Journal articles: 'High-resolution Regional Climate Model'

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Meissner, Cathérine Schädler, Hans-Jürgen Feldmann Panitz, and Christoph Kottmeier. "High-resolution sensitivity studies with the regional climate model COSMO-CLM." Meteorologische Zeitschrift 18, no. 5 (October 1, 2009): 543–57. http://dx.doi.org/10.1127/0941-2948/2009/0400.

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Suklitsch, Martin, Andreas Gobiet, Armin Leuprecht, and Christoph Frei. "High Resolution Sensitivity Studies with the Regional Climate Model CCLM in the Alpine Region." Meteorologische Zeitschrift 17, no. 4 (August 25, 2008): 467–76. http://dx.doi.org/10.1127/0941-2948/2008/0308.

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Mendoza, Pablo A., Balaji Rajagopalan, Martyn P. Clark, Kyoko Ikeda, and Roy M. Rasmussen. "Statistical Postprocessing of High-Resolution Regional Climate Model Output." Monthly Weather Review 143, no. 5 (May 1, 2015): 1533–53. http://dx.doi.org/10.1175/mwr-d-14-00159.1.

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Abstract Statistical postprocessing techniques have become essential tools for downscaling large-scale information to the point scale, and also for providing a better probabilistic characterization of hydrometeorological variables in simulation and forecasting applications at both short and long time scales. In this paper, the authors assess the utility of statistical postprocessing methods for generating probabilistic estimates of daily precipitation totals, using deterministic high-resolution outputs obtained with the Weather Research and Forecasting (WRF) Model. After a preliminary assessment of WRF simulations over a historical period, the performance of three postprocessing techniques is compared: multinomial logistic regression (MnLR), quantile regression (QR), and Bayesian model averaging (BMA)—all of which use WRF outputs as potential predictors. Results demonstrate that the WRF Model has skill in reproducing observed precipitation events, especially during fall/winter. Furthermore, it is shown that the spatial distribution of skill obtained from statistical postprocessing is closely linked with the quality of WRF precipitation outputs. A detailed comparison of statistical precipitation postprocessing approaches reveals that, although the poorest performance was obtained using MnLR, there is not an overall best technique. While QR should be preferred if skill (i.e., small probability forecast errors) and reliability (i.e., match between forecast probabilities and observed frequencies) are target properties, BMA is recommended in cases when discrimination (i.e., prediction of occurrence versus nonoccurrence) and statistical consistency (i.e., equiprobability of the observations within their ensemble distributions) are desired. Based on the results obtained here, the authors believe that future research should explore frameworks reconciling hierarchical Bayesian models with the use of the extreme value theory for high precipitation events.

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Berg, P., H. Feldmann, and H. J. Panitz. "Bias correction of high resolution regional climate model data." Journal of Hydrology 448-449 (July 2012): 80–92. http://dx.doi.org/10.1016/j.jhydrol.2012.04.026.

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Drost, Frank, James Renwick, B. Bhaskaran, Hilary Oliver, and James McGregor. "Simulation of New Zealand's climate using a high-resolution nested regional climate model." International Journal of Climatology 27, no. 9 (2007): 1153–69. http://dx.doi.org/10.1002/joc.1461.

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Wagner, Sven, Peter Berg, Gerd Schädler, and Harald Kunstmann. "High resolution regional climate model simulations for Germany: Part II—projected climate changes." Climate Dynamics 40, no. 1-2 (September 13, 2012): 415–27. http://dx.doi.org/10.1007/s00382-012-1510-1.

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Jadmiko, Syamsu Dwi, and Akhmad Faqih. "Dynamical Downscaling Luaran Global Climate Model (GCM) Menggunakan Model REGCM3 untuk Proyeksi Curah Hujan di Kabupaten Indramayu." Agromet 28, no. 1 (February 8, 2018): 9. http://dx.doi.org/10.29244/j.agromet.28.1.9-16.

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Future rainfall projection can be predicted by using Global Climate Model (GCM). In spite of low resolution, we are not able specifically to describe a local or regional information. Therefore, we applied downscaling technique of GCM output using Regional Climate Model (RCM). In this case, Regional Climate Model version 3 (RegCM3) is used to accomplish this purpose. RegCM3 is regional climate model which atmospheric properties are calculated by solving equations of motion and thermodynamics. Thus, RegCM3 is also called as dynamic downscaling model. RegCM3 has reliable capability to evaluate local or regional climate in high spatial resolution up to 10 × 10 km. In this study, dynamically downscaling techniques was applied to produce high spatial resolution (20 × 20 km) from GCM EH5OM output which commonly has rough spatial resolution (1.875<sup>o</sup> × 1.875<sup>o</sup>). Simulation show that future rainfall in Indramayu is relatively decreased compared to the baseline condition. Decreased rainfall generally occurs during the dry season (July-June-August/JJA) in a range 10-20%. Study of extreme daily rainfall indicates that there is no significant increase or decrease value.

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Demory, Marie-Estelle, Ségolène Berthou, Jesús Fernández, Silje L. Sørland, Roman Brogli, Malcolm J. Roberts, Urs Beyerle, et al. "European daily precipitation according to EURO-CORDEX regional climate models (RCMs) and high-resolution global climate models (GCMs) from the High-Resolution Model Intercomparison Project (HighResMIP)." Geoscientific Model Development 13, no. 11 (November 11, 2020): 5485–506. http://dx.doi.org/10.5194/gmd-13-5485-2020.

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Abstract. In this study, we evaluate a set of high-resolution (25–50 km horizontal grid spacing) global climate models (GCMs) from the High-Resolution Model Intercomparison Project (HighResMIP), developed as part of the EU-funded PRIMAVERA (Process-based climate simulation: Advances in high resolution modelling and European climate risk assessment) project, and from the EURO-CORDEX (Coordinated Regional Climate Downscaling Experiment) regional climate models (RCMs) (12–50 km horizontal grid spacing) over a European domain. It is the first time that an assessment of regional climate information using ensembles of both GCMs and RCMs at similar horizontal resolutions has been possible. The focus of the evaluation is on the distribution of daily precipitation at a 50 km scale under current climate conditions. Both the GCM and RCM ensembles are evaluated against high-quality gridded observations in terms of spatial resolution and station density. We show that both ensembles outperform GCMs from the 5th Coupled Model Intercomparison Project (CMIP5), which cannot capture the regional-scale precipitation distribution properly because of their coarse resolutions. PRIMAVERA GCMs generally simulate precipitation distributions within the range of EURO-CORDEX RCMs. Both ensembles perform better in summer and autumn in most European regions but tend to overestimate precipitation in winter and spring. PRIMAVERA shows improvements in the latter by reducing moderate-precipitation rate biases over central and western Europe. The spatial distribution of mean precipitation is also improved in PRIMAVERA. Finally, heavy precipitation simulated by PRIMAVERA agrees better with observations in most regions and seasons, while CORDEX overestimates precipitation extremes. However, uncertainty exists in the observations due to a potential undercatch error, especially during heavy-precipitation events. The analyses also confirm previous findings that, although the spatial representation of precipitation is improved, the effect of increasing resolution from 50 to 12 km horizontal grid spacing in EURO-CORDEX daily precipitation distributions is, in comparison, small in most regions and seasons outside mountainous regions and coastal regions. Our results show that both high-resolution GCMs and CORDEX RCMs provide adequate information to end users at a 50 km scale.

