The predictability of the extratropical stratosphere on monthly time-scales and its impact on the skill of tropospheric forecasts

This is the final version of the article. Available from Wiley via the DOI in this record.Extreme variability of the winter- and spring-time stratospheric polar vortex has been shown to affect extratropical tropospheric weather. Therefore, reducing stratospheric forecast error may be one way to improve the skill of tropospheric weather forecasts. In this review, the basis for this idea is examined. A range of studies of different stratospheric extreme vortex events shows that they can be skilfully forecasted beyond 5 days and into the sub-seasonal range (0–30 days) in some cases. Separate studies show that typical errors in forecasting a stratospheric extreme vortex event can alter tropospheric forecast skill by 5–7% in the extratropics on sub-seasonal time-scales. Thus understanding what limits stratospheric predictability is of significant interest to operational forecasting centres. Both limitations in forecasting tropospheric planetary waves and stratospheric model biases have been shown to be important in this context.This work is supported by the Natural Environmental Research Council (NERC) funded project Stratospheric Network for the Assessment of Predictability (SNAP) (Grant H5147600) and partially supported by the SPARC. ACP and RGH acknowledge funding through the EU ARISE project (Grant 284387) (EU-FP7). We also acknowledge Steven Pawson and Lawrence Coy from NASA for providing Figure 1. We wish to thank Lorenzo Polvani from Columbia University for providing Figure 4 and Amy Butler from NOAA for her contribution to Figure 5. We thank Adrian Simmons of ECMWF for his insightful review and two anonymous reviewers for their comments and suggestions that improved the quality of the manuscript

The Arctic predictability and prediction on seasonal-to-interannual timescales (APPOSITE) data set version 1

This is the final version of the article. Available from the publisher via the DOI in this record. Discussion paper (published on 15 Oct 2015)Recent decades have seen significant developments in seasonal-to-interannual timescale climate prediction capabilities. However, until recently the potential of such systems to predict Arctic climate had not been assessed. This paper describes a multi- 5 model predictability experiment which was run as part of the Arctic Predictability and Prediction On Seasonal to Inter-annual Timescales (APPOSITE) project. The main goal of APPOSITE was to quantify the timescales on which Arctic climate is predictable. In order to achieve this, a coordinated set of idealised initial-value predictability experiments, with seven general circulation models, was conducted. This was the first model 10 intercomparison project designed to quantify the predictability of Arctic climate on seasonal to inter-annual timescales. Here we present a description of the archived data set (which is available at the British Atmospheric Data Centre) and an update of the project's results. Although designed to address Arctic predictability, this data set could also be used to assess the predictability of other regions and modes of climate vari15 ability on these timescales, such as the El Niño Southern Oscillation.This work was supported by the Natural Environment Research Council (grant NE/I029447/1). Helge Goessling was supported by a fellowship of the German Research Foundation (DFG grant GO 2464/1-1). Data storage and processing capacity was kindly provided by the British Atmospheric Data Centre (BADC). Thanks to Yanjun Jiao (CCCma) for his assistance with the CanCM4 simulations and to Bill Merryfield for his comments on a draft of the pape

Revisiting Southern Hemisphere polar stratospheric temperature trends in WACCM: The role of dynamical forcing

The latest version of the Whole Atmosphere Community Climate Model (WACCM), which includes a new chemistry scheme and an updated parameterization of orographic gravity waves, produces temperature trends in the Antarctic lower stratosphere in excellent agreement with radiosonde observations for 1969-1998 as regards magnitude, location, timing, and persistence. The maximum trend, reached in November at 100hPa, is -4.42.8Kdecade(-1), which is a third smaller than the largest trend in the previous version of WACCM. Comparison with a simulation without the updated orographic gravity wave parameterization, together with analysis of the model's thermodynamic budget, reveals that the reduced trend is due to the effects of a stronger Brewer-Dobson circulation in the new simulations, which warms the polar cap. The effects are both direct (a trend in adiabatic warming in late spring) and indirect (a smaller trend in ozone, hence a smaller reduction in shortwave heating, due to the warmer environment)

The Arctic Predictability and Prediction on Seasonal-to-Interannual TimEscales (APPOSITE) data set version 1

