Arctic sea ice in Earth system models

The summer Arctic sea ice cover has retreated by about 50% in the last 30 years. This rate of change is unprecedented in Earth’s history and is at least partly due to an anthropogenic impact. It has critical implications for the climate worldwide. Earth system models are parameterized computer models that aim at simulating and projecting global climate and its change. Experiments with these models under common forcings and protocols are coordinated as part of the Coupled Model Intercomparison Project (CMIP). However, uncertainties in CMIP simulations of the Arctic climate are large in both, short- and long-term projections. This thesis investigates how observations can be used to improve Arctic climate simulations by analyzing and evaluating Arctic climate parameters in different types of climate simulations from Earth system models participating in CMIP Phase 5 (CMIP5). For this purpose, the Earth System Model Evaluation Tool (ESMValTool) developed by scientists worldwide to facilitate the challenging evaluation of Earth system models, is enhanced and applied. The thesis consists of two parts that are based on studies conducted by the thesis author. Part one investigates whether the initialization of Earth system model simulations with observations leads to more skillful predictions of Arctic climate. It focuses on the relatively new field of decadal climate predictions that have a forecast horizon of only 10 years. In contrast to long-term climate simulations, decadal climate predictions are initialized with observations so that the simulations start from the observed phase of natural variability. To analyze possible improvements in prediction skill by initialization, a complex verification system for retrospective decadal predictions (“hindcasts”) was developed, implemented into the ESMValTool, and applied to Arctic hindcasts of an Earth system model. It is shown that initialization improves surface temperature skill in the North Atlantic and along the east coast of Greenland, with root-mean-square errors against observations reduced by up to 50%. Accordingly, the skill for integrated sea ice area was increased in the Greenland Sea. However, these improvements were only found for winter and only for the first few forecast years after initialization. In other Arctic regions and other seasons, no significant improvements were found. On the contrary, especially in the marginal ice zone and the Kara and Barents Seas, there is significant degradation in skill by initialization. This can be explained by the so-called initialization shock, a mechanism that causes the model to drift to and overshoot its biased intrinsic climatology after the initialization. The thesis also identifies ways to improve decadal climate predictions in the Arctic, for example by including sea ice parameters in the initialization process and by additionally initializing the model in summer instead of only at the end of the year. Part two of the thesis focuses on the long-term development of the Arctic and is aimed at reducing uncertainties in multi-model Earth system model projections of the 21st century Arctic sea ice extent. The multiple diagnostic ensemble regression (MDER) method is used to constrain the CMIP5 projections with observations. The main advantage of the MDER method is the iterative regression algorithm that selects the diagnostics that best predict the future target variable from a set of given diagnostics and calculates a regression model to constrain the projections with observations. Applying this method to Arctic sea ice projections from 29 CMIP5 