79 research outputs found

    Influence of warming and atmospheric circulation changes on multidecadal European flood variability

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    International audienceEuropean flood frequency and intensity change on a multidecadal scale. Floods were more frequent in the 19th (central Europe) and early 20th century (western Europe) than during the mid-20th century and again more frequent since the 1970s. The causes of this variability are not well understood and the relation to climate change is unclear. Palaeoclimate studies from the northern Alps suggest that past flood-rich periods coincided with cold periods. In contrast, some studies suggest that more floods might occur in a future, warming world. Here we address the contribution of atmospheric circulation and of warming to multidecadal flood variability. For this, we use long series of annual peak streamflow, daily weather data, reanalyses, and reconstructions. We show that both changes in atmospheric circulation and moisture content affected multidecadal changes of annual peak streamflow in central and western Europe over the past two centuries. We find that during the 19th and early 20th century, atmospheric circulation changes led to high peak values of moisture flux convergence. The circulation was more conducive to strong and long-lasting precipitation events than in the mid-20th century. These changes are also partly reflected in the seasonal mean circulation and reproduced in atmospheric model simulations, pointing to a possible role of oceanic variability. For the period after 1980, increasing moisture content in a warming atmosphere led to extremely high moisture flux convergence. Thus, the main atmospheric driver of flood variability changed from atmospheric circulation variability to water vapour increase.La fréquence et l'intensité des inondations en Europe changent à une échelle multidécennale. Les inondations étaient plus fréquentes au 19ème (Europe centrale) et au début du 20ème siècle (Europe occidentale) qu'au milieu du 20ème siècle et à nouveau plus fréquentes depuis les années 1970. Les causes de cette variabilité ne sont pas bien comprises et la relation avec le changement climatique n'est pas claire. Les études paléoclimatiques menées dans les Alpes du Nord suggèrent que les périodes passées riches en inondations coïncidaient avec des périodes froides. En revanche, certaines études suggèrent que davantage d'inondations pourraient se produire dans un monde futur en réchauffement. Nous abordons ici la contribution de la circulation atmosphérique et du réchauffement à la variabilité multidécennale des inondations. Pour cela, nous utilisons de longues séries de débit maximal annuel, des données météorologiques quotidiennes, des réanalyses et des reconstructions climatiques. Nous montrons que les changements de la circulation atmosphérique et du contenu en humidité ont affecté les changements multidécennaux du débit maximal annuel en Europe centrale et occidentale au cours des deux derniers siècles. Nous constatons qu'au cours du 19ème et du début du 20ème siècle, les changements de la circulation atmosphérique ont conduit à des valeurs de pointe élevées de convergence du flux d'humidité. La circulation était plus propice à des événements de précipitations forts et durables qu'au milieu du 20e siècle. Ces changements se reflètent également en partie dans la circulation moyenne saisonnière et sont reproduits dans les simulations des modèles atmosphériques, ce qui indique un rôle possible de la variabilité océanique. Pour la période après 1980, l'augmentation de la teneur en humidité dans une atmosphère qui se réchauffe a conduit à une convergence extrêmement élevée des flux d'humidité. Ainsi, le principal moteur atmosphérique de la variabilité des crues est passé de la variabilité de la circulation atmosphérique à l'augmentation de la vapeur d'eau

    Rossby wave dynamics of the North Pacific extra-tropical response to El Niño: importance of the basic state in coupled GCMs

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    The extra-tropical response to El Nino in a "low" horizontal resolution coupled climate model, typical of the Intergovernmental Panel on Climate Change fourth assessment report simulations, is shown to have serious systematic errors. A high resolution configuration of the same model has a much improved response that is similar to observations. The errors in the low resolution model are traced to an incorrect representation of the atmospheric teleconnection mechanism that controls the extra-tropical sea surface temperatures (SSTs) during El Nino. This is due to an unrealistic atmospheric mean state, which changes the propagation characteristics of Rossby waves. These erroneous upper tropospheric circulation anomalies then induce erroneous surface circulation features over the North Pacific. The associated surface wind speed and direction errors create erroneous surface flux and upwelling anomalies which finally lead to the incorrect extra-tropical SST response to El Nino in the low resolution model. This highlights the sensitivity of the climate response to a single link in a chain of complex climatic processes. The correct representation of these processes in the high resolution model indicates the importance of horizontal resolution in resolving such processes

    An evaluation of the performance of the twentieth century reanalysis version 3

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    The performance of a new historical reanalysis, the NOAA–CIRES–DOE Twentieth Century Reanalysis version 3 (20CRv3), is evaluated via comparisons with other reanalyses and independent observations. This dataset provides global, 3-hourly estimates of the atmosphere from 1806 to 2015 by assimilating only surface pressure observations and prescribing sea surface temperature, sea ice concentration, and radiative forcings. Comparisons with independent observations, other reanalyses, and satellite products suggest that 20CRv3 can reliably produce atmospheric estimates on scales ranging from weather events to long-term climatic trends. Not only does 20CRv3 recreate a ‘‘best estimate’’ of the weather, including extreme events, it also provides an estimate of its confidence through the use of an ensemble. Surface pressure statistics suggest that these confidence estimates are reliable. Comparisons with independent upper-air observations in the Northern Hemisphere demonstrate that 20CRv3 has skill throughout the twentieth century. Upper-air fields from 20CRv3 in the late twentieth century and early twenty-first century correlate well with full-input reanalyses, and the correlation is predicted by the confidence fields from 20CRv3. The skill of analyzed 500-hPa geopotential heights from 20CRv3 for 1979–2015 is comparable to that of modern operational 3–4-day forecasts. Finally, 20CRv3 performs well on climate time scales. Long time series and multidecadal averages of mass, circulation, and precipitation fields agree well with modern reanalyses and station- and satellite-based products. 20CRv3 is also able to capture trends in tropospheric-layer temperatures that correlate well with independent products in the twentieth century, placing recent trends in a longer historical context.The research work of R. Przybylak and P. Wyszynski was supported by the National Science Centre, Poland (Grants DEC-2012/07/B/ST10/04002 and 2015/19/B/ST10/02933)

    Stochastic climate theory and modeling

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    Stochastic methods are a crucial area in contemporary climate research and are increasingly being used in comprehensive weather and climate prediction models as well as reduced order climate models. Stochastic methods are used as subgrid-scale parameterizations (SSPs) as well as for model error representation, uncertainty quantification, data assimilation, and ensemble prediction. The need to use stochastic approaches in weather and climate models arises because we still cannot resolve all necessary processes and scales in comprehensive numerical weather and climate prediction models. In many practical applications one is mainly interested in the largest and potentially predictable scales and not necessarily in the small and fast scales. For instance, reduced order models can simulate and predict large-scale modes. Statistical mechanics and dynamical systems theory suggest that in reduced order models the impact of unresolved degrees of freedom can be represented by suitable combinations of deterministic and stochastic components and non-Markovian (memory) terms. Stochastic approaches in numerical weather and climate prediction models also lead to the reduction of model biases. Hence, there is a clear need for systematic stochastic approaches in weather and climate modeling. In this review, we present evidence for stochastic effects in laboratory experiments. Then we provide an overview of stochastic climate theory from an applied mathematics perspective. We also survey the current use of stochastic methods in comprehensive weather and climate prediction models and show that stochastic parameterizations have the potential to remedy many of the current biases in these comprehensive models
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