scholarly journals Towards European-scale convection-resolving climate simulations with GPUs: a study with COSMO 4.19

2016 ◽  
Vol 9 (9) ◽  
pp. 3393-3412 ◽  
Author(s):  
David Leutwyler ◽  
Oliver Fuhrer ◽  
Xavier Lapillonne ◽  
Daniel Lüthi ◽  
Christoph Schär
Keyword(s):  
Graphics Processing Units ◽  
Climate Models ◽  
Climate Model ◽  
Deep Convection ◽  
Regional Climate ◽  
Small Scale ◽  
Moist Convection ◽  
Cosmo Model ◽  
Climate Simulations ◽  
Weather And Climate

Abstract. The representation of moist convection in climate models represents a major challenge, due to the small scales involved. Using horizontal grid spacings of O(1km), convection-resolving weather and climate models allows one to explicitly resolve deep convection. However, due to their extremely demanding computational requirements, they have so far been limited to short simulations and/or small computational domains. Innovations in supercomputing have led to new hybrid node designs, mixing conventional multi-core hardware and accelerators such as graphics processing units (GPUs). One of the first atmospheric models that has been fully ported to these architectures is the COSMO (Consortium for Small-scale Modeling) model.Here we present the convection-resolving COSMO model on continental scales using a version of the model capable of using GPU accelerators. The verification of a week-long simulation containing winter storm Kyrill shows that, for this case, convection-parameterizing simulations and convection-resolving simulations agree well. Furthermore, we demonstrate the applicability of the approach to longer simulations by conducting a 3-month-long simulation of the summer season 2006. Its results corroborate the findings found on smaller domains such as more credible representation of the diurnal cycle of precipitation in convection-resolving models and a tendency to produce more intensive hourly precipitation events. Both simulations also show how the approach allows for the representation of interactions between synoptic-scale and meso-scale atmospheric circulations at scales ranging from 1000 to 10 km. This includes the formation of sharp cold frontal structures, convection embedded in fronts and small eddies, or the formation and organization of propagating cold pools. Finally, we assess the performance gain from using heterogeneous hardware equipped with GPUs relative to multi-core hardware. With the COSMO model, we now use a weather and climate model that has all the necessary modules required for real-case convection-resolving regional climate simulations on GPUs.

scholarly journals Towards European-Scale Convection-Resolving Climate Simulations

2016 ◽  
Author(s):  
David Leutwyler ◽  
Oliver Fuhrer ◽  
Xavier Lapillonne ◽  
Daniel Lüthi ◽  
Christoph Schär
Keyword(s):  
Graphics Processing Units ◽  
Climate Models ◽  
Climate Model ◽  
Deep Convection ◽  
Moist Convection ◽  
Cold Pools ◽  
Cosmo Model ◽  
Climate Simulations ◽  
Graphics Processing ◽  
Horizontal Grid

Abstract. The representation of moist convection in climate models represents a major challenge, due to the small scales involved. Using horizontal grid spacings of O(1km), convection-resolving weather and climate models allow to explicitly resolve deep convection. However, due to their extremely demanding computational requirements, they have so far been limited to short simulations and/or small computational domains. Innovations in supercomputing have led to new hybrid node designs, mixing conventional multicore CPUs and accelerators such as graphics processing units (GPUs). One of the first atmospheric models that has been fully ported to these architectures is the COSMO model. Here we demonstrate the convection-resolving COSMO model on continental scales using a version of the model capable of using GPU accelerators. The verification of a week-long simulation containing winter storm Kyrill shows that, for this case, convection-parameterizing simulations and convection-resolving simulations agree well. Furthermore we demonstrate the applicability of the approach to longer simulations by conducting a three-month long simulation of the summer season 2006. Its results corroborate the findings found on smaller domains such as more credible representation of the diurnal cycle of precipitation in convection-resolving models and a tendency to produce more intensive hourly precipitation events. Both simulations also show how the approach allows for the representation of interactions between synoptic-scale and meso-scale atmospheric circulations at scales ranging from 1000 to 10 km. This includes the formation of sharp cold frontal structures, convection embedded in fronts and small eddies, or the formation and organization of propagating cold pools. Finally we assess the performance gain from using heterogeneous hardware equipped with GPUs with respect to multi-core hardware. With the COSMO model, we now use a climate model that has all the necessary modules required for real-case convection-resolving climate simulations on GPUs.

