estimates of volume transport in the Florida Current (FC), is tested in the Estimating the Circulation and Climate of the Ocean–Global Ocean Data Assimilation Experiment (ECCO–GODAE) estimation system for their impact on the estimate of the meridional overturning circulation (MOC) and the meridional heat transport in the Atlantic. An experimental setup covering the first deployment period of RAPID–MOCHA from March 2004 to March 2005 is used to test different strategies for incorporating these datasets. Incorporating both monthly means of the FC data and monthly means of the RAPID–MOCHA temperature and salinity measurements at the eastern and western boundaries of the basin as an observational constraint in a 1-yr experiment results in an adjustment to the reference estimate, which does not include these datasets, of approximately 1 Sv (1 Sv ≡ 106 m3 s−1) in the MOC at 26°N and the adjacent latitudes (approximately ±15°), with a larger northward branch of the MOC above 1000 m, compensated by a larger flow in the southward branch of the MOC between approximately 2000 and 3000 m. The meridional heat transport from 26°N to near 40°N is approximately 0.05 PW larger than in the reference experiment.

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Bente Tiedje, Armin Köhl, and Johanna Baehr

Abstract

This paper investigates the potential predictability of the meridional heat transport (MHT) in the North Atlantic on interannual time scales using hindcast ensembles based on an oceanic data assimilation product. The work analyzes the prognostic potential predictability (PPP), using the ocean synthesis of the German partner of the consortium for Estimating the Circulation and Climate of the Ocean (GECCO) as initial conditions and as boundary conditions. The PPP of the MHT varies with latitude: local maxima are apparent within the subpolar and the subtropical gyres, and a minimum is apparent at the boundary between the gyres. This PPP minimum can also be seen in the PPP structure of the Atlantic meridional overturning circulation (AMOC), although it is considerably less pronounced. The decomposition of the MHT shows that within the subpolar gyre, the gyre component of the MHT influences the PPP structure of the MHT. Within the subtropical gyre, the overturning component of the MHT characterizes the PPP structure of the MHT. At the boundary between the subpolar and the subtropical gyres, the dynamics of the Ekman heat transport limit the predictable lead times of the MHT. At most latitudes, variations in the velocity field control the PPP structure of the MHT. The PPP structure of the AMOC can also be classified into gyre and gyre-boundary regimes, but the predictable lead times within the gyres are only similar to those of the overturning component of the MHT. Overall, the analysis provides a reference point for the latitude dependence of the MHT’s PPP structure and relates it to the latitude dependence of the AMOC’s PPP structure.

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Johanna Baehr, David McInerney, Klaus Keller, and Jochem Marotzke

Abstract

Three methods are analyzed for the design of ocean observing systems to monitor the meridional overturning circulation (MOC) in the North Atlantic. Specifically, a continuous monitoring array to monitor the MOC at 1000 m at different latitudes is “deployed” into a numerical model. The authors compare array design methods guided by (i) physical intuition (heuristic array design), (ii) sequential optimization, and (iii) global optimization. The global optimization technique can recover the true global solution for the analyzed array design, while gradient-based optimization would be prone to misconverge. Both global optimization and heuristic array design yield considerably improved results over sequential array design. Global optimization always outperforms the heuristic array design in terms of minimizing the root-mean-square error. However, whether the results are physically meaningful is not guaranteed; the apparent success might merely represent a solution in which misfits compensate for each other accidentally. Testing the solution gained from global optimization in an independent dataset can provide crucial information about the solution’s robustness.

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Leonard F. Borchert, Wolfgang A. Müller, and Johanna Baehr

Abstract

An analysis of a three-member ensemble of initialized coupled simulations with the MPI-ESM-LR covering the period 1901–2010 shows that Atlantic northward ocean heat transport (OHT) at 50°N influences surface temperature variability in the North Atlantic region for several years. Three to ten years after strong OHT phases at 50°N, a characteristic pattern of sea surface temperature (SST) anomalies emerges: warm anomalies are found in the North Atlantic and cold anomalies emerge in the Gulf Stream region. This pattern originates from persistent upper-ocean heat content anomalies that originate from southward-propagating OHT anomalies in the North Atlantic. Interannual-to-decadal SST predictability of yearly initialized hindcasts is linked to this SST pattern: when ocean heat transport at 50°N is strong at the initialization of a hindcast, SST anomaly correlation coefficients in the northeast Atlantic at lead years 2–9 are significantly higher than when the ocean heat transport at 50°N is weak at initialization. Surface heat fluxes that mask the predictable low-frequency oceanic variability that influences SSTs in the northwest Atlantic after strong OHT phases, and in the northwest and northeast Atlantic after weak OHT phases at 50°N lead to zonally asymmetrically predictable SSTs 7–9 years ahead. This study shows that the interannual-to-decadal predictability of North Atlantic SSTs depends strongly on the strength of subpolar ocean heat transport at the start of a prediction, indicating that physical mechanisms need to be taken into account for actual temperature predictions.