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Walter, Andreas, Klaus Keuler, Daniela Jacob, Richard Knoche, Alexander Block, Sven Kotlarski, Gerhard Müller-Westermeier, Diana Rechid, and Wilfried Ahrens. "A high resolution reference data set of German wind velocity 19512001 and comparison with regional climate model results." Meteorologische Zeitschrift 15, no. 6 (December 20, 2006): 585–96. http://dx.doi.org/10.1127/0941-2948/2006/0162.

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Wang, Xiuquan, Guohe Huang, Qianguo Lin, and Jinliang Liu. "High-Resolution Probabilistic Projections of Temperature Changes over Ontario, Canada." Journal of Climate 27, no. 14 (July 10, 2014): 5259–84. http://dx.doi.org/10.1175/jcli-d-13-00717.1.

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Abstract Planning of mitigation and adaptation strategies to a changing climate can benefit from a good understanding of climate change impacts on human life and local society, which leads to an increasing requirement for reliable projections of future climate change at regional scales. This paper presents an ensemble of high-resolution regional climate simulations for the province of Ontario, Canada, developed with the Providing Regional Climates for Impacts Studies (PRECIS) modeling system. A Bayesian statistical model is proposed through an advance to the method proposed by Tebaldi et al. for generating probabilistic projections of temperature changes at gridpoint scale by treating the unknown quantities of interest as random variables to quantify their uncertainties in a statistical way. Observations for present climate and simulations from the ensemble are fed into the statistical model to derive posterior distributions of all the uncertain quantities through a Markov chain Monte Carlo (MCMC) sampling algorithm. Detailed analyses at 12 selected weather stations are conducted to investigate the practical significance of the proposed statistical model. Following that, maps of projected temperature changes at different probability levels are presented to help understand the spatial patterns across the entire province. The analysis shows that there is likely to be a significant warming trend throughout the twenty-first century. It also suggests that people in Ontario are very likely to suffer a change greater than 2°C to mean temperature in the forthcoming decades and very unlikely to suffer a change greater than 10°C to the end of this century.

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Gensini, Vittorio A., and Thomas L. Mote. "Estimations of Hazardous Convective Weather in the United States Using Dynamical Downscaling." Journal of Climate 27, no. 17 (August 28, 2014): 6581–89. http://dx.doi.org/10.1175/jcli-d-13-00777.1.

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Abstract High-resolution (4 km; hourly) regional climate modeling is utilized to resolve March–May hazardous convective weather east of the U.S. Continental Divide for a historical climate period (1980–90). A hazardous convective weather model proxy is used to depict occurrences of tornadoes, damaging thunderstorm wind gusts, and large hail at hourly intervals during the period of record. Through dynamical downscaling, the regional climate model does an admirable job of replicating the seasonal spatial shifts of hazardous convective weather occurrence during the months examined. Additionally, the interannual variability and diurnal progression of observed severe weather reports closely mimic cycles produced by the regional model. While this methodology has been tested in previous research, this is the first study to use coarse-resolution global climate model data to force a high-resolution regional model with continuous seasonal integration in the United States for purposes of resolving severe convection. Overall, it is recommended that dynamical downscaling play an integral role in measuring climatological distributions of severe weather, both in historical and future climates.

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Argüeso, D., J. P. Evans, and L. Fita. "Precipitation bias correction of very high resolution regional climate models." Hydrology and Earth System Sciences Discussions 10, no. 6 (June 25, 2013): 8145–65. http://dx.doi.org/10.5194/hessd-10-8145-2013.

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Abstract. Regional climate models are prone to biases in precipitation that are problematic for use in impact models such as hydrology models. A large number of methods have already been proposed aimed at correcting various moments of the rainfall distribution. They all require that the model produce the same or a higher number of rain days than the observational datasets, which are usually gridded datasets. Models have traditionally met this condition because their spatial resolution was coarser than the observational grids. But recent climate simulations use higher resolution than the gridded observational products and the models are likely to produce fewer rain days than the gridded observations. In this study, model output from a simulation at 2 km resolution are compared with gridded and in-situ observational datasets to determine whether the new scenario calls for revised methodologies. The gridded observations are found to be inadequate to correct the high-resolution model at daily timescales. A histogram equalisation bias correction method is selected and adapted to the use of stations, alleviating the problems associated with relatively low-resolution observational grids. The method is efficient at bias correcting both seasonal and daily characteristics of precipitation, providing more accurate information that is crucial for impact assessment studies.

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Argüeso, D., J. P. Evans, and L. Fita. "Precipitation bias correction of very high resolution regional climate models." Hydrology and Earth System Sciences 17, no. 11 (November 6, 2013): 4379–88. http://dx.doi.org/10.5194/hess-17-4379-2013.

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Abstract. Regional climate models are prone to biases in precipitation that are problematic for use in impact models such as hydrology models. A large number of methods have already been proposed aimed at correcting various moments of the rainfall distribution. They all require that the model produce the same or a higher number of rain days than the observational data sets, which are usually gridded data sets. Models have traditionally met this condition because their spatial resolution was coarser than the observational grids. But recent climate simulations use higher resolution and the models are likely to systematically produce fewer rain days than the gridded observations. In this study, model outputs from a simulation at 2 km resolution are compared with gridded and in situ observational data sets to determine whether the new scenario calls for revised methodologies. The gridded observations are found to be inadequate to correct the high-resolution model at daily timescales, because they are subjected to too frequent low intensity precipitation due to spatial averaging. A histogram equalisation bias correction method was adapted to the use of station, alleviating the problems associated with relative low-resolution observational grids. The wet-day frequency condition might not be satisfied for extremely dry biases, but the proposed approach substantially increases the applicability of bias correction to high-resolution models. The method is efficient at bias correcting both seasonal and daily characteristic of precipitation, providing more accurate information that is crucial for impact assessment studies.

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Kendon, Elizabeth J., Nigel M. Roberts, Catherine A. Senior, and Malcolm J. Roberts. "Realism of Rainfall in a Very High-Resolution Regional Climate Model." Journal of Climate 25, no. 17 (March 12, 2012): 5791–806. http://dx.doi.org/10.1175/jcli-d-11-00562.1.

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Abstract The realistic representation of rainfall on the local scale in climate models remains a key challenge. Realism encompasses the full spatial and temporal structure of rainfall, and is a key indicator of model skill in representing the underlying processes. In particular, if rainfall is more realistic in a climate model, there is greater confidence in its projections of future change. In this study, the realism of rainfall in a very high-resolution (1.5 km) regional climate model (RCM) is compared to a coarser-resolution 12-km RCM. This is the first time a convection-permitting model has been run for an extended period (1989–2008) over a region of the United Kingdom, allowing the characteristics of rainfall to be evaluated in a climatological sense. In particular, the duration and spatial extent of hourly rainfall across the southern United Kingdom is examined, with a key focus on heavy rainfall. Rainfall in the 1.5-km RCM is found to be much more realistic than in the 12-km RCM. In the 12-km RCM, heavy rain events are not heavy enough, and tend to be too persistent and widespread. While the 1.5-km model does have a tendency for heavy rain to be too intense, it still gives a much better representation of its duration and spatial extent. Long-standing problems in climate models, such as the tendency for too much persistent light rain and errors in the diurnal cycle, are also considerably reduced in the 1.5-km RCM. Biases in the 12-km RCM appear to be linked to deficiencies in the representation of convection.

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Cavicchia, Leone, and Hans von Storch. "The simulation of medicanes in a high-resolution regional climate model." Climate Dynamics 39, no. 9-10 (October 29, 2011): 2273–90. http://dx.doi.org/10.1007/s00382-011-1220-0.