scholarly journals Atmospheric Blocking and Mean Biases in Climate Models

2010 ◽  
Vol 23 (23) ◽  
pp. 6143-6152 ◽  
Author(s):  
Adam A. Scaife ◽  
Tim Woollings ◽  
Jeff Knight ◽  
Gill Martin ◽  
Tim Hinton
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Model ◽  
Horizontal Resolution ◽  
Small Scale ◽  
Model Data ◽  
Atmospheric Blocking ◽  
Intergovernmental Panel ◽  
Weather And Climate ◽  
Main Culprit

Abstract Models often underestimate blocking in the Atlantic and Pacific basins and this can lead to errors in both weather and climate predictions. Horizontal resolution is often cited as the main culprit for blocking errors due to poorly resolved small-scale variability, the upscale effects of which help to maintain blocks. Although these processes are important for blocking, the authors show that much of the blocking error diagnosed using common methods of analysis and current climate models is directly attributable to the climatological bias of the model. This explains a large proportion of diagnosed blocking error in models used in the recent Intergovernmental Panel for Climate Change report. Furthermore, greatly improved statistics are obtained by diagnosing blocking using climate model data corrected to account for mean model biases. To the extent that mean biases may be corrected in low-resolution models, this suggests that such models may be able to generate greatly improved levels of atmospheric blocking.

scholarly journals Regional Climate Models Add Value to Global Model Data: A Review and Selected Examples

2011 ◽  
Vol 92 (9) ◽  
pp. 1181-1192 ◽  
Author(s):  
Frauke Feser ◽  
Burkhardt Rockel ◽  
Hans von Storch ◽  
Jörg Winterfeldt ◽  
Matthias Zahn
Keyword(s):  
Regional Climate Model ◽  
Climate Models ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Spatial Scales ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Small Scale ◽  
Model Data ◽  
Polar Lows

An important challenge in current climate modeling is to realistically describe small-scale weather statistics, such as topographic precipitation and coastal wind patterns, or regional phenomena like polar lows. Global climate models simulate atmospheric processes with increasingly higher resolutions, but still regional climate models have a lot of advantages. They consume less computation time because of their limited simulation area and thereby allow for higher resolution both in time and space as well as for longer integration times. Regional climate models can be used for dynamical down-scaling purposes because their output data can be processed to produce higher resolved atmospheric fields, allowing the representation of small-scale processes and a more detailed description of physiographic details (such as mountain ranges, coastal zones, and details of soil properties). However, does higher resolution add value when compared to global model results? Most studies implicitly assume that dynamical downscaling leads to output fields that are superior to the driving global data, but little work has been carried out to substantiate these expectations. Here a series of articles is reviewed that evaluate the benefit of dynamical downscaling by explicitly comparing results of global and regional climate model data to the observations. These studies show that the regional climate model generally performs better for the medium spatial scales, but not always for the larger spatial scales. Regional models can add value, but only for certain variables and locations—particularly those influenced by regional specifics, such as coasts, or mesoscale dynamics, such as polar lows. Therefore, the decision of whether a regional climate model simulation is required depends crucially on the scientific question being addressed.

scholarly journals Convection-permitting regional climate simulations for representing floods in small and medium sized catchments in the Eastern Alps

2018 ◽  
Author(s):  
Christian Reszler ◽  
Matthew Blasie Switanek ◽  
Heimo Truhetz
Keyword(s):  
Regional Climate Model ◽  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Flash Flood ◽  
Regional Climate ◽  
Eastern Alps ◽  
Small Scale ◽  
Distributed Hydrological Model ◽  
Flood Generation

