ENSO mechanisms and interactions in a hybrid coupled recharge oscillator model

The El Niño Southern Oscillation (ENSO) mode is the most important source of interannual climate variability. It has its origin in the interactions of the atmosphere and the tropical Pacific Ocean but the teleconnections of ENSO reach far beyond the tropical Pacific. Although the understanding of ENSO has improved greatly, there are still aspects of ENSO that are not yet well understood. Some of these aspects are the seasonality of ENSO, i.e., the nature of ENSO events to peak in boreal winter, and the asymmetry of ENSO, i.e., the fact that El Niño events are, in general, stronger than La Niña events. Also, the possible effects of a changing climate on ENSO, and the influences of the tropical Indian and the tropical Atlantic Ocean on ENSO are areas of ongoing studies. For this work, the hybrid coupled model RECHOZ was developed consisting of the ECHAM5 atmospheric general circulation model, the simple recharge oscillator ocean model in the tropical Pacific and a simple mixed layer ocean model outside the tropical Pacific. Despite the simplistic and, by construction, linear representation of ocean dynamics in the RECHOZ model, it is able to simulate the main statistical features of El Niño, including variance, period, seasonality, skewness, and kurtosis. This model was used to study the seasonality of ENSO and the nonlinearities in the ENSO cycle. Analyses of the model show that atmospheric properties are responsible for the seasonality and nonlinearity of ENSO. A nonlinear relationship between the zonal wind stress and the sea surface temperature (SST) is causing the El Niño-La Niña asymmetry. With the aid of sensitivity experiments, the effects of changes in the mean state of the tropical Pacific on ENSO due to atmospheric feedbacks were studied and the influences of the tropical Indian and the tropical Atlantic Ocean on ENSO were analysed. Analyses of the sensitivity experiments show that changes in the mean state of the tropical Pacific have a strong influence on the amplitude and frequency of ENSO. However, the results strongly depend on the pattern of the changes. An El Ni~no-like warming pattern leads to a strong increase in ENSO variability and shifts the period of ENSO towards longer timescales. For the tropical Atlantic Ocean no clear influence on ENSO can be detected. In contrast, the tropical Indian Ocean has a strong damping effect on the SST variability in the tropical Pacific and reduces the period of the ENSO cycle

Influences of the tropical Indian and Atlantic Oceans on the predictability of ENSO

The El Niño Southern Oscillation (ENSO) is the leading mode of climate variability and predictable on interannual time scales. Recent studies suggest that the tropical Indian and Atlantic Oceans influence the dynamics and predictability of ENSO. Here we investigate these effects in a hybrid coupled model consisting of a full complexity atmospheric general circulation model (GCM) coupled to a strongly simplified linear 2-dimensional ENSO recharge oscillator ocean model. We find that the tropical Indian and Atlantic Oceans have distinct effects on the dynamics and predictability. The decoupling of the tropical Indian Ocean has a strong impact onto ENSO dynamics, but the initial conditions of it have only a small impact on the ENSO predictability. In contrast, initial conditions of the tropical Atlantic have a stronger impact on the predictability of ENSO, but the decoupling of the tropical Atlantic has almost no effect on the ENSO dynamics

ENSO Mechanismen und Wechselwirkungen in einem hybrid gekoppelten "Recharge Oscillator" Modell

