Resolution Regional Climate Model Research Articles

Projection of fire potential to future climate scenarios in the Alpine area: some methodological considerations

In Europe, wildfires are an issue not only for the Mediterranean area, but also in the Alpine regions in terms of increasing number of events and severity. In this study we evaluate the impact of climate change on the fire potential in the Alps in the past and in future scenarios. The Fine Fuel Moisture Code (FFMC) of the Canadian Forest Fire Danger Rating System, which successfully distinguishes among recorded fire/no fire events, is applied to projections of Regional Climate Models (RCMs) calculated on the SRES scenario A1B. We compare two different techniques: 1) a single model run of the COSMO-CLM RCM at 18 km resolution, and 2) a combination of 25-km resolution RCMs from the ENSEMBLES project, combined with the Multimodel SuperEnsemble technique and a new probabilistic Multimodel SuperEnsemble Dressing. The single-model RCM allows for a greater coherence among the input parameters, while the Multimodel techniques permit to reduce the model biases and to downscale to a higher resolution where long term records of observations are available. The projected changes with the Multimodel in the scenario give an estimation of increasing wildfire potential in the mid XXI century. In particular the frequency of severe wildfire potential days is shown to increase dramatically. The single (independent) COSMO model gives a weaker signal and in some regions of the study area the predicted changes are opposite to the ones by the Multimodel. This is mainly due to increasing precipitation amounts simulated especially in the northern parts of the Alps. However, there are also some individual models included in the Multimodel ensemble that show a similar signal. This confirms the ambiguity of any impact study based on a single climate model due to the uncertainty of the projections of the climate models.

Combining climatological and participatory approaches for assessing changes in extreme climatic indices at regional scale

This paper combines the climatological and societal perspectives for assessing future climatic extremes over Kangasabati River basin in India using an ensemble of four high resolution (25 km) regional climate model (RCM) simulations from 1970 to 2050. The relevant extreme indices and their thresholds are defined in consultation with stakeholders and are then compared using RCM simulations. To evaluate the performance of RCM in realistically representing atmospheric processes in the basin, model simulations driven with ERAInterim global re-analysis data from 1989 to 2008 are compared with observations. The models perform well in simulating seasonality, interannual variability and climatic extremes. Future climatic extremes are evaluated based on RCM simulations driven by GCMs, for present (1970–1999) and for the SRES A1B scenario for future (2021–2050) period. The analysis shows an intensification of majority of extremes as projected by future ensemble mean. The study suggests that there is a marked consistency in stakeholder observed changes in climate extremes and future predicted trends.

Downscaled climate change projections with uncertainty assessment over India using a high resolution multi-model approach

This study presents the possible regional climate change over South Asia with a focus over India as simulated by three very high resolution regional climate models (RCMs). One of the most striking results is a robust increase in monsoon precipitation by the end of the 21st century but regional differences in strength. First the ability of RCMs to simulate the monsoon climate is analyzed. For this purpose all three RCMs are forced with ECMWF reanalysis data for the period 1989–2008 at a horizontal resolution of ~25km. The results are compared against independent observations. In order to simulate future climate the models are driven by lateral boundary conditions from two global climate models (GCMs: ECHAM5-MPIOM and HadCM3) using the SRES A1B scenario, except for one RCM, which only used data from one GCM. The results are presented for the full transient simulation period 1970–2099 and also for several time slices. The analysis concentrates on precipitation and temperature over land. All models show a clear signal of gradually wide-spread warming throughout the 21st century. The ensemble-mean warming over India is 1.5°C at the end of 2050, whereas it is 3.9°C at the end of century with respect to 1970–1999. The pattern of projected precipitation changes shows considerable spatial variability, with an increase in precipitation over the peninsular of India and coastal areas and, either no change or decrease further inland. From the analysis of a larger ensemble of global climate models using the A1B scenario a wide spread warming (~3.2°C) and an overall increase (~8.5%) in mean monsoon precipitation by the end of the 21st century is very likely. The influence of the driving GCM on the projected precipitation change simulated with each RCM is as strong as the variability among the RCMs driven with one.