models, model uncertainty in multi-model mean results for the September Arctic sea ice extent could be reduced by up to 50%. Furthermore, model weighting reduces the projected multi-model mean sea ice extent by 20% (1 million km²) and predicts an earlier near-disappearance of Arctic sea ice by more than a decade (from 2076 to 2062) for a high greenhouse gas concentration scenario compared to the unweighted multi-model mean. This suggests a faster retreat of Arctic sea ice than previously estimated. However, the results of the thesis also show that model uncertainty – together with scenario uncertainty and irreducible internal variability – remains too large for exact predictions of the first year of near-disappearance of summer Arctic sea ice. The thesis demonstrates the potential of improving Earth system model simulations of Arctic climate with observations. Initializing climate models with observations is a relatively new field that needs to be further investigated. Finding emergent constraints for Arctic climate parameters may greatly enhance the potential reduction in uncertainties of multi-model projections when using observation-based analysis methods like MDER. While projection uncertainties are still large, most studies, including this thesis, suggest a more pessimistic future for Arctic sea ice. Hence, the conclusions are that mitigation strategies to reduce Arctic warming need to be intensified and that it becomes increasingly crucial to further improve the understanding of the Arctic climate system.Die Fläche des arktischen Sommermeereises hat in den letzten 30 Jahren um ca. 50% abgenommen. Diese Veränderungsrate ist in der Erdgeschichte beispiellos und ist zumindest teilweise auf einen anthropogenen Einfluss zurückzuführen. Die Veränderungen in der Arktis haben enorme Auswirkungen auf das weltweite Klima. Erdsystemmodelle sind parametrisierte Computermodelle, die das Ziel haben, das globale Klima und dessen Veränderungen zu simulieren. Das Coupled Model Intercomparison Project (CMIP) koordiniert die Forschungsexperimente mit diesen Modellen unter gemeinsamen Leitlinien. Die Unsicherheiten in den CMIP-Simulationen des arktischen Klimas groß sind – sowohl auf Kurzzeit- als auch auf Langzeitskala. In dieser Dissertation wird untersucht, wie Beobachtungen zur Verbesserung der arktischen Klimasimulationen genutzt werden können. Hierfür werden arktische Klimaparameter aus verschiedenen Typen von Klimasimulationen der Erdsystemmodelle aus CMIP Phase 5 (CMIP5) mit Beobachtungen analysiert und evaluiert. Zu diesem Zweck wird das Earth System Model Evaluation Tool (ESMValTool) erweitert und benutzt, das von Wissenschaftlern weltweit dazu entwickelt wird, die komplexe Evaluation von Erdsystemmodellen zu erleichtern. Die Dissertation besteht aus zwei Teilen, die auf Studien basieren, die vom Autor der Dissertation durchgeführt wurden. In Teil eins wird untersucht, ob die Initialisierung von Erdsystemmodellsimulationen mit Beobachtungen zu erhöhter Vorhersagegenauigkeit („Skill“) des arktischen Klimas führt. Der Fokus liegt dabei auf dem relativ neuen Feld der dekadischen Klimavorhersagen, die einen Vorhersagehorizont von nur zehn Jahren haben. Im Gegensatz zu Langzeitsimulationen werden dekadische Klimavorhersagen mit Beobachtungsdaten initialisiert, so dass sie von der beobachteten Phase natürlicher Klimaschwankung aus starten. Um mögliche Verbesserungen im Skill durch Initialisierung zu untersuchen, wurde ein vielschichtiges Verifikationssystem für retrospektive dekadische Klimavorhersagen („Hindcasts“) entwickelt, in das ESMValTool implementiert und auf dekadische Hindcasts