Abstract. Recent decades have seen significant developments in climate prediction capabilities at seasonal-to-interannual timescales. However, until recently the potential of such systems to predict Arctic climate had rarely been assessed. This paper describes a multi-model predictability experiment which was run as part of the Arctic Predictability and Prediction On Seasonal to Interannual Timescales (APPOSITE) project. The main goal of APPOSITE was to quantify the timescales on which Arctic climate is predictable. In order to achieve this, a coordinated set of idealised initial-value predictability experiments, with seven general circulation models, was conducted. This was the first model intercomparison project designed to quantify the predictability of Arctic climate on seasonal to interannual timescales. Here we present a description of the archived data set (which is available at the British Atmospheric Data Centre), an assessment of Arctic sea ice extent and volume predictability estimates in these models, and an investigation into to what extent predictability is dependent on the initial state. The inclusion of additional models expands the range of sea ice volume and extent predictability estimates, demonstrating that there is model diversity in the potential to make seasonal-to-interannual timescale predictions. We also investigate whether sea ice forecasts started from extreme high and low sea ice initial states exhibit higher levels of potential predictability than forecasts started from close to the models' mean state, and find that the result depends on the metric. Although designed to address Arctic predictability, we describe the archived data here so that others can use this data set to assess the predictability of other regions and modes of climate variability on these timescales, such as the El Niño–Southern Oscillation

Are Recent Arctic Ozone Losses Caused by Increasing Greenhouse Gases?

It has been suggested that the Arctic ozone losses observed in recent years might be a manifestation of climate change due to increasing greenhouse gases. We here offer evidence to the contrary, by focusing on the volume of polar stratospheric clouds (Vpsc), a convenient proxy for polar ozone loss whose simplicity allows for easily reproducible results. First, we analyze the time series of Vpsc in three reanalysis data sets and find no statistically significant trends in Vpsc–nor changes in their probability density functions–over the period 1979–2011. Second, we analyze Vpsc in a stratosphere-resolving chemistry-climate model forced uniquely with increasing greenhouse gases following the A1B scenario: here too, we find no significant changes in Vpsc over the entire 21st century. Taken together, these results strongly suggest that the sporadic high ozone losses in recent years have not been caused by increasing greenhouse gases

In vitro synergistic cytotoxicity of gemcitabine and pemetrexed and pharmacogenetic evaluation of response to gemcitabine in bladder cancer patients

The present study was performed to investigate the capability of gemcitabine and pemetrexed to synergistically interact with respect to cytotoxicity and apoptosis in T24 and J82 bladder cancer cells, and to establish a correlation between drug activity and gene expression of selected genes in tumour samples. The interaction between gemcitabine and pemetrexed was synergistic; indeed, pemetrexed favoured gemcitabine cytotoxicity by increasing cellular population in S-phase, reducing Akt phosphorylation as well as by inducing the expression of a major gemcitabine uptake system, the human equilibrative nucleoside transporter-1 (hENT1), and the key activating enzyme deoxycytidine kinase (dCK) in both cell lines. Bladder tumour specimens showed an heterogeneous gene expression pattern and patients with higher levels of dCK and hENT1 had better response. Moreover, human nucleoside concentrative transporter-1 was detectable only in 3/12 patients, two of whom presented a complete response to gemcitabine. These data provide evidence that the chemotherapeutic activity of the combination of gemcitabine and pemetrexed is synergistic against bladder cancer cells in vitro and that the assessment of the expression of genes involved in gemcitabine uptake and activation might be a possible determinant of bladder cancer response and may represent a new tool for treatment optimization

Seasonal to interannual Arctic sea-ice predictability in current GCMs

We establish the first inter-model comparison of seasonal to interannual predictability of present-day Arctic climate by performing coordinated sets of idealized ensemble predictions with four state-of-the-art global climate models. For Arctic sea-ice extent and volume, there is potential predictive skill for lead times of up to three years, and potential prediction errors have similar growth rates and magnitudes across the models. Spatial patterns of potential prediction errors differ substantially between the models, but some features are robust. Sea-ice concentration errors are largest in the marginal ice zone, and in winter they are almost zero away from the ice edge. Sea-ice thickness errors are amplified along the coasts of the Arctic Ocean, an effect that is dominated by sea-ice advection. These results give an upper bound on the ability of current global climate models to predict important aspects of Arctic climate

Will Arctic sea ice thickness initialization improve seasonal forecast skill?

Arctic sea ice thickness is thought to be an important predictor of Arctic sea ice extent. However, coupled seasonal forecast systems do not generally use sea ice thickness observations in their initialization and are therefore missing a potentially important source of additional skill. To investigate how large this source is, a set of ensemble potential predictability experiments with a global climate model, initialized with and without knowledge of the sea ice thickness initial state, have been run. These experiments show that accurate knowledge of the sea ice thickness field is crucially important for sea ice concentration and extent forecasts up to 8 months ahead, especially in summer. Perturbing sea ice thickness also has a significant impact on the forecast error in Arctic 2 m temperature a few months ahead. These results suggest that advancing capabilities to observe and assimilate sea ice thickness into coupled forecast systems could significantly increase skill