The Decadal Climate Prediction Project (DCPP) contribution to CMIP6

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 the fifth Coupled Model Intercomparison Project (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 a contribution to CMIP6 (Eyring et al., 2016) and to the WCRP Grand Challenge on Near Term Climate Prediction (Kushnir et al., 2016). 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, analysis and dissemination 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

Hyphal network whole field imaging allows for accurate estimation of anastomosis rates and branching dynamics of the filamentous fungus Podospora anserina

The success of filamentous fungi in colonizing most natural environments can be largely attributed to their ability to form an expanding interconnected network, the mycelium, or thallus, constituted by a collection of hyphal apexes in motion producing hyphae and subject to branching and fusion. In this work, we characterize the hyphal network expansion and the structure of the fungus Podospora anserina under controlled culture conditions. To this end, temporal series of pictures of the network dynamics are produced, starting from germinating ascospores and ending when the network reaches a few centimeters width, with a typical image resolution of several micrometers. The completely automated image reconstruction steps allow an easy post-processing and a quantitative analysis of the dynamics. The main features of the evolution of the hyphal network, such as the total length L of the mycelium, the number of "nodes" (or crossing points) N and the number of apexes A, can then be precisely quantified. Beyond these main features, the determination of the distribution of the intra-thallus surfaces (S; i; ) and the statistical analysis of some local measures of N, A and L give new insights on the dynamics of expanding fungal networks. Based on these results, we now aim at developing robust and versatile discrete/continuous mathematical models to further understand the key mechanisms driving the development of the fungus thallus

HighResMIP versions of EC-Earth: EC-Earth3P and EC-Earth3P-HR - Description, model computational performance and basic validation

A new global high-resolution coupled climate model, EC-Earth3P-HR has been developed by the EC-Earth consortium, with a resolution of approximately 40 km for the atmosphere and 0.25° for the ocean, alongside with a standard-resolution version of the model, EC-Earth3P (80 km atmosphere, 1.0 ° ocean). The model forcing and simulations follow the High Resolution Model Intercomparison Project (HighResMIP) protocol. According to this protocol, all simulations are made with both high and standard resolutions. The model has been optimized with respect to scalability, performance, data storage and post-processing. In accordance with the HighResMIP protocol, no specific tuning for the high-resolution version has been applied. Increasing horizontal resolution does not result in a general reduction of biases and overall improvement of the variability, and deteriorating impacts can be detected for specific regions and phenomena such as some Euro-Atlantic weather regimes, whereas others such as the El Niño-Southern Oscillation show a clear improvement in their spatial structure. The omission of specific tuning might be responsible for this. The shortness of the spin-up, as prescribed by the HighResMIP protocol, prevented the model from reaching equilibrium. The trend in the control and historical simulations, however, appeared to be similar, resulting in a warming trend, obtained by subtracting the control from the historical simulation, close to the observational one

Evaluation of global ocean–sea-ice model simulations based on the experimental protocols of the Ocean Model Intercomparison Project phase 2 (OMIP-2)

We present a new framework for global ocean- sea-ice model simulations based on phase 2 of the Ocean Model Intercomparison Project (OMIP-2), making use of the surface dataset based on the Japanese 55-year atmospheric reanalysis for driving ocean-sea-ice models (JRA55-do).We motivate the use of OMIP-2 over the framework for the first phase of OMIP (OMIP-1), previously referred to as the Coordinated Ocean-ice Reference Experiments (COREs), via the evaluation of OMIP-1 and OMIP-2 simulations from 11 state-of-the-science global ocean-sea-ice models. In the present evaluation, multi-model ensemble means and spreads are calculated separately for the OMIP-1 and OMIP-2 simulations and overall performance is assessed considering metrics commonly used by ocean modelers. Both OMIP-1 and OMIP-2 multi-model ensemble ranges capture observations in more than 80% of the time and region for most metrics, with the multi-model ensemble spread greatly exceeding the difference between the means of the two datasets. Many features, including some climatologically relevant ocean circulation indices, are very similar between OMIP-1 and OMIP- 2 simulations, and yet we could also identify key qualitative improvements in transitioning from OMIP-1 to OMIP- 2. For example, the sea surface temperatures of the OMIP- 2 simulations reproduce the observed global warming during the 1980s and 1990s, as well as the warming slowdown in the 2000s and the more recent accelerated warming, which were absent in OMIP-1, noting that the last feature is part of the design of OMIP-2 because OMIP-1 forcing stopped in 2009. A negative bias in the sea-ice concentration in summer of both hemispheres in OMIP-1 is significantly reduced in OMIP-2. The overall reproducibility of both seasonal and interannual variations in sea surface temperature and sea surface height (dynamic sea level) is improved in OMIP-2. These improvements represent a new capability of the OMIP-2 framework for evaluating processlevel responses using simulation results. Regarding the sensitivity of individual models to the change in forcing, the models show well-ordered responses for the metrics that are directly forced, while they show less organized responses for those that require complex model adjustments. Many of the remaining common model biases may be attributed either to errors in representing important processes in ocean-sea-ice models, some of which are expected to be reduced by using finer horizontal and/or vertical resolutions, or to shared biases and limitations in the atmospheric forcing. In particular, further efforts are warranted to resolve remaining issues in OMIP-2 such as the warm bias in the upper layer, the mismatch between the observed and simulated variability of heat content and thermosteric sea level before 1990s, and the erroneous representation of deep and bottom water formations and circulations. We suggest that such problems can be resolved through collaboration between those developing models (including parameterizations) and forcing datasets. Overall, the present assessment justifies our recommendation that future model development and analysis studies use the OMIP-2 framework