eines Erdsystemmodells angewendet. Es zeigt sich, dass die Initialisierung den Vorhersageskill von Oberflächentemperaturen im Nordatlantik und entlang der Ostküste von Grönland verbessert, wobei die mittlere quadratische Abweichung zu Beobachtungen um 50% reduziert wird. Dementsprechend hat sich der Skill für die integrierte Meereisfläche im Grönlandmeer erhöht. Allerdings zeigen sich diese Verbesserungen nur im Winter und nur für wenige Jahre nach Initialisierung. Tatsächlich zeigt sich eine signifikante Skillabnahme besonders am Meereisrand und in der Kara- und Barentssee. Dies kann mit dem sogenannten Initialisierungsschock erklärt werden - einem Mechanismus, der das Modell dazu bringt, nach der Initialisierung zu seinem verzerrten intrinsischen Klimazustand zurück zu driften und diesen zu überschießen. Die Arbeit zeigt Möglichkeiten auf, die dekadischen Klimavorhersagen in der Arktis zu verbessern, wie beispielsweise durch eine Berücksichtigung von Meereisparametern beim Initialisierungsprozess und eine zusätzliche Initialisierung des Modells im Sommer anstatt nur am Ende des Jahres. Teil zwei der Dissertation beschäftigt sich mit der Langzeitentwicklung der Arktis und hat das Ziel, Unsicherheiten in Klimaprojektionen der Entwicklung der arktischen Meereisausdehnung im 21. Jahrhundert zu reduzieren. Hierfür wird von der Multiple Diagnostic Ensemble Regression (MDER) Methode Gebrauch gemacht, die mithilfe von Beobachtungen die Bandbreite der CMIP5-Klimaprognosen einschränkt. Der zentrale Vorteil von MDER gegenüber anderen Analysemethoden ist der iterative Regressionsalgorithmus, der diejenigen Diagnostiken aus einer Menge an vorgegebenen Diagnostiken auswählt, die die Zukunftsvariable am besten vorhersagen können. Mit dem hieraus errechneten Regressionsmodell lassen sich dann die Prognosen mittels Beobachtungen einschränken. Durch die Anwendung dieser Methode auf Prognosen des arktischen Meereises von 29 CMIP5-Modellen konnte die Modellunsicherheit in Multimodellsimulationen der Ausdehnung des arktischen Septembermeereises um bis zu 50% verkleinert werden. Darüber hinaus zeigt das beschränkte Multimodellmittel im Vergleich zum ungewichteten Mittel eine um ca. eine Million km² (20%) kleinere Eisfläche und ein früheres Verschwinden des arktischen Sommermeereises um mehr als eine Dekade (von 2076 auf 2062) in einem hohen Treibhausgasszenario. Dies deutet auf einen schnelleren Rückgang des arktischen Meereises hin, als bisher angenommen. Allerdings zeigen die Ergebnisse der Dissertation auch, dass die Modellunsicherheit – zusammen mit nicht-reduzierbarer interner Klimavariabilität und Unsicherheiten durch verschiedene Annahmen zukünftiger Emissionsszenarien – zu groß für genaue Vorhersagen des Zeitpunkts bleibt, an dem das arktische Sommermeereis zum ersten Mal verschwindet. Die Dissertation legt das große Potential dar, Erdsystemmodellsimulationen des arktischen Klimas mittels Beobachtungen zu verbessern. Klimamodelle mit Beobachtungen zu initialisieren, ist ein relativ neues Forschungsgebiet, das noch weiterer Erforschung bedarf. Das Auffinden sogenannter „emergent constraints“ für arktische Klimaparameter kann die Reduktion von Unsicherheiten mittels beobachtungsbasierten Analysemethoden wie MDER stark verbessern. Obwohl Prognoseunsicherheiten noch immer groß sind, deuten die meisten Studien, so wie diese Arbeit, auf eine pessimistischere Zukunft des arktischen Meereises hin. Die Arbeit zeigt somit, dass Mitigationsstrategien gegen die arktische Klimaerwärmung weiter ausgebaut werden müssen und es zunehmend wichtiger wird, das Klimasystem der Arktis besser zu verstehen