Parameterization of High Resolution Vegetation Characteristics using Remote Sensing Products for the Nakdong River Watershed, Korea

Mesoscale regional climate models (RCMs), the primary tool for climate predictions, have recently increased in sophistication and are being run at increasingly higher resolutions to be also used in climate impact studies on ecosystems, particularly in agricultural crops. As satellite remote sensing observations of the earth terrestrial surface become available for assimilation in RCMs, it is possible to incorporate complex land surface processes, such as dynamics of state variables for hydrologic, agricultural and ecologic systems at the smaller scales. This study focuses on parameterization of vegetation characteristics specifically designed for high resolution RCM applications using various remote sensing products, such as Advanced Very High Resolution Radiometer (AVHRR), Système Pour l’Observation de la Terre-VEGETATION (SPOT-VGT) and Moderate Resolution Imaging Spectroradiometer (MODIS). The primary vegetative parameters, such as land surface characteristics (LCC), fractional vegetation cover (FVC), leaf area index (LAI) and surface albedo localization factors (SALF), are currently presented over the Nakdong River Watershed domain, Korea, based on 1-km remote sensing satellite data by using the Geographic Information System (GIS) software application tools. For future high resolution RCM modeling efforts on climate-crop interactions, this study has constructed the deriving parameters, such as FVC and SALF, following the existing methods and proposed the new interpolation methods to fill missing data with combining the regression equation and the time series trend function for time-variant parameters, such as LAI and NDVI data at 1-km scale.

Projected changes in climate extremes over China in the 21st century from a high resolution regional climate model (RegCM3)

Based on a consecutive simulation of the 21st century conducted by RegCM3, changes in climate extremes over China are investigated, following abasic validation of the model performances in simulating present climate. The model is one-way nested within the global model of CCSR/NIES/FRCGC MIROC3.2_hires. A total of 150-years (1951–2100) transient simulation is carried out at 25 km grid spacing under the IPCC SRES A1B scenario. The indices of the extremesincludes SU (summer days), FD (frost days), GSL (growing season length) for temperature, SDII (simple daily intensity index), R10 (no. of days with precipitation ⩽10 mm/d), and CDD (consecutive dry days) for precipitation. Results show that the model can reproduce both the spatial distribution and the values of the present day annual mean temperature and precipitationwell, and it also shows good performances in simulating the extreme indices. Following the significant warming, the indices of SU and GSL for warm events will increase while the indices of FD for cold events will decrease over China. Heavy precipitation events as measured by SDII and R10 show an general increase over the region, except the decrease of R10 in the Northeast and central Tibetan Plateau andless change or decrease of it along the middle and lower reaches of the Yangtze River. Decrease of drynessas measured by CDD over northern part of China while increase of it over the Tibetan Plateau, Sichuan Basin and other places in southern China are simulated by the model. This leads to the less change of the regional mean CDD in the time series in the 21st century unlike the other indices, which show clear trend of change following the time evolution.

Changes in mean and extreme climates over China with a 2°C global warming

Based on a 153-year (1948–2100) transient simulation of East Asian climate performed by a high resolution regional climate model (RegCM3) under the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) A1B scenario, the potential future changes in mean and extreme climates over China in association with a global warming of 2°C with respect to pre-industrial times are assessed in this study. Results show that annual temperature rises over the whole of China, with a greater magnitude of around 0.6°C compared to the global mean increase, at the time of a 2°C global warming. Large-scale surface warming gets stronger towards the high latitudes and on the Qinghai-Tibetan Plateau, while it is similar in magnitude but somewhat different in spatial pattern between seasons. Annual precipitation increases by 5.2%, and seasonal precipitation increases by 4.2%–8.5% with respect to the 1986–2005 climatology. At the large scale, apart from in boreal winter when precipitation increases in northern China but decreases in southern China, annual and seasonal precipitation increases in western and southeastern China but decreases over the rest of the country. Nationwide extreme warm (cold) temperature events increase (decrease). With respect to the 1986–2005 climatology, the country-averaged annual extreme precipitation events R5d, SDII, R95T, and R10 increase by 5.1 mm, 0.28 mm d−1, 6.6%, and 0.4 d respectively, and CDD decreases by 0.5 d. There is a large spatial variability in R10 and CDD changes.