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Berg, Peter, Sven Wagner, Harald Kunstmann, and Gerd Schädler. "High resolution regional climate model simulations for Germany: part I—validation." Climate Dynamics 40, no. 1-2 (September 4, 2012): 401–14. http://dx.doi.org/10.1007/s00382-012-1508-8.

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Önol, B. "Effects of coastal topography on climate: high-resolution simulation with a regional climate model." Climate Research 52 (March 22, 2012): 159–74. http://dx.doi.org/10.3354/cr01077.

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Gröger, Matthias, Christian Dieterich, Cyril Dutheil, H. E. Markus Meier, and Dmitry V. Sein. "Atmospheric rivers in CMIP5 climate ensembles downscaled with a high-resolution regional climate model." Earth System Dynamics 13, no. 1 (March 30, 2022): 613–31. http://dx.doi.org/10.5194/esd-13-613-2022.

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Abstract. Atmospheric rivers (ARs) are important drivers of hazardous precipitation levels and are often associated with intense floods. So far, the response of ARs to climate change in Europe has been investigated using global climate models within the CMIP5 framework. However, the spatial resolution of those models (1–3∘) is too coarse for an adequate assessment of local to regional precipitation patterns. Using a regional climate model with 0.22∘ resolution, we downscaled an ensemble consisting of 1 ERA-Interim (ERAI) reanalysis data hindcast simulation, 9 global historical, and 24 climate scenario simulations following greenhouse gas emission scenarios RCP2.6, RCP4.5, and RCP8.5. The performance of the climate model to simulate AR frequencies and AR-induced precipitation was tested against ERAI. Overall, we find a good agreement between the downscaled CMIP5 historical simulations and ERAI. However, the downscaled simulations better represented small-scale spatial characteristics. This was most evident over the terrain of the Iberian Peninsula, where the AR-induced precipitation pattern clearly reflected prominent east–west topographical elements, resulting in zonal bands of high and low AR impact. Over central Europe, the models simulated a smaller propagation distance of ARs toward eastern Europe than obtained using the ERAI data. Our models showed that ARs in a future warmer climate will be more frequent and more intense, especially in the higher-emission scenarios (RCP4.5, RCP8.5). However, assuming low emissions (RCP2.6), the related changes can be mostly mitigated. According to the high-emission scenario RCP8.5, AR-induced precipitation will increase by 20 %–40 % in western central Europe, whereas mean precipitation rates increase by a maximum of only 12 %. Over the Iberian Peninsula, AR-induced precipitation will slightly decrease (∼6 %) but the decrease in the mean rate will be larger (∼15 %). These changes will lead to an overall increased fractional contribution of ARs to heavy precipitation, with the greatest impact over the Iberian Peninsula (15 %–30 %) and western France (∼15 %). Likewise, the fractional share of yearly maximum precipitation attributable to ARs will increase over the Iberian Peninsula, the UK, and western France. Over Norway, average AR precipitation rates will decline by −5 % to −30 %, most likely due to dynamic changes, with ARs originating from latitudes > 60∘ N decreasing by up to 20 % and those originating south of 45∘ N increasing. This suggests that ARs over Norway will follow longer routes over the continent, such that additional moisture uptake will be impeded. By contrast, ARs from >60∘ N will take up moisture from the North Atlantic before making landfall over Norway. The found changes in the local AR pathway are probably driven by larger-scale circulation changes such as a change in dominating weather regimes and/or changes in the winter storm track over the North Atlantic.

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Alsarraf, Hussain. "Projected climate change over Kuwait simulated using a WRF high resolution regional climate model." International Journal of Global Warming 26, no. 2 (2022): 198. http://dx.doi.org/10.1504/ijgw.2022.10045039.

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Alsarraf, Hussain. "Projected climate change over Kuwait simulated using a WRF high resolution regional climate model." International Journal of Global Warming 26, no. 2 (2022): 198. http://dx.doi.org/10.1504/ijgw.2022.120844.

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Feldmann, Hendrik, Barbara Früh, Gerd Schädler, Hans-Jürgen Panitz, Klaus Keuler, Daniela Jacob, and Philip Lorenz. "Evaluation of the precipitation for South-western Germany from high resolution simulations with regional climate models." Meteorologische Zeitschrift 17, no. 4 (August 25, 2008): 455–65. http://dx.doi.org/10.1127/0941-2948/2008/0295.

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Lenaerts, Jan T. M., Stefan R. M. Ligtenberg, Brooke Medley, Willem Jan Van de Berg, Hannes Konrad, Julien P. Nicolas, J. Melchior Van Wessem, et al. "Climate and surface mass balance of coastal West Antarctica resolved by regional climate modelling." Annals of Glaciology 59, no. 76pt1 (November 27, 2017): 29–41. http://dx.doi.org/10.1017/aog.2017.42.

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ABSTRACTWest Antarctic climate and surface mass balance (SMB) records are sparse. To fill this gap, regional atmospheric climate modelling is useful, providing that such models are employed at sufficiently high horizontal resolution and coupled with a snow model. Here we present the results of a high-resolution (5.5 km) regional atmospheric climate model (RACMO2) simulation of coastal West Antarctica for the period 1979–2015. We evaluate the results with available in situ weather observations, remote-sensing estimates of surface melt, and SMB estimates derived from radar and firn cores. Moreover, results are compared with those from a lower-resolution version, to assess the added value of the resolution. The high-resolution model resolves small-scale climate variability invoked by topography, such as the relatively warm conditions over ice-shelf grounding zones, and local wind speed accelerations. Surface melt and SMB are well reproduced by RACMO2. This dataset will prove useful for picking ice core locations, converting elevation changes to mass changes, for driving ocean, ice-sheet and coupled models, and for attributing changes in the West Antarctic Ice Sheet and shelves to changes in atmospheric forcing.

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Sood, Abha. "Fresh-water discharge from Greenland using regional climate simulations." Annals of Glaciology 42 (2005): 95–100. http://dx.doi.org/10.3189/172756405781812880.

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AbstractThe annual mass budget of the Greenland ice sheet (1992) and the fresh-water flux from Greenland including the coasts is determined using high-resolution regional climate model (REMO) simulations. The climate model is modified to include processes such as lateral flow over Greenland using a newly developed routing scheme, the effect of sub-grid-scale surface heterogeneity (orography) on surface temperature and runoff and an improved snow and ice model for the Greenland ice sheet for surface processes on the ice sheet. The high-resolution (0.125˚ grid size) simulations of accumulation and runoff fields are also assessed compared to the lower-resolution (0.5˚ grid size) simulations.

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Tsegaw, Aynalem T., Marie Pontoppidan, Erle Kristvik, Knut Alfredsen, and Tone M. Muthanna. "Hydrological impacts of climate change on small ungauged catchments – results from a global climate model–regional climate model–hydrologic model chain." Natural Hazards and Earth System Sciences 20, no. 8 (August 10, 2020): 2133–55. http://dx.doi.org/10.5194/nhess-20-2133-2020.

