Impact of increased resolution on the representation of the Canary upwelling system in climate models

We investigate the representation of the Canary upwelling system (CUS) in six global coupled climate models operated at high and standard resolution as part of the High Resolution Model Intercomparison Project (HighResMIP). The models' performance in reproducing the observed CUS is assessed in terms of various upwelling indices based on sea surface temperature (SST), wind stress, and sea surface height, focusing on the effect of increasing model spatial resolution. Our analysis shows that possible improvement in upwelling representation due to the increased spatial resolution depends on the subdomain of the CUS considered. Strikingly, along the Iberian Peninsula region, which is the northernmost part of the CUS, the models show lower skill at higher resolution compared to their corresponding lower-resolution version in both components for all the indices analyzed in this study. In contrast, over the southernmost part of the CUS, from the north of Morocco to the Senegalese coast, the high-ocean- and high-atmosphere-resolution models simulate a more realistic upwelling than the standard-resolution models, which largely differ from the range of observational estimates. These results suggest that increasing resolution is not a sufficient condition to obtain a systematic improvement in the simulation of the upwelling phenomena as represented by the indices considered here, and other model improvements notably in terms of the physical parameterizations may also play a role.</p

On the range of future Sahel precipitation projections and the selection of a sub-sample of CMIP5 models for impact studies

The future evolution of the West African Monsoon is studied by analyzing 32 CMIP5 models under the rcp8.5 emission scenario. A hierarchical clustering method based on the simulated pattern of precipitation changes is used to classify the models. Four groups, which do not agree on the simple sign of future Sahel precipitation change, are obtained. We find that the inter-group differences are mainly associated with the large spread in (i) temperature increase over the Sahara and North Atlantic and in (ii) the strengthening of low and mid-level winds. A wetter Sahel is associated with a strong increase in temperature over the Sahara (>6°C), a northward shift of the monsoon system and a weakening of the African Easterly jet. A dryer Sahel is associated with subsidence anomalies, a strengthening of the 600 hPa wind speed, and a weaker warming over the Northern Hemisphere. Moreover, the western (central) Sahel is projected to become dryer (wetter) during the first months (last months) of the rainy season in a majority of models. We propose several methods to select a sub-sample of models that captures both the ensemble mean pattern and/or the spread of precipitation changes from the full ensemble. This methodology is useful in all the situations for which it is not possible to deal with a large ensemble of models, and in particular most impact studies. We show that no relationship exists between the climatological mean biases in precipitation and temperature and the future changes in the monsoon intensity. This indicates that the mean bias is therefore not a reliable metric for the model selection. For this reason, we propose several methodologies, based on the projected precipitation changes: The “diversity” method, which consists in the selection of one model from each group is the most appropriate to capture the spread in precipitation change. The “pattern selection” method, which consists in the selection of models in a single group allows to select models for the study of a specific pattern of precipitation change, for example the one that is the most representative of the full ensemble

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

Future projections of Mediterranean cyclone characteristics using the Med-CORDEX ensemble of coupled regional climate system models

Here, we analyze future projections of cyclone activity in the Mediterranean region at the end of the twenty-first century based on an ensemble of state-of-the-art fully-coupled Regional Climate System Models (RCSMs) from the Med-CORDEX initiative under the Representative Concentration Pathway (RCP) 8.5. Despite some noticeable biases, all the RCSMs capture spatial patterns and cyclone activity key characteristics in the region and thus all of them can be considered as plausible representations of the future evolution of Mediterranean cyclones. In general, the RCSMs show at the end of the twenty-first century a decrease in the number and an overall weakening of cyclones moving across the Mediterranean. Five out of seven RCSMs simulate also a decrease of the mean size of the systems. Moreover, in agreement with what already observed in CMIP5 projections for the area, the models suggest an increase in the Central part of the Mediterranean region and a decrease in the South-eastern part of the region in the cyclone-related wind speed and precipitation rate. These rather two opposite tendencies observed in the precipitation should compensate and amplify, respectively, the effect of the overall reduction of the frequency of cyclones on the water budget over the Central and South-eastern part of the region. A pronounced inter-model spread among the RCSMs emerges for the projected changes in the cyclone adjusted deepening rate, seasonal cycle occurrence and associated precipitation and wind patterns over some areas of the basin such as Ionian Sea and Iberian Peninsula. The differences observed appear to be determined by the driving Global Circulation Model (GCM) and influenced by the RCSM physics and internal variability. These results point to the importance of (1) better characterizing the range of plausible futures by relying on ensembles of models that explore well the existing diversity of GCMs and RCSMs as well as the climate natural variability and (2) better understanding the driving mechanisms of the future evolution of Mediterranean cyclones properties

Characterizing, modelling and understanding the climate variability of the deep water formation in the North-Western Mediterranean Sea

