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>

scholarly journals Selecting global climate models for regional climate change studies

2009 ◽  
Vol 106 (21) ◽  
pp. 8441-8446 ◽  
Author(s):  
D. W. Pierce ◽  
T. P. Barnett ◽  
B. D. Santer ◽  
P. J. Gleckler
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Global Climate ◽  
Regional Climate ◽  
Regional Climate Change ◽  
Global Climate Models

scholarly journals Effect of Model Resolution on Intense and Extreme Precipitationinthe Mediterranean Region

Atmosphere ◽  
2020 ◽  
Vol 11 (7) ◽  
pp. 699
Author(s):  
Dario Conte ◽  
Silvio Gualdi ◽  
Piero Lionello
Keyword(s):  
Climate Change ◽  
Mediterranean Region ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Model ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Global Climate Models ◽  
Total Precipitation ◽  
Model Resolution

This study explores the role of model resolution on the simulation of precipitation and on the estimate of its future change in the Mediterranean region. It compares the results of two regional climate models (RCMs, with two different horizontal grid resolutions, 0.44 and 0.11 degs, covering the whole Mediterranean region) and of the global climate model (GCM, 0.75 degs) that has provided the boundary conditions for them. The regional climate models include an interactive oceanic component with a resolution of 1/16 degs. The period 1960–2100 and the representative concentration pathways RCP4.5 and RCP8.5 are considered. The results show that, in the present climate, increasing resolution increases total precipitation and its extremes over steep orography, while it has the opposite effect over flat areas and the sea. Considering climate change, in all simulations, total precipitation will decrease over most of the considered domain except at the northern boundary, where it will increase. Extreme precipitation will increase over most of the northern Mediterranean region and decrease over the sea and some southern areas. Further, the overall probability of precipitation (frequency of wet days) significantly decreases over most of the region, but wet days will be characterized with precipitation intensity higher than the present. Our analysis shows that: (1) these projected changes are robust with respect to the considered range of model resolution; (2) increasing the resolution (within the considered resolution range) decreases the magnitude of these climate change effects. However, it is likely that resolution plays a less important role than other factors, such as the different physics of regional and global climate models. It remains to be investigated whether further increasing the resolution (and reaching the scale explicitly permitting convection) would change this conclusion.

scholarly journals An ensemble approach to assess hydrological models' contribution to uncertainties in the analysis of climate change impact on water resources

2013 ◽  
Vol 17 (2) ◽  
pp. 565-578 ◽  
Author(s):  
J. A. Velázquez ◽  
J. Schmid ◽  
S. Ricard ◽  
M. J. Muerth ◽  
B. Gauvin St-Denis ◽  
...  
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
Climate Models ◽  
Hydrological Model ◽  
Global Climate ◽  
Regional Climate ◽  
Structural Complexity ◽  
Global Climate Models ◽  
Hydrological Models ◽  
The Impact

Abstract. Over the recent years, several research efforts investigated the impact of climate change on water resources for different regions of the world. The projection of future river flows is affected by different sources of uncertainty in the hydro-climatic modelling chain. One of the aims of the QBic3 project (Québec-Bavarian International Collaboration on Climate Change) is to assess the contribution to uncertainty of hydrological models by using an ensemble of hydrological models presenting a diversity of structural complexity (i.e., lumped, semi distributed and distributed models). The study investigates two humid, mid-latitude catchments with natural flow conditions; one located in Southern Québec (Canada) and one in Southern Bavaria (Germany). Daily flow is simulated with four different hydrological models, forced by outputs from regional climate models driven by global climate models over a reference (1971–2000) and a future (2041–2070) period. The results show that, for our hydrological model ensemble, the choice of model strongly affects the climate change response of selected hydrological indicators, especially those related to low flows. Indicators related to high flows seem less sensitive on the choice of the hydrological model.

scholarly journals Climate Change Impact Assessment for Aji Basin Using Statistical Downscaling and Bias Correction of Climate Model Outputs

2016 ◽  
Vol 11 (2) ◽  
pp. 670-678 ◽  
Author(s):  
N. S Vithlani ◽  
H. D Rank
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Regional Climate ◽  
Climate Extremes ◽  
Global Climate Models ◽  
Climate Indices ◽  
Climate Projections

For the future projections Global climate models (GCMs) enable development of climate projections and relate greenhouse gas forcing to future potential climate states. When focusing it on smaller scales it exhibit some limitations to overcome this problem, regional climate models (RCMs) and other downscaling methods have been developed. To ensure statistics of the downscaled output matched the corresponding statistics of the observed data, bias correction was used. Quantify future changes of climate extremes were analyzed, based on these downscaled data from two RCMs grid points. Subset of indices and models, results of bias corrected model output and raw for the present day climate were compared with observation, which demonstrated that bias correction is important for RCM outputs. Bias correction directed agreements of extreme climate indices for future climate it does not correct for lag inverse autocorrelation and fraction of wet and dry days. But, it was observed that adjusting both the biases in the mean and variability, relatively simple non-linear correction, leads to better reproduction of observed extreme daily and multi-daily precipitation amounts. Due to climate change temperature and precipitation will increased day by day.