Abstract. Small scale floods are a consequence of high precipitation rates in small areas that can occur along frontal activity and convective storms. This situation is expected to become more severe due to a warming climate, when single precipitation events resulting from deep convection become more intense (Super Clausius-Clapeyron effect). Regional climate model (RCM) evaluations and inter-comparisons have shown that there is evidence that an increase in regional climate model resolution and in particular, at the convection permitting scale, will lead to a better representation of the spatial and temporal characteristics of heavy precipitation at small and medium scales. In this paper, the benefit of grid size reduction and bias correction in climate models are evaluated in their ability to properly represent flood generation in small and medium sized catchments. The climate models are coupled with a distributed hydrological model. The study area is the Eastern Alps, where small scale storms often occur along with heterogeneous rainfall distributions leading to a very local flash flood generation. The work is carried out in a small multi-model (ensemble) framework using two different RCMs (CCLM and WRF) in different grid sizes. Bias correction is performed by the use of the novel Scaled Distribution Mapping (SDM) method. The results show, that for small catchments (

Km-resolution climate models: prospects and challenges

2022 ◽  
Author(s):  
Christoph Schär
Keyword(s):  
Climate Change ◽  
Graphics Processing Units ◽  
Climate Models ◽  
Global Climate ◽  
Regional Climate ◽  
Heavy Precipitation ◽  
Wave Drag ◽  
Global Climate Models ◽  
Moist Convection ◽  
Climate Change Projections

<p>Currently major efforts are underway toward refining the horizontal grid spacing of climate models to about 1 km, using both global and regional climate models. There is the well-founded hope that this increase in resolution will improve climate models, as it enables replacing the parameterizations of moist convection and gravity-wave drag by explicit treatments. Results suggest that this approach has a high potential in improving the representation of the water cycle and extreme events, and in reducing uncertainties in climate change projections. The presentation will provide examples of these developments in the areas of heavy precipitation and severe weather events over Europe. In addition, it will be argued that km-resolution is a promising approach toward constraining uncertainties in global climate change projections, due to improvements in the representation of tropical and subtropical clouds. Work in the latter area has only recently started and results are highly encouraging.</p> <p>For a few years there have also been attempts to make km-resolution available in global climate models for decade-long simulations. Developing this approach requires a concerted effort. Key challenges include the exploitation of the next generation hardware architectures using accelerators (e.g. graphics processing units, GPUs), the development of suitable approaches to overcome the output avalanche, and the maintenance of the rapidly-developing model source codes on a number of different compute architectures. Despite these challenges, it will be argued that km-resolution GCMs with a capacity to run at 1 SYPD (simulated year per day), might be much closer than commonly believed.</p> <p>The presentation is largely based on a recent collaborative paper (Schär et al., 2020, BAMS, https://doi.org/10.1175/BAMS-D-18-0167.1) and ongoing studies. It will also present aspects of a recent Swiss project in this area (EXCLAIM, https://exclaim.ethz.ch/).</p>

The Soil Moisture–Precipitation Feedback in Simulations with Explicit and Parameterized Convection

2009 ◽  
Vol 22 (19) ◽  
pp. 5003-5020 ◽  
Author(s):  
Cathy Hohenegger ◽  
Peter Brockhaus ◽  
Christopher S. Bretherton ◽  
Christoph Schär
Keyword(s):  
Soil Moisture ◽  
Initial Conditions ◽  
Deep Convection ◽  
Regional Climate ◽  
Small Scale ◽  
Moist Convection ◽  
Stable Layer ◽  
Two Systems ◽  
Cloud Resolving Model ◽  
Convection Schemes