Observing, modelling and understanding the climate-scale variability of the deep water formation (DWF) in the North-Western Mediterranean Sea remains today very challenging. In this study, we first characterize the interannual variability of this phenomenon by a thorough reanalysis of observations in order to establish reference time series. These quantitative indicators include 31 observed years for the yearly maximum mixed layer depth over the period 1980–2013 and a detailed multi-indicator description of the period 2007–2013. Then a 1980–2013 hindcast simulation is performed with a fully-coupled regional climate system model including the high-resolution representation of the regional atmosphere, ocean, land-surface and rivers. The simulation reproduces quantitatively well the mean behaviour and the large interannual variability of the DWF phenomenon. The model shows convection deeper than 1000 m in 2/3 of the modelled winters, a mean DWF rate equal to 0.35 Sv with maximum values of 1.7 (resp. 1.6) Sv in 2013 (resp. 2005). Using the model results, the winter-integrated buoyancy loss over the Gulf of Lions is identified as the primary driving factor of the DWF interannual variability and explains, alone, around 50 % of its variance. It is itself explained by the occurrence of few stormy days during winter. At daily scale, the Atlantic ridge weather regime is identified as favourable to strong buoyancy losses and therefore DWF, whereas the positive phase of the North Atlantic oscillation is unfavourable. The driving role of the vertical stratification in autumn, a measure of the water column inhibition to mixing, has also been analyzed. Combining both driving factors allows to explain more than 70 % of the interannual variance of the phenomenon and in particular the occurrence of the five strongest convective years of the model (1981, 1999, 2005, 2009, 2013). The model simulates qualitatively well the trends in the deep waters (warming, saltening, increase in the dense water volume, increase in the bottom water density) despite an underestimation of the salinity and density trends. These deep trends come from a heat and salt accumulation during the 1980s and the 1990s in the surface and intermediate layers of the Gulf of Lions before being transferred stepwise towards the deep layers when very convective years occur in 1999 and later. The salinity increase in the near Atlantic Ocean surface layers seems to be the external forcing that finally leads to these deep trends. In the future, our results may allow to better understand the behaviour of the DWF phenomenon in Mediterranean Sea simulations in hindcast, forecast, reanalysis or future climate change scenario modes. The robustness of the obtained results must be however confirmed in multi-model studies

Artificial Intelligence and Digital Transformation: early lessons from the COVID-19 crisis

The COVID-19 pandemic has created an extraordinary medical, economic and social emergency. To contain the spread of the virus, many countries adopted a lock down policy closing schools and business and keeping people at home for several months. This resulted in a massive surge of activity online for education, business, public administration, research, social interaction. This report considers these recent developments and identifies some early lessons with respect to the present and future development of AI and digital transformation in Europe, focusing in particular on data, as this is an area of significant shifts in attitudes and policy. The report analyses the increasing use of AI in medicine and healthcare, the tensions in data sharing between individual rights and collective wellbeing, the search for technological solutions like contact tracing apps to help monitor the spread of the virus, and the potential concerns they raise. The forced transition to online showed the resilience of the Internet but also the disproportionate impact on already vulnerable groups like the elderly and children. The report concludes that the COVID-19 crisis has acted as a boost for AI adoption and data sharing, and created new opportunities. It has also amplified concerns for democracy and social inequality and showed Europe’s vulnerability on data and platforms, calling for action to address these crucial aspects.JRC.B.6-Digital Econom

Atlantic multidecadal variability and North Atlantic jet: a multi-model view from the decadal climate prediction project

The influence of the Atlantic Multidecadal Variability (AMV) on the North Atlantic storm track and eddy–driven jet in the winter season is assessed via a coordinated analysis of idealised simulations with state-of-the-art coupled models. Data used are obtained from a multi-model ensemble of AMV± experiments conducted in the framework of the Decadal Climate Prediction Project component C. These experiments are performed by nudging the surface of the Atlantic ocean to states defined by the superimposition of observed AMV± anomalies onto the model climatology. A robust extra-tropical response is found in the form of a wave-train extending from the Pacific to the Nordic seas. In the warm phase of the AMV compared to cold phase, the Atlantic storm track is typically contracted and less extended poleward and the low-level jet is shifted towards the equator in the Eastern Atlantic. Despite some robust features, the picture of an uncertain and model-dependent response of the Atlantic jet emerges and we demonstrate a link between model bias and the character of the jet response

Respective impacts of Arctic sea ice decline and increasing greenhouse gases concentration on Sahel precipitation

