[Show abstract][Hide abstract] ABSTRACT: We consider the impact of climate change on the wind energy resource of Ireland using an ensemble of regional climate model (RCM) simulations. The RCM used in this work is the Consortium for Small-scale Modelling–climate limited-area modelling (COSMO-CLM) model.
The COSMO-CLM model was evaluated by performing simulations of the past Irish climate, driven by European Centre for Medium-Range Weather Forecasts ERA-40 data, and comparing the output with observations. For the investigation of the influence of the future climate under different climate scenarios, the Max Planck Institute's global climate model, ECHAM5, was used to drive the COSMO-CLM model. Simulations are run for a control period 1961–2000 and future period 2021–2060. To add to the number of ensemble members, the control and future simulations were driven by different realizations of the ECHAM5 data. The future climate was simulated using the Intergovernmental Panel on Climate Change emission scenarios, A1B and B1.
The research was undertaken to consolidate, and as a continuation of, similar research using the Rossby Centre's RCA3 RCM to investigate the effects of climate change on the future wind energy resource of Ireland. The COSMO-CLM projections outlined in this study agree with the RCA3 projections, with both showing substantial increases in 60 m wind speed over Ireland during winter and decreases during summer. The projected changes of both studies were found to be statistically significant over most of Ireland. The agreement of the COSMO-CLM and RCA3 simulation results increases our confidence in the robustness of the projections. Copyright
[Show abstract][Hide abstract] ABSTRACT: The objective of this research is to get the best possible wind speed
forecasts for the wind energy industry by using an optimal combination
of well-established forecasting and post-processing methods. We start
with the ECMWF 51 member ensemble prediction system (EPS) which is
underdispersive and hence uncalibrated. We aim to produce wind speed
forecasts that are more accurate and calibrated than the EPS. The 51
members of the EPS are clustered to 8 weighted representative members
(RMs), chosen to minimize the within-cluster spread, while maximizing
the inter-cluster spread. The forecasts are then downscaled using two
limited area models, WRF and COSMO, at two resolutions, 14km and 3km.
This process creates four distinguishable ensembles which are used as
input to statistical post-processes requiring multi-model forecasts. Two
such processes are presented here. The first, Bayesian Model Averaging,
has been proven to provide more calibrated and accurate wind speed
forecasts than the ECMWF EPS using this multi-model input data. The
second, heteroscedastic censored regression is indicating positive
results also. We compare the two post-processing methods, applied to a
year of hindcast wind speed data around Ireland, using an array of
deterministic and probabilistic verification techniques, such as MAE,
CRPS, probability transform integrals and verification rank histograms,
to show which method provides the most accurate and calibrated
forecasts. However, the value of a forecast to an end-user cannot be
fully quantified by just the accuracy and calibration measurements
mentioned, as the relationship between skill and value is complex.
Capturing the full potential of the forecast benefits also requires
detailed knowledge of the end-users' weather sensitive decision-making
processes and most importantly the economic impact it will have on their
income. Finally, we present the continuous relative economic value of
both post-processing methods to identify which is more beneficial to the
wind energy industry of Ireland.
[Show abstract][Hide abstract] ABSTRACT: Two methods of post-processing the uncalibrated wind speed forecasts from the European Centre for Medium- Range Weather Forecasts (ECMWF) ensemble prediction system (EPS) are presented here. Both methods involve statistically post-processing the EPS or a downscaled version of it with Bayesian model averaging (BMA). The first method applies BMA directly to the EPS data. The second method involves clustering the EPS to eight representative members (RMs) and downscaling the data through two limited area models at two resolutions. Four weighted ensemble mean forecasts are produced and used as input to the BMA method. Both methods are tested against 13 meteorological stations around Ireland with 1 yr of forecast/observation data. Results show calibration and accuracy improvements using both methods, with the best results stemming from Method 2, which has comparatively low mean absolute error and continuous ranked probability scores.
[Show abstract][Hide abstract] ABSTRACT: We consider the impact of climate change on the wind energy resource of Ireland using an ensemble of Regional Climate Model (RCM) simulations. The RCM dynamically downscales the coarse information provided by the Global Climate Models (GCMs) and provides high resolution information, on a subdomain covering Ireland. The RCM used in this work is the Rossby Center's RCM (RCA3).
