Stratospheric influence on circulation changes in Southern Hemisphere troposphere in coupled climate models

The recent intensification of the circumpolar circulation in the SH troposphere in summer and autumn has been attributed to external forcing such as stratospheric ozone depletion and greenhouse gas (GHG) increases. Several studies have shown that climate models are able to simulate observed changes when forced by observed ozone trends or combined ozone and GHG trends. However, as some of these studies suffered from erroneously specified forcing, the reason for the circulation intensification remains debatable. Here, we re-approach this issue using data from 21 CMIP3 models. We demonstrate that only models that include ozone depletion simulate downward propagation of the circulation changes from the stratosphere to the troposphere similar to that observed, with GHG increases causing significant Antarctic geopotential height trends only in the lower troposphere. These changes are simulated by the majority of the ozone-forced models except those with the lowest vertical resolution between 300 hPa and 10 hPa. Citation: Karpechko, A. Yu., N. P. Gillett, G. J. Marshall, and A. A. Scaife (2008), Stratospheric influence on circulation changes in the Southern Hemisphere troposphere in coupled climate models, Geophys. Res. Lett., 35, L20806, doi: 10.1029/2008GL035354

Simple Statistical Probabilistic Forecasts of the winter NAO

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

Drivers and potential predictability of summer time North Atlantic polar front jet variability

The variability of the North Atlantic polar front jet stream is crucial in determining summer weather around the North Atlantic basin. Recent extreme summers in western Europe and North America have highlighted the need for greater understanding of this variability, in order to aid seasonal forecasting and mitigate societal, environmental and economic impacts. Here we find that simple linear regression and composite models based on a few predictable factors are able to explain up to 40% of summertime jet stream speed and latitude variability from 1955 onwards. Regression models covering the earlier part of the twentieth century are much less effective, presumably due to decreased availability of data, and increased uncertainty in observational reanalyses. Sea surface temperature (SST) forcings impact predominantly on jet speed, whereas solar and cryospheric forcings appear to influence jet latitude. The cryospheric associations come from the previous autumn, suggesting the survival of an ice-induced signal through the winter season, whereas solar influences lead jet variability by a few years. Wavelet coherence analysis identifies that associations fluctuate over the study period but it is not clear whether this is just internal variability or actual non-stationarity. Finally we identify areas for future research

Seasonal forecasting of groundwater levels in principal aquifers of the United Kingdom

To date, the majority of hydrological forecasting studies have focussed on using medium-range (3–15 days) weather forecasts to drive hydrological models and make predictions of future river flows. With recent developments in seasonal (1–3 months) weather forecast skill, such as those from the latest version of the UK Met Office global seasonal forecast system (GloSea5), there is now an opportunity to use similar methodologies to forecast groundwater levels in more slowly responding aquifers on seasonal timescales. This study uses seasonal rainfall forecasts and a lumped groundwater model to simulate groundwater levels at 21 locations in the United Kingdom up to three months into the future. The results indicate that the forecasts have skill; outperforming a persistence forecast and demonstrating reliability, resolution and discrimination. However, there is currently little to gain from using seasonal rainfall forecasts over using site climatology for this type of application. Furthermore, the forecasts are not able to capture extreme groundwater levels, primarily because of inadequacies in the driving rainfall forecasts. The findings also show that the origin of forecast skill, be it from the meteorological input, groundwater model or initial condition, is site specific and related to the groundwater response characteristics to rainfall and antecedent hydro-meteorological conditions

Long-range forecasts of UK winter hydrology

Seasonal river flow forecasts are beneficial for planning agricultural activities, river navigation, and for management of reservoirs for public water supply and hydropower generation. In the United Kingdom (UK), skilful seasonal river flow predictions have previously been limited to catchments in lowland (southern and eastern) regions. Here we show that skilful long-range forecasts of winter flows can now be achieved across the whole of the UK. This is due to a remarkable geographical complementarity between the regional geological and meteorological sources of predictability for river flows. Forecast skill derives from the hydrogeological memory of antecedent conditions in southern and eastern parts of the UK and from meteorological predictability in northern and western areas. Specifically, it is the predictions of the atmospheric circulation over the North Atlantic that provides the skill at the seasonal timescale. In addition, significant levels of skill in predicting the frequency of winter high flow events is demonstrated, which has the potential to allow flood adaptation measures to be put in place

