Dynamic Regional Climate Modeling and Sensitivity Experiments for the Northeast of Brazil

Böhm, Uwe; Gerstengarbe, Friedrich‐Wilhelm; Hauffe, Detlef; Kücken, Martin; Österle, Hermann; Werner, P.

doi:10.1007/978-3-642-55659-3_12

Cited by 8 publications

(5 citation statements)

References 4 publications

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“…For the regional simulations the climate model CCLM, the climate version of the non-hydrostatic limited-area operational weather prediction model of the Consortium on Small scale Modelling (COSMO), was used (Böhm et al, 2003;Rockel and Geyer, 2008 In all, three simulations are investigated in this study: a simulation forced with the ERA40-reanalysis data (Uppala et al, 2005) for the period 1993-1997, and two simulations forced with the ECHO-G global model. The first simulation is driven with pre-industrial conditions representing external forcings of 1750 AD (PI), the second one representing the present-day conditions of 1990 AD (PD).…”

Section: Climate Models and Experimental Setupmentioning

confidence: 99%

Comparison of 20th century and pre-industrial climate over South America in regional model simulations

2012

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Abstract. In this study, we assess how the anthropogenically induced increase in greenhouse gas concentrations affects the climate of central and southern South America. We utilise two regional climate simulations for present day (PD) and pre-industrial (PI) times. These simulations are compared to historical reconstructions in order to investigate the driving processes responsible for climatic changes between the different periods. The regional climate model is validated against observations for both re-analysis data and GCM-driven regional simulations for the second half of the 20th century. Model biases are also taken into account for the interpretation of the model results. The added value of the regional simulation over global-scale modelling relates to a better representation of hydrological processes that are particularly evident in the proximity of the Andes Mountains.Climatic differences between the simulated PD minus PI period agree qualitatively well with proxy-based temperature reconstructions, albeit the regional model overestimates the amplitude of the temperature increase. For precipitation the most important changes between the PD and PI simulation relate to a dipole pattern along the Andes Mountains with increased precipitation over the southern parts and reduced precipitation over the central parts. Here only a few regions show robust similarity with studies based on empirical evidence. However, from a dynamical point-of-view, atmospheric circulation changes related to an increase in high-latitude zonal wind speed simulated by the regional climate model are consistent with numerical modelling studies addressing changes in greenhouse gas concentrations.Our results indicate that besides the direct effect of greenhouse gas changes, large-scale changes in atmospheric circulation and sea surface temperatures also exert an influence on temperature and precipitation changes in southern South America. These combined changes in turn affect the relationship between climate and atmospheric circulation between PD and PI times and should be considered for the statistical reconstruction of climate indices calibrated within presentday climate data.

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Section: Climate Models and Experimental Setupmentioning

confidence: 99%

Comparison of 20th century and pre-industrial climate over South America in regional model simulations

2012

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“…Q 2 reaches a smaller value for CLM compared to the other regional model run which can be attributed to a better structural representation of the drought patterns. For details on the intercomparison of CLM and REMO and the major source of error for the latter see Böhm et al (2003). To improve the performance of CLM as compared to the global model ECHAM4 or the driving ECMWF analyses, however, the ability of the model to simulate precipitation must be enhanced.…”

Section: Agricultural Yield Loss Riskmentioning

confidence: 99%

Reliability of regional climate model simulations of extremes and of long-term climate

Böhm

Kücken²,

Hauffe³

et al. 2004

Nat. Hazards Earth Syst. Sci.

