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Christopher D. Warner, Adam A. Scaife, and Neal Butchart

Abstract

This paper investigates the vertical filtering of parameterized gravity wave pseudomomentum flux in the troposphere–stratosphere version of the Met Office Unified Model. Gravity wave forcing is parameterized using the Warner and McIntyre spectral gravity wave parameterization. The same amount of isotropic pseudomomentum flux per unit mass is launched from the planetary boundary layer at each grid point. The parameterization models the azimuthally dependent Doppler shifting and breaking of the gravity wave spectrum as it is filtered by the background atmosphere. The result is an anisotropic distribution of pseudomomentum flux among azimuthal sectors that varies greatly with altitude and location. This gives an idealized global climatology of nonorographic gravity waves. The filtering effect of the atmosphere in this climatology is diagnosed using the “zonal anisotropy.”

Results show areas where observational measurements could be targeted to find the most prominent features in the gravity wave field. Such areas include, for example, the summer stratosphere where zonal anisotropy is very large and where there is a significant localization in latitude and longitude of patches of high zonal anisotropy. Comparisons are also made with recent observational estimates of gravity wave fluxes and test whether wind filtering of a homogeneous, azimuthally isotropic source is enough to reproduce observed features of the gravity wave field.

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Hye-Mi Kim, Yoo-Geun Ham, and Adam A. Scaife

Abstract

The prediction skill and errors in surface temperature anomalies in initialized decadal hindcasts from phase 5 of the Coupled Model Intercomparison Project (CMIP5) are assessed using six ocean–atmosphere coupled models initialized every year from 1961 to 2008. The initialized hindcasts show relatively high prediction skill over the regions where external forcing dominates, indicating that a large portion of the prediction skill is due to the long-term trend. After removing the linear trend, high prediction skill is shown near the centers of action of the dominant decadal climate oscillations, such as the Pacific decadal oscillation (PDO) and Atlantic multidecadal oscillation (AMO). Low prediction skill appears over the tropical and eastern North Pacific Ocean where the predicted anomaly patterns associated with the PDO are systematically different in model and observations. By statistically correcting those systematic errors using a stepwise pattern projection method (SPPM) based on the data in an independent training period, the prediction skill of sea surface temperature (SST) is greatly enhanced over the North Pacific Ocean. The SST prediction skill over the North Pacific Ocean after the SPPM error correction is as high as that over the North Atlantic Ocean. In addition, the prediction skill in a single model after correction exceeds the skill of the multimodel ensemble (MME) mean before correction, implying that the MME method is not as effective in addressing systematic errors as the SPPM correction.

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Erik W. Kolstad, Stefan P. Sobolowski, and Adam A. Scaife

Abstract

Recent periods of extreme weather in Europe, such as the cold winter of 2009/10, have caused widespread impacts and were remarkable because of their persistence. It is therefore of great interest to improve the ability to forecast such events. Weather forecasts at midlatitudes generally show low skill beyond 5–10 days, but long-range forecast skill may increase during extended tropospheric blocking episodes or perturbations of the stratospheric polar vortex, which can affect midlatitude weather for several weeks at a time. Here a simple, linear approach is used to identify previously undocumented persistence in northern European summer and winter temperature anomalies in climate model simulations, corroborated by observations and reanalysis data. For instance, temperature anomalies of at least one standard deviation above or below climatology in March were found to be about 20%–120% more likely than normal if the preceding February was anomalous by 0.5–1.5 standard deviations (with the same sign). The corresponding range for April (i.e., persistence over two months) is between 20% and 80%. The persistence is observed irrespective of the data source or driving mechanisms, and the temperature itself is a more skillful predictor of the temperatures one month ahead than the stratospheric polar vortex or the NAO and even than both factors together. The results suggest potential to conditionally improve the skill of long-range forecasts and enhance recent advancements in dynamical seasonal prediction.