Arctic sea ice in Earth system models

The summer Arctic sea ice cover has retreated by about 50% in the last 30 years. This rate of change is unprecedented in Earth’s history and is at least partly due to an anthropogenic impact. It has critical implications for the climate worldwide. Earth system models are parameterized computer models that aim at simulating and projecting global climate and its change. Experiments with these models under common forcings and protocols are coordinated as part of the Coupled Model Intercomparison Project (CMIP). However, uncertainties in CMIP simulations of the Arctic climate are large in both, short- and long-term projections. This thesis investigates how observations can be used to improve Arctic climate simulations by analyzing and evaluating Arctic climate parameters in different types of climate simulations from Earth system models participating in CMIP Phase 5 (CMIP5). For this purpose, the Earth System Model Evaluation Tool (ESMValTool) developed by scientists worldwide to facilitate the challenging evaluation of Earth system models, is enhanced and applied. The thesis consists of two parts that are based on studies conducted by the thesis author. Part one investigates whether the initialization of Earth system model simulations with observations leads to more skillful predictions of Arctic climate. It focuses on the relatively new field of decadal climate predictions that have a forecast horizon of only 10 years. In contrast to long-term climate simulations, decadal climate predictions are initialized with observations so that the simulations start from the observed phase of natural variability. To analyze possible improvements in prediction skill by initialization, a complex verification system for retrospective decadal predictions (“hindcasts”) was developed, implemented into the ESMValTool, and applied to Arctic hindcasts of an Earth system model. It is shown that initialization improves surface temperature skill in the North Atlantic and along the east coast of Greenland, with root-mean-square errors against observations reduced by up to 50%. Accordingly, the skill for integrated sea ice area was increased in the Greenland Sea. However, these improvements were only found for winter and only for the first few forecast years after initialization. In other Arctic regions and other seasons, no significant improvements were found. On the contrary, especially in the marginal ice zone and the Kara and Barents Seas, there is significant degradation in skill by initialization. This can be explained by the so-called initialization shock, a mechanism that causes the model to drift to and overshoot its biased intrinsic climatology after the initialization. The thesis also identifies ways to improve decadal climate predictions in the Arctic, for example by including sea ice parameters in the initialization process and by additionally initializing the model in summer instead of only at the end of the year. Part two of the thesis focuses on the long-term development of the Arctic and is aimed at reducing uncertainties in multi-model Earth system model projections of the 21st century Arctic sea ice extent. The multiple diagnostic ensemble regression (MDER) method is used to constrain the CMIP5 projections with observations. The main advantage of the MDER method is the iterative regression algorithm that selects the diagnostics that best predict the future target variable from a set of given diagnostics and calculates a regression model to constrain the projections with observations. Applying this method to Arctic sea ice projections from 29 CMIP5 models, model uncertainty in multi-model mean results for the September Arctic sea ice extent could be reduced by up to 50%. Furthermore, model weighting reduces the projected multi-model mean sea ice extent by 20% (1 million km²) and predicts an earlier near-disappearance of Arctic sea ice by more than a decade (from 2076 to 2062) for a high greenhouse gas concentration scenario compared to the unweighted multi-model mean. This suggests a faster retreat of Arctic sea ice than previously estimated. However, the results of the thesis also show that model uncertainty – together with scenario uncertainty and irreducible internal variability – remains too large for exact predictions of the first year of near-disappearance of summer Arctic sea ice. The thesis demonstrates the potential of improving Earth system model simulations of Arctic climate with observations. Initializing climate models with observations is a relatively new field that needs to be further investigated. Finding emergent constraints for Arctic climate parameters may greatly enhance the potential reduction in uncertainties of multi-model projections when using observation-based analysis methods like MDER. While projection uncertainties are still large, most studies, including this thesis, suggest a more pessimistic future for Arctic sea ice. Hence, the conclusions are that mitigation strategies to reduce Arctic warming need to be intensified and that it becomes increasingly crucial to further improve the understanding of the Arctic climate system.Die Fläche des arktischen Sommermeereises hat in den letzten 30 Jahren um ca. 50% abgenommen. Diese Veränderungsrate ist in der Erdgeschichte beispiellos und ist zumindest teilweise auf einen anthropogenen Einfluss zurückzuführen. Die Veränderungen in der Arktis haben enorme Auswirkungen