Use Of Regional Climate Models Research Articles

Impact of RCM Spatial Resolution on the Reproduction of Local, Subdaily Precipitation

Abstract Many hydrological hazards are closely connected to local precipitation (extremes), especially in small and urban catchments. The use of regional climate model (RCM) data for small-scale hydrological climate change impact assessment has long been nearly unfeasible because of the low spatial resolution. The RCM resolution is, however, rapidly increasing, approaching the size of small catchments and thus potentially increasing the applicability of RCM data for this purpose. The objective of this study is to explore to what degree subhourly temporal precipitation statistics in an RCM converge to observed point statistics when gradually increasing the resolution from 50 to 6 km. This study uses precipitation simulated by RCA3 at seven locations in southern Sweden during 1995–2008. A positive impact of higher resolution was most clearly manifested in 10-yr intensity–duration–frequency (IDF) curves. At 50 km the intensities are underestimated by 50%–90%, but at 6 km they are nearly unbiased, when averaged over all locations and durations. Thus, at 6 km, RCA3 apparently generates low-frequency subdaily extremes that resemble the values found in point observations. Also, the reproduction of short-term variability and less extreme maxima were overall improved with increasing resolution. For monthly totals, a slightly increased overestimation with increasing resolution was found. The bias in terms of wet fraction and wet spell characteristics was overall not strongly dependent on resolution. These metrics are, however, influenced by the cutoff threshold used to separate between wet and dry time steps as well as the wet spell definition.

Configuration and validation of a mesoscale atmospheric model for simulating summertime rainfall in Central Alberta

ABSTRACTExtreme precipitation events in Central Alberta have overwhelmed hydraulic structures several times in recent years, and it is generally expected that rainfall intensity in this region will continue to increase over the next several decades. Accurate rainfall projections are thus needed to assess future flood risks and to mitigate the possible impacts of these changes. Such data may be obtained through the use of regional climate models (RCMs), and one in particular, the fifth‐generation NCAR/Penn State mesoscale atmospheric model (MM5), is investigated here. MM5 is used to dynamically downscale European Centre for Medium‐Range Weather Forecast (ECMWF) ERA‐Interim reanalysis data to evaluate its ability to accurately simulate rainfall in Central Alberta over two consecutive summers that represent contrasting precipitation regimes. It is determined that in complex terrain, different RCM preprocessing settings can result in vastly different input data which are used for the model simulation. After optimal preprocessing settings are identified, precipitation data from the resulting simulations are compared with data from Edmonton's local rain gauge network and a High Resolution Precipitation Product (HRPP), Climate Prediction Center (CPC) MORPHing technique (CMORPH). Precipitation data generated by MM5 reveal that this RCM can indeed distinguish between wet (2010) and dry (2009) years, but that simulated rainfall totals tend to be too high during May of both precipitation regimes, particularly during the dry year. This bias is partially attributed to the RCM's inaccurate simulation of available moisture in the presence of local terrain effects, and should be taken into consideration when making projections regarding possible changes to future precipitation conditions in Central Alberta and in other regions with similar climatology.

Impact of Climate Change on the Hydrology of Upper Tiber River Basin Using Bias Corrected Regional Climate Model

The use of regional climate model (RCM) outputs has been getting due attention in most European River basins because of the availability of large number of the models and modelling institutes in the continent; and the relative robustness the models to represent local climate. This paper presents the hydrological responses to climate change in the Upper Tiber River basin (Central Italy) using bias corrected daily regional climate model outputs. The hydrological analysis include both control (1961–1990) and future (2071–2100) climate scenarios. Three RCMs (RegCM, RCAO, and PROMES) that were forced by the same lateral boundary condition under A2 and B2 emission scenarios were used in this study. The projected climate variables from bias corrected models have shown that the precipitation and temperature tends to decrease and increase in summer season, respectively. The impact of climate change on the hydrology of the river basin was predicted using physically based Soil and Water Assessment Tool (SWAT). The SWAT model was first calibrated and validated using observed datasets at the sub-basin outlet. A total of six simulations were performed under each scenario and RCM combinations. The simulated result indicated that there is a significant annual and seasonal change in the hydrological water balance components. The annual water balance of the study area showed a decrease in surface runoff, aquifer recharge and total basin water yield under A2 scenario for RegCM and RCAO RCMs and an increase in PROMES RCM under B2 scenario. The overall hydrological behaviour of the basin indicated that there will be a reduction of water yield in the basin due to projected changes in temperature and precipitation. The changes in all other hydrological components are in agreement with the change in projected precipitation and temperature.

