Decadal predictability of the Atlantic meridional overturning circulation and climate in the IPSL-CM5A-LR model

This study explores the decadal potential predictability of the Atlantic Meridional Overturning Circulation (AMOC) as represented in the IPSL-CM5A-LR model, along with the predictability of associated oceanic and atmospheric fields. Using a 1000-year control run, we analyze the prognostic potential predictability (PPP) of the AMOC through ensembles of simulations with perturbed initial conditions. Based on a measure of the ensemble spread, the modelled AMOC has an average predictive skill of 8 years, with some degree of dependence on the AMOC initial state. Diagnostic potential predictability of surface temperature and precipitation is also identified in the control run and compared to the PPP. Both approaches clearly bring out the same regions exhibiting the highest predictive skill. Generally, surface temperature has the highest skill up to 2 decades in the far North Atlantic ocean. There are also weak signals over a few oceanic areas in the tropics and subtropics. Predictability over land is restricted to the coastal areas bordering oceanic predictable regions. Potential predictability at interannual and longer timescales is largely absent for precipitation in spite of weak signals identified mainly in the Nordic Seas. Regions of weak signals show some dependence on AMOC initial state. All the identified regions are closely linked to decadal AMOC fluctuations suggesting that the potential predictability of climate arises from the mechanisms controlling these fluctuations. Evidence for dependence on AMOC initial state also suggests that studying skills from case studies may prove more useful to understand predictability mechanisms than computing average skill from numerous start dates.

Advancements in decadal climate predictability: the role of nonoceanic drivers

We review recent progress in understanding the role of sea ice, land surface, stratosphere, and aerosols in decadal-scale predictability and discuss the perspectives for improving the predictive capabilities of current Earth system models (ESMs). These constituents have received relatively little attention because their contribution to the slow climatic manifold is controversial in comparison to that of the large heat capacity of the oceans. Furthermore, their initialization as well as their representation in state-of-the-art climate models remains a challenge. Numerous extraoceanic processes that could be active over the decadal range are proposed. Potential predictability associated with the aforementioned, poorly represented, and scarcely observed constituents of the climate system has been primarily inspected through numerical simulations performed under idealized experimental settings. The impact, however, on practical decadal predictions, conducted with realistically initialized full-fledged climate models, is still largely unexploited. Enhancing initial-value predictability through an improved model initialization appears to be a viable option for land surface, sea ice, and, marginally, the stratosphere. Similarly, capturing future aerosol emission storylines might lead to an improved representation of both global and regional short-term climatic changes. In addition to these factors, a key role on the overall predictive ability of ESMs is expected to be played by an accurate representation of processes associated with specific components of the climate system. These act as “signal carriers,” transferring across the climatic phase space the information associated with the initial state and boundary forcings, and dynamically bridging different (otherwise unconnected) subsystems. Through this mechanism, Earth system components trigger low-frequency variability modes, thus extending the predictability beyond the seasonal scale.

Examining the predictability of the Stratospheric Sudden Warming of January 2013 using multiple NWP systems

The first multi-model study to estimate the predictability of a boreal Sudden Stratospheric Warming (SSW) is performed using five NWP systems. During the 2012-2013 boreal winter, anomalous upward propagating planetary wave activity was observed towards the end of December, which followed by a rapid deceleration of the westerly circulation around 2 January 2013, and on 7 January 2013 the zonal mean zonal wind at 60°N and 10 hPa reversed to easterly. This stratospheric dynamical activity was followed by an equatorward shift of the tropospheric jet stream and by a high pressure anomaly over the North Atlantic, which resulted in severe cold conditions in the UK and Northern Europe. In most of the five models, the SSW event was predicted 10 days in advance. However, only some ensemble members in most of the models predicted weakening of westerly wind when the models were initialized 15 days in advance of the SSW. Further dynamical analysis of the SSW shows that this event was characterized by the anomalous planetary wave-1 amplification followed by the anomalous wave-2 amplification in the stratosphere, which resulted in a split vortex occurring between 6 January 2013 and 8 January 2013. The models have some success in reproducing wave-1 activity when initialized 15 days in advance, they but generally failed to produce the wave-2 activity during the final days of the event. Detailed analysis shows that models have reasonably good skill in forecasting tropospheric blocking features that stimulate wave-2 amplification in the troposphere, but they have limited skill in reproducing wave-2 amplification in the stratosphere.