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Abstract. Climate change is one of the greatest threats currently facing the world's environment. In Norway, a change in climate will strongly affect the pattern, frequency, and magnitudes of stream flows. However, it is challenging to quantify to what extent the change will affect the flow patterns and floods from small rural catchments due to the unavailability or inadequacy of hydro-meteorological data for the calibration of hydrological models and due to the tailoring of methods to a small-scale level. To provide meaningful climate impact studies at the level of small catchments, it is therefore beneficial to use high-spatial- and high-temporal-resolution climate projections as input to a high-resolution hydrological model. In this study, we used such a model chain to assess the impacts of climate change on the flow patterns and frequency of floods in small ungauged rural catchments in western Norway. We used a new high-resolution regional climate projection, with improved performance regarding the precipitation distribution, and a regionalized hydrological model (distance distribution dynamics) between a reference period (1981–2011) and a future period (2070–2100). The flow-duration curves for all study catchments show more wet periods in the future than during the reference period. The results also show that in the future period, the mean annual flow increases by 16 % to 33 %. The mean annual maximum floods increase by 29 % to 38 %, and floods of 2- to 200-year return periods increase by 16 % to 43 %. The results are based on the RCP8.5 scenario from a single climate model simulation tailored to the Bergen region in western Norway, and the results should be interpreted in this context. The results should therefore be seen in consideration of other scenarios for the region to address the uncertainty. Nevertheless, the study increases our knowledge and understanding of the hydrological impacts of climate change on small catchments in the Bergen area in the western part of Norway.

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Strandberg, Gustav, and Petter Lind. "The importance of horizontal model resolution on simulated precipitation in Europe – from global to regional models." Weather and Climate Dynamics 2, no. 1 (March 15, 2021): 181–204. http://dx.doi.org/10.5194/wcd-2-181-2021.

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Abstract. Precipitation is a key climate variable that affects large parts of society, especially in situations with excess amounts. Climate change projections show an intensified hydrological cycle through changes in intensity, frequency, and duration of precipitation events. Still, due to the complexity of precipitation processes and their large variability in time and space, climate models struggle to represent precipitation accurately. This study investigates the simulated precipitation in Europe in available climate model ensembles that cover a range of horizontal model resolutions. The ensembles used are global climate models (GCMs) from CMIP5 and CMIP6 (∼100–300 km horizontal grid spacing at mid-latitudes), GCMs from the PRIMAVERA project at sparse (∼80–160 km) and dense (∼25–50 km) grid spacing, and CORDEX regional climate models (RCMs) at sparse (∼50 km) and dense (∼12.5 km) grid spacing. The aim is to seasonally and regionally over Europe investigate the differences between models and model ensembles in the representation of the precipitation distribution in its entirety and through analysis of selected standard precipitation indices. In addition, the model ensemble performances are compared to gridded observations from E-OBS. The impact of model resolution on simulated precipitation is evident. Overall, in all seasons and regions the largest differences between resolutions are seen for moderate and high precipitation rates, where the largest precipitation rates are seen in the RCMs with the highest resolution (i.e. CORDEX 12.5 km) and the smallest rates in the CMIP GCMs. However, when compared to E-OBS, the high-resolution models most often overestimate high-intensity precipitation amounts, especially the CORDEX 12.5 km resolution models. An additional comparison to a regional data set of high quality lends, on the other hand, more confidence to the high-resolution model results. The effect of resolution is larger for precipitation indices describing heavy precipitation (e.g. maximum 1 d precipitation) than for indices describing the large-scale atmospheric circulation (e.g. the number of precipitation days), especially in regions with complex topography and in summer when precipitation is predominantly caused by convective processes. Importantly, the systematic differences between low resolution and high resolution also remain when all data are regridded to common grids of 0.5∘×0.5∘ and 2∘×2∘ prior to analysis. This shows that the differences are effects of model physics and better resolved surface properties and not due to the different grids on which the analysis is performed. PRIMAVERA high resolution and CORDEX low resolution give similar results as they are of similar resolution. Within the PRIMAVERA and CORDEX ensembles, there are clear differences between the low- and high-resolution simulations. Once reaching ∼50 km the difference between different models is often larger than between the low- and high-resolution versions of the same model. For indices describing precipitation days and heavy precipitation, the difference between two models can be twice as large as the difference between two resolutions, in both the PRIMAVERA and CORDEX ensembles. Even though increasing resolution improves the simulated precipitation in comparison to observations, the inter-model variability is still large, particularly in summer when smaller-scale processes and interactions are more prevalent and model formulations (such as convective parameterisations) become more important.

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Delworth, Thomas L., Anthony Rosati, Whit Anderson, Alistair J. Adcroft, V. Balaji, Rusty Benson, Keith Dixon, et al. "Simulated Climate and Climate Change in the GFDL CM2.5 High-Resolution Coupled Climate Model." Journal of Climate 25, no. 8 (April 10, 2012): 2755–81. http://dx.doi.org/10.1175/jcli-d-11-00316.1.

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Abstract The authors present results for simulated climate and climate change from a newly developed high-resolution global climate model [Geophysical Fluid Dynamics Laboratory Climate Model version 2.5 (GFDL CM2.5)]. The GFDL CM2.5 has an atmospheric resolution of approximately 50 km in the horizontal, with 32 vertical levels. The horizontal resolution in the ocean ranges from 28 km in the tropics to 8 km at high latitudes, with 50 vertical levels. This resolution allows the explicit simulation of some mesoscale eddies in the ocean, particularly at lower latitudes. Analyses are presented based on the output of a 280-yr control simulation; also presented are results based on a 140-yr simulation in which atmospheric CO2 increases at 1% yr−1 until doubling after 70 yr. Results are compared to GFDL CM2.1, which has somewhat similar physics but a coarser resolution. The simulated climate in CM2.5 shows marked improvement over many regions, especially the tropics, including a reduction in the double ITCZ and an improved simulation of ENSO. Regional precipitation features are much improved. The Indian monsoon and Amazonian rainfall are also substantially more realistic in CM2.5. The response of CM2.5 to a doubling of atmospheric CO2 has many features in common with CM2.1, with some notable differences. For example, rainfall changes over the Mediterranean appear to be tightly linked to topography in CM2.5, in contrast to CM2.1 where the response is more spatially homogeneous. In addition, in CM2.5 the near-surface ocean warms substantially in the high latitudes of the Southern Ocean, in contrast to simulations using CM2.1.

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Lucas-Picher, Philippe, Maria Wulff-Nielsen, Jens H. Christensen, Guðfinna Aðalgeirsdóttir, Ruth Mottram, and Sebastian B. Simonsen. "Very high resolution regional climate model simulations over Greenland: Identifying added value." Journal of Geophysical Research: Atmospheres 117, no. D2 (January 25, 2012): n/a. http://dx.doi.org/10.1029/2011jd016267.

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Bromwich, David H., Lesheng Bai, and Gudmundur G. Bjarnason. "High-Resolution Regional Climate Simulations over Iceland Using Polar MM5*." Monthly Weather Review 133, no. 12 (December 1, 2005): 3527–47. http://dx.doi.org/10.1175/mwr3049.1.

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Abstract High-resolution regional climate simulations of Iceland for 1991–2000 have been performed using the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesocale Model (MM5) modified for use in polar regions (Polar MM5) with three nested domains and short-duration integrations. The simulated results are compared with monthly mean surface observations from Iceland for 1991–2000 to demonstrate the high level of model performance; correlation coefficients exceed 0.9 for most variables considered. The simulation results are used to analyze the near-surface climate over Iceland. The simulated near-surface winds in winter are primarily katabatic. The land–sea-breeze circulation is clearly evident in summer. The land is colder than the ocean during winter, with a strong (weak) temperature gradient along the southern (northern) coast. This temperature pattern over the sloping terrain forces the katabatic wind. The diurnal cycle of near-surface air temperature is marked in summer over the land areas, which drives the land–sea breeze. The near-surface climate variations for extremes of the North Atlantic Oscillation (NAO) index during winter and summer result from the large-scale atmospheric advection conditions. The time-averaged mesoscale precipitation distribution over Iceland is reasonably well simulated by Polar MM5. Winter precipitation rates are double those during the summer, reflecting the much greater winter cyclonic activity. The simulated interannual precipitation variations during winter for 1991–2000 agree with those observed from snow accumulation measurements on the Vatnajökull ice cap. The winter precipitation decrease for 1991–2000 dominates the annual signal for all of Iceland except the northeastern and eastern parts where the precipitation increases. The large precipitation trends (decadal decrease of up to 50%) are caused by the eastward shift and weakening of the Icelandic low during the 1990s, as a result of changes in the NAO modulation of regional climate.