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Abstract. We present two case studies that demonstrate how a common evaluation methodology can be used to assess the reliability of regional climate model simulations from different fields of research. In Case I, we focused on the agricultural yield loss risk for maize in Northeastern Brazil during a drought linked to an El-Niño event. In Case II, the present-day regional climatic conditions in Europe for a 10-year period are simulated. To comprehensively evaluate the model results for both kinds of investigations, we developed a general methodology. On its basis, we elaborated and implemented modules to assess the quality of model results using both advanced visualization techniques and statistical algorithms. Besides univariate approaches for individual near-surface parameters, we used multivariate statistics to investigate multiple near-surface parameters of interest together. For the latter case, we defined generalized quality measures to quantify the model's accuracy. Furthermore, we elaborated a diagnosis tool applicable for atmospheric variables to assess the model's accuracy in representing the physical processes above the surface under various aspects. By means of this evaluation approach, it could be demonstrated in Case Study I that the accuracy of the applied regional climate model resides at the same level as that we found for another regional model and a global model. Excessive precipitation during the rainy season in coastal regions could be identified as a major contribution leading to this result. In Case Study II, we also identified the accuracy of the investigated mean characteristics for near-surface temperature and precipitation to be comparable to another regional model. In this case, an artificial modulation of the used initial and boundary data during preprocessing could be identified as the major source of error in the simulation. Altogether, the achieved results for the presented investigations indicate the potential of our methodology to be applied as a common test bed to different fields of research in regional climate modeling.

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“…The original core of COSMO-CLM or CCLM, was called Local MOdel (LM), developed by DWD for weather forecasting. The adopted LM version for climate purposes formed the COSMO-CLM (Böhm et al, 2003). COSMO-CLM is designed to simulate the regional climate at high spatial resolution, allowing researchers to study various aspects of the climate system, such as temperature, precipitation, and extreme events.…”

Section: Introductionmentioning

confidence: 99%

Climate Model Downscaling in Central Asia: A Dynamical and a Neural Network Approach

Fallah,

Menz,

Russo

et al. 2023

Preprint

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Abstract. To estimate future climate change impacts, usually high-resolution climate projections are necessary. Statistical and dynamical downscaling or a hybrid of both methods are mostly used to produce input datasets for impact modelers. In this study, we use the regional climate model (RCM) COSMO-CLM (CCLM) version 6.0 to identify the added value of dynamically downscaling a general circulation model (GCM) from the sixth phase of the Coupled Model Inter-comparison Project (CMIP6) and its climate change projections' signal over Central Asia (CA). We use the MPI-ESM1-2-HR (at 1° spatial resolution) to drive the CCLM (at 0.22° horizontal resolution) for the historical period of 1985–2014 and the projection period of 2019–2100 under three different shared socioeconomic pathways (SSPs): SSP1-2.6, SSP3-7.0 and SSP5-8.5 scenarios. Using the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) gridded observation dataset, we evaluate the CCLM performance over the historical period using a simulation driven by ERAInterim reanalysis. CCLM's added value, compared to its driving GCM, is significant over CA mountainous areas, which are at higher risk of extreme precipitation events. Furthermore, we downscale the CCLM for future climate projections. We present high-resolution maps of heavy precipitation changes based on CCLM and compare them with CMIP6 GCMs ensemble. Our analysis shows a significant increase in heavy precipitation intensity and frequency over CA areas that are already at risk of extreme climatic events in the present day. Finally, applying our single model high-resolution dynamical downscaling, we train a convolutional neural network (CNN) to map the low-resolution GCM simulations to the dynamically downscaled CCLM ones. We show that applied CNN could emulate the GCM-CCLM model chain over large CA areas. However, this specific emulator has shortcomings when applied to a new GCM-CCLM model chain. Our downscaling data and the pre-trained CNN model could be used by scientific communities interested in downscaling CMIP6 models and searching for a trade-off between the dynamical and statistical methods.

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Dynamic Regional Climate Modeling and Sensitivity Experiments for the Northeast of Brazil

Cited by 8 publications

References 4 publications

Comparison of 20th century and pre-industrial climate over South America in regional model simulations

Comparison of 20th century and pre-industrial climate over South America in regional model simulations

Reliability of regional climate model simulations of extremes and of long-term climate

Climate Model Downscaling in Central Asia: A Dynamical and a Neural Network Approach

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