Ocean and land forcing of the record-breaking Dust Bowl heat waves across central United States

International audienceThe severe drought of the 1930s Dust Bowl decade coincided with record-breaking summer heatwaves that contributed to the socioeconomic and ecological disaster over North America's Great Plains. It remains unresolved to what extent these exceptional heatwaves, hotter than in historically forced coupled climate model simulations, were forced by sea surface temperatures (SSTs) and exacerbated through human-induced deterioration of land cover. Here we show, using an atmospheric-only model, that anomalously warm North Atlantic SSTs enhance heatwave activity through an association with drier spring conditions resulting from weaker moisture transport. Model devegetation simulations, that represent the widespread exposure of bare soil in the 1930s, suggest human activity fueled stronger and more frequent heatwaves through greater evaporative drying in the warmer months. This study highlights the potential for the amplification of naturally occurring extreme events like droughts by vegetation feedbacks to create more extreme heatwaves in a warmer world

Evaluation of global ocean–sea-ice model simulations based on the experimental protocols of the Ocean Model Intercomparison Project phase 2 (OMIP-2)

We present a new framework for global ocean–sea-ice model simulations based on phase 2 of the Ocean Model Intercomparison Project (OMIP-2), making use of the surface dataset based on the Japanese 55-year atmospheric reanalysis for driving ocean–sea-ice models (JRA55-do). We motivate the use of OMIP-2 over the framework for the first phase of OMIP (OMIP-1), previously referred to as the Coordinated Ocean–ice Reference Experiments (COREs), via the evaluation of OMIP-1 and OMIP-2 simulations from 11 state-of-the-science global ocean–sea-ice models. In the present evaluation, multi-model ensemble means and spreads are calculated separately for the OMIP-1 and OMIP-2 simulations and overall performance is assessed considering metrics commonly used by ocean modelers. Both OMIP-1 and OMIP-2 multi-model ensemble ranges capture observations in more than 80 % of the time and region for most metrics, with the multi-model ensemble spread greatly exceeding the difference between the means of the two datasets. Many features, including some climatologically relevant ocean circulation indices, are very similar between OMIP-1 and OMIP-2 simulations, and yet we could also identify key qualitative improvements in transitioning from OMIP-1 to OMIP-2. For example, the sea surface temperatures of the OMIP-2 simulations reproduce the observed global warming during the 1980s and 1990s, as well as the warming slowdown in the 2000s and the more recent accelerated warming, which were absent in OMIP-1, noting that the last feature is part of the design of OMIP-2 because OMIP-1 forcing stopped in 2009. A negative bias in the sea-ice concentration in summer of both hemispheres in OMIP-1 is significantly reduced in OMIP-2. The overall reproducibility of both seasonal and interannual variations in sea surface temperature and sea surface height (dynamic sea level) is improved in OMIP-2. These improvements represent a new capability of the OMIP-2 framework for evaluating process-level responses using simulation results. Regarding the sensitivity of individual models to the change in forcing, the models show well-ordered responses for the metrics that are directly forced, while they show less organized responses for those that require complex model adjustments. Many of the remaining common model biases may be attributed either to errors in representing important processes in ocean–sea-ice models, some of which are expected to be reduced by using finer horizontal and/or vertical resolutions, or to shared biases and limitations in the atmospheric forcing. In particular, further efforts are warranted to resolve remaining issues in OMIP-2 such as the warm bias in the upper layer, the mismatch between the observed and simulated variability of heat content and thermosteric sea level before 1990s, and the erroneous representation of deep and bottom water formations and circulations. We suggest that such problems can be resolved through collaboration between those developing models (including parameterizations) and forcing datasets. Overall, the present assessment justifies our recommendation that future model development and analysis studies use the OMIP-2 framework.This research has been supported by the Integrated Research Program for Advancing Climate Models (TOUGOU) of the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan (grant nos. JPMXD0717935457 and JPMXD0717935561), the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) (grant no. 274762653), the Helmholtz Climate Initiative REKLIM (Regional Climate Change) and European Union's Horizon 2020 Research & Innovation program (grant nos. 727862 and 800154), the Research Council of Norway (EVA (grant no. 229771) and INES (grant no. 270061)), the US National Science Foundation (NSF) (grant no. 1852977), the National Natural Science Foundation of China (grant nos. 41931183 and 41976026), NOAA's Science Collaboration Program and administered by UCAR's Cooperative Programs for the Advancement of Earth System Science (CPAESS) (grant nos. NA16NWS4620043 and NA18NWS4620043B), and NOAA (grant no. NA18OAR4320123).Peer ReviewedPostprint (published version