A new method for the characterisation and verification of local spatial predictability for convective scale ensembles

The use of kilometre-scale ensembles in operational forecasting provides new challenges for forecast interpretation and evaluation to account for uncertainty on the convective scale. A new neighbourhood based method is presented for evaluating and characterising the local predictability variations from convective scale ensembles. Spatial scales over which ensemble forecasts agree (agreement scales, S^A) are calculated at each grid point ij, providing a map of the spatial agreement between forecasts. By comparing the average agreement scale obtained from ensemble member pairs (S^A(mm)_ij), with that between members and radar observations (S^A(mo)_ij), this approach allows the location-dependent spatial spread-skill relationship of the ensemble to be assessed. The properties of the agreement scales are demonstrated using an idealised experiment. To demonstrate the methods in an operational context the S^A(mm)_ij and S^A(mo)_ij are calculated for six convective cases run with the Met Office UK Ensemble Prediction System. The S^A(mm)_ij highlight predictability differences between cases, which can be linked to physical processes. Maps of S^A(mm)_ij are found to summarise the spatial predictability in a compact and physically meaningful manner that is useful for forecasting and for model interpretation. Comparison of S^A(mm)_ij and S^A(mo)_ij demonstrates the case-by-case and temporal variability of the spatial spread-skill, which can again be linked to physical processes.

Decadal predictability of regional scale wind speed and wind energy potentials over Central Europe

Decadal predictions on timescales from one year to one decade are gaining importance since this time frame falls within the planning horizon of politics, economy and society. The present study examines the decadal predictability of regional wind speed and wind energy potentials in three generations of the MiKlip (‘Mittelfristige Klimaprognosen’) decadal prediction system. The system is based on the global Max-Planck-Institute Earth System Model (MPI-ESM), and the three generations differ primarily in the ocean initialisation. Ensembles of uninitialised historical and yearly initialised hindcast experiments are used to assess the forecast skill for 10 m wind speeds and wind energy output (Eout) over Central Europe with lead times from one year to one decade. With this aim, a statistical-dynamical downscaling (SDD) approach is used for the regionalisation. Its added value is evaluated by comparison of skill scores for MPI-ESM large-scale wind speeds and SDD-simulated regional wind speeds. All three MPI-ESM ensemble generations show some forecast skill for annual mean wind speed and Eout over Central Europe on yearly and multi-yearly time scales. This forecast skill is mostly limited to the first years after initialisation. Differences between the three ensemble generations are generally small. The regionalisation preserves and sometimes increases the forecast skills of the global runs but results depend on lead time and ensemble generation. Moreover, regionalisation often improves the ensemble spread. Seasonal Eout skills are generally lower than for annual means. Skill scores are lowest during summer and persist longest in autumn. A large-scale westerly weather type with strong pressure gradients over Central Europe is identified as potential source of the skill for wind energy potentials, showing a similar forecast skill and a high correlation with Eout anomalies. These results are promising towards the establishment of a decadal prediction system for wind energy applications over Central Europe.

Predictability of the Arctic sea ice edge

Skillful sea ice forecasts from days to years ahead are becoming increasingly important for the operation and planning of human activities in the Arctic. Here we analyze the potential predictability of the Arctic sea ice edge in six climate models. We introduce the integrated ice-edge error (IIEE), a user-relevant verification metric defined as the area where the forecast and the “truth” disagree on the ice concentration being above or below 15%. The IIEE lends itself to decomposition into an absolute extent error, corresponding to the common sea ice extent error, and a misplacement error. We find that the often-neglected misplacement error makes up more than half of the climatological IIEE. In idealized forecast ensembles initialized on 1 July, the IIEE grows faster than the absolute extent error. This means that the Arctic sea ice edge is less predictable than sea ice extent, particularly in September, with implications for the potential skill of end-user relevant forecasts.

Global observations and forecast skill

The impact of selected observing systems on forecast skill is explored using the European Centre for Medium-Range Weather Forecasts (ECMWF) 40-yr reanalysis (ERA-40) system. Analyses have been produced for a surface-based observing system typical of the period prior to 1945/1950, a terrestrial-based observing system typical of the period 1950-1979 and a satellite-based observing system consisting of surface pressure and satellite observations. Global prediction experiments have been undertaken using these analyses as initial states, and which are available every 6 h, for the boreal winters of 1990/1991 and 2000/2001 and the summer of 2000, using a more recent version of the ECMWF model. The results show that for 500-hPa geopotential height, as a representative field, the terrestrial system in the Northern Hemisphere extratropics is only slightly inferior to the control system, which makes use of all observations for the analysis, and is also more accurate than the satellite system. There are indications that the skill of the terrestrial system worsens slightly and the satellite system improves somewhat between 1990/1991 and 2000/2001. The forecast skill in the Southern Hemisphere is dominated by the satellite information and this dominance is larger in the latter period. The overall skill is only slightly worse than that of the Northern Hemisphere. In the tropics (20 degrees S-20 degrees N), using the wind at 850 and 250 hPa as representative fields, the information content in the terrestrial and satellite systems is almost equal and complementary. The surface-based system has very limited skill restricted to the lower troposphere of the Northern Hemisphere. Predictability calculations show a potential for a further increase in predictive skill of 1-2 d in the extratropics of both hemispheres, but a potential for a major improvement of many days in the tropics. As well as the Eulerian perspective of predictability, the storm tracks have been calculated from all experiments and validated for the extratropics to provide a Lagrangian perspective.