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Gutowski, W. J., P. A. Ullrich, A. Hall, L. R. Leung, T. A. O’Brien, C. M. Patricola, R. W. Arritt, et al. "The Ongoing Need for High-Resolution Regional Climate Models: Process Understanding and Stakeholder Information." Bulletin of the American Meteorological Society 101, no. 5 (May 1, 2020): E664—E683. http://dx.doi.org/10.1175/bams-d-19-0113.1.

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ABSTRACT Regional climate modeling addresses our need to understand and simulate climatic processes and phenomena unresolved in global models. This paper highlights examples of current approaches to and innovative uses of regional climate modeling that deepen understanding of the climate system. High-resolution models are generally more skillful in simulating extremes, such as heavy precipitation, strong winds, and severe storms. In addition, research has shown that fine-scale features such as mountains, coastlines, lakes, irrigation, land use, and urban heat islands can substantially influence a region’s climate and its response to changing forcings. Regional climate simulations explicitly simulating convection are now being performed, providing an opportunity to illuminate new physical behavior that previously was represented by parameterizations with large uncertainties. Regional and global models are both advancing toward higher resolution, as computational capacity increases. However, the resolution and ensemble size necessary to produce a sufficient statistical sample of these processes in global models has proven too costly for contemporary supercomputing systems. Regional climate models are thus indispensable tools that complement global models for understanding physical processes governing regional climate variability and change. The deeper understanding of regional climate processes also benefits stakeholders and policymakers who need physically robust, high-resolution climate information to guide societal responses to changing climate. Key scientific questions that will continue to require regional climate models, and opportunities are emerging for addressing those questions.

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Pryor, S. C., G. Nikulin, and C. Jones. "Influence of spatial resolution on regional climate model derived wind climates." Journal of Geophysical Research: Atmospheres 117, no. D3 (February 14, 2012): n/a. http://dx.doi.org/10.1029/2011jd016822.

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Kunstmann, H., K. Schneider, R. Forkel, and R. Knoche. "Impact analysis of climate change for an Alpine catchment using high resolution dynamic downscaling of ECHAM4 time slices." Hydrology and Earth System Sciences 8, no. 6 (December 31, 2004): 1031–45. http://dx.doi.org/10.5194/hess-8-1031-2004.

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Abstract. Global climate change affects spatial and temporal patterns of precipitation and so has a major impact on surface and subsurface water balances. While global climate models are designed to describe climate change on global or continental scales, their resolution is too coarse for them to be suitable for describing regional climate change. Therefore, regional climate models are applied to downscale the coarse meteorological fields to a much higher spatial resolution to take account of regional climate phenomena. The changes of atmospheric state due to regional climate change must be translated into surface and sub-surface water fluxes so that the impact on water balances in specific catchments can be investigated. This can be achieved by the coupled regional climatic/hydrological simulations presented here. The non-hydrostatic regional climate model MCCM was used for dynamic downscaling for two time slices of a global climate model simulation with the GCM ECHAM4 (IPCC scenario IS92a, "business as usual") from 2.8° × 2.8° to 4 × 4 km2 resolution for the years 1991–1999 and 2031–2039. This allowed derivation of detailed maps showing changes in precipitation and temperature in a region of southern Germany and the central Alps. The performance of the downscaled ECHAM4 to reproduce the seasonality of precipitation in central Europe for the recent climate was investigated by comparison with dynamically downscaled ECMWF reanalyses in 20 × 20 km2 resolution. The downscaled ECHAM4 fields underestimate precipitation significantly in summer. The ratio of mean monthly downscaled ECHAM4 and ECMWF precipitation showed little variation, so it was used to adjust the course of precipitation for the ECHAM4/MCCM fields before it was applied in the hydrological model. The high resolution meteorological fields were aggregated to 8-hour time steps and applied to the distributed hydrological model WaSiM to simulate the water balance of the alpine catchment of the river Ammer (c. 700 km2) at 100 × 100 m2 resolution. To check the reliability of the coupled regional climatic/hydrological simulation results for the recent climate, they were compared with those of a station-based hydrological simulation for the period 1991–1999. This study shows the changes in the temperature and precipitation distributions in the catchment from the recent climate to the future climate scenario and how these will affect the frequency distribution of runoff. Keywords: coupled climate-hydrology simulations, dynamic downscaling, distributed hydrological modelling, ECHAM4 climate scenario, alpine hydrology

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Kirthiga, S. M., and N. R. Patel. "In-Season Wheat Yield Forecasting at High Resolution Using Regional Climate Model and Crop Model." AgriEngineering 4, no. 4 (October 30, 2022): 1054–75. http://dx.doi.org/10.3390/agriengineering4040066.

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In-season crop production forecasts at the regional or sub-regional scale are essential to aid in food security through early warning of harvest shortfall/surplus, tailoring crop management decisions and addressing climatic shock. Considering the efforts to establish a framework towards quantifying the crop yield prediction at regional scales are limited, we investigated the utility of combining crop model with the regional weather prediction model to forecast winter wheat yields over space. The exercise was performed for various lead-times in the regions of Punjab and Haryana for the years 2008–2009. A numerical weather prediction (NWP) model was used to generate micro-meteorological variables at different lead times (1-week, 2-weeks, 3-weeks and 5-weeks) ahead of crop harvest and used within the CERES-Wheat crop simulation model gridded framework at a spatial resolution of 10 km. Various scenarios of the yield forecasts were verified with district-wide reported yield values. Average deviations of −12 to 3% from the actual district-wise wheat yields were observed across the lead times. The 3-weeks-ahead yield forecasts yielded a maximum agreement index of 0.86 with a root mean squared error (RMSE) of 327.75 kg/ha and a relative deviation of −5.35%. The critical crop growth stages were found to be highly sensitive to the errors in the weather forecast, and thus made a huge impact on the predicted crop yields. The 5-weeks-ahead weather forecasts generated anomalous meteorological data during flowering and grain-filling crop growth stages, and thus had the highest negative impact on the simulated yields. The agreement index of the 5-week-ahead forecasts was 0.41 with an RMSE of 415.15 kg ha−1 and relative deviation of −2.77 ± 5.01. The proposed methodology showed significant forecast skill for extended space and time scale crop yield forecasting, offering scope for further research and practical applicability.

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Lenaerts, Jan T. M., Michiel R. van den Broeke, Jan M. van Wessem, Willem Jan van de Berg, Erik van Meijgaard, Lambertus H. van Ulft, and Marius Schaefer. "Extreme Precipitation and Climate Gradients in Patagonia Revealed by High-Resolution Regional Atmospheric Climate Modeling." Journal of Climate 27, no. 12 (June 5, 2014): 4607–21. http://dx.doi.org/10.1175/jcli-d-13-00579.1.