Kinematics of femtosecond laser-generated plasma expansion: Determination of sub-micron density gradient and collisionality evolution of over-critical laser plasmas

An optical diagnostic based on resonant absorption of laser light in a plasma is introduced and is used for the determination of density scale lengths in the range of 10 nm to >1 μm at the critical surface of an overdense plasma. This diagnostic is also used to extract the plasma collisional frequency, allowing inference of the temporally evolving plasma composition on the tens of femtosecond timescale. This is found to be characterized by two eras: the early time and short scale length expansion (L  0.1λ); this is consistent with a hydrogen plasma decoupling from the bulk target material. Density gradients and plasma parameters on this scale are of importance to plasma mirror optical performance and comment is made on this theme

Robust skill of decadal climate predictions

There is a growing need for skilful predictions of climate up to a decade ahead. Decadal climate predictions show high skill for surface temperature, but confidence in forecasts of precipitation and atmospheric circulation is much lower. Recent advances in seasonal and annual prediction show that the signal-to-noise ratio can be too small in climate models, requiring a very large ensemble to extract the predictable signal. Here, we reassess decadal prediction skill using a much larger ensemble than previously available, and reveal significant skill for precipitation over land and atmospheric circulation, in addition to surface temperature. We further propose a more powerful approach than used previously to evaluate the benefit of initialisation with observations, improving our understanding of the sources of skill. Our results show that decadal climate is more predictable than previously thought and will aid society to prepare for, and adapt to, ongoing climate variability and change.D.M.S., A.A.S., N.J.D., L.H. and R.E. were supported by the Met Office Hadley Centre Climate Programme funded by BEIS and Defra and by the European Commission Horizon 2020 EUCP project (GA 776613). L.P.C. was supported by the Spanish MINECO HIATUS (CGL2015-70353-R) project. F.J.D.R. was supported by the H2020 EUCP (GA 776613) and the Spanish MINECO CLINSA (CGL2017-85791-R) projects. W.A. M. and H.P. were supported by the German Ministry of Education and Research (BMBF) under the project MiKlip (grant 01LP1519A). The NCAR contribution was supported by the US National Oceanic and Atmospheric Administration (NOAA) Climate Program Office under Climate Variability and Predictability Program Grant NA13OAR4310138 and by the US National Science Foundation (NSF) Collaborative Research EaSM2 Grant OCE-1243015. The NCAR contribution is also based upon work supported by NCAR, which is a major facility sponsored by the US NSF under Cooperative Agreement No. 1852977. The Community Earth System Model Decadal Prediction Large Ensemble (CESM-DPLE) was generated using computational resources provided by the US National Energy Research Scientific Computing Center, which is supported by the Office of Science of the US Department of Energy under Contract DE-AC02-05CH11231, as well as by an Accelerated Scientific Discovery grant for Cheyenne (https://doi.org/10.5065/D6RX99HX) that was awarded by NCAR’s Computational and Information System Laboratory.Peer ReviewedPostprint (published version

How potentially predictable is northern European winter climate a season ahead?

We estimate the potential predictability of European winter temperature using factors based on physical studies of their influences on European winter climate. These influences include sea surface temperature patterns in different oceans, major tropical volcanoes, the quasi-biennial oscillation in the tropical stratosphere, and anthropogenic climate change. We first assess the predictive skill for winter mean temperature in northern Europe by evaluating statistical hindcasts made using multiple regression models of temperature for Europe for winter and the January-February season. We follow this up by extending the methodology to all of Europe on a 5° × 5° grid and include rainfall for completeness. These results can form the basis of practical prediction methods. However, our main aim is to develop ideas to act as a benchmark for improving the performance of dynamical climate models. Because we consider only potential predictability, many of the predictors have estimated values coincident with the winter season being forecast. However, in each case, these values are predictable on average with considerable skill in advance of the winter season. A key conclusion is that to reproduce the results of this paper, dynamical forecasting models will require a fully resolved stratosphere