Does increasing the spatial resolution of a regional climate model improve the simulated daily precipitation?

Three different resolution (50, 12, and 1.5 km) regional climate model simulations are compared in terms of their ability to simulate moderate and high daily pre- cipitation events over the southern United Kingdom. The convection-permitting 1.5-km simulation is carried out without convective parametrisation. As in previous studies, increasing resolution (especially from 50 to 12 km) is found to improve the representation of orographic precip- itation. The 50-km simulation underestimates mean pre- cipitation over the mountainous region of Wales, and event intensity tends to be too weak; this bias is reduced in both the 12- and 1.5-km simulations for both summer and winter. In south-east England lowlands where summer extremes are mostly convective, increasing resolution does not necessary lead to an improvement in the simulation. For the 12-km simulation, simulated daily extreme events are overly intense. Even though the average intensity of summer daily extremes is improved in the 1.5-km simu- lation, this simulation has a poorer mean bias with too many events exceeding high thresholds. Spatial density and clustering of summer extremes in south-east England are poorly simulated in both the 12- and 1.5-km simulations. In general, we have not found any clear evidence to show that the 1.5-km simulation is superior to the 12-km simulation, or vice versa at the daily level.

Validation of the present day annual cycle in heavy precipitation over the British Islands simulated by 14 RCMs

The representation of the annual cycle of heavy daily precipitation events across the United Kingdom within 14 regional climate models (RCMs) and the European observation data set (E‐OBS) over the 1961–2000 period is investigated. We model extreme precipitation as an inhomogeneous Poisson process with a non‐stationary threshold and use a sinusoidal model for the location and scale parameter of the corresponding generalized extreme value distribution and a constant shape parameter. First we fit the statistical model to the UK Met Office 5 km gridded precipitation data set (UKMO). Second the statistical model is fitted to 14 reanalysis driven 25 km resolution RCMs from the ENSEMBLES project and to E‐OBS. The resulting characteristics from the RCMs and from E‐OBS are compared with those from UKMO. We study the peak time of the annual cycle of the monthly return levels, the relative amplitude of their annual cycle and the relative bias of their absolute values. We show that the performance of the RCMs depends strongly on the region. The RCMs show deficits in modeling the characteristics of the annual cycle, especially in modeling its relative amplitude and mainly in Eastern England. However the peak time of the annual cycle is adequately simulated by most RCMs. E‐OBS exhibits considerable biases in the absolute values of all monthly return levels, but the relative amplitude and the phase of the annual cycle of heavy precipitation are well represented. Our results imply that studies which rely on the explicit annual cycle of simulated heavy precipitation should be carefully considered.

Development of climate change projections for small watersheds using multi-model ensemble simulation and stochastic weather generation

Regional climate models (RCMs) have been increasingly used for climate change studies at the watershed scale. However, their performance is strongly dependent upon their driving conditions, internal parameterizations and domain configurations. Also, the spatial resolution of RCMs often exceeds the scales of small watersheds. This study developed a two-step downscaling method to generate climate change projections for small watersheds through combining a weighted multi-RCM ensemble and a stochastic weather generator. The ensemble was built on a set of five model performance metrics and generated regional patterns of climate change as monthly shift terms. The stochastic weather generator then incorporated these shift terms into observed climate normals and produced synthetic future weather series at the watershed scale. This method was applied to the Assiniboia area in southern Saskatchewan, Canada. The ensemble led to reduced biases in temperature and precipitation projections through properly emphasizing models with good performance. Projection of precipitation occurrence was particularly improved through introducing a weight-based probability threshold. The ensemble-derived climate change scenario was well reproduced as local daily weather series by the stochastic weather generator. The proposed combination of dynamical downscaling and statistical downscaling can improve the reliability and resolution of future climate projection for small prairie watersheds. It is also an efficient solution to produce alternative series of daily weather conditions that are important inputs for examining watershed responses to climate change and associated uncertainties.