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Abstract This study uses output of a high-resolution (5.5 km) regional atmospheric climate model to describe the present-day (1979–2012) climate of Patagonia, with a particular focus on the surface mass balance (SMB) of the Patagonian ice fields. Through a comparison with available in situ observations, it is shown that the model is able to simulate the sharp climate gradients in western Patagonia. The southern Andes are an efficient barrier for the prevalent atmospheric flow, generating strong orographic uplift and precipitation throughout the entire year. The model suggests extreme orographic precipitation west of the Andes divide, with annual precipitation rates of >5 to 34 m w.e. (water equivalent), and a clear rain shadow east of the divide. These modeled precipitation rates are supported qualitatively by available precipitation stations and SMB estimates on the ice fields derived from firn cores. For the period 1979–2012, a slight atmospheric cooling at upper ice field elevations is found, leading to a small but insignificant increase in the ice field SMB.

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Geyer, B. "High resolution atmospheric reconstruction for Europe 1948–2012: coastDat2." Earth System Science Data Discussions 6, no. 2 (December 2, 2013): 779–809. http://dx.doi.org/10.5194/essdd-6-779-2013.

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Abstract. The coastDat data sets were produced to give a consistent and homogeneous database mainly for assessing weather statistics and long-term changes for Europe, especially in data sparse regions. A sequence of numerical models was employed to reconstruct all aspects of marine climate (such as storms, waves, surges etc.) over many decades. Here, we describe the atmospheric part of coastDat2 (Geyer and Rockel, 2013, doi:10.1594/WDCC/coastDat-2_COSMO-CLM). It consists of a regional climate reconstruction for entire Europe, including Baltic and North Sea and parts of the Atlantic. The simulation was done for 1948 to 2012 with a regional climate model and a horizontal grid size of 0.22° in rotated coordinates. Global reanalysis data were used as forcing and spectral nudging was applied. To meet the demands on the coastDat data set about 70 variables are stored hourly.

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Mote, Philip W., Myles R. Allen, Richard G. Jones, Sihan Li, Roberto Mera, David E. Rupp, Ahmed Salahuddin, and Dean Vickers. "Superensemble Regional Climate Modeling for the Western United States." Bulletin of the American Meteorological Society 97, no. 2 (February 1, 2016): 203–15. http://dx.doi.org/10.1175/bams-d-14-00090.1.

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Abstract Computing resources donated by volunteers have generated the first superensemble of regional climate model results, in which the Hadley Centre Regional Model, version 3P (HadRM3P), and Hadley Centre Atmosphere Model, version 3P (HadAM3P), were implemented for the western United States at 25-km resolution. Over 136,000 valid and complete 1-yr runs have been generated to date: about 126,000 for 1960–2009 using observed sea surface temperatures (SSTs) and 10,000 for 2030–49 using projected SSTs from a global model simulation. Ensemble members differ in initial conditions, model physics, and (potentially, for future runs) SSTs. This unprecedented confluence of high spatial resolution and large ensemble size allows high signal-to-noise ratio and more robust estimates of uncertainty. This paper describes the experiment, compares model output with observations, shows select results for climate change simulations, and gives examples of the strength of the large ensemble size.

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Russo, Emmanuele, Jonathan Buzan, Sebastian Lienert, Guillaume Jouvet, Patricio Velasquez Alvarez, Basil Davis, Patrick Ludwig, Fortunat Joos, and Christoph C. Raible. "High-resolution LGM climate of Europe and the Alpine region using the regional climate model WRF." Climate of the Past 20, no. 3 (March 12, 2024): 449–65. http://dx.doi.org/10.5194/cp-20-449-2024.

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Abstract. In this study we present a series of sensitivity experiments conducted for the Last Glacial Maximum (LGM, ∼21 ka) over Europe using the regional climate Weather Research and Forecasting model (WRF). Using a four-step two-way nesting approach, we are able to reach a convection-permitting horizontal resolution over the inner part of the study area, covering central Europe and the Alpine region. The main objective of the paper is to evaluate a model version including a series of new developments better suitable for the simulation of paleo-glacial time slices with respect to the ones employed in former studies. The evaluation of the model is conducted against newly available pollen-based reconstructions of the LGM European climate and takes into account the effect of two main sources of model uncertainty: a different height of continental glaciers at higher latitudes of the Northern Hemisphere and different land cover. Model results are in good agreement with evidence from the proxies, in particular for temperatures. Importantly, the consideration of different ensemble members for characterizing model uncertainty allows for increasing the agreement of the model against the proxy reconstructions that would be obtained when considering a single model realization. The spread of the produced ensemble is relatively small for temperature, besides areas surrounding glaciers in summer. On the other hand, differences between the different ensemble members are very pronounced for precipitation, in particular in winter over areas highly affected by moisture advection from the Atlantic. This highlights the importance of the considered sources of uncertainty for the study of European LGM climate and allows for determining where the results of a regional climate model (RCM) are more likely to be uncertain for the considered case study. Finally, the results are also used to assess the effect of convection-permitting resolutions, at both local and regional scales, under glacial conditions.

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Black, Benjamin A., Jean-François Lamarque, Daniel R. Marsh, Anja Schmidt, and Charles G. Bardeen. "Global climate disruption and regional climate shelters after the Toba supereruption." Proceedings of the National Academy of Sciences 118, no. 29 (July 6, 2021): e2013046118. http://dx.doi.org/10.1073/pnas.2013046118.

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The Toba eruption ∼74,000 y ago was the largest volcanic eruption since the start of the Pleistocene and represents an important test case for understanding the effects of large explosive eruptions on climate and ecosystems. However, the magnitude and repercussions of climatic changes driven by the eruption are strongly debated. High-resolution paleoclimate and archaeological records from Africa find little evidence for the disruption of climate or human activity in the wake of the eruption in contrast with a controversial link with a bottleneck in human evolution and climate model simulations predicting strong volcanic cooling for up to a decade after a Toba-scale eruption. Here, we use a large ensemble of high-resolution Community Earth System Model (CESM1.3) simulations to reconcile climate model predictions with paleoclimate records, accounting for uncertainties in the magnitude of Toba sulfur emissions with high and low emission scenarios. We find a near-zero probability of annual mean surface temperature anomalies exceeding 4 °C in most of Africa in contrast with near 100% probabilities of cooling this severe in Asia and North America for the high sulfur emission case. The likelihood of strong decreases in precipitation is low in most of Africa. Therefore, even Toba sulfur release at the upper range of plausible estimates remains consistent with the muted response in Africa indicated by paleoclimate proxies. Our results provide a probabilistic view of the uneven patterns of volcanic climate disruption during a crucial interval in human evolution, with implications for understanding the range of environmental impacts from past and future supereruptions.

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Paul, Frank, and Sven Kotlarski. "Forcing a Distributed Glacier Mass Balance Model with the Regional Climate Model REMO. Part II: Downscaling Strategy and Results for Two Swiss Glaciers." Journal of Climate 23, no. 6 (March 15, 2010): 1607–20. http://dx.doi.org/10.1175/2009jcli3345.1.