Abstract Moist convection is a key aspect of the extratropical summer climate and strongly affects the delicate balance of processes that determines the surface climate in response to larger-scale forcings. Previous studies using parameterized convection have found that the feedback between soil moisture and precipitation is predominantly positive (more precipitation over wet soils) over Europe. Here this feedback is investigated for one full month (July 2006) over the Alpine region using two different model configurations. The first one employs regional climate simulations performed with the Consortium for Small-Scale Modeling Model in Climate Mode (CCLM) on a grid spacing of 25 km. The second one uses the same model but integrated on a cloud-resolving grid of 2.2 km, allowing an explicit treatment of convection. Each configuration comprises one control and two sensitivity experiments. The latter start from perturbed soil moisture initial conditions. Comparison of the simulated soil moisture–precipitation feedback reveals significant differences between the two systems. The 25-km simulations sustain a strong positive feedback, while those at 2.2-km resolution are associated with a predominantly negative feedback. Thus the two systems yield not only different strengths of this key feedback but also different signs. This has important implications, with the cloud-resolving model exhibiting a shorter soil moisture memory and a smaller soil moisture–temperature feedback. Analysis shows that the different feedback signs relate to the sensitivity of the simulated convective development to the presence of a stable layer sitting on top of the planetary boundary layer. In the 2.2-km integrations, dry initial soil moisture conditions yield more vigorous thermals (owing to stronger daytime heating), which can more easily break through the stable air barrier, thereby leading to deep convection and ultimately to a negative soil moisture–precipitation feedback loop. In the 25-km integrations, deep convection is much less sensitive to the stable layer because of the design of the employed convective parameterization. The authors also show that there are considerable differences in the simulated soil moisture–precipitation feedback between low-resolution modeling frameworks using different cloud convection schemes.

scholarly journals Introduction. Stochastic physics and climate modelling

2008 ◽  
Vol 366 (1875) ◽  
pp. 2419-2425 ◽  
Author(s):  
T.N Palmer ◽  
P.D Williams
Keyword(s):  
Climate Models ◽  
Global Climate ◽  
Small Scale ◽  
Climate Modelling ◽  
Ocean Eddies ◽  
Bulk Formula ◽  
Climate Simulations ◽  
Weather And Climate ◽  
Research Findings ◽  
Stochastic Physics

Finite computing resources limit the spatial resolution of state-of-the-art global climate simulations to hundreds of kilometres. In neither the atmosphere nor the ocean are small-scale processes such as convection, clouds and ocean eddies properly represented. Climate simulations are known to depend, sometimes quite strongly, on the resulting bulk-formula representation of unresolved processes. Stochastic physics schemes within weather and climate models have the potential to represent the dynamical effects of unresolved scales in ways which conventional bulk-formula representations are incapable of so doing. The application of stochastic physics to climate modelling is a rapidly advancing, important and innovative topic. The latest research findings are gathered together in the Theme Issue for which this paper serves as the introduction.

scholarly journals Separating Dynamic and Thermodynamic Impacts of Climate Change on Daytime Convective Development over Land

2019 ◽  
Vol 32 (16) ◽  
pp. 5213-5234 ◽  
Author(s):  
Wojciech W. Grabowski ◽  
Andreas F. Prein
Keyword(s):  
Climate Change ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Deep Convection ◽  
Convective Available Potential Energy ◽  
Future Research ◽  
Small Scale ◽  
Moist Convection ◽  
The Impact

AbstractClimate change affects the dynamics and thermodynamics of moist convection. Changes in the dynamics concern, for instance, an increase of convection strength due to increases of convective available potential energy (CAPE). Thermodynamics involve increases in water vapor that the warmer atmosphere can hold and convection can work with. Small-scale simulations are conducted to separate these two components for daytime development of unorganized convection over land. The simulations apply a novel modeling technique referred to as the piggybacking (or master–slave) approach and consider the global climate model (GCM)-predicted change of atmospheric temperature and moisture profiles in the Amazon region at the end of the century under a business-as-usual scenario. The simulations show that the dynamic impact dominates because changes in cloudiness and rainfall come from cloud dynamics considerations, such as the change in CAPE and convective inhibition (CIN) combined with the impact of environmental relative humidity (RH) on deep convection. The small RH reduction between the current and future climate significantly affects the mean surface rain accumulation as it changes from a small reduction to a small increase when the RH decrease is eliminated. The thermodynamic impact on cloudiness and precipitation is generally small, with the extreme rainfall intensifying much less than expected from an atmospheric moisture increase. These results are discussed in the context of previous studies concerning climate change–induced modifications of moist convection. Future research directions applying the piggybacking method are discussed.

