A Machine Learning‐Based Approach to Quantify ENSO Sources of Predictability

A machine learning method is used to identify sources of long‐term ENSO predictability in the ocean (sea surface temperature (SST) and heat content) and the atmosphere (near‐surface zonal wind (U10)). Tropical SST represents the primary source of predictability skill. While U10 does not increase the skill when associated with SST, our analysis suggests U10 alone has apredictive skill comparable to that of SST between 11 and 21 months in advance, from late fall up to late spring. The long‐lead signal originates from coupled wind‐SST interactions across the Indian Ocean (IO) and propagates across the Pacific via an atmospheric bridge mechanism. A linear correlation analysis supports this mechanism, suggesting a precursor link between anomalies in SST in the western and wind in the eastern IO. Our results have important implications for ENSO predictions beyond 1 year ahead and identify the key role of U10 over the IO

Was the cold European winter 2009-2010 modified by anthropogenic climate change? An attribution study

An attribution study has been performed to investigate the degree to which the unusually cold European winter 2009-2010 was modified by anthropogenic climate change. Two different methods have been included for the attribution: one based on a large HadGEM3-A ensemble and one based on a statistical surrogate method. Both methods are evaluated by comparing simulated winter temperature means, trends, standard deviations, skewness, return periods, and 5 % quantiles with observations. While the surrogate method performs well, HadGEM3-A in general underestimates the trend in winter by a factor of 2/3. It has a mean cold bias dominated by the mountainous regions and also underestimates the cold 5 % quantile in many regions of Europe. Both methods show that the probability of experiencing a winter as cold as 2009-2010 has been reduced by approximately a factor of two due to anthropogenic changes. The method based on HadGEM3-A ensembles gives somewhat larger changes than the surrogate method because of differences in the definition of the unperturbed climate. The results are based on two diagnostics: the coldest day in winter and the largest continuous area with temperatures colder than twice the local standard deviation. The results are not sensitive to the choice of bias correction except in the mountainous regions. Previous results regarding the behavior of the measures of the changed probability have been extended. The counter-intuitive behavior for heavy-tailed distributions is found to hold for a range of measures and for events that become more rare in a changed climate

Multi-scale transport and exchange processes in the atmosphere over mountains. Programme and experiment

TEAMx is an international research programme that aims at improving the understanding of exchange processes in the atmosphere over mountains at multiple scales and at advancing the parameterizations of these processes in numerical models for weather and climate prediction–hence its acronyms stands for Multi-scale transport and exchange processes in the atmosphere over mountains – Programme and experiment. TEAMx is a bottom-up initiative promoted by a number of universities, research institutions and operational centres, internationally integrated through a Memorandum of Understanding between inter- ested parties. It is carried out by means of coordinated national, bi-national and multi-national research projects and supported by a Programme Coordination Office at the Department of Atmospheric and Cryospheric Sciences of the University of Innsbruck, Austria. The present document, compiled by the TEAMx Programme Coordination Office, provides a concise overview of the scientific scope of TEAMx. In the interest of accessibility and readability, the document aims at being self-contained and uses only a minimum of references to scientific literature. Greyboxes at the beginning of chapters list the literature sources that provide the scientific basis of the document. This largely builds on review articles published by the journal Atmosphere between 2018 and 2019, in a special issue on Atmospheric Processes over Complex Terrain. A few other important literature pieces have been referenced where appropriate. Interested readers are encouraged to examine the large body of literature summarized and referenced in these articles. Blue boxes have been added to most sub-chapters. Their purpose is to highlight key ideas and proposals for future collaborative research

Globally Significant CO2 Emissions From Katla, a Subglacial Volcano in Iceland

Volcanoes are a key natural source of CO2, but global estimates of volcanic CO2 flux are predominantly based on measurements from a fraction of world's actively degassing volcanoes. We combine high-precision airborne measurements from 2016 and 2017 with atmospheric dispersion modeling to quantify CO2 emissions from Katla, a major subglacial volcanic caldera in Iceland that last erupted 100 years ago but has been undergoing significant unrest in recent decades. Katla's sustained CO2 flux, 12–24 kt/d, is up to an order of magnitude greater than previous estimates of total CO2 release from Iceland's natural sources. Katla is one of the largest volcanic sources of CO2 on the planet, contributing up to 4% of global emissions from nonerupting volcanoes. Further measurements on subglacial volcanoes worldwide are urgently required to establish if Katla is exceptional, or if there is a significant previously unrecognized contribution to global CO2 emissions from natural sources