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David Fereday, Robin Chadwick, Jeff Knight, and Adam A. Scaife

Abstract

The IPCC Fifth Assessment Report highlighted large uncertainty in European precipitation changes in the coming century. This paper investigates the sources of intermodel differences using CMIP5 model European precipitation data. The contribution of atmospheric circulation to differences in precipitation trends is investigated by applying cluster analysis to daily mean sea level pressure (MSLP) data. The resulting classification is used to reconstruct monthly precipitation time series, thereby isolating the component of precipitation variability directly related to atmospheric circulation. Reconstructed observed precipitation and reconstructions of simulated historical and projection data are well correlated with the original precipitation series, showing that circulation variability accounts for a substantial fraction of European precipitation variability. Removing the reconstructed precipitation from the original precipitation leaves a residual component related to noncirculation effects (and any small remaining circulation effects). Intermodel spread in residual future European precipitation trends is substantially reduced compared to the spread of the original precipitation trends. Uncertainty in future atmospheric circulation accounts for more than half of the intermodel variance in twenty-first-century precipitation trends for winter months for both northern and southern Europe. Furthermore, a substantial part of this variance is related to different forced dynamical responses in different models and is therefore potentially reducible. These results highlight the importance of understanding future changes in atmospheric dynamics in achieving more robust projections of regional climate change. Finally, the possible dynamical mechanisms that may drive the future differences in regional circulation and precipitation are illustrated by examining simulated teleconnections with tropical precipitation.

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Adam A. Scaife, Carlo Buontempo, Mark Ringer, Mike Sanderson, Chris Gordon, and John F. B. Mitchell

Abstract

No Abstract available.

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Yixiong Lu, Tongwen Wu, Weihua Jie, Adam A. Scaife, Martin B. Andrews, and Jadwiga H. Richter

Abstract

It is well known that the stratospheric quasi-biennial oscillation (QBO) is forced by equatorial waves with different horizontal/vertical scales, including Kelvin waves, mixed Rossby–gravity (MRG) waves, inertial gravity waves (GWs), and mesoscale GWs, but the relative contribution of each wave is currently not very clear. Proper representation of these waves is critical to the simulation of the QBO in general circulation models (GCMs). In this study, the vertical resolution in the Beijing Climate Center Atmospheric General Circulation Model (BCC-AGCM) is increased to better represent large-scale waves, and a mesoscale GW parameterization scheme, which is coupled to the convective sources, is implemented to provide unresolved wave forcing of the QBO. Results show that BCC-AGCM can spontaneously generate the QBO with realistic periods, amplitudes, and asymmetric features between westerly and easterly phases. There are significant spatiotemporal variations of parameterized convective GWs, largely contributing to a great degree of variability in the simulated QBO. In the eastward wind shear of the QBO at 20 hPa, forcing provided by resolved waves is 0.1–0.2 m s−1 day−1 and forcing provided by parameterized GWs is ~0.15 m s−1 day−1. On the other hand, westward forcings by resolved waves and parameterized GWs are ~0.1 and 0.4–0.5 m s−1 day−1, respectively. It is inferred that the eastward forcing of the QBO is provided by both Kelvin waves and mesoscale convective GWs, whereas the westward forcing is largely provided by mesoscale GWs. MRG waves barely contribute to the formation of the QBO in the model.

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Blanca Ayarzagüena, Sarah Ineson, Nick J. Dunstone, Mark P. Baldwin, and Adam A. Scaife

Abstract

It is well established that El Niño–Southern Oscillation (ENSO) impacts the North Atlantic–European (NAE) climate, with the strongest influence in winter. In late winter, the ENSO signal travels via both tropospheric and stratospheric pathways to the NAE sector and often projects onto the North Atlantic Oscillation. However, this signal does not strengthen gradually during winter, and some studies have suggested that the ENSO signal is different between early and late winter and that the teleconnections involved in the early winter subperiod are not well understood. In this study, we investigate the ENSO teleconnection to NAE in early winter (November–December) and characterize the possible mechanisms involved in that teleconnection. To do so, observations, reanalysis data and the output of different types of model simulations have been used. We show that the intraseasonal winter shift of the NAE response to ENSO is detected for both El Niño and La Niña and is significant in both observations and initialized predictions, but it is not reproduced by free-running Coupled Model Intercomparison Project phase 5 (CMIP5) models. The teleconnection is established through the troposphere in early winter and is related to ENSO effects over the Gulf of Mexico and Caribbean Sea that appear in rainfall and reach the NAE region. CMIP5 model biases in equatorial Pacific ENSO sea surface temperature patterns and strength appear to explain the lack of signal in the Gulf of Mexico and Caribbean Sea and, hence, their inability to reproduce the intraseasonal shift of the ENSO signal over Europe.