Variability of the Atlantic thermohaline circulation described by three-dimensional empirical orthogonal functions

We describe the use of bivariate 3d empirical orthogonal functions (EOFs) in characterising low frequency variability of the Atlantic thermohaline circulation (THC) in the Hadley Centre global climate model, HadCM3. We find that the leading two modes are well correlated with an index of the meridional overturning circulation (MOC) on decadal timescales, with the leading mode alone accounting for 54% of the decadal variance. Episodes of coherent oscillations in the sub-space of the leading EOFs are identified; these episodes are of great interest for the predictability of the THC, and could indicate the existence of different regimes of natural variability. The mechanism identified for the multi-decadal variability is an internal ocean mode, dominated by changes in convection in the Nordic Seas, which lead the changes in the MOC by a few years. Variations in salinity transports from the Arctic and from the North Atlantic are the main feedbacks which control the oscillation. This mode has a weak feedback onto the atmosphere and hence a surface climatic influence. Interestingly, some of these climate impacts lead the changes in the overturning. There are also similarities to observed multi-decadal climate variability.

Modeled and observed atmospheric radiation balance during the West African dry season: role of mineral dust, biomass burning aerosol, and surface albedo

The global radiation balance of the atmosphere is still poorly observed, particularly at the surface. We investigate the observed radiation balance at (1) the surface using the ARM Mobile Facility in Niamey, Niger, and (2) the top of the atmosphere (TOA) over West Africa using data from the Geostationary Earth Radiation Budget (GERB) instrument on board Meteosat-8. Observed radiative fluxes are compared with predictions from the global numerical weather prediction (NWP) version of the Met Office Unified Model (MetUM). The evaluation points to major shortcomings in the NWP model's radiative fluxes during the dry season (December 2005 to April 2006) arising from (1) a lack of absorbing aerosol in the model (mineral dust and biomass burning aerosol) and (2) a poor specification of the surface albedo. A case study of the major Saharan dust outbreak of 6–12 March 2006 is used to evaluate a parameterization of mineral dust for use in the NWP models. The model shows good predictability of the large-scale flow out to 4–5 days with the dust parameterization providing reasonable dust uplift, spatial distribution, and temporal evolution for this strongly forced dust event. The direct radiative impact of the dust reduces net downward shortwave (SW) flux at the surface (TOA) by a maximum of 200 W m−2 (150 W m−2), with a SW heating of the atmospheric column. The impacts of dust on terrestrial radiation are smaller. Comparisons of TOA (surface) radiation balance with GERB (ARM) show the “dusty” forecasts reduce biases in the radiative fluxes and improve surface temperatures and vertical thermodynamic structure.

Can knowledge of the state of the stratosphere be used to improve statistical forecasts of the troposphere?

Recent analysis of the Arctic Oscillation (AO) in the stratosphere and troposphere has suggested that predictability of the state of the tropospheric AO may be obtained from the state of the stratospheric AO. However, much of this research has been of a purely qualitative nature. We present a more thorough statistical analysis of a long AO amplitude dataset which seeks to establish the magnitude of such a link. A relationship between the AO in the lower stratosphere and on the 1000 hPa surface on a 10-45 day time-scale is revealed. The relationship accounts for 5% of the variance of the 1000 hPa time series at its peak value and is significant at the 5% level. Over a similar time-scale the 1000 hPa time series accounts for 1% of itself and is not significant at the 5% level. Further investigation of the relationship reveals that it is only present during the winter season and in particular during February and March. It is also demonstrated that using stratospheric AO amplitude data as a predictor in a simple statistical model results in a gain of skill of 5% over a troposphere-only statistical model. This gain in skill is not repeated if an unrelated time series is included as a predictor in the model. Copyright © 2003 Royal Meteorological Society

Stratospheric Memory and Skill of Extended-Range Weather Forecasts

We use an empirical statistical model to demonstrate significant skill in making extended-range forecasts of the monthly-mean Arctic Oscillation (AO). Forecast skill derives from persistent circulation anomalies in the lowermost stratosphere and is greatest during boreal winter. A comparison to the Southern Hemisphere provides evidence that both the time scale and predictability of the AO depend on the presence of persistent circulation anomalies just above the tropopause. These circulation anomalies most likely affect the troposphere through changes to waves in the upper troposphere, which induce surface pressure changes that correspond to the AO.

