Abstract:
Abstract. Disentangling the effects of internal variability and anthropogenic forcing on regional climate trends remains a key challenge with far-reaching implications. Due to its largely unpredictable nature on timescales longer than a decade, internal climate variability limits the accuracy of climate model projections, introduces challenges in attributing past climate changes, and complicates climate model evaluation. Here, we highlight recent advances in climate modeling and physical understanding that have led to novel insights about these key issues. In particular, we synthesize new findings from large-ensemble simulations with Earth system models, observational large ensembles, and dynamical adjustment methodologies, with a focus on European climate.Keywords:
Forcing (mathematics)
Climate system
Climate science
Coupled Ocean-Atmosphere General Circulation Models (GCMs) provide the best estimates for assessing potential changes to our climate on a global scale out to the end of this century. Because coupled GCMs have a fairly coarse resolution they do not provide a detailed picture of climate (and climate change) at the local scale. Tasmania, due to its diverse geography and range of climate over a small area is a particularly difficult region for drawing conclusions regarding climate change when relying solely on GCMs. The foundation of the Climate Futures for Tasmania project is to take the output produced by multiple GCMs, using multiple climate change scenarios, and use this output as input into the Conformal Cubic Atmospheric Model (CCAM) to downscale the GCM output. CCAM is a full atmospheric global general circulation model, formulated using a conformal-cubic grid that covers the globe but can be stretched to provide higher resolution in the area of interest (Tasmania). By modelling the atmosphere at a much finer scale than is possible using a coupled GCM we can more accurately capture the processes that drive Tasmania's weather/climate, and thus can more clearly answer the question of how Tasmania's climate will change in the future. We present results that show the improvements in capturing the local-scale climate and climate drivers that can be achieved through downscaling, when compared to a gridded observational data set. The underlying assumption of this work is that a better simulated current climatology will also produce a more credible climate change signal.
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Abstract. Many climate extremes, including heatwaves and heavy precipitation events, are projected to worsen under climate change, with important impacts for society. Future projections required for adaptation are often based on climate model simulations. Given finite resources, trade-offs must be made concerning model resolution, ensemble size, and level of model complexity. Here we focus on the resolution component. A given resolution can be achieved over a region using either global climate models (GCMs) or at lower cost using regional climate models (RCMs) that dynamically downscale coarser GCMs. Both approaches to increasing resolution may better capture small-scale processes and features (downscaling effect), but increased GCM resolution may also improve the representation of the large-scale atmospheric circulation (upscaling effect). The size of this upscaling effect is therefore important for deciding modelling strategies. Here we evaluate the benefits of increased model resolution for both global and regional climate models for simulating temperature, precipitation, and wind extremes over Europe at resolutions that could currently be realistically used for coordinated sets of climate projections at the pan-European scale. First we examine the benefits of regional downscaling by comparing EURO-CORDEX simulations at 12.5 and 50 km resolution to their coarser CMIP5 driving simulations. Secondly, we compare global-scale HadGEM3-A simulations at three resolutions (130, 60, and 25 km). Finally, we separate out resolution-dependent differences for HadGEM3-A into downscaling and upscaling components using a circulation analogue technique. Results suggest limited benefits of increased resolution for heatwaves, except in reducing hot biases over mountainous regions. Precipitation extremes are sensitive to resolution, particularly over complex orography, with larger totals and heavier tails of the distribution at higher resolution, particularly in the CORDEX vs. CMIP5 analysis. CMIP5 models underestimate precipitation extremes, whilst CORDEX simulations overestimate compared to E-OBS, particularly at 12.5 km, but results are sensitive to the observational dataset used, with the MESAN reanalysis giving higher totals and heavier tails than E-OBS. Wind extremes are somewhat stronger and heavier tailed at higher resolution, except in coastal regions where large coastal grid boxes spread strong ocean winds further over land. The circulation analogue analysis suggests that differences with resolution for the HadGEM3-A GCM are primarily due to downscaling effects.