High resolution regional climate model simulations for Germany: Part II—projected climate changes

The projected climate change signals of a five-member high resolution ensemble, based on two global climate models (GCMs: ECHAM5 and CCCma3) and two regional climate models (RCMs: CLM and WRF) are analysed in this paper (Part II of a two part paper). In Part I the performance of the models for the control period are presented. The RCMs use a two nest procedure over Europe and Germany with a final spatial resolution of 7 km to downscale the GCM simulations for the present (1971–2000) and future A1B scenario (2021–2050) time periods. The ensemble was extended by earlier simulations with the RCM REMO (driven by ECHAM5, two realisations) at a slightly coarser resolution. The climate change signals are evaluated and tested for significance for mean values and the seasonal cycles of temperature and precipitation, as well as for the intensity distribution of precipitation and the numbers of dry days and dry periods. All GCMs project a significant warming over Europe on seasonal and annual scales and the projected warming of the GCMs is retained in both nests of the RCMs, however, with added small variations. The mean warming over Germany of all ensemble members for the fine nest is in the range of 0.8 and 1.3 K with an average of 1.1 K. For mean annual precipitation the climate change signal varies in the range of −2 to 9 % over Germany within the ensemble. Changes in the number of wet days are projected in the range of ±4 % on the annual scale for the future time period. For the probability distribution of precipitation intensity, a decrease of lower intensities and an increase of moderate and higher intensities is projected by most ensemble members. For the mean values, the results indicate that the projected temperature change signal is caused mainly by the GCM and its initial condition (realisation), with little impact from the RCM. For precipitation, in addition, the RCM affects the climate change signal significantly.

High resolution regional climate model simulations for Germany: part I—validation

A five-member ensemble of regional climate model (RCM) simulations for Europe, with a high resolution nest over Germany, is analysed in a two-part paper: Part I (the current paper) presents the performance of the models for the control period, and Part II presents results for near future climate changes. Two different RCMs, CLM and WRF, were used to dynamically downscale simulations with the ECHAM5 and CCCma3 global climate models (GCMs), as well as the ERA40-reanalysis for validation purposes. Three realisations of ECHAM5 and one with CCCma3 were downscaled with CLM, and additionally one realisation of ECHAM5 with WRF. An approach of double nesting was used, first to an approximately 50 km resolution for entire Europe and then to a domain of approximately 7 km covering Germany and its near surroundings. Comparisons of the fine nest simulations are made to earlier high resolution simulations for the region with the RCM REMO for two ECHAM5 realisations. Biases from the GCMs are generally carried over to the RCMs, which can then reduce or worsen the biases. The bias of the coarse nest is carried over to the fine nest but does not change in amplitude, i.e. the fine nest does not add additional mean bias to the simulations. The spatial pattern of the wet bias over central Europe is similar for all CLM simulations, and leads to a stronger bias in the fine nest simulations compared to that of WRF and REMO. The wet bias in the CLM model is found to be due to a too frequent drizzle, but for higher intensities the distributions are well simulated with both CLM and WRF at the 50 and 7 km resolutions. Also the spatial distributions are close to high resolution gridded observations. The REMO model has low biases in the domain averages over Germany and no drizzle problem, but has a shift in the mean precipitation patterns and a strong overestimation of higher intensities. The GCMs perform well in simulating the intensity distribution of precipitation at their own resolution, but the RCMs add value to the distributions when compared to observations at the fine nest resolution.