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Abstract Distributed glacier mass balance models are efficient tools for the assessment of climate change impacts on glaciers at regional scales and at high spatial resolution (25–100 m). In general, these models are driven by time series of meteorological parameters that are obtained from a climate station near a glacier or from climate model output. Because most glaciers are located in rugged mountain topography with a high spatial and temporal variability of the meteorological conditions, the challenge is to distribute the point data from a climate station or the gridbox values from a regional climate model (RCM) in an appropriate way to the terrain. Here an approach is presented that uses normalized grids at the resolution of the mass balance model to capture the spatial variability, and time series from a climate station (Robiei) and an RCM Regional Model (REMO) to provide a temporal forcing for the mass balance model. The test site near Nufenen Pass (Swiss Alps) covers two glaciers with direct mass balance measurements that are used to demonstrate the approach. The meteorological parameters (temperature, global radiation, and precipitation) are obtained for the years 1997–99 (at daily steps) from the climate station Robiei (1898 m MSL) and one grid box of the RCM REMO. The results of the mass balance model agree closely with the measured values and the specific differences in mass balance between the two glaciers and the two balance years are well captured. Despite the disparities in the meteorological forcing from the climate station and REMO, there are only small differences in the modeled mass balances. This gives confidence that the developed approach of coupling the coarse-resolution (18 km) RCM with the high-resolution (25 m) mass balance model is suitable and can be applied to other regions as well as to RCM scenario runs.

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Neal, Lucy S., Mohit Dalvi, Gerd Folberth, Rachel N. McInnes, Paul Agnew, Fiona M. O'Connor, Nicholas H. Savage, and Marie Tilbee. "A description and evaluation of an air quality model nested within global and regional composition-climate models using MetUM." Geoscientific Model Development 10, no. 11 (November 1, 2017): 3941–62. http://dx.doi.org/10.5194/gmd-10-3941-2017.

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Abstract. There is a clear need for the development of modelling frameworks for both climate change and air quality to help inform policies for addressing these issues simultaneously. This paper presents an initial attempt to develop a single modelling framework, by introducing a greater degree of consistency in the meteorological modelling framework by using a two-step, one-way nested configuration of models, from a global composition-climate model (GCCM) (140 km resolution) to a regional composition-climate model covering Europe (RCCM) (50 km resolution) and finally to a high (12 km) resolution model over the UK (AQUM). The latter model is used to produce routine air quality forecasts for the UK. All three models are based on the Met Office's Unified Model (MetUM). In order to better understand the impact of resolution on the downscaling of projections of future climate and air quality, we have used this nest of models to simulate a 5-year period using present-day emissions and under present-day climate conditions. We also consider the impact of running the higher-resolution model with higher spatial resolution emissions, rather than simply regridding emissions from the RCCM. We present an evaluation of the models compared to in situ air quality observations over the UK, plus a comparison against an independent 1 km resolution gridded dataset, derived from a combination of modelling and observations, effectively producing an analysis of annual mean surface pollutant concentrations. We show that using a high-resolution model over the UK has some benefits in improving air quality modelling, but that the use of higher spatial resolution emissions is important to capture local variations in concentrations, particularly for primary pollutants such as nitrogen dioxide and sulfur dioxide. For secondary pollutants such as ozone and the secondary component of PM10, the benefits of a higher-resolution nested model are more limited and reasons for this are discussed. This study highlights the point that the resolution of models is not the only factor in determining model performance – consistency between nested models is also important.

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Sharmila, S., K. J. E. Walsh, M. Thatcher, S. Wales, and S. Utembe. "Real World and Tropical Cyclone World. Part I: High-Resolution Climate Model Verification." Journal of Climate 33, no. 4 (February 15, 2020): 1455–72. http://dx.doi.org/10.1175/jcli-d-19-0078.1.

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AbstractRecent global climate models with sufficient resolution and physics offer a promising approach for simulating real-world tropical cyclone (TC) statistics and their changing relationship with climate. In the first part of this study, we examine the performance of a high-resolution (~40-km horizontal grid) global climate model, the atmospheric component of the Australian Community Climate and Earth System Simulator (ACCESS) based on the Met Office Unified Model (UM8.5) Global Atmosphere (GA6.0). The atmospheric model is forced with observed sea surface temperature, and 20 years of integrations (1990–2009) are analyzed for evaluating the simulated TC statistics compared with observations. The model reproduces the observed climatology, geographical distribution, and interhemispheric asymmetry of global TC formation rates reasonably well. The annual cycle of regional TC formation rates over most basins is also well captured. However, there are some regional biases in the geographical distribution of TC formation rates. To identify the sources of these biases, a suite of model-simulated large-scale climate conditions that critically modulate TC formation rates are further evaluated, including the assessment of a multivariate genesis potential index. Results indicate that the model TC genesis biases correspond well to the inherent biases in the simulated large-scale climatic states, although the relative effects on TC genesis of some variables differs between basins. This highlights the model’s mean-state dependency in simulating accurate TC formation rates.

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Ekman, Annica M. L. "Small-scale patterns of sulfate aerosol climate forcing simulated with a high-resolution regional climate model." Tellus B: Chemical and Physical Meteorology 54, no. 2 (March 2002): 143–62. http://dx.doi.org/10.3402/tellusb.v54i2.16655.

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EKMAN, ANNICA M. L. "Small-scale patterns of sulfate aerosol climate forcing simulated with a high-resolution regional climate model." Tellus B 54, no. 2 (April 2002): 143–62. http://dx.doi.org/10.1034/j.1600-0889.2002.00282.x.

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Geetha, Rajadurai, Andimuthu Ramachandran, J. Indumathi, Kandasamy Palanivelu, G. V. Uma, Prasanta Kumar Bal, and Perumal Thirumurugan. "Characterization of future climate extremes over Tamil Nadu, India, using high-resolution regional climate model simulation." Theoretical and Applied Climatology 138, no. 3-4 (May 25, 2019): 1297–309. http://dx.doi.org/10.1007/s00704-019-02901-0.

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Rajbhandari, Rupak, Arun Bhakta Shrestha, Santosh Nepal, Shahriar Wahid, and Guo-Yu Ren. "Extreme climate projections over the transboundary Koshi River Basin using a high resolution regional climate model." Advances in Climate Change Research 8, no. 3 (September 2017): 199–211. http://dx.doi.org/10.1016/j.accre.2017.08.006.

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Chaigneau, Alisée A., Guillaume Reffray, Aurore Voldoire, and Angélique Melet. "IBI-CCS: a regional high-resolution model to simulate sea level in western Europe." Geoscientific Model Development 15, no. 5 (March 10, 2022): 2035–62. http://dx.doi.org/10.5194/gmd-15-2035-2022.

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Abstract. Projections of coastal sea level (SL) changes are of great interest for coastal risk assessment and decision making. SL projections are typically produced using global climate models (GCMs), which cannot fully resolve SL changes at the coast due to their coarse resolution and lack of representation of some relevant processes (tides, atmospheric surface pressure forcing, waves). To overcome these limitations and refine projections at regional scales, GCMs can be dynamically downscaled through the implementation of a high-resolution regional climate model (RCM). In this study, we developed the IBI-CCS (Iberian–Biscay–Ireland Climate Change Scenarios) regional ocean model based on a 1/12∘ northeastern Atlantic Nucleus for European Modelling of the Ocean (NEMO) model configuration to dynamically downscale CNRM-CM6-1-HR, a GCM with a 1/4∘ resolution ocean model component participating in the sixth phase of the Coupled Model Intercomparison Project (CMIP6) by the Centre National de Recherches Météorologiques (CNRM). For a more complete representation of the processes driving coastal SL changes, tides and atmospheric surface pressure forcing are explicitly resolved in IBI-CCS in addition to the ocean general circulation. To limit the propagation of climate drifts and biases from the GCM into the regional simulations, several corrections are applied to the GCM fields used to force the RCM. The regional simulations are performed over the 1950 to 2100 period for two climate change scenarios (SSP1-2.6 and SSP5-8.5). To validate the dynamical downscaling method, the RCM and GCM simulations are compared to reanalyses and observations over the 1993–2014 period for a selection of ocean variables including SL. Results indicate that large-scale performance of IBI-CCS is better than that of the GCM thanks to the corrections applied to the RCM. Extreme SLs are also satisfactorily represented in the IBI-CCS historical simulation. Comparison of the RCM and GCM 21st century projections shows a limited impact of increased resolution (1/4 to 1/12∘) on SL changes. Overall, bias corrections have a moderate impact on projected coastal SL changes, except in the Mediterranean Sea, where GCM biases were substantial.