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Mikhail Dobrynin, Jens Murawski, Johanna Baehr, and Tatiana Ilyina

Abstract

Surface waves in the ocean respond to variability and changes of climate. Observations and modeling studies indicate trends in wave height over the past decades. Nevertheless, it is currently impossible to discern whether these trends are the result of climate variability or change. The output of an Earth system model (EC-EARTH) produced within phase 5 of the Coupled Model Intercomparison Project (CMIP5) is used here to force a global Wave Model (WAM) in order to study the response of waves to different climate regimes. A control simulation was run to determine the natural (unforced) model variability. A simplified fingerprint approach was used to calculate positive and negative limits of natural variability for wind speed and significant wave height, which were then compared to different (forced) climate regimes over the historical period (1850–2010) and in the future climate change scenario RCP8.5 (2010–2100). Detectable climate change signals were found in the current decade (2010–20) in the North Atlantic, equatorial Pacific, and Southern Ocean. Until the year 2060, climate change signals are detectable in 60% of the global ocean area. The authors show that climate change acts to generate detectable trends in wind speed and significant wave height that exceed the positive and the negative ranges of natural variability in different regions of the ocean. Moreover, in more than 3% of the ocean area, the climate change signal is reversible such that trends exceeded both positive and negative limits of natural variability at different points in time. These changes are attributed to local (due to local wind) and remote (due to swell) factors.

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Daniela I. V. Domeisen, Amy H. Butler, Kristina Fröhlich, Matthias Bittner, Wolfgang A. Müller, and Johanna Baehr

Abstract

Predictability on seasonal time scales over the North Atlantic–Europe region is assessed using a seasonal prediction system based on an initialized version of the Max Planck Institute Earth System Model (MPI-ESM). For this region, two of the dominant predictors on seasonal time scales are El Niño–Southern Oscillation (ENSO) and sudden stratospheric warming (SSW) events. Multiple studies have shown a potential for improved North Atlantic predictability for either predictor. Their respective influences are however difficult to disentangle, since the stratosphere is itself impacted by ENSO. Both El Niño and SSW events correspond to a negative signature of the North Atlantic Oscillation (NAO), which has a major influence on European weather.

This study explores the impact on Europe by separating the stratospheric pathway of the El Niño teleconnection. In the seasonal prediction system, the evolution of El Niño events is well captured for lead times of up to 6 months, and stratospheric variability is reproduced with a realistic frequency of SSW events. The model reproduces the El Niño teleconnection through the stratosphere, involving a deepened Aleutian low connected to a warm anomaly in the northern winter stratosphere. The stratospheric anomaly signal then propagates downward into the troposphere through the winter season. Predictability of 500-hPa geopotential height over Europe at lead times of up to 4 months is shown to be increased only for El Niño events that exhibit SSW events, and it is shown that the characteristic negative NAO signal is only obtained for winters also containing major SSW events for both the model and the reanalysis data.

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Johanna Baehr, Helmuth Haak, Steven Alderson, Stuart A. Cunningham, Johann H. Jungclaus, and Jochem Marotzke

Abstract

It is investigated how changes in the North Atlantic meridional overturning circulation (MOC) might be reliably detected within a few decades, using the observations provided by the RAPID-MOC 26°N array. Previously, detectability of MOC changes had been investigated with a univariate MOC time series exhibiting strong internal variability, which would prohibit the detection of MOC changes within a few decades. Here, a modification of K. Hasselmann’s fingerprint technique is used: (simulated) observations are projected onto a time-independent spatial pattern of natural variability to derive a time-dependent detection variable. The fixed spatial pattern of natural variability is derived by regressing the zonal density gradient along 26°N against the strength of the MOC at 26°N within the coupled ECHAM5/Max Planck Institute Ocean Model’s (MPI-OM) control climate simulation. This pattern is confirmed against the observed anomalies found between the 1957 and the 2004 hydrographic occupations of the section. Onto this fixed spatial pattern of natural variability, both the existing hydrographic observations and simulated observations mimicking the RAPID-MOC 26°N array in three realizations of the Intergovernmental Panel on Climate Change (IPCC) scenario A1B are projected. For a random observation error of 0.01 kg m−3, and only using zonal density gradients between 1700- and 3100-m depth, statistically significant detection occurs with 95% reliability after about 30 yr, in the model and climate change scenario analyzed here. Compared to using a single MOC time series as the detection variable, continuous observations of zonal density gradients reduce the detection time by 50%. For the five hydrographic occupations of the 26°N transect, none of the analyzed depth ranges shows a significant trend between 1957 and 2004, implying that there was no MOC trend over the past 50 yr.