Numerical Simulation of Climate Changes over North China by the Model RegCM3

AbstractUsing the high resolution regional climate model to estimate the regional future climate can help us to understand and assess the regional climate changes under the global warming. Based on the simulation conducted by National Climate Center with a high resolution regional climate model (RegCM3) at 25 km grid spacing nested within a global model of MIROC3.2_hires, this paper analyzes the changes in precipitation, temperature and some related extreme climate events (frost days, growing season length, number of heavy precipitation days, simple daily intensity index and max 5‐day precipitation amount) over North China under global warming. The simulation is carried out for the period of 1951–2100 following the observed greenhouse gases (1951–2000) and the IPCC SRES A1B emission scenario (2001–2100). The results suggest that the model is capable of simulating well the present (1981–2000) mean temperature, frost days and growing seasonal length in both spatial distributions and values; but with some discrepancies in values of present mean precipitation, number of heavy precipitation days, simple daily intensity index and max 5‐day precipitation amount. The RegCM3 overestimates the values of present mean precipitation and related extreme indices. Compared to the driving global model, significantly improvements have been shown by RegCM3 about the simulation of temperature, precipitation and extreme climate events. Increasing area averaged temperature and growing seasonal length, together with decreasing frost days during the period of 2010–2100 are simulated, at the same time an increasing trend of area averaged precipitation, number of heavy precipitation days, simple daily intensity index and max 5‐day precipitation amount is shown with the increasing greenhouse effect. In general, more obvious changes are expected to occur in the late 21st century (2081–2100) compared to the middle 21st century (2041–2060) from the spatial distributions.

On the simulation of Laurentian Great Lakes water levels under projections of global climate change

A new method is proposed to estimate future net basin supplies and lake levels for the Laurentian Great Lakes based on GCM projections of global climate change. The method first dynamically downscales the GCM simulation with a regional climate model, and then bias—corrects the simulated net basin supply in order to be used directly in a river—routing/lake level scheme. This technique addresses two weaknesses in the traditional approach, whereby observed sequences of climate variables are perturbed with fixed ratios or differences derived directly from GCMs in order to run evaporation and runoff models. Specifically, (1) land surface—atmosphere feedback processes are represented, and (2) changes in variability can be analyzed with the new approach. The method is demonstrated with a single, high resolution simulation, where small changes in future mean lake levels for all the upper Great Lakes are found, and an increase in seasonal range—especially for Lake Superior—is indicated. Analysis of a small ensemble of eight lower resolution regional climate model simulations supports these findings. In addition, a direct comparison with the traditional approach based on the same GCM projections used as the driving simulations in this ensemble shows that the new method indicates smaller declines in level for all the upper Great Lakes than has been reported previously based on the traditional method, though median differences are only a few centimetres in each case.

Near future changes of extreme precipitation over complex terrain in Central Europe derived from high resolution RCM ensemble simulations

AbstractAn ensemble of high‐resolution regional climate simulations is used to assess the effect of near future climate change on mean and extreme precipitation in a part of Central Europe with complex topography. The ensemble consists of high‐resolution simulations with the COSMO‐CLM (CLM) regional climate model (RCM) using several realizations of the driving general circulation models (GCMs) ECHAM5 and HadCM3. The study is focussed on the changes in the near future (2011–2040 compared to 1971–2000) which are relevant for planning purposes. The mean winter precipitation shows a spatially uniform increase, summertime mean precipitation is likely to decrease slightly. For extreme precipitation the simulations exhibit an increase on the average for both seasons, but for different reasons. The changes in winter are proportional to the increase in total precipitation, whereas in summer a broadening of the precipitation distribution is found. The spatial change patterns in summer are much more heterogeneous than in winter, with regions of significant increase and decrease sometimes close to each other. The plausibility of the findings is assessed in terms of ensemble consistency. The area mean changes found for the ensemble of CLM simulations were consistent with the change signals derived from a larger but coarser resolved ensembles using several RCMs and driving GCMs. In addition, it was found that the simulated near future precipitation changes in the study region during summer generally agree with trends observed during the last decades. Copyright © 2012 Royal Meteorological Society

Climate change impacts on Ontario wind power resource

Abstract Background The availability and reliability of wind power depend largely on current and future climate conditions, which may vary in the context of climate change. A high resolution regional climate model (PRECIS) was used for dynamic downscaling of the future wind speed over Ontario. The changes of wind power density and power production were further investigated through case studies. Results The spatial pattern and the magnitude of wind speed from PRECIS simulation, Wind Energy Atlas and the observation indicated a successful climate simulation. Climate modelling indicated that there would be a decrease of up to 5% in wind speed over southern Ontario from present to the period of 2071–2100. It was showed in the case studies that the changes of wind power production were not in proportion to the changes of average wind speed, due to the variations of wind speed distribution. Conclusion The decrease of projected wind speed would be more intense in A2 than in B2 scenario, showing statistically significant differences in the grid cell mean wind speed. The changes of wind power production may not be in proportion to the changes of average wind speed. It would be reasonable to develop onshore or offshore wind energy industry around Georgian Bay and James Bay, considering the projected increasing wind speeds within these areas.