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Anna Maidens, Alberto Arribas, Adam A. Scaife, Craig MacLachlan, Drew Peterson, and Jeff Knight

Abstract

December 2010 was unusual both in the strength of the negative North Atlantic Oscillation (NAO) intense atmospheric blocking and the associated record-breaking low temperatures over much of northern Europe. The negative North Atlantic Oscillation for November–January was predicted in October by 8 out of 11 World Meteorological Organization Global Producing Centres (WMO GPCs) of long-range forecasts. This paper examines whether the unusual strength of the NAO and temperature anomaly signals in early winter 2010 are attributable to slowly varying boundary conditions [El Niño–Southern Oscillation state, North Atlantic sea surface temperature (SST) tripole, Arctic sea ice extent, autumn Eurasian snow cover], and whether these were modeled in the Met Office Global Seasonal Forecasting System version 4 (GloSea4). Results from the real-time forecasts showed that a very robust signal was evident in both the surface pressure fields and temperature fields by the beginning of November. The historical reforecast set (hindcasts), used to calibrate and bias correct the real-time forecast, showed that the seasonal forecast model reproduces at least some of the observed physical mechanisms that drive the NAO. A series of ensembles of atmosphere-only experiments was constructed, using forecast SSTs and ice concentrations from November 2010. Each potential mechanism in turn was systematically isolated and removed, leading to the conclusion that the main mechanism responsible for the successful forecast of December 2010 was anomalous ocean heat content and associated SST anomalies in the North Atlantic.

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Richard J. Hall, Adam A. Scaife, Edward Hanna, Julie M. Jones, and Robert Erdélyi

Abstract

The variability of the North Atlantic Oscillation (NAO) is a key aspect of Northern Hemisphere atmospheric circulation and has a profound impact upon the weather of the surrounding landmasses. Recent success with dynamical forecasts predicting the winter NAO at lead times of a few months has the potential to deliver great socioeconomic impacts. Here, a linear regression model is found to provide skillful predictions of the winter NAO based on a limited number of statistical predictors. Identified predictors include El Niño, Arctic sea ice, Atlantic SSTs, and tropical rainfall. These statistical models can show significant skill when used to make out-of-sample forecasts, and the method is extended to produce probabilistic predictions of the winter NAO. The statistical hindcasts can achieve similar levels of skill to state-of-the-art dynamical forecast models, although out-of-sample predictions are less skillful, albeit over a small period. Forecasts over a longer out-of-sample period suggest there is true skill in the statistical models, comparable with that of dynamical forecasting models. They can be used both to help evaluate and to offer insight into the sources of predictability and limitations of dynamical models.

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Stefan Siegert, David B. Stephenson, Philip G. Sansom, Adam A. Scaife, Rosie Eade, and Alberto Arribas

Abstract

Predictability estimates of ensemble prediction systems are uncertain because of limited numbers of past forecasts and observations. To account for such uncertainty, this paper proposes a Bayesian inferential framework that provides a simple 6-parameter representation of ensemble forecasting systems and the corresponding observations. The framework is probabilistic and thus allows for quantifying uncertainty in predictability measures, such as correlation skill and signal-to-noise ratios. It also provides a natural way to produce recalibrated probabilistic predictions from uncalibrated ensembles forecasts.

The framework is used to address important questions concerning the skill of winter hindcasts of the North Atlantic Oscillation for 1992–2011 issued by the Met Office Global Seasonal Forecast System, version 5 (GloSea5), climate prediction system. Although there is much uncertainty in the correlation between ensemble mean and observations, there is strong evidence of skill: the 95% credible interval of the correlation coefficient of [0.19, 0.68] does not overlap zero. There is also strong evidence that the forecasts are not exchangeable with the observations: with over 99% certainty, the signal-to-noise ratio of the forecasts is smaller than the signal-to-noise ratio of the observations, which suggests that raw forecasts should not be taken as representative scenarios of the observations. Forecast recalibration is thus required, which can be coherently addressed within the proposed framework.

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