Deconstructing the climate change response of the Northern Hemisphere wintertime storm tracks

There are large uncertainties in the circulation response of the atmosphere to climate change. One manifestation of this is the substantial spread in projections for the extratropical storm tracks made by different state-of-the-art climate models. In this study we perform a series of sensitivity experiments, with the atmosphere component of a single climate model, in order to identify the causes of the differences between storm track responses in different models. In particular, the Northern Hemisphere wintertime storm tracks in the CMIP3 multi-model ensemble are considered. A number of potential physical drivers of storm track change are identified and their influence on the storm tracks is assessed. The experimental design aims to perturb the different physical drivers independently, by magnitudes representative of the range of values present in the CMIP3 model runs, and this is achieved via perturbations to the sea surface temperature and the sea-ice concentration forcing fields. We ask the question: can the spread of projections for the extratropical storm tracks present in the CMIP3 models be accounted for in a simple way by any of the identified drivers? The results suggest that, whilst the changes in the upper-tropospheric equator-to-pole temperature difference have an influence on the storm track response to climate change, the large spread of projections for the extratropical storm track present in the northern North Atlantic in particular is more strongly associated with changes in the lower-tropospheric equator-to-pole temperature difference

The roles of static stability and tropical-extratropical interactions in the summer interannual variability of the North Atlantic sector

Summer seasonal forecast skill in the North Atlantic sector is lower than winter skill. To identify potential controls on predictability, the sensitivity of North Atlantic baroclinicity to atmospheric drivers is quantified. Using ERA-INTERIM reanalysis data, North Atlantic storm-track baroclinicity is shown to be less sensitive to meridional temperature-gradient variability in summer. Static stability shapes the sector’s interannual variability by modulating the sensitivity of baroclinicity to variations in meridional temperature gradients and tropopause height and by modifying the baroclinicity itself. High static stability anomalies at upper levels result in more zonal extratropical cyclone tracks and higher eddy kinetic energy over the British Isles in the summertime. These static stability anomalies are not strongly related to the summer NAO; but they are correlated with the suppression of convection over the tropical Atlantic and with a poleward-shifted subtropical jet. These results suggest a non-local driver of North Atlantic variability. Furthermore, they imply that improved representations of convection over the south-eastern part of North America and the tropical Atlantic might improve summer seasonal forecast skill

Origin of Variability in Northern Hemisphere Winter Blocking on Interannual to Decadal Time Scales

Variability of mid-latitude blocking in the boreal winter northern hemisphere is investigated for the period 1960/61 to 2001/02 by means of relaxation experiments with the model of the European Center for Medium-Range Weather Forecasts. It is shown that there is pronounced interannual and decadal variability in blocking, especially over the Eurasian continent, consistent with previous studies. The relaxation experiments show that realistic variability in the tropics can account for a significant part of observed interannual blocking variability, but also that about half of the observed variability can only be explained by extratropical tropospheric variability. On the quasi-decadal time scale, extratropical sea surface temperature and sea-ice, in addition to tropical variability, play a more important role. The stratosphere, which has been shown to influence interannual variability of the North Atlantic Oscillation in previous studies, has no significant influence on blocking according to our analysis

The Met Office Global Coupled model 2.0 (GC2) configuration

The latest coupled configuration of the Met Office Unified Model (Global Coupled configuration 2, GC2) is presented. This paper documents the model components which make up the configuration (although the scientific description of these components is detailed elsewhere) and provides a description of the coupling between the components. The performance of GC2 in terms of its systematic errors is assessed using a variety of diagnostic techniques. The configuration is intended to be used by the Met Office and collaborating institutes across a range of timescales, with the seasonal forecast system (GloSea5) and climate projection system (HadGEM) being the initial users. In this paper GC2 is compared against the model currently used operationally in those two systems. Overall GC2 is shown to be an improvement on the configurations used currently, particularly in terms of modes of variability (e.g. mid-latitude and tropical cyclone intensities, the Madden–Julian Oscillation and El Niño Southern Oscillation). A number of outstanding errors are identified with the most significant being a considerable warm bias over the Southern Ocean and a dry precipitation bias in the Indian and West African summer monsoons. Research to address these is ongoing

Selecting CMIP5 GCMs for downscaling over multiple regions

The unprecedented availability of 6-hourly data from a multi-model GCM ensemble in the CMIP5 data archive presents the new opportunity to dynamically downscale multiple GCMs to develop high-resolution climate projections relevant to detailed assessment of climate vulnerability and climate change impacts. This enables the development of high resolution projections derived from the same set of models that are used to characterise the range of future climate changes at the global and large-scale, and as assessed in the IPCC AR5. However, the technical and human resource required to dynamically-downscale the full CMIP5 ensemble are significant and not necessary if the aim is to develop scenarios covering a representative range of future climate conditions relevant to a climate change risk assessment. This paper illustrates a methodology for selecting from the available CMIP5 models in order to identify a set of 8–10 GCMs for use in regional climate change assessments. The selection focuses on their suitability across multiple regions—Southeast Asia, Europe and Africa. The selection (a) avoids the inclusion of the least realistic models for each region and (b) simultaneously captures the maximum possible range of changes in surface temperature and precipitation for three continental-scale regions. We find that, of the CMIP5 GCMs with 6-hourly fields available, three simulate the key regional aspects of climate sufficiently poorly that we consider the projections from those models ‘implausible’ (MIROC-ESM, MIROC-ESM-CHEM, and IPSL-CM5B-LR). From the remaining models, we demonstrate a selection methodology which avoids the poorest models by including them in the set only if their exclusion would significantly reduce the range of projections sampled. The result of this process is a set of models suitable for using to generate downscaled climate change information for a consistent multi-regional assessment of climate change impacts and adaptation