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Model Run: Far future (2080 - 2100) (Far future (2080 - 2100)). The Self-Organizing Map Downscaling (SOMD) was developed at the Climate Systems Analysis Group (CSAG)[1], University of Cape Town. This is a leading empirical downscaled technique and provides meteorological station level response to global climate change forcing (See Hewitson and Crane (2006) for methodological details and Wilby et al. (2004) for a review of this and other statistical downscaling methodologies). Downscaling of a General Circulation Model (GCM) is accomplished by deriving the normative local response from the atmospheric state on a given day, as defined from historical observed data. [1] http://www.csag.uct.ac.za/
Forcing (mathematics)
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A mesoscale model (MM5)–based regional climate model (CMM5) integration driven by the Parallel Climate Model (PCM), a fully coupled atmosphere‐ocean‐land‐ice general circulation model (GCM), for the present (1986–1995) summer season climate is first compared with observations to study the CMM5's downscaling skill and uncertainty over the United States. The results indicate that the CMM5, with its finer resolution (30 km) and more comprehensive physics, simulates the present U.S. climate more accurately than the driving PCM, especially for precipitation, including summer mean patterns, diurnal cycles, and daily frequency distributions. Hence the CMM5 downscaling provides a credible means to improve GCM climate simulations. A parallel CMM5 integration driven by the PCM future (2041–2050) projection is then analyzed to determine the downscaling impact on regional climate changes. It is shown that the CMM5 generates climate change patterns very different from those predicted by the driving PCM. A key difference is a summer “warming hole” over the central United States in the CMM5 relative to the PCM. This study shows that the CMM5 downscaling can significantly reduce GCM biases in simulating the present climate and that this improvement has important consequences for future projections of regional climate changes. For both the present and future climate simulations, the CMM5 results are sensitive to the cumulus parameterization, with strong regional dependence. The deficiency in representing convection is likely the major reason for the PCM's unrealistic simulation of U.S. precipitation patterns and perhaps also for its large warming in the central United States.
MM5
Transient climate simulation
Climate commitment
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Citations (147)
<p>Empirical-statistical downscaling (ESD) methods are sparing regarding computational costs compared to dynamical downscaling models. Due to this advantage ESD can be applied in a short time frame and in a demand-based manner. It enables, e.g., the creation of ensembles of downscaled climate projections, which can be assessed either as stand-alone data set or to enhance ensembles based on dynamical methods. This helps improve the robustness of climatological statements for the purpose of climate impact research.</p><p>EPISODES is an ESD method for the regionalisation of output of general circulation models (GSMs). The initial development of EPISODES has been done at Deutscher Wetterdienst (DWD) for the area of Germany. Results of EPISODES results of CMIP5 projections are available for public download at the Earth System Grid Federation (ESGF). In the meantime, EPISODES has been extended for the downscaling of climate predictions on different timescales (decadal, seasonal, sub-seasonal) to meet the needs of climate data users for high spatial resolution datasets. Furthermore, is has been applied to a number of CMIP6 global projections.</p><p>In co-operation with the Zentralanstalt f&#252;r Meteorologie und Geodynamik (ZAMG) EPISODES is currently further developed and adjusted for handling besides the German area also the Alpine region. In addition to the interest of ZAMG to carry out downscaling with EPISODES for Austria, the complete coverage of the catchment areas of the Rhine, Elbe and Danube is a common interest of this cooperation.</p><p>The presentation will give an overview of the current status of EPISODES, show results, and provide an insight into recent developments.</p>
Regionalisation
Representative Concentration Pathways
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CR Climate Research Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsSpecials CR 07:129-149 (1996) - DOI: https://doi.org/10.3354/cr007129 Estimates of climate change in Southern Europe derived from dynamical climate model output Cubasch U, von Storch H, Waszkewitz J, Zorita E Three methods of downscaling are applied to climate change experiments to obtain regional climate information for Spain and the region designated as 'Southern Europe' by the Intergovernmental Panel on Climate Change (IPCC). The first method (direct interpolation of the grid points nearest the region analysed) gives a poor representation of the local climate. Its estimate of climate change simulated by different climate models is inconsistent. The success of the second method (time-slice), which uses a dynamical model to obtain the regional information, strongly depends on the horizontal resolution of the dynamical model. It provides the most reliable assessment of the regional climate, with the highest resolution. However, the computational expense of this high resolution limits the sample size. The third method (statistical downscaling) is an inexpensive tool for obtaining information on a regional scale. The problem is that this approach requires observational data sets to train the model. This limits the application of this method to well-observed quantities and regions. Both the time-slice and the statistical models indicate a lengthening of dry spells over Spain under CO2-doubling conditions. Downscaling · Regional climate change · Southern Europe Full text in pdf format PreviousNextExport citation RSS - Facebook - Tweet - linkedIn Cited by Published in CR Vol. 07, No. 2. Publication date: November 29, 1996 Print ISSN: 0936-577X; Online ISSN: 1616-1572 Copyright © 1996 Inter-Research.