Flaring efficiencies and NOx emission ratios measured for offshore oil and gas facilities in the North Sea

Gas flaring is a substantial global source of carbon emissions to atmosphere and is targeted as a route to mitigating the oil and gas sector carbon footprint due to the waste of resources involved. However, quantifying carbon emissions from flaring is resource-intensive, and no studies have yet assessed flaring emissions for offshore regions. In this work, we present carbon dioxide (CO2), methane (CH4), ethane (C2H6), and NOx (nitrogen oxide) data from 58 emission plumes identified as gas flaring, measured during aircraft campaigns over the North Sea (UK and Norway) in 2018 and 2019. Median combustion efficiency, the efficiency with which carbon in the flared gas is converted to CO2 in the emission plume, was 98.4% when accounting for C2H6 or 98.7% when only accounting for CH4. Higher combustion efficiencies were measured in the Norwegian sector of the North Sea compared with the UK sector. Destruction removal efficiencies (DREs), the efficiency with which an individual species is combusted, were 98.5% for CH4 and 97.9% for C2H6. Median NOx emission ratios were measured to be 0.003ppmppm-1CO2 and 0.26ppmppm-1CH4, and the median C2H6:CH4 ratio was measured to be 0.11ppmppm-1. The highest NOx emission ratios were observed from floating production storage and offloading (FPSO) vessels, although this could potentially be due to the presence of alternative NOx sources on board, such as diesel generators. The measurements in this work were used to estimate total emissions from the North Sea from gas flaring of 1.4Tgyr-1 CO2, 6.3Ggyr-1 CH4, 1.7Ggyr-1 C2H6 and 3.9Ggyr-1 NOx

Multiple perspectives on the attribution of the extreme European summer of 2012 to climate change

Summer 2012 was very wet in northern Europe, and unusually dry and hot in southern Europe. We use multiple approaches to determine whether anthropogenic forcing made the extreme European summer of 2012 more likely. Using a number of observation- and model-based methods, we find that there was an anthropogenic contribution to the extremes in southern Europe, with a qualitative consensus across all methodologies. There was a consensus across the methodologies that there has been a significant increase in the risk of hot summers in southern Europe with climate change. Most approaches also suggested a slight drying, but none of the results were statistically significant. The unusually wet summer in northern Europe was made more likely by the observed atmospheric circulation pattern in 2012, but no evidence was found for a long-term trend in circulation

Evaluation of the HadGEM3-A simulations in view of detection and attribution of human influence on extreme events in Europe

A detailed analysis is carried out to assess the HadGEM3-A global atmospheric model skill in simulating extreme temperatures, precipitation and storm surges in Europe in the view of their attribution to human influence. The analysis is performed based on an ensemble of 15 atmospheric simulations forced with observed Sea Surface Temperature of the 54 year period 1960-2013. These simulations, together with dual simulations without human influence in the forcing, are intended to be used in weather and climate event attribution. The analysis investigates the main processes leading to extreme events, including atmospheric circulation patterns, their links with temperature extremes, land-atmosphere and troposphere-stratosphere interactions. It also compares observed and simulated variability, trends and generalized extreme value theory parameters for temperature and precipitation. One of the most striking findings is the ability of the model to capture North Atlantic atmospheric weather regimes as obtained from a cluster analysis of sea level pressure fields. The model also reproduces the main observed weather patterns responsible for temperature and precipitation extreme events. However, biases are found in many physical processes. Slightly excessive drying may be the cause of an overestimated summer interannual variability and too intense heat waves, especially in central/northern Europe. However, this does not seem to hinder proper simulation of summer temperature trends. Cold extremes appear well simulated, as well as the underlying blocking frequency and stratosphere-troposphere interactions. Extreme precipitation amounts are overestimated and too variable. The atmospheric conditions leading to storm surges were also examined in the Baltics region. There, simulated weather conditions appear not to be leading to strong enough storm surges, but winds were found in very good agreement with reanalyses. The performance in reproducing atmospheric weather patterns indicates that biases mainly originate from local and regional physical processes. This makes local bias adjustment meaningful for climate change attribution