Subseasonal extremes of precipitation and active-break cycles of the Indian summer monsoon in a climate-change scenario

Changes to the behaviour of subseasonal precipitation extremes and active-break cycles of the Indian summer monsoon are assessed in this study using pre-industrial and 2 × CO2 integrations of the Hadley Centre coupled model HadCM3, which is able to simulate the monsoon seasonal cycle reasonably. At 2 × CO2, mean summer rainfall increases slightly, especially over central and northern India. The mean intensity of daily precipitation during the monsoon is found to increase, consistent with fewer wet days, and there are increases to heavy rain events beyond changes in the mean alone. The chance of reaching particular thresholds of heavy rainfall is found to approximately double over northern India, increasing the likelihood of damaging floods on a seasonal basis. The local distribution of such projections is uncertain, however, given the large spread in mean monsoon rainfall change and associated extremes amongst even the most recent coupled climate models. The measured increase of the heaviest precipitation events over India is found to be broadly in line with the degree of atmospheric warming and associated increases in specific humidity, lending a degree of predictability to changes in rainfall extremes. Active-break cycles of the Indian summer monsoon, important particularly due to their effect on agricultural output, are shown to be reasonably represented in HadCM3, in particular with some degree of northward propagation. We note an intensification of both active and break events, particularly when measured against the annual cycle, although there is no suggestion of any change to the duration or likelihood of monsoon breaks. Copyright © 2009 Royal Meteorological Society

On the influence of stratospheric water vapor changes on the tropospheric circulation

Observations suggest that the mixing ratio of water vapour in the stratosphere has increased by 20–50% between the 1960s and mid-1990s. Here we show that inclusion of such a stratospheric water vapour (SWV) increase in a state-of-the-art climate model modifies the circulation of the extratropical troposphere: the modeled increase in the North Atlantic Oscillation (NAO) index is 40% of the observed increase in NAO index between 1965 and 1995, suggesting that if the SWV trend is real, it explains a significant fraction of the observed NAO trend. Our results imply that SWV changes provide a novel mechanism for communicating the effects of large tropical volcanic eruptions and ENSO events to the extratropical troposphere over timescales of a few years, which provides a mechanism for interannual climate predictability. Finally, we discuss our results in the context of regional climate change associated with changes in methane emissions.

Stratospheric circulation in seasonal forecasting models: implications for seasonal prediction

Accurate seasonal forecasts rely on the presence of low frequency, predictable signals in the climate system which have a sufficiently well understood and significant impact on the atmospheric circulation. In the Northern European region, signals associated with seasonal scale variability such as ENSO, North Atlantic SST anomalies and the North Atlantic Oscillation have not yet proven sufficient to enable satisfactorily skilful dynamical seasonal forecasts. The winter-time circulations of the stratosphere and troposphere are highly coupled. It is therefore possible that additional seasonal forecasting skill may be gained by including a realistic stratosphere in models. In this study we assess the ability of five seasonal forecasting models to simulate the Northern Hemisphere extra-tropical winter-time stratospheric circulation. Our results show that all of the models have a polar night jet which is too weak and displaced southward compared to re-analysis data. It is shown that the models underestimate the number, magnitude and duration of periods of anomalous stratospheric circulation. Despite the poor representation of the general circulation of the stratosphere, the results indicate that there may be a detectable tropospheric response following anomalous circulation events in the stratosphere. However, the models fail to exhibit any predictability in their forecasts. These results highlight some of the deficiencies of current seasonal forecasting models with a poorly resolved stratosphere. The combination of these results with other recent studies which show a tropospheric response to stratospheric variability, demonstrates a real prospect for improving the skill of seasonal forecasts.

Land surface anomaly simulations and predictions with a climate model: an El Nino Southern Oscillation case study

The ability of climate models to reproduce and predict land surface anomalies is an important but little-studied topic. In this study, an atmosphere and ocean assimilation scheme is used to determine whether HadCM3 can reproduce and predict snow water equivalent and soil moisture during the 1997–1998 El Nino Southern Oscillation event. Soil moisture is reproduced more successfully, though both snow and soil moisture show some predictability at 1- and 4-month lead times. This result suggests that land surface anomalies may be reasonably well initialized for climate model predictions and hydrological applications using atmospheric assimilation methods over a period of time.