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Climate commitment
Interpolation
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Citations (153)
Abstract. Many climate extremes, including heatwaves and heavy precipitation events, are projected to worsen under climate change, with important impacts for society. Future projections, required for adaptation, are often based on climate model simulations. Given finite resources, trade-offs must be made concerning model resolution, ensemble size and level of model complexity. Here we focus on the resolution component. A given resolution can be achieved over a region using either global climate models (GCMs) or at lower cost using regional climate models (RCMs) that dynamically downscale coarser GCMs. Both approaches to increasing resolution may better capture small-scale processes and features (downscaling effect), but increased GCM resolution may also improve the representation of large-scale atmospheric circulation (upscaling effect). The size of this upscaling effect is therefore important for deciding modelling strategies. Here we evaluate the benefits of increased model resolution for both global and regional climate models for simulating temperature, precipitation and wind extremes over Europe at resolutions that could currently be realistically used for coordinated sets of climate projections at the pan-European scale. First we examine the benefits of regional downscaling by comparing EURO-CORDEX simulations at 12.5 and 50 km resolution to their coarser CMIP5 driving simulations. Secondly, we compare global scale HadGEM3-A simulations at three resolutions (130, 60 and 25 km). Finally, we separate out resolution dependent differences for HadGEM3-A into downscaling and upscaling components using a circulation analogue technique. Results suggest limited benefits of increased resolution for heatwaves, except in reducing hot biases over mountainous regions. Precipitation extremes are sensitive to resolution, particularly over complex orography, with larger totals and heavier tails of the distribution at higher resolution, particularly in the CORDEX vs CMIP5 analysis. CMIP5 models underestimate precipitation extremes, whilst CORDEX simulations overestimate compared to E-OBS, particularly at 12.5 km, but results are sensitive to the observational dataset used, with the MESAN reanalysis giving higher totals and heavier tails than E-OBS. Wind extremes are somewhat stronger and heavier tailed at higher resolution, except at coastal regions where large grid boxes spread strong ocean winds further over land. The circulation analogue analysis suggests that differences with resolution for the HadGEM3-A GCM are primarily due to downscaling effects.
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A suite of eighteen simulations over the U.S. and Mexico, representing combinations of two mesoscale regional climate models (RCMs), two driving global general circulation models (GCMs), and the historical and four future anthropogenic forcings were intercompared. The RCMs' downscaling reduces significantly driving GCMs' present‐climate biases and narrows inter‐model differences in representing climate sensitivity and hence in simulating the present and future climates. Very high spatial pattern correlations of the RCM minus GCM differences in precipitation and surface temperature between the present and future climates indicate that major model present‐climate biases are systematically propagated into future‐climate projections at regional scales. The total impacts of the biases on trend projections also depend strongly on regions and cannot be linearly removed. The result suggests that the nested RCM‐GCM approach that offers skill enhancement in representing the present climate also likely provides higher credibility in downscaling the future climate projection.
Circulation (fluid dynamics)
Climate extremes
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