Systematic Deletion of Homeobox Genes in Podospora anserina Uncovers Their Roles in Shaping the Fruiting Body

Higher fungi, which comprise ascomycetes and basidiomycetes, play major roles in the biosphere. Their evolutionary success may be due to the extended dikaryotic stage of their life cycle, which is the basis for their scientific name: the Dikarya. Dikaryosis is maintained by similar structures, the clamp in basidiomycetes and the crozier in ascomycetes. Homeodomain transcription factors are required for clamp formation in all basidiomycetes studied. We identified all the homeobox genes in the filamentous ascomycete fungus Podospora anserina and constructed deletion mutants for each of these genes and for a number of gene combinations. Croziers developed normally in these mutants, including those with up to six deleted homeogenes. However, some mutants had defects in maturation of the fruiting body, an effect that could be rescued by providing wild-type maternal hyphae. Analysis of mutants deficient in multiple homeogenes revealed interactions between the genes, suggesting that they operate as a complex network. Similar to their role in animals and plants, homeodomain transcription factors in ascomycetes are involved in shaping multicellular structures

The EC-Earth3 Earth system model for the Coupled Model Intercomparison Project 6

The Earth system model EC-Earth3 for contributions to CMIP6 is documented here, with its flexible coupling framework, major model configurations, a methodology for ensuring the simulations are comparable across different high-performance computing (HPC) systems, and with the physical performance of base configurations over the historical period. The variety of possible configurations and sub-models reflects the broad interests in the EC-Earth community. EC-Earth3 key performance metrics demonstrate physical behavior and biases well within the frame known from recent CMIP models. With improved physical and dynamic features, new Earth system model (ESM) components, community tools, and largely improved physical performance compared to the CMIP5 version, EC-Earth3 represents a clear step forward for the only European community ESM. We demonstrate here that EC-Earth3 is suited for a range of tasks in CMIP6 and beyond

Wood Utilization Is Dependent on Catalase Activities in the Filamentous Fungus Podospora anserina

Catalases are enzymes that play critical roles in protecting cells against the toxic effects of hydrogen peroxide. They are implicated in various physiological and pathological conditions but some of their functions remain unclear. In order to decipher the role(s) of catalases during the life cycle of Podospora anserina, we analyzed the role of the four monofunctional catalases and one bifunctional catalase-peroxidase genes present in its genome. The five genes were deleted and the phenotypes of each single and all multiple mutants were investigated. Intriguingly, although the genes are differently expressed during the life cycle, catalase activity is dispensable during both vegetative growth and sexual reproduction in laboratory conditions. Catalases are also not essential for cellulose or fatty acid assimilation. In contrast, they are strictly required for efficient utilization of more complex biomass like wood shavings by allowing growth in the presence of lignin. The secreted CATB and cytosolic CAT2 are the major catalases implicated in peroxide resistance, while CAT2 is the major player during complex biomass assimilation. Our results suggest that P. anserina produces external H2O2 to assimilate complex biomass and that catalases are necessary to protect the cells during this process. In addition, the phenotypes of strains lacking only one catalase gene suggest that a decrease of catalase activity improves the capacity of the fungus to degrade complex biomass

Current and emerging developments in subseasonal to decadal prediction

Weather and climate variations of subseasonal to decadal timescales can have enormous social, economic and environmental impacts, making skillful predictions on these timescales 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) timescales, these include high-impact meteorological events such as tropical cyclones, extratropical storms, floods, droughts, and heat and cold waves. On seasonal to decadal (S2D) timescales, while the focus remains broadly 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 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 correct, 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 Prograame (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