The RCA3 model is evaluated by performing simulations of the past Irish climate, driven by European Center for Medium-Range Weather Forecasts ERA-40 data, and by comparing the output to observations. Results confirm that the output of the RCA3 model exhibits reasonable and realistic features as documented in the historical wind data record. For the investigation of the influence of the future climate under different climate scenarios, the Max Plank Institute's GCM, European Center Hamburg Model, is used to drive the RCA3 model. Simulations are run for a control period 1961-2000 and future period 2021-2060. The future climate was simulated using the four Intergovernmental Panel on Climate Change emission scenarios A1B, A2, B1 and B2. The results for the downscaled simulations show a substantial overall increase in the energy content of the wind for the future winter months and a decrease during the summer months. The projected changes for summer and winter were found to be statistically significant over most of Ireland. However, the projected changes should be viewed with caution since the climate change signal is of similar magnitude to the variability of the evaluation and control simulations. Copyright
[Show abstract][Hide abstract] ABSTRACT: At the Meteorology & Climate Centre at University College Dublin, we
are using the CLM-Community's COSMO-CLM Regional Climate Model (RCM) and
the WRF RCM (developed at NCAR) to simulate the climate of Ireland at
high spatial resolution. To address the issue of model uncertainty, a
Multi-Model Ensemble (MME) approach is used. The ensemble method uses
different RCMs, driven by several Global Climate Models (GCMs), to
simulate climate change. Through the MME approach, the uncertainty in
the RCM projections is quantified, enabling us to estimate the
probability density function of predicted changes, and providing a
measure of confidence in the predictions. The RCMs were validated by
performing a 20-year simulation of the Irish climate (1981-2000), driven
by ECMWF ERA-40 global re-analysis data, and comparing the output to
observations. Results confirm that the output of the RCMs exhibit
reasonable and realistic features as documented in the historical data
record. Projections for the future Irish climate were generated by
downscaling the Max Planck Institute's ECHAM5 GCM, the UK Met Office
HadGEM2-ES GCM and the CGCM3.1 GCM from the Canadian Centre for Climate
Modelling. Simulations were run for a reference period 1961-2000 and
future period 2021-2060. The future climate was simulated using the A1B,
A2, B1, RCP 4.5 & RCP 8.5 greenhouse gas emission scenarios.
Results for the downscaled simulations show a substantial overall
increase in precipitation and wind speed for the future winter months
and a decrease during the summer months. The predicted annual change in
temperature is approximately 1.1°C over Ireland. To date, all RCM
projections are in general agreement, thus increasing our confidence in
the robustness of the results.
[Show abstract][Hide abstract] ABSTRACT: This project aims to produce the best possible wind speed forecasts for
the wind energy industry by using an optimal combination of
well-established forecasting and post-processing methods. We start with
the ECMWF 51 member ensemble prediction system (EPS) and produce a more
accurate forecast than the ensemble mean. The 51 members are clustered
to 8 weighted representative members (RMs) using a clustering technique.