auf das weltweite Klima. Erdsystemmodelle sind parametrisierte Computermodelle, die das Ziel haben, das globale Klima und dessen Veränderungen zu simulieren. Das Coupled Model Intercomparison Project (CMIP) koordiniert die Forschungsexperimente mit diesen Modellen unter gemeinsamen Leitlinien. Die Unsicherheiten in den CMIP-Simulationen des arktischen Klimas groß sind – sowohl auf Kurzzeit- als auch auf Langzeitskala. In dieser Dissertation wird untersucht, wie Beobachtungen zur Verbesserung der arktischen Klimasimulationen genutzt werden können. Hierfür werden arktische Klimaparameter aus verschiedenen Typen von Klimasimulationen der Erdsystemmodelle aus CMIP Phase 5 (CMIP5) mit Beobachtungen analysiert und evaluiert. Zu diesem Zweck wird das Earth System Model Evaluation Tool (ESMValTool) erweitert und benutzt, das von Wissenschaftlern weltweit dazu entwickelt wird, die komplexe Evaluation von Erdsystemmodellen zu erleichtern. Die Dissertation besteht aus zwei Teilen, die auf Studien basieren, die vom Autor der Dissertation durchgeführt wurden. In Teil eins wird untersucht, ob die Initialisierung von Erdsystemmodellsimulationen mit Beobachtungen zu erhöhter Vorhersagegenauigkeit („Skill“) des arktischen Klimas führt. Der Fokus liegt dabei auf dem relativ neuen Feld der dekadischen Klimavorhersagen, die einen Vorhersagehorizont von nur zehn Jahren haben. Im Gegensatz zu Langzeitsimulationen werden dekadische Klimavorhersagen mit Beobachtungsdaten initialisiert, so dass sie von der beobachteten Phase natürlicher Klimaschwankung aus starten. Um mögliche Verbesserungen im Skill durch Initialisierung zu untersuchen, wurde ein vielschichtiges Verifikationssystem für retrospektive dekadische Klimavorhersagen („Hindcasts“) entwickelt, in das ESMValTool implementiert und auf dekadische Hindcasts eines Erdsystemmodells angewendet. Es zeigt sich, dass die Initialisierung den Vorhersageskill von Oberflächentemperaturen im Nordatlantik und entlang der Ostküste von Grönland verbessert, wobei die mittlere quadratische Abweichung zu Beobachtungen um 50% reduziert wird. Dementsprechend hat sich der Skill für die integrierte Meereisfläche im Grönlandmeer erhöht. Allerdings zeigen sich diese Verbesserungen nur im Winter und nur für wenige Jahre nach Initialisierung. Tatsächlich zeigt sich eine signifikante Skillabnahme besonders am Meereisrand und in der Kara- und Barentssee. Dies kann mit dem sogenannten Initialisierungsschock erklärt werden - einem Mechanismus, der das Modell dazu bringt, nach der Initialisierung zu seinem verzerrten intrinsischen Klimazustand zurück zu driften und diesen zu überschießen. Die Arbeit zeigt Möglichkeiten auf, die dekadischen Klimavorhersagen in der Arktis zu verbessern, wie beispielsweise durch eine Berücksichtigung von Meereisparametern beim Initialisierungsprozess und eine zusätzliche Initialisierung des Modells im Sommer anstatt nur am Ende des Jahres. Teil zwei der Dissertation beschäftigt sich mit der Langzeitentwicklung der Arktis und hat das Ziel, Unsicherheiten in Klimaprojektionen der Entwicklung der arktischen Meereisausdehnung im 21. Jahrhundert zu reduzieren. Hierfür wird von der Multiple Diagnostic Ensemble Regression (MDER) Methode Gebrauch gemacht, die mithilfe von Beobachtungen die Bandbreite der CMIP5-Klimaprognosen einschränkt. Der zentrale Vorteil von MDER gegenüber anderen Analysemethoden ist der iterative Regressionsalgorithmus, der diejenigen Diagnostiken aus einer Menge an vorgegebenen Diagnostiken auswählt, die die Zukunftsvariable am besten vorhersagen können. Mit dem hieraus errechneten Regressionsmodell lassen sich dann die Prognosen mittels Beobachtungen einschränken. Durch die Anwendung dieser Methode auf Prognosen des arktischen Meereises von 29 CMIP5-Modellen konnte die Modellunsicherheit in Multimodellsimulationen der Ausdehnung des arktischen Septembermeereises um bis zu 50% verkleinert werden. Darüber hinaus zeigt das beschränkte Multimodellmittel im Vergleich zum ungewichteten Mittel eine um ca. eine Million km² (20%) kleinere Eisfläche und ein früheres Verschwinden des arktischen Sommermeereises um mehr als eine Dekade (von 2076 auf 2062) in einem hohen Treibhausgasszenario. Dies deutet auf einen schnelleren Rückgang des arktischen Meereises hin, als bisher angenommen. Allerdings zeigen die Ergebnisse der Dissertation auch, dass die Modellunsicherheit – zusammen mit nicht-reduzierbarer interner Klimavariabilität und Unsicherheiten durch verschiedene Annahmen zukünftiger Emissionsszenarien – zu groß für genaue Vorhersagen des Zeitpunkts bleibt, an dem das arktische Sommermeereis zum ersten Mal verschwindet. Die Dissertation legt das große Potential dar, Erdsystemmodellsimulationen des arktischen Klimas mittels Beobachtungen zu verbessern. Klimamodelle mit Beobachtungen zu initialisieren, ist ein relativ neues Forschungsgebiet, das noch weiterer Erforschung bedarf. Das Auffinden sogenannter „emergent constraints“ für arktische Klimaparameter kann die Reduktion von Unsicherheiten mittels beobachtungsbasierten Analysemethoden wie MDER stark verbessern. Obwohl Prognoseunsicherheiten noch immer groß sind, deuten die meisten Studien, so wie diese Arbeit, auf eine pessimistischere Zukunft des arktischen Meereises hin. Die Arbeit zeigt somit, dass Mitigationsstrategien gegen die arktische Klimaerwärmung weiter ausgebaut werden müssen und es zunehmend wichtiger wird, das Klimasystem der Arktis besser zu verstehen