The predictive state: Science, territory and the future of the Indian climate.

Acts of scientific calculation have long been considered central to the formation of the modern nation state, yet the transnational spaces of knowledge generation and political action associated with climate change seem to challenge territorial modes of political order. This article explores the changing geographies of climate prediction through a study of the ways in which climate change is rendered knowable at the national scale in India. The recent controversy surrounding an erroneous prediction of melting Himalayan glaciers by the Intergovernmental Panel on Climate Change provides a window onto the complex and, at times, antagonistic relationship between the Panel and Indian political and scientific communities. The Indian reaction to the error, made public in 2009, drew upon a national history of contestation around climate change science and corresponded with the establishment of a scientific assessment network, the Indian Network for Climate Change Assessment, which has given the state a new platform on which to bring together knowledge about the future climate. I argue that the Indian Network for Climate Change Assessment is indicative of the growing use of regional climate models within longer traditions of national territorial knowledge-making, allowing a rescaling of climate change according to local norms and practices of linking scientific knowledge to political action. I illustrate the complex co-production of the epistemic and the normative in climate politics, but also seek to show how co-productionist understandings of science and politics can function as strategic resources in the ongoing negotiation of social order. In this case, scientific rationalities and modes of environmental governance contribute to the contested epistemic construction of territory and the evolving spatiality of the modern nation state under a changing climate.

Regional climate models downscaling in the Alpine area with multimodel superensemble

Abstract. The climatic scenarios show a strong signal of warming in the Alpine area already for the mid-XXI century. The climate simulations, however, even when obtained with regional climate models (RCMs), are affected by strong errors when compared with observations, due both to their difficulties in representing the complex orography of the Alps and to limitations in their physical parametrization. Therefore, the aim of this work is to reduce these model biases by using a specific post processing statistic technique, in order to obtain a more suitable projection of climate change scenarios in the Alpine area. For our purposes we used a selection of regional climate models (RCMs) runs which were developed in the framework of the ENSEMBLES project. They were carefully chosen with the aim to maximise the variety of leading global climate models and of the RCMs themselves, calculated on the SRES scenario A1B. The reference observations for the greater Alpine area were extracted from the European dataset E-OBS (produced by the ENSEMBLES project), which have an available resolution of 25 km. For the study area of Piedmont daily temperature and precipitation observations (covering the period from 1957 to the present) were carefully gridded on a 14 km grid over Piedmont region through the use of an optimal interpolation technique. Hence, we applied the multimodel superensemble technique to temperature fields, reducing the high biases of RCMs temperature field compared to observations in the control period. We also proposed the application of a brand new probabilistic multimodel superensemble dressing technique, already applied to weather forecast models successfully, to RCMS: the aim was to estimate precipitation fields, with careful description of precipitation probability density functions conditioned to the model outputs. This technique allowed for reducing the strong precipitation overestimation, arising from the use of RCMs, over the Alpine chain and to reproduce well the monthly behaviour of precipitation in the control period.