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Valmassoi, Arianna, Jimy Dudhia, Silvana Di Sabatino, and Francesco Pilla. "Regional Climate Impacts of Irrigation in Northern Italy Using a High Resolution Model." Atmosphere 11, no. 1 (January 6, 2020): 72. http://dx.doi.org/10.3390/atmos11010072.

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Irrigation is crucial for sustaining agriculture in certain regions; however, there are effects on the local climate. Previous studies discussed that the irrigation signal might depend on the geographical region as well as the synoptic and climatic conditions. The work presented here aims to investigate the mechanisms behind changes in the irrigation impact on the local conditions depending on synoptic changes. Different to previous works, this employs convection-permitting simulations. Irrigation processes are parameterized in three different ways depending on the evaporative loss. The region of focus is in northern Italy (Po Valley), which is of interest for both the soil-atmosphere coupling strength and widely used irrigation. The simulation period is Summer 2015 (May–July), which includes a heatwave month (July) and an average month (June). The results show how irrigation prevented the drying out of the soil layers during the heatwave. This influences the surface flux partition differently, by increasing moisture flux and decreasing the sensible heat flux. In general, the irrigation impact magnitude, with respect to the control simulation, is more than double in July compared to June. This study discusses climate implications for the region, such as the impact of widespread irrigation on the vegetation health, the heatwave feedback mechanism, atmospheric pollution, and human heat discomfort.

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Srivastava, Rohit, and Ruchita Shah. "Study of Monsoonal Features Using Regional Climate Model over Heterogeneous Monsoon Dominated Region." E3S Web of Conferences 101 (2019): 03004. http://dx.doi.org/10.1051/e3sconf/201910103004.

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Global warming is an increase in average global temperature of the earth which lead to climate change. Heterogeneity in the earth-atmosphere system becomes difficult to capture at low resolution (1°x1°) by satellite. Such features may be captured by using high resolution model such as regional climate model (0.5°x 0.5°). This type of study is quite important for a monsoon dominated country like India where Indo-Gangetic Plains (IGP) faces highest heterogeneity due to its geographic location. Present study compares high resolution model features with satellite data over IGP for monsoon season during a normal rainfall year 2010 to understand the actual performance of model. Almost whole IGP simulates relative humidity (RH) with wide range (~50-100%), whereas satellite shows it with narrow range (~60-80%) during September, 2010. Thus model is able to pick the features which were missed by satellite. Hence further model simulation extends over India and adjoining oceanic regions which simulates data of southwest monsoon with high (~70-100%) RH, high (~0.4-0.7) cloud fraction (CF) and low (~80-200 W/m2) outgoing longwave radiation (OLR) over Arabian Sea during June, 2010. Such type of study can be useful to understand heterogeneity at regional scale with the help of high resolution model generated data.

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LYRA, Andre de Arruda, Sin Chan CHOU, and Gilvan de Oliveira SAMPAIO. "Sensitivity of the Amazon biome to high resolution climate change projections." Acta Amazonica 46, no. 2 (June 2016): 175–88. http://dx.doi.org/10.1590/1809-4392201502225.

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ABSTRACT: Despite the reduction in deforestation rate in recent years, the impact of global warming by itself can cause changes in vegetation cover. The objective of this work was to investigate the possible changes on the major Brazilian biome, the Amazon Rainforest, under different climate change scenarios. The dynamic vegetation models may simulate changes in vegetation distribution and the biogeochemical processes due to climate change. Initially, the Inland dynamic vegetation model was forced with initial and boundary conditions provided by CFSR and the Eta regional climate model driven by the historical simulation of HadGEM2-ES. These simulations were validated using the Santarém tower data. In the second part, we assess the impact of a future climate change on the Amazon biome by applying the Inland model forced with regional climate change projections. The projections show that some areas of rainforest in the Amazon region are replaced by deciduous forest type and grassland in RCP4.5 scenario and only by grassland in RCP8.5 scenario at the end of this century. The model indicates a reduction of approximately 9% in the area of tropical forest in RCP4.5 scenario and a further reduction in the RCP8.5 scenario of about 50% in the eastern region of Amazon. Although the increase of CO2 atmospheric concentration may favour the growth of trees, the projections of Eta-HadGEM2-ES show increase of temperature and reduction of rainfall in the Amazon region, which caused the forest degradation in these simulations.

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Nakamura, Masaomi, Sachie Kaneda, Yasutaka Wakazuki, Chiashi Muroi, Akihiro Hashimoto, Teruyuki Kato, Akira Noda, Masanori Yoshizaki, and Kazuaki Yasunaga. "Effects of Global Warming on Heavy Rainfall During the Baiu Season Projected by a Cloud-System-Resolving Model." Journal of Disaster Research 3, no. 1 (February 1, 2008): 15–24. http://dx.doi.org/10.20965/jdr.2008.p0015.

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Under the Kyosei-4 Project, unprecedented high resolution global and regional climate models were developed on the Earth Simulator to investigate the effect of global warming on tropical cyclones, baiu frontal rainfall systems, and heavy rainfall events that could not be resolved using conventional climate models.For the regional climate model, a nonhydrostatic model (NHM) with a horizontal resolution of 5 km was developed to be used in the simulation of heavy rainfall during the baiu season in Japan. Simulations in June and July were executed for 10 years in present and future global warming climates. It was found that, due to global warming, mean rainfall is projected to increase except in eastern and northern Japan, the frequency of heavy rainfall events would increase and its increment rate become higher for heavier rainfall, and return values for extreme rainfall would grow.Experiments using an NHM with a horizontal resolution of 1 km were conducted to study the effects of resolution. Compared to 5 km resolution, it expresses the organization of rainfall systems causing heavy rainfall and the appearance-frequency distribution of rainfall for variable intensities more realistically.

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Maples, Amy, Maurice McHugh, and Erica Brown. "Downscaled climate models in complex topographical regions: relevancy for water utility planning." Journal of Water and Climate Change 5, no. 4 (May 5, 2014): 540–55. http://dx.doi.org/10.2166/wcc.2014.036.

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Abstract:

Water resource managers are interested in planning for future climate change scenarios, but global climate models are too coarse for water resource planning and running scenarios through dynamic downscaled regional climate models can be overly time-consuming. For this experiment, we conceptually illustrate that regional climate models can reproduce observed data for the San Francisco area, skipping a time-intensive intermediate step. To determine whether skipping the step would negatively affect output, we downscaled 13 months of National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis (NNRP2) data from native to 50, 40, and 20-km resolution using the regional climate model RegCM3. Outputs relevant to water planners, temperature and precipitation were compared with a high resolution observed dataset, which indicated that this configuration of RegCM3 can produce downscaled data with high correlations to observed data for this domain. The high correlations indicate that this domain can be simulated with a high spatial resolution ratio (1:14), without the need for the intermediate step. This study is a proof of concept that high resolution data can be obtained more efficiently for water agencies considering possible climate scenarios in planning for their future water supply. However, additional analysis is necessary before information can be obtained from downscaled models for decision-relevant use.

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Journal articles on the topic 'High-resolution Regional Climate Model'

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