scholarly journals Convection permitting regional climate change simulations for understanding future climate and informing decision making in Africa

2021 ◽  
pp. 1-46
Author(s):  
Catherine A Senior ◽  
John H Marsham ◽  
Sègoléne Berthou ◽  
Laura E Burgin ◽  
Sonja S Folwell ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Model ◽  
Rainfall Variability ◽  
Regional Climate ◽  
Critical Role ◽  
Future Climate ◽  
Small Scale ◽  
Moist Convection ◽  
Continental Scale ◽  
The Impact

AbstractPan-Africa convection-permitting regional climate model simulations have been performed to study the impact of high resolution and the explicit representation of atmospheric moist convection on the present and future climate of Africa. These unique simulations have allowed European and African climate scientists to understand the critical role that the representation of convection plays in the ability of a contemporary climate model to capture climate and climate change, including many impact relevant aspects such as rainfall variability and extremes. There are significant improvements in not only the small-scale characteristics of rainfall such as its intensity and diurnal cycle, but also in the large-scale circulation. Similarly effects of explicit convection affect not only projected changes in rainfall extremes, dry-spells and high winds, but also continental-scale circulation and regional rainfall accumulations. The physics underlying such differences are in many cases expected to be relevant to all models that use parameterized convection. In some cases physical understanding of small-scale change mean that we can provide regional decision makers with new scales of information across a range of sectors. We demonstrate the potential value of these simulations both as scientific tools to increase climate process understanding and, when used with other models, for direct user applications. We describe how these ground-breaking simulations have been achieved under the UK Government’s Future Climate for Africa Programme. We anticipate a growing number of such simulations, which we advocate should become a routine component of climate projection, and encourage international co-ordination of such computationally, and human-resource expensive simulations as effectively as possible.

scholarly journals Kilometer-Scale Climate Models: Prospects and Challenges

2020 ◽  
Vol 101 (5) ◽  
pp. E567-E587 ◽  
Author(s):  
Christoph Schär ◽  
Oliver Fuhrer ◽  
Andrea Arteaga ◽  
Nikolina Ban ◽  
Christophe Charpilloz ◽  
...  
Keyword(s):  
Graphics Processing Units ◽  
Climate Models ◽  
Water Cycle ◽  
Weather Prediction ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Horizontal Resolution ◽  
Limited Area ◽  
Moist Convection ◽  
Continental Scale

Abstract Currently major efforts are underway toward refining the horizontal resolution (or grid spacing) of climate models to about 1 km, using both global and regional climate models (GCMs and RCMs). Several groups have succeeded in conducting kilometer-scale multiweek GCM simulations and decadelong continental-scale RCM simulations. There is the well-founded hope that this increase in resolution represents a quantum jump in climate modeling, as it enables replacing the parameterization of moist convection by an explicit treatment. It is expected that this will improve the simulation of the water cycle and extreme events and reduce uncertainties in climate change projections. While kilometer-scale resolution is commonly employed in limited-area numerical weather prediction, enabling it on global scales for extended climate simulations requires a concerted effort. In this paper, we exploit an RCM that runs entirely on graphics processing units (GPUs) and show examples that highlight the prospects of this approach. A particular challenge addressed in this paper relates to the growth in output volumes. It is argued that the data avalanche of high-resolution simulations will make it impractical or impossible to store the data. Rather, repeating the simulation and conducting online analysis will become more efficient. A prototype of this methodology is presented. It makes use of a bit-reproducible model version that ensures reproducible simulations across hardware architectures, in conjunction with a data virtualization layer as a common interface for output analyses. An assessment of the potential of these novel approaches will be provided.

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