The impact of climate change on Sahel precipitation is uncertain and has to be widely documented. Recently, it has been shown that Arctic sea ice loss leverages the global warming effects worldwide, suggesting a potential impact of Arctic sea ice decline on tropical regions. However, defining the specific roles of increasing greenhouse gases (GHG) concentration and declining Arctic sea ice extent on Sahel climate is not straightforward since the former impacts the latter. We avoid this dependency by analysing idealized experiments performed with the CNRM-CM5 coupled model. Results show that the increase in GHG concentration explains most of the Sahel precipitation change. We found that the impact due to Arctic sea ice loss depends on the level of atmospheric GHG concentration. When the GHG concentration is relatively low (values representative of 1980s), then the impact is moderate over the Sahel. However, when the concentration in GHG is levelled up, then Arctic sea ice loss leads to increased Sahel precipitation. In this particular case the ocean-land meridional gradient of temperature strengthens, allowing a more intense monsoon circulation. We linked the non-linearity of Arctic sea ice decline impact with differences in temperature and sea level pressure changes over the North Atlantic Ocean. We argue that the impact of the Arctic sea ice loss will become more relevant with time, in the context of climate change

Coupled climate response to Atlantic Multidecadal Variability in a multi-model multi-resolution ensemble

North Atlantic sea surface temperatures (SSTs) underwent pronounced multidecadal variability during the 20th and early 21st century. We examine the impacts of this Atlantic Multidecadal Variability (AMV), also referred to as the Atlantic Multidecadal Oscillation (AMO), on climate in an ensemble of five coupled climate models at both low and high spatial resolution. We use a SST nudging scheme specified by the Coupled Model Intercomparision Project's Decadal Climate Prediction Project Component C (CMIP6 DCPP-C) to impose a persistent positive/negative phase of the AMV in the North Atlantic in coupled model simulations; SSTs are free to evolve outside this region. The large-scale seasonal mean response to the positive AMV involves widespread warming over Eurasia and the Americas, with a pattern of cooling over the Pacific Ocean similar to the Pacific Decadal Oscillation (PDO), together with a northward displacement of the inter-tropical convergence zone (ITCZ). The accompanying changes in global atmospheric circulation lead to widespread changes in precipitation. We use Analysis of Variance (ANOVA) to demonstrate that this large-scale climate response is accompanied by significant differences between models in how they respond to the common AMV forcing, particularly in the tropics. These differences may arise from variations in North Atlantic air-sea heat fluxes between models despite a common North Atlantic SST forcing pattern. We cannot detect a widespread effect of increased model horizontal resolution in this climate response, with the exception of the ITCZ, which shifts further northwards in the positive phase of the AMV in the higher resolution configurations

Artificial Intelligence: A European Perspective

We are only at the beginning of a rapid period of transformation of our economy and society due to the convergence of many digital technologies. Artificial Intelligence (AI) is central to this change and offers major opportunities to improve our lives. The recent developments in AI are the result of increased processing power, improvements in algorithms and the exponential growth in the volume and variety of digital data. Many applications of AI have started entering into our every-day lives, from machine translations, to image recognition, and music generation, and are increasingly deployed in industry, government, and commerce. Connected and autonomous vehicles, and AI-supported medical diagnostics are areas of application that will soon be commonplace. There is strong global competition on AI among the US, China, and Europe. The US leads for now but China is catching up fast and aims to lead by 2030. For the EU, it is not so much a question of winning or losing a race but of finding the way of embracing the opportunities offered by AI in a way that is human-centred, ethical, secure, and true to our core values. The EU Member States and the European Commission are developing coordinated national and European strategies, recognising that only together we can succeed. We can build on our areas of strength including excellent research, leadership in some industrial sectors like automotive and robotics, a solid legal and regulatory framework, and very rich cultural diversity also at regional and sub-regional levels. It is generally recognised that AI can flourish only if supported by a robust computing infrastructure and good quality data: • With respect to computing, we identified a window of opportunity for Europe to invest in the emerging new paradigm of computing distributed towards the edges of the network, in addition to centralised facilities. This will support also the future deployment of 5G and the Internet of Things. • With respect to data, we argue in favour of learning from successful Internet companies, opening access to data and developing interactivity with the users rather than just broadcasting data. In this way, we can develop ecosystems of public administrations, firms, and civil society enriching the data to make it fit for AI applications responding to European needs. We should embrace the opportunities afforded by AI but not uncritically. The black box characteristics of most leading AI techniques make them opaque even to specialists. AI systems are currently limited to narrow and well-defined tasks, and their technologies inherit imperfections from their human creators, such as the well-recognised bias effect present in data. We should challenge the shortcomings of AI and work towards strong evaluation strategies, transparent and reliable systems, and good human-AI interactions. Ethical and secure-by-design algorithms are crucial to build trust in this disruptive technology, but we also need a broader engagement of civil society on the values to be embedded in AI and the directions for future development. This social engagement should be part of the effort to strengthen our resilience at all levels from local, to national and European, across institutions, industry and civil society. Developing local ecosystems of skills, computing, data, and applications can foster the engagement of local communities, respond to their needs, harness local creativity and knowledge, and build a human-centred, diverse, and socially driven AI. We still know very little about how AI will impact the way we think, make decisions, relate to each other, and how it will affect our jobs. This uncertainty can be a source of concern but is also a sign of opportunity. The future is not yet written. We can shape it based on our collective vision of what future we would like to have. But we need to act together and act fast.JRC.B.6-Digital Econom