The 8 RMs are chosen to minimize the within-cluster spread, while
maximizing the inter-cluster spread. The forecasts are then downscaled
using two limited area models, WRF and COSMO, at two resolutions, 14km
and 3km. Numerical weather prediction is far from perfect and each of
the ensemble member forecasts contains errors, both systematic and
chaotic. Systematic errors can be minimized with statistical
post-processing. We apply four adaptive post-processing methods to each
forecast which require only a short training period. The weighted
ensemble mean of the post-processed ensembles is used as the input to a
Bayesian Model Averaging (BMA) system. Each ensemble forecast
probability density function (PDF) is weighted based on how well it has
performed over a training period. The weighted PDFs are then summed to
form the BMA PDF which represents the probability of all possible wind
speeds and has been proven to outperform the ensemble mean. We present a
detailed description of the above process and detail some preliminary
[Show abstract][Hide abstract] ABSTRACT: In recent years, uncertainties in climate model projections have become of great interest because a wide range of future projection have become available from a combination of various emission scenarios and different climate models. The quality of the future projections of climate change depend critically on the ability of the Global and Regional Climate Models to reproduce the present-day climate. Due to the coarse resolution of global models, high-resolution regional studies are essential for impact studies and adaptation strategies in Ireland. Particularly, the representation of extreme events requires us to use high-resolution Regional Climate Models (RCMs). Moreover, the interaction between the atmosphere and ocean has to be included in RCMs in a more sophisticated way; this is especially important for accurately reproducing the climate over Ireland, which is surrounded by the Atlantic Ocean and Irish Sea. The RCA_NEMO model, an interactive flux coupled regional atmosphere-ocean model was developed in this study. This model, which combines two well-known components, the Rossby Center regional climate model (RCA) and the ocean model NEMO, together with the OASIS3 coupler, is fully parallel and can introduce the interaction between the atmosphere and ocean into climate simulations in a sophisticated manner. The model has been demonstrated to run long simulations (1960-1990) without flux correction. The monthly mean value between 1961 and 1990 is fully evaluated against analysis/observations. Mean sea level pressure, which is strongly associated with cyclone activity, has been reproduced well by both models, except in April. The atmosphere-only run has a weaker pressure gradient over Ireland. Comparing the output with the UKCIP observational data, the coupled model more accurately represents the climate of Ireland. Not only are the basic characteristics reproduced by the coupled run; the wet bias in the midlands and the dry bias in the southwest of Ireland are also decreased in the coupled run. Although there is no simple relationship between the monthly mean precipitation and extreme events, the heavy precipitation events over land has been improved by the coupled model, too. The coupled model has different performance at different times of the year. The 2 metre temperature has been slightly overestimated by the coupled model in wet months (January and October). However, the 2 metre temperature has been improved in the dry season (April and July). In conclusion, the coupled RCA_NEMO model is an effective way to accurately reproduce the current climate of Ireland. Further improvements will be developed in the next stage of this research, and will be used to investigate climate change in Ireland under a range of different climate scenarios.
[Show abstract][Hide abstract] ABSTRACT: Accurate forecasting of available energy is crucial for the efficient management and use of wind power in the national power grid. With energy output critically dependent upon wind strength there is a need to reduce the errors associated wind forecasting. The objective of this research is to get the best possible wind forecasts for the wind energy industry. To achieve this goal, three methods are being applied. First, a mesoscale numerical weather prediction (NWP) model called WRF (Weather Research and Forecasting) is being used to predict wind values over Ireland. Currently, a gird resolution of 10km is used and higher model resolutions are being evaluated to establish whether they are economically viable given the forecast skill improvement they produce. Second, the WRF model is being used in conjunction with ECMWF (European Centre for Medium-Range Weather Forecasts) ensemble forecasts to produce a probabilistic weather forecasting product. Due to the chaotic nature of the atmosphere, a single, deterministic weather forecast can only have limited skill. The ECMWF ensemble methods produce an ensemble of 51 global forecasts, twice a day, by perturbing initial conditions of a 'control' forecast which is the best estimate of the initial state of the atmosphere. This method provides an indication of the reliability of the forecast and a quantitative basis for probabilistic forecasting. The limitation of ensemble forecasting lies in the fact that the perturbed model runs behave differently under different weather patterns and each model run is equally likely to be closest to the observed weather situation. Models have biases, and involve assumptions about physical processes and forcing factors such as underlying topography. Third, Bayesian Model Averaging (BMA) is being applied to the output from the ensemble forecasts in order to statistically post-process the results and achieve a better wind forecasting system. BMA is a promising technique that will offer calibrated probabilistic wind forecasts which will be invaluable in wind energy management. In brief, this method turns the ensemble forecasts into a calibrated predictive probability distribution. Each ensemble member is provided with a 'weight' determined by its relative predictive skill over a training period of around 30 days. Verification of data is carried out using observed wind data from operational wind farms. These are then compared to existing forecasts produced by ECMWF and Met Eireann in relation to skill scores. We are developing decision-making models to show the benefits achieved using the data produced by our wind energy forecasting system. An energy trading model will be developed, based on the rules currently used by the Single Electricity Market Operator for energy trading in Ireland. This trading model will illustrate the potential for financial savings by using the forecast data generated by this research.