A machine learning-based approach to quantify ENSO sources of predictability

A machine learning method is used to identify sources of long-term ENSO predictability in the ocean (sea surface temperature (SST) and heat content) and the atmosphere (near-surface zonal wind (U10)). The interplay between predictors and the geographical regions contributing to the skill are determined. Tropical SST represents the primary source of predictability skill up to 1 year ahead, while U10 plays an essential role between 11-21 months in advance, from late fall up to late spring. The long-lead signal originates from coupled wind-SST interactions across the Indian Ocean (IO) and propagates across the Pacific via an atmospheric bridge mechanism. A linear correlation analysis supports this mechanism, suggesting a precursor link between anomalies in SST in the western and wind in the eastern IO. Our results have important implications for ENSO predictions beyond one year ahead and identify the critical role of U10 over the IO

Evaluation of Weather Noise and Its Role in Climate Model Simulations

Abstract The relationship between coupled atmosphere–ocean general circulation model simulations and uncoupled simulations made with specified SST and sea ice is investigated using the Community Climate System Model, version 3. Experiments are carried out in a perfect model framework. Two closely related questions are investigated: 1) whether the statistics of the atmospheric weather noise in the atmospheric model are the same as in the coupled model, and 2) whether the atmospheric model reproduces the SST-forced response of the coupled model. The weather noise in both the coupled and uncoupled simulations is found by removing the forced response, as determined from the uncoupled ensemble, from the atmospheric field. The weather-noise variance is generally not distinguishable between the coupled and uncoupled simulations. However, variances of the total fields differ between the coupled and uncoupled simulations, since there is constructive or destructive interference between the SST-forced response and weather noise in the coupled model but no correlation between the SST-forced and weather-noise components in the uncoupled model simulations. Direct regression estimates of the forced response show little difference between the coupled and uncoupled simulations. Differences in local correlations are explained by weather noise because weather noise forces SST in the coupled simulation only. The results demonstrate and explain an important intrinsic difference in precipitation statistics between the coupled and uncoupled simulations. This difference could have consequences for the design of dynamical downscaling experiments and for tuning general circulation models

Speciation of VOC emissions related to offshore North Sea oil and gas production

The North Sea is Europe's key oil and gas (O&G) basin with the output currently meeting 3-4 % of global oil supply. Despite this, there are few observational constraints on the nature of atmospheric emissions from this region, with most information derived from bottom-up inventory estimates. This study reports on airborne measurements of volatile organic compounds (VOCs) emitted from O&G producing regions in the North Sea. VOC source emission signatures for the primary extraction 5 products from offshore fields (oil, gas, condensate, mixed) were determined in four geographic regions. Measured iso-pentane to n-pentane (iC 5 /nC 5) ratios were 0.89-1.24 for all regions, used as a confirmatory indicator of O&G activities. Light alkanes (ethane, propane, butane, pentane) were the dominant species emitted in all four regions, however total OH reactivity was dominated by unsaturated species, such as 1,3-butadiene, despite their relatively low abundance. Benzene to toluene ratios indicated the influence of possible terrestrial combustion sources of emissions in the Southern, gas-producing region of the 10 North Sea, seen only during south or south-westerly wind episodes. However, all other regions showed a characteristic signature of O&G operations. Correlations between ethane (C 2 H 6) and methane (CH 4), confirmed O&G production to be the primary CH 4 source. The enhancement ratio (∆C 2 H 6 /∆CH 4) ranged between 0.03-0.18, indicating a spatial dependence on emissions with both wet and dry CH 4 emission sources. The excess mole fraction demonstrated that deepwater oil extraction resulted in a greater proportion of emissions of higher carbon number alkanes relative to CH 4 , whereas gas extraction, typically from 15 shallow waters, resulted in a less complex mix of emissions dominated by CH 4. The VOC source profiles measured were similar to those in the UK National Atmospheric Emissions Inventory (NAEI) for oil production, with consistency between the molar ratios of light alkanes to propane. The largest discrepancies between observations and the inventory were for mono-aromatic compounds, highlighting that these species are not currently fully captured in the inventory. hese results demonstrate the applicability of VOC measurements to distinguish unique sources within the O&G sector and give an overview of VOC speciation over the North Sea