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Bo Pang
,
Riyu Lu
,
A. Scaife Adam
, and
Rongcai Ren

Abstract

This study identifies that cold surges over the South China Sea (SCS) have experienced a significant change on decadal time scales. The results indicate that cold surges occur more frequently after the early 2000s than before and are at least partially explained by changes in circulation patterns. Both the negative phase of the Scandinavian (SCA) pattern and the cold phase of the interdecadal Pacific oscillation (IPO) can induce increased cold surges and the IPO effect dominates in recent decades. When the IPO shifts to its cold phase, low-level cyclones are induced over the western North Pacific through a Gill response. The northeasterlies along the northwest flank of the cyclones further lead to intensified cold surges over the SCS. The above processes can be reproduced in coupled models, suggesting a robust connection between the IPO and cold surges. The present findings highlight the role of tropical forcing and bring insight into understanding of the future climate variability and change over East Asia during boreal winter.

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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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Adam A. Scaife
,
Tim Woollings
,
Jeff Knight
,
Gill Martin
, and
Tim Hinton

Abstract

Models often underestimate blocking in the Atlantic and Pacific basins and this can lead to errors in both weather and climate predictions. Horizontal resolution is often cited as the main culprit for blocking errors due to poorly resolved small-scale variability, the upscale effects of which help to maintain blocks. Although these processes are important for blocking, the authors show that much of the blocking error diagnosed using common methods of analysis and current climate models is directly attributable to the climatological bias of the model. This explains a large proportion of diagnosed blocking error in models used in the recent Intergovernmental Panel for Climate Change report. Furthermore, greatly improved statistics are obtained by diagnosing blocking using climate model data corrected to account for mean model biases. To the extent that mean biases may be corrected in low-resolution models, this suggests that such models may be able to generate greatly improved levels of atmospheric blocking.

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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.

Open access
Bo Pang
,
Adam A. Scaife
,
Riyu Lu
, and
Rongcai Ren

Abstract

This study investigates the stratosphere–troposphere coupling associated with the Scandinavian (SCA) pattern in boreal winter. The results indicate that the SCA impacts stratospheric circulation but that its positive and negative phases have different effects. The positive phase of the SCA (SCA+) pattern is restricted to the troposphere, but the negative phase (SCA) extends to the upper stratosphere. The asymmetry between phases is also visible in the lead–lag evolution of the stratosphere and troposphere. Prominent stratospheric anomalies are found to be intensified following SCA+ events, but prior to SCA events. Further analysis reveals that the responses are associated with upward propagation of planetary waves, especially wavenumber 1, which is asymmetric between SCA phases. The wave amplitudes in the stratosphere, originating from the troposphere, are enhanced after the SCA+ events and before the SCA events. Furthermore, the anomalous planetary wave activity can be understood through its interference with climatological stationary waves. Constructive wave interference is accompanied by clear upward propagation in the SCA+ events, while destructive interference suppresses stratospheric waves in the SCA events. Our results also reveal that the SCA+ events are more likely to be followed by sudden stratospheric warming (SSW) events, because of the deceleration of stratospheric westerlies following the SCA+ events.

Open access
Bo Pang
,
Adam A. Scaife
,
Riyu Lu
,
Rongcai Ren
, and
Xiaoxuan Zhao

Abstract

This study investigates the interdecadal variation of the Scandinavian (SCA) pattern and corresponding drivers during the boreal winter. It is found that the SCA pattern experiences a prominent regime shift from its negative to positive phase in the early 2000s based on several reanalyses. This interdecadal change contributes to an extensive cooling over Siberia after the early 2000s, revealing its importance for recent variations of climate over Eurasia. The outputs from 35 coupled models within phase 6 of the Coupled Model Intercomparison Project (CMIP6) are also analyzed. The results show that the interdecadal change of the SCA is weak in response to external forcings but can be largely explained by internal variability associated with a change of precipitation over the tropical Atlantic. Further analysis indicates that the enhanced tropical convection induces poleward propagation of Rossby waves and further results in an intensification of geopotential height over the Scandinavian Peninsula during the transition to positive SCA phases. These findings imply a contribution of tropical forcing to the observed interdecadal strengthening of the SCA around the early 2000s and offer an insight into the understanding of future climate change over the Eurasian continent.

Restricted access
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.

Full access
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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