Simple uncertainty frameworks for selecting weighting schemes and interpreting multimodel ensemble climate change experiments

Future climate change projections are often derived from ensembles of simulations from multiple global circulation models using heuristic weighting schemes. This study provides a more rigorous justification for this by introducing a nested family of three simple analysis of variance frameworks. Statistical frameworks are essential in order to quantify the uncertainty associated with the estimate of the mean climate change response. The most general framework yields the “one model, one vote” weighting scheme often used in climate projection. However, a simpler additive framework is found to be preferable when the climate change response is not strongly model dependent. In such situations, the weighted multimodel mean may be interpreted as an estimate of the actual climate response, even in the presence of shared model biases. Statistical significance tests are derived to choose the most appropriate framework for specific multimodel ensemble data. The framework assumptions are explicit and can be checked using simple tests and graphical techniques. The frameworks can be used to test for evidence of nonzero climate response and to construct confidence intervals for the size of the response. The methodology is illustrated by application to North Atlantic storm track data from the Coupled Model Intercomparison Project phase 5 (CMIP5) multimodel ensemble. Despite large variations in the historical storm tracks, the cyclone frequency climate change response is not found to be model dependent over most of the region. This gives high confidence in the response estimates. Statistically significant decreases in cyclone frequency are found on the flanks of the North Atlantic storm track and in the Mediterranean basin

Challenges and opportunities for improved understanding of regional climate dynamics

Dynamical processes in the atmosphere and ocean are central to determining the large-scale drivers of regional climate change, yet their predictive understanding is poor. Here, we identify three frontline challenges in climate dynamics where significant progress can be made to inform adaptation: response of storms, blocks and jet streams to external forcing; basin-to-basin and tropical–extratropical teleconnections; and the development of non-linear predictive theory. We highlight opportunities and techniques for making immediate progress in these areas, which critically involve the development of high-resolution coupled model simulations, partial coupling or pacemaker experiments, as well as the development and use of dynamical metrics and exploitation of hierarchies of models

Decadal predictions with the HiGEM high resolution global coupled climate model: description and basic evaluation

This paper describes the development and basic evaluation of decadal predictions produced using the HiGEM coupled climate model. HiGEM is a higher resolution version of the HadGEM1 Met Office Unified Model. The horizontal resolution in HiGEM has been increased to 1.25◦ × 0.83◦ in longitude and latitude for the atmosphere, and 1/3◦ × 1/3◦ globally for the ocean. The HiGEM decadal predictions are initialised using an anomaly assimilation scheme that relaxes anomalies of ocean temperature and salinity to observed anomalies. 10 year hindcasts are produced for 10 start dates (1960, 1965,..., 2000, 2005). To determine the relative contributions to prediction skill from initial conditions and external forcing, the HiGEM decadal predictions are compared to uninitialised HiGEM transient experiments. The HiGEM decadal predictions have substantial skill for predictions of annual mean surface air temperature and 100 m upper ocean temperature. For lead times up to 10 years, anomaly correlations (ACC) over large areas of the North Atlantic Ocean, the Western Pacific Ocean and the Indian Ocean exceed values of 0.6. Initialisation of the HiGEM decadal predictions significantly increases skill over regions of the Atlantic Ocean,the Maritime Continent and regions of the subtropical North and South Pacific Ocean. In particular, HiGEM produces skillful predictions of the North Atlantic subpolar gyre for up to 4 years lead time (with ACC > 0.7), which are significantly larger than the uninitialised HiGEM transient experiments

Reconstruction of Lamb weather type series back to the eighteenth century

The Lamb weather type series is a subjective catalogue of daily atmospheric patterns and flow directions over the British Isles, covering the period 1861–1996. Based on synoptic maps, meteorologists have empirically classified surface pressure patterns over this area, which is a key area for the progression of Atlantic storm tracks towards Europe. We apply this classification to a set of daily pressure series from a few stations from western Europe, in order to reconstruct and to extend this daily weather type series back to 1781. We describe a statistical framework which provides, for each day, the weather types consistent enough with the observed pressure pattern, and their respective probability. Overall, this technique can correctly reconstruct almost 75% of the Lamb daily types, when simplified to the seven main weather types. The weather type series are described and compared to the original series for the winter season only. Since the low frequency variability of synoptic conditions is directly related to the North Atlantic Oscillation (NAO), we derive from the weather type series an NAO index for winter. An interesting feature is a larger multidecadal variability during the nineteenth century than during the twentieth century

Clouds, circulation and climate sensitivity

Fundamental puzzles of climate science remain unsolved because of our limited understanding of how clouds, circulation and climate interact. One example is our inability to provide robust assessments of future global and regional climate changes. However, ongoing advances in our capacity to observe, simulate and conceptualize the climate system now make it possible to fill gaps in our knowledge. We argue that progress can be accelerated by focusing research on a handful of important scientific questions that have become tractable as a result of recent advances. We propose four such questions below; they involve understanding the role of cloud feedbacks and convective organization in climate, and the factors that control the position, the strength and the variability of the tropical rain belts and the extratropical storm tracks