[Show abstract][Hide abstract] ABSTRACT: At the Meteorology & Climate Centre at University College Dublin, we are using the CLM-Community's COSMO-CLM Regional Climate Model (RCM) and the WRF RCM (developed at NCAR) to simulate the climate of Ireland at 7km resolution. The RCM models were validated by performing a 20-year simulation of the Irish climate (1981-2000), driven at the lateral boundaries by ECMWF ERA-40 global re-analysis data, and comparing the output to observations. Results confirm that the output of the RCM models exhibit reasonable and realistic features as documented in the historical data record. Validation results will be presented for wind, temperature and precipitation. Projections for the future Irish climate were generated by downscaling the Max Planck Institute's ECHAM5 global climate model data using the COSMO-CLM RCM. Simulations were run for a reference period 1961-2000 and future period 2021-2060. The future climate was simulated using the A1B & B1 greenhouse gas emission scenarios. Results for the downscaled simulations show a substantial overall increase in wind speeds for the future winter months and a decrease during the summer months. The projected changes for summer and winter were found to be statistically significant over most of Ireland. Future projections for temperature and precipitation will also be presented.
[Show abstract][Hide abstract] ABSTRACT: The dynamics of non-divergent flow on a rotating sphere are described by the conservation of absolute vorticity. The analytical study of the non-linear barotropic vorticity equation is greatly facilitated by the expansion of the solution in spherical harmonics and truncation at low order. The normal modes are the well-known Rossby–Haurwitz (RH) waves, which represent the natural oscillations of the system. Triads of RH waves, which satisfy conditions for resonance, are of critical importance for the distribution of energy in the atmosphere.We show how non-linear interactions of resonant RH triads may result in dynamic instability of large-scale components. We also demonstrate a mathematical equivalence between the equations for an orographically forced triad and a simple mechanical system, the forced-damped swinging spring. This equivalence yields insight concerning the bounded response to a constant forcing in the absence of damping. An examination of triad interactions in atmospheric reanalysis data would be of great interest.
[Show abstract][Hide abstract] ABSTRACT: A study of nine Irish catchments was carried out to quantify the expected impact of climate change on hydrology in Ireland. Boundary data from the European Centre Hamburg Model Version 5 (ECHAM 5) general circulation model were used to force the Rossby Centre Atmosphere Model (RCA3) regional climate model, producing dynamically downscaled precipitation and temperature data under past and future climate scenarios. This data was used to force the HBV-Light conceptual rainfall-runoff model to simulate stream flow in the reference period (1961–2000) and in the future (2021–2060) under the Special Report on Emissions Scenarios (SRES) A1B scenario. A Monte-Carlo approach to calibration was used to obtain 100 parameter sets which reproduced observed stream flow well. Use of an ensemble provided results in terms of a range rather than a single value. Results suggested an amplification of the seasonal cycle across the country, driven by increased winter precipitation, decreased summer precipitation and increased temperature. The expected changes in mean winter and summer flows as well as annual maximum daily mean flow varied depending on catchment characteristics and the timing and magnitude of expected changes in precipitation in each catchment.
Full-text · Article · Jul 2008 · Journal of Hydrology
[Show abstract][Hide abstract] ABSTRACT: simulations with the Rossby Centre regional climate model RCA3. The model domain comprises large parts of the North Atlantic and the adjacent continents, RCA3 is driven by ECHAM5-OM1 general circulation model data for May to December from 1985 to 2000 and May to December from 2085 to 2100 assuming the SRES-A2 emission scenario. We apply an objective algorithm to identify and track tropical and extratropical cyclones, as well as extratropical transition. The simulation indicates increase in the count of strong hurricanes and extratropical cyclones. Contrasting, and generally weaker, changes are seen for the less extreme events. Decreases of 18% in the count of extratropical cyclones and 13% in the count of tropical cyclones with wind speeds of >= 18 m s(-1) can be found. Furthermore, there is a pronounced shift in the tracks of hurricanes and their extratropical transition in November and December-more hurricanes are seen over the Gulf of Mexico, the Caribbean Sea and the western Sargasso Sea and less over the southern North Atlantic.