The El Nino Southern Oscillation (ENSO) mode is the most important phenomenon of interannual climate variability. The phenomenon has its origin in the interactions of the atmosphere and the tropical Pacific Ocean but the teleconnections of ENSO reach far beyond the tropical Pacific. Although the understanding of ENSO has improved greatly, there are still aspects of ENSO that are not yet well understood. Some of these aspects are the seasonality of ENSO, i.e., the nature of El Nino events to peak in boreal winter, and the asymmetry of ENSO, i.e., the fact that El Nino events are, in general, stronger than La Nina events. Also, the possible effects of a changing climate on ENSO, and the influences of the tropical Indian and the tropical Atlantic Ocean on ENSO are areas of ongoing studies. For this work, the hybrid coupled model RECHOZ was developed consisting of the ECHAM5 atmospheric general circulation model, a simple mixed layer ocean model outside the tropical Pacific and the simple recharge oscillator ocean model in the tropical Pacific. Despite the simplistic and, by construction, linear representation of ocean dynamics in the RECHOZ model, it is able to simulate the main statistical features of El Nino, including variance, period, seasonality, skewness, and kurtosis. This model was used to study the seasonality of ENSO and the nonlinearities in the ENSO cycle. Analyses of the model show that atmospheric properties are responsible for the seasonality and nonlinearity of ENSO. A nonlinear relationship between the zonal wind stress and sea surface temperature (SST) is causing the El Nino-La Nina asymmetry. With the aid of sensitivity experiments, the effects of changes in the mean state of the tropical Pacific on ENSO due to atmospheric feedbacks were studied and the influences of the tropical Indian and the tropical Atlantic Ocean on ENSO were analysed. Analyses of the sensitivity experiments show that changes in the mean state of the tropical Pacific have a strong influence on the amplitude and frequency of ENSO. However, the results strongly depend on the pattern of the changes. An El Nino-like warming pattern leads to a strong increase in ENSO variability and shifts the period of ENSO towards longer timescales. For the tropical Atlantic Ocean no clear influence on ENSO can be detected. In contrast the tropical Indian Ocean has a strong damping effect on the SST variability in the tropical Pacific and reduces the period of the ENSO cycle.Der El Nino Southern Oscillation (ENSO) Mode ist das bei weitem wichtigste Phänomen zwischenjährlicher Klimavariabilität. ENSO hat seinen Ursprung in den Wechselwirkungen der Atmosphäre und des tropischen Pazifischen Ozeans, aber die Fernwirkungen reichen weit über den tropischen Pazifik hinaus. Obwohl sich das Verständnis des ENSO Phänomens stark verbessert hat, sind einige Aspekte ENSOs noch immer nicht gut verstanden. Einige dieser Aspekte sind die Saisonalität ENSOs, d.h. die Eigenschaft ENSOs im nordhemisphärischen Winter den Höhepunkt zu erreichen, und die Asymmetrie ENSOs, d.h. die Tatsache, dass El Nino Ereignisse für gewöhnlich stärker ausfallen als La Nina Ereignisse. Ebenso sind die möglichen Effekte des Klimawandels auf ENSO und die Einflüsse des tropischen Indischen und des tropischen Atlantischen Ozeans auf ENSO Gegenstand aktueller Forschung. Im Rahmen dieser Arbeit wurde das hybrid gekoppelte Modell RECHOZ entwickelt, das aus dem allgemeinen atmosphärischen Zirkulationsmodell ECHAM5, dem einfachen Recharge Oscillator Ozeanmodell im tropischen Pazifik und einem einfachen Durchmischungsschicht-Model außerhalb des tropischen Pazifiks besteht. Trotz der einfachen und per Konstruktion linearen Darstellung der Ozeandynamik im RECHOZ-Modell ist das Modell in der Lage, die wesentlichen statistischen Eigenschaften ENSOs, wie die Varianz, die Periode, die Saisonalität, die Schiefe und die Kurtosis, zu reproduzieren. Dieses Modells wurde verwendet, um die Saisonalität ENSOs und die Nichtlinearität im ENSO-Zyklus zu untersuchen. Analysen der Modellergebnisse zeigen, dass atmosphärische Eigenschaften für die Saisonalität und Nichtlinearität ENSOs verantwortlich sind. Ein nichtlinearer Zusammenhang zwischen der zonalen Windschubspannung und der Meeresoberflächentemperatur verursacht die El Nino-La Nina Asymmetrie. Mithilfe von Sensitivitätsexperimenten wurden die Effekte von Veränderungen im mittleren Zustand des tropischen Pazifiks auf ENSO durch atmosphärische Rückkopplungen und die Einflüsse des tropischen Indischen und des tropischen Atlantischen Ozeans auf ENSO analysiert. Untersuchungen der Sensitivitätsexperimente zeigen, dass Veränderungen im mittleren Zustand des tropischen Pazifiks einen starken Einfluss auf die Amplitude und Frequenz von ENSO haben. Allerdings sind die Auswirkungen stark von dem Muster der Veränderungen abhängig. Ein El Nino-artiges Erwärmungsmuster führt zu einem starken Anstieg der ENSO Variabilität und verschiebt die Periode ENSOs zu längeren Zeiträumen. Für den tropischen Atlantik kann kein deutlicher Einfluss auf ENSO festgestellt werden. Im Gegensatz dazu hat der tropische Indische Ozean einen stark dämpfenden Effekt auf die SST Variabilität im tropischen Pazifik und verringert die Periode des ENSO-Zyklus

The role of local atmospheric forcing on the modulation of the ocean mixed layer depth in reanalysis and a coupled single column ocean model

The role of the local atmospheric forcing on the ocean mixed layer depth (MLD) over the global oceans is studied using ocean reanalysis data products and a single-column ocean model coupled to an atmospheric general circulation model. The focus of this study is on how the annual mean and the seasonal cycle of the MLD relate to various forcing characteristics in different parts of the world's ocean, and how anomalous variations in the monthly mean MLD relate to anomalous atmospheric forcings. By analysing both ocean reanalysis data and the single-column ocean model, regions with different dominant forcings and different mean and variability characteristics of the MLD can be identified. Many of the global oceans' MLD characteristics appear to be directly linked to different atmospheric forcing characteristics at different locations. Here, heating and wind-stress are identified as the main drivers; in some, mostly coastal, regions the atmospheric salinity forcing also contributes. The annual mean MLD is more closely related to the annual mean wind-stress and the MLD seasonality is more closely to the seasonality in heating. The single-column ocean model, however, also points out that the MLD characteristics over most global ocean regions, and in particular the tropics and subtropics, cannot be maintained by local atmospheric forcings only, but are also a result of ocean dynamics that are not simulated in a single-column ocean model. Thus, lateral ocean dynamics are essentially in correctly simulating observed MLD