Two-Center Interferences in Dielectronic Transitions in H₂⁺+ He Collisions

Molecular two-center interferences in a collision induced excitation of H2+ projectile ions, with simultaneous ionization of helium target atoms, are investigated in a kinematically complete experiment. In the process under investigation, the helium atom is singly ionized and simultaneously the molecular hydrogen ion is dissociated. Different collision mechanisms are identified and interference fringes emerging from a correlated first-order mechanism and from an independent second-order process are observed

Pulse length dependence of photoelectron circular dichroism

We investigate photoelectron circular dichroism (PECD) with coherent light sources whose pulse durations range from femtoseconds to nanoseconds. To that end, we employed an optical parametric amplifier, an ultraviolet optical pulse shaper, and a nanosecond dye laser, all centered around a wavelength of 380 nm. A multiphoton ionization experiment on the gas-phase chiral prototype fenchone found that PECD measured via the 3s intermediate resonance is about 15% and robust over five orders of magnitude of the pulse duration. PECD remains robust despite ongoing molecular dynamics such as rotation, vibration, and internal conversion. We used the Lindblad equation to model the molecular dynamics. Under the assumption of a cascading internal conversion, from the 3p to the 3s and further to the ground state, we estimated the lifetimes of the internal conversion processes in the 100 fs regime

Earth System Model Evaluation Tool (ESMValTool) v2.0 - diagnostics for emergent constraints and future projections from Earth system models in CMIP

The Earth System Model Evaluation Tool (ESMValTool), a community diagnostics and performance metrics tool for evaluation and analysis of Earth system models (ESMs) is designed to facilitate a more comprehensive and rapid comparison of single or multiple models participating in the Coupled Model Intercomparison Project (CMIP). The ESM results can be compared against observations or reanalysis data as well as against other models including predecessor versions of the same model. The updated and extended version 2.0 of the ESMValTool includes several new analysis scripts such as large-scale diagnostics for evaluation of ESMs as well as diagnostics for extreme events, regional model and impact evaluation. In this paper, the newly implemented climate metrics such as effective climate sensitivity (ECS) and transient climate response (TCR) as well as emergent constraints for various climate-relevant feedbacks and diagnostics for future projections from ESMs are described and illustrated with examples using results from the well-established model ensemble CMIP5. The emergent constraints implemented include constraints on ECS, snow-albedo effect, climate-carbon cycle feedback, hydrologic cycle intensification, future Indian summer monsoon precipitation, and year of disappearance of summer Arctic sea ice. The diagnostics included in ESMValTool v2.0 to analyze future climate projections from ESMs further include analysis scripts to reproduce selected figures of chapter 12 of the Intergovernmental Panel on Climate Change's (IPCC) Fifth Assessment report (AR5) and various multi-model statistics

Benchmarking CMIP5 models with a subset of ESA CCI Phase 2 data using the ESMValTool

The Coupled Model Intercomparison Project (CMIP) is now moving into its sixth phase and aims at a more routine evaluation of the models as soon as the model output is published to the Earth System Grid Federation (ESGF). To meet this goal the Earth System Model Evaluation Tool (ESMValTool), a community diagnostics and performance metrics tool for the systematic evaluation of Earth system models (ESMs) in CMIP, has been developed and a first version (1.0) released as open source software in 2015. Here, an enhanced version of the ESMValTool is presented that exploits a subset of Essential Climate Variables (ECVs) from the European Space Agency's Climate Change Initiative (ESA CCI) Phase 2 and this version is used to demonstrate the value of the data for model evaluation. This subset includes consistent, long-term time series of ECVs obtained from harmonized, reprocessed products from different satellite instruments for sea surface temperature, sea ice, cloud, soil moisture, land cover, aerosol, ozone, and greenhouse gases. The ESA CCI data allow 'extending the calculation of performance metrics as summary statistics for some variables and add an important alternative data set in other cases where observations are already available. The provision of uncertainty estimates on a per grid basis for the ESA CCI data sets is used in a new extended version of the Taylor diagram and provides important additional information for a more objective evaluation of the models. In our analysis we place a specific focus on the comparability of model and satellite data both in time and space. The ESA CCI data are well suited for an evaluation of results from global climate models across ESM compartments as well as an analysis of long-term trends, variability and change in the context of a changing climate. The enhanced version of the ESMValTool is released as open source software and ready to support routine model evaluation in CMIP6 and at individual modeling centers. (C) 2017 Elsevier Inc. All rights reserved.Peer reviewe

ESMValTool (v1.0) – a community diagnostic and performance metrics tool for routine evaluation of Earth system models in CMIP