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Adrian M. Tompkins, María Inés Ortiz De Zárate, Ramiro I. Saurral, Carolina Vera, Celeste Saulo, William J. Merryfield, Michael Sigmond, Woo-Sung Lee, Johanna Baehr, Alain Braun, Amy Butler, Michel Déqué, Francisco J. Doblas-Reyes, Margaret Gordon, Adam A. Scaife, Yukiko Imada, Masayoshi Ishii, Tomoaki Ose, Ben Kirtman, Arun Kumar, Wolfgang A. Müller, Anna Pirani, Tim Stockdale, Michel Rixen, and Tamaki Yasuda
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William J. Merryfield, Johanna Baehr, Lauriane Batté, Emily J. Becker, Amy H. Butler, Caio A. S. Coelho, Gokhan Danabasoglu, Paul A. Dirmeyer, Francisco J. Doblas-Reyes, Daniela I. V. Domeisen, Laura Ferranti, Tatiana Ilynia, Arun Kumar, Wolfgang A. Müller, Michel Rixen, Andrew W. Robertson, Doug M. Smith, Yuhei Takaya, Matthias Tuma, Frederic Vitart, Christopher J. White, Mariano S. Alvarez, Constantin Ardilouze, Hannah Attard, Cory Baggett, Magdalena A. Balmaseda, Asmerom F. Beraki, Partha S. Bhattacharjee, Roberto Bilbao, Felipe M. de Andrade, Michael J. DeFlorio, Leandro B. Díaz, Muhammad Azhar Ehsan, Georgios Fragkoulidis, Sam Grainger, Benjamin W. Green, Momme C. Hell, Johnna M. Infanti, Katharina Isensee, Takahito Kataoka, Ben P. Kirtman, Nicholas P. Klingaman, June-Yi Lee, Kirsten Mayer, Roseanna McKay, Jennifer V. Mecking, Douglas E. Miller, Nele Neddermann, Ching Ho Justin Ng, Albert Ossó, Klaus Pankatz, Simon Peatman, Kathy Pegion, Judith Perlwitz, G. Cristina Recalde-Coronel, Annika Reintges, Christoph Renkl, Balakrishnan Solaraju-Murali, Aaron Spring, Cristiana Stan, Y. Qiang Sun, Carly R. Tozer, Nicolas Vigaud, Steven Woolnough, and Stephen Yeager

Abstract

Weather and climate variations on subseasonal to decadal time scales can have enormous social, economic, and environmental impacts, making skillful predictions on these time scales a valuable tool for decision-makers. As such, there is a growing interest in the scientific, operational, and applications communities in developing forecasts to improve our foreknowledge of extreme events. On subseasonal to seasonal (S2S) time scales, these include high-impact meteorological events such as tropical cyclones, extratropical storms, floods, droughts, and heat and cold waves. On seasonal to decadal (S2D) time scales, while the focus broadly remains similar (e.g., on precipitation, surface and upper-ocean temperatures, and their effects on the probabilities of high-impact meteorological events), understanding the roles of internal variability and externally forced variability such as anthropogenic warming in forecasts also becomes important. The S2S and S2D communities share common scientific and technical challenges. These include forecast initialization and ensemble generation; initialization shock and drift; understanding the onset of model systematic errors; bias correction, calibration, and forecast quality assessment; model resolution; atmosphere–ocean coupling; sources and expectations for predictability; and linking research, operational forecasting, and end-user needs. In September 2018 a coordinated pair of international conferences, framed by the above challenges, was organized jointly by the World Climate Research Programme (WCRP) and the World Weather Research Programme (WWRP). These conferences surveyed the state of S2S and S2D prediction, ongoing research, and future needs, providing an ideal basis for synthesizing current and emerging developments in these areas that promise to enhance future operational services. This article provides such a synthesis.

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