Potential for small scale added value of RCM’s downscaled climate change signal

In recent decades, the need of future climate information at local scales have pushed the climate modelling community to perform increasingly higher resolution simulations and to develop alternative approaches to obtain fine-scale climatic information. In this article, various nested regional climate model (RCM) simulations have been used to try to identify regions across North America where high-resolution downscaling generates fine-scale details in the climate projection derived using the “delta method”. Two necessary conditions were identified for an RCM to produce added value (AV) over lower resolution atmosphere-ocean general circulation models in the fine-scale component of the climate change (CC) signal. First, the RCM-derived CC signal must contain some non-negligible fine-scale information—independently of the RCM ability to produce AV in the present climate. Second, the uncertainty related with the estimation of this fine-scale information should be relatively small compared with the information itself in order to suggest that RCMs are able to simulate robust fine-scale features in the CC signal. Clearly, considering necessary (but not sufficient) conditions means that we are studying the “potential” of RCMs to add value instead of the AV, which preempts and avoids any discussion of the actual skill and hence the need for hindcast comparisons. The analysis concentrates on the CC signal obtained from the seasonal-averaged temperature and precipitation fields and shows that the fine-scale variability of the CC signal is generally small compared to its large-scale component, suggesting that little AV can be expected for the time-averaged fields. For the temperature variable, the largest potential for fine-scale added value appears in coastal regions mainly related with differential warming in land and oceanic surfaces. Fine-scale features can account for nearly 60 % of the total CC signal in some coastal regions although for most regions the fine scale contributions to the total CC signal are of around ∼5 %. For the precipitation variable, fine scales contribute to a change of generally less than 15 % of the seasonal-averaged precipitation in present climate with a continental North American average of ∼5 % in both summer and winter seasons. In the case of precipitation, uncertainty due to sampling issues may further dilute the information present in the downscaled fine scales. These results suggest that users of RCM simulations for climate change studies in a delta method framework have little high-resolution information to gain from RCMs at least if they limit themselves to the study of first-order statistical moments. Other possible benefits arising from the use of RCMs—such as in the large scale of the downscaled fields– were not explored in this research.

Bias correction of regional climate model simulations for hydrological climate-change impact studies: Review and evaluation of different methods

Despite the increasing use of regional climate model (RCM) simulations in hydrological climate-change impact studies, their application is challenging due to the risk of considerable biases. To dea ...

On the use of regional climate models: Implications of climate change for viticulture in Serbia

Climate projections obtained from the coupled regional climate model EBU–POM (Eta Belgrade University – Princeton Ocean Model) driven by the Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios (SRES), showed that the vineyard regions of Serbia tend to become warmer and dryer toward the end of 21st century. To evaluate how Serbian viticulture could be affected by a projected climate regime, several climatic variables and agro-climatic indices describing the suitability of a particular area for grapevine production were calculated, after a statistical bias correction was applied to the daily temperature and precipitation data from EBU–POM outputs. Comparison between climatic variables and agro-climatic indices for the reference period 1961–1990 and predicted values for the 2001–2030 period (under the SRES A1B scenario) and the 2071–2100 period (under the SRES A2 scenario) was made for 18 climatological stations placed mostly within, but also outside traditional viticultural regions. According to the obtained change trends it is likely that no significant disturbances in Serbian viticulture will occur over the next few decades, but considerable changes are expected by the end of the 21st century. Warmer and prolonged growing season with greater heat accumulation and longer frost-free period with decline in frost frequency would likely affect the yield and ripening potential of grapes and induce shifts in varietal suitability and wine styles. Projected changes may bring on the need for additional vineyard irrigation, but also open up the possibility that marginal and elevated areas, previously too cool for cultivation of grapevines, become climatically suited for viticulture.

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-

Climate Signals on the Regional Scale Derived with a Statistical Method: Relevance of the Driving Model’s Resolution

When assessing the magnitude of climate signals in a regional scale, a host of optional approaches is feasible. This encompasses the use of regional climate models (RCM), nested into global climate models (GCM) for an area of interest as well as employing empirical statistical downscaling methods (ESD). In this context the question is addressed: Is an empirical statistical downscaling method capable of yielding results that are comparable to those by dynamical RCMs? Based on the presented ESD method, the comparison of RCM and ESD results show a high amount of agreement. In addition the empirical statistical downscaling can be applied directly to a GCM or a GCM-RCM cascade. The paper aims at comparing the consequences of employing various CGM-RCM-ESD combinations on the derived future changes of temperature and precipitation. This adds to the insight on the scale dependency of downscaling strategies. Results for one GCM with several scenario runs driving several RCMs with and without subsequent empirical statistical downscaling are presented. It is shown that there are only small differences between using the GCM results directly or as a GCM-RCM-ESD cascade.