A community diagnostics and performance metrics tool for the evaluation of Earth system models (ESMs) has been developed that allows for routine comparison of single or multiple models, either against predecessor versions or against observations. The priority of the effort so far has been to target specific scientific themes focusing on selected essential climate variables (ECVs), a range of known systematic biases common to ESMs, such as coupled tropical climate variability, monsoons, Southern Ocean processes, continental dry biases, and soil hydrology–climate interactions, as well as atmospheric CO2 budgets, tropospheric and stratospheric ozone, and tropospheric aerosols. The tool is being developed in such a way that additional analyses can easily be added. A set of standard namelists for each scientific topic reproduces specific sets of diagnostics or performance metrics that have demonstrated their importance in ESM evaluation in the peer-reviewed literature. The Earth System Model Evaluation Tool (ESMValTool) is a community effort open to both users and developers encouraging open exchange of diagnostic source code and evaluation results from the Coupled Model Intercomparison Project (CMIP) ensemble. This will facilitate and improve ESM evaluation beyond the state-of-the-art and aims at supporting such activities within CMIP and at individual modelling centres. Ultimately, we envisage running the ESMValTool alongside the Earth System Grid Federation (ESGF) as part of a more routine evaluation of CMIP model simulations while utilizing observations available in standard formats (obs4MIPs) or provided by the user

Creative destruction in science

Drawing on the concept of a gale of creative destruction in a capitalistic economy, we argue that initiatives to assess the robustness of findings in the organizational literature should aim to simultaneously test competing ideas operating in the same theoretical space. In other words, replication efforts should seek not just to support or question the original findings, but also to replace them with revised, stronger theories with greater explanatory power. Achieving this will typically require adding new measures, conditions, and subject populations to research designs, in order to carry out conceptual tests of multiple theories in addition to directly replicating the original findings. To illustrate the value of the creative destruction approach for theory pruning in organizational scholarship, we describe recent replication initiatives re-examining culture and work morality, working parents\u2019 reasoning about day care options, and gender discrimination in hiring decisions. Significance statement It is becoming increasingly clear that many, if not most, published research findings across scientific fields are not readily replicable when the same method is repeated. Although extremely valuable, failed replications risk leaving a theoretical void\u2014 reducing confidence the original theoretical prediction is true, but not replacing it with positive evidence in favor of an alternative theory. We introduce the creative destruction approach to replication, which combines theory pruning methods from the field of management with emerging best practices from the open science movement, with the aim of making replications as generative as possible. In effect, we advocate for a Replication 2.0 movement in which the goal shifts from checking on the reliability of past findings to actively engaging in competitive theory testing and theory building. Scientific transparency statement The materials, code, and data for this article are posted publicly on the Open Science Framework, with links provided in the article

Verification of Temperature and Sea Ice in the MiKlip Decadal Climate Predictions with the ESMValTool

Decadal climate predictions, that aim at predicting the time horizon of the next10-30 years, are a relatively new field of research. An open science topic is whetherthe initialization of the climate model simulations with observations of the slowly-varying components of the climate system results in more accurate near-term pre-dictions compared to uninitialized long-term simulations. To address this sciencequestion, Goddard et al. [2013] introduced a verification system for decadal ex-periments that enables a quantitative assessment of the model performance fromthe decadal predictions compared to observations and to uninitialized long-termsimulations.The goal of this thesis is to assess the possible additional predictive skill for near-surface temperature and sea-ice concentrations in the decadal simulations of theMax Planck Institute Earth System Model (MPI-ESM) compared to the uninitial-ized long-term simulations. To allow this assessment, the verification frameworkfrom Goddard et al. (2013) is implemented into the Earth System Model Valida-tion Tool (ESMValTool). The ESMValTool is a software tool developed by multipleinstitutions that aims at improving routine Earth system model (ESM) evaluation.For this work, in particular the anomaly correlation skill, reliability and accuracy ofthe simulations are evaluated and tested against each other, the model’s uninitial-ized long-term simulations, and observations.No further prediction skill in global mean near-surface temperature is found fordecadal hindcasts (i.e., retrospective forecasts) in comparison to the long-termsimulations, except for the initialization year 1. In the following years, the decadalhindcasts drift to their preferred biased model state resulting in a prediction skillthat is similar to that of the long-term simulations. On regional scales however,certain areas such as southwest of the South American continent and the NorthAtlantic Ocean show a significantly higher predictive skill. This regionality alsotranslates to sea ice.Further studies are required that expand the proposed metrics and include dif-ferent variables and additional climate models to provide a concluding answer tothe question of whether the initialization of climate models can lead to a higherpredictability of near-future climate change