scholarly journals New Tendencies in Global Climate Change and Their Effects on the Climate of Hungary

Hadmérnök ◽  
2019 ◽  
Vol 14 (1) ◽  
pp. 99-107
Author(s):  
László Földi ◽  
László Halász
Keyword(s):  
Climate Change ◽  
Carbon Dioxide ◽  
Greenhouse Gases ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Regional Climate ◽  
Global Climate Models ◽  
Lower Atmosphere ◽  
The Earth

Defining the term of climate, we investigate the role of natural causes and effects of human activities in climate change. The temperature of the Earth is determined by the balance between the amount of radiation energy received from the Sun and that emitted from the surface of the Earth towards the outer space. Greenhouse gases in the atmosphere, including water vapor, carbon dioxide, methane and nitrous oxides, act to make the surface much warmer, because they absorb and emit heat energy in all directions (including downwards), keeping Earth’s surface and lower atmosphere warm. The primary cause of climate change is the burning of fossil fuels, such as oil and coal, which emits greenhouse gases into the atmosphere – primarily carbon dioxide. We give a review about the activity of the Intergovernmental Panel on Climate Change and the United Nations Climate Change Conferences. Shortly investigate the different global climate models and some regional climate models. Finally discuss the results of regional climate model simulations for the Carpathian Basin.

Empirical-Statistical Downscaling: Nonlinear Statistical Downscaling

2018 ◽  
Author(s):  
Aristita Busuioc ◽  
Alexandru Dumitrescu
Keyword(s):  
Climate Change ◽  
Statistical Downscaling ◽  
Large Scale ◽  
Climate Models ◽  
Dynamical Downscaling ◽  
Global Climate ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Global Climate Models ◽  
Local Climate

This is an advance summary of a forthcoming article in the Oxford Research Encyclopedia of Climate Science. Please check back later for the full article.The concept of statistical downscaling or empirical-statistical downscaling became a distinct and important scientific approach in climate science in recent decades, when the climate change issue and assessment of climate change impact on various social and natural systems have become international challenges. Global climate models are the best tools for estimating future climate conditions. Even if improvements can be made in state-of-the art global climate models, in terms of spatial resolution and their performance in simulation of climate characteristics, they are still skillful only in reproducing large-scale feature of climate variability, such as global mean temperature or various circulation patterns (e.g., the North Atlantic Oscillation). However, these models are not able to provide reliable information on local climate characteristics (mean temperature, total precipitation), especially on extreme weather and climate events. The main reason for this failure is the influence of local geographical features on the local climate, as well as other factors related to surrounding large-scale conditions, the influence of which cannot be correctly taken into consideration by the current dynamical global models.Impact models, such as hydrological and crop models, need high resolution information on various climate parameters on the scale of a river basin or a farm, scales that are not available from the usual global climate models. Downscaling techniques produce regional climate information on finer scale, from global climate change scenarios, based on the assumption that there is a systematic link between the large-scale and local climate. Two types of downscaling approaches are known: a) dynamical downscaling is based on regional climate models nested in a global climate model; and b) statistical downscaling is based on developing statistical relationships between large-scale atmospheric variables (predictors), available from global climate models, and observed local-scale variables of interest (predictands).Various types of empirical-statistical downscaling approaches can be placed approximately in linear and nonlinear groupings. The empirical-statistical downscaling techniques focus more on details related to the nonlinear models—their validation, strengths, and weaknesses—in comparison to linear models or the mixed models combining the linear and nonlinear approaches. Stochastic models can be applied to daily and sub-daily precipitation in Romania, with a comparison to dynamical downscaling. Conditional stochastic models are generally specific for daily or sub-daily precipitation as predictand.A complex validation of the nonlinear statistical downscaling models, selection of the large-scale predictors, model ability to reproduce historical trends, extreme events, and the uncertainty related to future downscaled changes are important issues. A better estimation of the uncertainty related to downscaled climate change projections can be achieved by using ensembles of more global climate models as drivers, including their ability to simulate the input in downscaling models. Comparison between future statistical downscaled climate signals and those derived from dynamical downscaling driven by the same global model, including a complex validation of the regional climate models, gives a measure of the reliability of downscaled regional climate changes.

scholarly journals A regional climate modelling projection ensemble experiment – NARCliM

2013 ◽  
Vol 6 (3) ◽  
pp. 5117-5139 ◽  
Author(s):  
J. P. Evans ◽  
F. Ji ◽  
C. Lee ◽  
P. Smith ◽  
D. Argüeso ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Planning Process ◽  
Global Climate ◽  
Regional Climate ◽  
Global Climate Models ◽  
Regional Climate Modelling ◽  
Climate Projections ◽  
Climate Modelling ◽  
User Engagement