Role of wind stress in driving SST biases in the tropical Atlantic

Coupled climate models used for long-term future climate projections and seasonal or decadal predictions share a systematic and persistent warm sea surface temperature (SST) bias in the tropical Atlantic. This study attempts to better understand the physical mechanisms responsible for the development of systematic biases in the tropical Atlantic using the so-called Transpose-CMIP protocol in a multi-model context. Six global climate models have been used to perform seasonal forecasts starting both in May and February over the period 2000-2009. In all models, the growth of SST biases is rapid. Significant biases are seen in the first month of forecast and, by six months, the root-mean-square SST bias is 80% of the climatological bias. These control experiments show that the equatorial warm SST bias is not driven by surface heat flux biases in all models, whereas in the south-eastern Atlantic the solar heat flux could explain the setup of an initial warm bias in the first few days. A set of sensitivity experiments with prescribed wind stress confirm the leading role of wind stress biases in driving the equatorial SST bias, even if the amplitude of the SST bias is model dependent. A reduced SST bias leads to a reduced precipitation bias locally, but there is no robust remote effect on West African Monsoon rainfall. Over the south-eastern part of the basin, local wind biases tend to have an impact on the local SST bias (except in the high resolution model). However, there is also a non-local effect of equatorial wind correction in two models. This can be explained by sub-surface advection of water from the equator, which is colder when the bias in equatorial wind stress is corrected. In terms of variability, it is also shown that improving the mean state in the equatorial Atlantic leads to a beneficial intensification of the Bjerknes feedback loop. In conclusion, we show a robust effect of wind stress biases on tropical mean climate and variability in multiple climate models

El Nino and La Nina amplitude asymmetry caused by atmospheric feedbacks

Interannual variability of tropical Pacific sea surface temperatures (SST) has an asymmetry with stronger positive events, El Niño, and weaker negative events, La Niña, which is generally attributed to processes in the ocean. Here we present evidence from a new hybrid coupled model that the asymmetry and seasonality of El Niño can be caused by nonlinear and seasonally varying atmospheric feedbacks. The model consists of the ECHAM5 global atmospheric general circulation model (GCM) coupled to the 2-dimensional El Niño linear recharge oscillator ocean model in the tropical Pacific and a mixed layer ocean elsewhere. Despite the models simplistic and, by construction, linear representation of the ocean dynamics, it is able to simulate the main statistical features of El Niño including period, seasonality, skewness, and kurtosis. Analyses of the model show that a nonlinear relationship between zonal wind stress and SST is causing the El Niño-La Niña asymmetry

Earth Virtualization Engines -- A Technical Perspective

Participants of the Berlin Summit on Earth Virtualization Engines (EVEs) discussed ideas and concepts to improve our ability to cope with climate change. EVEs aim to provide interactive and accessible climate simulations and data for a wide range of users. They combine high-resolution physics-based models with machine learning techniques to improve the fidelity, efficiency, and interpretability of climate projections. At their core, EVEs offer a federated data layer that enables simple and fast access to exabyte-sized climate data through simple interfaces. In this article, we summarize the technical challenges and opportunities for developing EVEs, and argue that they are essential for addressing the consequences of climate change

Assessment of Uncertainties in Scenario Simulations of Biogeochemical Cycles in the Baltic Sea

Following earlier regional assessment studies, such as the Assessment of Climate Change for the Baltic Sea Basin and the North Sea Region Climate Change Assessment, knowledge acquired from available literature about future scenario simulations of biogeochemical cycles in the Baltic Sea and their uncertainties is assessed. The identification and reduction of uncertainties of scenario simulations are issues for marine management. For instance, it is important to know whether nutrient load abatement will meet its objectives of restored water quality status in future climate or whether additional measures are required. However, uncertainties are large and their sources need to be understood to draw conclusions about the effectiveness of measures. The assessment of sources of uncertainties in projections of biogeochemical cycles based on authors' own expert judgment suggests that the biggest uncertainties are caused by (1) unknown current and future bioavailable nutrient loads from land and atmosphere, (2) the experimental setup (including the spin up strategy), (3) differences between the projections of global and regional climate models, in particular, with respect to the global mean sea level rise and regional water cycle, (4) differing model-specific responses of the simulated biogeochemical cycles to long-term changes in external nutrient loads and climate of the Baltic Sea region, and (5) unknown future greenhouse gas emissions. Regular assessments of the models' skill (or quality compared to observations) for the Baltic Sea region and the spread in scenario simulations (differences among projected changes) as well as improvement of dynamical downscaling methods are recommended.Peer reviewe

Assessment of eutrophication abatement scenarios for the Baltic Sea by multi-model ensemble simulations

Peer reviewe

Earth Virtualization Engines: a technical perspective

Participants of the Berlin Summit on Earth Virtualization Engines (EVEs) discussed ideas and concepts to improve our ability to cope with climate change. EVEs aim to provide interactive and accessible climate simulations and data for a wide range of users. They combine high-resolution physics-based models with machine learning techniques to improve the fidelity, efficiency, and interpretability of climate projections. At their core, EVEs offer a federated data layer that enables simple and fast access to exabyte-sized climate data through simple interfaces. In this article, we summarize the technical challenges and opportunities for developing EVEs, and argue that they are essential for addressing the consequences of climate change