On the suitability of regional climate models for reconstructing climatologies

This paper discusses the potential of Regional Climate Models (RCMs) as reanalysis tools by presenting a reconstruction of the European climate using several RCMs with diverse physical parameterizations. The use of RCMs is intended to increase the spatial resolution of the analysis provided by Global Models through dynamic downscaling. At the same time, the use of several models allows us to characterize the uncertainties, as these can be estimated from the spread of the ensemble. When the RCMs are nested in reanalyses instead of in a Global Model it is possible to create climatologies of unprecedented robustness for variables such as temperature, precipitation, wind speed, and humidity, among others. While these climatologies are subject to further improvement as methods and computing power evolve, they point the way forward to the development of atmospheric information products suitable for a variety of studies including education, agriculture, renewable energies and climate change research, biogeography, insurance, risk assessment, hydrology, and regional planning.

State‐of‐the‐art with regional climate models

AbstractRegional climate models are used by a large number of groups, for more or less all regions of the world. Regional climate models are complementary to global climate models. A typical use of regional climate models is to add further detail to global climate analyses or simulations, or to study climate processes in more detail than global models allow. The relationship between global and regional climate models is much akin to that of global and regional weather forecasting models. Over the past 20 years, the development of regional climate models has led to increased resolution, longer model runs, and steps towards regional climate system models. During recent years, community efforts have started to emerge in earnest, which can be expected to further advance the state‐of‐the‐art in regional climate modeling. Applications of regional climate models span both the past and possible future climates, facilitating climate impact studies, information and support to climate policy, and adaptation. Copyright © 2010 John Wiley & Sons, Inc.This article is categorized under: Climate Models and Modeling > Earth System Models

Evaluation of the skill and added value of a reanalysis‐driven regional simulation for Alpine temperature

AbstractTo determine whether the use of regional climate models improves the representation of climate is a crucial topic in climate modelling. An improvement over coarser scale models is expected especially in areas with complex orography or along coastlines. However, some studies have shown no clear added value for regional climate models. In this study a high‐resolution regional climate simulation performed with REMO 5.0 (regional model) over the whole of Europe over the period 1958–1998 is analysed for 2‐m temperature over the European Alps and their surroundings called the Greater Alpine region (GAR). The simulation is driven by perfect boundary conditions at the lateral boundaries provided by the ERA40 reanalysis and spectral nudging of the large‐scale wind fields towards ERA40 values for the upper layers inside the model domain. The added value of the regional simulation (1/6° resolution) is analysed with respect to the driving reanalysis (1.125° resolution).Both the REMO simulation and the ERA40 reanalysis are validated against different station datasets of monthly and daily mean 2‐m temperature. Correlation analysis shows that the temporal variability of temperature is well represented by both REMO and ERA40, whereas both show considerable biases. The REMO bias reaches 3 K in summer in regions known to experience a problem with summer drying in a number of regional models. The comparison of REMO and ERA40 shows that an added value of the former exists for all regions in winter. For the regions surrounding the Alps, the added value is absent in summer, whereas in the inner Alpine subregions with most complex orography, REMO performs better than ERA40 during the whole year. The only moderate value added by REMO in this hindcast set‐up may be partly explicable by the fact that meteorological measurements are assimilated in the ERA40 reanalysis but not in the REMO simulation. Copyright © 2009 Royal Meteorological Society

The application of Regional Climate Model output for the simulation of high-mountain permafrost scenarios

This study investigates the possibilities and limitations of using Regional Climate Model (RCM) output for the simulation of alpine permafrost scenarios. It focuses on the general problem of scale mismatch between RCMs and impact models and, in particular, the special challenges that arise when driving an impact model in topographically complex high-mountain environments with the output of an RCM. Two approaches are introduced that take into account the special difficulties in such areas, and thus enable the use of RCM for alpine permafrost scenario modelling. Intended as an initial example, they are applied at the area of Corvatsch (Upper Engadine, Switzerland) in order to demonstrate and discuss the application of the two approaches, rather than to provide an assessment of future changes in permafrost occurrence. There are still many uncertainties and inaccuracies inherent in climate and impact models, which increase when driving one model with the output of the other. Nevertheless, our study shows that the use of RCMs offers new and promising perspectives for the simulation of high-mountain permafrost scenarios.