A regional response in mean westerly circulation and rainfall to projected climate warming over Tasmania, Australia

Coupled ocean–atmosphere general circulation models (GCMs) lack sufficient resolution to model the regional detail of changes to mean circulation and rainfall with projected climate warming. In this paper, changes in mean circulation and rainfall in GCMs are compared to those in a variable resolution regional climate model, the Conformal Cubic Atmospheric Model (CCAM), under a high greenhouse gas emissions scenario. The study site is Tasmania, Australia, which is positioned within the mid-latitude westerlies of the southern hemisphere. CCAM projects a different response in mean sea level pressure and mid-latitude westerly circulation to climate warming to the GCMs used as input, and shows greater regional detail of the boundaries between regions of increasing and decreasing rainfall. Changes in mean circulation dominate the mean rainfall response in western Tasmania, whereas changes to rainfall in the East Coast are less related to mean circulation changes. CCAM projects an amplification of the dominant westerly circulation over Tasmania and this amplifies the seasonal cycle of wet winters and dry summers in the west. There is a larger change in the strength than in the incidence of westerly circulation and rainfall events. We propose the regional climate model displays a more sensitive atmospheric response to the different rates of warming of land and sea than the GCMs as input. The regional variation in these results highlight the need for dynamical downscaling of coupled general circulation models to finely resolve the influence of mean circulation and boundaries between regions of projected increases and decreases in rainfall.

Bias correction of high resolution regional climate model data

Summary Bias correction of varying complexity – from simple scaling and additive corrections to more advanced histogram equalisation (HE) corrections – is applied to high resolution (7 km) regional climate model (RCM) simulations. The aim of the study is to compare different methods that are easily implemented and applied to the data, and to assess the applicability and impact of the bias correction depending on the type of bias. The model bias is determined by comparison to a new gridded high resolution (1 km) data set of temperature and precipitation, which is also used as reference for the corrections. The performance of the different methods depends on the type of bias of the model, and on the investigated statistic. Whereas simpler methods correct the first moment of the distributions, they can have adverse effects on higher moments. The HE method corrects also higher moments, but approximations of the transfer function are necessary when applying the method to other data than the calibration data. Here, an empirical transfer function with linear fits to the tails is compared to a version where the complete function is approximated by a linear fit. The latter is thus limited to corrections of the first and second moments of the distribution. While making the transfer function more generally applicable, these approximations also limit the performance of the HE method. For the current model biases, the linear approximation is found suitable for precipitation, but for temperature it is not able to correct the whole distribution. The lower performance of the linear correction is most pronounced in summer, and is likely due to a difference in skewness between the model and observational data. Further limitations of the HE method are due to the need for long time series in order to have robust distributions for calculating the transfer function. Theoretical approximations of the required length of the calibration period were performed by using different sampling sizes drawn from a known distribution. The excerise show that about 30 year long time series are needed to have reasonable accuracy for the estimation of variance, when also corrections of the annual cycle is required.