Abstract. Including the impacts of climate change in decision making and planning processes is a challenge facing many regional governments including the New South Wales (NSW) and Australian Capital Territory (ACT) governments in Australia. NARCliM (NSW/ACT Regional Climate Modelling project) is a regional climate modelling project that aims to provide a comprehensive and consistent set of climate projections that can be used by all relevant government departments when considering climate change. To maximise end user engagement and ensure outputs are relevant to the planning process, a series of stakeholder workshops were run to define key aspects of the model experiment including spatial resolution, time slices, and output variables. As with all such experiments, practical considerations limit the number of ensembles members that can be simulated such that choices must be made concerning which Global Climate Models (GCMs) to downscale from, and which Regional Climate Models (RCMs) to downscale with. Here a methodology for making these choices is proposed that aims to sample the uncertainty in both GCMs and RCMs, as well as spanning the range of future climate projections present in the full GCM ensemble. The created ensemble provides a more robust view of future regional climate changes.

scholarly journals Analysis of the observed and modelled budburst dates of grapevine in Croatia

2021 ◽  
Author(s):  
Branimir Omazić ◽  
Maja Telišman Prtenjak ◽  
Ivan Prša ◽  
Marko Karoglan
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Global Climate ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Climatic Conditions ◽  
Global Climate Models ◽  
Statistical Characteristics ◽  
Base Temperature ◽  
Spring Frost

<p>Since changes in temperatures and precipitation significantly affect the biosphere, viticulture as an important economic branch in the moderate latitudes (e.g., mainly between 35°N and 55°N) is strongly influenced by climate change. The most commonly analysed/modelled phenological phases of grapevines are budburst (beginning of grapevine seasonal growth), flowering (crucial for the reproductive cycle) and veraison (initiation of the ripening). Resent studies indicate that budburst is greatly regulated by temperature. Due to climate change and temperature increase, budburst dates show trends in earlier occurrences at several available stations throughout Croatia which increases the vulnerability of the grapevine to the spring frost.</p><p>The aim of this study is to determine trends and changes in budburst date, their statistical characteristics at available stations in period 1961-2020 in Croatia. We focus on four grapevine varieties, two white (Graševina and Chardonnay) and two red (Merlot and Plavac Mali) and performance of statistical models (GDD, Riou’s model and BRIN model) that predict budburst dates in the present climate. For this purpose an effect of the dormancy period and base temperature on the simulated budburst date have been explored. The study is further extended to future climatic conditions using statistical and numerical climate models. Therefore, a daily output from three CORDEX Regional Climate Models’ (RCMs) simulations (CLMcom-CCLM4-8-17, SMHI-RCA4, CNRM-ALADIN5.3) for Croatian domain are used. All RCMs are forced by Global Climate Models (GCMs) with a moderate (RCP4.5) and a high-end (RCP8.5) green-house gass (GHG) scenario(s) and all the simulations have horizontal grid spacing of 0.11°. Results indicate further earlier appearance of budburst regardless of varieties.</p>

scholarly journals Regional climate change projections from NA-CORDEX and their relation to climate sensitivity

2020 ◽  
Vol 162 (2) ◽  
pp. 645-665
Author(s):  
Melissa S. Bukovsky ◽  
Linda O. Mearns
Keyword(s):  
Climate Change ◽  
Climate Sensitivity ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Regional Climate ◽  
Full Range ◽  
Regional Scale ◽  
Global Climate Models ◽  
Continental Scale

Abstract The climate sensitivity of global climate models (GCMs) strongly influences projected climate change due to increased atmospheric carbon dioxide. Reasonably, the climate sensitivity of a GCM may be expected to affect dynamically downscaled projections. However, there has been little examination of the effect of the climate sensitivity of GCMs on regional climate model (RCM) ensembles. Therefore, we present projections of temperature and precipitation from the ensemble of projections produced as a part of the North American branch of the international Coordinated Regional Downscaling Experiment (NA-CORDEX) in the context of their relationship to the climate sensitivity of their parent GCMs. NA-CORDEX simulations were produced at 50-km and 25-km resolutions with multiple RCMs which downscaled multiple GCMs that spanned nearly the full range of climate sensitivity available in the CMIP5 archive. We show that climate sensitivity is a very important source of spread in the NA-CORDEX ensemble, particularly for temperature. Temperature projections correlate with driving GCM climate sensitivity annually and seasonally across North America not only at a continental scale but also at a local-to-regional scale. Importantly, the spread in temperature projections would be reduced if only low, mid, or high climate sensitivity simulations were considered, or if only the ensemble mean were considered. Precipitation projections correlate with climate sensitivity, but only at a continental scale during the cold season, due to the increasing influence of other processes at finer scales. Additionally, it is shown that the RCMs do alter the projection space sampled by their driving GCMs.

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