[Show abstract][Hide abstract] ABSTRACT: Numerical simulation of an ever-increasing range of geophysical phenomena is adding enormously to our understand-ing of complex processes in the Earth system. The consequences for mankind of ongoing climate change will be far-reach-ing. Earth System Models are capable of replicating climate regimes of past millennia and are the best means we have of predicting the future of our climate. The basic ideas of numerical forecasting and climate modeling were developed about a century ago, long before the first electronic computer was constructed. There were several major practical obstacles to be overcome before numerical pre-diction could be put into practice. A fuller understanding of atmospheric dynamics allowed the development of simplified systems of equations; regular radiosonde observations of the free atmosphere and, later, satellite data, provided the initial conditions; stable finite difference schemes were developed; and powerful electronic computers provided a practical means of carrying out the prodigious calculations required to predict the changes in the weather. Progress in weather forecasting and in climate modeling over the past 50 years has been dramatic. In this presentation, we will trace the history of computer forecasting through the ENIAC integrations to the present day. The useful range of deterministic prediction is increasing by about one day each decade, and our understanding of climate change is growing rapidly as Earth System Models of ever-increasing sophistication are developed.
Preview · Article · Mar 2008 · Journal of Computational Physics
[Show abstract][Hide abstract] ABSTRACT: 1] The influence of an increased sea surface temperature (SST) on the frequency and intensity of cyclones over the North Atlantic is investigated using two data sets from simulations with the Rossby Centre regional climate model RCA3. The model domain comprises large parts of the North Atlantic and the adjacent continents. RCA3 is driven by reanalysis data for May to December 1985–2000 at the lateral and lower boundaries, using SST and lateral boundary temperatures. A realistic interannual variation in tropical storm and hurricane counts is simulated. In an idealized sensitivity experiment, SSTs and boundary condition temperatures at all levels are increased by 1 K to ensure that we can distinguish the SST from other factors influencing the development of cyclones. An increase in the count of strong hurricanes is simulated. There is not much change in the location of hurricanes. Generally weaker changes are seen in the extratropical region and for the less extreme events. Increases of 9% in the count of extratropical cyclones and 39% in the count of tropical cyclones with wind speeds of at least 18 m/s are found. (2008), Regional model simulation of North Atlantic cyclones: Present climate and idealized response to increased sea surface temperature, J. Geophys. Res., 113, D02107, doi:10.1029/2006JD008213.
Full-text · Article · Jan 2008 · Journal of Geophysical Research Atmospheres
[Show abstract][Hide abstract] ABSTRACT: The Regional Ocean Model System (ROMS) of Rutgers University is used to investigate the influence of anthropogenic climate change on storm surges over Irish waters, particularly on the extreme values. Two experiments were performed to confirm the validity of the approach in the current climate: the first focused on hindcasting the surge generated by a storm in early 2002 while the second provided surge statistics by running the model for the period 1990–2002; in both cases ROMS was driven with ERA-40 forcing fields. The results show that the model is capable of simulating both specific surge events and surge climate statistics with reasonable accuracy (order of 10 cm). Model outputs were also compared spatially against satellite altimetry data, corrected for long wavelength errors, from 1993 to 2001. The ROMS model consistently reproduces the sea level changes in the Irish Sea, and over the waters to the south and west of Ireland. For the investigation of the impact of the climate change on storm surges, the same configuration of ROMS was driven by atmospheric forcing fields downscaled from ECHAM5/OM1 data for the past (1961–1990) and future (2031–2060; SRES A1B greenhouse gas scenario); the downscaled data were produced using the Rossby Centre Regional Atmosphere model (RCA3). The results show an increase in storm surge events around Irish coastal areas in the future projection, except along the south Irish coast; there is also a significant increase in the height of the extreme surges along the west and east coasts, with most of the extreme surges occurring in wintertime.