Climate change in China in the 21st century as simulated by a high resolution regional climate model

Climate change in the 21st century over China is simulated using the Abdus Salam International Centre for Theoretical Physics (ICTP) Regional Climate Model version 3 (RegCM3). The model is one-way nested within the global model CCSR/NIES/FRCGC MIROC3.2_hires (Center for Climate System Research/National Institute for Environmental Studies/Frontier Research Center for Global Change/Model for Interdisciplinary Research on Climate). A 150-year (1951–2100) transient simulation is conducted at 25 km grid spacing, under the Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios (IPCC SRES) A1B scenario. Simulations of present climate conditions in China by RegCM3 are compared against observations to assess model performance. Results show that RegCM3 reproduces the observed spatial structure of surface air temperature and precipitation well. Changes in mean temperature and precipitation in December–January–February (DJF) and June–July–August (JJA) during the middle and end of the 21st century are analyzed. Significant future warming is simulated by RegCM3. This warming becomes greater with time, and increased warming is simulated at high latitude and high altitude (Tibetan Plateau) areas. In the middle of the 21st century in DJF, a general increase of precipitation is found in most areas, except over the Tibetan Plateau. Precipitation changes in JJA show an increase over northwest China and a decrease over the Tibetan Plateau. There is a mixture of positive and negative changes in eastern China. The change pattern at the end of the century is generally consistent with that in mid century, except in some small areas, and the magnitude of change is usually larger. In addition, the simulation is compared with a previous simulation of the RegCM3 driven by a different global model, to address uncertainties of the projected climate change in China.

Very high resolution regional climate model simulations over Greenland: Identifying added value

This study presents two simulations of the climate over Greenland with the regional climate model (RCM) HIRHAM5 at 0.05° and 0.25° resolution driven at the lateral boundaries by the ERA‐Interim reanalysis for the period 1989–2009. These simulations are validated against observations from meteorological stations (Danish Meteorological Institute) at the coast and automatic weather stations on the ice sheet (Greenland Climate Network). Generally, the temperature and precipitation biases are small, indicating a realistic simulation of the climate over Greenland that is suitable to drive ice sheet models. However, the bias between the simulations and the few available observations does not reduce with higher resolution. This is partly explained by the lack of observations in regions where the higher resolution is expected to improve the simulated climate. The RCM simulations show that the temperature has increased the most in the northern part of Greenland and at lower elevations over the period 1989–2009. Higher resolution increases the relief variability in the model topography and causes the simulated precipitation to be larger on the coast and smaller over the main ice sheet compared to the lower‐resolution simulation. The higher‐resolution simulation likely represents the Greenlandic climate better, but the lack of observations makes it difficult to validate fully. The detailed temperature and precipitation fields that are generated with the higher resolution are recommended for producing adequate forcing fields for ice sheet models, particularly for their improved simulation of the processes occurring at the steep margins of the ice sheet.

Regional simulation with the PRECIS Model

During 2006 the Geophysics Department of the University of Chile conducted a series of high resolution regional climate model (RCM) simulations for Chile’s National Environmental Commission (CONAMA). The study employed a dynamical model (PRECIS) to produce spatially detailed climate projections for Chile under two different emissions scenarios. The results of the simulations were made freely available for use by the scientific community, industry and policy makers. Today the PRECIS database remains an im-portant resource for the analysis of climate change in Chile, especially in areas such as Patagonia where observations are sparse and climate variables such as temperature and precipitation exhibit extremely sharp spatial gradients that are not resolved by global models. In this presentation we provide an overview of the PRECIS project. We begin with a brief discus-sion of the motivation (section 2) and configuration (section 3) for the regional simulations. In section 4 selected PRECIS results are shown focusing on temperature and precipitation predictions in the Magellan region.MOTIVATIONMost current knowledge about how the world’s climate is likely to change as a result of increasing greenhouse gas emissions is based on numerical simulations using global climate models (GCMs). GCMs describe important physical elements and processes in the atmosphere, oceans and land surface that make up the climate system. The conclusions of the latest IPCC Assessment report were based on simulations from over 20 different global models operated by a similar number of scientific agencies worldwide. A major disadvantage of GCMs is their spatial scale, which is typically a few hundred kilo-meters in resolution. In order to study the impacts of climate change, it is necessary to predict changes on much finer scales. One of the techniques for doing so is through the use of Regional Climate Models (RCMs), which have the potential to improve the representation of the climate information used to assess vulnerability to climate change.The need for RCM’s is especially evident in Chile where, due to the sharp differences in eleva-